Early tests
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Lysandre Debut
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Коммит
870320a24e
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@ -11,6 +11,15 @@ from .file_utils import add_start_docstrings
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logger = logging.getLogger(__name__)
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ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP = {
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'albert-base': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-base-pytorch_model.bin",
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'albert-large': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-large-pytorch_model.bin",
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'albert-xlarge': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xlarge-pytorch_model.bin",
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'albert-xxlarge': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xxlarge-pytorch_model.bin",
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}
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def load_tf_weights_in_albert(model, config, tf_checkpoint_path):
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""" Load tf checkpoints in a pytorch model."""
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try:
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@ -39,6 +48,7 @@ def load_tf_weights_in_albert(model, config, tf_checkpoint_path):
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for name, array in zip(names, arrays):
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original_name = name
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name = name.replace("ffn_1", "ffn")
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name = name.replace("/bert/", "/albert/")
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name = name.replace("ffn/intermediate/output", "ffn_output")
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name = name.replace("attention_1", "attention")
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name = name.replace("cls/predictions", "predictions")
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@ -114,29 +124,6 @@ class AlbertAttention(BertSelfAttention):
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self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
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self.pruned_heads = set()
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def prune_heads(self, heads):
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if len(heads) == 0:
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return
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mask = torch.ones(self.num_attention_heads, self.attention_head_size)
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heads = set(heads) - self.pruned_heads # Convert to set and emove already pruned heads
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for head in heads:
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# Compute how many pruned heads are before the head and move the index accordingly
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head = head - sum(1 if h < head else 0 for h in self.pruned_heads)
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mask[head] = 0
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mask = mask.view(-1).contiguous().eq(1)
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index = torch.arange(len(mask))[mask].long()
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# Prune linear layers
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self.query = prune_linear_layer(self.query, index)
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self.key = prune_linear_layer(self.key, index)
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self.value = prune_linear_layer(self.value, index)
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self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
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# Update hyper params and store pruned heads
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self.num_attention_heads = self.num_attention_heads - len(heads)
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self.all_head_size = self.attention_head_size * self.num_attention_heads
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self.pruned_heads = self.pruned_heads.union(heads)
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def forward(self, input_ids, attention_mask=None, head_mask=None):
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mixed_query_layer = self.query(input_ids)
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mixed_key_layer = self.key(input_ids)
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@ -225,7 +212,7 @@ class AlbertLayerGroup(nn.Module):
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layer_attentions = layer_attentions + (layer_output[1],)
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if self.output_hidden_states:
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layer_hidden_states = layer_hidden_states + (hidden_states,)
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layer_hidden_states = layer_hidden_states + (hidden_states,)
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outputs = (hidden_states,)
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if self.output_hidden_states:
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@ -367,6 +354,8 @@ class AlbertModel(BertModel):
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self.pooler = nn.Linear(config.hidden_size, config.hidden_size)
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self.pooler_activation = nn.Tanh()
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self.init_weights()
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def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None):
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if attention_mask is None:
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@ -422,33 +411,41 @@ class AlbertForMaskedLM(BertPreTrainedModel):
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list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
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Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
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"""
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config_class = AlbertConfig
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pretrained_model_archive_map = ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP
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load_tf_weights = load_tf_weights_in_albert
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base_model_prefix = "albert"
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def __init__(self, config):
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super(AlbertForMaskedLM, self).__init__(config)
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self.config = config
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self.bert = AlbertModel(config)
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self.albert = AlbertModel(config)
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self.LayerNorm = nn.LayerNorm(config.embedding_size)
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self.bias = nn.Parameter(torch.zeros(config.vocab_size))
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self.dense = nn.Linear(config.hidden_size, config.embedding_size)
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self.word_embeddings = nn.Linear(config.embedding_size, config.vocab_size)
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self.decoder = nn.Linear(config.embedding_size, config.vocab_size)
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self.activation = ACT2FN[config.hidden_act]
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self.init_weights()
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self.tie_weights()
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def tie_weights(self):
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""" Make sure we are sharing the input and output embeddings.
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Export to TorchScript can't handle parameter sharing so we are cloning them instead.
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"""
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self._tie_or_clone_weights(self.classifier.word_embeddings,
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self.transformer.embeddings.word_embeddings)
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self._tie_or_clone_weights(self.decoder,
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self.albert.embeddings.word_embeddings)
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def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None,
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masked_lm_labels=None):
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outputs = self.bert(input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None)
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outputs = self.albert(input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None)
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sequence_outputs = outputs[0]
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hidden_states = self.dense(sequence_outputs)
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hidden_states = self.activation(hidden_states)
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hidden_states = self.LayerNorm(hidden_states)
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prediction_scores = self.word_embeddings(hidden_states)
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prediction_scores = self.decoder(hidden_states)
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outputs = (prediction_scores,) + outputs[2:] # Add hidden states and attention if they are here
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if masked_lm_labels is not None:
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@ -0,0 +1,191 @@
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# coding=utf-8
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# Copyright 2018 The Google AI Language Team Authors.
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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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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import unittest
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import shutil
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import pytest
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from transformers import is_torch_available
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from .modeling_common_test import (CommonTestCases, ids_tensor)
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from .configuration_common_test import ConfigTester
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if is_torch_available():
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from transformers import (AlbertConfig, AlbertModel, AlbertForMaskedLM)
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from transformers.modeling_albert import ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP
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else:
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pytestmark = pytest.mark.skip("Require Torch")
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class AlbertModelTest(CommonTestCases.CommonModelTester):
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all_model_classes = (AlbertModel, AlbertForMaskedLM) if is_torch_available() else ()
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test_pruning = False
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test_head_masking = False
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class AlbertModelTester(object):
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def __init__(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=5,
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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 = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
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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 = AlbertConfig(
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vocab_size_or_config_json_file=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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return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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def check_loss_output(self, result):
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self.parent.assertListEqual(
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list(result["loss"].size()),
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[])
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def create_and_check_albert_model(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
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model = AlbertModel(config=config)
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model.eval()
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sequence_output, pooled_output = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
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sequence_output, pooled_output = model(input_ids, token_type_ids=token_type_ids)
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sequence_output, pooled_output = model(input_ids)
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result = {
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"sequence_output": sequence_output,
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"pooled_output": pooled_output,
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}
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self.parent.assertListEqual(
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list(result["sequence_output"].size()),
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[self.batch_size, self.seq_length, self.hidden_size])
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self.parent.assertListEqual(list(result["pooled_output"].size()), [self.batch_size, self.hidden_size])
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def create_and_check_albert_for_masked_lm(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
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model = AlbertForMaskedLM(config=config)
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model.eval()
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loss, prediction_scores = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, masked_lm_labels=token_labels)
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result = {
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"loss": loss,
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"prediction_scores": prediction_scores,
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}
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self.parent.assertListEqual(
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list(result["prediction_scores"].size()),
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[self.batch_size, self.seq_length, self.vocab_size])
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self.check_loss_output(result)
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def prepare_config_and_inputs_for_common(self):
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config_and_inputs = self.prepare_config_and_inputs()
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(config, input_ids, token_type_ids, input_mask,
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sequence_labels, token_labels, choice_labels) = 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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def setUp(self):
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self.model_tester = AlbertModelTest.AlbertModelTester(self)
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self.config_tester = ConfigTester(self, config_class=AlbertConfig, 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_albert_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_albert_model(*config_and_inputs)
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def test_for_masked_lm(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_albert_for_masked_lm(*config_and_inputs)
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@pytest.mark.slow
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def test_model_from_pretrained(self):
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cache_dir = "/tmp/transformers_test/"
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for model_name in list(ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
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model = AlbertModel.from_pretrained(model_name, cache_dir=cache_dir)
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shutil.rmtree(cache_dir)
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self.assertIsNotNone(model)
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if __name__ == "__main__":
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unittest.main()
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