Early tests

transformers/modeling_albert.py

@@ -11,6 +11,15 @@ from .file_utils import add_start_docstrings
 
 logger = logging.getLogger(__name__)
 
+
+ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP = {
+    'albert-base': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-base-pytorch_model.bin",
+    'albert-large': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-large-pytorch_model.bin",
+    'albert-xlarge': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xlarge-pytorch_model.bin",
+    'albert-xxlarge': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xxlarge-pytorch_model.bin",
+}
+
+
 def load_tf_weights_in_albert(model, config, tf_checkpoint_path):
     """ Load tf checkpoints in a pytorch model."""
     try:
@@ -39,6 +48,7 @@ def load_tf_weights_in_albert(model, config, tf_checkpoint_path):
     for name, array in zip(names, arrays):
         original_name = name
         name = name.replace("ffn_1", "ffn")
+        name = name.replace("/bert/", "/albert/")
         name = name.replace("ffn/intermediate/output", "ffn_output")
         name = name.replace("attention_1", "attention")   
         name = name.replace("cls/predictions", "predictions")
@@ -114,29 +124,6 @@ class AlbertAttention(BertSelfAttention):
         self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
         self.pruned_heads = set()
 
-    def prune_heads(self, heads):
-        if len(heads) == 0:
-            return
-        mask = torch.ones(self.num_attention_heads, self.attention_head_size)
-        heads = set(heads) - self.pruned_heads  # Convert to set and emove already pruned heads
-        for head in heads:
-            # Compute how many pruned heads are before the head and move the index accordingly
-            head = head - sum(1 if h < head else 0 for h in self.pruned_heads)
-            mask[head] = 0
-        mask = mask.view(-1).contiguous().eq(1)
-        index = torch.arange(len(mask))[mask].long()
-
-        # Prune linear layers
-        self.query = prune_linear_layer(self.query, index)
-        self.key = prune_linear_layer(self.key, index)
-        self.value = prune_linear_layer(self.value, index)
-        self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
-
-        # Update hyper params and store pruned heads
-        self.num_attention_heads = self.num_attention_heads - len(heads)
-        self.all_head_size = self.attention_head_size * self.num_attention_heads
-        self.pruned_heads = self.pruned_heads.union(heads)
-
     def forward(self, input_ids, attention_mask=None, head_mask=None):
         mixed_query_layer = self.query(input_ids)
         mixed_key_layer = self.key(input_ids)
@@ -225,7 +212,7 @@ class AlbertLayerGroup(nn.Module):
                 layer_attentions = layer_attentions + (layer_output[1],)
 
         if self.output_hidden_states:
-                layer_hidden_states = layer_hidden_states + (hidden_states,)
+            layer_hidden_states = layer_hidden_states + (hidden_states,)
 
         outputs = (hidden_states,)
         if self.output_hidden_states:
@@ -367,6 +354,8 @@ class AlbertModel(BertModel):
         self.pooler = nn.Linear(config.hidden_size, config.hidden_size)
         self.pooler_activation = nn.Tanh()
 
+        self.init_weights()
+
 
     def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None):
         if attention_mask is None:
@@ -422,33 +411,41 @@ class AlbertForMaskedLM(BertPreTrainedModel):
             list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
             Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
     """
-    
+
+    config_class = AlbertConfig
+    pretrained_model_archive_map = ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP
+    load_tf_weights = load_tf_weights_in_albert
+    base_model_prefix = "albert"
+
     def __init__(self, config):
         super(AlbertForMaskedLM, self).__init__(config)
 
         self.config = config
-        self.bert = AlbertModel(config)
+        self.albert = AlbertModel(config)
         self.LayerNorm = nn.LayerNorm(config.embedding_size)
         self.bias = nn.Parameter(torch.zeros(config.vocab_size))
         self.dense = nn.Linear(config.hidden_size, config.embedding_size)
-        self.word_embeddings = nn.Linear(config.embedding_size, config.vocab_size)
+        self.decoder = nn.Linear(config.embedding_size, config.vocab_size)
         self.activation = ACT2FN[config.hidden_act]
 
+        self.init_weights()
+        self.tie_weights()
+
     def tie_weights(self):
         """ Make sure we are sharing the input and output embeddings.
             Export to TorchScript can't handle parameter sharing so we are cloning them instead.
         """
-        self._tie_or_clone_weights(self.classifier.word_embeddings,
-                                   self.transformer.embeddings.word_embeddings)
+        self._tie_or_clone_weights(self.decoder,
+                                   self.albert.embeddings.word_embeddings)
 
     def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None,
                 masked_lm_labels=None):
-        outputs = self.bert(input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None)
+        outputs = self.albert(input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None)
         sequence_outputs = outputs[0]
         hidden_states = self.dense(sequence_outputs)
         hidden_states = self.activation(hidden_states)
         hidden_states = self.LayerNorm(hidden_states)
-        prediction_scores = self.word_embeddings(hidden_states)
+        prediction_scores = self.decoder(hidden_states)
 
         outputs = (prediction_scores,) + outputs[2:]  # Add hidden states and attention if they are here
         if masked_lm_labels is not None:

transformers/tests/modeling_albert_test.py

+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import unittest
+import shutil
+import pytest
+
+from transformers import is_torch_available
+
+from .modeling_common_test import (CommonTestCases, ids_tensor)
+from .configuration_common_test import ConfigTester
+
+if is_torch_available():
+    from transformers import (AlbertConfig, AlbertModel, AlbertForMaskedLM)
+    from transformers.modeling_albert import ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP
+else:
+    pytestmark = pytest.mark.skip("Require Torch")
+
+
+class AlbertModelTest(CommonTestCases.CommonModelTester):
+
+    all_model_classes = (AlbertModel, AlbertForMaskedLM) if is_torch_available() else ()
+    test_pruning = False
+    test_head_masking = False
+
+    class AlbertModelTester(object):
+
+        def __init__(self,
+                     parent,
+                     batch_size=13,
+                     seq_length=7,
+                     is_training=True,
+                     use_input_mask=True,
+                     use_token_type_ids=True,
+                     use_labels=True,
+                     vocab_size=99,
+                     hidden_size=32,
+                     num_hidden_layers=5,
+                     num_attention_heads=4,
+                     intermediate_size=37,
+                     hidden_act="gelu",
+                     hidden_dropout_prob=0.1,
+                     attention_probs_dropout_prob=0.1,
+                     max_position_embeddings=512,
+                     type_vocab_size=16,
+                     type_sequence_label_size=2,
+                     initializer_range=0.02,
+                     num_labels=3,
+                     num_choices=4,
+                     scope=None,
+                    ):
+            self.parent = parent
+            self.batch_size = batch_size
+            self.seq_length = seq_length
+            self.is_training = is_training
+            self.use_input_mask = use_input_mask
+            self.use_token_type_ids = use_token_type_ids
+            self.use_labels = use_labels
+            self.vocab_size = vocab_size
+            self.hidden_size = hidden_size
+            self.num_hidden_layers = num_hidden_layers
+            self.num_attention_heads = num_attention_heads
+            self.intermediate_size = intermediate_size
+            self.hidden_act = hidden_act
+            self.hidden_dropout_prob = hidden_dropout_prob
+            self.attention_probs_dropout_prob = attention_probs_dropout_prob
+            self.max_position_embeddings = max_position_embeddings
+            self.type_vocab_size = type_vocab_size
+            self.type_sequence_label_size = type_sequence_label_size
+            self.initializer_range = initializer_range
+            self.num_labels = num_labels
+            self.num_choices = num_choices
+            self.scope = scope
+
+        def prepare_config_and_inputs(self):
+            input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
+
+            input_mask = None
+            if self.use_input_mask:
+                input_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
+
+            token_type_ids = None
+            if self.use_token_type_ids:
+                token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
+
+            sequence_labels = None
+            token_labels = None
+            choice_labels = None
+            if self.use_labels:
+                sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
+                token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
+                choice_labels = ids_tensor([self.batch_size], self.num_choices)
+
+            config = AlbertConfig(
+                vocab_size_or_config_json_file=self.vocab_size,
+                hidden_size=self.hidden_size,
+                num_hidden_layers=self.num_hidden_layers,
+                num_attention_heads=self.num_attention_heads,
+                intermediate_size=self.intermediate_size,
+                hidden_act=self.hidden_act,
+                hidden_dropout_prob=self.hidden_dropout_prob,
+                attention_probs_dropout_prob=self.attention_probs_dropout_prob,
+                max_position_embeddings=self.max_position_embeddings,
+                type_vocab_size=self.type_vocab_size,
+                initializer_range=self.initializer_range)
+
+            return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
+
+        def check_loss_output(self, result):
+            self.parent.assertListEqual(
+                list(result["loss"].size()),
+                [])
+
+        def create_and_check_albert_model(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
+            model = AlbertModel(config=config)
+            model.eval()
+            sequence_output, pooled_output = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
+            sequence_output, pooled_output = model(input_ids, token_type_ids=token_type_ids)
+            sequence_output, pooled_output = model(input_ids)
+
+            result = {
+                "sequence_output": sequence_output,
+                "pooled_output": pooled_output,
+            }
+            self.parent.assertListEqual(
+                list(result["sequence_output"].size()),
+                [self.batch_size, self.seq_length, self.hidden_size])
+            self.parent.assertListEqual(list(result["pooled_output"].size()), [self.batch_size, self.hidden_size])
+
+
+        def create_and_check_albert_for_masked_lm(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
+            model = AlbertForMaskedLM(config=config)
+            model.eval()
+            loss, prediction_scores = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, masked_lm_labels=token_labels)
+            result = {
+                "loss": loss,
+                "prediction_scores": prediction_scores,
+            }
+            self.parent.assertListEqual(
+                list(result["prediction_scores"].size()),
+                [self.batch_size, self.seq_length, self.vocab_size])
+            self.check_loss_output(result)
+
+
+        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
+
+    def setUp(self):
+        self.model_tester = AlbertModelTest.AlbertModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=AlbertConfig, hidden_size=37)
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    def test_albert_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_albert_model(*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_albert_for_masked_lm(*config_and_inputs)
+
+    @pytest.mark.slow
+    def test_model_from_pretrained(self):
+        cache_dir = "/tmp/transformers_test/"
+        for model_name in list(ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
+            model = AlbertModel.from_pretrained(model_name, cache_dir=cache_dir)
+            shutil.rmtree(cache_dir)
+            self.assertIsNotNone(model)
+
+if __name__ == "__main__":
+    unittest.main()