# 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 collections import json import random import re import modeling import six import tensorflow as tf class BertModelTest(tf.test.TestCase): class BertModelTester(object): def __init__(self, parent, batch_size=13, seq_length=7, is_training=True, use_input_mask=True, use_token_type_ids=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, initializer_range=0.02, 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.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.initializer_range = initializer_range self.scope = scope def create_model(self): input_ids = BertModelTest.ids_tensor([self.batch_size, self.seq_length], self.vocab_size) input_mask = None if self.use_input_mask: input_mask = BertModelTest.ids_tensor( [self.batch_size, self.seq_length], vocab_size=2) token_type_ids = None if self.use_token_type_ids: token_type_ids = BertModelTest.ids_tensor( [self.batch_size, self.seq_length], self.type_vocab_size) config = modeling.BertConfig( vocab_size=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) model = modeling.BertModel( config=config, is_training=self.is_training, input_ids=input_ids, input_mask=input_mask, token_type_ids=token_type_ids, scope=self.scope) outputs = { "embedding_output": model.get_embedding_output(), "sequence_output": model.get_sequence_output(), "pooled_output": model.get_pooled_output(), "all_encoder_layers": model.get_all_encoder_layers(), } return outputs def check_output(self, result): self.parent.assertAllEqual( result["embedding_output"].shape, [self.batch_size, self.seq_length, self.hidden_size]) self.parent.assertAllEqual( result["sequence_output"].shape, [self.batch_size, self.seq_length, self.hidden_size]) self.parent.assertAllEqual(result["pooled_output"].shape, [self.batch_size, self.hidden_size]) def test_default(self): self.run_tester(BertModelTest.BertModelTester(self)) def test_config_to_json_string(self): config = modeling.BertConfig(vocab_size=99, hidden_size=37) obj = json.loads(config.to_json_string()) self.assertEqual(obj["vocab_size"], 99) self.assertEqual(obj["hidden_size"], 37) def run_tester(self, tester): with self.test_session() as sess: ops = tester.create_model() init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer()) sess.run(init_op) output_result = sess.run(ops) tester.check_output(output_result) self.assert_all_tensors_reachable(sess, [init_op, ops]) @classmethod def ids_tensor(cls, shape, vocab_size, rng=None, name=None): """Creates a random int32 tensor of the shape within the vocab size.""" if rng is None: rng = random.Random() total_dims = 1 for dim in shape: total_dims *= dim values = [] for _ in range(total_dims): values.append(rng.randint(0, vocab_size - 1)) return tf.constant(value=values, dtype=tf.int32, shape=shape, name=name) def assert_all_tensors_reachable(self, sess, outputs): """Checks that all the tensors in the graph are reachable from outputs.""" graph = sess.graph ignore_strings = [ "^.*/assert_less_equal/.*$", "^.*/dilation_rate$", "^.*/Tensordot/concat$", "^.*/Tensordot/concat/axis$", "^testing/.*$", ] ignore_regexes = [re.compile(x) for x in ignore_strings] unreachable = self.get_unreachable_ops(graph, outputs) filtered_unreachable = [] for x in unreachable: do_ignore = False for r in ignore_regexes: m = r.match(x.name) if m is not None: do_ignore = True if do_ignore: continue filtered_unreachable.append(x) unreachable = filtered_unreachable self.assertEqual( len(unreachable), 0, "The following ops are unreachable: %s" % (" ".join([x.name for x in unreachable]))) @classmethod def get_unreachable_ops(cls, graph, outputs): """Finds all of the tensors in graph that are unreachable from outputs.""" outputs = cls.flatten_recursive(outputs) output_to_op = collections.defaultdict(list) op_to_all = collections.defaultdict(list) assign_out_to_in = collections.defaultdict(list) for op in graph.get_operations(): for x in op.inputs: op_to_all[op.name].append(x.name) for y in op.outputs: output_to_op[y.name].append(op.name) op_to_all[op.name].append(y.name) if str(op.type) == "Assign": for y in op.outputs: for x in op.inputs: assign_out_to_in[y.name].append(x.name) assign_groups = collections.defaultdict(list) for out_name in assign_out_to_in.keys(): name_group = assign_out_to_in[out_name] for n1 in name_group: assign_groups[n1].append(out_name) for n2 in name_group: if n1 != n2: assign_groups[n1].append(n2) seen_tensors = {} stack = [x.name for x in outputs] while stack: name = stack.pop() if name in seen_tensors: continue seen_tensors[name] = True if name in output_to_op: for op_name in output_to_op[name]: if op_name in op_to_all: for input_name in op_to_all[op_name]: if input_name not in stack: stack.append(input_name) expanded_names = [] if name in assign_groups: for assign_name in assign_groups[name]: expanded_names.append(assign_name) for expanded_name in expanded_names: if expanded_name not in stack: stack.append(expanded_name) unreachable_ops = [] for op in graph.get_operations(): is_unreachable = False all_names = [x.name for x in op.inputs] + [x.name for x in op.outputs] for name in all_names: if name not in seen_tensors: is_unreachable = True if is_unreachable: unreachable_ops.append(op) return unreachable_ops @classmethod def flatten_recursive(cls, item): """Flattens (potentially nested) a tuple/dictionary/list to a list.""" output = [] if isinstance(item, list): output.extend(item) elif isinstance(item, tuple): output.extend(list(item)) elif isinstance(item, dict): for (_, v) in six.iteritems(item): output.append(v) else: return [item] flat_output = [] for x in output: flat_output.extend(cls.flatten_recursive(x)) return flat_output if __name__ == "__main__": tf.test.main()