tensorflow - 如何保存使用来自 Tensorflow 1.xx 的 .meta 检查点模型作为一部分的 Tensorflow 2.0 模型？

Question

我用 tensorflow 1.15 训练了模型并保存为检查点(使用 .meta 、 .index 和 .data 文件)。

我需要的是在此图的开头和结尾添加一些额外的操作。其中一些操作仅存在于 tensorflow 2.0 和 tensorflow_text 2.0 中。之后我想为 tensorflow-serving 保存这个模型。

我试图做的是:使用 tensorflow 2.0 我将它保存为这样的 .pb 文件。

trained_checkpoint_prefix = 'path/to/model'
export_dir = os.path.join('path/to/export', '0')

graph = tf.Graph()
with tf.compat.v1.Session(graph=graph) as sess:
    # Restore from checkpoint
    loader = tf.compat.v1.train.import_meta_graph(trained_checkpoint_prefix + '.meta')
    loader.restore(sess, trained_checkpoint_prefix)

    # Export checkpoint to SavedModel
    builder = tf.compat.v1.saved_model.builder.SavedModelBuilder(export_dir)

    classification_signature = tf.compat.v1.saved_model.signature_def_utils.build_signature_def(
        inputs={
            'token_indices': get_tensor_info('token_indices_ph:0'),
            'token_mask': get_tensor_info('token_mask_ph:0'),
            'y_mask': get_tensor_info('y_mask_ph:0'),
        },
        outputs={'probas': get_tensor_info('ner/Softmax:0'), 'seq_lengths': get_tensor_info('ner/Sum:0')},
        method_name='predict',
    )

    builder.add_meta_graph_and_variables(sess,
                                         [tf.saved_model.TRAINING, tf.saved_model.SERVING],
                                         strip_default_attrs=True, saver=loader,
                                         signature_def_map={'predict': classification_signature}) # , clear_devices=True)
    builder.save()  

之后，我创建了一个 tf.keras.Model 来加载 .pb 模型并执行我需要的所有员工:

import os
from pathlib import Path

import tensorflow as tf
import tensorflow_text as tf_text


class BertPipeline(tf.keras.Model):
    def __init__(self):
        super().__init__()

        vocab_file = Path('path/to/vocab.txt')
        vocab = vocab_file.read_text().split('\n')[:-1]
        self.vocab_table = self.create_table(vocab)

        export_dir = 'path/to/pb/model'
        self.model = tf.saved_model.load(export_dir)

        self.bert_tokenizer = BertTokenizer(
            self.vocab_table,
            max_chars_per_token=15,
                token_out_type=tf.int64
            ,
            lower_case=True,
        )

        self.to_dense = tf_text.keras.layers.ToDense()

    def call(self, texts):
        tokens = self.bert_tokenizer.tokenize(texts)
        tokens = tf.cast(tokens, dtype=tf.int32)

        mask = self.make_mask(tokens)
        token_ids = self.make_token_ids(tokens)

        token_indices = self.to_dense(token_ids)
        token_mask = self.to_dense(tf.ones_like(mask))
        y_mask = self.to_dense(mask)

        res = self.model.signatures['predict'](
            token_indices=token_indices,
            token_mask=token_mask,
            y_mask=y_mask,
        )

        starts_range = tf.range(0, tf.shape(res['seq_lengths'])[0]) * tf.shape(res['probas'])[1]
        row_splits = tf.reshape(
            tf.stack(
                [
                    starts_range,
                    starts_range + res['seq_lengths'],
                ],
                axis=1,
            ),
            [-1],
        )

        row_splits = tf.concat(
            [
                row_splits,
                tf.expand_dims(tf.shape(res['probas'])[0] * tf.shape(res['probas'])[1], 0),
            ],
            axis=0,
        )

        probas = tf.RaggedTensor.from_row_splits(
            tf.reshape(res['probas'], [-1, 2]),
            row_splits,
        )[::2]

        probas

        return probas

    def make_mask(self, tokens):
        masked_suff = tf.concat(
            [
                tf.ones_like(tokens[:, :, :1], dtype=tf.int32),
                tf.zeros_like(tokens[:, :, 1:], dtype=tf.int32),
            ],
            axis=-1,
        )

        joined_mask = self.join_wordpieces(masked_suff)
        return tf.concat(
            [
                tf.zeros_like(joined_mask[:, :1], dtype=tf.int32),
                joined_mask,
                tf.zeros_like(joined_mask[:, :1], dtype=tf.int32),
            ],
            axis=-1,
        )

    def make_token_ids(self, tokens):
        joined_tokens = self.join_wordpieces(tokens)

        return tf.concat(
            [
                tf.fill(
                    [joined_tokens.nrows(), 1],
                    tf.dtypes.cast(
                        self.vocab_table.lookup(tf.constant('[CLS]')),
                        dtype=tf.int32,
                    )
                ),
                self.join_wordpieces(tokens),
                tf.fill(
                    [joined_tokens.nrows(), 1],
                    tf.dtypes.cast(
                        self.vocab_table.lookup(tf.constant('[SEP]')),
                        dtype=tf.int32,
                    )
                ),
            ],
            axis=-1,
        )


    def join_wordpieces(self, wordpieces):
        return tf.RaggedTensor.from_row_splits(
            wordpieces.flat_values, tf.gather(wordpieces.values.row_splits,
                                              wordpieces.row_splits))

    def create_table(self, vocab, num_oov=1):
        init = tf.lookup.KeyValueTensorInitializer(
            vocab,
            tf.range(tf.size(vocab, out_type=tf.int64), dtype=tf.int64),
            key_dtype=tf.string,
            value_dtype=tf.int64)
        return tf.lookup.StaticVocabularyTable(init, num_oov, lookup_key_dtype=tf.string)

当我调用此代码时，它可以完美运行:

bert_pipeline = BertPipeline()
print(bbert_pipeline(["Some test string", "another string"]))

---
<tf.RaggedTensor [[[0.17896245419979095, 0.8210375308990479], [0.8825045228004456, 0.11749550700187683], [0.9141901731491089, 0.0858098641037941]], [[0.2768123149871826, 0.7231876850128174], [0.9391192197799683, 0.060880810022354126]]]>

但我不知道如何保存它。如果我理解正确 tf.keras.Model 不要将 self.model 和 self.bert_tokenizer 作为模型的一部分。如果我调用 bert_pipeline.summary() 则没有操作:

bert_pipeline.build([])
bert_pipeline.summary()

---
Model: "bert_pipeline_3"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
to_dense (ToDense)           multiple                  0         
=================================================================
Total params: 0
Trainable params: 0
Non-trainable params: 0
_________________________________________________________________

此外，我尝试使用显式 tensorflow.compat.v1 和 Session 使用 Graph 运行它，但在这种情况下，我无法正确加载模型。与 import tensorflow.compat.v1 as tf 和样板代码相同的 tensorflow 1.xx 代码无法初始化某些变量:

# tf.saved_model.load(export_dir) changed to tf.saved_model.load_v2(export_dir) above

import tensorflow.compat.v1 as tf
graph = tf.Graph()
with tf.Session(graph=graph) as sess:
    bert_pipeline = BertPipeline()
    texts = tf.placeholder(tf.string, shape=[None], name='texts')

    res_tensor = bert_pipeline(texts)

    sess.run(tf.tables_initializer())
    sess.run(tf.global_variables_initializer())

    sess.run(res_tensor, feed_dict={texts: ["Some test string", "another string"]})

---
FailedPreconditionError                   Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/client/session.py in _do_call(self, fn, *args)
   1364     try:
-> 1365       return fn(*args)
   1366     except errors.OpError as e:

/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/client/session.py in _run_fn(feed_dict, fetch_list, target_list, options, run_metadata)
   1349       return self._call_tf_sessionrun(options, feed_dict, fetch_list,
-> 1350                                       target_list, run_metadata)
   1351 

/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/client/session.py in _call_tf_sessionrun(self, options, feed_dict, fetch_list, target_list, run_metadata)
   1442                                             fetch_list, target_list,
-> 1443                                             run_metadata)
   1444 

FailedPreconditionError: [_Derived_]{{function_node __inference_pruned_77348}} {{function_node __inference_pruned_77348}} Attempting to use uninitialized value bert/encoder/layer_3/attention/self/query/kernel
     [[{{node bert/encoder/layer_3/attention/self/query/kernel/read}}]]
     [[bert_pipeline/StatefulPartitionedCall]]

During handling of the above exception, another exception occurred:

FailedPreconditionError                   Traceback (most recent call last)
<ipython-input-15-5a0a45327337> in <module>
     21     sess.run(tf.global_variables_initializer())
     22 
---> 23     sess.run(res_tensor, feed_dict={texts: ["Some test string", "another string"]})
     24 
     25 #     print(res)

/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/client/session.py in run(self, fetches, feed_dict, options, run_metadata)
    954     try:
    955       result = self._run(None, fetches, feed_dict, options_ptr,
--> 956                          run_metadata_ptr)
    957       if run_metadata:
    958         proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)

/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/client/session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
   1178     if final_fetches or final_targets or (handle and feed_dict_tensor):
   1179       results = self._do_run(handle, final_targets, final_fetches,
-> 1180                              feed_dict_tensor, options, run_metadata)
   1181     else:
   1182       results = []

/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/client/session.py in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)
   1357     if handle is None:
   1358       return self._do_call(_run_fn, feeds, fetches, targets, options,
-> 1359                            run_metadata)
   1360     else:
   1361       return self._do_call(_prun_fn, handle, feeds, fetches)

/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/client/session.py in _do_call(self, fn, *args)
   1382                     '\nsession_config.graph_options.rewrite_options.'
   1383                     'disable_meta_optimizer = True')
-> 1384       raise type(e)(node_def, op, message)
   1385 
   1386   def _extend_graph(self):
FailedPreconditionError: [_Derived_]  Attempting to use uninitialized value bert/encoder/layer_3/attention/self/query/kernel
     [[{{node bert/encoder/layer_3/attention/self/query/kernel/read}}]]
     [[bert_pipeline/StatefulPartitionedCall]]

请，如果您有一些想法如何解决我保存图形的方法，或者您知道如何做得更好 - 请告诉我。谢谢!

Answer 1

我解决了。首先，我无法使用 tf.keras .我用了

import tensorflow.compat.v1 as tf

除此之外，我使用了 .meta , .index和 bla bla 检查点，而不是 '.pb'。

我使用的主要内容描述如下: Tensorflow: How to replace a node in a calculation graph?

我制作了 2 个不同的图表，然后将它们合并，就像在这部分代码中一样:

def _build_model(self):
    with tf.Graph().as_default() as g_1:
        self.lookup_table = self._make_lookup_table()

        init_table = tf.initialize_all_tables()

        self.bert_tokenizer = BertTokenizer(
            self.lookup_table,
            max_chars_per_token=15,
            token_out_type=tf.int64,
            lower_case=True,
        )

        self.texts_ph = tf.placeholder(tf.string, shape=(None,), name="texts_ph")  # input

        words_without_name, tokens_int_64 = self.bert_tokenizer.tokenize(self.texts_ph)
        words = words_without_name.to_tensor(default_value='', name='tokens')

        tokens = tf.cast(tokens_int_64, dtype=tf.int32)

        mask = self._make_mask(tokens)
        token_ids = self._make_token_ids(tokens)

        self.token_indices = token_ids.to_tensor(default_value=0, name='token_indices')  # output 1
        self.token_mask = tf.ones_like(mask).to_tensor(default_value=0, name='token_mask') # output 2
        self.y_mask = mask.to_tensor(default_value=0, name='y_mask') # output 3

    with tf.Graph().as_default() as g_2:
        sess = tf.Session()
        path_to_model = 'path/to/model'
        self._load_model(sess, path_to_model)

        token_indices_2 = g_2.get_tensor_by_name('token_indices_ph:0'),
        token_mask_2 = g_2.get_tensor_by_name('token_mask_ph:0'),
        y_mask_2 = g_2.get_tensor_by_name('y_mask_ph:0'),

        probas = g_2.get_tensor_by_name('ner/Softmax:0')
        seq_lengths = g_2.get_tensor_by_name('ner/Sum:0')

        exclude_scopes = ('Optimizer', 'learning_rate', 'momentum', 'EMA/BackupVariables')
        all_vars = variables._all_saveable_objects()
        self.vars_to_save = [var for var in all_vars if all(sc not in var.name for sc in exclude_scopes)]
        self.saver = tf.train.Saver(self.vars_to_save

    g_1_def = g_1.as_graph_def()
    g_2_def = g_2.as_graph_def()

    with tf.Graph().as_default() as g_combined:
        self.texts = tf.placeholder(tf.string, shape=(None,), name="texts")

        y1, y2, y3, self.init_table, self.words = tf.import_graph_def(
           g_1_def, input_map={"texts_ph:0": self.texts},
           return_elements=["token_indices/GatherV2:0", "token_mask/GatherV2:0", "y_mask/GatherV2:0", 'init_all_tables', 'tokens/GatherV2:0'],
           name='',
        )

        self.dense_probas, self.lengths = tf.import_graph_def(
            g_2_def, input_map={"token_indices_ph:0": y1, "token_mask_ph:0": y2, "y_mask_ph:0": y3},
            return_elements=["ner/Softmax:0", "ner/Sum:0"],
            name='',
        )

        self.sess = tf.Session(graph=g_combined)
        self.graph = g_combined

        self.sess.run(self.init_table)

        vars_dict_to_save = {v.name[:-2]: g_2.get_tensor_by_name(v.name) for v in self.vars_to_save}
        self.saver.restore(self.sess, path_to_model)

你可能注意到我调用 self._load_model(sess, path_to_model)加载模型，创建 saver使用所需的变量，然后再次加载模型 self.saver.save(sess, path_to_model) .需要第一次加载才能读取预先保存的图形并访问它的张量。第二个需要在另一个 session 中加载权重 g_combined合并图。我认为有一种方法可以在不从磁盘加载数据两次的情况下做到这一点，但它有效，我不想破坏它:-)。

更重要的一件事是 vars_dict_to_save .需要这个字典来在图中加载的权重和张量之间进行映射。

之后，您拥有包含所有操作的完整图形，因此您可以这样调用它:

def __call__(self, texts):
    lengths, words, probs = self.sess.run(
        [self.lengths, self.words, self.dense_probas],
        feed_dict={
            self.texts: texts
        },
    )
    return lengths, words, probs

注意执行 __call__方法。它使用我用合并图创建的 session 。

一旦您拥有加载权重的完整图表，就可以轻松导出图表以供服务:

def export(self, export_dir):
    with self.graph.as_default():
        builder = tf.saved_model.builder.SavedModelBuilder(export_dir)

        predict_signature = tf.saved_model.signature_def_utils.predict_signature_def(
            inputs={
                'texts': self.texts,
            },
            outputs={
                'lengths': self.lengths,
                'tokens': self.words,
                'probs': self.dense_probas,
            },
        )

        builder.add_meta_graph_and_variables(
            self.sess,
            [tf.saved_model.SERVING],
            strip_default_attrs=True,
            signature_def_map={'predict': predict_signature},
            saver=self.saver,
            main_op=self.init_table,
        )
        builder.save()

有几个重要的时刻:
- 使用合并图 .as_default() - 使用与合并图相同的 session 。
- 使用与在合并图中加载权重相同的保护程序。
- 添加主 main_op如果您有需要初始化的表。

如果它对某人有帮助，我会很高兴:-)。这对我来说不是微不足道的，我花了很多时间让它发挥作用。

附言 BertTokenizer在此代码中与来自 tensorflow_text 的此类略有不同，但与问题无关。