尝试使用Cloud TPU恢复更新的BERT模型检查点时出现InfeedEnqueueTuple问题

Question

如果您能提供以下帮助，我将不胜感激，提前感谢。我复制了谷歌伯特关于微调的笔记本，并使用Cloud TPU和Bucket在上面训练了SQUAD数据集。开发集上的预测是可以的，所以我在本地下载了检查点、model.ckpt.meta、model.ckpt.index和model.ckpt.数据文件，并尝试使用代码进行恢复

sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
saver = tf.train.import_meta_graph(META_FILE) # META_FILE being path to .meta
saver.restore(sess, 'model.ckpt')

然而，我得到了错误：

op_def = op_dict[node.op]
KeyError: 'InfeedEnqueueTuple'

我认为它是Cloud TPU工具的一部分，我应该继续使用Cloud TPU，所以我尝试了以下(参考(：

# code from cells before includes
...
tf.contrib.cloud.configure_gcs(session, credentials=auth_info)
...
tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver(TPU_ADDRESS)
run_config = tf.contrib.tpu.RunConfig(
cluster=tpu_cluster_resolver,
model_dir=OUTPUT_DIR,
save_checkpoints_steps=SAVE_CHECKPOINTS_STEPS,
tpu_config=tf.contrib.tpu.TPUConfig(
iterations_per_loop=ITERATIONS_PER_LOOP,
num_shards=NUM_TPU_CORES,
per_host_input_for_training=tf.contrib.tpu.InputPipelineConfig.PER_HOST_V2))
...

问题单元格：

"""
# not valid checkpoint error. <bucket> placeholder for cloud bucket name
sess = tf.Session()
META_FILE = "gs://<bucket>/bert/models/bertsquad/model.ckpt-10949.meta"
CKPT_FILE = "gs://<bucket>/bert/models/bertsquad/model.ckpt"
saver = tf.train.import_meta_graph(META_FILE)
saver.restore(sess, CKPT_FILE)
"""
from google.cloud import storage
from tensorflow import MetaGraphDef
client = storage.Client(project="agent-helper-4a014")
bucket = client.get_bucket(<bucket>)
metafile = "bert/models/bertsquad/model.ckpt-10949.meta"
# using full path gs://<bucket>/bert/models/bertsquad doesn't work
blob = bucket.get_blob(metafile)
#blob = bucket.blob(metafile)
#model_graph = blob.download_to_filename("model.ckpt")
model_graph = blob.download_as_string()
mgd = MetaGraphDef()
mgd.ParseFromString(model_graph)
with tf.Session() as sess:
saver = tf.train.import_meta_graph(mgd, clear_devices=True)
init_checkpoint = saver.restore(sess, 'model.ckpt')

这反过来又产生了以下错误：

InvalidArgumentError (see above for traceback): Restoring from checkpoint failed. This is most likely due to a mismatch between the current graph and the graph from the checkpoint. Please ensure that you have not altered the graph expected based on the checkpoint. Original error:
No OpKernel was registered to support Op 'InfeedEnqueueTuple' with these attrs.  Registered devices: [CPU,XLA_CPU], Registered kernels:
<no registered kernels>
[[node input_pipeline_task0/while/InfeedQueue/enqueue/0 (defined at <ipython-input-67-e4b52b7b5944>:21)  = InfeedEnqueueTuple[_class=["loc:@input_pipeline_task0/while/IteratorGetNext"], device_ordinal=0, dtypes=[DT_INT32, DT_INT32, DT_INT32, DT_INT32, DT_INT32, DT_INT32], shapes=[[2], [2,384], [2,384], [2,384], [2], [2]], _device="/job:worker/task:0/device:CPU:0"](input_pipeline_task0/while/IteratorGetNext, input_pipeline_task0/while/IteratorGetNext:1, input_pipeline_task0/while/IteratorGetNext:2, input_pipeline_task0/while/IteratorGetNext:3, input_pipeline_task0/while/IteratorGetNext:4, input_pipeline_task0/while/IteratorGetNext:5)]]

Answer 1

如果你的动机是预测，那么只需给出保存检查点和元文件的model_dir位置(必须是GCS存储桶(。代码将不会再次进行训练(因为检查点是为训练步骤的数量而保存的，并且模型图中没有变化(。它将直接跳转到预测。

但是，如果您的用例真的想保存检查点，并且只为推断而恢复它，那么请遵循以下步骤：

• 手动为每个层创建与原始模型相同的模型网络，或者使用保存的.meta文件使用tf.train.import()函数重新创建网络，如下所示：

saver = tf.train.import_meta_graph('<filename>.meta')

• 现在，使用saver.restore(sess, 'model.ckpt')恢复检查点

注意：要将检查点恢复到的模型图应该与保存这些检查点的原始图完全相同。

希望这能解决你的问题。