简而言之，我遵循TF Transformer中的教程。
在此issue中保存模型时，其他人会发生此问题。
我的问题是1）如何编写正确的签名以保存模型？
2）是否需要为模型@tf.function中的每个call函数添加class。
# tutorial: https://www.tensorflow.org/api_docs/python/tf/saved_model/save
infer_signature = transformer.call.get_concrete_function(
tf.TensorSpec(shape=[None, None], dtype=tf.int64, name='encoder_input'), # encoder_input
tf.TensorSpec(shape=[None, None], dtype=tf.int64, name='tar_input'), # tar_input
tf.TensorSpec(shape=None, dtype=tf.bool, name='train'), # training
tf.TensorSpec(shape=[4, None], dtype=tf.float32, name='enc_padding_mask'), # enc_padding_mask
tf.TensorSpec(shape=[4, None], dtype=tf.float32, name='combined_mask'), # combined_mask
tf.TensorSpec(shape=[4, None], dtype=tf.float32, name='dec_padding_mask') # dec_padding_mask
)
saved_path = './saved_model'
transformer.save(saved_path, signatures=infer_signature)
# OR => tf.saved_model.save(transformer, saved_path, signatures=infer_signature)
错误：
_________________________________________________________________
Traceback (most recent call last):
File "/Users/xiaofengwu/Google Drive/intern-zq/intern_notes/tvm_prj/tf_ocr_model_impl/transformer_tf_2/transformer_tf2.py", line 1038, in <module>
transformer.save(saved_path, signatures=infer_signature)
File "/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py", line 1979, in save
signatures, options)
File "/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/keras/saving/save.py", line 134, in save_model
signatures, options)
File "/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/keras/saving/saved_model/save.py", line 80, in save
save_lib.save(model, filepath, signatures, options)
File "/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/saved_model/save.py", line 976, in save
obj, export_dir, signatures, options, meta_graph_def)
File "/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/saved_model/save.py", line 1050, in _build_meta_graph
signature_serialization.canonicalize_signatures(signatures))
File "/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/saved_model/signature_serialization.py", line 137, in canonicalize_signatures
**tensor_spec_signature)
File "/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 1073, in _get_concrete_function_garbage_collected
self._initialize(args, kwargs, add_initializers_to=initializers)
File "/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 697, in _initialize
*args, **kwds))
File "/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 2855, in _get_concrete_function_internal_garbage_collected
graph_function, _, _ = self._maybe_define_function(args, kwargs)
File "/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3213, in _maybe_define_function
graph_function = self._create_graph_function(args, kwargs)
File "/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3075, in _create_graph_function
capture_by_value=self._capture_by_value),
File "/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py", line 986, in func_graph_from_py_func
func_outputs = python_func(*func_args, **func_kwargs)
File "/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 600, in wrapped_fn
return weak_wrapped_fn().__wrapped__(*args, **kwds)
File "/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py", line 973, in wrapper
raise e.ag_error_metadata.to_exception(e)
ValueError: in user code:
/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/saved_model/signature_serialization.py:126 signature_wrapper *
structured_outputs, signature_function.name, signature_key)
/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/saved_model/signature_serialization.py:187 _normalize_outputs **
.format(original_outputs, function_name, signature_key))
ValueError: Got non-flat outputs '(<tf.Tensor 'StatefulPartitionedCall:0' shape=(None, None, 980) dtype=float32>, {'decoder_layer1_block1': <tf.Tensor 'StatefulPartitionedCall:1' shape=(None, 8, 4, None) dtype=float32>, 'decoder_layer1_block2': <tf.Tensor 'StatefulPartitionedCall:2' shape=(None, 8, 4, None) dtype=float32>, 'decoder_layer2_block1': <tf.Tensor 'StatefulPartitionedCall:3' shape=(None, 8, 4, None) dtype=float32>, 'decoder_layer2_block2': <tf.Tensor 'StatefulPartitionedCall:4' shape=(None, 8, 4, None) dtype=float32>, 'decoder_layer3_block1': <tf.Tensor 'StatefulPartitionedCall:5' shape=(None, 8, 4, None) dtype=float32>, 'decoder_layer3_block2': <tf.Tensor 'StatefulPartitionedCall:6' shape=(None, 8, 4, None) dtype=float32>, 'decoder_layer4_block1': <tf.Tensor 'StatefulPartitionedCall:7' shape=(None, 8, 4, None) dtype=float32>, 'decoder_layer4_block2': <tf.Tensor 'StatefulPartitionedCall:8' shape=(None, 8, 4, None) dtype=float32>})' from 'b'__inference_call_54652'' for SavedModel signature 'serving_default'. Signatures have one Tensor per output, so to have predictable names Python functions used to generate these signatures should avoid outputting Tensors in nested structures.
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