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20 hours ago •
tensorflow
tensorflow
Unable to use query_with_exclusions with undefined length of exclusions
Hi, I am trying to save a model with query_with_exclusions with an indefinite length of exclusions (because every user might have seen more or fewer posts....):
recommendations_k_21 = tf.function(lambda user_id: final_model(queries=user_id, k=21))
recommendations_k_21_exclusions = tf.function(lambda user_id, exclusions: final_model.query_with_exclusions(queries=user_id, k=21, exclusions=exclusions))
# Call them to create concrete functions.
_, ids = recommendations_k_21(tf.constant("b6bfd5af-d21e-4726-860c-dc663c34e20f"))
_, ids = recommendations_k_21_exclusions(tf.constant(["b6bfd5af-d21e-4726-860c-dc663c34e20f"]), tf.constant([["1234"]]))
signatures = {"recommendations_k_21": recommendations_k_21.get_concrete_function(user_id=tf.TensorSpec(shape=(), dtype=tf.string)), "recommendations_k_21_exclusions": recommendations_k_21_exclusions.get_concrete_function(user_id=tf.TensorSpec(shape=(1,), dtype=tf.string), exclusions=tf.TensorSpec(shape=(1, None), dtype=tf.string))}
tf.saved_model.save(final_model, MODEL_STORAGE_PATH, signatures)
However, I get the following error:
WARNING:tensorflow:Model was constructed with shape (None,) for input KerasTensor(type_spec=TensorSpec(shape=(None,), dtype=tf.string, name='string_lookup_input'), name='string_lookup_input', description="created by layer 'string_lookup_input'"), but it was called on an input with incompatible shape ().
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Input In [275], in <cell line: 8>()
5 _, ids = recommendations_k_21(tf.constant("b6bfd5af-d21e-4726-860c-dc663c34e20f"))
6 _, ids = recommendations_k_21_exclusions(tf.constant(["b6bfd5af-d21e-4726-860c-dc663c34e20f"]), tf.constant([["1234"]]))
----> 8 signatures = {"recommendations_k_21": recommendations_k_21.get_concrete_function(user_id=tf.TensorSpec(shape=(), dtype=tf.string)), "recommendations_k_21_exclusions": recommendations_k_21_exclusions.get_concrete_function(user_id=tf.TensorSpec(shape=(1,), dtype=tf.string), exclusions=tf.TensorSpec(shape=(1, None), dtype=tf.string))}
10 tf.saved_model.save(final_model, MODEL_STORAGE_PATH, signatures)
File /opt/conda/lib/python3.10/site-packages/tensorflow/python/eager/def_function.py:1239, in Function.get_concrete_function(self, *args, **kwargs)
1237 def get_concrete_function(self, *args, **kwargs):
1238 # Implements GenericFunction.get_concrete_function.
-> 1239 concrete = self._get_concrete_function_garbage_collected(*args, **kwargs)
1240 concrete._garbage_collector.release() # pylint: disable=protected-access
1241 return concrete
File /opt/conda/lib/python3.10/site-packages/tensorflow/python/eager/def_function.py:1230, in Function._get_concrete_function_garbage_collected(self, *args, **kwargs)
1225 return self._stateless_fn._get_concrete_function_garbage_collected( # pylint: disable=protected-access
1226 *args, **kwargs)
1227 elif self._stateful_fn is not None:
1228 # In this case we have not created variables on the first call. So we can
1229 # run the first trace but we should fail if variables are created.
-> 1230 concrete = self._stateful_fn._get_concrete_function_garbage_collected( # pylint: disable=protected-access
1231 *args, **kwargs)
1232 if self._created_variables:
1233 raise ValueError("Creating variables on a non-first call to a function"
1234 " decorated with tf.function.")
File /opt/conda/lib/python3.10/site-packages/tensorflow/python/eager/function.py:2533, in Function._get_concrete_function_garbage_collected(self, *args, **kwargs)
2531 args, kwargs = None, None
2532 with self._lock:
-> 2533 graph_function, _ = self._maybe_define_function(args, kwargs)
2534 seen_names = set()
2535 captured = object_identity.ObjectIdentitySet(
2536 graph_function.graph.internal_captures)
File /opt/conda/lib/python3.10/site-packages/tensorflow/python/eager/function.py:2711, in Function._maybe_define_function(self, args, kwargs)
2708 cache_key = self._function_cache.generalize(cache_key)
2709 (args, kwargs) = cache_key._placeholder_value() # pylint: disable=protected-access
-> 2711 graph_function = self._create_graph_function(args, kwargs)
2712 self._function_cache.add(cache_key, cache_key_deletion_observer,
2713 graph_function)
2715 return graph_function, filtered_flat_args
File /opt/conda/lib/python3.10/site-packages/tensorflow/python/eager/function.py:2627, in Function._create_graph_function(self, args, kwargs)
2622 missing_arg_names = [
2623 "%s_%d" % (arg, i) for i, arg in enumerate(missing_arg_names)
2624 ]
2625 arg_names = base_arg_names + missing_arg_names
2626 graph_function = ConcreteFunction(
-> 2627 func_graph_module.func_graph_from_py_func(
2628 self._name,
2629 self._python_function,
2630 args,
2631 kwargs,
2632 self.input_signature,
2633 autograph=self._autograph,
2634 autograph_options=self._autograph_options,
2635 arg_names=arg_names,
2636 capture_by_value=self._capture_by_value),
2637 self._function_attributes,
2638 spec=self.function_spec,
2639 # Tell the ConcreteFunction to clean up its graph once it goes out of
2640 # scope. This is not the default behavior since it gets used in some
2641 # places (like Keras) where the FuncGraph lives longer than the
2642 # ConcreteFunction.
2643 shared_func_graph=False)
2644 return graph_function
File /opt/conda/lib/python3.10/site-packages/tensorflow/python/framework/func_graph.py:1141, in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, acd_record_initial_resource_uses)
1138 else:
1139 _, original_func = tf_decorator.unwrap(python_func)
-> 1141 func_outputs = python_func(*func_args, **func_kwargs)
1143 # invariant: `func_outputs` contains only Tensors, CompositeTensors,
1144 # TensorArrays and `None`s.
1145 func_outputs = nest.map_structure(
1146 convert, func_outputs, expand_composites=True)
File /opt/conda/lib/python3.10/site-packages/tensorflow/python/eager/def_function.py:677, in Function._defun_with_scope.<locals>.wrapped_fn(*args, **kwds)
673 with default_graph._variable_creator_scope(scope, priority=50): # pylint: disable=protected-access
674 # __wrapped__ allows AutoGraph to swap in a converted function. We give
675 # the function a weak reference to itself to avoid a reference cycle.
676 with OptionalXlaContext(compile_with_xla):
--> 677 out = weak_wrapped_fn().__wrapped__(*args, **kwds)
678 return out
File /opt/conda/lib/python3.10/site-packages/tensorflow/python/framework/func_graph.py:1127, in func_graph_from_py_func.<locals>.autograph_handler(*args, **kwargs)
1125 except Exception as e: # pylint:disable=broad-except
1126 if hasattr(e, "ag_error_metadata"):
-> 1127 raise e.ag_error_metadata.to_exception(e)
1128 else:
1129 raise
File /opt/conda/lib/python3.10/site-packages/tensorflow/python/framework/func_graph.py:1116, in func_graph_from_py_func.<locals>.autograph_handler(*args, **kwargs)
1114 # TODO(mdan): Push this block higher in tf.function's call stack.
1115 try:
-> 1116 return autograph.converted_call(
1117 original_func,
1118 args,
1119 kwargs,
1120 options=autograph.ConversionOptions(
1121 recursive=True,
1122 optional_features=autograph_options,
1123 user_requested=True,
1124 ))
1125 except Exception as e: # pylint:disable=broad-except
1126 if hasattr(e, "ag_error_metadata"):
File /opt/conda/lib/python3.10/site-packages/tensorflow/python/autograph/impl/api.py:439, in converted_call(f, args, kwargs, caller_fn_scope, options)
437 try:
438 if kwargs is not None:
--> 439 result = converted_f(*effective_args, **kwargs)
440 else:
441 result = converted_f(*effective_args)
File /tmp/__autograph_generated_fileh578f4h6.py:5, in outer_factory.<locals>.inner_factory.<locals>.<lambda>(user_id, exclusions)
4 def inner_factory(ag__):
----> 5 tf__lam = lambda user_id, exclusions: ag__.with_function_scope(lambda lscope: ag__.converted_call(final_model.query_with_exclusions, (), dict(queries=user_id, k=21, exclusions=exclusions), lscope), 'lscope', ag__.ConversionOptions(recursive=True, user_requested=True, optional_features=(), internal_convert_user_code=True))
6 return tf__lam
File /opt/conda/lib/python3.10/site-packages/tensorflow/python/autograph/core/function_wrappers.py:113, in with_function_scope(thunk, scope_name, options)
111 """Inline version of the FunctionScope context manager."""
112 with FunctionScope('lambda_', scope_name, options) as scope:
--> 113 return thunk(scope)
File /tmp/__autograph_generated_fileh578f4h6.py:5, in outer_factory.<locals>.inner_factory.<locals>.<lambda>(lscope)
4 def inner_factory(ag__):
----> 5 tf__lam = lambda user_id, exclusions: ag__.with_function_scope(lambda lscope: ag__.converted_call(final_model.query_with_exclusions, (), dict(queries=user_id, k=21, exclusions=exclusions), lscope), 'lscope', ag__.ConversionOptions(recursive=True, user_requested=True, optional_features=(), internal_convert_user_code=True))
6 return tf__lam
File /opt/conda/lib/python3.10/site-packages/tensorflow/python/autograph/impl/api.py:331, in converted_call(f, args, kwargs, caller_fn_scope, options)
329 if conversion.is_in_allowlist_cache(f, options):
330 logging.log(2, 'Allowlisted %s: from cache', f)
--> 331 return _call_unconverted(f, args, kwargs, options, False)
333 if ag_ctx.control_status_ctx().status == ag_ctx.Status.DISABLED:
334 logging.log(2, 'Allowlisted: %s: AutoGraph is disabled in context', f)
File /opt/conda/lib/python3.10/site-packages/tensorflow/python/autograph/impl/api.py:458, in _call_unconverted(f, args, kwargs, options, update_cache)
455 return f.__self__.call(args, kwargs)
457 if kwargs is not None:
--> 458 return f(*args, **kwargs)
459 return f(*args)
File /opt/conda/lib/python3.10/site-packages/tensorflow/python/util/traceback_utils.py:153, in filter_traceback.<locals>.error_handler(*args, **kwargs)
151 except Exception as e:
152 filtered_tb = _process_traceback_frames(e.__traceback__)
--> 153 raise e.with_traceback(filtered_tb) from None
154 finally:
155 del filtered_tb
File /tmp/__autograph_generated_filef8r31nzk.py:34, in outer_factory.<locals>.inner_factory.<locals>.tf__query_with_exclusions(self, queries, exclusions, k)
32 retval_ = ag__.UndefinedReturnValue()
33 k = ag__.if_exp(ag__.ld(k) is not None, lambda : ag__.ld(k), lambda : ag__.ld(self)._k, 'k is not None')
---> 34 adjusted_k = ag__.ld(k) + ag__.ld(exclusions).shape[1]
35 (x, y) = ag__.converted_call(ag__.ld(self), (), dict(queries=ag__.ld(queries), k=ag__.ld(adjusted_k)), fscope)
36 try:
TypeError: in user code:
File "/tmp/ipykernel_162/1740288157.py", line 2, in None *
lambda user_id, exclusions: final_model.query_with_exclusions(queries=user_id, k=21, exclusions=exclusions)
File "/opt/conda/lib/python3.10/site-packages/tensorflow_recommenders/layers/factorized_top_k.py", line 287, in query_with_exclusions *
adjusted_k = k + exclusions.shape[1]
TypeError: unsupported operand type(s) for +: 'int' and 'NoneType'
What is my mistake here? I got the idea from https://github.com/tensorflow/recommenders/issues/298#issuecomment-918412784.
Im wondering if your problem is since you did not define anything in the shape?
signatures = {"recommendations_k_21": recommendations_k_21.get_concrete_function(user_id=tf.TensorSpec(shape=(), dtype=tf.string)),
