Description
Error while run Train Module in t5_tf_huggingface .it gives division error while calculating lose in train_step() function
Truncation was not explicitly activated but max_length is provided a specific value, please use truncation=True to explicitly truncate examples to max length. Defaulting to 'longest_first' truncation strategy. If you encode pairs of sequences (GLUE-style) with the tokenizer you can select this strategy more precisely by providing a specific strategy to truncation.
WARNING:tensorflow:The parameters output_attentions, output_hidden_states and use_cache cannot be updated when calling a model.They have to be set to True/False in the config object (i.e.: config=XConfig.from_pretrained('name', output_attentions=True)).
WARNING:tensorflow:AutoGraph could not transform <bound method Socket.send of <zmq.sugar.socket.Socket object at 0x000001E91FD18AC0>> and will run it as-is.
Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, export AUTOGRAPH_VERBOSITY=10) and attach the full output.
Cause: module, class, method, function, traceback, frame, or code object was expected, got cython_function_or_method
To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert
WARNING:tensorflow:AutoGraph could not transform <bound method Socket.send of <zmq.sugar.socket.Socket object at 0x000001E91FD18AC0>> and will run it as-is.
Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, export AUTOGRAPH_VERBOSITY=10) and attach the full output.
Cause: module, class, method, function, traceback, frame, or code object was expected, got cython_function_or_method
To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert
WARNING: AutoGraph could not transform <bound method Socket.send of <zmq.sugar.socket.Socket object at 0x000001E91FD18AC0>> and will run it as-is.
Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, export AUTOGRAPH_VERBOSITY=10) and attach the full output.
Cause: module, class, method, function, traceback, frame, or code object was expected, got cython_function_or_method
To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert
WARNING:tensorflow:The parameters output_attentions, output_hidden_states and use_cache cannot be updated when calling a model.They have to be set to True/False in the config object (i.e.: config=XConfig.from_pretrained('name', output_attentions=True)).
WARNING:tensorflow:The parameter return_dict cannot be set in graph mode and will always be set to True.
WARNING:tensorflow:The parameter return_dict cannot be set in graph mode and will always be set to True.
WARNING:tensorflow:The parameters output_attentions, output_hidden_states and use_cache cannot be updated when calling a model.They have to be set to True/False in the config object (i.e.: config=XConfig.from_pretrained('name', output_attentions=True)).
WARNING:tensorflow:The parameters output_attentions, output_hidden_states and use_cache cannot be updated when calling a model.They have to be set to True/False in the config object (i.e.: config=XConfig.from_pr
65B8
etrained('name', output_attentions=True)).
WARNING:tensorflow:The parameter return_dict cannot be set in graph mode and will always be set to True.
WARNING:tensorflow:The parameter return_dict cannot be set in graph mode and will always be set to True.
Tensor("strided_slice_3:0", shape=(16, 149), dtype=int32)
StagingError Traceback (most recent call last)
in
14 for i, (input_ids, input_mask, y) in enumerate(train_ds):
15 # training
---> 16 train_step(input_ids, input_mask, y)
17
18 # validation
~\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\eager\def_function.py in call(self, *args, **kwds)
826 tracing_count = self.experimental_get_tracing_count()
827 with trace.Trace(self._name) as tm:
--> 828 result = self._call(*args, **kwds)
829 compiler = "xla" if self._experimental_compile else "nonXla"
830 new_tracing_count = self.experimental_get_tracing_count()
~\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\eager\def_function.py in _call(self, *args, **kwds)
869 # This is the first call of call, so we have to initialize.
870 initializers = []
--> 871 self._initialize(args, kwds, add_initializers_to=initializers)
872 finally:
873 # At this point we know that the initialization is complete (or less
~\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\eager\def_function.py in _initialize(self, args, kwds, add_initializers_to)
723 self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph)
724 self._concrete_stateful_fn = (
--> 725 self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access
726 *args, **kwds))
727
~\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\eager\function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
2967 args, kwargs = None, None
2968 with self._lock:
-> 2969 graph_function, _ = self._maybe_define_function(args, kwargs)
2970 return graph_function
2971
~\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\eager\function.py in _maybe_define_function(self, args, kwargs)
3359
3360 self._function_cache.missed.add(call_context_key)
-> 3361 graph_function = self._create_graph_function(args, kwargs)
3362 self._function_cache.primary[cache_key] = graph_function
3363
~\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\eager\function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
3194 arg_names = base_arg_names + missing_arg_names
3195 graph_function = ConcreteFunction(
-> 3196 func_graph_module.func_graph_from_py_func(
3197 self._name,
3198 self._python_function,
~\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\framework\func_graph.py 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, override_flat_arg_shapes)
988 _, original_func = tf_decorator.unwrap(python_func)
989
--> 990 func_outputs = python_func(*func_args, **func_kwargs)
991
992 # invariant: func_outputs contains only Tensors, CompositeTensors,
~\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\eager\def_function.py in wrapped_fn(*args, **kwds)
632 xla_context.Exit()
633 else:
--> 634 out = weak_wrapped_fn().wrapped(*args, **kwds)
635 return out
636
~\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\framework\func_graph.py in wrapper(*args, **kwargs)
975 except Exception as e: # pylint:disable=broad-except
976 if hasattr(e, "ag_error_metadata"):
--> 977 raise e.ag_error_metadata.to_exception(e)
978 else:
979 raise
StagingError: in user code:
<ipython-input-11-c9eecf4c3d2e>:14 train_step *
loss = loss_object(y[:, 1:], predictions)
C:\Users\Rohith\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\keras\losses.py:152 __call__ **
losses = call_fn(y_true, y_pred)
C:\Users\Rohith\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\keras\losses.py:256 call **
return ag_fn(y_true, y_pred, **self._fn_kwargs)
C:\Users\Rohith\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\util\dispatch.py:201 wrapper
return target(*args, **kwargs)
C:\Users\Rohith\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\keras\losses.py:1568 sparse_categorical_crossentropy
return K.sparse_categorical_crossentropy(
C:\Users\Rohith\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\util\dispatch.py:201 wrapper
return target(*args, **kwargs)
C:\Users\Rohith\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\keras\backend.py:4915 sparse_categorical_crossentropy
axis %= output_rank
ZeroDivisionError: integer division or modulo by zero
Please help me . Thanks