Created by: sbl1996

PaddlePaddle: 1.8.1

I have tried to use optimizer = fluid.contrib.mixed_precision.decorate(optimizer, init_loss_scaling=128.0, use_dynamic_loss_scaling=True) directly, but got the following error while calling model.fit.

I can't find any documentation, examples, or issues of that.

Code to reproduce is here.

Epoch 1/200
---------------------------------------------------------------------------AttributeError                            Traceback (most recent call last)<ipython-input-7-b712d38173b4> in <module>
----> 1 model.fit(train_loader, test_loader, epochs=epochs, save_dir="./checkpoint", save_freq=100, eval_freq=10)
~/work/hapi/hapi/model.py in fit(self, train_data, eval_data, batch_size, epochs, eval_freq, log_freq, save_dir, save_freq, verbose, drop_last, shuffle, num_workers, callbacks)
   1305 
   1306             cbks.on_epoch_begin(epoch)
-> 1307             logs = self._run_one_epoch(train_loader, cbks, 'train')
   1308             cbks.on_epoch_end(epoch, logs)
   1309 
~/work/hapi/hapi/model.py in _run_one_epoch(self, data_loader, callbacks, mode, logs)
   1609             if mode != 'test':
   1610                 outs = getattr(self, mode + '_batch')(data[:len(self._inputs)],
-> 1611                                                       data[len(self._inputs):])
   1612                 # losses
   1613                 loss = outs[0] if self._metrics else outs
~/work/hapi/hapi/model.py in train_batch(self, inputs, labels)
    752               print(loss)
    753         """
--> 754         return self._adapter.train_batch(inputs, labels)
    755 
    756     def eval_batch(self, inputs, labels=None):
~/work/hapi/hapi/model.py in train_batch(self, inputs, labels)
    507             final_loss.backward()
    508 
--> 509         self.model._optimizer.minimize(final_loss)
    510         self.model.clear_gradients()
    511         metrics = []
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/contrib/mixed_precision/decorator.py in minimize(self, loss, startup_program, parameter_list, no_grad_set)
    209             startup_program=startup_program,
    210             parameter_list=parameter_list,
--> 211             no_grad_set=no_grad_set)
    212 
    213         optimize_ops = self.apply_gradients(scaled_params_grads)
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/contrib/mixed_precision/decorator.py in backward(self, loss, startup_program, parameter_list, no_grad_set, callbacks)
    139         # Change the op_role_var attr for some ops, so that gradients
    140         # transferred across GPUs can be FP16.
--> 141         update_role_var_grad(self._train_program, self._params_grads)
    142         scaled_params_grads = []
    143         for p, g in self._params_grads:
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/contrib/mixed_precision/fp16_utils.py in update_role_var_grad(main_prog, params_grads)
    292     OPTIMIZE = core.op_proto_and_checker_maker.OpRole.Optimize
    293     for p, g in params_grads:
--> 294         op = g.op
    295         if g.dtype == core.VarDesc.VarType.FP32 and op.type == 'cast':
    296             role = op.attr('op_role')
AttributeError: 'paddle.fluid.core_avx.VarBase' object has no attribute 'op'