[GitHub] zheng-da opened a new issue #12269: get memory error when running a model exported from gluon model zoo

[GitBox <gi...@apache.org>]

zheng-da opened a new issue #12269: get memory error when running a model exported from gluon model zoo
URL: https://github.com/apache/incubator-mxnet/issues/12269
 
 
   I tried to export resnet from the Gluon model zoo and run it in the Symbol executor, but got a serious memory error. The memory error is also non-deterministic. Here is the minimal code. I need to run it multiple times to reproduce the error.
   
   ```python
   import numpy as np
   import mxnet as mx
   import copy
   import random
   import itertools
   from numpy.testing import assert_allclose, assert_array_equal
   from mxnet.test_utils import *
   from mxnet.gluon.model_zoo.vision import get_model
   
   def test_make_subgraph():
       def create_weights(shapes, names):
           nd_dict = {}
           assert len(shapes) == len(names)
           for i in range(len(shapes)):
               nd_dict[names[i]] = mx.nd.array(np.ones(shapes[i]), ctx=default_context())
           return nd_dict
   
       def make_subgraph4(stype):
           model = get_model('resnet18_v1')
           model.hybridize()
           model.initialize()
           shape = (1, 3, 32, 32)
           data = mx.nd.random.normal(shape=shape)
           out = model(data)
           model.export('resnet18')
           orig = mx.sym.load('resnet18-symbol.json')
   
           arg_shapes, out_shapes, aux_shapes = orig.infer_shape(data=shape)
           weight_shapes = arg_shapes[1:]
           weight_names = orig.list_arguments()[1:]
           weight_dict = create_weights(weight_shapes, weight_names)
           aux_dict = create_weights(aux_shapes, orig.list_auxiliary_states())
   
           arr = mx.nd.random.uniform(-1, 1, shape=shape, ctx=default_context()).tostype(stype)
           arg_dict = weight_dict
           arg_dict['data'] = arr
           return (orig, arg_dict, aux_dict)
   
       orig, inputs, aux_states = make_subgraph4('default')
       all_inputs = copy.deepcopy(inputs)
       all_inputs.update(aux_states)
       args_grad = {key : mx.nd.empty(shape=all_inputs[key].shape) for key in all_inputs.keys()}
       e1 = orig.bind(ctx=default_context(), args=all_inputs, args_grad=args_grad,
                      aux_states=all_inputs)
       args_grad = {key : mx.nd.empty(shape=all_inputs[key].shape) for key in all_inputs.keys()}
       e1.forward()
       mx.nd.waitall()
   
       out_grads = [mx.nd.random.uniform(-1, 1, shape=out.shape, ctx=default_context())
                    for out in e1.outputs]
       e1.backward(out_grads)
       mx.nd.waitall()
   
   test_make_subgraph()
   ```

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