new_export_func (#824)

* new_export_func

* add_test

* remove_kernel_prune

* add_test_&_update_clear_ss

paddleslim/core/dygraph.py

@@ -64,6 +64,7 @@ def extract_vars(inputs):
                     f"Variable is excepted, but get an element with type({type(_value)}) from inputs whose type is dict. And the key of element is {_key}."
                 )
     elif isinstance(inputs, (tuple, list)):
+
         for _value in inputs:
             vars.extend(extract_vars(_value))
     if len(vars) == 0:
@@ -99,7 +100,6 @@ def dygraph2program(layer,
                     extract_outputs_fn=None,
                     dtypes=None):
     assert isinstance(layer, Layer)
-
     extract_inputs_fn = extract_inputs_fn if extract_inputs_fn is not None else extract_vars
     extract_outputs_fn = extract_outputs_fn if extract_outputs_fn is not None else extract_vars
     tracer = _dygraph_tracer()._get_program_desc_tracer()
@@ -116,6 +116,7 @@ def dygraph2program(layer,
         else:
             inputs = to_variables(inputs)
             input_var_list = extract_inputs_fn(inputs)
+
         original_outputs = layer(*inputs)
         # 'original_outputs' may be dict, so we should convert it to list of varibles.
         # And should not create new varibles in 'extract_vars'.

paddleslim/nas/ofa/get_sub_model.py

@@ -17,7 +17,7 @@ import paddle
 from paddle.fluid import core
 from .layers_base import BaseBlock
 
-__all__ = ['get_prune_params_config', 'prune_params', 'check_search_space']
+__all__ = ['check_search_space']
 
 WEIGHT_OP = [
     'conv2d', 'linear', 'embedding', 'conv2d_transpose', 'depthwise_conv2d'
@@ -28,63 +28,6 @@ CONV_TYPES = [
 ]
 
 
-def get_prune_params_config(graph, origin_model_config):
-    """ Convert config of search space to parameters' prune config.
-    """
-    param_config = {}
-    precedor = None
-    for op in graph.ops():
-        ### TODO(ceci3):
-        ### 1. fix config when this op is concat by graph.pre_ops(op)
-        ### 2. add kernel_size in config
-        for inp in op.all_inputs():
-            n_ops = graph.next_ops(op)
-            if inp._var.name in origin_model_config.keys():
-                if 'expand_ratio' in origin_model_config[
-                        inp._var.name] or 'channel' in origin_model_config[
-                            inp._var.name]:
-                    key = 'channel' if 'channel' in origin_model_config[
-                        inp._var.name] else 'expand_ratio'
-                    tmp = origin_model_config[inp._var.name][key]
-                    if len(inp._var.shape) > 1:
-                        if inp._var.name in param_config.keys():
-                            param_config[inp._var.name].append(tmp)
-                        ### first op
-                        else:
-                            param_config[inp._var.name] = [precedor, tmp]
-                    else:
-                        param_config[inp._var.name] = [tmp]
-                    precedor = tmp
-                else:
-                    precedor = None
-            for n_op in n_ops:
-                for next_inp in n_op.all_inputs():
-                    if next_inp._var.persistable == True:
-                        if next_inp._var.name in origin_model_config.keys():
-                            if 'expand_ratio' in origin_model_config[
-                                    next_inp._var.
-                                    name] or 'channel' in origin_model_config[
-                                        next_inp._var.name]:
-                                key = 'channel' if 'channel' in origin_model_config[
-                                    next_inp._var.name] else 'expand_ratio'
-                                tmp = origin_model_config[next_inp._var.name][
-                                    key]
-                                pre = tmp if precedor is None else precedor
-                                if len(next_inp._var.shape) > 1:
-                                    param_config[next_inp._var.name] = [pre]
-                                else:
-                                    param_config[next_inp._var.name] = [tmp]
-                            else:
-                                if len(next_inp._var.
-                                       shape) > 1 and precedor != None:
-                                    param_config[
-                                        next_inp._var.name] = [precedor, None]
-                        else:
-                            param_config[next_inp._var.name] = [precedor]
-
-    return param_config
-
-
 def get_actual_shape(transform, channel):
     if transform == None:
         channel = int(channel)
@@ -96,66 +39,6 @@ def get_actual_shape(transform, channel):
     return channel
 
 
-def prune_params(model, param_config, super_model_sd=None):
-    """ Prune parameters according to the config.
-        Parameters:
-            model(paddle.nn.Layer): instance of model.
-            param_config(dict): prune config of each weight.
-            super_model_sd(dict, optional): parameters come from supernet. If super_model_sd is not None, transfer parameters from this dict to model; otherwise, prune model from itself.
-    """
-    for l_name, sublayer in model.named_sublayers():
-        if isinstance(sublayer, BaseBlock):
-            continue
-        for p_name, param in sublayer.named_parameters(include_sublayers=False):
-            t_value = param.value().get_tensor()
-            value = np.array(t_value).astype("float32")
-
-            if super_model_sd != None:
-                name = l_name + '.' + p_name
-                super_t_value = super_model_sd[name].value().get_tensor()
-                super_value = np.array(super_t_value).astype("float32")
-                super_model_sd.pop(name)
-
-            if param.name in param_config.keys():
-                if len(param_config[param.name]) > 1:
-                    in_exp = param_config[param.name][0]
-                    out_exp = param_config[param.name][1]
-                    if sublayer.__class__.__name__.lower() in CONV_TYPES:
-                        in_chn = get_actual_shape(in_exp, value.shape[1])
-                        out_chn = get_actual_shape(out_exp, value.shape[0])
-                        prune_value = super_value[:out_chn, :in_chn, ...] \
-                                         if super_model_sd != None else value[:out_chn, :in_chn, ...]
-                    else:
-                        in_chn = get_actual_shape(in_exp, value.shape[0])
-                        out_chn = get_actual_shape(out_exp, value.shape[1])
-                        prune_value = super_value[:in_chn, :out_chn, ...] \
-                                         if super_model_sd != None else value[:in_chn, :out_chn, ...]
-                else:
-                    out_chn = get_actual_shape(param_config[param.name][0],
-                                               value.shape[0])
-                    prune_value = super_value[:out_chn, ...] \
-                                     if super_model_sd != None else value[:out_chn, ...]
-
-            else:
-                prune_value = super_value if super_model_sd != None else value
-
-            p = t_value._place()
-            if p.is_cpu_place():
-                place = core.CPUPlace()
-            elif p.is_cuda_pinned_place():
-                place = core.CUDAPinnedPlace()
-            else:
-                place = core.CUDAPlace(p.gpu_device_id())
-            t_value.set(prune_value, place)
-            if param.trainable:
-                param.clear_gradient()
-
-    ### initialize param which not in sublayers, such as create persistable inputs by create_parameters
-    if super_model_sd != None and len(super_model_sd) != 0:
-        for k, v in super_model_sd.items():
-            setattr(model, k, v)
-
-
 def _is_depthwise(op):
     """Check if this op is depthwise conv. Only Cin == Cout == groups be consider as depthwise conv.
        The shape of input and the shape of output in depthwise conv must be same in superlayer,

paddleslim/nas/ofa/layers.py

@@ -177,6 +177,7 @@ class SuperConv2D(nn.Conv2D):
             data_format=data_format)
 
         self.candidate_config = candidate_config
+        self.cur_config = None
         if len(candidate_config.items()) != 0:
             for k, v in candidate_config.items():
                 candidate_config[k] = list(set(v))
@@ -314,7 +315,7 @@ class SuperConv2D(nn.Conv2D):
             bias = self.bias[:weight_out_nc]
         else:
             bias = self.bias
-
+        self.cur_config['prune_dim'] = list(weight.shape)
         out = F.conv2d(
             input,
             weight,
@@ -482,6 +483,7 @@ class SuperConv2DTranspose(nn.Conv2DTranspose):
             data_format=data_format)
 
         self.candidate_config = candidate_config
+        self.cur_config = None
         if len(self.candidate_config.items()) != 0:
             for k, v in candidate_config.items():
                 candidate_config[k] = list(set(v))
@@ -620,7 +622,7 @@ class SuperConv2DTranspose(nn.Conv2DTranspose):
             bias = self.bias[:weight_out_nc]
         else:
             bias = self.bias
-
+        self.cur_config['prune_dim'] = list(weight.shape)
         out = F.conv2d_transpose(
             input,
             weight,
@@ -733,6 +735,7 @@ class SuperSeparableConv2D(nn.Layer):
         ])
 
         self.candidate_config = candidate_config
+        self.cur_config = None
         self.expand_ratio = candidate_config[
             'expand_ratio'] if 'expand_ratio' in candidate_config else None
         self.base_output_dim = self.conv[0]._out_channels
@@ -784,7 +787,7 @@ class SuperSeparableConv2D(nn.Layer):
             bias = self.conv[2].bias[:out_nc]
         else:
             bias = self.conv[2].bias
-
+        self.cur_config['prune_dim'] = list(weight.shape)
         conv1_out = F.conv2d(
             norm_out,
             weight,
@@ -864,6 +867,7 @@ class SuperLinear(nn.Linear):
         self._in_features = in_features
         self._out_features = out_features
         self.candidate_config = candidate_config
+        self.cur_config = None
         self.expand_ratio = candidate_config[
             'expand_ratio'] if 'expand_ratio' in candidate_config else None
         self.base_output_dim = self._out_features
@@ -896,7 +900,7 @@ class SuperLinear(nn.Linear):
             bias = self.bias[:out_nc]
         else:
             bias = self.bias
-
+        self.cur_config['prune_dim'] = list(weight.shape)
         out = F.linear(x=input, weight=weight, bias=bias, name=self.name)
         return out
 
@@ -945,6 +949,7 @@ class SuperBatchNorm2D(nn.BatchNorm2D):
         super(SuperBatchNorm2D, self).__init__(
             num_features, momentum, epsilon, weight_attr, bias_attr,
             data_format, use_global_stats, name)
+        self.cur_config = None
 
     def forward(self, input):
         self._check_data_format(self._data_format)
@@ -956,7 +961,7 @@ class SuperBatchNorm2D(nn.BatchNorm2D):
         bias = self.bias[:feature_dim]
         mean = self._mean[:feature_dim]
         variance = self._variance[:feature_dim]
-
+        self.cur_config = {'prune_dim': feature_dim}
         return F.batch_norm(
             input,
             mean,
@@ -982,6 +987,7 @@ class SuperSyncBatchNorm(nn.SyncBatchNorm):
         super(SuperSyncBatchNorm,
               self).__init__(num_features, momentum, epsilon, weight_attr,
                              bias_attr, data_format, name)
+        self.cur_config = None
 
     def forward(self, input):
 
@@ -995,6 +1001,7 @@ class SuperSyncBatchNorm(nn.SyncBatchNorm):
         mean_out = mean
         # variance and variance out share the same memory
         variance_out = variance
+        self.cur_config = {'prune_dim': feature_dim}
 
         attrs = ("momentum", self._momentum, "epsilon", self._epsilon,
                  "is_test", not self.training, "data_layout", self._data_format,
@@ -1049,6 +1056,7 @@ class SuperInstanceNorm2D(nn.InstanceNorm2D):
         super(SuperInstanceNorm2D, self).__init__(num_features, epsilon,
                                                   momentum, weight_attr,
                                                   bias_attr, data_format, name)
+        self.cur_config = None
 
     def forward(self, input):
         self._check_input_dim(input)
@@ -1060,7 +1068,7 @@ class SuperInstanceNorm2D(nn.InstanceNorm2D):
         else:
             scale = self.scale[:feature_dim]
             bias = self.bias[:feature_dim]
-
+        self.cur_config = {'prune_dim': feature_dim}
         return F.instance_norm(input, scale, bias, eps=self._epsilon)
 
 
@@ -1112,6 +1120,7 @@ class SuperLayerNorm(nn.LayerNorm):
                  name=None):
         super(SuperLayerNorm, self).__init__(normalized_shape, epsilon,
                                              weight_attr, bias_attr, name)
+        self.cur_config = None
 
     def forward(self, input):
         ### TODO(ceci3): fix if normalized_shape is not a single number
@@ -1127,6 +1136,8 @@ class SuperLayerNorm(nn.LayerNorm):
             bias = self.bias[:feature_dim]
         else:
             bias = None
+        self.cur_config = {'prune_dim': feature_dim}
+
         out, _, _ = core.ops.layer_norm(input, weight, bias, 'epsilon',
                                         self._epsilon, 'begin_norm_axis',
                                         begin_norm_axis)
@@ -1191,6 +1202,7 @@ class SuperEmbedding(nn.Embedding):
                                              padding_idx, sparse, weight_attr,
                                              name)
         self.candidate_config = candidate_config
+        self.cur_config = None
         self.expand_ratio = candidate_config[
             'expand_ratio'] if 'expand_ratio' in candidate_config else None
         self.base_output_dim = self._embedding_dim
@@ -1216,6 +1228,7 @@ class SuperEmbedding(nn.Embedding):
             out_nc = self._embedding_dim
 
         weight = self.weight[:, :out_nc]
+        self.cur_config = {'prune_dim': list(weight.shape)}
         return F.embedding(
             input,
             weight=weight,

paddleslim/nas/ofa/ofa.py

@@ -27,11 +27,14 @@ else:
     from .layers import SuperConv2D, SuperLinear
     Layer = paddle.nn.Layer
     DataParallel = paddle.DataParallel
-from .layers_base import BaseBlock
+from .layers_base import BaseBlock, Block
 from .utils.utils import search_idx
 from ...common import get_logger
 from ...core import GraphWrapper, dygraph2program
-from .get_sub_model import get_prune_params_config, prune_params, check_search_space, broadcast_search_space
+from .get_sub_model import check_search_space, broadcast_search_space
+from paddle.fluid import core
+from paddle.fluid.framework import Variable
+import numbers
 
 _logger = get_logger(__name__, level=logging.INFO)
 
@@ -459,35 +462,41 @@ class OFA(OFABase):
     def search(self, eval_func, condition):
         pass
 
-    def _export_sub_model_config(self, origin_model, config, input_shapes,
-                                 input_dtypes):
-        param2name = {}
-        for name, sublayer in origin_model.named_sublayers():
-            for param in sublayer.parameters(include_sublayers=False):
-                if name.split('.')[-1] == 'fn':
-                    ### if sublayer is Block, the name of the param.name has 'fn', the config always donnot have 'fn'
-                    param2name[param.name] = name[:-3]
-                else:
-                    param2name[param.name] = name
+    def _get_model_pruned_weight(self):
 
-        program = dygraph2program(
-            origin_model, inputs=input_shapes, dtypes=input_dtypes)
-        graph = GraphWrapper(program)
+        pruned_param = {}
+        for l_name, sublayer in self.model.named_sublayers():
 
-        same_config, _ = check_search_space(graph)
-        if same_config != None:
-            broadcast_search_space(same_config, param2name, config)
+            if getattr(sublayer, 'cur_config', None) == None:
+                continue
 
-        origin_model_config = {}
-        for name, sublayer in origin_model.named_sublayers():
-            if isinstance(sublayer, BaseBlock):
-                sublayer = sublayer.fn
-            for param in sublayer.parameters(include_sublayers=False):
-                if name in config.keys():
-                    origin_model_config[param.name] = config[name]
+            assert 'prune_dim' in sublayer.cur_config, 'The laycer {} do not have prune_dim in cur_config.'.format(
+                l_name)
+            prune_shape = sublayer.cur_config['prune_dim']
+
+            for p_name, param in sublayer.named_parameters(
+                    include_sublayers=False):
+                origin_param = param.value().get_tensor()
+                param = np.array(origin_param).astype("float32")
+
+                name = l_name + '.' + p_name
+                if isinstance(prune_shape, list):
 
-        param_prune_config = get_prune_params_config(graph, origin_model_config)
-        return param_prune_config
+                    if len(param.shape) == 4:
+                        pruned_param[name] = param[:prune_shape[0], :
+                                                   prune_shape[1], :, :]
+                    elif len(param.shape) == 2:
+                        pruned_param[name] = param[:prune_shape[0], :
+                                                   prune_shape[1]]
+                    else:
+                        if isinstance(sublayer, SuperLinear):
+                            pruned_param[name] = param[:prune_shape[1]]
+                        else:
+                            pruned_param[name] = param[:prune_shape[0]]
+                else:
+                    pruned_param[name] = param[:prune_shape]
+
+        return pruned_param
 
     def export(self,
                config,
@@ -510,17 +519,72 @@ class OFA(OFABase):
               config = {'conv2d_0': {'expand_ratio': 2}, 'conv2d_1': {'expand_ratio': 2}}
               origin_model = ofa_model.export(origin_model, config, input_shapes=[1, 3, 28, 28], input_dtypes=['float32'])
         """
-        super_sd = None
+        self.set_net_config(config)
+        self.model.eval()
+
+        def build_input(input_size, dtypes):
+            if isinstance(input_size, list) and all(
+                    isinstance(i, numbers.Number) for i in input_size):
+                if isinstance(dtypes, list):
+                    dtype = dtypes[0]
+                else:
+                    dtype = dtypes
+                return paddle.cast(paddle.rand(list(input_size)), dtype)
+            if isinstance(input_size, dict):
+                inputs = {}
+                if isinstance(dtypes, list):
+                    dtype = dtypes[0]
+                else:
+                    dtype = dtypes
+                for key, value in input_size.items():
+                    inputs[key] = paddle.cast(paddle.rand(list(value)), dtype)
+                return inputs
+            if isinstance(input_size, list):
+                return [
+                    build_input(i, dtype)
+                    for i, dtype in zip(input_size, dtypes)
+                ]
+
+        data = build_input(input_shapes, input_dtypes)
+
+        if isinstance(data, list):
+            self.forward(*data)
+        else:
+            self.forward(data)
+        super_model_state_dict = None
         if load_weights_from_supernet and origin_model != None:
-            super_sd = remove_model_fn(origin_model, self.model.state_dict())
+            super_model_state_dict = remove_model_fn(origin_model,
+                                                     self.model.state_dict())
 
         if origin_model == None:
             origin_model = self.model
+
         origin_model = origin_model._layers if isinstance(
             origin_model, DataParallel) else origin_model
-        param_config = self._export_sub_model_config(origin_model, config,
-                                                     input_shapes, input_dtypes)
-        prune_params(origin_model, param_config, super_sd)
+
+        _logger.info("Start to get pruned params, please wait...")
+        pruned_param = self._get_model_pruned_weight()
+        pruned_state_dict = remove_model_fn(origin_model, pruned_param)
+        _logger.info("Start to get pruned model, please wait...")
+        for l_name, sublayer in origin_model.named_sublayers():
+            for p_name, param in sublayer.named_parameters(
+                    include_sublayers=False):
+                name = l_name + '.' + p_name
+                t_value = param.value().get_tensor()
+                if name in pruned_state_dict:
+                    p = t_value._place()
+                    if p.is_cpu_place():
+                        place = core.CPUPlace()
+                    elif p.is_cuda_pinned_place():
+                        place = core.CUDAPinnedPlace()
+                    else:
+                        place = core.CUDAPlace(p.gpu_device_id())
+                    t_value.set(pruned_state_dict[name], place)
+
+        if super_model_state_dict != None and len(super_model_state_dict) != 0:
+            for k, v in super_model_state_dict.items():
+                setattr(origin_model, k, v)
+
         return origin_model
 
     @property
@@ -566,11 +630,26 @@ class OFA(OFABase):
         input_shapes = []
         input_dtypes = []
         for n in inputs:
-            input_shapes.append(n.shape)
-            input_dtypes.append(n.numpy().dtype)
-        for n, v in kwargs.items():
-            input_shapes.append(v.shape)
-            input_dtypes.append(v.numpy().dtype)
+            if isinstance(n, Variable):
+                input_shapes.append(n)
+                input_dtypes.append(n.numpy().dtype)
+
+        for key, val in kwargs.items():
+            if isinstance(val, Variable):
+                input_shapes.append(val)
+                input_dtypes.append(val.numpy().dtype)
+            elif isinstance(val, dict):
+                input_shape = {}
+                input_dtype = {}
+                for k, v in val.items():
+                    input_shape[k] = v
+                    input_dtype[k] = v.numpy().dtype
+                input_shapes.append(input_shape)
+                input_dtypes.append(input_dtype)
+            else:
+                _logger.error(
+                    "Cannot figure out the type of inputs! Right now, the type of inputs can be only Variable or dict."
+                )
 
         ### find shortcut block using static model
         model_to_traverse = self.model._layers if isinstance(
@@ -674,11 +753,9 @@ class OFA(OFABase):
         _logger.debug("Current config is {}".format(self.current_config))
         if 'depth' in self.current_config:
             kwargs['depth'] = self.current_config['depth']
-
         if self._broadcast:
             broadcast_search_space(self._same_ss, self._param2key,
                                    self.current_config)
-
         student_output = self.model.forward(*inputs, **kwargs)
 
         if self._add_teacher:

tests/test_ofa.py

@@ -142,10 +142,19 @@ class ModelConv2(nn.Layer):
 class ModelLinear(nn.Layer):
     def __init__(self):
         super(ModelLinear, self).__init__()
-        with supernet(expand_ratio=(1.0, 2.0, 4.0)) as ofa_super:
+        with supernet(expand_ratio=(1, 2, 4)) as ofa_super:
             models = []
             models += [nn.Embedding(num_embeddings=64, embedding_dim=64)]
-            models += [nn.Linear(64, 128)]
+            weight_attr = paddle.ParamAttr(
+                learning_rate=0.5,
+                regularizer=paddle.regularizer.L2Decay(1.0),
+                trainable=True)
+            bias_attr = paddle.ParamAttr(
+                initializer=paddle.nn.initializer.Constant(value=1.0))
+            models += [
+                nn.Linear(
+                    64, 128, weight_attr=weight_attr, bias_attr=bias_attr)
+            ]
             models += [nn.LayerNorm(128)]
             models += [nn.Linear(128, 256)]
             models = ofa_super.convert(models)
@@ -402,16 +411,7 @@ class TestExport(unittest.TestCase):
         self.ofa_model = OFA(model)
 
     def test_ofa(self):
-        config = {
-            'embedding_1': {
-                'expand_ratio': (2.0)
-            },
-            'linear_3': {
-                'expand_ratio': (2.0)
-            },
-            'linear_4': {},
-            'linear_5': {}
-        }
+        config = self.ofa_model._sample_config(task='expand_ratio', phase=None)
         origin_dict = {}
         for name, param in self.origin_model.named_parameters():
             origin_dict[name] = param.shape
@@ -459,9 +459,29 @@ class TestExportCase1(unittest.TestCase):
         outs, _ = self.ofa_model(self.data)
         self.config = self.ofa_model.current_config
 
-    def test_export_model(self):
-        self.ofa_model.export(
+    def test_export_model_linear1(self):
+        ex_model = self.ofa_model.export(
             self.config, input_shapes=[[3, 64]], input_dtypes=['int64'])
+        ex_model(self.data)
+        assert len(self.ofa_model.ofa_layers) == 4
+
+
+class TestExportCase2(unittest.TestCase):
+    def setUp(self):
+        model = ModelLinear()
+        data_np = np.random.random((3, 64)).astype(np.int64)
+        self.data = paddle.to_tensor(data_np)
+        self.ofa_model = OFA(model)
+        self.ofa_model.set_epoch(0)
+        outs, _ = self.ofa_model(self.data)
+        self.config = self.ofa_model.current_config
+
+    def test_export_model_linear2(self):
+        config = self.ofa_model._sample_config(
+            task='expand_ratio', phase=None, sample_type='smallest')
+        ex_model = self.ofa_model.export(
+            config, input_shapes=[[3, 64]], input_dtypes=['int64'])
+        ex_model(self.data)
         assert len(self.ofa_model.ofa_layers) == 4
 
 

tests/test_ofa_v2.py

@@ -81,6 +81,25 @@ class ModelShortcut(nn.Layer):
         return z
 
 
+class ModelInputDict(nn.Layer):
+    def __init__(self):
+        super(ModelInputDict, self).__init__()
+        self.conv0 = nn.Sequential(
+            nn.Conv2D(3, 12, 1), nn.BatchNorm2D(12), nn.ReLU())
+        self.conv1 = nn.Sequential(
+            nn.Conv2D(12, 12, 1), nn.BatchNorm2D(12), nn.ReLU())
+        self.conv2 = nn.Sequential(
+            nn.Conv2D(12, 12, 1), nn.BatchNorm2D(12), nn.ReLU())
+        self.conv3 = nn.Sequential(
+            nn.Conv2D(12, 12, 1), nn.BatchNorm2D(12), nn.ReLU())
+
+    def forward(self, x, data):
+        x = self.conv1(self.conv0(x))
+        y = self.conv2(x)
+        y = y + data['data']
+        return self.conv3(y)
+
+
 class TestOFAV2(unittest.TestCase):
     def setUp(self):
         model = ModelV1()
@@ -93,7 +112,6 @@ class TestOFAV2(unittest.TestCase):
         self.ofa_model.set_epoch(0)
         self.ofa_model.set_task('expand_ratio')
         out, _ = self.ofa_model(self.images)
-        print(self.ofa_model.get_current_config)
 
 
 class TestOFAV2Export(unittest.TestCase):
@@ -151,5 +169,34 @@ class TestShortcutSkiplayersCase2(TestShortcutSkiplayers):
         assert list(self.ofa_model._ofa_layers.keys()) == ['conv1.0', 'out.0']
 
 
+class TestInputDict(unittest.TestCase):
+    def setUp(self):
+        model = ModelInputDict()
+
+        sp_net_config = supernet(expand_ratio=[0.5, 1.0])
+        self.model = Convert(sp_net_config).convert(model)
+        self.images = paddle.randn(shape=[2, 3, 32, 32], dtype='float32')
+        self.images2 = {
+            'data': paddle.randn(
+                shape=[2, 12, 32, 32], dtype='float32')
+        }
+        default_run_config = {'skip_layers': ['conv1.0', 'conv2.0']}
+        self.run_config = RunConfig(**default_run_config)
+
+        self.ofa_model = OFA(self.model, run_config=self.run_config)
+        self.ofa_model._clear_search_space(self.images, data=self.images2)
+
+    def test_export(self):
+
+        config = self.ofa_model._sample_config(
+            task="expand_ratio", sample_type="smallest")
+        self.ofa_model.export(
+            config,
+            input_shapes=[[1, 3, 32, 32], {
+                'data': [1, 12, 32, 32]
+            }],
+            input_dtypes=['float32', 'float32'])
+
+
 if __name__ == '__main__':
     unittest.main()