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From GitBox <...@apache.org>
Subject [GitHub] [singa] joddiy commented on issue #691: Add save and load method for Module class
Date Sat, 16 May 2020 07:49:42 GMT

joddiy commented on issue #691:
URL: https://github.com/apache/singa/issues/691#issuecomment-629604531


   > **Updated on May 15 Night**
   > 
   > ```python
   > class Layer:
   >    def get_params(self):
   >        """the params of this layer and sublayers as a dict;  param name is: layername.param
   >            e.g., self.W = Tensor(), self.b=Tensor()
   >                   name of W and b is  like conv1.W and conv1.b  
   >        """
   > 
   >     def get_states(self):
   >        """states of this layer as sublayers that are necessary for model training/evaluation/inference.
   >            the states include the params and others, e.g., the running mean and var
of batchnorm.
   >       """
   > 
   > class Module(Layer):   
   >   def compile(self ...):
   >      """set the name of each layer and sublayers, which will be used to create the
dict 
   >           for get_params and get_states. Then no need to manually config the layer
name 
   >          the __init__ method of a layer.
   >  
   >         For instance,
   >         class Blk(Layer):
   >              def __init__(self):
   >                   self.conv1= Conv2d()
   >                   self.conv2 = Conv2d()
   > 
   >         class MyModel(Module):
   >               def __init__(self):         
   >                  self.blk1 = Blk() --> blk1.conv1, blk1.conv2
   >                  self.blk2 = Blk()  --> blk2.conv1, blk2.conv2
   >    """
   > 
   >   # high priority
   >   def save_states(self, fpath, aux_states={}):
   >       """Save states.
   >       
   >       Args:
   >          fpath: output file path (without the extension)
   >          aux_states(dict): values are standard data types or Tensor, 
   >                                    e.g., epoch ID, learning rate, optimizer states
   >       """
   >       states = get_states() + aux_states + input_placeholders
   >       tensor_dict = {}
   >       for k, v in states:
   >            if type(v) is Tensor:
   >              tensor_dict[k] = v
   >              states[k] = {'shape': v.shape, 'dtype': v.dtype}
   >       save states as json file
   >       save tensor_dict via numpy or hdf5 or protobuf
   >       zip the output files
   > 
   >   def load_states(self, fpath, dev, use_graph=True, graph_alg='sequence'):
   >       """Load the model onto dev
   >    
   >        Args:
   >          path: input file path (without the extension)
   >        Returns:
   >           dict 
   >       ```
   >       unzip the input file
   >       load the json file --> states
   >       load the tensor files --> tensor_dict
   >       put the tensors into states
   >       states --> model_states + input_placeholders + aux_states
   >       self.compile(input_placeholders, dev, use_graph, graph_alg)
   >      model.set_states(model_states) 
   >      return the rest states as a dict
   > 
   > # lower priority
   > def save(fpath, model):
   >     attributes <-- model
   >     replace all tensors in attributes --> {'shape': v.shape, 'dtype': v.dtype}
   >     dump the tensors via numpy or protobuf or hdf5
   >     dump model via pickle
   >     zip the output files
   > 
   > def load(fpath, dev, use_graph, graph_alg):
   >      unzip the input file
   >      load model via pickle
   >      load tensors 
   >      restore the tensors in model attributes
   >      return the model
   > 
   > 
   > # handle ONNX 
   > def to_onnx(model):
   >     return a onnx model 
   > 
   > class SONNXModel(Module):
   >      def __init__(self, onnx_model):
   >           self.store_output = store_output
   >           for layer_name, layer_config in get_layer(onnx_model):
   >               self.__dict__[layer_name] = CreateLayer(...)
   > 
   >     def forward(self, aux_output):
   >           run forward according to onnx graph 
   >          return the last output + aux_output
   > 
   > class MyModel(SONNXModel):
   >      def __init__(self, onnx):
   >           super.__init__(onnx)
   >           self.layer1 = Conv()
   >           self.layer2 = Conv()
   > 
   >      def forward(self, x):
   >            x1, x2 = super.forward(x, aux_output)
   >            x = self.layer1.forward(x2)
   >            return self.layer2.forward(x1) + x
   > 
   >       def train_one_batch(self, x, y):
   >            y_ = self.forward(x)
   >            ....
   > ```
   > 
   > Clarification:
   > 
   > * Params: layer parameters (Tensor) that are updated via SGD. `Layer.get_params()`
   > * States: Params + other variables that are necessary for model evaluation/inference.
Superset of params.  `Layer.get_states()`
   > * Attributes: members of a class instance `class.__dict__`. Superset of states.
   
   # handle ONNX 
   def to_onnx(model):
       return a onnx model 
   
   class SONNXModel(Module):
   	def __init__(self, onnx_model):
   		singa_rep = sonnx.prepare(onnx_model, device=dev, batchsize=1)
   		for layer_name, layer in singa_rep.layers:
   			self.__dict__[layer_name] = layer
   		# store weights here as numpy
   		for weith_name, weight in singa_rep.weights:
   			self.weights[weith_name] = weight
   		# store layer info such as input and output name(only weights)
   		for layer_name, layer_info in singa_rep.layer_infos:
   			self.layer_infos[layer_name] = layer_info
   
       def forward(self, aux_output):
           # run forward according to onnx graph 
           return the last output + aux_output
   
       def compile(self)
       	# init weights
       	super.compile(self)
       	# set weights' value
   		for layer_name, layer in self.__dict__:
   			input_info, output_info = self.layer_infos[layer_name]
   			for input_name in input_info:
   				layer.set_weight(self.weights[input_name])
   
   class MyModel(SONNXModel):
        def __init__(self, onnx):
             super.__init__(onnx)
             self.layer1 = Conv()
             self.layer2 = Conv()
   
        def forward(self, x):
              x1, x2 = super.forward(x, aux_output)
              x = self.layer1.forward(x2)
              return self.layer2.forward(x1) + x
   
         def train_one_batch(self, x, y):
              y_ = self.forward(x)
              ....
   ```
   How about this one, we pareses onnx by `soon.prepare`(Backend), it returns a `singa_rep`(BackendRep),
and the  singa_rep contains the layers, weights and input_output_info, we store the layers
in self.__dict__. When we compile the model, first we call super() to init the params, then
we set its value from the onnx loaded weights.


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