support bert finetune

x2paddle/core/op_mapper.py

@@ -52,6 +52,24 @@ def export_paddle_param(param, param_name, dir):
 def run_net(param_dir="./"):
     import os
     inputs, outputs = x2paddle_net()
+
+    ops = fluid.default_main_program().global_block().ops
+    used_vars = list()
+    for op in ops:
+        used_vars += op.input_arg_names
+
+    tmp = list()
+    for input in inputs:
+        if isinstance(input, list):
+            for ipt in input:
+                if ipt.name not in used_vars:
+                    continue
+                tmp.append(ipt)
+        else:
+            if input.name not in used_vars:
+                continue
+            tmp.append(input)
+    inputs = tmp
     for i, out in enumerate(outputs):
         if isinstance(out, list):
             for out_part in out:
@@ -121,12 +139,30 @@ class OpMapper(object):
         import model
         try:
             inputs, outputs = model.x2paddle_net()
+
+            ops = fluid.default_main_program().global_block().ops
+            used_vars = list()
+            for op in ops:
+                used_vars += op.input_arg_names
+
             for i, out in enumerate(outputs):
                 if isinstance(out, list):
                     for out_part in out:
                         outputs.append(out_part)
                     del outputs[i]
-            input_names = [input.name for input in inputs]
+
+            input_names = list()
+            for input in inputs:
+                if isinstance(input, list):
+                    for ipt in input:
+                        if ipt.name not in used_vars:
+                            continue
+                        input_names.append(ipt.name)
+                else:
+                    if input.name not in used_vars:
+                        continue
+                    input_names.append(input.name)
+
             exe = fluid.Executor(fluid.CPUPlace())
             exe.run(fluid.default_startup_program())
 

x2paddle/decoder/tf_decoder.py

@@ -48,7 +48,7 @@ class TFGraphNode(GraphNode):
 
     @property
     def out_shapes(self):
-        if self.layer_type == "OneShotIterator":
+        if self.layer_type == "OneShotIterator" or self.layer_type == "IteratorV2":
             values = self.layer.attr["output_shapes"].list.shape
         else:
             values = self.layer.attr["_output_shapes"].list.shape
@@ -115,7 +115,7 @@ class TFGraph(Graph):
     def __init__(self, model, data_format="NHWC"):
         super(TFGraph, self).__init__(model)
         self.identity_map = dict()
-        self.multi_out_ops = ['Split', 'SplitV']
+        self.multi_out_ops = ['Split', 'SplitV', 'IteratorV2']
         self.tf_data_format = data_format
 
     def build(self):
@@ -335,7 +335,7 @@ class TFDecoder(object):
         graph_def = cp.deepcopy(graph_def)
         input_map = dict()
         for layer in graph_def.node:
-            if layer.op != "Placeholder" and layer.op != "OneShotIterator":
+            if layer.op != "Placeholder" and layer.op != "OneShotIterator" and layer.op != "IteratorV2":
                 continue
             graph_node = TFGraphNode(layer)
             dtype = graph_node.layer.attr['dtype'].type

x2paddle/op_mapper/tf_op_mapper_nhwc.py

@@ -70,7 +70,7 @@ class TFOpMapperNHWC(OpMapper):
 
         not_placeholder = list()
         for name in self.graph.input_nodes:
-            if self.graph.get_node(name).layer_type != "Placeholder":
+            if self.graph.get_node(name).layer_type != "Placeholder" and self.graph.get_node(name).layer_type != "OneShotIterator" and self.graph.get_node(name).layer_type != "IteratorV2":
                 not_placeholder.append(name)
         for name in not_placeholder:
             idx = self.graph.input_nodes.index(name)
@@ -79,7 +79,7 @@ class TFOpMapperNHWC(OpMapper):
         unsupported_ops = set()
         sys.stderr.write("Total nodes: {}\n".format(len(self.graph.topo_sort)))
         for i, node_name in enumerate(self.graph.topo_sort):
-            sys.stderr.write("\rConverting node {} ...     ".format(i + 1))
+            sys.stderr.write("\rConverting node {} name: {:50}...     ".format(i + 1, node_name))
             node = self.graph.get_node(node_name)
             op = node.layer_type
             if op in self.directly_map_ops:
@@ -94,11 +94,12 @@ class TFOpMapperNHWC(OpMapper):
                 if len(unsupported_ops) > 0:
                     continue
                 func = getattr(self, op)
-                try:
                 func(node)
-                except Exception as e:
-                    print(str(e))
-                    unsupported_ops.add(op)
+#                try:
+#                    func(node)
+#                except Exception as e:
+#                    print(str(e))
+#                    unsupported_ops.add(op)
             else:
                 unsupported_ops.add(op)
         if len(unsupported_ops) > 0:
@@ -710,6 +711,10 @@ class TFOpMapperNHWC(OpMapper):
     def BatchMatMul(self, node):
         return self.MatMul(node)
 
+    def BatchMatMulV2(self, node):
+        return self.MatMul(node)
+
+
     def ArgMax(self, node):
         input = self.graph.get_node(node.layer.input[0], copy=True)
         axis = self.graph.get_node(node.layer.input[1], copy=True)
@@ -810,13 +815,14 @@ class TFOpMapperNHWC(OpMapper):
         input = self.graph.get_node(node.layer.input[0], copy=True)
         begin = self.graph.get_node(node.layer.input[1], copy=True)
         size = self.graph.get_node(node.layer.input[2], copy=True)
+
         self.add_omit_nodes(begin.layer_name, node.layer_name)
         self.add_omit_nodes(size.layer_name, node.layer_name)
         if begin.layer_type == "Const":
             begin = begin.value.tolist()
         else:
             begin = self.decoder.infer_tensor(begin).tolist()
-        if size.layer_type == "const":
+        if size.layer_type == "Const":
             size = size.value.tolist()
         else:
             size = self.decoder.infer_tensor(size).tolist()
@@ -1173,3 +1179,52 @@ class TFOpMapperNHWC(OpMapper):
                                   inputs=inputs,
                                   output=node,
                                   param_attr=attr)
+
+    def OneShotIterator(self, node):
+        return self.Placeholder(node)
+
+    def IteratorV2(self, node):
+        dtype_map = {
+            1: "float32",
+            3: "int32",
+            4: "uint8",
+            9: "int64",
+            10: "bool"
+        }
+        shapes = node.out_shapes
+        dtypes = node.layer.attr['output_types'].list.type
+        node.fluid_code.add_note("{} = [0] * {}".format(node.layer_name, len(shapes)))
+        for i, shape in enumerate(shapes):
+            attr = {
+                'dtype': string(dtype_map[dtypes[i]]),
+                'shape': shape,
+                'name': string("{}_{}".format(node.layer_name, i)),
+                'append_batch_size': False
+            } 
+            output = "{}[{}]".format(node.layer_name, i)
+            node.fluid_code.add_layer("data",
+                                      inputs=None,
+                                      output=output,
+                                      param_attr=attr)
+
+    def Fill(self, node):
+        dims = self.graph.get_node(node.layer.input[0], copy=True)
+        value = self.graph.get_node(node.layer.input[1], copy=True)
+        assert dims.layer_type == 'Const', "Only support Const parameter in Fill OP"
+        assert value.layer_type == 'Const', "Only support Const parameter in Fill OP"
+        self.add_omit_nodes(dims.layer_name, node.layer_name)
+        self.add_omit_nodes(value.layer_name, node.layer_name)
+        dims = dims.value.tolist()
+        value = value.value
+
+        initializer = "Constant({})".format(value)
+        attr = {
+            'dtype': string(node.dtype),
+            'shape': dims,
+            'name': string(node.layer_name),
+            'default_initializer': initializer
+        }
+        node.fluid_code.add_layer("create_parameter",
+                                  inputs=None,
+                                  output=node,
+                                  param_attr=attr)