fix for keras bert

x2paddle/decoder/tf_decoder.py

@@ -60,7 +60,7 @@ class TFGraphNode(GraphNode):
 
     @property
     def dtype(self):
-        keys = ['dtype', 'Tidx', 'T', 'DstT']
+        keys = ['dtype', 'T', 'DstT']
         for k in keys:
             dtype = self.layer.attr[k].type
             if dtype > 0:

x2paddle/op_mapper/tf_op_mapper.py

@@ -744,7 +744,7 @@ class TFOpMapper(OpMapper):
 
         input_names = [i.name for i in inputs]
         for i, ipt in enumerate(inputs):
-            if node.dtype == 'bool':
+            if ipt.dtype == 'bool':
                 cast_name = gen_name('concat', 'cast')
                 program.add_layer(
                     kernel="fluid.layers.cast",
@@ -1213,9 +1213,17 @@ class TFOpMapper(OpMapper):
         attr["dim"] = reduce_idx.value.tolist()
         attr["keep_dim"] = node.get_attr("keep_dims")
 
+        input_name = input.name
+        if input.dtype != "bool":
+            input_name = gen_name("all", "cast")
+            program.add_layer(
+                "fluid.layers.cast",
+                inputs={"x": input.name},
+                outputs=[input_name],
+                dtype=string("bool"))
         program.add_layer(
             "fluid.layers.reduce_all",
-            inputs={"input": input.name},
+            inputs={"input": input_name},
             outputs=[node.name],
             **attr)
 

x2paddle/optimizer/tensorflow/batch_norm.py

@@ -8,6 +8,7 @@ class BatchNormOpt:
         pass
 
     def run(self, graph):
+        print("Optimize: BatchNormOpt...")
         layers = copy.deepcopy(graph.layers)
         for layer_id, layer in layers.items():
             if layer.kernel != "fluid.layers.elementwise_add":

x2paddle/optimizer/tensorflow/bias.py

@@ -13,6 +13,7 @@ class BiasOpt:
         ]
 
     def run(self, graph):
+        print("Optimize: BiasOpt...")
         layers = copy.deepcopy(graph.layers)
         for layer_id, layer in layers.items():
             if layer.kernel in self.conv_layers or layer.kernel == "fluid.layers.transpose":

x2paddle/optimizer/tensorflow/transpose.py

@@ -36,6 +36,7 @@ class TransposeOpt:
         return count
 
     def run(self, graph):
+        print("Optimize: TransposeOpt...")
         total_layer_num = len(graph.layers)
         scanned_layers = set()
         optimized_transpose_layers = list()