- a more comprehensive test graph

- add op
the first tensor still fails

Author: neil-tan

tests/tflm/tflite_export/conftest.py

@@ -9,49 +9,91 @@
 def simple_tflm_graph():
     ugraph = uTensorGraph()
 
-    weight_op = OperationInfo(
-        name = "weight_const",
+    mock_input_op0 = OperationInfo(
+        name = "mock_input_const0",
         op_type = "Const",
         backend = "tensorflow",
         ugraph = ugraph,
         op_attr = dict(),
         input_tensors = [],
         output_tensors = []
     )
-    #weight_op.op_attr["value"] = np.array([1,2,3,4], dtype=np.int8)
-    weight_op.op_attr["value"] = np.array([10,20,30,40], dtype=np.float32)
-    weight_op.op_attr["shape"] = [1,4]
-
-    weight = TensorInfo(
-        name = "weight",
-        op_name = "weight_const",
-        dtype = np.dtype("float32"),
-        shape = weight_op.op_attr["shape"],
+    mock_input_op0.op_attr["value"] = np.array([[2],[4],[6],[8]], dtype=np.float32)
+    mock_input_op0.op_attr["shape"] = [4,1]
+
+    input0 = TensorInfo(
+        name = "input0",
+        op_name = "mock_input_const0",
+        dtype = mock_input_op0.op_attr["value"].dtype,
+        shape = mock_input_op0.op_attr["shape"],
         ugraph = ugraph
     )
-    weight_op.output_tensors = [weight]
 
-    mock_input_op = OperationInfo(
-        name = "mock_input_const",
+    mock_input_op0.output_tensors = [input0]
+
+    mock_input1_op = OperationInfo(
+        name = "mock_input_const1",
         op_type = "Const",
         backend = "tensorflow",
         ugraph = ugraph,
         op_attr = dict(),
         input_tensors = [],
         output_tensors = []
     )
-    mock_input_op.op_attr["value"] = np.array([[2],[4],[6],[8]], dtype=np.float32)
-    mock_input_op.op_attr["shape"] = [4,1]
+    mock_input1_op.op_attr["value"] = np.array([[2],[4],[6],[8]], dtype=np.float32)
+    mock_input1_op.op_attr["shape"] = [4,1]
 
     input1 = TensorInfo(
         name = "input1",
-        op_name = "mock_input_const",
-        dtype = mock_input_op.op_attr["value"].dtype,
-        shape = mock_input_op.op_attr["shape"],
+        op_name = "mock_input_const1",
+        dtype = mock_input1_op.op_attr["value"].dtype,
+        shape = mock_input1_op.op_attr["shape"],
+        ugraph = ugraph
+    )
+
+    mock_input1_op.output_tensors = [input1]
+
+    add_output = TensorInfo(
+        name = "add_out",
+        op_name = "add0",
+        dtype = mock_input_op0.op_attr["value"].dtype,
+        shape = mock_input_op0.op_attr["shape"],
         ugraph = ugraph
     )
 
-    mock_input_op.output_tensors = [input1]
+    add_op = OperationInfo(
+        name = "add0",
+        op_type = "ADD",
+        backend = "tensorflow",
+        ugraph = ugraph,
+        op_attr = dict(),
+        input_tensors = [input0, input1],
+        output_tensors = [add_output]
+    )
+
+    ugraph.ops_info["ADD0"] = add_op
+
+    weight_op = OperationInfo(
+        name = "weight_const",
+        op_type = "Const",
+        backend = "tensorflow",
+        ugraph = ugraph,
+        op_attr = dict(),
+        input_tensors = [],
+        output_tensors = []
+    )
+    #weight_op.op_attr["value"] = np.array([1,2,3,4], dtype=np.int8)
+    weight_op.op_attr["value"] = np.array([10,20,30,40], dtype=np.float32)
+    weight_op.op_attr["shape"] = [1,4]
+
+    weight = TensorInfo(
+        name = "weight",
+        op_name = "weight_const",
+        dtype = np.dtype("float32"),
+        shape = weight_op.op_attr["shape"],
+        ugraph = ugraph
+    )
+    weight_op.output_tensors = [weight]
 
     bias_op = OperationInfo(
         name = "bias_const",
@@ -94,7 +136,7 @@ def simple_tflm_graph():
         ugraph = ugraph
     )
 
-    fc1_op.input_tensors = [input1, weight, bias]
+    fc1_op.input_tensors = [add_output, weight, bias]
     fc1_op.output_tensors = [output]
 
     ugraph.ops_info["FC1"] = fc1_op
@@ -105,4 +147,4 @@ def simple_tflm_graph():
     #ugraph = prune_graph(ugraph)
 
     #return: ugraph, input tensors, output tensors
-    return [ugraph, [], ["input1", "weight", "bias", "output"]]
+    return [ugraph, ["input0", "input1"], ["weight", "bias", "output"]]

tests/tflm/tflite_export/test_write.py

@@ -86,7 +86,7 @@ def test_tflite_fb_write(hybrid_quant_output):
     ugraph = exporter.transform(sample_ugraph)
     model_content = exporter.output()
 
-    #print_tflite_graph(model_content)
+    print_tflite_graph(model_content)
 
     # referece_model_content = open('/Users/neitan01/Documents/tflm/sinExample/sine_model.tflite', "rb").read()
     # print_tflite_graph(referece_model_content)
@@ -96,11 +96,17 @@ def test_tflite_fb_write(hybrid_quant_output):
     test_model.allocate_tensors()
     test_model.invoke()
 
+    print("0 :", test_model.get_tensor(0))
+    print("1 :", test_model.get_tensor(1))
+    print("2 :", test_model.get_tensor(2))
+    print("3 :", test_model.get_tensor(3))
+
+
     print(test_model.get_tensor_details())
-    print("1 :", test_model.get_tensor(test_model.get_output_details()[0]["index"]))
-    print("2 :", test_model.get_tensor(test_model.get_output_details()[1]["index"]))
-    print("3 :", test_model.get_tensor(test_model.get_output_details()[2]["index"]))
-    print("out :", test_model.get_tensor(test_model.get_output_details()[3]["index"]))
+    print("out0 :", test_model.get_tensor(test_model.get_output_details()[0]["index"]))
+    print("out1 :", test_model.get_tensor(test_model.get_output_details()[1]["index"]))
+    print("out2 :", test_model.get_tensor(test_model.get_output_details()[2]["index"]))
+
 
     test_pass = True
     assert test_pass, 'error message here'

utensor_cgen/transformer/tflite_exporter.py

@@ -38,6 +38,15 @@ def get_fullyconnected_builtin_option(fbuilder, op_info):
 
   return obj, BuiltinOptions.FullyConnectedOptions
 
+def get_add_builtin_option(fbuilder, op_info):
+
+  tflite.AddOptions.AddOptionsStart(fbuilder)
+  #FIXME: node fusion and select an activation function here
+  tflite.AddOptions.AddOptionsAddFusedActivationFunction(fbuilder, ActivationFunctionType.NONE)
+  obj = tflite.AddOptions.AddOptionsEnd(fbuilder)
+
+  return obj, BuiltinOptions.AddOptions
+
 def tensor_type_lookup(numpy_dtype):
   TensorType = tflite.TensorType.TensorType
   lookup_map = dict()
@@ -249,6 +258,7 @@ def add_Op(self, op_info):
     ##TODO: add op factory to deal with op options
     op_option_factory = dict()
     op_option_factory["FULLY_CONNECTED"] = get_fullyconnected_builtin_option
+    op_option_factory["ADD"] = get_add_builtin_option
 
     builtin_opt_func = op_option_factory[op_info.op_type]
 
@@ -314,11 +324,11 @@ def __create_variable_tensors(self, ugraph):
 
       tflite.Tensor.TensorStart(self.fbuilder)
       tflite.Tensor.TensorAddShape(self.fbuilder, shape_vec)
-      #tflite.Tensor.TensorAddType(self.fbuilder, TensorType.INT8) #TODO: tensor type conversion here
       tflite.Tensor.TensorAddType(self.fbuilder, tensor_type_lookup(tensor_info.dtype))
       tflite.Tensor.TensorAddName(self.fbuilder, tensor_name)
       #tflite.Tensor.TensorAddQuantization(self.fbuilder, q_param)
       tflite.Tensor.TensorAddIsVariable(self.fbuilder, True)
+      #tflite.Tensor.TensorAddIsVariable(self.fbuilder, False) #FIXME: TOCO outputs False here, no idea why
 
       self.tensor_index[tensor_info.name] = tflite.Tensor.TensorEnd(self.fbuilder)