Automated sync from github.com/tensorflow/tensorflow

codegen/examples/hello_world/hello_world_model.cc

@@ -122,7 +122,8 @@ struct Node0_0 {
       .activation = kTfLiteActRelu,
       .weights_format = kTfLiteFullyConnectedWeightsFormatDefault,
       .keep_num_dims = false,
-      .asymmetric_quantize_inputs = false};
+      .asymmetric_quantize_inputs = false,
+      .quantized_bias_type = kTfLiteNoType};
 } node_0_0;
 
 struct Node0_1 {
@@ -139,7 +140,8 @@ struct Node0_1 {
       .activation = kTfLiteActRelu,
       .weights_format = kTfLiteFullyConnectedWeightsFormatDefault,
       .keep_num_dims = false,
-      .asymmetric_quantize_inputs = false};
+      .asymmetric_quantize_inputs = false,
+      .quantized_bias_type = kTfLiteNoType};
 } node_0_1;
 
 struct Node0_2 {
@@ -156,7 +158,8 @@ struct Node0_2 {
       .activation = kTfLiteActNone,
       .weights_format = kTfLiteFullyConnectedWeightsFormatDefault,
       .keep_num_dims = false,
-      .asymmetric_quantize_inputs = false};
+      .asymmetric_quantize_inputs = false,
+      .quantized_bias_type = kTfLiteNoType};
 } node_0_2;
 
 struct Tensor0_0Dims {

codegen/operators/constants.py

@@ -26,3 +26,25 @@
     schema_fb.ActivationFunctionType.TANH: "kTfLiteActTanh",
     schema_fb.ActivationFunctionType.SIGN_BIT: "kTfLiteActSignBit",
 }
+
+TFLITE_TYPE: Dict[int, str] = {
+    0: "kTfLiteNoType",
+    1: "kTfLiteFloat32",
+    2: "kTfLiteInt32",
+    3: "kTfLiteUInt8",
+    4: "kTfLiteInt64",
+    5: "kTfLiteString",
+    6: "kTfLiteBool",
+    7: "kTfLiteInt16",
+    8: "kTfLiteComplex64",
+    9: "kTfLiteInt8",
+    10: "kTfLiteFloat16",
+    11: "kTfLiteFloat64",
+    12: "kTfLiteComplex128",
+    13: "kTfLiteUInt64",
+    14: "kTfLiteResource",
+    15: "kTfLiteVariant",
+    16: "kTfLiteUInt32",
+    17: "kTfLiteUInt16",
+    18: "kTfLiteInt4",
+}

codegen/operators/fully_connected.py

@@ -43,11 +43,14 @@ def generate_c_builtin_data(self) -> str:
         "    .activation = ${activation},\n"
         "    .weights_format = ${weights_format},\n"
         "    .keep_num_dims = ${keep_num_dims},\n"
-        "    .asymmetric_quantize_inputs = ${asymmetric_quantize_inputs}};")
+        "    .asymmetric_quantize_inputs = ${asymmetric_quantize_inputs},\n"
+        "    .quantized_bias_type = ${quantized_bias_type}};")
     return builtin_template.substitute(
         activation=constants.ACTIVATION_FUNCS[
             self._builtin_options.fusedActivationFunction],
         weights_format=_WEIGHTS_FORMATS[self._builtin_options.weightsFormat],
         keep_num_dims=utils.bool_to_c_str(self._builtin_options.keepNumDims),
         asymmetric_quantize_inputs=utils.bool_to_c_str(
-            self._builtin_options.asymmetricQuantizeInputs))
+            self._builtin_options.asymmetricQuantizeInputs),
+        quantized_bias_type=constants.TFLITE_TYPE[
+            self._builtin_options.quantizedBiasType])

tensorflow/lite/core/api/flatbuffer_conversions.cc

@@ -1305,6 +1305,9 @@ TfLiteStatus ParseConv2D(const Operator* op, ErrorReporter* error_reporter,
 
     params->dilation_width_factor = schema_params->dilation_w_factor();
     params->dilation_height_factor = schema_params->dilation_h_factor();
+    TF_LITE_ENSURE_STATUS(
+        ConvertTensorType(schema_params->quantized_bias_type(),
+                          &params->quantized_bias_type, error_reporter));
   } else {
     // TODO(b/157480169): We should either return kTfLiteError or fill in some
     // reasonable defaults in the params struct. We are not doing so until we
@@ -1519,7 +1522,9 @@ TfLiteStatus ParseFullyConnected(const Operator* op,
     params->keep_num_dims = schema_params->keep_num_dims();
     params->asymmetric_quantize_inputs =
         schema_params->asymmetric_quantize_inputs();
-
+    TF_LITE_ENSURE_STATUS(
+        ConvertTensorType(schema_params->quantized_bias_type(),
+                          &params->quantized_bias_type, error_reporter));
     switch (schema_params->weights_format()) {
       case FullyConnectedOptionsWeightsFormat_DEFAULT:
         params->weights_format = kTfLiteFullyConnectedWeightsFormatDefault;
@@ -2450,6 +2455,9 @@ TfLiteStatus ParseTransposeConv(const Operator* op,
 
     params->activation =
         ConvertActivation(transpose_conv_params->fused_activation_function());
+    TF_LITE_ENSURE_STATUS(
+        ConvertTensorType(transpose_conv_params->quantized_bias_type(),
+                          &params->quantized_bias_type, error_reporter));
   } else {
     // TODO(b/157480169): We should either return kTfLiteError or fill in some
     // reasonable defaults in the params struct. We are not doing so until we

tensorflow/lite/core/c/builtin_op_data.h

@@ -91,6 +91,10 @@ typedef struct {
   // Note: Version 2 supports dilation values not equal to 1.
   int dilation_width_factor;
   int dilation_height_factor;
+
+  // Parameters for CONV_2D version 7 or above.
+  // Used to determine the default value for the quantized bias.
+  TfLiteType quantized_bias_type;
 } TfLiteConvParams;
 
 typedef struct {
@@ -194,6 +198,10 @@ typedef struct {
   // If set to true and the weights are quantized, then non constant inputs
   // are quantized at evaluation time with asymmetric quantization.
   bool asymmetric_quantize_inputs;
+
+  // Parameters for FullyConnected version 10 or above.
+  // Used to determine the default value for the quantized bias.
+  TfLiteType quantized_bias_type;
 } TfLiteFullyConnectedParams;
 
 typedef enum {
@@ -431,6 +439,10 @@ typedef struct {
 
   // Parameters supported by version 4:
   TfLiteFusedActivation activation;
+
+  // Parameters for TransposeConv version 5 or above.
+  // Used to determine the default value for the quantized bias.
+  TfLiteType quantized_bias_type;
 } TfLiteTransposeConvParams;
 
 typedef struct {

tensorflow/lite/micro/kernels/conv_test.cc

@@ -53,6 +53,7 @@ static TfLiteConvParams common_conv_params = {
     kTfLiteActNone,       // activation
     1,                    // dilation_width_factor
     1,                    // dilation_height_factor
+    kTfLiteNoType         // quantized_bias_type
 };
 
 }  // namespace
@@ -420,8 +421,8 @@ TF_LITE_MICRO_TEST(SimpleTestQuantized16x8PerChannelRelu632bBias) {
 TF_LITE_MICRO_TEST(Kernel1x1QuantizedPerChannel) {
   // conv params:
   // padding, stride_<width,height>, activation, dilation_<width, height>
-  TfLiteConvParams conv_params = {kTfLitePaddingValid, 1, 1,
-                                  kTfLiteActNone,      1, 1};
+  TfLiteConvParams conv_params = {
+      kTfLitePaddingValid, 1, 1, kTfLiteActNone, 1, 1, kTfLiteNoType};
 
   int input_shape[] = {4, 1, 2, 2, 4};  // [len,N,H,W,C]
   constexpr int input_elements =
@@ -473,8 +474,8 @@ TF_LITE_MICRO_TEST(Kernel1x1QuantizedPerChannel) {
 TF_LITE_MICRO_TEST(Kernel1x1QuantizedPerChannelRelu6) {
   // conv params:
   // padding, stride_<width,height>, activation, dilation_<width, height>
-  TfLiteConvParams conv_params = {kTfLitePaddingValid, 1, 1,
-                                  kTfLiteActRelu6,     1, 1};
+  TfLiteConvParams conv_params = {
+      kTfLitePaddingValid, 1, 1, kTfLiteActRelu6, 1, 1, kTfLiteNoType};
 
   int input_shape[] = {4, 1, 2, 2, 4};  // [len,N,H,W,C]
   constexpr int input_elements =
@@ -526,8 +527,8 @@ TF_LITE_MICRO_TEST(Kernel1x1QuantizedPerChannelRelu6) {
 TF_LITE_MICRO_TEST(Kernel1x1Quantized16x8PerChannelRelu6) {
   // conv params:
   // padding, stride_<width,height>, activation, dilation_<width, height>
-  TfLiteConvParams conv_params = {kTfLitePaddingValid, 1, 1,
-                                  kTfLiteActRelu6,     1, 1};
+  TfLiteConvParams conv_params = {
+      kTfLitePaddingValid, 1, 1, kTfLiteActRelu6, 1, 1, kTfLiteNoType};
 
   int input_shape[] = {4, 1, 2, 2, 4};  // [len,N,H,W,C]
   const int input_elements = 1 * 2 * 2 * 4;

tensorflow/lite/micro/kernels/fully_connected_test.cc

@@ -247,7 +247,8 @@ TfLiteStatus ValidateFullyConnectedGoldens(
     const TfLiteFusedActivation activation, const float tolerance,
     const int output_len, const T* golden, T* output_data) {
   TfLiteFullyConnectedParams builtin_data = {
-      activation, kTfLiteFullyConnectedWeightsFormatDefault, false, false};
+      activation, kTfLiteFullyConnectedWeightsFormatDefault, false, false,
+      kTfLiteNoType};
 
   // Avoid variable length array warning.
   constexpr int inputs_array_len = 4;

tensorflow/lite/micro/kernels/transpose_conv_test.cc

@@ -53,7 +53,8 @@ static TfLiteConvParams common_conv_params = {kTfLitePaddingSame,  // padding
                                               1,  // stride_height
                                               kTfLiteActNone,
                                               1,
-                                              1};
+                                              1,
+                                              kTfLiteNoType};
 
 template <typename T>
 TfLiteStatus InvokeTransposeConv(TfLiteTensor* tensors, int tensors_size,
@@ -253,7 +254,8 @@ TF_LITE_MICRO_TEST(fusedRELUTest) {
                                   1,                   // stride_height
                                   kTfLiteActRelu,
                                   1,
-                                  1};
+                                  1,
+                                  kTfLiteNoType};
 
   TF_LITE_MICRO_EXPECT_EQ(
       kTfLiteOk, tflite::testing::TestTransposeConvFloat(
@@ -276,7 +278,8 @@ TF_LITE_MICRO_TEST(AccuracyWithFusedActivationTest) {
                                   3,                   // stride_height
                                   kTfLiteActRelu,
                                   1,
-                                  1};
+                                  1,
+                                  kTfLiteNoType};
 
   TF_LITE_MICRO_EXPECT_EQ(
       kTfLiteOk, tflite::testing::TestTransposeConvFloat(
@@ -304,7 +307,8 @@ TF_LITE_MICRO_TEST(MultiChannelBiasWithFusedActivationTest) {
                                   2,                    // stride_height
                                   kTfLiteActRelu,
                                   1,
-                                  1};
+                                  1,
+                                  kTfLiteNoType};
 
   TF_LITE_MICRO_EXPECT_EQ(
       kTfLiteOk,

tensorflow/lite/micro/memory_arena_threshold_test.cc

@@ -97,7 +97,7 @@ constexpr int kTestConvModelOnlyTotalSize = 9488;
 // Tail size contributed by the conv model excluding the
 // RecordingMicroAllocator's overhead
 // TODO(b/207157610): replace magic number that depends on OPs
-constexpr int kTestConvModelOnlyTailSize = 1744;
+constexpr int kTestConvModelOnlyTailSize = 1816;
 constexpr int kTestConvModelPersistentTfLiteTensorDataSize = 128;
 constexpr int kTestConvModelPersistentBufferDataSize = 728;
 #else
@@ -108,7 +108,7 @@ constexpr int kTestConvModelOnlyTotalSize = 9760;
 // Tail size contributed by the conv model excluding the
 // RecordingMicroAllocator's overhead
 // TODO(b/207157610): replace magic number that depends on OPs
-constexpr int kTestConvModelOnlyTailSize = 2016;
+constexpr int kTestConvModelOnlyTailSize = 2088;
 constexpr int kTestConvModelPersistentTfLiteTensorDataSize = 224;
 constexpr int kTestConvModelPersistentBufferDataSize = 720;
 #endif

tensorflow/lite/python/schema_py_generated.py

@@ -2640,7 +2640,14 @@ def DilationHFactor(self):
             return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
         return 1
 
-def Conv2DOptionsStart(builder): builder.StartObject(6)
+    # Conv2DOptions
+    def QuantizedBiasType(self):
+        o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(16))
+        if o != 0:
+            return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
+        return 0
+
+def Conv2DOptionsStart(builder): builder.StartObject(7)
 def Start(builder):
     return Conv2DOptionsStart(builder)
 def Conv2DOptionsAddPadding(builder, padding): builder.PrependInt8Slot(0, padding, 0)
@@ -2661,6 +2668,9 @@ def AddDilationWFactor(builder, dilationWFactor):
 def Conv2DOptionsAddDilationHFactor(builder, dilationHFactor): builder.PrependInt32Slot(5, dilationHFactor, 1)
 def AddDilationHFactor(builder, dilationHFactor):
     return Conv2DOptionsAddDilationHFactor(builder, dilationHFactor)
+def Conv2DOptionsAddQuantizedBiasType(builder, quantizedBiasType): builder.PrependInt8Slot(6, quantizedBiasType, 0)
+def AddQuantizedBiasType(builder, quantizedBiasType):
+    return Conv2DOptionsAddQuantizedBiasType(builder, quantizedBiasType)
 def Conv2DOptionsEnd(builder): return builder.EndObject()
 def End(builder):
     return Conv2DOptionsEnd(builder)
@@ -2675,6 +2685,7 @@ def __init__(self):
         self.fusedActivationFunction = 0  # type: int
         self.dilationWFactor = 1  # type: int
         self.dilationHFactor = 1  # type: int
+        self.quantizedBiasType = 0  # type: int
 
     @classmethod
     def InitFromBuf(cls, buf, pos):
@@ -2698,6 +2709,7 @@ def _UnPack(self, conv2doptions):
         self.fusedActivationFunction = conv2doptions.FusedActivationFunction()
         self.dilationWFactor = conv2doptions.DilationWFactor()
         self.dilationHFactor = conv2doptions.DilationHFactor()
+        self.quantizedBiasType = conv2doptions.QuantizedBiasType()
 
     # Conv2DOptionsT
     def Pack(self, builder):
@@ -2708,6 +2720,7 @@ def Pack(self, builder):
         Conv2DOptionsAddFusedActivationFunction(builder, self.fusedActivationFunction)
         Conv2DOptionsAddDilationWFactor(builder, self.dilationWFactor)
         Conv2DOptionsAddDilationHFactor(builder, self.dilationHFactor)
+        Conv2DOptionsAddQuantizedBiasType(builder, self.quantizedBiasType)
         conv2doptions = Conv2DOptionsEnd(builder)
         return conv2doptions
 # automatically generated by the FlatBuffers compiler, do not modify
@@ -4512,7 +4525,14 @@ def AsymmetricQuantizeInputs(self):
             return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
         return False
 
-def FullyConnectedOptionsStart(builder): builder.StartObject(4)
+    # FullyConnectedOptions
+    def QuantizedBiasType(self):
+        o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12))
+        if o != 0:
+            return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
+        return 0
+
+def FullyConnectedOptionsStart(builder): builder.StartObject(5)
 def Start(builder):
     return FullyConnectedOptionsStart(builder)
 def FullyConnectedOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(0, fusedActivationFunction, 0)
@@ -4527,6 +4547,9 @@ def AddKeepNumDims(builder, keepNumDims):
 def FullyConnectedOptionsAddAsymmetricQuantizeInputs(builder, asymmetricQuantizeInputs): builder.PrependBoolSlot(3, asymmetricQuantizeInputs, 0)
 def AddAsymmetricQuantizeInputs(builder, asymmetricQuantizeInputs):
     return FullyConnectedOptionsAddAsymmetricQuantizeInputs(builder, asymmetricQuantizeInputs)
+def FullyConnectedOptionsAddQuantizedBiasType(builder, quantizedBiasType): builder.PrependInt8Slot(4, quantizedBiasType, 0)
+def AddQuantizedBiasType(builder, quantizedBiasType):
+    return FullyConnectedOptionsAddQuantizedBiasType(builder, quantizedBiasType)
 def FullyConnectedOptionsEnd(builder): return builder.EndObject()
 def End(builder):
     return FullyConnectedOptionsEnd(builder)
@@ -4539,6 +4562,7 @@ def __init__(self):
         self.weightsFormat = 0  # type: int
         self.keepNumDims = False  # type: bool
         self.asymmetricQuantizeInputs = False  # type: bool
+        self.quantizedBiasType = 0  # type: int
 
     @classmethod
     def InitFromBuf(cls, buf, pos):
@@ -4560,6 +4584,7 @@ def _UnPack(self, fullyConnectedOptions):
         self.weightsFormat = fullyConnectedOptions.WeightsFormat()
         self.keepNumDims = fullyConnectedOptions.KeepNumDims()
         self.asymmetricQuantizeInputs = fullyConnectedOptions.AsymmetricQuantizeInputs()
+        self.quantizedBiasType = fullyConnectedOptions.QuantizedBiasType()
 
     # FullyConnectedOptionsT
     def Pack(self, builder):
@@ -4568,6 +4593,7 @@ def Pack(self, builder):
         FullyConnectedOptionsAddWeightsFormat(builder, self.weightsFormat)
         FullyConnectedOptionsAddKeepNumDims(builder, self.keepNumDims)
         FullyConnectedOptionsAddAsymmetricQuantizeInputs(builder, self.asymmetricQuantizeInputs)
+        FullyConnectedOptionsAddQuantizedBiasType(builder, self.quantizedBiasType)
         fullyConnectedOptions = FullyConnectedOptionsEnd(builder)
         return fullyConnectedOptions
 # automatically generated by the FlatBuffers compiler, do not modify
@@ -16436,7 +16462,14 @@ def FusedActivationFunction(self):
             return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
         return 0
 
-def TransposeConvOptionsStart(builder): builder.StartObject(4)
+    # TransposeConvOptions
+    def QuantizedBiasType(self):
+        o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12))
+        if o != 0:
+            return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
+        return 0
+
+def TransposeConvOptionsStart(builder): builder.StartObject(5)
 def Start(builder):
     return TransposeConvOptionsStart(builder)
 def TransposeConvOptionsAddPadding(builder, padding): builder.PrependInt8Slot(0, padding, 0)
@@ -16451,6 +16484,9 @@ def AddStrideH(builder, strideH):
 def TransposeConvOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(3, fusedActivationFunction, 0)
 def AddFusedActivationFunction(builder, fusedActivationFunction):
     return TransposeConvOptionsAddFusedActivationFunction(builder, fusedActivationFunction)
+def TransposeConvOptionsAddQuantizedBiasType(builder, quantizedBiasType): builder.PrependInt8Slot(4, quantizedBiasType, 0)
+def AddQuantizedBiasType(builder, quantizedBiasType):
+    return TransposeConvOptionsAddQuantizedBiasType(builder, quantizedBiasType)
 def TransposeConvOptionsEnd(builder): return builder.EndObject()
 def End(builder):
     return TransposeConvOptionsEnd(builder)
@@ -16463,6 +16499,7 @@ def __init__(self):
         self.strideW = 0  # type: int
         self.strideH = 0  # type: int
         self.fusedActivationFunction = 0  # type: int
+        self.quantizedBiasType = 0  # type: int
 
     @classmethod
     def InitFromBuf(cls, buf, pos):
@@ -16484,6 +16521,7 @@ def _UnPack(self, transposeConvOptions):
         self.strideW = transposeConvOptions.StrideW()
         self.strideH = transposeConvOptions.StrideH()
         self.fusedActivationFunction = transposeConvOptions.FusedActivationFunction()
+        self.quantizedBiasType = transposeConvOptions.QuantizedBiasType()
 
     # TransposeConvOptionsT
     def Pack(self, builder):
@@ -16492,6 +16530,7 @@ def Pack(self, builder):
         TransposeConvOptionsAddStrideW(builder, self.strideW)
         TransposeConvOptionsAddStrideH(builder, self.strideH)
         TransposeConvOptionsAddFusedActivationFunction(builder, self.fusedActivationFunction)
+        TransposeConvOptionsAddQuantizedBiasType(builder, self.quantizedBiasType)
         transposeConvOptions = TransposeConvOptionsEnd(builder)
         return transposeConvOptions
 # automatically generated by the FlatBuffers compiler, do not modify