Rollback of BroadcastTo op additions (part 3)
Rolling back until discussion about builtin ops schema issue is discussed.
PiperOrigin-RevId: 322891511
Change-Id: Idf64195f89855cf6201cef9d5fd0ec24db403ec1
diff --git a/RELEASE.md b/RELEASE.md
index a4f3643..00c056b 100644
--- a/RELEASE.md
+++ b/RELEASE.md
@@ -64,8 +64,7 @@
True, the function may use type annotations to optimize the tracing
performance.
* `tf.lite`:
- * Better support for ops with high-dimensional broadcasting inputs by adding
- `BroadcastTo` ops when necessary.
+ * <ADD RELEASE NOTES HERE>
* `tf.random`:
* <ADD RELEASE NOTES HERE>
* Math and Linear Algebra:
diff --git a/tensorflow/lite/builtin_ops.h b/tensorflow/lite/builtin_ops.h
index c644072..8514028 100644
--- a/tensorflow/lite/builtin_ops.h
+++ b/tensorflow/lite/builtin_ops.h
@@ -153,7 +153,6 @@
kTfLiteBuiltinDensify = 124,
kTfLiteBuiltinSegmentSum = 125,
kTfLiteBuiltinBatchMatmul = 126,
- kTfLiteBuiltinBroadcastTo = 127,
} TfLiteBuiltinOperator;
#ifdef __cplusplus
diff --git a/tensorflow/lite/c/common.c b/tensorflow/lite/c/common.c
index 4bbbcbb..0264f42 100644
--- a/tensorflow/lite/c/common.c
+++ b/tensorflow/lite/c/common.c
@@ -219,29 +219,6 @@
return "Unknown type";
}
-// Size of string is not constant, return 0 in such case.
-int TfLiteTypeGetSize(TfLiteType type) {
- switch (type) {
- case kTfLiteUInt8:
- case kTfLiteInt8:
- return 1;
- case kTfLiteBool:
- return sizeof(bool);
- case kTfLiteInt16:
- case kTfLiteFloat16:
- return 2;
- case kTfLiteFloat32:
- case kTfLiteInt32:
- return 4;
- case kTfLiteInt64:
- case kTfLiteComplex64:
- case kTfLiteFloat64:
- return 8;
- default:
- return 0;
- }
-}
-
TfLiteDelegate TfLiteDelegateCreate() {
TfLiteDelegate d = {
.data_ = NULL,
diff --git a/tensorflow/lite/c/common.h b/tensorflow/lite/c/common.h
index 615b5fb..3398d17 100644
--- a/tensorflow/lite/c/common.h
+++ b/tensorflow/lite/c/common.h
@@ -284,9 +284,6 @@
// Return the name of a given type, for error reporting purposes.
const char* TfLiteTypeGetName(TfLiteType type);
-// Return the size of given type in bytes. Return 0 in in case of string.
-int TfLiteTypeGetSize(TfLiteType type);
-
// SupportedQuantizationTypes.
typedef enum TfLiteQuantizationType {
// No quantization.
diff --git a/tensorflow/lite/core/api/flatbuffer_conversions.cc b/tensorflow/lite/core/api/flatbuffer_conversions.cc
index 059ad97..0652c64 100644
--- a/tensorflow/lite/core/api/flatbuffer_conversions.cc
+++ b/tensorflow/lite/core/api/flatbuffer_conversions.cc
@@ -820,7 +820,6 @@
case BuiltinOperator_SCATTER_ND:
case BuiltinOperator_DENSIFY:
case BuiltinOperator_SEGMENT_SUM:
- case BuiltinOperator_BROADCAST_TO:
return kTfLiteOk;
}
return kTfLiteError;
diff --git a/tensorflow/lite/kernels/BUILD b/tensorflow/lite/kernels/BUILD
index 3157081..4351a2c 100644
--- a/tensorflow/lite/kernels/BUILD
+++ b/tensorflow/lite/kernels/BUILD
@@ -491,7 +491,6 @@
"batch_to_space_nd.cc",
"bidirectional_sequence_lstm.cc",
"bidirectional_sequence_rnn.cc",
- "broadcast_to.cc",
"cast.cc",
"ceil.cc",
"comparisons.cc",
@@ -986,19 +985,6 @@
)
cc_test(
- name = "broadcast_to_test",
- size = "small",
- srcs = ["broadcast_to_test.cc"],
- deps = [
- ":builtin_ops",
- ":test_main",
- ":test_util",
- "//tensorflow/lite:framework",
- "@com_google_googletest//:gtest",
- ],
-)
-
-cc_test(
name = "cast_test",
size = "small",
srcs = ["cast_test.cc"],
diff --git a/tensorflow/lite/kernels/broadcast_to.cc b/tensorflow/lite/kernels/broadcast_to.cc
deleted file mode 100644
index 0e7baca..0000000
--- a/tensorflow/lite/kernels/broadcast_to.cc
+++ /dev/null
@@ -1,136 +0,0 @@
-/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-#include "tensorflow/lite/kernels/internal/reference/broadcast_to.h"
-
-#include <string.h>
-
-#include <cstdint>
-#include <memory>
-
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/kernels/internal/tensor.h"
-#include "tensorflow/lite/kernels/kernel_util.h"
-
-namespace tflite {
-namespace ops {
-namespace builtin {
-namespace broadcastto {
-
-constexpr int kInputTensor = 0;
-constexpr int kShapeTensor = 1;
-constexpr int kOutputTensor = 0;
-constexpr int kMaxDims = 8;
-
-struct BroadcastToContext {
- BroadcastToContext(TfLiteContext* context, TfLiteNode* node) {
- input = GetInput(context, node, kInputTensor);
- shape = GetInput(context, node, kShapeTensor);
- output = GetOutput(context, node, kOutputTensor);
- }
- const TfLiteTensor* input;
- const TfLiteTensor* shape;
- TfLiteTensor* output;
-};
-
-TfLiteStatus ResizeOutputTensor(TfLiteContext* context,
- BroadcastToContext* op_context) {
- // Ensures the shape is 1D tensor.
- TF_LITE_ENSURE_EQ(context, NumDimensions(op_context->shape), 1);
-
- // Ensure output dims is not less than input dims.
- int input_num_dims = NumDimensions(op_context->input);
- int output_num_dims = SizeOfDimension(op_context->shape, 0);
- TF_LITE_ENSURE_MSG(context, input_num_dims <= output_num_dims,
- "Output shape must be broadcastable from input shape.");
- TF_LITE_ENSURE_MSG(context, output_num_dims <= kMaxDims,
- "BroadcastTo only supports 1-8D tensor.");
-
- // Check if output shape is broadcastable from input shape.
- auto get_shape_data = [op_context](int i) -> int32_t {
- if (op_context->shape->type == kTfLiteInt32) {
- return GetTensorData<int32_t>(op_context->shape)[i];
- } else {
- return GetTensorData<int64_t>(op_context->shape)[i];
- }
- };
-
- int extending_dims = output_num_dims - input_num_dims;
- for (int idx = 0; idx < input_num_dims; ++idx) {
- TF_LITE_ENSURE_MSG(context,
- (SizeOfDimension(op_context->input, idx) == 1 ||
- SizeOfDimension(op_context->input, idx) ==
- get_shape_data(extending_dims + idx)),
- "Output shape must be broadcastable from input shape.");
- }
- // Resizing the shape of the output tensor.
- TfLiteIntArray* output_shape = TfLiteIntArrayCreate(output_num_dims);
- std::unique_ptr<TfLiteIntArray, void (*)(TfLiteIntArray*)>
- scoped_output_shape(output_shape, TfLiteIntArrayFree);
- for (int idx = 0; idx < output_num_dims; ++idx) {
- output_shape->data[idx] = get_shape_data(idx);
- }
-
- return context->ResizeTensor(context, op_context->output,
- scoped_output_shape.release());
-}
-
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
- TF_LITE_ENSURE(context, NumInputs(node) == 2);
- TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
- TF_LITE_ENSURE_MSG(context,
- (NumDimensions(GetInput(context, node, 0)) <= kMaxDims),
- "BroadcastTo only supports 1-8D tensor.");
-
- BroadcastToContext op_context(context, node);
- TF_LITE_ENSURE(context, op_context.shape->type == kTfLiteInt32 ||
- op_context.shape->type == kTfLiteInt64);
- TF_LITE_ENSURE_EQ(context, op_context.input->type, op_context.output->type);
-
- // Not yet support string type due to the use of memcopy with fixed size.
- TF_LITE_ENSURE(context, op_context.input->type != kTfLiteString);
-
- if (IsConstantTensor(op_context.shape)) {
- return ResizeOutputTensor(context, &op_context);
- }
-
- SetTensorToDynamic(op_context.output);
- return kTfLiteOk;
-}
-
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
- BroadcastToContext op_context(context, node);
- if (IsDynamicTensor(op_context.output)) {
- TF_LITE_ENSURE_OK(context, ResizeOutputTensor(context, &op_context));
- }
-
- // BroadcastTo op support upto 8 dims, matching the support of Tensorflow.
- reference_ops::BroadcastTo<kMaxDims>(
- GetTensorShape(op_context.input), op_context.input->data.raw,
- GetTensorShape(op_context.output), op_context.output->data.raw,
- op_context.input->type);
- return kTfLiteOk;
-}
-
-} // namespace broadcastto
-
-TfLiteRegistration* Register_BROADCAST_TO() {
- static TfLiteRegistration r = {nullptr, nullptr, broadcastto::Prepare,
- broadcastto::Eval};
- return &r;
-}
-
-} // namespace builtin
-} // namespace ops
-} // namespace tflite
diff --git a/tensorflow/lite/kernels/broadcast_to_test.cc b/tensorflow/lite/kernels/broadcast_to_test.cc
deleted file mode 100644
index a36ed35..0000000
--- a/tensorflow/lite/kernels/broadcast_to_test.cc
+++ /dev/null
@@ -1,255 +0,0 @@
-/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-#include <cstdint>
-#include <vector>
-
-#include <gtest/gtest.h>
-#include "tensorflow/lite/interpreter.h"
-#include "tensorflow/lite/kernels/register.h"
-#include "tensorflow/lite/kernels/test_util.h"
-#include "tensorflow/lite/model.h"
-
-namespace tflite {
-namespace {
-using ::testing::ElementsAreArray;
-
-template <class InputType, class ShapeType = int32_t>
-class BroadcastToOpModel : public SingleOpModel {
- public:
- // BroadcastTo with dynamic shape.
- BroadcastToOpModel(std::initializer_list<int> input_shape,
- std::initializer_list<int> shape_shape) {
- input_ = AddInput({GetTensorType<InputType>(), input_shape});
- shape_ = AddInput({GetTensorType<ShapeType>(), shape_shape});
- output_ = AddOutput(GetTensorType<InputType>());
- SetBuiltinOp(BuiltinOperator_BROADCAST_TO,
- BuiltinOptions_BroadcastToOptions,
- CreateBroadcastToOptions(builder_).Union());
- BuildInterpreter({input_shape, shape_shape});
- }
-
- // BroadcastTo with const shape.
- BroadcastToOpModel(std::initializer_list<int> input_shape,
- std::initializer_list<int> shape_shape,
- std::initializer_list<ShapeType> shape_values) {
- input_ = AddInput({GetTensorType<InputType>(), input_shape});
- shape_ =
- AddConstInput(GetTensorType<ShapeType>(), shape_values, shape_shape);
- output_ = AddOutput(GetTensorType<InputType>());
- SetBuiltinOp(BuiltinOperator_BROADCAST_TO,
- BuiltinOptions_BroadcastToOptions,
- CreateBroadcastToOptions(builder_).Union());
- BuildInterpreter({input_shape, shape_shape});
- }
-
- void SetInput(std::initializer_list<InputType> data) {
- PopulateTensor(input_, data);
- }
-
- void SetShape(std::initializer_list<ShapeType> data) {
- PopulateTensor(shape_, data);
- }
-
- std::vector<InputType> GetOutput() {
- return ExtractVector<InputType>(output_);
- }
- std::vector<int> GetOutputShape() { return GetTensorShape(output_); }
-
- protected:
- int input_;
- int shape_;
- int output_;
-};
-
-template <typename T>
-class BroadcastToOpTest : public ::testing::Test {};
-
-using DataTypes = ::testing::Types<float, uint8_t, int8_t, int16_t, int32_t>;
-TYPED_TEST_SUITE(BroadcastToOpTest, DataTypes);
-
-#ifdef GTEST_HAS_DEATH_TEST
-TYPED_TEST(BroadcastToOpTest, ShapeMustBe1D) {
- EXPECT_DEATH(
- BroadcastToOpModel<TypeParam>({2, 3, 4, 4}, {2, 2}, {2, 3, 4, 4}), "");
- // Non-constant Shape tensor.
- BroadcastToOpModel<TypeParam> m({2, 3, 4, 4}, {2, 2});
- m.SetShape({2, 3, 4, 4});
- EXPECT_THAT(m.InvokeUnchecked(), kTfLiteError);
-}
-
-TYPED_TEST(BroadcastToOpTest, TooManyDimensions) {
- EXPECT_DEATH(BroadcastToOpModel<TypeParam>({1, 2, 3, 4, 5, 6, 7, 8, 9}, {9},
- {2, 2, 3, 4, 5, 6, 7, 8, 9}),
- "BroadcastTo only supports 1-8D tensor.");
- EXPECT_DEATH(BroadcastToOpModel<TypeParam>({1, 2, 3, 4, 5, 6, 7, 8, 9}, {9}),
- "BroadcastTo only supports 1-8D tensor.");
-}
-
-TYPED_TEST(BroadcastToOpTest, MismatchDimension) {
- EXPECT_DEATH(BroadcastToOpModel<TypeParam>({2, 4, 1, 2}, {4}, {2, 4, 1, 3}),
- "Output shape must be broadcastable from input shape.");
- EXPECT_DEATH(
- BroadcastToOpModel<TypeParam>({2, 4, 1, 2, 3}, {4}, {2, 4, 1, 2}),
- "Output shape must be broadcastable from input shape.");
-
- // Non-constant Shape tensor.
- BroadcastToOpModel<TypeParam> m1({2, 4, 1, 2}, {4});
- m1.SetShape({2, 3, 4, 4});
- EXPECT_THAT(m1.InvokeUnchecked(), kTfLiteError);
- BroadcastToOpModel<TypeParam> m2({2, 4, 1, 2}, {5});
- m2.SetShape({1, 2, 3, 4, 4});
- EXPECT_THAT(m2.InvokeUnchecked(), kTfLiteError);
-}
-#endif
-
-TYPED_TEST(BroadcastToOpTest, BroadcastTo1DConstTest) {
- BroadcastToOpModel<TypeParam> m({1}, {1}, {4});
- m.SetInput({3});
- m.Invoke();
- EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({4}));
- EXPECT_THAT(m.GetOutput(), ElementsAreArray({3, 3, 3, 3}));
-}
-
-TYPED_TEST(BroadcastToOpTest, BroadcastTo4DConstTest) {
- BroadcastToOpModel<TypeParam> m({1, 1, 1, 2}, {4}, {1, 1, 2, 2});
- m.SetInput({3, 4});
- m.Invoke();
- EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 1, 2, 2}));
- EXPECT_THAT(m.GetOutput(), ElementsAreArray({3, 4, 3, 4}));
-}
-
-TYPED_TEST(BroadcastToOpTest, BroadcastTo8DConstTest) {
- BroadcastToOpModel<TypeParam> m({1, 1, 1, 1, 1, 1, 2, 1}, {8},
- {1, 1, 1, 1, 1, 1, 2, 2});
- m.SetInput({3, 4});
- m.Invoke();
- EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 1, 1, 1, 1, 1, 2, 2}));
- EXPECT_THAT(m.GetOutput(), ElementsAreArray({3, 3, 4, 4}));
-}
-
-TYPED_TEST(BroadcastToOpTest, BroadcastTo1DDynamicTest) {
- BroadcastToOpModel<TypeParam> m({1}, {1});
- m.SetInput({3});
- m.SetShape({4});
- m.Invoke();
- EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({4}));
- EXPECT_THAT(m.GetOutput(), ElementsAreArray({3, 3, 3, 3}));
-}
-
-TYPED_TEST(BroadcastToOpTest, BroadcastTo4DDynamicTest) {
- BroadcastToOpModel<TypeParam> m({1, 1, 1, 2}, {4});
- m.SetInput({3, 4});
- m.SetShape({1, 1, 2, 2});
- m.Invoke();
- EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 1, 2, 2}));
- EXPECT_THAT(m.GetOutput(), ElementsAreArray({3, 4, 3, 4}));
-}
-
-TYPED_TEST(BroadcastToOpTest, BroadcastTo8DDynamicTest) {
- BroadcastToOpModel<TypeParam> m({1, 1, 1, 1, 1, 1, 2, 1}, {8});
- m.SetInput({3, 4});
- m.SetShape({1, 1, 1, 1, 1, 1, 2, 2});
- m.Invoke();
- EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 1, 1, 1, 1, 1, 2, 2}));
- EXPECT_THAT(m.GetOutput(), ElementsAreArray({3, 3, 4, 4}));
-}
-
-TYPED_TEST(BroadcastToOpTest, ComplexBroadcast4DConstTest) {
- BroadcastToOpModel<TypeParam> m({1, 3, 1, 2}, {4}, {3, 3, 2, 2});
- m.SetInput({1, 2, 3, 4, 5, 6});
- m.Invoke();
- EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({3, 3, 2, 2}));
- EXPECT_THAT(
- m.GetOutput(),
- ElementsAreArray({1, 2, 1, 2, 3, 4, 3, 4, 5, 6, 5, 6, 1, 2, 1, 2, 3, 4,
- 3, 4, 5, 6, 5, 6, 1, 2, 1, 2, 3, 4, 3, 4, 5, 6, 5, 6}));
-}
-
-TYPED_TEST(BroadcastToOpTest, ComplexBroadcast4DDynamicTest) {
- BroadcastToOpModel<TypeParam> m({1, 3, 1, 2}, {4});
- m.SetInput({1, 2, 3, 4, 5, 6});
- m.SetShape({3, 3, 2, 2});
- m.Invoke();
- EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({3, 3, 2, 2}));
- EXPECT_THAT(
- m.GetOutput(),
- ElementsAreArray({1, 2, 1, 2, 3, 4, 3, 4, 5, 6, 5, 6, 1, 2, 1, 2, 3, 4,
- 3, 4, 5, 6, 5, 6, 1, 2, 1, 2, 3, 4, 3, 4, 5, 6, 5, 6}));
-}
-
-TYPED_TEST(BroadcastToOpTest, ComplexBroadcast6DConstTest) {
- BroadcastToOpModel<TypeParam> m({1, 2, 1, 3, 1, 2}, {6}, {2, 2, 1, 3, 2, 2});
- m.SetInput({1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12});
- m.Invoke();
- EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({2, 2, 1, 3, 2, 2}));
- EXPECT_THAT(m.GetOutput(),
- ElementsAreArray({1, 2, 1, 2, 3, 4, 3, 4, 5, 6, 5, 6,
- 7, 8, 7, 8, 9, 10, 9, 10, 11, 12, 11, 12,
- 1, 2, 1, 2, 3, 4, 3, 4, 5, 6, 5, 6,
- 7, 8, 7, 8, 9, 10, 9, 10, 11, 12, 11, 12}));
-}
-
-TYPED_TEST(BroadcastToOpTest, ComplexBroadcast6DDynamicTest) {
- BroadcastToOpModel<TypeParam> m({1, 2, 1, 3, 1, 2}, {6});
- m.SetInput({1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12});
- m.SetShape({2, 2, 1, 3, 2, 2});
- m.Invoke();
- EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({2, 2, 1, 3, 2, 2}));
- EXPECT_THAT(m.GetOutput(),
- ElementsAreArray({1, 2, 1, 2, 3, 4, 3, 4, 5, 6, 5, 6,
- 7, 8, 7, 8, 9, 10, 9, 10, 11, 12, 11, 12,
- 1, 2, 1, 2, 3, 4, 3, 4, 5, 6, 5, 6,
- 7, 8, 7, 8, 9, 10, 9, 10, 11, 12, 11, 12}));
-}
-
-TYPED_TEST(BroadcastToOpTest, ExtendingShape4DConstTest) {
- BroadcastToOpModel<TypeParam> m({3, 1, 2}, {4}, {3, 3, 2, 2});
- m.SetInput({1, 2, 3, 4, 5, 6});
- m.Invoke();
- EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({3, 3, 2, 2}));
- EXPECT_THAT(
- m.GetOutput(),
- ElementsAreArray({1, 2, 1, 2, 3, 4, 3, 4, 5, 6, 5, 6, 1, 2, 1, 2, 3, 4,
- 3, 4, 5, 6, 5, 6, 1, 2, 1, 2, 3, 4, 3, 4, 5, 6, 5, 6}));
-}
-
-TYPED_TEST(BroadcastToOpTest, NoBroadcastingConstTest) {
- BroadcastToOpModel<TypeParam> m({3, 1, 2}, {3}, {3, 1, 2});
- m.SetInput({1, 2, 3, 4, 5, 6});
- m.Invoke();
- EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({3, 1, 2}));
- EXPECT_THAT(m.GetOutput(), ElementsAreArray({1, 2, 3, 4, 5, 6}));
-}
-
-TYPED_TEST(BroadcastToOpTest, Int64ShapeConstTest) {
- BroadcastToOpModel<TypeParam, int64_t> m({1, 1, 1, 1, 1, 1, 2, 1}, {8},
- {1, 1, 1, 1, 1, 1, 2, 2});
- m.SetInput({3, 4});
- m.Invoke();
- EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 1, 1, 1, 1, 1, 2, 2}));
- EXPECT_THAT(m.GetOutput(), ElementsAreArray({3, 3, 4, 4}));
-}
-
-TYPED_TEST(BroadcastToOpTest, Int64ShapeDDynamicTest) {
- BroadcastToOpModel<TypeParam, int64_t> m({1, 1, 1, 1, 1, 1, 2, 1}, {8});
- m.SetInput({3, 4});
- m.SetShape({1, 1, 1, 1, 1, 1, 2, 2});
- m.Invoke();
- EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 1, 1, 1, 1, 1, 2, 2}));
- EXPECT_THAT(m.GetOutput(), ElementsAreArray({3, 3, 4, 4}));
-}
-
-} // namespace
-} // namespace tflite
diff --git a/tensorflow/lite/kernels/builtin_op_kernels.h b/tensorflow/lite/kernels/builtin_op_kernels.h
index fea25f8..1c73f06 100644
--- a/tensorflow/lite/kernels/builtin_op_kernels.h
+++ b/tensorflow/lite/kernels/builtin_op_kernels.h
@@ -39,7 +39,6 @@
TfLiteRegistration* Register_BATCH_MATMUL();
TfLiteRegistration* Register_BIDIRECTIONAL_SEQUENCE_LSTM();
TfLiteRegistration* Register_BIDIRECTIONAL_SEQUENCE_RNN();
-TfLiteRegistration* Register_BROADCAST_TO();
TfLiteRegistration* Register_CAST();
TfLiteRegistration* Register_CEIL();
TfLiteRegistration* Register_CONCATENATION();
diff --git a/tensorflow/lite/kernels/internal/BUILD b/tensorflow/lite/kernels/internal/BUILD
index 075c1da..5acabeb 100644
--- a/tensorflow/lite/kernels/internal/BUILD
+++ b/tensorflow/lite/kernels/internal/BUILD
@@ -441,7 +441,6 @@
"reference/arg_min_max.h",
"reference/batch_matmul.h",
"reference/binary_function.h",
- "reference/broadcast_to.h",
"reference/ceil.h",
"reference/comparisons.h",
"reference/concatenation.h",
diff --git a/tensorflow/lite/kernels/internal/common.h b/tensorflow/lite/kernels/internal/common.h
index 10cb164..c45aff9 100644
--- a/tensorflow/lite/kernels/internal/common.h
+++ b/tensorflow/lite/kernels/internal/common.h
@@ -665,13 +665,6 @@
indexes[4] * desc.strides[4];
}
-inline int SubscriptToIndex(const NdArrayDesc<8>& desc, int indexes[8]) {
- return indexes[0] * desc.strides[0] + indexes[1] * desc.strides[1] +
- indexes[2] * desc.strides[2] + indexes[3] * desc.strides[3] +
- indexes[4] * desc.strides[4] + indexes[5] * desc.strides[5] +
- indexes[6] * desc.strides[6] + indexes[7] * desc.strides[7];
-}
-
// Given the dimensions of the operands for an element-wise binary broadcast,
// adjusts them so that they can be directly iterated over with simple loops.
// Returns the adjusted dims as instances of NdArrayDesc in 'desc0_out' and
diff --git a/tensorflow/lite/kernels/internal/reference/broadcast_to.h b/tensorflow/lite/kernels/internal/reference/broadcast_to.h
deleted file mode 100644
index 69f4531..0000000
--- a/tensorflow/lite/kernels/internal/reference/broadcast_to.h
+++ /dev/null
@@ -1,90 +0,0 @@
-/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_BROADCAST_TO_H_
-#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_BROADCAST_TO_H_
-
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/kernels/internal/common.h"
-
-namespace tflite {
-namespace reference_ops {
-template <int N>
-void BroadcastImpl(const NdArrayDesc<N>& input_desc, const char* input_data,
- const NdArrayDesc<N>& output_desc, char* output_data,
- int indexes[N], int dim, const int last_broadcasting_dim,
- const int type_size) {
- // Copy data from input to output.
- if (dim == last_broadcasting_dim) {
- int copy_size = output_desc.strides[dim] * type_size;
- const char* data_src =
- input_data + SubscriptToIndex(input_desc, indexes) * type_size;
- char* data_dst =
- output_data + SubscriptToIndex(output_desc, indexes) * type_size;
- for (int i = 0; i < output_desc.extents[dim]; ++i, data_dst += copy_size) {
- memcpy(data_dst, data_src, copy_size);
- }
- return;
- }
-
- // Recursive call to find the next broadcasting.
- for (indexes[dim] = 0; indexes[dim] < input_desc.extents[dim];
- ++indexes[dim]) {
- BroadcastImpl<N>(input_desc, input_data, output_desc, output_data, indexes,
- dim + 1, last_broadcasting_dim, type_size);
- }
-
- // Duplicate data in output tensor.
- indexes[dim] = 0;
- if (input_desc.extents[dim] != output_desc.extents[dim]) {
- int copy_size = output_desc.strides[dim] * type_size;
- char* data_src =
- output_data + SubscriptToIndex(output_desc, indexes) * type_size;
- char* data_dst = data_src + copy_size;
- for (int i = 1; i < output_desc.extents[dim]; ++i, data_dst += copy_size) {
- memcpy(data_dst, data_src, copy_size);
- }
- }
-}
-
-template <int N>
-inline void BroadcastTo(const RuntimeShape& unextended_input_shape,
- const char* input_data,
- const RuntimeShape& unextended_output_shape,
- char* output_data, TfLiteType data_type) {
- NdArrayDesc<N> input_desc;
- NdArrayDesc<N> output_desc;
- CopyDimsToDesc(RuntimeShape::ExtendedShape(N, unextended_input_shape),
- &input_desc);
- CopyDimsToDesc(RuntimeShape::ExtendedShape(N, unextended_output_shape),
- &output_desc);
-
- // Get the last dimension has broadcasting. At this dimension, the data is
- // copied from input tensor to output tensor.
- int last_broadcast_dim = 0;
- for (int i = N - 1; i > 0; --i) {
- if (input_desc.extents[i] != output_desc.extents[i]) {
- last_broadcast_dim = i;
- break;
- }
- }
-
- // Broadcasting using memcpy.
- int indexes[N] = {0};
- BroadcastImpl<N>(input_desc, input_data, output_desc, output_data, indexes, 0,
- last_broadcast_dim, TfLiteTypeGetSize(data_type));
-}
-} // namespace reference_ops
-} // namespace tflite
-#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_BROADCAST_TO_H_
diff --git a/tensorflow/lite/kernels/register.cc b/tensorflow/lite/kernels/register.cc
index da7480d..275340e 100644
--- a/tensorflow/lite/kernels/register.cc
+++ b/tensorflow/lite/kernels/register.cc
@@ -292,7 +292,6 @@
AddBuiltin(BuiltinOperator_BATCH_MATMUL, Register_BATCH_MATMUL(),
/* min_version = */ 1,
/* max_version = */ 2);
- AddBuiltin(BuiltinOperator_BROADCAST_TO, Register_BROADCAST_TO());
AddCustom("NumericVerify", tflite::ops::custom::Register_NUMERIC_VERIFY());
// TODO(andrewharp, ahentz): Move these somewhere more appropriate so that
// custom ops aren't always included by default.
diff --git a/tensorflow/lite/kernels/register_ref.cc b/tensorflow/lite/kernels/register_ref.cc
index b1dd31a..233520e 100644
--- a/tensorflow/lite/kernels/register_ref.cc
+++ b/tensorflow/lite/kernels/register_ref.cc
@@ -139,7 +139,6 @@
TfLiteRegistration* Register_SELECT_V2();
TfLiteRegistration* Register_SEGMENT_SUM();
TfLiteRegistration* Register_BATCH_MATMUL_REF();
-TfLiteRegistration* Register_BROADCAST_TO();
namespace {
@@ -208,7 +207,6 @@
Register_SPACE_TO_BATCH_ND_REF());
AddBuiltin(BuiltinOperator_BATCH_TO_SPACE_ND,
Register_BATCH_TO_SPACE_ND_REF());
- AddBuiltin(BuiltinOperator_BROADCAST_TO, Register_BROADCAST_TO());
AddBuiltin(BuiltinOperator_MUL, Register_MUL_REF());
AddBuiltin(BuiltinOperator_L2_NORMALIZATION, Register_L2NORM_REF());
AddBuiltin(BuiltinOperator_LOCAL_RESPONSE_NORMALIZATION,
diff --git a/tensorflow/lite/schema/schema.fbs b/tensorflow/lite/schema/schema.fbs
index 949d769..878acde 100644
--- a/tensorflow/lite/schema/schema.fbs
+++ b/tensorflow/lite/schema/schema.fbs
@@ -349,8 +349,7 @@
SELECT_V2 = 123,
DENSIFY = 124,
SEGMENT_SUM = 125,
- BATCH_MATMUL = 126,
- BROADCAST_TO = 127
+ BATCH_MATMUL = 126
}
@@ -456,8 +455,7 @@
SelectV2Options,
DensifyOptions,
SegmentSumOptions,
- BatchMatMulOptions,
- BroadcastToOptions
+ BatchMatMulOptions
}
enum Padding : byte { SAME, VALID }
@@ -977,9 +975,6 @@
adj_y:bool;
}
-table BroadcastToOptions {
-}
-
// An OperatorCode can be an enum value (BuiltinOperator) if the operator is a
// builtin, or a string if the operator is custom.
table OperatorCode {
diff --git a/tensorflow/lite/schema/schema_generated.h b/tensorflow/lite/schema/schema_generated.h
index 7bf79e5..a6117dc 100755
--- a/tensorflow/lite/schema/schema_generated.h
+++ b/tensorflow/lite/schema/schema_generated.h
@@ -349,9 +349,6 @@
struct BatchMatMulOptions;
struct BatchMatMulOptionsT;
-struct BroadcastToOptions;
-struct BroadcastToOptionsT;
-
struct OperatorCode;
struct OperatorCodeT;
@@ -784,12 +781,11 @@
BuiltinOperator_DENSIFY = 124,
BuiltinOperator_SEGMENT_SUM = 125,
BuiltinOperator_BATCH_MATMUL = 126,
- BuiltinOperator_BROADCAST_TO = 127,
BuiltinOperator_MIN = BuiltinOperator_ADD,
- BuiltinOperator_MAX = BuiltinOperator_BROADCAST_TO
+ BuiltinOperator_MAX = BuiltinOperator_BATCH_MATMUL
};
-inline const BuiltinOperator (&EnumValuesBuiltinOperator())[128] {
+inline const BuiltinOperator (&EnumValuesBuiltinOperator())[127] {
static const BuiltinOperator values[] = {
BuiltinOperator_ADD,
BuiltinOperator_AVERAGE_POOL_2D,
@@ -917,14 +913,13 @@
BuiltinOperator_SELECT_V2,
BuiltinOperator_DENSIFY,
BuiltinOperator_SEGMENT_SUM,
- BuiltinOperator_BATCH_MATMUL,
- BuiltinOperator_BROADCAST_TO
+ BuiltinOperator_BATCH_MATMUL
};
return values;
}
inline const char * const *EnumNamesBuiltinOperator() {
- static const char * const names[129] = {
+ static const char * const names[128] = {
"ADD",
"AVERAGE_POOL_2D",
"CONCATENATION",
@@ -1052,14 +1047,13 @@
"DENSIFY",
"SEGMENT_SUM",
"BATCH_MATMUL",
- "BROADCAST_TO",
nullptr
};
return names;
}
inline const char *EnumNameBuiltinOperator(BuiltinOperator e) {
- if (flatbuffers::IsOutRange(e, BuiltinOperator_ADD, BuiltinOperator_BROADCAST_TO)) return "";
+ if (flatbuffers::IsOutRange(e, BuiltinOperator_ADD, BuiltinOperator_BATCH_MATMUL)) return "";
const size_t index = static_cast<size_t>(e);
return EnumNamesBuiltinOperator()[index];
}
@@ -1167,12 +1161,11 @@
BuiltinOptions_DensifyOptions = 99,
BuiltinOptions_SegmentSumOptions = 100,
BuiltinOptions_BatchMatMulOptions = 101,
- BuiltinOptions_BroadcastToOptions = 102,
BuiltinOptions_MIN = BuiltinOptions_NONE,
- BuiltinOptions_MAX = BuiltinOptions_BroadcastToOptions
+ BuiltinOptions_MAX = BuiltinOptions_BatchMatMulOptions
};
-inline const BuiltinOptions (&EnumValuesBuiltinOptions())[103] {
+inline const BuiltinOptions (&EnumValuesBuiltinOptions())[102] {
static const BuiltinOptions values[] = {
BuiltinOptions_NONE,
BuiltinOptions_Conv2DOptions,
@@ -1275,14 +1268,13 @@
BuiltinOptions_SelectV2Options,
BuiltinOptions_DensifyOptions,
BuiltinOptions_SegmentSumOptions,
- BuiltinOptions_BatchMatMulOptions,
- BuiltinOptions_BroadcastToOptions
+ BuiltinOptions_BatchMatMulOptions
};
return values;
}
inline const char * const *EnumNamesBuiltinOptions() {
- static const char * const names[104] = {
+ static const char * const names[103] = {
"NONE",
"Conv2DOptions",
"DepthwiseConv2DOptions",
@@ -1385,14 +1377,13 @@
"DensifyOptions",
"SegmentSumOptions",
"BatchMatMulOptions",
- "BroadcastToOptions",
nullptr
};
return names;
}
inline const char *EnumNameBuiltinOptions(BuiltinOptions e) {
- if (flatbuffers::IsOutRange(e, BuiltinOptions_NONE, BuiltinOptions_BroadcastToOptions)) return "";
+ if (flatbuffers::IsOutRange(e, BuiltinOptions_NONE, BuiltinOptions_BatchMatMulOptions)) return "";
const size_t index = static_cast<size_t>(e);
return EnumNamesBuiltinOptions()[index];
}
@@ -1805,10 +1796,6 @@
static const BuiltinOptions enum_value = BuiltinOptions_BatchMatMulOptions;
};
-template<> struct BuiltinOptionsTraits<tflite::BroadcastToOptions> {
- static const BuiltinOptions enum_value = BuiltinOptions_BroadcastToOptions;
-};
-
struct BuiltinOptionsUnion {
BuiltinOptions type;
void *value;
@@ -2649,14 +2636,6 @@
return type == BuiltinOptions_BatchMatMulOptions ?
reinterpret_cast<const tflite::BatchMatMulOptionsT *>(value) : nullptr;
}
- tflite::BroadcastToOptionsT *AsBroadcastToOptions() {
- return type == BuiltinOptions_BroadcastToOptions ?
- reinterpret_cast<tflite::BroadcastToOptionsT *>(value) : nullptr;
- }
- const tflite::BroadcastToOptionsT *AsBroadcastToOptions() const {
- return type == BuiltinOptions_BroadcastToOptions ?
- reinterpret_cast<const tflite::BroadcastToOptionsT *>(value) : nullptr;
- }
};
bool VerifyBuiltinOptions(flatbuffers::Verifier &verifier, const void *obj, BuiltinOptions type);
@@ -9331,46 +9310,6 @@
flatbuffers::Offset<BatchMatMulOptions> CreateBatchMatMulOptions(flatbuffers::FlatBufferBuilder &_fbb, const BatchMatMulOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct BroadcastToOptionsT : public flatbuffers::NativeTable {
- typedef BroadcastToOptions TableType;
- BroadcastToOptionsT() {
- }
-};
-
-struct BroadcastToOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
- typedef BroadcastToOptionsT NativeTableType;
- bool Verify(flatbuffers::Verifier &verifier) const {
- return VerifyTableStart(verifier) &&
- verifier.EndTable();
- }
- BroadcastToOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(BroadcastToOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<BroadcastToOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const BroadcastToOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct BroadcastToOptionsBuilder {
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- explicit BroadcastToOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
- : fbb_(_fbb) {
- start_ = fbb_.StartTable();
- }
- BroadcastToOptionsBuilder &operator=(const BroadcastToOptionsBuilder &);
- flatbuffers::Offset<BroadcastToOptions> Finish() {
- const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<BroadcastToOptions>(end);
- return o;
- }
-};
-
-inline flatbuffers::Offset<BroadcastToOptions> CreateBroadcastToOptions(
- flatbuffers::FlatBufferBuilder &_fbb) {
- BroadcastToOptionsBuilder builder_(_fbb);
- return builder_.Finish();
-}
-
-flatbuffers::Offset<BroadcastToOptions> CreateBroadcastToOptions(flatbuffers::FlatBufferBuilder &_fbb, const BroadcastToOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
struct OperatorCodeT : public flatbuffers::NativeTable {
typedef OperatorCode TableType;
tflite::BuiltinOperator builtin_code;
@@ -9810,9 +9749,6 @@
const tflite::BatchMatMulOptions *builtin_options_as_BatchMatMulOptions() const {
return builtin_options_type() == tflite::BuiltinOptions_BatchMatMulOptions ? static_cast<const tflite::BatchMatMulOptions *>(builtin_options()) : nullptr;
}
- const tflite::BroadcastToOptions *builtin_options_as_BroadcastToOptions() const {
- return builtin_options_type() == tflite::BuiltinOptions_BroadcastToOptions ? static_cast<const tflite::BroadcastToOptions *>(builtin_options()) : nullptr;
- }
const flatbuffers::Vector<uint8_t> *custom_options() const {
return GetPointer<const flatbuffers::Vector<uint8_t> *>(VT_CUSTOM_OPTIONS);
}
@@ -10253,10 +10189,6 @@
return builtin_options_as_BatchMatMulOptions();
}
-template<> inline const tflite::BroadcastToOptions *Operator::builtin_options_as<tflite::BroadcastToOptions>() const {
- return builtin_options_as_BroadcastToOptions();
-}
-
struct OperatorBuilder {
flatbuffers::FlatBufferBuilder &fbb_;
flatbuffers::uoffset_t start_;
@@ -13724,29 +13656,6 @@
_adj_y);
}
-inline BroadcastToOptionsT *BroadcastToOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
- auto _o = new BroadcastToOptionsT();
- UnPackTo(_o, _resolver);
- return _o;
-}
-
-inline void BroadcastToOptions::UnPackTo(BroadcastToOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
- (void)_o;
- (void)_resolver;
-}
-
-inline flatbuffers::Offset<BroadcastToOptions> BroadcastToOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const BroadcastToOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
- return CreateBroadcastToOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<BroadcastToOptions> CreateBroadcastToOptions(flatbuffers::FlatBufferBuilder &_fbb, const BroadcastToOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
- (void)_rehasher;
- (void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const BroadcastToOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
- return tflite::CreateBroadcastToOptions(
- _fbb);
-}
-
inline OperatorCodeT *OperatorCode::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
auto _o = new OperatorCodeT();
UnPackTo(_o, _resolver);
@@ -14556,10 +14465,6 @@
auto ptr = reinterpret_cast<const tflite::BatchMatMulOptions *>(obj);
return verifier.VerifyTable(ptr);
}
- case BuiltinOptions_BroadcastToOptions: {
- auto ptr = reinterpret_cast<const tflite::BroadcastToOptions *>(obj);
- return verifier.VerifyTable(ptr);
- }
default: return true;
}
}
@@ -14982,10 +14887,6 @@
auto ptr = reinterpret_cast<const tflite::BatchMatMulOptions *>(obj);
return ptr->UnPack(resolver);
}
- case BuiltinOptions_BroadcastToOptions: {
- auto ptr = reinterpret_cast<const tflite::BroadcastToOptions *>(obj);
- return ptr->UnPack(resolver);
- }
default: return nullptr;
}
}
@@ -15396,10 +15297,6 @@
auto ptr = reinterpret_cast<const tflite::BatchMatMulOptionsT *>(value);
return CreateBatchMatMulOptions(_fbb, ptr, _rehasher).Union();
}
- case BuiltinOptions_BroadcastToOptions: {
- auto ptr = reinterpret_cast<const tflite::BroadcastToOptionsT *>(value);
- return CreateBroadcastToOptions(_fbb, ptr, _rehasher).Union();
- }
default: return 0;
}
}
@@ -15810,10 +15707,6 @@
value = new tflite::BatchMatMulOptionsT(*reinterpret_cast<tflite::BatchMatMulOptionsT *>(u.value));
break;
}
- case BuiltinOptions_BroadcastToOptions: {
- value = new tflite::BroadcastToOptionsT(*reinterpret_cast<tflite::BroadcastToOptionsT *>(u.value));
- break;
- }
default:
break;
}
@@ -16326,11 +16219,6 @@
delete ptr;
break;
}
- case BuiltinOptions_BroadcastToOptions: {
- auto ptr = reinterpret_cast<tflite::BroadcastToOptionsT *>(value);
- delete ptr;
- break;
- }
default: break;
}
value = nullptr;
@@ -16394,4 +16282,4 @@
} // namespace tflite
-#endif // FLATBUFFERS_GENERATED_SCHEMA_TFLITE_H_
\ No newline at end of file
+#endif // FLATBUFFERS_GENERATED_SCHEMA_TFLITE_H_
diff --git a/tensorflow/lite/toco/model.h b/tensorflow/lite/toco/model.h
index 2478ca6f..b42fed6 100644
--- a/tensorflow/lite/toco/model.h
+++ b/tensorflow/lite/toco/model.h
@@ -43,7 +43,6 @@
kAveragePool,
kBatchMatMul,
kBatchNormalization,
- kBroadcastTo,
kCeil,
kConv,
kConcatenation,
diff --git a/tensorflow/lite/toco/tflite/op_version.cc b/tensorflow/lite/toco/tflite/op_version.cc
index 3793bb5..b16f282 100644
--- a/tensorflow/lite/toco/tflite/op_version.cc
+++ b/tensorflow/lite/toco/tflite/op_version.cc
@@ -63,7 +63,6 @@
{{OperatorType::kBatchToSpaceND, 1}, "1.6.0"},
{{OperatorType::kBatchToSpaceND, 2}, "1.14.0"},
{{OperatorType::kBatchMatMul, 1}, kPendingReleaseOpVersion},
- {{OperatorType::kBroadcastTo, 1}, kPendingReleaseOpVersion},
{{OperatorType::kCast, 1}, "1.5.0"},
{{OperatorType::kConcatenation, 1}, "1.5.0"},
{{OperatorType::kConcatenation, 2}, "1.14.0"},
diff --git a/tensorflow/lite/tools/benchmark/experimental/c/c_api_types.h b/tensorflow/lite/tools/benchmark/experimental/c/c_api_types.h
index 615b5fb..3398d17 100644
--- a/tensorflow/lite/tools/benchmark/experimental/c/c_api_types.h
+++ b/tensorflow/lite/tools/benchmark/experimental/c/c_api_types.h
@@ -284,9 +284,6 @@
// Return the name of a given type, for error reporting purposes.
const char* TfLiteTypeGetName(TfLiteType type);
-// Return the size of given type in bytes. Return 0 in in case of string.
-int TfLiteTypeGetSize(TfLiteType type);
-
// SupportedQuantizationTypes.
typedef enum TfLiteQuantizationType {
// No quantization.
diff --git a/tensorflow/lite/tools/versioning/runtime_version.cc b/tensorflow/lite/tools/versioning/runtime_version.cc
index 8938c0b..c2e3f27 100644
--- a/tensorflow/lite/tools/versioning/runtime_version.cc
+++ b/tensorflow/lite/tools/versioning/runtime_version.cc
@@ -59,7 +59,6 @@
{{BuiltinOperator_AVERAGE_POOL_2D, 3}, "2.3.0"},
{{BuiltinOperator_BATCH_MATMUL, 1}, "2.3.0"},
{{BuiltinOperator_BATCH_MATMUL, 2}, "2.3.0"},
- {{BuiltinOperator_BROADCAST_TO, 1}, kPendingReleaseVersion},
{{BuiltinOperator_CONV_2D, 1}, "1.5.0"},
{{BuiltinOperator_CONV_2D, 2}, "1.14.0"},
{{BuiltinOperator_CONV_2D, 3}, "1.14.0"},
diff --git a/tensorflow/lite/tools/versioning/runtime_version_test.cc b/tensorflow/lite/tools/versioning/runtime_version_test.cc
index df1ca46..c32de22 100644
--- a/tensorflow/lite/tools/versioning/runtime_version_test.cc
+++ b/tensorflow/lite/tools/versioning/runtime_version_test.cc
@@ -47,7 +47,7 @@
EXPECT_NE(runtime_version, "")
<< "Please add the version " << version << " of "
<< tflite::EnumNamesBuiltinOperator()[op_code]
- << " to runtime_version.cc";
+ << " runtime_version.cc";
}
}
}
