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Add const-tensor checks #3141

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5 changes: 4 additions & 1 deletion tensorflow/lite/micro/kernels/broadcast_to.cc
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@@ -1,4 +1,4 @@
/* Copyright 2022 The TensorFlow Authors. All Rights Reserved.
/* Copyright 2025 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.
Expand Down Expand Up @@ -95,6 +95,9 @@ TfLiteStatus BroadcastToPrepare(TfLiteContext* context, TfLiteNode* node) {
// the same as TFLite.
TF_LITE_ENSURE(context, input->type != kTfLiteString);

TF_LITE_ENSURE_MSG(context, IsConstantTensor(shape),
"Non-constant >shape< tensor is not supported");

TF_LITE_ENSURE_STATUS(ValidateOutputTensor(context, input, shape, output));
micro_context->DeallocateTempTfLiteTensor(input);
micro_context->DeallocateTempTfLiteTensor(shape);
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4 changes: 3 additions & 1 deletion tensorflow/lite/micro/kernels/broadcast_to_test.cc
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@@ -1,4 +1,4 @@
/* Copyright 2022 The TensorFlow Authors. All Rights Reserved.
/* Copyright 2025 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.
Expand Down Expand Up @@ -44,6 +44,8 @@ tflite::micro::KernelRunner CreateBroadcastToTestRunner(

tensors[0] = CreateTensor(input_data, IntArrayFromInts(input_shape));
tensors[1] = CreateTensor(dims_data, IntArrayFromInts(dims_shape));
// shape must be a const tensor
tensors[1].allocation_type = kTfLiteMmapRo;
tensors[2] = CreateTensor(output_data, IntArrayFromInts(output_shape));

// The output type matches the value type.
Expand Down
8 changes: 3 additions & 5 deletions tensorflow/lite/micro/kernels/expand_dims.cc
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@@ -1,4 +1,4 @@
/* Copyright 2021 The TensorFlow Authors. All Rights Reserved.
/* Copyright 2025 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.
Expand Down Expand Up @@ -99,10 +99,8 @@ TfLiteStatus ExpandDimsPrepare(TfLiteContext* context, TfLiteNode* node) {
micro_context->AllocateTempOutputTensor(node, kOutputTensor);
TF_LITE_ENSURE(context, output != nullptr);
output->type = input->type;
if (IsDynamicTensor(axis)) {
MicroPrintf("DynamicTensor is not yet supported by Expand_Dims.");
return kTfLiteError;
}
TF_LITE_ENSURE_MSG(context, IsConstantTensor(axis),
"Non-constant >axis< tensor is not supported");
TF_LITE_ENSURE_OK(context, VerifyTensorDim(context, input, axis, output));

micro_context->DeallocateTempTfLiteTensor(input);
Expand Down
4 changes: 3 additions & 1 deletion tensorflow/lite/micro/kernels/expand_dims_test.cc
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@@ -1,4 +1,4 @@
/* Copyright 2021 The TensorFlow Authors. All Rights Reserved.
/* Copyright 2025 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.
Expand Down Expand Up @@ -57,6 +57,8 @@ micro::KernelRunner CreateExpandDimsKernelRunner(

tensors[kDimsTensorIndex] = CreateTensor(input_data, in_dims);
tensors[kAxisTensorIndex] = CreateTensor(axis_data, ax_dims);
// axis must be a const tensor
tensors[kAxisTensorIndex].allocation_type = kTfLiteMmapRo;
tensors[kOutputTensorIndex] = CreateTensor(output_data, out_dims, true);

TfLiteIntArray* inputs_array =
Expand Down
13 changes: 6 additions & 7 deletions tensorflow/lite/micro/kernels/fill.cc
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@@ -1,4 +1,4 @@
/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
/* Copyright 2025 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.
Expand Down Expand Up @@ -87,12 +87,11 @@ TfLiteStatus FillPrepare(TfLiteContext* context, TfLiteNode* node) {
// The dimension of the output tensor is known in model already.
TFLITE_DCHECK(output->dims != nullptr);

if (dims->data.data != nullptr) {
// When the dims tensor is specified in model already (i.e. is not an
// activation tensor), the dims tensor must match the output tensor shape.
// As a byproduct, ensures the dims tensor is of an integer type.
TF_LITE_ENSURE_OK(context, EnsureEq(context, output->dims, dims));
}
TF_LITE_ENSURE_MSG(context, IsConstantTensor(dims),
"Non-constant >dims< tensor is not supported");
// The dims tensor must match the output tensor shape.
// As a byproduct, ensures the dims tensor is of an integer type.
TF_LITE_ENSURE_OK(context, EnsureEq(context, output->dims, dims));

micro_context->DeallocateTempTfLiteTensor(dims);
micro_context->DeallocateTempTfLiteTensor(value);
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21 changes: 13 additions & 8 deletions tensorflow/lite/micro/kernels/fill_test.cc
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@@ -1,4 +1,4 @@
/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
/* Copyright 2025 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.
Expand Down Expand Up @@ -44,6 +44,10 @@ tflite::micro::KernelRunner CreateFillTestRunner(
static TfLiteTensor tensors[3];

tensors[0] = CreateTensor(dims_data, IntArrayFromInts(dims_shape));
if (dims_data != nullptr) {
// dims must be a const tensor
tensors[0].allocation_type = kTfLiteMmapRo;
}
tensors[1] = CreateTensor(value_data, IntArrayFromInts(value_shape));
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Better to have this condition after all the tensor creations are done?

tensors[2] = CreateTensor(output_data, IntArrayFromInts(output_shape));

Expand Down Expand Up @@ -154,15 +158,13 @@ TF_LITE_MICRO_TEST(FillInt8Int32Dims) {
output_data);
}

// Verify the FILL still works when the input dims tensor is an activation
// tensor (i.e. has not prepopulated value). Fill a 2x2x2 tensor with a int8
// scalar value.
TF_LITE_MICRO_TEST(FillInt8NoInputDimsData) {
TF_LITE_MICRO_TEST(FillInt8NonConstDimsTensorFail) {
constexpr int kDim1 = 2;
constexpr int kDim2 = 2;
constexpr int kDim3 = 2;

// The dims tensor with unknown data. Note that shape is always known.
// Simulate the dims tensor with dynamic data. Note that shape is always
// known.
int dims_shape[] = {1, 3};
int32_t* dims_data = nullptr;

Expand All @@ -172,8 +174,11 @@ TF_LITE_MICRO_TEST(FillInt8NoInputDimsData) {
int output_shape[] = {3, kDim1, kDim2, kDim3};
int8_t output_data[kDim1 * kDim2 * kDim3];

TestFill(dims_shape, dims_data, value_shape, value_data, output_shape,
output_data);
tflite::micro::KernelRunner runner =
CreateFillTestRunner(dims_shape, dims_data, value_shape, value_data,
output_shape, output_data);

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I just want to understand few things here.

  • Is this test was successful previously?
  • Do we want to make this test fail in the prepare stage?
  • Why can't we use TestFill() API here aswell?

TF_LITE_MICRO_EXPECT_EQ(runner.InitAndPrepare(), kTfLiteError);
}

TF_LITE_MICRO_TEST(FillFloatInt32Dims) {
Expand Down
8 changes: 7 additions & 1 deletion tensorflow/lite/micro/kernels/strided_slice_common.cc
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@@ -1,4 +1,4 @@
/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
/* Copyright 2025 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.
Expand Down Expand Up @@ -141,6 +141,12 @@ TfLiteStatus StridedSlicePrepare(TfLiteContext* context, TfLiteNode* node) {
StridedSliceContext op_context(context, node);
TF_LITE_ENSURE_MSG(context, op_context.dims <= kMaxDim,
"input dim should not exceed 4");
TF_LITE_ENSURE_MSG(context, IsConstantTensor(op_context.begin),
"Non-constant >begin< tensor is not supported");
TF_LITE_ENSURE_MSG(context, IsConstantTensor(op_context.end),
"Non-constant >end< tensor is not supported");
TF_LITE_ENSURE_MSG(context, IsConstantTensor(op_context.strides),
"Non-constant >strides< tensor is not supported");
auto params = BuildStridedSliceParams(&op_context);
memcpy(op_params, &params, sizeof(StridedSliceParams));
return CheckOutputSize(context, &op_context);
Expand Down
16 changes: 15 additions & 1 deletion tensorflow/lite/micro/kernels/strided_slice_test.cc
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Original file line number Diff line number Diff line change
@@ -1,4 +1,4 @@
/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
/* Copyright 2025 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.
Expand All @@ -12,6 +12,8 @@ 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/micro/kernels/strided_slice.h"

#include <cstdint>

#include "tensorflow/lite/c/builtin_op_data.h"
Expand Down Expand Up @@ -82,6 +84,12 @@ void TestStridedSliceFloat(int* input_shape, int* begin_shape, int* end_shape,
CreateTensor(strides_data, strides_dims),
CreateTensor(output_data, output_dims),
};
// begin must be a const tensor
tensors[kStridedSliceBeginTensor].allocation_type = kTfLiteMmapRo;
// end must be a const tensor
tensors[kStridedSliceEndTensor].allocation_type = kTfLiteMmapRo;
// strides must be a const tensor
tensors[kStridedSliceStridesTensor].allocation_type = kTfLiteMmapRo;

ValidateStridedSliceGoldens(tensors, tensors_size, expected_output,
output_data, ElementCount(*output_dims),
Expand Down Expand Up @@ -116,6 +124,12 @@ void TestStridedSliceQuantized(int* input_shape, int* begin_shape,
CreateTensor(strides_data, strides_dims),
CreateQuantizedTensor(output_data, output_dims, 1.0, zero_point),
};
// begin must be a const tensor
tensors[kStridedSliceBeginTensor].allocation_type = kTfLiteMmapRo;
// end must be a const tensor
tensors[kStridedSliceEndTensor].allocation_type = kTfLiteMmapRo;
// strides must be a const tensor
tensors[kStridedSliceStridesTensor].allocation_type = kTfLiteMmapRo;

ValidateStridedSliceGoldens(tensors, tensors_size, expected_output,
output_data, ElementCount(*output_dims),
Expand Down
2 changes: 2 additions & 0 deletions tensorflow/lite/micro/kernels/transpose_common.cc
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Expand Up @@ -34,6 +34,8 @@ TfLiteStatus TransposePrepare(TfLiteContext* context, TfLiteNode* node) {
"Transpose op only supports 1D-5D input arrays.");
TF_LITE_ENSURE_TYPES_EQ(context, op_context.input->type,
op_context.output->type);
TF_LITE_ENSURE_MSG(context, IsConstantTensor(op_context.perm),
"Non-constant >perm< tensor is not supported");

int dims = NumDimensions(op_context.input);
const int32_t* perm_data = GetTensorData<int32_t>(op_context.perm);
Expand Down
3 changes: 3 additions & 0 deletions tensorflow/lite/micro/kernels/transpose_test.cc
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Expand Up @@ -18,6 +18,7 @@ limitations under the License.
#include "tensorflow/lite/c/builtin_op_data.h"
#include "tensorflow/lite/c/common.h"
#include "tensorflow/lite/micro/kernels/kernel_runner.h"
#include "tensorflow/lite/micro/kernels/transpose.h"
#include "tensorflow/lite/micro/micro_utils.h"
#include "tensorflow/lite/micro/test_helpers.h"
#include "tensorflow/lite/micro/testing/micro_test.h"
Expand Down Expand Up @@ -123,6 +124,8 @@ void TestTranspose(int* input_dims_data, T* input_data, int* output_dims_data,
CreateTensor(params->perm, perm_dims),
CreateTensor(output_data, output_dims),
};
// perm must be a const tensor
tensors[kTransposePermTensor].allocation_type = kTfLiteMmapRo;

TF_LITE_MICRO_EXPECT_EQ(
kTfLiteOk, ValidateTranspose(tensors, tensors_size, expected_output_data,
Expand Down
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