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Add INT32 support to SUB #3037

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1541736
Add INT32 support to SUB
HemanthSai7 Jan 17, 2025
b2a8b94
Merge branch 'main' into sub_int32
HemanthSai7 Feb 16, 2025
a726500
Merge remote-tracking branch 'upstream/main' into sub_int32
HemanthSai7 Mar 7, 2025
80082a1
Update SUB kernel and license year
HemanthSai7 Mar 7, 2025
5bc3e4d
Added if on TestSubInt32 to pass tests
HemanthSai7 Mar 8, 2025
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89 changes: 63 additions & 26 deletions tensorflow/lite/micro/kernels/sub.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -36,39 +36,76 @@ void* SubInit(TfLiteContext* context, const char* buffer, size_t length) {
return context->AllocatePersistentBuffer(context, sizeof(OpDataSub));
}

void EvalSub(TfLiteContext* context, TfLiteNode* node, TfLiteSubParams* params,
const OpDataSub* data, const TfLiteEvalTensor* input1,
const TfLiteEvalTensor* input2, TfLiteEvalTensor* output) {
float output_activation_min, output_activation_max;
CalculateActivationRange(params->activation, &output_activation_min,
&output_activation_max);
tflite::ArithmeticParams op_params;
SetActivationParams(output_activation_min, output_activation_max, &op_params);
if (data->requires_broadcast) {
tflite::reference_ops::BroadcastSubSlow(
op_params, tflite::micro::GetTensorShape(input1),
tflite::micro::GetTensorData<float>(input1),
tflite::micro::GetTensorShape(input2),
tflite::micro::GetTensorData<float>(input2),
tflite::micro::GetTensorShape(output),
tflite::micro::GetTensorData<float>(output));
} else {
tflite::reference_ops::SubWithActivation(
op_params, tflite::micro::GetTensorShape(input1),
tflite::micro::GetTensorData<float>(input1),
tflite::micro::GetTensorShape(input2),
tflite::micro::GetTensorData<float>(input2),
tflite::micro::GetTensorShape(output),
tflite::micro::GetTensorData<float>(output));
TfLiteStatus EvalSub(TfLiteContext* context, TfLiteNode* node,
TfLiteSubParams* params, const OpDataSub* data,
const TfLiteEvalTensor* input1,
const TfLiteEvalTensor* input2, TfLiteEvalTensor* output) {
switch (output->type) {
case kTfLiteFloat32: {
float output_activation_min, output_activation_max;
CalculateActivationRange(params->activation, &output_activation_min,
&output_activation_max);
tflite::ArithmeticParams op_params;
SetActivationParams(output_activation_min, output_activation_max,
&op_params);
if (data->requires_broadcast) {
tflite::reference_ops::BroadcastSubSlow(
op_params, tflite::micro::GetTensorShape(input1),
tflite::micro::GetTensorData<float>(input1),
tflite::micro::GetTensorShape(input2),
tflite::micro::GetTensorData<float>(input2),
tflite::micro::GetTensorShape(output),
tflite::micro::GetTensorData<float>(output));
} else {
tflite::reference_ops::SubWithActivation(
op_params, tflite::micro::GetTensorShape(input1),
tflite::micro::GetTensorData<float>(input1),
tflite::micro::GetTensorShape(input2),
tflite::micro::GetTensorData<float>(input2),
tflite::micro::GetTensorShape(output),
tflite::micro::GetTensorData<float>(output));
}
} break;
case kTfLiteInt32: {
int32_t output_activation_min, output_activation_max;
CalculateActivationRange(params->activation, &output_activation_min,
&output_activation_max);
tflite::ArithmeticParams op_params;
SetActivationParams(output_activation_min, output_activation_max,
&op_params);
if (data->requires_broadcast) {
tflite::reference_ops::BroadcastSubSlow(
op_params, tflite::micro::GetTensorShape(input1),
tflite::micro::GetTensorData<int32_t>(input1),
tflite::micro::GetTensorShape(input2),
tflite::micro::GetTensorData<int32_t>(input2),
tflite::micro::GetTensorShape(output),
tflite::micro::GetTensorData<int32_t>(output));
} else {
tflite::reference_ops::SubWithActivation(
op_params, tflite::micro::GetTensorShape(input1),
tflite::micro::GetTensorData<int32_t>(input1),
tflite::micro::GetTensorShape(input2),
tflite::micro::GetTensorData<int32_t>(input2),
tflite::micro::GetTensorShape(output),
tflite::micro::GetTensorData<int32_t>(output));
}
} break;
default:
MicroPrintf("Type %s (%d) not supported.",
TfLiteTypeGetName(output->type), output->type);
return kTfLiteError;
}

return kTfLiteOk;
}

TfLiteStatus EvalSubQuantized(TfLiteContext* context, TfLiteNode* node,
TfLiteSubParams* params, const OpDataSub* data,
const TfLiteEvalTensor* input1,
const TfLiteEvalTensor* input2,
TfLiteEvalTensor* output) {
tflite::ArithmeticParams op_params;
tflite::ArithmeticParams op_params = {};
op_params.left_shift = data->left_shift;
op_params.input1_offset = data->input1_offset;
op_params.input1_multiplier = data->input1_multiplier;
Expand Down Expand Up @@ -147,7 +184,7 @@ TfLiteStatus SubEval(TfLiteContext* context, TfLiteNode* node) {
TFLITE_DCHECK(node->user_data != nullptr);
const OpDataSub& data = *(static_cast<const OpDataSub*>(node->user_data));

if (output->type == kTfLiteFloat32) {
if (output->type == kTfLiteFloat32 || output->type == kTfLiteInt32) {
EvalSub(context, node, params, &data, input1, input2, output);
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@ddavis-2015 ddavis-2015 Mar 4, 2025 •
edited
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Could you add a TF_LITE_ENSURE_OK check here (line 188). It will make the code more consistent.

} else if (output->type == kTfLiteInt8 || output->type == kTfLiteInt16) {
TF_LITE_ENSURE_OK(context, EvalSubQuantized(context, node, params, &data,
Expand Down
33 changes: 33 additions & 0 deletions tensorflow/lite/micro/kernels/sub_test.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -105,6 +105,27 @@ void TestSubFloat(int* input1_dims_data, const float* input1_data,
ElementCount(*output_dims), activation);
}

void TestSubInt32(int* input1_dims_data, const int32_t* input1_data,
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You will need #if !defined(XTENSA) around this method also to prevent the unused function warning (which is promoted to an error).

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done @ddavis-2015 .

int* input2_dims_data, const int32_t* input2_data,
int* output_dims_data, const int32_t* expected_output,
TfLiteFusedActivation activation, int32_t* output_data) {
TfLiteIntArray* input1_dims = IntArrayFromInts(input1_dims_data);
TfLiteIntArray* input2_dims = IntArrayFromInts(input2_dims_data);
TfLiteIntArray* output_dims = IntArrayFromInts(output_dims_data);

constexpr int inputs_size = 2;
constexpr int outputs_size = 1;
constexpr int tensors_size = inputs_size + outputs_size;
TfLiteTensor tensors[tensors_size] = {
CreateTensor(input1_data, input1_dims),
CreateTensor(input2_data, input2_dims),
CreateTensor(output_data, output_dims),
};

ValidateSubGoldens(tensors, tensors_size, expected_output, output_data,
ElementCount(*output_dims), activation);
}

template <typename T>
void TestSubQuantized(int* input1_dims_data, const float* input1_data,
T* input1_quantized, float input1_scale,
Expand Down Expand Up @@ -219,6 +240,18 @@ TF_LITE_MICRO_TEST(FloatSubWithScalarBroadcast) {
}
}

TF_LITE_MICRO_TEST(Int32SubNoActivation) {
int inout_shape[] = {4, 1, 2, 2, 1};
const int32_t input1_values[] = {-2, 2147483646, -1, 1146622854};
const int32_t input2_values[] = {3, 1, -2147483647, -726978367};
const int32_t golden_values[] = {-5, 2147483645, 2147483646, 1873601221};
const int kOutputDimsCount = 4;
int32_t output_data[kOutputDimsCount];
tflite::testing::TestSubInt32(inout_shape, input1_values, inout_shape,
input2_values, inout_shape, golden_values,
kTfLiteActNone, output_data);
}

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This code will need #if !defined(XTENSA) around it in order to pass the CI tests.

TF_LITE_MICRO_TEST(QuantizedSubNoActivationInt8) {
const float scales[] = {0.25, 0.5, 1.0};
const int zero_points[] = {-10, 4, 13};
Expand Down
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