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Enable HiFi SIMD for SUB operator #3088

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53 changes: 49 additions & 4 deletions tensorflow/lite/micro/kernels/xtensa/sub.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -36,14 +36,55 @@ 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) {
TfLiteStatus 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 HIFI_VFPU && (defined(HIFI3) || defined(HIFI4) || defined(HIFI5))
const RuntimeShape extended_input1_shape =
RuntimeShape::ExtendedShape(5, tflite::micro::GetTensorShape(input1));
const RuntimeShape extended_input2_shape =
RuntimeShape::ExtendedShape(5, tflite::micro::GetTensorShape(input2));
const RuntimeShape extended_output_shape =
RuntimeShape::ExtendedShape(5, tflite::micro::GetTensorShape(output));
const int* input1_dims = extended_input1_shape.DimsData();
const int* input2_dims = extended_input2_shape.DimsData();
const int* output_dims = extended_output_shape.DimsData();

int inp1_off = 0;
int inp2_off = 0;
int out_off = output_dims[1] * output_dims[2] * output_dims[3] * output_dims[4];
if (input1_dims[0] > 1) {
inp1_off =
input1_dims[1] * input1_dims[2] * input1_dims[3] * input1_dims[4];
}
if (input2_dims[0] > 1) {
inp2_off =
input2_dims[1] * input2_dims[2] * input2_dims[3] * input2_dims[4];
}

for (int b = 0; b < output_dims[0]; b++) {
int err = xa_nn_elm_sub_broadcast_4D_f32xf32_f32(
tflite::micro::GetTensorData<float>(output) + b * out_off,
output_dims + 1,
tflite::micro::GetTensorData<float>(input1) + b * inp1_off,
input1_dims + 1,
tflite::micro::GetTensorData<float>(input2) + b * inp2_off,
input2_dims + 1);
TF_LITE_ENSURE(context, err == 0);
}

float* output_data = tflite::micro::GetTensorData<float>(output);
xa_nn_vec_activation_min_max_f32_f32(
output_data, output_data, op_params.float_activation_min,
op_params.float_activation_max, (output_dims[0] * out_off));
#else // HIFI_VFPU && (defined(HIFI3) || defined(HIFI4) || defined(HIFI5))
if (data->requires_broadcast) {
tflite::reference_ops::BroadcastSubSlow(
op_params, tflite::micro::GetTensorShape(input1),
Expand All @@ -61,6 +102,9 @@ void EvalSub(TfLiteContext* context, TfLiteNode* node, TfLiteSubParams* params,
tflite::micro::GetTensorShape(output),
tflite::micro::GetTensorData<float>(output));
}
#endif

return kTfLiteOk;
}

TfLiteStatus EvalSubQuantized(TfLiteContext* context, TfLiteNode* node,
Expand Down Expand Up @@ -229,7 +273,8 @@ TfLiteStatus SubEval(TfLiteContext* context, TfLiteNode* node) {
const OpDataSub& data = *(static_cast<const OpDataSub*>(node->user_data));

if (output->type == kTfLiteFloat32) {
EvalSub(context, node, params, &data, input1, input2, output);
TF_LITE_ENSURE_OK(
context, EvalSub(context, node, params, &data, input1, input2, output));
} else if (output->type == kTfLiteInt8 || output->type == kTfLiteInt16) {
TF_LITE_ENSURE_OK(context, EvalSubQuantized(context, node, params, &data,
input1, input2, output));
Expand Down