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Add 16bit support for unpack, transpose and comparisons operators #3028

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Add 16bit support for unpack, transpose and comparisons operators #3028

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5e0b839
Add 16bit support for unpack, transpose and comparisons operators
rameshkunasi Dec 24, 2024
611d82b
Added support for FC16x8 per channel quantization
rameshkunasi Dec 24, 2024
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13 changes: 13 additions & 0 deletions tensorflow/lite/micro/kernels/comparisons.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -286,6 +286,19 @@ TfLiteStatus GreaterEval(TfLiteContext* context, TfLiteNode* node) {
tflite::micro::GetTensorData<int8_t>(input2), output_shape,
output_data);
break;
case kTfLiteInt16:
requires_broadcast
? reference_ops::Broadcast4DSlowGreaterWithScaling(
data->params, input1_shape,
tflite::micro::GetTensorData<int16_t>(input1), input2_shape,
tflite::micro::GetTensorData<int16_t>(input2), output_shape,
output_data)
: reference_ops::GreaterWithScaling(
data->params, input1_shape,
tflite::micro::GetTensorData<int16_t>(input1), input2_shape,
tflite::micro::GetTensorData<int16_t>(input2), output_shape,
output_data);
break;
default:
MicroPrintf("Type %s (%d) not supported.",
TfLiteTypeGetName(input1->type), input1->type);
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102 changes: 86 additions & 16 deletions tensorflow/lite/micro/kernels/fully_connected.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -238,25 +238,95 @@ TfLiteStatus FullyConnectedEval(TfLiteContext* context, TfLiteNode* node) {
case kTfLiteInt16: {
switch (filter->type) {
case kTfLiteInt8: {
tflite::reference_integer_ops::FullyConnected(
FullyConnectedParamsQuantized(data),
tflite::micro::GetTensorShape(input),
tflite::micro::GetTensorData<int16_t>(input),
tflite::micro::GetTensorShape(filter),
if (bias == nullptr || bias->type == kTfLiteInt32) {
data.is_per_channel
? tflite::reference_integer_ops::FullyConnectedPerChannel(
FullyConnectedParamsQuantized(data),
data.per_channel_output_multiplier,
reinterpret_cast<const int*>(data.per_channel_output_shift),
tflite::micro::GetTensorShape(input),
tflite::micro::GetTensorData<int16_t>(input),
tflite::micro::GetTensorShape(filter),
#ifdef USE_TFLM_COMPRESSION
tflite::micro::GetTensorData<int8_t>(micro_context, filter,
weights_comp_td,
data.weights_scratch_index),
tflite::micro::GetTensorShape(bias),
tflite::micro::GetOptionalTensorData<int64_t>(
micro_context, bias, bias_comp_td, data.bias_scratch_index),
tflite::micro::GetTensorData<int8_t>(micro_context, filter,
weights_comp_td,
data.weights_scratch_index),
tflite::micro::GetTensorShape(bias),
tflite::micro::GetOptionalTensorData<int32_t>(
micro_context, bias, bias_comp_td,
data.bias_scratch_index),
#else // USE_TFLM_COMPRESSION
tflite::micro::GetTensorData<int8_t>(filter),
tflite::micro::GetTensorShape(bias),
tflite::micro::GetOptionalTensorData<int64_t>(bias),
tflite::micro::GetTensorData<int8_t>(filter),
tflite::micro::GetTensorShape(bias),
tflite::micro::GetOptionalTensorData<int32_t>(bias),
#endif // USE_TFLM_COMPRESSION
tflite::micro::GetTensorShape(output),
tflite::micro::GetTensorData<int16_t>(output));
tflite::micro::GetTensorShape(output),
tflite::micro::GetTensorData<int16_t>(output))
: tflite::reference_integer_ops::FullyConnected(
FullyConnectedParamsQuantized(data),
tflite::micro::GetTensorShape(input),
tflite::micro::GetTensorData<int16_t>(input),
tflite::micro::GetTensorShape(filter),
#ifdef USE_TFLM_COMPRESSION
tflite::micro::GetTensorData<int8_t>(micro_context, filter,
weights_comp_td,
data.weights_scratch_index),
tflite::micro::GetTensorShape(bias),
tflite::micro::GetOptionalTensorData<int32_t>(
micro_context, bias, bias_comp_td,
data.bias_scratch_index),
#else // USE_TFLM_COMPRESSION
tflite::micro::GetTensorData<int8_t>(filter),
tflite::micro::GetTensorShape(bias),
tflite::micro::GetOptionalTensorData<int32_t>(bias),
#endif // USE_TFLM_COMPRESSION
tflite::micro::GetTensorShape(output),
tflite::micro::GetTensorData<int16_t>(output));
} else if (bias->type == kTfLiteInt64) {
data.is_per_channel
? tflite::reference_integer_ops::FullyConnectedPerChannel(
FullyConnectedParamsQuantized(data),
data.per_channel_output_multiplier,
reinterpret_cast<const int*>(data.per_channel_output_shift),
tflite::micro::GetTensorShape(input),
tflite::micro::GetTensorData<int16_t>(input),
tflite::micro::GetTensorShape(filter),
#ifdef USE_TFLM_COMPRESSION
tflite::micro::GetTensorData<int8_t>(micro_context, filter,
weights_comp_td,
data.weights_scratch_index),
tflite::micro::GetTensorShape(bias),
tflite::micro::GetOptionalTensorData<int64_t>(
micro_context, bias, bias_comp_td,
data.bias_scratch_index),
#else // USE_TFLM_COMPRESSION
tflite::micro::GetTensorData<int8_t>(filter),
tflite::micro::GetTensorShape(bias),
tflite::micro::GetOptionalTensorData<int64_t>(bias),
#endif // USE_TFLM_COMPRESSION
tflite::micro::GetTensorShape(output),
tflite::micro::GetTensorData<int16_t>(output))
: tflite::reference_integer_ops::FullyConnected(
FullyConnectedParamsQuantized(data),
tflite::micro::GetTensorShape(input),
tflite::micro::GetTensorData<int16_t>(input),
tflite::micro::GetTensorShape(filter),
#ifdef USE_TFLM_COMPRESSION
tflite::micro::GetTensorData<int8_t>(micro_context, filter,
weights_comp_td,
data.weights_scratch_index),
tflite::micro::GetTensorShape(bias),
tflite::micro::GetOptionalTensorData<int64_t>(
micro_context, bias, bias_comp_td,
data.bias_scratch_index),
#else // USE_TFLM_COMPRESSION
tflite::micro::GetTensorData<int8_t>(filter),
tflite::micro::GetTensorShape(bias),
tflite::micro::GetOptionalTensorData<int64_t>(bias),
#endif // USE_TFLM_COMPRESSION
tflite::micro::GetTensorShape(output),
tflite::micro::GetTensorData<int16_t>(output));
}
break;
}
default: {
Expand Down
11 changes: 8 additions & 3 deletions tensorflow/lite/micro/kernels/fully_connected_common.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -95,9 +95,14 @@ TfLiteStatus CalculateOpDataFullyConnected(
filter->quantization.params);
const int per_channel_quantization_size = affine_quantization->scale->size;

// Currently only Int8 is supported for per channel quantization.
TF_LITE_ENSURE(context,
input->type == kTfLiteInt8 && filter->type != kTfLiteInt4);
// Currently only Int8/Int16 are supported for per channel quantization.
TF_LITE_ENSURE(
context,
(input->type == kTfLiteInt8 && filter->type != kTfLiteInt4) ||
(input->type == kTfLiteInt16 && filter->type != kTfLiteInt4));

TF_LITE_ENSURE_EQ(context, affine_quantization->scale->size,
per_channel_quantization_size);

TF_LITE_ENSURE_EQ(
context, per_channel_quantization_size,
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8 changes: 7 additions & 1 deletion tensorflow/lite/micro/kernels/transpose.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -103,10 +103,16 @@ TfLiteStatus TransposeEval(TfLiteContext* context, TfLiteNode* node) {
tflite::micro::GetTensorShape(output),
tflite::micro::GetTensorData<int8_t>(output));
break;
case kTfLiteInt16:
reference_ops::Transpose(params, tflite::micro::GetTensorShape(input),
tflite::micro::GetTensorData<int16_t>(input),
tflite::micro::GetTensorShape(output),
tflite::micro::GetTensorData<int16_t>(output));
break;
default:
MicroPrintf(
"Type %s is currently not supported by Transpose. "
"Only float32 and int8 is supported",
"Only float32, int8 and int16 is supported",
TfLiteTypeGetName(input->type));
return kTfLiteError;
}
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3 changes: 3 additions & 0 deletions tensorflow/lite/micro/kernels/unpack.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -89,6 +89,9 @@ TfLiteStatus UnpackEval(TfLiteContext* context, TfLiteNode* node) {
case kTfLiteInt8: {
return UnpackImpl<int8_t>(context, node, input, data->num, data->axis);
}
case kTfLiteInt16: {
return UnpackImpl<int16_t>(context, node, input, data->num, data->axis);
}
default: {
MicroPrintf("Type '%s' is not supported by unpack.",
TfLiteTypeGetName(input->type));
Expand Down
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