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Generalize circular buffer operator.

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PiperOrigin-RevId: 343587123
Change-Id: Ifaea8ffe1e067b4119e529053aa13874c87c984b
This commit is contained in:
Nat Jeffries 2020-11-20 16:34:01 -08:00 committed by TensorFlower Gardener
parent 0957e53d4a
commit 05ae49f2f6
1 changed files with 29 additions and 30 deletions
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59
tensorflow/lite/micro/kernels/circular_buffer.cc
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@ -65,45 +65,49 @@ struct OpData {
int cycles_max; int cycles_max;
}; };
// These constants represent constants specific to the music detect model.
// They exist until (b/132070898) is fixed.
constexpr int kMaxOpDataSize = 7;
int op_data_counter = 0;
OpData op_data_array[kMaxOpDataSize];
} // namespace } // namespace
void Free(TfLiteContext* context, void* buffer) { op_data_counter = 0; } void* Init(TfLiteContext* context, const char* buffer, size_t length) {
TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
return context->AllocatePersistentBuffer(context, sizeof(OpData));
}
TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
const TfLiteTensor* input = GetInput(context, node, kInputTensor); const TfLiteTensor* input = GetInput(context, node, kInputTensor);
TF_LITE_ENSURE(context, input != nullptr);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor); TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
TF_LITE_ENSURE(context, output != nullptr);
TFLITE_DCHECK(node->user_data != nullptr);
OpData* op_data = static_cast<OpData*>(node->user_data);
TF_LITE_ENSURE(context, input != nullptr); TF_LITE_ENSURE(context, input != nullptr);
TF_LITE_ENSURE(context, output != nullptr); TF_LITE_ENSURE(context, output != nullptr);
TF_LITE_ENSURE_EQ(context, 1, output->dims->data[0]); TF_LITE_ENSURE_EQ(context, input->dims->data[0], output->dims->data[0]);
TF_LITE_ENSURE_EQ(context, 1, input->dims->data[0]);
TF_LITE_ENSURE_EQ(context, 1, input->dims->data[1]); TF_LITE_ENSURE_EQ(context, 1, input->dims->data[1]);
TF_LITE_ENSURE_EQ(context, 1, output->dims->data[2]); TF_LITE_ENSURE_EQ(context, input->dims->data[2], output->dims->data[2]);
TF_LITE_ENSURE_EQ(context, 1, input->dims->data[2]);
TF_LITE_ENSURE_EQ(context, output->dims->data[3], input->dims->data[3]); TF_LITE_ENSURE_EQ(context, output->dims->data[3], input->dims->data[3]);
TF_LITE_ENSURE_TYPES_EQ(context, input->type, output->type); TF_LITE_ENSURE_TYPES_EQ(context, input->type, output->type);
// The circular buffer custom operator currently only supports int8_t. // The circular buffer custom operator currently only supports int8.
TF_LITE_ENSURE_TYPES_EQ(context, input->type, kTfLiteInt8); TF_LITE_ENSURE_TYPES_EQ(context, input->type, kTfLiteInt8);
// TODO(b/132070898): Use statically slotted OpData structures until a // The last circular buffer layer simply accumulates outputs, and does not run
// scratch memory API is ready. // periodically.
TFLITE_DCHECK_LE(op_data_counter, kMaxOpDataSize);
OpData* op_data = &op_data_array[op_data_counter++];
// The last circular buffer layer (length 5) simply accumulates outputs, and
// does not run periodically.
// TODO(b/150001379): Move this special case logic to the tflite flatbuffer. // TODO(b/150001379): Move this special case logic to the tflite flatbuffer.
if (output->dims->data[1] == 5) { static int cb_prepare_count = 0;
cb_prepare_count++;
// These checks specifically work for the only two streaming models supported
// on TFLM. They use the shape of the output tensor along with the layer
// number to determine if the circular buffer period should be 1 or 2.
// These models are outlined int the following documents:
// https://docs.google.com/document/d/1lc_G2ZFhjiKFo02UHjBaljye1xsL0EkfybkaVELEE3Q/edit?usp=sharing
// https://docs.google.com/document/d/1pGc42PuWyrk-Jy1-9qeqtggvsmHr1ifz8Lmqfpr2rKA/edit?usp=sharing
if (output->dims->data[1] == 5 || output->dims->data[1] == 13 ||
(cb_prepare_count == 5 && output->dims->data[2] == 2 &&
output->dims->data[3] == 96)) {
op_data->cycles_max = 1; op_data->cycles_max = 1;
cb_prepare_count = 0;
} else { } else {
op_data->cycles_max = 2; op_data->cycles_max = 2;
} }
@ -127,10 +131,11 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
TfLiteEvalTensor* output = TfLiteEvalTensor* output =
tflite::micro::GetEvalOutput(context, node, kOutputTensor); tflite::micro::GetEvalOutput(context, node, kOutputTensor);
TFLITE_DCHECK(node->user_data != nullptr);
OpData* data = reinterpret_cast<OpData*>(node->user_data); OpData* data = reinterpret_cast<OpData*>(node->user_data);
int num_slots = output->dims->data[1]; int num_slots = output->dims->data[1];
int depth = output->dims->data[3]; int depth = output->dims->data[2] * output->dims->data[3];
if (input->type == kTfLiteInt8) { if (input->type == kTfLiteInt8) {
EvalInt8(tflite::micro::GetTensorData<int8_t>(input), num_slots, depth, EvalInt8(tflite::micro::GetTensorData<int8_t>(input), num_slots, depth,
@ -148,12 +153,6 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
return static_cast<TfLiteStatus>(kTfLiteAbort); return static_cast<TfLiteStatus>(kTfLiteAbort);
} }
// If prepare is ever called more than one time (for example, when testing the
// ambient model, the interpreter is created a few times), this op data
// counter needs to be reset so that future instances do not overrun this op
// data array.
op_data_counter = 0;
data->cycles_until_run = data->cycles_max; data->cycles_until_run = data->cycles_max;
return kTfLiteOk; return kTfLiteOk;
@ -162,8 +161,8 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
} // namespace circular_buffer } // namespace circular_buffer
TfLiteRegistration* Register_CIRCULAR_BUFFER() { TfLiteRegistration* Register_CIRCULAR_BUFFER() {
static TfLiteRegistration r = {/*init=*/nullptr, static TfLiteRegistration r = {/*init=*/circular_buffer::Init,
/*free=*/circular_buffer::Free, /*free=*/nullptr,
/*prepare=*/circular_buffer::Prepare, /*prepare=*/circular_buffer::Prepare,
/*invoke=*/circular_buffer::Eval, /*invoke=*/circular_buffer::Eval,
/*profiling_string=*/nullptr, /*profiling_string=*/nullptr,