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16x8 reference operators: ADD/SUB - inference always using general case + tests for versioning.

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Change-Id: Ib24a2c2b837eaeb4868afdf1445aa332965a41af
This commit is contained in:
Elena Zhelezina 2020-08-12 11:57:04 +01:00
parent 8c3b7438db
commit 01da0c850b
8 changed files with 142 additions and 52 deletions
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44
tensorflow/lite/kernels/add.cc
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@ -110,7 +110,13 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
// 8bit -> 8bit general quantized path, with general rescalings
// as well as, int16 -> int16 with general rescalings
bool pot_scale_int16 = true;
// There are two implementations of ADD operator in case of
// 16bit input/output depending on whether the scale parameter is
// the power of 2 or not. Currently only implementation for
// general case is used, but we need to use another implementation
// for older versions.
bool general_scale_int16 = false;
bool input1_scale_is_pot = false;
bool input2_scale_is_pot = false;
@ -122,31 +128,31 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
if (input1->type == kTfLiteInt16 && input2->type == kTfLiteInt16 &&
output->type == kTfLiteInt16) {
// In case of 16-bit, there are two implementation:
// the scale parameter is a general number
// the scale parameter is POT and
// zero_point is zero for inputs/output.
pot_scale_int16 = (input1->params.zero_point == 0) &&
(input2->params.zero_point == 0) &&
(output->params.zero_point == 0);
general_scale_int16 = !params || !params->pot_scale_int16;
input1_scale_is_pot =
CheckedLog2(input1->params.scale, &input1_scale_log2_rounded);
if (!general_scale_int16) {
// Do preparation in the case of the scale parameter is power of 2.
input2_scale_is_pot =
CheckedLog2(input2->params.scale, &input2_scale_log2_rounded);
input1_scale_is_pot =
CheckedLog2(input1->params.scale, &input1_scale_log2_rounded);
output_scale_is_pot =
CheckedLog2(output->params.scale, &output_scale_log2_rounded);
input2_scale_is_pot =
CheckedLog2(input2->params.scale, &input2_scale_log2_rounded);
pot_scale_int16 &=
input1_scale_is_pot && input2_scale_is_pot && output_scale_is_pot;
output_scale_is_pot =
CheckedLog2(output->params.scale, &output_scale_log2_rounded);
general_scale_int16 =
!input1_scale_is_pot || !input2_scale_is_pot ||
!output_scale_is_pot || input1->params.zero_point != 0 ||
input2->params.zero_point != 0 || output->params.zero_point != 0;
}
}
data->pot_scale_int16 = pot_scale_int16;
data->pot_scale_int16 = !general_scale_int16;
if (output->type == kTfLiteUInt8 || output->type == kTfLiteInt8 ||
!pot_scale_int16) {
general_scale_int16) {
// 8bit -> 8bit general quantized path, with general rescalings
// as well as, 16bit -> 16bit with general rescalings
data->input1_offset = -input1->params.zero_point;
@ -156,7 +162,7 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
// The shift is set to 15 for 16-bit and 20 in case of 8-bit, accordingly.
// In case of 16-bit we have 65535 << 15 which is less than 1 << 31,
// therefore the addition will still fit in a 32 bit accumulator.
data->left_shift = !pot_scale_int16 ? 15 : 20;
data->left_shift = general_scale_int16 ? 15 : 20;
const double twice_max_input_scale =
2 * std::max(input1->params.scale, input2->params.scale);
const double real_input1_multiplier =

2
tensorflow/lite/kernels/register.cc
View File

@ -90,7 +90,7 @@ BuiltinOpResolver::BuiltinOpResolver() {
/* max_version = */ 3);
AddBuiltin(BuiltinOperator_ADD, Register_ADD(),
/* min_version */ 1,
/* max_version */ 4);
/* max_version */ 3);
AddBuiltin(BuiltinOperator_SPACE_TO_BATCH_ND, Register_SPACE_TO_BATCH_ND(),
/* min_version = */ 1,
/* max_version = */ 3);

43
tensorflow/lite/kernels/sub.cc
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@ -236,7 +236,13 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
// 8bit -> 8bit general quantized path, with general rescalings
// as well as, 16bit -> 16bit with general rescalings
bool pot_scale_int16 = true;
// There are two implementations of SUB operator in case of
// 16bit input depending on whether the scale parameter is
// the power of 2 or not. Currently only implementation for
// general case is used, but we need to use another implementation
// for older versions.
bool general_scale_int16 = false;
bool input1_scale_is_pot = false;
bool input2_scale_is_pot = false;
@ -248,31 +254,30 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
if (input1->type == kTfLiteInt16 && input2->type == kTfLiteInt16 &&
output->type == kTfLiteInt16) {
// In case of 16-bit, there are two implementation:
// the scale parameter is a general number
// the scale parameter is POT and
// zero_point is zero for inputs/output.
pot_scale_int16 = (input1->params.zero_point == 0) &&
(input2->params.zero_point == 0) &&
(output->params.zero_point == 0);
general_scale_int16 = !params || !params->pot_scale_int16;
input1_scale_is_pot =
CheckedLog2(input1->params.scale, &input1_scale_log2_rounded);
if (!general_scale_int16) {
// Do preparation in the case of the scale parameter is power of 2.
input1_scale_is_pot =
CheckedLog2(input1->params.scale, &input1_scale_log2_rounded);
input2_scale_is_pot =
CheckedLog2(input2->params.scale, &input2_scale_log2_rounded);
input2_scale_is_pot =
CheckedLog2(input2->params.scale, &input2_scale_log2_rounded);
output_scale_is_pot =
CheckedLog2(output->params.scale, &output_scale_log2_rounded);
output_scale_is_pot =
CheckedLog2(output->params.scale, &output_scale_log2_rounded);
pot_scale_int16 &=
input1_scale_is_pot && input2_scale_is_pot && output_scale_is_pot;
general_scale_int16 =
!input1_scale_is_pot || !input2_scale_is_pot ||
!output_scale_is_pot || input1->params.zero_point != 0 ||
input2->params.zero_point != 0 || output->params.zero_point != 0;
}
}
data->pot_scale_int16 = pot_scale_int16;
data->pot_scale_int16 = !general_scale_int16;
if (output->type == kTfLiteUInt8 || output->type == kTfLiteInt8 ||
!pot_scale_int16) {
general_scale_int16) {
TF_LITE_ENSURE_OK(context, PrepareGeneralSubOp(context, input1, input2,
output, params, data, -1));
} else if (output->type == kTfLiteInt16) {
@ -388,7 +393,7 @@ void EvalQuantized(TfLiteContext* context, TfLiteNode* node,
}
} else if (!data->pot_scale_int16) {
if (need_broadcast) {
TF_LITE_SUB(reference_ops, BroadcastAdd4DSlow, int16_t);
TF_LITE_SUB(reference_ops, BroadcastSubSlow, int16_t);
} else {
reference_ops::Add(op_params, GetTensorShape(input1),
GetTensorData<int16_t>(input1), GetTensorShape(input2),

6
tensorflow/lite/schema/schema.fbs
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@ -583,8 +583,8 @@ table ConcatenationOptions {
table AddOptions {
fused_activation_function:ActivationFunctionType;
// Parameters supported by version 4.
pot_scale_int16:bool = true;
// Parameters supported by version 3.
pot_scale_int16:bool = false;
}
table MulOptions {
@ -707,7 +707,7 @@ table DepthToSpaceOptions {
table SubOptions {
fused_activation_function:ActivationFunctionType;
// Parameters supported by version 5
pot_scale_int16:bool = true;
pot_scale_int16:bool = false;
}
table DivOptions {

1
tensorflow/lite/tools/optimize/operator_property.cc
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@ -913,6 +913,7 @@ OperatorProperty GetOperatorProperty(const ModelT* model, int subgraph_index,
property.inputs = {{0, {}}, {1, {}}};
property.outputs = {{0, {}}};
property.version = 2;
property.restrict_same_input_output_scale = true;
break;
case BuiltinOperator_SUM:
property.inputs = {{0, {}}};

40
tensorflow/lite/tools/optimize/quantize_model.cc
View File

@ -467,6 +467,44 @@ TfLiteStatus ApplyConstraints(
return kTfLiteOk;
}
// In case of int16 activations, there are two implementations of kernels for
// ADD/SUB operators. We set the builtin option pot_scale_int16
// during quantization so that from now only the general case implementation is
// used.
void SetOperatorPropertyADDSUBOperator(ModelT* model,
const TensorType& activations_type) {
if (activations_type != TensorType_INT16) {
// This is needed only in case of int16 activations.
return;
}
for (int subgraph_idx = 0, end = model->subgraphs.size(); subgraph_idx < end;
subgraph_idx++) {
SubGraphT* subgraph = model->subgraphs.at(subgraph_idx).get();
// Iterate backward to avoid messing with index.
for (int op_idx = subgraph->operators.size() - 1; op_idx >= 0; op_idx--) {
OperatorT* op = subgraph->operators[op_idx].get();
OperatorCodeT* op_code = model->operator_codes[op->opcode_index].get();
if (op_code && op_code->builtin_code == BuiltinOperator_ADD) {
{
auto* options = op->builtin_options.AsAddOptions();
if (options) {
options->pot_scale_int16 = false;
}
}
}
if (op_code && op_code->builtin_code == BuiltinOperator_SUB) {
{
auto* options = op->builtin_options.AsSubOptions();
if (options) {
options->pot_scale_int16 = false;
}
}
}
}
}
}
std::vector<std::pair<int, operator_property::TensorProperty>> GetInputs(
const OperatorT* op, operator_property::OperatorProperty property) {
std::vector<std::pair<int, operator_property::TensorProperty>> inputs;
@ -1347,7 +1385,7 @@ TfLiteStatus QuantizeModel(flatbuffers::FlatBufferBuilder* builder,
utils::SetOperatorCodeVersion(model);
TF_LITE_ENSURE_STATUS(SetInputAndOutputTypes(
model, input_type, output_type, activations_type, error_reporter));
SetOperatorPropertyADDSUBOperator(model, activations_type);
flatbuffers::Offset<Model> output_model_location =
Model::Pack(*builder, model);
FinishModelBuffer(*builder, output_model_location);

33
tensorflow/lite/tools/optimize/quantize_model_test.cc
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@ -998,19 +998,25 @@ TEST_F(QuantizeMultiInputAddWithReshapeTest, VerifyAddQuantization) {
EXPECT_EQ(model_.operator_codes[1]->version, 1);
}
class QuantizeConstInputTest : public QuantizeModelTest {
class QuantizeConstInputTest : public QuantizeModelTest,
public testing::WithParamInterface<TensorType> {
protected:
QuantizeConstInputTest() {
input_model_ = ReadModel(internal::kConstInputAddModel);
readonly_model_ = input_model_->GetModel();
readonly_model_->UnPackTo(&model_);
}
};
TEST_F(QuantizeConstInputTest, VerifyConstOpInput) {
auto status = QuantizeModelAllOperators(&builder_, &model_, TensorType_INT8,
TensorType_INT8, false,
TensorType_INT8, &error_reporter_);
TensorType tensor_type_;
};
INSTANTIATE_TEST_SUITE_P(QuantizeConstInputTestInst, QuantizeConstInputTest,
testing::ValuesIn({TensorType_INT8,
TensorType_INT16}));
TEST_P(QuantizeConstInputTest, VerifyConstOpInput) {
auto status =
QuantizeModelAllOperators(&builder_, &model_, tensor_type_, tensor_type_,
false, tensor_type_, &error_reporter_);
ASSERT_EQ(kTfLiteOk, status);
// Verify ConstOp is quantized.
@ -1030,17 +1036,26 @@ TEST_F(QuantizeConstInputTest, VerifyConstOpInput) {
for (size_t input_idx = 0; input_idx < 2; ++input_idx) {
EXPECT_EQ(subgraph->tensors[op->inputs[input_idx]].get()->type,
TensorType_INT8);
tensor_type_);
}
EXPECT_EQ(subgraph->tensors[op->outputs[0]].get()->type, TensorType_INT8);
EXPECT_EQ(subgraph->tensors[op->outputs[0]].get()->type, tensor_type_);
// check op and versioning.
EXPECT_EQ(model_.operator_codes.size(), 1);
EXPECT_EQ(model_.operator_codes[0]->builtin_code, BuiltinOperator_ADD);
EXPECT_EQ(model_.operator_codes[0]->version, 2);
}
// check that in case of int16 activations, pot_scale_int16 parameter is set
// to false.
if (tensor_type_ == TensorType_INT16) {
EXPECT_EQ(subgraph->operators[0]
.get()
->builtin_options.AsAddOptions()
->pot_scale_int16,
false);
}
}
class QuantizeArgMaxTest : public QuantizeModelTest {
protected:
QuantizeArgMaxTest() {

25
tensorflow/lite/tools/versioning/op_version_test.cc
View File

@ -301,10 +301,35 @@ TEST(OpVersionTest, VersioningSumTest) {
}
TEST(OpVersionTest, VersioningAddTest) {
OpSignature fake_op_sig = {
.op = BuiltinOperator_ADD,
.input_types = std::vector<TensorType>{TensorType_INT16},
.output_types = std::vector<TensorType>{TensorType_INT16}
};
fake_op_sig.options.addsub.pot_scale_int16 = false;
EXPECT_EQ(GetBuiltinOperatorVersion(fake_op_sig), 3);
SimpleVersioningTest(BuiltinOperator_ADD);
}
TEST(OpVersionTest, VersioningSubTest) {
OpSignature fake_op_sig = {
.op = BuiltinOperator_SUB,
.input_types = std::vector<TensorType>{TensorType_INT16},
.output_types = std::vector<TensorType>{TensorType_INT16}
};
fake_op_sig.options.addsub.pot_scale_int16 = false;
EXPECT_EQ(GetBuiltinOperatorVersion(fake_op_sig), 5);
fake_op_sig.input_types = std::vector<TensorType>{TensorType_INT64};
EXPECT_EQ(GetBuiltinOperatorVersion(fake_op_sig), 4);
fake_op_sig.input_types = std::vector<TensorType>{TensorType_INT8};
fake_op_sig.output_types = std::vector<TensorType>{TensorType_INT8};
fake_op_sig.options.addsub.need_broadcast = true;
fake_op_sig.options.addsub.num_dims = 5;
EXPECT_EQ(GetBuiltinOperatorVersion(fake_op_sig), 3);
SimpleVersioningTest(BuiltinOperator_SUB);
}