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Added versioning to ADD/SUB + some rework of the existing code.

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This commit is contained in:
Elena Zhelezina 2020-03-26 15:08:25 +00:00
parent 4180f945a7
commit dd0d9e8f03
7 changed files with 114 additions and 23 deletions
tensorflow/lite
kernels
add.ccregister.ccsub.cc
toco/tflite
op_version.cc
tools/versioning
BUILDop_version.ccop_version.h

27
tensorflow/lite/kernels/add.cc
View File

@ -100,19 +100,26 @@ 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_16bit = false;
bool pot_scale_16bit = true;
bool input1_scale_is_pot = false;
bool input2_scale_is_pot = false;
bool output_scale_is_pot = false;
int input1_scale_log2_rounded;
int input2_scale_log2_rounded;
int output_scale_log2_rounded;
int input1_scale_log2_rounded{0};
int input2_scale_log2_rounded{0};
int output_scale_log2_rounded{0};
if (input1->type == kTfLiteInt16 && input2->type == kTfLiteInt16 &&
output->type == kTfLiteInt16) {
// Check that param scale is POT
// 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_16bit = (input1->params.zero_point == 0) &&
(input2->params.zero_point == 0) &&
(output->params.zero_point == 0);
input1_scale_is_pot =
CheckedLog2(input1->params.scale, &input1_scale_log2_rounded);
@ -122,14 +129,14 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
output_scale_is_pot =
CheckedLog2(output->params.scale, &output_scale_log2_rounded);
pot_scale_16bit = input1_scale_log2_rounded && input2_scale_log2_rounded &&
output_scale_log2_rounded;
pot_scale_16bit &=
input1_scale_is_pot && input2_scale_is_pot && output_scale_is_pot;
}
data->pot_scale_16bit = pot_scale_16bit;
if (output->type == kTfLiteUInt8 || output->type == kTfLiteInt8 ||
pot_scale_16bit) {
!pot_scale_16bit) {
// 8bit -> 8bit general quantized path, with general rescalings
// as well as, 16bit -> 16bit with general rescalings
data->input1_offset = -input1->params.zero_point;
@ -139,7 +146,7 @@ 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_16bit ? 15 : 20;
data->left_shift = !pot_scale_16bit ? 15 : 20;
const double twice_max_input_scale =
2 * std::max(input1->params.scale, input2->params.scale);
const double real_input1_multiplier =
@ -252,7 +259,7 @@ TfLiteStatus EvalAddQuantized(TfLiteContext* context, TfLiteNode* node,
const TfLiteTensor* input2,
TfLiteTensor* output) {
if (output->type == kTfLiteUInt8 || output->type == kTfLiteInt8 ||
data->pot_scale_16bit) {
!data->pot_scale_16bit) {
tflite::ArithmeticParams op_params;
op_params.left_shift = data->left_shift;
op_params.input1_offset = data->input1_offset;

4
tensorflow/lite/kernels/register.cc
View File

@ -88,7 +88,7 @@ BuiltinOpResolver::BuiltinOpResolver() {
/* max_version */ 2);
AddBuiltin(BuiltinOperator_ADD, Register_ADD(),
/* min_version */ 1,
/* max_version */ 2);
/* max_version */ 4);
AddBuiltin(BuiltinOperator_SPACE_TO_BATCH_ND, Register_SPACE_TO_BATCH_ND(),
/* min_version */ 1,
/* max_version */ 3);
@ -139,7 +139,7 @@ BuiltinOpResolver::BuiltinOpResolver() {
AddBuiltin(BuiltinOperator_DIV, Register_DIV());
AddBuiltin(BuiltinOperator_SUB, Register_SUB(),
/* min_version */ 1,
/* max_version */ 3);
/* max_version */ 5);
AddBuiltin(BuiltinOperator_SPLIT, Register_SPLIT(), /* min_version */ 1,
/* max_version */ 3);
AddBuiltin(BuiltinOperator_SPLIT_V, Register_SPLIT_V(),

25
tensorflow/lite/kernels/sub.cc
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@ -225,19 +225,26 @@ 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_16bit = false;
bool pot_scale_16bit = true;
bool input1_scale_is_pot = false;
bool input2_scale_is_pot = false;
bool output_scale_is_pot = false;
int input1_scale_log2_rounded;
int input2_scale_log2_rounded;
int output_scale_log2_rounded;
int input1_scale_log2_rounded{0};
int input2_scale_log2_rounded{0};
int output_scale_log2_rounded{0};
if (input1->type == kTfLiteInt16 && input2->type == kTfLiteInt16 &&
output->type == kTfLiteInt16) {
// Check that param scale is POT
// 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_16bit = (input1->params.zero_point == 0) &&
(input2->params.zero_point == 0) &&
(output->params.zero_point == 0);
input1_scale_is_pot =
CheckedLog2(input1->params.scale, &input1_scale_log2_rounded);
@ -247,14 +254,14 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
output_scale_is_pot =
CheckedLog2(output->params.scale, &output_scale_log2_rounded);
pot_scale_16bit = input1_scale_log2_rounded && input2_scale_log2_rounded &&
output_scale_log2_rounded;
pot_scale_16bit &=
input1_scale_is_pot && input2_scale_is_pot && output_scale_is_pot;
}
data->pot_scale_16bit = pot_scale_16bit;
if (output->type == kTfLiteUInt8 || output->type == kTfLiteInt8 ||
pot_scale_16bit) {
!pot_scale_16bit) {
TF_LITE_ENSURE_OK(context, PrepareGeneralSubOp(context, input1, input2,
output, params, data, -1));
} else if (output->type == kTfLiteInt16) {
@ -348,7 +355,7 @@ void EvalQuantized(TfLiteContext* context, TfLiteNode* node,
} else {
TF_LITE_SUB(reference_integer_ops, Add, int8_t);
}
} else if (data->pot_scale_16bit) {
} else if (!data->pot_scale_16bit) {
if (need_broadcast) {
TF_LITE_SUB(reference_ops, BroadcastAdd4DSlow, int16_t);
} else {

5
tensorflow/lite/toco/tflite/op_version.cc
View File

@ -49,11 +49,16 @@ string GetMinimumRuntimeVersionForModel(const Model& model) {
{{OperatorType::kDepthwiseConv, 3}, "1.14.0"},
{{OperatorType::kAdd, 1}, "1.5.0"},
{{OperatorType::kAdd, 2}, "1.14.0"},
{{OperatorType::kAdd, 3}, "1.15.0"},
{{OperatorType::kAdd, 4}, kPendingReleaseOpVersion},
{{OperatorType::kAddN, 1}, "1.14.0"},
{{OperatorType::kSpaceToBatchND, 1}, "1.6.0"},
{{OperatorType::kSpaceToBatchND, 2}, "1.14.0"},
{{OperatorType::kSub, 1}, "1.6.0"},
{{OperatorType::kSub, 2}, "1.14.0"},
{{OperatorType::kSub, 3}, "1.15.0"},
{{OperatorType::kSub, 4}, "1.15.0"},
{{OperatorType::kSub, 5}, kPendingReleaseOpVersion},
{{OperatorType::kDiv, 1}, "1.6.0"},
{{OperatorType::kBatchToSpaceND, 1}, "1.6.0"},
{{OperatorType::kBatchToSpaceND, 2}, "1.14.0"},

1
tensorflow/lite/tools/versioning/BUILD
View File

@ -22,6 +22,7 @@ cc_library(
"//tensorflow/core:tflite_portable_logging",
"//tensorflow/lite:minimal_logging",
"//tensorflow/lite/kernels/internal:compatibility",
"//tensorflow/lite/kernels/internal:quantization_util",
"//tensorflow/lite/schema:schema_fbs",
"//tensorflow/lite/schema:schema_fbs_with_mutable",
"@com_google_absl//absl/memory",

72
tensorflow/lite/tools/versioning/op_version.cc
View File

@ -24,6 +24,7 @@ limitations under the License.
#include "absl/strings/str_split.h"
#include "tensorflow/core/platform/logging.h"
#include "tensorflow/lite/kernels/internal/compatibility.h"
#include "tensorflow/lite/kernels/internal/quantization_util.h"
namespace tflite {
namespace {
@ -359,7 +360,29 @@ int GetBuiltinOperatorVersion(const OpSignature& op_sig) {
}
return 1;
case BuiltinOperator_ADD:
if (op_sig.input_types.at(0) == TensorType_INT16 &&
op_sig.output_types.at(0) == TensorType_INT16) {
if (op_sig.options.addsub.pot_scale_int16) {
return 4;
} else {
return 3;
}
}
if (op_sig.input_types.at(0) == TensorType_INT8) {
return 2;
}
return 1;
case BuiltinOperator_SUB:
if (op_sig.input_types.at(0) == TensorType_INT16 &&
op_sig.output_types.at(0) == TensorType_INT16) {
if (op_sig.options.addsub.pot_scale_int16) {
return 5;
} else {
return 4;
}
}
if (op_sig.options.broadcast.need_broadcast &&
op_sig.options.broadcast.num_dims > 4) {
return 3;
@ -370,7 +393,6 @@ int GetBuiltinOperatorVersion(const OpSignature& op_sig) {
return 1;
case BuiltinOperator_AVERAGE_POOL_2D:
case BuiltinOperator_ADD:
case BuiltinOperator_CONCATENATION:
case BuiltinOperator_MAX_POOL_2D:
case BuiltinOperator_PAD:
@ -487,6 +509,53 @@ OpSignature GetOpSignature(const OperatorCode* op_code, const Operator* op,
}
} break;
case BuiltinOperator_ADD:
case BuiltinOperator_SUB: {
op_sig.options.addsub.pot_scale_int16 = false;
const Tensor* input1_tensor =
subgraph->tensors()->Get(op->inputs()->Get(0));
const Tensor* input2_tensor =
subgraph->tensors()->Get(op->inputs()->Get(1));
const Tensor* output_tensor =
subgraph->tensors()->Get(op->outputs()->Get(0));
const QuantizationParameters* input1_quant =
input1_tensor->quantization();
const QuantizationParameters* input2_quant =
input2_tensor->quantization();
const QuantizationParameters* output_quant =
output_tensor->quantization();
if (input1_quant && input1_quant->scale() &&
input1_quant->scale()->Length() && input2_quant &&
input2_quant->scale() && input2_quant->scale()->Length() &&
output_quant && output_quant->scale() &&
output_quant->scale()->Length()) {
float input1_scale = input1_quant->scale()->Get(0);
float input2_scale = input2_quant->scale()->Get(0);
float output_scale = output_quant->scale()->Get(0);
int scale_log2_rounded = 0;
bool input1_scale_is_pot =
CheckedLog2(input1_scale, &scale_log2_rounded);
bool input2_scale_is_pot =
CheckedLog2(input2_scale, &scale_log2_rounded);
bool output_scale_is_pot =
CheckedLog2(output_scale, &scale_log2_rounded);
op_sig.options.addsub.pot_scale_int16 =
input1_scale_is_pot && input2_scale_is_pot && output_scale_is_pot;
}
if (op_code->builtin_code() == BuiltinOperator_SUB) {
op_sig.options.broadcast.need_broadcast =
!HaveSameShapes(subgraph, op, 0, 1);
op_sig.options.broadcast.num_dims =
std::max(GetNumDims(subgraph, op, 0), GetNumDims(subgraph, op, 1));
}
} break;
case BuiltinOperator_LSTM: {
auto lstm_option = op->builtin_options_as_LSTMOptions();
if (lstm_option) {
@ -512,7 +581,6 @@ OpSignature GetOpSignature(const OperatorCode* op_code, const Operator* op,
op_sig.options.space_batch.num_dims = GetNumDims(subgraph, op, 0);
} break;
case BuiltinOperator_SUB:
case BuiltinOperator_MAXIMUM:
case BuiltinOperator_MINIMUM: {
op_sig.options.broadcast.need_broadcast =

3
tensorflow/lite/tools/versioning/op_version.h
View File

@ -59,6 +59,9 @@ typedef struct {
int32_t num_dims;
bool need_broadcast;
} broadcast;
struct {
bool pot_scale_int16;
} addsub;
} options;
} OpSignature;