common/QuantUtils.h - platform/packages/modules/NeuralNetworks - Git at Google

 // Quantized calculation utilities.
 // TODO(vddang): Replace this with tensorflow/lite/kernels/internal/tensor_utils(common).h
 // after TFLite module has been synced.

 #ifndef ANDROID_FRAMEWORKS_ML_NN_COMMON_QUANTUTILS_H
 #define ANDROID_FRAMEWORKS_ML_NN_COMMON_QUANTUTILS_H

 #include <public/gemmlowp.h>

 #include <limits>
 #include <memory>

 #include "LegacyUtils.h"
 #include "OperationsUtils.h"

 namespace android {
 namespace nn {

 inline int32_t MultiplyByQuantizedMultiplier(int32_t x, int32_t quantized_multiplier, int shift) {
     using gemmlowp::RoundingDivideByPOT;
     using gemmlowp::SaturatingRoundingDoublingHighMul;
     int left_shift = shift > 0 ? shift : 0;
     int right_shift = shift > 0 ? 0 : -shift;
     return RoundingDivideByPOT(
             SaturatingRoundingDoublingHighMul(x * (1 << left_shift), quantized_multiplier),
             right_shift);
 }

 template <typename T>
 void MatrixBatchVectorMultiplyAccumulate(const int8_t* input, const int32_t* bias,
                                          const int8_t* input_to_gate_weights, int32_t multiplier,
                                          int32_t shift, int32_t n_batch, int32_t n_input,
                                          int32_t n_output, int32_t output_zp, T* output) {
     const int16_t output_max = std::numeric_limits<T>::max();
     const int16_t output_min = std::numeric_limits<T>::min();
     for (int batch = 0; batch < n_batch; ++batch) {
         for (int row = 0; row < n_output; ++row) {
             int32_t acc = bias[row];
             for (int col = 0; col < n_input; ++col) {
                 int8_t input_val = input[batch * n_input + col];
                 int8_t weights_val = input_to_gate_weights[row * n_input + col];
                 acc += input_val * weights_val;
             }
             acc = MultiplyByQuantizedMultiplier(acc, multiplier, shift);
             acc += output_zp;
             acc += output[batch * n_output + row];
             if (acc > output_max) {
                 acc = output_max;
             }
             if (acc < output_min) {
                 acc = output_min;
             }
             output[batch * n_output + row] = static_cast<T>(acc);
         }
     }
 }

 template <typename T>
 int CountLeadingZeros(T integer_input) {
     static_assert(std::is_unsigned<T>::value, "Only unsigned integer types handled.");
 #if defined(__GNUC__)
     return integer_input ? __builtin_clz(integer_input) : std::numeric_limits<T>::digits;
 #else
     if (integer_input == 0) {
         return std::numeric_limits<T>::digits;
     }

     const T one_in_leading_positive = static_cast<T>(1) << (std::numeric_limits<T>::digits - 1);
     int leading_zeros = 0;
     while (integer_input < one_in_leading_positive) {
         integer_input <<= 1;
         ++leading_zeros;
     }
     return leading_zeros;
 #endif
 }

 inline bool GetInvSqrtQuantizedMultiplierExp(int32_t input, int reverse_shift,
                                              int32_t* output_inv_sqrt, int* output_shift) {
     NN_RET_CHECK_GE(input, 0);
     if (input <= 1) {
         // Handle the input value 1 separately to avoid overflow in that case
         // in the general computation below. Also handle 0 as if it
         // were a 1. 0 is an invalid input here (divide by zero) and 1 is a valid
         // but rare/unrealistic input value. We can expect both to occur in some
         // incompletely trained models, but probably not in fully trained models.
         *output_inv_sqrt = std::numeric_limits<std::int32_t>::max();
         *output_shift = 0;
         return true;
     }

     *output_shift = 11;
     while (input >= (1 << 29)) {
         input /= 4;
         ++*output_shift;
     }
     const unsigned max_left_shift_bits = CountLeadingZeros(static_cast<uint32_t>(input)) - 1;
     const unsigned max_left_shift_bit_pairs = max_left_shift_bits / 2;
     const unsigned left_shift_bit_pairs = max_left_shift_bit_pairs - 1;
     *output_shift -= left_shift_bit_pairs;
     input <<= 2 * left_shift_bit_pairs;
     NN_RET_CHECK_GE(input, (1 << 27));
     NN_RET_CHECK_LT(input, (1 << 29));
     using gemmlowp::FixedPoint;
     using gemmlowp::Rescale;
     using gemmlowp::SaturatingRoundingMultiplyByPOT;
     // Using 3 integer bits gives us enough room for the internal arithmetic in
     // this Newton-Raphson iteration.
     using F3 = FixedPoint<int32_t, 3>;
     using F0 = FixedPoint<int32_t, 0>;
     const F3 fixedpoint_input = F3::FromRaw(input >> 1);
     const F3 fixedpoint_half_input = SaturatingRoundingMultiplyByPOT<-1>(fixedpoint_input);
     const F3 fixedpoint_half_three =
             GEMMLOWP_CHECKED_FIXEDPOINT_CONSTANT(F3, (1 << 28) + (1 << 27), 1.5);
     // Newton-Raphson iteration
     // Naive unoptimized starting guess: x = 1
     F3 x = F3::One();
     // Naive unoptimized number of iterations: 5
     for (int i = 0; i < 5; i++) {
         const F3 x3 = Rescale<3>(x * x * x);
         x = Rescale<3>(fixedpoint_half_three * x - fixedpoint_half_input * x3);
     }
     const F0 fixedpoint_half_sqrt_2 =
             GEMMLOWP_CHECKED_FIXEDPOINT_CONSTANT(F0, 1518500250, std::sqrt(2.) / 2.);
     x = x * fixedpoint_half_sqrt_2;
     *output_inv_sqrt = x.raw();
     if (*output_shift < 0) {
         *output_inv_sqrt <<= -*output_shift;
         *output_shift = 0;
     }
     // Convert right shift (right is positive) to left shift.
     *output_shift *= reverse_shift;
     return true;
 }

 void ApplyLayerNorm(const int16_t* input, const int16_t* layer_norm_weights, const int32_t* bias,
                     int32_t layer_norm_scale_a, int32_t layer_norm_scale_b, int32_t variance_limit,
                     int n_batch, int n_input, int16_t* output);

 void MatrixScalarMultiplyAccumulate(const int8_t* matrix, int32_t scalar, int32_t n_row,
                                     int32_t n_col, int32_t* output);

 bool PrecomputeZeroPointTimesWeightWithBias(int32_t zero_point, const int8_t* weight_tensor,
                                             const Shape& weight_shape, const int32_t* bias_tensor,
                                             std::unique_ptr<int32_t[]>* output);

 void ApplySigmoid(const int16_t* input, int32_t n_batch, int32_t n_input, int16_t* output);

 template <int IntegerBits>
 void ApplyTanh(const int16_t* input, int32_t n_batch, int32_t n_input, int16_t* output) {
     using FX = gemmlowp::FixedPoint<std::int16_t, IntegerBits>;
     using F0 = gemmlowp::FixedPoint<std::int16_t, 0>;
     for (int batch = 0; batch < n_batch; ++batch) {
         for (int i = 0; i < n_input; ++i) {
             const int index = batch * n_input + i;
             FX tanh_input = FX::FromRaw(input[index]);
             F0 tanh_output = gemmlowp::tanh(tanh_input);
             output[index] = tanh_output.raw();
         }
     }
 }

 inline void ApplyTanh(int32_t integer_bits, const int16_t* input, int32_t n_batch, int32_t n_input,
                       int16_t* output) {
     assert(integer_bits <= 6);
 #define DISPATCH_TANH(i)                               \
     case i:                                            \
         ApplyTanh<i>(input, n_batch, n_input, output); \
         break;
     switch (integer_bits) {
         DISPATCH_TANH(0);
         DISPATCH_TANH(1);
         DISPATCH_TANH(2);
         DISPATCH_TANH(3);
         DISPATCH_TANH(4);
         DISPATCH_TANH(5);
         DISPATCH_TANH(6);
         default:
             return;
     }
 #undef DISPATCH_TANH
 }

 void CwiseMul(const int16_t* input_1, const int16_t* input_2, int n_batch, int n_input, int shift,
               int16_t* output);
 void CwiseMul(const int16_t* input_1, const int16_t* input_2, int32_t multiplier, int32_t shift,
               int32_t n_batch, int32_t n_input, int32_t output_zp, int8_t* output);

 bool CheckedLog2(const float x, int* log2_result);

 void CwiseAdd(const int16_t* input_1, const int16_t* input_2, int n_batch, int n_input,
               int16_t* output);

 inline void Sub1Vector(const int16_t* vector, int v_size, int16_t* result) {
     static const int16_t kOne = 32767;
     for (int v = 0; v < v_size; v++) {
         *result++ = kOne - *vector++;
     }
 }

 void CwiseClipping(int16_t* input, const int16_t clipping_value, int32_t n_batch, int32_t n_input);

 void CwiseClipping(int8_t* input, const int8_t clipping_value, int32_t n_batch, int32_t n_input);

 void VectorBatchVectorCwiseProductAccumulate(const int16_t* vector, int v_size,
                                              const int16_t* batch_vector, int n_batch,
                                              int32_t multiplier, int shift, int16_t* result);

 }  // namespace nn
 }  // namespace android

 #endif  // ANDROID_FRAMEWORKS_ML_NN_COMMON_QUANTUTILS_H
	// Quantized calculation utilities.
	// TODO(vddang): Replace this with tensorflow/lite/kernels/internal/tensor_utils(common).h
	// after TFLite module has been synced.

	#ifndef ANDROID_FRAMEWORKS_ML_NN_COMMON_QUANTUTILS_H
	#define ANDROID_FRAMEWORKS_ML_NN_COMMON_QUANTUTILS_H

	#include <public/gemmlowp.h>

	#include <limits>
	#include <memory>

	#include "LegacyUtils.h"
	#include "OperationsUtils.h"

	namespace android {
	namespace nn {

	inline int32_t MultiplyByQuantizedMultiplier(int32_t x, int32_t quantized_multiplier, int shift) {
	using gemmlowp::RoundingDivideByPOT;
	using gemmlowp::SaturatingRoundingDoublingHighMul;
	int left_shift = shift > 0 ? shift : 0;
	int right_shift = shift > 0 ? 0 : -shift;
	return RoundingDivideByPOT(
	SaturatingRoundingDoublingHighMul(x * (1 << left_shift), quantized_multiplier),
	right_shift);
	}

	template <typename T>
	void MatrixBatchVectorMultiplyAccumulate(const int8_t* input, const int32_t* bias,
	const int8_t* input_to_gate_weights, int32_t multiplier,
	int32_t shift, int32_t n_batch, int32_t n_input,
	int32_t n_output, int32_t output_zp, T* output) {
	const int16_t output_max = std::numeric_limits<T>::max();
	const int16_t output_min = std::numeric_limits<T>::min();
	for (int batch = 0; batch < n_batch; ++batch) {
	for (int row = 0; row < n_output; ++row) {
	int32_t acc = bias[row];
	for (int col = 0; col < n_input; ++col) {
	int8_t input_val = input[batch * n_input + col];
	int8_t weights_val = input_to_gate_weights[row * n_input + col];
	acc += input_val * weights_val;
	}
	acc = MultiplyByQuantizedMultiplier(acc, multiplier, shift);
	acc += output_zp;
	acc += output[batch * n_output + row];
	if (acc > output_max) {
	acc = output_max;
	}
	if (acc < output_min) {
	acc = output_min;
	}
	output[batch * n_output + row] = static_cast<T>(acc);
	}
	}
	}

	template <typename T>
	int CountLeadingZeros(T integer_input) {
	static_assert(std::is_unsigned<T>::value, "Only unsigned integer types handled.");
	#if defined(__GNUC__)
	return integer_input ? __builtin_clz(integer_input) : std::numeric_limits<T>::digits;
	#else
	if (integer_input == 0) {
	return std::numeric_limits<T>::digits;
	}

	const T one_in_leading_positive = static_cast<T>(1) << (std::numeric_limits<T>::digits - 1);
	int leading_zeros = 0;
	while (integer_input < one_in_leading_positive) {
	integer_input <<= 1;
	++leading_zeros;
	}
	return leading_zeros;
	#endif
	}

	inline bool GetInvSqrtQuantizedMultiplierExp(int32_t input, int reverse_shift,
	int32_t* output_inv_sqrt, int* output_shift) {
	NN_RET_CHECK_GE(input, 0);
	if (input <= 1) {
	// Handle the input value 1 separately to avoid overflow in that case
	// in the general computation below. Also handle 0 as if it
	// were a 1. 0 is an invalid input here (divide by zero) and 1 is a valid
	// but rare/unrealistic input value. We can expect both to occur in some
	// incompletely trained models, but probably not in fully trained models.
	*output_inv_sqrt = std::numeric_limits<std::int32_t>::max();
	*output_shift = 0;
	return true;
	}

	*output_shift = 11;
	while (input >= (1 << 29)) {
	input /= 4;
	++*output_shift;
	}
	const unsigned max_left_shift_bits = CountLeadingZeros(static_cast<uint32_t>(input)) - 1;
	const unsigned max_left_shift_bit_pairs = max_left_shift_bits / 2;
	const unsigned left_shift_bit_pairs = max_left_shift_bit_pairs - 1;
	*output_shift -= left_shift_bit_pairs;
	input <<= 2 * left_shift_bit_pairs;
	NN_RET_CHECK_GE(input, (1 << 27));
	NN_RET_CHECK_LT(input, (1 << 29));
	using gemmlowp::FixedPoint;
	using gemmlowp::Rescale;
	using gemmlowp::SaturatingRoundingMultiplyByPOT;
	// Using 3 integer bits gives us enough room for the internal arithmetic in
	// this Newton-Raphson iteration.
	using F3 = FixedPoint<int32_t, 3>;
	using F0 = FixedPoint<int32_t, 0>;
	const F3 fixedpoint_input = F3::FromRaw(input >> 1);
	const F3 fixedpoint_half_input = SaturatingRoundingMultiplyByPOT<-1>(fixedpoint_input);
	const F3 fixedpoint_half_three =
	GEMMLOWP_CHECKED_FIXEDPOINT_CONSTANT(F3, (1 << 28) + (1 << 27), 1.5);
	// Newton-Raphson iteration
	// Naive unoptimized starting guess: x = 1
	F3 x = F3::One();
	// Naive unoptimized number of iterations: 5
	for (int i = 0; i < 5; i++) {
	const F3 x3 = Rescale<3>(x * x * x);
	x = Rescale<3>(fixedpoint_half_three * x - fixedpoint_half_input * x3);
	}
	const F0 fixedpoint_half_sqrt_2 =
	GEMMLOWP_CHECKED_FIXEDPOINT_CONSTANT(F0, 1518500250, std::sqrt(2.) / 2.);
	x = x * fixedpoint_half_sqrt_2;
	*output_inv_sqrt = x.raw();
	if (*output_shift < 0) {
	output_inv_sqrt <<= -output_shift;
	*output_shift = 0;
	}
	// Convert right shift (right is positive) to left shift.
	output_shift = reverse_shift;
	return true;
	}

	void ApplyLayerNorm(const int16_t* input, const int16_t* layer_norm_weights, const int32_t* bias,
	int32_t layer_norm_scale_a, int32_t layer_norm_scale_b, int32_t variance_limit,
	int n_batch, int n_input, int16_t* output);

	void MatrixScalarMultiplyAccumulate(const int8_t* matrix, int32_t scalar, int32_t n_row,
	int32_t n_col, int32_t* output);

	bool PrecomputeZeroPointTimesWeightWithBias(int32_t zero_point, const int8_t* weight_tensor,
	const Shape& weight_shape, const int32_t* bias_tensor,
	std::unique_ptr<int32_t[]>* output);

	void ApplySigmoid(const int16_t* input, int32_t n_batch, int32_t n_input, int16_t* output);

	template <int IntegerBits>
	void ApplyTanh(const int16_t* input, int32_t n_batch, int32_t n_input, int16_t* output) {
	using FX = gemmlowp::FixedPoint<std::int16_t, IntegerBits>;
	using F0 = gemmlowp::FixedPoint<std::int16_t, 0>;
	for (int batch = 0; batch < n_batch; ++batch) {
	for (int i = 0; i < n_input; ++i) {
	const int index = batch * n_input + i;
	FX tanh_input = FX::FromRaw(input[index]);
	F0 tanh_output = gemmlowp::tanh(tanh_input);
	output[index] = tanh_output.raw();
	}
	}
	}

	inline void ApplyTanh(int32_t integer_bits, const int16_t* input, int32_t n_batch, int32_t n_input,
	int16_t* output) {
	assert(integer_bits <= 6);
	#define DISPATCH_TANH(i) \
	case i: \
	ApplyTanh<i>(input, n_batch, n_input, output); \
	break;
	switch (integer_bits) {
	DISPATCH_TANH(0);
	DISPATCH_TANH(1);
	DISPATCH_TANH(2);
	DISPATCH_TANH(3);
	DISPATCH_TANH(4);
	DISPATCH_TANH(5);
	DISPATCH_TANH(6);
	default:
	return;
	}
	#undef DISPATCH_TANH
	}

	void CwiseMul(const int16_t* input_1, const int16_t* input_2, int n_batch, int n_input, int shift,
	int16_t* output);
	void CwiseMul(const int16_t* input_1, const int16_t* input_2, int32_t multiplier, int32_t shift,
	int32_t n_batch, int32_t n_input, int32_t output_zp, int8_t* output);

	bool CheckedLog2(const float x, int* log2_result);

	void CwiseAdd(const int16_t* input_1, const int16_t* input_2, int n_batch, int n_input,
	int16_t* output);

	inline void Sub1Vector(const int16_t* vector, int v_size, int16_t* result) {
	static const int16_t kOne = 32767;
	for (int v = 0; v < v_size; v++) {
	result++ = kOne - vector++;
	}
	}

	void CwiseClipping(int16_t* input, const int16_t clipping_value, int32_t n_batch, int32_t n_input);

	void CwiseClipping(int8_t* input, const int8_t clipping_value, int32_t n_batch, int32_t n_input);

	void VectorBatchVectorCwiseProductAccumulate(const int16_t* vector, int v_size,
	const int16_t* batch_vector, int n_batch,
	int32_t multiplier, int shift, int16_t* result);

	} // namespace nn
	} // namespace android

	#endif // ANDROID_FRAMEWORKS_ML_NN_COMMON_QUANTUTILS_H