| /* Copyright 2018 The TensorFlow Authors. All Rights Reserved. |
| |
| Licensed under the Apache License, Version 2.0 (the "License"); |
| you may not use this file except in compliance with the License. |
| You may obtain a copy of the License at |
| |
| http://www.apache.org/licenses/LICENSE-2.0 |
| |
| Unless required by applicable law or agreed to in writing, software |
| distributed under the License is distributed on an "AS IS" BASIS, |
| WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| See the License for the specific language governing permissions and |
| limitations under the License. |
| ==============================================================================*/ |
| #ifndef TENSORFLOW_CONTRIB_LITE_KERNELS_INTERNAL_OPTIMIZED_OPTIMIZED_OPS_H_ |
| #define TENSORFLOW_CONTRIB_LITE_KERNELS_INTERNAL_OPTIMIZED_OPTIMIZED_OPS_H_ |
| |
| #include <assert.h> |
| #include <stdint.h> |
| #include <sys/types.h> |
| #include <algorithm> |
| #include <cmath> |
| #include <limits> |
| #include <memory> |
| #include <tuple> |
| #include <type_traits> |
| |
| #include "Eigen/Core" |
| #include "unsupported/Eigen/CXX11/Tensor" |
| #include "fixedpoint/fixedpoint.h" |
| #include "public/gemmlowp.h" |
| #include "tensorflow/contrib/lite/kernels/internal/common.h" |
| #include "tensorflow/contrib/lite/kernels/internal/quantization_util.h" |
| #include "tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h" |
| #include "tensorflow/contrib/lite/kernels/internal/round.h" |
| #include "tensorflow/contrib/lite/kernels/internal/strided_slice_logic.h" |
| #include "tensorflow/contrib/lite/kernels/internal/tensor_utils.h" |
| #include "tensorflow/contrib/lite/kernels/internal/types.h" |
| |
| namespace tflite { |
| namespace optimized_ops { |
| |
| // Unoptimized reference ops: |
| using reference_ops::ArgMax; |
| using reference_ops::ArgMinMax; |
| using reference_ops::Broadcast4DSlowGreater; |
| using reference_ops::Broadcast4DSlowGreaterEqual; |
| using reference_ops::Broadcast4DSlowGreaterEqualWithScaling; |
| using reference_ops::Broadcast4DSlowGreaterWithScaling; |
| using reference_ops::Broadcast4DSlowLess; |
| using reference_ops::Broadcast4DSlowLessEqual; |
| using reference_ops::Broadcast4DSlowLessEqualWithScaling; |
| using reference_ops::Broadcast4DSlowLessWithScaling; |
| using reference_ops::BroadcastAdd4DSlow; |
| using reference_ops::BroadcastGreater; |
| using reference_ops::BroadcastGreaterEqual; |
| using reference_ops::BroadcastLess; |
| using reference_ops::BroadcastLessEqual; |
| using reference_ops::BroadcastMul4DSlow; |
| using reference_ops::BroadcastSub4DSlow; |
| using reference_ops::Concatenation; |
| using reference_ops::DepthConcatenation; |
| using reference_ops::Dequantize; |
| using reference_ops::Div; |
| using reference_ops::FakeQuant; |
| using reference_ops::Gather; |
| using reference_ops::Greater; |
| using reference_ops::GreaterEqual; |
| using reference_ops::GreaterEqualWithScaling; |
| using reference_ops::GreaterWithScaling; |
| using reference_ops::Less; |
| using reference_ops::LessEqual; |
| using reference_ops::LessEqualWithScaling; |
| using reference_ops::LessWithScaling; |
| using reference_ops::Mean; |
| using reference_ops::RankOneSelect; |
| using reference_ops::Relu1; |
| using reference_ops::Relu6; |
| using reference_ops::ReluX; |
| using reference_ops::Select; |
| using reference_ops::SpaceToBatchND; |
| using reference_ops::Split; |
| using reference_ops::StridedSlice; |
| using reference_ops::TensorFlowSplit; |
| using reference_ops::Transpose; |
| |
| // TODO(b/80247582) Remove this constant. |
| // This will be phased out as the shifts are revised with more thought. Use of a |
| // constant enables us to track progress on this work. |
| // |
| // Used to convert from old-style shifts (right) to new-style (left). |
| static constexpr int kReverseShift = -1; |
| |
| // Make a local VectorMap typedef allowing to map a float array |
| // as a Eigen vector expression. The std::conditional here is to |
| // construct the suitable Eigen type for the constness of the |
| // data. Indeed, for const data, we need to produce |
| // Eigen::Map<const Eigen::Matrix<float, ...>> |
| // and not the more straightforward |
| // Eigen::Map<Eigen::Matrix<const float, ...>> |
| template <typename Scalar> |
| using VectorMap = typename std::conditional< |
| std::is_const<Scalar>::value, |
| Eigen::Map<const Eigen::Matrix<typename std::remove_const<Scalar>::type, |
| Eigen::Dynamic, 1>>, |
| Eigen::Map<Eigen::Matrix<Scalar, Eigen::Dynamic, 1>>>::type; |
| |
| template <typename Scalar> |
| VectorMap<Scalar> MapAsVector(Scalar* data, const RuntimeShape& shape) { |
| const int size = shape.FlatSize(); |
| return VectorMap<Scalar>(data, size, 1); |
| } |
| |
| template <typename Scalar, int N> |
| VectorMap<Scalar> MapAsVector(Scalar* data, const Dims<N>& dims) { |
| const int size = FlatSize(dims); |
| return VectorMap<Scalar>(data, size, 1); |
| } |
| |
| // Make a local VectorMap typedef allowing to map a float array |
| // as a Eigen matrix expression. The same explanation as for VectorMap |
| // above also applies here. |
| template <typename Scalar> |
| using MatrixMap = typename std::conditional< |
| std::is_const<Scalar>::value, |
| Eigen::Map<const Eigen::Matrix<typename std::remove_const<Scalar>::type, |
| Eigen::Dynamic, Eigen::Dynamic>>, |
| Eigen::Map<Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic>>>::type; |
| |
| template <typename Scalar> |
| MatrixMap<Scalar> MapAsMatrixWithLastDimAsRows(Scalar* data, |
| const RuntimeShape& shape) { |
| const int dims_count = shape.DimensionsCount(); |
| const int rows = shape.Dims(dims_count - 1); |
| const int cols = FlatSizeSkipDim(shape, dims_count - 1); |
| return MatrixMap<Scalar>(data, rows, cols); |
| } |
| |
| template <typename Scalar> |
| MatrixMap<Scalar> MapAsMatrixWithFirstDimAsCols(Scalar* data, |
| const RuntimeShape& shape) { |
| const int cols = shape.Dims(0); |
| const int rows = FlatSizeSkipDim(shape, 0); |
| return MatrixMap<Scalar>(data, rows, cols); |
| } |
| |
| template <typename Scalar, int N> |
| MatrixMap<Scalar> MapAsMatrixWithFirstDimAsRows(Scalar* data, |
| const Dims<N>& dims) { |
| const int rows = dims.sizes[0]; |
| int cols = 1; |
| for (int d = 1; d < N; d++) { |
| cols *= dims.sizes[d]; |
| } |
| return MatrixMap<Scalar>(data, rows, cols); |
| } |
| |
| template <typename Scalar, int N> |
| MatrixMap<Scalar> MapAsMatrixWithLastDimAsCols(Scalar* data, |
| const Dims<N>& dims) { |
| const int cols = dims.sizes[N - 1]; |
| int rows = 1; |
| for (int d = 0; d < N - 1; d++) { |
| rows *= dims.sizes[d]; |
| } |
| return MatrixMap<Scalar>(data, rows, cols); |
| } |
| |
| template <typename Scalar> |
| using ArrayMap = typename std::conditional< |
| std::is_const<Scalar>::value, |
| Eigen::Map<const Eigen::Array<typename std::remove_const<Scalar>::type, |
| Eigen::Dynamic, Eigen::Dynamic>>, |
| Eigen::Map<Eigen::Array<Scalar, Eigen::Dynamic, Eigen::Dynamic>>>::type; |
| |
| template <typename Scalar, int N> |
| ArrayMap<Scalar> MapAsArrayWithFirstDimAsRows(Scalar* data, |
| const Dims<N>& dims) { |
| const int rows = dims.sizes[0]; |
| int cols = 1; |
| for (int d = 1; d < N; d++) { |
| cols *= dims.sizes[d]; |
| } |
| return ArrayMap<Scalar>(data, rows, cols); |
| } |
| |
| template <typename Scalar> |
| ArrayMap<Scalar> MapAsArrayWithLastDimAsRows(Scalar* data, |
| const RuntimeShape& shape) { |
| const int dims_count = shape.DimensionsCount(); |
| const int rows = shape.Dims(dims_count - 1); |
| const int cols = FlatSizeSkipDim(shape, dims_count - 1); |
| return ArrayMap<Scalar>(data, rows, cols); |
| } |
| |
| // Copied from tensorflow/core/framework/tensor_types.h |
| template <typename T, int NDIMS = 1, typename IndexType = Eigen::DenseIndex> |
| struct TTypes { |
| // Rank-1 tensor (vector) of scalar type T. |
| typedef Eigen::TensorMap<Eigen::Tensor<T, 1, Eigen::RowMajor, IndexType>, |
| Eigen::Aligned> |
| Flat; |
| typedef Eigen::TensorMap< |
| Eigen::Tensor<const T, 2, Eigen::RowMajor, IndexType>> |
| UnalignedConstMatrix; |
| }; |
| |
| // TODO(b/80418076): Move to legacy ops file, update invocations. |
| // Legacy. |
| // TODO(b/62193649): this function is only needed as long |
| // as we have the --variable_batch hack. |
| template <typename Scalar, int N> |
| MatrixMap<Scalar> MapAsMatrixWithGivenNumberOfRows(Scalar* data, |
| const Dims<N>& dims, |
| int rows) { |
| const int flatsize = FlatSize(dims); |
| TFLITE_DCHECK((flatsize % rows) == 0); |
| const int cols = flatsize / rows; |
| return MatrixMap<Scalar>(data, rows, cols); |
| } |
| |
| // TODO(b/62193649): this function is only needed as long |
| // as we have the --variable_batch hack. |
| template <typename Scalar> |
| MatrixMap<Scalar> MapAsMatrixWithGivenNumberOfRows(Scalar* data, |
| const RuntimeShape& shape, |
| int rows) { |
| const int flatsize = shape.FlatSize(); |
| TFLITE_DCHECK_EQ(flatsize % rows, 0); |
| const int cols = flatsize / rows; |
| return MatrixMap<Scalar>(data, rows, cols); |
| } |
| |
| // This is like the template-parameter version, except that the power-of-two is |
| // passed as a function parameter. The template version is to be preferred, |
| // since some target hardware optimizations depend on the range of the exponent. |
| template <typename IntegerType> |
| IntegerType SaturatingRoundingMultiplyByPOTParam(IntegerType x, int exponent) { |
| if (exponent == 0) { |
| return x; |
| } |
| using ScalarIntegerType = |
| typename gemmlowp::FixedPointRawTypeTraits<IntegerType>::ScalarRawType; |
| const IntegerType min = |
| gemmlowp::Dup<IntegerType>(std::numeric_limits<ScalarIntegerType>::min()); |
| const IntegerType max = |
| gemmlowp::Dup<IntegerType>(std::numeric_limits<ScalarIntegerType>::max()); |
| const int ScalarIntegerTypeBits = 8 * sizeof(ScalarIntegerType); |
| |
| const std::int32_t threshold = |
| ((1 << (ScalarIntegerTypeBits - 1 - exponent)) - 1); |
| const IntegerType positive_mask = |
| gemmlowp::MaskIfGreaterThan(x, gemmlowp::Dup<IntegerType>(threshold)); |
| const IntegerType negative_mask = |
| gemmlowp::MaskIfLessThan(x, gemmlowp::Dup<IntegerType>(-threshold)); |
| |
| IntegerType result = gemmlowp::ShiftLeft(x, exponent); |
| result = gemmlowp::SelectUsingMask(positive_mask, max, result); |
| result = gemmlowp::SelectUsingMask(negative_mask, min, result); |
| return result; |
| } |
| |
| // This is like the template-parameter version, except that the power-of-two is |
| // passed as a function parameter. See raw-integer version for further comments. |
| template <typename tRawType, int tIntegerBits> |
| gemmlowp::FixedPoint<tRawType, tIntegerBits> |
| SaturatingRoundingMultiplyByPOTParam( |
| gemmlowp::FixedPoint<tRawType, tIntegerBits> a, int exponent) { |
| return gemmlowp::FixedPoint<tRawType, tIntegerBits>::FromRaw( |
| SaturatingRoundingMultiplyByPOTParam(a.raw(), exponent)); |
| } |
| |
| inline bool AreSameDims(const Dims<4>& dims1, const Dims<4>& dims2) { |
| for (int i = 0; i < 4; i++) { |
| if (dims1.sizes[i] != dims2.sizes[i]) { |
| return false; |
| } |
| } |
| return true; |
| } |
| |
| inline void AddBiasAndEvalActivationFunction(float output_activation_min, |
| float output_activation_max, |
| const RuntimeShape& bias_shape, |
| const float* bias_data, |
| const RuntimeShape& array_shape, |
| float* array_data) { |
| #ifdef USE_NEON |
| gemmlowp::ScopedProfilingLabel label("AddBiasAndEvalActivationFunction"); |
| const int bias_size = bias_shape.FlatSize(); |
| const int array_size = array_shape.FlatSize(); |
| TFLITE_DCHECK_EQ((array_size % bias_size), 0); |
| float* array_ptr = array_data; |
| float* array_end_ptr = array_ptr + array_size; |
| const auto activation_min = vdupq_n_f32(output_activation_min); |
| const auto activation_max = vdupq_n_f32(output_activation_max); |
| for (; array_ptr != array_end_ptr; array_ptr += bias_size) { |
| int i = 0; |
| for (; i <= bias_size - 16; i += 16) { |
| auto b0 = vld1q_f32(bias_data + i); |
| auto b1 = vld1q_f32(bias_data + i + 4); |
| auto b2 = vld1q_f32(bias_data + i + 8); |
| auto b3 = vld1q_f32(bias_data + i + 12); |
| auto a0 = vld1q_f32(array_ptr + i); |
| auto a1 = vld1q_f32(array_ptr + i + 4); |
| auto a2 = vld1q_f32(array_ptr + i + 8); |
| auto a3 = vld1q_f32(array_ptr + i + 12); |
| auto x0 = vaddq_f32(a0, b0); |
| auto x1 = vaddq_f32(a1, b1); |
| auto x2 = vaddq_f32(a2, b2); |
| auto x3 = vaddq_f32(a3, b3); |
| x0 = vmaxq_f32(activation_min, x0); |
| x1 = vmaxq_f32(activation_min, x1); |
| x2 = vmaxq_f32(activation_min, x2); |
| x3 = vmaxq_f32(activation_min, x3); |
| x0 = vminq_f32(activation_max, x0); |
| x1 = vminq_f32(activation_max, x1); |
| x2 = vminq_f32(activation_max, x2); |
| x3 = vminq_f32(activation_max, x3); |
| vst1q_f32(array_ptr + i, x0); |
| vst1q_f32(array_ptr + i + 4, x1); |
| vst1q_f32(array_ptr + i + 8, x2); |
| vst1q_f32(array_ptr + i + 12, x3); |
| } |
| for (; i <= bias_size - 4; i += 4) { |
| auto b = vld1q_f32(bias_data + i); |
| auto a = vld1q_f32(array_ptr + i); |
| auto x = vaddq_f32(a, b); |
| x = vmaxq_f32(activation_min, x); |
| x = vminq_f32(activation_max, x); |
| vst1q_f32(array_ptr + i, x); |
| } |
| for (; i < bias_size; i++) { |
| array_ptr[i] = ActivationFunctionWithMinMax(array_ptr[i] + bias_data[i], |
| output_activation_min, |
| output_activation_max); |
| } |
| } |
| #else // not NEON |
| gemmlowp::ScopedProfilingLabel label("AddBiasAndEvalActivationFunction"); |
| const int bias_size = bias_shape.FlatSize(); |
| const int array_size = array_shape.FlatSize(); |
| TFLITE_DCHECK_EQ((array_size % bias_size), 0); |
| for (int array_offset = 0; array_offset < array_size; |
| array_offset += bias_size) { |
| for (int i = 0; i < bias_size; i++) { |
| array_data[array_offset + i] = ActivationFunctionWithMinMax( |
| array_data[array_offset + i] + bias_data[i], output_activation_min, |
| output_activation_max); |
| } |
| } |
| #endif |
| } |
| |
| // TODO(b/80418076): Move to legacy ops file, update invocations. |
| // Legacy. |
| inline void AddBiasAndEvalActivationFunction(const float* bias_data, |
| const Dims<4>& bias_dims, |
| float* array_data, |
| const Dims<4>& array_dims, |
| float output_activation_min, |
| float output_activation_max) { |
| AddBiasAndEvalActivationFunction(output_activation_min, output_activation_max, |
| DimsToShape(bias_dims), bias_data, |
| DimsToShape(array_dims), array_data); |
| } |
| |
| // Note: This to be converted to RuntimeShapes along with Conv. |
| // legacy, for compatibility with old checked-in code |
| template <FusedActivationFunctionType Ac> |
| void AddBiasAndEvalActivationFunction(const float* bias_data, |
| const Dims<4>& bias_dims, |
| float* array_data, |
| const Dims<4>& array_dims) { |
| float output_activation_min, output_activation_max; |
| GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); |
| AddBiasAndEvalActivationFunction(bias_data, bias_dims, array_data, array_dims, |
| output_activation_min, |
| output_activation_max); |
| } |
| |
| template <typename Lhs, typename Rhs, typename Result> |
| void Gemm(const Eigen::MatrixBase<Lhs>& lhs, const Eigen::MatrixBase<Rhs>& rhs, |
| Eigen::MatrixBase<Result>* result) { |
| if (rhs.cols() == 1) { |
| gemmlowp::ScopedProfilingLabel label("GEMV"); |
| result->col(0).noalias() = lhs * rhs.col(0); |
| } else { |
| gemmlowp::ScopedProfilingLabel label("GEMM"); |
| result->noalias() = lhs * rhs; |
| } |
| } |
| |
| inline void optimized_ops_preload_l1_stream(const uint8* ptr) { |
| #ifdef GEMMLOWP_ARM_64 |
| asm volatile("prfm pldl1strm, [%[ptr]]\n" ::[ptr] "r"(ptr) :); |
| #else |
| gemmlowp::Prefetch(ptr); |
| #endif |
| } |
| |
| inline void optimized_ops_preload_l1_keep(const uint8* ptr) { |
| #ifdef GEMMLOWP_ARM_64 |
| asm volatile("prfm pldl1keep, [%[ptr]]\n" ::[ptr] "r"(ptr) :); |
| #else |
| gemmlowp::Prefetch(ptr); |
| #endif |
| } |
| |
| #ifdef GEMMLOWP_NEON |
| // In the common case of batch size 1, a fully-connected node degenerates |
| // to a matrix*vector product. LSTM cells contain a fully-connected node; |
| // when quantized, this becomes a special type of GEMV operation where |
| // the output is 16bit-quantized, thus needs its own special path. |
| inline void GEMVForLstmCell(const RuntimeShape& input_shape, |
| const uint8* input_data, |
| const RuntimeShape& weights_shape, |
| const uint8* weights_data, uint8 weights_zero_point, |
| const RuntimeShape& bias_shape, |
| const int32* bias_data, int32 accum_multiplier, |
| int accum_shift, const RuntimeShape& output_shape, |
| int16* output_data) { |
| gemmlowp::ScopedProfilingLabel label("GEMVForLstmCell"); |
| TFLITE_DCHECK_GE(input_shape.DimensionsCount(), 1); |
| TFLITE_DCHECK_GE(weights_shape.DimensionsCount(), 2); |
| TFLITE_DCHECK_GE(output_shape.DimensionsCount(), 1); |
| const int output_dim_count = output_shape.DimensionsCount(); |
| const int weights_dim_count = weights_shape.DimensionsCount(); |
| TFLITE_DCHECK_EQ(FlatSizeSkipDim(output_shape, output_dim_count - 1), 1); |
| const int input_size = FlatSizeSkipDim(input_shape, 0); |
| const int output_size = MatchingDim(weights_shape, weights_dim_count - 2, |
| output_shape, output_dim_count - 1); |
| // This special fast path for quantized LSTM cells does not try to support |
| // odd sizes that we haven't encountered in any LSTM cell, that would |
| // require special code (that would go untested until any LSTM cell |
| // exercises it). We just guard our assumptions about size evenness with |
| // the following assertions. |
| TFLITE_DCHECK(!(output_size % 4)); |
| TFLITE_DCHECK(!(input_size % 8)); |
| const int32* bias_ptr = bias_data; |
| int16* output_ptr = output_data; |
| for (int out = 0; out < output_size; out += 4) { |
| int32x4_t acc_0 = vdupq_n_s32(0); |
| int32x4_t acc_1 = vdupq_n_s32(0); |
| int32x4_t acc_2 = vdupq_n_s32(0); |
| int32x4_t acc_3 = vdupq_n_s32(0); |
| const int16x8_t input_offset_vec = vdupq_n_s16(-128); |
| const int16x8_t weights_offset_vec = vdupq_n_s16(-weights_zero_point); |
| int in = 0; |
| // Handle 16 levels of depth at a time. |
| for (; in <= input_size - 16; in += 16) { |
| const uint8x16_t input_val_u8 = vld1q_u8(input_data + in); |
| const uint8* weights_ptr = weights_data + in + out * input_size; |
| uint8x16_t weights_val_u8_0 = vld1q_u8(weights_ptr + 0 * input_size); |
| uint8x16_t weights_val_u8_1 = vld1q_u8(weights_ptr + 1 * input_size); |
| uint8x16_t weights_val_u8_2 = vld1q_u8(weights_ptr + 2 * input_size); |
| uint8x16_t weights_val_u8_3 = vld1q_u8(weights_ptr + 3 * input_size); |
| int16x8_t input_val_0, input_val_1; |
| const uint8x8_t low = vget_low_u8(input_val_u8); |
| const uint8x8_t high = vget_high_u8(input_val_u8); |
| input_val_0 = vreinterpretq_s16_u16(vmovl_u8(low)); |
| input_val_1 = vreinterpretq_s16_u16(vmovl_u8(high)); |
| input_val_0 = vaddq_s16(input_val_0, input_offset_vec); |
| input_val_1 = vaddq_s16(input_val_1, input_offset_vec); |
| int16x8_t weights_val_0_0, weights_val_1_0, weights_val_2_0, |
| weights_val_3_0; |
| int16x8_t weights_val_0_1, weights_val_1_1, weights_val_2_1, |
| weights_val_3_1; |
| weights_val_0_0 = vaddq_s16( |
| vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(weights_val_u8_0))), |
| weights_offset_vec); |
| weights_val_0_1 = vaddq_s16( |
| vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(weights_val_u8_0))), |
| weights_offset_vec); |
| weights_val_1_0 = vaddq_s16( |
| vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(weights_val_u8_1))), |
| weights_offset_vec); |
| weights_val_1_1 = vaddq_s16( |
| vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(weights_val_u8_1))), |
| weights_offset_vec); |
| weights_val_2_0 = vaddq_s16( |
| vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(weights_val_u8_2))), |
| weights_offset_vec); |
| weights_val_2_1 = vaddq_s16( |
| vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(weights_val_u8_2))), |
| weights_offset_vec); |
| weights_val_3_0 = vaddq_s16( |
| vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(weights_val_u8_3))), |
| weights_offset_vec); |
| weights_val_3_1 = vaddq_s16( |
| vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(weights_val_u8_3))), |
| weights_offset_vec); |
| acc_0 = vmlal_s16(acc_0, vget_low_s16(weights_val_0_0), |
| vget_low_s16(input_val_0)); |
| acc_1 = vmlal_s16(acc_1, vget_low_s16(weights_val_1_0), |
| vget_low_s16(input_val_0)); |
| acc_2 = vmlal_s16(acc_2, vget_low_s16(weights_val_2_0), |
| vget_low_s16(input_val_0)); |
| acc_3 = vmlal_s16(acc_3, vget_low_s16(weights_val_3_0), |
| vget_low_s16(input_val_0)); |
| acc_0 = vmlal_s16(acc_0, vget_high_s16(weights_val_0_0), |
| vget_high_s16(input_val_0)); |
| acc_1 = vmlal_s16(acc_1, vget_high_s16(weights_val_1_0), |
| vget_high_s16(input_val_0)); |
| acc_2 = vmlal_s16(acc_2, vget_high_s16(weights_val_2_0), |
| vget_high_s16(input_val_0)); |
| acc_3 = vmlal_s16(acc_3, vget_high_s16(weights_val_3_0), |
| vget_high_s16(input_val_0)); |
| acc_0 = vmlal_s16(acc_0, vget_low_s16(weights_val_0_1), |
| vget_low_s16(input_val_1)); |
| acc_1 = vmlal_s16(acc_1, vget_low_s16(weights_val_1_1), |
| vget_low_s16(input_val_1)); |
| acc_2 = vmlal_s16(acc_2, vget_low_s16(weights_val_2_1), |
| vget_low_s16(input_val_1)); |
| acc_3 = vmlal_s16(acc_3, vget_low_s16(weights_val_3_1), |
| vget_low_s16(input_val_1)); |
| acc_0 = vmlal_s16(acc_0, vget_high_s16(weights_val_0_1), |
| vget_high_s16(input_val_1)); |
| acc_1 = vmlal_s16(acc_1, vget_high_s16(weights_val_1_1), |
| vget_high_s16(input_val_1)); |
| acc_2 = vmlal_s16(acc_2, vget_high_s16(weights_val_2_1), |
| vget_high_s16(input_val_1)); |
| acc_3 = vmlal_s16(acc_3, vget_high_s16(weights_val_3_1), |
| vget_high_s16(input_val_1)); |
| } |
| // Handle 8 levels of depth at a time. |
| for (; in < input_size; in += 8) { |
| const uint8x8_t input_val_u8 = vld1_u8(input_data + in); |
| const uint8* weights_ptr = weights_data + in + out * input_size; |
| uint8x8_t weights_val_u8_0 = vld1_u8(weights_ptr + 0 * input_size); |
| uint8x8_t weights_val_u8_1 = vld1_u8(weights_ptr + 1 * input_size); |
| uint8x8_t weights_val_u8_2 = vld1_u8(weights_ptr + 2 * input_size); |
| uint8x8_t weights_val_u8_3 = vld1_u8(weights_ptr + 3 * input_size); |
| int16x8_t input_val; |
| input_val = vreinterpretq_s16_u16(vmovl_u8(input_val_u8)); |
| input_val = vaddq_s16(input_val, input_offset_vec); |
| int16x8_t weights_val_0, weights_val_1, weights_val_2, weights_val_3; |
| weights_val_0 = |
| vaddq_s16(vreinterpretq_s16_u16(vmovl_u8(weights_val_u8_0)), |
| weights_offset_vec); |
| weights_val_1 = |
| vaddq_s16(vreinterpretq_s16_u16(vmovl_u8(weights_val_u8_1)), |
| weights_offset_vec); |
| weights_val_2 = |
| vaddq_s16(vreinterpretq_s16_u16(vmovl_u8(weights_val_u8_2)), |
| weights_offset_vec); |
| weights_val_3 = |
| vaddq_s16(vreinterpretq_s16_u16(vmovl_u8(weights_val_u8_3)), |
| weights_offset_vec); |
| acc_0 = vmlal_s16(acc_0, vget_low_s16(weights_val_0), |
| vget_low_s16(input_val)); |
| acc_1 = vmlal_s16(acc_1, vget_low_s16(weights_val_1), |
| vget_low_s16(input_val)); |
| acc_2 = vmlal_s16(acc_2, vget_low_s16(weights_val_2), |
| vget_low_s16(input_val)); |
| acc_3 = vmlal_s16(acc_3, vget_low_s16(weights_val_3), |
| vget_low_s16(input_val)); |
| acc_0 = vmlal_s16(acc_0, vget_high_s16(weights_val_0), |
| vget_high_s16(input_val)); |
| acc_1 = vmlal_s16(acc_1, vget_high_s16(weights_val_1), |
| vget_high_s16(input_val)); |
| acc_2 = vmlal_s16(acc_2, vget_high_s16(weights_val_2), |
| vget_high_s16(input_val)); |
| acc_3 = vmlal_s16(acc_3, vget_high_s16(weights_val_3), |
| vget_high_s16(input_val)); |
| } |
| // Horizontally reduce accumulators |
| int32x2_t pairwise_reduced_acc_0, pairwise_reduced_acc_1, |
| pairwise_reduced_acc_2, pairwise_reduced_acc_3; |
| pairwise_reduced_acc_0 = |
| vpadd_s32(vget_low_s32(acc_0), vget_high_s32(acc_0)); |
| pairwise_reduced_acc_1 = |
| vpadd_s32(vget_low_s32(acc_1), vget_high_s32(acc_1)); |
| pairwise_reduced_acc_2 = |
| vpadd_s32(vget_low_s32(acc_2), vget_high_s32(acc_2)); |
| pairwise_reduced_acc_3 = |
| vpadd_s32(vget_low_s32(acc_3), vget_high_s32(acc_3)); |
| const int32x2_t reduced_lo = |
| vpadd_s32(pairwise_reduced_acc_0, pairwise_reduced_acc_1); |
| const int32x2_t reduced_hi = |
| vpadd_s32(pairwise_reduced_acc_2, pairwise_reduced_acc_3); |
| int32x4_t reduced = vcombine_s32(reduced_lo, reduced_hi); |
| // Add bias values. |
| int32x4_t bias_vec = vld1q_s32(bias_ptr); |
| bias_ptr += 4; |
| reduced = vaddq_s32(reduced, bias_vec); |
| int left_shift = accum_shift > 0 ? accum_shift : 0; |
| int right_shift = accum_shift > 0 ? 0 : -accum_shift; |
| reduced = vshlq_s32(reduced, vdupq_n_s32(left_shift)); |
| // Multiply by the fixed-point multiplier. |
| reduced = vqrdmulhq_n_s32(reduced, accum_multiplier); |
| // Rounding-shift-right. |
| using gemmlowp::RoundingDivideByPOT; |
| reduced = RoundingDivideByPOT(reduced, right_shift); |
| // Narrow values down to 16 bit signed. |
| const int16x4_t res16 = vqmovn_s32(reduced); |
| vst1_s16(output_ptr, res16); |
| output_ptr += 4; |
| } |
| } |
| #endif |
| |
| #ifdef GEMMLOWP_NEON |
| inline void GEMVForLstmCellWithSymmetricRange( |
| const RuntimeShape& input_shape, const uint8* input_data, |
| const RuntimeShape& weights_shape, const uint8* weights_data, |
| const RuntimeShape& bias_shape, const int32* bias_data, |
| int32 accum_multiplier, int accum_shift, const RuntimeShape& output_shape, |
| int16* output_data) { |
| gemmlowp::ScopedProfilingLabel label("GEMVForLstmCellWithSymmetricRange"); |
| TFLITE_DCHECK_GE(input_shape.DimensionsCount(), 1); |
| TFLITE_DCHECK_GE(weights_shape.DimensionsCount(), 2); |
| TFLITE_DCHECK_GE(output_shape.DimensionsCount(), 1); |
| const int output_dim_count = output_shape.DimensionsCount(); |
| const int weights_dim_count = weights_shape.DimensionsCount(); |
| TFLITE_DCHECK_EQ(FlatSizeSkipDim(output_shape, output_dim_count - 1), 1); |
| const int input_size = FlatSizeSkipDim(input_shape, 0); |
| const int output_size = MatchingDim(weights_shape, weights_dim_count - 2, |
| output_shape, output_dim_count - 1); |
| // This special fast path for quantized LSTM cells does not try to support |
| // odd sizes that we haven't encountered in any LSTM cell, that would |
| // require special code (that would go untested until any LSTM cell |
| // exercises it). We just guard our assumptions about size evenness with |
| // the following assertions. |
| TFLITE_DCHECK(!(output_size % 4)); |
| TFLITE_DCHECK(!(input_size % 64)); |
| const int32* bias_ptr = bias_data; |
| int16* output_ptr = output_data; |
| const uint8x16_t signbit = vdupq_n_u8(0x80); |
| for (int in = 0; in < input_size; in += 32) { |
| optimized_ops_preload_l1_keep(input_data + in); |
| } |
| const int left_shift = accum_shift > 0 ? accum_shift : 0; |
| const int right_shift = accum_shift > 0 ? 0 : -accum_shift; |
| for (int out = 0; out < output_size; out += 4) { |
| // Load the bias values |
| int32x4_t bias_vec = vld1q_s32(bias_ptr); |
| bias_ptr += 4; |
| |
| // Clear accumulators. We use 2 accumulator registers per row, |
| // for 4 rows. row_accumRN is the N-th accumulator for row R. |
| int32x4_t row_accum00 = vdupq_n_s32(0); |
| int32x4_t row_accum01 = vdupq_n_s32(0); |
| int32x4_t row_accum10 = vdupq_n_s32(0); |
| int32x4_t row_accum11 = vdupq_n_s32(0); |
| int32x4_t row_accum20 = vdupq_n_s32(0); |
| int32x4_t row_accum21 = vdupq_n_s32(0); |
| int32x4_t row_accum30 = vdupq_n_s32(0); |
| int32x4_t row_accum31 = vdupq_n_s32(0); |
| |
| // kReadAhead parametrizes how far ahead we prefetch weights into L1 cache. |
| const int kReadAhead = 512; |
| // Prefetch the first weights values. |
| for (int k = 0; k < kReadAhead; k += 64) { |
| optimized_ops_preload_l1_stream(weights_data + (out + 0) * input_size + |
| k); |
| optimized_ops_preload_l1_stream(weights_data + (out + 1) * input_size + |
| k); |
| optimized_ops_preload_l1_stream(weights_data + (out + 2) * input_size + |
| k); |
| optimized_ops_preload_l1_stream(weights_data + (out + 3) * input_size + |
| k); |
| } |
| // Loop along the rows, handling 64 bytes per iteration because that's |
| // cache line size on most current ARM-architecture CPUs. |
| for (int in = 0; in < input_size; in += 64) { |
| // Prefetch some future weights values. |
| optimized_ops_preload_l1_stream(weights_data + (out + 0) * input_size + |
| in + kReadAhead); |
| optimized_ops_preload_l1_stream(weights_data + (out + 1) * input_size + |
| in + kReadAhead); |
| optimized_ops_preload_l1_stream(weights_data + (out + 2) * input_size + |
| in + kReadAhead); |
| optimized_ops_preload_l1_stream(weights_data + (out + 3) * input_size + |
| in + kReadAhead); |
| |
| // We will use 2 local 16-bit accumulators per row, for 2 rows. |
| // See below (*) for the rationale of processing only 2 rows at a time. |
| // local_accumRN is the N-th local accumulator for row R. |
| int16x8_t local_accum00; |
| int16x8_t local_accum01; |
| int16x8_t local_accum10; |
| int16x8_t local_accum11; |
| |
| // Load 64 bytes of input activations values. Convert to signed int8 |
| // by flipping the sign bit (i.e. subtracting 128, the required |
| // zero_point value). |
| int8x16_t input0 = vreinterpretq_s8_u8( |
| veorq_u8(signbit, vld1q_u8(input_data + in + 16 * 0))); |
| int8x16_t input1 = vreinterpretq_s8_u8( |
| veorq_u8(signbit, vld1q_u8(input_data + in + 16 * 1))); |
| int8x16_t input2 = vreinterpretq_s8_u8( |
| veorq_u8(signbit, vld1q_u8(input_data + in + 16 * 2))); |
| int8x16_t input3 = vreinterpretq_s8_u8( |
| veorq_u8(signbit, vld1q_u8(input_data + in + 16 * 3))); |
| |
| // Beginning of the core accumulation. Notice how while we have 4 |
| // rows to process, this code is taking care of only 2 rows at a time, |
| // thus being divided into two parts looking similar ("Rows 0 and 1" and |
| // "Rows 2 and 3"). |
| // |
| // (*) The rationale for handling only 2 rows at a time is to avoid |
| // cache aliasing issues on 4-way set-associative L1-cache CPUs, such |
| // as Cortex-A53. With sufficiently large, power-of-two matrix dimensions, |
| // we may find ourselves in a situation where rows alias each other in |
| // the L1 cache, and moreover may also mutually alias with the input |
| // activations. If we try to load 4 rows at a time, together with the |
| // input activations, that may be 5 mutually-aliasing vectors, resulting |
| // in constant mutual eviction from L1 cache. Handling 2 rows at a time |
| // here largely mitigates these issues, and seems at least to be very |
| // effective on Cortex-A53: |
| // Before After |
| // big (Cortex-A73) 2.85 ms 2.85 ms |
| // little (Cortex-A53) 11.0 ms 5.16 ms |
| |
| // Rows 0 and 1: |
| // Load 64 bytes of weights values from each row. Convert to signed int8 |
| // by flipping the sign bit (i.e. subtracting 128, the required |
| // zero_point value). |
| int8x16_t weights00 = vreinterpretq_s8_u8(veorq_u8( |
| signbit, |
| vld1q_u8(weights_data + (out + 0) * input_size + in + 16 * 0))); |
| int8x16_t weights01 = vreinterpretq_s8_u8(veorq_u8( |
| signbit, |
| vld1q_u8(weights_data + (out + 0) * input_size + in + 16 * 1))); |
| int8x16_t weights02 = vreinterpretq_s8_u8(veorq_u8( |
| signbit, |
| vld1q_u8(weights_data + (out + 0) * input_size + in + 16 * 2))); |
| int8x16_t weights03 = vreinterpretq_s8_u8(veorq_u8( |
| signbit, |
| vld1q_u8(weights_data + (out + 0) * input_size + in + 16 * 3))); |
| int8x16_t weights10 = vreinterpretq_s8_u8(veorq_u8( |
| signbit, |
| vld1q_u8(weights_data + (out + 1) * input_size + in + 16 * 0))); |
| int8x16_t weights11 = vreinterpretq_s8_u8(veorq_u8( |
| signbit, |
| vld1q_u8(weights_data + (out + 1) * input_size + in + 16 * 1))); |
| int8x16_t weights12 = vreinterpretq_s8_u8(veorq_u8( |
| signbit, |
| vld1q_u8(weights_data + (out + 1) * input_size + in + 16 * 2))); |
| int8x16_t weights13 = vreinterpretq_s8_u8(veorq_u8( |
| signbit, |
| vld1q_u8(weights_data + (out + 1) * input_size + in + 16 * 3))); |
| // Multiply-accumulate into local 16-bit accumulators. |
| // We can accumulate two products without overflow because weights are |
| // required to never be -128, so each product is at most 127^2 in absolute |
| // value. |
| local_accum00 = vmull_s8(vget_low_s8(weights00), vget_low_s8(input0)); |
| local_accum01 = vmull_s8(vget_low_s8(weights01), vget_low_s8(input1)); |
| local_accum10 = vmull_s8(vget_low_s8(weights10), vget_low_s8(input0)); |
| local_accum11 = vmull_s8(vget_low_s8(weights11), vget_low_s8(input1)); |
| local_accum00 = vmlal_s8(local_accum00, vget_high_s8(weights00), |
| vget_high_s8(input0)); |
| local_accum01 = vmlal_s8(local_accum01, vget_high_s8(weights01), |
| vget_high_s8(input1)); |
| local_accum10 = vmlal_s8(local_accum10, vget_high_s8(weights10), |
| vget_high_s8(input0)); |
| local_accum11 = vmlal_s8(local_accum11, vget_high_s8(weights11), |
| vget_high_s8(input1)); |
| // Pairwise add and accumulate into 32-bit accumulators |
| row_accum00 = vpadalq_s16(row_accum00, local_accum00); |
| row_accum01 = vpadalq_s16(row_accum01, local_accum01); |
| row_accum10 = vpadalq_s16(row_accum10, local_accum10); |
| row_accum11 = vpadalq_s16(row_accum11, local_accum11); |
| // Multiply-accumulate into local 16-bit accumulators. |
| // We can accumulate two products without overflow because weights are |
| // required to never be -128, so each product is at most 127^2 in absolute |
| // value. |
| local_accum00 = vmull_s8(vget_low_s8(weights02), vget_low_s8(input2)); |
| local_accum01 = vmull_s8(vget_low_s8(weights03), vget_low_s8(input3)); |
| local_accum10 = vmull_s8(vget_low_s8(weights12), vget_low_s8(input2)); |
| local_accum11 = vmull_s8(vget_low_s8(weights13), vget_low_s8(input3)); |
| local_accum00 = vmlal_s8(local_accum00, vget_high_s8(weights02), |
| vget_high_s8(input2)); |
| local_accum01 = vmlal_s8(local_accum01, vget_high_s8(weights03), |
| vget_high_s8(input3)); |
| local_accum10 = vmlal_s8(local_accum10, vget_high_s8(weights12), |
| vget_high_s8(input2)); |
| local_accum11 = vmlal_s8(local_accum11, vget_high_s8(weights13), |
| vget_high_s8(input3)); |
| // Pairwise add and accumulate into 32-bit accumulators |
| row_accum00 = vpadalq_s16(row_accum00, local_accum00); |
| row_accum01 = vpadalq_s16(row_accum01, local_accum01); |
| row_accum10 = vpadalq_s16(row_accum10, local_accum10); |
| row_accum11 = vpadalq_s16(row_accum11, local_accum11); |
| |
| // Rows 2 and 3: |
| // Load 64 bytes of weights values from each row. Convert to signed int8 |
| // by flipping the sign bit (i.e. subtracting 128, the required |
| // zero_point value). |
| weights00 = vreinterpretq_s8_u8(veorq_u8( |
| signbit, |
| vld1q_u8(weights_data + (out + 2) * input_size + in + 16 * 0))); |
| weights01 = vreinterpretq_s8_u8(veorq_u8( |
| signbit, |
| vld1q_u8(weights_data + (out + 2) * input_size + in + 16 * 1))); |
| weights02 = vreinterpretq_s8_u8(veorq_u8( |
| signbit, |
| vld1q_u8(weights_data + (out + 2) * input_size + in + 16 * 2))); |
| weights03 = vreinterpretq_s8_u8(veorq_u8( |
| signbit, |
| vld1q_u8(weights_data + (out + 2) * input_size + in + 16 * 3))); |
| weights10 = vreinterpretq_s8_u8(veorq_u8( |
| signbit, |
| vld1q_u8(weights_data + (out + 3) * input_size + in + 16 * 0))); |
| weights11 = vreinterpretq_s8_u8(veorq_u8( |
| signbit, |
| vld1q_u8(weights_data + (out + 3) * input_size + in + 16 * 1))); |
| weights12 = vreinterpretq_s8_u8(veorq_u8( |
| signbit, |
| vld1q_u8(weights_data + (out + 3) * input_size + in + 16 * 2))); |
| weights13 = vreinterpretq_s8_u8(veorq_u8( |
| signbit, |
| vld1q_u8(weights_data + (out + 3) * input_size + in + 16 * 3))); |
| // Multiply-accumulate into local 16-bit accumulators. |
| // We can accumulate two products without overflow because weights are |
| // required to never be -128, so each product is at most 127^2 in absolute |
| // value. |
| local_accum00 = vmull_s8(vget_low_s8(weights00), vget_low_s8(input0)); |
| local_accum01 = vmull_s8(vget_low_s8(weights01), vget_low_s8(input1)); |
| local_accum10 = vmull_s8(vget_low_s8(weights10), vget_low_s8(input0)); |
| local_accum11 = vmull_s8(vget_low_s8(weights11), vget_low_s8(input1)); |
| local_accum00 = vmlal_s8(local_accum00, vget_high_s8(weights00), |
| vget_high_s8(input0)); |
| local_accum01 = vmlal_s8(local_accum01, vget_high_s8(weights01), |
| vget_high_s8(input1)); |
| local_accum10 = vmlal_s8(local_accum10, vget_high_s8(weights10), |
| vget_high_s8(input0)); |
| local_accum11 = vmlal_s8(local_accum11, vget_high_s8(weights11), |
| vget_high_s8(input1)); |
| // Pairwise add and accumulate into 32-bit accumulators |
| row_accum20 = vpadalq_s16(row_accum20, local_accum00); |
| row_accum21 = vpadalq_s16(row_accum21, local_accum01); |
| row_accum30 = vpadalq_s16(row_accum30, local_accum10); |
| row_accum31 = vpadalq_s16(row_accum31, local_accum11); |
| // Multiply-accumulate into local 16-bit accumulators. |
| // We can accumulate two products without overflow because weights are |
| // required to never be -128, so each product is at most 127^2 in absolute |
| // value. |
| local_accum00 = vmull_s8(vget_low_s8(weights02), vget_low_s8(input2)); |
| local_accum01 = vmull_s8(vget_low_s8(weights03), vget_low_s8(input3)); |
| local_accum10 = vmull_s8(vget_low_s8(weights12), vget_low_s8(input2)); |
| local_accum11 = vmull_s8(vget_low_s8(weights13), vget_low_s8(input3)); |
| local_accum00 = vmlal_s8(local_accum00, vget_high_s8(weights02), |
| vget_high_s8(input2)); |
| local_accum01 = vmlal_s8(local_accum01, vget_high_s8(weights03), |
| vget_high_s8(input3)); |
| local_accum10 = vmlal_s8(local_accum10, vget_high_s8(weights12), |
| vget_high_s8(input2)); |
| local_accum11 = vmlal_s8(local_accum11, vget_high_s8(weights13), |
| vget_high_s8(input3)); |
| // Pairwise add and accumulate into 32-bit accumulators |
| row_accum20 = vpadalq_s16(row_accum20, local_accum00); |
| row_accum21 = vpadalq_s16(row_accum21, local_accum01); |
| row_accum30 = vpadalq_s16(row_accum30, local_accum10); |
| row_accum31 = vpadalq_s16(row_accum31, local_accum11); |
| } |
| |
| row_accum00 = vaddq_s32(row_accum00, row_accum01); |
| row_accum10 = vaddq_s32(row_accum10, row_accum11); |
| row_accum20 = vaddq_s32(row_accum20, row_accum21); |
| row_accum30 = vaddq_s32(row_accum30, row_accum31); |
| // Horizontally reduce accumulators |
| int32x2_t pairwise_reduced_acc_0, pairwise_reduced_acc_1, |
| pairwise_reduced_acc_2, pairwise_reduced_acc_3; |
| pairwise_reduced_acc_0 = |
| vpadd_s32(vget_low_s32(row_accum00), vget_high_s32(row_accum00)); |
| pairwise_reduced_acc_1 = |
| vpadd_s32(vget_low_s32(row_accum10), vget_high_s32(row_accum10)); |
| pairwise_reduced_acc_2 = |
| vpadd_s32(vget_low_s32(row_accum20), vget_high_s32(row_accum20)); |
| pairwise_reduced_acc_3 = |
| vpadd_s32(vget_low_s32(row_accum30), vget_high_s32(row_accum30)); |
| const int32x2_t reduced_lo = |
| vpadd_s32(pairwise_reduced_acc_0, pairwise_reduced_acc_1); |
| const int32x2_t reduced_hi = |
| vpadd_s32(pairwise_reduced_acc_2, pairwise_reduced_acc_3); |
| int32x4_t reduced = vcombine_s32(reduced_lo, reduced_hi); |
| // Add bias values. |
| reduced = vaddq_s32(reduced, bias_vec); |
| reduced = vshlq_s32(reduced, vdupq_n_s32(left_shift)); |
| // Multiply by the fixed-point multiplier. |
| reduced = vqrdmulhq_n_s32(reduced, accum_multiplier); |
| // Rounding-shift-right. |
| using gemmlowp::RoundingDivideByPOT; |
| reduced = RoundingDivideByPOT(reduced, right_shift); |
| // Narrow values down to 16 bit signed. |
| const int16x4_t res16 = vqmovn_s32(reduced); |
| vst1_s16(output_ptr, res16); |
| output_ptr += 4; |
| } |
| } |
| #endif |
| |
| inline void FullyConnected( |
| const FullyConnectedParams& params, const RuntimeShape& input_shape, |
| const float* input_data, const RuntimeShape& weights_shape, |
| const float* weights_data, const RuntimeShape& bias_shape, |
| const float* bias_data, const RuntimeShape& output_shape, |
| float* output_data) { |
| gemmlowp::ScopedProfilingLabel label("FullyConnected"); |
| const float output_activation_min = params.float_activation_min; |
| const float output_activation_max = params.float_activation_max; |
| |
| // TODO(b/62193649): this convoluted shape computation (determining |
| // input_rows from the weights_dims, then MapAsMatrixWithGivenNumberOfRows) |
| // is because the current --variable_batch hack consists in overwriting the |
| // 3rd dimension with the runtime batch size, as we don't keep track for each |
| // array of which dimension is the batch dimension in it. |
| // When that is fixed, this should become: |
| // const auto input_matrix_map = |
| // MapAsMatrixWithFirstDimAsRows(input_data, input_dims); |
| const int dims_count = weights_shape.DimensionsCount(); |
| const int input_rows = weights_shape.Dims(dims_count - 1); |
| const auto input_matrix_map = |
| MapAsMatrixWithGivenNumberOfRows(input_data, input_shape, input_rows); |
| const auto filter_matrix_map = |
| MapAsMatrixWithLastDimAsRows(weights_data, weights_shape); |
| auto output_matrix_map = |
| MapAsMatrixWithLastDimAsRows(output_data, output_shape); |
| |
| Gemm(filter_matrix_map.transpose(), input_matrix_map, &output_matrix_map); |
| AddBiasAndEvalActivationFunction(output_activation_min, output_activation_max, |
| bias_shape, bias_data, output_shape, |
| output_data); |
| } |
| |
| // TODO(b/80418076): Move to legacy ops file, update invocations. |
| // Legacy. |
| inline void FullyConnected(const float* input_data, const Dims<4>& input_dims, |
| const float* weights_data, |
| const Dims<4>& weights_dims, const float* bias_data, |
| const Dims<4>& bias_dims, |
| float output_activation_min, |
| float output_activation_max, float* output_data, |
| const Dims<4>& output_dims) { |
| tflite::FullyConnectedParams op_params; |
| op_params.float_activation_min = output_activation_min; |
| op_params.float_activation_max = output_activation_max; |
| |
| FullyConnected(op_params, DimsToShape(input_dims), input_data, |
| DimsToShape(weights_dims), weights_data, |
| DimsToShape(bias_dims), bias_data, DimsToShape(output_dims), |
| output_data); |
| } |
| |
| // legacy, for compatibility with old checked-in code |
| template <FusedActivationFunctionType Ac> |
| void FullyConnected(const float* input_data, const Dims<4>& input_dims, |
| const float* weights_data, const Dims<4>& weights_dims, |
| const float* bias_data, const Dims<4>& bias_dims, |
| float* output_data, const Dims<4>& output_dims) { |
| float output_activation_min, output_activation_max; |
| GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); |
| FullyConnected(input_data, input_dims, weights_data, weights_dims, bias_data, |
| bias_dims, output_activation_min, output_activation_max, |
| output_data, output_dims); |
| } |
| |
| #ifdef USE_NEON |
| inline void FullyConnectedAsGEMV( |
| const RuntimeShape& input_shape, const uint8* input_data, |
| int32 input_offset, const RuntimeShape& filter_shape, |
| const uint8* filter_data, int32 filter_offset, |
| const RuntimeShape& bias_shape, const int32* bias_data, int32 output_offset, |
| int32 output_multiplier, int output_shift, int32 output_activation_min, |
| int32 output_activation_max, const RuntimeShape& output_shape, |
| uint8* output_data) { |
| gemmlowp::ScopedProfilingLabel label("FullyConnectedAsGEMV/8bit"); |
| TFLITE_DCHECK_GE(input_shape.DimensionsCount(), 1); |
| TFLITE_DCHECK_GE(filter_shape.DimensionsCount(), 2); |
| TFLITE_DCHECK_GE(output_shape.DimensionsCount(), 1); |
| const int output_dim_count = output_shape.DimensionsCount(); |
| const int filter_dim_count = filter_shape.DimensionsCount(); |
| TFLITE_DCHECK_EQ(FlatSizeSkipDim(output_shape, output_dim_count - 1), 1); |
| const int input_size = FlatSizeSkipDim(input_shape, 0); |
| const int output_size = MatchingDim(filter_shape, filter_dim_count - 2, |
| output_shape, output_dim_count - 1); |
| static constexpr int kPeel = 4; |
| const bool shift_left = (output_shift > 0); |
| for (int k = 0; k < input_size; k += 64) { |
| optimized_ops_preload_l1_stream(input_data + k); |
| } |
| for (int k = 0; k < kPeel * input_size; k += 64) { |
| optimized_ops_preload_l1_stream(filter_data + k); |
| } |
| TFLITE_DCHECK(!(output_size % kPeel)); |
| const int32* bias_ptr = bias_data; |
| uint8* output_ptr = output_data; |
| for (int out = 0; out < output_size; out += kPeel) { |
| int32x4_t acc[kPeel]; |
| for (int k = 0; k < kPeel; k++) { |
| acc[k] = vdupq_n_s32(0); |
| } |
| const int16x8_t input_offset_vec = vdupq_n_s16(input_offset); |
| const int16x8_t filter_offset_vec = vdupq_n_s16(filter_offset); |
| int in = 0; |
| for (; in <= input_size - 16; in += 16) { |
| const uint8x16_t input_val_u8 = vld1q_u8(input_data + in); |
| uint8x16_t filter_val_u8[kPeel]; |
| for (int k = 0; k < kPeel; k++) { |
| const uint8* filter_ptr = filter_data + in + (out + k) * input_size; |
| filter_val_u8[k] = vld1q_u8(filter_ptr); |
| optimized_ops_preload_l1_stream(filter_ptr + 64); |
| } |
| int16x8_t input_val[2]; |
| const uint8x8_t low = vget_low_u8(input_val_u8); |
| const uint8x8_t high = vget_high_u8(input_val_u8); |
| input_val[0] = vreinterpretq_s16_u16(vmovl_u8(low)); |
| input_val[1] = vreinterpretq_s16_u16(vmovl_u8(high)); |
| input_val[0] = vaddq_s16(input_val[0], input_offset_vec); |
| input_val[1] = vaddq_s16(input_val[1], input_offset_vec); |
| int16x8_t filter_val[kPeel][2]; |
| for (int k = 0; k < kPeel; k++) { |
| const uint8x8_t low = vget_low_u8(filter_val_u8[k]); |
| const uint8x8_t high = vget_high_u8(filter_val_u8[k]); |
| filter_val[k][0] = vreinterpretq_s16_u16(vmovl_u8(low)); |
| filter_val[k][1] = vreinterpretq_s16_u16(vmovl_u8(high)); |
| filter_val[k][0] = vaddq_s16(filter_val[k][0], filter_offset_vec); |
| filter_val[k][1] = vaddq_s16(filter_val[k][1], filter_offset_vec); |
| } |
| for (int p = 0; p < 2; p++) { |
| for (int k = 0; k < kPeel; k++) { |
| acc[k] = vmlal_s16(acc[k], vget_low_s16(filter_val[k][p]), |
| vget_low_s16(input_val[p])); |
| } |
| for (int k = 0; k < kPeel; k++) { |
| acc[k] = vmlal_s16(acc[k], vget_high_s16(filter_val[k][p]), |
| vget_high_s16(input_val[p])); |
| } |
| } |
| } |
| for (; in <= input_size - 8; in += 8) { |
| const uint8x8_t input_val_u8 = vld1_u8(input_data + in); |
| uint8x8_t filter_val_u8[kPeel]; |
| for (int k = 0; k < kPeel; k++) { |
| const uint8* filter_ptr = filter_data + in + (out + k) * input_size; |
| filter_val_u8[k] = vld1_u8(filter_ptr); |
| } |
| int16x8_t input_val; |
| input_val = vreinterpretq_s16_u16(vmovl_u8(input_val_u8)); |
| input_val = vaddq_s16(input_val, input_offset_vec); |
| int16x8_t filter_val[kPeel]; |
| for (int k = 0; k < kPeel; k++) { |
| filter_val[k] = vreinterpretq_s16_u16(vmovl_u8(filter_val_u8[k])); |
| filter_val[k] = vaddq_s16(filter_val[k], filter_offset_vec); |
| } |
| for (int k = 0; k < kPeel; k++) { |
| acc[k] = vmlal_s16(acc[k], vget_low_s16(filter_val[k]), |
| vget_low_s16(input_val)); |
| } |
| for (int k = 0; k < kPeel; k++) { |
| acc[k] = vmlal_s16(acc[k], vget_high_s16(filter_val[k]), |
| vget_high_s16(input_val)); |
| } |
| } |
| if (in < input_size) { |
| int32 buf[4 * kPeel]; |
| for (int k = 0; k < 4; k++) { |
| vst1q_s32(buf + 4 * k, acc[k]); |
| } |
| for (; in < input_size; in++) { |
| int lane = (in + 8 - input_size) % 4; |
| const int32 input_val = input_data[in] + input_offset; |
| for (int k = 0; k < kPeel; k++) { |
| int32 filter_val = |
| filter_data[in + (out + k) * input_size] + filter_offset; |
| buf[lane + 4 * k] += filter_val * input_val; |
| } |
| } |
| for (int k = 0; k < 4; k++) { |
| acc[k] = vld1q_s32(buf + 4 * k); |
| } |
| } |
| |
| // Horizontally reduce accumulators |
| int32x2_t pairwise_reduced_acc[kPeel]; |
| for (int k = 0; k < kPeel; k++) { |
| pairwise_reduced_acc[k] = |
| vpadd_s32(vget_low_s32(acc[k]), vget_high_s32(acc[k])); |
| } |
| static_assert(kPeel == 4, "the code below currently assumes kPeel = 4"); |
| const int32x2_t reduced_lo = |
| vpadd_s32(pairwise_reduced_acc[0], pairwise_reduced_acc[1]); |
| const int32x2_t reduced_hi = |
| vpadd_s32(pairwise_reduced_acc[2], pairwise_reduced_acc[3]); |
| int32x4_t reduced = vcombine_s32(reduced_lo, reduced_hi); |
| // Add bias values. |
| int32x4_t bias_vec = vld1q_s32(bias_ptr); |
| bias_ptr += 4; |
| reduced = vaddq_s32(reduced, bias_vec); |
| if (shift_left) { |
| const int32 multiplier_power_of_two = 1 << output_shift; |
| reduced = vmulq_n_s32(reduced, multiplier_power_of_two); |
| reduced = vqrdmulhq_n_s32(reduced, output_multiplier); |
| } else { |
| // Multiply by the fixed-point multiplier. |
| reduced = vqrdmulhq_n_s32(reduced, output_multiplier); |
| // Rounding-shift-right. |
| using gemmlowp::RoundingDivideByPOT; |
| reduced = RoundingDivideByPOT(reduced, -output_shift); |
| } |
| // Add the output offset. |
| const int32x4_t output_offset_vec = vdupq_n_s32(output_offset); |
| reduced = vaddq_s32(reduced, output_offset_vec); |
| // Narrow values down to 16 bit signed. |
| const int16x4_t res16 = vqmovn_s32(reduced); |
| // Narrow values down to 8 bit unsigned, saturating. |
| uint8x8_t res8 = vqmovun_s16(vcombine_s16(res16, res16)); |
| // Apply the clamping from the activation function |
| res8 = vmax_u8(res8, vdup_n_u8(output_activation_min)); |
| res8 = vmin_u8(res8, vdup_n_u8(output_activation_max)); |
| // Store results to destination. Assumes 32bit alignment. |
| vst1_lane_u32(reinterpret_cast<uint32*>(output_ptr), |
| vreinterpret_u32_u8(res8), 0); |
| output_ptr += kPeel; |
| } |
| } |
| #endif // USE_NEON |
| |
| struct GemmlowpOutputPipeline { |
| typedef gemmlowp::VectorMap<const int32, gemmlowp::VectorShape::Col> |
| ColVectorMap; |
| typedef std::tuple<gemmlowp::OutputStageBiasAddition<ColVectorMap>, |
| gemmlowp::OutputStageScaleInt32ByFixedPointAndExponent, |
| gemmlowp::OutputStageClamp, |
| gemmlowp::OutputStageSaturatingCastToUint8> |
| Pipeline; |
| static Pipeline MakeExp(const int32* bias_data, int output_rows, |
| int32 output_offset, int32 output_multiplier, |
| int output_left_shift, int32 output_activation_min, |
| int32 output_activation_max) { |
| ColVectorMap bias_vector(bias_data, output_rows); |
| gemmlowp::OutputStageBiasAddition<ColVectorMap> bias_addition_stage; |
| bias_addition_stage.bias_vector = bias_vector; |
| gemmlowp::OutputStageScaleInt32ByFixedPointAndExponent quantize_down_stage; |
| quantize_down_stage.result_offset_after_shift = output_offset; |
| quantize_down_stage.result_fixedpoint_multiplier = output_multiplier; |
| quantize_down_stage.result_exponent = output_left_shift; |
| gemmlowp::OutputStageClamp clamp_stage; |
| clamp_stage.min = output_activation_min; |
| clamp_stage.max = output_activation_max; |
| gemmlowp::OutputStageSaturatingCastToUint8 saturating_cast_stage; |
| return std::make_tuple(bias_addition_stage, quantize_down_stage, |
| clamp_stage, saturating_cast_stage); |
| } |
| }; |
| |
| inline void FullyConnected( |
| const FullyConnectedParams& params, const RuntimeShape& input_shape, |
| const uint8* input_data, const RuntimeShape& filter_shape, |
| const uint8* filter_data, const RuntimeShape& bias_shape, |
| const int32* bias_data, const RuntimeShape& output_shape, |
| uint8* output_data, gemmlowp::GemmContext* gemm_context) { |
| gemmlowp::ScopedProfilingLabel label("FullyConnected/8bit"); |
| const int32 input_offset = params.input_offset; |
| const int32 filter_offset = params.weights_offset; |
| const int32 output_offset = params.output_offset; |
| const int32 output_multiplier = params.output_multiplier; |
| const int output_shift = params.output_shift; |
| const int32 output_activation_min = params.quantized_activation_min; |
| const int32 output_activation_max = params.quantized_activation_max; |
| TFLITE_DCHECK_GE(filter_shape.DimensionsCount(), 2); |
| TFLITE_DCHECK_GE(output_shape.DimensionsCount(), 1); |
| // TODO(benoitjacob): This really should be: |
| // const int batches = ArraySize(output_dims, 1); |
| // but the current --variable_batch hack consists in overwriting the 3rd |
| // dimension with the runtime batch size, as we don't keep track for each |
| // array of which dimension is the batch dimension in it. |
| const int output_dim_count = output_shape.DimensionsCount(); |
| const int filter_dim_count = filter_shape.DimensionsCount(); |
| const int batches = FlatSizeSkipDim(output_shape, output_dim_count - 1); |
| #ifdef USE_NEON |
| const int output_size = MatchingDim(filter_shape, filter_dim_count - 2, |
| output_shape, output_dim_count - 1); |
| if (batches == 1 && !(output_size % 4)) { |
| return FullyConnectedAsGEMV( |
| input_shape, input_data, input_offset, filter_shape, filter_data, |
| filter_offset, bias_shape, bias_data, output_offset, output_multiplier, |
| output_shift, output_activation_min, output_activation_max, |
| output_shape, output_data); |
| } |
| #endif // USE_NEON |
| const int filter_rows = filter_shape.Dims(filter_dim_count - 2); |
| const int filter_cols = filter_shape.Dims(filter_dim_count - 1); |
| TFLITE_DCHECK_EQ(filter_shape.FlatSize(), filter_rows * filter_cols); |
| const int output_rows = output_shape.Dims(output_dim_count - 1); |
| TFLITE_DCHECK_EQ(output_rows, filter_rows); |
| TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_rows); |
| |
| gemmlowp::MatrixMap<const uint8, gemmlowp::MapOrder::RowMajor> filter_matrix( |
| filter_data, output_rows, filter_cols, filter_cols); |
| gemmlowp::MatrixMap<const uint8, gemmlowp::MapOrder::ColMajor> input_matrix( |
| input_data, filter_cols, batches, filter_cols); |
| gemmlowp::MatrixMap<uint8, gemmlowp::MapOrder::ColMajor> output_matrix( |
| output_data, output_rows, batches, output_rows); |
| const auto& output_pipeline = GemmlowpOutputPipeline::MakeExp( |
| bias_data, output_rows, output_offset, output_multiplier, output_shift, |
| output_activation_min, output_activation_max); |
| gemmlowp::GemmWithOutputPipeline<uint8, uint8, |
| gemmlowp::L8R8WithLhsNonzeroBitDepthParams>( |
| gemm_context, filter_matrix, input_matrix, &output_matrix, filter_offset, |
| input_offset, output_pipeline); |
| } |
| |
| // TODO(b/80418076): Move to legacy ops file, update invocations. |
| // Legacy. |
| inline void FullyConnected(const uint8* input_data, const Dims<4>& input_dims, |
| int32 input_offset, const uint8* filter_data, |
| const Dims<4>& filter_dims, int32 filter_offset, |
| const int32* bias_data, const Dims<4>& bias_dims, |
| int32 output_offset, int32 output_multiplier, |
| int output_shift, int32 output_activation_min, |
| int32 output_activation_max, uint8* output_data, |
| const Dims<4>& output_dims, |
| gemmlowp::GemmContext* gemm_context) { |
| tflite::FullyConnectedParams op_params; |
| op_params.input_offset = input_offset; |
| op_params.weights_offset = filter_offset; |
| op_params.output_offset = output_offset; |
| op_params.output_multiplier = output_multiplier; |
| // Legacy ops used mixed left and right shifts. Now all are +ve-means-left. |
| op_params.output_shift = kReverseShift * output_shift; |
| op_params.quantized_activation_min = output_activation_min; |
| op_params.quantized_activation_max = output_activation_max; |
| |
| FullyConnected(op_params, DimsToShape(input_dims), input_data, |
| DimsToShape(filter_dims), filter_data, DimsToShape(bias_dims), |
| bias_data, DimsToShape(output_dims), output_data, |
| gemm_context); |
| } |
| |
| inline void FullyConnected( |
| const FullyConnectedParams& params, const RuntimeShape& input_shape, |
| const uint8* input_data, const RuntimeShape& filter_shape, |
| const uint8* filter_data, const RuntimeShape& bias_shape, |
| const int32* bias_data_int32, const RuntimeShape& output_shape, |
| int16* output_data, gemmlowp::GemmContext* gemm_context) { |
| gemmlowp::ScopedProfilingLabel label("FullyConnected/Uint8Int16"); |
| const int32 input_offset = params.input_offset; |
| const int32 filter_offset = params.weights_offset; |
| const int32 output_offset = params.output_offset; |
| const int32 output_multiplier = params.output_multiplier; |
| const int output_shift = params.output_shift; |
| const int32 output_activation_min = params.quantized_activation_min; |
| const int32 output_activation_max = params.quantized_activation_max; |
| // This is a copy of the reference implementation. We do not currently have a |
| // properly optimized version. |
| (void)gemm_context; // only used in properly optimized code. |
| TFLITE_DCHECK_LE(output_activation_min, output_activation_max); |
| TFLITE_DCHECK_EQ(output_offset, 0); |
| TFLITE_DCHECK_GE(filter_shape.DimensionsCount(), 2); |
| TFLITE_DCHECK_GE(output_shape.DimensionsCount(), 1); |
| |
| // TODO(benoitjacob): This really should be: |
| // const int batches = ArraySize(output_dims, 1); |
| // but the current --variable_batch hack consists in overwriting the 3rd |
| // dimension with the runtime batch size, as we don't keep track for each |
| // array of which dimension is the batch dimension in it. |
| const int output_dim_count = output_shape.DimensionsCount(); |
| const int filter_dim_count = filter_shape.DimensionsCount(); |
| const int batches = FlatSizeSkipDim(output_shape, output_dim_count - 1); |
| const int output_depth = MatchingDim(filter_shape, filter_dim_count - 2, |
| output_shape, output_dim_count - 1); |
| const int accum_depth = filter_shape.Dims(filter_dim_count - 1); |
| |
| // Implementation of the fully connected node suited to the inside of an LSTM |
| // cell. The operands are 8-bit integers, the accumulators are internally |
| // 32bit integers, and the output is 16-bit fixed-point with 3 integer bits so |
| // the output range is [-2^3, 2^3] == [-8, 8]. The rationale for that |
| // is explained in the function comment above. |
| #ifdef GEMMLOWP_NEON |
| if (batches == 1 && input_offset == -128 && output_activation_min == -32768 && |
| output_activation_max == 32767) { |
| if (filter_offset == -128 && !(output_depth % 4) && !(accum_depth % 64)) { |
| GEMVForLstmCellWithSymmetricRange( |
| input_shape, input_data, filter_shape, filter_data, bias_shape, |
| bias_data_int32, output_multiplier, output_shift, output_shape, |
| output_data); |
| return; |
| } |
| if (!(output_depth % 4) && !(accum_depth % 8)) { |
| GEMVForLstmCell(input_shape, input_data, filter_shape, filter_data, |
| filter_offset, bias_shape, bias_data_int32, |
| output_multiplier, output_shift, output_shape, |
| output_data); |
| return; |
| } |
| } |
| #endif |
| gemmlowp::MatrixMap<const uint8, gemmlowp::MapOrder::RowMajor> weights_matrix( |
| filter_data, output_depth, accum_depth); |
| gemmlowp::MatrixMap<const uint8, gemmlowp::MapOrder::ColMajor> input_matrix( |
| input_data, accum_depth, batches); |
| gemmlowp::MatrixMap<int16, gemmlowp::MapOrder::ColMajor> output_matrix( |
| output_data, output_depth, batches); |
| typedef gemmlowp::VectorMap<const int32, gemmlowp::VectorShape::Col> |
| ColVectorMap; |
| ColVectorMap bias_vector(bias_data_int32, output_depth); |
| gemmlowp::OutputStageBiasAddition<ColVectorMap> bias_addition_stage; |
| bias_addition_stage.bias_vector = bias_vector; |
| gemmlowp::OutputStageScaleInt32ByFixedPointAndExponent scale_stage; |
| scale_stage.result_offset_after_shift = 0; |
| scale_stage.result_fixedpoint_multiplier = output_multiplier; |
| // Note that this shift is negated wrt ordinary FC. |
| scale_stage.result_exponent = output_shift; |
| gemmlowp::OutputStageClamp clamp_stage; |
| clamp_stage.min = output_activation_min; |
| clamp_stage.max = output_activation_max; |
| gemmlowp::OutputStageSaturatingCastToInt16 saturating_cast_int16_stage; |
| auto output_pipeline = |
| std::make_tuple(bias_addition_stage, scale_stage, clamp_stage, |
| saturating_cast_int16_stage); |
| gemmlowp::GemmWithOutputPipeline<uint8, int16, |
| gemmlowp::L8R8WithLhsNonzeroBitDepthParams>( |
| gemm_context, weights_matrix, input_matrix, &output_matrix, filter_offset, |
| input_offset, output_pipeline); |
| } |
| |
| // TODO(b/80418076): Move to legacy ops file, update invocations. |
| // Legacy. |
| inline void FullyConnected( |
| const uint8* input_data, const Dims<4>& input_dims, int32 input_offset, |
| const uint8* filter_data, const Dims<4>& filter_dims, int32 filter_offset, |
| const int32* bias_data_int32, const Dims<4>& bias_dims, int32 output_offset, |
| int32 output_multiplier, int output_shift, int32 output_activation_min, |
| int32 output_activation_max, int16* output_data, const Dims<4>& output_dims, |
| gemmlowp::GemmContext* gemm_context) { |
| tflite::FullyConnectedParams op_params; |
| op_params.input_offset = input_offset; |
| op_params.weights_offset = filter_offset; |
| op_params.output_offset = output_offset; |
| op_params.output_multiplier = output_multiplier; |
| // Legacy ops used mixed left and right shifts. Now all are +ve-means-left. |
| op_params.output_shift = kReverseShift * output_shift; |
| op_params.quantized_activation_min = output_activation_min; |
| op_params.quantized_activation_max = output_activation_max; |
| |
| FullyConnected(op_params, DimsToShape(input_dims), input_data, |
| DimsToShape(filter_dims), filter_data, DimsToShape(bias_dims), |
| bias_data_int32, DimsToShape(output_dims), output_data, |
| gemm_context); |
| } |
| |
| // TODO(b/80418076): Move to legacy ops file, update invocations. |
| // legacy, for compatibility with old checked-in code |
| template <FusedActivationFunctionType Ac> |
| void FullyConnected(const uint8* input_data, const Dims<4>& input_dims, |
| int32 input_offset, const uint8* filter_data, |
| const Dims<4>& filter_dims, int32 filter_offset, |
| const int32* bias_data, const Dims<4>& bias_dims, |
| int32 output_offset, int32 output_multiplier, |
| int output_shift, int32 output_activation_min, |
| int32 output_activation_max, uint8* output_data, |
| const Dims<4>& output_dims, |
| gemmlowp::GemmContext* gemm_context) { |
| static_assert(Ac == FusedActivationFunctionType::kNone || |
| Ac == FusedActivationFunctionType::kRelu || |
| Ac == FusedActivationFunctionType::kRelu6 || |
| Ac == FusedActivationFunctionType::kRelu1, |
| ""); |
| FullyConnected(input_data, input_dims, input_offset, filter_data, filter_dims, |
| filter_offset, bias_data, bias_dims, output_offset, |
| output_multiplier, output_shift, output_activation_min, |
| output_activation_max, output_data, output_dims, gemm_context); |
| } |
| |
| // Internal function doing the actual arithmetic work for |
| // ShuffledFullyConnected. |
| // May be called either directly by it (single-threaded case) or may be used |
| // as the 'task' for worker threads to run (multi-threaded case, see |
| // ShuffledFullyConnectedWorkerTask below). |
| inline void ShuffledFullyConnectedWorkerImpl( |
| const uint8* shuffled_input_workspace_data, |
| const int8* shuffled_weights_data, int batches, int output_depth, |
| int output_stride, int accum_depth, const int32* bias_data, |
| int32 output_multiplier, int output_shift, int16* output_data) { |
| #if defined USE_NEON |
| const int8* shuffled_weights_ptr = shuffled_weights_data; |
| if (batches == 1) { |
| const int right_shift = output_shift > 0 ? 0 : -output_shift; |
| const int left_shift = output_shift > 0 ? output_shift : 0; |
| for (int c = 0; c < output_depth; c += 4) { |
| // Accumulation loop. |
| int32x4_t row_accum0 = vdupq_n_s32(0); |
| int32x4_t row_accum1 = vdupq_n_s32(0); |
| int32x4_t row_accum2 = vdupq_n_s32(0); |
| int32x4_t row_accum3 = vdupq_n_s32(0); |
| for (int d = 0; d < accum_depth; d += 16) { |
| int8x16_t weights0 = vld1q_s8(shuffled_weights_ptr + 0); |
| int8x16_t weights1 = vld1q_s8(shuffled_weights_ptr + 16); |
| int8x16_t weights2 = vld1q_s8(shuffled_weights_ptr + 32); |
| int8x16_t weights3 = vld1q_s8(shuffled_weights_ptr + 48); |
| shuffled_weights_ptr += 64; |
| int8x16_t input = |
| vreinterpretq_s8_u8(vld1q_u8(shuffled_input_workspace_data + d)); |
| int16x8_t local_accum0 = |
| vmull_s8(vget_low_s8(weights0), vget_low_s8(input)); |
| int16x8_t local_accum1 = |
| vmull_s8(vget_low_s8(weights1), vget_low_s8(input)); |
| int16x8_t local_accum2 = |
| vmull_s8(vget_low_s8(weights2), vget_low_s8(input)); |
| int16x8_t local_accum3 = |
| vmull_s8(vget_low_s8(weights3), vget_low_s8(input)); |
| local_accum0 = |
| vmlal_s8(local_accum0, vget_high_s8(weights0), vget_high_s8(input)); |
| local_accum1 = |
| vmlal_s8(local_accum1, vget_high_s8(weights1), vget_high_s8(input)); |
| local_accum2 = |
| vmlal_s8(local_accum2, vget_high_s8(weights2), vget_high_s8(input)); |
| local_accum3 = |
| vmlal_s8(local_accum3, vget_high_s8(weights3), vget_high_s8(input)); |
| row_accum0 = vpadalq_s16(row_accum0, local_accum0); |
| row_accum1 = vpadalq_s16(row_accum1, local_accum1); |
| row_accum2 = vpadalq_s16(row_accum2, local_accum2); |
| row_accum3 = vpadalq_s16(row_accum3, local_accum3); |
| } |
| // Horizontally reduce accumulators |
| int32x2_t pairwise_reduced_acc_0, pairwise_reduced_acc_1, |
| pairwise_reduced_acc_2, pairwise_reduced_acc_3; |
| pairwise_reduced_acc_0 = |
| vpadd_s32(vget_low_s32(row_accum0), vget_high_s32(row_accum0)); |
| pairwise_reduced_acc_1 = |
| vpadd_s32(vget_low_s32(row_accum1), vget_high_s32(row_accum1)); |
| pairwise_reduced_acc_2 = |
| vpadd_s32(vget_low_s32(row_accum2), vget_high_s32(row_accum2)); |
| pairwise_reduced_acc_3 = |
| vpadd_s32(vget_low_s32(row_accum3), vget_high_s32(row_accum3)); |
| const int32x2_t reduced_lo = |
| vpadd_s32(pairwise_reduced_acc_0, pairwise_reduced_acc_1); |
| const int32x2_t reduced_hi = |
| vpadd_s32(pairwise_reduced_acc_2, pairwise_reduced_acc_3); |
| int32x4_t reduced = vcombine_s32(reduced_lo, reduced_hi); |
| // Add bias values. |
| int32x4_t bias_vec = vld1q_s32(bias_data + c); |
| reduced = vaddq_s32(reduced, bias_vec); |
| reduced = vshlq_s32(reduced, vdupq_n_s32(left_shift)); |
| // Multiply by the fixed-point multiplier. |
| reduced = vqrdmulhq_n_s32(reduced, output_multiplier); |
| // Rounding-shift-right. |
| using gemmlowp::RoundingDivideByPOT; |
| reduced = RoundingDivideByPOT(reduced, right_shift); |
| // Narrow values down to 16 bit signed. |
| const int16x4_t res16 = vqmovn_s32(reduced); |
| vst1_s16(output_data + c, res16); |
| } |
| } else if (batches == 4) { |
| const int right_shift = output_shift > 0 ? 0 : -output_shift; |
| const int left_shift = output_shift > 0 ? output_shift : 0; |
| for (int c = 0; c < output_depth; c += 4) { |
| const int8* shuffled_input_ptr = |
| reinterpret_cast<const int8*>(shuffled_input_workspace_data); |
| // Accumulation loop. |
| int32x4_t row_accum00 = vdupq_n_s32(0); |
| int32x4_t row_accum10 = vdupq_n_s32(0); |
| int32x4_t row_accum20 = vdupq_n_s32(0); |
| int32x4_t row_accum30 = vdupq_n_s32(0); |
| int32x4_t row_accum01 = vdupq_n_s32(0); |
| int32x4_t row_accum11 = vdupq_n_s32(0); |
| int32x4_t row_accum21 = vdupq_n_s32(0); |
| int32x4_t row_accum31 = vdupq_n_s32(0); |
| int32x4_t row_accum02 = vdupq_n_s32(0); |
| int32x4_t row_accum12 = vdupq_n_s32(0); |
| int32x4_t row_accum22 = vdupq_n_s32(0); |
| int32x4_t row_accum32 = vdupq_n_s32(0); |
| int32x4_t row_accum03 = vdupq_n_s32(0); |
| int32x4_t row_accum13 = vdupq_n_s32(0); |
| int32x4_t row_accum23 = vdupq_n_s32(0); |
| int32x4_t row_accum33 = vdupq_n_s32(0); |
| for (int d = 0; d < accum_depth; d += 16) { |
| int8x16_t weights0 = vld1q_s8(shuffled_weights_ptr + 0); |
| int8x16_t weights1 = vld1q_s8(shuffled_weights_ptr + 16); |
| int8x16_t weights2 = vld1q_s8(shuffled_weights_ptr + 32); |
| int8x16_t weights3 = vld1q_s8(shuffled_weights_ptr + 48); |
| shuffled_weights_ptr += 64; |
| int8x16_t input0 = vld1q_s8(shuffled_input_ptr + 0); |
| int8x16_t input1 = vld1q_s8(shuffled_input_ptr + 16); |
| int8x16_t input2 = vld1q_s8(shuffled_input_ptr + 32); |
| int8x16_t input3 = vld1q_s8(shuffled_input_ptr + 48); |
| shuffled_input_ptr += 64; |
| int16x8_t local_accum0, local_accum1, local_accum2, local_accum3; |
| #define TFLITE_SHUFFLED_FC_ACCUM(B) \ |
| local_accum0 = vmull_s8(vget_low_s8(weights0), vget_low_s8(input##B)); \ |
| local_accum1 = vmull_s8(vget_low_s8(weights1), vget_low_s8(input##B)); \ |
| local_accum2 = vmull_s8(vget_low_s8(weights2), vget_low_s8(input##B)); \ |
| local_accum3 = vmull_s8(vget_low_s8(weights3), vget_low_s8(input##B)); \ |
| local_accum0 = \ |
| vmlal_s8(local_accum0, vget_high_s8(weights0), vget_high_s8(input##B)); \ |
| local_accum1 = \ |
| vmlal_s8(local_accum1, vget_high_s8(weights1), vget_high_s8(input##B)); \ |
| local_accum2 = \ |
| vmlal_s8(local_accum2, vget_high_s8(weights2), vget_high_s8(input##B)); \ |
| local_accum3 = \ |
| vmlal_s8(local_accum3, vget_high_s8(weights3), vget_high_s8(input##B)); \ |
| row_accum0##B = vpadalq_s16(row_accum0##B, local_accum0); \ |
| row_accum1##B = vpadalq_s16(row_accum1##B, local_accum1); \ |
| row_accum2##B = vpadalq_s16(row_accum2##B, local_accum2); \ |
| row_accum3##B = vpadalq_s16(row_accum3##B, local_accum3); |
| |
| TFLITE_SHUFFLED_FC_ACCUM(0) |
| TFLITE_SHUFFLED_FC_ACCUM(1) |
| TFLITE_SHUFFLED_FC_ACCUM(2) |
| TFLITE_SHUFFLED_FC_ACCUM(3) |
| |
| #undef TFLITE_SHUFFLED_FC_ACCUM |
| } |
| // Horizontally reduce accumulators |
| |
| #define TFLITE_SHUFFLED_FC_STORE(B) \ |
| { \ |
| int32x2_t pairwise_reduced_acc_0, pairwise_reduced_acc_1, \ |
| pairwise_reduced_acc_2, pairwise_reduced_acc_3; \ |
| pairwise_reduced_acc_0 = \ |
| vpadd_s32(vget_low_s32(row_accum0##B), vget_high_s32(row_accum0##B)); \ |
| pairwise_reduced_acc_1 = \ |
| vpadd_s32(vget_low_s32(row_accum1##B), vget_high_s32(row_accum1##B)); \ |
| pairwise_reduced_acc_2 = \ |
| vpadd_s32(vget_low_s32(row_accum2##B), vget_high_s32(row_accum2##B)); \ |
| pairwise_reduced_acc_3 = \ |
| vpadd_s32(vget_low_s32(row_accum3##B), vget_high_s32(row_accum3##B)); \ |
| const int32x2_t reduced_lo = \ |
| vpadd_s32(pairwise_reduced_acc_0, pairwise_reduced_acc_1); \ |
| const int32x2_t reduced_hi = \ |
| vpadd_s32(pairwise_reduced_acc_2, pairwise_reduced_acc_3); \ |
| int32x4_t reduced = vcombine_s32(reduced_lo, reduced_hi); \ |
| int32x4_t bias_vec = vld1q_s32(bias_data + c); \ |
| reduced = vaddq_s32(reduced, bias_vec); \ |
| reduced = vshlq_s32(reduced, vdupq_n_s32(left_shift)); \ |
| reduced = vqrdmulhq_n_s32(reduced, output_multiplier); \ |
| using gemmlowp::RoundingDivideByPOT; \ |
| reduced = RoundingDivideByPOT(reduced, right_shift); \ |
| const int16x4_t res16 = vqmovn_s32(reduced); \ |
| vst1_s16(output_data + c + B * output_stride, res16); \ |
| } |
| |
| TFLITE_SHUFFLED_FC_STORE(0); |
| TFLITE_SHUFFLED_FC_STORE(1); |
| TFLITE_SHUFFLED_FC_STORE(2); |
| TFLITE_SHUFFLED_FC_STORE(3); |
| |
| #undef TFLITE_SHUFFLED_FC_STORE |
| } |
| } else { |
| TFLITE_DCHECK(false); |
| return; |
| } |
| #else |
| if (batches == 1) { |
| int16* output_ptr = output_data; |
| // Shuffled weights have had their sign bit (0x80) pre-flipped (xor'd) |
| // so that just reinterpreting them as int8 values is equivalent to |
| // subtracting 128 from them, thus implementing for free the subtraction of |
| // the zero_point value 128. |
| const int8* shuffled_weights_ptr = |
| reinterpret_cast<const int8*>(shuffled_weights_data); |
| // Likewise, we preshuffled and pre-xored the input data above. |
| const int8* shuffled_input_data = |
| reinterpret_cast<const int8*>(shuffled_input_workspace_data); |
| for (int c = 0; c < output_depth; c += 4) { |
| // Internal accumulation. |
| // Initialize accumulator with the bias-value. |
| int32 accum[4] = {0}; |
| // Accumulation loop. |
| for (int d = 0; d < accum_depth; d += 16) { |
| for (int i = 0; i < 4; i++) { |
| for (int j = 0; j < 16; j++) { |
| int8 input_val = shuffled_input_data[d + j]; |
| int8 weights_val = *shuffled_weights_ptr++; |
| accum[i] += weights_val * input_val; |
| } |
| } |
| } |
| for (int i = 0; i < 4; i++) { |
| // Add bias value |
| int acc = accum[i] + bias_data[c + i]; |
| // Down-scale the final int32 accumulator to the scale used by our |
| // (16-bit, typically 3 integer bits) fixed-point format. The quantized |
| // multiplier and shift here have been pre-computed offline |
| // (e.g. by toco). |
| acc = |
| MultiplyByQuantizedMultiplier(acc, output_multiplier, output_shift); |
| // Saturate, cast to int16, and store to output array. |
| acc = std::max(acc, -32768); |
| acc = std::min(acc, 32767); |
| output_ptr[c + i] = acc; |
| } |
| } |
| } else if (batches == 4) { |
| int16* output_ptr = output_data; |
| // Shuffled weights have had their sign bit (0x80) pre-flipped (xor'd) |
| // so that just reinterpreting them as int8 values is equivalent to |
| // subtracting 128 from them, thus implementing for free the subtraction of |
| // the zero_point value 128. |
| const int8* shuffled_weights_ptr = |
| reinterpret_cast<const int8*>(shuffled_weights_data); |
| // Likewise, we preshuffled and pre-xored the input data above. |
| const int8* shuffled_input_data = |
| reinterpret_cast<const int8*>(shuffled_input_workspace_data); |
| for (int c = 0; c < output_depth; c += 4) { |
| const int8* shuffled_input_ptr = shuffled_input_data; |
| // Accumulation loop. |
| // Internal accumulation. |
| // Initialize accumulator with the bias-value. |
| int32 accum[4][4]; |
| for (int i = 0; i < 4; i++) { |
| for (int b = 0; b < 4; b++) { |
| accum[i][b] = 0; |
| } |
| } |
| for (int d = 0; d < accum_depth; d += 16) { |
| for (int i = 0; i < 4; i++) { |
| for (int b = 0; b < 4; b++) { |
| for (int j = 0; j < 16; j++) { |
| int8 input_val = shuffled_input_ptr[16 * b + j]; |
| int8 weights_val = shuffled_weights_ptr[16 * i + j]; |
| accum[i][b] += weights_val * input_val; |
| } |
| } |
| } |
| shuffled_input_ptr += 64; |
| shuffled_weights_ptr += 64; |
| } |
| for (int i = 0; i < 4; i++) { |
| for (int b = 0; b < 4; b++) { |
| // Add bias value |
| int acc = accum[i][b] + bias_data[c + i]; |
| // Down-scale the final int32 accumulator to the scale used by our |
| // (16-bit, typically 3 integer bits) fixed-point format. The |
| // quantized multiplier and shift here have been pre-computed offline |
| // (e.g. by toco). |
| acc = MultiplyByQuantizedMultiplier(acc, output_multiplier, |
| output_shift); |
| // Saturate, cast to int16, and store to output array. |
| acc = std::max(acc, -32768); |
| acc = std::min(acc, 32767); |
| output_ptr[b * output_stride + c + i] = acc; |
| } |
| } |
| } |
| } else { |
| TFLITE_DCHECK(false); |
| return; |
| } |
| #endif |
| } |
| |
| // Wraps ShuffledFullyConnectedWorkerImpl into a Task class |
| // to allow using gemmlowp's threadpool. |
| struct ShuffledFullyConnectedWorkerTask : gemmlowp::Task { |
| ShuffledFullyConnectedWorkerTask(const uint8* input_data, |
| const int8* shuffled_weights_data, |
| int batches, int output_depth, |
| int output_stride, int accum_depth, |
| const int32* bias_data, |
| int32 output_multiplier, int output_shift, |
| int16* output_data) |
| : input_data_(input_data), |
| shuffled_weights_data_(shuffled_weights_data), |
| batches_(batches), |
| output_depth_(output_depth), |
| output_stride_(output_stride), |
| accum_depth_(accum_depth), |
| bias_data_(bias_data), |
| output_multiplier_(output_multiplier), |
| output_shift_(output_shift), |
| output_data_(output_data) {} |
| |
| void Run() override { |
| ShuffledFullyConnectedWorkerImpl( |
| input_data_, shuffled_weights_data_, batches_, output_depth_, |
| output_stride_, accum_depth_, bias_data_, output_multiplier_, |
| output_shift_, output_data_); |
| } |
| |
| const uint8* input_data_; |
| const int8* shuffled_weights_data_; |
| int batches_; |
| int output_depth_; |
| int output_stride_; |
| int accum_depth_; |
| const int32* bias_data_; |
| int32 output_multiplier_; |
| int output_shift_; |
| int16* output_data_; |
| }; |
| |
| inline void ShuffledFullyConnected( |
| const FullyConnectedParams& params, const RuntimeShape& input_shape, |
| const uint8* input_data, const RuntimeShape& weights_shape, |
| const uint8* shuffled_weights_data, const RuntimeShape& bias_shape, |
| const int32* bias_data, const RuntimeShape& output_shape, |
| int16* output_data, uint8* shuffled_input_workspace_data, |
| gemmlowp::GemmContext* gemm_context) { |
| gemmlowp::ScopedProfilingLabel label("ShuffledFullyConnected/8bit"); |
| const int32 output_multiplier = params.output_multiplier; |
| const int output_shift = params.output_shift; |
| const int32 output_activation_min = params.quantized_activation_min; |
| const int32 output_activation_max = params.quantized_activation_max; |
| (void)gemm_context; // only used in optimized code. |
| TFLITE_DCHECK_EQ(output_activation_min, -32768); |
| TFLITE_DCHECK_EQ(output_activation_max, 32767); |
| TFLITE_DCHECK_GE(input_shape.DimensionsCount(), 1); |
| TFLITE_DCHECK_GE(weights_shape.DimensionsCount(), 2); |
| TFLITE_DCHECK_GE(output_shape.DimensionsCount(), 1); |
| // TODO(benoitjacob): This really should be: |
| // const int batches = ArraySize(output_dims, 1); |
| // but the current --variable_batch hack consists in overwriting the 3rd |
| // dimension with the runtime batch size, as we don't keep track for each |
| // array of which dimension is the batch dimension in it. |
| const int output_dim_count = output_shape.DimensionsCount(); |
| const int weights_dim_count = weights_shape.DimensionsCount(); |
| const int batches = FlatSizeSkipDim(output_shape, output_dim_count - 1); |
| const int output_depth = MatchingDim(weights_shape, weights_dim_count - 2, |
| output_shape, output_dim_count - 1); |
| const int accum_depth = weights_shape.Dims(weights_dim_count - 1); |
| TFLITE_DCHECK((accum_depth % 16) == 0); |
| TFLITE_DCHECK((output_depth % 4) == 0); |
| // Shuffled weights have had their sign bit (0x80) pre-flipped (xor'd) |
| // so that just reinterpreting them as int8 values is equivalent to |
| // subtracting 128 from them, thus implementing for free the subtraction of |
| // the zero_point value 128. |
| const int8* int8_shuffled_weights_data = |
| reinterpret_cast<const int8*>(shuffled_weights_data); |
| |
| // Shuffling and xoring of input activations into the workspace buffer |
| if (batches == 1) { |
| #ifdef USE_NEON |
| const uint8x16_t signbit = vdupq_n_u8(0x80); |
| for (int i = 0; i < accum_depth; i += 16) { |
| uint8x16_t val = vld1q_u8(input_data + i); |
| val = veorq_u8(val, signbit); |
| vst1q_u8(shuffled_input_workspace_data + i, val); |
| } |
| #else |
| for (int i = 0; i < accum_depth; i++) { |
| shuffled_input_workspace_data[i] = input_data[i] ^ 0x80; |
| } |
| #endif |
| } else if (batches == 4) { |
| uint8* shuffled_input_workspace_ptr = shuffled_input_workspace_data; |
| int c = 0; |
| #ifdef USE_NEON |
| const uint8x16_t signbit = vdupq_n_u8(0x80); |
| for (c = 0; c < accum_depth; c += 16) { |
| const uint8* src_data_ptr = input_data + c; |
| uint8x16_t val0 = vld1q_u8(src_data_ptr + 0 * accum_depth); |
| uint8x16_t val1 = vld1q_u8(src_data_ptr + 1 * accum_depth); |
| uint8x16_t val2 = vld1q_u8(src_data_ptr + 2 * accum_depth); |
| uint8x16_t val3 = vld1q_u8(src_data_ptr + 3 * accum_depth); |
| val0 = veorq_u8(val0, signbit); |
| val1 = veorq_u8(val1, signbit); |
| val2 = veorq_u8(val2, signbit); |
| val3 = veorq_u8(val3, signbit); |
| vst1q_u8(shuffled_input_workspace_ptr + 0, val0); |
| vst1q_u8(shuffled_input_workspace_ptr + 16, val1); |
| vst1q_u8(shuffled_input_workspace_ptr + 32, val2); |
| vst1q_u8(shuffled_input_workspace_ptr + 48, val3); |
| shuffled_input_workspace_ptr += 64; |
| } |
| #else |
| for (c = 0; c < accum_depth; c += 16) { |
| for (int b = 0; b < 4; b++) { |
| const uint8* src_data_ptr = input_data + b * accum_depth + c; |
| for (int j = 0; j < 16; j++) { |
| uint8 src_val = *src_data_ptr++; |
| // Flip the sign bit, so that the kernel will only need to |
| // reinterpret these uint8 values as int8, getting for free the |
| // subtraction of the zero_point value 128. |
| uint8 dst_val = src_val ^ 0x80; |
| *shuffled_input_workspace_ptr++ = dst_val; |
| } |
| } |
| } |
| #endif |
| } else { |
| TFLITE_DCHECK(false); |
| return; |
| } |
| |
| static constexpr int kKernelRows = 4; |
| const int thread_count = gemmlowp::HowManyThreads<kKernelRows>( |
| gemm_context->max_num_threads(), output_depth, batches, accum_depth); |
| if (thread_count == 1) { |
| // Single-thread case: do the computation on the current thread, don't |
| // use a threadpool |
| ShuffledFullyConnectedWorkerImpl( |
| shuffled_input_workspace_data, int8_shuffled_weights_data, batches, |
| output_depth, output_depth, accum_depth, bias_data, output_multiplier, |
| output_shift, output_data); |
| return; |
| } |
| |
| // Multi-threaded case: use the gemmlowp context's threadpool. |
| TFLITE_DCHECK_GT(thread_count, 1); |
| std::vector<gemmlowp::Task*> tasks(thread_count); |
| const int kRowsPerWorker = |
| gemmlowp::RoundUp<kKernelRows>(output_depth / thread_count); |
| int row_start = 0; |
| for (int i = 0; i < thread_count; i++) { |
| int row_end = std::min(output_depth, row_start + kRowsPerWorker); |
| tasks[i] = new ShuffledFullyConnectedWorkerTask( |
| shuffled_input_workspace_data, |
| int8_shuffled_weights_data + row_start * accum_depth, batches, |
| row_end - row_start, output_depth, accum_depth, bias_data + row_start, |
| output_multiplier, output_shift, output_data + row_start); |
| row_start = row_end; |
| } |
| TFLITE_DCHECK_EQ(row_start, output_depth); |
| gemm_context->workers_pool()->Execute(tasks); |
| } |
| |
| // TODO(b/80418076): Move to legacy ops file, update invocations. |
| // Legacy. |
| inline void ShuffledFullyConnected( |
| const uint8* input_data, const Dims<4>& input_dims, |
| const uint8* shuffled_weights_data, const Dims<4>& weights_dims, |
| const int32* bias_data, const Dims<4>& bias_dims, int32 output_multiplier, |
| int output_shift, int32 output_activation_min, int32 output_activation_max, |
| int16* output_data, const Dims<4>& output_dims, |
| uint8* shuffled_input_workspace_data, gemmlowp::GemmContext* gemm_context) { |
| tflite::FullyConnectedParams op_params; |
| op_params.output_multiplier = output_multiplier; |
| // Legacy ops used mixed left and right shifts. Now all are +ve-means-left. |
| op_params.output_shift = kReverseShift * output_shift; |
| op_params.quantized_activation_min = output_activation_min; |
| op_params.quantized_activation_max = output_activation_max; |
| |
| ShuffledFullyConnected(op_params, DimsToShape(input_dims), input_data, |
| DimsToShape(weights_dims), shuffled_weights_data, |
| DimsToShape(bias_dims), bias_data, |
| DimsToShape(output_dims), output_data, |
| shuffled_input_workspace_data, gemm_context); |
| } |
| |
| template <typename T> |
| inline void ExtractPatchIntoBufferColumn(const RuntimeShape& input_shape, int w, |
| int h, int b, int kheight, int kwidth, |
| int stride_width, int stride_height, |
| int pad_width, int pad_height, |
| int in_width, int in_height, |
| int in_depth, int single_buffer_length, |
| int buffer_id, const T* in_data, |
| T* conv_buffer_data, uint8 zero_byte) { |
| gemmlowp::ScopedProfilingLabel label("ExtractPatchIntoBufferColumn"); |
| TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); |
| // This chunk of code reshapes all the inputs corresponding to |
| // output (b, h, w) to a column vector in conv_buffer(:, buffer_id). |
| const int kwidth_times_indepth = kwidth * in_depth; |
| const int inwidth_times_indepth = in_width * in_depth; |
| const int ih_ungated_start = h * stride_height - pad_height; |
| const int ih_ungated_end = (ih_ungated_start + kheight); |
| const int ih_end = std::min(ih_ungated_end, in_height); |
| const int iw_ungated_start = w * stride_width - pad_width; |
| const int iw_ungated_end = (iw_ungated_start + kwidth); |
| const int iw_end = std::min(iw_ungated_end, in_width); |
| // If the patch is off the edge of the input image, skip writing those rows |
| // and columns from the patch into the output array. |
| const int h_offset = std::max(0, -ih_ungated_start); |
| const int w_offset = std::max(0, -iw_ungated_start); |
| const int ih_start = std::max(0, ih_ungated_start); |
| const int iw_start = std::max(0, iw_ungated_start); |
| const int single_row_num = |
| std::min(kwidth - w_offset, in_width - iw_start) * in_depth; |
| const int output_row_offset = (buffer_id * single_buffer_length); |
| int out_offset = |
| output_row_offset + (h_offset * kwidth + w_offset) * in_depth; |
| int in_offset = Offset(input_shape, b, ih_start, iw_start, 0); |
| |
| // Express all of the calculations as padding around the input patch. |
| const int top_padding = h_offset; |
| const int bottom_padding = (ih_ungated_end - ih_end); |
| const int left_padding = w_offset; |
| const int right_padding = (iw_ungated_end - iw_end); |
| assert(single_row_num == |
| ((kwidth - (left_padding + right_padding)) * in_depth)); |
| |
| // Write out zeroes to the elements representing the top rows of the input |
| // patch that are off the edge of the input image. |
| if (top_padding > 0) { |
| const int top_row_elements = (top_padding * kwidth * in_depth); |
| memset(conv_buffer_data + output_row_offset, zero_byte, |
| (top_row_elements * sizeof(T))); |
| } |
| |
| // If the patch is on the interior of the input image horizontally, just copy |
| // over the rows sequentially, otherwise add zero padding at the start or end. |
| if ((left_padding == 0) && (right_padding == 0)) { |
| for (int ih = ih_start; ih < ih_end; ++ih) { |
| memcpy(conv_buffer_data + out_offset, in_data + in_offset, |
| single_row_num * sizeof(T)); |
| out_offset += kwidth_times_indepth; |
| in_offset += inwidth_times_indepth; |
| } |
| } else { |
| for (int ih = ih_start; ih < ih_end; ++ih) { |
| if (left_padding > 0) { |
| const int left_start = (out_offset - (left_padding * in_depth)); |
| memset(conv_buffer_data + left_start, zero_byte, |
| (left_padding * in_depth * sizeof(T))); |
| } |
| memcpy(conv_buffer_data + out_offset, in_data + in_offset, |
| single_row_num * sizeof(T)); |
| if (right_padding > 0) { |
| const int right_start = (out_offset + single_row_num); |
| memset(conv_buffer_data + right_start, zero_byte, |
| (right_padding * in_depth * sizeof(T))); |
| } |
| out_offset += kwidth_times_indepth; |
| in_offset += inwidth_times_indepth; |
| } |
| } |
| |
| // If the bottom of the patch falls off the input image, pad the values |
| // representing those input rows with zeroes. |
| if (bottom_padding > 0) { |
| const int bottom_row_elements = (bottom_padding * kwidth * in_depth); |
| const int bottom_start = |
| output_row_offset + |
| ((top_padding + (ih_end - ih_start)) * kwidth * in_depth); |
| memset(conv_buffer_data + bottom_start, zero_byte, |
| (bottom_row_elements * sizeof(T))); |
| } |
| } |
| |
| // TODO(b/80418076): Move to legacy ops file, update invocations. |
| // Legacy. |
| template <typename T> |
| inline void ExtractPatchIntoBufferColumn( |
| const Dims<4>& input_dims, int w, int h, int b, int kheight, int kwidth, |
| int stride_width, int stride_height, int pad_width, int pad_height, |
| int in_width, int in_height, int in_depth, int single_buffer_length, |
| int buffer_id, const T* in_data, T* conv_buffer_data, uint8 zero_byte) { |
| ExtractPatchIntoBufferColumn( |
| DimsToShape(input_dims), w, h, b, kheight, kwidth, stride_width, |
| stride_height, pad_width, pad_height, in_width, in_height, in_depth, |
| single_buffer_length, buffer_id, in_data, conv_buffer_data, zero_byte); |
| } |
| |
| template <typename T> |
| void DilatedIm2col(const ConvParams& params, uint8 zero_byte, |
| const RuntimeShape& input_shape, const T* input_data, |
| const RuntimeShape& filter_shape, |
| const RuntimeShape& output_shape, T* im2col_data) { |
| const int stride_width = params.stride_width; |
| const int stride_height = params.stride_height; |
| const int dilation_width_factor = params.dilation_width_factor; |
| const int dilation_height_factor = params.dilation_height_factor; |
| const int pad_width = params.padding_values.width; |
| const int pad_height = params.padding_values.height; |
| TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); |
| TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4); |
| TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); |
| |
| // For dilated convolution, the input pixels are not contiguous therefore we |
| // can't use the same opitimizations as Im2Col(). Though note this code would |
| // work fine for the non-dilated case too (though likely a bit slower). |
| gemmlowp::ScopedProfilingLabel label("DilatedIm2col"); |
| TFLITE_DCHECK(dilation_width_factor != 1 || dilation_height_factor != 1); |
| TFLITE_DCHECK(im2col_data); |
| const int batches = MatchingDim(input_shape, 0, output_shape, 0); |
| const int input_height = input_shape.Dims(1); |
| const int input_width = input_shape.Dims(2); |
| const int input_depth = MatchingDim(input_shape, 3, filter_shape, 3); |
| const int filter_height = filter_shape.Dims(1); |
| const int filter_width = filter_shape.Dims(2); |
| const int output_height = output_shape.Dims(1); |
| const int output_width = output_shape.Dims(2); |
| MatchingDim(output_shape, 3, filter_shape, 0); |
| |
| // Construct the MxN sized im2col matrix. |
| // The rows M, are sub-ordered B x H x W |
| const RuntimeShape row_shape({1, batches, output_height, output_width}); |
| // The columns, N, are sub-ordered Kh x Kw x Din |
| const RuntimeShape col_shape({1, filter_height, filter_width, input_depth}); |
| // Use dimensions M and N to construct dims for indexing directly into im2col |
| const RuntimeShape im2col_shape( |
| {1, 1, row_shape.FlatSize(), col_shape.FlatSize()}); |
| |
| // Loop through the output rows (B x H x W) |
| for (int batch = 0; batch < batches; ++batch) { |
| for (int out_y = 0; out_y < output_height; ++out_y) { |
| for (int out_x = 0; out_x < output_width; ++out_x) { |
| // Each im2col row is an output pixel. Arrange the input data in this |
| // row in an order we can conveniently multiply with the filter data. |
| int row_offset = Offset(row_shape, 0, batch, out_y, out_x); |
| const int in_x_origin = (out_x * stride_width) - pad_width; |
| const int in_y_origin = (out_y * stride_height) - pad_height; |
| // Loop through all the pixels of the filter (Kh x Kw) |
| for (int filter_y = 0; filter_y < filter_height; ++filter_y) { |
| const int in_y = in_y_origin + dilation_height_factor * filter_y; |
| if ((in_y >= 0) && (in_y < input_height)) { |
| // Filter row is within the input data. |
| // Loop through all the filter pixels in this row. |
| for (int filter_x = 0; filter_x < filter_width; ++filter_x) { |
| const int in_x = in_x_origin + dilation_width_factor * filter_x; |
| int col_offset = Offset(col_shape, 0, filter_y, filter_x, 0); |
| T* dst = im2col_data + |
| Offset(im2col_shape, 0, 0, row_offset, col_offset); |
| if ((in_x >= 0) && (in_x < input_width)) { |
| // Filter pixel is within the input, copy the input data. |
| T const* src = |
| input_data + Offset(input_shape, batch, in_y, in_x, 0); |
| memcpy(dst, src, input_depth * sizeof(T)); |
| } else { |
| // Filter pixel is outside the input, zero it out. |
| memset(dst, zero_byte, input_depth * sizeof(T)); |
| } |
| } |
| } else { |
| // Filter row is outside the input, zero out the entire filter row. |
| int col_offset = Offset(col_shape, 0, filter_y, 0, 0); |
| T* dst = im2col_data + |
| Offset(im2col_shape, 0, 0, row_offset, col_offset); |
| memset(dst, zero_byte, filter_width * input_depth * sizeof(T)); |
| } |
| } |
| } |
| } |
| } |
| } |
| |
| // TODO(b/80418076): Move to legacy ops file, update invocations. |
| // Legacy. |
| template <typename T> |
| void DilatedIm2col(const T* input_data, const Dims<4>& input_dims, |
| const Dims<4>& filter_dims, int stride_width, |
| int stride_height, int dilation_width_factor, |
| int dilation_height_factor, int pad_width, int pad_height, |
| const Dims<4>& output_dims, uint8 zero_byte, |
| T* im2col_data) { |
| tflite::ConvParams op_params; |
| // Padding type is ignored, but still set. |
| op_params.padding_type = PaddingType::kSame; |
| op_params.padding_values.width = pad_width; |
| op_params.padding_values.height = pad_height; |
| op_params.stride_width = stride_width; |
| op_params.stride_height = stride_height; |
| op_params.dilation_width_factor = dilation_width_factor; |
| op_params.dilation_height_factor = dilation_height_factor; |
| |
| DilatedIm2col(op_params, zero_byte, DimsToShape(input_dims), input_data, |
| DimsToShape(filter_dims), DimsToShape(output_dims), |
| im2col_data); |
| } |
| |
| template <typename T> |
| void Im2col(const ConvParams& params, int kheight, int kwidth, uint8 zero_byte, |
| const RuntimeShape& input_shape, const T* input_data, |
| const RuntimeShape& output_shape, T* output_data) { |
| gemmlowp::ScopedProfilingLabel label("Im2col"); |
| const int stride_width = params.stride_width; |
| const int stride_height = params.stride_height; |
| const int pad_width = params.padding_values.width; |
| const int pad_height = params.padding_values.height; |
| TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); |
| TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); |
| |
| const int batches = MatchingDim(input_shape, 0, output_shape, 0); |
| const int input_depth = input_shape.Dims(3); |
| const int input_width = input_shape.Dims(2); |
| const int input_height = input_shape.Dims(1); |
| const int output_depth = output_shape.Dims(3); |
| const int output_width = output_shape.Dims(2); |
| const int output_height = output_shape.Dims(1); |
| |
| int buffer_id = 0; |
| // Loop over the output nodes. |
| for (int b = 0; b < batches; ++b) { |
| for (int h = 0; h < output_height; ++h) { |
| for (int w = 0; w < output_width; ++w) { |
| ExtractPatchIntoBufferColumn( |
| input_shape, w, h, b, kheight, kwidth, stride_width, stride_height, |
| pad_width, pad_height, input_width, input_height, input_depth, |
| output_depth, buffer_id, input_data, output_data, zero_byte); |
| ++buffer_id; |
| } |
| } |
| } |
| } |
| |
| // TODO(b/80418076): Move to legacy ops file, update invocations. |
| // Legacy. |
| template <typename T> |
| void Im2col(const T* input_data, const Dims<4>& input_dims, int stride_width, |
| int stride_height, int pad_width, int pad_height, int kheight, |
| int kwidth, uint8 zero_byte, T* output_data, |
| const Dims<4>& output_dims) { |
| tflite::ConvParams op_params; |
| // Padding type is ignored, but still set. |
| op_params.padding_type = PaddingType::kSame; |
| op_params.padding_values.width = pad_width; |
| op_params.padding_values.height = pad_height; |
| op_params.stride_width = stride_width; |
| op_params.stride_height = stride_height; |
| op_params.dilation_width_factor = 1; |
| op_params.dilation_height_factor = 1; |
| |
| Im2col(op_params, kheight, kwidth, zero_byte, DimsToShape(input_dims), |
| input_data, DimsToShape(output_dims), output_data); |
| } |
| |
| // legacy, for compatibility with old checked-in code |
| template <typename T> |
| void Im2col(const T* input_data, const Dims<4>& input_dims, int stride, |
| int pad_width, int pad_height, int kheight, int kwidth, |
| uint8 zero_byte, T* output_data, const Dims<4>& output_dims) { |
| Im2col(input_data, input_dims, stride, stride, pad_width, pad_height, kheight, |
| kwidth, zero_byte, output_data, output_dims); |
| } |
| |
| inline void Conv(const ConvParams& params, const RuntimeShape& input_shape, |
| const float* input_data, const RuntimeShape& filter_shape, |
| const float* filter_data, const RuntimeShape& bias_shape, |
| const float* bias_data, const RuntimeShape& output_shape, |
| float* output_data, const RuntimeShape& im2col_shape, |
| float* im2col_data) { |
| const int stride_width = params.stride_width; |
| const int stride_height = params.stride_height; |
| const int dilation_width_factor = params.dilation_width_factor; |
| const int dilation_height_factor = params.dilation_height_factor; |
| const float output_activation_min = params.float_activation_min; |
| const float output_activation_max = params.float_activation_max; |
| TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); |
| TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4); |
| TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); |
| |
| (void)im2col_data; |
| (void)im2col_shape; |
| gemmlowp::ScopedProfilingLabel label("Conv"); |
| |
| // NB: static_cast<float>(0x00000000h) == 0.0f |
| const uint8 float_zero_byte = 0x00; |
| const float* gemm_input_data = nullptr; |
| const RuntimeShape* gemm_input_shape = nullptr; |
| const int filter_width = filter_shape.Dims(2); |
| const int filter_height = filter_shape.Dims(1); |
| const bool need_dilated_im2col = |
| dilation_width_factor != 1 || dilation_height_factor != 1; |
| const bool need_im2col = stride_width != 1 || stride_height != 1 || |
| filter_width != 1 || filter_height != 1; |
| if (need_dilated_im2col) { |
| DilatedIm2col(params, float_zero_byte, input_shape, input_data, |
| filter_shape, output_shape, im2col_data); |
| gemm_input_data = im2col_data; |
| gemm_input_shape = &im2col_shape; |
| } else if (need_im2col) { |
| TFLITE_DCHECK(im2col_data); |
| Im2col(params, filter_height, filter_width, float_zero_byte, input_shape, |
| input_data, im2col_shape, im2col_data); |
| gemm_input_data = im2col_data; |
| gemm_input_shape = &im2col_shape; |
| } else { |
| // TODO(aselle): We need to make sure to not send im2col if it is not |
| // needed. |
| TFLITE_DCHECK(!im2col_data); |
| gemm_input_data = input_data; |
| gemm_input_shape = &input_shape; |
| } |
| |
| const auto im2col_matrix_map = |
| MapAsMatrixWithLastDimAsRows(gemm_input_data, *gemm_input_shape); |
| const auto filter_matrix_map = |
| MapAsMatrixWithFirstDimAsCols(filter_data, filter_shape); |
| auto output_matrix_map = |
| MapAsMatrixWithLastDimAsRows(output_data, output_shape); |
| |
| Gemm(filter_matrix_map.transpose(), im2col_matrix_map, &output_matrix_map); |
| |
| AddBiasAndEvalActivationFunction(output_activation_min, output_activation_max, |
| bias_shape, bias_data, output_shape, |
| output_data); |
| } |
| |
| // TODO(b/80418076): Move to legacy ops file, update invocations. |
| // Legacy. |
| inline void Conv(const float* input_data, const Dims<4>& input_dims, |
| const float* filter_data, const Dims<4>& filter_dims, |
| const float* bias_data, const Dims<4>& bias_dims, |
| int stride_width, int stride_height, int dilation_width_factor, |
| int dilation_height_factor, int pad_width, int pad_height, |
| float output_activation_min, float output_activation_max, |
| float* output_data, const Dims<4>& output_dims, |
| float* im2col_data, const Dims<4>& im2col_dims) { |
| tflite::ConvParams op_params; |
| // Padding type is ignored, but still set. |
| op_params.padding_type = PaddingType::kSame; |
| op_params.padding_values.width = pad_width; |
| op_params.padding_values.height = pad_height; |
| op_params.stride_width = stride_width; |
| op_params.stride_height = stride_height; |
| op_params.dilation_width_factor = dilation_width_factor; |
| op_params.dilation_height_factor = dilation_height_factor; |
| op_params.float_activation_min = output_activation_min; |
| op_params.float_activation_max = output_activation_max; |
| |
| Conv(op_params, DimsToShape(input_dims), input_data, DimsToShape(filter_dims), |
| filter_data, DimsToShape(bias_dims), bias_data, DimsToShape(output_dims), |
| output_data, DimsToShape(im2col_dims), im2col_data); |
| } |
| |
| inline void HybridConv(const ConvParams& params, float* scaling_factors_ptr, |
| const RuntimeShape& input_shape, |
| const int8_t* input_data, |
| const RuntimeShape& filter_shape, |
| const int8_t* filter_data, |
| const RuntimeShape& bias_shape, const float* bias_data, |
| const RuntimeShape& output_shape, float* output_data, |
| const RuntimeShape& im2col_shape, int8_t* im2col_data) { |
| const int stride_width = params.stride_width; |
| const int stride_height = params.stride_height; |
| const float output_activation_min = params.float_activation_min; |
| const float output_activation_max = params.float_activation_max; |
| TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); |
| TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4); |
| TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); |
| |
| const int batch_size = input_shape.Dims(0); |
| const int filter_width = filter_shape.Dims(2); |
| const int filter_height = filter_shape.Dims(1); |
| |
| const int8_t* gemm_input_data = nullptr; |
| int num_input; |
| const bool need_im2col = stride_width != 1 || stride_height != 1 || |
| filter_width != 1 || filter_height != 1; |
| |
| if (need_im2col) { |
| TFLITE_DCHECK(im2col_data); |
| // symmetric quantization assumes zero point of 0. |
| const int input_zero_point = 0; |
| |
| Im2col(params, filter_height, filter_width, input_zero_point, input_shape, |
| input_data, im2col_shape, im2col_data); |
| gemm_input_data = im2col_data; |
| num_input = im2col_shape.FlatSize(); |
| } else { |
| TFLITE_DCHECK(!im2col_data); |
| gemm_input_data = input_data; |
| num_input = input_shape.FlatSize(); |
| } |
| |
| // Flatten 4D matrices into 2D matrices for matrix multiplication. |
| |
| // Flatten so that each filter has its own row. |
| const int filter_rows = filter_shape.Dims(0); |
| const int filter_cols = FlatSizeSkipDim(filter_shape, 0); |
| |
| // In MatrixBatchVectorMultiplyAccumulate, each output value is the |
| // dot product of one row of the first matrix with one row of the second |
| // matrix. Therefore, the number of cols in each matrix are equivalent. |
| // |
| // After Im2Col, each input patch becomes a row. |
| const int gemm_input_cols = filter_cols; |
| const int gemm_input_rows = num_input / gemm_input_cols; |
| |
| const int output_cols = output_shape.Dims(3); |
| const int output_rows = FlatSizeSkipDim(output_shape, 3); |
| TFLITE_DCHECK_EQ(output_cols, filter_rows); |
| TFLITE_DCHECK_EQ(output_rows, gemm_input_rows); |
| TFLITE_DCHECK_EQ(bias_shape.Dims(3), output_cols); |
| TFLITE_DCHECK_EQ(bias_shape.Dims(2), 1); |
| TFLITE_DCHECK_EQ(bias_shape.Dims(1), 1); |
| TFLITE_DCHECK_EQ(bias_shape.Dims(0), 1); |
| |
| // MatrixBatchVectorMultiplyAccumulate assumes that each row of the second |
| // input matrix has its own scale factor. This code duplicates the scale |
| // factors for each row in the same batch. |
| const int rows_per_batch = gemm_input_rows / batch_size; |
| for (int i = gemm_input_rows - 1; i >= 0; --i) { |
| scaling_factors_ptr[i] = scaling_factors_ptr[i / rows_per_batch]; |
| } |
| |
| tensor_utils::ZeroVector(output_data, output_rows * output_cols); |
| |
| tensor_utils::MatrixBatchVectorMultiplyAccumulate( |
| filter_data, filter_rows, filter_cols, gemm_input_data, |
| scaling_factors_ptr, /*n_batch=*/gemm_input_rows, output_data, |
| /*result_stride=*/1); |
| |
| AddBiasAndEvalActivationFunction(output_activation_min, output_activation_max, |
| bias_shape, bias_data, output_shape, |
| output_data); |
| } |
| |
| // TODO(b/80418076): Move to legacy ops file, update invocations. |
| // Legacy. |
| inline void HybridConv(const int8_t* input_data, const Dims<4>& input_dims, |
| const int8_t* filter_data, const Dims<4>& filter_dims, |
| const float* bias_data, const Dims<4>& bias_dims, |
| int stride_width, int stride_height, int pad_width, |
| int pad_height, float* scaling_factors_ptr, |
| float output_activation_min, float output_activation_max, |
| float* output_data, const Dims<4>& output_dims, |
| int8_t* im2col_data, const Dims<4>& im2col_dims) { |
| tflite::ConvParams op_params; |
| // Padding type is ignored, but still set. |
| op_params.padding_type = PaddingType::kSame; |
| op_params.padding_values.width = pad_width; |
| op_params.padding_values.height = pad_height; |
| op_params.stride_width = stride_width; |
| op_params.stride_height = stride_height; |
| op_params.float_activation_min = output_activation_min; |
| op_params.float_activation_max = output_activation_max; |
| |
| HybridConv(op_params, scaling_factors_ptr, DimsToShape(input_dims), |
| input_data, DimsToShape(filter_dims), filter_data, |
| DimsToShape(bias_dims), bias_data, DimsToShape(output_dims), |
| output_data, DimsToShape(im2col_dims), im2col_data); |
| } |
| |
| // TODO(b/80418076): Move to legacy ops file, update invocations. |
| // Legacy. |
| template <FusedActivationFunctionType Ac> |
| void Conv(const float* input_data, const Dims<4>& input_dims, |
| const float* filter_data, const Dims<4>& filter_dims, |
| const float* bias_data, const Dims<4>& bias_dims, int stride_width, |
| int stride_height, int dilation_width_factor, |
| int dilation_height_factor, int pad_width, int pad_height, |
| float* output_data, const Dims<4>& output_dims, float* im2col_data, |
| const Dims<4>& im2col_dims) { |
| float output_activation_min, output_activation_max; |
| GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); |
| Conv(input_data, input_dims, filter_data, filter_dims, bias_data, bias_dims, |
| stride_width, stride_height, dilation_width_factor, |
| dilation_height_factor, pad_width, pad_height, output_activation_min, |
| output_activation_max, output_data, output_dims, im2col_data, |
| im2col_dims); |
| } |
| |
| // TODO(b/80418076): Move to legacy ops file, update invocations. |
| // legacy, for compatibility with old checked-in code |
| template <FusedActivationFunctionType Ac> |
| void Conv(const float* input_data, const Dims<4>& input_dims, |
| const float* filter_data, const Dims<4>& filter_dims, |
| const float* bias_data, const Dims<4>& bias_dims, int stride_width, |
| int stride_height, int pad_width, int pad_height, float* output_data, |
| const Dims<4>& output_dims, float* im2col_data, |
| const Dims<4>& im2col_dims) { |
| float output_activation_min, output_activation_max; |
| GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); |
| Conv(input_data, input_dims, filter_data, filter_dims, bias_data, bias_dims, |
| stride_width, stride_height, 1, 1, pad_width, pad_height, |
| output_activation_min, output_activation_max, output_data, output_dims, |
| im2col_data, im2col_dims); |
| } |
| |
| // TODO(b/80418076): Move to legacy ops file, update invocations. |
| // legacy, for compatibility with old checked-in code |
| template <FusedActivationFunctionType Ac> |
| void Conv(const float* input_data, const Dims<4>& input_dims, |
| const float* filter_data, const Dims<4>& filter_dims, |
| const float* bias_data, const Dims<4>& bias_dims, int stride, |
| int pad_width, int pad_height, float* output_data, |
| const Dims<4>& output_dims, float* im2col_data, |
| const Dims<4>& im2col_dims) { |
| Conv<Ac>(input_data, input_dims, filter_data, filter_dims, bias_data, |
| bias_dims, stride, stride, 1, 1, pad_width, pad_height, output_data, |
| output_dims, im2col_data, im2col_dims); |
| } |
| |
| inline void Conv(const ConvParams& params, const RuntimeShape& input_shape, |
| const uint8* input_data, const RuntimeShape& filter_shape, |
| const uint8* filter_data, const RuntimeShape& bias_shape, |
| const int32* bias_data, const RuntimeShape& output_shape, |
| uint8* output_data, const RuntimeShape& im2col_shape, |
| uint8* im2col_data, gemmlowp::GemmContext* gemm_context) { |
| gemmlowp::ScopedProfilingLabel label("Conv/8bit"); |
| const int stride_width = params.stride_width; |
| const int stride_height = params.stride_height; |
| const int dilation_width_factor = params.dilation_width_factor; |
| const int dilation_height_factor = params.dilation_height_factor; |
| const int32 input_offset = params.input_offset; |
| const int32 filter_offset = params.weights_offset; |
| const int32 output_offset = params.output_offset; |
| const int32 output_multiplier = params.output_multiplier; |
| const int output_shift = params.output_shift; |
| const int32 output_activation_min = params.quantized_activation_min; |
| const int32 output_activation_max = params.quantized_activation_max; |
| TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); |
| TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4); |
| TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); |
| |
| const uint8* gemm_input_data = nullptr; |
| const RuntimeShape* gemm_input_shape = nullptr; |
| const int filter_width = filter_shape.Dims(2); |
| const int filter_height = filter_shape.Dims(1); |
| const bool need_dilated_im2col = |
| dilation_width_factor != 1 || dilation_height_factor != 1; |
| const bool need_im2col = stride_width != 1 || stride_height != 1 || |
| filter_width != 1 || filter_height != 1; |
| if (need_dilated_im2col) { |
| TFLITE_DCHECK(im2col_data); |
| const int input_zero_point = -input_offset; |
| TFLITE_DCHECK_GE(input_zero_point, 0); |
| TFLITE_DCHECK_LE(input_zero_point, 255); |
| DilatedIm2col(params, input_zero_point, input_shape, input_data, |
| filter_shape, output_shape, im2col_data); |
| gemm_input_data = im2col_data; |
| gemm_input_shape = &im2col_shape; |
| } else if (need_im2col) { |
| TFLITE_DCHECK(im2col_data); |
| const int input_zero_point = -input_offset; |
| TFLITE_DCHECK_GE(input_zero_point, 0); |
| TFLITE_DCHECK_LE(input_zero_point, 255); |
| Im2col(params, filter_height, filter_width, input_zero_point, input_shape, |
| input_data, im2col_shape, im2col_data); |
| gemm_input_data = im2col_data; |
| gemm_input_shape = &im2col_shape; |
| } else { |
| TFLITE_DCHECK(!im2col_data); |
| gemm_input_data = input_data; |
| gemm_input_shape = &input_shape; |
| } |
| |
| const int gemm_input_rows = gemm_input_shape->Dims(3); |
| // Using FlatSizeSkipDim causes segfault in some contexts (see b/79927784). |
| // The root cause has not yet been identified though. Same applies below for |
| // the other calls commented out. This is a partial rollback of cl/196819423. |
| // const int gemm_input_cols = FlatSizeSkipDim(*gemm_input_shape, 3); |
| const int gemm_input_cols = gemm_input_shape->Dims(0) * |
| gemm_input_shape->Dims(1) * |
| gemm_input_shape->Dims(2); |
| const int filter_rows = filter_shape.Dims(0); |
| // See b/79927784. |
| // const int filter_cols = FlatSizeSkipDim(filter_shape, 0); |
| const int filter_cols = |
| filter_shape.Dims(1) * filter_shape.Dims(2) * filter_shape.Dims(3); |
| const int output_rows = output_shape.Dims(3); |
| // See b/79927784. |
| // const int output_cols = FlatSizeSkipDim(output_shape, 3); |
| const int output_cols = |
| output_shape.Dims(0) * output_shape.Dims(1) * output_shape.Dims(2); |
| TFLITE_DCHECK_EQ(output_rows, filter_rows); |
| TFLITE_DCHECK_EQ(output_cols, gemm_input_cols); |
| TFLITE_DCHECK_EQ(filter_cols, gemm_input_rows); |
| TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_rows); |
| gemmlowp::MatrixMap<const uint8, gemmlowp::MapOrder::RowMajor> filter_matrix( |
| filter_data, filter_rows, filter_cols); |
| gemmlowp::MatrixMap<const uint8, gemmlowp::MapOrder::ColMajor> input_matrix( |
| gemm_input_data, gemm_input_rows, gemm_input_cols); |
| gemmlowp::MatrixMap<uint8, gemmlowp::MapOrder::ColMajor> output_matrix( |
| output_data, output_rows, output_cols); |
| const auto& output_pipeline = GemmlowpOutputPipeline::MakeExp( |
| bias_data, output_rows, output_offset, output_multiplier, output_shift, |
| output_activation_min, output_activation_max); |
| gemmlowp::GemmWithOutputPipeline<uint8, uint8, |
| gemmlowp::L8R8WithLhsNonzeroBitDepthParams>( |
| gemm_context, filter_matrix, input_matrix, &output_matrix, filter_offset, |
| input_offset, output_pipeline); |
| } |
| |
| // TODO(b/80418076): Move to legacy ops file, update invocations. |
| // Legacy. |
| inline void Conv(const uint8* input_data, const Dims<4>& input_dims, |
| int32 input_offset, const uint8* filter_data, |
| const Dims<4>& filter_dims, int32 filter_offset, |
| const int32* bias_data, const Dims<4>& bias_dims, |
| int stride_width, int stride_height, int dilation_width_factor, |
| int dilation_height_factor, int pad_width, int pad_height, |
| int32 output_offset, int32 output_multiplier, int output_shift, |
| int32 output_activation_min, int32 output_activation_max, |
| uint8* output_data, const Dims<4>& output_dims, |
| uint8* im2col_data, const Dims<4>& im2col_dims, |
| gemmlowp::GemmContext* gemm_context) { |
| tflite::ConvParams op_params; |
| // Padding type is ignored, but still set. |
| op_params.padding_type = PaddingType::kSame; |
| op_params.padding_values.width = pad_width; |
| op_params.padding_values.height = pad_height; |
| op_params.stride_width = stride_width; |
| op_params.stride_height = stride_height; |
| op_params.dilation_width_factor = dilation_width_factor; |
| op_params.dilation_height_factor = dilation_height_factor; |
| op_params.input_offset = input_offset; |
| op_params.weights_offset = filter_offset; |
| op_params.output_offset = output_offset; |
| op_params.output_multiplier = output_multiplier; |
| // Legacy ops used mixed left and right shifts. Now all are +ve-means-left. |
| op_params.output_shift = kReverseShift * output_shift; |
| op_params.quantized_activation_min = output_activation_min; |
| op_params.quantized_activation_max = output_activation_max; |
| |
| Conv(op_params, DimsToShape(input_dims), input_data, DimsToShape(filter_dims), |
| filter_data, DimsToShape(bias_dims), bias_data, DimsToShape(output_dims), |
| output_data, DimsToShape(im2col_dims), im2col_data, gemm_context); |
| } |
| |
| // TODO(b/80418076): Move to legacy ops file, update invocations. |
| // Legacy. |
| inline void Conv(const uint8* input_data, const Dims<4>& input_dims, |
| int32 input_offset, const uint8* filter_data, |
| const Dims<4>& filter_dims, int32 filter_offset, |
| const int32* bias_data, const Dims<4>& bias_dims, |
| int stride_width, int stride_height, int pad_width, |
| int pad_height, int32 output_offset, int32 output_multiplier, |
| int output_shift, int32 output_activation_min, |
| int32 output_activation_max, uint8* output_data, |
| const Dims<4>& output_dims, uint8* im2col_data, |
| const Dims<4>& im2col_dims, |
| gemmlowp::GemmContext* gemm_context) { |
| Conv(input_data, input_dims, input_offset, filter_data, filter_dims, |
| filter_offset, bias_data, bias_dims, stride_width, stride_height, 1, 1, |
| pad_width, pad_height, output_offset, output_multiplier, output_shift, |
| output_activation_min, output_activation_max, output_data, output_dims, |
| im2col_data, im2col_dims, gemm_context); |
| } |
| |
| // TODO(b/80418076): Move to legacy ops file, update invocations. |
| // legacy, for compatibility with old checked-in code |
| template <FusedActivationFunctionType Ac> |
| inline void Conv(const uint8* input_data, const Dims<4>& input_dims, |
| int32 input_offset, const uint8* filter_data, |
| const Dims<4>& filter_dims, int32 filter_offset, |
| const int32* bias_data, const Dims<4>& bias_dims, |
| int stride_width, int stride_height, int pad_width, |
| int pad_height, int32 output_offset, int32 output_multiplier, |
| int output_shift, int32 output_activation_min, |
| int32 output_activation_max, uint8* output_data, |
| const Dims<4>& output_dims, uint8* im2col_data, |
| const Dims<4>& im2col_dims, |
| gemmlowp::GemmContext* gemm_context) { |
| static_assert(Ac == FusedActivationFunctionType::kNone || |
| Ac == FusedActivationFunctionType::kRelu || |
| Ac == FusedActivationFunctionType::kRelu6 || |
| Ac == FusedActivationFunctionType::kRelu1, |
| ""); |
| if (Ac == FusedActivationFunctionType::kNone) { |
| TFLITE_DCHECK_EQ(output_activation_min, 0); |
| TFLITE_DCHECK_EQ(output_activation_max, 255); |
| } |
| Conv(input_data, input_dims, input_offset, filter_data, filter_dims, |
| filter_offset, bias_data, bias_dims, stride_width, stride_height, |
| pad_width, pad_height, output_offset, output_multiplier, output_shift, |
| output_activation_min, output_activation_max, output_data, output_dims, |
| im2col_data, im2col_dims, gemm_context); |
| } |
| |
| // TODO(b/80418076): Move to legacy ops file, update invocations. |
| // legacy, for compatibility with old checked-in code |
| template <FusedActivationFunctionType Ac> |
| void Conv(const uint8* input_data, const Dims<4>& input_dims, |
| int32 input_offset, const uint8* filter_data, |
| const Dims<4>& filter_dims, int32 filter_offset, |
| const int32* bias_data, const Dims<4>& bias_dims, int stride, |
| int pad_width, int pad_height, int32 output_offset, |
| int32 output_multiplier, int output_shift, |
| int32 output_activation_min, int32 output_activation_max, |
| uint8* output_data, const Dims<4>& output_dims, uint8* im2col_data, |
| const Dims<4>& im2col_dims, gemmlowp::GemmContext* gemm_context) { |
| static_assert(Ac == FusedActivationFunctionType::kNone || |
| Ac == FusedActivationFunctionType::kRelu || |
| Ac == FusedActivationFunctionType::kRelu6 || |
| Ac == FusedActivationFunctionType::kRelu1, |
| ""); |
| Conv(input_data, input_dims, input_offset, filter_data, filter_dims, |
| filter_offset, bias_data, bias_dims, stride, stride, pad_width, |
| pad_height, output_offset, output_multiplier, output_shift, |
| output_activation_min, output_activation_max, output_data, output_dims, |
| im2col_data, im2col_dims, gemm_context); |
| } |
| |
| // TODO(b/80418076): Move to legacy ops file, update invocations. |
| // legacy, for compatibility with old checked-in code |
| template <FusedActivationFunctionType Ac, typename T> |
| void Im2col(const T* input_data, const Dims<4>& input_dims, int stride, |
| int pad_width, int pad_height, int kheight, int kwidth, |
| uint8 zero_byte, T* output_data, const Dims<4>& output_dims) { |
| Im2col(input_data, input_dims, stride, stride, pad_width, pad_height, kheight, |
| kwidth, zero_byte, output_data, output_dims); |
| } |
| |
| // legacy, for compatibility with old checked-in code |
| template <FusedActivationFunctionType Ac> |
| void ConvAsGemm(const float* input_data, const Dims<4>& input_dims, |
| const float* filter_data, const Dims<4>& filter_dims, |
| const float* bias_data, const Dims<4>& bias_dims, |
| float* output_data, const Dims<4>& output_dims) { |
| gemmlowp::ScopedProfilingLabel label("ConvAsGemm"); |
| |
| const auto input_matrix_map = |
| MapAsMatrixWithFirstDimAsRows(input_data, input_dims); |
| const auto filter_matrix_map = |
| MapAsMatrixWithLastDimAsCols(filter_data, filter_dims); |
| auto output_matrix_map = |
| MapAsMatrixWithFirstDimAsRows(output_data, output_dims); |
| |
| Gemm(filter_matrix_map.transpose(), input_matrix_map, &output_matrix_map); |
| |
| AddBiasAndEvalActivationFunction<Ac>(bias_data, bias_dims, output_data, |
| output_dims); |
| } |
| |
| // TODO(b/80418076): Move to legacy ops file, update invocations. |
| // legacy, for compatibility with old checked-in code |
| template <FusedActivationFunctionType Ac> |
| void ConvAsGemm(const uint8* input_data, const Dims<4>& input_dims, |
| int32 input_offset, const uint8* filter_data, |
| const Dims<4>& filter_dims, int32 filter_offset, |
| const int32* bias_data, const Dims<4>& bias_dims, |
| int32 output_offset, int32 output_multiplier, int output_shift, |
| int32 output_activation_min, int32 output_activation_max, |
| uint8* output_data, const Dims<4>& output_dims, |
| gemmlowp::GemmContext* gemm_context) { |
| gemmlowp::ScopedProfilingLabel label("ConvAsGemm/8bit"); |
| static_assert(Ac == FusedActivationFunctionType::kNone || |
| Ac == FusedActivationFunctionType::kRelu || |
| Ac == FusedActivationFunctionType::kRelu6 || |
| Ac == FusedActivationFunctionType::kRelu1, |
| ""); |
| const int input_rows = input_dims.sizes[0]; |
| const int input_cols = FlatSizeSkipDim(input_dims, 0); |
| const int filter_rows = filter_dims.sizes[3]; |
| const int filter_cols = FlatSizeSkipDim(filter_dims, 3); |
| const int output_rows = output_dims.sizes[0]; |
| const int output_cols = FlatSizeSkipDim(output_dims, 0); |
| TFLITE_DCHECK_EQ(output_rows, filter_rows); |
| TFLITE_DCHECK_EQ(output_cols, input_cols); |
| TFLITE_DCHECK_EQ(filter_cols, input_rows); |
| TFLITE_DCHECK_EQ(bias_dims.sizes[0], output_rows); |
| TFLITE_DCHECK_EQ(bias_dims.sizes[1], 1); |
| TFLITE_DCHECK_EQ(bias_dims.sizes[2], 1); |
| TFLITE_DCHECK_EQ(bias_dims.sizes[3], 1); |
| gemmlowp::MatrixMap<const uint8, gemmlowp::MapOrder::RowMajor> filter_matrix( |
| filter_data, output_rows, filter_cols, filter_cols); |
| gemmlowp::MatrixMap<const uint8, gemmlowp::MapOrder::ColMajor> input_matrix( |
| input_data, filter_cols, output_cols, filter_cols); |
| gemmlowp::MatrixMap<uint8, gemmlowp::MapOrder::ColMajor> output_matrix( |
| output_data, output_rows, output_cols, output_rows); |
| const auto& output_pipeline = GemmlowpOutputPipeline::MakeExp( |
| bias_data, output_rows, output_offset, output_multiplier, -output_shift, |
| output_activation_min, output_activation_max); |
| gemmlowp::GemmWithOutputPipeline<uint8, uint8, |
| gemmlowp::L8R8WithLhsNonzeroBitDepthParams>( |
| gemm_context, filter_matrix, input_matrix, &output_matrix, filter_offset, |
| input_offset, output_pipeline); |
| } |
| |
| template <typename T> |
| inline void DepthToSpace(const tflite::DepthToSpaceParams& op_params, |
| const RuntimeShape& unextended_input_shape, |
| const T* input_data, |
| const RuntimeShape& unextended_output_shape, |
| T* output_data) { |
| gemmlowp::ScopedProfilingLabel label("DepthToSpace"); |
| |
| TFLITE_DCHECK_LE(unextended_input_shape.DimensionsCount(), 4); |
| TFLITE_DCHECK_LE(unextended_output_shape.DimensionsCount(), 4); |
| const RuntimeShape input_shape = |
| RuntimeShape::ExtendedShape(4, unextended_input_shape); |
| const RuntimeShape output_shape = |
| RuntimeShape::ExtendedShape(4, unextended_output_shape); |
| |
| const int input_depth = input_shape.Dims(3); |
| const int input_width = input_shape.Dims(2); |
| const int input_height = input_shape.Dims(1); |
| |
| const int output_depth = output_shape.Dims(3); |
| const int batch_size = output_shape.Dims(0); |
| |
| // Number of continuous values that we can copy in one interation. |
| const int stride = op_params.block_size * output_depth; |
| |
| for (int batch = 0; batch < batch_size; ++batch) { |
| for (int in_h = 0; in_h < input_height; ++in_h) { |
| const T* input_ptr = input_data + Offset(input_shape, batch, in_h, 0, 0); |
| for (int offset_h = 0; offset_h < op_params.block_size; ++offset_h) { |
| const T* src = input_ptr; |
| for (int in_w = 0; in_w < input_width; ++in_w) { |
| memcpy(output_data, src, stride * sizeof(T)); |
| output_data += stride; |
| src += input_depth; |
| } |
| input_ptr += stride; |
| } |
| } |
| } |
| } |
| |
| template <typename T> |
| inline void SpaceToDepth(const tflite::SpaceToDepthParams& op_params, |
| const RuntimeShape& unextended_input_shape, |
| const T* input_data, |
| const RuntimeShape& unextended_output_shape, |
| T* output_data) { |
| gemmlowp::ScopedProfilingLabel label("SpaceToDepth"); |
| |
| TFLITE_DCHECK_LE(unextended_input_shape.DimensionsCount(), 4); |
| TFLITE_DCHECK_LE(unextended_output_shape.DimensionsCount(), 4); |
| const RuntimeShape input_shape = |
| RuntimeShape::ExtendedShape(4, unextended_input_shape); |
| const RuntimeShape output_shape = |
| RuntimeShape::ExtendedShape(4, unextended_output_shape); |
| |
| const int output_depth = output_shape.Dims(3); |
| const int output_width = output_shape.Dims(2); |
| const int output_height = output_shape.Dims(1); |
| |
| const int input_depth = input_shape.Dims(3); |
| const int batch_size = input_shape.Dims(0); |
| |
| // Number of continuous values that we can copy in one interation. |
| const int stride = op_params.block_size * input_depth; |
| |
| for (int batch = 0; batch < batch_size; ++batch) { |
| for (int out_h = 0; out_h < output_height; ++out_h) { |
| T* output_ptr = output_data + Offset(output_shape, batch, out_h, 0, 0); |
| for (int offset_h = 0; offset_h < op_params.block_size; ++offset_h) { |
| T* dst = output_ptr; |
| for (int out_w = 0; out_w < output_width; ++out_w) { |
| memcpy(dst, input_data, stride * sizeof(T)); |
| input_data += stride; |
| dst += output_depth; |
| } |
| output_ptr += stride; |
| } |
| } |
| } |
| } |
| |
| inline void Relu(const RuntimeShape& input_shape, const float* input_data, |
| const RuntimeShape& output_shape, float* output_data) { |
| gemmlowp::ScopedProfilingLabel label("Relu (not fused)"); |
| |
| const auto input = MapAsVector(input_data, input_shape); |
| auto output = MapAsVector(output_data, output_shape); |
| output = input.cwiseMax(0.0f); |
| } |
| |
| inline void L2Normalization(const tflite::L2NormalizationParams& op_params, |
| const RuntimeShape& input_shape, |
| const float* input_data, |
| const RuntimeShape& output_shape, |
| float* output_data) { |
| gemmlowp::ScopedProfilingLabel label("L2Normalization"); |
| const int trailing_dim = input_shape.DimensionsCount() - 1; |
| const int outer_size = |
| MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape); |
| const int depth = |
| MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim); |
| for (int i = 0; i < outer_size; ++i) { |
| float squared_l2_norm = 0; |
| for (int c = 0; c < depth; ++c) { |
| const float val = input_data[c]; |
| squared_l2_norm += val * val; |
| } |
| const float l2_norm = std::sqrt(squared_l2_norm); |
| for (int c = 0; c < depth; ++c) { |
| *output_data = *input_data / l2_norm; |
| ++output_data; |
| ++input_data; |
| } |
| } |
| } |
| |
| inline void GetInvSqrtQuantizedMultiplierExp(int32 input, |
| int32* output_inv_sqrt, |
| int* output_shift) { |
| *output_shift = 11; |
| while (input >= (1 << 29)) { |
| input /= 4; |
| ++*output_shift; |
| } |
| TFLITE_DCHECK_GT(input, 0); |
| const unsigned max_left_shift_bits = |
| CountLeadingZeros(static_cast<uint32>(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; |
| TFLITE_DCHECK_GE(input, (1 << 27)); |
| TFLITE_DCHECK_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, 3>; |
| using F0 = FixedPoint<int32, 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 *= kReverseShift; |
| } |
| |
| inline void L2Normalization(const tflite::L2NormalizationParams& op_params, |
| const RuntimeShape& input_shape, |
| const uint8* input_data, |
| const RuntimeShape& output_shape, |
| uint8* output_data) { |
| gemmlowp::ScopedProfilingLabel label("L2Normalization/8bit"); |
| const int trailing_dim = input_shape.DimensionsCount() - 1; |
| const int depth = |
| MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim); |
| const int outer_size = |
| MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape); |
| const int32 input_zero_point = op_params.input_zero_point; |
| for (int i = 0; i < outer_size; ++i) { |
| int32 square_l2_norm = 0; |
| for (int c = 0; c < depth; c++) { |
| // Note that input_data advances by depth in the second pass below. |
| int32 diff = input_data[c] - input_zero_point; |
| square_l2_norm += diff * diff; |
| } |
| int32 inv_l2norm_multiplier; |
| int inv_l2norm_shift; |
| GetInvSqrtQuantizedMultiplierExp(square_l2_norm, &inv_l2norm_multiplier, |
| &inv_l2norm_shift); |
| |
| for (int c = 0; c < depth; c++) { |
| int32 diff = *input_data - input_zero_point; |
| int32 rescaled_diff = MultiplyByQuantizedMultiplierSmallerThanOneExp( |
| 128 * diff, inv_l2norm_multiplier, inv_l2norm_shift); |
| int32 unclamped_output_val = 128 + rescaled_diff; |
| int32 output_val = std::min(255, std::max(0, unclamped_output_val)); |
| *output_data = static_cast<uint8>(output_val); |
| ++input_data; |
| ++output_data; |
| } |
| } |
| } |
| |
| inline void Add(const ArithmeticParams& params, |
| const RuntimeShape& input1_shape, const float* input1_data, |
| const RuntimeShape& input2_shape, const float* input2_data, |
| const RuntimeShape& output_shape, float* output_data) { |
| gemmlowp::ScopedProfilingLabel label("Add"); |
| |
| int i = 0; |
| const int size = MatchingFlatSize(input1_shape, input2_shape, output_shape); |
| #ifdef USE_NEON |
| const auto activation_min = vdupq_n_f32(params.float_activation_min); |
| const auto activation_max = vdupq_n_f32(params.float_activation_max); |
| for (; i <= size - 16; i += 16) { |
| auto a10 = vld1q_f32(input1_data + i); |
| auto a11 = vld1q_f32(input1_data + i + 4); |
| auto a12 = vld1q_f32(input1_data + i + 8); |
| auto a13 = vld1q_f32(input1_data + i + 12); |
| auto a20 = vld1q_f32(input2_data + i); |
| auto a21 = vld1q_f32(input2_data + i + 4); |
| auto a22 = vld1q_f32(input2_data + i + 8); |
| auto a23 = vld1q_f32(input2_data + i + 12); |
| auto x0 = vaddq_f32(a10, a20); |
| auto x1 = vaddq_f32(a11, a21); |
| auto x2 = vaddq_f32(a12, a22); |
| auto x3 = vaddq_f32(a13, a23); |
| x0 = vmaxq_f32(activation_min, x0); |
| x1 = vmaxq_f32(activation_min, x1); |
| x2 = vmaxq_f32(activation_min, x2); |
| x3 = vmaxq_f32(activation_min, x3); |
| x0 = vminq_f32(activation_max, x0); |
| x1 = vminq_f32(activation_max, x1); |
| x2 = vminq_f32(activation_max, x2); |
| x3 = vminq_f32(activation_max, x3); |
| vst1q_f32(output_data + i, x0); |
| vst1q_f32(output_data + i + 4, x1); |
| vst1q_f32(output_data + i + 8, x2); |
| vst1q_f32(output_data + i + 12, x3); |
| } |
| for (; i <= size - 4; i += 4) { |
| auto a1 = vld1q_f32(input1_data + i); |
| auto a2 = vld1q_f32(input2_data + i); |
| auto x = vaddq_f32(a1, a2); |
| x = vmaxq_f32(activation_min, x); |
| x = vminq_f32(activation_max, x); |
| vst1q_f32(output_data + i, x); |
| } |
| #endif // NEON |
| |
| for (; i < size; i++) { |
| auto x = input1_data[i] + input2_data[i]; |
| output_data[i] = ActivationFunctionWithMinMax( |
| x, params.float_activation_min, params.float_activation_max); |
| } |
| } |
| |
| // Element-wise add that can often be used for inner loop of broadcast add as |
| // well as the non-broadcast add. |
| inline void AddElementwise(int size, const ArithmeticParams& params, |
| const uint8* input1_data, const uint8* input2_data, |
| uint8* output_data) { |
| int i = 0; |
| TFLITE_DCHECK_GT(params.input1_offset, -256); |
| TFLITE_DCHECK_GT(params.input2_offset, -256); |
| TFLITE_DCHECK_LT(params.input1_offset, 256); |
| TFLITE_DCHECK_LT(params.input2_offset, 256); |
| #ifdef USE_NEON |
| const auto output_activation_min_vector = |
| vdup_n_u8(params.quantized_activation_min); |
| const auto output_activation_max_vector = |
| vdup_n_u8(params.quantized_activation_max); |
| for (; i <= size - 8; i += 8) { |
| const auto input1_val_original = vld1_u8(input1_data + i); |
| const auto input2_val_original = vld1_u8(input2_data + i); |
| const auto input1_val_s16 = |
| vreinterpretq_s16_u16(vmovl_u8(input1_val_original)); |
| const auto input2_val_s16 = |
| vreinterpretq_s16_u16(vmovl_u8(input2_val_original)); |
| const auto input1_val = |
| vaddq_s16(input1_val_s16, vdupq_n_s16(params.input1_offset)); |
| const auto input2_val = |
| vaddq_s16(input2_val_s16, vdupq_n_s16(params.input2_offset)); |
| const auto input1_val_high = vget_high_s16(input1_val); |
| const auto input1_val_low = vget_low_s16(input1_val); |
| const auto input2_val_high = vget_high_s16(input2_val); |
| const auto input2_val_low = vget_low_s16(input2_val); |
| auto x11 = vmovl_s16(input1_val_low); |
| auto x12 = vmovl_s16(input1_val_high); |
| auto x21 = vmovl_s16(input2_val_low); |
| auto x22 = vmovl_s16(input2_val_high); |
| const auto left_shift_dup = vdupq_n_s32(params.left_shift); |
| x11 = vshlq_s32(x11, left_shift_dup); |
| x12 = vshlq_s32(x12, left_shift_dup); |
| x21 = vshlq_s32(x21, left_shift_dup); |
| x22 = vshlq_s32(x22, left_shift_dup); |
| x11 = vqrdmulhq_n_s32(x11, params.input1_multiplier); |
| x12 = vqrdmulhq_n_s32(x12, params.input1_multiplier); |
| x21 = vqrdmulhq_n_s32(x21, params.input2_multiplier); |
| x22 = vqrdmulhq_n_s32(x22, params.input2_multiplier); |
| const auto input1_shift_dup = vdupq_n_s32(params.input1_shift); |
| const auto input2_shift_dup = vdupq_n_s32(params.input2_shift); |
| x11 = vshlq_s32(x11, input1_shift_dup); |
| x12 = vshlq_s32(x12, input1_shift_dup); |
| x21 = vshlq_s32(x21, input2_shift_dup); |
| x22 = vshlq_s32(x22, input2_shift_dup); |
| auto s1 = vaddq_s32(x11, x21); |
| auto s2 = vaddq_s32(x12, x22); |
| s1 = vqrdmulhq_n_s32(s1, params.output_multiplier); |
| s2 = vqrdmulhq_n_s32(s2, params.output_multiplier); |
| using gemmlowp::RoundingDivideByPOT; |
| s1 = RoundingDivideByPOT(s1, -params.output_shift); |
| s2 = RoundingDivideByPOT(s2, -params.output_shift); |
| const auto s1_narrowed = vmovn_s32(s1); |
| const auto s2_narrowed = vmovn_s32(s2); |
| const auto s = vaddq_s16(vcombine_s16(s1_narrowed, s2_narrowed), |
| vdupq_n_s16(params.output_offset)); |
| const auto clamped = |
| vmax_u8(output_activation_min_vector, |
| vmin_u8(output_activation_max_vector, vqmovun_s16(s))); |
| vst1_u8(output_data + i, clamped); |
| } |
| #endif // NEON |
| |
| for (; i < size; ++i) { |
| const int32 input1_val = params.input1_offset + input1_data[i]; |
| const int32 input2_val = params.input2_offset + input2_data[i]; |
| const int32 shifted_input1_val = input1_val * (1 << params.left_shift); |
| const int32 shifted_input2_val = input2_val * (1 << params.left_shift); |
| const int32 scaled_input1_val = |
| MultiplyByQuantizedMultiplierSmallerThanOneExp( |
| shifted_input1_val, params.input1_multiplier, params.input1_shift); |
| const int32 scaled_input2_val = |
| MultiplyByQuantizedMultiplierSmallerThanOneExp( |
| shifted_input2_val, params.input2_multiplier, params.input2_shift); |
| const int32 raw_sum = scaled_input1_val + scaled_input2_val; |
| const int32 raw_output = |
| MultiplyByQuantizedMultiplierSmallerThanOneExp( |
| raw_sum, params.output_multiplier, params.output_shift) + |
| params.output_offset; |
| const int32 clamped_output = |
| std::min(params.quantized_activation_max, |
| std::max(params.quantized_activation_min, raw_output)); |
| output_data[i] = static_cast<uint8>(clamped_output); |
| } |
| } |
| |
| inline void Add(const ArithmeticParams& params, |
| const RuntimeShape& input1_shape, const uint8* input1_data, |
| const RuntimeShape& input2_shape, const uint8* input2_data, |
| const RuntimeShape& output_shape, uint8* output_data) { |
| TFLITE_DCHECK_LE(params.quantized_activation_min, |
| params.quantized_activation_max); |
| gemmlowp::ScopedProfilingLabel label("Add/8bit"); |
| const int flat_size = |
| MatchingFlatSize(input1_shape, input2_shape, output_shape); |
| |
| TFLITE_DCHECK_GT(params.input1_offset, -256); |
| TFLITE_DCHECK_GT(params.input2_offset, -256); |
| TFLITE_DCHECK_LT(params.input1_offset, 256); |
| TFLITE_DCHECK_LT(params.input2_offset, 256); |
| AddElementwise(flat_size, params, input1_data, input2_data, output_data); |
| } |
| |
| inline void Add(const ArithmeticParams& params, |
| const RuntimeShape& input1_shape, const int16* input1_data, |
| const RuntimeShape& input2_shape, const int16* input2_data, |
| const RuntimeShape& output_shape, int16* output_data) { |
| gemmlowp::ScopedProfilingLabel label("Add/Int16"); |
| TFLITE_DCHECK_LE(params.quantized_activation_min, |
| params.quantized_activation_max); |
| |
| const int input1_shift = params.input1_shift; |
| const int flat_size = |
| MatchingFlatSize(output_shape, input1_shape, input2_shape); |
| const int16 output_activation_min = params.quantized_activation_min; |
| const int16 output_activation_max = params.quantized_activation_max; |
| |
| TFLITE_DCHECK(input1_shift == 0 || params.input2_shift == 0); |
| TFLITE_DCHECK_LE(input1_shift, 0); |
| TFLITE_DCHECK_LE(params.input2_shift, 0); |
| const int16* not_shift_input = input1_shift == 0 ? input1_data : input2_data; |
| const int16* shift_input = input1_shift == 0 ? input2_data : input1_data; |
| const int input_right_shift = |
| input1_shift == 0 ? -params.input2_shift : -input1_shift; |
| |
| for (int i = 0; i < flat_size; i++) { |
| // F0 uses 0 integer bits, range [-1, 1]. |
| using F0 = gemmlowp::FixedPoint<std::int16_t, 0>; |
| |
| F0 input_ready_scaled = F0::FromRaw(not_shift_input[i]); |
| F0 scaled_input = F0::FromRaw( |
| gemmlowp::RoundingDivideByPOT(shift_input[i], input_right_shift)); |
| F0 result = gemmlowp::SaturatingAdd(scaled_input, input_ready_scaled); |
| const int16 raw_output = result.raw(); |
| const int16 clamped_output = std::min( |
| output_activation_max, std::max(output_activation_min, raw_output)); |
| output_data[i] = clamped_output; |
| } |
| } |
| |
| inline void Add(const ArithmeticParams& params, |
| const RuntimeShape& input1_shape, const int32* input1_data, |
| const RuntimeShape& input2_shape, const int32* input2_data, |
| const RuntimeShape& output_shape, int32* output_data) { |
| gemmlowp::ScopedProfilingLabel label("Add/int32"); |
| |
| auto input1_map = MapAsVector(input1_data, input1_shape); |
| auto input2_map = MapAsVector(input2_data, input2_shape); |
| auto output_map = MapAsVector(output_data, output_shape); |
| if (input1_shape == input2_shape) { |
| output_map.array() = input1_map.array() + input2_map.array(); |
| } else if (input2_shape.FlatSize() == 1) { |
| auto scalar = input2_data[0]; |
| output_map.array() = input1_map.array() + scalar; |
| } else if (input1_shape.FlatSize() == 1) { |
| auto scalar = input1_data[0]; |
| output_map.array() = scalar + input2_map.array(); |
| } else { |
| // Should not come here. |
| TFLITE_DCHECK(false); |
| } |
| output_map = output_map.cwiseMax(params.quantized_activation_min); |
| output_map = output_map.cwiseMin(params.quantized_activation_max); |
| } |
| |
| inline void BroadcastAddFivefold(const ArithmeticParams& unswitched_params, |
| const RuntimeShape& unswitched_input1_shape, |
| const uint8* unswitched_input1_data, |
| const RuntimeShape& unswitched_input2_shape, |
| const uint8* unswitched_input2_data, |
| const RuntimeShape& output_shape, |
| uint8* output_data) { |
| gemmlowp::ScopedProfilingLabel label("BroadcastAddFivefold/8bit"); |
| |
| ArithmeticParams switched_params = unswitched_params; |
| switched_params.input1_offset = unswitched_params.input2_offset; |
| switched_params.input1_multiplier = unswitched_params.input2_multiplier; |
| switched_params.input1_shift = unswitched_params.input2_shift; |
| switched_params.input2_offset = unswitched_params.input1_offset; |
| switched_params.input2_multiplier = unswitched_params.input1_multiplier; |
| switched_params.input2_shift = unswitched_params.input1_shift; |
| |
| const bool use_unswitched = |
| unswitched_params.broadcast_category == |
| tflite::BroadcastableOpCategory::kFirstInputBroadcastsFast; |
| |
| const ArithmeticParams& params = |
| use_unswitched ? unswitched_params : switched_params; |
| const uint8* input1_data = |
| use_unswitched ? unswitched_input1_data : unswitched_input2_data; |
| const uint8* input2_data = |
| use_unswitched ? unswitched_input2_data : unswitched_input1_data; |
| |
| // Fivefold nested loops. The second input resets its position for each |
| // iteration of the second loop. The first input resets its position at the |
| // beginning of the fourth loop. The innermost loop is an elementwise add of |
| // sections of the arrays. |
| uint8* output_data_ptr = output_data; |
| const uint8* input1_data_ptr = input1_data; |
| const uint8* input2_data_reset = input2_data; |
| int y0 = params.broadcast_shape[0]; |
| int y1 = params.broadcast_shape[1]; |
| int y2 = params.broadcast_shape[2]; |
| int y3 = params.broadcast_shape[3]; |
| int y4 = params.broadcast_shape[4]; |
| for (int i0 = 0; i0 < y0; ++i0) { |
| const uint8* input2_data_ptr; |
| for (int i1 = 0; i1 < y1; ++i1) { |
| input2_data_ptr = input2_data_reset; |
| for (int i2 = 0; i2 < y2; ++i2) { |
| for (int i3 = 0; i3 < y3; ++i3) { |
| AddElementwise(y4, params, input1_data_ptr, input2_data_ptr, |
| output_data_ptr); |
| input2_data_ptr += y4; |
| output_data_ptr += y4; |
| } |
| input1_data_ptr += y4; |
| } |
| } |
| input2_data_reset = input2_data_ptr; |
| } |
| } |
| |
| inline void Mul(const ArithmeticParams& params, |
| const RuntimeShape& input1_shape, const float* input1_data, |
| const RuntimeShape& input2_shape, const float* input2_data, |
| const RuntimeShape& output_shape, float* output_data) { |
| gemmlowp::ScopedProfilingLabel label("Mul"); |
| const float output_activation_min = params.float_activation_min; |
| const float output_activation_max = params.float_activation_max; |
| |
| int i = 0; |
| const int size = MatchingFlatSize(input1_shape, input2_shape, output_shape); |
| #ifdef USE_NEON |
| const auto activation_min = vdupq_n_f32(output_activation_min); |
| const auto activation_max = vdupq_n_f32(output_activation_max); |
| for (; i <= size - 16; i += 16) { |
| auto a10 = vld1q_f32(input1_data + i); |
| auto a11 = vld1q_f32(input1_data + i + 4); |
| auto a12 = vld1q_f32(input1_data + i + 8); |
| auto a13 = vld1q_f32(input1_data + i + 12); |
| auto a20 = vld1q_f32(input2_data + i); |
| auto a21 = vld1q_f32(input2_data + i + 4); |
| auto a22 = vld1q_f32(input2_data + i + 8); |
| auto a23 = vld1q_f32(input2_data + i + 12); |
| auto x0 = vmulq_f32(a10, a20); |
| auto x1 = vmulq_f32(a11, a21); |
| auto x2 = vmulq_f32(a12, a22); |
| auto x3 = vmulq_f32(a13, a23); |
| |
| x0 = vmaxq_f32(activation_min, x0); |
| x1 = vmaxq_f32(activation_min, x1); |
| x2 = vmaxq_f32(activation_min, x2); |
| x3 = vmaxq_f32(activation_min, x3); |
| x0 = vminq_f32(activation_max, x0); |
| x1 = vminq_f32(activation_max, x1); |
| x2 = vminq_f32(activation_max, x2); |
| x3 = vminq_f32(activation_max, x3); |
| |
| vst1q_f32(output_data + i, x0); |
| vst1q_f32(output_data + i + 4, x1); |
| vst1q_f32(output_data + i + 8, x2); |
| vst1q_f32(output_data + i + 12, x3); |
| } |
| for (; i <= size - 4; i += 4) { |
| auto a1 = vld1q_f32(input1_data + i); |
| auto a2 = vld1q_f32(input2_data + i); |
| auto x = vmulq_f32(a1, a2); |
| |
| x = vmaxq_f32(activation_min, x); |
| x = vminq_f32(activation_max, x); |
| |
| vst1q_f32(output_data + i, x); |
| } |
| #endif // NEON |
| |
| for (; i < size; i++) { |
| auto x = input1_data[i] * input2_data[i]; |
| output_data[i] = ActivationFunctionWithMinMax(x, output_activation_min, |
| output_activation_max); |
| } |
| } |
| |
| inline void Mul(const ArithmeticParams& params, |
| const RuntimeShape& input1_shape, const int32* input1_data, |
| const RuntimeShape& input2_shape, const int32* input2_data, |
| const RuntimeShape& output_shape, int32* output_data) { |
| gemmlowp::ScopedProfilingLabel label("Mul/int32/activation"); |
| |
| const int flat_size = |
| MatchingFlatSize(input1_shape, input2_shape, output_shape); |
| const int32 output_activation_min = params.quantized_activation_min; |
| const int32 output_activation_max = params.quantized_activation_max; |
| for (int i = 0; i < flat_size; ++i) { |
| output_data[i] = ActivationFunctionWithMinMax( |
| input1_data[i] * input2_data[i], output_activation_min, |
| output_activation_max); |
| } |
| } |
| |
| inline void MulNoActivation(const ArithmeticParams& params, |
| const RuntimeShape& input1_shape, |
| const int32* input1_data, |
| const RuntimeShape& input2_shape, |
| const int32* input2_data, |
| const RuntimeShape& output_shape, |
| int32* output_data) { |
| gemmlowp::ScopedProfilingLabel label("Mul/int32"); |
| |
| auto input1_map = MapAsVector(input1_data, input1_shape); |
| auto input2_map = MapAsVector(input2_data, input2_shape); |
| auto output_map = MapAsVector(output_data, output_shape); |
| if (input1_shape == input2_shape) { |
| output_map.array() = input1_map.array() * input2_map.array(); |
| } else if (input2_shape.FlatSize() == 1) { |
| auto scalar = input2_data[0]; |
| output_map.array() = input1_map.array() * scalar; |
| } else if (input1_shape.FlatSize() == 1) { |
| auto scalar = input1_data[0]; |
| output_map.array() = scalar * input2_map.array(); |
| } else { |
| // Should not come here. |
| TFLITE_DCHECK(false); |
| } |
| } |
| |
| inline void Mul(const ArithmeticParams& params, |
| const RuntimeShape& input1_shape, const int16* input1_data, |
| const RuntimeShape& input2_shape, const int16* input2_data, |
| const RuntimeShape& output_shape, int16* output_data) { |
| gemmlowp::ScopedProfilingLabel label("Mul/Int16/NoActivation"); |
| // This is a copy of the reference implementation. We do not currently have a |
| // properly optimized version. |
| |
| const int flat_size = |
| MatchingFlatSize(input1_shape, input2_shape, output_shape); |
| |
| for (int i = 0; i < flat_size; i++) { |
| // F0 uses 0 integer bits, range [-1, 1]. |
| using F0 = gemmlowp::FixedPoint<std::int16_t, 0>; |
| |
| F0 unclamped_result = |
| F0::FromRaw(input1_data[i]) * F0::FromRaw(input2_data[i]); |
| output_data[i] = unclamped_result.raw(); |
| } |
| } |
| |
| inline void Mul(const ArithmeticParams& params, |
| const RuntimeShape& input1_shape, const int16* input1_data, |
| const RuntimeShape& input2_shape, const int16* input2_data, |
| const RuntimeShape& output_shape, uint8* output_data) { |
| gemmlowp::ScopedProfilingLabel label("Mul/Int16Uint8"); |
| // This is a copy of the reference implementation. We do not currently have a |
| // properly optimized version. |
| const int32 output_activation_min = params.quantized_activation_min; |
| const int32 output_activation_max = params.quantized_activation_max; |
| const int32 output_offset = params.output_offset; |
| TFLITE_DCHECK_LE(output_activation_min, output_activation_max); |
| |
| const int flat_size = |
| MatchingFlatSize(input1_shape, input2_shape, output_shape); |
| |
| for (int i = 0; i < flat_size; i++) { |
| // F0 uses 0 integer bits, range [-1, 1]. |
| using F0 = gemmlowp::FixedPoint<std::int16_t, 0>; |
| |
| F0 unclamped_result = |
| F0::FromRaw(input1_data[i]) * F0::FromRaw(input2_data[i]); |
| int16 rescaled_result = |
| gemmlowp::RoundingDivideByPOT(unclamped_result.raw(), 8); |
| int16 clamped_result = |
| std::min<int16>(output_activation_max - output_offset, rescaled_result); |
| clamped_result = |
| std::max<int16>(output_activation_min - output_offset, clamped_result); |
| output_data[i] = output_offset + clamped_result; |
| } |
| } |
| |
| // Element-wise mul that can often be used for inner loop of broadcast Mul as |
| // well as the non-broadcast Mul. |
| inline void MulElementwise(int size, const ArithmeticParams& params, |
| const uint8* input1_data, const uint8* input2_data, |
| uint8* output_data) { |
| int i = 0; |
| TFLITE_DCHECK_GT(params.input1_offset, -256); |
| TFLITE_DCHECK_LT(params.input1_offset, 256); |
| TFLITE_DCHECK_GT(params.input2_offset, -256); |
| TFLITE_DCHECK_LT(params.input2_offset, 256); |
| TFLITE_DCHECK_GT(params.output_offset, -256); |
| TFLITE_DCHECK_LT(params.output_offset, 256); |
| #ifdef USE_NEON |
| const auto input1_offset_vector = vdupq_n_s16(params.input1_offset); |
| const auto input2_offset_vector = vdupq_n_s16(params.input2_offset); |
| const auto output_offset_vector = vdupq_n_s16(params.output_offset); |
| const auto output_activation_min_vector = |
| vdup_n_u8(params.quantized_activation_min); |
| const auto output_activation_max_vector = |
| vdup_n_u8(params.quantized_activation_max); |
| for (; i <= size - 8; i += 8) { |
| // We load / store 8 at a time, multiplying as two sets of 4 int32s. |
| const auto input1_val_original = vld1_u8(input1_data + i); |
| const auto input2_val_original = vld1_u8(input2_data + i); |
| const auto input1_val_s16 = |
| vreinterpretq_s16_u16(vmovl_u8(input1_val_original)); |
| const auto input2_val_s16 = |
| vreinterpretq_s16_u16(vmovl_u8(input2_val_original)); |
| const auto input1_val = vaddq_s16(input1_val_s16, input1_offset_vector); |
| const auto input2_val = vaddq_s16(input2_val_s16, input2_offset_vector); |
| |
| const auto input1_val_low = vget_low_s16(input1_val); |
| const auto input1_val_high = vget_high_s16(input1_val); |
| const auto input2_val_low = vget_low_s16(input2_val); |
| const auto input2_val_high = vget_high_s16(input2_val); |
| |
| auto p1 = vmull_s16(input2_val_low, input1_val_low); |
| auto p2 = vmull_s16(input2_val_high, input1_val_high); |
| |
| p1 = vqrdmulhq_n_s32(p1, params.output_multiplier); |
| p2 = vqrdmulhq_n_s32(p2, params.output_multiplier); |
| using gemmlowp::RoundingDivideByPOT; |
| p1 = RoundingDivideByPOT(p1, -params.output_shift); |
| p2 = RoundingDivideByPOT(p2, -params.output_shift); |
| |
| const auto p1_narrowed = vmovn_s32(p1); |
| const auto p2_narrowed = vmovn_s32(p2); |
| const auto p = |
| vaddq_s16(vcombine_s16(p1_narrowed, p2_narrowed), output_offset_vector); |
| const auto clamped = |
| vmax_u8(output_activation_min_vector, |
| vmin_u8(output_activation_max_vector, vqmovun_s16(p))); |
| vst1_u8(output_data + i, clamped); |
| } |
| #endif // NEON |
| |
| for (; i < size; ++i) { |
| const int32 input1_val = params.input1_offset + input1_data[i]; |
| const int32 input2_val = params.input2_offset + input2_data[i]; |
| const int32 unclamped_result = |
| params.output_offset + |
| MultiplyByQuantizedMultiplierSmallerThanOneExp(input1_val * input2_val, |
| params.output_multiplier, |
| params.output_shift); |
| const int32 clamped_output = |
| std::min(params.quantized_activation_max, |
| std::max(params.quantized_activation_min, unclamped_result)); |
| output_data[i] = static_cast<uint8>(clamped_output); |
| } |
| } |
| |
| // Broadcast mul that can often be used for inner loop of broadcast Mul. |
| inline void MulSimpleBroadcast(int size, const ArithmeticParams& params, |
| const uint8 broadcast_value, |
| const uint8* input2_data, uint8* output_data) { |
| const int16 input1_val = params.input1_offset + broadcast_value; |
| |
| int i = 0; |
| TFLITE_DCHECK_GT(params.input1_offset, -256); |
| TFLITE_DCHECK_LT(params.input1_offset, 256); |
| TFLITE_DCHECK_GT(params.input2_offset, -256); |
| TFLITE_DCHECK_LT(params.input2_offset, 256); |
| TFLITE_DCHECK_GT(params.output_offset, -256); |
| TFLITE_DCHECK_LT(params.output_offset, 256); |
| #ifdef USE_NEON |
| const auto input2_offset_vector = vdupq_n_s16(params.input2_offset); |
| const auto output_offset_vector = vdupq_n_s16(params.output_offset); |
| const auto output_activation_min_vector = |
| vdup_n_u8(params.quantized_activation_min); |
| const auto output_activation_max_vector = |
| vdup_n_u8(params.quantized_activation_max); |
| for (; i <= size - 8; i += 8) { |
| // We load / store 8 at a time, multiplying as two sets of 4 int32s. |
| const auto input2_val_original = vld1_u8(input2_data + i); |
| const auto input2_val_s16 = |
| vreinterpretq_s16_u16(vmovl_u8(input2_val_original)); |
| const auto input2_val = vaddq_s16(input2_val_s16, input2_offset_vector); |
| |
| const auto input2_val_low = vget_low_s16(input2_val); |
| const auto input2_val_high = vget_high_s16(input2_val); |
| |
| auto p1 = vmull_n_s16(input2_val_low, input1_val); |
| auto p2 = vmull_n_s16(input2_val_high, input1_val); |
| |
| p1 = vqrdmulhq_n_s32(p1, params.output_multiplier); |
| p2 = vqrdmulhq_n_s32(p2, params.output_multiplier); |
| using gemmlowp::RoundingDivideByPOT; |
| p1 = RoundingDivideByPOT(p1, -params.output_shift); |
| p2 = RoundingDivideByPOT(p2, -params.output_shift); |
| |
| const auto p1_narrowed = vmovn_s32(p1); |
| const auto p2_narrowed = vmovn_s32(p2); |
| const auto p = |
| vaddq_s16(vcombine_s16(p1_narrowed, p2_narrowed), output_offset_vector); |
| const auto clamped = |
| vmax_u8(output_activation_min_vector, |
| vmin_u8(output_activation_max_vector, vqmovun_s16(p))); |
| vst1_u8(output_data + i, clamped); |
| } |
| #endif // NEON |
| |
| for (; i < size; ++i) { |
| const int32 input2_val = params.input2_offset + input2_data[i]; |
| const int32 unclamped_result = |
| params.output_offset + |
| MultiplyByQuantizedMultiplierSmallerThanOneExp(input1_val * input2_val, |
| params.output_multiplier, |
| params.output_shift); |
| const int32 clamped_output = |
| std::min(params.quantized_activation_max, |
| std::max(params.quantized_activation_min, unclamped_result)); |
| output_data[i] = static_cast<uint8>(clamped_output); |
| } |
| } |
| |
| inline void Mul(const ArithmeticParams& params, |
| const RuntimeShape& input1_shape, const uint8* input1_data, |
| const RuntimeShape& input2_shape, const uint8* input2_data, |
| const RuntimeShape& output_shape, uint8* output_data) { |
| TFLITE_DCHECK_LE(params.quantized_activation_min, |
| params.quantized_activation_max); |
| gemmlowp::ScopedProfilingLabel label("Mul/8bit"); |
| const int flat_size = |
| MatchingFlatSize(input1_shape, input2_shape, output_shape); |
| |
| MulElementwise(flat_size, params, input1_data, input2_data, output_data); |
| } |
| |
| inline void BroadcastMulFivefold(const ArithmeticParams& unswitched_params, |
| const RuntimeShape& unswitched_input1_shape, |
| const uint8* unswitched_input1_data, |
| const RuntimeShape& unswitched_input2_shape, |
| const uint8* unswitched_input2_data, |
| const RuntimeShape& output_shape, |
| uint8* output_data) { |
| gemmlowp::ScopedProfilingLabel label("BroadcastMulFivefold/8bit"); |
| |
| ArithmeticParams switched_params = unswitched_params; |
| switched_params.input1_offset = unswitched_params.input2_offset; |
| switched_params.input2_offset = unswitched_params.input1_offset; |
| |
| const bool use_unswitched = |
| unswitched_params.broadcast_category == |
| tflite::BroadcastableOpCategory::kFirstInputBroadcastsFast; |
| |
| const ArithmeticParams& params = |
| use_unswitched ? unswitched_params : switched_params; |
| const uint8* input1_data = |
| use_unswitched ? unswitched_input1_data : unswitched_input2_data; |
| const uint8* input2_data = |
| use_unswitched ? unswitched_input2_data : unswitched_input1_data; |
| |
| // Fivefold nested loops. The second input resets its position for each |
| // iteration of the second loop. The first input resets its position at the |
| // beginning of the fourth loop. The innermost loop is an elementwise Mul of |
| // sections of the arrays. |
| uint8* output_data_ptr = output_data; |
| const uint8* input1_data_ptr = input1_data; |
| const uint8* input2_data_reset = input2_data; |
| int y0 = params.broadcast_shape[0]; |
| int y1 = params.broadcast_shape[1]; |
| int y2 = params.broadcast_shape[2]; |
| int y3 = params.broadcast_shape[3]; |
| int y4 = params.broadcast_shape[4]; |
| if (y4 > 1) { |
| for (int i0 = 0; i0 < y0; ++i0) { |
| const uint8* input2_data_ptr; |
| for (int i1 = 0; i1 < y1; ++i1) { |
| input2_data_ptr = input2_data_reset; |
| for (int i2 = 0; i2 < y2; ++i2) { |
| for (int i3 = 0; i3 < y3; ++i3) { |
| MulElementwise(y4, params, input1_data_ptr, input2_data_ptr, |
| output_data_ptr); |
| input2_data_ptr += y4; |
| output_data_ptr += y4; |
| } |
| input1_data_ptr += y4; |
| } |
| } |
| input2_data_reset = input2_data_ptr; |
| } |
| } else { |
| for (int i0 = 0; i0 < y0; ++i0) { |
| const uint8* input2_data_ptr; |
| for (int i1 = 0; i1 < y1; ++i1) { |
| input2_data_ptr = input2_data_reset; |
| for (int i2 = 0; i2 < y2; ++i2) { |
| MulSimpleBroadcast(y3, params, *input1_data_ptr, input2_data_ptr, |
| output_data_ptr); |
| input2_data_ptr += y3; |
| output_data_ptr += y3; |
| ++input1_data_ptr; |
| } |
| } |
| input2_data_reset = input2_data_ptr; |
| } |
| } |
| } |
| |
| // TODO(jiawen): We can implement BroadcastDiv on buffers of arbitrary |
| // dimensionality if the runtime code does a single loop over one dimension |
| // that handles broadcasting as the base case. The code generator would then |
| // generate max(D1, D2) nested for loops. |
| // TODO(benoitjacob): BroadcastDiv is intentionally duplicated from |
| // reference_ops.h. Once an optimized version is implemented and NdArrayDesc<T> |
| // is no longer referenced in this file, move NdArrayDesc<T> from types.h to |
| // reference_ops.h. |
| template <typename T> |
| void BroadcastDiv4DSlow(const ArithmeticParams& params, |
| const RuntimeShape& unextended_input1_shape, |
| const T* input1_data, |
| const RuntimeShape& unextended_input2_shape, |
| const T* input2_data, |
| const RuntimeShape& unextended_output_shape, |
| T* output_data) { |
| gemmlowp::ScopedProfilingLabel label("BroadcastDiv4DSlow"); |
| T output_activation_min; |
| T output_activation_max; |
| GetActivationParams(params, &output_activation_min, &output_activation_max); |
| |
| TFLITE_DCHECK_LE(unextended_input1_shape.DimensionsCount(), 4); |
| TFLITE_DCHECK_LE(unextended_input2_shape.DimensionsCount(), 4); |
| TFLITE_DCHECK_LE(unextended_output_shape.DimensionsCount(), 4); |
| const RuntimeShape output_shape = |
| RuntimeShape::ExtendedShape(4, unextended_output_shape); |
| |
| NdArrayDesc<4> desc1; |
| NdArrayDesc<4> desc2; |
| NdArrayDescsForElementwiseBroadcast(unextended_input1_shape, |
| unextended_input2_shape, &desc1, &desc2); |
| |
| // In Tensorflow, the dimensions are canonically named (batch_number, row, |
| // col, channel), with extents (batches, height, width, depth), with the |
| // trailing dimension changing most rapidly (channels has the smallest stride, |
| // typically 1 element). |
| // |
| // In generated C code, we store arrays with the dimensions reversed. The |
| // first dimension has smallest stride. |
| // |
| // We name our variables by their Tensorflow convention, but generate C code |
| // nesting loops such that the innermost loop has the smallest stride for the |
| // best cache behavior. |
| for (int b = 0; b < output_shape.Dims(0); ++b) { |
| for (int y = 0; y < output_shape.Dims(1); ++y) { |
| for (int x = 0; x < output_shape.Dims(2); ++x) { |
| for (int c = 0; c < output_shape.Dims(3); ++c) { |
| output_data[Offset(output_shape, b, y, x, c)] = |
| ActivationFunctionWithMinMax( |
| input1_data[SubscriptToIndex(desc1, b, y, x, c)] / |
| input2_data[SubscriptToIndex(desc2, b, y, x, c)], |
| output_activation_min, output_activation_max); |
| } |
| } |
| } |
| } |
| } |
| |
| // TODO(b/80418076): Move to legacy ops file, update invocations. |
| // Legacy Dims<4>. |
| template <typename T> |
| void BroadcastDiv(const T* input1_data, const Dims<4>& input1_dims, |
| const T* input2_data, const Dims<4>& input2_dims, |
| T output_activation_min, T output_activation_max, |
| T* output_data, const Dims<4>& output_dims) { |
| tflite::ArithmeticParams op_params; |
| SetActivationParams(output_activation_min, output_activation_max, &op_params); |
| |
| BroadcastDiv4DSlow(op_params, DimsToShape(input1_dims), input1_data, |
| DimsToShape(input2_dims), input2_data, |
| DimsToShape(output_dims), output_data); |
| } |
| |
| // TODO(aselle): This is not actually optimized yet. |
| inline void SubNonBroadcast(const ArithmeticParams& params, |
| const RuntimeShape& input1_shape, |
| const float* input1_data, |
| const RuntimeShape& input2_shape, |
| const float* input2_data, |
| const RuntimeShape& output_shape, |
| float* output_data) { |
| gemmlowp::ScopedProfilingLabel label("SubNonBroadcast"); |
| const int flat_size = |
| MatchingFlatSize(input1_shape, input2_shape, output_shape); |
| for (int i = 0; i < flat_size; ++i) { |
| output_data[i] = ActivationFunctionWithMinMax( |
| input1_data[i] - input2_data[i], params.float_activation_min, |
| params.float_activation_max); |
| } |
| } |
| |
| inline void SubWithActivation(const ArithmeticParams& params, |
| const RuntimeShape& input1_shape, |
| const int32* input1_data, |
| const RuntimeShape& input2_shape, |
| const int32* input2_data, |
| const RuntimeShape& output_shape, |
| int32* output_data) { |
| gemmlowp::ScopedProfilingLabel label("SubWithActivation/int32"); |
| const int flat_size = |
| MatchingFlatSize(input1_shape, input2_shape, input2_shape); |
| for (int i = 0; i < flat_size; ++i) { |
| output_data[i] = ActivationFunctionWithMinMax( |
| input1_data[i] - input2_data[i], params.quantized_activation_min, |
| params.quantized_activation_max); |
| } |
| } |
| |
| inline void SubWithActivation(const ArithmeticParams& params, |
| const RuntimeShape& input1_shape, |
| const float* input1_data, |
| const RuntimeShape& input2_shape, |
| const float* input2_data, |
| const RuntimeShape& output_shape, |
| float* output_data) { |
| gemmlowp::ScopedProfilingLabel label("SubWithActivation/float"); |
| const int flat_size = |
| MatchingFlatSize(input1_shape, input2_shape, input2_shape); |
| for (int i = 0; i < flat_size; ++i) { |
| output_data[i] = ActivationFunctionWithMinMax( |
| input1_data[i] - input2_data[i], params.float_activation_min, |
| params.float_activation_max); |
| } |
| } |
| |
| template <typename T> |
| void Sub(const ArithmeticParams& params, const RuntimeShape& input1_shape, |
| const T* input1_data, const RuntimeShape& input2_shape, |
| const T* input2_data, const RuntimeShape& output_shape, |
| T* output_data) { |
| gemmlowp::ScopedProfilingLabel label("Sub"); |
| |
| auto input1_map = MapAsVector(input1_data, input1_shape); |
| auto input2_map = MapAsVector(input2_data, input2_shape); |
| auto output_map = MapAsVector(output_data, output_shape); |
| if (input1_shape == input2_shape) { |
| output_map.array() = input1_map.array() - input2_map.array(); |
| } else if (input1_shape.FlatSize() == 1) { |
| auto scalar = input1_data[0]; |
| output_map.array() = scalar - input2_map.array(); |
| } else if (input2_shape.FlatSize() == 1) { |
| auto scalar = input2_data[0]; |
| output_map.array() = input1_map.array() - scalar; |
| } else { |
| BroadcastSub4DSlow(params, input1_shape, input1_data, input2_shape, |
| input2_data, output_shape, output_data); |
| } |
| } |
| |
| inline void LstmCell( |
| const LstmCellParams& params, const RuntimeShape& unextended_input_shape, |
| const float* input_data, const RuntimeShape& unextended_prev_activ_shape, |
| const float* prev_activ_data, const RuntimeShape& weights_shape, |
| const float* weights_data, const RuntimeShape& unextended_bias_shape, |
| const float* bias_data, const RuntimeShape& unextended_prev_state_shape, |
| const float* prev_state_data, |
| const RuntimeShape& unextended_output_state_shape, float* output_state_data, |
| const RuntimeShape& unextended_output_activ_shape, float* output_activ_data, |
| const RuntimeShape& unextended_concat_temp_shape, float* concat_temp_data, |
| const RuntimeShape& unextended_activ_temp_shape, float* activ_temp_data) { |
| gemmlowp::ScopedProfilingLabel label("LstmCell"); |
| TFLITE_DCHECK_LE(unextended_input_shape.DimensionsCount(), 4); |
| TFLITE_DCHECK_LE(unextended_prev_activ_shape.DimensionsCount(), 4); |
| TFLITE_DCHECK_LE(unextended_bias_shape.DimensionsCount(), 4); |
| TFLITE_DCHECK_LE(unextended_prev_state_shape.DimensionsCount(), 4); |
| TFLITE_DCHECK_LE(unextended_output_state_shape.DimensionsCount(), 4); |
| TFLITE_DCHECK_LE(unextended_output_activ_shape.DimensionsCount(), 4); |
| TFLITE_DCHECK_LE(unextended_concat_temp_shape.DimensionsCount(), 4); |
| TFLITE_DCHECK_LE(unextended_activ_temp_shape.DimensionsCount(), 4); |
| const RuntimeShape input_shape = |
| RuntimeShape::ExtendedShape(4, unextended_input_shape); |
| const RuntimeShape prev_activ_shape = |
| RuntimeShape::ExtendedShape(4, unextended_prev_activ_shape); |
| const RuntimeShape bias_shape = |
| RuntimeShape::ExtendedShape(4, unextended_bias_shape); |
| const RuntimeShape prev_state_shape = |
| RuntimeShape::ExtendedShape(4, unextended_prev_state_shape); |
| const RuntimeShape output_state_shape = |
| RuntimeShape::ExtendedShape(4, unextended_output_state_shape); |
| const RuntimeShape output_activ_shape = |
| RuntimeShape::ExtendedShape(4, unextended_output_activ_shape); |
| const RuntimeShape concat_temp_shape = |
| RuntimeShape::ExtendedShape(4, unextended_concat_temp_shape); |
| const RuntimeShape activ_temp_shape = |
| RuntimeShape::ExtendedShape(4, unextended_activ_temp_shape); |
| TFLITE_DCHECK_GE(weights_shape.DimensionsCount(), 2); |
| |
| const int weights_dim_count = weights_shape.DimensionsCount(); |
| MatchingDim( // batches |
| input_shape, 0, prev_activ_shape, 0, prev_state_shape, 0, |
| output_state_shape, 0, output_activ_shape, 0); |
| MatchingDim( // height |
| input_shape, 1, prev_activ_shape, 1, prev_state_shape, 1, |
| output_state_shape, 1, output_activ_shape, 1); |
| MatchingDim( // width |
| input_shape, 2, prev_activ_shape, 2, prev_state_shape, 2, |
| output_state_shape, 2, output_activ_shape, 2); |
| const int input_depth = input_shape.Dims(3); |
| const int prev_activ_depth = prev_activ_shape.Dims(3); |
| const int total_input_depth = prev_activ_depth + input_depth; |
| TFLITE_DCHECK_EQ(weights_shape.Dims(weights_dim_count - 1), |
| total_input_depth); |
| TFLITE_DCHECK_EQ(FlatSizeSkipDim(bias_shape, 3), 1); |
| const int intern_activ_depth = |
| MatchingDim(weights_shape, weights_dim_count - 2, bias_shape, 3); |
| TFLITE_DCHECK_EQ(weights_shape.FlatSize(), |
| intern_activ_depth * total_input_depth); |
| TFLITE_DCHECK_EQ(intern_activ_depth % 4, 0); |
| const int output_depth = |
| MatchingDim(prev_state_shape, 3, prev_activ_shape, 3, output_state_shape, |
| 3, output_activ_shape, 3); |
| TFLITE_DCHECK_EQ(output_depth, intern_activ_depth / 4); |
| |
| // Concatenate prev_activ and input data together |
| std::vector<float const*> concat_input_arrays_data; |
| std::vector<RuntimeShape const*> concat_input_arrays_shapes; |
| concat_input_arrays_data.push_back(input_data); |
| concat_input_arrays_data.push_back(prev_activ_data); |
| concat_input_arrays_shapes.push_back(&input_shape); |
| concat_input_arrays_shapes.push_back(&prev_activ_shape); |
| tflite::ConcatenationParams concat_params; |
| concat_params.axis = 3; |
| concat_params.inputs_count = concat_input_arrays_data.size(); |
| Concatenation(concat_params, &(concat_input_arrays_shapes[0]), |
| &(concat_input_arrays_data[0]), concat_temp_shape, |
| concat_temp_data); |
| |
| // Fully connected |
| tflite::FullyConnectedParams fc_params; |
| fc_params.float_activation_min = std::numeric_limits<float>::lowest(); |
| fc_params.float_activation_max = std::numeric_limits<float>::max(); |
| FullyConnected(fc_params, concat_temp_shape, concat_temp_data, weights_shape, |
| weights_data, bias_shape, bias_data, activ_temp_shape, |
| activ_temp_data); |
| |
| // Map raw arrays to Eigen arrays so we can use Eigen's optimized array |
| // operations. |
| ArrayMap<float> activ_temp_map = |
| MapAsArrayWithLastDimAsRows(activ_temp_data, activ_temp_shape); |
| auto input_gate_sm = activ_temp_map.block(0 * output_depth, 0, output_depth, |
| activ_temp_map.cols()); |
| auto new_input_sm = activ_temp_map.block(1 * output_depth, 0, output_depth, |
| activ_temp_map.cols()); |
| auto forget_gate_sm = activ_temp_map.block(2 * output_depth, 0, output_depth, |
| activ_temp_map.cols()); |
| auto output_gate_sm = activ_temp_map.block(3 * output_depth, 0, output_depth, |
| activ_temp_map.cols()); |
| ArrayMap<const float> prev_state_map = |
| MapAsArrayWithLastDimAsRows(prev_state_data, prev_state_shape); |
| ArrayMap<float> output_state_map = |
| MapAsArrayWithLastDimAsRows(output_state_data, output_state_shape); |
| ArrayMap<float> output_activ_map = |
| MapAsArrayWithLastDimAsRows(output_activ_data, output_activ_shape); |
| |
| // Combined memory state and final output calculation |
| gemmlowp::ScopedProfilingLabel label2("MemoryStateAndFinalOutput"); |
| output_state_map = |
| input_gate_sm.unaryExpr(Eigen::internal::scalar_sigmoid_op<float>()) * |
| new_input_sm.tanh() + |
| forget_gate_sm.unaryExpr(Eigen::internal::scalar_sigmoid_op<float>()) * |
| prev_state_map; |
| output_activ_map = |
| output_gate_sm.unaryExpr(Eigen::internal::scalar_sigmoid_op<float>()) * |
| output_state_map.tanh(); |
| } |
| |
| // TODO(b/80418076): Move to legacy ops file, update invocations. |
| // Legacy. |
| inline void LstmCell(const float* input_data, const Dims<4>& input_dims, |
| const float* prev_activ_data, |
| const Dims<4>& prev_activ_dims, const float* weights_data, |
| const Dims<4>& weights_dims, const float* bias_data, |
| const Dims<4>& bias_dims, const float* prev_state_data, |
| const Dims<4>& prev_state_dims, float* output_state_data, |
| const Dims<4>& output_state_dims, float* output_activ_data, |
| const Dims<4>& output_activ_dims, float* concat_temp_data, |
| const Dims<4>& concat_temp_dims, float* activ_temp_data, |
| const Dims<4>& activ_temp_dims) { |
| tflite::LstmCellParams op_params; |
| // Float LSTM cell does not need parameters to be set: leave untouched. |
| |
| LstmCell(op_params, DimsToShape(input_dims), input_data, |
| DimsToShape(prev_activ_dims), prev_activ_data, |
| DimsToShape(weights_dims), weights_data, DimsToShape(bias_dims), |
| bias_data, DimsToShape(prev_state_dims), prev_state_data, |
| DimsToShape(output_state_dims), output_state_data, |
| DimsToShape(output_activ_dims), output_activ_data, |
| DimsToShape(concat_temp_dims), concat_temp_data, |
| DimsToShape(activ_temp_dims), activ_temp_data); |
| } |
| |
| // Quantized LSTM cell. Currently just a copy of the reference impl in |
| // reference_ops.h. See the big function comment there, not replicating it |
| // here. |
| template <int StateIntegerBits> |
| inline void LstmCell( |
| const LstmCellParams& params, const RuntimeShape& unextended_input_shape, |
| const uint8* input_data_uint8, |
| const RuntimeShape& unextended_prev_activ_shape, |
| const uint8* prev_activ_data_uint8, const RuntimeShape& weights_shape, |
| const uint8* weights_data_uint8, const RuntimeShape& unextended_bias_shape, |
| const int32* bias_data_int32, |
| const RuntimeShape& unextended_prev_state_shape, |
| const int16* prev_state_data_int16, |
| const RuntimeShape& unextended_output_state_shape, |
| int16* output_state_data_int16, |
| const RuntimeShape& unextended_output_activ_shape, |
| uint8* output_activ_data_uint8, |
| const RuntimeShape& unextended_concat_temp_shape, |
| uint8* concat_temp_data_uint8, |
| const RuntimeShape& unextended_activ_temp_shape, |
| int16* activ_temp_data_int16, gemmlowp::GemmContext* gemm_context) { |
| gemmlowp::ScopedProfilingLabel label( |
| "LstmCell/quantized (8bit external, 16bit internal)"); |
| int32 weights_zero_point = params.weights_zero_point; |
| int32 accum_multiplier = params.accum_multiplier; |
| int accum_shift = params.accum_shift; |
| TFLITE_DCHECK_LE(unextended_input_shape.DimensionsCount(), 4); |
| TFLITE_DCHECK_LE(unextended_prev_activ_shape.DimensionsCount(), 4); |
| TFLITE_DCHECK_LE(unextended_bias_shape.DimensionsCount(), 4); |
| TFLITE_DCHECK_LE(unextended_prev_state_shape.DimensionsCount(), 4); |
| TFLITE_DCHECK_LE(unextended_output_state_shape.DimensionsCount(), 4); |
| TFLITE_DCHECK_LE(unextended_output_activ_shape.DimensionsCount(), 4); |
| TFLITE_DCHECK_LE(unextended_concat_temp_shape.DimensionsCount(), 4); |
| TFLITE_DCHECK_LE(unextended_activ_temp_shape.DimensionsCount(), 4); |
| const RuntimeShape input_shape = |
| RuntimeShape::ExtendedShape(4, unextended_input_shape); |
| const RuntimeShape prev_activ_shape = |
| RuntimeShape::ExtendedShape(4, unextended_prev_activ_shape); |
| const RuntimeShape bias_shape = |
| RuntimeShape::ExtendedShape(4, unextended_bias_shape); |
| const RuntimeShape prev_state_shape = |
| RuntimeShape::ExtendedShape(4, unextended_prev_state_shape); |
| const RuntimeShape output_state_shape = |
| RuntimeShape::ExtendedShape(4, unextended_output_state_shape); |
| const RuntimeShape output_activ_shape = |
| RuntimeShape::ExtendedShape(4, unextended_output_activ_shape); |
| const RuntimeShape concat_temp_shape = |
| RuntimeShape::ExtendedShape(4, unextended_concat_temp_shape); |
| const RuntimeShape activ_temp_shape = |
| RuntimeShape::ExtendedShape(4, unextended_activ_temp_shape); |
| TFLITE_DCHECK_GE(weights_shape.DimensionsCount(), 2); |
| |
| // Gather dimensions information, and perform consistency checks. |
| const int weights_dim_count = weights_shape.DimensionsCount(); |
| const int outer_size = MatchingFlatSizeSkipDim( |
| input_shape, 3, prev_activ_shape, prev_state_shape, output_state_shape, |
| output_activ_shape); |
| const int input_depth = input_shape.Dims(3); |
| const int prev_activ_depth = prev_activ_shape.Dims(3); |
| const int total_input_depth = prev_activ_depth + input_depth; |
| TFLITE_DCHECK_EQ(weights_shape.Dims(weights_dim_count - 1), |
| total_input_depth); |
| const int intern_activ_depth = |
| MatchingDim(weights_shape, weights_dim_count - 2, bias_shape, 3); |
| TFLITE_DCHECK_EQ(weights_shape.FlatSize(), |
| intern_activ_depth * total_input_depth); |
| TFLITE_DCHECK_EQ(FlatSizeSkipDim(bias_shape, 3), 1); |
| TFLITE_DCHECK_EQ(intern_activ_depth % 4, 0); |
| const int output_depth = |
| MatchingDim(prev_state_shape, 3, prev_activ_shape, 3, output_state_shape, |
| 3, output_activ_shape, 3); |
| TFLITE_DCHECK_EQ(output_depth, intern_activ_depth / 4); |
| const int fc_batches = FlatSizeSkipDim(activ_temp_shape, 3); |
| const int fc_output_depth = |
| MatchingDim(weights_shape, weights_dim_count - 2, activ_temp_shape, 3); |
| const int fc_accum_depth = total_input_depth; |
| TFLITE_DCHECK_EQ(fc_output_depth, 4 * output_depth); |
| |
| // Depth-concatenate prev_activ and input data together. |
| uint8 const* concat_input_arrays_data[2] = {input_data_uint8, |
| prev_activ_data_uint8}; |
| const RuntimeShape* concat_input_arrays_shapes[2] = {&input_shape, |
| &prev_activ_shape}; |
| tflite::ConcatenationParams concat_params; |
| concat_params.axis = 3; |
| concat_params.inputs_count = 2; |
| Concatenation(concat_params, concat_input_arrays_shapes, |
| concat_input_arrays_data, concat_temp_shape, |
| concat_temp_data_uint8); |
| |
| // Implementation of the fully connected node inside the LSTM cell. |
| // The operands are 8-bit integers, the accumulators are internally 32bit |
| // integers, and the output is 16-bit fixed-point with 3 integer bits so |
| // the output range is [-2^3, 2^3] == [-8, 8]. The rationale for that |
| // is explained in the function comment above. |
| bool gemm_already_performed = false; |
| #ifdef GEMMLOWP_NEON |
| if (fc_batches == 1 && !(fc_output_depth % 4) && !(fc_accum_depth % 8)) { |
| GEMVForLstmCell(concat_temp_shape, concat_temp_data_uint8, weights_shape, |
| weights_data_uint8, weights_zero_point, bias_shape, |
| bias_data_int32, accum_multiplier, accum_shift, |
| activ_temp_shape, activ_temp_data_int16); |
| gemm_already_performed = true; |
| } |
| #endif |
| if (!gemm_already_performed) { |
| gemmlowp::MatrixMap<const uint8, gemmlowp::MapOrder::RowMajor> |
| weights_matrix(weights_data_uint8, fc_output_depth, fc_accum_depth); |
| gemmlowp::MatrixMap<const uint8, gemmlowp::MapOrder::ColMajor> input_matrix( |
| concat_temp_data_uint8, fc_accum_depth, fc_batches); |
| gemmlowp::MatrixMap<int16, gemmlowp::MapOrder::ColMajor> output_matrix( |
| activ_temp_data_int16, fc_output_depth, fc_batches); |
| typedef gemmlowp::VectorMap<const int32, gemmlowp::VectorShape::Col> |
| ColVectorMap; |
| ColVectorMap bias_vector(bias_data_int32, fc_output_depth); |
| gemmlowp::OutputStageBiasAddition<ColVectorMap> bias_addition_stage; |
| bias_addition_stage.bias_vector = bias_vector; |
| gemmlowp::OutputStageScaleInt32ByFixedPointAndExponent scale_stage; |
| scale_stage.result_offset_after_shift = 0; |
| scale_stage.result_fixedpoint_multiplier = accum_multiplier; |
| scale_stage.result_exponent = accum_shift; |
| gemmlowp::OutputStageSaturatingCastToInt16 saturating_cast_int16_stage; |
| auto output_pipeline = std::make_tuple(bias_addition_stage, scale_stage, |
| saturating_cast_int16_stage); |
| gemmlowp::GemmWithOutputPipeline< |
| uint8, int16, gemmlowp::L8R8WithLhsNonzeroBitDepthParams>( |
| gemm_context, weights_matrix, input_matrix, &output_matrix, |
| -weights_zero_point, -128, output_pipeline); |
| } |
| |
| // Rest of the LSTM cell: tanh and logistic math functions, and some adds |
| // and muls, all done in 16-bit fixed-point. |
| const int16* input_gate_input_ptr = activ_temp_data_int16; |
| const int16* input_modulation_gate_input_ptr = |
| activ_temp_data_int16 + output_depth; |
| const int16* forget_gate_input_ptr = activ_temp_data_int16 + 2 * output_depth; |
| const int16* output_gate_input_ptr = activ_temp_data_int16 + 3 * output_depth; |
| const int16* prev_state_ptr = prev_state_data_int16; |
| int16* output_state_data_ptr = output_state_data_int16; |
| uint8* output_activ_data_ptr = output_activ_data_uint8; |
| |
| for (int b = 0; b < outer_size; ++b) { |
| int c = 0; |
| #ifdef GEMMLOWP_NEON |
| for (; c <= output_depth - 8; c += 8) { |
| // Define the fixed-point data types that we will use here. All use |
| // int16 as the underlying integer type i.e. all are 16-bit fixed-point. |
| // They only differ by the number of integral vs. fractional bits, |
| // determining the range of values that they can represent. |
| // |
| // F0 uses 0 integer bits, range [-1, 1]. |
| // This is the return type of math functions such as tanh, logistic, |
| // whose range is in [-1, 1]. |
| using F0 = gemmlowp::FixedPoint<int16x8_t, 0>; |
| // F3 uses 3 integer bits, range [-8, 8]. |
| // This is the range of the previous fully-connected node's output, |
| // which is our input here. |
| using F3 = gemmlowp::FixedPoint<int16x8_t, 3>; |
| // FS uses StateIntegerBits integer bits, range [-2^StateIntegerBits, |
| // 2^StateIntegerBits]. It's used to represent the internal state, whose |
| // number of integer bits is currently dictated by the model. See comment |
| // on the StateIntegerBits template parameter above. |
| using FS = gemmlowp::FixedPoint<int16x8_t, StateIntegerBits>; |
| // Implementation of input gate, using fixed-point logistic function. |
| F3 input_gate_input = F3::FromRaw(vld1q_s16(input_gate_input_ptr)); |
| input_gate_input_ptr += 8; |
| F0 input_gate_output = gemmlowp::logistic(input_gate_input); |
| // Implementation of input modulation gate, using fixed-point tanh |
| // function. |
| F3 input_modulation_gate_input = |
| F3::FromRaw(vld1q_s16(input_modulation_gate_input_ptr)); |
| input_modulation_gate_input_ptr += 8; |
| F0 input_modulation_gate_output = |
| gemmlowp::tanh(input_modulation_gate_input); |
| // Implementation of forget gate, using fixed-point logistic function. |
| F3 forget_gate_input = F3::FromRaw(vld1q_s16(forget_gate_input_ptr)); |
| forget_gate_input_ptr += 8; |
| F0 forget_gate_output = gemmlowp::logistic(forget_gate_input); |
| // Implementation of output gate, using fixed-point logistic function. |
| F3 output_gate_input = F3::FromRaw(vld1q_s16(output_gate_input_ptr)); |
| output_gate_input_ptr += 8; |
| F0 output_gate_output = gemmlowp::logistic(output_gate_input); |
| // Implementation of internal multiplication nodes, still in fixed-point. |
| F0 input_times_input_modulation = |
| input_gate_output * input_modulation_gate_output; |
| FS prev_state = FS::FromRaw(vld1q_s16(prev_state_ptr)); |
| prev_state_ptr += 8; |
| FS prev_state_times_forget_state = forget_gate_output * prev_state; |
| // Implementation of internal addition node, saturating. |
| FS new_state = gemmlowp::SaturatingAdd( |
| gemmlowp::Rescale<StateIntegerBits>(input_times_input_modulation), |
| prev_state_times_forget_state); |
| // Implementation of last internal Tanh node, still in fixed-point. |
| // Since a Tanh fixed-point implementation is specialized for a given |
| // number or integer bits, and each specialization can have a substantial |
| // code size, and we already used above a Tanh on an input with 3 integer |
| // bits, and per the table in the above function comment there is no |
| // significant accuracy to be lost by clamping to [-8, +8] for a |
| // 3-integer-bits representation, let us just do that. This helps people |
| // porting this to targets where code footprint must be minimized. |
| F3 new_state_f3 = gemmlowp::Rescale<3>(new_state); |
| F0 output_activ_int16 = output_gate_output * gemmlowp::tanh(new_state_f3); |
| // Store the new internal state back to memory, as 16-bit integers. |
| // Note: here we store the original value with StateIntegerBits, not |
| // the rescaled 3-integer-bits value fed to tanh. |
| vst1q_s16(output_state_data_ptr, new_state.raw()); |
| output_state_data_ptr += 8; |
| // Down-scale the output activations to 8-bit integers, saturating, |
| // and store back to memory. |
| int16x8_t rescaled_output_activ = |
| gemmlowp::RoundingDivideByPOT(output_activ_int16.raw(), 8); |
| int8x8_t int8_output_activ = vqmovn_s16(rescaled_output_activ); |
| uint8x8_t uint8_output_activ = |
| vadd_u8(vdup_n_u8(128), vreinterpret_u8_s8(int8_output_activ)); |
| vst1_u8(output_activ_data_ptr, uint8_output_activ); |
| output_activ_data_ptr += 8; |
| } |
| #endif |
| for (; c < output_depth; ++c) { |
| // Define the fixed-point data types that we will use here. All use |
| // int16 as the underlying integer type i.e. all are 16-bit fixed-point. |
| // They only differ by the number of integral vs. fractional bits, |
| // determining the range of values that they can represent. |
| // |
| // F0 uses 0 integer bits, range [-1, 1]. |
| // This is the return type of math functions such as tanh, logistic, |
| // whose range is in [-1, 1]. |
| using F0 = gemmlowp::FixedPoint<std::int16_t, 0>; |
| // F3 uses 3 integer bits, range [-8, 8]. |
| // This is the range of the previous fully-connected node's output, |
| // which is our input here. |
| using F3 = gemmlowp::FixedPoint<std::int16_t, 3>; |
| // FS uses StateIntegerBits integer bits, range [-2^StateIntegerBits, |
| // 2^StateIntegerBits]. It's used to represent the internal state, whose |
| // number of integer bits is currently dictated by the model. See comment |
| // on the StateIntegerBits template parameter above. |
| using FS = gemmlowp::FixedPoint<std::int16_t, StateIntegerBits>; |
| // Implementation of input gate, using fixed-point logistic function. |
| F3 input_gate_input = F3::FromRaw(*input_gate_input_ptr++); |
| F0 input_gate_output = gemmlowp::logistic(input_gate_input); |
| // Implementation of input modulation gate, using fixed-point tanh |
| // function. |
| F3 input_modulation_gate_input = |
| F3::FromRaw(*input_modulation_gate_input_ptr++); |
| F0 input_modulation_gate_output = |
| gemmlowp::tanh(input_modulation_gate_input); |
| // Implementation of forget gate, using fixed-point logistic function. |
| F3 forget_gate_input = F3::FromRaw(*forget_gate_input_ptr++); |
| F0 forget_gate_output = gemmlowp::logistic(forget_gate_input); |
| // Implementation of output gate, using fixed-point logistic function. |
| F3 output_gate_input = F3::FromRaw(*output_gate_input_ptr++); |
| F0 output_gate_output = gemmlowp::logistic(output_gate_input); |
| // Implementation of internal multiplication nodes, still in fixed-point. |
| F0 input_times_input_modulation = |
| input_gate_output * input_modulation_gate_output; |
| FS prev_state = FS::FromRaw(*prev_state_ptr++); |
| FS prev_state_times_forget_state = forget_gate_output * prev_state; |
| // Implementation of internal addition node, saturating. |
| FS new_state = gemmlowp::SaturatingAdd( |
| gemmlowp::Rescale<StateIntegerBits>(input_times_input_modulation), |
| prev_state_times_forget_state); |
| // Implementation of last internal Tanh node, still in fixed-point. |
| // Since a Tanh fixed-point implementation is specialized for a given |
| // number or integer bits, and each specialization can have a substantial |
| // code size, and we already used above a Tanh on an input with 3 integer |
| // bits, and per the table in the above function comment there is no |
| // significant accuracy to be lost by clamping to [-8, +8] for a |
| // 3-integer-bits representation, let us just do that. This helps people |
| // porting this to targets where code footprint must be minimized. |
| F3 new_state_f3 = gemmlowp::Rescale<3>(new_state); |
| F0 output_activ_int16 = output_gate_output * gemmlowp::tanh(new_state_f3); |
| // Store the new internal state back to memory, as 16-bit integers. |
| // Note: here we store the original value with StateIntegerBits, not |
| // the rescaled 3-integer-bits value fed to tanh. |
| *output_state_data_ptr++ = new_state.raw(); |
| // Down-scale the output activations to 8-bit integers, saturating, |
| // and store back to memory. |
| int16 rescaled_output_activ = |
| gemmlowp::RoundingDivideByPOT(output_activ_int16.raw(), 8); |
| int16 clamped_output_activ = |
| std::max<int16>(-128, std::min<int16>(127, rescaled_output_activ)); |
| *output_activ_data_ptr++ = 128 + clamped_output_activ; |
| } |
| input_gate_input_ptr += 3 * output_depth; |
| input_modulation_gate_input_ptr += 3 * output_depth; |
| forget_gate_input_ptr += 3 * output_depth; |
| output_gate_input_ptr += 3 * output_depth; |
| } |
| } |
| |
| // TODO(b/80418076): Move to legacy ops file, update invocations. |
| // Legacy. |
| template <int StateIntegerBits> |
| void LstmCell(const uint8* input_data_uint8, const Dims<4>& input_dims, |
| const uint8* prev_activ_data_uint8, |
| const Dims<4>& prev_activ_dims, const uint8* weights_data_uint8, |
| const Dims<4>& weights_dims, const int32* bias_data_int32, |
| const Dims<4>& bias_dims, const int16* prev_state_data_int16, |
| const Dims<4>& prev_state_dims, int16* output_state_data_int16, |
| const Dims<4>& output_state_dims, uint8* output_activ_data_uint8, |
| const Dims<4>& output_activ_dims, uint8* concat_temp_data_uint8, |
| const Dims<4>& concat_temp_dims, int16* activ_temp_data_int16, |
| const Dims<4>& activ_temp_dims, int32 weights_zero_point, |
| int32 accum_multiplier, int accum_shift, |
| gemmlowp::GemmContext* gemm_context) { |
| tflite::LstmCellParams op_params; |
| op_params.weights_zero_point = weights_zero_point; |
| op_params.accum_multiplier = accum_multiplier; |
| op_params.accum_shift = accum_shift; |
| |
| LstmCell<StateIntegerBits>( |
| op_params, DimsToShape(input_dims), input_data_uint8, |
| DimsToShape(prev_activ_dims), prev_activ_data_uint8, |
| DimsToShape(weights_dims), weights_data_uint8, DimsToShape(bias_dims), |
| bias_data_int32, DimsToShape(prev_state_dims), prev_state_data_int16, |
| DimsToShape(output_state_dims), output_state_data_int16, |
| DimsToShape(output_activ_dims), output_activ_data_uint8, |
| DimsToShape(concat_temp_dims), concat_temp_data_uint8, |
| DimsToShape(activ_temp_dims), activ_temp_data_int16, gemm_context); |
| } |
| |
| inline int NodeOffset(int b, int h, int w, int height, int width) { |
| return (b * height + h) * width + w; |
| } |
| |
| inline void AveragePool(const PoolParams& params, |
| const RuntimeShape& input_shape, |
| const float* input_data, |
| const RuntimeShape& output_shape, float* output_data) { |
| gemmlowp::ScopedProfilingLabel label("AveragePool"); |
| TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); |
| TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); |
| const int batches = MatchingDim(input_shape, 0, output_shape, 0); |
| const int input_height = input_shape.Dims(1); |
| const int input_width = input_shape.Dims(2); |
| const int output_height = output_shape.Dims(1); |
| const int output_width = output_shape.Dims(2); |
| const int stride_height = params.stride_height; |
| const int stride_width = params.stride_width; |
| |
| // TODO(benoitjacob) make this a proper reference impl without Eigen! |
| const auto in_mat = MapAsMatrixWithLastDimAsRows(input_data, input_shape); |
| auto out_mat = MapAsMatrixWithLastDimAsRows(output_data, output_shape); |
| // TODO(benoitjacob) get rid of the dynamic memory allocation here! |
| Eigen::VectorXf out_count(out_mat.cols()); |
| out_count.setZero(); |
| // Prefill the output to 0. |
| out_mat.setZero(); |
| for (int b = 0; b < batches; ++b) { |
| for (int h = 0; h < input_height; ++h) { |
| for (int w = 0; w < input_width; ++w) { |
| // (h_start, h_end) * (w_start, w_end) is the range that the input |
| // vector projects to. |
| int hpad = h + params.padding_values.height; |
| int wpad = w + params.padding_values.width; |
| int h_start = (hpad < params.filter_height) |
| ? 0 |
| : (hpad - params.filter_height) / stride_height + 1; |
| int h_end = std::min(hpad / stride_height + 1, output_height); |
| int w_start = (wpad < params.filter_width) |
| ? 0 |
| : (wpad - params.filter_width) / stride_width + 1; |
| int w_end = std::min(wpad / stride_width + 1, output_width); |
| // compute elementwise sum |
| for (int ph = h_start; ph < h_end; ++ph) { |
| for (int pw = w_start; pw < w_end; ++pw) { |
| int out_offset = NodeOffset(b, ph, pw, output_height, output_width); |
| out_mat.col(out_offset) += |
| in_mat.col(NodeOffset(b, h, w, input_height, input_width)); |
| out_count(out_offset)++; |
| } |
| } |
| } |
| } |
| } |
| // Divide the output by the actual number of elements being averaged over |
| TFLITE_DCHECK_GT(out_count.minCoeff(), 0); |
| out_mat.array().rowwise() /= out_count.transpose().array(); |
| |
| const int flat_size = output_shape.FlatSize(); |
| for (int i = 0; i < flat_size; ++i) { |
| output_data[i] = ActivationFunctionWithMinMax(output_data[i], |
| params.float_activation_min, |
| params.float_activation_max); |
| } |
| } |
| |
| inline void AveragePool(const PoolParams& params, |
| const RuntimeShape& input_shape, |
| const uint8* input_data, |
| const RuntimeShape& output_shape, uint8* output_data) { |
| gemmlowp::ScopedProfilingLabel label("AveragePool/8bit"); |
| TFLITE_DCHECK_LE(params.quantized_activation_min, |
| params.quantized_activation_max); |
| TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); |
| TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); |
| const int batches = MatchingDim(input_shape, 0, output_shape, 0); |
| const int depth = MatchingDim(input_shape, 3, output_shape, 3); |
| const int input_height = input_shape.Dims(1); |
| const int input_width = input_shape.Dims(2); |
| const int output_height = output_shape.Dims(1); |
| const int output_width = output_shape.Dims(2); |
| const int stride_height = params.stride_height; |
| const int stride_width = params.stride_width; |
| for (int batch = 0; batch < batches; ++batch) { |
| for (int out_y = 0; out_y < output_height; ++out_y) { |
| for (int out_x = 0; out_x < output_width; ++out_x) { |
| const int in_x_origin = |
| (out_x * stride_width) - params.padding_values.width; |
| const int in_y_origin = |
| (out_y * stride_height) - params.padding_values.height; |
| const int filter_x_start = std::max(0, -in_x_origin); |
| const int filter_x_end = |
| std::min(params.filter_width, input_width - in_x_origin); |
| const int filter_y_start = std::max(0, -in_y_origin); |
| const int filter_y_end = |
| std::min(params.filter_height, input_height - in_y_origin); |
| const int filter_count = |
| (filter_x_end - filter_x_start) * (filter_y_end - filter_y_start); |
| // 1280 required by Inception v3 |
| static constexpr int kAccBufferMaxSize = 2048; |
| TFLITE_DCHECK_LE(depth, kAccBufferMaxSize); |
| uint16 acc[kAccBufferMaxSize]; |
| memset(acc, 0, depth * sizeof(acc[0])); |
| const uint8* input_ptr = |
| input_data + |
| depth * (in_x_origin + |
| input_width * (in_y_origin + input_height * batch)); |
| for (int fy = filter_y_start; fy < filter_y_end; fy++) { |
| const uint8* input_row_ptr = |
| input_ptr + depth * (fy * input_width + filter_x_start); |
| for (int fx = filter_x_start; fx < filter_x_end; fx++) { |
| int channel = 0; |
| #ifdef USE_NEON |
| for (; channel <= depth - 16; channel += 16) { |
| uint16x8_t acc_reg[2]; |
| for (int i = 0; i < 2; i++) { |
| acc_reg[i] = vld1q_u16(acc + channel + 8 * i); |
| } |
| uint8x16_t input_reg = vld1q_u8(input_row_ptr); |
| input_row_ptr += 16; |
| acc_reg[0] = vaddw_u8(acc_reg[0], vget_low_u8(input_reg)); |
| acc_reg[1] = vaddw_u8(acc_reg[1], vget_high_u8(input_reg)); |
| for (int i = 0; i < 2; i++) { |
| vst1q_u16(acc + channel + 8 * i, acc_reg[i]); |
| } |
| } |
| for (; channel <= depth - 8; channel += 8) { |
| uint16x8_t acc_reg = vld1q_u16(acc + channel); |
| uint8x8_t input_reg = vld1_u8(input_row_ptr); |
| input_row_ptr += 8; |
| acc_reg = vaddw_u8(acc_reg, input_reg); |
| vst1q_u16(acc + channel, acc_reg); |
| } |
| #endif |
| for (; channel < depth; ++channel) { |
| acc[channel] += *input_row_ptr++; |
| } |
| } |
| } |
| uint8* output_ptr = |
| output_data + Offset(output_shape, batch, out_y, out_x, 0); |
| int channel = 0; |
| #ifdef USE_NEON |
| #define AVGPOOL_DIVIDING_BY(FILTER_COUNT) \ |
| if (filter_count == FILTER_COUNT) { \ |
| for (; channel <= depth - 8; channel += 8) { \ |
| uint16 buf[8]; \ |
| for (int i = 0; i < 8; i++) { \ |
| buf[i] = (acc[channel + i] + FILTER_COUNT / 2) / FILTER_COUNT; \ |
| } \ |
| uint8x8_t buf8 = vqmovn_u16(vld1q_u16(buf)); \ |
| buf8 = vmin_u8(buf8, vdup_n_u8(params.quantized_activation_max)); \ |
| buf8 = vmax_u8(buf8, vdup_n_u8(params.quantized_activation_min)); \ |
| vst1_u8(output_ptr + channel, buf8); \ |
| } \ |
| } |
| AVGPOOL_DIVIDING_BY(9) |
| AVGPOOL_DIVIDING_BY(15) |
| #undef AVGPOOL_DIVIDING_BY |
| for (; channel <= depth - 8; channel += 8) { |
| uint16 buf[8]; |
| for (int i = 0; i < 8; i++) { |
| buf[i] = (acc[channel + i] + filter_count / 2) / filter_count; |
| } |
| uint8x8_t buf8 = vqmovn_u16(vld1q_u16(buf)); |
| buf8 = vmin_u8(buf8, vdup_n_u8(params.quantized_activation_max)); |
| buf8 = vmax_u8(buf8, vdup_n_u8(params.quantized_activation_min)); |
| vst1_u8(output_ptr + channel, buf8); |
| } |
| #endif |
| for (; channel < depth; ++channel) { |
| uint16 a = (acc[channel] + filter_count / 2) / filter_count; |
| a = std::max<uint16>(a, params.quantized_activation_min); |
| a = std::min<uint16>(a, params.quantized_activation_max); |
| output_ptr[channel] = static_cast<uint8>(a); |
| } |
| } |
| } |
| } |
| } |
| |
| inline void MaxPool(const PoolParams& params, const RuntimeShape& input_shape, |
| const float* input_data, const RuntimeShape& output_shape, |
| float* output_data) { |
| gemmlowp::ScopedProfilingLabel label("MaxPool"); |
| TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); |
| TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); |
| const int batches = MatchingDim(input_shape, 0, output_shape, 0); |
| const int input_height = input_shape.Dims(1); |
| const int input_width = input_shape.Dims(2); |
| const int output_height = output_shape.Dims(1); |
| const int output_width = output_shape.Dims(2); |
| const int stride_height = params.stride_height; |
| const int stride_width = params.stride_width; |
| |
| const auto in_mat = MapAsMatrixWithLastDimAsRows(input_data, input_shape); |
| auto out_mat = MapAsMatrixWithLastDimAsRows(output_data, output_shape); |
| // Prefill the output to minimum representable float value |
| out_mat.setConstant(std::numeric_limits<float>::lowest()); |
| for (int b = 0; b < batches; ++b) { |
| for (int h = 0; h < input_height; ++h) { |
| for (int w = 0; w < input_width; ++w) { |
| // (h_start, h_end) * (w_start, w_end) is the range that the input |
| // vector projects to. |
| int hpad = h + params.padding_values.height; |
| int wpad = w + params.padding_values.width; |
| int h_start = (hpad < params.filter_height) |
| ? 0 |
| : (hpad - params.filter_height) / stride_height + 1; |
| int h_end = std::min(hpad / stride_height + 1, output_height); |
| int w_start = (wpad < params.filter_width) |
| ? 0 |
| : (wpad - params.filter_width) / stride_width + 1; |
| int w_end = std::min(wpad / stride_width + 1, output_width); |
| // compute elementwise sum |
| for (int ph = h_start; ph < h_end; ++ph) { |
| for (int pw = w_start; pw < w_end; ++pw) { |
| int out_offset = NodeOffset(b, ph, pw, output_height, output_width); |
| out_mat.col(out_offset) = |
| out_mat.col(out_offset) |
| .cwiseMax(in_mat.col( |
| NodeOffset(b, h, w, input_height, input_width))); |
| } |
| } |
| } |
| } |
| } |
| const int flat_size = output_shape.FlatSize(); |
| for (int i = 0; i < flat_size; ++i) { |
| output_data[i] = ActivationFunctionWithMinMax(output_data[i], |
| params.float_activation_min, |
| params.float_activation_max); |
| } |
| } |
| |
| inline void MaxPool(const PoolParams& params, const RuntimeShape& input_shape, |
| const uint8* input_data, const RuntimeShape& output_shape, |
| uint8* output_data) { |
| gemmlowp::ScopedProfilingLabel label("MaxPool/8bit"); |
| TFLITE_DCHECK_LE(params.quantized_activation_min, |
| params.quantized_activation_max); |
| TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); |
| TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); |
| const int batches = MatchingDim(input_shape, 0, output_shape, 0); |
| const int depth = MatchingDim(input_shape, 3, output_shape, 3); |
| const int input_height = input_shape.Dims(1); |
| const int input_width = input_shape.Dims(2); |
| const int output_height = output_shape.Dims(1); |
| const int output_width = output_shape.Dims(2); |
| const int stride_height = params.stride_height; |
| const int stride_width = params.stride_width; |
| for (int batch = 0; batch < batches; ++batch) { |
| for (int out_y = 0; out_y < output_height; ++out_y) { |
| for (int out_x = 0; out_x < output_width; ++out_x) { |
| const int in_x_origin = |
| (out_x * stride_width) - params.padding_values.width; |
| const int in_y_origin = |
| (out_y * stride_height) - params.padding_values.height; |
| const int filter_x_start = std::max(0, -in_x_origin); |
| const int filter_x_end = |
| std::min(params.filter_width, input_width - in_x_origin); |
| const int filter_y_start = std::max(0, -in_y_origin); |
| const int filter_y_end = |
| std::min(params.filter_height, input_height - in_y_origin); |
| // 2048 required by Inception v3 |
| static constexpr int kAccBufferMaxSize = 2048; |
| TFLITE_DCHECK_LE(depth, kAccBufferMaxSize); |
| uint8 acc[kAccBufferMaxSize]; |
| memset(acc, 0, depth * sizeof(acc[0])); |
| const uint8* input_ptr = |
| input_data + |
| depth * (in_x_origin + |
| input_width * (in_y_origin + input_height * batch)); |
| for (int fy = filter_y_start; fy < filter_y_end; fy++) { |
| const uint8* input_row_ptr = |
| input_ptr + depth * (fy * input_width + filter_x_start); |
| for (int fx = filter_x_start; fx < filter_x_end; fx++) { |
| int channel = 0; |
| #ifdef USE_NEON |
| for (; channel <= depth - 16; channel += 16) { |
| uint8x16_t acc_reg = vld1q_u8(acc + channel); |
| uint8x16_t input_reg = vld1q_u8(input_row_ptr); |
| input_row_ptr += 16; |
| acc_reg = vmaxq_u8(acc_reg, input_reg); |
| vst1q_u8(acc + channel, acc_reg); |
| } |
| |
| for (; channel <= depth - 8; channel += 8) { |
| uint8x8_t acc_reg = vld1_u8(acc + channel); |
| uint8x8_t input_reg = vld1_u8(input_row_ptr); |
| input_row_ptr += 8; |
| acc_reg = vmax_u8(acc_reg, input_reg); |
| vst1_u8(acc + channel, acc_reg); |
| } |
| #endif |
| for (; channel < depth; ++channel) { |
| acc[channel] = std::max(acc[channel], *input_row_ptr++); |
| } |
| } |
| } |
| uint8* output_ptr = |
| output_data + Offset(output_shape, batch, out_y, out_x, 0); |
| int channel = 0; |
| #ifdef USE_NEON |
| for (; channel <= depth - 16; channel += 16) { |
| uint8x16_t a = vld1q_u8(acc + channel); |
| a = vminq_u8(a, vdupq_n_u8(params.quantized_activation_max)); |
| a = vmaxq_u8(a, vdupq_n_u8(params.quantized_activation_min)); |
| vst1q_u8(output_ptr + channel, a); |
| } |
| for (; channel <= depth - 8; channel += 8) { |
| uint8x8_t a = vld1_u8(acc + channel); |
| a = vmin_u8(a, vdup_n_u8(params.quantized_activation_max)); |
| a = vmax_u8(a, vdup_n_u8(params.quantized_activation_min)); |
| vst1_u8(output_ptr + channel, a); |
| } |
| #endif |
| for (; channel < depth; ++channel) { |
| uint8 a = acc[channel]; |
| a = std::max<uint8>(a, params.quantized_activation_min); |
| a = std::min<uint8>(a, params.quantized_activation_max); |
| output_ptr[channel] = static_cast<uint8>(a); |
| } |
| } |
| } |
| } |
| } |
| |
| inline void L2Pool(const PoolParams& params, const RuntimeShape& input_shape, |
| const float* input_data, const RuntimeShape& output_shape, |
| float* output_data) { |
| gemmlowp::ScopedProfilingLabel label("L2Pool"); |
| TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); |
| TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); |
| const int batches = MatchingDim(input_shape, 0, output_shape, 0); |
| const int input_height = input_shape.Dims(1); |
| const int input_width = input_shape.Dims(2); |
| const int output_height = output_shape.Dims(1); |
| const int output_width = output_shape.Dims(2); |
| const int stride_height = params.stride_height; |
| const int stride_width = params.stride_width; |
| // Actually carry out L2 Pool. Code is written in forward mode: we go through |
| // the input values once, and write to all the pooled regions that it maps to. |
| const auto in_mat = MapAsMatrixWithLastDimAsRows(input_data, input_shape); |
| auto out_mat = MapAsMatrixWithLastDimAsRows(output_data, output_shape); |
| Eigen::VectorXf in_square(in_mat.rows()); |
| Eigen::VectorXf out_count(out_mat.cols()); |
| out_count.setZero(); |
| // Prefill the output to 0. |
| out_mat.setZero(); |
| for (int b = 0; b < batches; ++b) { |
| for (int h = 0; h < input_height; ++h) { |
| for (int w = 0; w < input_width; ++w) { |
| // (h_start, h_end) * (w_start, w_end) is the range that the input |
| // vector projects to. |
| const int hpad = h + params.padding_values.height; |
| const int wpad = w + params.padding_values.width; |
| const int h_start = |
| (hpad < params.filter_height) |
| ? 0 |
| : (hpad - params.filter_height) / stride_height + 1; |
| const int h_end = std::min(hpad / stride_height + 1, output_height); |
| const int w_start = |
| (wpad < params.filter_width) |
| ? 0 |
| : (wpad - params.filter_width) / stride_width + 1; |
| const int w_end = std::min(wpad / stride_width + 1, output_width); |
| // pre-compute square |
| const int in_offset = w + input_width * (h + input_height * b); |
| in_square = |
| in_mat.col(in_offset).array() * in_mat.col(in_offset).array(); |
| // compute elementwise sum of squares |
| for (int ph = h_start; ph < h_end; ++ph) { |
| for (int pw = w_start; pw < w_end; ++pw) { |
| const int out_offset = pw + output_width * (ph + output_height * b); |
| out_mat.col(out_offset) += in_square; |
| out_count(out_offset)++; |
| } |
| } |
| } |
| } |
| } |
| |
| out_count = out_count.array().inverse(); |
| out_mat = |
| (out_mat.array().rowwise() * out_count.transpose().array()).cwiseSqrt(); |
| |
| const int flat_size = output_shape.FlatSize(); |
| for (int i = 0; i < flat_size; ++i) { |
| output_data[i] = ActivationFunctionWithMinMax(output_data[i], |
| params.float_activation_min, |
| params.float_activation_max); |
| } |
| } |
| |
| inline void LocalResponseNormalization( |
| const tflite::LocalResponseNormalizationParams& op_params, |
| const RuntimeShape& input_shape, const float* input_data, |
| const RuntimeShape& output_shape, float* output_data) { |
| gemmlowp::ScopedProfilingLabel label("LocalResponseNormalization"); |
| MatchingFlatSize(input_shape, output_shape); |
| |
| const auto data_in = MapAsMatrixWithLastDimAsRows(input_data, input_shape); |
| auto data_out = MapAsMatrixWithLastDimAsRows(output_data, output_shape); |
| |
| // Carry out local response normalization, vector by vector. |
| // Since the data are stored column major, making row-wise operation |
| // probably not memory efficient anyway, we do an explicit for loop over |
| // the columns. |
| const int double_range = op_params.range * 2; |
| Eigen::VectorXf padded_square(data_in.rows() + double_range); |
| padded_square.setZero(); |
| for (int r = 0; r < data_in.cols(); ++r) { |
| // Do local response normalization for data_in(:, r) |
| // first, compute the square and store them in buffer for repeated use |
| padded_square.block(op_params.range, 0, data_in.rows(), 1) = |
| data_in.col(r).cwiseProduct(data_in.col(r)) * op_params.alpha; |
| // Then, compute the scale and writes them to data_out |
| float accumulated_scale = 0; |
| for (int i = 0; i < double_range; ++i) { |
| accumulated_scale += padded_square(i); |
| } |
| for (int i = 0; i < data_in.rows(); ++i) { |
| accumulated_scale += padded_square(i + double_range); |
| data_out(i, r) = op_params.bias + accumulated_scale; |
| accumulated_scale -= padded_square(i); |
| } |
| } |
| |
| // In a few cases, the pow computation could benefit from speedups. |
| if (op_params.beta == 1) { |
| data_out.array() = data_in.array() * data_out.array().inverse(); |
| } else if (op_params.beta == 0.5) { |
| data_out.array() = data_in.array() * data_out.array().sqrt().inverse(); |
| } else { |
| data_out.array() = data_in.array() * data_out.array().pow(-op_params.beta); |
| } |
| } |
| |
| inline void Softmax(const SoftmaxParams& params, |
| const RuntimeShape& input_shape, const float* input_data, |
| const RuntimeShape& output_shape, float* output_data) { |
| gemmlowp::ScopedProfilingLabel label("Softmax"); |
| MatchingFlatSize(input_shape, output_shape); |
| |
| const auto in_mat = MapAsMatrixWithLastDimAsRows(input_data, input_shape); |
| auto out_mat = MapAsMatrixWithLastDimAsRows(output_data, output_shape); |
| // Compute the exponential first, removing the max coefficient for numerical |
| // stability. |
| out_mat = |
| (in_mat.rowwise() - in_mat.colwise().maxCoeff()).array() * params.beta; |
| // We are separating out the exp function so that exp can be vectorized. |
| out_mat = out_mat.array().exp(); |
| // Normalize to get the activations. |
| Eigen::Array<float, 1, Eigen::Dynamic> scale = |
| out_mat.array().colwise().sum().inverse(); |
| out_mat.array().rowwise() *= scale; |
| } |
| |
| // TODO(b/80418076): Move to legacy ops file, update invocations. |
| // Legacy. |
| inline void Softmax(const float* input_data, const RuntimeShape& input_shape, |
| float beta, float* output_data, |
| const RuntimeShape& output_shape) { |
| SoftmaxParams params; |
| params.beta = beta; |
| Softmax(params, input_shape, input_data, output_shape, output_data); |
| } |
| |
| inline void Softmax(const SoftmaxParams& params, |
| const RuntimeShape& input_shape, const uint8* input_data, |
| const RuntimeShape& output_shape, uint8* output_data) { |
| const int32 input_beta_multiplier = params.input_multiplier; |
| const int32 input_beta_left_shift = params.input_left_shift; |
| const int diff_min = params.diff_min; |
| // The representation chosen for the input to the exp() function is Q5.26. |
| // We need to leave extra space since values that we skip might be as large as |
| // -32 before multiplying by input_beta_multiplier, and therefore as large as |
| // -16 afterwards. Note that exp(-8) is definitely not insignificant to |
| // accumulation, but exp(-16) definitely is. |
| static const int kScaledDiffIntegerBits = 5; |
| static const int kAccumulationIntegerBits = 12; |
| using FixedPointScaledDiff = |
| gemmlowp::FixedPoint<int32, kScaledDiffIntegerBits>; |
| using FixedPointAccum = gemmlowp::FixedPoint<int32, kAccumulationIntegerBits>; |
| using FixedPoint0 = gemmlowp::FixedPoint<int32, 0>; |
| |
| gemmlowp::ScopedProfilingLabel label("Softmax/8bit"); |
| const int trailing_dim = input_shape.DimensionsCount() - 1; |
| const int outer_size = |
| MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape); |
| const int depth = |
| MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim); |
| |
| for (int b = 0; b < outer_size; ++b) { |
| const uint8* input_data_ptr = input_data + b * depth; |
| uint8* output_data_ptr = output_data + b * depth; |
| |
| // Determine the largest entry in the current row |
| uint8 max_in_row = 0; |
| { |
| int c = 0; |
| #ifdef USE_NEON |
| uint8x16_t max16_0 = vdupq_n_u8(0); |
| uint8x16_t max16_1 = vdupq_n_u8(0); |
| for (; c <= depth - 32; c += 32) { |
| max16_0 = vmaxq_u8(max16_0, vld1q_u8(input_data_ptr + c + 0)); |
| max16_1 = vmaxq_u8(max16_1, vld1q_u8(input_data_ptr + c + 16)); |
| } |
| uint8x16_t max16 = vmaxq_u8(max16_0, max16_1); |
| if (c <= depth - 16) { |
| max16 = vmaxq_u8(max16, vld1q_u8(input_data_ptr + c)); |
| c += 16; |
| } |
| uint8x8_t max8 = vmax_u8(vget_low_u8(max16), vget_high_u8(max16)); |
| if (c <= depth - 8) { |
| max8 = vmax_u8(max8, vld1_u8(input_data_ptr + c)); |
| c += 8; |
| } |
| uint8x8_t max4 = vmax_u8(max8, vext_u8(max8, max8, 4)); |
| uint8x8_t max2 = vmax_u8(max4, vext_u8(max4, max4, 2)); |
| uint8x8_t max1 = vpmax_u8(max2, max2); |
| max_in_row = vget_lane_u8(max1, 0); |
| #endif |
| for (; c < depth; ++c) { |
| max_in_row = std::max(max_in_row, input_data_ptr[c]); |
| } |
| } |
| |
| #ifdef USE_NEON |
| using FixedPointAccumInt32x4 = |
| gemmlowp::FixedPoint<int32x4_t, kAccumulationIntegerBits>; |
| using FixedPointScaledDiffInt32x4 = |
| gemmlowp::FixedPoint<int32x4_t, kScaledDiffIntegerBits>; |
| using FixedPoint0Int32x4 = gemmlowp::FixedPoint<int32x4_t, 0>; |
| FixedPoint0Int32x4 input_beta_multiplier_f0 = |
| FixedPoint0Int32x4::FromScalarRaw(input_beta_multiplier); |
| int16x8_t max_in_row_s16 = vdupq_n_s16(max_in_row); |
| #endif |
| |
| // Compute the sum of exponentials of the differences of entries in the |
| // current row from the largest entry in the current row. |
| FixedPointAccum sum_of_exps = FixedPointAccum::Zero(); |
| { |
| int c = 0; |
| #ifdef USE_NEON |
| int32x4_t diff_min_s32 = vdupq_n_s32(diff_min); |
| FixedPointAccumInt32x4 sum_of_exps_0 = FixedPointAccumInt32x4::Zero(); |
| FixedPointAccumInt32x4 sum_of_exps_1 = FixedPointAccumInt32x4::Zero(); |
| FixedPointAccumInt32x4 zeros = FixedPointAccumInt32x4::Zero(); |
| for (; c <= depth - 8; c += 8) { |
| uint16x8_t input_u16 = vmovl_u8(vld1_u8(input_data_ptr + c)); |
| int16x8_t input_diff_s16 = |
| vsubq_s16(vreinterpretq_s16_u16(input_u16), max_in_row_s16); |
| int32x4_t input_diff_s32_0 = vmovl_s16(vget_low_s16(input_diff_s16)); |
| int32x4_t input_diff_s32_1 = vmovl_s16(vget_high_s16(input_diff_s16)); |
| int32x4_t mask_0 = |
| gemmlowp::MaskIfGreaterThanOrEqual(input_diff_s32_0, diff_min_s32); |
| int32x4_t mask_1 = |
| gemmlowp::MaskIfGreaterThanOrEqual(input_diff_s32_1, diff_min_s32); |
| FixedPointScaledDiffInt32x4 scaled_diff_0 = |
| input_beta_multiplier_f0 * |
| FixedPointScaledDiffInt32x4::FromRaw( |
| gemmlowp::ShiftLeft(input_diff_s32_0, input_beta_left_shift)); |
| FixedPointScaledDiffInt32x4 scaled_diff_1 = |
| input_beta_multiplier_f0 * |
| FixedPointScaledDiffInt32x4::FromRaw( |
| gemmlowp::ShiftLeft(input_diff_s32_1, input_beta_left_shift)); |
| FixedPointAccumInt32x4 exps_0 = |
| gemmlowp::Rescale<kAccumulationIntegerBits>( |
| exp_on_negative_values(scaled_diff_0)); |
| FixedPointAccumInt32x4 exps_1 = |
| gemmlowp::Rescale<kAccumulationIntegerBits>( |
| exp_on_negative_values(scaled_diff_1)); |
| FixedPointAccumInt32x4 masked_exps_0 = |
| SelectUsingMask(mask_0, exps_0, zeros); |
| FixedPointAccumInt32x4 masked_exps_1 = |
| SelectUsingMask(mask_1, exps_1, zeros); |
| sum_of_exps_0 = sum_of_exps_0 + masked_exps_0; |
| sum_of_exps_1 = sum_of_exps_1 + masked_exps_1; |
| } |
| int32x4_t sum_of_exps_reduced_4 = (sum_of_exps_0 + sum_of_exps_1).raw(); |
| int32x2_t sum_of_exps_reduced_2 = |
| vadd_s32(vget_low_s32(sum_of_exps_reduced_4), |
| vget_high_s32(sum_of_exps_reduced_4)); |
| int32x2_t sum_of_exps_reduced_1 = |
| vpadd_s32(sum_of_exps_reduced_2, sum_of_exps_reduced_2); |
| sum_of_exps = |
| FixedPointAccum::FromRaw(vget_lane_s32(sum_of_exps_reduced_1, 0)); |
| #endif |
| for (; c < depth; ++c) { |
| int32 input_diff = static_cast<int32>(input_data_ptr[c]) - max_in_row; |
| if (input_diff >= diff_min) { |
| const int32 input_diff_rescaled = |
| MultiplyByQuantizedMultiplierGreaterThanOne( |
| input_diff, input_beta_multiplier, input_beta_left_shift); |
| const FixedPointScaledDiff scaled_diff_f8 = |
| FixedPointScaledDiff::FromRaw(input_diff_rescaled); |
| sum_of_exps = |
| sum_of_exps + gemmlowp::Rescale<kAccumulationIntegerBits>( |
| exp_on_negative_values(scaled_diff_f8)); |
| } |
| } |
| } |
| |
| // Compute the fixed-point multiplier and shift that we need to apply to |
| // perform a division by the above-computed sum-of-exponentials. |
| int32 fixed_sum_of_exps = sum_of_exps.raw(); |
| int headroom_plus_one = |
| CountLeadingZeros(static_cast<uint32>(fixed_sum_of_exps)); |
| // This is the number of bits to the left of the binary point above 1.0. |
| // Consider fixed_sum_of_exps=1.25. In that case shifted_scale=0.8 and |
| // no later adjustment will be needed. |
| int num_bits_over_unit = kAccumulationIntegerBits - headroom_plus_one; |
| int32 shifted_sum_minus_one = static_cast<int32>( |
| (static_cast<uint32>(fixed_sum_of_exps) << headroom_plus_one) - |
| (static_cast<uint32>(1) << 31)); |
| FixedPoint0 shifted_scale = gemmlowp::one_over_one_plus_x_for_x_in_0_1( |
| FixedPoint0::FromRaw(shifted_sum_minus_one)); |
| |
| // Compute the quotients of exponentials of differences of entries in the |
| // current row from the largest entry, over the previously-computed sum of |
| // exponentials. |
| { |
| int c = 0; |
| #ifdef USE_NEON |
| int16x8_t diff_min_s16 = vdupq_n_s16(diff_min); |
| for (; c <= depth - 8; c += 8) { |
| uint16x8_t input_u16 = vmovl_u8(vld1_u8(input_data_ptr + c)); |
| int16x8_t input_diff_s16 = |
| vsubq_s16(vreinterpretq_s16_u16(input_u16), max_in_row_s16); |
| int32x4_t input_diff_s32_0 = vmovl_s16(vget_low_s16(input_diff_s16)); |
| int32x4_t input_diff_s32_1 = vmovl_s16(vget_high_s16(input_diff_s16)); |
| uint8x8_t mask = vmovn_u16(vcgeq_s16(input_diff_s16, diff_min_s16)); |
| FixedPointScaledDiffInt32x4 scaled_diff_0 = |
| input_beta_multiplier_f0 * |
| FixedPointScaledDiffInt32x4::FromRaw( |
| gemmlowp::ShiftLeft(input_diff_s32_0, input_beta_left_shift)); |
| FixedPointScaledDiffInt32x4 scaled_diff_1 = |
| input_beta_multiplier_f0 * |
| FixedPointScaledDiffInt32x4::FromRaw( |
| gemmlowp::ShiftLeft(input_diff_s32_1, input_beta_left_shift)); |
| FixedPoint0Int32x4 exp_0 = exp_on_negative_values(scaled_diff_0); |
| FixedPoint0Int32x4 exp_1 = exp_on_negative_values(scaled_diff_1); |
| int32x4_t output_s32_0 = gemmlowp::RoundingDivideByPOT( |
| vqrdmulhq_n_s32(exp_0.raw(), shifted_scale.raw()), |
| num_bits_over_unit + 31 - 8); |
| int32x4_t output_s32_1 = gemmlowp::RoundingDivideByPOT( |
| vqrdmulhq_n_s32(exp_1.raw(), shifted_scale.raw()), |
| num_bits_over_unit + 31 - 8); |
| int16x8_t output_s16 = |
| vcombine_s16(vqmovn_s32(output_s32_0), vqmovn_s32(output_s32_1)); |
| uint8x8_t output_u8 = vqmovun_s16(output_s16); |
| uint8x8_t masked_output = vbsl_u8(mask, output_u8, vdup_n_u8(0)); |
| vst1_u8(output_data_ptr + c, masked_output); |
| } |
| #endif |
| for (; c < depth; ++c) { |
| int32 input_diff = static_cast<int32>(input_data_ptr[c]) - max_in_row; |
| if (input_diff >= diff_min) { |
| const int32 input_diff_rescaled = |
| MultiplyByQuantizedMultiplierGreaterThanOne( |
| input_diff, input_beta_multiplier, input_beta_left_shift); |
| const FixedPointScaledDiff scaled_diff_f8 = |
| FixedPointScaledDiff::FromRaw(input_diff_rescaled); |
| |
| FixedPoint0 exp_in_0 = exp_on_negative_values(scaled_diff_f8); |
| int32 unsat_output = gemmlowp::RoundingDivideByPOT( |
| (shifted_scale * exp_in_0).raw(), num_bits_over_unit + 31 - 8); |
| |
| output_data_ptr[c] = std::max(std::min(unsat_output, 255), 0); |
| |
| } else { |
| output_data_ptr[c] = 0; |
| } |
| } |
| } |
| } |
| } |
| |
| // TODO(b/80418076): Move to legacy ops file, update invocations. |
| // Legacy. |
| inline void Softmax(const uint8* input_data, const RuntimeShape& input_shape, |
| int32 input_beta_multiplier, int32 input_beta_left_shift, |
| int diff_min, uint8* output_data, |
| const RuntimeShape& output_shape) { |
| SoftmaxParams params; |
| params.input_multiplier = input_beta_multiplier; |
| params.input_left_shift = input_beta_left_shift; |
| params.diff_min = diff_min; |
| Softmax(params, input_shape, input_data, output_shape, output_data); |
| } |
| |
| // TODO(myenik): This is the same as the reference implementation, not actually |
| // optimized yet. |
| inline void LogSoftmax(const SoftmaxParams& params, |
| const RuntimeShape& input_shape, const float* input_data, |
| const RuntimeShape& output_shape, float* output_data) { |
| gemmlowp::ScopedProfilingLabel label("LogSoftmax"); |
| const int trailing_dim = input_shape.DimensionsCount() - 1; |
| const int outer_size = |
| MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape); |
| const int depth = |
| MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim); |
| |
| for (int i = 0; i < outer_size; ++i) { |
| const float* block_input_data = input_data + i * depth; |
| float* block_output_data = output_data + i * depth; |
| // Find max element value which we'll use to ensure numerical stability |
| // taking advantage of the following equality: |
| // log(exp(x[i])/sum(exp(x[i]))) == log(exp(x[i]+C)/sum(exp(x[i]+C))) |
| float max = std::numeric_limits<float>::lowest(); |
| for (int c = 0; c < depth; ++c) { |
| max = std::max(max, block_input_data[c]); |
| } |
| |
| // Compute sum. |
| float sum = 0.f; |
| for (int c = 0; c < depth; ++c) { |
| sum += std::exp(block_input_data[c] - max); |
| } |
| |
| // Compute result. |
| const float log_sum = std::log(sum); |
| for (int c = 0; c < depth; ++c) { |
| block_output_data[c] = block_input_data[c] - max - log_sum; |
| } |
| } |
| } |
| |
| // TODO(b/80418076): Move to legacy ops file, update invocations. |
| // Legacy |
| inline void LogSoftmax(const float* input_data, const RuntimeShape& input_shape, |
| float* output_data, const RuntimeShape& output_shape) { |
| SoftmaxParams params; |
| // No params currently used for float LogSoftmax. |
| LogSoftmax(params, input_shape, input_data, output_shape, output_data); |
| } |
| |
| template <int OutputIntegerBits, int InputIntegerBits> |
| inline gemmlowp::FixedPoint<int32, OutputIntegerBits> |
| log_x_for_x_greater_than_or_equal_to_1_impl( |
| gemmlowp::FixedPoint<int32, InputIntegerBits> input_val) { |
| // assert(__builtin_clz(0u) >= std::numeric_limits<uint32>::digits - 1); |
| // assert(__builtin_clz(0u) <= std::numeric_limits<uint32>::digits); |
| using FixedPoint0 = gemmlowp::FixedPoint<int32, 0>; |
| // The reason for accumulating the result with an extra bit of headroom is |
| // that z_pow_2_adj * log_2 might be saturated, and adding num_scaled * |
| // recip_denom will otherwise introduce an error. |
| static constexpr int kAccumIntegerBits = OutputIntegerBits + 1; |
| using FixedPointAccum = gemmlowp::FixedPoint<int32, kAccumIntegerBits>; |
| |
| const FixedPoint0 log_2 = GEMMLOWP_CHECKED_FIXEDPOINT_CONSTANT( |
| FixedPoint0, 1488522236, std::log(2.0)); |
| const FixedPoint0 sqrt_sqrt_half = GEMMLOWP_CHECKED_FIXEDPOINT_CONSTANT( |
| FixedPoint0, 1805811301, std::sqrt(std::sqrt(0.5))); |
| const FixedPoint0 sqrt_half = GEMMLOWP_CHECKED_FIXEDPOINT_CONSTANT( |
| FixedPoint0, 1518500250, std::sqrt(0.5)); |
| const FixedPoint0 one_quarter = |
| GEMMLOWP_CHECKED_FIXEDPOINT_CONSTANT(FixedPoint0, 536870912, 1.0 / 4.0); |
| |
| const FixedPoint0 alpha_n = GEMMLOWP_CHECKED_FIXEDPOINT_CONSTANT( |
| FixedPoint0, 117049297, 11.0 / 240.0 * std::sqrt(std::sqrt(2.0))); |
| const FixedPoint0 alpha_d = GEMMLOWP_CHECKED_FIXEDPOINT_CONSTANT( |
| FixedPoint0, 127690142, 1.0 / 20.0 * std::sqrt(std::sqrt(2.0))); |
| const FixedPoint0 alpha_i = GEMMLOWP_CHECKED_FIXEDPOINT_CONSTANT( |
| FixedPoint0, 1057819769, |
| 2.0 / std::sqrt(std::sqrt(2.0)) - std::sqrt(std::sqrt(2.0))); |
| const FixedPoint0 alpha_f = GEMMLOWP_CHECKED_FIXEDPOINT_CONSTANT( |
| FixedPoint0, 638450708, 1.0 / 4.0 * std::sqrt(std::sqrt(2.0))); |
| |
| const FixedPointAccum shifted_quarter = |
| gemmlowp::Rescale<kAccumIntegerBits>(one_quarter); |
| |
| // Reinterpret the input value as Q0.31, because we will figure out the |
| // required shift "ourselves" instead of using, say, Rescale. |
| FixedPoint0 z_a = FixedPoint0::FromRaw(input_val.raw()); |
| // z_a_pow_2 = input_integer_bits - z_a_headroom; |
| int z_a_headroom_plus_1 = CountLeadingZeros(static_cast<uint32>(z_a.raw())); |
| FixedPoint0 r_a_tmp = |
| SaturatingRoundingMultiplyByPOTParam(z_a, (z_a_headroom_plus_1 - 1)); |
| const int32 r_a_raw = |
| SaturatingRoundingMultiplyByPOTParam((r_a_tmp * sqrt_half).raw(), 1); |
| // z_pow_2_adj = max(z_pow_2_a - 0.75, z_pow_2_b - 0.25); |
| // z_pow_2_adj = max(InputIntegerBits - z_a_headroom_plus_1 + 0.25, |
| // InputIntegerBits - z_b_headroom - 0.25); |
| const FixedPointAccum z_a_pow_2_adj = SaturatingAddNonGemmlowp( |
| FixedPointAccum::FromRaw(SaturatingRoundingMultiplyByPOTParam( |
| InputIntegerBits - z_a_headroom_plus_1, 31 - kAccumIntegerBits)), |
| shifted_quarter); |
| |
| // z_b is treated like z_a, but premultiplying by sqrt(0.5). |
| FixedPoint0 z_b = z_a * sqrt_half; |
| int z_b_headroom = CountLeadingZeros(static_cast<uint32>(z_b.raw())) - 1; |
| const int32 r_b_raw = |
| SaturatingRoundingMultiplyByPOTParam(z_a.raw(), z_b_headroom); |
| const FixedPointAccum z_b_pow_2_adj = SaturatingSub( |
| FixedPointAccum::FromRaw(SaturatingRoundingMultiplyByPOTParam( |
| InputIntegerBits - z_b_headroom, 31 - kAccumIntegerBits)), |
| shifted_quarter); |
| |
| const FixedPoint0 r = FixedPoint0::FromRaw(std::min(r_a_raw, r_b_raw)); |
| const FixedPointAccum z_pow_2_adj = FixedPointAccum::FromRaw( |
| std::max(z_a_pow_2_adj.raw(), z_b_pow_2_adj.raw())); |
| |
| const FixedPoint0 p = gemmlowp::RoundingHalfSum(r, sqrt_sqrt_half); |
| FixedPoint0 q = r - sqrt_sqrt_half; |
| q = q + q; |
| |
| const FixedPoint0 common_sq = q * q; |
| const FixedPoint0 num = q * r + q * common_sq * alpha_n; |
| const FixedPoint0 denom_minus_one_0 = |
| p * (alpha_i + q + alpha_d * common_sq) + alpha_f * q; |
| const FixedPoint0 recip_denom = |
| one_over_one_plus_x_for_x_in_0_1(denom_minus_one_0); |
| |
| const FixedPointAccum num_scaled = gemmlowp::Rescale<kAccumIntegerBits>(num); |
| return gemmlowp::Rescale<OutputIntegerBits>(z_pow_2_adj * log_2 + |
| num_scaled * recip_denom); |
| } |
| |
| // Minimum output bits to accommodate log of maximum input range. It actually |
| // does not matter if one considers, say, [-64,64] or [-64,64). |
| // |
| // For example, run this through Octave: |
| // [0:127; ... |
| // ceil(log(abs( log(2.^(0:127))+1 ))/log(2)); ... |
| // ceil(log(abs( log(2.^(0:127))+1 ))/log(2))] |
| constexpr int min_log_x_output_bits(int input_bits) { |
| return input_bits > 90 |
| ? 7 |
| : input_bits > 44 |
| ? 6 |
| : input_bits > 21 |
| ? 5 |
| : input_bits > 10 |
| ? 4 |
| : input_bits > 4 ? 3 : input_bits > 1 ? 2 : 1; |
| } |
| |
| template <int OutputIntegerBits, int InputIntegerBits> |
| inline gemmlowp::FixedPoint<int32, OutputIntegerBits> |
| log_x_for_x_greater_than_or_equal_to_1( |
| gemmlowp::FixedPoint<int32, InputIntegerBits> input_val) { |
| static_assert( |
| OutputIntegerBits >= min_log_x_output_bits(InputIntegerBits), |
| "Output integer bits must be sufficent to accommodate logs of inputs."); |
| return log_x_for_x_greater_than_or_equal_to_1_impl<OutputIntegerBits, |
| InputIntegerBits>( |
| input_val); |
| } |
| |
| // Currently just a copy of the reference code. |
| inline void LogSoftmax(const SoftmaxParams& params, |
| const RuntimeShape& input_shape, const uint8* input_data, |
| const RuntimeShape& output_shape, uint8* output_data) { |
| gemmlowp::ScopedProfilingLabel label("LogSoftmax/Uint8"); |
| const int32 input_multiplier = params.input_multiplier; |
| const int32 input_left_shift = params.input_left_shift; |
| const int32 reverse_scaling_divisor = params.reverse_scaling_divisor; |
| const int32 reverse_scaling_right_shift = params.reverse_scaling_right_shift; |
| const int diff_min = params.diff_min; |
| // The representation chosen for the input to the exp() function is Q5.26. |
| // We need to leave extra space since values that we skip might be as large as |
| // -32 before multiplying by input_beta_multiplier, and therefore as large as |
| // -16 afterwards. Note that exp(-8) is definitely not insignificant to |
| // accumulation, but exp(-16) definitely is. |
| static constexpr int kScaledDiffIntegerBits = 5; |
| static constexpr int kAccumulationIntegerBits = 12; |
| static constexpr int kOutputIntegerBits = 4; |
| using FixedPointScaledDiff = |
| gemmlowp::FixedPoint<int32, kScaledDiffIntegerBits>; |
| using FixedPointAccum = gemmlowp::FixedPoint<int32, kAccumulationIntegerBits>; |
| using FixedPoint0 = gemmlowp::FixedPoint<int32, 0>; |
| |
| const int trailing_dim = input_shape.DimensionsCount() - 1; |
| const int outer_size = |
| MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape); |
| const int depth = |
| MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim); |
| |
| for (int i = 0; i < outer_size; ++i) { |
| const uint8* block_input_data = input_data + i * depth; |
| uint8* block_output_data = output_data + i * depth; |
| uint8 max_in_row = 0; |
| for (int c = 0; c < depth; ++c) { |
| max_in_row = std::max(max_in_row, block_input_data[c]); |
| } |
| |
| FixedPointAccum sum_of_exps = FixedPointAccum::Zero(); |
| for (int c = 0; c < depth; ++c) { |
| int32 input_diff = static_cast<int32>(block_input_data[c]) - max_in_row; |
| if (input_diff >= diff_min) { |
| const int32 input_diff_rescaled = |
| MultiplyByQuantizedMultiplierGreaterThanOne( |
| input_diff, input_multiplier, input_left_shift); |
| const FixedPointScaledDiff scaled_diff_f8 = |
| FixedPointScaledDiff::FromRaw(input_diff_rescaled); |
| sum_of_exps = sum_of_exps + gemmlowp::Rescale<kAccumulationIntegerBits>( |
| exp_on_negative_values(scaled_diff_f8)); |
| } |
| } |
| |
| const int32 fixed_log_sum_of_exps = |
| log_x_for_x_greater_than_or_equal_to_1<kScaledDiffIntegerBits>( |
| sum_of_exps) |
| .raw(); |
| |
| // rescaled_diff_min is smallest representable in |
| // Q(kScaledDiffIntegerBits).(31-kScaledDiffIntegerBits) plus the |
| // log-sub-exps that will be subtracted in the loop. |
| // |
| // The thresholds diff_min, etc are negative. |
| const int rescaled_diff_min = |
| fixed_log_sum_of_exps + std::numeric_limits<int32>::lowest(); |
| const int adjusted_diff_min = |
| std::max(diff_min - 1, // Note use of > below instead of >= above. |
| MultiplyByQuantizedMultiplierSmallerThanOneExp( |
| rescaled_diff_min, reverse_scaling_divisor, |
| -reverse_scaling_right_shift)); |
| |
| for (int c = 0; c < depth; ++c) { |
| int32 input_diff = static_cast<int32>(block_input_data[c]) - max_in_row; |
| if (input_diff > adjusted_diff_min) { |
| const int32 input_diff_rescaled = |
| MultiplyByQuantizedMultiplierGreaterThanOne( |
| input_diff, input_multiplier, input_left_shift); |
| int32 unsat_output = |
| gemmlowp::RoundingDivideByPOT( |
| (input_diff_rescaled - fixed_log_sum_of_exps), |
| 31 - kScaledDiffIntegerBits - kOutputIntegerBits) + |
| 255; |
| |
| block_output_data[c] = static_cast<uint8>( |
| std::max(std::min(unsat_output, static_cast<int32>(255)), 0)); |
| } else { |
| // Set output to smallest value. |
| block_output_data[c] = 0; |
| } |
| } |
| } |
| } |
| |
| // TODO(b/80418076): Move to legacy ops file, update invocations. |
| // Legacy. |
| inline void LogSoftmax(const uint8* input_data, const RuntimeShape& input_shape, |
| int32 input_multiplier, int32 input_left_shift, |
| int32 reverse_scaling_divisor, |
| int32 reverse_scaling_right_shift, int diff_min, |
| uint8* output_data, const RuntimeShape& output_shape) { |
| SoftmaxParams params; |
| params.input_multiplier = input_multiplier; |
| params.input_left_shift = input_left_shift; |
| params.reverse_scaling_divisor = reverse_scaling_divisor; |
| params.reverse_scaling_right_shift = reverse_scaling_right_shift; |
| params.diff_min = diff_min; |
| LogSoftmax(params, input_shape, input_data, output_shape, output_data); |
| } |
| |
| inline void Logistic(const RuntimeShape& input_shape, const float* input_data, |
| const RuntimeShape& output_shape, float* output_data) { |
| gemmlowp::ScopedProfilingLabel label("Logistic"); |
| auto input_map = MapAsVector(input_data, input_shape); |
| auto output_map = MapAsVector(output_data, output_shape); |
| output_map.array() = |
| input_map.array().unaryExpr(Eigen::internal::scalar_sigmoid_op<float>()); |
| } |
| |
| // Convenience version that allows, for example, generated-code calls to be |
| // uniform between data types. |
| inline void Logistic(const LogisticParams&, const RuntimeShape& input_shape, |
| const float* input_data, const RuntimeShape& output_shape, |
| float* output_data) { |
| // Drop params: not needed. |
| Logistic(input_shape, input_data, output_shape, output_data); |
| } |
| |
| inline void Logistic(const LogisticParams& params, |
| const RuntimeShape& input_shape, const uint8* input_data, |
| const RuntimeShape& output_shape, uint8* output_data) { |
| gemmlowp::ScopedProfilingLabel label("Logistic/Uint8"); |
| const int32 input_zero_point = params.input_zero_point; |
| const int32 input_range_radius = params.input_range_radius; |
| const int32 input_multiplier = params.input_multiplier; |
| const int input_left_shift = params.input_left_shift; |
| const int size = MatchingFlatSize(input_shape, output_shape); |
| |
| int c = 0; |
| #ifdef USE_NEON |
| // Handle 16 values at a time |
| for (; c <= size - 16; c += 16) { |
| // Read input uint8 values, cast to int16 and subtract input_zero_point |
| uint8x16_t input_val_u8 = vld1q_u8(input_data + c); |
| int16x8_t input_val_centered_0 = |
| vsubq_s16(vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(input_val_u8))), |
| vdupq_n_s16(input_zero_point)); |
| int16x8_t input_val_centered_1 = |
| vsubq_s16(vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(input_val_u8))), |
| vdupq_n_s16(input_zero_point)); |
| |
| // Prepare the bit masks that we will use at the end to implement the logic |
| // that was expressed in the scalar code with branching: |
| // if (input_val_centered < -input_range_radius) { |
| // output_val = 0; |
| // } else if (input_val_centered > input_range_radius) { |
| // output_val = 255; |
| // } else { |
| // ... |
| uint16x8_t mask_rightclamp_0 = |
| vcgtq_s16(input_val_centered_0, vdupq_n_s16(input_range_radius)); |
| uint16x8_t mask_rightclamp_1 = |
| vcgtq_s16(input_val_centered_1, vdupq_n_s16(input_range_radius)); |
| uint16x8_t mask_leftclamp_0 = |
| vcgeq_s16(input_val_centered_0, vdupq_n_s16(-input_range_radius)); |
| uint16x8_t mask_leftclamp_1 = |
| vcgeq_s16(input_val_centered_1, vdupq_n_s16(-input_range_radius)); |
| uint8x16_t mask_rightclamp = vcombine_u8(vshrn_n_u16(mask_rightclamp_0, 8), |
| vshrn_n_u16(mask_rightclamp_1, 8)); |
| uint8x16_t mask_leftclamp = vcombine_u8(vshrn_n_u16(mask_leftclamp_0, 8), |
| vshrn_n_u16(mask_leftclamp_1, 8)); |
| |
| // This performs what is expressed in the scalar code as |
| // const int32 input_val_rescaled = |
| // MultiplyByQuantizedMultiplierGreaterThanOne( |
| // input_val_centered, input_multiplier, input_left_shift); |
| int32x4_t input_val_rescaled_0 = |
| vshlq_s32(vmovl_s16(vget_low_s16(input_val_centered_0)), |
| vdupq_n_s32(input_left_shift)); |
| int32x4_t input_val_rescaled_1 = |
| vshlq_s32(vmovl_s16(vget_high_s16(input_val_centered_0)), |
| vdupq_n_s32(input_left_shift)); |
| int32x4_t input_val_rescaled_2 = |
| vshlq_s32(vmovl_s16(vget_low_s16(input_val_centered_1)), |
| vdupq_n_s32(input_left_shift)); |
| int32x4_t input_val_rescaled_3 = |
| vshlq_s32(vmovl_s16(vget_high_s16(input_val_centered_1)), |
| vdupq_n_s32(input_left_shift)); |
| input_val_rescaled_0 = |
| vqrdmulhq_n_s32(input_val_rescaled_0, input_multiplier); |
| input_val_rescaled_1 = |
| vqrdmulhq_n_s32(input_val_rescaled_1, input_multiplier); |
| input_val_rescaled_2 = |
| vqrdmulhq_n_s32(input_val_rescaled_2, input_multiplier); |
| input_val_rescaled_3 = |
| vqrdmulhq_n_s32(input_val_rescaled_3, input_multiplier); |
| |
| // Invoke gemmlowp::logistic on FixedPoint wrapping int32x4_t |
| using FixedPoint4 = gemmlowp::FixedPoint<int32x4_t, 4>; |
| using FixedPoint0 = gemmlowp::FixedPoint<int32x4_t, 0>; |
| const FixedPoint4 input_val_f4_0 = |
| FixedPoint4::FromRaw(input_val_rescaled_0); |
| const FixedPoint4 input_val_f4_1 = |
| FixedPoint4::FromRaw(input_val_rescaled_1); |
| const FixedPoint4 input_val_f4_2 = |
| FixedPoint4::FromRaw(input_val_rescaled_2); |
| const FixedPoint4 input_val_f4_3 = |
| FixedPoint4::FromRaw(input_val_rescaled_3); |
| const FixedPoint0 output_val_f0_0 = gemmlowp::logistic(input_val_f4_0); |
| const FixedPoint0 output_val_f0_1 = gemmlowp::logistic(input_val_f4_1); |
| const FixedPoint0 output_val_f0_2 = gemmlowp::logistic(input_val_f4_2); |
| const FixedPoint0 output_val_f0_3 = gemmlowp::logistic(input_val_f4_3); |
| |
| // Divide by 2^23 as in the scalar code |
| using gemmlowp::RoundingDivideByPOT; |
| int32x4_t output_val_s32_0 = RoundingDivideByPOT(output_val_f0_0.raw(), 23); |
| int32x4_t output_val_s32_1 = RoundingDivideByPOT(output_val_f0_1.raw(), 23); |
| int32x4_t output_val_s32_2 = RoundingDivideByPOT(output_val_f0_2.raw(), 23); |
| int32x4_t output_val_s32_3 = RoundingDivideByPOT(output_val_f0_3.raw(), 23); |
| |
| // Cast output values to uint8, saturating |
| int16x8_t output_val_s16_0 = vcombine_s16(vqmovn_s32(output_val_s32_0), |
| vqmovn_s32(output_val_s32_1)); |
| int16x8_t output_val_s16_1 = vcombine_s16(vqmovn_s32(output_val_s32_2), |
| vqmovn_s32(output_val_s32_3)); |
| uint8x16_t output_val_u8 = vcombine_u8(vqmovun_s16(output_val_s16_0), |
| vqmovun_s16(output_val_s16_1)); |
| |
| // Perform the bit-masking with the bit masks computed at the beginning, |
| // see the comment there. |
| output_val_u8 = vorrq_u8(output_val_u8, mask_rightclamp); |
| output_val_u8 = vandq_u8(output_val_u8, mask_leftclamp); |
| |
| // Store back to memory |
| vst1q_u8(output_data + c, output_val_u8); |
| } |
| #endif |
| // Leftover loop: handle one value at a time with scalar code. |
| for (; c < size; ++c) { |
| const uint8 input_val_u8 = input_data[c]; |
| const int32 input_val_centered = |
| static_cast<int32>(input_val_u8) - input_zero_point; |
| uint8 output_val; |
| if (input_val_centered < -input_range_radius) { |
| output_val = 0; |
| } else if (input_val_centered > input_range_radius) { |
| output_val = 255; |
| } else { |
| const int32 input_val_rescaled = |
| MultiplyByQuantizedMultiplierGreaterThanOne( |
| input_val_centered, input_multiplier, input_left_shift); |
| using FixedPoint4 = gemmlowp::FixedPoint<int32, 4>; |
| using FixedPoint0 = gemmlowp::FixedPoint<int32, 0>; |
| const FixedPoint4 input_val_f4 = FixedPoint4::FromRaw(input_val_rescaled); |
| const FixedPoint0 output_val_f0 = gemmlowp::logistic(input_val_f4); |
| using gemmlowp::RoundingDivideByPOT; |
| int32 output_val_s32 = RoundingDivideByPOT(output_val_f0.raw(), 23); |
| if (output_val_s32 == 256) { |
| output_val_s32 = 255; |
| } |
| TFLITE_DCHECK_GE(output_val_s32, 0); |
| TFLITE_DCHECK_LE(output_val_s32, 255); |
| output_val = static_cast<uint8>(output_val_s32); |
| } |
| output_data[c] = output_val; |
| } |
| } |
| |
| // TODO(b/80418076): Move to legacy ops file, update invocations. |
| // Legacy. |
| inline void Logistic(const uint8* input_data, const RuntimeShape& input_shape, |
| int32 input_zero_point, int32 input_range_radius, |
| int32 input_multiplier, int input_left_shift, |
| uint8* output_data, const RuntimeShape& output_shape) { |
| LogisticParams params; |
| params.input_zero_point = input_zero_point; |
| params.input_range_radius = input_range_radius; |
| params.input_multiplier = input_multiplier; |
| params.input_left_shift = input_left_shift; |
| Logistic(params, input_shape, input_data, output_shape, output_data); |
| } |
| |
| inline void Logistic(const LogisticParams& params, |
| const RuntimeShape& input_shape, const int16* input_data, |
| const RuntimeShape& output_shape, int16* output_data) { |
| gemmlowp::ScopedProfilingLabel label("Logistic/Int16"); |
| const int flat_size = MatchingFlatSize(input_shape, output_shape); |
| |
| for (int i = 0; i < flat_size; i++) { |
| } |
| |
| int c = 0; |
| const int16* input_data_ptr = input_data; |
| int16* output_data_ptr = output_data; |
| #ifdef GEMMLOWP_NEON |
| { |
| // F0 uses 0 integer bits, range [-1, 1]. |
| // This is the return type of math functions such as tanh, logistic, |
| // whose range is in [-1, 1]. |
| using F0 = gemmlowp::FixedPoint<int16x8_t, 0>; |
| // F3 uses 3 integer bits, range [-8, 8], the input range expected here. |
| using F3 = gemmlowp::FixedPoint<int16x8_t, 3>; |
| |
| for (; c <= flat_size - 16; c += 16) { |
| F3 input0 = F3::FromRaw(vld1q_s16(input_data_ptr)); |
| F3 input1 = F3::FromRaw(vld1q_s16(input_data_ptr + 8)); |
| F0 output0 = gemmlowp::logistic(input0); |
| F0 output1 = gemmlowp::logistic(input1); |
| vst1q_s16(output_data_ptr, output0.raw()); |
| vst1q_s16(output_data_ptr + 8, output1.raw()); |
| |
| input_data_ptr += 16; |
| output_data_ptr += 16; |
| } |
| for (; c <= flat_size - 8; c += 8) { |
| F3 input = F3::FromRaw(vld1q_s16(input_data_ptr)); |
| F0 output = gemmlowp::logistic(input); |
| vst1q_s16(output_data_ptr, output.raw()); |
| |
| input_data_ptr += 8; |
| output_data_ptr += 8; |
| } |
| } |
| #endif |
| { |
| // F0 uses 0 integer bits, range [-1, 1]. |
| // This is the return type of math functions such as tanh, logistic, |
| // whose range is in [-1, 1]. |
| using F0 = gemmlowp::FixedPoint<std::int16_t, 0>; |
| // F3 uses 3 integer bits, range [-8, 8], the input range expected here. |
| using F3 = gemmlowp::FixedPoint<std::int16_t, 3>; |
| |
| for (; c < flat_size; ++c) { |
| F3 input = F3::FromRaw(*input_data_ptr); |
| F0 output = gemmlowp::logistic(input); |
| *output_data_ptr = output.raw(); |
| |
| ++input_data_ptr; |
| ++output_data_ptr; |
| } |
| } |
| } |
| |
| // TODO(b/80418076): Move to legacy ops file, update invocations. |
| // Legacy version. |
| inline void Logistic(const RuntimeShape& input_shape, const int16* input_data, |
| const RuntimeShape& output_shape, int16* output_data) { |
| LogisticParams params; |
| // No params currently needed by int16 Logistic. |
| Logistic(params, input_shape, input_data, output_shape, output_data); |
| } |
| |
| // TODO(b/80418076): Move to legacy ops file, update invocations. |
| // Legacy version. |
| inline void Logistic(const int16* input_data, const RuntimeShape& input_shape, |
| int16* output_data, const RuntimeShape& output_shape) { |
| LogisticParams params; |
| // No params currently needed by int16 Logistic. |
| Logistic(params, input_shape, input_data, output_shape, output_data); |
| } |
| |
| inline void Tanh(const RuntimeShape& input_shape, const float* input_data, |
| const RuntimeShape& output_shape, float* output_data) { |
| gemmlowp::ScopedProfilingLabel label("Tanh"); |
| auto input_map = MapAsVector(input_data, input_shape); |
| auto output_map = MapAsVector(output_data, output_shape); |
| output_map.array() = input_map.array().tanh(); |
| } |
| |
| // Convenience version that allows, for example, generated-code calls to be |
| // uniform between data types. |
| inline void Tanh(const TanhParams&, const RuntimeShape& input_shape, |
| const float* input_data, const RuntimeShape& output_shape, |
| float* output_data) { |
| // Drop params: not needed. |
| Tanh(input_shape, input_data, output_shape, output_data); |
| } |
| |
| inline void Tanh(const TanhParams& params, const RuntimeShape& input_shape, |
| const uint8* input_data, const RuntimeShape& output_shape, |
| uint8* output_data) { |
| // Note that this is almost the exact same code as in Logistic(). |
| gemmlowp::ScopedProfilingLabel label("Tanh"); |
| const int32 input_zero_point = params.input_zero_point; |
| const int32 input_range_radius = params.input_range_radius; |
| const int32 input_multiplier = params.input_multiplier; |
| const int input_left_shift = params.input_left_shift; |
| const int size = MatchingFlatSize(input_shape, output_shape); |
| |
| int c = 0; |
| int32_t output_zero_point = 128; |
| #ifdef USE_NEON |
| // Handle 16 values at a time |
| for (; c <= size - 16; c += 16) { |
| // Read input uint8 values, cast to int16 and subtract input_zero_point |
| uint8x16_t input_val_u8 = vld1q_u8(input_data + c); |
| int16x8_t input_val_centered_0 = |
| vsubq_s16(vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(input_val_u8))), |
| vdupq_n_s16(input_zero_point)); |
| int16x8_t input_val_centered_1 = |
| vsubq_s16(vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(input_val_u8))), |
| vdupq_n_s16(input_zero_point)); |
| |
| // Prepare the bit masks that we will use at the end to implement the logic |
| // that was expressed in the scalar code with branching: |
| // if (input_val_centered < -input_range_radius) { |
| // output_val = 0; |
| // } else if (input_val_centered > input_range_radius) { |
| // output_val = 255; |
| // } else { |
| // ... |
| uint16x8_t mask_rightclamp_0 = |
| vcgtq_s16(input_val_centered_0, vdupq_n_s16(input_range_radius)); |
| uint16x8_t mask_rightclamp_1 = |
| vcgtq_s16(input_val_centered_1, vdupq_n_s16(input_range_radius)); |
| uint16x8_t mask_leftclamp_0 = |
| vcgeq_s16(input_val_centered_0, vdupq_n_s16(-input_range_radius)); |
| uint16x8_t mask_leftclamp_1 = |
| vcgeq_s16(input_val_centered_1, vdupq_n_s16(-input_range_radius)); |
| uint8x16_t mask_rightclamp = vcombine_u8(vshrn_n_u16(mask_rightclamp_0, 8), |
| vshrn_n_u16(mask_rightclamp_1, 8)); |
| uint8x16_t mask_leftclamp = vcombine_u8(vshrn_n_u16(mask_leftclamp_0, 8), |
| vshrn_n_u16(mask_leftclamp_1, 8)); |
| |
| // This performs what is expressed in the scalar code as |
| // const int32 input_val_rescaled = |
| // MultiplyByQuantizedMultiplierGreaterThanOne( |
| // input_val_centered, input_multiplier, input_left_shift); |
| int32x4_t input_val_rescaled_0 = |
| vshlq_s32(vmovl_s16(vget_low_s16(input_val_centered_0)), |
| vdupq_n_s32(input_left_shift)); |
| int32x4_t input_val_rescaled_1 = |
| vshlq_s32(vmovl_s16(vget_high_s16(input_val_centered_0)), |
| vdupq_n_s32(input_left_shift)); |
| int32x4_t input_val_rescaled_2 = |
| vshlq_s32(vmovl_s16(vget_low_s16(input_val_centered_1)), |
| vdupq_n_s32(input_left_shift)); |
| int32x4_t input_val_rescaled_3 = |
| vshlq_s32(vmovl_s16(vget_high_s16(input_val_centered_1)), |
| vdupq_n_s32(input_left_shift)); |
| input_val_rescaled_0 = |
| vqrdmulhq_n_s32(input_val_rescaled_0, input_multiplier); |
| input_val_rescaled_1 = |
| vqrdmulhq_n_s32(input_val_rescaled_1, input_multiplier); |
| input_val_rescaled_2 = |
| vqrdmulhq_n_s32(input_val_rescaled_2, input_multiplier); |
| input_val_rescaled_3 = |
| vqrdmulhq_n_s32(input_val_rescaled_3, input_multiplier); |
| |
| // Invoke gemmlowp::tanh on FixedPoint wrapping int32x4_t |
| using FixedPoint4 = gemmlowp::FixedPoint<int32x4_t, 4>; |
| using FixedPoint0 = gemmlowp::FixedPoint<int32x4_t, 0>; |
| const FixedPoint4 input_val_f4_0 = |
| FixedPoint4::FromRaw(input_val_rescaled_0); |
| const FixedPoint4 input_val_f4_1 = |
| FixedPoint4::FromRaw(input_val_rescaled_1); |
| const FixedPoint4 input_val_f4_2 = |
| FixedPoint4::FromRaw(input_val_rescaled_2); |
| const FixedPoint4 input_val_f4_3 = |
| FixedPoint4::FromRaw(input_val_rescaled_3); |
| const FixedPoint0 output_val_f0_0 = gemmlowp::tanh(input_val_f4_0); |
| const FixedPoint0 output_val_f0_1 = gemmlowp::tanh(input_val_f4_1); |
| const FixedPoint0 output_val_f0_2 = gemmlowp::tanh(input_val_f4_2); |
| const FixedPoint0 output_val_f0_3 = gemmlowp::tanh(input_val_f4_3); |
| |
| // Divide by 2^24 as in the scalar code |
| using gemmlowp::RoundingDivideByPOT; |
| int32x4_t output_val_s32_0 = RoundingDivideByPOT(output_val_f0_0.raw(), 24); |
| int32x4_t output_val_s32_1 = RoundingDivideByPOT(output_val_f0_1.raw(), 24); |
| int32x4_t output_val_s32_2 = RoundingDivideByPOT(output_val_f0_2.raw(), 24); |
| int32x4_t output_val_s32_3 = RoundingDivideByPOT(output_val_f0_3.raw(), 24); |
| |
| // Add the output zero point |
| int32x4_t output_zero_point_s32 = vdupq_n_s32(output_zero_point); |
| output_val_s32_0 = vaddq_s32(output_val_s32_0, output_zero_point_s32); |
| output_val_s32_1 = vaddq_s32(output_val_s32_1, output_zero_point_s32); |
| output_val_s32_2 = vaddq_s32(output_val_s32_2, output_zero_point_s32); |
| output_val_s32_3 = vaddq_s32(output_val_s32_3, output_zero_point_s32); |
| |
| // Cast output values to uint8, saturating |
| int16x8_t output_val_s16_0 = vcombine_s16(vqmovn_s32(output_val_s32_0), |
| vqmovn_s32(output_val_s32_1)); |
| int16x8_t output_val_s16_1 = vcombine_s16(vqmovn_s32(output_val_s32_2), |
| vqmovn_s32(output_val_s32_3)); |
| uint8x16_t output_val_u8 = vcombine_u8(vqmovun_s16(output_val_s16_0), |
| vqmovun_s16(output_val_s16_1)); |
| |
| // Perform the bit-masking with the bit masks computed at the beginning, |
| // see the comment there. |
| output_val_u8 = vorrq_u8(output_val_u8, mask_rightclamp); |
| output_val_u8 = vandq_u8(output_val_u8, mask_leftclamp); |
| |
| // Store back to memory |
| vst1q_u8(output_data + c, output_val_u8); |
| } |
| #endif |
| // Leftover loop: handle one value at a time with scalar code. |
| for (; c < size; ++c) { |
| const uint8 input_val_u8 = input_data[c]; |
| const int32 input_val_centered = |
| static_cast<int32>(input_val_u8) - input_zero_point; |
| uint8 output_val; |
| if (input_val_centered < -input_range_radius) { |
| output_val = 0; |
| } else if (input_val_centered > input_range_radius) { |
| output_val = 255; |
| } else { |
| const int32 input_val_rescaled = |
| MultiplyByQuantizedMultiplierGreaterThanOne( |
| input_val_centered, input_multiplier, input_left_shift); |
| using FixedPoint4 = gemmlowp::FixedPoint<int32, 4>; |
| using FixedPoint0 = gemmlowp::FixedPoint<int32, 0>; |
| const FixedPoint4 input_val_f4 = FixedPoint4::FromRaw(input_val_rescaled); |
| const FixedPoint0 output_val_f0 = gemmlowp::tanh(input_val_f4); |
| using gemmlowp::RoundingDivideByPOT; |
| int32 output_val_s32 = RoundingDivideByPOT(output_val_f0.raw(), 24); |
| output_val_s32 += output_zero_point; |
| if (output_val_s32 == 256) { |
| output_val_s32 = 255; |
| } |
| TFLITE_DCHECK_GE(output_val_s32, 0); |
| TFLITE_DCHECK_LE(output_val_s32, 255); |
| output_val = static_cast<uint8>(output_val_s32); |
| } |
| output_data[c] = output_val; |
| } |
| } |
| |
| // TODO(b/80418076): Move to legacy ops file, update invocations. |
| // Legacy. |
| inline void Tanh(const uint8* input_data, const RuntimeShape& input_shape, |
| int32 input_zero_point, int32 input_range_radius, |
| int32 input_multiplier, int input_left_shift, |
| uint8* output_data, const RuntimeShape& output_shape) { |
| TanhParams params; |
| params.input_zero_point = input_zero_point; |
| params.input_range_radius = input_range_radius; |
| params.input_multiplier = input_multiplier; |
| params.input_left_shift = input_left_shift; |
| Tanh(params, input_shape, input_data, output_shape, output_data); |
| } |
| |
| inline void Tanh(const TanhParams& params, const RuntimeShape& input_shape, |
| const int16* input_data, const RuntimeShape& output_shape, |
| int16* output_data) { |
| gemmlowp::ScopedProfilingLabel label("Tanh/Int16"); |
| const int input_left_shift = params.input_left_shift; |
| // Support for shifts is limited until we have a parameterized version of |
| // SaturatingRoundingMultiplyByPOT(). |
| TFLITE_DCHECK_GE(input_left_shift, 0); |
| TFLITE_DCHECK_LE(input_left_shift, 1); |
| |
| const int flat_size = MatchingFlatSize(input_shape, output_shape); |
| |
| int c = 0; |
| const int16* input_data_ptr = input_data; |
| int16* output_data_ptr = output_data; |
| #ifdef GEMMLOWP_NEON |
| { |
| // F0 uses 0 integer bits, range [-1, 1]. |
| // This is the return type of math functions such as tanh, logistic, |
| // whose range is in [-1, 1]. |
| using F0 = gemmlowp::FixedPoint<int16x8_t, 0>; |
| // F3 uses 3 integer bits, range [-8, 8], the input range expected here. |
| using F3 = gemmlowp::FixedPoint<int16x8_t, 3>; |
| |
| if (input_left_shift == 0) { |
| for (; c <= flat_size - 16; c += 16) { |
| F3 input0 = F3::FromRaw(vld1q_s16(input_data_ptr)); |
| F3 input1 = F3::FromRaw(vld1q_s16(input_data_ptr + 8)); |
| F0 output0 = gemmlowp::tanh(input0); |
| F0 output1 = gemmlowp::tanh(input1); |
| vst1q_s16(output_data_ptr, output0.raw()); |
| vst1q_s16(output_data_ptr + 8, output1.raw()); |
| |
| input_data_ptr += 16; |
| output_data_ptr += 16; |
| } |
| for (; c <= flat_size - 8; c += 8) { |
| F3 input = F3::FromRaw(vld1q_s16(input_data_ptr)); |
| F0 output = gemmlowp::tanh(input); |
| vst1q_s16(output_data_ptr, output.raw()); |
| |
| input_data_ptr += 8; |
| output_data_ptr += 8; |
| } |
| } else { |
| for (; c <= flat_size - 16; c += 16) { |
| F3 input0 = F3::FromRaw(gemmlowp::SaturatingRoundingMultiplyByPOT<1>( |
| vld1q_s16(input_data_ptr))); |
| F3 input1 = F3::FromRaw(gemmlowp::SaturatingRoundingMultiplyByPOT<1>( |
| vld1q_s16(input_data_ptr + 8))); |
| F0 output0 = gemmlowp::tanh(input0); |
| F0 output1 = gemmlowp::tanh(input1); |
| vst1q_s16(output_data_ptr, output0.raw()); |
| vst1q_s16(output_data_ptr + 8, output1.raw()); |
| |
| input_data_ptr += 16; |
| output_data_ptr += 16; |
| } |
| for (; c <= flat_size - 8; c += 8) { |
| F3 input = F3::FromRaw(gemmlowp::SaturatingRoundingMultiplyByPOT<1>( |
| vld1q_s16(input_data_ptr))); |
| F0 output = gemmlowp::tanh(input); |
| vst1q_s16(output_data_ptr, output.raw()); |
| |
| input_data_ptr += 8; |
| output_data_ptr += 8; |
| } |
| } |
| } |
| #endif |
| { |
| // F0 uses 0 integer bits, range [-1, 1]. |
| // This is the return type of math functions such as tanh, logistic, |
| // whose range is in [-1, 1]. |
| using F0 = gemmlowp::FixedPoint<std::int16_t, 0>; |
| // F3 uses 3 integer bits, range [-8, 8], the input range expected here. |
| using F3 = gemmlowp::FixedPoint<std::int16_t, 3>; |
| |
| if (input_left_shift == 0) { |
| for (; c < flat_size; ++c) { |
| F3 input = F3::FromRaw(*input_data_ptr); |
| F0 output = gemmlowp::tanh(input); |
| *output_data_ptr = output.raw(); |
| |
| ++input_data_ptr; |
| ++output_data_ptr; |
| } |
| } else { |
| for (; c < flat_size; ++c) { |
| F3 input = F3::FromRaw( |
| gemmlowp::SaturatingRoundingMultiplyByPOT<1>(*input_data_ptr)); |
| F0 output = gemmlowp::tanh(input); |
| *output_data_ptr = output.raw(); |
| |
| ++input_data_ptr; |
| ++output_data_ptr; |
| } |
| } |
| } |
| } |
| |
| // TODO(b/80418076): Move to legacy ops file, update invocations. |
| // Legacy. |
| inline void Tanh(const int16* input_data, const RuntimeShape& input_shape, |
| int input_left_shift, int16* output_data, |
| const RuntimeShape& output_shape) { |
| TanhParams params; |
| params.input_left_shift = input_left_shift; |
| Tanh(params, input_shape, input_data, output_shape, output_data); |
| } |
| |
| template <typename SrcT, typename DstT> |
| inline void Cast(const RuntimeShape& input_shape, const SrcT* input_data, |
| const RuntimeShape& output_shape, DstT* output_data) { |
| gemmlowp::ScopedProfilingLabel label("Cast"); |
| auto input_map = MapAsVector(input_data, input_shape); |
| auto output_map = MapAsVector(output_data, output_shape); |
| output_map.array() = input_map.array().template cast<DstT>(); |
| } |
| |
| inline void Floor(const RuntimeShape& input_shape, const float* input_data, |
| const RuntimeShape& output_shape, float* output_data) { |
| gemmlowp::ScopedProfilingLabel label("Floor"); |
| auto input_map = MapAsVector(input_data, input_shape); |
| auto output_map = MapAsVector(output_data, output_shape); |
| output_map.array() = Eigen::floor(input_map.array()); |
| } |
| |
| #ifdef USE_NEON |
| inline void ResizeBilinearKernel(const float* input_ptr, int32 depth, |
| float scale, float* output_ptr) { |
| int ic = 0; |
| // Handle 32 input channels at a time. |
| for (; ic <= depth - 32; ic += 32) { |
| float32x4x2_t input[4]; |
| for (int i = 0; i < 4; i++) { |
| input[i].val[0] = vld1q_f32(input_ptr + 8 * i); |
| input[i].val[1] = vld1q_f32(input_ptr + 8 * i + 4); |
| } |
| float32x4x2_t acc[4]; |
| for (int i = 0; i < 4; i++) { |
| acc[i].val[0] = vld1q_f32(output_ptr + 8 * i); |
| acc[i].val[1] = vld1q_f32(output_ptr + 8 * i + 4); |
| } |
| for (int i = 0; i < 4; i++) { |
| acc[i].val[0] = vmlaq_n_f32(acc[i].val[0], input[i].val[0], scale); |
| acc[i].val[1] = vmlaq_n_f32(acc[i].val[1], input[i].val[1], scale); |
| } |
| for (int i = 0; i < 4; i++) { |
| vst1q_f32(output_ptr, acc[i].val[0]); |
| vst1q_f32(output_ptr + 4, acc[i].val[1]); |
| output_ptr += 8; |
| } |
| input_ptr += 32; |
| } |
| // Handle 16 input channels at a time. |
| for (; ic <= depth - 16; ic += 16) { |
| float32x4x2_t input[2]; |
| for (int i = 0; i < 2; i++) { |
| input[i].val[0] = vld1q_f32(input_ptr + 8 * i); |
| input[i].val[1] = vld1q_f32(input_ptr + 8 * i + 4); |
| } |
| float32x4x2_t acc[2]; |
| for (int i = 0; i < 2; i++) { |
| acc[i].val[0] = vld1q_f32(output_ptr + 8 * i); |
| acc[i].val[1] = vld1q_f32(output_ptr + 8 * i + 4); |
| } |
| for (int i = 0; i < 2; i++) { |
| acc[i].val[0] = vmlaq_n_f32(acc[i].val[0], input[i].val[0], scale); |
| acc[i].val[1] = vmlaq_n_f32(acc[i].val[1], input[i].val[1], scale); |
| } |
| for (int i = 0; i < 2; i++) { |
| vst1q_f32(output_ptr, acc[i].val[0]); |
| vst1q_f32(output_ptr + 4, acc[i].val[1]); |
| output_ptr += 8; |
| } |
| input_ptr += 16; |
| } |
| // Handle 8 input channels at a time. |
| for (; ic <= depth - 8; ic += 8) { |
| float32x4x2_t input; |
| input.val[0] = vld1q_f32(input_ptr); |
| input.val[1] = vld1q_f32(input_ptr + 4); |
| |
| float32x4x2_t acc; |
| acc.val[0] = vld1q_f32(output_ptr); |
| acc.val[1] = vld1q_f32(output_ptr + 4); |
| acc.val[0] = vmlaq_n_f32(acc.val[0], input.val[0], scale); |
| acc.val[1] = vmlaq_n_f32(acc.val[1], input.val[1], scale); |
| |
| vst1q_f32(output_ptr, acc.val[0]); |
| vst1q_f32(output_ptr + 4, acc.val[1]); |
| |
| input_ptr += 8; |
| output_ptr += 8; |
| } |
| // Handle 4 input channels at a time. |
| for (; ic <= depth - 4; ic += 4) { |
| float32x4_t input = vld1q_f32(input_ptr); |
| float32x4_t acc = vld1q_f32(output_ptr); |
| |
| acc = vmlaq_n_f32(acc, input, scale); |
| vst1q_f32(output_ptr, acc); |
| |
| input_ptr += 4; |
| output_ptr += 4; |
| } |
| // Handle 1 input channel at a time. |
| for (; ic < depth; ic++) { |
| *output_ptr += *input_ptr * scale; |
| output_ptr++; |
| input_ptr++; |
| } |
| } |
| #else |
| inline void ResizeBilinearKernel(const float* input_ptr, int32 depth, |
| float scale, float* output_ptr) { |
| for (int32 i = 0; i < depth; i++) { |
| *output_ptr += *input_ptr * scale; |
| output_ptr++; |
| input_ptr++; |
| } |
| } |
| #endif |
| |
| inline void ResizeBilinearKernel2x2(int32 x0, int32 x1, int32 y0, int32 y1, |
| int32 x, int32 y, int32 depth, int32 batch, |
| const RuntimeShape& input_shape, |
| const float* input_data, |
| const RuntimeShape& output_shape, |
| float* output_data) { |
| TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); |
| TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); |
| const int32 input_width = input_shape.Dims(2); |
| const int32 output_width = output_shape.Dims(2); |
| |
| const int32 input_x_offset = (x1 - x0) * depth; |
| const int32 input_y_offset = (y1 - y0) * depth * input_width; |
| const int32 output_x_offset = depth; |
| const int32 output_y_offset = depth * output_width; |
| |
| #ifdef USE_NEON |
| TFLITE_DCHECK(x1 >= x0); |
| TFLITE_DCHECK(y1 >= y0); |
| |
| int ic = 0; |
| // Handle 8 input channels at a time. |
| for (; ic <= depth - 8; ic += 8) { |
| const float* input_ptr = nullptr; |
| |
| float32x4x2_t x0y0; |
| input_ptr = &input_data[Offset(input_shape, batch, y0, x0, ic)]; |
| x0y0.val[0] = vld1q_f32(input_ptr); |
| x0y0.val[1] = vld1q_f32(input_ptr + 4); |
| |
| float32x4x2_t x1y0; |
| input_ptr += input_x_offset; |
| x1y0.val[0] = vld1q_f32(input_ptr); |
| x1y0.val[1] = vld1q_f32(input_ptr + 4); |
| |
| float32x4x2_t x0y1; |
| input_ptr += -input_x_offset + input_y_offset; |
| x0y1.val[0] = vld1q_f32(input_ptr); |
| x0y1.val[1] = vld1q_f32(input_ptr + 4); |
| |
| float32x4x2_t x1y1; |
| input_ptr += input_x_offset; |
| x1y1.val[0] = vld1q_f32(input_ptr); |
| x1y1.val[1] = vld1q_f32(input_ptr + 4); |
| |
| // Top left corner. |
| float* output_ptr = &output_data[Offset(output_shape, batch, y, x, ic)]; |
| vst1q_f32(output_ptr, x0y0.val[0]); |
| vst1q_f32(output_ptr + 4, x0y0.val[1]); |
| |
| // Top right corner. |
| output_ptr += output_x_offset; |
| float32x4x2_t tr; |
| tr.val[0] = vaddq_f32(x0y0.val[0], x1y0.val[0]); |
| tr.val[1] = vaddq_f32(x0y0.val[1], x1y0.val[1]); |
| tr.val[0] = vmulq_n_f32(tr.val[0], 0.5f); |
| tr.val[1] = vmulq_n_f32(tr.val[1], 0.5f); |
| |
| vst1q_f32(output_ptr, tr.val[0]); |
| vst1q_f32(output_ptr + 4, tr.val[1]); |
| |
| // Bottom left corner. |
| output_ptr += -output_x_offset + output_y_offset; |
| float32x4x2_t bl; |
| bl.val[0] = vaddq_f32(x0y0.val[0], x0y1.val[0]); |
| bl.val[1] = vaddq_f32(x0y0.val[1], x0y1.val[1]); |
| bl.val[0] = vmulq_n_f32(bl.val[0], 0.5f); |
| bl.val[1] = vmulq_n_f32(bl.val[1], 0.5f); |
| vst1q_f32(output_ptr, bl.val[0]); |
| vst1q_f32(output_ptr + 4, bl.val[1]); |
| |
| // Bottom right corner. |
| output_ptr += output_x_offset; |
| float32x4x2_t br; |
| br.val[0] = vaddq_f32(x1y0.val[0], x1y1.val[0]); |
| br.val[1] = vaddq_f32(x1y0.val[1], x1y1.val[1]); |
| br.val[0] = vmlaq_n_f32(bl.val[0], br.val[0], 0.5f); |
| br.val[1] = vmlaq_n_f32(bl.val[1], br.val[1], 0.5f); |
| br.val[0] = vmulq_n_f32(br.val[0], 0.5f); |
| br.val[1] = vmulq_n_f32(br.val[1], 0.5f); |
| vst1q_f32(output_ptr, br.val[0]); |
| vst1q_f32(output_ptr + 4, br.val[1]); |
| } |
| // Handle 4 input channels at a time. |
| for (; ic <= depth - 4; ic += 4) { |
| const float* input_ptr = |
| &input_data[Offset(input_shape, batch, y0, x0, ic)]; |
| float32x4_t x0y0 = vld1q_f32(input_ptr); |
| float32x4_t x1y0 = vld1q_f32(input_ptr + input_x_offset); |
| float32x4_t x0y1 = vld1q_f32(input_ptr + input_y_offset); |
| float32x4_t x1y1 = vld1q_f32(input_ptr + input_x_offset + input_y_offset); |
| |
| // Top left corner. |
| float* output_ptr = &output_data[Offset(output_shape, batch, y, x, ic)]; |
| vst1q_f32(output_ptr, x0y0); |
| |
| // Top right corner. |
| output_ptr += output_x_offset; |
| float32x4_t tr = vaddq_f32(x0y0, x1y0); |
| tr = vmulq_n_f32(tr, 0.5f); |
| vst1q_f32(output_ptr, tr); |
| |
| // Bottom left corner. |
| output_ptr += -output_x_offset + output_y_offset; |
| float32x4_t bl = vaddq_f32(x0y0, x0y1); |
| bl = vmulq_n_f32(bl, 0.5f); |
| vst1q_f32(output_ptr, bl); |
| |
| // Bottom right corner. |
| output_ptr += output_x_offset; |
| float32x4_t br = vaddq_f32(x1y0, x1y1); |
| br = vmlaq_n_f32(bl, br, 0.5f); |
| br = vmulq_n_f32(br, 0.5f); |
| vst1q_f32(output_ptr, br); |
| } |
| // Handle one input channel at a time. |
| for (; ic < depth; ic++) { |
| const int32 input_offset = Offset(input_shape, batch, y0, x0, ic); |
| |
| float x0y0 = input_data[input_offset]; |
| float x1y0 = input_data[input_offset + input_x_offset]; |
| float x0y1 = input_data[input_offset + input_y_offset]; |
| float x1y1 = input_data[input_offset + input_x_offset + input_y_offset]; |
| |
| // Top left corner. |
| const int32 output_offset = Offset(output_shape, batch, y, x, ic); |
| output_data[output_offset] = x0y0; |
| |
| // Top right corner. |
| output_data[output_offset + output_x_offset] = (x0y0 + x1y0) / 2; |
| |
| // Bottom left corner. |
| float output = (x0y0 + x0y1) / 2; |
| output_data[output_offset + output_y_offset] = output; |
| |
| // Bottom right corner. |
| output_data[output_offset + output_x_offset + output_y_offset] = |
| (output + ((x1y0 + x1y1) / 2)) / 2; |
| } |
| #else |
| for (int ch = 0; ch < depth; ch++) { |
| const int32 input_offset = Offset(input_shape, batch, y0, x0, ch); |
| |
| float x0y0 = input_data[input_offset]; |
| float x1y0 = input_data[input_offset + input_x_offset]; |
| float x0y1 = input_data[input_offset + input_y_offset]; |
| float x1y1 = input_data[input_offset + input_x_offset + input_y_offset]; |
| |
| // Top left corner. |
| const int32 output_offset = Offset(output_shape, batch, y, x, ch); |
| output_data[output_offset] = x0y0; |
| |
| // Top right corner. |
| output_data[output_offset + output_x_offset] = (x0y0 + x1y0) / 2; |
| |
| // Bottom left corner. |
| float output = (x0y0 + x0y1) / 2; |
| output_data[output_offset + output_y_offset] = output; |
| |
| // Bottom right corner. |
| output_data[output_offset + output_x_offset + output_y_offset] = |
| (output + ((x1y0 + x1y1) / 2)) / 2; |
| } |
| #endif |
| } |
| |
| inline void ResizeBilinear2x2(int32 batches, int32 input_height, |
| int32 input_width, int32 depth, |
| int32 output_height, int32 output_width, |
| const RuntimeShape& input_shape, |
| const float* input_data, |
| const RuntimeShape& output_shape, |
| float* output_data) { |
| for (int b = 0; b < batches; b++) { |
| for (int y0 = 0, y = 0; y <= output_height - 2; y += 2, y0++) { |
| for (int x0 = 0, x = 0; x <= output_width - 2; x += 2, x0++) { |
| int32 x1 = std::min(x0 + 1, input_width - 1); |
| int32 y1 = std::min(y0 + 1, input_height - 1); |
| ResizeBilinearKernel2x2(x0, x1, y0, y1, x, y, depth, b, input_shape, |
| input_data, output_shape, output_data); |
| } |
| } |
| } |
| } |
| |
| inline void ResizeBilinearGeneric( |
| int32 batches, int32 input_height, int32 input_width, int32 depth, |
| int32 output_height, int32 output_width, float height_scale, |
| float width_scale, const RuntimeShape& input_shape, const float* input_data, |
| const RuntimeShape& output_shape, float* output_data) { |
| memset(output_data, 0, |
| batches * output_height * output_width * depth * sizeof(float)); |
| |
| int32 output_offset = 0; |
| for (int b = 0; b < batches; ++b) { |
| for (int y = 0; y < output_height; ++y) { |
| float input_y = y * height_scale; |
| int32 y0 = static_cast<int32>(std::floor(input_y)); |
| int32 y1 = std::min(y0 + 1, input_height - 1); |
| for (int x = 0; x < output_width; ++x) { |
| float input_x = x * width_scale; |
| int32 x0 = static_cast<int32>(input_x); |
| int32 x1 = std::min(x0 + 1, input_width - 1); |
| float* output_ptr = &output_data[output_offset]; |
| |
| // Run kernel on the 4 corners of the bilinear resize algorithm. |
| int32 input_offset = Offset(input_shape, b, y0, x0, 0); |
| float scale = (1 - (input_y - y0)) * (1 - (input_x - x0)); |
| const float* input_ptr = &input_data[input_offset]; |
| ResizeBilinearKernel(input_ptr, depth, scale, output_ptr); |
| |
| input_offset = Offset(input_shape, b, y0, x1, 0); |
| scale = (1 - (input_y - y0)) * (input_x - x0); |
| input_ptr = &input_data[input_offset]; |
| ResizeBilinearKernel(input_ptr, depth, scale, output_ptr); |
| |
| input_offset = Offset(input_shape, b, y1, x0, 0); |
| scale = (input_y - y0) * (1 - (input_x - x0)); |
| input_ptr = &input_data[input_offset]; |
| ResizeBilinearKernel(input_ptr, depth, scale, output_ptr); |
| |
| input_offset = Offset(input_shape, b, y1, x1, 0); |
| scale = (input_y - y0) * (input_x - x0); |
| input_ptr = &input_data[input_offset]; |
| ResizeBilinearKernel(input_ptr, depth, scale, output_ptr); |
| |
| output_offset += depth; |
| } |
| } |
| } |
| } |
| |
| template <typename T> |
| inline void ResizeBilinearGenericSmallChannel( |
| int32 batches, int32 input_height, int32 input_width, int32 depth, |
| int32 output_height, int32 output_width, float height_scale, |
| float width_scale, const RuntimeShape& input_shape, const T* input_data, |
| const RuntimeShape& output_shape, T* output_data) { |
| memset(output_data, 0, |
| batches * output_height * output_width * depth * sizeof(T)); |
| |
| T* output_ptr = &output_data[0]; |
| for (int b = 0; b < batches; ++b) { |
| for (int y = 0; y < output_height; ++y) { |
| float input_y = y * height_scale; |
| int32 y0 = static_cast<int32>(std::floor(input_y)); |
| int32 y1 = std::min(y0 + 1, input_height - 1); |
| for (int x = 0; x < output_width; ++x) { |
| float input_x = x * width_scale; |
| int32 x0 = static_cast<int32>(input_x); |
| int32 x1 = std::min(x0 + 1, input_width - 1); |
| |
| int32 input_offset[4] = {Offset(input_shape, b, y0, x0, 0), |
| Offset(input_shape, b, y0, x1, 0), |
| Offset(input_shape, b, y1, x0, 0), |
| Offset(input_shape, b, y1, x1, 0)}; |
| float scale[4] = {(1 - (input_y - y0)) * (1 - (input_x - x0)), |
| (1 - (input_y - y0)) * (input_x - x0), |
| (input_y - y0) * (1 - (input_x - x0)), |
| (input_y - y0) * (input_x - x0)}; |
| |
| for (int d = 0; d < depth; d++) { |
| const T* input_ptr = &input_data[d]; |
| *output_ptr++ = static_cast<T>(input_ptr[input_offset[0]] * scale[0] + |
| input_ptr[input_offset[1]] * scale[1] + |
| input_ptr[input_offset[2]] * scale[2] + |
| input_ptr[input_offset[3]] * scale[3]); |
| } |
| } |
| } |
| } |
| } |
| |
| inline void ResizeBilinear(const tflite::ResizeBilinearParams& op_params, |
| const RuntimeShape& unextended_input_shape, |
| const float* input_data, |
| const RuntimeShape& output_size_shape, |
| const int32* output_size_data, |
| const RuntimeShape& unextended_output_shape, |
| float* output_data) { |
| gemmlowp::ScopedProfilingLabel label("ResizeBilinear"); |
| TFLITE_DCHECK_LE(unextended_input_shape.DimensionsCount(), 4); |
| TFLITE_DCHECK_LE(unextended_output_shape.DimensionsCount(), 4); |
| const RuntimeShape input_shape = |
| RuntimeShape::ExtendedShape(4, unextended_input_shape); |
| const RuntimeShape output_shape = |
| RuntimeShape::ExtendedShape(4, unextended_output_shape); |
| |
| int32 batches = MatchingDim(input_shape, 0, output_shape, 0); |
| int32 input_height = input_shape.Dims(1); |
| int32 input_width = input_shape.Dims(2); |
| int32 depth = MatchingDim(input_shape, 3, output_shape, 3); |
| |
| TFLITE_DCHECK_EQ(output_size_shape.FlatSize(), 2); |
| int32 output_height = output_size_data[0]; |
| int32 output_width = output_size_data[1]; |
| |
| // Specialize for 2x2 upsample. |
| if (!op_params.align_corners && output_height == 2 * input_height && |
| output_width == 2 * input_width) { |
| ResizeBilinear2x2(batches, input_height, input_width, depth, output_height, |
| output_width, input_shape, input_data, output_shape, |
| output_data); |
| } else { |
| float height_scale = static_cast<float>(input_height) / output_height; |
| float width_scale = static_cast<float>(input_width) / output_width; |
| if (op_params.align_corners && output_height > 1) { |
| height_scale = static_cast<float>(input_height - 1) / (output_height - 1); |
| } |
| if (op_params.align_corners && output_width > 1) { |
| width_scale = static_cast<float>(input_width - 1) / (output_width - 1); |
| } |
| |
| ResizeBilinearGeneric(batches, input_height, input_width, depth, |
| output_height, output_width, height_scale, |
| width_scale, input_shape, input_data, output_shape, |
| output_data); |
| } |
| } |
| |
| // TODO(prabhumk): This is not a real quantized bilinear. It does not use int8 |
| // or int16 arithmetic. |
| inline void ResizeBilinear(const tflite::ResizeBilinearParams& op_params, |
| const RuntimeShape& unextended_input_shape, |
| const uint8* input_data, |
| const RuntimeShape& output_size_shape, |
| const int32* output_size_data, |
| const RuntimeShape& unextended_output_shape, |
| uint8* output_data) { |
| gemmlowp::ScopedProfilingLabel label("ResizeBilinear"); |
| TFLITE_DCHECK_LE(unextended_input_shape.DimensionsCount(), 4); |
| TFLITE_DCHECK_LE(unextended_output_shape.DimensionsCount(), 4); |
| const RuntimeShape input_shape = |
| RuntimeShape::ExtendedShape(4, unextended_input_shape); |
| const RuntimeShape output_shape = |
| RuntimeShape::ExtendedShape(4, unextended_output_shape); |
| |
| int32 batches = MatchingDim(input_shape, 0, output_shape, 0); |
| int32 input_height = input_shape.Dims(1); |
| int32 input_width = input_shape.Dims(2); |
| int32 depth = MatchingDim(input_shape, 3, output_shape, 3); |
| |
| TFLITE_DCHECK_EQ(output_size_shape.FlatSize(), 2); |
| int32 output_height = output_size_data[0]; |
| int32 output_width = output_size_data[1]; |
| |
| float height_scale = |
| (op_params.align_corners && output_height > 1) |
| ? (static_cast<float>(input_height - 1) / (output_height - 1)) |
| : (static_cast<float>(input_height) / output_height); |
| |
| float width_scale = |
| (op_params.align_corners && output_width > 1) |
| ? (static_cast<float>(input_width - 1) / (output_width - 1)) |
| : (static_cast<float>(input_width) / output_width); |
| |
| ResizeBilinearGenericSmallChannel<uint8>( |
| batches, input_height, input_width, depth, output_height, output_width, |
| height_scale, width_scale, input_shape, input_data, output_shape, |
| output_data); |
| } |
| |
| // Helper methods for BatchToSpaceND. |
| // `spatial_index_dim` specifies post-crop offset index in this spatial |
| // dimension, i.e. spatial offset introduced by flattening batch to spatial |
| // dimension minus the crop size at beginning. `block_shape_dim` is the block |
| // size in current dimension. `input_dim` and `output_dim` are input and output |
| // size of BatchToSpaceND operation in current dimension. |
| // Output start index is inclusive and end index is exclusive. |
| inline void GetIndexRange(int spatial_index_dim, int block_shape_dim, |
| int input_dim, int output_dim, int* start_index, |
| int* end_index) { |
| // (*start_index) * block_shape_dim is effectively rounded up to the next |
| // multiple of block_shape_dim by the integer division. |
| *start_index = |
| std::max(0, (-spatial_index_dim + block_shape_dim - 1) / block_shape_dim); |
| // Similarly, (*end_index) * block_shape_dim is rounded up too (note that |
| // end_index is exclusive). |
| *end_index = std::min( |
| input_dim, |
| (output_dim - spatial_index_dim + block_shape_dim - 1) / block_shape_dim); |
| } |
| |
| template <typename T> |
| inline void BatchToSpaceND( |
| const RuntimeShape& unextended_input1_shape, const T* input1_data, |
| const RuntimeShape& unextended_input2_shape, const int32* block_shape_data, |
| const RuntimeShape& unextended_input3_shape, const int32* crops_data, |
| const RuntimeShape& unextended_output_shape, T* output_data) { |
| gemmlowp::ScopedProfilingLabel label("BatchToSpaceND"); |
| |
| TFLITE_DCHECK_LE(unextended_input1_shape.DimensionsCount(), 4); |
| TFLITE_DCHECK_LE(unextended_output_shape.DimensionsCount(), 4); |
| const RuntimeShape input1_shape = |
| RuntimeShape::ExtendedShape(4, unextended_input1_shape); |
| const RuntimeShape output_shape = |
| RuntimeShape::ExtendedShape(4, unextended_output_shape); |
| |
| const int output_width = output_shape.Dims(2); |
| const int output_height = output_shape.Dims(1); |
| const int output_batch_size = output_shape.Dims(0); |
| |
| const int depth = input1_shape.Dims(3); |
| const int input_width = input1_shape.Dims(2); |
| const int input_height = input1_shape.Dims(1); |
| const int input_batch_size = input1_shape.Dims(0); |
| |
| const int block_shape_width = block_shape_data[1]; |
| const int block_shape_height = block_shape_data[0]; |
| const int crops_top = crops_data[0]; |
| const int crops_left = crops_data[2]; |
| |
| for (int in_batch = 0; in_batch < input_batch_size; ++in_batch) { |
| const int out_batch = in_batch % output_batch_size; |
| const int spatial_offset = in_batch / output_batch_size; |
| |
| int in_h_start = 0; |
| int in_h_end = 0; |
| // GetIndexRange ensures start and end indices are in [0, output_height). |
| GetIndexRange(spatial_offset / block_shape_width - crops_top, |
| block_shape_height, input_height, output_height, &in_h_start, |
| &in_h_end); |
| |
| for (int in_h = in_h_start; in_h < in_h_end; ++in_h) { |
| const int out_h = in_h * block_shape_height + |
| spatial_offset / block_shape_width - crops_top; |
| TFLITE_DCHECK_GE(out_h, 0); |
| TFLITE_DCHECK_LT(out_h, output_height); |
| |
| int in_w_start = 0; |
| int in_w_end = 0; |
| // GetIndexRange ensures start and end indices are in [0, output_width). |
| GetIndexRange(spatial_offset % block_shape_width - crops_left, |
| block_shape_width, input_width, output_width, &in_w_start, |
| &in_w_end); |
| |
| for (int in_w = in_w_start; in_w < in_w_end; ++in_w) { |
| const int out_w = in_w * block_shape_width + |
| spatial_offset % block_shape_width - crops_left; |
| TFLITE_DCHECK_GE(out_w, 0); |
| TFLITE_DCHECK_LT(out_w, output_width); |
| T* out = output_data + Offset(output_shape, out_batch, out_h, out_w, 0); |
| const T* in = |
| input1_data + Offset(input1_shape, in_batch, in_h, in_w, 0); |
| memcpy(out, in, depth * sizeof(T)); |
| } |
| } |
| } |
| } |
| |
| template <typename T> |
| void TypedMemset(void* ptr, T value, size_t num) { |
| // Optimization for common cases where memset() will suffice. |
| if (value == 0 || std::is_same<T, uint8_t>::value) { |
| memset(ptr, value, num * sizeof(T)); |
| } else { |
| // Default implementation for cases where memset() will not preserve the |
| // bytes, e.g., typically when sizeof(T) > sizeof(uint8_t). |
| char* pos = static_cast<char*>(ptr); |
| for (size_t i = 0; i < num; ++i) { |
| memcpy(pos, &value, sizeof(T)); |
| pos = pos + sizeof(T); |
| } |
| } |
| } |
| |
| // There are two versions of pad: Pad and PadV2. In PadV2 there is a second |
| // scalar input that provides the padding value. Therefore pad_value_ptr can be |
| // equivalent to a simple input1_data. For Pad, it should point to a zero |
| // value. |
| // |
| // Note that two typenames are required, so that T=P=int32 is considered a |
| // specialization distinct from P=int32. |
| template <typename T, typename P> |
| inline void PadImpl(const tflite::PadParams& op_params, |
| const RuntimeShape& input_shape, const T* input_data, |
| const P* pad_value_ptr, const RuntimeShape& output_shape, |
| T* output_data) { |
| gemmlowp::ScopedProfilingLabel label("Pad"); |
| const RuntimeShape ext_input_shape = |
| RuntimeShape::ExtendedShape(4, input_shape); |
| const RuntimeShape ext_output_shape = |
| RuntimeShape::ExtendedShape(4, output_shape); |
| TFLITE_DCHECK_LE(op_params.left_padding_count, 4); |
| TFLITE_DCHECK_LE(op_params.right_padding_count, 4); |
| |
| // Runtime calls are currently fixed at 4 dimensions. Copy inputs so |
| // we can pad them to 4 dims (yes, we are "padding the padding"). |
| std::vector<int> left_padding_copy(4, 0); |
| const int left_padding_extend = 4 - op_params.left_padding_count; |
| for (int i = 0; i < op_params.left_padding_count; ++i) { |
| left_padding_copy[left_padding_extend + i] = op_params.left_padding[i]; |
| } |
| std::vector<int> right_padding_copy(4, 0); |
| const int right_padding_extend = 4 - op_params.right_padding_count; |
| for (int i = 0; i < op_params.right_padding_count; ++i) { |
| right_padding_copy[right_padding_extend + i] = op_params.right_padding[i]; |
| } |
| |
| const int output_batch = ext_output_shape.Dims(0); |
| const int output_height = ext_output_shape.Dims(1); |
| const int output_width = ext_output_shape.Dims(2); |
| const int output_depth = ext_output_shape.Dims(3); |
| |
| const int left_b_padding = left_padding_copy[0]; |
| const int left_h_padding = left_padding_copy[1]; |
| const int left_w_padding = left_padding_copy[2]; |
| const int left_d_padding = left_padding_copy[3]; |
| |
| const int right_b_padding = right_padding_copy[0]; |
| const int right_h_padding = right_padding_copy[1]; |
| const int right_w_padding = right_padding_copy[2]; |
| const int right_d_padding = right_padding_copy[3]; |
| |
| const int input_depth = ext_input_shape.Dims(3); |
| const T pad_value = *pad_value_ptr; |
| |
| if (left_b_padding != 0) { |
| TypedMemset<T>( |
| output_data, pad_value, |
| left_b_padding * output_height * output_width * output_depth); |
| } |
| for (int out_b = left_b_padding; out_b < output_batch - right_b_padding; |
| ++out_b) { |
| if (left_h_padding != 0) { |
| TypedMemset<T>(output_data + Offset(ext_output_shape, out_b, 0, 0, 0), |
| pad_value, left_h_padding * output_width * output_depth); |
| } |
| for (int out_h = left_h_padding; out_h < output_height - right_h_padding; |
| ++out_h) { |
| if (left_w_padding != 0) { |
| TypedMemset<T>( |
| output_data + Offset(ext_output_shape, out_b, out_h, 0, 0), |
| pad_value, left_w_padding * output_depth); |
| } |
| for (int out_w = left_w_padding; out_w < output_width - right_w_padding; |
| ++out_w) { |
| if (left_d_padding != 0) { |
| TypedMemset<T>( |
| output_data + Offset(ext_output_shape, out_b, out_h, out_w, 0), |
| pad_value, left_d_padding); |
| } |
| |
| T* out = output_data + |
| Offset(ext_output_shape, out_b, out_h, out_w, left_d_padding); |
| const T* in = input_data + |
| Offset(ext_input_shape, out_b - left_b_padding, |
| out_h - left_h_padding, out_w - left_w_padding, 0); |
| memcpy(out, in, input_depth * sizeof(T)); |
| |
| if (right_d_padding != 0) { |
| TypedMemset<T>( |
| output_data + Offset(ext_output_shape, out_b, out_h, out_w, |
| output_depth - right_d_padding), |
| pad_value, right_d_padding); |
| } |
| } |
| if (right_w_padding != 0) { |
| TypedMemset<T>(output_data + Offset(ext_output_shape, out_b, out_h, |
| output_width - right_w_padding, 0), |
| pad_value, right_w_padding * output_depth); |
| } |
| } |
| if (right_h_padding != 0) { |
| TypedMemset<T>( |
| output_data + Offset(ext_output_shape, out_b, |
| output_height - right_h_padding, 0, 0), |
| pad_value, right_h_padding * output_width * output_depth); |
| } |
| } |
| if (right_b_padding != 0) { |
| TypedMemset<T>( |
| output_data + |
| Offset(ext_output_shape, output_batch - right_b_padding, 0, 0, 0), |
| pad_value, |
| right_b_padding * output_height * output_width * output_depth); |
| } |
| } |
| |
| template <typename T, typename P> |
| inline void Pad(const tflite::PadParams& op_params, |
| const RuntimeShape& input_shape, const T* input_data, |
| const P* pad_value_ptr, const RuntimeShape& output_shape, |
| T* output_data) { |
| PadImpl(op_params, input_shape, input_data, pad_value_ptr, output_shape, |
| output_data); |
| } |
| |
| // The second (pad-value) input can be int32 when, say, the first is uint8. |
| template <typename T> |
| inline void Pad(const tflite::PadParams& op_params, |
| const RuntimeShape& input_shape, const T* input_data, |
| const int32* pad_value_ptr, const RuntimeShape& output_shape, |
| T* output_data) { |
| const T converted_pad_value = static_cast<T>(*pad_value_ptr); |
| PadImpl(op_params, input_shape, input_data, &converted_pad_value, |
| output_shape, output_data); |
| } |
| |
| // This version avoids conflicting template matching. |
| template <> |
| inline void Pad(const tflite::PadParams& op_params, |
| const RuntimeShape& input_shape, const int32* input_data, |
| const int32* pad_value_ptr, const RuntimeShape& output_shape, |
| int32* output_data) { |
| PadImpl(op_params, input_shape, input_data, pad_value_ptr, output_shape, |
| output_data); |
| } |
| |
| template <typename T> |
| inline void Slice(const tflite::SliceParams& op_params, |
| const RuntimeShape& input_shape, const T* input_data, |
| const RuntimeShape& output_shape, T* output_data) { |
| gemmlowp::ScopedProfilingLabel label("Slice"); |
| const RuntimeShape ext_shape = RuntimeShape::ExtendedShape(4, input_shape); |
| // TODO(dkalenichenko): This op only supports 4D tensors or smaller. |
| TFLITE_DCHECK_LE(op_params.begin_count, 4); |
| TFLITE_DCHECK_LE(op_params.size_count, 4); |
| const int begin_count = op_params.begin_count; |
| const int size_count = op_params.size_count; |
| // We front-pad the begin and size vectors. |
| const int start_b = 4 - begin_count > 0 ? 0 : op_params.begin[0]; |
| const int stop_b = (4 - size_count > 0 || op_params.size[0] == -1) |
| ? ext_shape.Dims(0) - start_b |
| : start_b + op_params.size[0]; |
| const int start_h = begin_count < 3 ? 0 : op_params.begin[begin_count - 3]; |
| const int stop_h = (size_count < 3 || op_params.size[size_count - 3] == -1) |
| ? ext_shape.Dims(1) - start_h |
| : start_h + op_params.size[size_count - 3]; |
| const int start_w = begin_count < 2 ? 0 : op_params.begin[begin_count - 2]; |
| const int stop_w = (size_count < 2 || op_params.size[size_count - 2] == -1) |
| ? ext_shape.Dims(2) - start_w |
| : start_w + op_params.size[size_count - 2]; |
| const int start_d = begin_count < 1 ? 0 : op_params.begin[begin_count - 1]; |
| const int stop_d = (size_count < 1 || op_params.size[size_count - 1] == -1) |
| ? ext_shape.Dims(3) - start_d |
| : start_d + op_params.size[size_count - 1]; |
| |
| T* out_ptr = output_data; |
| for (int in_b = start_b; in_b < stop_b; ++in_b) { |
| for (int in_h = start_h; in_h < stop_h; ++in_h) { |
| for (int in_w = start_w; in_w < stop_w; ++in_w) { |
| const int len = stop_d - start_d; |
| memcpy(out_ptr, |
| input_data + Offset(ext_shape, in_b, in_h, in_w, start_d), |
| len * sizeof(T)); |
| out_ptr += len; |
| } |
| } |
| } |
| } |
| |
| template <typename T> |
| void Minimum(const RuntimeShape& input1_shape, const T* input1_data, |
| const T* input2_data, const RuntimeShape& output_shape, |
| T* output_data) { |
| gemmlowp::ScopedProfilingLabel label("TensorFlowMinimum"); |
| auto input1_map = MapAsVector(input1_data, input1_shape); |
| auto output_map = MapAsVector(output_data, output_shape); |
| auto min_value = input2_data[0]; |
| output_map.array() = input1_map.array().min(min_value); |
| } |
| |
| // Convenience version that allows, for example, generated-code calls to be |
| // the same as other binary ops. |
| template <typename T> |
| inline void Minimum(const RuntimeShape& input1_shape, const T* input1_data, |
| const RuntimeShape&, const T* input2_data, |
| const RuntimeShape& output_shape, T* output_data) { |
| // Drop shape of second input: not needed. |
| Minimum(input1_shape, input1_data, input2_data, output_shape, output_data); |
| } |
| |
| template <typename T> |
| void Maximum(const RuntimeShape& input1_shape, const T* input1_data, |
| const T* input2_data, const RuntimeShape& output_shape, |
| T* output_data) { |
| gemmlowp::ScopedProfilingLabel label("TensorFlowMaximum"); |
| auto input1_map = MapAsVector(input1_data, input1_shape); |
| auto output_map = MapAsVector(output_data, output_shape); |
| auto max_value = input2_data[0]; |
| output_map.array() = input1_map.array().max(max_value); |
| } |
| |
| // Convenience version that allows, for example, generated-code calls to be |
| // the same as other binary ops. |
| template <typename T> |
| inline void Maximum(const RuntimeShape& input1_shape, const T* input1_data, |
| const RuntimeShape&, const T* input2_data, |
| const RuntimeShape& output_shape, T* output_data) { |
| // Drop shape of second input: not needed. |
| Maximum(input1_shape, input1_data, input2_data, output_shape, output_data); |
| } |
| |
| template <typename T> |
| void TransposeIm2col(const ConvParams& params, uint8 zero_byte, |
| const RuntimeShape& input_shape, const T* input_data, |
| const RuntimeShape& filter_shape, |
| const RuntimeShape& output_shape, T* im2col_data) { |
| gemmlowp::ScopedProfilingLabel label("TransposeIm2col"); |
| const int stride_width = params.stride_width; |
| const int stride_height = params.stride_height; |
| const int pad_width = params.padding_values.width; |
| const int pad_height = params.padding_values.height; |
| TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); |
| TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4); |
| TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); |
| TFLITE_DCHECK(im2col_data); |
| |
| const int batches = MatchingDim(input_shape, 0, output_shape, 0); |
| const int input_height = input_shape.Dims(1); |
| const int input_width = input_shape.Dims(2); |
| const int input_depth = MatchingDim(input_shape, 3, filter_shape, 0); |
| const int filter_height = filter_shape.Dims(1); |
| const int filter_width = filter_shape.Dims(2); |
| const int output_height = output_shape.Dims(1); |
| const int output_width = output_shape.Dims(2); |
| MatchingDim(output_shape, 3, filter_shape, 3); // output_depth |
| |
| // Construct the MxN sized im2col matrix. |
| // The rows M, are sub-ordered B x H x W |
| const RuntimeShape row_shape({1, batches, output_height, output_width}); |
| // The columns, N, are sub-ordered Kh x Kw x Din |
| const RuntimeShape col_shape({1, filter_height, filter_width, input_depth}); |
| // Use dimensions M and N to construct dims for indexing directly into im2col |
| const RuntimeShape im2col_shape( |
| {1, 1, row_shape.FlatSize(), col_shape.FlatSize()}); |
| |
| // Build the im2col matrix by looping through all the input pixels, |
| // computing their influence on the output, rather than looping through all |
| // the output pixels. We therefore must initialize the im2col array to zero. |
| // This is potentially inefficient because we subsequently overwrite bytes |
| // set here. However, in practice memset is very fast and costs negligible. |
| memset(im2col_data, zero_byte, im2col_shape.FlatSize() * sizeof(T)); |
| |
| // Loop through the output batches |
| for (int batch = 0; batch < batches; ++batch) { |
| // Loop through input pixels one at a time. |
| for (int in_y = 0; in_y < input_height; ++in_y) { |
| for (int in_x = 0; in_x < input_width; ++in_x) { |
| // Loop through the output pixels it will influence |
| const int out_x_origin = (in_x * stride_width) - pad_width; |
| const int out_y_origin = (in_y * stride_height) - pad_height; |
| for (int filter_y = 0; filter_y < filter_height; ++filter_y) { |
| const int out_y = out_y_origin + filter_y; |
| // Is output pixel within height bounds? |
| if ((out_y >= 0) && (out_y < output_height)) { |
| for (int filter_x = 0; filter_x < filter_width; ++filter_x) { |
| const int out_x = out_x_origin + filter_x; |
| // Is output pixel within width bounds? |
| if ((out_x >= 0) && (out_x < output_width)) { |
| // Copy the input elements of this pixel |
| T const* src = |
| input_data + Offset(input_shape, batch, in_y, in_x, 0); |
| int row_offset = Offset(row_shape, 0, batch, out_y, out_x); |
| int col_offset = Offset(col_shape, 0, filter_y, filter_x, 0); |
| T* dst = im2col_data + |
| Offset(im2col_shape, 0, 0, row_offset, col_offset); |
| memcpy(dst, src, input_depth * sizeof(T)); |
| } |
| } |
| } |
| } |
| } |
| } |
| } |
| } |
| |
| // TODO(b/80418076): Move to legacy ops file, update invocations. |
| // Legacy. |
| template <typename T> |
| void TransposeIm2col(const T* input_data, const Dims<4>& input_dims, |
| const Dims<4>& filter_dims, int stride_width, |
| int stride_height, int pad_width, int pad_height, |
| const Dims<4>& output_dims, uint8 zero_byte, |
| T* im2col_data) { |
| tflite::ConvParams op_params; |
| // Padding type is ignored, but still set. |
| op_params.padding_type = PaddingType::kSame; |
| op_params.padding_values.width = pad_width; |
| op_params.padding_values.height = pad_height; |
| op_params.stride_width = stride_width; |
| op_params.stride_height = stride_height; |
| |
| TransposeIm2col(op_params, zero_byte, DimsToShape(input_dims), input_data, |
| DimsToShape(filter_dims), DimsToShape(output_dims), |
| im2col_data); |
| } |
| |
| inline void TransposeConv( |
| const ConvParams& params, const RuntimeShape& input_shape, |
| const float* input_data, const RuntimeShape& filter_shape, |
| const float* filter_data, const RuntimeShape& output_shape, |
| float* output_data, const RuntimeShape& im2col_shape, float* im2col_data) { |
| gemmlowp::ScopedProfilingLabel label("TransposeConv"); |
| |
| // Note we could use transposed weights with forward conv for unstrided |
| // cases. But we are already getting good performance with this code as-is. |
| TFLITE_DCHECK(im2col_data); |
| TransposeIm2col(params, 0, input_shape, input_data, filter_shape, |
| output_shape, im2col_data); |
| |
| const auto im2col_matrix_map = |
| MapAsMatrixWithLastDimAsRows(im2col_data, im2col_shape); |
| const auto filter_matrix_map = |
| MapAsMatrixWithFirstDimAsCols(filter_data, filter_shape); |
| auto output_matrix_map = |
| MapAsMatrixWithLastDimAsRows(output_data, output_shape); |
| |
| Gemm(filter_matrix_map.transpose(), im2col_matrix_map, &output_matrix_map); |
| } |
| |
| // TODO(b/80418076): Move to legacy ops file, update invocations. |
| // Legacy. |
| inline void TransposeConv(const float* input_data, const Dims<4>& input_dims, |
| const float* filter_data, const Dims<4>& filter_dims, |
| int stride_width, int stride_height, int pad_width, |
| int pad_height, float* output_data, |
| const Dims<4>& output_dims, float* im2col_data, |
| const Dims<4>& im2col_dims) { |
| tflite::ConvParams op_params; |
| // Padding type is ignored, but still set. |
| op_params.padding_type = PaddingType::kSame; |
| op_params.padding_values.width = pad_width; |
| op_params.padding_values.height = pad_height; |
| op_params.stride_width = stride_width; |
| op_params.stride_height = stride_height; |
| |
| TransposeConv(op_params, DimsToShape(input_dims), input_data, |
| DimsToShape(filter_dims), filter_data, DimsToShape(output_dims), |
| output_data, DimsToShape(im2col_dims), im2col_data); |
| } |
| |
| } // namespace optimized_ops |
| } // namespace tflite |
| |
| #if defined OPTIMIZED_OPS_H__IGNORE_DEPRECATED_DECLARATIONS |
| #undef OPTIMIZED_OPS_H__IGNORE_DEPRECATED_DECLARATIONS |
| #pragma GCC diagnostic pop |
| #endif |
| |
| #endif // TENSORFLOW_CONTRIB_LITE_KERNELS_INTERNAL_OPTIMIZED_OPTIMIZED_OPS_H_ |