diff options
Diffstat (limited to 'tensorflow/contrib/lite/kernels/internal/optimized/legacy_optimized_ops.h')
-rw-r--r-- | tensorflow/contrib/lite/kernels/internal/optimized/legacy_optimized_ops.h | 312 |
1 files changed, 294 insertions, 18 deletions
diff --git a/tensorflow/contrib/lite/kernels/internal/optimized/legacy_optimized_ops.h b/tensorflow/contrib/lite/kernels/internal/optimized/legacy_optimized_ops.h index 7816752132..d5503073a7 100644 --- a/tensorflow/contrib/lite/kernels/internal/optimized/legacy_optimized_ops.h +++ b/tensorflow/contrib/lite/kernels/internal/optimized/legacy_optimized_ops.h @@ -55,15 +55,262 @@ inline void Relu(const float* input_data, const Dims<4>& input_dims, DimsToShape(output_dims)); } +// legacy, for compatibility with old checked-in code +template <FusedActivationFunctionType Ac> +void Add(const float* input1_data, const Dims<4>& input1_dims, + const float* input2_data, const Dims<4>& input2_dims, + float* output_data, const Dims<4>& output_dims) { + float output_activation_min, output_activation_max; + GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); + + tflite::ArithmeticParams op_params; + op_params.float_activation_min = output_activation_min; + op_params.float_activation_max = output_activation_max; + Add(op_params, DimsToShape(input1_dims), input1_data, + DimsToShape(input2_dims), input2_data, DimsToShape(output_dims), + output_data); +} + +template <FusedActivationFunctionType Ac> +inline void Add(int left_shift, const uint8* input1_data, + const Dims<4>& input1_dims, int32 input1_offset, + int32 input1_multiplier, int input1_shift, + const uint8* input2_data, const Dims<4>& input2_dims, + int32 input2_offset, int32 input2_multiplier, int input2_shift, + 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) { + constexpr int kReverseShift = -1; + static_assert(Ac == FusedActivationFunctionType::kNone || + Ac == FusedActivationFunctionType::kRelu || + Ac == FusedActivationFunctionType::kRelu6 || + Ac == FusedActivationFunctionType::kRelu1, + ""); + TFLITE_DCHECK_LE(output_activation_min, output_activation_max); + if (Ac == FusedActivationFunctionType::kNone) { + TFLITE_DCHECK_EQ(output_activation_min, 0); + TFLITE_DCHECK_EQ(output_activation_max, 255); + } + + tflite::ArithmeticParams op_params; + op_params.left_shift = left_shift; + op_params.input1_offset = input1_offset; + op_params.input1_multiplier = input1_multiplier; + op_params.input1_shift = kReverseShift * input1_shift; + op_params.input2_offset = input2_offset; + op_params.input2_multiplier = input2_multiplier; + op_params.input2_shift = kReverseShift * input2_shift; + op_params.output_offset = output_offset; + op_params.output_multiplier = output_multiplier; + op_params.output_shift = kReverseShift * output_shift; + op_params.quantized_activation_min = output_activation_min; + op_params.quantized_activation_max = output_activation_max; + Add(op_params, DimsToShape(input1_dims), input1_data, + DimsToShape(input2_dims), input2_data, DimsToShape(output_dims), + output_data); +} + +template <FusedActivationFunctionType Ac> +void Add(const int32* input1_data, const Dims<4>& input1_dims, + const int32* input2_data, const Dims<4>& input2_dims, + int32* output_data, const Dims<4>& output_dims) { + gemmlowp::ScopedProfilingLabel label("Add/int32"); + TFLITE_DCHECK(Ac == FusedActivationFunctionType::kNone); + + tflite::ArithmeticParams op_params; + op_params.quantized_activation_min = std::numeric_limits<int32>::min(); + op_params.quantized_activation_max = std::numeric_limits<int32>::max(); + Add(op_params, DimsToShape(input1_dims), input1_data, + DimsToShape(input2_dims), input2_data, DimsToShape(output_dims), + output_data); +} + +template <typename T> +void BroadcastAdd(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; + op_params.float_activation_min = output_activation_min; + op_params.float_activation_max = output_activation_max; + BroadcastAdd4DSlow(op_params, DimsToShape(input1_dims), input1_data, + DimsToShape(input2_dims), input2_data, + DimsToShape(output_dims), output_data); +} + +template <FusedActivationFunctionType Ac> +inline void BroadcastAdd(int left_shift, const uint8* input1_data, + const Dims<4>& input1_dims, int32 input1_offset, + int32 input1_multiplier, int input1_shift, + const uint8* input2_data, const Dims<4>& input2_dims, + int32 input2_offset, int32 input2_multiplier, + int input2_shift, 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) { + constexpr int kReverseShift = -1; + static_assert(Ac == FusedActivationFunctionType::kNone || + Ac == FusedActivationFunctionType::kRelu || + Ac == FusedActivationFunctionType::kRelu6 || + Ac == FusedActivationFunctionType::kRelu1, + ""); + TFLITE_DCHECK_LE(output_activation_min, output_activation_max); + if (Ac == FusedActivationFunctionType::kNone) { + TFLITE_DCHECK_EQ(output_activation_min, 0); + TFLITE_DCHECK_EQ(output_activation_max, 255); + } + + tflite::ArithmeticParams op_params; + op_params.left_shift = left_shift; + op_params.input1_offset = input1_offset; + op_params.input1_multiplier = input1_multiplier; + op_params.input1_shift = kReverseShift * input1_shift; + op_params.input2_offset = input2_offset; + op_params.input2_multiplier = input2_multiplier; + op_params.input2_shift = kReverseShift * input2_shift; + op_params.output_offset = output_offset; + op_params.output_multiplier = output_multiplier; + op_params.output_shift = kReverseShift * output_shift; + op_params.quantized_activation_min = output_activation_min; + op_params.quantized_activation_max = output_activation_max; + BroadcastAdd4DSlow(op_params, DimsToShape(input1_dims), input1_data, + DimsToShape(input2_dims), input2_data, + DimsToShape(output_dims), output_data); +} + +template <FusedActivationFunctionType Ac> +inline void BroadcastAddFivefold( + int y0, int y1, int y2, int y3, int y4, int left_shift, + const uint8* input1_data, const Dims<4>& input1_dims, int32 input1_offset, + int32 input1_multiplier, int input1_shift, const uint8* input2_data, + const Dims<4>& input2_dims, int32 input2_offset, int32 input2_multiplier, + int input2_shift, 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) { + constexpr int kReverseShift = -1; + static_assert(Ac == FusedActivationFunctionType::kNone || + Ac == FusedActivationFunctionType::kRelu || + Ac == FusedActivationFunctionType::kRelu6 || + Ac == FusedActivationFunctionType::kRelu1, + ""); + TFLITE_DCHECK_LE(output_activation_min, output_activation_max); + if (Ac == FusedActivationFunctionType::kNone) { + TFLITE_DCHECK_EQ(output_activation_min, 0); + TFLITE_DCHECK_EQ(output_activation_max, 255); + } + tflite::ArithmeticParams op_params; + op_params.broadcast_category = + tflite::BroadcastableOpCategory::kFirstInputBroadcastsFast; + op_params.left_shift = left_shift; + op_params.input1_offset = input1_offset; + op_params.input1_multiplier = input1_multiplier; + op_params.input1_shift = kReverseShift * input1_shift; + op_params.input2_offset = input2_offset; + op_params.input2_multiplier = input2_multiplier; + op_params.input2_shift = kReverseShift * input2_shift; + op_params.output_offset = output_offset; + op_params.output_multiplier = output_multiplier; + op_params.output_shift = kReverseShift * output_shift; + op_params.quantized_activation_min = output_activation_min; + op_params.quantized_activation_max = output_activation_max; + op_params.broadcast_shape[4] = y0; + op_params.broadcast_shape[3] = y1; + op_params.broadcast_shape[2] = y2; + op_params.broadcast_shape[1] = y3; + op_params.broadcast_shape[0] = y4; + BroadcastAddFivefold(op_params, DimsToShape(input1_dims), input1_data, + DimsToShape(input2_dims), input2_data, + DimsToShape(output_dims), output_data); +} + +// legacy, for compatibility with old checked-in code +template <FusedActivationFunctionType Ac, typename T> +void BroadcastAdd(const T* input1_data, const Dims<4>& input1_dims, + const T* input2_data, const Dims<4>& input2_dims, + T* output_data, const Dims<4>& output_dims) { + T output_activation_min, output_activation_max; + GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); + + BroadcastAdd(input1_data, input1_dims, input2_data, input2_dims, + output_activation_min, output_activation_max, output_data, + output_dims); +} + +template <FusedActivationFunctionType Ac> +inline void Add(const int16* input1_data, const Dims<4>& input1_dims, + int input1_shift, const int16* input2_data, + const Dims<4>& input2_dims, int input2_shift, + int16 output_activation_min, int16 output_activation_max, + int16* output_data, const Dims<4>& output_dims) { + constexpr int kReverseShift = -1; + static_assert(Ac == FusedActivationFunctionType::kNone || + Ac == FusedActivationFunctionType::kRelu || + Ac == FusedActivationFunctionType::kRelu6 || + Ac == FusedActivationFunctionType::kRelu1, + ""); + TFLITE_DCHECK_LE(output_activation_min, output_activation_max); + if (Ac == FusedActivationFunctionType::kNone) { + TFLITE_DCHECK_EQ(output_activation_min, -32768); + TFLITE_DCHECK_EQ(output_activation_max, 32767); + } + + tflite::ArithmeticParams op_params; + op_params.input1_shift = kReverseShift * input1_shift; + op_params.input2_shift = kReverseShift * input2_shift; + op_params.quantized_activation_min = output_activation_min; + op_params.quantized_activation_max = output_activation_max; + Add(op_params, DimsToShape(input1_dims), input1_data, + DimsToShape(input2_dims), input2_data, DimsToShape(output_dims), + output_data); +} + +inline void Sub(const float* input1_data, const Dims<4>& input1_dims, + const float* input2_data, const Dims<4>& input2_dims, + float* output_data, const Dims<4>& output_dims) { + float output_activation_min, output_activation_max; + GetActivationMinMax(FusedActivationFunctionType::kNone, + &output_activation_min, &output_activation_max); + tflite::ArithmeticParams op_params; + op_params.float_activation_min = output_activation_min; + op_params.float_activation_max = output_activation_max; + Sub(op_params, DimsToShape(input1_dims), input1_data, + DimsToShape(input2_dims), input2_data, DimsToShape(output_dims), + output_data); +} + +template <typename T> +void Sub(const T* input1_data, const Dims<4>& input1_dims, const T* input2_data, + const Dims<4>& input2_dims, T* output_data, + const Dims<4>& output_dims) { + T output_activation_min, output_activation_max; + GetActivationMinMax(FusedActivationFunctionType::kNone, + &output_activation_min, &output_activation_max); + tflite::ArithmeticParams op_params; + op_params.quantized_activation_min = output_activation_min; + op_params.quantized_activation_max = output_activation_max; + Sub(op_params, DimsToShape(input1_dims), input1_data, + DimsToShape(input2_dims), input2_data, DimsToShape(output_dims), + output_data); +} + inline void AveragePool(const float* input_data, const Dims<4>& input_dims, int stride_width, int stride_height, int pad_width, int pad_height, int kwidth, int kheight, float output_activation_min, float output_activation_max, float* output_data, const Dims<4>& output_dims) { - AveragePool(input_data, DimsToShape(input_dims), stride_width, stride_height, - pad_width, pad_height, kwidth, kheight, output_activation_min, - output_activation_max, output_data, DimsToShape(output_dims)); + tflite::PoolParams params; + params.stride_height = stride_height; + params.stride_width = stride_width; + params.filter_height = kheight; + params.filter_width = kwidth; + params.padding_values.height = pad_height; + params.padding_values.width = pad_width; + params.float_activation_min = output_activation_min; + params.float_activation_max = output_activation_max; + AveragePool(params, DimsToShape(input_dims), input_data, + DimsToShape(output_dims), output_data); } // legacy, for compatibility with old checked-in code @@ -96,10 +343,17 @@ inline void AveragePool(const uint8* input_data, const Dims<4>& input_dims, int32 output_activation_min, int32 output_activation_max, uint8* output_data, const Dims<4>& output_dims) { - AveragePool(input_data, DimsToShape(input_dims), stride_width, stride_height, - pad_width, pad_height, filter_width, filter_height, - output_activation_min, output_activation_max, output_data, - DimsToShape(output_dims)); + tflite::PoolParams params; + params.stride_height = stride_height; + params.stride_width = stride_width; + params.filter_height = filter_height; + params.filter_width = filter_width; + params.padding_values.height = pad_height; + params.padding_values.width = pad_width; + params.quantized_activation_min = output_activation_min; + params.quantized_activation_max = output_activation_max; + AveragePool(params, DimsToShape(input_dims), input_data, + DimsToShape(output_dims), output_data); } // legacy, for compatibility with old checked-in code @@ -140,9 +394,17 @@ inline void MaxPool(const float* input_data, const Dims<4>& input_dims, int pad_height, int kwidth, int kheight, float output_activation_min, float output_activation_max, float* output_data, const Dims<4>& output_dims) { - MaxPool(input_data, DimsToShape(input_dims), stride_width, stride_height, - pad_width, pad_height, kwidth, kheight, output_activation_min, - output_activation_max, output_data, DimsToShape(output_dims)); + tflite::PoolParams params; + params.stride_height = stride_height; + params.stride_width = stride_width; + params.filter_height = kheight; + params.filter_width = kwidth; + params.padding_values.height = pad_height; + params.padding_values.width = pad_width; + params.float_activation_min = output_activation_min; + params.float_activation_max = output_activation_max; + MaxPool(params, DimsToShape(input_dims), input_data, DimsToShape(output_dims), + output_data); } // legacy, for compatibility with old checked-in code @@ -172,10 +434,17 @@ inline void MaxPool(const uint8* input_data, const Dims<4>& input_dims, int pad_height, int filter_width, int filter_height, int32 output_activation_min, int32 output_activation_max, uint8* output_data, const Dims<4>& output_dims) { - MaxPool(input_data, DimsToShape(input_dims), stride_width, stride_height, - pad_width, pad_height, filter_width, filter_height, - output_activation_min, output_activation_max, output_data, - DimsToShape(output_dims)); + PoolParams params; + params.stride_height = stride_height; + params.stride_width = stride_width; + params.filter_height = filter_height; + params.filter_width = filter_width; + params.padding_values.height = pad_height; + params.padding_values.width = pad_width; + params.quantized_activation_min = output_activation_min; + params.quantized_activation_max = output_activation_max; + MaxPool(params, DimsToShape(input_dims), input_data, DimsToShape(output_dims), + output_data); } // legacy, for compatibility with old checked-in code @@ -215,10 +484,17 @@ inline void L2Pool(const float* input_data, const Dims<4>& input_dims, int pad_height, int filter_width, int filter_height, float output_activation_min, float output_activation_max, float* output_data, const Dims<4>& output_dims) { - L2Pool(input_data, DimsToShape(input_dims), stride_width, stride_height, - pad_width, pad_height, filter_width, filter_height, - output_activation_min, output_activation_max, output_data, - DimsToShape(output_dims)); + PoolParams params; + params.stride_height = stride_height; + params.stride_width = stride_width; + params.filter_height = filter_height; + params.filter_width = filter_width; + params.padding_values.height = pad_height; + params.padding_values.width = pad_width; + params.float_activation_min = output_activation_min; + params.float_activation_max = output_activation_max; + L2Pool(params, DimsToShape(input_dims), input_data, DimsToShape(output_dims), + output_data); } // legacy, for compatibility with old checked-in code |