COMPMID-2407: Add (logistic and tanh) activation support for QSYMM16 for NEON

Change-Id: Ib89c9cfe12975e51d1710af736c73ce79e667363
Signed-off-by: giuros01 <giuseppe.rossini@arm.com>
Reviewed-on: https://review.mlplatform.org/c/1412
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Manuel Bottini <manuel.bottini@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>

8 files changed, 255 insertions, 18 deletions

diff --git a/arm_compute/core/NEON/NESymm.h b/arm_compute/core/NEON/NESymm.h
index 0479753426..364a317bc7 100644
--- a/arm_compute/core/NEON/NESymm.h
+++ b/arm_compute/core/NEON/NESymm.h
@@ -102,5 +102,52 @@ inline int16_t finalize_quantization_int16(int32_t in_value, int result_fixedpoi
 
     return out_s16;
 }
+
+/** Dequantize a neon vector holding 8 16-bit quantized values.
+ *
+ * @param[in] qv    Input values to be dequantized.
+ * @param[in] scale Quantization scale
+ *
+ * @return Dequantized values in a neon vector
+ */
+inline float32x4x2_t vdequantize_int16(const int16x8_t &qv, float scale)
+{
+    const float32x4_t   vscale = vdupq_n_f32(scale);
+    const float32x4x2_t vdequantized_input =
+    {
+        {
+            vmulq_f32(vcvtq_f32_s32(vmovl_s16(vget_low_s16(qv))), vscale),
+            vmulq_f32(vcvtq_f32_s32(vmovl_s16(vget_high_s16(qv))), vscale)
+        }
+    };
+    return vdequantized_input;
+}
+
+/** Quantize a neon vector holding 8 floating point values.
+ *
+ * @param[in] qv    Input values to be quantized.
+ * @param[in] scale Quantization scale
+ *
+ * @return A neon vector holding the quantized values
+ */
+inline int16x8_t vquantize_int16(const float32x4x2_t &qv, float scale)
+{
+    const float32x4_t vinvscale = vdupq_n_f32(1.f / scale);
+
+    const int32x4x2_t rf =
+    {
+        {
+#ifdef __aarch64__
+            vcvtnq_s32_f32(vmulq_f32(qv.val[0], vinvscale)),
+            vcvtnq_s32_f32(vmulq_f32(qv.val[1], vinvscale))
+#else  //__aarch64__
+            vcvtq_s32_f32(vmulq_f32(qv.val[0], vinvscale)),
+            vcvtq_s32_f32(vmulq_f32(qv.val[1], vinvscale))
+#endif //__aarch64__
+        }
+    };
+    return vcombine_s16(vqmovn_s32(rf.val[0]), vqmovn_s32(rf.val[1]));
+}
+
 } // namespace arm_compute
 #endif // __ARM_COMPUTE_NESYMM_H__
diff --git a/arm_compute/core/NEON/kernels/NEActivationLayerKernel.h b/arm_compute/core/NEON/kernels/NEActivationLayerKernel.h
index 9381beaded..5e87bd76a5 100644
--- a/arm_compute/core/NEON/kernels/NEActivationLayerKernel.h
+++ b/arm_compute/core/NEON/kernels/NEActivationLayerKernel.h
@@ -58,7 +58,7 @@ public:
      * @note If the output tensor is a nullptr, the activation function will be performed in-place
      *
      * @param[in, out] input           Source tensor. In case of @p output tensor = nullptr, this tensor will store the result
-     *                                 of the activation function. Data types supported: QASYMM8/F16/F32.
+     *                                 of the activation function. Data types supported: QASYMM8/QSYMM16/F16/F32.
      * @param[out]     output          Destination tensor. Data type supported: same as @p input
      * @param[in]      activation_info Activation layer information.
      */
@@ -66,7 +66,7 @@ public:
     /** Static function to check if given info will lead to a valid configuration of @ref NEActivationLayerKernel
      *
      * @param[in] input    Source tensor info. In case of @p output tensor info = nullptr, this tensor will store the result
-     *                     of the activation function. Data types supported: QASYMM8/F16/F32.
+     *                     of the activation function. Data types supported: QASYMM8/QSYMM16/F16/F32.
      * @param[in] output   Destination tensor info. Data type supported: same as @p input
      * @param[in] act_info Activation layer information.
      *
@@ -97,6 +97,12 @@ private:
      */
     template <ActivationLayerInfo::ActivationFunction F, typename T>
     typename std::enable_if<std::is_same<T, qasymm8_t>::value, void>::type activation(const Window &window);
+    /** Function to apply an activation function on a tensor.
+     *
+     * @param[in] window Region on which to execute the kernel
+     */
+    template <ActivationLayerInfo::ActivationFunction F, typename T>
+    typename std::enable_if<std::is_same<T, qsymm16_t>::value, void>::type activation(const Window &window);
 
 private:
     ITensor                      *_input;
diff --git a/arm_compute/core/QuantizationInfo.h b/arm_compute/core/QuantizationInfo.h
index dcfdd6ba16..1c49cd29ed 100644
--- a/arm_compute/core/QuantizationInfo.h
+++ b/arm_compute/core/QuantizationInfo.h
@@ -34,6 +34,7 @@ namespace arm_compute
 {
 using qasymm8_t = uint8_t; /**< 8 bit quantized asymmetric scalar value */
 using qsymm8_t  = int8_t;  /**< 8 bit quantized symmetric scalar value */
+using qsymm16_t = int16_t; /**< 16 bit quantized symmetric scalar value */
 
 /** Quantization info when assuming per layer quantization */
 struct UniformQuantizationInfo
@@ -350,6 +351,5 @@ inline float dequantize_qsymm16(int16_t value, const QuantizationInfo &qinfo)
 {
     return dequantize_qsymm16(value, qinfo.uniform());
 }
-
 } // namespace arm_compute
-#endif /*__ARM_COMPUTE_QUANTIZATION_INFO_H__ */
\ No newline at end of file
+#endif /*__ARM_COMPUTE_QUANTIZATION_INFO_H__ */
diff --git a/arm_compute/runtime/NEON/functions/NEActivationLayer.h b/arm_compute/runtime/NEON/functions/NEActivationLayer.h
index 588de04332..c0b5f7ab37 100644
--- a/arm_compute/runtime/NEON/functions/NEActivationLayer.h
+++ b/arm_compute/runtime/NEON/functions/NEActivationLayer.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2017-2018 ARM Limited.
+ * Copyright (c) 2017-2019 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -44,7 +44,7 @@ public:
      * @note If the output tensor is a nullptr or is equal to the input, the activation function will be performed in-place
      *
      * @param[in, out] input           Source tensor. In case of @p output tensor = nullptr, this tensor will store the result
-     *                                 of the activation function. Data types supported: QASYMM8/F16/F32.
+     *                                 of the activation function. Data types supported: QASYMM8/QSYMM16/F16/F32.
      * @param[out]     output          Destination tensor. Data type supported: same as @p input
      * @param[in]      activation_info Activation layer parameters.
      */
@@ -52,7 +52,7 @@ public:
     /** Static function to check if given info will lead to a valid configuration of @ref NEActivationLayer
      *
      * @param[in] input    Source tensor info. In case of @p output tensor info = nullptr, this tensor will store the result
-     *                     of the activation function. Data types supported: QASYMM8/F16/F32.
+     *                     of the activation function. Data types supported: QASYMM8/QSYMM16/F16/F32.
      * @param[in] output   Destination tensor info. Data type supported: same as @p input
      * @param[in] act_info Activation layer information.
      *
diff --git a/src/core/NEON/kernels/NEActivationLayerKernel.cpp b/src/core/NEON/kernels/NEActivationLayerKernel.cpp
index 64342512a0..3953305996 100644
--- a/src/core/NEON/kernels/NEActivationLayerKernel.cpp
+++ b/src/core/NEON/kernels/NEActivationLayerKernel.cpp
@@ -29,6 +29,7 @@
 #include "arm_compute/core/NEON/NEAsymm.h"
 #include "arm_compute/core/NEON/NEFixedPoint.h"
 #include "arm_compute/core/NEON/NEMath.h"
+#include "arm_compute/core/NEON/NESymm.h"
 #include "arm_compute/core/NEON/wrapper/wrapper.h"
 #include "arm_compute/core/TensorInfo.h"
 #include "arm_compute/core/Utils.h"
@@ -47,9 +48,9 @@ namespace
 Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const ActivationLayerInfo &activation_info)
 {
     ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(input);
-    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::QASYMM8, DataType::F16, DataType::F32);
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::QASYMM8, DataType::QSYMM16, DataType::F16, DataType::F32);
 
-    static std::set<ActivationLayerInfo::ActivationFunction> qs8_supported_activations =
+    static std::set<ActivationLayerInfo::ActivationFunction> qasymm8_supported_activations =
     {
         ActivationLayerInfo::ActivationFunction::RELU,
         ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU,
@@ -57,15 +58,26 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, c
         ActivationLayerInfo::ActivationFunction::LOGISTIC,
         ActivationLayerInfo::ActivationFunction::TANH
     };
+    static std::set<ActivationLayerInfo::ActivationFunction> qsymm16_supported_activations =
+    {
+        ActivationLayerInfo::ActivationFunction::LOGISTIC,
+        ActivationLayerInfo::ActivationFunction::TANH
+    };
     const DataType                                data_type = input->data_type();
     const QuantizationInfo                       &oq_info   = (output != nullptr) ? output->quantization_info() : input->quantization_info();
     const ActivationLayerInfo::ActivationFunction f_act     = activation_info.activation();
 
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG(is_data_type_quantized_asymmetric(data_type) && (qs8_supported_activations.count(f_act) == 0),
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG(is_data_type_quantized_asymmetric(data_type) && (qasymm8_supported_activations.count(f_act) == 0),
                                     "For QASYMM8 only tanh, logistic, relu and lower/upper bounded relu are supported");
+
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG(is_data_type_quantized_symmetric(data_type) && (qsymm16_supported_activations.count(f_act) == 0),
+                                    "For QSYMM16 only tanh and logistic are supported");
     ARM_COMPUTE_RETURN_ERROR_ON(is_data_type_quantized_asymmetric(data_type) && (f_act == ActivationLayerInfo::ActivationFunction::TANH) && (oq_info != QuantizationInfo(1.f / 128.f, 128)));
     ARM_COMPUTE_RETURN_ERROR_ON(is_data_type_quantized_asymmetric(data_type) && (f_act == ActivationLayerInfo::ActivationFunction::LOGISTIC) && (oq_info != QuantizationInfo(1.f / 256.f, 0)));
 
+    ARM_COMPUTE_RETURN_ERROR_ON(is_data_type_quantized_symmetric(data_type) && (f_act == ActivationLayerInfo::ActivationFunction::TANH) && (oq_info != QuantizationInfo(1.f / 32768.f, 0)));
+    ARM_COMPUTE_RETURN_ERROR_ON(is_data_type_quantized_symmetric(data_type) && (f_act == ActivationLayerInfo::ActivationFunction::LOGISTIC) && (oq_info != QuantizationInfo(1.f / 32768.f, 0)));
+
     // Checks performed when output is configured
     if((output != nullptr) && (output->total_size() != 0))
     {
@@ -163,11 +175,21 @@ void NEActivationLayerKernel::configure(ITensor *input, ITensor *output, Activat
         { ActivationFunction::IDENTITY, &NEActivationLayerKernel::activation<ActivationFunction::IDENTITY, qasymm8_t> },
     };
 
+    // Activation functions : QSYMM16
+    static std::map<ActivationFunction, ActivationFunctionExecutorPtr> act_map_qsymm16 =
+    {
+        { ActivationFunction::LOGISTIC, &NEActivationLayerKernel::activation<ActivationFunction::LOGISTIC, qsymm16_t> },
+        { ActivationFunction::TANH, &NEActivationLayerKernel::activation<ActivationFunction::TANH, qsymm16_t> },
+    };
+
     switch(input->info()->data_type())
     {
         case DataType::QASYMM8:
             _func = act_map_qasymm8[activation_info.activation()];
             break;
+        case DataType::QSYMM16:
+            _func = act_map_qsymm16[activation_info.activation()];
+            break;
         case DataType::F32:
             _func = act_map_f32[activation_info.activation()];
             break;
@@ -469,6 +491,105 @@ typename std::enable_if<std::is_same<T, qasymm8_t>::value, void>::type NEActivat
     input, output);
 }
 
+template <ActivationLayerInfo::ActivationFunction F, typename T>
+typename std::enable_if<std::is_same<T, qsymm16_t>::value, void>::type NEActivationLayerKernel::activation(const Window &window)
+{
+    const int                window_step_x  = 16 / sizeof(T);
+    const auto               window_start_x = static_cast<int>(window.x().start());
+    const auto               window_end_x   = static_cast<int>(window.x().end());
+    const ActivationFunction act            = F;
+
+    Window win_collapsed = window.collapse_if_possible(window, Window::DimZ);
+    win_collapsed.set(Window::DimX, Window::Dimension(0, 1, 1));
+
+    Iterator input(_input, win_collapsed);
+    Iterator output(_output, win_collapsed);
+
+    const UniformQuantizationInfo qi_in    = _input->info()->quantization_info().uniform();
+    const UniformQuantizationInfo qi_out   = _output->info()->quantization_info().uniform();
+    const auto                    vconst_1 = vdupq_n_f32(1.f);
+    const float32x4_t             va_f32   = vdupq_n_f32(_act_info.a());
+    const float32x4_t             vb_f32   = vdupq_n_f32(_act_info.b());
+    const float                   a_f32    = _act_info.a();
+    const float                   b_f32    = _act_info.b();
+
+    execute_window_loop(win_collapsed, [&](const Coordinates &)
+    {
+        const auto input_ptr  = reinterpret_cast<const T *>(input.ptr());
+        const auto output_ptr = reinterpret_cast<T *>(output.ptr());
+
+        wrapper::traits::neon_bitvector_t<T, wrapper::traits::BitWidth::W128> tmp;
+        ARM_COMPUTE_UNUSED(tmp);
+
+        // Compute S elements per iteration
+        int x = window_start_x;
+        for(; x <= (window_end_x - window_step_x); x += window_step_x)
+        {
+            const auto vin = wrapper::vloadq(input_ptr + x);
+            if(act == ActivationFunction::LOGISTIC)
+            {
+                // De-quantize
+                const auto vin_deq = vdequantize_int16(vin, qi_in.scale);
+                // Perform activation
+                const float32x4x2_t tmp_dep =
+                {
+                    {
+                        wrapper::vdiv(vconst_1, wrapper::vadd(vconst_1, wrapper::vexpq(wrapper::vneg(vin_deq.val[0])))),
+                        wrapper::vdiv(vconst_1, wrapper::vadd(vconst_1, wrapper::vexpq(wrapper::vneg(vin_deq.val[1])))),
+                    }
+                };
+                // Re-quantize to new output space
+                tmp = vquantize_int16(tmp_dep, qi_out.scale);
+            }
+            else if(act == ActivationFunction::TANH)
+            {
+                // De-quantize
+                const auto vin_deq = vdequantize_int16(vin, qi_in.scale);
+                // Perform activation
+                const float32x4x2_t tmp_dep =
+                {
+                    {
+                        wrapper::vmul(va_f32, wrapper::vtanh(wrapper::vmul(vin_deq.val[0], vb_f32))),
+                        wrapper::vmul(va_f32, wrapper::vtanh(wrapper::vmul(vin_deq.val[1], vb_f32))),
+                    }
+                };
+                // Re-quantize to new output space
+                tmp = vquantize_int16(tmp_dep, qi_out.scale);
+            }
+            else
+            {
+                ARM_COMPUTE_ERROR("Unsupported activation function");
+            }
+            wrapper::vstore(output_ptr + x, tmp);
+        }
+
+        // Compute left-over elements
+        for(; x < window_end_x; ++x)
+        {
+            T in = *(reinterpret_cast<const T *>(input_ptr + x));
+            T tmp;
+            if(act == ActivationFunction::LOGISTIC)
+            {
+                float tmp_f = dequantize_qsymm16(in, qi_in.scale);
+                tmp_f       = 1.f / (1.f + std::exp(-tmp_f));
+                tmp         = quantize_qsymm16(tmp_f, qi_out);
+            }
+            else if(act == ActivationFunction::TANH)
+            {
+                float tmp_f = dequantize_qsymm16(in, qi_in.scale);
+                tmp_f       = a_f32 * std::tanh(b_f32 * tmp_f);
+                tmp         = quantize_qsymm16(tmp_f, qi_out);
+            }
+            else
+            {
+                ARM_COMPUTE_ERROR("Unsupported activation function");
+            }
+            *(output_ptr + x) = tmp;
+        }
+    },
+    input, output);
+}
+
 Status NEActivationLayerKernel::validate(const ITensorInfo *input, const ITensorInfo *output, const ActivationLayerInfo &act_info)
 {
     ARM_COMPUTE_UNUSED(act_info);
diff --git a/tests/validation/NEON/ActivationLayer.cpp b/tests/validation/NEON/ActivationLayer.cpp
index a5030b94b7..1174a055c7 100644
--- a/tests/validation/NEON/ActivationLayer.cpp
+++ b/tests/validation/NEON/ActivationLayer.cpp
@@ -104,6 +104,8 @@ constexpr AbsoluteTolerance<uint8_t> tolerance_qasymm8(0);
 constexpr AbsoluteTolerance<uint8_t> tolerance_qasymm8(1);
 #endif // defined(__aarch64__)
 
+constexpr AbsoluteTolerance<int16_t> tolerance_qsymm16(1);
+
 /** CNN data types */
 const auto CNNDataTypes = framework::dataset::make("DataType",
 {
@@ -233,7 +235,6 @@ const auto QuantizedActivationFunctionsDataset = framework::dataset::make("Activ
                                                                                                   ActivationLayerInfo::ActivationFunction::LOGISTIC,
                                                                                                   ActivationLayerInfo::ActivationFunction::TANH
                                                                                                 });
-
 const auto QuantizedActivationDataset = combine(combine(framework::dataset::make("InPlace", { false }), QuantizedActivationFunctionsDataset),
                                                 framework::dataset::make("AlphaBeta", { 0.5f, 1.f }));
 
@@ -256,6 +257,32 @@ FIXTURE_DATA_TEST_CASE(RunLarge, NEActivationLayerQuantizedFixture<uint8_t>, fra
     validate(Accessor(_target), _reference, tolerance_qasymm8);
 }
 TEST_SUITE_END() // QASYMM8
+
+/** Input data sets. */
+const auto Int16QuantizedActivationFunctionsDataset = framework::dataset::make("ActivationFunction", { ActivationLayerInfo::ActivationFunction::LOGISTIC,
+                                                                                                       ActivationLayerInfo::ActivationFunction::TANH
+                                                                                                     });
+const auto Int16QuantizedActivationDataset = combine(combine(framework::dataset::make("InPlace", { false }), Int16QuantizedActivationFunctionsDataset),
+                                                     framework::dataset::make("AlphaBeta", { 0.5f, 1.f }));
+
+TEST_SUITE(QSYMM16)
+FIXTURE_DATA_TEST_CASE(RunSmall, NEActivationLayerQuantizedFixture<int16_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::SmallShapes(), Int16QuantizedActivationDataset),
+                                                                                                                        framework::dataset::make("DataType",
+                                                                                                                                DataType::QSYMM16)),
+                                                                                                                        framework::dataset::make("QuantizationInfo", { QuantizationInfo(1.f / 32768.f, 0.f) })))
+{
+    // Validate output
+    validate(Accessor(_target), _reference, tolerance_qsymm16);
+}
+FIXTURE_DATA_TEST_CASE(RunLarge, NEActivationLayerQuantizedFixture<int16_t>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeShapes(), Int16QuantizedActivationDataset),
+                                                                                                                      framework::dataset::make("DataType",
+                                                                                                                              DataType::QSYMM16)),
+                                                                                                                      framework::dataset::make("QuantizationInfo", { QuantizationInfo(1.f / 32768.f, 0.f) })))
+{
+    // Validate output
+    validate(Accessor(_target), _reference, tolerance_qsymm16);
+}
+TEST_SUITE_END() // QSYMM16
 TEST_SUITE_END() // Quantized
 
 TEST_SUITE_END() // ActivationLayer
diff --git a/tests/validation/fixtures/ActivationLayerFixture.h b/tests/validation/fixtures/ActivationLayerFixture.h
index 464382a1ec..4aaf8e7ce3 100644
--- a/tests/validation/fixtures/ActivationLayerFixture.h
+++ b/tests/validation/fixtures/ActivationLayerFixture.h
@@ -52,11 +52,11 @@ public:
         ActivationLayerInfo info(function, alpha_beta, alpha_beta);
 
         _in_place                 = in_place;
-        _output_quantization_info = calculate_output_quantization_info(info, quantization_info);
-        _input_quantization_info  = in_place ? _output_quantization_info : quantization_info;
         _data_type                = data_type;
-        _function                 = function;
+        _output_quantization_info = calculate_output_quantization_info(_data_type, info, quantization_info);
+        _input_quantization_info  = in_place ? _output_quantization_info : quantization_info;
 
+        _function  = function;
         _target    = compute_target(shape, info);
         _reference = compute_reference(shape, info);
     }
@@ -73,7 +73,7 @@ protected:
             std::uniform_real_distribution<> distribution(min_bound, max_bound);
             library->fill(tensor, distribution, 0);
         }
-        else if(is_data_type_quantized_asymmetric(tensor.data_type()))
+        else if(is_data_type_quantized_asymmetric(tensor.data_type()) || (is_data_type_quantized_symmetric(tensor.data_type())))
         {
             library->fill_tensor_uniform(tensor, 0);
         }
@@ -141,14 +141,39 @@ protected:
     }
 
 private:
-    QuantizationInfo calculate_output_quantization_info(const ActivationLayerInfo &act_info, const QuantizationInfo &default_qinfo)
+    QuantizationInfo calculate_output_quantization_info(DataType dt, const ActivationLayerInfo &act_info, const QuantizationInfo &default_qinfo)
     {
+        auto qasymm8_max = float(std::numeric_limits<uint8_t>::max()) + 1.f;
+        auto qsymm16_max = float(std::numeric_limits<int16_t>::max()) + 1.f;
+
         switch(act_info.activation())
         {
             case ActivationLayerInfo::ActivationFunction::TANH:
-                return QuantizationInfo(1.f / 128.f, 128);
+                if(dt == DataType::QSYMM16)
+                {
+                    return QuantizationInfo(1.f / qsymm16_max, 0);
+                }
+                else if(dt == DataType::QASYMM8)
+                {
+                    return QuantizationInfo(1.f / (0.5 * qasymm8_max), int(0.5 * qasymm8_max));
+                }
+                else
+                {
+                    return default_qinfo;
+                }
             case ActivationLayerInfo::ActivationFunction::LOGISTIC:
-                return QuantizationInfo(1.f / 256.f, 0);
+                if(dt == DataType::QSYMM16)
+                {
+                    return QuantizationInfo(1.f / qsymm16_max, 0);
+                }
+                else if(dt == DataType::QASYMM8)
+                {
+                    return QuantizationInfo(1.f / qasymm8_max, 0);
+                }
+                else
+                {
+                    return default_qinfo;
+                }
             default:
                 return default_qinfo;
         }
diff --git a/tests/validation/reference/ActivationLayer.cpp b/tests/validation/reference/ActivationLayer.cpp
index f5e98aa7e8..f573d12df8 100644
--- a/tests/validation/reference/ActivationLayer.cpp
+++ b/tests/validation/reference/ActivationLayer.cpp
@@ -65,6 +65,17 @@ SimpleTensor<uint8_t> activation_layer<uint8_t>(const SimpleTensor<uint8_t> &src
     return dst;
 }
 
+template <>
+SimpleTensor<int16_t> activation_layer<int16_t>(const SimpleTensor<int16_t> &src, ActivationLayerInfo info, const QuantizationInfo &oq_info)
+{
+    const QuantizationInfo dst_qinfo = oq_info.empty() ? src.quantization_info() : oq_info;
+
+    SimpleTensor<float>   src_tmp = convert_from_symmetric(src);
+    SimpleTensor<float>   dst_tmp = activation_layer<float>(src_tmp, info);
+    SimpleTensor<int16_t> dst     = convert_to_symmetric<int16_t>(dst_tmp, dst_qinfo);
+    return dst;
+}
+
 template SimpleTensor<float> activation_layer(const SimpleTensor<float> &src, ActivationLayerInfo info, const QuantizationInfo &oq_info);
 template SimpleTensor<half> activation_layer(const SimpleTensor<half> &src, ActivationLayerInfo info, const QuantizationInfo &oq_info);
 } // namespace reference