COMPMID-3046: Add CLRequantizationLayerKernel

Change-Id: I034f5aa023642f2323372495ddd14fc62b4c12e0
Signed-off-by: Manuel Bottini <manuel.bottini@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/2681
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Giorgio Arena <giorgio.arena@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>

2 files changed, 70 insertions, 13 deletions

diff --git a/src/core/CL/cl_kernels/quantization_layer.cl b/src/core/CL/cl_kernels/quantization_layer.cl
index 41d9957150..cfb2bb65f7 100644
--- a/src/core/CL/cl_kernels/quantization_layer.cl
+++ b/src/core/CL/cl_kernels/quantization_layer.cl
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2017-2019 ARM Limited.
+ * Copyright (c) 2017-2020 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -29,7 +29,7 @@
 
 #if defined(VEC_SIZE) && defined(DATA_TYPE_IN) && defined(DATA_TYPE_OUT) && defined(SCALE) && defined(OFFSET) && defined(MIN_QUANT_VAL) && defined(MAX_QUANT_VAL)
 
-/** This performs the quantization of floating point inputs to 8-bit unsigned integers.
+/** This performs the quantization of floating point inputs or 8-bit quantized integers to 8-bit integers.
  *
  * @note Input data type should be given as a preprocessor argument using -DDATA_TYPE_IN=type. e.g. -DDATA_TYPE=short
  * @note Output data type should be given as a preprocessor argument using -DDATA_TYPE_OUT=type. e.g. -DDATA_TYPE=short
@@ -38,8 +38,9 @@
  * @note Quantization offset should be given as a preprocessor argument using -DOFFSET=offset. e.g. -DOFFSET=125
  * @note Minimum value for quantized type should be given as a preprocessor argument using -DMIN_QUANT_VAL=value. e.g. -DMIN_QUANT_VAL=0
  * @note Maximum value for quantized type should be given as a preprocessor argument using -DMAX_QUANT_VAL=value. e.g. -DMAXIN_QUANT_VAL=255
+ * @note If the input data type if a floating point (F16 or F32) the preprocessor argument should be give as -DIS_FLOAT
  *
- * @param[in]  input_ptr                            Pointer to the source tensor. Supported data types: F32
+ * @param[in]  input_ptr                            Pointer to the source tensor. Supported data types: QASYMM8/QASYMM8_SIGNED/F16/F32
  * @param[in]  input_stride_x                       Stride of the source tensor in X dimension (in bytes)
  * @param[in]  input_step_x                         input_stride_x * number of elements along X processed per workitem(in bytes)
  * @param[in]  input_stride_y                       Stride of the source tensor in Y dimension (in bytes)
@@ -47,7 +48,7 @@
  * @param[in]  input_stride_z                       Stride of the source tensor in Z dimension (in bytes)
  * @param[in]  input_step_z                         input_stride_z * number of elements along Z processed per workitem(in bytes)
  * @param[in]  input_offset_first_element_in_bytes  The offset of the first element in the source tensor
- * @param[out] output_ptr                           Pointer to the destination tensor. Supported data types: U8
+ * @param[out] output_ptr                           Pointer to the destination tensor. Supported data types: QASYMM8/QASYMM8_SIGNED
  * @param[in]  output_stride_x                      Stride of the destination tensor in X dimension (in bytes)
  * @param[in]  output_step_x                        output_stride_x * number of elements along X processed per workitem(in bytes)
  * @param[in]  output_stride_y                      Stride of the destination tensor in Y dimension (in bytes)
@@ -72,16 +73,30 @@ __kernel void quantization_layer(
     output.ptr -= max(xi - (int)LAST_ACCESSED_X, 0) * output_stride_x;
 
     // Load data
+#if defined(IS_FLOAT)
+    // Load data
     VEC_DATA_TYPE(DATA_TYPE_IN, VEC_SIZE)
-    val = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE_IN *)input.ptr);
+    val_float = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE_IN *)input.ptr);
 
     // Create scale and offset vectors
     const VEC_DATA_TYPE(DATA_TYPE_IN, VEC_SIZE) vscale = SCALE;
-    const VEC_DATA_TYPE(int, VEC_SIZE) voffset         = OFFSET;
+    const VEC_DATA_TYPE(int, VEC_SIZE) voffset  = OFFSET;
+#else // defined(IS_FLOAT)
+    // Load data
+    VEC_DATA_TYPE(DATA_TYPE_IN, VEC_SIZE)
+    val = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE_IN *)input.ptr);
+
+    const VEC_DATA_TYPE(float, VEC_SIZE)
+    val_float = CONVERT(val, VEC_DATA_TYPE(float, VEC_SIZE));
+
+    // Create scale and offset vectors
+    const VEC_DATA_TYPE(float, VEC_SIZE) vscale = SCALE;
+    const VEC_DATA_TYPE(int, VEC_SIZE) voffset  = OFFSET;
+#endif // defined(IS_FLOAT)
 
     // Quantize
     VEC_DATA_TYPE(int, VEC_SIZE)
-    res = CLAMP(CONVERT_RTE_VEC(val / vscale, int, VEC_SIZE) + voffset, MIN_QUANT_VAL, MAX_QUANT_VAL);
+    res = CLAMP(CONVERT_RTE_VEC(val_float / vscale, int, VEC_SIZE) + voffset, MIN_QUANT_VAL, MAX_QUANT_VAL);
 
     // Store result
     VSTORE(VEC_SIZE)
@@ -90,4 +105,4 @@ __kernel void quantization_layer(
     *((__global DATA_TYPE_OUT *)(output.ptr)) = (DATA_TYPE_OUT)CLAMP(CONVERT_RTE(((float) * (__global DATA_TYPE_IN *)input.ptr) / ((float)SCALE), int) + (int)OFFSET, MIN_QUANT_VAL, MAX_QUANT_VAL);
 #endif // defined(VEC_SIZE) && defined(LAST_ACCESSED_X)
 }
-#endif //defined(VEC_SIZE) && defined(DATA_TYPE_IN) && defined(DATA_TYPE_OUT) && defined(SCALE) && defined(OFFSET) && defined(MIN_QUANT_VAL) && defined(MAX_QUANT_VAL)
+#endif // defined(VEC_SIZE) && defined(DATA_TYPE_IN) && defined(DATA_TYPE_OUT) && defined(SCALE) && defined(OFFSET) && defined(MIN_QUANT_VAL) && defined(MAX_QUANT_VAL)
diff --git a/src/core/CL/kernels/CLQuantizationLayerKernel.cpp b/src/core/CL/kernels/CLQuantizationLayerKernel.cpp
index 3d7aff0712..ab3b5d271d 100644
--- a/src/core/CL/kernels/CLQuantizationLayerKernel.cpp
+++ b/src/core/CL/kernels/CLQuantizationLayerKernel.cpp
@@ -41,7 +41,7 @@ namespace
 Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output)
 {
     ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output);
-    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32, DataType::F16);
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::F32, DataType::F16);
     ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input);
 
     // Output must always be initialized
@@ -62,8 +62,7 @@ std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITen
     const bool multi_access_x = (input_width_x / vec_size_x > 0);
     if(multi_access_x)
     {
-        win.set(Window::DimX,
-                Window::Dimension(win.x().start(), ceil_to_multiple(win.x().end(), vec_size_x), vec_size_x));
+        win.set(Window::DimX, Window::Dimension(win.x().start(), ceil_to_multiple(win.x().end(), vec_size_x), vec_size_x));
     }
 
     Coordinates coord;
@@ -99,10 +98,53 @@ void CLQuantizationLayerKernel::configure(const ICLTensor *input, ICLTensor *out
     const UniformQuantizationInfo qinfo            = output->info()->quantization_info().uniform();
     const DataType                output_data_type = output->info()->data_type();
 
+    float   scale_to_apply  = qinfo.scale;
+    int32_t offset_to_apply = qinfo.offset;
+    if(is_data_type_quantized_asymmetric(_input->info()->data_type()))
+    {
+        /*
+         * In case of requantization of a quantized input tensor to an output tensor with another quantization
+         * instead of of apply dequantization and then a quantization functions, we just compute new scale and
+         * offset to apply.
+         *
+         * Assuming:
+         *   - q_i as input quantized value
+         *   - q_o as output quantized value
+         *   - z_i as input quantization offset value
+         *   - z_o as output quantization offset value
+         *   - s_i as input quantization scale value
+         *   - s_o as output quantization scale value
+         *   - z_n as new quantization offset value
+         *   - s_n as new quantization scale value
+         *
+         * q_o = ( q_i - z_i ) * s_i / s_o + z_o
+         *
+         * We can rewrite the formula as:
+         *
+         * q_o = ( q_i * s_i / s_o ) - z_i * s_i / s_o + z_o
+         *
+         * q_o = q_i / s_n + z_n
+         *
+         * Where:
+         *
+         * s_n = s_o / s_i
+         *
+         * z_n = - z_i * s_i / s_o + z_o
+         *
+         */
+        const UniformQuantizationInfo qinfo_in = _input->info()->quantization_info().uniform();
+        scale_to_apply /= qinfo_in.scale;
+        // In order to minimize flooring we convert the offset to a float,
+        // then compute the new offset in the float domain,
+        // finally we convert it back as int32_t
+        offset_to_apply -= static_cast<int32_t>(static_cast<float>(qinfo_in.offset) * qinfo_in.scale / qinfo.scale);
+    }
+
     // Create kernel
     CLBuildOptions build_opts;
-    build_opts.add_option("-DSCALE=" + float_to_string_with_full_precision(qinfo.scale));
-    build_opts.add_option("-DOFFSET=" + support::cpp11::to_string(qinfo.offset));
+    build_opts.add_option_if(is_data_type_float(_input->info()->data_type()), "-DIS_FLOAT");
+    build_opts.add_option("-DSCALE=" + float_to_string_with_full_precision(scale_to_apply));
+    build_opts.add_option("-DOFFSET=" + support::cpp11::to_string(offset_to_apply));
     build_opts.add_option("-DVEC_SIZE=" + support::cpp11::to_string(vec_size_x));
     build_opts.add_option("-DDATA_TYPE_IN=" + get_cl_type_from_data_type(input->info()->data_type()));
     build_opts.add_option("-DDATA_TYPE_OUT=" + get_cl_type_from_data_type(output_data_type));