COMPMID-1936: Add support for QASYMM8 in CLQuantizeLayer.

Change-Id: I9aa1f1f1753bcdee6a74ec15b4fb366f823788b4
Signed-off-by: Usama Arif <usama.arif@arm.com>
Reviewed-on: https://review.mlplatform.org/c/850
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>

2 files changed, 90 insertions, 80 deletions

diff --git a/src/core/CL/cl_kernels/quantization_layer.cl b/src/core/CL/cl_kernels/quantization_layer.cl
index 80ea54012f..7ae34ef71a 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 ARM Limited.
+ * Copyright (c) 2017-2019 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -23,53 +23,63 @@
  */
 #include "helpers.h"
 
+#define CONVERT_RTE(x, type) (convert_##type##_rte((x)))
+#define CONVERT_RTE_VEC_STR(x, type, size) (convert_##type##size##_rte((x)))
+#define CONVERT_RTE_VEC(x, type, size) CONVERT_RTE_VEC_STR(x, type, size)
+
+#if defined(VEC_SIZE) && defined(DATA_TYPE) && defined(SCALE) && defined(OFFSET)
+
 /** This performs the quantization of floating point inputs to 8-bit unsigned integers.
  *
- * @param[in]  input_ptr                             Pointer to the source image. Supported data types: F32
- * @param[in]  input_stride_x                        Stride of the source image 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 image in Y dimension (in bytes)
- * @param[in]  input_step_y                          input_stride_y * number of elements along Y processed per workitem(in bytes)
- * @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 image
- * @param[out] output_ptr                            Pointer to the destination image. Supported data types: U8
- * @param[in]  output_stride_x                       Stride of the destination image 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 image in Y dimension (in bytes)
- * @param[in]  output_step_y                         output_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in]  output_stride_z                       Stride of the source tensor in Z dimension (in bytes)
- * @param[in]  output_step_z                         output_stride_z * number of elements along Z processed per workitem(in bytes)
- * @param[in]  output_offset_first_element_in_bytes  The offset of the first element in the destination image
- * @param[in]  min_max_ptr                           Pointer to the min/max vector. Minimum value in position 0, maximum value in position 1. Supported data types: F32.
- * @param[in]  min_max_stride_x                      Stride of the min/max vector in X dimension (in bytes)
- * @param[in]  min_max_step_x                        min_max_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in]  min_max_offset_first_element_in_bytes The offset of the first element in the min/max vector
+ * @param[in]  input_ptr                            Pointer to the source tensor. Supported data types: 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)
+ * @param[in]  input_step_y                         input_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @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[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)
+ * @param[in]  output_step_y                        output_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in]  output_stride_z                      Stride of the source tensor in Z dimension (in bytes)
+ * @param[in]  output_step_z                        output_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in]  output_offset_first_element_in_bytes The offset of the first element in the destination tensor
  */
 __kernel void quantization_layer(
     TENSOR3D_DECLARATION(input),
-    TENSOR3D_DECLARATION(output),
-    VECTOR_DECLARATION(min_max))
+    TENSOR3D_DECLARATION(output))
 {
     // Get pixels pointer
     Tensor3D input  = CONVERT_TO_TENSOR3D_STRUCT(input);
     Tensor3D output = CONVERT_TO_TENSOR3D_STRUCT(output);
 
-    // min_max_value.s0 = min, min_max_value.s1 = max
-    const float2 min_max_value = vload2(0, (__global float *)(min_max_ptr + min_max_offset_first_element_in_bytes));
-
-    const float4 vmin   = (float4)min_max_value.s0;
-    const float4 vrange = (float4)(min_max_value.s1 - min_max_value.s0);
+#if defined(VEC_SIZE) && defined(LAST_ACCESSED_X)
+    // Check if access on width gets out of bounds
+    // If it does shift access vector to access elements within bounds
+    const int xi = (int)(get_global_id(0) * VEC_SIZE);
+    input.ptr -= max(xi - (int)LAST_ACCESSED_X, 0) * input_stride_x;
+    output.ptr -= max(xi - (int)LAST_ACCESSED_X, 0) * output_stride_x;
 
     // Load data
-    float4 data = vload4(0, (__global float *)input.ptr);
+    VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)
+    val = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)input.ptr);
 
-    // Map float values to range [0.0, 1.0]
-    data = (data - vmin) / vrange;
+    // Create scale and offset vectors
+    const VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE) vscale = SCALE;
+    const VEC_DATA_TYPE(int, VEC_SIZE) voffset      = OFFSET;
 
-    // Quantize and saturate
-    uchar4 res = convert_uchar4_sat(data * 256.0f);
+    // Quantize
+    VEC_DATA_TYPE(int, VEC_SIZE)
+    res = CLAMP(CONVERT_RTE_VEC(val / vscale, int, VEC_SIZE) + voffset, 0, 255);
 
-    // Store result
-    vstore4(res, 0, (__global uchar *)output.ptr);
+    //Store result
+    VSTORE(VEC_SIZE)
+    (CONVERT(res, VEC_DATA_TYPE(uchar, VEC_SIZE)), 0, (__global uchar *)output.ptr);
+#else  //!defined(VEC_SIZE) || !defined(LAST_ACCESSED_X)
+    *((__global uchar *)(output.ptr)) = (uchar)CLAMP(CONVERT_RTE(((float) * (__global DATA_TYPE *)input.ptr) / ((float)SCALE), int) + (int)OFFSET, 0, 255);
+#endif // defined(VEC_SIZE) && defined(LAST_ACCESSED_X)
 }
+#endif //defined(VEC_SIZE) && defined(DATA_TYPE) && defined(SCALE) && defined(OFFSET)
diff --git a/src/core/CL/kernels/CLQuantizationLayerKernel.cpp b/src/core/CL/kernels/CLQuantizationLayerKernel.cpp
index 9028b0f604..374b22eab1 100644
--- a/src/core/CL/kernels/CLQuantizationLayerKernel.cpp
+++ b/src/core/CL/kernels/CLQuantizationLayerKernel.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2017-2018 ARM Limited.
+ * Copyright (c) 2017-2019 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -26,6 +26,7 @@
 #include "arm_compute/core/AccessWindowStatic.h"
 #include "arm_compute/core/CL/CLHelpers.h"
 #include "arm_compute/core/CL/CLKernelLibrary.h"
+#include "arm_compute/core/CL/CLValidate.h"
 #include "arm_compute/core/CL/ICLTensor.h"
 #include "arm_compute/core/TensorInfo.h"
 #include "arm_compute/core/Utils.h"
@@ -36,73 +37,76 @@ using namespace arm_compute;
 
 namespace
 {
-Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const ITensorInfo *min_max)
+Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output)
 {
-    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output, min_max);
-    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
-    ARM_COMPUTE_RETURN_ERROR_ON(input->num_dimensions() < 3);
-
-    if(output->tensor_shape().total_size() > 0)
+    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_F16_UNSUPPORTED(input);
+    if((output != nullptr) && (output->total_size() != 0))
     {
-        ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8);
+        ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QASYMM8);
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output);
     }
 
     return Status{};
 }
 
-std::tuple<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output, ITensorInfo *min_max)
+std::tuple<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output)
 {
-    // Output tensor auto initialization if not yet initialized
-    auto_init_if_empty(*output, input->tensor_shape(), 1, DataType::U8);
-
-    constexpr unsigned int num_elems_processed_per_iteration = 4;
-
-    // Configure window
-    Window                 win = calculate_max_window(*input, Steps(num_elems_processed_per_iteration));
-    AccessWindowHorizontal input_access(input, 0, num_elems_processed_per_iteration);
-    AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration);
-    AccessWindowStatic     min_max_access(min_max, 0, 0, 2, min_max->dimension(1));
+    // Configure kernel window
+    Window win = calculate_max_window(*input, Steps());
 
-    // Update window and padding
-    bool window_changed = update_window_and_padding(win, input_access, output_access, min_max_access);
+    // Output tensor auto initialization if not yet initialized
+    auto_init_if_empty(*output, input->tensor_shape(), 1, DataType::QASYMM8);
 
-    output_access.set_valid_region(win, input->valid_region());
+    Coordinates coord;
+    coord.set_num_dimensions(output->num_dimensions());
+    output->set_valid_region(ValidRegion(coord, output->tensor_shape()));
 
-    Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
-    return std::make_tuple(err, win);
+    return std::make_tuple(Status{}, win);
 }
 } // namespace
 
 CLQuantizationLayerKernel::CLQuantizationLayerKernel()
-    : _input(nullptr), _output(nullptr), _min_max(nullptr)
+    : _input(nullptr), _output(nullptr)
 {
 }
 
-void CLQuantizationLayerKernel::configure(const ICLTensor *input, ICLTensor *output, ICLTensor *min_max)
+void CLQuantizationLayerKernel::configure(const ICLTensor *input, ICLTensor *output)
 {
-    ARM_COMPUTE_ERROR_ON_NULLPTR(input, output, min_max);
-    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), min_max->info()));
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
+    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info()));
 
-    _input   = input;
-    _output  = output;
-    _min_max = min_max;
+    _input  = input;
+    _output = output;
 
-    // Create kernel
-    _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("quantization_layer"));
+    const int  vec_size_x     = 16 / input->info()->element_size();
+    const int  input_width_x  = input->info()->tensor_shape().x();
+    const bool multi_access_x = (input_width_x / vec_size_x > 0);
 
-    // Configure kernel window
-    auto win_config = validate_and_configure_window(input->info(), output->info(), min_max->info());
-
-    ARM_COMPUTE_ERROR_THROW_ON(std::get<0>(win_config));
+    // Create and update the window (if needed)
+    Window win = calculate_max_window(*input->info());
+    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));
+    }
+    ICLKernel::configure_internal(win);
 
-    ICLKernel::configure_internal(std::get<1>(win_config));
+    // Create kernel
+    CLBuildOptions build_opts;
+    build_opts.add_option("-DSCALE=" + float_to_string_with_full_precision(output->info()->quantization_info().scale));
+    build_opts.add_option("-DOFFSET=" + support::cpp11::to_string(output->info()->quantization_info().offset));
+    build_opts.add_option("-DVEC_SIZE=" + support::cpp11::to_string(vec_size_x));
+    build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type()));
+    build_opts.add_option_if(multi_access_x, "-DLAST_ACCESSED_X=" + support::cpp11::to_string(std::max<int>(input_width_x - vec_size_x, 0)));
+    _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("quantization_layer", build_opts.options()));
 }
 
-Status CLQuantizationLayerKernel::validate(const ITensorInfo *input, const ITensorInfo *output, const ITensorInfo *min_max)
+Status CLQuantizationLayerKernel::validate(const ITensorInfo *input, const ITensorInfo *output)
 {
-    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, min_max));
-    ARM_COMPUTE_RETURN_ON_ERROR(std::get<0>(validate_and_configure_window(input->clone().get(), output->clone().get(), min_max->clone().get())));
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output));
+    ARM_COMPUTE_RETURN_ON_ERROR(std::get<0>(validate_and_configure_window(input->clone().get(), output->clone().get())));
 
     return Status{};
 }
@@ -117,13 +121,9 @@ void CLQuantizationLayerKernel::run(const Window &window, cl::CommandQueue &queu
 
     do
     {
-        Window slice_min_max = slice.shift_dimensions(2);
-        slice_min_max.set(Window::DimX, Window::Dimension(0, 1, 1));
-
         unsigned int idx = 0;
         add_3D_tensor_argument(idx, _input, slice);
         add_3D_tensor_argument(idx, _output, slice);
-        add_1D_tensor_argument(idx, _min_max, slice_min_max);
         enqueue(queue, *this, slice);
     }
     while(window_collapsed.slide_window_slice_3D(slice));