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>

1 files changed, 45 insertions, 45 deletions

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));