COMPMID-1068 Create validate method to CLDepthWiseConvolution

Change-Id: I3301b66a8a072c6ecd0d7f2dabef350017b55ac4
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/128677
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>

1 files changed, 155 insertions, 118 deletions

diff --git a/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.cpp b/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.cpp
index 1997a901fe..e4ad97faca 100644
--- a/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.cpp
+++ b/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.cpp
@@ -39,116 +39,57 @@
 using namespace arm_compute;
 using namespace arm_compute::misc::shape_calculator;
 
-CLDepthwiseConvolutionLayer3x3NCHWKernel::CLDepthwiseConvolutionLayer3x3NCHWKernel()
-    : _conv_stride_x(0), _conv_pad_top(0)
+namespace
 {
-}
-
-BorderSize CLDepthwiseConvolutionLayer3x3NCHWKernel::border_size() const
+Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier,
+                          const ActivationLayerInfo &act_info)
 {
-    return _border_size;
-}
-
-void CLDepthwiseConvolutionLayer3x3NCHWKernel::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info,
-                                                         unsigned int        depth_multiplier,
-                                                         ActivationLayerInfo act_info)
-{
-    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32);
-    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
-    ARM_COMPUTE_ERROR_ON(weights->info()->dimension(0) != 3 || weights->info()->dimension(1) != 3);
-
-    bool is_qasymm = is_data_type_quantized_asymmetric(input->info()->data_type());
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32);
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG(act_info.enabled() && ((input->data_type() != DataType::QASYMM8) || ((act_info.activation() != ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU)
+                                                                                                         && (act_info.activation() != ActivationLayerInfo::ActivationFunction::BOUNDED_RELU)
+                                                                                                         && (act_info.activation() != ActivationLayerInfo::ActivationFunction::RELU))),
+                                    "For QASYMM8 only relu, lower bounded relu and lower-upper bounded relu are supported");
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
+    ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(0) != 3 || weights->dimension(1) != 3);
+    ARM_COMPUTE_RETURN_ERROR_ON((input->dimension(2) * depth_multiplier) != output->dimension(2));
+    ARM_COMPUTE_RETURN_ERROR_ON(conv_info.stride().first < 1 || conv_info.stride().first > 3);
+
+    const bool is_qasymm = is_data_type_quantized_asymmetric(input->data_type());
 
     if(biases != nullptr)
     {
         if(is_qasymm)
         {
-            ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::S32);
+            ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::S32);
         }
         else
         {
-            ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases);
+            ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases);
         }
-        ARM_COMPUTE_ERROR_ON(biases->info()->dimension(0) != weights->info()->dimension(2));
-        ARM_COMPUTE_ERROR_ON(biases->info()->num_dimensions() > 1);
+        ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(2));
+        ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
     }
 
-    // Get convolved dimensions
-    const TensorShape output_shape = compute_depthwise_convolution_shape(*input->info(), *weights->info(), conv_info, depth_multiplier);
-
-    // Output auto inizialitation if not yet initialized
-    auto_init_if_empty(*output->info(),
-                       output_shape,
-                       1,
-                       input->info()->data_type(),
-                       input->info()->fixed_point_position(),
-                       input->info()->quantization_info());
-
-    ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS(output->info()->tensor_shape(), output_shape);
-    ARM_COMPUTE_ERROR_ON(output->info()->dimension(2) != weights->info()->dimension(2));
-
-    _input         = input;
-    _output        = output;
-    _weights       = weights;
-    _biases        = biases;
-    _conv_stride_x = conv_info.stride().first;
-    _conv_stride_y = conv_info.stride().second;
-    _conv_pad_left = conv_info.pad_left();
-    _conv_pad_top  = conv_info.pad_top();
-    _border_size   = BorderSize(_conv_pad_top, conv_info.pad_right(), conv_info.pad_bottom(), _conv_pad_left);
-
-    // Set build options
-    ARM_COMPUTE_ERROR_ON(_conv_stride_x < 1 || _conv_stride_x > 3);
-    CLBuildOptions build_opts;
-    build_opts.add_option("-DDEPTH_MULTIPLIER=" + support::cpp11::to_string(depth_multiplier));
-    build_opts.add_option("-DCONV_STRIDE_X=" + support::cpp11::to_string(_conv_stride_x));
-    build_opts.add_option_if(_biases != nullptr, "-DHAS_BIAS");
-
-    if(is_qasymm)
+    if(output->total_size() != 0)
     {
-        float multiplier        = _input->info()->quantization_info().scale * _weights->info()->quantization_info().scale / _output->info()->quantization_info().scale;
-        int   output_multiplier = 0;
-        int   output_shift      = 0;
-        quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift);
-
-        build_opts.add_option("-DCONV_STRIDE_Y=" + support::cpp11::to_string(_conv_stride_y));
-        build_opts.add_option("-DINPUT_OFFSET=" + support::cpp11::to_string(-_input->info()->quantization_info().offset));
-        build_opts.add_option("-DWEIGHTS_OFFSET=" + support::cpp11::to_string(-_weights->info()->quantization_info().offset));
-        build_opts.add_option("-DOUTPUT_OFFSET=" + support::cpp11::to_string(_output->info()->quantization_info().offset));
-        build_opts.add_option("-DK_OFFSET=" + support::cpp11::to_string(9 * input->info()->quantization_info().offset * weights->info()->quantization_info().offset));
-        build_opts.add_option("-DOUTPUT_MULTIPLIER=" + support::cpp11::to_string(output_multiplier));
-        build_opts.add_option("-DOUTPUT_SHIFT=" + support::cpp11::to_string(output_shift));
-
-        if(act_info.enabled())
-        {
-            const int a_val = input->info()->quantization_info().quantize(act_info.a(), RoundingPolicy::TO_NEAREST_UP);
-            const int b_val = input->info()->quantization_info().quantize(act_info.b(), RoundingPolicy::TO_NEAREST_UP);
-            const int o1    = input->info()->quantization_info().offset;
-
-            build_opts.add_option("-DFUSED_ACTIVATION=" + lower_string(string_from_activation_func(act_info.activation())));
-            build_opts.add_option("-DA_VAL=" + support::cpp11::to_string(a_val));
-            build_opts.add_option("-DB_VAL=" + support::cpp11::to_string(b_val));
-            build_opts.add_option("-DCONST_0=" + support::cpp11::to_string(o1));
+        const TensorShape output_shape = compute_depthwise_convolution_shape(*input, *weights, conv_info, depth_multiplier);
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), output_shape);
+    }
 
-            if(output != nullptr)
-            {
-                const float s1 = input->info()->quantization_info().scale;
-                const float s2 = output->info()->quantization_info().scale;
-                const int   o2 = output->info()->quantization_info().offset;
+    return Status{};
+}
 
-                if(o1 != o2 || s1 != s2)
-                {
-                    build_opts.add_option("-DS1_VAL=" + float_to_string_with_full_precision(s1));
-                    build_opts.add_option("-DS2_VAL=" + float_to_string_with_full_precision(s2));
-                    build_opts.add_option("-DO1_VAL=" + support::cpp11::to_string(o1));
-                    build_opts.add_option("-DO2_VAL=" + support::cpp11::to_string(o2));
-                }
-            }
-        }
-    }
+std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *weights, ITensorInfo *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier,
+                                                        GPUTarget gpu_target, std::string &kernel_name)
+{
+    // Output auto inizialitation if not yet initialized
+    const TensorShape output_shape = compute_depthwise_convolution_shape(*input, *weights, conv_info, depth_multiplier);
+    auto_init_if_empty(*output, input->clone()->set_tensor_shape(output_shape));
 
-    const GPUTarget gpu_target = get_target();
-    const bool      is_bifrost = gpu_target_is_in(gpu_target, GPUTarget::G71, GPUTarget::G72, GPUTarget::G51, GPUTarget::G51BIG, GPUTarget::G51LIT, GPUTarget::TNOX);
+    const unsigned int conv_stride_x = conv_info.stride().first;
+    const unsigned int conv_stride_y = conv_info.stride().second;
+    const bool         is_qasymm     = is_data_type_quantized_asymmetric(input->data_type());
+    const bool         is_bifrost    = get_arch_from_target(gpu_target) == GPUTarget::BIFROST;
 
     // Configure kernel window
     unsigned int num_elems_read_per_iteration_x    = 0;
@@ -156,16 +97,13 @@ void CLDepthwiseConvolutionLayer3x3NCHWKernel::configure(const ICLTensor *input,
     unsigned int num_elems_written_per_iteration_x = 0;
     unsigned int num_elems_written_per_iteration_y = 0;
 
-    // Create kernel
-    std::string kernel_name;
-
-    if(input->info()->data_type() == DataType::F16)
+    if(input->data_type() == DataType::F16)
     {
         kernel_name                       = "depthwise_convolution_3x3_f16";
-        num_elems_written_per_iteration_x = 8 / data_size_from_type(input->info()->data_type());
+        num_elems_written_per_iteration_x = 8 / data_size_from_type(input->data_type());
         num_elems_written_per_iteration_y = 1;
         num_elems_read_per_iteration_y    = 3;
-        switch(_conv_stride_x)
+        switch(conv_stride_x)
         {
             case 1:
                 num_elems_read_per_iteration_x = 8;
@@ -177,12 +115,12 @@ void CLDepthwiseConvolutionLayer3x3NCHWKernel::configure(const ICLTensor *input,
                 num_elems_read_per_iteration_x = 16;
                 break;
             default:
-                num_elems_read_per_iteration_x = 3 + (num_elems_written_per_iteration_x - 1) * _conv_stride_x;
+                num_elems_read_per_iteration_x = 3 + (num_elems_written_per_iteration_x - 1) * conv_stride_x;
                 break;
         }
         if(is_bifrost)
         {
-            if(_conv_stride_x == 1 && _conv_stride_y == 1)
+            if(conv_stride_x == 1 && conv_stride_y == 1)
             {
                 kernel_name                       = "depthwise_convolution_3x3_stridex1_stridey1_bifrost_f16";
                 num_elems_read_per_iteration_x    = 8;
@@ -190,7 +128,7 @@ void CLDepthwiseConvolutionLayer3x3NCHWKernel::configure(const ICLTensor *input,
                 num_elems_read_per_iteration_y    = 6;
                 num_elems_written_per_iteration_y = 4;
             }
-            else if(_conv_stride_x == 2 && _conv_stride_y == 2)
+            else if(conv_stride_x == 2 && conv_stride_y == 2)
             {
                 kernel_name                       = "depthwise_convolution_3x3_stridex2_stridey2_bifrost_f16";
                 num_elems_read_per_iteration_x    = 10;
@@ -200,9 +138,9 @@ void CLDepthwiseConvolutionLayer3x3NCHWKernel::configure(const ICLTensor *input,
             }
         }
     }
-    else if(input->info()->data_type() == DataType::F32 && is_bifrost)
+    else if(input->data_type() == DataType::F32 && is_bifrost)
     {
-        if(_conv_stride_x == 1 && _conv_stride_y == 1)
+        if(conv_stride_x == 1 && conv_stride_y == 1)
         {
             kernel_name                       = "depthwise_convolution_3x3_stridex1_stridey1_bifrost_f32";
             num_elems_read_per_iteration_x    = 4;
@@ -210,7 +148,7 @@ void CLDepthwiseConvolutionLayer3x3NCHWKernel::configure(const ICLTensor *input,
             num_elems_written_per_iteration_x = 2;
             num_elems_written_per_iteration_y = 4;
         }
-        else if(_conv_stride_x == 2 && _conv_stride_y == 2)
+        else if(conv_stride_x == 2 && conv_stride_y == 2)
         {
             kernel_name                       = "depthwise_convolution_3x3_stridex2_stridey2_bifrost_f32";
             num_elems_read_per_iteration_x    = 6;
@@ -221,35 +159,123 @@ void CLDepthwiseConvolutionLayer3x3NCHWKernel::configure(const ICLTensor *input,
         else
         {
             kernel_name                       = "depthwise_convolution_3x3";
-            num_elems_written_per_iteration_x = 8 / data_size_from_type(input->info()->data_type());
+            num_elems_written_per_iteration_x = 8 / data_size_from_type(input->data_type());
             num_elems_written_per_iteration_y = 1;
-            num_elems_read_per_iteration_x    = 3 + (num_elems_written_per_iteration_x - 1) * _conv_stride_x;
+            num_elems_read_per_iteration_x    = 3 + (num_elems_written_per_iteration_x - 1) * conv_stride_x;
             num_elems_read_per_iteration_y    = 3;
         }
     }
     else
     {
         kernel_name                       = is_qasymm ? "depthwise_convolution_3x3_quantized_nchw" : "depthwise_convolution_3x3";
-        num_elems_written_per_iteration_x = 8 / data_size_from_type(input->info()->data_type());
-        num_elems_written_per_iteration_y = (is_qasymm && _conv_stride_y < 3) ? (2 / _conv_stride_y) : 1;
-        num_elems_read_per_iteration_x    = 3 + (num_elems_written_per_iteration_x - 1) * _conv_stride_x;
+        num_elems_written_per_iteration_x = 8 / data_size_from_type(input->data_type());
+        num_elems_written_per_iteration_y = (is_qasymm && conv_stride_y < 3) ? (2 / conv_stride_y) : 1;
+        num_elems_read_per_iteration_x    = 3 + (num_elems_written_per_iteration_x - 1) * conv_stride_x;
         num_elems_read_per_iteration_y    = num_elems_written_per_iteration_y + 2;
     }
 
     // Create window and update padding
-    Window win = calculate_max_window(*output->info(), Steps(num_elems_written_per_iteration_x, num_elems_written_per_iteration_y));
+    Window win = calculate_max_window(*output, Steps(num_elems_written_per_iteration_x, num_elems_written_per_iteration_y));
 
-    AccessWindowRectangle input_access(input->info(), -_conv_pad_left, -_conv_pad_top,
+    AccessWindowRectangle input_access(input, -conv_info.pad_left(), -conv_info.pad_top(),
                                        num_elems_read_per_iteration_x, num_elems_read_per_iteration_y,
-                                       _conv_stride_x, _conv_stride_y);
-    AccessWindowStatic    weights_access(weights->info(), 0, 0, 3, 3);
-    AccessWindowRectangle output_access(output->info(), 0, 0, num_elems_written_per_iteration_x, num_elems_written_per_iteration_y);
+                                       conv_stride_x, conv_stride_y);
+    AccessWindowStatic    weights_access(weights, 0, 0, 3, 3);
+    AccessWindowRectangle output_access(output, 0, 0, num_elems_written_per_iteration_x, num_elems_written_per_iteration_y);
+
+    bool window_changed = update_window_and_padding(win, input_access, weights_access, output_access);
+
+    output_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape()));
+
+    Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
+    return std::make_pair(err, win);
+}
+} // namespace
+
+CLDepthwiseConvolutionLayer3x3NCHWKernel::CLDepthwiseConvolutionLayer3x3NCHWKernel()
+    : _conv_stride_x(0), _conv_pad_top(0)
+{
+}
+
+BorderSize CLDepthwiseConvolutionLayer3x3NCHWKernel::border_size() const
+{
+    return _border_size;
+}
 
-    update_window_and_padding(win, input_access, weights_access, output_access);
+void CLDepthwiseConvolutionLayer3x3NCHWKernel::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info,
+                                                         unsigned int        depth_multiplier,
+                                                         ActivationLayerInfo act_info)
+{
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
+
+    bool is_qasymm = is_data_type_quantized_asymmetric(input->info()->data_type());
+
+    _input         = input;
+    _output        = output;
+    _weights       = weights;
+    _biases        = biases;
+    _conv_stride_x = conv_info.stride().first;
+    _conv_stride_y = conv_info.stride().second;
+    _conv_pad_left = conv_info.pad_left();
+    _conv_pad_top  = conv_info.pad_top();
+    _border_size   = BorderSize(_conv_pad_top, conv_info.pad_right(), conv_info.pad_bottom(), _conv_pad_left);
 
-    output_access.set_valid_region(win, ValidRegion(Coordinates(), output->info()->tensor_shape()));
+    // Set build options
+    CLBuildOptions build_opts;
+    build_opts.add_option("-DDEPTH_MULTIPLIER=" + support::cpp11::to_string(depth_multiplier));
+    build_opts.add_option("-DCONV_STRIDE_X=" + support::cpp11::to_string(_conv_stride_x));
+    build_opts.add_option_if(_biases != nullptr, "-DHAS_BIAS");
 
-    ICLKernel::configure(win);
+    if(is_qasymm)
+    {
+        float multiplier        = _input->info()->quantization_info().scale * _weights->info()->quantization_info().scale / _output->info()->quantization_info().scale;
+        int   output_multiplier = 0;
+        int   output_shift      = 0;
+        quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift);
+
+        build_opts.add_option("-DCONV_STRIDE_Y=" + support::cpp11::to_string(_conv_stride_y));
+        build_opts.add_option("-DINPUT_OFFSET=" + support::cpp11::to_string(-_input->info()->quantization_info().offset));
+        build_opts.add_option("-DWEIGHTS_OFFSET=" + support::cpp11::to_string(-_weights->info()->quantization_info().offset));
+        build_opts.add_option("-DOUTPUT_OFFSET=" + support::cpp11::to_string(_output->info()->quantization_info().offset));
+        build_opts.add_option("-DK_OFFSET=" + support::cpp11::to_string(9 * input->info()->quantization_info().offset * weights->info()->quantization_info().offset));
+        build_opts.add_option("-DOUTPUT_MULTIPLIER=" + support::cpp11::to_string(output_multiplier));
+        build_opts.add_option("-DOUTPUT_SHIFT=" + support::cpp11::to_string(output_shift));
+
+        if(act_info.enabled())
+        {
+            const int a_val = input->info()->quantization_info().quantize(act_info.a(), RoundingPolicy::TO_NEAREST_UP);
+            const int b_val = input->info()->quantization_info().quantize(act_info.b(), RoundingPolicy::TO_NEAREST_UP);
+            const int o1    = input->info()->quantization_info().offset;
+
+            build_opts.add_option("-DFUSED_ACTIVATION=" + lower_string(string_from_activation_func(act_info.activation())));
+            build_opts.add_option("-DA_VAL=" + support::cpp11::to_string(a_val));
+            build_opts.add_option("-DB_VAL=" + support::cpp11::to_string(b_val));
+            build_opts.add_option("-DCONST_0=" + support::cpp11::to_string(o1));
+
+            if(output != nullptr)
+            {
+                const float s1 = input->info()->quantization_info().scale;
+                const float s2 = output->info()->quantization_info().scale;
+                const int   o2 = output->info()->quantization_info().offset;
+
+                if(o1 != o2 || s1 != s2)
+                {
+                    build_opts.add_option("-DS1_VAL=" + float_to_string_with_full_precision(s1));
+                    build_opts.add_option("-DS2_VAL=" + float_to_string_with_full_precision(s2));
+                    build_opts.add_option("-DO1_VAL=" + support::cpp11::to_string(o1));
+                    build_opts.add_option("-DO2_VAL=" + support::cpp11::to_string(o2));
+                }
+            }
+        }
+    }
+
+    // Configure kernel window
+    std::string     kernel_name;
+    const GPUTarget gpu_target = get_target();
+
+    auto win_config = validate_and_configure_window(input->info(), weights->info(), output->info(), conv_info, depth_multiplier, gpu_target, kernel_name);
+    ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
+    ICLKernel::configure(win_config.second);
 
     _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options()));
 
@@ -269,6 +295,17 @@ void CLDepthwiseConvolutionLayer3x3NCHWKernel::configure(const ICLTensor *input,
     _config_id += support::cpp11::to_string(output->info()->dimension(1));
 }
 
+Status CLDepthwiseConvolutionLayer3x3NCHWKernel::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
+                                                          unsigned int        depth_multiplier,
+                                                          ActivationLayerInfo act_info, GPUTarget gpu_target)
+{
+    std::string kernel_name;
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, weights, biases, output, conv_info, depth_multiplier, act_info));
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), weights->clone().get(), output->clone().get(), conv_info, depth_multiplier, gpu_target, kernel_name).first);
+
+    return Status{};
+}
+
 void CLDepthwiseConvolutionLayer3x3NCHWKernel::run(const Window &window, cl::CommandQueue &queue)
 {
     ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);