Add support for cl_image in CLDirectConvolutionLayer
- The cl_image object can be used for the weights
- cl_image can only work for f32/f16
- Fix the implicit padding on the first dimension X
Resolves COMPMID-4341
Change-Id: I04e0901c69e7765c42afceca38c4a840645b9123
Signed-off-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/5393
Reviewed-by: Giorgio Arena <giorgio.arena@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
diff --git a/src/core/gpu/cl/kernels/ClDirectConvolutionKernel.cpp b/src/core/gpu/cl/kernels/ClDirectConvolutionKernel.cpp
index c6ca084..bf26477 100644
--- a/src/core/gpu/cl/kernels/ClDirectConvolutionKernel.cpp
+++ b/src/core/gpu/cl/kernels/ClDirectConvolutionKernel.cpp
@@ -33,12 +33,13 @@
#include "arm_compute/core/utils/misc/ShapeCalculator.h"
#include "arm_compute/core/utils/quantization/AsymmHelpers.h"
#include "src/core/AccessWindowStatic.h"
+#include "src/core/CL/CLUtils.h"
#include "src/core/CL/CLValidate.h"
+#include "src/core/CL/gemm/CLGEMMHelpers.h"
#include "src/core/helpers/AutoConfiguration.h"
#include "src/core/helpers/WindowHelpers.h"
#include "support/Cast.h"
#include "support/StringSupport.h"
-
namespace arm_compute
{
namespace opencl
@@ -276,7 +277,11 @@
if(data_layout == DataLayout::NHWC)
{
const unsigned int vec_size = std::min(static_cast<unsigned int>(dst->tensor_shape()[0]), 4u);
- const unsigned int num_rows = dst->tensor_shape()[0] > 16 ? 2u : 1U;
+ unsigned int num_rows = 1U;
+ if(dst->tensor_shape()[0] > 16)
+ {
+ num_rows = src->data_type() == DataType::F32 ? 2U : 4U;
+ }
// Create window and update padding
Window win = calculate_max_window(output_shape, Steps(vec_size, num_rows));
@@ -318,6 +323,50 @@
ARM_COMPUTE_ERROR("Not supported");
}
}
+
+bool export_to_cl_image_support(ITensorInfo *tensor, GPUTarget gpu_target, DataLayout data_layout)
+{
+ if(tensor->tensor_shape()[0] % 4 || (data_layout != DataLayout::NHWC))
+ {
+ return false;
+ }
+
+ // If not floating point
+ if(!is_data_type_float(tensor->data_type()))
+ {
+ return false;
+ }
+
+ if(gpu_target == GPUTarget::G71 || get_arch_from_target(gpu_target) == GPUTarget::MIDGARD)
+ {
+ return false;
+ }
+
+ // Check if the cl_khr_image2d_from_buffer extension is supported on the target platform
+ if(!image2d_from_buffer_supported(CLKernelLibrary::get().get_device()))
+ {
+ return false;
+ }
+
+ // Check cl image pitch alignment
+ if(get_cl_image_pitch_alignment(CLKernelLibrary::get().get_device()) == 0)
+ {
+ return false;
+ }
+
+ const size_t image_w = tensor->tensor_shape()[0] / 4;
+ const size_t image_h = tensor->tensor_shape()[1] * tensor->tensor_shape()[2] * tensor->tensor_shape()[3];
+ const size_t max_image_w = CLKernelLibrary::get().get_device().getInfo<CL_DEVICE_IMAGE2D_MAX_WIDTH>();
+ const size_t max_image_h = CLKernelLibrary::get().get_device().getInfo<CL_DEVICE_IMAGE2D_MAX_HEIGHT>();
+
+ if(image_w > max_image_w || image_h > max_image_h)
+ {
+ return false;
+ }
+
+ return true;
+}
+
} // namespace
BorderSize ClDirectConvolutionKernel::border_size() const
@@ -365,13 +414,19 @@
kernel_name << "direct_convolution_nhwc";
- const unsigned int n0 = win_config.second.x().step();
- const unsigned int m0 = win_config.second.y().step();
+ const unsigned int n0 = win_config.second.x().step();
+ const unsigned int m0 = win_config.second.y().step();
+ const unsigned int k0 = adjust_vec_size(8u, src->dimension(channel_idx));
+ const unsigned int partial_store_n0 = dst->dimension(channel_idx) % n0;
+ const unsigned int pad_left = conv_info.pad_left();
+ const unsigned int pad_top = conv_info.pad_top();
+ const bool export_to_cl_image = export_to_cl_image_support(weights, gpu_target, _data_layout);
- const unsigned int k0 = adjust_vec_size(8u, src->dimension(channel_idx));
- const unsigned int partial_store_n0 = dst->dimension(channel_idx) % n0;
- const unsigned int pad_left = conv_info.pad_left();
- const unsigned int pad_top = conv_info.pad_top();
+ // Update the padding for the weights tensor if we can export to cl_image
+ if(export_to_cl_image)
+ {
+ arm_compute::cl_gemm::update_padding_for_cl_image(weights);
+ }
if(biases != nullptr)
{
@@ -390,7 +445,7 @@
build_options.add_option("-DDST_HEIGHT=" + support::cpp11::to_string(dst->dimension(height_idx)));
build_options.add_option("-DDST_CHANNELS=" + support::cpp11::to_string(dst->dimension(channel_idx)));
build_options.add_option("-DDST_DATA_TYPE=" + get_cl_type_from_data_type(dst->data_type()));
- build_options.add_option("-DWEI_TENSOR_TYPE=BUFFER");
+ build_options.add_option_if_else(export_to_cl_image, "-DWEI_TENSOR_TYPE=IMAGE", "-DWEI_TENSOR_TYPE=BUFFER");
build_options.add_option("-DWEI_WIDTH=" + support::cpp11::to_string(weights->dimension(width_idx)));
build_options.add_option("-DWEI_HEIGHT=" + support::cpp11::to_string(weights->dimension(height_idx)));
build_options.add_option("-DWEI_DATA_TYPE=" + get_cl_type_from_data_type(weights->data_type()));
@@ -533,13 +588,32 @@
if(_data_layout == DataLayout::NHWC)
{
- const size_t dim_y_collapsed = ceil_to_multiple(dst->info()->dimension(1) * dst->info()->dimension(2), slice.y().step());
+ cl::Image2D weights_cl_image;
+
+ const size_t dim_y_collapsed = ceil_to_multiple(dst->info()->dimension(1) * dst->info()->dimension(2), slice.y().step());
+ const bool export_to_cl_image = export_to_cl_image_support(weights->info(), get_target(), _data_layout);
+
slice.set(Window::DimY, Window::Dimension(0, dim_y_collapsed, slice.y().step()));
slice.set(Window::DimZ, Window::Dimension(0, dst->info()->dimension(3), 1));
+ if(export_to_cl_image)
+ {
+ const size_t image_w = weights->info()->dimension(0) / 4;
+ const size_t image_h = weights->info()->dimension(1) * weights->info()->dimension(2) * weights->info()->dimension(3);
+ const TensorShape shape2d(image_w, image_h);
+ const size_t image_row_pitch = weights->info()->strides_in_bytes()[1];
+
+ // Export cl_buffer to cl_image
+ weights_cl_image = create_image2d_from_buffer(CLKernelLibrary::get().context(), weights->cl_buffer(), shape2d, weights->info()->data_type(), image_row_pitch);
+ }
+
unsigned int idx = 0;
add_4D_tensor_argument(idx, src, slice);
add_4D_tensor_argument(idx, dst, slice);
+ if(export_to_cl_image)
+ {
+ _kernel.setArg(idx++, weights_cl_image);
+ }
add_4D_tensor_argument(idx, weights, slice);
if(biases != nullptr)
{