Remove padding from direct convolution - OpenCL
- Refactor direct convolution for NHWC
- Remove old kernels for NHWC
- Change the heuristic in CLConvolutionLayer.cpp. The new direct
convolution implementation is faster than FFT
Resolves COMPMID-3908
Change-Id: Iee15ce7b04e21847b6eaae5c6d3c1b18180e7efc
Signed-off-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/4876
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
diff --git a/src/core/CL/kernels/CLDirectConvolutionLayerKernel.cpp b/src/core/CL/kernels/CLDirectConvolutionLayerKernel.cpp
index 8884521..91ff35b 100644
--- a/src/core/CL/kernels/CLDirectConvolutionLayerKernel.cpp
+++ b/src/core/CL/kernels/CLDirectConvolutionLayerKernel.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017-2020 Arm Limited.
+ * Copyright (c) 2017-2021 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -53,8 +53,6 @@
const int channel_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL);
ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights->dimension(width_idx) != weights->dimension(height_idx), "Weights should have same width and height");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights->dimension(width_idx) != 1 && weights->dimension(width_idx) != 3 && weights->dimension(width_idx) != 5 && weights->dimension(width_idx) != 9,
- "Kernel sizes other than 1x1, 3x3, 5x5 or 9x9 are not supported");
ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights->dimension(channel_idx) != input->dimension(channel_idx),
"Weights feature map dimension should match the respective input's one");
ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights->num_dimensions() > 4, "Weights can be at most 4 dimensional");
@@ -63,6 +61,20 @@
&& std::get<0>(conv_info.stride()) > 2,
"Strides larger than 2 not supported for 3x3, 5x5, 9x9 convolution.");
+ if(data_layout == DataLayout::NCHW)
+ {
+ if(is_data_type_quantized(input->data_type()))
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights->dimension(width_idx) != 1 && weights->dimension(width_idx) != 3 && weights->dimension(width_idx) != 5 && weights->dimension(width_idx) != 9,
+ "Kernel sizes other than 1x1, 3x3, 5x5 or 9x9 are not supported with quantised data types");
+ }
+ else
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights->dimension(width_idx) != 1 && weights->dimension(width_idx) != 3 && weights->dimension(width_idx) != 5,
+ "Kernel sizes other than 1x1, 3x3 or 5x5 are not supported with float data types");
+ }
+ }
+
if(biases != nullptr)
{
if(is_data_type_quantized_asymmetric(input->data_type()))
@@ -102,8 +114,8 @@
return Status{};
}
-inline bool can_run_optimized_kernel_for_bifrost(GPUTarget gpu_target, unsigned int conv_stride_x, unsigned int conv_stride_y, unsigned int kernel_size,
- DataType data_type, DataLayout data_layout)
+inline bool can_run_optimized_kernel_for_bifrost_nchw(GPUTarget gpu_target, unsigned int conv_stride_x, unsigned int conv_stride_y, unsigned int kernel_size,
+ DataType data_type, DataLayout data_layout)
{
return gpu_target_is_in(gpu_target,
GPUTarget::G71, GPUTarget::G72, GPUTarget::G76,
@@ -115,29 +127,16 @@
&& (data_layout == DataLayout::NCHW);
}
-inline bool can_run_optimized_kernel_for_bifrost_nhwc(GPUTarget gpu_target, unsigned int conv_stride_x, unsigned int conv_stride_y, unsigned int kernel_size,
- DataType data_type, DataLayout data_layout)
-{
- return gpu_target_is_in(gpu_target,
- GPUTarget::G71, GPUTarget::G72, GPUTarget::G76,
- GPUTarget::G51, GPUTarget::G51BIG, GPUTarget::G51LIT,
- GPUTarget::G52, GPUTarget::G52LIT)
- && (kernel_size == 9)
- && (conv_stride_x == 1) && (conv_stride_y == 1)
- && (data_type == DataType::F32)
- && (data_layout == DataLayout::NHWC);
-}
-
-inline void setup_num_elems(unsigned int &num_elems_read_per_iteration_x, unsigned int &num_elems_read_per_iteration_y,
- unsigned int &num_elems_written_per_iteration_x, unsigned int &num_elems_written_per_iteration_y,
- unsigned int kernel_size, const PadStrideInfo &conv_info, const GPUTarget target, ITensorInfo *input)
+inline void setup_num_elems_nchw(unsigned int &num_elems_read_per_iteration_x, unsigned int &num_elems_read_per_iteration_y,
+ unsigned int &num_elems_written_per_iteration_x, unsigned int &num_elems_written_per_iteration_y,
+ unsigned int kernel_size, const PadStrideInfo &conv_info, const GPUTarget target, ITensorInfo *input)
{
const DataType data_type = input->data_type();
const DataLayout data_layout = input->data_layout();
unsigned int conv_stride_x = std::get<0>(conv_info.stride());
unsigned int conv_stride_y = std::get<1>(conv_info.stride());
- const bool run_optimized_bifrost = can_run_optimized_kernel_for_bifrost(target, conv_stride_x, conv_stride_y, kernel_size, data_type, data_layout);
+ const bool run_optimized_bifrost = can_run_optimized_kernel_for_bifrost_nchw(target, conv_stride_x, conv_stride_y, kernel_size, data_type, data_layout);
if(run_optimized_bifrost)
{
@@ -174,7 +173,7 @@
}
}
}
- else if(data_layout == DataLayout::NCHW)
+ else
{
num_elems_read_per_iteration_y = kernel_size;
num_elems_written_per_iteration_x = 8;
@@ -253,97 +252,13 @@
ARM_COMPUTE_ERROR("Invalid direct convolution size");
}
}
- else // data_layout == NHWC
- {
- const bool run_optimized_bifrost_nhwc = can_run_optimized_kernel_for_bifrost_nhwc(target, conv_stride_x, conv_stride_y, kernel_size, data_type, data_layout);
-
- num_elems_written_per_iteration_x = 1;
-
- if(run_optimized_bifrost_nhwc)
- {
- num_elems_read_per_iteration_x = 4;
- }
- else
- {
- num_elems_read_per_iteration_x = 1;
- }
-
- switch(kernel_size)
- {
- case 1:
- switch(conv_stride_x)
- {
- case 1:
- num_elems_read_per_iteration_y = 8;
- num_elems_written_per_iteration_y = 8;
- break;
- case 2:
- num_elems_read_per_iteration_y = 16;
- num_elems_written_per_iteration_y = 8;
- break;
- default:
- ARM_COMPUTE_ERROR("Invalid convolution stride X");
- }
- break;
- case 3:
- switch(conv_stride_x)
- {
- case 1:
- num_elems_read_per_iteration_y = 10;
- num_elems_written_per_iteration_y = 8;
- break;
- case 2:
- num_elems_read_per_iteration_y = 17;
- num_elems_written_per_iteration_y = 8;
- break;
- default:
- ARM_COMPUTE_ERROR("Invalid convolution stride X");
- }
- break;
- case 5:
- switch(conv_stride_x)
- {
- case 1:
- num_elems_read_per_iteration_y = 12;
- num_elems_written_per_iteration_y = 8;
- break;
- case 2:
- num_elems_read_per_iteration_y = 20;
- num_elems_written_per_iteration_y = 8;
- break;
- default:
- ARM_COMPUTE_ERROR("Invalid convolution stride X");
- }
- break;
- case 9:
- switch(conv_stride_x)
- {
- case 1:
- num_elems_read_per_iteration_y = 16;
- num_elems_written_per_iteration_y = 8;
- break;
- case 2:
- num_elems_read_per_iteration_y = 24;
- num_elems_written_per_iteration_y = 8;
- break;
- default:
- ARM_COMPUTE_ERROR("Invalid convolution stride X");
- }
- break;
- default:
- ARM_COMPUTE_ERROR("Not implemented.");
- break;
- }
- }
}
std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *weights, ITensorInfo *output, const PadStrideInfo &conv_info, const GPUTarget target)
{
- const DataLayout data_layout = input->data_layout();
- const int width_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
- const unsigned int kernel_size = weights->dimension(width_idx);
+ const DataLayout data_layout = input->data_layout();
- // Get convolved dimensions
+ // Get output shape
TensorShape output_shape = misc::shape_calculator::compute_deep_convolution_shape(*input, *weights, conv_info);
// Output auto inizialitation if not yet initialized
@@ -352,38 +267,39 @@
input->data_type(),
input->quantization_info());
- unsigned int num_elems_read_per_iteration_x = 0;
- unsigned int num_elems_read_per_iteration_y = 0;
- unsigned int num_elems_written_per_iteration_x = 0;
- unsigned int num_elems_written_per_iteration_y = 0;
-
- unsigned int conv_pad_left = conv_info.pad_left();
- unsigned int conv_pad_top = conv_info.pad_top();
- unsigned int conv_stride_x = std::get<0>(conv_info.stride());
- unsigned int conv_stride_y = std::get<1>(conv_info.stride());
-
- setup_num_elems(num_elems_read_per_iteration_x, num_elems_read_per_iteration_y,
- num_elems_written_per_iteration_x, num_elems_written_per_iteration_y,
- kernel_size, conv_info, target, input);
-
- // Create window and update padding
- bool window_changed = false;
- Window win = calculate_max_window(*output, Steps(num_elems_written_per_iteration_x, num_elems_written_per_iteration_y));
-
if(data_layout == DataLayout::NHWC)
{
- AccessWindowStatic input_access(input, 0, -conv_pad_left,
- ceil_to_multiple(input->dimension(0), num_elems_read_per_iteration_x),
- ceil_to_multiple(input->dimension(1) + conv_info.pad_right(), num_elems_read_per_iteration_y));
- AccessWindowStatic weights_access(weights, 0, 0, ceil_to_multiple(weights->dimension(0), num_elems_read_per_iteration_x), weights->dimension(1));
- AccessWindowRectangle output_access(output, 0, 0, num_elems_written_per_iteration_x, num_elems_written_per_iteration_y);
- 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{};
+ const unsigned int vec_size = std::min(static_cast<unsigned int>(output->tensor_shape()[0]), 4u);
+
+ // Create window and update padding
+ Window win = calculate_max_window(*output, Steps(vec_size, 1U));
+ output->set_valid_region(ValidRegion(Coordinates(), output->tensor_shape()));
+ Status err = Status{};
return std::make_pair(err, win);
}
else if(data_layout == DataLayout::NCHW)
{
+ const int width_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
+ const unsigned int kernel_size = weights->dimension(width_idx);
+
+ unsigned int num_elems_read_per_iteration_x = 0;
+ unsigned int num_elems_read_per_iteration_y = 0;
+ unsigned int num_elems_written_per_iteration_x = 0;
+ unsigned int num_elems_written_per_iteration_y = 0;
+
+ unsigned int conv_pad_left = conv_info.pad_left();
+ unsigned int conv_pad_top = conv_info.pad_top();
+ unsigned int conv_stride_x = std::get<0>(conv_info.stride());
+ unsigned int conv_stride_y = std::get<1>(conv_info.stride());
+
+ setup_num_elems_nchw(num_elems_read_per_iteration_x, num_elems_read_per_iteration_y,
+ num_elems_written_per_iteration_x, num_elems_written_per_iteration_y,
+ kernel_size, conv_info, target, input);
+
+ // Create window and update padding
+ bool window_changed = false;
+ Window win = calculate_max_window(*output, Steps(num_elems_written_per_iteration_x, num_elems_written_per_iteration_y));
+
AccessWindowRectangle input_access(input, -conv_pad_left, -conv_pad_top, num_elems_read_per_iteration_x, num_elems_read_per_iteration_y, conv_stride_x, conv_stride_y);
AccessWindowStatic weights_access(weights, 0, 0, kernel_size, kernel_size);
AccessWindowRectangle output_access(output, 0, 0, num_elems_written_per_iteration_x, num_elems_written_per_iteration_y);
@@ -419,25 +335,7 @@
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
- _data_layout = input->info()->data_layout();
- const int width_idx = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::WIDTH);
- const int height_idx = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::HEIGHT);
- const int channel_idx = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::CHANNEL);
-
- const unsigned int kernel_size = weights->info()->dimension(width_idx);
- const DataType data_type = input->info()->data_type();
-
- // Get convolved dimensions
- TensorShape output_shape = misc::shape_calculator::compute_deep_convolution_shape(*input->info(), *weights->info(), conv_info);
-
- // Output auto inizialitation if not yet initialized
- auto_init_if_empty(*output->info(),
- output_shape,
- 1,
- input->info()->data_type(),
- input->info()->quantization_info());
-
- // Perform validation step
+ // Perform validation
ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(),
weights->info(),
(biases != nullptr) ? biases->info() : nullptr,
@@ -446,72 +344,64 @@
_conv_stride_x = std::get<0>(conv_info.stride());
_conv_stride_y = std::get<1>(conv_info.stride());
+ _data_layout = input->info()->data_layout();
+ _input = input;
+ _weights = weights;
+ _output = output;
+ _biases = biases;
- if(_data_layout == DataLayout::NHWC)
- {
- _border_size = BorderSize(conv_info.pad_left(), 0, conv_info.pad_right(), 0);
- }
- else if(_data_layout == DataLayout::NCHW)
- {
- _border_size = BorderSize(conv_info.pad_top(), conv_info.pad_right(), conv_info.pad_bottom(), conv_info.pad_left());
- }
- else
- {
- ARM_COMPUTE_ERROR("Not supported");
- }
-
- _input = input;
- _weights = weights;
- _output = output;
- _biases = biases;
+ const unsigned int width_idx = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::WIDTH);
+ const unsigned int height_idx = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::HEIGHT);
+ const unsigned int channel_idx = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::CHANNEL);
+ const unsigned int kernel_size = weights->info()->dimension(width_idx);
+ const DataType data_type = input->info()->data_type();
const GPUTarget gpu_target = get_target();
+ // Configure kernel window
+ auto win_config = validate_and_configure_window(input->info(), weights->info(), output->info(), conv_info, gpu_target);
+ ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
+ ICLKernel::configure_internal(win_config.second);
+
std::stringstream kernel_name;
- kernel_name << "direct_convolution" << kernel_size << "x" << kernel_size;
+ CLBuildOptions build_options;
+
if(_data_layout == DataLayout::NHWC)
{
- kernel_name << "_" << lower_string(string_from_data_layout(_data_layout));
- }
+ _border_size = BorderSize();
- CLBuildOptions build_options;
- build_options.add_option_if(_biases != nullptr, std::string("-DHAS_BIAS"));
+ kernel_name << "direct_convolution_nhwc";
- const bool run_optimized_for_bifrost = can_run_optimized_kernel_for_bifrost(gpu_target, _conv_stride_x, _conv_stride_y, kernel_size, data_type, _data_layout);
+ const unsigned int n0 = win_config.second.x().step();
+ const unsigned int m0 = win_config.second.y().step();
+ const unsigned int k0 = std::min(static_cast<unsigned int>(_input->info()->dimension(channel_idx)), 16u);
+ const unsigned int partial_store_n0 = _output->info()->dimension(channel_idx) % n0;
+ const unsigned int partial_store_m0 = _output->info()->dimension(channel_idx) % m0;
+ const unsigned int pad_left = conv_info.pad_left();
+ const unsigned int pad_top = conv_info.pad_top();
- if(run_optimized_for_bifrost)
- {
- build_options.add_option(std::string("-DWEIGHTS_DEPTH=" + support::cpp11::to_string(_weights->info()->dimension(channel_idx))));
-
- kernel_name << "_f32_bifrost";
- _kernel = create_kernel(compile_context, kernel_name.str(), build_options.options());
- }
- else
- {
- build_options.add_option(std::string("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type)));
- build_options.add_option(std::string("-DDATA_SIZE=" + get_data_size_from_data_type(data_type)));
- build_options.add_option(std::string("-DWEIGHTS_DEPTH=" + support::cpp11::to_string(_weights->info()->dimension(channel_idx))));
- build_options.add_option(std::string("-DSTRIDE_X=" + support::cpp11::to_string(_conv_stride_x)));
- if(_data_layout == DataLayout::NHWC)
- {
- const bool run_optimized_for_bifrost_nhwc = can_run_optimized_kernel_for_bifrost_nhwc(gpu_target, _conv_stride_x, _conv_stride_y, kernel_size, data_type, _data_layout);
- build_options.add_option(std::string("-DDATA_LAYOUT_NHWC=1"));
- build_options.add_option(std::string("-DDST_HEIGHT=" + support::cpp11::to_string(_output->info()->dimension(height_idx))));
- build_options.add_option(std::string("-DDST_WIDTH=" + support::cpp11::to_string(_output->info()->dimension(width_idx))));
- build_options.add_option(std::string("-DSRC_HEIGHT=" + support::cpp11::to_string(_input->info()->dimension(height_idx))));
- build_options.add_option(std::string("-DSRC_WIDTH=" + support::cpp11::to_string(_input->info()->dimension(width_idx))));
- build_options.add_option(std::string("-DPAD_LEFT=" + support::cpp11::to_string(conv_info.pad_left())));
- build_options.add_option(std::string("-DPAD_TOP=" + support::cpp11::to_string(conv_info.pad_top())));
- build_options.add_option(std::string("-DPAD_BOTTOM=" + support::cpp11::to_string(conv_info.pad_bottom())));
- build_options.add_option(std::string("-DSTRIDE_Y=" + support::cpp11::to_string(_conv_stride_y)));
- if(run_optimized_for_bifrost_nhwc)
- {
- const unsigned int num_elems_read_per_iteration_x = 4;
- _border_size.right = num_elems_read_per_iteration_x;
- build_options.add_option("-DVEC_SIZE=" + support::cpp11::to_string(num_elems_read_per_iteration_x));
- }
- }
- build_options.add_option(std::string("-DDATA_TYPE_PROMOTED=" + get_cl_type_from_data_type(data_type)));
+ build_options.add_option_if(_biases != nullptr, std::string("-DHAS_BIAS"));
+ build_options.add_option_if(_biases != nullptr, std::string("-DBIA_DATA_TYPE=" + get_cl_type_from_data_type(_biases->info()->data_type())));
+ build_options.add_option("-DSRC_WIDTH=" + support::cpp11::to_string(_input->info()->dimension(width_idx)));
+ build_options.add_option("-DSRC_HEIGHT=" + support::cpp11::to_string(_input->info()->dimension(height_idx)));
+ build_options.add_option("-DSRC_CHANNELS=" + support::cpp11::to_string(_input->info()->dimension(channel_idx)));
+ build_options.add_option("-DSRC_DATA_TYPE=" + get_cl_type_from_data_type(_input->info()->data_type()));
+ build_options.add_option("-DDST_WIDTH=" + support::cpp11::to_string(_output->info()->dimension(width_idx)));
+ build_options.add_option("-DDST_HEIGHT=" + support::cpp11::to_string(_output->info()->dimension(height_idx)));
+ build_options.add_option("-DDST_CHANNELS=" + support::cpp11::to_string(_output->info()->dimension(channel_idx)));
+ build_options.add_option("-DDST_DATA_TYPE=" + get_cl_type_from_data_type(_output->info()->data_type()));
+ build_options.add_option("-DWEI_WIDTH=" + support::cpp11::to_string(_weights->info()->dimension(width_idx)));
+ build_options.add_option("-DWEI_HEIGHT=" + support::cpp11::to_string(_weights->info()->dimension(height_idx)));
+ build_options.add_option("-DWEI_DATA_TYPE=" + get_cl_type_from_data_type(_weights->info()->data_type()));
+ build_options.add_option("-DSTRIDE_X=" + support::cpp11::to_string(_conv_stride_x));
+ build_options.add_option("-DSTRIDE_Y=" + support::cpp11::to_string(_conv_stride_y));
+ build_options.add_option("-DPAD_LEFT=" + support::cpp11::to_string(pad_left));
+ build_options.add_option("-DPAD_TOP=" + support::cpp11::to_string(pad_top));
+ build_options.add_option("-DN0=" + support::cpp11::to_string(n0));
+ build_options.add_option("-DM0=" + support::cpp11::to_string(m0));
+ build_options.add_option("-DK0=" + support::cpp11::to_string(k0));
+ build_options.add_option("-DPARTIAL_STORE_N0=" + support::cpp11::to_string(partial_store_n0));
+ build_options.add_option("-DPARTIAL_STORE_M0=" + support::cpp11::to_string(partial_store_m0));
if(is_data_type_quantized(data_type))
{
@@ -523,33 +413,74 @@
int output_multiplier = 0;
int output_shift = 0;
quantization::calculate_quantized_multiplier(multiplier, &output_multiplier, &output_shift);
- build_options.add_option("-DOUTPUT_MULTIPLIER=" + support::cpp11::to_string(output_multiplier));
- build_options.add_option("-DOUTPUT_SHIFT=" + support::cpp11::to_string(output_shift));
- build_options.add_option("-DKERNEL_SIZE=" + support::cpp11::to_string(kernel_size));
-
- // Create kernel
- _kernel = create_kernel(compile_context, "direct_convolution_quantized", build_options.options());
-
- // Set static kernel arguments
- unsigned int idx = 3 * num_arguments_per_3D_tensor() + ((_biases != nullptr) ? num_arguments_per_1D_tensor() : 0) + 1;
- _kernel.setArg(idx++, -iqinfo.offset);
- _kernel.setArg(idx++, -wqinfo.offset);
- _kernel.setArg(idx++, oqinfo.offset);
+ build_options.add_option("-DIS_QUANTISED");
+ build_options.add_option("-DDST_MULTIPLIER=" + support::cpp11::to_string(output_multiplier));
+ build_options.add_option("-DDST_SHIFT=" + support::cpp11::to_string(output_shift));
+ build_options.add_option("-DSRC_OFFSET=" + support::cpp11::to_string(-iqinfo.offset));
+ build_options.add_option("-DWEI_OFFSET=" + support::cpp11::to_string(-wqinfo.offset));
+ build_options.add_option("-DDST_OFFSET=" + support::cpp11::to_string(oqinfo.offset));
+ build_options.add_option("-DACC_DATA_TYPE=" + get_cl_type_from_data_type(DataType::S32));
}
else
{
- // Create kernel
- _kernel = create_kernel(compile_context, kernel_name.str(), build_options.options());
+ build_options.add_option("-DACC_DATA_TYPE=" + get_cl_type_from_data_type(data_type));
+ build_options.add_option("-DSRC_OFFSET=" + support::cpp11::to_string(0));
+ build_options.add_option("-DWEI_OFFSET=" + support::cpp11::to_string(0));
+ build_options.add_option("-DDST_OFFSET=" + support::cpp11::to_string(0));
+ build_options.add_option("-DACC_DATA_TYPE=" + get_cl_type_from_data_type(data_type));
+ }
+ }
+ else
+ {
+ _border_size = BorderSize(conv_info.pad_top(), conv_info.pad_right(), conv_info.pad_bottom(), conv_info.pad_left());
+
+ kernel_name << "direct_convolution" << kernel_size << "x" << kernel_size;
+
+ build_options.add_option_if(_biases != nullptr, std::string("-DHAS_BIAS"));
+
+ const bool run_optimized_for_bifrost = can_run_optimized_kernel_for_bifrost_nchw(gpu_target, _conv_stride_x, _conv_stride_y, kernel_size, data_type, _data_layout);
+
+ if(run_optimized_for_bifrost)
+ {
+ build_options.add_option(std::string("-DWEIGHTS_DEPTH=" + support::cpp11::to_string(_weights->info()->dimension(channel_idx))));
+
+ kernel_name << "_f32_bifrost";
+ }
+ else
+ {
+ build_options.add_option(std::string("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type)));
+ build_options.add_option(std::string("-DDATA_SIZE=" + get_data_size_from_data_type(data_type)));
+ build_options.add_option(std::string("-DWEIGHTS_DEPTH=" + support::cpp11::to_string(_weights->info()->dimension(channel_idx))));
+ build_options.add_option(std::string("-DSTRIDE_X=" + support::cpp11::to_string(_conv_stride_x)));
+ build_options.add_option(std::string("-DDATA_TYPE_PROMOTED=" + get_cl_type_from_data_type(data_type)));
+
+ if(is_data_type_quantized(data_type))
+ {
+ const UniformQuantizationInfo iqinfo = _input->info()->quantization_info().uniform();
+ const UniformQuantizationInfo wqinfo = _weights->info()->quantization_info().uniform();
+ const UniformQuantizationInfo oqinfo = _output->info()->quantization_info().uniform();
+
+ float multiplier = iqinfo.scale * wqinfo.scale / oqinfo.scale;
+ int output_multiplier = 0;
+ int output_shift = 0;
+ quantization::calculate_quantized_multiplier(multiplier, &output_multiplier, &output_shift);
+ build_options.add_option("-DOUTPUT_MULTIPLIER=" + support::cpp11::to_string(output_multiplier));
+ build_options.add_option("-DOUTPUT_SHIFT=" + support::cpp11::to_string(output_shift));
+ build_options.add_option("-DKERNEL_SIZE=" + support::cpp11::to_string(kernel_size));
+ build_options.add_option("-DINPUT_OFFSET=" + support::cpp11::to_string(-iqinfo.offset));
+ build_options.add_option("-DWEIGHTS_OFFSET=" + support::cpp11::to_string(-wqinfo.offset));
+ build_options.add_option("-DOUTPUT_OFFSET=" + support::cpp11::to_string(oqinfo.offset));
+
+ kernel_name.str("direct_convolution_quantized");
+ }
}
}
- // Configure kernel window
- auto win_config = validate_and_configure_window(input->info(), weights->info(), output->info(), conv_info, gpu_target);
- ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
- ICLKernel::configure_internal(win_config.second);
+ _kernel = create_kernel(compile_context, kernel_name.str(), build_options.options());
// Set config_id for enabling LWS tuning
- _config_id = "direct_convolution_";
+ _config_id = kernel_name.str();
+ _config_id += "_";
_config_id += lower_string(string_from_data_type(data_type));
_config_id += "_";
_config_id += support::cpp11::to_string(kernel_size);
@@ -588,38 +519,58 @@
ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window);
// Get initial windows
- Window slice = window.first_slice_window_3D();
- Window win_in = window;
+ Window slice = window.first_slice_window_3D();
- win_in.adjust(Window::DimX, -_border_size.left, true);
- win_in.adjust(Window::DimY, -_border_size.top, true);
-
- const int width_idx = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::WIDTH);
- const int height_idx = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::HEIGHT);
-
- win_in.set_dimension_step(width_idx, window[width_idx].step() * _conv_stride_x);
- win_in.set_dimension_step(height_idx, window[height_idx].step() * _conv_stride_y);
-
- Window slice_in = win_in.first_slice_window_3D();
- unsigned int idx1 = 2 * num_arguments_per_3D_tensor();
- add_3D_tensor_argument(idx1, _weights, slice);
-
- if(_biases != nullptr)
+ if(_data_layout == DataLayout::NHWC)
{
- Window slice_biases;
- slice_biases.use_tensor_dimensions(_biases->info()->tensor_shape());
- add_1D_tensor_argument(idx1, _biases, slice_biases);
- }
+ slice.set(Window::DimY, Window::Dimension(0, _output->info()->dimension(1) * _output->info()->dimension(2), 1));
+ slice.set(Window::DimZ, Window::Dimension(0, _output->info()->dimension(3), 1));
- _kernel.setArg(idx1++, static_cast<unsigned int>(_weights->info()->strides_in_bytes()[3]));
-
- do
- {
unsigned int idx = 0;
- add_3D_tensor_argument(idx, _input, slice_in);
+ add_3D_tensor_argument(idx, _input, slice);
add_3D_tensor_argument(idx, _output, slice);
+ add_3D_tensor_argument(idx, _weights, slice);
+ if(_biases != nullptr)
+ {
+ add_1D_tensor_argument(idx, _biases, slice);
+ }
+ _kernel.setArg(idx++, static_cast<unsigned int>(_weights->info()->strides_in_bytes()[3]));
enqueue(queue, *this, slice, lws_hint());
}
- while(window.slide_window_slice_3D(slice) && win_in.slide_window_slice_3D(slice_in));
+ else
+ {
+ Window win_in = window;
+
+ win_in.adjust(Window::DimX, -_border_size.left, true);
+ win_in.adjust(Window::DimY, -_border_size.top, true);
+
+ const int width_idx = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::WIDTH);
+ const int height_idx = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::HEIGHT);
+
+ win_in.set_dimension_step(width_idx, window[width_idx].step() * _conv_stride_x);
+ win_in.set_dimension_step(height_idx, window[height_idx].step() * _conv_stride_y);
+
+ Window slice_in = win_in.first_slice_window_3D();
+ unsigned int idx1 = 2 * num_arguments_per_3D_tensor();
+ add_3D_tensor_argument(idx1, _weights, slice);
+
+ if(_biases != nullptr)
+ {
+ Window slice_biases;
+ slice_biases.use_tensor_dimensions(_biases->info()->tensor_shape());
+ add_1D_tensor_argument(idx1, _biases, slice_biases);
+ }
+
+ _kernel.setArg(idx1++, static_cast<unsigned int>(_weights->info()->strides_in_bytes()[3]));
+
+ do
+ {
+ unsigned int idx = 0;
+ add_3D_tensor_argument(idx, _input, slice_in);
+ add_3D_tensor_argument(idx, _output, slice);
+ enqueue(queue, *this, slice, lws_hint());
+ }
+ while(window.slide_window_slice_3D(slice) && win_in.slide_window_slice_3D(slice_in));
+ }
}
} // namespace arm_compute