#include "arm_compute/core/Error.h"
#include "arm_compute/core/FixedPoint.h"
#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/core/Utils.h"
#include "arm_compute/core/Validate.h"
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, input1);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input0, input1);
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(input1->num_dimensions() > 3, "The number of dimensions for the matrix B must be <= 3");
if(!is_interleaved_transposed)
{
}
inline std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input0, ITensorInfo *input1, ITensorInfo *output,
- bool is_interleaved_transposed, GPUTarget gpu_target,
+ bool is_interleaved_transposed, const GEMMReshapeInfo &reshape_info, GPUTarget gpu_target,
ElementsProcessed &num_elements_processed)
{
bool window_changed = false;
unsigned int &num_elems_processed_per_iteration_x = num_elements_processed[0];
unsigned int &num_elems_processed_per_iteration_y = num_elements_processed[1];
+ // Output tensor auto inizialitation if not yet initialized
+ auto_init_if_empty(*output, input0->clone()->set_tensor_shape(compute_mm_shape(*input0, *input1, is_interleaved_transposed, reshape_info)));
+
if(is_interleaved_transposed)
{
// Configure kernel window
win = calculate_max_window(*output, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
AccessWindowRectangle input0_access(input0, 0, 0, num_elems_processed_per_iteration_y, 1, 1.f, 0.25f);
- AccessWindowTranspose input1_access(input1, 0, 0, num_elems_processed_per_iteration_x, 1, 0.f, 0.25f);
+ AccessWindowStatic input1_access(input1, 0, 0,
+ ceil_to_multiple(input1->dimension(0), num_elems_processed_per_iteration_x),
+ ceil_to_multiple(input1->dimension(1), num_elems_processed_per_iteration_y));
AccessWindowRectangle output_access(output, 0, 0, num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y);
window_changed = update_window_and_padding(win, input0_access, input1_access, output_access);
num_elems_processed_per_iteration_y = std::min(static_cast<int>(output->dimension(1)), 4);
// Create kernels according to the architecture, data type and input size.
- if(gpu_target == GPUTarget::BIFROST && data_type == DataType::F32)
+ GPUTarget arch_target = get_arch_from_target(gpu_target);
+ if(arch_target == GPUTarget::BIFROST && data_type == DataType::F32)
{
num_elems_processed_per_iteration_x = (input1->dimension(0) <= 1000 && input0->num_dimensions() == 1) ? 2 : 4;
}
output_access.set_valid_region(win, ValidRegion(coord, output->tensor_shape()));
}
+ // Collapse along the Z direction
+ // This collapse needs to be here in order to tune the Z dimension of LWS
+ Window collapsed = win;
+ const unsigned int dimension_to_collapse = std::min(static_cast<unsigned int>(output->num_dimensions()), 2u);
+ collapsed = win.collapse(win, dimension_to_collapse);
+
Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
- return std::make_pair(err, win);
+ return std::make_pair(err, collapsed);
}
} // namespace
CLGEMMMatrixMultiplyKernel::CLGEMMMatrixMultiplyKernel()
- : _input0(nullptr), _input1(nullptr), _output(nullptr)
+ : _input0(nullptr), _input1(nullptr), _output(nullptr), _slide_matrix_b(true)
{
}
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input0, input1, output);
- // Output tensor auto inizialitation if not yet initialized
- TensorShape tensor_shape{ input0->info()->tensor_shape() };
- tensor_shape.set(0, is_interleaved_transposed ? reshape_info.n() : input1->info()->dimension(0));
- tensor_shape.set(1, is_interleaved_transposed ? reshape_info.m() : input0->info()->dimension(1));
-
- auto_init_if_empty(*output->info(), input0->info()->clone()->set_tensor_shape(tensor_shape));
-
// Perform validate step
ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input0->info(), input1->info(), output->info(), is_interleaved_transposed, reshape_info));
- _input0 = input0;
- _input1 = input1;
- _output = output;
+ _input0 = input0;
+ _input1 = input1;
+ _output = output;
+ _slide_matrix_b = _input1->info()->num_dimensions() >= _input0->info()->num_dimensions();
const DataType data_type = input0->info()->data_type();
const int fp_pos = input0->info()->fixed_point_position();
// Get target architecture
- GPUTarget arch_target = get_arch_from_target(get_target());
+ GPUTarget gpu_target = get_target();
// Configure LWS hint
- if(arch_target == GPUTarget::BIFROST && input1->info()->dimension(1) == 24)
- {
- // LWS optimized for the 11x11 AlexNet convolution on Bifrost.
- _lws_hint = cl::NDRange(2, 2);
- }
- else if(output->info()->dimension(1) == 196)
- {
- _lws_hint = cl::NDRange(1, 7);
- }
- else
+ switch(gpu_target)
{
- _lws_hint = cl::NDRange(8, 8);
+ case GPUTarget::MIDGARD:
+ case GPUTarget::T600:
+ case GPUTarget::T700:
+ case GPUTarget::T800:
+ if(output->info()->dimension(1) == 196)
+ {
+ _lws_hint = cl::NDRange(1, 7);
+ }
+ else
+ {
+ _lws_hint = cl::NDRange(8, 8);
+ }
+ break;
+ case GPUTarget::G71:
+ case GPUTarget::G72:
+ case GPUTarget::G51:
+ case GPUTarget::G51BIG:
+ case GPUTarget::G51LIT:
+ case GPUTarget::TNOX:
+ if(input1->info()->dimension(1) == 24)
+ {
+ // LWS optimized for the 11x11 AlexNet convolution on Bifrost.
+ _lws_hint = cl::NDRange(2, 2);
+ }
+ else if(output->info()->dimension(1) == 196)
+ {
+ _lws_hint = cl::NDRange(1, 7);
+ }
+ else
+ {
+ _lws_hint = cl::NDRange(8, 8);
+ }
+ break;
+ default:
+ _lws_hint = cl::NullRange;
}
ElementsProcessed num_elements_processed{};
// Configure kernel window
- auto win_config = validate_and_configure_window(input0->info(), input1->info(), output->info(), is_interleaved_transposed, arch_target, num_elements_processed);
+ auto win_config = validate_and_configure_window(input0->info(), input1->info(), output->info(), is_interleaved_transposed, reshape_info, gpu_target, num_elements_processed);
ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
ICLKernel::configure(win_config.second);
"-DALPHA=" + float_to_string_with_full_precision(alpha));
}
+ // Do not slide matrix B if _slide_matrix_b = false
+ build_opts.add_option_if(!_slide_matrix_b, "-DMATRIX_B_DEPTH=" + support::cpp11::to_string(input1->info()->dimension(2)));
+
+ const bool is_bifrost = get_arch_from_target(gpu_target) == GPUTarget::BIFROST;
+
std::string kernel_name;
if(is_interleaved_transposed)
{
build_opts.add_option("-DMULT_TRANSPOSE1XW_WIDTH=" + support::cpp11::to_string(mult_transpose1xW_width));
build_opts.add_option("-DMULT_INTERLEAVE4X4_HEIGHT=" + support::cpp11::to_string(mult_interleave4x4_height));
- if(data_type == DataType::F32)
+ if(is_data_type_float(data_type) && is_bifrost)
{
- kernel_name = "gemm_mm_interleaved_transposed_f32_" + string_from_target(arch_target);
+ kernel_name = "gemm_mm_interleaved_transposed_" + lower_string(string_from_data_type(data_type)) + "_bifrost";
}
else
{
else // The input tensors have not been reshaped
{
build_opts.add_option("-DCOLS_A=" + support::cpp11::to_string(input0->info()->dimension(0)));
+ build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type));
// Create kernels according to the architecture, data type and input size.
- if(arch_target == GPUTarget::BIFROST && data_type == DataType::F32)
+ if(is_data_type_float(data_type) && is_bifrost)
{
- // The first kernel is optimized for the case of 1000 or less output elements (e.g. FC8 of AlexNet and VGG-16, and
- // FC1 of Inception v3). The second kernel is optimized for the case of greater than 1000 output elements (e.g.
- // FC6 and FC7 of AlexNet and VGG-16).
- kernel_name = (input1->info()->dimension(0) <= 1000 && input0->info()->num_dimensions() == 1) ? "gemm_mm_floating_point_f32_bifrost_1000" : "gemm_mm_floating_point_f32_bifrost";
+ kernel_name = "gemm_mm_floating_point";
+
+ if(input0->info()->num_dimensions() != 1)
+ {
+ kernel_name += "_" + lower_string(string_from_data_type(data_type)) + "_bifrost";
+ }
+ else if(input1->info()->dimension(0) <= 1000 && data_type == DataType::F32)
+ {
+ // The first kernel is optimized for the case of 1000 or less output elements (e.g. FC8 of AlexNet and VGG-16, and
+ // FC1 of Inception v3). The second kernel is optimized for the case of greater than 1000 output elements (e.g.
+ // FC6 and FC7 of AlexNet and VGG-16).
+ kernel_name += "_" + lower_string(string_from_data_type(data_type)) + "_bifrost_1000";
+ }
// The work-group size equal to the Bifrost quad size has been proved to be optimal for these kernels
// via exhaustive autotuning over a range of representative layer configurations.
}
else // (MIDGARD and F32) or (F16)
{
- build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type));
kernel_name = "gemm_mm_floating_point";
}
build_opts.add_option("-DNUM_ELEMS_PROCESSED_PER_THREAD_Y=" + support::cpp11::to_string(num_elements_processed.y()));
_config_id += "_";
_config_id += support::cpp11::to_string(output->info()->dimension(0));
_config_id += "_";
+ _config_id += support::cpp11::to_string(output->info()->dimension(2));
+ _config_id += "_";
+ _config_id += support::cpp11::to_string(output->info()->dimension(3));
+ _config_id += "_";
_config_id += (is_interleaved_transposed ? support::cpp11::to_string(input1->info()->dimension(0)) : support::cpp11::to_string(input1->info()->dimension(1)));
}
input1->clone().get(),
output->clone().get(),
is_interleaved_transposed,
+ reshape_info,
gpu_target,
num_elements_processed)
.first);
ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window);
- Window slice = window.first_slice_window_2D();
+ if(_input1->info()->num_dimensions() < 3)
+ {
+ // The stride_z for matrix B must be zero if we do not slice
+ ARM_COMPUTE_ERROR_ON(_input1->info()->strides_in_bytes()[3] != 0);
+ }
+
+ Window slice = window.first_slice_window_3D();
Window slice_matrix_b = slice;
slice_matrix_b.set(Window::DimX, Window::Dimension(0, 1, 1));
{
Window slice_b = slice;
// Don't slice matrix B along the z dimension if matrix B has just 2 dimensions and matrix A more than 2
- // This scenario can happen when the the matrix multiplication is used to perform a convolution operation
- if(_input1->info()->num_dimensions() < 3)
+ // This scenario can happen when the matrix multiplication is used to perform a convolution operation
+ if(!_slide_matrix_b)
{
slice_b = slice_matrix_b;
}
add_2D_tensor_argument(idx, _input0, slice);
add_2D_tensor_argument(idx, _input1, slice_b);
add_2D_tensor_argument(idx, _output, slice);
+ _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_input0->info()->strides_in_bytes()[2]));
+ _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_input1->info()->strides_in_bytes()[2]));
+ _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_output->info()->strides_in_bytes()[2]));
enqueue(queue, *this, slice, _lws_hint);
}
- while(window.slide_window_slice_2D(slice));
+ while(window.slide_window_slice_3D(slice));
}
projects / platform / upstream / armcl.git / blobdiff