COMPMID-2053: Fuse bias addition with CLGEMMMatrixMultiplyReshapedKernel
Change-Id: I5bfd38c94a6fd18a1cba2104f7e1b04e7bef6ec2
Signed-off-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
Reviewed-on: https://review.mlplatform.org/c/1359
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
diff --git a/src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedKernel.cpp b/src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedKernel.cpp
index 59afa47..4436726 100644
--- a/src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedKernel.cpp
+++ b/src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedKernel.cpp
@@ -56,8 +56,9 @@
{
using ElementsProcessed = Steps;
-Status validate_arguments(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output, float alpha, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info,
- const GEMMReshapeInfo &gemm_info)
+Status validate_arguments(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, float alpha, float beta, const GEMMLHSMatrixInfo &lhs_info,
+ const GEMMRHSMatrixInfo &rhs_info,
+ const GEMMReshapeInfo &gemm_info)
{
ARM_COMPUTE_UNUSED(alpha);
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input0, input1, output);
@@ -86,6 +87,22 @@
tensor_shape1.set(0, n);
tensor_shape1.set(1, k);
+ if(input2 != nullptr && !(helpers::float_ops::is_zero(beta)))
+ {
+ const int input2_dim0 = static_cast<int>(input2->dimension(0));
+ const int input2_dim1 = static_cast<int>(input2->dimension(1));
+
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input2, input1);
+ if(gemm_info.broadcast_bias())
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG((input2_dim1 != 1 || input2_dim0 != n), "Incorrect dimension of bias matrix which is to be broadcasted");
+ }
+ else
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG((input2_dim0 != n || input2_dim1 != m), "Incorrect dimension of bias matrix");
+ }
+ }
+
const TensorInfo tensor_info0 = input0->clone()->set_tensor_shape(tensor_shape0);
const TensorInfo tensor_info1 = input1->clone()->set_tensor_shape(tensor_shape1);
@@ -105,7 +122,8 @@
return Status{};
}
-std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input0, ITensorInfo *input1, ITensorInfo *output, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info,
+std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input0, ITensorInfo *input1, ITensorInfo *input2, ITensorInfo *output, const GEMMLHSMatrixInfo &lhs_info,
+ const GEMMRHSMatrixInfo &rhs_info,
const GEMMReshapeInfo &gemm_info, ElementsProcessed &num_elements_processed)
{
unsigned int &num_elems_processed_per_iteration_x = num_elements_processed[0];
@@ -152,8 +170,24 @@
ceil_to_multiple(output->dimension(0), num_elems_processed_per_iteration_x),
output->dimension(1) + bottom_pad);
- window_changed = update_window_and_padding(win, input0_access, input1_access) || // window used by the execute_window_loop
- update_window_and_padding(win_out, output_access); // window used to update the padding requirements of output tensor
+ if(input2 != nullptr)
+ {
+ const int bias_processed_per_iteration_x = num_elems_processed_per_iteration_x;
+
+ const int bias_processed_per_iteration_y = gemm_info.broadcast_bias() ? 1 : num_elems_processed_per_iteration_y;
+
+ AccessWindowStatic input2_access(input2, 0, 0,
+ ceil_to_multiple(input2->dimension(0), bias_processed_per_iteration_x),
+ ceil_to_multiple(input2->dimension(1), bias_processed_per_iteration_y));
+
+ window_changed = update_window_and_padding(win, input0_access, input1_access, input2_access) || // window used by the execute_window_loop
+ update_window_and_padding(win_out, output_access); // window used to update the padding requirements of output tensor
+ }
+ else
+ {
+ window_changed = update_window_and_padding(win, input0_access, input1_access) || // window used by the execute_window_loop
+ update_window_and_padding(win_out, output_access); // window used to update the padding requirements of output tensor
+ }
output_access.set_valid_region(win_out, ValidRegion(Coordinates(0, 0), output->tensor_shape()));
@@ -169,23 +203,28 @@
} // namespace
CLGEMMMatrixMultiplyReshapedKernel::CLGEMMMatrixMultiplyReshapedKernel()
- : _input0(nullptr), _input1(nullptr), _output(nullptr), _slide_matrix_b(true), _reinterpret_output_as_3d(false), _k(1), _use_dummy_work_items(false)
+ : _input0(nullptr), _input1(nullptr), _input2(nullptr), _output(nullptr), _slide_matrix_b(true), _reinterpret_output_as_3d(false), _k(1), _use_dummy_work_items(false), _add_bias(false),
+ _broadcast_bias(false)
{
}
-void CLGEMMMatrixMultiplyReshapedKernel::configure(const ICLTensor *input0, const ICLTensor *input1, ICLTensor *output, float alpha, const GEMMLHSMatrixInfo &lhs_info,
+void CLGEMMMatrixMultiplyReshapedKernel::configure(const ICLTensor *input0, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, float alpha, float beta,
+ const GEMMLHSMatrixInfo &lhs_info,
const GEMMRHSMatrixInfo &rhs_info, const GEMMReshapeInfo &gemm_info)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input0, input1, output);
- ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input0->info(), input1->info(), output->info(), alpha, lhs_info, rhs_info, gemm_info));
+ ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input0->info(), input1->info(), (input2 != nullptr ? input2->info() : nullptr), output->info(), alpha, beta, lhs_info, rhs_info, gemm_info));
_input0 = input0;
_input1 = input1;
+ _input2 = helpers::float_ops::is_zero(beta) ? nullptr : input2;
_output = output;
_reinterpret_output_as_3d = (gemm_info.depth_output_gemm3d() != 0);
_k = gemm_info.k();
_use_dummy_work_items = preferred_dummy_work_items_support(CLKernelLibrary::get().get_device());
+ _add_bias = _input2 != nullptr;
+ _broadcast_bias = gemm_info.broadcast_bias();
// Check if we need to slide the matrix B
const unsigned int num_dimensions_input0 = _input0->info()->num_dimensions();
@@ -194,7 +233,7 @@
ElementsProcessed num_elements_processed{};
// Configure kernel window
- auto win_config = validate_and_configure_window(input0->info(), input1->info(), output->info(), lhs_info, rhs_info, gemm_info, num_elements_processed);
+ auto win_config = validate_and_configure_window(input0->info(), input1->info(), input2 != nullptr ? input2->info() : nullptr, output->info(), lhs_info, rhs_info, gemm_info, num_elements_processed);
ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
ICLKernel::configure_internal(win_config.second);
@@ -202,9 +241,12 @@
CLBuildOptions build_opts;
build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(input0->info()->data_type()));
build_opts.add_option_if(!(helpers::float_ops::is_one(alpha)), "-DALPHA=" + float_to_string_with_full_precision(alpha));
+ build_opts.add_option_if(_input2 != nullptr, "-DBETA=" + float_to_string_with_full_precision(beta));
+ build_opts.add_option_if(helpers::float_ops::is_one(beta), "-DUNIT_BETA");
build_opts.add_option_if(_reinterpret_output_as_3d, "-DREINTERPRET_OUTPUT_AS_3D");
build_opts.add_option_if(_reinterpret_output_as_3d, "-DHEIGHT_GEMM3D=" + support::cpp11::to_string(output->info()->dimension(1)));
build_opts.add_option_if(_reinterpret_output_as_3d, "-DDEPTH_GEMM3D=" + support::cpp11::to_string(output->info()->dimension(2)));
+ build_opts.add_option_if(gemm_info.broadcast_bias(), "-DBROADCAST_BIAS");
build_opts.add_option_if(!_slide_matrix_b, "-DMATRIX_B_DEPTH=" + support::cpp11::to_string(input1->info()->dimension(2)));
build_opts.add_option_if(lhs_info.interleave, "-DLHS_INTERLEAVE");
build_opts.add_option_if(rhs_info.interleave, "-DRHS_INTERLEAVE");
@@ -227,6 +269,8 @@
// Set config_id for enabling LWS tuning
_config_id = kernel_name;
_config_id += "_";
+ _config_id += (_add_bias ? "add_bias_" : "");
+ _config_id += (_broadcast_bias ? "broadcast_bias_" : "");
_config_id += (_reinterpret_output_as_3d ? "3do_" : "");
_config_id += lower_string(string_from_data_type(input0->info()->data_type()));
_config_id += "_";
@@ -253,13 +297,15 @@
_config_id += support::cpp11::to_string(rhs_info.interleave);
}
-Status CLGEMMMatrixMultiplyReshapedKernel::validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output, float alpha, const GEMMLHSMatrixInfo &lhs_info,
+Status CLGEMMMatrixMultiplyReshapedKernel::validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, float alpha, float beta,
+ const GEMMLHSMatrixInfo &lhs_info,
const GEMMRHSMatrixInfo &rhs_info, const GEMMReshapeInfo &gemm_info)
{
ElementsProcessed num_elements_processed{};
- ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input0, input1, output, alpha, lhs_info, rhs_info, gemm_info));
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input0, input1, input2, output, alpha, beta, lhs_info, rhs_info, gemm_info));
ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input0->clone().get(),
input1->clone().get(),
+ input2 != nullptr ? input2->clone().get() : nullptr,
output->clone().get(),
lhs_info,
rhs_info,
@@ -290,7 +336,15 @@
if(_reinterpret_output_as_3d)
{
// Pass bottom paddings to the kernel if the output has to be reinterpreted as 3D tensor
- const unsigned int idx0 = 3 * num_arguments_per_2D_tensor() + 4;
+ unsigned int idx0;
+ if(_add_bias)
+ {
+ idx0 = 4 * num_arguments_per_2D_tensor() + 5;
+ }
+ else
+ {
+ idx0 = 3 * num_arguments_per_2D_tensor() + 4;
+ }
const unsigned int total_cross_plane_pad = _output->info()->padding().top + _output->info()->padding().bottom;
_kernel.setArg<cl_uint>(idx0, static_cast<unsigned int>(total_cross_plane_pad));
}
@@ -308,10 +362,18 @@
unsigned int idx = 0;
add_2D_tensor_argument(idx, _input0, slice);
add_2D_tensor_argument(idx, _input1, slice_b);
+ if(_add_bias)
+ {
+ add_2D_tensor_argument(idx, _input2, slice);
+ }
add_2D_tensor_argument(idx, _output, slice);
_kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_k));
_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]));
+ if(_add_bias)
+ {
+ _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_input2->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(), _use_dummy_work_items);
}