COMPMID-2096: Refactor the CLGEMMLowp function selection (heuristic)
Change-Id: I15a8b39e0354d3b6686ed4cc8c361782c0512037
Signed-off-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
Reviewed-on: https://review.mlplatform.org/c/1410
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: VidhyaSudhan Loganathan <vidhyasudhan.loganathan@arm.com>
diff --git a/src/core/CL/kernels/CLGEMMLowpMatrixMultiplyKernel.cpp b/src/core/CL/kernels/CLGEMMLowpMatrixMultiplyKernel.cpp
index 1a1a4b7..cda7a83 100644
--- a/src/core/CL/kernels/CLGEMMLowpMatrixMultiplyKernel.cpp
+++ b/src/core/CL/kernels/CLGEMMLowpMatrixMultiplyKernel.cpp
@@ -55,63 +55,38 @@
{
using ElementsProcessed = Steps;
-Status validate_arguments(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output, bool is_interleaved_transposed, const GEMMReshapeInfo &reshape_info)
+Status validate_arguments(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output, const GEMMReshapeInfo &gemm_info)
{
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input0, input1, output);
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::QASYMM8);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, input1);
ARM_COMPUTE_RETURN_ERROR_ON_MSG(input0->num_dimensions() > 4, "The number of dimensions for the matrix A must be <= 4");
ARM_COMPUTE_RETURN_ERROR_ON_MSG(input1->num_dimensions() > 3, "The number of dimensions for the matrix B must be <= 3");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(is_interleaved_transposed && reshape_info.reinterpret_input_as_3d(), "The input tensor cannot be reinterpreted as 3D if is_interleaved_transposed is true");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(input1->num_dimensions() > 2 && reshape_info.reinterpret_input_as_3d(), "The input1 tensor cannot have more than 2 dimensions if input0 has to be reinterpreted as 3D");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(input1->num_dimensions() > 2 && gemm_info.reinterpret_input_as_3d(), "The input1 tensor cannot have more than 2 dimensions if input0 has to be reinterpreted as 3D");
- if(!is_interleaved_transposed)
+ const int m = gemm_info.m();
+ const int n = gemm_info.n();
+ const int k = gemm_info.k();
+
+ ARM_COMPUTE_UNUSED(m);
+ ARM_COMPUTE_UNUSED(n);
+ ARM_COMPUTE_UNUSED(k);
+
+ ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(0) != static_cast<unsigned int>(k));
+ ARM_COMPUTE_RETURN_ERROR_ON(input1->dimension(0) != static_cast<unsigned int>(n));
+ ARM_COMPUTE_RETURN_ERROR_ON(input1->dimension(1) != static_cast<unsigned int>(k));
+ if(gemm_info.reinterpret_input_as_3d())
{
- ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(0) != input1->dimension(1));
+ ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(1) * input0->dimension(2) != static_cast<unsigned int>(m));
}
else
{
- GEMMRHSMatrixInfo rhs_info;
- GEMMLHSMatrixInfo lhs_info;
- const int m = reshape_info.m();
- const int n = reshape_info.n();
- const int k = reshape_info.k();
- const int mult_transpose1xW_width = reshape_info.mult_transpose1xW_width();
- const int mult_interleave4x4_height = reshape_info.mult_interleave4x4_height();
- const bool unroll_block = dot8_supported(CLKernelLibrary::get().get_device());
-
- rhs_info.n0 = 16 / input1->element_size();
- rhs_info.k0 = 1;
- rhs_info.h0 = mult_transpose1xW_width;
- rhs_info.interleave = false;
- rhs_info.transpose = false;
- lhs_info.m0 = 4;
- lhs_info.k0 = 4;
- lhs_info.v0 = mult_interleave4x4_height;
- lhs_info.interleave = true;
- lhs_info.transpose = !unroll_block;
-
- TensorShape tensor_shape0{ input0->tensor_shape() };
- tensor_shape0.set(0, k);
- tensor_shape0.set(1, m);
-
- TensorShape tensor_shape1{ input1->tensor_shape() };
- tensor_shape1.set(0, n);
- tensor_shape1.set(1, k);
-
- const TensorInfo tensor_info0 = input0->clone()->set_tensor_shape(tensor_shape0);
- const TensorInfo tensor_info1 = input1->clone()->set_tensor_shape(tensor_shape1);
-
- const TensorInfo tensor_info_reshaped0 = input0->clone()->set_tensor_shape(compute_lhs_reshaped_shape(tensor_info0, lhs_info));
- const TensorInfo tensor_info_reshaped1 = input1->clone()->set_tensor_shape(compute_rhs_reshaped_shape(tensor_info1, rhs_info));
-
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input0, &tensor_info_reshaped0);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input1, &tensor_info_reshaped1);
+ ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(1) != static_cast<unsigned int>(m));
}
if(output->total_size() != 0)
{
- const TensorInfo tensor_info_output = output->clone()->set_tensor_shape(compute_mm_shape(*input0, *input1, is_interleaved_transposed, reshape_info));
+ const TensorInfo tensor_info_output = output->clone()->set_tensor_shape(compute_mm_shape(*input0, *input1, false, gemm_info));
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &tensor_info_output);
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::S32);
}
@@ -119,14 +94,12 @@
return Status{};
}
-std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input0, ITensorInfo *input1, ITensorInfo *output, bool is_interleaved_transposed,
- const GEMMReshapeInfo &reshape_info, ElementsProcessed &num_elements_processed)
+std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input0, ITensorInfo *input1, ITensorInfo *output, const GEMMReshapeInfo &gemm_info, ElementsProcessed &num_elements_processed)
{
- const bool is_dot8_supported = dot8_supported(CLKernelLibrary::get().get_device());
unsigned int &num_elems_processed_per_iteration_x = num_elements_processed[0];
unsigned int &num_elems_processed_per_iteration_y = num_elements_processed[1];
- bool reinterpret_input_as_3d = reshape_info.reinterpret_input_as_3d();
- bool reinterpret_output_as_3d = (reshape_info.depth_output_gemm3d() != 0);
+ bool reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d();
+ bool reinterpret_output_as_3d = (gemm_info.depth_output_gemm3d() != 0);
Window win{};
Window win_out{};
@@ -141,7 +114,7 @@
}
// 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)).set_data_type(DataType::S32));
+ auto_init_if_empty(*output, input0->clone()->set_tensor_shape(compute_mm_shape(*input0, *input1, false, gemm_info)).set_data_type(DataType::S32));
TensorInfo tmp_info(*output);
@@ -154,66 +127,32 @@
tmp_info.set_tensor_shape(tmp_shape);
}
- // Check if the output tensor is a vector. If so,the kernel runs the vector-matrix multiplication
- if(is_interleaved_transposed)
- {
- // reinterpret_input_as_3d is not supported if is_interleaved_transposed is set
- ARM_COMPUTE_ERROR_ON(reshape_info.reinterpret_input_as_3d());
+ // Special case for 1xN, 2xN, 3xN and 4xN input0 tensor. num_elems_processed_per_iteration_x
+ // Note: if the dot product instruction is available, the 8x2 tile has to be used
+ num_elems_processed_per_iteration_x = 4;
+ num_elems_processed_per_iteration_y = std::min(static_cast<int>(output->dimension(1)), 4);
- // Configure kernel window
- num_elems_processed_per_iteration_x = 4;
- num_elems_processed_per_iteration_y = 4;
+ // Note: bottom paddings are calculated manually as the output can be reinterpreted as 3D tensor
+ // The only way to set properly the paddings, it is to set those explicitly through the AccessWindowStatic
+ const int m = reinterpret_input_as_3d ? input0->tensor_shape()[1] * input0->tensor_shape()[2] : input0->tensor_shape()[1];
+ const int bottom_pad = (num_elems_processed_per_iteration_y - (m % num_elems_processed_per_iteration_y)) % num_elems_processed_per_iteration_y;
- // Note: bottom paddings are calculated manually as the output can be reinterpreted as 3D tensor
- // The only way to set properly the paddings, it is to set those explicitly through the AccessWindowStatic
- const int m = reshape_info.m();
- const int bottom_pad = (num_elems_processed_per_iteration_y - (m % num_elems_processed_per_iteration_y)) % num_elems_processed_per_iteration_y;
+ // Configure window
+ win = calculate_max_window(tmp_info, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
+ win_out = calculate_max_window(*output, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
- win = calculate_max_window(tmp_info, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
- win_out = calculate_max_window(*output, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
+ AccessWindowStatic input0_access(input0, 0, 0, input0->dimension(0), input0->dimension(1) + bottom_pad);
+ AccessWindowStatic input1_access(input1, 0, 0, ceil_to_multiple(input1->dimension(0), num_elems_processed_per_iteration_x), input1->dimension(1));
+ AccessWindowStatic output_access(output, 0, 0,
+ ceil_to_multiple(output->dimension(0), num_elems_processed_per_iteration_x),
+ output->dimension(1) + bottom_pad);
- AccessWindowRectangle input0_access(input0, 0, 0, num_elems_processed_per_iteration_y, 1, 1.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));
- AccessWindowStatic output_access(output, 0, 0,
- 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
- 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()));
- }
- else
- {
- // Special case for 1xN, 2xN, 3xN and 4xN input0 tensor. num_elems_processed_per_iteration_x
- // Note: if the dot product instruction is available, the 8x2 tile has to be used
- num_elems_processed_per_iteration_x = is_dot8_supported ? 8 : 4;
- num_elems_processed_per_iteration_y = std::min(static_cast<int>(output->dimension(1)), is_dot8_supported ? 2 : 4);
-
- // Note: bottom paddings are calculated manually as the output can be reinterpreted as 3D tensor
- // The only way to set properly the paddings, it is to set those explicitly through the AccessWindowStatic
- const int m = reinterpret_input_as_3d ? input0->tensor_shape()[1] * input0->tensor_shape()[2] : input0->tensor_shape()[1];
- const int bottom_pad = (num_elems_processed_per_iteration_y - (m % num_elems_processed_per_iteration_y)) % num_elems_processed_per_iteration_y;
-
- // Configure window
- win = calculate_max_window(tmp_info, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
- win_out = calculate_max_window(*output, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
-
- AccessWindowStatic input0_access(input0, 0, 0, input0->dimension(0), input0->dimension(1) + bottom_pad);
- AccessWindowStatic input1_access(input1, 0, 0, ceil_to_multiple(input1->dimension(0), num_elems_processed_per_iteration_x), input1->dimension(1));
- AccessWindowStatic output_access(output, 0, 0,
- 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
-
- Coordinates coord;
- coord.set_num_dimensions(output->num_dimensions());
- output_access.set_valid_region(win_out, ValidRegion(coord, output->tensor_shape()));
- }
+ Coordinates coord;
+ coord.set_num_dimensions(output->num_dimensions());
+ output_access.set_valid_region(win_out, 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
@@ -231,17 +170,17 @@
{
}
-void CLGEMMLowpMatrixMultiplyKernel::configure(const ICLTensor *input0, const ICLTensor *input1, ICLTensor *output, bool is_interleaved_transposed, const GEMMReshapeInfo &reshape_info)
+void CLGEMMLowpMatrixMultiplyKernel::configure(const ICLTensor *input0, const ICLTensor *input1, ICLTensor *output, 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(), is_interleaved_transposed, reshape_info));
+ ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input0->info(), input1->info(), output->info(), gemm_info));
_input0 = input0;
_input1 = input1;
_output = output;
- _reinterpret_input_as_3d = reshape_info.reinterpret_input_as_3d();
- _reinterpret_output_as_3d = (reshape_info.depth_output_gemm3d() != 0);
+ _reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d();
+ _reinterpret_output_as_3d = (gemm_info.depth_output_gemm3d() != 0);
// In case both input and output have to be reinterpreted as 3D tensors,
// force reinterpret_input_as_3d and reinterpret_output_as_3d to be false.
@@ -257,16 +196,11 @@
ElementsProcessed num_elements_processed{};
- // Get target architecture
- GPUTarget arch_target = get_arch_from_target(get_target());
-
// Configure kernel window
- auto win_config = validate_and_configure_window(input0->info(), input1->info(), output->info(), is_interleaved_transposed, reshape_info, num_elements_processed);
+ auto win_config = validate_and_configure_window(input0->info(), input1->info(), output->info(), gemm_info, num_elements_processed);
ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
ICLKernel::configure_internal(win_config.second);
- const bool is_dot8_supported = dot8_supported(CLKernelLibrary::get().get_device());
-
// Create build options
std::string kernel_name(" ");
CLBuildOptions build_opts;
@@ -275,38 +209,18 @@
build_opts.add_option_if(_reinterpret_input_as_3d || _reinterpret_output_as_3d, "-DHEIGHT_GEMM3D=" + support::cpp11::to_string(output->info()->dimension(1)));
build_opts.add_option_if(_reinterpret_input_as_3d || _reinterpret_output_as_3d, "-DDEPTH_GEMM3D=" + support::cpp11::to_string(output->info()->dimension(2)));
build_opts.add_option_if(!_slide_matrix_b, "-DMATRIX_B_DEPTH=" + support::cpp11::to_string(input1->info()->dimension(2)));
+ build_opts.add_option("-DCOLS_A=" + support::cpp11::to_string(input0->info()->dimension(0)));
+ build_opts.add_option("-DNUM_ELEMS_PROCESSED_PER_THREAD_X=" + support::cpp11::to_string(num_elements_processed.x()));
+ build_opts.add_option("-DNUM_ELEMS_PROCESSED_PER_THREAD_Y=" + support::cpp11::to_string(num_elements_processed.y()));
- if(is_interleaved_transposed)
- {
- const int mult_transpose1xW_width = reshape_info.mult_transpose1xW_width();
- const int mult_interleave4x4_height = reshape_info.mult_interleave4x4_height();
-
- // Note: The computation tile has the x dimension equal to 4 which is less than the transpose_width (16)
- // In order to access correctly the elements from the transposed matrix B, we need to pass
- // the correct step which is calculated as (16 * mult_transpose1xW_width) / 4)
-
- build_opts.add_option("-DCOLS_B=" + support::cpp11::to_string(input1->info()->dimension(0)));
- build_opts.add_option("-DMULT_TRANSPOSE1XW_WIDTH=" + support::cpp11::to_string(mult_transpose1xW_width));
- build_opts.add_option("-DTRANSPOSE1XW_WIDTH_STEP=" + support::cpp11::to_string(4 * mult_transpose1xW_width));
- build_opts.add_option("-DMULT_INTERLEAVE4X4_HEIGHT=" + support::cpp11::to_string(mult_interleave4x4_height));
-
- kernel_name = "gemmlowp_mm_interleaved_transposed_" + string_from_target(arch_target) + (is_dot8_supported ? "_dot8" : "");
- }
- else
- {
- build_opts.add_option("-DCOLS_A=" + support::cpp11::to_string(input0->info()->dimension(0)));
- build_opts.add_option("-DNUM_ELEMS_PROCESSED_PER_THREAD_X=" + support::cpp11::to_string(num_elements_processed.x()));
- build_opts.add_option("-DNUM_ELEMS_PROCESSED_PER_THREAD_Y=" + support::cpp11::to_string(num_elements_processed.y()));
-
- kernel_name = "gemmlowp_mm_" + string_from_target(arch_target) + (is_dot8_supported ? "_dot8" : "");
- }
+ kernel_name = "gemmlowp_mm_midgard";
// Create kernel
_kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options()));
// Set config_id for enabling LWS tuning
- _config_id = "gemmlowp_";
- _config_id += (is_interleaved_transposed ? "reshaped_" : "");
+ _config_id = kernel_name;
+ _config_id += "_";
_config_id += (_reinterpret_input_as_3d ? "3di_" : "");
_config_id += (_reinterpret_output_as_3d ? "3do_" : "");
_config_id += lower_string(string_from_data_type(input0->info()->data_type()));
@@ -314,19 +228,16 @@
_config_id += support::cpp11::to_string(output->info()->dimension(1));
_config_id += "_";
_config_id += support::cpp11::to_string(output->info()->dimension(0));
- _config_id += "_";
- _config_id += (is_interleaved_transposed ? support::cpp11::to_string(input1->info()->dimension(0)) : support::cpp11::to_string(input1->info()->dimension(1)));
}
-Status CLGEMMLowpMatrixMultiplyKernel::validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output, bool is_interleaved_transposed, const GEMMReshapeInfo &reshape_info)
+Status CLGEMMLowpMatrixMultiplyKernel::validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output, const GEMMReshapeInfo &gemm_info)
{
ElementsProcessed num_elements_processed{};
- ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input0, input1, output, is_interleaved_transposed, reshape_info));
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input0, input1, output, gemm_info));
ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input0->clone().get(),
input1->clone().get(),
output->clone().get(),
- is_interleaved_transposed,
- reshape_info,
+ gemm_info,
num_elements_processed)
.first);