Blame - src/core/CL/kernels/CLGEMMMatrixMultiplyNativeKernel.cpp - ml/ComputeLibrary

Status validate_arguments(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output, float alpha, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info,

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const GEMMReshapeInfo &gemm_info)

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{

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ARM_COMPUTE_UNUSED(alpha);

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ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input0, input1, output);

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ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::F32, DataType::F16);

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ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, input1);

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ARM_COMPUTE_RETURN_ERROR_ON_MSG(input0->num_dimensions() > 4, "The number of dimensions for the LHS matrix must be <= 4");

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ARM_COMPUTE_RETURN_ERROR_ON_MSG(input1->num_dimensions() > 3, "The number of dimensions for the RHS matrix must be <= 3");

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ARM_COMPUTE_RETURN_ERROR_ON_MSG(((rhs_info.k0 & (rhs_info.k0 - 1)) && rhs_info.k0 != 3), "Only 2,3,4,8,16 are supported for k0");

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ARM_COMPUTE_RETURN_ERROR_ON(rhs_info.k0 > 16);

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ARM_COMPUTE_RETURN_ERROR_ON(lhs_info.m0 < 1 || lhs_info.m0 > 8);

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ARM_COMPUTE_RETURN_ERROR_ON_MSG(((rhs_info.n0 & (rhs_info.n0 - 1)) && rhs_info.n0 != 3), "Only 2,3,4,8,16 are supported for n0");

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const int m = gemm_info.m();

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const int n = gemm_info.n();

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const int k = gemm_info.k();

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ARM_COMPUTE_UNUSED(m);

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ARM_COMPUTE_UNUSED(n);

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ARM_COMPUTE_UNUSED(k);

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ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(0) != static_cast<unsigned int>(k));

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ARM_COMPUTE_RETURN_ERROR_ON(input1->dimension(0) != static_cast<unsigned int>(n));

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ARM_COMPUTE_RETURN_ERROR_ON(input1->dimension(1) != static_cast<unsigned int>(k));

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if(gemm_info.reinterpret_input_as_3d())

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{

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ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(1) * input0->dimension(2) != static_cast<unsigned int>(m));

}

else

{

ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(1) != static_cast<unsigned int>(m));

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}

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if(output->total_size() != 0)

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{

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const TensorInfo tensor_info_output = output->clone()->set_tensor_shape(compute_mm_shape(*input0, *input1, gemm_info));

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ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &tensor_info_output);

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ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, output);

}

return Status{};

}

std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input0, ITensorInfo *input1, ITensorInfo *output, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info,

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const GEMMReshapeInfo &gemm_info, ElementsProcessed &num_elements_processed)

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{

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unsigned int &num_elems_processed_per_iteration_x = num_elements_processed[0];

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unsigned int &num_elems_processed_per_iteration_y = num_elements_processed[1];

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bool reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d();

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bool reinterpret_output_as_3d = (gemm_info.depth_output_gemm3d() != 0);

Window win{};

Window win_out{};

bool window_changed = false;

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// In case both input and output have to be reinterpreted as 3D tensors,

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// force reinterpret_input_as_3d and reinterpret_output_as_3d to be false.

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if(reinterpret_input_as_3d == reinterpret_output_as_3d)

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{

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reinterpret_output_as_3d = false;

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}

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// Output tensor auto initialization if not yet initialized

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auto_init_if_empty(*output, input0->clone()->set_tensor_shape(compute_mm_shape(*input0, *input1, gemm_info)));

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TensorInfo tmp_info(*output);

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if(reinterpret_output_as_3d)

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{

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// Since the output tensor has to be reinterpreted as 3D and the execute window is based on a 2D GEMM,

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// the window needs to be constructed on the 2D collapsed version of the tensor

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TensorShape tmp_shape(output->tensor_shape());

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tmp_shape.collapse(2U, 1U);

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tmp_info.set_tensor_shape(tmp_shape);

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}

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// Configure kernel window

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num_elems_processed_per_iteration_x = rhs_info.n0;

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num_elems_processed_per_iteration_y = lhs_info.m0;

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// Note: bottom paddings are calculated manually as the output can be reinterpreted as 3D tensor

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// The only way to set properly the paddings, it is to set those explicitly through the AccessWindowStatic

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const int m = gemm_info.m();

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const int bottom_pad = (num_elems_processed_per_iteration_y - (m % num_elems_processed_per_iteration_y)) % num_elems_processed_per_iteration_y;

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win = calculate_max_window(tmp_info, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));

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win_out = calculate_max_window(*output, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));

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AccessWindowStatic input0_access(input0, 0, 0,

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input0->dimension(0),

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input0->dimension(1) + bottom_pad);

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AccessWindowStatic input1_access(input1, 0, 0,

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ceil_to_multiple(input1->dimension(0), num_elems_processed_per_iteration_x),

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input1->dimension(1));

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AccessWindowStatic output_access(output, 0, 0,

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ceil_to_multiple(output->dimension(0), num_elems_processed_per_iteration_x),

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output->dimension(1) + bottom_pad);

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window_changed = update_window_and_padding(win, input0_access, input1_access) || // window used by the execute_window_loop

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update_window_and_padding(win_out, output_access); // window used to update the padding requirements of output tensor

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output_access.set_valid_region(win_out, ValidRegion(Coordinates(), output->tensor_shape()));

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// Collapse along the Z direction

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// This collapse needs to be here in order to tune the Z dimension of LWS

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Window collapsed = win;

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const unsigned int dimension_to_collapse = std::min(static_cast<unsigned int>(output->num_dimensions()), 2u);

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collapsed = win.collapse(win, dimension_to_collapse);

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Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};

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return std::make_pair(err, collapsed);

}

} // namespace

CLGEMMMatrixMultiplyNativeKernel::CLGEMMMatrixMultiplyNativeKernel()

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: _input0(nullptr), _input1(nullptr), _output(nullptr), _slide_matrix_b(true), _reinterpret_input_as_3d(false), _reinterpret_output_as_3d(false), _use_dummy_work_items(false)

{

}

void CLGEMMMatrixMultiplyNativeKernel::configure(const ICLTensor *input0, const ICLTensor *input1, ICLTensor *output, float alpha, const GEMMLHSMatrixInfo &lhs_info,

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const GEMMRHSMatrixInfo &rhs_info, const GEMMReshapeInfo &gemm_info)

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{

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ARM_COMPUTE_ERROR_ON_NULLPTR(input0, input1, output);

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ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input0->info(), input1->info(), output->info(), alpha, lhs_info, rhs_info, gemm_info));

_input0 = input0;

_input1 = input1;

_output = output;

_reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d();

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_reinterpret_output_as_3d = (gemm_info.depth_output_gemm3d() != 0);

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_use_dummy_work_items = preferred_dummy_work_items_support(CLKernelLibrary::get().get_device());

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// In case both input and output have to be reinterpreted as 3D tensors,

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// force reinterpret_input_as_3d and reinterpret_output_as_3d to be false.

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if(_reinterpret_input_as_3d == _reinterpret_output_as_3d)

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{

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_reinterpret_input_as_3d = false;

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_reinterpret_output_as_3d = false;

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}

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// Check if we need to slide the matrix B

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const unsigned int num_dimensions_input0 = _input0->info()->num_dimensions();

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_slide_matrix_b = (_input1->info()->num_dimensions() >= num_dimensions_input0);

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ElementsProcessed num_elements_processed{};

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// Configure kernel window

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auto win_config = validate_and_configure_window(input0->info(), input1->info(), output->info(), lhs_info, rhs_info, gemm_info, num_elements_processed);

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ARM_COMPUTE_ERROR_THROW_ON(win_config.first);

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ICLKernel::configure_internal(win_config.second);

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// Create build options

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CLBuildOptions build_opts;

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build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(input0->info()->data_type()));

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build_opts.add_option_if(std::abs(1.0f - alpha) > 0.00001f, "-DALPHA=" + float_to_string_with_full_precision(alpha));

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build_opts.add_option_if(_reinterpret_input_as_3d, "-DREINTERPRET_INPUT_AS_3D");

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build_opts.add_option_if(_reinterpret_output_as_3d, "-DREINTERPRET_OUTPUT_AS_3D");

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build_opts.add_option_if(_reinterpret_input_as_3d || _reinterpret_output_as_3d, "-DHEIGHT_GEMM3D=" + support::cpp11::to_string(output->info()->dimension(1)));

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build_opts.add_option_if(_reinterpret_input_as_3d || _reinterpret_output_as_3d, "-DDEPTH_GEMM3D=" + support::cpp11::to_string(output->info()->dimension(2)));

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build_opts.add_option_if(!_slide_matrix_b, "-DMATRIX_B_DEPTH=" + support::cpp11::to_string(input1->info()->dimension(2)));

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build_opts.add_option_if(_use_dummy_work_items, "-DDUMMY_WORK_ITEMS");

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build_opts.add_option("-DM=" + support::cpp11::to_string(input0->info()->dimension(1)));

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build_opts.add_option("-DN=" + support::cpp11::to_string(gemm_info.n()));

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build_opts.add_option("-DK=" + support::cpp11::to_string(gemm_info.k()));

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build_opts.add_option("-DM0=" + support::cpp11::to_string(lhs_info.m0));

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build_opts.add_option("-DN0=" + support::cpp11::to_string(rhs_info.n0));

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build_opts.add_option("-DK0=" + support::cpp11::to_string(rhs_info.k0));

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std::string kernel_name("gemm_mm_native");

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// Create kernel

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_kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options()));

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// Set config_id for enabling LWS tuning

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_config_id = kernel_name;

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_config_id += "_";

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_config_id += (_reinterpret_input_as_3d ? "3di_" : "");

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_config_id += (_reinterpret_output_as_3d ? "3do_" : "");

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_config_id += lower_string(string_from_data_type(input0->info()->data_type()));

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_config_id += "_";

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_config_id += support::cpp11::to_string(output->info()->dimension(1));

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_config_id += "_";

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_config_id += support::cpp11::to_string(output->info()->dimension(0));

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_config_id += "_";

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_config_id += support::cpp11::to_string(gemm_info.k());

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_config_id += "_";

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_config_id += support::cpp11::to_string(output->info()->dimension(2));

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_config_id += "_";

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_config_id += support::cpp11::to_string(lhs_info.m0);

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_config_id += "_";

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_config_id += support::cpp11::to_string(rhs_info.n0);

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_config_id += "_";

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_config_id += support::cpp11::to_string(rhs_info.k0);

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}

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Status CLGEMMMatrixMultiplyNativeKernel::validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output, float alpha, const GEMMLHSMatrixInfo &lhs_info,

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const GEMMRHSMatrixInfo &rhs_info, const GEMMReshapeInfo &gemm_info)

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{

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ElementsProcessed num_elements_processed{};

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ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input0, input1, output, alpha, lhs_info, rhs_info, gemm_info));

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ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input0->clone().get(),

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input1->clone().get(),

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output->clone().get(),

lhs_info,

rhs_info,

gemm_info,

num_elements_processed)

.first);

return Status{};

}

void CLGEMMMatrixMultiplyNativeKernel::run(const Window &window, cl::CommandQueue &queue)

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{

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ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);

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ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window);

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if(_input1->info()->num_dimensions() < 3)

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{

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// The stride_z for matrix B must be zero if we do not slice

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ARM_COMPUTE_ERROR_ON(_input1->info()->strides_in_bytes()[3] != 0);

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}

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Window slice = window.first_slice_window_3D();

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Window slice_matrix_b = slice;

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slice_matrix_b.set(Window::DimX, Window::Dimension(0, 1, 1));

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slice_matrix_b.set(Window::DimY, Window::Dimension(0, 1, 1));

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if(_reinterpret_input_as_3d)

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{

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// Pass bottom paddings to the kernel if the input has to be reinterpreted as 3D tensor

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const unsigned int idx0 = 3 * num_arguments_per_2D_tensor() + 3;

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const unsigned int total_cross_plane_pad = _input0->info()->padding().top + _input0->info()->padding().bottom;

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_kernel.setArg<cl_uint>(idx0, static_cast<unsigned int>(total_cross_plane_pad));

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}

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if(_reinterpret_output_as_3d)

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{

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// Pass bottom paddings to the kernel if the output has to be reinterpreted as 3D tensor

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const unsigned int idx0 = 3 * num_arguments_per_2D_tensor() + 3 + (_reinterpret_input_as_3d ? 1 : 0);

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const unsigned int total_cross_plane_pad = _output->info()->padding().top + _output->info()->padding().bottom;

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_kernel.setArg<cl_uint>(idx0, static_cast<unsigned int>(total_cross_plane_pad));

}

do

{

Window slice_b = slice;

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// Don't slice matrix B along the z dimension if matrix B has just 2 dimensions and matrix A more than 2

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// This scenario can happen when the matrix multiplication is used to perform a convolution operation

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if(!_slide_matrix_b)

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{

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slice_b = slice_matrix_b;

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}

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unsigned int idx = 0;

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add_2D_tensor_argument(idx, _input0, slice);

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add_2D_tensor_argument(idx, _input1, slice_b);

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add_2D_tensor_argument(idx, _output, slice);

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_kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_input0->info()->strides_in_bytes()[2]));

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_kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_input1->info()->strides_in_bytes()[2]));

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_kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_output->info()->strides_in_bytes()[2]));

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enqueue(queue, *this, slice, lws_hint(), _use_dummy_work_items);

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}

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while(window.slide_window_slice_3D(slice));

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}

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} // namespace arm_compute