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

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

56

const GEMMReshapeInfo &gemm_info)

57

{

58

ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input0, input1, output);

59

ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::QASYMM8);

60

ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, input1);

61

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

62

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

63

ARM_COMPUTE_RETURN_ERROR_ON(lhs_info.k0 != rhs_info.k0);

64

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

65

ARM_COMPUTE_RETURN_ERROR_ON(lhs_info.k0 > 16);

66

ARM_COMPUTE_RETURN_ERROR_ON(lhs_info.m0 < 2 || lhs_info.m0 > 8);

67

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");

68

69

const int m = gemm_info.m();

70

const int n = gemm_info.n();

71

const int k = gemm_info.k();

72

73

ARM_COMPUTE_UNUSED(m);

74

ARM_COMPUTE_UNUSED(n);

75

ARM_COMPUTE_UNUSED(k);

76

77

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

78

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

79

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

80

if(gemm_info.reinterpret_input_as_3d())

81

{

82

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));

87

}

88

89

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

90

{

91

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

92

ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &tensor_info_output);

93

ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::S32);

}

return Status{};

}

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

100

const GEMMReshapeInfo &gemm_info, ElementsProcessed &num_elements_processed)

101

{

102

unsigned int &num_elems_processed_per_iteration_x = num_elements_processed[0];

103

unsigned int &num_elems_processed_per_iteration_y = num_elements_processed[1];

104

bool reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d();

105

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

Window win{};

Window win_out{};

bool window_changed = false;

110

111

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

112

// force reinterpret_output_as_3d to be false.

113

if(reinterpret_input_as_3d == reinterpret_output_as_3d)

114

{

115

reinterpret_output_as_3d = false;

116

}

117

118

// Output tensor auto initialization if not yet initialized

119

auto_init_if_empty(*output, input0->clone()->set_tensor_shape(compute_mm_shape(*input0, *input1, gemm_info)).set_data_type(DataType::S32));

120

121

TensorInfo tmp_info(*output);

122

123

if(reinterpret_output_as_3d)

124

{

125

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

126

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

127

TensorShape tmp_shape(output->tensor_shape());

128

tmp_shape.collapse(2U, 1U);

129

tmp_info.set_tensor_shape(tmp_shape);

130

}

131

132

// Configure kernel window

133

num_elems_processed_per_iteration_x = rhs_info.n0;

134

num_elems_processed_per_iteration_y = lhs_info.m0;

135

136

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

137

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

138

const int m = reinterpret_output_as_3d ? gemm_info.m() : input0->dimension(1);

139

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

140

141

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

142

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

143

144

AccessWindowStatic input0_access(input0, 0, 0,

145

input0->dimension(0),

146

input0->dimension(1) + bottom_pad);

147

AccessWindowStatic input1_access(input1, 0, 0,

148

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

149

input1->dimension(1));

150

AccessWindowStatic output_access(output, 0, 0,

151

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

152

output->dimension(1) + bottom_pad);

153

154

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

155

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

156

157

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

158

159

// Collapse along the Z direction

160

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

161

Window collapsed = win;

162

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

163

collapsed = win.collapse(win, dimension_to_collapse);

164

165

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

166

return std::make_pair(err, collapsed);

}

} // namespace

CLGEMMLowpMatrixMultiplyNativeKernel::CLGEMMLowpMatrixMultiplyNativeKernel()

171

: _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 CLGEMMLowpMatrixMultiplyNativeKernel::configure(const ICLTensor *input0, const ICLTensor *input1, ICLTensor *output, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info,

176

const GEMMReshapeInfo &gemm_info)

177

{

178

ARM_COMPUTE_ERROR_ON_NULLPTR(input0, input1, output);

179

180

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

_input0 = input0;

_input1 = input1;

_output = output;

_reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d();

186

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

187

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

188

189

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

190

// force reinterpret_input_as_3d and reinterpret_output_as_3d to be false.

191

if(_reinterpret_input_as_3d == _reinterpret_output_as_3d)

192

{

193

_reinterpret_input_as_3d = false;

194

_reinterpret_output_as_3d = false;

195

}

196

197

// Check if we need to slide the matrix B

198

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

199

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

200

201

ElementsProcessed num_elements_processed{};

202

203

// Configure kernel window

204

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

205

ARM_COMPUTE_ERROR_THROW_ON(win_config.first);

206

ICLKernel::configure_internal(win_config.second);

207

208

// Create build options

209

CLBuildOptions build_opts;

210

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

211

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

212

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

213

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

214

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

215

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

216

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

217

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

218

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

219

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

220

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

221

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

222

223

std::string kernel_name("gemmlowp_mm_native");

224

225

// Create kernel

226

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

227

228

// Set config_id for enabling LWS tuning

229

_config_id = kernel_name;

230

_config_id += "_";

231

_config_id += dot8_supported(CLKernelLibrary::get().get_device()) ? "_dot8" : "";

232

_config_id += "_";

233

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

234

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

235

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

236

_config_id += "_";

237

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

238

_config_id += "_";

239

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

240

_config_id += "_";

241

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

242

_config_id += "_";

243

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

244

_config_id += "_";

245

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

246

_config_id += "_";

247

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

248

}

249

250

Status CLGEMMLowpMatrixMultiplyNativeKernel::validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output, const GEMMLHSMatrixInfo &lhs_info,

251

const GEMMRHSMatrixInfo &rhs_info, const GEMMReshapeInfo &gemm_info)

252

{

253

ElementsProcessed num_elements_processed{};

254

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

255

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

256

input1->clone().get(),

257

output->clone().get(),

lhs_info,

rhs_info,

gemm_info,

num_elements_processed)

.first);

return Status{};

}

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

268

{

269

ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);

270

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

271

272

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

273

{

274

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

275

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

276

}

277

278

Window slice = window.first_slice_window_3D();

279

Window slice_matrix_b = slice;

280

281

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

282

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

283

284

if(_reinterpret_input_as_3d)

285

{

286

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

287

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

288

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

289

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

290

}

291

292

if(_reinterpret_output_as_3d)

293

{

294

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

295

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

296

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

297

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

}

do

{

Window slice_b = slice;

303

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

304

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

305

if(!_slide_matrix_b)

306

{

307

slice_b = slice_matrix_b;

308

}

309

310

unsigned int idx = 0;

311

add_2D_tensor_argument(idx, _input0, slice);

312

add_2D_tensor_argument(idx, _input1, slice_b);

313

add_2D_tensor_argument(idx, _output, slice);

314

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

315

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

316

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

317

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

318

}

319

while(window.slide_window_slice_3D(slice));

320

}

321

} // namespace arm_compute