diff options
Diffstat (limited to 'src/gpu/cl/kernels/ClPool2dKernel.cpp')
| -rw-r--r-- | src/gpu/cl/kernels/ClPool2dKernel.cpp | 184 |
1 files changed, 108 insertions, 76 deletions
diff --git a/src/gpu/cl/kernels/ClPool2dKernel.cpp b/src/gpu/cl/kernels/ClPool2dKernel.cpp index a1afc585e0..41ab4d6922 100644 --- a/src/gpu/cl/kernels/ClPool2dKernel.cpp +++ b/src/gpu/cl/kernels/ClPool2dKernel.cpp @@ -28,6 +28,7 @@ #include "arm_compute/core/utils/helpers/AdjustVecSize.h" #include "arm_compute/core/utils/misc/ShapeCalculator.h" #include "arm_compute/core/utils/StringUtils.h" + #include "src/core/CL/CLValidate.h" #include "src/core/helpers/AutoConfiguration.h" #include "src/core/helpers/WindowHelpers.h" @@ -43,37 +44,47 @@ using namespace arm_compute::misc::shape_calculator; namespace { -Status validate_arguments(const ITensorInfo *src, const ITensorInfo *dst, const PoolingLayerInfo &pool_info, const ITensorInfo *indices) +Status validate_arguments(const ITensorInfo *src, + const ITensorInfo *dst, + const PoolingLayerInfo &pool_info, + const ITensorInfo *indices) { ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src, dst); ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(src); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::F16, DataType::F32); - ARM_COMPUTE_RETURN_ERROR_ON_MSG((is_data_type_quantized_asymmetric(src->data_type()) && pool_info.pool_type == PoolingType::L2), - "Unsupported combination of parameters!"); - - const auto data_layout = pool_info.data_layout == DataLayout::UNKNOWN ? src->data_layout() : pool_info.data_layout; - const int idx_width = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); - const int idx_height = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); - const bool is_global_pooling = pool_info.is_global_pooling; - unsigned int pool_size_x = is_global_pooling ? src->dimension(idx_width) : pool_info.pool_size.width; - unsigned int pool_size_y = is_global_pooling ? src->dimension(idx_height) : pool_info.pool_size.height; - int output_width = 0; - int output_height = 0; - - ARM_COMPUTE_RETURN_ERROR_ON_MSG(is_pool_region_entirely_outside_input(pool_info), "Pooling region that is entirely outside input tensor is unsupported"); - - std::tie(output_width, output_height) = scaled_dimensions_signed(src->tensor_shape()[idx_width], src->tensor_shape()[idx_height], - pool_size_x, pool_size_y, pool_info.pad_stride_info); - ARM_COMPUTE_RETURN_ERROR_ON_MSG((output_width < 1 || output_height < 1), "Calculated output dimension size is invalid"); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, + DataType::F16, DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON_MSG( + (is_data_type_quantized_asymmetric(src->data_type()) && pool_info.pool_type == PoolingType::L2), + "Unsupported combination of parameters!"); + + const auto data_layout = pool_info.data_layout == DataLayout::UNKNOWN ? src->data_layout() : pool_info.data_layout; + const int idx_width = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); + const int idx_height = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); + const bool is_global_pooling = pool_info.is_global_pooling; + unsigned int pool_size_x = is_global_pooling ? src->dimension(idx_width) : pool_info.pool_size.width; + unsigned int pool_size_y = is_global_pooling ? src->dimension(idx_height) : pool_info.pool_size.height; + int output_width = 0; + int output_height = 0; + + ARM_COMPUTE_RETURN_ERROR_ON_MSG(is_pool_region_entirely_outside_input(pool_info), + "Pooling region that is entirely outside input tensor is unsupported"); + + std::tie(output_width, output_height) = + scaled_dimensions_signed(src->tensor_shape()[idx_width], src->tensor_shape()[idx_height], pool_size_x, + pool_size_y, pool_info.pad_stride_info); + ARM_COMPUTE_RETURN_ERROR_ON_MSG((output_width < 1 || output_height < 1), + "Calculated output dimension size is invalid"); // Check indices - if(indices) + if (indices) { ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F16, DataType::F32); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(pool_info.pool_type != PoolingType::MAX, "Pooling indices only supported for MAX pooling method"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG((pool_info.pool_size != Size2D(2, 2)), "Pooling indices only supported for pool size 2x2"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(pool_info.pool_type != PoolingType::MAX, + "Pooling indices only supported for MAX pooling method"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG((pool_info.pool_size != Size2D(2, 2)), + "Pooling indices only supported for pool size 2x2"); - if(indices->total_size() != 0) + if (indices->total_size() != 0) { TensorInfo idx_info(TensorInfo(compute_pool_shape(*src, pool_info), 1, DataType::U32)); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(indices, &idx_info); @@ -81,7 +92,7 @@ Status validate_arguments(const ITensorInfo *src, const ITensorInfo *dst, const } // Checks performed when dst is configured - if(dst->total_size() != 0) + if (dst->total_size() != 0) { ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, dst); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(src, dst); @@ -98,42 +109,47 @@ ClPool2dKernel::ClPool2dKernel() _type = CLKernelType::POOL; } -void ClPool2dKernel::configure(const ClCompileContext &compile_context, ITensorInfo *src, ITensorInfo *dst, const PoolingLayerInfo &pool_info, ITensorInfo *indices) +void ClPool2dKernel::configure(const ClCompileContext &compile_context, + ITensorInfo *src, + ITensorInfo *dst, + const PoolingLayerInfo &pool_info, + ITensorInfo *indices) { ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst); ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src, dst, pool_info, indices)); - auto padding_info = get_padding_info({ src, dst, indices }); + auto padding_info = get_padding_info({src, dst, indices}); // Auto init if empty TensorShape out_shape = compute_pool_shape(*src, pool_info); auto_init_if_empty(*dst, src->clone()->set_tensor_shape(out_shape)); - if(indices) + if (indices) { auto_init_if_empty(*indices, src->clone()->set_tensor_shape(out_shape).set_data_type(DataType::U32)); } // Set instance variables - _pool_info = pool_info; - _data_layout = pool_info.data_layout == DataLayout::UNKNOWN ? src->data_layout() : pool_info.data_layout; - _num_elems_processed_per_iteration = (_data_layout == DataLayout::NCHW) ? 1 : ((dst->data_type() == DataType::F32) ? 2 : 4); + _pool_info = pool_info; + _data_layout = pool_info.data_layout == DataLayout::UNKNOWN ? src->data_layout() : pool_info.data_layout; + _num_elems_processed_per_iteration = + (_data_layout == DataLayout::NCHW) ? 1 : ((dst->data_type() == DataType::F32) ? 2 : 4); _num_elems_processed_per_iteration = adjust_vec_size(_num_elems_processed_per_iteration, dst->dimension(0)); - int pool_stride_x = 0; - int pool_stride_y = 0; - const PoolingType pool_type = pool_info.pool_type; - const int idx_width = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::WIDTH); - const int idx_height = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::HEIGHT); - const int idx_channel = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::CHANNEL); - const int idx_batch_size = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::BATCHES); - const int pool_size_x = pool_info.is_global_pooling ? src->dimension(idx_width) : pool_info.pool_size.width; - const int pool_size_y = pool_info.is_global_pooling ? src->dimension(idx_height) : pool_info.pool_size.height; - const PadStrideInfo pad_stride_info = pool_info.pad_stride_info; - const bool exclude_padding = pool_info.exclude_padding; + int pool_stride_x = 0; + int pool_stride_y = 0; + const PoolingType pool_type = pool_info.pool_type; + const int idx_width = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::WIDTH); + const int idx_height = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::HEIGHT); + const int idx_channel = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::CHANNEL); + const int idx_batch_size = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::BATCHES); + const int pool_size_x = pool_info.is_global_pooling ? src->dimension(idx_width) : pool_info.pool_size.width; + const int pool_size_y = pool_info.is_global_pooling ? src->dimension(idx_height) : pool_info.pool_size.height; + const PadStrideInfo pad_stride_info = pool_info.pad_stride_info; + const bool exclude_padding = pool_info.exclude_padding; std::tie(pool_stride_x, pool_stride_y) = pad_stride_info.stride(); - const int pool_pad_top = pad_stride_info.pad_top(); - const int pool_pad_left = pad_stride_info.pad_left(); - const DataType data_type = src->data_type(); + const int pool_pad_top = pad_stride_info.pad_top(); + const int pool_pad_left = pad_stride_info.pad_left(); + const DataType data_type = src->data_type(); // Set build options CLBuildOptions build_opts; @@ -148,20 +164,23 @@ void ClPool2dKernel::configure(const ClCompileContext &compile_context, ITensorI build_opts.add_option("-DPOOL_SIZE_Y=" + support::cpp11::to_string(pool_size_y)); build_opts.add_option("-DSRC_WIDTH=" + support::cpp11::to_string(src->dimension(idx_width))); build_opts.add_option("-DSRC_HEIGHT=" + support::cpp11::to_string(src->dimension(idx_height))); - build_opts.add_option("-DMAX_WIDTH=" + support::cpp11::to_string(src->dimension(idx_width) + (exclude_padding ? 0 : pool_pad_left))); - build_opts.add_option("-DMAX_HEIGHT=" + support::cpp11::to_string(src->dimension(idx_height) + (exclude_padding ? 0 : pool_pad_top))); + build_opts.add_option("-DMAX_WIDTH=" + + support::cpp11::to_string(src->dimension(idx_width) + (exclude_padding ? 0 : pool_pad_left))); + build_opts.add_option("-DMAX_HEIGHT=" + + support::cpp11::to_string(src->dimension(idx_height) + (exclude_padding ? 0 : pool_pad_top))); // Tensor paddings are used to calculate the indicies for MAX pooling - if(pool_info.pool_size == Size2D(2, 2) && pool_type == PoolingType::MAX && indices && is_data_type_float(data_type)) + if (pool_info.pool_size == Size2D(2, 2) && pool_type == PoolingType::MAX && indices && + is_data_type_float(data_type)) { build_opts.add_option("-DSRC_BATCH=" + support::cpp11::to_string(src->tensor_shape().total_size_lower(3))); } - if(is_data_type_quantized_asymmetric(data_type)) + if (is_data_type_quantized_asymmetric(data_type)) { build_opts.add_option("-DQUANTIZED"); - if(src->quantization_info() != dst->quantization_info()) + if (src->quantization_info() != dst->quantization_info()) { const UniformQuantizationInfo iq_info = src->quantization_info().uniform(); const UniformQuantizationInfo oq_info = dst->quantization_info().uniform(); @@ -174,9 +193,9 @@ void ClPool2dKernel::configure(const ClCompileContext &compile_context, ITensorI } // Set the initial value for the pooling operation accordingly with the data type - if(pool_type == PoolingType::MAX) + if (pool_type == PoolingType::MAX) { - if(is_data_type_quantized(data_type)) + if (is_data_type_quantized(data_type)) { PixelValue type_min{}; std::tie(type_min, std::ignore) = get_min_max(data_type); @@ -184,7 +203,9 @@ void ClPool2dKernel::configure(const ClCompileContext &compile_context, ITensorI } else { - std::string initial_value = pool_info.use_inf_as_limit ? "(-INFINITY)" : float_to_string_with_full_precision(std::numeric_limits<float>::lowest()); + std::string initial_value = pool_info.use_inf_as_limit + ? "(-INFINITY)" + : float_to_string_with_full_precision(std::numeric_limits<float>::lowest()); build_opts.add_option("-DINITIAL_VALUE=" + initial_value); } } @@ -195,22 +216,25 @@ void ClPool2dKernel::configure(const ClCompileContext &compile_context, ITensorI } // Create kernel - switch(_data_layout) + switch (_data_layout) { case DataLayout::NCHW: { const auto use_fp_mixed_precision = (data_type == DataType::F16) && pool_info.fp_mixed_precision; const auto use_wider_accumulator = use_fp_mixed_precision && (pool_type != PoolingType::MAX); - const auto acc_data_type = get_cl_type_from_data_type(use_wider_accumulator ? DataType::F32 : (is_data_type_quantized(data_type) ? DataType::S32 : data_type)); + const auto acc_data_type = get_cl_type_from_data_type( + use_wider_accumulator ? DataType::F32 + : (is_data_type_quantized(data_type) ? DataType::S32 : data_type)); build_opts.add_option("-DACC_DATA_TYPE=" + acc_data_type); build_opts.add_option_if(use_wider_accumulator, "-DFP_MIXED_PRECISION"); - if(pool_type != PoolingType::MAX) + if (pool_type != PoolingType::MAX) { build_opts.add_option_if(exclude_padding, "-DEXCLUDE_PADDING"); } - if(pool_info.pool_size == Size2D(2, 2) && pool_type == PoolingType::MAX && indices && is_data_type_float(data_type)) + if (pool_info.pool_size == Size2D(2, 2) && pool_type == PoolingType::MAX && indices && + is_data_type_float(data_type)) { // For max pooling with pool2x2, store indicies which will be used in max unpooling std::string kernel_name = "pooling_layer_2_nchw_indices"; @@ -226,18 +250,19 @@ void ClPool2dKernel::configure(const ClCompileContext &compile_context, ITensorI case DataLayout::NHWC: { // Floating point mixed precision is support on F16 only - const auto use_fp_mixed_precision = (data_type == DataType::F16) && pool_info.fp_mixed_precision && pool_type != PoolingType::MAX; + const auto use_fp_mixed_precision = + (data_type == DataType::F16) && pool_info.fp_mixed_precision && pool_type != PoolingType::MAX; // Wider accumulation is required to avoid accuracy loss // Case 1: Floating point mixed precision (fp16 src data and fp32 accumulation) // Cast 2: Quantized (int8/uint8 src data and int32 accumulation ) DataType acc_data_type = data_type; - if(use_fp_mixed_precision) + if (use_fp_mixed_precision) { acc_data_type = DataType::F32; } - else if(is_data_type_quantized(data_type) && pool_type != PoolingType::MAX) + else if (is_data_type_quantized(data_type) && pool_type != PoolingType::MAX) { acc_data_type = DataType::S32; } @@ -250,8 +275,9 @@ void ClPool2dKernel::configure(const ClCompileContext &compile_context, ITensorI build_opts.add_option("-DDST_HEIGHT=" + support::cpp11::to_string(dst->dimension(idx_height))); build_opts.add_option("-DDST_CHANNELS=" + support::cpp11::to_string(dst->dimension(idx_channel))); build_opts.add_option("-DDST_BATCH_SIZE=" + support::cpp11::to_string(dst->dimension(idx_batch_size))); - build_opts.add_option("-DVEC_SIZE_LEFTOVER=" + support::cpp11::to_string(src->dimension(0) % _num_elems_processed_per_iteration)); - if(pool_info.pool_size == Size2D(2, 2) && is_data_type_float(data_type)) + build_opts.add_option("-DVEC_SIZE_LEFTOVER=" + + support::cpp11::to_string(src->dimension(0) % _num_elems_processed_per_iteration)); + if (pool_info.pool_size == Size2D(2, 2) && is_data_type_float(data_type)) { build_opts.add_option_if(indices != nullptr && pool_type == PoolingType::MAX, "-DEXTRACT_MAX_INDEX"); @@ -260,7 +286,9 @@ void ClPool2dKernel::configure(const ClCompileContext &compile_context, ITensorI } else { - std::string kernel_name = is_data_type_quantized_asymmetric(data_type) ? "pooling_layer_MxN_quantized_nhwc" : "pooling_layer_MxN_nhwc"; + std::string kernel_name = is_data_type_quantized_asymmetric(data_type) + ? "pooling_layer_MxN_quantized_nhwc" + : "pooling_layer_MxN_nhwc"; _kernel = create_kernel(compile_context, kernel_name, build_opts.options()); } break; @@ -290,7 +318,10 @@ void ClPool2dKernel::configure(const ClCompileContext &compile_context, ITensorI ARM_COMPUTE_ERROR_ON(has_padding_changed(padding_info)); } -Status ClPool2dKernel::validate(const ITensorInfo *src, const ITensorInfo *dst, const PoolingLayerInfo &pool_info, const ITensorInfo *indices) +Status ClPool2dKernel::validate(const ITensorInfo *src, + const ITensorInfo *dst, + const PoolingLayerInfo &pool_info, + const ITensorInfo *indices) { ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src, dst, pool_info, indices)); return Status{}; @@ -301,18 +332,19 @@ void ClPool2dKernel::run_op(ITensorPack &tensors, const Window &window, cl::Comm ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window); - unsigned int pool_stride_x = 0; - unsigned int pool_stride_y = 0; + unsigned int pool_stride_x = 0; + unsigned int pool_stride_y = 0; std::tie(pool_stride_x, pool_stride_y) = _pool_info.pad_stride_info.stride(); - const auto src = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC)); - auto dst = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST_0)); - auto indices = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST_1)); + const auto src = + utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC)); + auto dst = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST_0)); + auto indices = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST_1)); // Collapse window Window window_collapsed = window.collapse_if_possible(ICLKernel::window(), Window::DimZ); - switch(_data_layout) + switch (_data_layout) { case DataLayout::NCHW: { @@ -323,13 +355,12 @@ void ClPool2dKernel::run_op(ITensorPack &tensors, const Window &window, cl::Comm unsigned int idx = 0; add_3D_tensor_argument(idx, src, slice); add_3D_tensor_argument(idx, dst, slice); - if(indices && is_data_type_float(src->info()->data_type()) && (_pool_info.pool_size == Size2D(2, 2))) + if (indices && is_data_type_float(src->info()->data_type()) && (_pool_info.pool_size == Size2D(2, 2))) { add_3D_tensor_argument(idx, indices, slice); } enqueue(queue, *this, slice, lws_hint()); - } - while(window_collapsed.slide_window_slice_3D(slice)); + } while (window_collapsed.slide_window_slice_3D(slice)); break; } case DataLayout::NHWC: @@ -338,7 +369,8 @@ void ClPool2dKernel::run_op(ITensorPack &tensors, const Window &window, cl::Comm Window slice = window_collapsed.first_slice_window_4D(); Window in_slice = window_collapsed.first_slice_window_4D(); - in_slice.set(Window::DimX, Window::Dimension(0, src->info()->dimension(0), _num_elems_processed_per_iteration)); + in_slice.set(Window::DimX, + Window::Dimension(0, src->info()->dimension(0), _num_elems_processed_per_iteration)); in_slice.set(Window::DimY, Window::Dimension(0, src->info()->dimension(1), pool_stride_x)); in_slice.set(Window::DimZ, Window::Dimension(0, src->info()->dimension(2), pool_stride_y)); in_slice.set(3, Window::Dimension(0, batch_size, 1)); @@ -348,13 +380,13 @@ void ClPool2dKernel::run_op(ITensorPack &tensors, const Window &window, cl::Comm unsigned int idx = 0; add_4D_tensor_argument(idx, src, in_slice); add_4D_tensor_argument(idx, dst, slice); - if(indices && is_data_type_float(src->info()->data_type()) && (_pool_info.pool_type == PoolingType::MAX) && (_pool_info.pool_size == Size2D(2, 2))) + if (indices && is_data_type_float(src->info()->data_type()) && + (_pool_info.pool_type == PoolingType::MAX) && (_pool_info.pool_size == Size2D(2, 2))) { add_4D_tensor_argument(idx, indices, slice); } enqueue(queue, *this, slice, lws_hint()); - } - while(window.slide_window_slice_4D(slice) && window.slide_window_slice_4D(in_slice)); + } while (window.slide_window_slice_4D(slice) && window.slide_window_slice_4D(in_slice)); break; } default: |