| /* |
| * Copyright (c) 2022 Arm Limited. |
| * |
| * SPDX-License-Identifier: MIT |
| * |
| * Permission is hereby granted, free of charge, to any person obtaining a copy |
| * of this software and associated documentation files (the "Software"), to |
| * deal in the Software without restriction, including without limitation the |
| * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or |
| * sell copies of the Software, and to permit persons to whom the Software is |
| * furnished to do so, subject to the following conditions: |
| * |
| * The above copyright notice and this permission notice shall be included in all |
| * copies or substantial portions of the Software. |
| * |
| * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR |
| * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, |
| * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE |
| * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER |
| * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, |
| * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE |
| * SOFTWARE. |
| */ |
| #ifdef ENABLE_EXPERIMENTAL_DYNAMIC_FUSION |
| #include "arm_compute/core/utils/misc/ShapeCalculator.h" |
| |
| #include "src/core/CL/CLValidate.h" |
| #include "src/core/experimental/dynamic_fusion/ClKernelBuildingAPI.h" |
| #include "src/core/experimental/dynamic_fusion/WorkloadImpl/ClKernelGraph.h" |
| |
| #include "support/Cast.h" |
| |
| namespace arm_compute |
| { |
| namespace experimental |
| { |
| namespace dynamic_fusion |
| { |
| Status ClDirectConv2dKernel::generate(ClKernelBlueprint &bp) const |
| { |
| const auto input = _tensors.get_const_tensor(TensorType::ACL_SRC_0); |
| const auto weight = _tensors.get_const_tensor(TensorType::ACL_SRC_1); |
| const auto bias = _tensors.get_const_tensor(TensorType::ACL_SRC_2); |
| const auto dst = _tensors.get_const_tensor(TensorType::ACL_DST_0); |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input, weight, dst); |
| ArgumentID input_id; |
| add_tensor(bp, input->desc, input_id, input->id); |
| ArgumentID weight_id; |
| add_tensor(bp, weight->desc, weight_id, weight->id); |
| ArgumentID bias_id = g_arg_placeholder; |
| if(bias != nullptr) |
| { |
| add_tensor(bp, bias->desc, bias_id, bias->id); |
| } |
| ArgumentID dst_id; |
| add_tensor(bp, dst->desc, dst_id, dst->id); |
| |
| add_kcomp_direct_conv2d(bp, desc, input_id, weight_id, bias_id, dst_id); |
| return Status{}; |
| } |
| Status ClDirectConv2dKernel::validate(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const ClDirectConv2dKernelDescriptor &conv2d_desc) |
| { |
| // 1. Check validity |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src, weights, dst); |
| // Matching data type |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, weights); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, dst); |
| if(biases != nullptr) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, biases); |
| } |
| |
| // Matching data layout |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(src, weights); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(src, dst); |
| if(biases != nullptr) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(src, biases); |
| } |
| |
| // All tensor infos are initialized |
| ARM_COMPUTE_RETURN_ERROR_ON(src->tensor_shape().total_size() == 0); |
| ARM_COMPUTE_RETURN_ERROR_ON(weights->tensor_shape().total_size() == 0); |
| ARM_COMPUTE_RETURN_ERROR_ON(dst->tensor_shape().total_size() == 0); |
| if(biases != nullptr) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON(biases->tensor_shape().total_size() == 0); |
| } |
| // Device requirements are met |
| ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(src); |
| // weights shape is correct |
| const DataLayout data_layout = src->data_layout(); |
| const int channel_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights->dimension(channel_idx) != src->dimension(channel_idx), "Weights feature map dimension should match the respective src's one"); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights->num_dimensions() > 4, "Weights can be at most 4 dimensional"); |
| |
| // dst shape is correct |
| PadStrideInfo legacy_pad_stride(conv2d_desc.conv2d.stride.x(), conv2d_desc.conv2d.stride.y(), conv2d_desc.conv2d.pad.left, conv2d_desc.conv2d.pad.right, conv2d_desc.conv2d.pad.top, |
| conv2d_desc.conv2d.pad.bottom, DimensionRoundingType{}); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(dst->tensor_shape(), |
| misc::shape_calculator::compute_deep_convolution_shape(*src, *weights, legacy_pad_stride)); |
| |
| // biases shape is correct |
| if(biases != nullptr) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(biases->dimension(0) != weights->dimension(3), |
| "Biases size and number of dst feature maps should match"); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(biases->num_dimensions() > 1, |
| "Biases should be one dimensional"); |
| } |
| |
| // 2. Check support level |
| // Data type |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F16, DataType::F32); |
| // Data layout |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_LAYOUT_NOT_IN(src, DataLayout::NHWC); |
| |
| return Status{}; |
| } |
| |
| bool ClDirectConv2dKernel::operator==(const ClKernel &other) const |
| { |
| const auto converted = *utils::cast::polymorphic_downcast<const ClDirectConv2dKernel *>(&other); |
| return config() == other.config() && tensors() == other.tensors() && desc == converted.desc; |
| } |
| |
| Status ClElementwiseKernel::generate(ClKernelBlueprint &bp) const |
| { |
| const auto lhs = _tensors.get_const_tensor(TensorType::ACL_SRC_0); |
| const auto rhs = _tensors.get_const_tensor(TensorType::ACL_SRC_1); |
| const auto dst = _tensors.get_const_tensor(TensorType::ACL_DST_0); |
| ARM_COMPUTE_ERROR_ON_NULLPTR(lhs, rhs, dst); |
| ArgumentID lhs_id; |
| add_tensor(bp, lhs->desc, lhs_id, lhs->id); |
| ArgumentID rhs_id; |
| add_tensor(bp, rhs->desc, rhs_id, rhs->id); |
| ArgumentID dst_id; |
| add_tensor(bp, dst->desc, dst_id, dst->id); |
| |
| add_kcomp_eltwise_op(bp, desc, lhs_id, rhs_id, dst_id); |
| return Status{}; |
| } |
| |
| Status ClElementwiseKernel::validate(const ITensorInfo *lhs, const ITensorInfo *rhs, const ITensorInfo *dst) |
| { |
| // 1. Check validity |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(lhs, rhs, dst); |
| |
| // Matching data type |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(lhs, rhs); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(lhs, dst); |
| |
| // Matching data layout |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(lhs, rhs); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(lhs, dst); |
| |
| // All tensor infos are initialized |
| ARM_COMPUTE_RETURN_ERROR_ON(lhs->tensor_shape().total_size() == 0); |
| ARM_COMPUTE_RETURN_ERROR_ON(rhs->tensor_shape().total_size() == 0); |
| ARM_COMPUTE_RETURN_ERROR_ON(dst->tensor_shape().total_size() == 0); |
| |
| // Device requirements are met |
| ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(lhs); |
| |
| const bool in_place = (lhs == dst) || (rhs == dst); |
| const bool src0_in_place = in_place && (lhs == dst); |
| |
| // dst shape is correct |
| const TensorShape out_shape = TensorShape::broadcast_shape(lhs->tensor_shape(), rhs->tensor_shape()); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(out_shape.total_size() == 0, "Inputs are not broadcast compatible"); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(detail::have_different_dimensions(out_shape, dst->tensor_shape(), 0), "Wrong shape for dst"); |
| if(in_place) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(detail::have_different_dimensions(out_shape, src0_in_place ? lhs->tensor_shape() : rhs->tensor_shape(), 0), |
| "Wrong shape for dst, cannot do in_place calculation"); |
| } |
| |
| // 2. Check support level |
| |
| // Data type |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(lhs, 1, DataType::F32, DataType::F16); |
| |
| // Data layout |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_LAYOUT_NOT_IN(lhs, DataLayout::NHWC); |
| |
| return Status{}; |
| } |
| |
| bool ClElementwiseKernel::operator==(const ClKernel &other) const |
| { |
| const auto converted = *utils::cast::polymorphic_downcast<const ClElementwiseKernel *>(&other); |
| return config() == other.config() && tensors() == other.tensors() && desc == converted.desc; |
| } |
| |
| Status ClFloorKernel::generate(ClKernelBlueprint &bp) const |
| { |
| const auto src = _tensors.get_const_tensor(TensorType::ACL_SRC_0); |
| const auto dst = _tensors.get_const_tensor(TensorType::ACL_DST_0); |
| ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst); |
| ArgumentID src_id; |
| add_tensor(bp, src->desc, src_id, src->id); |
| ArgumentID dst_id; |
| add_tensor(bp, dst->desc, dst_id, dst->id); |
| |
| add_kcomp_floor(bp, desc, src_id, dst_id); |
| return Status{}; |
| } |
| |
| Status ClFloorKernel::validate(const ITensorInfo *src, const ITensorInfo *dst) |
| { |
| // 1. Check validity |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src, dst); |
| |
| // Matching data type |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, dst); |
| |
| // Matching data layout |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(src, dst); |
| |
| // All tensor infos are initialized |
| ARM_COMPUTE_RETURN_ERROR_ON(src->tensor_shape().total_size() == 0); |
| ARM_COMPUTE_RETURN_ERROR_ON(dst->tensor_shape().total_size() == 0); |
| |
| // Device requirements are met |
| ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(src); |
| |
| // dst shape is correct |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(detail::have_different_dimensions(src->tensor_shape(), dst->tensor_shape(), 0), "Wrong shape for dst"); |
| |
| // 2. Check support level |
| |
| // Data type |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F32, DataType::F16); |
| |
| // Data layout |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_LAYOUT_NOT_IN(src, DataLayout::NHWC); |
| |
| return Status{}; |
| } |
| |
| bool ClFloorKernel::operator==(const ClKernel &other) const |
| { |
| const auto converted = *utils::cast::polymorphic_downcast<const ClFloorKernel *>(&other); |
| return config() == other.config() && tensors() == other.tensors() && desc == converted.desc; |
| } |
| |
| std::vector<const ClKernel *> traverse(const ClKernelGraph &graph) |
| { |
| std::vector<const ClKernel *> kernels; |
| const auto sorted = graph.graph.topological_sort(); |
| for(const auto &pack : sorted.second) |
| { |
| kernels.push_back(graph.kernels.at(pack.op).get()); |
| } |
| return kernels; |
| } |
| |
| std::vector<ClKernel *> traverse(ClKernelGraph &graph) |
| { |
| std::vector<ClKernel *> kernels; |
| const auto sorted = graph.graph.topological_sort(); |
| for(const auto &pack : sorted.second) |
| { |
| kernels.push_back(graph.kernels.at(pack.op).get()); |
| } |
| return kernels; |
| } |
| } // namespace dynamic_fusion |
| } // namespace experimental |
| } // namespace arm_compute |
| #endif /* ENABLE_EXPERIMENTAL_DYNAMIC_FUSION */ |