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Diffstat (limited to 'src/core/experimental/dynamic_fusion/WorkloadImpl/ClKernelGraph.cpp')
| -rw-r--r-- | src/core/experimental/dynamic_fusion/WorkloadImpl/ClKernelGraph.cpp | 219 |
1 files changed, 219 insertions, 0 deletions
diff --git a/src/core/experimental/dynamic_fusion/WorkloadImpl/ClKernelGraph.cpp b/src/core/experimental/dynamic_fusion/WorkloadImpl/ClKernelGraph.cpp new file mode 100644 index 0000000000..8aaf0946bb --- /dev/null +++ b/src/core/experimental/dynamic_fusion/WorkloadImpl/ClKernelGraph.cpp @@ -0,0 +1,219 @@ +/* + * 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. + */ +#ifndef ENABLE_EXPERIMENTAL_DYNAMIC_FUSION +#error "This experimental feature must be enabled with -DENABLE_EXPERIMENTAL_DYNAMIC_FUSION" +#endif /* 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 ClAddKernel::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_add(bp, desc, lhs_id, rhs_id, dst_id); + return Status{}; +} + +Status ClAddKernel::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 ClAddKernel::operator==(const ClKernel &other) const +{ + const auto converted = *utils::cast::polymorphic_downcast<const ClAddKernel *>(&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
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