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[onert/ruy] Move operation visitors to separate layer files (#16269)
This commit moves Conv2D and FullyConnected visitor implementations from KernelGenerator.cc to their respective operation layer files. It also updates Validator.h to use visitor declarations instead of inline implementations. ONE-DCO-1.0-Signed-off-by: Hyeongseok Oh <hseok82.oh@samsung.com>
1 parent 6701cbc commit 7da2f65

4 files changed

Lines changed: 97 additions & 79 deletions

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‎runtime/onert/backend/ruy/KernelGenerator.cc‎

Lines changed: 1 addition & 77 deletions
Original file line numberDiff line numberDiff line change
@@ -16,9 +16,6 @@
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#include "KernelGenerator.h"
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#include "ops/Conv2DLayer.h"
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#include "ops/FullyConnectedLayer.h"
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#include <backend/Backend.h>
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#include <backend/IConfig.h>
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#include <memory>
@@ -72,79 +69,6 @@ KernelGenerator::KernelGenerator(const ir::Graph &graph,
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// DO NOTHING
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}
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void KernelGenerator::visit(const ir::operation::Conv2D &node)
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{
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using ir::operation::Conv2D;
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const auto ofm_index{node.getOutputs().at(0)};
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const auto ifm_index{node.getInputs().at(Conv2D::Input::INPUT)};
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const auto ker_index{node.getInputs().at(Conv2D::Input::KERNEL)};
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const auto bias_index{node.getInputs().at(Conv2D::Input::BIAS)};
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auto ofm_tensor = _tensor_reg->getPortableTensor(ofm_index);
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auto ifm_tensor = _tensor_reg->getPortableTensor(ifm_index);
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auto ker_tensor = _tensor_reg->getPortableTensor(ker_index);
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auto bias_tensor = _tensor_reg->getPortableTensor(bias_index);
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const auto stride = node.param().stride;
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const auto activation = node.param().activation;
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const auto &param_padding = node.param().padding;
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const auto dilation = node.param().dilation;
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auto fn = std::make_unique<ops::ConvolutionLayer>();
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if (_ctx.at(ifm_index).info().isDynamic() || _ctx.at(ker_index).info().isDynamic())
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{
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fn->configure(ifm_tensor, ker_tensor, bias_tensor, param_padding.type, param_padding.param.left,
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param_padding.param.right, param_padding.param.top, param_padding.param.bottom,
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stride.horizontal, stride.vertical, dilation.width_factor, dilation.height_factor,
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activation, ofm_tensor, _external_context);
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_return_fn = std::move(fn);
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return;
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}
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const auto ifm_shape = _ctx.at(ifm_index).shape().asFeature();
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const auto ofm_shape = _ctx.at(ofm_index).shape().asFeature();
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// Kernel format is [depth_out, kernel_height, kernel_width, depth_in].
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const auto &ker_shape = _ctx.at(ker_index).shape();
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const auto ker_height = ker_shape.dim(1);
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const auto ker_width = ker_shape.dim(2);
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const auto padding =
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ir::calculatePadding(param_padding, ifm_shape, ofm_shape, stride, ker_width, ker_height,
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dilation.width_factor, dilation.height_factor);
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fn->configure(ifm_tensor, ker_tensor, bias_tensor, param_padding.type, padding.left,
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padding.right, padding.top, padding.bottom, stride.horizontal, stride.vertical,
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dilation.width_factor, dilation.height_factor, activation, ofm_tensor,
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_external_context);
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_return_fn = std::move(fn);
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}
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void KernelGenerator::visit(const ir::operation::FullyConnected &node)
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{
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using ir::operation::FullyConnected;
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const auto output_index{node.getOutputs().at(0)};
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const auto input_index{node.getInputs().at(FullyConnected::Input::INPUT)};
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const auto weight_index{node.getInputs().at(FullyConnected::Input::WEIGHT)};
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const auto bias_index{node.getInputs().at(FullyConnected::Input::BIAS)};
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const auto activation = node.param().activation;
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const auto weights_format = node.param().weights_format;
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if (weights_format != ir::FullyConnectedWeightsFormat::Default)
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throw std::runtime_error("Unsupported FullyConnected Weights Format");
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auto output_tensor = _tensor_reg->getPortableTensor(output_index);
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auto input_tensor = _tensor_reg->getPortableTensor(input_index);
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auto weight_tensor = _tensor_reg->getPortableTensor(weight_index);
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auto bias_tensor = bias_index.undefined() ? nullptr : _tensor_reg->getPortableTensor(bias_index);
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auto fn = std::make_unique<ops::FullyConnectedLayer>();
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fn->configure(input_tensor, weight_tensor, bias_tensor, activation, output_tensor,
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_external_context);
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_return_fn = std::move(fn);
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}
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// Visitors for each operation is in ops/<InternalName>Layer.cc file
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} // namespace onert::backend::ruy

‎runtime/onert/backend/ruy/Validator.h‎

Lines changed: 1 addition & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -30,8 +30,7 @@ class Validator : public backend::ValidatorBase
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Validator(const ir::Graph &graph) : backend::ValidatorBase(graph) {}
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private:
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#define OP(InternalName) \
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void visit(const ir::operation::InternalName &) override { _supported = true; }
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#define OP(InternalName) void visit(const ir::operation::InternalName &) override;
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#include "Operation.lst"
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#undef OP
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};

‎runtime/onert/backend/ruy/ops/Conv2DLayer.cc‎

Lines changed: 59 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -16,9 +16,68 @@
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#include "Conv2DLayer.h"
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#include "../KernelGenerator.h"
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#include "../Tensor.h"
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#include "../Validator.h"
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#include "ir/Padding.h"
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25+
namespace onert::backend::ruy
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{
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void Validator::visit(const ir::operation::Conv2D &) { _supported = true; }
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void KernelGenerator::visit(const ir::operation::Conv2D &node)
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{
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using ir::operation::Conv2D;
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const auto ofm_index{node.getOutputs().at(0)};
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const auto ifm_index{node.getInputs().at(Conv2D::Input::INPUT)};
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const auto ker_index{node.getInputs().at(Conv2D::Input::KERNEL)};
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const auto bias_index{node.getInputs().at(Conv2D::Input::BIAS)};
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auto ofm_tensor = _tensor_reg->getPortableTensor(ofm_index);
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auto ifm_tensor = _tensor_reg->getPortableTensor(ifm_index);
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auto ker_tensor = _tensor_reg->getPortableTensor(ker_index);
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auto bias_tensor = _tensor_reg->getPortableTensor(bias_index);
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const auto stride = node.param().stride;
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const auto activation = node.param().activation;
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const auto &param_padding = node.param().padding;
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const auto dilation = node.param().dilation;
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auto fn = std::make_unique<ops::ConvolutionLayer>();
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if (_ctx.at(ifm_index).info().isDynamic() || _ctx.at(ker_index).info().isDynamic())
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{
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fn->configure(ifm_tensor, ker_tensor, bias_tensor, param_padding.type, param_padding.param.left,
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param_padding.param.right, param_padding.param.top, param_padding.param.bottom,
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stride.horizontal, stride.vertical, dilation.width_factor, dilation.height_factor,
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activation, ofm_tensor, _external_context);
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_return_fn = std::move(fn);
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return;
59+
}
60+
const auto ifm_shape = _ctx.at(ifm_index).shape().asFeature();
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const auto ofm_shape = _ctx.at(ofm_index).shape().asFeature();
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// Kernel format is [depth_out, kernel_height, kernel_width, depth_in].
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const auto &ker_shape = _ctx.at(ker_index).shape();
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const auto ker_height = ker_shape.dim(1);
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const auto ker_width = ker_shape.dim(2);
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const auto padding =
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ir::calculatePadding(param_padding, ifm_shape, ofm_shape, stride, ker_width, ker_height,
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dilation.width_factor, dilation.height_factor);
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fn->configure(ifm_tensor, ker_tensor, bias_tensor, param_padding.type, padding.left,
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padding.right, padding.top, padding.bottom, stride.horizontal, stride.vertical,
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dilation.width_factor, dilation.height_factor, activation, ofm_tensor,
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_external_context);
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76+
_return_fn = std::move(fn);
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}
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} // namespace onert::backend::ruy
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namespace onert::backend::ruy::ops
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{
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ConvolutionLayer::ConvolutionLayer()

‎runtime/onert/backend/ruy/ops/FullyConnectedLayer.cc‎

Lines changed: 36 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -16,10 +16,46 @@
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#include "FullyConnectedLayer.h"
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#include "../KernelGenerator.h"
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#include "../Tensor.h"
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#include "../Validator.h"
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#include <ruy/operation/FullyConnected.h>
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#include <ruy/TensorUtils.h>
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namespace onert::backend::ruy
27+
{
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void Validator::visit(const ir::operation::FullyConnected &) { _supported = true; }
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31+
void KernelGenerator::visit(const ir::operation::FullyConnected &node)
32+
{
33+
using ir::operation::FullyConnected;
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const auto output_index{node.getOutputs().at(0)};
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const auto input_index{node.getInputs().at(FullyConnected::Input::INPUT)};
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const auto weight_index{node.getInputs().at(FullyConnected::Input::WEIGHT)};
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const auto bias_index{node.getInputs().at(FullyConnected::Input::BIAS)};
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const auto activation = node.param().activation;
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const auto weights_format = node.param().weights_format;
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if (weights_format != ir::FullyConnectedWeightsFormat::Default)
42+
throw std::runtime_error("Unsupported FullyConnected Weights Format");
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auto output_tensor = _tensor_reg->getPortableTensor(output_index);
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auto input_tensor = _tensor_reg->getPortableTensor(input_index);
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auto weight_tensor = _tensor_reg->getPortableTensor(weight_index);
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auto bias_tensor = bias_index.undefined() ? nullptr : _tensor_reg->getPortableTensor(bias_index);
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auto fn = std::make_unique<ops::FullyConnectedLayer>();
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fn->configure(input_tensor, weight_tensor, bias_tensor, activation, output_tensor,
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_external_context);
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54+
_return_fn = std::move(fn);
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}
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} // namespace onert::backend::ruy
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namespace onert::backend::ruy::ops
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{
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