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Added grouped conv2d layout inference (#419) 

Signed-off-by: Chen Xin <jack.chen@verisilicon.com>

Co-authored-by: Chen Xin <jack.chen@verisilicon.com>
This commit is contained in:
chxin66 2022-06-28 14:52:26 +08:00 committed by GitHub
parent d716a1a9f0
commit 3e8d5e3493
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2 changed files with 123 additions and 0 deletions
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2
src/tim/transform/layout_inference.cc
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@ -27,6 +27,7 @@
#include "tim/transform/layout_inference.h"
#include "ops/conv2d_layout_inference.h"
#include "ops/grouped_conv2d_layout_inference.h"
#include "ops/reduce_layout_inference.h"
#include "ops/elementwise_layout_inference.h"
#include "ops/activation_layout_inference.h"
@ -205,6 +206,7 @@ std::vector<std::shared_ptr<vx::Tensor>> HandleLayoutInfer(
std::vector<std::shared_ptr<vx::Tensor>> next_tensors;
switch (op_id) {
REGIST_LAYOUT_INFERENCE(VSI_NN_OP_CONV2D, Conv2d);
REGIST_LAYOUT_INFERENCE(VSI_NN_OP_GROUPED_CONV2D, GroupedConv2d);
REGIST_LAYOUT_INFERENCE(VSI_NN_OP_RELU, Relu);
REGIST_LAYOUT_INFERENCE(VSI_NN_OP_RELU1, Relu1);
REGIST_LAYOUT_INFERENCE(VSI_NN_OP_RELU6, Relu6);

121
src/tim/transform/ops/grouped_conv2d_layout_inference.h Normal file
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@ -0,0 +1,121 @@
/****************************************************************************
*
* Copyright (c) 2020 Vivante Corporation
*
* 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 TIM_LAYOUT_INFER_GROUPED_CONV2D_LAYOUT_INFERENCE_H_
#define TIM_LAYOUT_INFER_GROUPED_CONV2D_LAYOUT_INFERENCE_H_
#include "tim/vx/ops/groupedconv2d.h"
#include "direct_map_op_impl.h"
#include "permute_vector.h"
#include "ops/op_layout_inference.h"
namespace tim {
namespace transform {
class GroupedConv2dLayoutInfer : public OpLayoutInfer {
public:
GroupedConv2dLayoutInfer(
const std::shared_ptr<vx::Operation> op,
std::shared_ptr<layout_inference_impl::LayoutInferContext>& context)
: OpLayoutInfer(op, context) {}
void OnInputs(
std::vector<std::shared_ptr<vx::Tensor>>& next_tensors) override {
vx::DataLayout layout = op_->impl()->layout_;
auto required_pv = MakeShared(4);
if (layout == vx::DataLayout::CWHN) {
required_pv = std::make_shared<PermuteVector<4>>(kCWHN2WHCN);
}
auto input_tensors = op_->impl()->InputsTensor();
for (const auto& in : input_tensors) {
std::shared_ptr<vx::Tensor> infer_tensor;
std::shared_ptr<IPermuteVector> trans_pv;
if (in->IsConstTensor() &&
!(in->GetSpec().attr_ & vx::TensorAttribute::INPUT)) {
// For bias
if (in->GetShape().size() == 1) {
infer_tensor = context_->infer_graph_->CreateTensor(
in->GetSpec(), in->GetDataRef());
trans_pv = MakeShared(1);
} else {
// For input/weight
if (!required_pv->IsAligned()) {
auto src_grouped_conv2d = std::static_pointer_cast<vx::ops::GroupedConv2d>(op_);
// Support TVM Kernel Layout
if (src_grouped_conv2d->KernelDataLayout() == vx::DataLayout::OcIcWH) {
trans_pv = std::make_shared<PermuteVector<4>>(kOcIcWH2WHIcOc);
infer_tensor = PermuteConstTensor(
in, trans_pv);
} else if (src_grouped_conv2d->KernelDataLayout() == vx::DataLayout::IcOcWH) {
trans_pv = std::make_shared<PermuteVector<4>>(kIcOcWH2WHIcOc);
infer_tensor = PermuteConstTensor(
in, trans_pv);
} else {
infer_tensor = PermuteConstTensor(in, required_pv);
trans_pv = required_pv;
}
} else {
infer_tensor = context_->infer_graph_->CreateTensor(
in->GetSpec(), in->GetDataRef());
trans_pv = MakeShared(required_pv->Rank());
}
}
} else {
// For bias
if (in->GetShape().size() == 1) {
infer_tensor = context_->GetMapedTensor(in);
trans_pv = MakeShared(1);
} else {
// For input/weight
auto pv = context_->GetPermuteVector(in);
auto final_pv = pv->Reverse()->Add(required_pv);
if (!final_pv->IsAligned()) {
infer_tensor =
InsertPermute(context_->GetMapedTensor(in), final_pv);
trans_pv = required_pv;
} else {
infer_tensor = context_->GetMapedTensor(in);
trans_pv = pv;
}
}
}
context_->UpdateTensorMap(in, infer_tensor);
context_->SetPermuteVector(in, trans_pv);
}
auto conv2d = op_->Clone(context_->infer_graph_);
auto otensor_infer = CreateOutputsTensor(required_pv);
for (const auto& i_src : input_tensors) {
(*conv2d).BindInput(context_->GetMapedTensor(i_src));
}
(*conv2d).BindOutput(otensor_infer[0]);
context_->SetPermuteVector(op_->impl()->OutputsTensor()[0], required_pv);
// Add out tensor of src_graph into next_tensor
next_tensors.push_back(op_->impl()->OutputsTensor()[0]);
}
};
} // namespace transform
} // namespace tim
#endif