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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,93 @@ | ||
| // | ||
| // NPUBatchMatMul.cpp | ||
| // MNN | ||
| // | ||
| // Created by MNN on b'2020/10/15'. | ||
| // Copyright © 2018, Alibaba Group Holding Limited | ||
| // | ||
|
|
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| #include "NPUBatchMatMul.hpp" | ||
| #include "NPUBackend.hpp" | ||
|
|
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| using namespace std; | ||
|
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| namespace MNN { | ||
|
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| NPUBatchMatMul::NPUBatchMatMul(MNN::Backend *b, const MNN::Op *op, const std::vector<Tensor *> &inputs, const std::vector<MNN::Tensor *> &outputs) : NPUCommonExecution(b, op) { | ||
| auto opName = mOp->name()->str(); | ||
|
|
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| bool isConst0 = TensorUtils::getDescribe(inputs[0])->usage==Tensor::InsideDescribe::Usage::CONSTANT; | ||
| bool isConst1 = TensorUtils::getDescribe(inputs[1])->usage==Tensor::InsideDescribe::Usage::CONSTANT; | ||
|
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| Tensor* input = nullptr; | ||
| if (isConst0 && !isConst1){ | ||
| input = inputs[0]; | ||
| } | ||
| if (!isConst0 && isConst1){ | ||
| input = inputs[1]; | ||
| } | ||
| if (input != nullptr) { | ||
| mConst = ge::op::Const(opName + "_w_const"); | ||
| ge::TensorPtr filter = std::make_shared<ge::Tensor>(); | ||
| vector<int64_t> dims; | ||
| for (int32_t i = 0; i < input->buffer().dimensions; i++) { | ||
| dims.push_back(input->buffer().dim[i].extent); | ||
| } | ||
| ge::TensorDesc fdesc(ge::Shape(dims), ge::FORMAT_NCHW, ge::DT_FLOAT); | ||
| if (input->getType().code == halide_type_int && input->getType().bits == 32) { | ||
| fdesc.SetDataType(ge::DT_INT32); | ||
| filter->SetData((uint8_t *)input->host<int32_t>(), input->elementSize() * sizeof(int32_t)); | ||
| } else { | ||
| filter->SetData((uint8_t *)input->host<float>(), input->elementSize() * sizeof(float)); | ||
| } | ||
| filter->SetTensorDesc(fdesc); | ||
| mConst.set_attr_value(filter); | ||
| } | ||
|
|
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| } | ||
|
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| ErrorCode NPUBatchMatMul::onResize(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) { | ||
| mNpuBackend->setNetworkInput(inputs, mOp); | ||
| auto opName = mOp->name()->str(); | ||
| bool isConst0 = TensorUtils::getDescribe(inputs[0])->usage==Tensor::InsideDescribe::Usage::CONSTANT; | ||
| bool isConst1 = TensorUtils::getDescribe(inputs[1])->usage==Tensor::InsideDescribe::Usage::CONSTANT; | ||
| auto param = mOp->main_as_BatchMatMulParam(); | ||
| shared_ptr<hiai::op::BatchMatMul> batchMatMul(new hiai::op::BatchMatMul(opName)); | ||
| if (isConst0 && !isConst1) { | ||
| auto inputIndex1 = mOp->inputIndexes()->data()[1]; | ||
| auto iops1 = mNpuBackend->mGrapMap[inputIndex1]; | ||
| auto xOp1 = iops1.back().first; | ||
| (*batchMatMul) | ||
| .set_input_x1(mConst) | ||
| .set_input_x2(*xOp1.get()) | ||
| .set_attr_adj_x1(param->adjX()) | ||
| .set_attr_adj_x2(param->adjY()); | ||
| } else if (!isConst0 && isConst1) { | ||
| auto inputIndex = mOp->inputIndexes()->data()[0]; | ||
| auto iops = mNpuBackend->mGrapMap[inputIndex]; | ||
| auto xOp = iops.back().first; | ||
| (*batchMatMul) | ||
| .set_input_x1(*xOp.get()) | ||
| .set_input_x2(mConst) | ||
| .set_attr_adj_x1(param->adjX()) | ||
| .set_attr_adj_x2(param->adjY()); | ||
| } else { | ||
| auto inputIndex = mOp->inputIndexes()->data()[0]; | ||
| auto iops = mNpuBackend->mGrapMap[inputIndex]; | ||
| auto xOp = iops.back().first; | ||
| auto inputIndex1 = mOp->inputIndexes()->data()[1]; | ||
| auto iops1 = mNpuBackend->mGrapMap[inputIndex1]; | ||
| auto xOp1 = iops1.back().first; | ||
| (*batchMatMul) | ||
| .set_input_x1(*xOp.get()) | ||
| .set_input_x2(*xOp1.get()) | ||
| .set_attr_adj_x1(param->adjX()) | ||
| .set_attr_adj_x2(param->adjY()); | ||
| } | ||
| mNpuBackend->setOutputOps(mOp, {batchMatMul}, outputs); | ||
| return NO_ERROR; | ||
| } | ||
|
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| NPUCreatorRegister<TypedCreator<NPUBatchMatMul>> __BatchMatMul_op(OpType_BatchMatMul); | ||
|
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| } // namespace MNN |
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