| |
| // Copyright (c) 2020, ARM Limited. |
| // |
| // Licensed under the Apache License, Version 2.0 (the "License"); |
| // you may not use this file except in compliance with the License. |
| // You may obtain a copy of the License at |
| // |
| // http://www.apache.org/licenses/LICENSE-2.0 |
| // |
| // Unless required by applicable law or agreed to in writing, software |
| // distributed under the License is distributed on an "AS IS" BASIS, |
| // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| // See the License for the specific language governing permissions and |
| // limitations under the License. |
| |
| #include "data_layout.h" |
| #include "quant_util.h" |
| |
| using namespace TosaReference; |
| using namespace Eigen; |
| using namespace tosa; |
| |
| template <int Rank, DType Dtype> |
| OpConcat<Rank, Dtype>::OpConcat(TosaAttributeBase* attribute_, TosaQuantInfoBase* qinfo_, uint64_t id_) |
| : GraphNode(Op_CONCAT, id_) |
| { |
| setRequiredOperands(2, 1); |
| setRequiredRank(1, 6); |
| |
| INIT_ATTRIBUTE(Axis); |
| } |
| |
| template <int Rank, DType Dtype> |
| OpConcat<Rank, Dtype>::~OpConcat() |
| { |
| if (attribute) |
| delete attribute; |
| } |
| |
| template <int Rank, DType Dtype> |
| int OpConcat<Rank, Dtype>::checkTensorAttributes() |
| { |
| if (validateRequiredOperands()) |
| return 1; |
| |
| if (validateRequiredRank(inputs[0]) || validateRequiredRank(outputs[0])) |
| { |
| return 1; |
| } |
| |
| // output and input must be the same types and rank |
| // inputs[0] and inputs[1] should also match type and rank |
| if (inputs[0]->matchRankType(*outputs[0]) || inputs[1]->matchRankType(*outputs[0])) |
| { |
| printNodeValidationError("Concat operator input ranks and types must match"); |
| return 1; |
| } |
| |
| lhs = dynamic_cast<TosaReference::TensorTemplate<TIn>*>(inputs[0]); |
| rhs = dynamic_cast<TosaReference::TensorTemplate<TIn>*>(inputs[1]); |
| out = dynamic_cast<TosaReference::TensorTemplate<TOut>*>(outputs[0]); |
| |
| if (attribute->axis() < 0 || (size_t)attribute->axis() >= rhs->getShape().size()) |
| { |
| printNodeValidationError("Axis is beyond input tensor rank"); |
| return 1; |
| } |
| |
| return 0; |
| } |
| |
| template <int Rank, DType Dtype> |
| int OpConcat<Rank, Dtype>::eval() |
| { |
| |
| int32_t reversed_axis = Rank - 1 - attribute->axis(); |
| |
| for (int32_t d = 0; d < Rank; d++) |
| { |
| reverser[d] = Rank - 1 - d; |
| } |
| |
| TIn lhs_reversed = lhs->getTensor().shuffle(reverser); |
| TIn rhs_reversed = rhs->getTensor().shuffle(reverser); |
| |
| TIn reversed_result = lhs_reversed.concatenate(rhs_reversed, reversed_axis); |
| out->getTensor() = reversed_result.shuffle(reverser); |
| // out->getTensor() = lhs->getTensor().concatenate(rhs->getTensor(), axis); |
| |
| return GraphNode::eval(); |
| } |
| |
| template <int Rank, DType Dtype> |
| OpPad<Rank, Dtype>::OpPad(TosaAttributeBase* attribute_, TosaQuantInfoBase* qinfo_, uint64_t id_) |
| : GraphNode(Op_PAD, id_) |
| { |
| setRequiredOperands(2, 1); |
| setRequiredRank(0, 6); |
| |
| INIT_QINFO(Pad); |
| } |
| |
| template <int Rank, DType Dtype> |
| OpPad<Rank, Dtype>::~OpPad() |
| { |
| if (qinfo) |
| delete qinfo; |
| } |
| |
| template <int Rank, DType Dtype> |
| int OpPad<Rank, Dtype>::checkTensorAttributes() |
| { |
| if (validateRequiredOperands()) |
| return 1; |
| |
| if (validateRequiredRank(inputs[0]) || validateRequiredRank(outputs[0])) |
| { |
| return 1; |
| } |
| |
| // output and input must be the same types |
| if (inputs[0]->matchRankType(*outputs[0])) |
| { |
| printNodeValidationError("Failure to match input and output type and rank"); |
| return 1; |
| } |
| |
| in = dynamic_cast<TosaReference::TensorTemplate<TIn>*>(inputs[0]); |
| out = dynamic_cast<TosaReference::TensorTemplate<TOut>*>(outputs[0]); |
| TosaReference::TensorTemplate<ETensor2<int32_t>>* paddings = |
| dynamic_cast<TosaReference::TensorTemplate<ETensor2<int32_t>>*>(inputs[1]); |
| |
| for (int i = 0; i < Rank; i++) |
| { |
| paddings_array[i] = std::make_pair(paddings->getTensor()(i, 0), paddings->getTensor()(i, 1)); |
| } |
| |
| return 0; |
| } |
| |
| template <int Rank, DType Dtype> |
| int OpPad<Rank, Dtype>::eval() |
| { |
| InEigenType pad_value = 0; |
| if (this->qinfo) |
| { |
| pad_value = (InEigenType)this->qinfo->input_zp(); |
| } |
| |
| this->out->getTensor() = this->in->getTensor().pad(this->paddings_array, pad_value); |
| |
| return GraphNode::eval(); |
| } |
| |
| template <int InRank, int OutRank, DType Dtype> |
| OpReshape<InRank, OutRank, Dtype>::OpReshape(TosaAttributeBase* attribute_, TosaQuantInfoBase* qinfo_, uint64_t id_) |
| : GraphNode(Op_RESHAPE, id_) |
| { |
| setRequiredOperands(1, 1); |
| setRequiredRank(0, 6); |
| |
| INIT_ATTRIBUTE(Reshape); |
| } |
| |
| template <int InRank, int OutRank, DType Dtype> |
| OpReshape<InRank, OutRank, Dtype>::~OpReshape() |
| { |
| if (attribute) |
| delete attribute; |
| } |
| |
| template <int InRank, int OutRank, DType Dtype> |
| int OpReshape<InRank, OutRank, Dtype>::checkTensorAttributes() |
| { |
| uint32_t minusOneCount = 0; |
| |
| if (validateRequiredOperands()) |
| return 1; |
| |
| if (validateRequiredRank(inputs[0]) || validateRequiredRank(outputs[0])) |
| { |
| return 1; |
| } |
| |
| // output and input must be the same types |
| if (inputs[0]->matchType(*outputs[0])) |
| { |
| printNodeValidationError("OpReshape: Input and output types must match"); |
| return 1; |
| } |
| |
| for (uint32_t d = 0; d < OutRank; d++) |
| { |
| if (attribute->shape()[d] == -1) |
| { |
| minusOneCount++; |
| } |
| } |
| |
| if (minusOneCount > 1) |
| { |
| printNodeValidationError("OpReshape: new shape has more than one -1 dimension"); |
| return 1; |
| } |
| |
| in = dynamic_cast<TosaReference::TensorTemplate<TIn>*>(inputs[0]); |
| out = dynamic_cast<TosaReference::TensorTemplate<TOut>*>(outputs[0]); |
| |
| return 0; |
| } |
| |
| template <int InRank, int OutRank, DType Dtype> |
| int OpReshape<InRank, OutRank, Dtype>::eval() |
| { |
| uint32_t remainingSize = in->getElementCount(); |
| |
| // If there is a -1 dimension, find the remainder in one pass over the output shape |
| for (int32_t d = 0; d < OutRank; d++) |
| { |
| if (attribute->shape()[d] != -1) |
| { |
| remainingSize = remainingSize / attribute->shape()[d]; |
| } |
| } |
| |
| for (int32_t d = 0; d < OutRank; d++) |
| { |
| array_shape[d] = attribute->shape()[OutRank - 1 - d]; |
| out_reverser[d] = OutRank - 1 - d; |
| |
| // Jam in the remainder here |
| if (array_shape[d] == -1) |
| { |
| array_shape[d] = remainingSize; |
| } |
| } |
| |
| for (int32_t d = 0; d < InRank; d++) |
| { |
| in_reverser[d] = InRank - 1 - d; |
| } |
| |
| // Eigen Tensor is col-major, and we're referencing row-major result |
| // need to reverse it to row-major before reshape, and perform another reverse afterward |
| |
| // input tensor rank 0 can't do .shuffle(), need to be handled otherwise |
| TIn in_reversed; |
| if (InRank > 1) |
| { |
| in_reversed = in->getTensor().shuffle(in_reverser); |
| } |
| else |
| { |
| in_reversed = in->getTensor(); |
| } |
| |
| TOut in_reshaped = in_reversed.reshape(array_shape); |
| |
| // output tensor can be rank 0, .reshape() and .shuffle() don't work, need to be handled otherwise |
| if (OutRank > 1) |
| { |
| out->getTensor() = in_reshaped.shuffle(out_reverser); |
| } |
| else |
| { |
| out->getTensor() = in_reshaped; |
| } |
| |
| return GraphNode::eval(); |
| } |
| |
| template <int Rank, DType Dtype> |
| OpReverse<Rank, Dtype>::OpReverse(TosaAttributeBase* attribute_, TosaQuantInfoBase* qinfo_, uint64_t id_) |
| : GraphNode(Op_REVERSE, id_) |
| { |
| setRequiredOperands(1, 1); |
| setRequiredRank(1, 6); |
| |
| INIT_ATTRIBUTE(Axis); |
| } |
| |
| template <int Rank, DType Dtype> |
| OpReverse<Rank, Dtype>::~OpReverse() |
| { |
| if (attribute) |
| delete attribute; |
| } |
| |
| template <int Rank, DType Dtype> |
| int OpReverse<Rank, Dtype>::checkTensorAttributes() |
| { |
| if (validateRequiredOperands()) |
| return 1; |
| |
| if (validateRequiredRank(inputs[0]) || validateRequiredRank(outputs[0])) |
| { |
| return 1; |
| } |
| |
| // output and input must be the same types |
| if (inputs[0]->matchRankTypeShape(*outputs[0])) |
| { |
| printNodeValidationError("Failure to match input and output rank/type/shape"); |
| return 1; |
| } |
| |
| in = dynamic_cast<TosaReference::TensorTemplate<TIn>*>(inputs[0]); |
| out = dynamic_cast<TosaReference::TensorTemplate<TOut>*>(outputs[0]); |
| |
| ASSERT_MEM(in && out); |
| |
| if (attribute->axis() < 0 || attribute->axis() >= inputs[0]->getRank()) |
| { |
| printNodeValidationError("Reverse axis must between [0, input_rank - 1]"); |
| return 1; |
| } |
| |
| // transform list of axis into true or false list |
| // e.g. rank=4, axis=[1,2], reverse array would be [false, true, true, false] |
| for (int i = 0; i < Rank; i++) |
| { |
| reverse_array[i] = false; |
| } |
| reverse_array[attribute->axis()] = true; |
| |
| return 0; |
| } |
| |
| template <int Rank, DType Dtype> |
| int OpReverse<Rank, Dtype>::eval() |
| { |
| out->getTensor() = in->getTensor().reverse(reverse_array); |
| |
| return GraphNode::eval(); |
| } |
| |
| template <int Rank, DType Dtype> |
| OpSlice<Rank, Dtype>::OpSlice(TosaAttributeBase* attribute_, TosaQuantInfoBase* qinfo_, uint64_t id_) |
| : GraphNode(Op_SLICE, id_) |
| { |
| setRequiredOperands(1, 1); |
| setRequiredRank(0, 6); |
| |
| INIT_ATTRIBUTE(Slice); |
| } |
| |
| template <int Rank, DType Dtype> |
| OpSlice<Rank, Dtype>::~OpSlice() |
| { |
| if (attribute) |
| delete attribute; |
| } |
| |
| template <int Rank, DType Dtype> |
| int OpSlice<Rank, Dtype>::checkTensorAttributes() |
| { |
| if (validateRequiredOperands()) |
| return 1; |
| |
| if (validateRequiredRank(inputs[0]) || validateRequiredRank(outputs[0])) |
| { |
| return 1; |
| } |
| |
| // output and input must be the same types |
| if (inputs[0]->matchType(*outputs[0])) |
| { |
| printNodeValidationError("Failure to match input and output type"); |
| return 1; |
| } |
| |
| in = dynamic_cast<TosaReference::TensorTemplate<TIn>*>(inputs[0]); |
| out = dynamic_cast<TosaReference::TensorTemplate<TOut>*>(outputs[0]); |
| |
| for (size_t i = 0; i < attribute->begin().size(); i++) |
| { |
| begin_array[i] = attribute->begin()[i]; |
| } |
| |
| for (size_t i = 0; i < attribute->size().size(); i++) |
| { |
| if (attribute->size()[i] != 0) |
| { |
| size_array[i] = attribute->size()[i]; |
| } |
| else |
| { |
| // Tensorflow assigns a zero size to dimensions that are kept |
| // Eigen expects size to be the full size of the dimension |
| size_array[i] = in->getTensor().dimension(0); |
| } |
| } |
| |
| return 0; |
| } |
| |
| template <int Rank, DType Dtype> |
| int OpSlice<Rank, Dtype>::eval() |
| { |
| out->getTensor() = in->getTensor().slice(begin_array, size_array); |
| |
| return GraphNode::eval(); |
| } |
| |
| template <int Rank, DType Dtype> |
| OpTileBase<Rank, Dtype>::OpTileBase(TosaAttributeBase* attribute_, TosaQuantInfoBase* qinfo_, uint64_t id_) |
| : GraphNode(Op_TILE, id_) |
| { |
| setRequiredOperands(1, 1); |
| setRequiredRank(0, 6); |
| |
| INIT_ATTRIBUTE(Tile); |
| } |
| |
| template <int Rank, DType Dtype> |
| OpTileBase<Rank, Dtype>::~OpTileBase() |
| { |
| if (attribute) |
| delete attribute; |
| } |
| |
| template <int Rank, DType Dtype> |
| int OpTileBase<Rank, Dtype>::checkTensorAttributes() |
| { |
| if (validateRequiredOperands()) |
| return 1; |
| |
| if (validateRequiredRank(inputs[0]) || validateRequiredRank(outputs[0])) |
| { |
| return 1; |
| } |
| |
| // output and input must be the same ranks and types |
| if (inputs[0]->matchRankType(*outputs[0])) |
| { |
| printNodeValidationError("Failure to match input and output rank or type"); |
| return 1; |
| } |
| |
| in = dynamic_cast<TosaReference::TensorTemplate<TIn>*>(inputs[0]); |
| out = dynamic_cast<TosaReference::TensorTemplate<TOut>*>(outputs[0]); |
| |
| if (attribute->multiples().size() != Rank) |
| { |
| printNodeValidationError("1D list 'multiples' must have size equal to input rank"); |
| return 1; |
| } |
| |
| for (int32_t d = 0; d < Rank; d++) |
| { |
| if (in->getShape()[d] * attribute->multiples()[d] != out->getShape()[d]) |
| { |
| printNodeValidationError("unexpected output shape"); |
| return 1; |
| } |
| } |
| |
| return 0; |
| } |
| |
| template <int Rank, DType Dtype> |
| int OpTile<Rank, Dtype>::eval() |
| { |
| // primary template shouldn't be called |
| FATAL_ERROR_NODE("OpTile rank=%i, dtype=%s: not implemented yet", Rank, EnumNamesDType()[Dtype]); |
| } |
| |
| template <DType Dtype> |
| int OpTile<1, Dtype>::eval() |
| { |
| for (int32_t od0 = 0; od0 < this->out->getShape()[0]; od0++) |
| { |
| int32_t id0 = od0 % this->in->getShape()[0]; |
| this->out->getTensor()(od0) = this->in->getTensor()(id0); |
| } |
| |
| return GraphNode::eval(); |
| } |
| |
| template <DType Dtype> |
| int OpTile<2, Dtype>::eval() |
| { |
| for (int32_t od0 = 0; od0 < this->out->getShape()[0]; od0++) |
| { |
| int32_t id0 = od0 % this->in->getShape()[0]; |
| for (int32_t od1 = 0; od1 < this->out->getShape()[1]; od1++) |
| { |
| int32_t id1 = od1 % this->in->getShape()[1]; |
| this->out->getTensor()(od0, od1) = this->in->getTensor()(id0, id1); |
| } |
| } |
| |
| return GraphNode::eval(); |
| } |
| |
| template <DType Dtype> |
| int OpTile<3, Dtype>::eval() |
| { |
| for (int32_t od0 = 0; od0 < this->out->getShape()[0]; od0++) |
| { |
| int32_t id0 = od0 % this->in->getShape()[0]; |
| for (int32_t od1 = 0; od1 < this->out->getShape()[1]; od1++) |
| { |
| int32_t id1 = od1 % this->in->getShape()[1]; |
| for (int32_t od2 = 0; od2 < this->out->getShape()[2]; od2++) |
| { |
| int32_t id2 = od2 % this->in->getShape()[2]; |
| this->out->getTensor()(od0, od1, od2) = this->in->getTensor()(id0, id1, id2); |
| } |
| } |
| } |
| |
| return GraphNode::eval(); |
| } |
| |
| template <DType Dtype> |
| int OpTile<4, Dtype>::eval() |
| { |
| for (int32_t od0 = 0; od0 < this->out->getShape()[0]; od0++) |
| { |
| int32_t id0 = od0 % this->in->getShape()[0]; |
| for (int32_t od1 = 0; od1 < this->out->getShape()[1]; od1++) |
| { |
| int32_t id1 = od1 % this->in->getShape()[1]; |
| for (int32_t od2 = 0; od2 < this->out->getShape()[2]; od2++) |
| { |
| int32_t id2 = od2 % this->in->getShape()[2]; |
| for (int32_t od3 = 0; od3 < this->out->getShape()[3]; od3++) |
| { |
| int32_t id3 = od3 % this->in->getShape()[3]; |
| this->out->getTensor()(od0, od1, od2, od3) = this->in->getTensor()(id0, id1, id2, id3); |
| } |
| } |
| } |
| } |
| |
| return GraphNode::eval(); |
| } |
| |
| template <int Rank, DType Dtype> |
| OpTranspose<Rank, Dtype>::OpTranspose(TosaAttributeBase* attribute_, TosaQuantInfoBase* qinfo_, uint64_t id_) |
| : GraphNode(Op_TRANSPOSE, id_) |
| { |
| setRequiredOperands(2, 1); |
| setRequiredRank(0, 6); |
| } |
| |
| template <int Rank, DType Dtype> |
| OpTranspose<Rank, Dtype>::~OpTranspose() |
| {} |
| |
| template <int Rank, DType Dtype> |
| int OpTranspose<Rank, Dtype>::checkTensorAttributes() |
| { |
| if (validateRequiredOperands()) |
| return 1; |
| |
| if (validateRequiredRank(inputs[0]) || validateRequiredRank(outputs[0])) |
| { |
| return 1; |
| } |
| |
| // output and input must be the same types |
| if (inputs[0]->matchRankType(*outputs[0])) |
| { |
| printNodeValidationError("Failure to match input and output rank and type"); |
| return 1; |
| } |
| |
| if (inputs[0]->getElementCount() != outputs[0]->getElementCount()) |
| { |
| printNodeValidationError("Failure to match input and output total element count"); |
| return 1; |
| } |
| |
| in = dynamic_cast<TosaReference::TensorTemplate<TIn>*>(inputs[0]); |
| out = dynamic_cast<TosaReference::TensorTemplate<TOut>*>(outputs[0]); |
| perm_tensor = dynamic_cast<TosaReference::TensorTemplate<ETensor1<int32_t>>*>(inputs[1]); |
| |
| return 0; |
| } |
| |
| template <int Rank, DType Dtype> |
| int OpTranspose<Rank, Dtype>::eval() |
| { |
| for (int32_t d = 0; d < Rank; d++) |
| { |
| perm_array[d] = this->perm_tensor->getTensor().data()[d]; |
| } |
| |
| out->getTensor() = in->getTensor().shuffle(perm_array); |
| |
| return GraphNode::eval(); |
| } |
| |
| // template explicit instantiation |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpConcat, FLOAT) |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpConcat, AINT8) |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpConcat, INT8) |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpConcat, INT16) |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpConcat, INT32) |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpConcat, BOOL) |
| |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpPad, FLOAT); |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpPad, AINT8); |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpPad, INT8); |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpPad, INT16); |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpPad, INT32); |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpPad, BOOL); |
| |
| DEF_INSTANTIATE_RESHAPE(OpReshape, FLOAT); |
| DEF_INSTANTIATE_RESHAPE(OpReshape, AINT8); |
| DEF_INSTANTIATE_RESHAPE(OpReshape, INT8); |
| DEF_INSTANTIATE_RESHAPE(OpReshape, INT16); |
| DEF_INSTANTIATE_RESHAPE(OpReshape, INT32); |
| DEF_INSTANTIATE_RESHAPE(OpReshape, BOOL); |
| |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpReverse, FLOAT); |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpReverse, AINT8); |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpReverse, INT8); |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpReverse, INT16); |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpReverse, INT32); |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpReverse, BOOL); |
| |
| DEF_INSTANTIATE_RANK0_6_ONE_RANK_ONE_TYPE(OpSlice, FLOAT); |
| DEF_INSTANTIATE_RANK0_6_ONE_RANK_ONE_TYPE(OpSlice, AINT8); |
| DEF_INSTANTIATE_RANK0_6_ONE_RANK_ONE_TYPE(OpSlice, INT8); |
| DEF_INSTANTIATE_RANK0_6_ONE_RANK_ONE_TYPE(OpSlice, INT16); |
| DEF_INSTANTIATE_RANK0_6_ONE_RANK_ONE_TYPE(OpSlice, INT32); |
| DEF_INSTANTIATE_RANK0_6_ONE_RANK_ONE_TYPE(OpSlice, BOOL); |
| |
| DEF_INSTANTIATE_RANK0_6_ONE_RANK_ONE_TYPE(OpTile, FLOAT); |
| DEF_INSTANTIATE_RANK0_6_ONE_RANK_ONE_TYPE(OpTile, AINT8); |
| DEF_INSTANTIATE_RANK0_6_ONE_RANK_ONE_TYPE(OpTile, INT8); |
| DEF_INSTANTIATE_RANK0_6_ONE_RANK_ONE_TYPE(OpTile, INT16); |
| DEF_INSTANTIATE_RANK0_6_ONE_RANK_ONE_TYPE(OpTile, INT32); |
| DEF_INSTANTIATE_RANK0_6_ONE_RANK_ONE_TYPE(OpTile, BOOL); |
| |
| DEF_INSTANTIATE_RANK0_6_ONE_RANK_ONE_TYPE(OpTranspose, FLOAT); |
| DEF_INSTANTIATE_RANK0_6_ONE_RANK_ONE_TYPE(OpTranspose, AINT8); |
| DEF_INSTANTIATE_RANK0_6_ONE_RANK_ONE_TYPE(OpTranspose, INT8); |
| DEF_INSTANTIATE_RANK0_6_ONE_RANK_ONE_TYPE(OpTranspose, INT16); |
| DEF_INSTANTIATE_RANK0_6_ONE_RANK_ONE_TYPE(OpTranspose, INT32); |
| DEF_INSTANTIATE_RANK0_6_ONE_RANK_ONE_TYPE(OpTranspose, BOOL); |