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Diffstat (limited to 'reference_model/src/ops/data_layout.cc')
| -rw-r--r-- | reference_model/src/ops/data_layout.cc | 644 |
1 files changed, 644 insertions, 0 deletions
diff --git a/reference_model/src/ops/data_layout.cc b/reference_model/src/ops/data_layout.cc new file mode 100644 index 0000000..32029b9 --- /dev/null +++ b/reference_model/src/ops/data_layout.cc @@ -0,0 +1,644 @@ + +// 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); |