| |
| // Copyright (c) 2020-2023, 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(SubgraphTraverser* sgt_, |
| TosaAttributeBase* attribute_, |
| uint64_t id_) |
| : GraphNode(sgt_, Op_CONCAT, id_) |
| { |
| setRequiredOperands(-1, 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 (inputs.empty()) |
| { |
| printNodeValidationError("Concat operator must have at least one input tensor"); |
| return 1; |
| } |
| |
| int32_t num_inputs = inputs.size(); |
| |
| // output and input must be the same types and rank |
| for (int32_t i = 0; i < num_inputs; i++) |
| { |
| if (inputs[i]->matchRankType(*outputs[0])) |
| { |
| printNodeValidationError("OpConcat: input ranks and types must match"); |
| return 1; |
| } |
| ins.push_back(dynamic_cast<TosaReference::TensorTemplate<TIn>*>(inputs[i])); |
| } |
| |
| if (attribute->axis() < 0 || (size_t)attribute->axis() >= Rank) |
| { |
| printNodeValidationError("OpConcat: axis is beyond output tensor rank"); |
| return 1; |
| } |
| |
| int32_t output_dim_on_axis = 0; |
| for (int32_t j = 0; j < num_inputs; j++) |
| { |
| for (int32_t i = 0; i < Rank; i++) |
| { |
| int32_t input_dim = inputs[j]->getShape()[i]; |
| if (i == attribute->axis()) |
| { |
| output_dim_on_axis += input_dim; |
| } |
| else if (input_dim != outputs[0]->getShape()[i]) |
| { |
| printNodeValidationError("OpConcat: input dimension not matching output dimension"); |
| return 1; |
| } |
| } |
| } |
| |
| ERROR_IF(output_dim_on_axis != outputs[0]->getShape()[attribute->axis()], |
| "OpConcat: sum of input dimension on axis not equal to output dimension on axis"); |
| |
| out = dynamic_cast<TosaReference::TensorTemplate<TOut>*>(outputs[0]); |
| |
| 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 result = ins[0]->getTensor().shuffle(reverser); |
| |
| for (size_t i = 1; i < ins.size(); i++) |
| { |
| TIn in_reversed = ins[i]->getTensor().shuffle(reverser); |
| TIn temp = result.concatenate(in_reversed, reversed_axis); |
| result = temp; |
| } |
| out->getTensor() = result.shuffle(reverser); |
| |
| return GraphNode::eval(); |
| } |
| |
| template <int Rank, DType Dtype> |
| OpPad<Rank, Dtype>::OpPad(SubgraphTraverser* sgt_, |
| TosaAttributeBase* attribute_, |
| uint64_t id_) |
| : GraphNode(sgt_, Op_PAD, id_) |
| { |
| setRequiredOperands(1, 1); |
| setRequiredRank(1, 6); |
| |
| INIT_ATTRIBUTE(Pad); |
| } |
| |
| template <int Rank, DType Dtype> |
| OpPad<Rank, Dtype>::~OpPad() |
| { |
| } |
| |
| 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]); |
| ASSERT_MEM(in && out); |
| |
| // padding in spec is 2D array in shape of [Rank, 2] |
| // Reference model implement this as 1D array of [Rank * 2], with ordering: |
| // [Rank0_front, Rank0_back, Rank1_front, Rank1_back, ..., Rank(N-1)_front, Rank(N-1)_back] |
| ERROR_IF(attribute->padding().size() != (Rank * 2), "OpPad: padding length needs to be (rank(input1) * 2)"); |
| |
| for (int i = 0; i < Rank; i++) |
| { |
| int32_t pad_front = attribute->padding()[2 * i]; |
| int32_t pad_back = attribute->padding()[2 * i + 1]; |
| ERROR_IF((pad_front < 0) || (pad_back < 0), "OpPad: padding can't be smaller than 0"); |
| ERROR_IF(out->getShape()[i] != pad_front + in->getShape()[i] + pad_back, |
| "OpPad: output shape not equal to input plus padding"); |
| paddings_array[i] = std::make_pair(pad_front, pad_back); |
| } |
| |
| return 0; |
| } |
| |
| template <int Rank, DType Dtype> |
| int OpPad<Rank, Dtype>::eval() |
| { |
| InEigenType pad_value = 0; |
| |
| switch (Dtype) |
| { |
| case DType_BOOL: |
| case DType_INT8: |
| case DType_INT16: |
| case DType_INT32: |
| pad_value = (InEigenType)attribute->pad_const_int(); |
| break; |
| case DType_FP16: |
| case DType_BF16: |
| case DType_FP32: |
| pad_value = (InEigenType)attribute->pad_const_fp(); |
| break; |
| default: |
| printNodeValidationError("Unsupported data type"); |
| break; |
| } |
| |
| 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(SubgraphTraverser* sgt_, |
| TosaAttributeBase* attribute_, |
| uint64_t id_) |
| : GraphNode(sgt_, 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() |
| { |
| 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; |
| } |
| |
| ERROR_IF(inputs[0]->getElementCount() != outputs[0]->getElementCount(), |
| "Input tensor size does not match output tensor size"); |
| |
| for (uint32_t d = 0; d < OutRank; d++) |
| { |
| ERROR_IF(attribute->new_shape()[d] != outputs[0]->getShape()[d], |
| "OpReshape: new_shape doesn't match output shape"); |
| } |
| |
| 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() |
| { |
| for (int32_t d = 0; d < OutRank; d++) |
| { |
| array_shape[d] = attribute->new_shape()[OutRank - 1 - d]; |
| out_reverser[d] = OutRank - 1 - d; |
| } |
| |
| 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(SubgraphTraverser* sgt_, |
| TosaAttributeBase* attribute_, |
| uint64_t id_) |
| : GraphNode(sgt_, 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(SubgraphTraverser* sgt_, |
| TosaAttributeBase* attribute_, |
| uint64_t id_) |
| : GraphNode(sgt_, Op_SLICE, id_) |
| { |
| setRequiredOperands(1, 1); |
| setRequiredRank(1, 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]->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]); |
| |
| ASSERT_MEM(in && out); |
| |
| ERROR_IF((int32_t)attribute->start().size() != in->getRank(), |
| "OpSlice: begin array length needs to be rank(input)"); |
| ERROR_IF((int32_t)attribute->size().size() != in->getRank(), "OpSlice: size array length needs to be rank(input)"); |
| |
| for (int32_t i = 0; i < in->getRank(); i++) |
| { |
| int32_t b = attribute->start()[i]; |
| int32_t s = attribute->size()[i]; |
| ERROR_IF(b < 0 || b >= in->getShape()[i], "OpSlice: start out of boundary"); |
| ERROR_IF((b + s) < 0 || (b + s) > in->getShape()[i], "OpSlice: (start+size) out of boundary"); |
| ERROR_IF(s <= 0, "OpSlice: output must be positive"); |
| ERROR_IF(s != out->getShape()[i], "OpSlice: size doesn't match output tensor dimension"); |
| begin_array[i] = b; |
| size_array[i] = s; |
| } |
| |
| 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(SubgraphTraverser* sgt_, |
| TosaAttributeBase* attribute_, |
| uint64_t id_) |
| : GraphNode(sgt_, Op_TILE, id_) |
| { |
| setRequiredOperands(1, 1); |
| setRequiredRank(1, 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]); |
| |
| ASSERT_MEM(in && out); |
| |
| 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++) |
| { |
| ERROR_IF(in->getShape()[d] * attribute->multiples()[d] != out->getShape()[d], |
| "Output shape not equal to input * multiples;") |
| } |
| |
| return 0; |
| } |
| |
| template <int Rank, DType Dtype> |
| int OpTile<Rank, Dtype>::eval() |
| { |
| // primary template shouldn't be called |
| FATAL_ERROR("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 <DType Dtype> |
| int OpTile<5, 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]; |
| for (int32_t od4 = 0; od4 < this->out->getShape()[4]; od4++) |
| { |
| int32_t id4 = od4 % this->in->getShape()[4]; |
| this->out->getTensor()(od0, od1, od2, od3, od4) = |
| this->in->getTensor()(id0, id1, id2, id3, id4); |
| } |
| } |
| } |
| } |
| } |
| |
| return GraphNode::eval(); |
| } |
| |
| template <DType Dtype> |
| int OpTile<6, 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]; |
| for (int32_t od4 = 0; od4 < this->out->getShape()[4]; od4++) |
| { |
| int32_t id4 = od4 % this->in->getShape()[4]; |
| for (int32_t od5 = 0; od5 < this->out->getShape()[5]; od5++) |
| { |
| int32_t id5 = od5 % this->in->getShape()[5]; |
| this->out->getTensor()(od0, od1, od2, od3, od4, od5) = |
| this->in->getTensor()(id0, id1, id2, id3, id4, id5); |
| } |
| } |
| } |
| } |
| } |
| } |
| |
| return GraphNode::eval(); |
| } |
| |
| template <int Rank, DType Dtype> |
| OpTranspose<Rank, Dtype>::OpTranspose(SubgraphTraverser* sgt_, |
| TosaAttributeBase* attribute_, |
| uint64_t id_) |
| : GraphNode(sgt_, Op_TRANSPOSE, id_) |
| { |
| setRequiredOperands(1, 1); |
| setRequiredRank(1, 6); |
| |
| INIT_ATTRIBUTE(Transpose); |
| } |
| |
| template <int Rank, DType Dtype> |
| OpTranspose<Rank, Dtype>::~OpTranspose() |
| { |
| if (attribute) delete attribute; |
| } |
| |
| 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]); |
| |
| ASSERT_MEM(in && out); |
| |
| ERROR_IF(attribute->perms().size() != Rank, "OpTranspose: perms array size needs to match rank(input)"); |
| |
| std::array<bool, Rank> index_used; |
| index_used.fill(false); |
| for (int32_t d = 0; d < Rank; d++) |
| { |
| int32_t index = attribute->perms()[d]; |
| ERROR_IF(index < 0 or index >= Rank, "OpTranspose: index out of boundary"); |
| ERROR_IF(index_used[index], "OpTranspose: index duplicated in perm attribute"); |
| index_used[index] = true; |
| ERROR_IF(in->getShape()[index] != out->getShape()[d], "OpTranspose: input output shape mismatch"); |
| perm_array[d] = index; |
| } |
| |
| return 0; |
| } |
| |
| template <int Rank, DType Dtype> |
| int OpTranspose<Rank, Dtype>::eval() |
| { |
| out->getTensor() = in->getTensor().shuffle(perm_array); |
| |
| return GraphNode::eval(); |
| } |
| |
| // template explicit instantiation |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpConcat, FP16) |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpConcat, BF16) |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpConcat, FP32) |
| 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, FP16); |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpPad, BF16); |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpPad, FP32); |
| 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, FP16); |
| DEF_INSTANTIATE_RESHAPE(OpReshape, BF16); |
| DEF_INSTANTIATE_RESHAPE(OpReshape, FP32); |
| 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, FP16); |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpReverse, BF16); |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpReverse, FP32); |
| 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_RANK1_6_ONE_RANK_ONE_TYPE(OpSlice, FP16); |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpSlice, BF16); |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpSlice, FP32); |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpSlice, INT8); |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpSlice, INT16); |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpSlice, INT32); |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpSlice, BOOL); |
| |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpTileBase, FP16); |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpTileBase, BF16); |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpTileBase, FP32); |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpTileBase, INT8); |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpTileBase, INT16); |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpTileBase, INT32); |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpTileBase, BOOL); |
| |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpTile, FP16); |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpTile, BF16); |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpTile, FP32); |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpTile, INT8); |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpTile, INT16); |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpTile, INT32); |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpTile, BOOL); |
| |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpTranspose, FP16); |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpTranspose, BF16); |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpTranspose, FP32); |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpTranspose, INT8); |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpTranspose, INT16); |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpTranspose, INT32); |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpTranspose, BOOL); |