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Diffstat (limited to 'reference_model/src/ops/type_conversion.cc')
| -rw-r--r-- | reference_model/src/ops/type_conversion.cc | 299 |
1 files changed, 299 insertions, 0 deletions
diff --git a/reference_model/src/ops/type_conversion.cc b/reference_model/src/ops/type_conversion.cc new file mode 100644 index 0000000..61a19f4 --- /dev/null +++ b/reference_model/src/ops/type_conversion.cc @@ -0,0 +1,299 @@ + +// 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 "type_conversion.h" +#include "quant_util.h" +#include "template_types.h" +#include <cmath> + +using namespace TosaReference; +using namespace Eigen; +using namespace tosa; + +template <int Rank, DType InDtype, DType OutDtype> +OpRescale<Rank, InDtype, OutDtype>::OpRescale(TosaAttributeBase* attribute_, TosaQuantInfoBase* qinfo_, uint64_t id_) + : GraphNode(Op_RESCALE, id_) +{ + setRequiredOperands(1, 1); + setRequiredRank(0, 6); + INIT_ATTRIBUTE(Rescale); +} + +template <int Rank, DType InDtype, DType OutDtype> +OpRescale<Rank, InDtype, OutDtype>::~OpRescale() +{ + if (attribute) + delete attribute; +} + +template <int Rank, DType InDtype, DType OutDtype> +int OpRescale<Rank, InDtype, OutDtype>::checkTensorAttributes() +{ + if (validateRequiredOperands()) + return 1; + + if (validateRequiredRank(inputs[0]) || validateRequiredRank(outputs[0])) + { + return 1; + } + + // output and input must be the same rank and size + if (inputs[0]->matchRankSize(*outputs[0])) + { + printNodeValidationError("OpRescale: input and output rank/size must match"); + return 1; + } + + in = dynamic_cast<TosaReference::TensorTemplate<TIn>*>(inputs[0]); + out = dynamic_cast<TosaReference::TensorTemplate<TOut>*>(outputs[0]); + + ASSERT_MEM(in && out); + + return 0; +} + +template <int Rank, DType InDtype, DType OutDtype> +int OpRescale<Rank, InDtype, OutDtype>::eval() +{ + int32_t input_zp = attribute->input_zp(); + int32_t output_zp = attribute->output_zp(); + std::vector<int32_t> multiplier = attribute->multiplier(); + std::vector<int32_t> shift = attribute->shift(); + //bool scale32 = attribute->scale32(); + bool double_round = attribute->double_round(); + bool per_channel = attribute->per_channel(); + + if (TosaReference::TypeChecker::is_symmetric(InDtype)) + { + if (input_zp != 0) + { + FATAL_ERROR_NODE("input tensor is symmetric type %s but zeropoint is %d instead of 0", + EnumNamesDType()[InDtype], input_zp); + } + } + + if (TosaReference::TypeChecker::is_symmetric(OutDtype)) + { + if (output_zp != 0) + { + FATAL_ERROR_NODE("output tensor is symmetric type %s but zeropoint is %d instead of 0", + EnumNamesDType()[OutDtype], output_zp); + } + } + + // reshape [d0, d1, ..., dn] into [d0 * d1 ..., dn] + Eigen::array<Eigen::Index, 2> shape_2d; + shape_2d[0] = 1; + if (Rank > 0) + { + for (int i = 0; i < Rank - 1; i++) + { + shape_2d[0] *= this->in->getShape()[i]; + } + shape_2d[1] = this->in->getShape()[Rank - 1]; + } + else + { + shape_2d[1] = 1; + } + ETensor2<InEigenType> input_reshaped = this->in->getTensor().reshape(shape_2d); + + ETensor2<OutEigenType> output_2d(shape_2d); + + // TODO: pass scale32 in when 16-bit mode implemented + if (per_channel) + { + ETensor2<InEigenType> curr_channel_slice_prescaled; + ETensor2<OutEigenType> curr_channel_slice_postscaled; + int32_t channel_multiplier, channel_shift; + Eigen::array<Eigen::Index, 2> begin, size; + size = Eigen::array<Eigen::Index, 2>({ shape_2d[0], 1 }); + for (int32_t i = 0; i < shape_2d[1]; i++) + { + begin = Eigen::array<Eigen::Index, 2>({ 0, i }); + curr_channel_slice_prescaled = input_reshaped.slice(begin, size); + channel_multiplier = multiplier[i]; + channel_shift = shift[i]; + curr_channel_slice_postscaled = + curr_channel_slice_prescaled.unaryExpr([input_zp, output_zp, channel_multiplier, channel_shift, + double_round](InEigenType in_val) -> OutEigenType { + InEigenType input_zp_shifted = in_val - (InEigenType)input_zp; + int32_t scaled = TosaReference::QuantUtil<InDtype>::apply_scale( + input_zp_shifted, channel_multiplier, channel_shift, double_round); + OutEigenType out_val = (OutEigenType)(scaled + output_zp); + out_val = std::max<OutEigenType>(out_val, QMin); + out_val = std::min<OutEigenType>(out_val, QMax); + return out_val; + }); + + for (int32_t j = 0; j < shape_2d[0]; j++) + { + output_2d(j, i) = curr_channel_slice_postscaled(j, 0); + } + } + } + else + { + int32_t tensor_multiplier = multiplier[0]; + int32_t tensor_shift = shift[0]; + output_2d = input_reshaped.unaryExpr( + [input_zp, output_zp, tensor_multiplier, tensor_shift, double_round](InEigenType in_val) -> OutEigenType { + InEigenType input_zp_shifted = in_val - (InEigenType)input_zp; + int32_t scaled = TosaReference::QuantUtil<InDtype>::apply_scale(input_zp_shifted, tensor_multiplier, + tensor_shift, double_round); + OutEigenType out_val = (OutEigenType)(scaled + output_zp); + out_val = std::max<OutEigenType>(out_val, QMin); + out_val = std::min<OutEigenType>(out_val, QMax); + return out_val; + }); + } + + // reshape [d0 * d1 ..., dn] back to [d0, d1, ..., dn] + Eigen::array<Eigen::Index, Rank> output_shape; + for (int i = 0; i < Rank; i++) + { + output_shape[i] = this->out->getShape()[i]; + } + this->out->getTensor() = output_2d.reshape(output_shape); + + return GraphNode::eval(); +} + +template <int Rank, DType InDtype, DType OutDtype> +OpCast<Rank, InDtype, OutDtype>::OpCast(TosaAttributeBase* attribute_, TosaQuantInfoBase* qinfo_, uint64_t id_) + : GraphNode(Op_CAST, id_) +{ + setRequiredOperands(1, 1); + setRequiredRank(0, 6); +} + +template <int Rank, DType InDtype, DType OutDtype> +OpCast<Rank, InDtype, OutDtype>::~OpCast() +{} + +template <int Rank, DType InDtype, DType OutDtype> +int OpCast<Rank, InDtype, OutDtype>::checkTensorAttributes() +{ + if (validateRequiredOperands()) + return 1; + + if (validateRequiredRank(inputs[0]) || validateRequiredRank(outputs[0])) + { + return 1; + } + + // output and input must be the same rank and size + if (inputs[0]->matchRankSize(*outputs[0])) + { + printNodeValidationError("OpCast: input and output rank/size must match"); + return 1; + } + + in = dynamic_cast<TosaReference::TensorTemplate<TIn>*>(inputs[0]); + out = dynamic_cast<TosaReference::TensorTemplate<TOut>*>(outputs[0]); + + ASSERT_MEM(in && out); + + return 0; +} + +template <int Rank, DType InDtype, DType OutDtype> +int OpCast<Rank, InDtype, OutDtype>::eval() +{ + this->out->getTensor() = this->in->getTensor().unaryExpr(cast_helper.get_fcn()); + + return GraphNode::eval(); +} + +template <DType InDtype, DType OutDtype> +CastHelper<InDtype, OutDtype>::CastHelper() +{ + fcn = [](InEigenType in) -> OutEigenType { + OutEigenType out = (OutEigenType)in; // implicit sign_extend() if sizeof(out_t) >= sizeof(in_t) + int64_t mask = (1L << OutBits) - 1; + out = out & mask; + return out; + }; +} + +template <DType InDtype> +CastHelper<InDtype, DType_BOOL>::CastHelper() +{ + fcn = [](InEigenType in) -> bool { return (in != 0) ? true : false; }; +} + +template <DType OutDtype> +CastHelper<DType_BOOL, OutDtype>::CastHelper() +{ + fcn = [](bool in) -> OutEigenType { + OutEigenType out = in ? (OutEigenType)1 : (OutEigenType)0; + return out; + }; +} + +template <DType InDtype> +CastHelper<InDtype, DType_FLOAT>::CastHelper() +{ + fcn = [](InEigenType in) -> float { + float out = (OutEigenType)in; // default cast to float is round_to_nearest_float() + return out; + }; +} + +template <DType OutDtype> +CastHelper<DType_FLOAT, OutDtype>::CastHelper() +{ + fcn = [](float in) -> OutEigenType { + OutEigenType out = std::round(in); + out = std::max<OutEigenType>(out, OutMin); + out = std::min<OutEigenType>(out, OutMax); + return out; + }; +} + +// template explicit instantiation +DEF_INSTANTIATE_RANK0_6_ONE_RANK_TWO_TYPE(OpCast, BOOL, INT8); +DEF_INSTANTIATE_RANK0_6_ONE_RANK_TWO_TYPE(OpCast, BOOL, INT16); +DEF_INSTANTIATE_RANK0_6_ONE_RANK_TWO_TYPE(OpCast, BOOL, INT32); +DEF_INSTANTIATE_RANK0_6_ONE_RANK_TWO_TYPE(OpCast, INT8, BOOL); +DEF_INSTANTIATE_RANK0_6_ONE_RANK_TWO_TYPE(OpCast, INT8, INT16); +DEF_INSTANTIATE_RANK0_6_ONE_RANK_TWO_TYPE(OpCast, INT8, INT32); +DEF_INSTANTIATE_RANK0_6_ONE_RANK_TWO_TYPE(OpCast, INT8, FLOAT); +DEF_INSTANTIATE_RANK0_6_ONE_RANK_TWO_TYPE(OpCast, INT16, BOOL); +DEF_INSTANTIATE_RANK0_6_ONE_RANK_TWO_TYPE(OpCast, INT16, INT8); +DEF_INSTANTIATE_RANK0_6_ONE_RANK_TWO_TYPE(OpCast, INT16, INT32); +DEF_INSTANTIATE_RANK0_6_ONE_RANK_TWO_TYPE(OpCast, INT16, FLOAT); +DEF_INSTANTIATE_RANK0_6_ONE_RANK_TWO_TYPE(OpCast, INT32, BOOL); +DEF_INSTANTIATE_RANK0_6_ONE_RANK_TWO_TYPE(OpCast, INT32, INT8); +DEF_INSTANTIATE_RANK0_6_ONE_RANK_TWO_TYPE(OpCast, INT32, INT16); +DEF_INSTANTIATE_RANK0_6_ONE_RANK_TWO_TYPE(OpCast, INT32, FLOAT); +DEF_INSTANTIATE_RANK0_6_ONE_RANK_TWO_TYPE(OpCast, FLOAT, INT8); +DEF_INSTANTIATE_RANK0_6_ONE_RANK_TWO_TYPE(OpCast, FLOAT, INT16); +DEF_INSTANTIATE_RANK0_6_ONE_RANK_TWO_TYPE(OpCast, FLOAT, INT32); + +DEF_INSTANTIATE_RANK0_6_ONE_RANK_TWO_TYPE(OpRescale, AINT8, AINT8); +DEF_INSTANTIATE_RANK0_6_ONE_RANK_TWO_TYPE(OpRescale, AINT8, INT16); +DEF_INSTANTIATE_RANK0_6_ONE_RANK_TWO_TYPE(OpRescale, AINT8, INT32); +DEF_INSTANTIATE_RANK0_6_ONE_RANK_TWO_TYPE(OpRescale, INT16, AINT8); +DEF_INSTANTIATE_RANK0_6_ONE_RANK_TWO_TYPE(OpRescale, INT16, INT16); +DEF_INSTANTIATE_RANK0_6_ONE_RANK_TWO_TYPE(OpRescale, INT16, INT32); +DEF_INSTANTIATE_RANK0_6_ONE_RANK_TWO_TYPE(OpRescale, INT32, AINT8); +DEF_INSTANTIATE_RANK0_6_ONE_RANK_TWO_TYPE(OpRescale, INT32, INT16); +DEF_INSTANTIATE_RANK0_6_ONE_RANK_TWO_TYPE(OpRescale, INT32, INT32); +DEF_INSTANTIATE_RANK0_6_ONE_RANK_TWO_TYPE(OpRescale, INT48, AINT8); +DEF_INSTANTIATE_RANK0_6_ONE_RANK_TWO_TYPE(OpRescale, INT48, INT16); +DEF_INSTANTIATE_RANK0_6_ONE_RANK_TWO_TYPE(OpRescale, INT48, INT32); +DEF_INSTANTIATE_RANK0_6_ONE_RANK_TWO_TYPE(OpRescale, UINT8, AINT8); +DEF_INSTANTIATE_RANK0_6_ONE_RANK_TWO_TYPE(OpRescale, AINT8, UINT8); |