| |
| // Copyright (c) 2020-2022, 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> |
| #include "half.hpp" |
| |
| using namespace TosaReference; |
| using namespace Eigen; |
| using namespace tosa; |
| |
| template <int Rank, DType InDtype, DType OutDtype> |
| OpRescale<Rank, InDtype, OutDtype>::OpRescale(SubgraphTraverser* sgt_, |
| TosaAttributeBase* attribute_, |
| uint64_t id_) |
| : GraphNode(sgt_, 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); |
| |
| if ((InDtype != DType_INT8) && (InDtype != DType_UINT8) && (InDtype != DType_UINT16) && (attribute->input_zp() != 0)) |
| { |
| printNodeValidationError("OpRescale: Input DType not INT8/UINT8/UINT16 and zero point not 0"); |
| return 1; |
| } |
| |
| if ((OutDtype != DType_INT8) && (OutDtype != DType_UINT8) && (OutDtype != DType_UINT16) && (attribute->output_zp() != 0)) |
| { |
| printNodeValidationError("OpRescale: Output DType not INT8/UINT8/UINT16 and zero point not 0"); |
| return 1; |
| } |
| |
| if ((InDtype == DType_UINT16) && ((attribute->input_zp() != 0) && (attribute->input_zp() != 32768))) |
| { |
| printNodeValidationError("OpRescale: Input DType UINT16 and zero point not 0 or 32768"); |
| return 1; |
| } |
| |
| if ((OutDtype == DType_UINT16) && ((attribute->output_zp() != 0) && (attribute->output_zp() != 32768))) |
| { |
| printNodeValidationError("OpRescale: Output DType UINT16 and zero point not 0 or 32768"); |
| return 1; |
| } |
| |
| if (attribute->scale32() && (InDtype == DType_INT48)) |
| { |
| printNodeValidationError("OpRescale: Scale set to true but input type is INT48"); |
| return 1; |
| } |
| |
| if ((!attribute->scale32()) && attribute->double_round()) |
| { |
| printNodeValidationError("OpRescale: Scale set to false but double round set to true"); |
| return 1; |
| } |
| |
| 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(); |
| |
| // 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); |
| |
| 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 }); |
| try |
| { |
| 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, scale32](InEigenType in_val) -> OutEigenType { |
| InEigenType input_zp_shifted = in_val - (InEigenType)input_zp; |
| int32_t scaled; |
| if (scale32) |
| scaled = TosaReference::QuantUtil::apply_scale_32(input_zp_shifted, channel_multiplier, |
| channel_shift, double_round); |
| else |
| scaled = TosaReference::QuantUtil::apply_scale_16(input_zp_shifted, channel_multiplier, |
| channel_shift); |
| 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); |
| } |
| } |
| } |
| catch (std::string desc) |
| { |
| REQUIRE(false, "OpRescale apply_scale_32/16() fails: %s.", desc.c_str()); |
| } |
| } |
| else |
| { |
| int32_t tensor_multiplier = multiplier[0]; |
| int32_t tensor_shift = shift[0]; |
| try |
| { |
| output_2d = input_reshaped.unaryExpr([input_zp, output_zp, tensor_multiplier, tensor_shift, double_round, |
| scale32](InEigenType in_val) -> OutEigenType { |
| InEigenType input_zp_shifted = in_val - (InEigenType)input_zp; |
| int32_t scaled; |
| if (scale32) |
| scaled = TosaReference::QuantUtil::apply_scale_32(input_zp_shifted, tensor_multiplier, tensor_shift, |
| double_round); |
| else |
| scaled = |
| TosaReference::QuantUtil::apply_scale_16(input_zp_shifted, tensor_multiplier, tensor_shift); |
| 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; |
| }); |
| } |
| catch (std::string desc) |
| { |
| REQUIRE(false, "OpRescale apply_scale_32/16() fails: %s.", desc.c_str()); |
| } |
| } |
| |
| // 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(SubgraphTraverser* sgt_, |
| TosaAttributeBase* attribute_, |
| uint64_t id_) |
| : GraphNode(sgt_, 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) |
| 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_FP16>::CastHelper() |
| { |
| fcn = [](InEigenType in) -> float { |
| half_float::half out = half_float::half_cast<half_float::half, InEigenType>(in); // Cast to half_float |
| return half_float::half_cast<float, half_float::half>(out); // Cast to float (underlying FP16 EigenType) |
| }; |
| } |
| |
| template <DType OutDtype> |
| CastHelper<DType_FP16, OutDtype>::CastHelper() |
| { |
| // Assuming InEigenType = float. |
| fcn = [](float in) -> OutEigenType { |
| // Perform initial rounding in half-precision then cast back to float |
| half_float::half h = half_float::half_cast<half_float::half, float>(in); |
| h = std::round(h); |
| OutEigenType out = half_float::half_cast<float, half_float::half>(h); |
| out = std::max<OutEigenType>(out, OutMin); |
| out = std::min<OutEigenType>(out, OutMax); |
| 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, FP16); |
| 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, FP16); |
| 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, FP16); |
| DEF_INSTANTIATE_RANK0_6_ONE_RANK_TWO_TYPE(OpCast, INT32, FLOAT); |
| DEF_INSTANTIATE_RANK0_6_ONE_RANK_TWO_TYPE(OpCast, FP16, INT8); |
| DEF_INSTANTIATE_RANK0_6_ONE_RANK_TWO_TYPE(OpCast, FP16, INT16); |
| DEF_INSTANTIATE_RANK0_6_ONE_RANK_TWO_TYPE(OpCast, FP16, INT32); |
| 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, INT8, INT8); |
| DEF_INSTANTIATE_RANK0_6_ONE_RANK_TWO_TYPE(OpRescale, INT8, INT16); |
| DEF_INSTANTIATE_RANK0_6_ONE_RANK_TWO_TYPE(OpRescale, INT8, INT32); |
| DEF_INSTANTIATE_RANK0_6_ONE_RANK_TWO_TYPE(OpRescale, INT16, INT8); |
| 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, INT8); |
| 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, INT8); |
| 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, INT8); |
| DEF_INSTANTIATE_RANK0_6_ONE_RANK_TWO_TYPE(OpRescale, UINT8, INT16); |
| DEF_INSTANTIATE_RANK0_6_ONE_RANK_TWO_TYPE(OpRescale, UINT16, INT16); |
| DEF_INSTANTIATE_RANK0_6_ONE_RANK_TWO_TYPE(OpRescale, INT8, UINT8); |
| DEF_INSTANTIATE_RANK0_6_ONE_RANK_TWO_TYPE(OpRescale, INT16, UINT8); |
| DEF_INSTANTIATE_RANK0_6_ONE_RANK_TWO_TYPE(OpRescale, INT16, UINT16); |