// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. // // 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. #pragma once #include #include #include #include #include #include #include "lite/core/op_lite.h" #include "lite/core/tensor.h" namespace paddle { namespace lite { namespace subgraph { namespace xpu { // Graph and node is defined to collect all of converted XTCL IR nodes class Node { public: enum class Role { kVar = 0, kConst, kData, }; Node(std::shared_ptr data, PrecisionType precision, DataLayoutType layout, Role role) : data_(data), precision_(precision), layout_(layout), role_(role) {} Node(PrecisionType precision, DataLayoutType layout, Role role) : precision_(precision), layout_(layout), role_(role) {} void set_data(std::shared_ptr data) { data_ = data; } void set_precision(PrecisionType precision) { precision_ = precision; } void set_layout(DataLayoutType layout) { layout_ = layout; } void set_role(Role role) { role_ = role; } std::shared_ptr data() { return data_; } PrecisionType precision() const { return precision_; } DataLayoutType layout() const { return layout_; } Role role() const { return role_; } bool is_var() const { return role_ == Role::kVar; } bool is_const() const { return role_ == Role::kConst; } bool is_data() const { return role_ == Role::kData; } private: std::shared_ptr data_{nullptr}; PrecisionType precision_{PRECISION(kFloat)}; DataLayoutType layout_{DATALAYOUT(kNCHW)}; Role role_{Role::kVar}; }; class Graph { public: int Add(const std::string& name, std::shared_ptr node); // Variable node std::shared_ptr Add(const std::string& name, const xtcl::xExpr& layer, PrecisionType precision = PRECISION(kFloat), DataLayoutType layout = DATALAYOUT(kNCHW)); // Const or data node std::shared_ptr Add(const std::string& name, const Tensor& tensor, std::vector shape, PrecisionType precision = PRECISION(kFloat), DataLayoutType layout = DATALAYOUT(kNCHW)); std::shared_ptr Add(const std::string& name, const Tensor& tensor, PrecisionType precision = PRECISION(kFloat), DataLayoutType layout = DATALAYOUT(kNCHW)) { return Add(name, tensor, tensor.dims().Vectorize(), precision, layout); } std::shared_ptr Add(const std::string& name, const Tensor& tensor, DDim dims, PrecisionType precision = PRECISION(kFloat), DataLayoutType layout = DATALAYOUT(kNCHW)) { return Add(name, tensor, dims.Vectorize(), precision, layout); } // Const node template std::shared_ptr Add(const std::string& name, const std::vector& data, std::vector shape = {}, DataLayoutType layout = DATALAYOUT(kNCHW)) { const std::type_info& info = typeid(T); PrecisionType precision = PRECISION(kFloat); if (info == typeid(float)) { precision = PRECISION(kFloat); } else if (info == typeid(int8_t)) { precision = PRECISION(kFloat); } else if (info == typeid(int32_t)) { precision = PRECISION(kInt32); } else { LOG(FATAL) << "[XPU] Unknow data type " << info.name(); } if (shape.empty()) { shape = {static_cast(data.size())}; } else { int size = 1; for (auto i : shape) { size *= i; } CHECK_EQ(data.size(), size); } Tensor tensor; tensor.Resize(shape); tensor.set_persistable(true); std::memcpy(reinterpret_cast(tensor.mutable_data()), reinterpret_cast(data.data()), data.size() * sizeof(T)); return Add(name, tensor, precision, layout); } template std::shared_ptr Add(const std::string& name, const std::vector& data, DDim dims, DataLayoutType layout = DATALAYOUT(kNCHW)) { return Add(name, data, dims.Vectorize(), layout); } template std::shared_ptr Add(const std::string& name, T value, std::vector shape = {1}, DataLayoutType layout = DATALAYOUT(kNCHW)) { int64_t size = 1; for (auto i : shape) { size *= i; } std::vector data(size, value); return Add(name, data, shape, layout); } template std::shared_ptr Add(const std::string& name, T value, DDim dims, DataLayoutType layout = DATALAYOUT(kNCHW)) { return Add(name, value, dims.Vectorize(), layout); } // Data node std::shared_ptr Add(const std::string& name, std::vector shape, PrecisionType precision = PRECISION(kFloat), DataLayoutType layout = DATALAYOUT(kNCHW)); std::shared_ptr Add(const std::string& name, DDim dims, PrecisionType precision = PRECISION(kFloat), DataLayoutType layout = DATALAYOUT(kNCHW)) { return Add(name, dims.Vectorize(), precision, layout); } std::shared_ptr Get(const std::string& name) { CHECK(Has(name)) << "[XPU] Node " << name << " not found."; return nodes_.at(name).back(); } bool Has(const std::string& name) { return nodes_.find(name) != nodes_.end(); } public: // XPU network builder and constant tensors xtcl::network::xNetworkBuilder builder_; xtcl::network::xTensorCompiler::ParamNDArrayMap params_; private: std::unordered_map>> nodes_; }; } // namespace xpu } // namespace subgraph } // namespace lite } // namespace paddle