/* Copyright (c) 2018 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 #include #include "common/enforce.h" #include "framework/data_layout.h" #include "framework/tensor_base.h" #include "memory/t_malloc.h" namespace paddle_mobile { namespace framework { class LoDTensor; class Tensor : public TensorBase { public: Tensor() {} template Tensor(std::vector input, DDim ddim) { PADDLE_MOBILE_ENFORCE( input.size() == framework::product(ddim), "input vector'length should be equal to tensor's length"); auto input_ptr = mutable_data(ddim); for (int i = 0; i < input.size(); ++i) { input_ptr[i] = input[i]; } } Tensor(const Tensor &inTensor) { this->dims_ = inTensor.dims_; this->holder_ = inTensor.holder_; this->offset_ = inTensor.offset_; } /*! Resize the dimensions of the memory block. */ inline Tensor &Resize(const DDim &dims) { dims_ = dims; return *this; } /*! The internal of two tensors share the same memory block. */ inline Tensor &ShareDataWith(const Tensor &src) { src.check_memory_size(); if (holder_.get() != src.holder_.get()) { *this = src; } return *this; } inline void *mutable_data(std::type_index type) { if (holder_ != nullptr) { holder_->set_type(type); } PADDLE_MOBILE_ENFORCE(numel() >= 0, "the Tensor's numel must >=0.") int64_t size = numel() * SizeOfType(type); if (holder_ == nullptr || holder_->size() < size + offset_) { holder_.reset(new PlaceholderImpl(size, type)); offset_ = 0; } return reinterpret_cast( reinterpret_cast(holder_->ptr()) + offset_); } /** * @brief Return a pointer to mutable memory block. * @note If not exist, then allocation. */ template inline T *mutable_data() { static_assert(std::is_pod::value, "T must be POD"); return reinterpret_cast(mutable_data(typeid(T))); } /** * @brief Return a pointer to mutable memory block. * * @param[in] dims The dimensions of the memory block. * @param[in] place The place of the memory block. * * @note If not exist, then allocation. */ template inline T *mutable_data(DDim dims) { static_assert(std::is_pod::value, "T must be POD"); Resize(dims); return mutable_data(); } /** * @brief Return a sub-tensor of the given tensor. * * @param[in] begin_idx The index of the start row(inclusive) to * slice. * The index number begins from 0. * @param[in] end_idx The index of the end row(exclusive) to * slice. * The index number begins from 0. */ inline Tensor Slice(int begin_idx, int end_idx) const { check_memory_size(); PADDLE_MOBILE_ENFORCE(begin_idx >= 0, "The start row index must be greater than 0.") PADDLE_MOBILE_ENFORCE(end_idx <= dims_[0], "The end row index is out of bound.") PADDLE_MOBILE_ENFORCE( begin_idx < end_idx, "The start row index must be lesser than the end row index") if (dims_[0] == 1) { return *this; } else { size_t base = numel() / dims_[0]; Tensor dst; dst.holder_ = holder_; DDim dst_dims = dims_; dst_dims[0] = end_idx - begin_idx; dst.Resize(dst_dims); dst.offset_ = offset_ + begin_idx * base * SizeOfType(type()); return dst; } } /*! Return a pointer to mutable memory block. */ template inline T *data() { check_memory_size(); PADDLE_MOBILE_ENFORCE( (std::is_same::value || holder_->type().hash_code() == typeid(T).hash_code()), "Tensor holds the wrong type, it holds %s, requested %s", this->holder_->type().name(), typeid(T).name()); return reinterpret_cast(reinterpret_cast(holder_->ptr()) + offset_); } /*! Return a pointer to constant memory block. */ template inline const T *data() const { check_memory_size(); PADDLE_MOBILE_ENFORCE( (std::is_same::value || holder_->type().hash_code() == typeid(T).hash_code()), "Tensor holds the wrong type, it holds %s, requested %s", this->holder_->type().name(), typeid(T).name()); return reinterpret_cast( reinterpret_cast(holder_->ptr()) + offset_); } private: struct PlaceholderImpl : public Placeholder { PlaceholderImpl(size_t size, std::type_index type) : ptr_(static_cast(memory::Alloc(size)), memory::PODDeleter()), size_(size), type_(type) { PADDLE_MOBILE_ENFORCE(ptr_ != nullptr, "Insufficient memory to allocation"); } virtual size_t size() const { return size_; } virtual void *ptr() const { return static_cast(ptr_.get()); } virtual std::type_index type() const { return type_; } virtual void set_type(std::type_index type) { type_ = type; } std::unique_ptr> ptr_; /*! the size of memory block. */ size_t size_; /* the current type of memory */ std::type_index type_; }; #ifdef PADDLE_MOBILE_FPGA public: // NOLINT inline void reset_data_ptr(void *p) { ((PlaceholderImpl *)(holder_.get()))->ptr_.reset((uint8_t *)p); // NOLINT } inline void *init(std::type_index type) { if (holder_ != nullptr) { holder_->set_type(type); } PADDLE_MOBILE_ENFORCE(numel() >= 0, "the Tensor's numel must >=0.") int64_t size = 1 * SizeOfType(type); if (holder_ == nullptr || holder_->size() < size + offset_) { holder_.reset(new PlaceholderImpl(size, type)); offset_ = 0; } return reinterpret_cast( reinterpret_cast(holder_->ptr()) + offset_); } float scale[2]; // scale[0]= MAX/127.0, scale[1]= 127.0/MAX #endif }; #ifdef PADDLE_MOBILE_DEBUG inline Print &operator<<(Print &printer, const Tensor &tensor) { printer << " dims: " << tensor.dims() << "\n"; int stride = tensor.numel() / 20; stride = stride > 0 ? stride : 1; #ifndef PADDLE_MOBILE_FPGA for (int i = 0; i < tensor.numel(); i += stride) { if (tensor.type() == typeid(float)) { printer << tensor.data()[i] << " "; } else if (tensor.type() == typeid(int32_t)) { printer << tensor.data()[i] << " "; } else if (tensor.type() == typeid(int64_t)) { printer << tensor.data()[i] << " "; } else if (tensor.type() == typeid(int8_t)) { printer << static_cast(tensor.data()[i]) << " "; } else if (tensor.type() == typeid(int32_t)) { printer << tensor.data()[i] << " "; } } #endif return printer; } #endif inline Tensor ReshapeToMatrix(const Tensor &src, int num_col_dims) { Tensor res; res.ShareDataWith(src); res.Resize(flatten_to_2d(src.dims(), num_col_dims)); return res; } } // namespace framework } // namespace paddle_mobile