Introduce ITensor instead of Blob (#16048)
* Introduce ITensor * Added new allocator * Hide ITensor from dev api * Changed some python tests * Remove deprecated API from sample * Fixed warnings * Skiped unsupported tests * Fixed exception message * Fixed template func tests * Fixed incorrect tests * Fixed comments and move ITensor to developer API * Fixed CI issue * Fixed allocated tensor * Fixed docs and windows warning * Fixed set shape for strided tensors * Fixed build and some comments * Introduce remote tensor * Fixed code style * Fixed build * Remove static assert method * Remove fail type * Added device name API * Try to fix GPU remote tests * Added debug output * Try to fix GPU tests * Fixed comments * Fixed build * Added additional element type check * Revert some comment changes
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Ilya Churaev authored and 
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@@ -15,7 +15,6 @@
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#include "format_reader_ptr.h"
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#include "samples/slog.hpp"
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#include "shared_tensor_allocator.hpp"
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#include "utils.hpp"
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template <typename T>
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@@ -31,10 +30,8 @@ ov::Tensor create_tensor_from_image(const std::vector<std::string>& files,
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const benchmark_app::InputInfo& inputInfo,
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const std::string& inputName,
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std::string* filenames_used = nullptr) {
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size_t tensor_size =
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std::accumulate(inputInfo.dataShape.begin(), inputInfo.dataShape.end(), 1, std::multiplies<size_t>());
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auto allocator = std::make_shared<SharedTensorAllocator>(tensor_size * sizeof(T));
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auto data = reinterpret_cast<T*>(allocator->get_buffer());
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auto tensor = ov::Tensor(inputInfo.type, inputInfo.dataShape);
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auto data = tensor.data<T>();
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/** Collect images data ptrs **/
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std::vector<std::shared_ptr<uint8_t>> vreader;
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@@ -90,7 +87,6 @@ ov::Tensor create_tensor_from_image(const std::vector<std::string>& files,
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}
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}
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auto tensor = ov::Tensor(inputInfo.type, inputInfo.dataShape, ov::Allocator(allocator));
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return tensor;
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}
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@@ -103,8 +99,8 @@ ov::Tensor create_tensor_from_numpy(const std::vector<std::string>& files,
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std::string* filenames_used = nullptr) {
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size_t tensor_size =
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std::accumulate(inputInfo.dataShape.begin(), inputInfo.dataShape.end(), 1, std::multiplies<size_t>());
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auto allocator = std::make_shared<SharedTensorAllocator>(tensor_size * sizeof(T));
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auto data = reinterpret_cast<T*>(allocator->get_buffer());
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auto tensor = ov::Tensor(inputInfo.type, inputInfo.dataShape);
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auto data = tensor.data<T>();
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std::vector<std::shared_ptr<unsigned char>> numpy_array_pointers;
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numpy_array_pointers.reserve(batchSize);
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@@ -150,7 +146,7 @@ ov::Tensor create_tensor_from_numpy(const std::vector<std::string>& files,
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}
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}
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return ov::Tensor(inputInfo.type, inputInfo.dataShape, ov::Allocator(allocator));
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return tensor;
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}
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template <typename T>
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@@ -160,8 +156,8 @@ ov::Tensor create_tensor_im_info(const std::pair<size_t, size_t>& image_size,
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const std::string& inputName) {
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size_t tensor_size =
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std::accumulate(inputInfo.dataShape.begin(), inputInfo.dataShape.end(), 1, std::multiplies<size_t>());
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auto allocator = std::make_shared<SharedTensorAllocator>(tensor_size * sizeof(T));
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auto data = reinterpret_cast<T*>(allocator->get_buffer());
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auto tensor = ov::Tensor(inputInfo.type, inputInfo.dataShape);
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char* data = static_cast<char*>(tensor.data());
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size_t infoBatchSize = 1;
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if (!inputInfo.layout.empty() && ov::layout::has_batch(inputInfo.layout)) {
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@@ -176,15 +172,14 @@ ov::Tensor create_tensor_im_info(const std::pair<size_t, size_t>& image_size,
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for (size_t i = 0; i < iminfoSize; i++) {
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size_t index = b * iminfoSize + i;
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if (0 == i)
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data[index] = static_cast<T>(image_size.first);
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data[index] = static_cast<char>(image_size.first);
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else if (1 == i)
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data[index] = static_cast<T>(image_size.second);
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data[index] = static_cast<char>(image_size.second);
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else
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data[index] = 1;
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data[index] = static_cast<char>(1);
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}
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}
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auto tensor = ov::Tensor(inputInfo.type, inputInfo.dataShape, ov::Allocator(allocator));
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return tensor;
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}
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@@ -197,8 +192,8 @@ ov::Tensor create_tensor_from_binary(const std::vector<std::string>& files,
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std::string* filenames_used = nullptr) {
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size_t tensor_size =
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std::accumulate(inputInfo.dataShape.begin(), inputInfo.dataShape.end(), 1, std::multiplies<size_t>());
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auto allocator = std::make_shared<SharedTensorAllocator>(tensor_size * sizeof(T));
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char* data = allocator->get_buffer();
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auto tensor = ov::Tensor(inputInfo.type, inputInfo.dataShape);
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char* data = static_cast<char*>(tensor.data());
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size_t binaryBatchSize = 1;
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if (!inputInfo.layout.empty() && ov::layout::has_batch(inputInfo.layout)) {
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binaryBatchSize = batchSize;
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@@ -245,7 +240,6 @@ ov::Tensor create_tensor_from_binary(const std::vector<std::string>& files,
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}
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}
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auto tensor = ov::Tensor(inputInfo.type, inputInfo.dataShape, ov::Allocator(allocator));
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return tensor;
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}
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@@ -255,8 +249,8 @@ ov::Tensor create_tensor_random(const benchmark_app::InputInfo& inputInfo,
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T rand_max = std::numeric_limits<uint8_t>::max()) {
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size_t tensor_size =
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std::accumulate(inputInfo.dataShape.begin(), inputInfo.dataShape.end(), 1, std::multiplies<size_t>());
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auto allocator = std::make_shared<SharedTensorAllocator>(tensor_size * sizeof(T));
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auto data = reinterpret_cast<T*>(allocator->get_buffer());
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auto tensor = ov::Tensor(inputInfo.type, inputInfo.dataShape);
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auto data = tensor.data<T>();
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std::mt19937 gen(0);
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uniformDistribution<T2> distribution(rand_min, rand_max);
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@@ -264,7 +258,6 @@ ov::Tensor create_tensor_random(const benchmark_app::InputInfo& inputInfo,
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data[i] = static_cast<T>(distribution(gen));
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}
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auto tensor = ov::Tensor(inputInfo.type, inputInfo.dataShape, ov::Allocator(allocator));
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return tensor;
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}
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@@ -1,42 +0,0 @@
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// Copyright (C) 2018-2023 Intel Corporation
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// SPDX-License-Identifier: Apache-2.0
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//
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#pragma once
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#include "openvino/runtime/allocator.hpp"
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class SharedTensorAllocator : public ov::AllocatorImpl {
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public:
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SharedTensorAllocator(size_t sizeBytes) : size(sizeBytes) {
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data = new char[size];
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}
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~SharedTensorAllocator() {
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delete[] data;
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}
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virtual void* allocate(const size_t bytes, const size_t) override {
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return bytes <= this->size ? (void*)data : nullptr;
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}
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void deallocate(void* handle, const size_t bytes, const size_t) override {
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if (handle == data) {
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delete[] data;
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data = nullptr;
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}
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}
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bool is_equal(const AllocatorImpl& other) const override {
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auto other_tensor_allocator = dynamic_cast<const SharedTensorAllocator*>(&other);
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return other_tensor_allocator != nullptr && other_tensor_allocator == this;
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}
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char* get_buffer() {
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return data;
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}
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private:
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char* data;
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size_t size;
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};
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