286 lines
12 KiB
C++
286 lines
12 KiB
C++
/*
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* Copyright (c) 2018-2019 Arm Limited.
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*
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* SPDX-License-Identifier: MIT
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*
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* Permission is hereby granted, free of charge, to any person obtaining a copy
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* of this software and associated documentation files (the "Software"), to
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* deal in the Software without restriction, including without limitation the
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* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
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* sell copies of the Software, and to permit persons to whom the Software is
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* furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in all
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* copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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* SOFTWARE.
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*/
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#include "arm_compute/runtime/CL/CLTensorAllocator.h"
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#include "arm_compute/core/utils/misc/MMappedFile.h"
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#include "arm_compute/runtime/CL/CLScheduler.h"
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#include "arm_compute/runtime/CL/functions/CLActivationLayer.h"
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#include "arm_compute/runtime/MemoryGroup.h"
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#include "tests/CL/CLAccessor.h"
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#include "tests/Globals.h"
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#include "tests/framework/Asserts.h"
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#include "tests/framework/Macros.h"
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#include "tests/validation/Validation.h"
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#include "tests/validation/reference/ActivationLayer.h"
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#include <memory>
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#include <random>
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namespace arm_compute
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{
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namespace test
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{
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namespace validation
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{
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namespace
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{
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cl_mem import_malloc_memory_helper(void *ptr, size_t size)
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{
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const cl_import_properties_arm import_properties[] =
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{
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CL_IMPORT_TYPE_ARM,
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CL_IMPORT_TYPE_HOST_ARM,
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0
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};
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cl_int err = CL_SUCCESS;
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cl_mem buf = clImportMemoryARM(CLKernelLibrary::get().context().get(), CL_MEM_READ_WRITE, import_properties, ptr, size, &err);
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ARM_COMPUTE_ASSERT(err == CL_SUCCESS);
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return buf;
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}
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} // namespace
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TEST_SUITE(CL)
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TEST_SUITE(UNIT)
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TEST_SUITE(TensorAllocator)
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/** Validates import memory interface when importing cl buffer objects */
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TEST_CASE(ImportMemoryBuffer, framework::DatasetMode::ALL)
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{
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// Init tensor info
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const TensorInfo info(TensorShape(24U, 16U, 3U), 1, DataType::F32);
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// Allocate memory buffer
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const size_t total_size = info.total_size();
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auto buf = cl::Buffer(CLScheduler::get().context(), CL_MEM_READ_WRITE, total_size);
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// Negative case : Import nullptr
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CLTensor t1;
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t1.allocator()->init(info);
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ARM_COMPUTE_EXPECT(!bool(t1.allocator()->import_memory(cl::Buffer())), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(t1.info()->is_resizable(), framework::LogLevel::ERRORS);
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// Negative case : Import memory to a tensor that is memory managed
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CLTensor t2;
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MemoryGroup mg;
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t2.allocator()->set_associated_memory_group(&mg);
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ARM_COMPUTE_EXPECT(!bool(t2.allocator()->import_memory(buf)), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(t2.info()->is_resizable(), framework::LogLevel::ERRORS);
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// Negative case : Invalid buffer size
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CLTensor t3;
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const TensorInfo info_neg(TensorShape(32U, 16U, 3U), 1, DataType::F32);
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t3.allocator()->init(info_neg);
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ARM_COMPUTE_EXPECT(!bool(t3.allocator()->import_memory(buf)), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(t3.info()->is_resizable(), framework::LogLevel::ERRORS);
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// Positive case : Set raw pointer
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CLTensor t4;
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t4.allocator()->init(info);
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ARM_COMPUTE_EXPECT(bool(t4.allocator()->import_memory(buf)), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(!t4.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(t4.cl_buffer().get() == buf.get(), framework::LogLevel::ERRORS);
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t4.allocator()->free();
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ARM_COMPUTE_EXPECT(t4.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(t4.cl_buffer().get() != buf.get(), framework::LogLevel::ERRORS);
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}
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/** Validates import memory interface when importing malloced memory */
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TEST_CASE(ImportMemoryMalloc, framework::DatasetMode::ALL)
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{
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// Check if import extension is supported
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if(!device_supports_extension(CLKernelLibrary::get().get_device(), "cl_arm_import_memory_host"))
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{
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return;
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}
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else
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{
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const ActivationLayerInfo act_info(ActivationLayerInfo::ActivationFunction::RELU);
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const TensorShape shape = TensorShape(24U, 16U, 3U);
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const DataType data_type = DataType::F32;
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// Create tensor
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const TensorInfo info(shape, 1, data_type);
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CLTensor tensor;
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tensor.allocator()->init(info);
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// Create and configure activation function
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CLActivationLayer act_func;
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act_func.configure(&tensor, nullptr, act_info);
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// Allocate and import tensor
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const size_t total_size_in_elems = tensor.info()->tensor_shape().total_size();
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const size_t total_size_in_bytes = tensor.info()->total_size();
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const size_t alignment = CLKernelLibrary::get().get_device().getInfo<CL_DEVICE_GLOBAL_MEM_CACHELINE_SIZE>();
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size_t space = total_size_in_bytes + alignment;
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auto raw_data = support::cpp14::make_unique<uint8_t[]>(space);
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void *aligned_ptr = raw_data.get();
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support::cpp11::align(alignment, total_size_in_bytes, aligned_ptr, space);
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cl::Buffer wrapped_buffer(import_malloc_memory_helper(aligned_ptr, total_size_in_bytes));
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ARM_COMPUTE_EXPECT(bool(tensor.allocator()->import_memory(wrapped_buffer)), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(!tensor.info()->is_resizable(), framework::LogLevel::ERRORS);
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// Fill tensor
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std::uniform_real_distribution<float> distribution(-5.f, 5.f);
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std::mt19937 gen(library->seed());
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auto *typed_ptr = reinterpret_cast<float *>(aligned_ptr);
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for(unsigned int i = 0; i < total_size_in_elems; ++i)
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{
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typed_ptr[i] = distribution(gen);
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}
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// Execute function and sync
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act_func.run();
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CLScheduler::get().sync();
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// Validate result by checking that the input has no negative values
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for(unsigned int i = 0; i < total_size_in_elems; ++i)
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{
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ARM_COMPUTE_EXPECT(typed_ptr[i] >= 0, framework::LogLevel::ERRORS);
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}
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// Release resources
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tensor.allocator()->free();
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ARM_COMPUTE_EXPECT(tensor.info()->is_resizable(), framework::LogLevel::ERRORS);
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}
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}
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#if !defined(BARE_METAL)
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/** Validates import memory interface when importing memory mapped objects */
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TEST_CASE(ImportMemoryMappedFile, framework::DatasetMode::ALL)
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{
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// Check if import extension is supported
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if(!device_supports_extension(CLKernelLibrary::get().get_device(), "cl_arm_import_memory_host"))
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{
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return;
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}
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else
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{
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const ActivationLayerInfo act_info(ActivationLayerInfo::ActivationFunction::RELU);
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const TensorShape shape = TensorShape(24U, 16U, 3U);
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const DataType data_type = DataType::F32;
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// Create tensor
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const TensorInfo info(shape, 1, data_type);
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CLTensor tensor;
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tensor.allocator()->init(info);
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// Create and configure activation function
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CLActivationLayer act_func;
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act_func.configure(&tensor, nullptr, act_info);
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// Get number of elements
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const size_t total_size_in_elems = tensor.info()->tensor_shape().total_size();
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const size_t total_size_in_bytes = tensor.info()->total_size();
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// Create file
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std::ofstream output_file("test_mmap_import.bin", std::ios::binary | std::ios::out);
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output_file.seekp(total_size_in_bytes - 1);
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output_file.write("", 1);
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output_file.close();
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// Map file
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utils::mmap_io::MMappedFile mmapped_file("test_mmap_import.bin", 0 /** Whole file */, 0);
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ARM_COMPUTE_EXPECT(mmapped_file.is_mapped(), framework::LogLevel::ERRORS);
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unsigned char *data = mmapped_file.data();
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cl::Buffer wrapped_buffer(import_malloc_memory_helper(data, total_size_in_bytes));
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ARM_COMPUTE_EXPECT(bool(tensor.allocator()->import_memory(wrapped_buffer)), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(!tensor.info()->is_resizable(), framework::LogLevel::ERRORS);
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// Fill tensor
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std::uniform_real_distribution<float> distribution(-5.f, 5.f);
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std::mt19937 gen(library->seed());
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auto *typed_ptr = reinterpret_cast<float *>(data);
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for(unsigned int i = 0; i < total_size_in_elems; ++i)
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{
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typed_ptr[i] = distribution(gen);
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}
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// Execute function and sync
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act_func.run();
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CLScheduler::get().sync();
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// Validate result by checking that the input has no negative values
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for(unsigned int i = 0; i < total_size_in_elems; ++i)
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{
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ARM_COMPUTE_EXPECT(typed_ptr[i] >= 0, framework::LogLevel::ERRORS);
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}
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// Release resources
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tensor.allocator()->free();
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ARM_COMPUTE_EXPECT(tensor.info()->is_resizable(), framework::LogLevel::ERRORS);
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}
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}
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#endif // !defined(BARE_METAL)
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/** Validates symmetric per channel quantization */
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TEST_CASE(Symm8PerChannelQuantizationInfo, framework::DatasetMode::ALL)
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{
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// Create tensor
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CLTensor tensor;
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const std::vector<float> scale = { 0.25f, 1.4f, 3.2f, 2.3f, 4.7f };
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const TensorInfo info(TensorShape(32U, 16U), 1, DataType::QSYMM8_PER_CHANNEL, QuantizationInfo(scale));
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tensor.allocator()->init(info);
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// Check quantization information
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ARM_COMPUTE_EXPECT(!tensor.info()->quantization_info().empty(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(!tensor.info()->quantization_info().scale().empty(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(tensor.info()->quantization_info().scale().size() == scale.size(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(tensor.info()->quantization_info().offset().empty(), framework::LogLevel::ERRORS);
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CLQuantization quantization = tensor.quantization();
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ARM_COMPUTE_ASSERT(quantization.scale != nullptr);
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ARM_COMPUTE_ASSERT(quantization.offset != nullptr);
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// Check OpenCL quantization arrays before allocating
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ARM_COMPUTE_EXPECT(quantization.scale->max_num_values() == 0, framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(quantization.offset->max_num_values() == 0, framework::LogLevel::ERRORS);
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// Check OpenCL quantization arrays after allocating
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tensor.allocator()->allocate();
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ARM_COMPUTE_EXPECT(quantization.scale->max_num_values() == scale.size(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(quantization.offset->max_num_values() == 0, framework::LogLevel::ERRORS);
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// Validate that the scale values are the same
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auto cl_scale_buffer = quantization.scale->cl_buffer();
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void *mapped_ptr = CLScheduler::get().queue().enqueueMapBuffer(cl_scale_buffer, CL_TRUE, CL_MAP_READ, 0, scale.size());
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auto cl_scale_ptr = static_cast<float *>(mapped_ptr);
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for(unsigned int i = 0; i < scale.size(); ++i)
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{
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ARM_COMPUTE_EXPECT(cl_scale_ptr[i] == scale[i], framework::LogLevel::ERRORS);
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}
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CLScheduler::get().queue().enqueueUnmapMemObject(cl_scale_buffer, mapped_ptr);
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}
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TEST_SUITE_END() // TensorAllocator
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TEST_SUITE_END() // UNIT
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TEST_SUITE_END() // CL
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} // namespace validation
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} // namespace test
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} // namespace arm_compute
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