mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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16d2467ea8
* Update TensorRT-LLM --------- Co-authored-by: Denis Kayshev <topenkoff@gmail.com> Co-authored-by: akhoroshev <arthoroshev@gmail.com> Co-authored-by: Patrick Reiter Horn <patrick.horn@gmail.com> Update
124 lines
4.2 KiB
C++
124 lines
4.2 KiB
C++
/*
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* Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#ifndef TOP_LEVEL_DIR
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#error "Define TOP_LEVEL_DIR"
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#endif
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#include "tensorrt_llm/runtime/bufferManager.h"
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#include "tensorrt_llm/runtime/iBuffer.h"
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#include "tensorrt_llm/runtime/iTensor.h"
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#include "tensorrt_llm/runtime/utils/numpyUtils.h"
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#include <gmock/gmock.h>
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#include <gtest/gtest.h>
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#include <cstddef>
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#include <filesystem>
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#include <numeric>
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#include <string>
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using namespace tensorrt_llm::runtime;
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namespace tc = tensorrt_llm::common;
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namespace fs = std::filesystem;
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class UtilsTest : public ::testing::Test // NOLINT(cppcoreguidelines-pro-type-member-init)
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{
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protected:
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void SetUp() override
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{
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mDeviceCount = tc::getDeviceCount();
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if (mDeviceCount == 0)
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GTEST_SKIP();
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mStream = std::make_unique<CudaStream>();
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mManager = std::make_unique<BufferManager>(mStream);
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}
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void TearDown() override {}
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int mDeviceCount;
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std::unique_ptr<BufferManager> mManager;
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BufferManager::CudaStreamPtr mStream;
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};
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TEST_F(UtilsTest, LoadNpy)
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{
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auto const testResourcePath = fs::path{TOP_LEVEL_DIR} / "cpp/tests/resources";
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auto const inputFile = testResourcePath / "data/input_tokens.npy";
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auto loadedTensor = utils::loadNpy(*mManager, inputFile.string(), MemoryType::kCPU);
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ASSERT_EQ(loadedTensor->getSize(), 96);
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EXPECT_EQ(loadedTensor->getShape().nbDims, 2);
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EXPECT_EQ(loadedTensor->getShape().d[0], 8);
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EXPECT_EQ(loadedTensor->getShape().d[1], 12);
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}
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TEST_F(UtilsTest, LoadStoreNpy)
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{
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auto dims = ITensor::makeShape({2, 3, 4});
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auto constexpr dataType = nvinfer1::DataType::kFLOAT;
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ITensor::SharedPtr tensor{BufferManager::cpu(dims, dataType)};
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auto tensorRange = BufferRange<float>(*tensor);
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std::iota(tensorRange.begin(), tensorRange.end(), 0);
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std::string filename{"tensor.npy"};
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utils::saveNpy(*mManager, *tensor, filename);
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auto loadedTensor = utils::loadNpy(*mManager, filename, MemoryType::kCPU);
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ASSERT_EQ(loadedTensor->getSize(), tensor->getSize());
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EXPECT_EQ(loadedTensor->getShape().nbDims, tensor->getShape().nbDims);
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EXPECT_EQ(loadedTensor->getShape().d[0], tensor->getShape().d[0]);
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EXPECT_EQ(loadedTensor->getShape().d[1], tensor->getShape().d[1]);
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EXPECT_EQ(loadedTensor->getShape().d[2], tensor->getShape().d[2]);
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auto loadedTensorRange = BufferRange<float>(*loadedTensor);
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for (size_t i = 0; i < tensor->getSize(); ++i)
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{
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EXPECT_EQ(loadedTensorRange[i], tensorRange[i]);
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}
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}
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TEST_F(UtilsTest, LoadStoreNpyGPU)
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{
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auto dims = ITensor::makeShape({2, 3, 4});
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auto constexpr dataType = nvinfer1::DataType::kFLOAT;
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ITensor::SharedPtr tensor{BufferManager::cpu(dims, dataType)};
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auto tensorRange = BufferRange<float>(*tensor);
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std::iota(tensorRange.begin(), tensorRange.end(), 0);
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auto deviceTensor = mManager->copyFrom(*tensor, MemoryType::kGPU);
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std::string filename{"tensor.npy"};
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utils::saveNpy(*mManager, *deviceTensor, filename);
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auto loadedTensor = utils::loadNpy(*mManager, filename, MemoryType::kGPU);
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ASSERT_EQ(loadedTensor->getSize(), tensor->getSize());
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EXPECT_EQ(loadedTensor->getShape().nbDims, tensor->getShape().nbDims);
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EXPECT_EQ(loadedTensor->getShape().d[0], tensor->getShape().d[0]);
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EXPECT_EQ(loadedTensor->getShape().d[1], tensor->getShape().d[1]);
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EXPECT_EQ(loadedTensor->getShape().d[2], tensor->getShape().d[2]);
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auto hostTensor = mManager->copyFrom(*loadedTensor, MemoryType::kCPU);
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auto loadedTensorRange = BufferRange<float>(*hostTensor);
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for (size_t i = 0; i < tensor->getSize(); ++i)
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{
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EXPECT_EQ(loadedTensorRange[i], tensorRange[i]);
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}
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}
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