TensorRT-LLMs/cpp/tensorrt_llm/runtime/iBuffer.cpp
xiweny c076a02b38
[TRTLLM-4629] [feat] Add support of CUDA13 and sm103 devices (#7568)
Signed-off-by: Xiwen Yu <13230610+VALLIS-NERIA@users.noreply.github.com>
Signed-off-by: Tian Zheng <29906817+Tom-Zheng@users.noreply.github.com>
Signed-off-by: Daniel Stokes <dastokes@nvidia.com>
Signed-off-by: Zhanrui Sun <zhanruis@nvidia.com>
Signed-off-by: Xiwen Yu <xiweny@nvidia.com>
Signed-off-by: Jiagan Cheng <jiaganc@nvidia.com>
Signed-off-by: Yiqing Yan <yiqingy@nvidia.com>
Signed-off-by: Bo Deng <deemod@nvidia.com>
Signed-off-by: ZhanruiSunCh <184402041+ZhanruiSunCh@users.noreply.github.com>
Signed-off-by: xiweny <13230610+VALLIS-NERIA@users.noreply.github.com>
Co-authored-by: Tian Zheng <29906817+Tom-Zheng@users.noreply.github.com>
Co-authored-by: Daniel Stokes <dastokes@nvidia.com>
Co-authored-by: Zhanrui Sun <zhanruis@nvidia.com>
Co-authored-by: Jiagan Cheng <jiaganc@nvidia.com>
Co-authored-by: Yiqing Yan <yiqingy@nvidia.com>
Co-authored-by: Bo Deng <deemod@nvidia.com>
Co-authored-by: Zhanrui Sun <184402041+ZhanruiSunCh@users.noreply.github.com>
2025-09-16 09:56:18 +08:00

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/*
* Copyright (c) 2022-2024, NVIDIA CORPORATION. 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.
*/
#include "tensorrt_llm/runtime/iBuffer.h"
#include "tensorrt_llm/runtime/iTensor.h"
#include "tensorrt_llm/runtime/tllmBuffers.h"
#include "tensorrt_llm/common/assert.h"
#include "tensorrt_llm/common/cudaUtils.h"
#include "tensorrt_llm/runtime/bufferView.h"
#include <cuda_runtime_api.h>
#include <memory>
using namespace tensorrt_llm::runtime;
MemoryType IBuffer::memoryType(void const* data)
{
cudaPointerAttributes attributes{};
TLLM_CUDA_CHECK(::cudaPointerGetAttributes(&attributes, data));
switch (attributes.type)
{
case cudaMemoryTypeHost: return MemoryType::kPINNEDPOOL;
case cudaMemoryTypeDevice: return MemoryType::kGPU;
case cudaMemoryTypeManaged: return MemoryType::kUVM;
case cudaMemoryTypeUnregistered: return MemoryType::kCPU;
}
TLLM_THROW("Unsupported memory type");
}
IBuffer::UniquePtr IBuffer::slice(IBuffer::SharedPtr buffer, std::size_t offset, std::size_t size)
{
return std::make_unique<BufferView>(std::move(buffer), offset, size);
}
IBuffer::UniquePtr IBuffer::wrap(void* data, nvinfer1::DataType type, std::size_t size, std::size_t capacity)
{
TLLM_CHECK_WITH_INFO(size <= capacity, "Requested size is larger than capacity");
auto memoryType = IBuffer::memoryType(data);
IBuffer::UniquePtr result;
auto const capacityInBytes = capacity * BufferDataType(type).getSize();
switch (memoryType)
{
case MemoryType::kPINNED:
result.reset(new GenericBuffer<PinnedBorrowingAllocator>( // NOLINT(modernize-make-unique)
capacity, type, PinnedBorrowingAllocator(data, capacityInBytes)));
break;
case MemoryType::kPINNEDPOOL:
result.reset(new GenericBuffer<PinnedPoolBorrowingAllocator>( // NOLINT(modernize-make-unique)
capacity, type, PinnedPoolBorrowingAllocator(data, capacityInBytes)));
break;
case MemoryType::kCPU:
result.reset( // NOLINT(modernize-make-unique)
new GenericBuffer<CpuBorrowingAllocator>(capacity, type, CpuBorrowingAllocator(data, capacityInBytes)));
break;
case MemoryType::kGPU:
result.reset( // NOLINT(modernize-make-unique)
new GenericBuffer<GpuBorrowingAllocator>(capacity, type, GpuBorrowingAllocator(data, capacityInBytes)));
break;
default: TLLM_THROW("Unknown memory type");
}
result->resize(size);
return result;
}
std::ostream& tensorrt_llm::runtime::operator<<(std::ostream& output, IBuffer const& buffer)
{
auto data = const_cast<IBuffer&>(buffer).data();
auto tensor = ITensor::wrap(data, buffer.getDataType(),
ITensor::makeShape({static_cast<SizeType32>(buffer.getSize())}), buffer.getCapacity());
return output << *tensor;
}
char const* IBuffer::getDataTypeName(DataType dataType)
{
switch (dataType)
{
case nvinfer1::DataType::kINT64: return DataTypeTraits<nvinfer1::DataType::kINT64>::name;
case nvinfer1::DataType::kINT32: return DataTypeTraits<nvinfer1::DataType::kINT32>::name;
case nvinfer1::DataType::kFLOAT: return DataTypeTraits<nvinfer1::DataType::kFLOAT>::name;
case nvinfer1::DataType::kBF16: return DataTypeTraits<nvinfer1::DataType::kBF16>::name;
case nvinfer1::DataType::kHALF: return DataTypeTraits<nvinfer1::DataType::kHALF>::name;
case nvinfer1::DataType::kBOOL: return DataTypeTraits<nvinfer1::DataType::kBOOL>::name;
case nvinfer1::DataType::kUINT8: return DataTypeTraits<nvinfer1::DataType::kUINT8>::name;
case nvinfer1::DataType::kINT8: return DataTypeTraits<nvinfer1::DataType::kINT8>::name;
case nvinfer1::DataType::kFP8: return DataTypeTraits<nvinfer1::DataType::kFP8>::name;
case nvinfer1::DataType::kINT4: [[fallthrough]] /* do nothing */;
case nvinfer1::DataType::kFP4: [[fallthrough]] /* do nothing */;
default: TLLM_THROW("Unknown data type");
}
}
char const* IBuffer::getDataTypeName() const
{
return getDataTypeName(getDataType());
}
char const* IBuffer::getMemoryTypeName() const
{
switch (getMemoryType())
{
case MemoryType::kPINNED: return MemoryTypeString<MemoryType::kPINNED>::value;
case MemoryType::kPINNEDPOOL: return MemoryTypeString<MemoryType::kPINNEDPOOL>::value;
case MemoryType::kCPU: return MemoryTypeString<MemoryType::kCPU>::value;
case MemoryType::kGPU: return MemoryTypeString<MemoryType::kGPU>::value;
case MemoryType::kUVM: return MemoryTypeString<MemoryType::kUVM>::value;
}
TLLM_THROW("Unknown memory type");
}