Instructions to use xThr45hx/TensorRT-LLM-Windows-RTX40 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- TensorRT
How to use xThr45hx/TensorRT-LLM-Windows-RTX40 with TensorRT:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
File size: 15,522 Bytes
00db36f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 | #ifdef _WIN32
// Windows stubs: GDRCopy/NUMA-backed host-accessible device memory not
// available on Windows. All allocations fail / are no-ops; callers must
// rely on normal cudaMalloc/cudaMallocManaged paths instead.
#include "hostAccessibleDeviceAllocator.h"
#include <cstdlib>
#include <cstring>
namespace tensorrt_llm::runtime
{
bool HostAccessibleDeviceAllocator::mAllowManagedFallback = false;
HostAccessibleDeviceAllocator& HostAccessibleDeviceAllocator::getInstance()
{
static HostAccessibleDeviceAllocator instance;
return instance;
}
bool HostAccessibleDeviceAllocator::isSupported()
{
return false;
}
void* HostAccessibleDeviceAllocator::allocate(size_t /*memorySize*/)
{
return nullptr;
}
void HostAccessibleDeviceAllocator::free(void* /*ptr*/) {}
void* HostAccessibleDeviceAllocator::getHostPtr(void* /*devPtr*/)
{
return nullptr;
}
void HostAccessibleDeviceAllocator::memcpyToDevice(void* dst, void const* src, size_t size)
{
std::memcpy(dst, src, size);
}
void HostAccessibleDeviceAllocator::init() {}
void HostAccessibleDeviceAllocator::shutdown() {}
void HostAccessibleDeviceAllocator::IncRefCount() {}
void HostAccessibleDeviceAllocator::DecRefCount() {}
void HostAccessibleDeviceAllocator::recordAllocation(
void* /*devPtr*/, size_t /*memorySize*/, void* /*hostPtr*/, gdrcopy::GdrMemDesc* /*memDesc*/)
{
}
HostAccessibleDeviceAllocator::AllocationInfo
HostAccessibleDeviceAllocator::getAllocationInfoFromHostPtr(void const* /*hostPtr*/)
{
return {0, nullptr, nullptr, nullptr};
}
HostAccessibleDeviceAllocator::AllocationInfo
HostAccessibleDeviceAllocator::getAllocationInfoFromDevPtr(void const* /*devPtr*/)
{
return {0, nullptr, nullptr, nullptr};
}
} // namespace tensorrt_llm::runtime
#else
/*
* 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 <string.h>
#include <cstddef>
#include <cstdlib>
#include <cuda_runtime_api.h>
#ifndef _WIN32
#include <numaif.h>
#endif
#include <sys/mman.h>
#include "gdrwrap.h"
#include "hostAccessibleDeviceAllocator.h"
#include "topologyDetector.h"
#include "tensorrt_llm/common/cudaUtils.h"
#include "tensorrt_llm/common/envUtils.h"
#include "tensorrt_llm/common/logger.h"
namespace tensorrt_llm::runtime
{
class NumaHugePagePoolAllocator
{
public:
NumaHugePagePoolAllocator(NumaHugePagePoolAllocator const&) = delete;
void operator=(NumaHugePagePoolAllocator const&) = delete;
static NumaHugePagePoolAllocator& getInstance();
void* allocate(size_t memorySize);
void free(void* ptr);
private:
static constexpr size_t kHugePageSize = 512LL * 1024 * 1024;
static constexpr size_t kAlignSize = 1024;
static constexpr size_t kReservedVirtualMemorySize = 256LL * 1024 * 1024 * 1024;
NumaHugePagePoolAllocator() = default;
void maybeInit();
void shutdown();
uint8_t* mMmapBasePtr = nullptr; // allocated memory address range, not aligned.
uint8_t* mBasePtr = nullptr; // aligned memory address range.
size_t mAllocatedSize = 0; // aligned to kAlignSize
size_t mMappedSize = 0; // aligned to kHugePageSize
int mDevId = -1;
int mGpuMemNumaId = -1;
std::mutex mMutex{};
bool mIsInited = false;
};
NumaHugePagePoolAllocator& NumaHugePagePoolAllocator::getInstance()
{
static NumaHugePagePoolAllocator instance;
instance.maybeInit();
return instance;
}
static void allocateAlignedHugePage(void* hintAddr, size_t sizeBytes, int numaNodeId)
{
void* alignedAddr
= mmap(hintAddr, sizeBytes, PROT_READ | PROT_WRITE, MAP_PRIVATE | MAP_ANONYMOUS | MAP_FIXED, -1, 0);
if (alignedAddr == MAP_FAILED)
{
TLLM_THROW("mmap aligned failed.");
return;
}
TLLM_CHECK_WITH_INFO(alignedAddr == hintAddr, "alignedAddr=%p, but hintAddr=%p", alignedAddr, hintAddr);
void* addr = alignedAddr;
// NUMA bind
unsigned long nodemask = 1UL << numaNodeId;
long mbind_ret = mbind(addr, sizeBytes, MPOL_BIND, &nodemask, sizeof(nodemask) * 8, 0);
if (mbind_ret != 0)
{
TLLM_THROW("mbind failed.");
munmap(addr, sizeBytes);
return;
}
// Request THP
if (madvise(addr, sizeBytes, MADV_HUGEPAGE) != 0)
{
TLLM_THROW("madvise(MADV_HUGEPAGE) failed.");
}
// Touch memory to actually allocate
memset(addr, 0, sizeBytes);
}
void* NumaHugePagePoolAllocator::allocate(size_t memorySize)
{
std::unique_lock<std::mutex> lock(mMutex);
size_t alignedMemorySize = tensorrt_llm::common::divUp(memorySize, kAlignSize) * kAlignSize;
size_t totalAllocatedSize = mAllocatedSize + alignedMemorySize;
if (totalAllocatedSize > mMappedSize)
{
// we need to map new pages.
size_t newMapSize
= tensorrt_llm::common::divUp(totalAllocatedSize - mMappedSize, kHugePageSize) * kHugePageSize;
if (mMappedSize != 0)
{
TLLM_CUDA_CHECK(cudaHostUnregister(mBasePtr));
}
allocateAlignedHugePage(mBasePtr + mMappedSize, newMapSize, mGpuMemNumaId);
mMappedSize += newMapSize;
TLLM_CUDA_CHECK(cudaHostRegister(mBasePtr, mMappedSize, cudaHostRegisterDefault));
}
uint8_t* ptr = mBasePtr + mAllocatedSize;
mAllocatedSize += alignedMemorySize;
return ptr;
}
void NumaHugePagePoolAllocator::free(void* ptr)
{
// TODO: we don't actually free up memory since reuse is not implemented, and our use case is for weights, which are
// not released until exit.
(void) ptr;
}
void NumaHugePagePoolAllocator::maybeInit()
{
std::unique_lock<std::mutex> lock(mMutex);
if (mIsInited)
{
return;
}
TLLM_CUDA_CHECK(cudaGetDevice(&mDevId));
mGpuMemNumaId = TopologyDetector::getInstance().getCurrentGpuMemoryNumaId();
TLLM_CHECK_WITH_INFO(mGpuMemNumaId >= 0, "NUMA memory not supported.");
// allocate a range of virtual address
mMmapBasePtr = static_cast<uint8_t*>(
mmap(NULL, kReservedVirtualMemorySize + kHugePageSize, PROT_NONE, MAP_PRIVATE | MAP_ANONYMOUS, -1, 0));
// aligned to huge page boundary
size_t offset = reinterpret_cast<uint64_t>(mMmapBasePtr) % kHugePageSize;
mBasePtr = mMmapBasePtr;
if (offset > 0)
{
mBasePtr += kHugePageSize - offset;
}
mIsInited = true;
}
void NumaHugePagePoolAllocator::shutdown()
{
munmap(mMmapBasePtr, kReservedVirtualMemorySize + kHugePageSize);
}
bool HostAccessibleDeviceAllocator::mAllowManagedFallback = false;
bool HostAccessibleDeviceAllocator::isSupported()
{
if (!tensorrt_llm::common::getEnvEplbForceGdrcopy()
&& TopologyDetector::getInstance().getCurrentGpuMemoryNumaId() >= 0)
{
// we are on systems that GPU memory is also a NUMA node.
return true;
}
if (!tensorrt_llm::runtime::gdrcopy::isInitialized() && !tensorrt_llm::runtime::gdrcopy::initialize())
{
// system don't support GDRCopy.
return mAllowManagedFallback;
}
return true;
}
void HostAccessibleDeviceAllocator::init()
{
TLLM_CHECK(mIsInited == false);
if (getenv("TLLM_HOST_ACCESSIBLE_ALLOW_MANAGED_FALLBACK") != nullptr)
{
if (std::string(getenv("TLLM_HOST_ACCESSIBLE_ALLOW_MANAGED_FALLBACK")) == "1")
{
mAllowManagedFallback = true;
}
}
TLLM_CUDA_CHECK(cudaGetDevice(&mDevId));
if (tensorrt_llm::common::getEnvEplbForceGdrcopy())
{
mGpuMemNumaId = -1;
TLLM_LOG_INFO("Force using GDRCopy for EPLB, ignore NUMA node for GPU memory.");
}
else
{
mGpuMemNumaId = TopologyDetector::getInstance().getCurrentGpuMemoryNumaId();
}
if (mGpuMemNumaId < 0)
{
// We only use GDRCopy when there is no NUMA node for GPU memory.
bool gdrCopyInitedSuccess = true;
if (!tensorrt_llm::runtime::gdrcopy::isInitialized() && !tensorrt_llm::runtime::gdrcopy::initialize())
{
gdrCopyInitedSuccess = false;
}
if (gdrCopyInitedSuccess)
{
mGdrHandle = tensorrt_llm::runtime::gdrcopy::open();
}
}
mIsInited = true;
}
void HostAccessibleDeviceAllocator::shutdown()
{
if (mIsInited == false)
{
return;
}
// We should close GDRCopy handle in the last MoeLoadBalancer,
// But there might be some allocated memory not freed, so we can't close GDRCopy handle.
// So for now, we don't close GDRCopy handle.
#if 0
if (mGdrHandle != nullptr) {
tensorrt_llm::runtime::gdrcopy::close(mGdrHandle);
mGdrHandle = nullptr;
}
#endif
mIsInited = false;
}
HostAccessibleDeviceAllocator& HostAccessibleDeviceAllocator::getInstance()
{
static HostAccessibleDeviceAllocator instance;
return instance;
}
void HostAccessibleDeviceAllocator::IncRefCount()
{
std::lock_guard<std::mutex> lock(mRefMutex);
if (mLoadBalancerCount == 0)
{
init();
}
mLoadBalancerCount++;
}
void HostAccessibleDeviceAllocator::DecRefCount()
{
std::lock_guard<std::mutex> lock(mRefMutex);
mLoadBalancerCount--;
if (mLoadBalancerCount == 0)
{
shutdown();
}
}
void HostAccessibleDeviceAllocator::recordAllocation(
void* devPtr, size_t memorySize, void* hostPtr, gdrcopy::GdrMemDesc* memDesc)
{
std::unique_lock<std::shared_mutex> lock(mAllocationsMutex);
mDeviceAllocations[devPtr] = {memorySize, hostPtr, devPtr, memDesc};
mHostAllocations[hostPtr] = {memorySize, hostPtr, devPtr, memDesc};
}
HostAccessibleDeviceAllocator::AllocationInfo HostAccessibleDeviceAllocator::getAllocationInfoFromHostPtr(
void const* hostPtr)
{
std::shared_lock<std::shared_mutex> lock(mAllocationsMutex);
if (mHostAllocations.empty())
{
return HostAccessibleDeviceAllocator::AllocationInfo{0, nullptr, nullptr, nullptr};
}
auto it = mHostAllocations.upper_bound(hostPtr);
if (it == mHostAllocations.begin())
{
return HostAccessibleDeviceAllocator::AllocationInfo{0, nullptr, nullptr, nullptr};
;
}
--it;
return it->second;
}
HostAccessibleDeviceAllocator::AllocationInfo HostAccessibleDeviceAllocator::getAllocationInfoFromDevPtr(
void const* devPtr)
{
std::shared_lock<std::shared_mutex> lock(mAllocationsMutex);
if (mDeviceAllocations.empty())
{
return HostAccessibleDeviceAllocator::AllocationInfo{0, nullptr, nullptr, nullptr};
}
auto it = mDeviceAllocations.upper_bound(devPtr);
if (it == mDeviceAllocations.begin())
{
return HostAccessibleDeviceAllocator::AllocationInfo{0, nullptr, nullptr, nullptr};
;
}
--it;
return it->second;
}
void* HostAccessibleDeviceAllocator::getHostPtr(void* devPtr)
{
auto allocationInfo = getAllocationInfoFromDevPtr(devPtr);
if (allocationInfo.devPtr == nullptr)
{
return nullptr;
}
void* recordedDevPtr = allocationInfo.devPtr;
size_t recordedSize = allocationInfo.size;
void* recordedHostPtr = allocationInfo.hostPtr;
auto pDev = static_cast<char*>(devPtr);
auto pRecordedDev = static_cast<char*>(recordedDevPtr);
if (pDev >= pRecordedDev && pDev < (pRecordedDev + recordedSize))
{
ptrdiff_t offset = pDev - pRecordedDev;
return static_cast<char*>(recordedHostPtr) + offset;
}
return nullptr;
}
void HostAccessibleDeviceAllocator::memcpyToDevice(void* dst, void const* src, size_t size)
{
if (mGdrHandle != nullptr)
{
auto allocationInfo = getAllocationInfoFromHostPtr(dst);
TLLM_CHECK(allocationInfo.hostPtr != nullptr);
TLLM_CHECK(allocationInfo.memDesc != nullptr);
tensorrt_llm::runtime::gdrcopy::copy_to_mapping(allocationInfo.memDesc->gdrMh, dst, src, size);
}
else
{
memcpy(dst, src, size);
}
}
void* HostAccessibleDeviceAllocator::allocate(size_t memorySize)
{
int currentDevId = -1;
TLLM_CUDA_CHECK(cudaGetDevice(¤tDevId));
TLLM_CHECK_WITH_INFO(currentDevId == mDevId,
"HostAccessibleDeviceAllocator is not initialized for the current device, currentDevId=%d, mDevId=%d",
currentDevId, mDevId);
TLLM_CHECK_WITH_INFO(isSupported(), "HostAccessibleDeviceAllocator is not supported on the current system.");
void* devPtr = nullptr;
void* hostPtr = nullptr;
gdrcopy::GdrMemDesc* memDesc = nullptr;
if (mGpuMemNumaId >= 0)
{
// devPtr = TopologyDetector::getInstance().allocateCurrentGpuNumaMemory(memorySize);
devPtr = NumaHugePagePoolAllocator::getInstance().allocate(memorySize);
hostPtr = devPtr;
}
else if (mGdrHandle)
{
gdrcopy::gdrCudaMalloc(&hostPtr, &devPtr, memorySize, &memDesc, mGdrHandle);
}
else
{
TLLM_CHECK_WITH_INFO(
mAllowManagedFallback, "HostAccessibleDeviceAllocator is not supported on the current system.");
TLLM_CUDA_CHECK(cudaMallocManaged(&devPtr, memorySize));
TLLM_CUDA_CHECK(cudaMemAdvise(
devPtr, memorySize, cudaMemAdviseSetPreferredLocation, {cudaMemLocationTypeDevice, currentDevId}));
hostPtr = devPtr;
}
recordAllocation(devPtr, memorySize, hostPtr, memDesc);
return devPtr;
}
void HostAccessibleDeviceAllocator::free(void* ptr)
{
std::unique_lock<std::shared_mutex> lock(mAllocationsMutex);
auto it = mDeviceAllocations.find(ptr);
if (it != mDeviceAllocations.end())
{
auto const& allocInfo = it->second;
if (allocInfo.memDesc)
{
gdrcopy::gdrCudaFree(allocInfo.memDesc, mGdrHandle);
}
else if (mGpuMemNumaId >= 0)
{
// TopologyDetector::getInstance().freeCurrentGpuNumaMemory(const_cast<void*>(it->first), allocInfo.size);
NumaHugePagePoolAllocator::getInstance().free(const_cast<void*>(it->first));
}
else
{
TLLM_CHECK_WITH_INFO(
mAllowManagedFallback, "HostAccessibleDeviceAllocator is not supported on the current system.");
TLLM_CUDA_CHECK(cudaFree(ptr));
}
void* hostPtr = it->second.hostPtr;
TLLM_CHECK_WITH_INFO(mHostAllocations.count(hostPtr) == 1, "host pointer not recorded.");
mDeviceAllocations.erase(it);
mHostAllocations.erase(hostPtr);
}
else
{
TLLM_LOG_WARNING("Attempted to free a pointer that was not allocated by HostAccessibleDeviceAllocator.");
}
}
} // namespace tensorrt_llm::runtime
#endif // _WIN32
|