Vllm cuda out of memory. OutOfMemoryError: CUDA out of memory.

Vllm cuda out of memory Hardware I'm encountering an issue when using the VLLM library in Python. 00 MiB (GPU 0; 39. This can happen due to various reasons, including large model sizes, insufficient hardware resources, or inefficient memory management during inference. Efficient . 59 GiB. Tried to allocate 826. device) torch. Tried to allocate 140. vLLM seamlessly supports many Hugging Face models, Well when you get CUDA OOM I'm afraid you can only restart the notebook/re-run your script. 81 MiB is free. 0 --model mistralai/Mixtral-8x7B torch. 75 GiB total capacity; 13. 25 GiB memory in use. 35 GiB of which 804. collect() torch. 5，vllm-flash-attn 2. When finish inference on the first model, how to release this model and release GPU memory to load another one, since directly reloading may cause CUDA OUT OF MEMORY for it doesn't vllm-project > vllm [Misc]: OOM (CUDA Out Of Memory) when running LLMs in WSL using vLLM about vllm HOT 7 CLOSED BooleanMind commented on October 3, 2024 [Misc]: OOM (CUDA Out Of Memory) when running LLMs in WSL using vLLM. Speculative decoding in vLLM. 40 Python version: 3. Of the allocated memory 13. 12. GPU 0 has a total capacity of 11. See documentation for Memory Management and torch. Modified 1 year, 7 months ago. 11 GiB of which 1018. 0 Clang version: 19. 00 MiB (GPU 0; 8. 19 MiB is free. 91 GiB. Your GPU doesn't have enough memory for the size of the inputs you are using. A typical usage for DL applications would be: 1. Compile with TORCH_USE_CUDA_DSA to enable device If you are running out of memory, consider decreasing `gpu_memory_utilization` or enforcing eager mode. 00 MiB is free. Process 353470 has 46. The full exception stack is: How can I fix a CUDA Out of Memory Exception while saving a PyTorch model? Ask Question Asked 1 year, 7 months ago. Memory utilization is not exactly balance between all GPUs as we mix tensor parallelism and pipeline parallelism. 4 ROCM used to build PyTorch: N/A OS: Ubuntu 22. python3 -m vllm. 78 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. 3. 5 torch 2. Process 103776 has 15. 64 GiB total capacity; 22. Your current environment VLLM image: v0. You switched accounts on another tab or window. i'm using hugging face estimators. This virtually increases the GPU memory space you can use to hold the model weights, at the cost of CPU-GPU data transfer for every forward pass. py:980] CUDA graphs can take additional 1~3 GiB memory per GPU. They both have 39. 7 (main, Nov 6 2024, 18:29:01) [GCC 14. Of the allocated memory 20. cc:324] Failed to initialize Python stub: Runtim The kernel launch is failing probably because of the memory out-of-bounds accesses that are being reported because you are running your code with cuda-memcheck. 18 GiB of which 302. No response. ] 相信这个错误大家都不陌生，在使用 GPU 进行单机单卡，单机多卡的训练任务中，经常遇到该报错。 由于 OOM 是很明显的内存不足告警，通常情况下笔者发现错误就直接去 kill 其他任务或者加内存了，很少关心任务中 You can either use the ipc=host flag or --shm-size flag to allow the container to access the host’s shared memory. Using these for the API server will not function as expected. I can't find any more relevant information in the documentation. OutOfMemoryError: CUDA out of memory, when i run this python : from vllm import LLM, SamplingParams @jibowang it seems like you have other processes running on the same GPU as vLLM. Quantization: GPTQ, AWQ, INT4, INT8, and FP8 here is the training part of my code and the criterion_T is a self-defined loss function in this paper Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels and here is the code of the paper code, my criterion_T’s loss is the ‘Truncated-Loss. See documentation for Memory Management and vLLM Build Flags: CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled GPU Topology: [4mGPU0 CPU Affinity NUMA Affinity GPU NUMA ID [0m GPU0 X 0-7 0 N/A. ' Does it mean that I need to find a suitable value for gpu_memory_utilization or is there any other things going wrong? vLLM is a fast and easy-to-use library for LLM inference and serving. import torch. (less than 3 GiBs are left on the device) My system is a Ubuntu 22. Here are some effective strategies to debug these issues: Enable Detailed Logging. 39 GiB memory in use. This requires no more than 26, maybe 27 GiB of memory. model_runner. 096gb when loading import os from vllm import LLM, SamplingParams os. 9, it came with OOM. Tried to allocate 72. Inevitable-Start-653 A high-throughput and memory-efficient inference and serving engine for LLMs - SoheylM/vllm-localllm-cuda12. Of the allocated memory 7. I only wish it weren't written in Python. varlen_fwd RuntimeError: CUDA error: an illegal memory access was encountered Compile with TORCH_USE_CUDA_DSA to enable device-side assertions. when i run individual gpu, it works fine. enforce_eager – Whether to enforce eager 192. The 2xA10G large already provide 48GB VRAM, but out of memory still occurred, how could I fix this? torch. 77 GiB of A high-throughput and memory-efficient inference and serving engine for LLMs - MooreThreads/vllm_musa [2023/06] Serving vLLM On any Cloud with SkyPilot. 7 ( torch. 35 Python version: 3. Building vLLM with aarch64 and CUDA (GH200), For example, when you use WSL it only assigns 50% of the total memory by default, so using export MAX_JOBS=1 can avoid compiling multiple files simultaneously and running out of memory. 72 GiB of which 94. 0-4ubuntu2) 14. GPU 0 has a total capacity of 79. Tried to allocate 304. Note that, you need to instal vllm package under Linux by: pip install vllm. 4 hardware: RTX4090 gpu driver: You signed out in another tab or window. 0-1ubuntu1~22. You can also reduce the `max_num_seqs` as needed to decrease memory usage. Optionally, use the CUDA_VISIBLE_DEVICES environment variable to specify the GPUs. Process 252091 has 21. 7. 25 GiB. 95 GiB already allocated; 132. 94 MiB free; 6. Thanks for the comment! Fortunately, it seems like the issue is not happening after upgrading pytorch version to 1. GPU 0 has a total capacty of 39. 45 GiB total capacity; 38. def process_batch(batch: List[str]) -> List[Dict[str, str]]: llm = init_llm() predictor = LLMPredictor(llm) return predictor I aslo meet torch. 8，vllm 0. Tried to allocate 1002. Kubernetes users should avoid naming their services vllm to prevent conflicts with environment variables set by Kubernetes. You signed out in another tab or window. You can also reduce the max_num_seqs as needed A10 , 测试了meta 官方的llama2-13b-chat 加载正常，但是加载Llama2-Chinese-13b-Chat 出现CUDA out of memory from vllm import LLM, SamplingParams prompts = [ "Hello, my name is", "The president of the United States is", RuntimeError: CUDA out of memory. Continuous batching of incoming requests. This will check if your GPU drivers are installed and the PyTorch version: 2. Also INFO 10-15 06:53:46 model_runner. 0 lm_eval 0. 4 A100 + CUDA 12. Would be grateful for any insights! stackoverflow. Process 2381604 has 78. 98 GiB of which 1. 8, it came with 'No available memory for the cache blocks. but I keep getting the error: RuntimeError: CUDA out of memory. Including non-PyTorch memory, this process has 11. However, if you run with 3 GPU nodes you will still observe a CUDA OOM. py’ in that code the bug occur in the line You signed in with another tab or window. Including non-PyTorch memory, this process has 17179869184. I printed out the results of the torch. 3 to also see vllm pre-allocate that memory before loading the model. 88 MiB is free. Process 3179876 has 1. 25 MiB free; 11. Git Product home page Git Product. 30 GiB memory in use. 15 GiB is allocated by PyTorch, and 1. py:858] CUDA graphs can take additional 1~3 GiB memory per GPU. (more than 200GB), and I found this will cause OOM (not cuda memory) Skip to content. 20 GiB already allocated; 139. 36 GiB reserved in total by PyTorch) If reserved gpu_memory_utilization: float: The ratio (between 0 and 1) of GPU memory to reserve for the model weights, activations, and KV cache. profile_run() may incorrectly compute the free GPU memory, depending on the value of max-num-batched-tokens. 04. enforce_eager – Whether to enforce eager execution. Managing variables properly is crucial in PyTorch to prevent memory issues. Tried to allocate 1. additionally, whenever I run in combination of CUDA_VISIBLE_DEVICES number 1, always get the below message or something similar. 63 “OutOfMemoryError: CUDA out of memory. Process 18600 has 78. 98 GiB of which 338. max_context_len_to_capture – Maximum context You can also reduce the `max_num_seqs` as needed to decrease memory usage. Keyword Definition Example; torch. Supported Hardware for Quantization Kernels. If you are running out of memory, consider decreasing `gpu_memory_utilization` or enforcing eager mode. 20 GiB of which 13. 61 GiB memory in use. Open A100 + CUDA 12. about vllm HOT 5 CLOSED tristandevs commented on October 9, 2024 1 [Bug]: torch. 4 ROCM used to build PyTorch: N/A OS: Ubuntu 24. What is the issue? When I try the llama3 model I get out of memory errors. . 8gb safetensors, and 37. 28 GiB is allocated by PyTorch, and 51. Comments (7) mgoin commented on October 3, 2024 2 . 9 --trust-remote-code --tensor-parallel-size 2 --max-model-len 37776 I believe --gpu-memory-utilization is not "fully" respected. Tried to allocate 96. malloc(10000000) vLLM is a fast and easy-to-use library for LLM inference and serving. Tried to allocate 64. 17 GiB memory in use. Use mixed precision training. py:680] CUDA graphs can take additional 1~3 GiB memory per GPU. 75 GiB of which 72. 31 MiB is INFO 08-21 08:31:11 model_runner. 71 GiB already allocated; 190. Fast model execution with CUDA/HIP graph. GPU Any idea? Hello folks, recently I started benchmarking 7b / 8b LLMs using lm-eval-harness and it's very clear to me that the vllm backend is a lot faster than the hf accelerate backend by virtue of using more memory. py:1221] CUDA graphs can take additional 1~3 GiB memory per GPU. If False, we will use CUDA graph and eager execution in hybrid. 62 GiB memory in use. Batching makes this more pronounced. 50 MiB free; 23. 3 Model Input Dumps No response 🐛 Describe the bug Description: Out of Memory (OOM) Issues During MMLU Evaluation with lm_eval #10325. The steps for checking this are: Use nvidia-smi in the terminal. vllm doesn't [Bug]: torch. 76 MiB already allocated; 6. For debugging consider passing CUDA_LAUNCH_BLOCKING=1. However, I just post one solution here when using VLLM. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid OutOfMemoryError: CUDA out of memory. 36. 38 GiB already allocated; 6. If reserved but unallocated memory is large try setting max_split_size_mb to avoid fragmentation. Check out a 1-click example to start the vLLM demo, Support NVIDIA CUDA and AMD ROCm. To achieve better latency. 10 (x86_64) GCC version: (Ubuntu 14. All environment variables in vLLM are prefixed with VLLM_. vllm also needs to allocate some GPU mem for KVCache (both for the target model and the draft model), so the consumed mem is larger than you thought. Install Ray cluster using kuberay with one head and one worker pod Use the command below to load model with single GPU by setting --tensor-parallel-siz torch. 3. cpu_offload_gb – The size (GiB) of CPU memory to use for offloading the model weights. CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect. If you are running out of memory, consider decreasing I am using vLLM to serve the model. It has run successfully and responds correctly. With larger --num-scheduler-steps we are likely holding on to a lot more CUDA tensors causing an out-of-memory issue. A INFO 06-18 06:28:40 model_runner. CUDA out of memory. Process 13236 has 8. 1 ROCM used to build PyTorch: N/A OS: Ubuntu 22. Tried to allocate 3. Tried to allocate 982. Navigation Menu Toggle navigation. ', then later request can NOT be processed, it means, async engine was dead and need to restart vllm engine for continue service. GPU How would you like to use vllm I'm running a eval framework that's evaluating multiple models. Repositories Users Hot Words ; Hot Users ; 2024 1 [Bug]: torch. 77 GiB is allocated by PyTorch, I am writing to seek your expertise and assistance regarding an issue I encountered while attempting to perform full-finetuning of the LLAMA-3-8B model using a Multi-GPU environment with two A100 8 RuntimeError: CUDA out of memory. Tried to allocate 448. torch. 1 70B GPTQ and get cuda out of memory on A6000 48GB, when LLAMA3 70B GPTQ is working great. Mixed precision training is a technique that uses lower-precision data types for some parts of the computation to reduce memory usage and speed up training. llms import VLLM Out of Memory (OOM) errors in vLLM occur when the system runs out of available memory resources while attempting to allocate memory for model operations. 00 MiB (GPU 0; 23. 11 GiB is free. The problem occurs when I try to instantiate a LLM object inside a [Bug]: torch. If you are using too many data augmentation techniques, you can try reducing the number of transformations or using less memory-intensive techniques. 60 GiB memory in use. 00 GiB total capacity; 142. Process 38354 has 14. _C' PyTorch version: 2. input_q = torch. 31 MiB free; 38. Explore solutions for Vllm CUDA out of memory errors, optimizing performance and resource management effectively. 11 GiB memory in use. 67 GiB memory in use. [ torch. one config of hyperparams (or, in general, operations that Debugging. we can catch the cuda OOM exception (maybe other exception for AMD devices) and abort current and other running request, then later request Including non-PyTorch memory, this process has 23. 7 has CUDA Graphs enabled by default (i. Specifically, when I create a VLLM model object inside a function, I run into memory problems and cannot clear the GPU memory effectively, even after deleting objects and using torch. 36 GiB already allocated; 194. Increase gpu_memory_utilization. Process 258755 has 23. try: torch. 44 MiB is free. Process 13238 has 3. vLLM is fast with: State-of-the-art serving throughput. 7-mixtral-8x7b-GPTQ This version is also uncensored I'm encountering an issue when using the VLLM library in Python. 00 MiB (GPU 1; 11. 1. 06 GiB memory in use. Legend: X = Self torch. memory_summary() method to get a human-readable printout of the memory allocator statistics for a given device. 43 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try I tried to deploy a triton (using version: 23. Be cautious with the VLLM_PORT and VLLM_HOST_IP variables, as they are intended for internal usage only. openai. Tried to allocate 134. ones(self. 2' and ' vllm 0. 00 MiB is reserved by PyTorch but unallocated. Stack Overflow for Teams Where developers & technologists share private knowledge with coworkers; Advertising & Talent Reach devs & technologists worldwide about your product, service or employer brand; OverflowAI GenAI features for Teams; OverflowAPI Train & fine-tune LLMs; Labs The future of collective knowledge sharing; About the company vLLM 0. 50 MiB is free. Otherwise, too small values may cause out-of-memory (OOM) errors. OutOfMemoryError: CUDA out of memory, when i run this python : from vllm My vllm inference program runs well for most models with the environment of 'transformers=4. 36 GiB already allocated; 272. 07) ensemble model with python backend and custom fine tuned Llama2 model and I am getting: I0815 16:40:55. Tried to allocate 462. 12 GiB is allocated by PyTorch, and 80. 662153 1 pb_stub. GPU info in Colab T4 runtime This command worked for me: python3 -m vllm. entrypoints. 00 MiB (GPU 0; 12. The vLLM pre-allocates GPU cache by using gpu_memory_utilization% of memory. py:1395 -- SIGTERM handler is not set because current thread is OutOfMemoryError: CUDA out of memory. """ # Profile the memory usage of the model and get the maximum number of # cache blocks that can be allocated with the remaining free memory. We will use OpenVPN for this setup. If you are running out of memory, consider decreasing gpu_memory_utilization or enforcing eager mode. Comments (5) tristandevs commented on October 9, 2024 . 39 GiB of which 17. py:1406] CUDA graphs can take additional 13 GiB memory per GPU. 32 GiB is free. GPU 0 has a total capacty of 79. Including non-PyTorch memory, this process has 28. Check out a 1-click example to start the vLLM demo, and the blog post for the story behind vLLM development on the clouds. 77 GiB total capacity; 10. If reserved but unallocated memory is large A10 , 测试了meta 官方的llama2-13b-chat 加载正常，但是加载Llama2-Chinese-13b-Chat 出现CUDA out of memory from vllm import LLM, SamplingParams prompts = [ "Hello, my name is", "The president of the United States is", You signed in with another tab or window. Check out a 1-click example to start the vLLM demo, Optimized CUDA kernels; vLLM is flexible and easy to use with: 硬件环境：4090+i9-14900f 操作系统：ubuntu 22. 按照教程运行，也把vllm版本降到0. Your current environment vllm 0. 94 MiB is free. By increasing this utilization, Please try out this feature and let us know your feedback via GitHub issues! previous. num_groups, [rank0]: ^^^^^ [rank0]: torch. This is somehow also not happening so I assume my parameter is not respected when loading a model as CUDA out of memory. Tried to allocate 926. empty_cache() torch. 50 GiB. GPU 0 has a total capacity of 31. next. GPU 0 has a total capacty of 14. # Getting a human-readable printout of the memory allocator statistics. Reduce batch size to 1, reduce generation length to 1 token. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF Well, CUDA is not fully deterministicso there's that. 0a0+git2e4abc8 Is debug build: torch. [2023/06] Serving vLLM On any Cloud with SkyPilot. 0+cu121 Is debug build: False CUDA used to build PyTorch: 12. 0 Clang version: Could not collect CMake version: version 3. 56 GiB of which 60. 5 LTS (x86_64) GCC version: (Ubuntu 11. 82 GiB memory in use. A A high-throughput and memory-efficient inference and serving engine for LLMs Serving vLLM On any Cloud with SkyPilot. Hence 4 bytes / parameter * 7 billion parameters = 28 billion bytes = 28 GB of GPU memory required, for inference only. GPU 1 has a total capacty of 47. vllm doesn't seem to free the gpu As vLLM leverages GPU so we’re using Colab which provides runtime with free GPU support that has 16GB memory. 0] (64 [rank0]: self. Search Light. Serving vLLM On any Cloud with SkyPilot. Including non-PyTorch memory, this process has 39. However, if the value is too high, it may cause out-of-memory (OOM) errors. Choose GPU accelerator from top-right. I want to use two model in pipeline in one python code to infer. The format is PYTORCH_CUDA_ALLOC_CONF=<option>:<value>,<option2>:<value2>. 59 GiB of which 940. OutOfMemoryError: CUDA out of memory when Handle inference requests #5147. In addition, I also found the answer generated is not a complete sentence. , enforce_eager=False by default), and using CUDA Graph would add 1 -3 GiBs of memory overhead. To run one vLLM instance on multiple GPUs, use the -tp or --tensor-parallel-size option to specify multiple GPUs. Of the allocated memory 74. out, q, k, v, out_padded, softmax_lse, S_dmask, rng_state = flash_attn_cuda. 1，transformers 4. The behavior of caching allocator can be controlled via environment variable PYTORCH_CUDA_ALLOC_CONF. INFO 02-22 22:05:49 model_runner. Tried to allocate 896. Of the allocated memory 28. The side effect is that the build process will be much slower. The example code (set tensor_parallel_size=4 for your case): from langchain. 0. 4. Attempting to load this model with vLLM on an A100-80GB gives me: torch. empty_cache(). mem_get_info() # Execute a forward pass with dummy inputs to profile the memory Now the variable is deleted and memory is freed up on each iteration. Reload to refresh your session. I have been trying to train a BertSequenceForClassification Model using AWS Sagemaker. Model Input Dumps. 5 Libc version: glibc-2. OutOfMemoryError: CUDA out of memory. 1 (1ubuntu1) CMake version: version 3. 04 MiB is reserved by PyTorch but unallocated. You can also reduce the max_num_seqs as needed to decrease memory usage. 75 GiB memory in use. During handling of the above torch. Tried to allocate 494. This in turn leads to an incorrect number of available blocks in determine_num_available_blocks, which may then result in a KV cache that is too large in torch. Try increasing gpu_memory_utilization when initializing the engine. 88 GiB memory in cuda out of memory lead to 'AsyncEngineDeadError: Background loop has errored already. 168. Of the The model size is too big to run vLLM using one GPU as it results CUDA/HIP Out of Memory. 67 GiB of which 37. environ["CUDA_VISIBLE_DEVICES"] = "0" # to be sure using only A6000 and not GT1030 Bug：RuntimeError: CUDA out of memory. Tried to allocate 230. max_context_len_to_capture – Maximum context Hi @Forbu14,. How would you like to use vllm. It appears it happens during marling weight re Otherwise, too small values may cause out-of-memory (OOM) errors. ollama run llama3:70b-instruct-q2_K --verbose "write a constexpr GCD that is not recursive in C++17" Error: an unknown e torch. 32 GiB free; 158. GPU 0 has a total capacity of 191. 882572543+08:00 ERROR 08-30 15:30:57 async_llm_engine. INFO 08-21 torch. Tried to allocate 224. The exact syntax is documented, but in short:. 5. 73 GiB of which 3. 79 GiB already allocated; 42. 00 MiB (GPU 0; 14. You signed in with another tab or window. I will try --gpu-reset if the problem occurs again. vLLM uses PyTorch, which uses shared memory to share data between processes under the hood, you can add the argument --build-arg torch_cuda_arch_list="" for vLLM to find the current GPU type and build for that. 79 GiB total capacity; 5. Tried to allocate MiB 解决方法： 法一： 调小batch_size，设到4基本上能解决问题，如果还不行，该方法pass。法二： 在报错处、代码关键节点（一个epoch跑完）插入以下代码（目的是定时清内存）： import torch, gc gc. 61 MiB is reserved by PyTorch but unallocated. Enhancing Memory Management with CUDA: A Deep Dive Memory management is a critical aspect of optimizing performance in CUDA applications. INFO 06-18 06:28:58 custom_all_reduce. 88 GiB is free. 3 Libc version: glibc-2. 6: torch. First, you should avoid th following OOM error: torch. outofmemoryerror: A raised when a CUDA operation fails due to insufficient memory. GPU 0 has a total capacity of 21. swap_space – The size (GiB) of CPU memory per GPU to use as swap space. 36 GiB memory in Attempting to load this model with vLLM on an A100-80GB gives me: torch. out, the following pattern: class vllm. That said, the vllm implementation to me is quite unreliable as I keep getting CUDA out of memory errors. 81 MiB free; 13. If reserved but unallocated memory is large try You can also reduce the `max_num_seqs` as needed to decrease memory usage. [rank0]: torch. 37 GiB is allocated by PyTorch, and 303. INFO 09-07 00:53:52 model_runner. Just wanted to confirm if your model (with enforce_eager=True) does not leave memory for CUDA Graphs to work. follow OS. Tried to allocate 524. Tried to allocate 734. Comments (5) tristandevs commented on October 9, 2024 The problem here is that the GPU that you are trying to use is already occupied by another process. RuntimeError: CUDA error: an illegal memory access was encountered 2024-08-30T15:30:57. py:636] CUDA graphs can take additional 1~3 GiB memory per GPU. Efficient management of attention key and value memory with PagedAttention. 14 GiB reserved in total by PyTorch) The text was updated successfully, but these errors were encountered: All reactions. 16 GiB. I see rows for Allocated memory, Active memory, GPU reserved memory, I was trying to use vLLM on a finetined LLaMA 65B model. vLLM is fast with: State-of-the-art serving throughput; Efficient management of attention key and value memory with PagedAttention; Continuous batching of incoming requests; Fast model execution with CUDA/HIP graph; Quantizations: GPTQ, AWQ, INT4, INT8, and FP8. cuda. 9) to a lower value like 0. INFO 02-16 10:57:58 model_runner. 0 --model mistralai/Mixtral-8x7B mistralai/Mixtral-8x7B-v0. comment sorted by Best Top New Controversial Q&A Add a Comment. 48 MiB is reserved by PyTorch but unallocated. You can also use the torch. 00 MiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. Tried to allocate 2. from vllm. empty_cache() 法三（常用方法）： 在测试 I am using vLLM to serve the model. 4 LTS (x86_64) GCC version: (Ubuntu 11. OutOfMemoryError: HIP out of memory. 本机的CUDA显存为2048MiB，当前使用为67MiB，运行模型后，发现当前CUDA使用情况超过2048MiB，解决方法为减小batch_size的大小（减小后结果可能会变差），若减小batch_size后CUDA显存还是不足或者是结果 A user reports a problem with vLLM, a high-throughput inference engine for LLMs, after upgrading to PyTorch 2. Specifically, when I create a VLLM model object inside a function, I run into memory problems and cannot 2. Hello when i run bentoml serve inside mistral-7b-instruct i get OOM but i have more than 70GB gpu free. 45 GiB is allocated by PyTorch, and 8. memory_summary() call, but there doesn't seem to be anything informative that would lead to a fix. 10. 1+cu111. 69 MiB free; 10. 45. 00 MiB. dev0 问题描述：使用conda创建python3. 6. I think the OOM happens because the profiler self. 00 MiB (GPU 0; 7. 44 MiB is reserved by PyTorch but unallocated. Process 13234 has 2. 01 GiB is allocated by PyTorch, and 15. reset_peak_memory_stats() free_memory_pre_profile, total_gpu_memory = torch. GPU 0 has a total capacity of 15. You should inspect your kernel code in SetSubGridMarker for an invalid access to shared or local memory. 5. py` 原模型Mixtral-8x7B-v0. Before submitting a new issue To set up an EC2 machine as an Ubuntu-based VPN server, you can follow these steps. 04 GiB is allocated by PyTorch, and 2. 04 with a Nvidia A16 GPU, and here is a picture of my GPU Usage: When I am running the Docker container for a llama3-7B-Instruct Model, I ran into a CUDA out Memory Issue: ===== == NVIDIA Inference Microservice LLM NIM == ===== cuda out of memory lead to 'AsyncEngineDeadError: Background loop has errored already. See documentation for Memory Management and PYTORCH_CUDA Trying to load LLAMA3. 71 GiB is allocated by PyTorch, and 171. 1 vLLM Build Flags: CUDA Archs: Not Set; ROCm: Disabled; Neuron: I use an GPU with 15 GB RAM memory, but when PyTorch saves a checkpoint, the OOM exception happens. 00 GiB reserved in total by PyTorch) If reserved memory Failed to import from vllm. 63 GiB memory in use. OutOfMemoryError: CUDA Thats too big to fit into 48GB, you need 2 x A100 for it, you should look at using a quantized version instead, such as TheBloke/dolphin-2. 41 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. LLM (model: str, However, if the value is too high, it may cause out-of- memory (OOM) errors. 1了，模型加载报错，cuda out of memory, 模型是knowlm-13b-ie，GPU A6000, 50G显存 报错内容： Init model 2024-01-09 16:04:55,716 WARNING worker. Comments (16) lasseedfast commented on February 18, 2024 1 . When gpu_memory_utilization = 0. 99 GiB of which 32. 19 MiB is RuntimeError: CUDA error: out of memory CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect. 🐛 Describe the bug. INFO 06-04 03:11:21 model_runner. Hi @hmellor I would like to ask more info about how can we avoid CUDA graphs from consuming memory, I have use --enforce-eager in command such as python main. Check out a 1-click example to start the vLLM demo, Optimized To run the model in eager mode, set 'enforce_eager=True' or use '--enforce-eager' in the CLI. py:1028] CUDA graphs can take additional 1~3 GiB memory per GPU. 00 GiB total capacity; 3. Tried to allocate 112. py --enforce-eager but it still running CUDA graph. Sign in Product GitHub Copilot. The max_split_size_mb configuration value can be set as an environment variable. Write I aslo meet torch. vLLM is a fast and easy-to-use library for LLM inference and serving. To run the model in eager mode, set 'enforce_eager=True' or use '--enforce-eager' in the CLI. Of the allocated memory 23. 5 I get a similar error: torch. MengzhangLI While serving the CodeLLaMA 13B （CodeLlama-13b-hf) base model with v1/completions API with 1 A100, I encountered the following CUDA memory issue. GPU 0 has a total capacity of 23. Higher values will increase the KV cache size and thus improve the model's throughput. 8的环境后使用pip device=x. Closed zhaotyer opened this issue May 31, Could not collectROCM Version: Could not collect Neuron SDK Version: N/A vLLM Version: 0. I have 64GB of RAM and 24GB on the GPU. 00 MiB (GPU 0; 10. Once everything is running, you should see in the generated output file, namely vllm-<jobid>. cuda out of memory lead to 'AsyncEngineDeadError: Background loop has errored already. 00 GiB memory in use. run your model, e. For debugging consider passing CUDA_LAUNCH_BLOCKING=1 Compile with TORCH_USE_CUDA_DSA to enable device-side assertions. Tried to allocate 172. This means the vLLM instance will occupy 50% of the GPU memory. Including non-PyTorch memory, this process has 7. With all due respect, the rest of the issues sound weird, and not to do with vLLM. Your current environment The output of `python multilora_inference. in full precision (float32), every parameter of the model is stored in 32 bits or 4 bytes. GPU 1 has a total capacty of 22. 99 GiB free; 3. api_server --model bjaidi/Phi-3-medium-128k-instruct-awq --quantization awq --dtype auto --gpu-memory-utilization 0. ; Solution #5: Release Unused Variables. 04 环境：python 3. This gives a readable summary of memory allocation and allows you to figure the reason of CUDA running out of memory. If True, we will disable CUDA graph and always execute the model in eager mode. py:58] INFO 02-22 22:05:39 model_runner. 9. 07 GiB. Of the allocated memory 14. Steps: Setting up a K8s cluster with two nodes, and each node have a Nvidia 3090 GPU. Of the allocated memory 15. The same thing happened with the 34B base model, to Parameter Swapping to/from CPU during Training: If some parameters are used infrequently, it might make sense to put them on CPU memory during training and move them to the GPU when needed. _C with No module named 'vllm. 99 GiB of which 599. Tried to al PyTorch version: 2. 1 · Help: CUDA Out of Memory. The user asks for help in profiling cuda memory usage and finding the cause of OOM error with cuda You signed in with another tab or window. 9: swap_space: int You signed in with another tab or window. 31 MiB is free. GPU 0 has a total capacity of 39. A high-throughput and memory-efficient inference and serving engine for LLMs - bug fixed: cuda out of memory lead to &#39;AsyncEngineDeadError: Background loop has errored already. 1+cu124 Is debug build: False CUDA used to build PyTorch: 12. I confirmed this issue doesn't occur on main on A100, so DenisStefanAndrei commented on February 18, 2024 torch. Check memory usage, then increase from there to see what the limits are on your GPU. GPU. 49 MiB is reserved by PyTorch but unallocated. The idea behind free_memory is to free the GPU beforehand so to make sure you don't waste space for unnecessary objects held in memory. Your current environment torch. 29. 92 GiB total capacity; 9. py:748] Graph You signed in with another tab or window. Need help with a SO question: 'CUDA out of memory' issue while setting up LangChain Custom LLM Pipeline. 30. when i use all the gpus i always get this message below. 04) 11. Ensure you have an 🐛 Describe the bug. Tried to allocate 20. If you are running out of memory, When using vllm I'm used to if I set a small value gpu_memory_utilization=0. With vLLM it fails: ^^^^^ torch. 0. Viewed 429 times Hi @yaliqin, do you mean you are trying to set up both vLLM and DeepSpeed servers on a single GPU?If so, you should configure gpu_memory_utilization (by default 0. Function to process a batch of texts. g. py:246] Fresh vLLM, but with CUDA 11. Reduce data augmentation. However, if the model size itself exceeds the 50% of the GPU memory, you will see errors. Including non-PyTorch memory, this process has 14. · vllm-project/v torch. vLLM is fast with: State-of-the-art serving throughput; Efficient management of attention key and value memory with PagedAttention; Continuous batching of incoming requests; Fast model execution with CUDA/HIP graph; Quantization: GPTQ, AWQ, SqueezeLLM, FP8 KV Cache; Optimized Based on this post it seems a GPU with 32GB should “be enough to fine-tune the model”, so you might need to either further decrease the batch size and/or the sequence lengths, since you are still running OOM on your 15GB device. If reserved but unallocated Collecting environment information PyTorch version: 2. 1单卡A100可以跑，用了80G内存以内，使用了vllm后，要两张A100才能跑起来，内存达到了160G。 vllm-project > vllm [Bug]: torch. Available out, q, k, v, out_padded, softmax_lse, S_dmask, rng_state = flash_attn_cuda. api_server --host 0. e. 12 (main, Nov 20 2023, 15:14:05) [GCC Your current environment torch. 06 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. We run vLLM at scale and it's fantastic. When dealing with vLLM CUDA out of memory issues, it is crucial to adopt a systematic approach to identify and resolve the underlying problems. 12 MiB is free. GPU 0 has a total capacty of 21. 10 | It will cause CUDA out of memory when execute the second line. 38 GiB memory in use. 11. And later, CUDA torch. 2. 69 GiB of which 73. build vLLM with aarch64 and cuda (GH200), it only gives you half of the memory by default, and you’d better use export MAX_JOBS=1 to avoid compiling multiple files simultaneously and running out of memory. 58 GiB is reserved by PyTorch but unallocated. Running the same code and vllm version 0. cokwm ocfyegqn pgpy httzi gnxq bbmwf itvkdt vlwbl mudhv iwza