Vllm cuda out of memory. You switched accounts on another tab or window.

Vllm cuda out of memory 67 GiB of which 37. 00 MiB (GPU 0; 10. 12 (main, Nov 20 2023, 15:14:05) [GCC Your current environment torch. We will use OpenVPN for this setup. I only wish it weren't written in Python. A A high-throughput and memory-efficient inference and serving engine for LLMs Serving vLLM On any Cloud with SkyPilot. 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. py:980] CUDA graphs can take additional 1~3 GiB memory per GPU. Of the allocated memory 28. 0 Clang version: Could not collect CMake version: version 3. 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. 31 MiB free; 38. You should inspect your kernel code in SetSubGridMarker for an invalid access to shared or local memory. 1 ROCM used to build PyTorch: N/A OS: Ubuntu 22. Hello when i run bentoml serve inside mistral-7b-instruct i get OOM but i have more than 70GB gpu free. Try increasing gpu_memory_utilization when initializing the engine. 4 LTS (x86_64) GCC version: (Ubuntu 11. Tried to allocate 734. 29. device) torch. 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. Tried to allocate 112. 5 LTS (x86_64) GCC version: (Ubuntu 11. 0+cu121 Is debug build: False CUDA used to build PyTorch: 12. When dealing with vLLM CUDA out of memory issues, it is crucial to adopt a systematic approach to identify and resolve the underlying problems. GPU 0 has a total capacty of 21. 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. 79 GiB already allocated; 42. I see rows for Allocated memory, Active memory, GPU reserved memory, I was trying to use vLLM on a finetined LLaMA 65B model. 4. 01 GiB is allocated by PyTorch, and 15. 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. 662153 1 pb_stub. 0-4ubuntu2) 14. When gpu_memory_utilization = 0. RuntimeError: CUDA error: an illegal memory access was encountered 2024-08-30T15:30:57. 3 to also see vllm pre-allocate that memory before loading the model. 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. 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. Fast model execution with CUDA/HIP graph. By increasing this utilization, Please try out this feature and let us know your feedback via GitHub issues! previous. 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. 99 GiB free; 3. 71 GiB already allocated; 190. 59 GiB of which 940. Continuous batching of incoming requests. Process 2381604 has 78. How would you like to use vllm. follow OS. 3. memory_summary() method to get a human-readable printout of the memory allocator statistics for a given device. With vLLM it fails: ^^^^^ torch. 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. 60 GiB memory in use. Supported Hardware for Quantization Kernels. 14 GiB reserved in total by PyTorch) The text was updated successfully, but these errors were encountered: All reactions. 00 MiB (GPU 0; 7. GPU info in Colab T4 runtime This command worked for me: python3 -m vllm. 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. num_groups, [rank0]: ^^^^^ [rank0]: torch. import torch. 43 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try I tried to deploy a triton (using version: 23. The 2xA10G large already provide 48GB VRAM, but out of memory still occurred, how could I fix this? torch. 25 MiB free; 11. py:1221] CUDA graphs can take additional 1~3 GiB memory per GPU. 00 GiB total capacity; 142. Process 3179876 has 1. py:1395 -- SIGTERM handler is not set because current thread is OutOfMemoryError: CUDA out of memory. LLM (model: str, However, if the value is too high, it may cause out-of- memory (OOM) errors. 5. What is the issue? When I try the llama3 model I get out of memory errors. Reload to refresh your session. 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. 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. 04 GiB is allocated by PyTorch, and 2. 67 GiB memory in use. 00 GiB memory in use. 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. Including non-PyTorch memory, this process has 39. First, you should avoid th following OOM error: torch. Of the allocated memory 74. 36 GiB already allocated; 272. 38 GiB memory in use. e. 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. 77 GiB total capacity; 10. INFO 08-21 torch. _C with No module named 'vllm. 36. 95 GiB already allocated; 132. Increase gpu_memory_utilization. Tried to allocate 1002. 0. Of the allocated memory 23. py` 原模型Mixtral-8x7B-v0. 2' and ' vllm 0. This requires no more than 26, maybe 27 GiB of memory. 04) 11. empty_cache() torch. 75 GiB memory in use. 04. py:246] Fresh vLLM, but with CUDA 11. 7 (main, Nov 6 2024, 18:29:01) [GCC 14. Thanks for the comment! Fortunately, it seems like the issue is not happening after upgrading pytorch version to 1. cuda. 11 GiB is free. 5，vllm-flash-attn 2. vLLM is fast with: State-of-the-art serving throughput. Of the The model size is too big to run vLLM using one GPU as it results CUDA/HIP Out of Memory. Your current environment The output of `python multilora_inference. 00 GiB total capacity; 3. 81 MiB is free. · vllm-project/v torch. 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. 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. 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. 11 GiB of which 1018. 04 MiB is reserved by PyTorch but unallocated. Comments (5) tristandevs commented on October 9, 2024 . . Process 38354 has 14. I have been trying to train a BertSequenceForClassification Model using AWS Sagemaker. enforce_eager – Whether to enforce eager execution. Tried to al PyTorch version: 2. A INFO 06-18 06:28:40 model_runner. 00 MiB is reserved by PyTorch but unallocated. 44 MiB is free. 72 GiB of which 94. INFO 02-16 10:57:58 model_runner. GPU 0 has a total capacity of 11. With all due respect, the rest of the issues sound weird, and not to do with vLLM. Ensure you have an 🐛 Describe the bug. Your current environment vllm 0. However, if the value is too high, it may cause out-of-memory (OOM) errors. 2. However, if you run with 3 GPU nodes you will still observe a CUDA OOM. Including non-PyTorch memory, this process has 11. It appears it happens during marling weight re Otherwise, too small values may cause out-of-memory (OOM) errors. Of the allocated memory 13. profile_run() may incorrectly compute the free GPU memory, depending on the value of max-num-batched-tokens. 63 “OutOfMemoryError: CUDA out of memory. 4 hardware: RTX4090 gpu driver: You signed out in another tab or window. INFO 02-22 22:05:49 model_runner. GPU 1 has a total capacty of 47. GPU 0 has a total capacty of 14. 25 GiB memory in use. Function to process a batch of texts. With larger --num-scheduler-steps we are likely holding on to a lot more CUDA tensors causing an out-of-memory issue. try: torch. 32 GiB is free. 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. 9) to a lower value like 0. Your current environment torch. Use mixed precision training. GPU 1 has a total capacty of 22. Enhancing Memory Management with CUDA: A Deep Dive Memory management is a critical aspect of optimizing performance in CUDA applications. 32 GiB free; 158. 40 Python version: 3. 00 MiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. dev0 问题描述：使用conda创建python3. Higher values will increase the KV cache size and thus improve the model's throughput. Comments (7) mgoin commented on October 3, 2024 2 . The example code (set tensor_parallel_size=4 for your case): from langchain. 76 MiB already allocated; 6. next. Managing variables properly is crucial in PyTorch to prevent memory issues. I will try --gpu-reset if the problem occurs again. 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. Reduce data augmentation. Search Light. 1 70B GPTQ and get cuda out of memory on A6000 48GB, when LLAMA3 70B GPTQ is working great. Efficient management of attention key and value memory with PagedAttention. entrypoints. If you are running out of memory, consider decreasing `gpu_memory_utilization` or enforcing eager mode. Also INFO 10-15 06:53:46 model_runner. max_context_len_to_capture – Maximum context You can also reduce the `max_num_seqs` as needed to decrease memory usage. 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. You signed in with another tab or window. This can happen due to various reasons, including large model sizes, insufficient hardware resources, or inefficient memory management during inference. You can also use the torch. 69 MiB free; 10. Tried to allocate 230. I confirmed this issue doesn't occur on main on A100, so DenisStefanAndrei commented on February 18, 2024 torch. 10. 0. Tried to allocate 64. Sign in Product GitHub Copilot. but I keep getting the error: RuntimeError: CUDA out of memory. 39 GiB memory in use. 88 GiB is free. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid OutOfMemoryError: CUDA out of memory. Including non-PyTorch memory, this process has 28. 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. 🐛 Describe the bug. 07) ensemble model with python backend and custom fine tuned Llama2 model and I am getting: I0815 16:40:55. Tried to allocate 896. vllm doesn't [Bug]: torch. Tried to allocate 448. 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. During handling of the above torch. cpu_offload_gb – The size (GiB) of CPU memory to use for offloading the model weights. 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. py --enforce-eager but it still running CUDA graph. 00 MiB (GPU 0; 39. 00 MiB (GPU 0; 14. 3 Model Input Dumps No response 🐛 Describe the bug Description: Out of Memory (OOM) Issues During MMLU Evaluation with lm_eval #10325. The problem occurs when I try to instantiate a LLM object inside a [Bug]: torch. GPU 0 has a total capacity of 23. when i use all the gpus i always get this message below. GPU 0 has a total capacity of 191. 39 GiB of which 17. py:858] CUDA graphs can take additional 1~3 GiB memory per GPU. Of the allocated memory 14. 71 GiB is allocated by PyTorch, and 171. GPU 0 has a total capacity of 15. Batching makes this more pronounced. 06 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. Tried to allocate 72. 35 Python version: 3. 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. 99 GiB of which 599. See documentation for Memory Management and PYTORCH_CUDA Trying to load LLAMA3. Serving vLLM On any Cloud with SkyPilot. 096gb when loading import os from vllm import LLM, SamplingParams os. Your current environment VLLM image: v0. 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. 882572543+08:00 ERROR 08-30 15:30:57 async_llm_engine. 94 MiB free; 6. No response. 5 Libc version: glibc-2. Of the allocated memory 15. If you are using too many data augmentation techniques, you can try reducing the number of transformations or using less memory-intensive techniques. python3 -m vllm. [ torch. OutOfMemoryError: CUDA out of memory when Handle inference requests #5147. 63 GiB memory in use. Tried to allocate 134. 35 GiB of which 804. 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. 64 GiB total capacity; 22. py:58] INFO 02-22 22:05:39 model_runner. However, if the model size itself exceeds the 50% of the GPU memory, you will see errors. This gives a readable summary of memory allocation and allows you to figure the reason of CUDA running out of memory. 79 GiB total capacity; 5. 1，transformers 4. py:1406] CUDA graphs can take additional 13 GiB memory per GPU. Explore solutions for Vllm CUDA out of memory errors, optimizing performance and resource management effectively. py:636] CUDA graphs can take additional 1~3 GiB memory per GPU. They both have 39. model_runner. 3 Libc version: glibc-2. Tried to allocate 462. 36 GiB already allocated; 194. 75 GiB of which 72. 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. 58 GiB is reserved by PyTorch but unallocated. This means the vLLM instance will occupy 50% of the GPU memory. 16 GiB. malloc(10000000) vLLM is a fast and easy-to-use library for LLM inference and serving. 30 GiB memory in use. py:1028] CUDA graphs can take additional 1~3 GiB memory per GPU. Write I aslo meet torch. Check memory usage, then increase from there to see what the limits are on your GPU. 31 MiB is free. api_server --model bjaidi/Phi-3-medium-128k-instruct-awq --quantization awq --dtype auto --gpu-memory-utilization 0. Tried to allocate 1. py’ in that code the bug occur in the line You signed in with another tab or window. 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. 0 --model mistralai/Mixtral-8x7B mistralai/Mixtral-8x7B-v0. ones(self. To achieve better latency. 4 A100 + CUDA 12. 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. If you are running out of memory, consider decreasing gpu_memory_utilization or enforcing eager mode. [rank0]: torch. Navigation Menu Toggle navigation. 20 GiB already allocated; 139. If reserved but unallocated Collecting environment information PyTorch version: 2. Just wanted to confirm if your model (with enforce_eager=True) does not leave memory for CUDA Graphs to work. Hardware I'm encountering an issue when using the VLLM library in Python. 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. 75 GiB total capacity; 13. 9: swap_space: int You signed in with another tab or window. Note that, you need to instal vllm package under Linux by: pip install vllm. GPU 0 has a total capacty of 39. 92 GiB total capacity; 9. Tried to allocate 140. 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. 50 MiB is free. about vllm HOT 5 CLOSED tristandevs commented on October 9, 2024 1 [Bug]: torch. 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. The max_split_size_mb configuration value can be set as an environment variable. Reduce batch size to 1, reduce generation length to 1 token. Process 13236 has 8. Inevitable-Start-653 A high-throughput and memory-efficient inference and serving engine for LLMs - SoheylM/vllm-localllm-cuda12. INFO 06-18 06:28:58 custom_all_reduce. 82 GiB memory in use. 00 MiB (GPU 0; 8. 28 GiB is allocated by PyTorch, and 51. The behavior of caching allocator can be controlled via environment variable PYTORCH_CUDA_ALLOC_CONF. 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. Model Input Dumps. Tried to allocate 926. For debugging consider passing CUDA_LAUNCH_BLOCKING=1. GPU 0 has a total capacity of 31. # Getting a human-readable printout of the memory allocator statistics. collect() torch. You can also reduce the max_num_seqs as needed to decrease memory usage. 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. swap_space – The size (GiB) of CPU memory per GPU to use as swap space. Kubernetes users should avoid naming their services vllm to prevent conflicts with environment variables set by Kubernetes. 00 GiB reserved in total by PyTorch) If reserved memory Failed to import from vllm. The format is PYTORCH_CUDA_ALLOC_CONF=<option>:<value>,<option2>:<value2>. Open A100 + CUDA 12. Process 353470 has 46. Process 18600 has 78. ', then later request can NOT be processed, it means, async engine was dead and need to restart vllm engine for continue service. torch. 06 GiB memory in use. 88 MiB is free. 50 GiB. All environment variables in vLLM are prefixed with VLLM_. Your GPU doesn't have enough memory for the size of the inputs you are using. reset_peak_memory_stats() free_memory_pre_profile, total_gpu_memory = torch. Efficient . memory_summary() call, but there doesn't seem to be anything informative that would lead to a fix. 15 GiB is allocated by PyTorch, and 1. Using these for the API server will not function as expected. py:748] Graph You signed in with another tab or window. The steps for checking this are: Use nvidia-smi in the terminal. Available out, q, k, v, out_padded, softmax_lse, S_dmask, rng_state = flash_attn_cuda. ; Solution #5: Release Unused Variables. Tried to allocate 494. Repositories Users Hot Words ; Hot Users ; 2024 1 [Bug]: torch. 62 GiB memory in use. Including non-PyTorch memory, this process has 17179869184. Specifically, when I create a VLLM model object inside a function, I run into memory problems and cannot 2. Git Product home page Git Product. when i run individual gpu, it works fine. Tried to allocate 982. The exact syntax is documented, but in short:. 8gb safetensors, and 37. If you are running out of memory, When using vllm I'm used to if I set a small value gpu_memory_utilization=0. Be cautious with the VLLM_PORT and VLLM_HOST_IP variables, as they are intended for internal usage only. INFO 06-04 03:11:21 model_runner. Otherwise, too small values may cause out-of-memory (OOM) errors. 61 GiB memory in use. 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. I want to use two model in pipeline in one python code to infer. vLLM is a fast and easy-to-use library for LLM inference and serving. 1+cu124 Is debug build: False CUDA used to build PyTorch: 12. Closed zhaotyer opened this issue May 31, Could not collectROCM Version: Could not collect Neuron SDK Version: N/A vLLM Version: 0. (more than 200GB), and I found this will cause OOM (not cuda memory) Skip to content. You switched accounts on another tab or window. GPU How would you like to use vllm I'm running a eval framework that's evaluating multiple models. Tried to allocate 20. , enforce_eager=False by default), and using CUDA Graph would add 1 -3 GiBs of memory overhead. 45 GiB total capacity; 38. 168. Tried to allocate 3. 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. 8, it came with 'No available memory for the cache blocks. To run one vLLM instance on multiple GPUs, use the -tp or --tensor-parallel-size option to specify multiple GPUs. 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. You can also reduce the `max_num_seqs` as needed to decrease memory usage. 10 | It will cause CUDA out of memory when execute the second line. 78 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. 0 Clang version: 19. Hence 4 bytes / parameter * 7 billion parameters = 28 billion bytes = 28 GB of GPU memory required, for inference only. 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. Keyword Definition Example; torch. 1+cu111. I printed out the results of the torch. 7 has CUDA Graphs enabled by default (i. 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. Before submitting a new issue To set up an EC2 machine as an Ubuntu-based VPN server, you can follow these steps. Attempting to load this model with vLLM on an A100-80GB gives me: torch. 30. 4 ROCM used to build PyTorch: N/A OS: Ubuntu 24. 12. _C' PyTorch version: 2. INFO 09-07 00:53:52 model_runner. Check out a 1-click example to start the vLLM demo, and the blog post for the story behind vLLM development on the clouds. 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. 1 · Help: CUDA Out of Memory. OutOfMemoryError: CUDA out of memory. We run vLLM at scale and it's fantastic. I can't find any more relevant information in the documentation. i'm using hugging face estimators. To run the model in eager mode, set 'enforce_eager=True' or use '--enforce-eager' in the CLI. I think the OOM happens because the profiler self. def process_batch(batch: List[str]) -> List[Dict[str, str]]: llm = init_llm() predictor = LLMPredictor(llm) return predictor I aslo meet torch. empty_cache(). 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. 44 MiB is reserved by PyTorch but unallocated. Including non-PyTorch memory, this process has 7. one config of hyperparams (or, in general, operations that Debugging. 9. (less than 3 GiBs are left on the device) My system is a Ubuntu 22. 7 ( torch. outofmemoryerror: A raised when a CUDA operation fails due to insufficient memory. 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. 3. 0a0+git2e4abc8 Is debug build: torch. GPU 0 has a total capacty of 79. Tried to allocate 524. 8，vllm 0. Once everything is running, you should see in the generated output file, namely vllm-<jobid>. GPU 0 has a total capacity of 21. Check out a 1-click example to start the vLLM demo, Support NVIDIA CUDA and AMD ROCm. 61 MiB is reserved by PyTorch but unallocated. 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. comment sorted by Best Top New Controversial Q&A Add a Comment. 12 MiB is free. Process 13234 has 2. The side effect is that the build process will be much slower. ' 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. run your model, e. 37 GiB is allocated by PyTorch, and 303. 0 lm_eval 0. 31 MiB is INFO 08-21 08:31:11 model_runner. Need help with a SO question: 'CUDA out of memory' issue while setting up LangChain Custom LLM Pipeline. GPU. additionally, whenever I run in combination of CUDA_VISIBLE_DEVICES number 1, always get the below message or something similar. 48 MiB is reserved by PyTorch but unallocated. 5. GPU 0 has a total capacity of 39. 81 MiB free; 13. 5 I get a similar error: torch. A typical usage for DL applications would be: 1. Including non-PyTorch memory, this process has 14. 38 GiB already allocated; 6. You signed out in another tab or window. 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. out, the following pattern: class vllm. 56 GiB of which 60. 0] (64 [rank0]: self. Process 258755 has 23. ollama run llama3:70b-instruct-q2_K --verbose "write a constexpr GCD that is not recursive in C++17" Error: an unknown e torch. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF Well, CUDA is not fully deterministicso there's that. 1 (1ubuntu1) CMake version: version 3. Choose GPU accelerator from top-right. Tried to allocate 304. 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. Would be grateful for any insights! stackoverflow. 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. OutOfMemoryError: HIP out of memory. Here are some effective strategies to debug these issues: Enable Detailed Logging. max_context_len_to_capture – Maximum context Hi @Forbu14,. 98 GiB of which 1. Steps: Setting up a K8s cluster with two nodes, and each node have a Nvidia 3090 GPU. varlen_fwd RuntimeError: CUDA error: an illegal memory access was encountered Compile with TORCH_USE_CUDA_DSA to enable device-side assertions. I have 64GB of RAM and 24GB on the GPU. If False, we will use CUDA graph and eager execution in hybrid. In addition, I also found the answer generated is not a complete sentence. 36 GiB memory in Attempting to load this model with vLLM on an A100-80GB gives me: torch. Legend: X = Self torch. 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. Process 103776 has 15. 00 MiB. 99 GiB of which 32. 8的环境后使用pip device=x. For debugging consider passing CUDA_LAUNCH_BLOCKING=1 Compile with TORCH_USE_CUDA_DSA to enable device-side assertions. See documentation for Memory Management and torch. Of the allocated memory 7. 25 GiB. Speculative decoding in vLLM. 73 GiB of which 3. Of the allocated memory 20. 07 GiB. input_q = torch. 04 环境：python 3. 69 GiB of which 73. 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. 按照教程运行，也把vllm版本降到0. in full precision (float32), every parameter of the model is stored in 32 bits or 4 bytes. 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. If reserved but unallocated memory is large try You can also reduce the `max_num_seqs` as needed to decrease memory usage. environ["CUDA_VISIBLE_DEVICES"] = "0" # to be sure using only A6000 and not GT1030 Bug：RuntimeError: CUDA out of memory. 49 MiB is reserved by PyTorch but unallocated. 0-1ubuntu1~22. 98 GiB of which 338. 4 ROCM used to build PyTorch: N/A OS: Ubuntu 22. 00 MiB (GPU 0; 23. 00 MiB is free. 12 GiB is allocated by PyTorch, and 80. ] 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. It has run successfully and responds correctly. 1. [2023/06] Serving vLLM On any Cloud with SkyPilot. 94 MiB is free. g. If True, we will disable CUDA graph and always execute the model in eager mode. Tried to allocate 224. 5 torch 2. 45. 11. """ # Profile the memory usage of the model and get the maximum number of # cache blocks that can be allocated with the remaining free memory. 0 --model mistralai/Mixtral-8x7B torch. 18 GiB of which 302. Process 13238 has 3. 88 GiB memory in cuda out of memory lead to 'AsyncEngineDeadError: Background loop has errored already. 41 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. However, I just post one solution here when using VLLM. 50 MiB free; 23. Running the same code and vllm version 0. 6. 20 GiB of which 13. cuda out of memory lead to 'AsyncEngineDeadError: Background loop has errored already. out, q, k, v, out_padded, softmax_lse, S_dmask, rng_state = flash_attn_cuda. 6: torch. 11 GiB memory in use. 59 GiB. Modified 1 year, 7 months ago. api_server --host 0. from vllm. Comments (16) lasseedfast commented on February 18, 2024 1 . py:680] CUDA graphs can take additional 1~3 GiB memory per GPU. 9, it came with OOM. empty_cache() 法三（常用方法）： 在测试 I am using vLLM to serve the model. 45 GiB is allocated by PyTorch, and 8. Tried to allocate 826. Memory utilization is not exactly balance between all GPUs as we mix tensor parallelism and pipeline parallelism. 7. 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. If reserved but unallocated memory is large try setting max_split_size_mb to avoid fragmentation. openai. CUDA out of memory. GPU 0 has a total capacity of 79. Optionally, use the CUDA_VISIBLE_DEVICES environment variable to specify the GPUs. This is somehow also not happening so I assume my parameter is not respected when loading a model as CUDA out of memory. That said, the vllm implementation to me is quite unreliable as I keep getting CUDA out of memory errors. The vLLM pre-allocates GPU cache by using gpu_memory_utilization% of memory. This will check if your GPU drivers are installed and the PyTorch version: 2. 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. Tried to allocate 2. 00 MiB (GPU 0; 12. 91 GiB. Process 252091 has 21. And later, CUDA torch. 17 GiB memory in use. 19 MiB is free. Tried to allocate 172. enforce_eager – Whether to enforce eager 192. 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. 10 (x86_64) GCC version: (Ubuntu 14. mfuaba kyjmu hvdsex jipq mzxj czl abu stsmth kaod vlcihx