Vllm cuda out of memory 2024-01-15T20:32:13. vLLM is designed to occupy all the GPU memory for storing KV cache blocks. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. Check memory usage, then increase from there to see what the limits are on your GPU. next. 38 GiB already allocated; 6. Legend: torch. 31 MiB is You need to explicitly clear the allocated memory on cuda via torch. Supported Hardware for Quantization Kernels. By increasing this utilization, you can provide more KV cache space. And FastChat produces this error as it is loading the last few checkpoint shards: torch. GPU 0 has a total capacity of 47. 00 GiB total capacity; 3. 12 MiB is free. Modified 1 year, 6 months ago. See documentation for Memory Management and PYTORCH_CUDA How can I fix a CUDA Out of Memory Exception while saving a PyTorch model? Ask Question Asked 1 year, 6 months ago. 99 GiB free; 3. from vllm. The example code (set tensor_parallel_size=4 for your case): from langchain. 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. ', then later request can NOT be processed, it means, async engine was dead and need to restart vllm engine for continue service. 78 GiB reserved in total by PyTorch) If reserved memory is >> allocated I'm encountering an issue when using the VLLM library in Python. 01 GiB is allocated by PyTorch, and 15. 39 GiB of which 17. Here are some effective strategies to debug these issues: Enable Detailed Logging. Include these lines into your run_vllm_eval() function: I’m trying to run llama2 13b model with rope scaling on the AWS g4dn. [Bug]: Out of Memory (OOM) Issues During MMLU Evaluation with lm_eval #10325. Tried to allocate 734. 4. 76 GiB total capacity; 4. 7-mixtral-8x7b-GPTQ This version is also uncensored torch. 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. Several issues can lead to out of memory errors in CUDA operations: Insufficient GPU Memory: Ensure that your GPU has None of these methods worked. 00 GiB memory in use. However, I just post one solution here when using VLLM. 1. Tried to allocate 14. Your current environment vllm 0. But i had lots of accuracy loss on this. The steps for checking this are: Use nvidia-smi in the terminal. If reserved but unallocated memory is large try setting max_split_size_mb to avoid fragmentation. Reduce batch size to 1, reduce generation length to 1 token. about vllm HOT 5 CLOSED tristandevs commented on October 9, 2024 1 [Bug]: torch. post1 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. Comments (5) tristandevs commented on October 9, 2024. It has run successfully and responds correctly. 54 GiB of which 1. We will use OpenVPN for this setup. Note that, you need to instal vllm package under Linux by: pip install vllm. Just wanted to confirm if your model (with To set up an EC2 machine as an Ubuntu-based VPN server, you can follow these steps. 5 (50% utilization, or can be set even lower) when initialize the LLM class to reduce the memory footprint. 5 torch 2. Of the allocated memory 78. 41 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. 56 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. CUDA out of memory. 94 MiB free; 6. You can pass in the gpu_memory_utilization=0. Including non-PyTorch memory, this process has 17179869184. 95 vllm减小显存 | vllm小模型大显存问题 INFO 07-16 20:48:26 model_runner. . Explore solutions for Vllm CUDA out of memory errors, optimizing performance and resource management effectively. Tried to allocate 2. Of the allocated memory 14. cuda out of memory lead to 'AsyncEngineDeadError: Background loop has errored already. My vllm inference program runs well for most models with the environment of 'transformers=4. 2. OutOfMemoryError: CUDA out of memory. GPU 0 has a total capacty of 39. Closed zhaotyer opened this issue May 31, Could not collectROCM Version: Could not collect Neuron SDK Version: N/A vLLM Version: 0. 00 MiB (GPU 0; 12. llms import VLLM When dealing with vLLM CUDA out of memory issues, it is crucial to adopt a systematic approach to identify and resolve the underlying problems. getting CUDA out of memory. If i am setting min and max pixels that is given by huggingface, The model takes maximum 24GB worth cuda memory. This gives a readable summary of memory allocation and allows you to figure the reason of CUDA running out of memory. If not set i am getting cuda out of memory in A100 80GB machine also. See documentation for Memory Management and vllm-project > vllm [Bug]: torch. 00 GiB. py:928] CUDA graphs can take additional 1 ~3 GiB memory per GPU. 7 has CUDA Graphs enabled by default (i. You can also reduce the ` max_num_seqs ` as needed to decrease memory usage. , enforce_eager=False by default), and using CUDA Graph would add 1 -3 GiBs of memory overhead. 59 GiB of which 940. Please try out this feature and let us know your feedback via GitHub issues! previous. reset_peak_memory_stats(). ( torch. Of the allocated memory 45. 58 GiB is free. Process 353470 has 46. 726720287Z torch. Tried to @jibowang it seems like you have other processes running on the same GPU as vLLM. 00 GiB (GPU 0; 11. Tried to allocate 1002. 61 GiB is allocated by PyTorch, and 6. def process_batch(batch: List[str]) -> List[Dict[str, str]]: llm = init_llm() predictor = LLMPredictor(llm) return predictor I am getting accuracy loss to set the min and max pixels for this model. 1: Attention formula. 0 lm_eval 0. I printed out the results of the torch. Attempting to load this model with vLLM on an A100-80GB gives me: torch. The formula consists of three variables. entrypoints. 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. 97 GiB memory in use. This means vLLM 0. See New(old) problem 🙂 torch. 78 MiB is reserved by PyTorch but unallocated. That said, the vllm implementation to me is quite unreliable as I keep getting CUDA out of memory errors. GPU 0 has a total capacty of 79. 35 GiB of which 804. GPU. 87 GiB already allocated; 5. Process 3889394 has 31. Ensure you have an Fig. 3 Model Input Dumps No response 🐛 Describe the bug Description: When using lm_eval for MMLU accuracy evaluation tasks, I frequently encounter OOM errors. The problem occurs when I try to instantiate a LLM object inside a Increase gpu_memory_utilization. 61 GiB memory in use. 94 GiB memory in use. api_server --model bjaidi/Phi-3-medium-128k-instruct-awq --quantization awq --dtype auto --gpu-memory-utilization 0. 58 GiB is reserved by PyTorch but unallocated. I see rows for Allocated memory, Active memory, GPU reserved memory, 按照教程运行，也把vllm版本降到0. 9) to a lower value like 0. 94 MiB is free. Of the allocated memory 20. 00 MiB. [Bug]: torch. py` 原模型Mixtral-8x7B-v0. RuntimeError: CUDA error: out of memory Compile with TORCH_USE_CUDA_DSA to enable device-side assertions. The full exception stack is: [rank0]: torch. Tried to allocate 926. GPU Your GPU doesn't have enough memory for the size of the inputs you are using. OutOfMemoryError: CUDA I'm encountering CUDA out of memory on cold starts. Speculative decoding in vLLM. 30 GiB memory in use. 6. If you are running out of memory, consider decreasing ` gpu_memory_utilization ` or enforcing eager mode. 20 GiB already allocated; 139. 12xlarge machine with has 4 gpus with 16 GB VRAM each but getting cuda out of memory error. Viewed 428 times 1 I am fine-tuning an LLM model. Tried to allocate 50. memory_summary() call, but there doesn't seem to be anything informative that would lead to a fix. Including non-PyTorch memory, this process has 14. 49 MiB is reserved by PyTorch but unallocated. 79 GiB total capacity; 5. 17 GiB memory in use. 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. Open cuda out of memory lead to 'AsyncEngineDeadError: Background loop has errored already. Tried to allocate 826. 5. 4 A100 + CUDA 12. 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. vLLM just kills the terminal as the model is almost done downloading its weights. py:1395 -- SIGTERM handler is not set because current thread is Function to process a batch of texts. 5 (50% First, you should avoid th following OOM error: torch. When dealing with vLLM CUDA out of memory issues, Common Causes of Out of Memory Errors. 10 MiB is reserved by PyTorch but unallocated. 04 GiB is allocated by PyTorch, and 2. empty_cache(). The vLLM pre-allocates GPU cache by using gpu_memory_utilization% of memory. 88 GiB is free. 38 GiB is allocated by PyTorch, and 115. GPU 0 has a total capacity of 15. I am not sure why vLLM is as memory hungry as you The problem here is that the GPU that you are trying to use is already occupied by another process. This will check if your GPU drivers are installed and the load of the GPUS. In the autoregressive process of the model, it will compute an attention formula to select a key word as an output. vllm日志输出的内容，你可以 Hello when i run bentoml serve inside mistral-7b-instruct i get OOM but i have more than 70GB gpu free. Including non-PyTorch memory, this process has 45. Tried to allocate 462. cuda. 00 MiB (GPU 0; 7. Your current environment The output of `python multilora_inference. 18 GiB of which 302. 99 GiB of which 32. 7+cu118'. OutOfMemoryError: CUDA out of memory when Handle inference requests #5147. e. Here is the error log. 19 MiB is free. GPU 1 has a total capacty of 47. Including non-PyTorch memory, this process has 23. 9 --trust-remote-code --tensor-parallel-size 2 --max-model-len 37776 vLLM Version: 0. I use an GPU with 15 GB RAM memory, but when PyTorch saves a checkpoint, the OOM exception happens. OutOfMemoryError: CUDA out of memory. And later, CUDA torch. 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。 torch. Tried to allocate 112. torch. 42 GiB free; 5. 1 vLLM Build Flags: CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled GPU Topology: GPU0 GPU1 NIC0 NIC1 CPU This command worked for me: python3 -m vllm. Tried to allocate 494. Btw, the text-generation-webui can load the model successfully. 2' and 'vllm 0. 0. Including non-PyTorch memory, this process has 78. 36. GPU Any idea? @jibowang it seems like you have other processes running on the same GPU as vLLM. llwih nct zvfyp ytumtu aitc arc mmmtm vhmuf gmjldlc hdytp