Vllm cuda out of memory. Closed zhaotyer opened this issue May 31, .

Vllm cuda out of memory 4. 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. RuntimeError: CUDA error: out of memory Compile with TORCH_USE_CUDA_DSA to enable device-side assertions. Including non-PyTorch memory, this process has 14. Including non-PyTorch memory, this process has 45. 94 GiB memory in use. Tried to allocate 112. 38 GiB already allocated; 6. Ensure you have an Fig. Process 353470 has 46. Please try out this feature and let us know your feedback via GitHub issues! previous. In the autoregressive process of the model, it will compute an attention formula to select a key word as an output. Your current environment The output of `python multilora_inference. 19 MiB is free. 99 GiB free; 3. 39 GiB of which 17. ( torch. OutOfMemoryError: CUDA out of memory. GPU 0 has a total capacty of 79. Tried to allocate 1002. 36. py:1395 -- SIGTERM handler is not set because current thread is Function to process a batch of texts. Tried to allocate 2. 726720287Z torch. See New(old) problem 🙂 torch. [Bug]: Out of Memory (OOM) Issues During MMLU Evaluation with lm_eval #10325. 4 A100 + CUDA 12. 1: Attention formula. This means vLLM 0. 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. 17 GiB memory in use. Explore solutions for Vllm CUDA out of memory errors, optimizing performance and resource management effectively. OutOfMemoryError: CUDA out of memory. 00 MiB (GPU 0; 12. 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. I use an GPU with 15 GB RAM memory, but when PyTorch saves a checkpoint, the OOM exception happens. Including non-PyTorch memory, this process has 17179869184. 12xlarge machine with has 4 gpus with 16 GB VRAM each but getting cuda out of memory error. I printed out the results of the torch. 9) to a lower value like 0. 88 GiB is free. 12 MiB is free. The steps for checking this are: Use nvidia-smi in the terminal. 2. 01 GiB is allocated by PyTorch, and 15. py` 原模型Mixtral-8x7B-v0. 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. 7 has CUDA Graphs enabled by default (i. 54 GiB of which 1. Tried to allocate 14. Process 3889394 has 31. You can pass in the gpu_memory_utilization=0. It has run successfully and responds correctly. Comments (5) tristandevs commented on October 9, 2024. 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. 04 GiB is allocated by PyTorch, and 2. py:928] CUDA graphs can take additional 1 ~3 GiB memory per GPU. Including non-PyTorch memory, this process has 78. 00 GiB (GPU 0; 11. Here are some effective strategies to debug these issues: Enable Detailed Logging. 59 GiB of which 940. See documentation for Memory Management and vllm-project > vllm [Bug]: torch. 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. But i had lots of accuracy loss on this. My vllm inference program runs well for most models with the environment of 'transformers=4. 2024-01-15T20:32:13. 20 GiB already allocated; 139. Of the allocated memory 20. 41 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. Tried to @jibowang it seems like you have other processes running on the same GPU as vLLM. 78 MiB is reserved by PyTorch but unallocated. 95 vllm减小显存 | vllm小模型大显存问题 INFO 07-16 20:48:26 model_runner. vLLM just kills the terminal as the model is almost done downloading its weights. Legend: torch. You can also reduce the ` max_num_seqs ` as needed to decrease memory usage. 7-mixtral-8x7b-GPTQ This version is also uncensored torch. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. 00 GiB total capacity; 3. empty_cache(). 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. 6. 00 MiB (GPU 0; 7. 31 MiB is You need to explicitly clear the allocated memory on cuda via torch. 30 GiB memory in use. OutOfMemoryError: CUDA I'm encountering CUDA out of memory on cold starts. However, I just post one solution here when using VLLM. 00 GiB. And FastChat produces this error as it is loading the last few checkpoint shards: torch. This gives a readable summary of memory allocation and allows you to figure the reason of CUDA running out of memory. The full exception stack is: [rank0]: 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. api_server --model bjaidi/Phi-3-medium-128k-instruct-awq --quantization awq --dtype auto --gpu-memory-utilization 0. 18 GiB of which 302. 61 GiB memory in use. GPU. memory_summary() call, but there doesn't seem to be anything informative that would lead to a fix. 97 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. 58 GiB is free. If you are running out of memory, consider decreasing ` gpu_memory_utilization ` or enforcing eager mode. 94 MiB free; 6. 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. torch. GPU 0 has a total capacty of 39. 5 (50% utilization, or can be set even lower) when initialize the LLM class to reduce the memory footprint. 2' and 'vllm 0. 42 GiB free; 5. 00 MiB. Here is the error log. That said, the vllm implementation to me is quite unreliable as I keep getting CUDA out of memory errors. By increasing this utilization, you can provide more KV cache space. 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. 7+cu118'. 5. , enforce_eager=False by default), and using CUDA Graph would add 1 -3 GiBs of memory overhead. from vllm. 99 GiB of which 32. 38 GiB is allocated by PyTorch, and 115. 5 (50% First, you should avoid th following OOM error: torch. Your current environment vllm 0. 35 GiB of which 804. Including non-PyTorch memory, this process has 23. We will use OpenVPN for this setup. vLLM is designed to occupy all the GPU memory for storing KV cache blocks. GPU 0 has a total capacity of 47. Attempting to load this model with vLLM on an A100-80GB gives me: 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. 1. Of the allocated memory 78. Speculative decoding in vLLM. Btw, the text-generation-webui can load the model successfully. Supported Hardware for Quantization Kernels. GPU 1 has a total capacty of 47. Of the allocated memory 14. about vllm HOT 5 CLOSED tristandevs commented on October 9, 2024 1 [Bug]: torch. CUDA out of memory. Reduce batch size to 1, reduce generation length to 1 token. 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. Note that, you need to instal vllm package under Linux by: pip install vllm. 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. 94 MiB is free. When dealing with vLLM CUDA out of memory issues, Common Causes of Out of Memory Errors. cuda. Tried to allocate 50. If reserved but unallocated memory is large try setting max_split_size_mb to avoid fragmentation. If not set i am getting cuda out of memory in A100 80GB machine also. 3 Model Input Dumps No response 🐛 Describe the bug Description: When using lm_eval for MMLU accuracy evaluation tasks, I frequently encounter OOM errors. 00 GiB memory in use. 78 GiB reserved in total by PyTorch) If reserved memory is >> allocated I'm encountering an issue when using the VLLM library in Python. And later, CUDA torch. Tried to allocate 926. 49 MiB is reserved by PyTorch but unallocated. This will check if your GPU drivers are installed and the load of the GPUS. GPU Any idea? @jibowang it seems like you have other processes running on the same GPU as vLLM. 10 MiB is reserved by PyTorch but unallocated. Closed zhaotyer opened this issue May 31, Could not collectROCM Version: Could not collect Neuron SDK Version: N/A vLLM Version: 0. Tried to allocate 462. GPU 0 has a total capacity of 15. 56 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. 61 GiB is allocated by PyTorch, and 6. [Bug]: torch. ', then later request can NOT be processed, it means, async engine was dead and need to restart vllm engine for continue service. The example code (set tensor_parallel_size=4 for your case): from langchain. getting CUDA out of memory. 0 lm_eval 0. Viewed 428 times 1 I am fine-tuning an LLM model. 0. e. OutOfMemoryError: CUDA out of memory when Handle inference requests #5147. Check memory usage, then increase from there to see what the limits are on your GPU. 58 GiB is reserved by PyTorch but unallocated. Include these lines into your run_vllm_eval() function: I’m trying to run llama2 13b model with rope scaling on the AWS g4dn. GPU Your GPU doesn't have enough memory for the size of the inputs you are using. 76 GiB total capacity; 4. 9 --trust-remote-code --tensor-parallel-size 2 --max-model-len 37776 vLLM Version: 0. 79 GiB total capacity; 5. The vLLM pre-allocates GPU cache by using gpu_memory_utilization% of memory. 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. Tried to allocate 734. The formula consists of three variables. The problem occurs when I try to instantiate a LLM object inside a Increase gpu_memory_utilization. If i am setting min and max pixels that is given by huggingface, The model takes maximum 24GB worth cuda memory. entrypoints. Tried to allocate 494. 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. Open cuda out of memory lead to 'AsyncEngineDeadError: Background loop has errored already. Modified 1 year, 6 months ago. 5 torch 2. Tried to allocate 826. reset_peak_memory_stats(). . next. cuda out of memory lead to 'AsyncEngineDeadError: Background loop has errored already. 87 GiB already allocated; 5. Of the allocated memory 45. pygauk hnmxn vxww pbrom uus kljxoy hbkte tqp nfw tleezv