CUDA OOM with deepspeed - torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 224.00 MiB. GPU 0 has a total capacity of 47.40 GiB of which 209.12 MiB is free
Hello,i have used google before but nothing helped me.When i run my script with “deepspeed test_deepspeed.py” i get the following errors.When i wath with nvidia-smi i get only my two processes each per GPU.How can i handle this OOM Cuda error? Whan i run with llama-3.1-8b everything works fine.My server has two RTX 6000 Ada. 512Gb Ram, 2x AMD Epyc 9124 (16C/32T). I think this is quiete enough for ofloading while fintuning.Can you give me a hint where my error is? My idea is to quantizie it to 8bit and the run the fine-tune. It should work as translator at the end.Thanks a lot guys! Appreciate your help.[2024-12-14 09:35:04,046] [INFO] [real_accelerator.py:219:get_accelerator] Setting ds_accelerator to cuda (auto detect)Belegter GPU-Speicher: 0.00 GiBReservierter GPU-Speicher: 0.00 GiB[2024-12-14 09:35:04,672] [INFO] [real_accelerator.py:219:get_accelerator] Setting ds_accelerator to cuda (auto detect)low_cpu_mem_usagewas None, now default to True since model is quantized.Loading checkpoint shards:   0%|                                                                                                                                                                                      | 0/30 [00:00<?, ?it/s]Belegter GPU-Speicher: 0.00 GiBReservierter GPU-Speicher: 0.00 GiBlow_cpu_mem_usagewas None, now default to True since model is quantized.Loading checkpoint shards:  33%|█████████████████████████████████████████████████████████▋                                                                                                                   | 10/30 [00:19<00:38,  1.93s/it]Traceback (most recent call last):File “/root/scripts/test_deepspeed.py”, line 51, inmodel = AutoModelForCausalLM.from_pretrained(^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^File “/root/venv/lib/python3.11/site-packages/transformers/models/auto/auto_factory.py”, line 564, in from_pretrainedreturn model_class.from_pretrained(^^^^^^^^^^^^^^^^^^^^^^^^^^^^File “/root/venv/lib/python3.11/site-packages/transformers/modeling_utils.py”, line 4264, in from_pretrained) = cls._load_pretrained_model(^^^^^^^^^^^^^^^^^^^^^^^^^^^File “/root/venv/lib/python3.11/site-packages/transformers/modeling_utils.py”, line 4777, in _load_pretrained_modelnew_error_msgs, offload_index, state_dict_index = _load_state_dict_into_meta_model(^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^File “/root/venv/lib/python3.11/site-packages/transformers/modeling_utils.py”, line 944, in _load_state_dict_into_meta_modelREDACTED_TOKEN.create_quantized_param(model, param, param_name, param_device, state_dict, unexpected_keys)File “/root/venv/lib/python3.11/site-packages/transformers/quantizers/quantizer_bnb_8bit.py”, line 226, in create_quantized_paramnew_value = bnb.nn.Int8Params(new_value, requires_grad=False, **kwargs).to(target_device)^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^File “/root/venv/lib/python3.11/site-packages/bitsandbytes/nn/modules.py”, line 626, in toreturn self.cuda(device)^^^^^^^^^^^^^^^^^File “/root/venv/lib/python3.11/site-packages/bitsandbytes/nn/modules.py”, line 589, in cudaCB, SCB, _ = bnb.functional.int8_vectorwise_quant(B)^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^File “/root/venv/lib/python3.11/site-packages/bitsandbytes/functional.py”, line 2777, in int8_vectorwise_quantout_row = torch.empty(A.shape, device=A.device, dtype=torch.int8)^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 224.00 MiB. GPU 0 has a total capacity of 47.40 GiB of which 209.12 MiB is free. Including non-PyTorch memory, this process has 24.66 GiB memory in use. Process 36676 has 22.52 GiB memory in use. Of the allocated memory 24.14 GiB is allocated by PyTorch, and 112.35 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (CUDA semantics — PyTorch 2.5 documentation)Loading checkpoint shards:  30%|████████████████████████████████████████████████████▏                                                                                                                         | 9/30 [00:18<00:44,  2.10s/it]Traceback (most recent call last):File “/root/scripts/test_deepspeed.py”, line 51, inmodel = AutoModelForCausalLM.from_pretrained(^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^File “/root/venv/lib/python3.11/site-packages/transformers/models/auto/auto_factory.py”, line 564, in from_pretrainedreturn model_class.from_pretrained(^^^^^^^^^^^^^^^^^^^^^^^^^^^^File “/root/venv/lib/python3.11/site-packages/transformers/modeling_utils.py”, line 4264, in from_pretrained) = cls._load_pretrained_model(^^^^^^^^^^^^^^^^^^^^^^^^^^^File “/root/venv/lib/python3.11/site-packages/transformers/modeling_utils.py”, line 4777, in _load_pretrained_modelnew_error_msgs, offload_index, state_dict_index = _load_state_dict_into_meta_model(^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^File “/root/venv/lib/python3.11/site-packages/transformers/modeling_utils.py”, line 944, in _load_state_dict_into_meta_modelREDACTED_TOKEN.create_quantized_param(model, param, param_name, param_device, state_dict, unexpected_keys)File “/root/venv/lib/python3.11/site-packages/transformers/quantizers/quantizer_bnb_8bit.py”, line 226, in create_quantized_paramnew_value = bnb.nn.Int8Params(new_value, requires_grad=False, **kwargs).to(target_device)^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^File “/root/venv/lib/python3.11/site-packages/bitsandbytes/nn/modules.py”, line 626, in toreturn self.cuda(device)^^^^^^^^^^^^^^^^^File “/root/venv/lib/python3.11/site-packages/bitsandbytes/nn/modules.py”, line 589, in cudaCB, SCB, _ = bnb.functional.int8_vectorwise_quant(B)^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^File “/root/venv/lib/python3.11/site-packages/bitsandbytes/functional.py”, line 2777, in int8_vectorwise_quantout_row = torch.empty(A.shape, device=A.device, dtype=torch.int8)^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 224.00 MiB. GPU 0 has a total capacity of 47.40 GiB of which 209.12 MiB is free. Process 36675 has 24.66 GiB memory in use. Including non-PyTorch memory, this process has 22.52 GiB memory in use. Of the allocated memory 21.97 GiB is allocated by PyTorch, and 137.30 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (CUDA semantics — PyTorch 2.5 documentation)Deepspeed config:{“zero_optimization”: {“stage”: 3,“offload_optimizer”: {“device”: “cpu”,“pin_memory”: true},“offload_param”: {“device”: “cpu”,“pin_memory”: true},“overlap_comm”: true,“contiguous_gradients”: true},“bf16”: {“enabled”: true},“train_micro_batch_size_per_gpu”: “auto”,“gradient_accumulation_steps”: “auto”,“gradient_clipping”: 1.0}My Python script:import osos.environ[“PYTORCH_CUDA_ALLOC_CONF”] = “expandable_segments:True”import logginglogging.basicConfig(level=logging.INFO)from datasets import load_datasetLade die JSONL-Datei als Datasetdataset = load_dataset(“json”, data_files=“reitsport_lexikon.jsonl”)Zeige ein Beispiel anprint(dataset[‘train’][0])from transformers import AutoTokenizerTokenizer für das gewünschte Modell ladentokenizer = AutoTokenizer.from_pretrained(“meta-llama/Llama-3.3-70B-Instruct”)tokenizer.pad_token = tokenizer.eos_tokendef tokenize_function(examples):tokens = tokenizer(examples[“text”], padding=“max_length”, truncation=True, max_length=512)tokens[“labels”] = tokens[“input_ids”].copy()return tokenstokenized_dataset = dataset.map(tokenize_function, batched=True)from transformers import AutoModelForCausalLM, TrainingArguments, Trainer, BitsAndBytesConfigfrom peft import get_peft_model, LoraConfig, TaskTypeimport deepspeedimport torchtorch.cuda.empty_cache()torch.cuda.reset_peak_memory_stats()print(f"Belegter GPU-Speicher: {torch.cuda.memory_allocated() / 10243:.2f} GiB")print(f"Reservierter GPU-Speicher: {torch.cuda.memory_reserved() / 10243:.2f} GiB")Konfiguration für 8-Bit-Quantisierungbnb_config = BitsAndBytesConfig(load_in_8bit=True  # 8-Bit-Quantisierung aktivieren)Modell laden#model= AutoModelForCausalLM.from_pretrained(“meta-llama/Llama-3.3-70B-Instruct”, torch_dtype=“auto”)model = AutoModelForCausalLM.from_pretrained(“meta-llama/Llama-3.3-70B-Instruct”,quantization_config=bnb_config)LoRA-Konfigurationlora_config = LoraConfig(task_type=TaskType.CAUSAL_LM,r=8,lora_alpha=32,lora_dropout=0.1)model = get_peft_model(model, lora_config)Trainingsargumente mit DeepSpeed-Konfigurationtraining_args = TrainingArguments(output_dir=“llama_reitsport_finetuned”,gradient_accumulation_steps=8,    # Gradientenakkumulation erhöhenper_device_train_batch_size=1,num_train_epochs=20,eval_strategy=“epoch”,save_strategy=“epoch”,learning_rate=2e-4,bf16=True,deepspeed=“test_ds_config.json”,  # DeepSpeed-Konfigurationsdatei einbinden)Eval-Dataset erstellen (analog zum Train-Dataset)small_train_dataset = tokenized_dataset[“train”].shuffle(seed=42).select(range(len(tokenized_dataset[“train”])))small_eval_dataset = tokenized_dataset[“train”].shuffle(seed=42).select(range(len(tokenized_dataset[“train”])))Trainer erstellen mit eval_datasettrainer = Trainer(model=model,args=training_args,train_dataset=small_train_dataset,eval_dataset=small_eval_dataset,)print(f"Belegter GPU-Speicher: {torch.cuda.memory_allocated() / 10243:.2f} GiB")print(f"Reservierter GPU-Speicher: {torch.cuda.memory_reserved() / 10243:.2f} GiB")import gcgc.collect()Training startentrainer.train()print(f"Belegter GPU-Speicher: {torch.cuda.memory_allocated() / 10243:.2f} GiB")print(f"Reservierter GPU-Speicher: {torch.cuda.memory_reserved() / 10243:.2f} GiB")