MIkumikumi0116
commented
1 year ago Describe the bug When I am training my model, everything goes well in the first batch, but an error occurs in the second batch:
RuntimeError: Trying to backward through the graph a second time (or directly access saved tensors after they have already been freed). Saved intermediate values of the graph are freed when you call .backward() or autograd.grad(). Specify retain_graph=True if you need to backward through the graph a second time or if you need to access saved tensors after calling backward.To Reproduce I am fine-tuning llama2 using my own implementation of LoRA. Each lora linear in my llama2 has different LoRA rank. Each linear in my model is replaced to
class Adaptive_Linear(torch.nn.Module): def __init__( self, model_arg: dict, # batch size, max seqlen, vocab size, etc. name: str, # example: layer_list.0.attention.key lora_rank_dict: dict, # lora rank of each linear in_features: int, out_features: int ): super().__init__() self.name = name self.model_arg = model_arg self.linear = torch.nn.Linear(in_features, out_features, bias = False) self.lora_rank = lora_rank_dict[self.name] if self.lora_rank > 0: self.lora_dropout = torch.nn.Dropout(model_arg.lora_dropout) self.lora_a_linear = torch.nn.Linear(in_features, self.lora_rank, bias = False) self.lora_b_linear = torch.nn.Linear(self.lora_rank, out_features, bias = False) self.lora_a_linear.weight.data = torch.rand_like(self.lora_a_linear.weight.data) - 1 * 2 self.lora_b_linear.weight.data = torch.zeros_like(self.lora_b_linear.weight.data) def trainable_parameters(self): # only train lora matrix # the trainable parameters of my model are trainable_parameters # of all Adaptive_Linear connected together using itertools.chain. return iter((self.lora_a_linear.weight, self.lora_b_linear.weight)) def forward(self, x): hidden = self.linear(x) if self.lora_rank > 0: input = self.lora_dropout(x) lora_a = self.lora_a_linear(input) lora_b = self.lora_b_linear(lora_a) output = hidden + lora_b else: output = hidden return outputMy dataset is
class Dataset(torch.utils.data.Dataset): def __init__(self, model_arg, dataset_path): self.model_arg = model_arg self.dataset_path = dataset_path self.dataset = torch.load(self.dataset_path) # List[Tuple] list of tuples of (input token list, output token list, label) token and label are int self.dataset = [data_item for data_item in self.dataset if len(data_item[0]) >= MIN_SEQ_LEN and len(data_item[0]) <= MAX_SEQ_LEN] def __len__(self): return len(self.dataset) def __getitem__(self, index): input_token, output_token, label = self.dataset return input_token, output_token, labelCollate function to pad tokens of a batch to same length
collate_fn(batch): input_token_list, output_token_list, label_list = zip(*batch) padded_input_token = torch.nn.utils.rnn.pad_sequence([seq for seq in input_token_list], batch_first = True) padded_output_token = torch.nn.utils.rnn.pad_sequence([seq for seq in output_token_list], batch_first = True) return padded_input_token, padded_output_token, label_listInitialize model and deepspeed
model = Llama_2_With_Adaptive_Lora(model_arg, lora_rank_dict) model.load_state_dict(state_dict) for name, parameter in model.named_parameters(): if not ('lora' in name): parameter.requires_grad = False engine, optimizer, train_dataloader, _= deepspeed.initialize( model = model, model_parameters = model.trainable_parameters(), training_data = train_dataset, args = args, # local_rank only collate_fn = collate_fn, config = deepspeed_config )Deepspeed config
{ "train_micro_batch_size_per_gpu": 1, "gradient_accumulation_steps": 8, "gradient_clipping": 1.0, "steps_per_print": 10, "wall_clock_breakdown": false, "memory_breakdown": false, "optimizer": { "type": "AdamW", "params": { "lr": 2e-5, "betas": [ 0.9, 0.95 ], "eps": 1e-8, "weight_decay": 1e-4 } }, "scheduler": { "type": "WarmupDecayLR", "params": { "warmup_min_lr": 0, "warmup_max_lr": 2e-5, "warmup_num_steps": 1000, "total_num_steps": 10000 } }, "bf16": { "enabled": true, "loss_scale": 0, "loss_scale_window": 1000, "initial_scale_power": 16, "hysteresis": 2, "min_loss_scale": 1 }, "zero_optimization": { "stage": 3, "offload_param": { "device": "cpu", "pin_memory": true }, "offload_optimizer": { "device": "cpu", "pin_memory": true }, "allgather_partitions": true, "allgather_bucket_size": 1e7, "reduce_scatter": true, "reduce_bucket_size": 1e7, "overlap_comm": true, "contiguous_gradients": true, "sub_group_size": 1e9, "stage3_prefetch_bucket_size": 1e7, "stage3_param_persistence_threshold": 1e5, "stage3_max_live_parameters": 1e8, "stage3_max_reuse_distance": 1e8, "stage3_gather_16bit_weights_on_model_save": true }, "activation_checkpointing": { "partition_activations": true, "number_checkpoints": 100, "cpu_checkpointing": true, "contiguous_memory_optimization": true, "synchronize_checkpoint_boundary": false, "profile": true }, "data_types": { "grad_accum_dtype":"bf16" } }Main code of training
engine.train() for input_token, target_token, _ in train_dataloader: input_token = input_token. to(engine.device) target_token = target_token.to(engine.device) pred_logits = engine(input_token) target_pos = (target_token != PAD_ID) # only calculate loss on non-pad token pred_logits = pred_logits[ target_pos].view(-1, model_arg.vocab_size) target_token = target_token[target_pos].view(-1) loss = torch.nn.functional.cross_entropy(pred_logits, target_token) engine.backward(loss) engine.step()Expected behavior Fix the error
Trying to backward through the graph a second timeds_report output
[2023-10-17 22:28:50,665] [INFO] [real_accelerator.py:158:get_accelerator] Setting ds_accelerator to cuda (auto detect) -------------------------------------------------- DeepSpeed C++/CUDA extension op report -------------------------------------------------- NOTE: Ops not installed will be just-in-time (JIT) compiled at runtime if needed. Op compatibility means that your system meet the required dependencies to JIT install the op. -------------------------------------------------- JIT compiled ops requires ninja ninja .................. [OKAY] -------------------------------------------------- op name ................ installed .. compatible -------------------------------------------------- [WARNING] async_io requires the dev libaio .so object and headers but these were not found. [WARNING] async_io: please install the libaio-dev package with apt [WARNING] If libaio is already installed (perhaps from source), try setting the CFLAGS and LDFLAGS environment variables to where it can be found. async_io ............... [NO] ....... [NO] fused_adam ............. [NO] ....... [OKAY] cpu_adam ............... [NO] ....... [OKAY] cpu_adagrad ............ [NO] ....... [OKAY] cpu_lion ............... [NO] ....... [OKAY] [WARNING] Please specify the CUTLASS repo directory as environment variable $CUTLASS_PATH evoformer_attn ......... [NO] ....... [NO] fused_lamb ............. [NO] ....... [OKAY] fused_lion ............. [NO] ....... [OKAY] quantizer .............. [NO] ....... [OKAY] random_ltd ............. [NO] ....... [OKAY] [WARNING] sparse_attn requires a torch version >= 1.5 and < 2.0 but detected 2.1 [WARNING] using untested triton version (2.1.0), only 1.0.0 is known to be compatible sparse_attn ............ [NO] ....... [NO] spatial_inference ...... [NO] ....... [OKAY] transformer ............ [NO] ....... [OKAY] stochastic_transformer . [NO] ....... [OKAY] transformer_inference .. [NO] ....... [OKAY] -------------------------------------------------- DeepSpeed general environment info: torch install path ............... ['/home/user/miniconda3/envs/llama2/lib/python3.11/site-packages/torch'] torch version .................... 2.1.0+cu121 deepspeed install path ........... ['/home/user/miniconda3/envs/llama2/lib/python3.11/site-packages/deepspeed'] deepspeed info ................... 0.11.1, unknown, unknown torch cuda version ............... 12.1 torch hip version ................ None nvcc version ..................... 11.6 deepspeed wheel compiled w. ...... torch 2.0, cuda 11.7 shared memory (/dev/shm) size .... 125.77 GBScreenshots No Screenshots
System info (please complete the following information): OS: Ubuntu 20.04.4 GPU: 1x RTX 3090ti Interconnects: No Python version: 3.11.4
Launcher context
PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:64 deepspeed --include localhost:0 train.pyDocker context No docker
Additional context Complete deepspeed output In Reproduce, I simplified some of the code, so the error traceback is slightly different from Reproduce.
[2023-10-17 22:38:23,225] [INFO] [real_accelerator.py:158:get_accelerator] Setting ds_accelerator to cuda (auto detect) [2023-10-17 22:38:24,055] [WARNING] [runner.py:203:fetch_hostfile] Unable to find hostfile, will proceed with training with local resources only. [2023-10-17 22:38:24,056] [INFO] [runner.py:570:main] cmd = /home/user/miniconda3/envs/llama2/bin/python -u -m deepspeed.launcher.launch --world_info=eyJsb2NhbGhvc3QiOiBbNV19 --master_addr=127.0.0.1 --master_port=29500 --enable_each_rank_log=None train.py [2023-10-17 22:38:26,035] [INFO] [real_accelerator.py:158:get_accelerator] Setting ds_accelerator to cuda (auto detect) [2023-10-17 22:38:26,860] [INFO] [launch.py:145:main] WORLD INFO DICT: {'localhost': [5]} [2023-10-17 22:38:26,860] [INFO] [launch.py:151:main] nnodes=1, num_local_procs=1, node_rank=0 [2023-10-17 22:38:26,860] [INFO] [launch.py:162:main] global_rank_mapping=defaultdict(<class 'list'>, {'localhost': [0]}) [2023-10-17 22:38:26,860] [INFO] [launch.py:163:main] dist_world_size=1 [2023-10-17 22:38:26,860] [INFO] [launch.py:165:main] Setting CUDA_VISIBLE_DEVICES=5 [2023-10-17 22:38:28,800] [INFO] [real_accelerator.py:158:get_accelerator] Setting ds_accelerator to cuda (auto detect) [2023-10-17 22:38:31,345] [INFO] [comm.py:637:init_distributed] cdb=None [2023-10-17 22:38:31,346] [INFO] [comm.py:668:init_distributed] Initializing TorchBackend in DeepSpeed with backend nccl [2023-10-17 22:41:14,468] [INFO] [logging.py:96:log_dist] [Rank 0] DeepSpeed info: version=0.11.1, git-hash=unknown, git-branch=unknown [2023-10-17 22:41:20,391] [INFO] [logging.py:96:log_dist] [Rank 0] DeepSpeed Flops Profiler Enabled: False �[93m [WARNING] �[0m cpu_adam cuda is missing or is incompatible with installed torch, only cpu ops can be compiled! Using /home/user/.cache/torch_extensions/py311_cu121 as PyTorch extensions root... Emitting ninja build file /home/user/.cache/torch_extensions/py311_cu121/cpu_adam/build.ninja... Building extension module cpu_adam... Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N) ninja: no work to do. Loading extension module cpu_adam... Time to load cpu_adam op: 2.476133108139038 seconds Adam Optimizer #0 is created with AVX512 arithmetic capability. Config: alpha=0.000020, betas=(0.900000, 0.950000), weight_decay=0.000100, adam_w=1 [2023-10-17 22:41:25,279] [INFO] [logging.py:96:log_dist] [Rank 0] Using DeepSpeed Optimizer param name adamw as basic optimizer [2023-10-17 22:41:25,280] [INFO] [logging.py:96:log_dist] [Rank 0] Removing param_group that has no 'params' in the basic Optimizer [2023-10-17 22:41:25,313] [INFO] [logging.py:96:log_dist] [Rank 0] DeepSpeed Basic Optimizer = DeepSpeedCPUAdam [2023-10-17 22:41:25,313] [INFO] [utils.py:56:is_zero_supported_optimizer] Checking ZeRO support for optimizer=DeepSpeedCPUAdam type=<class 'deepspeed.ops.adam.cpu_adam.DeepSpeedCPUAdam'> [2023-10-17 22:41:25,313] [INFO] [logging.py:96:log_dist] [Rank 0] Creating fp16 ZeRO stage 3 optimizer, MiCS is enabled False, Hierarchical params gather False [2023-10-17 22:41:25,313] [INFO] [logging.py:96:log_dist] [Rank 0] Creating torch.bfloat16 ZeRO stage 3 optimizer [2023-10-17 22:41:25,766] [INFO] [utils.py:802:see_memory_usage] Stage 3 initialize beginning [2023-10-17 22:41:25,767] [INFO] [utils.py:803:see_memory_usage] MA 12.99 GB Max_MA 12.99 GB CA 13.01 GB Max_CA 13 GB [2023-10-17 22:41:25,767] [INFO] [utils.py:810:see_memory_usage] CPU Virtual Memory: used = 186.86 GB, percent = 74.3% [2023-10-17 22:41:25,771] [INFO] [stage3.py:126:__init__] Reduce bucket size 50000000 [2023-10-17 22:41:25,771] [INFO] [stage3.py:127:__init__] Prefetch bucket size 50,000,000 [2023-10-17 22:41:26,225] [INFO] [utils.py:802:see_memory_usage] DeepSpeedZeRoOffload initialize [begin] [2023-10-17 22:41:26,225] [INFO] [utils.py:803:see_memory_usage] MA 12.99 GB Max_MA 12.99 GB CA 13.01 GB Max_CA 13 GB [2023-10-17 22:41:26,225] [INFO] [utils.py:810:see_memory_usage] CPU Virtual Memory: used = 187.52 GB, percent = 74.6% Parameter Offload: Total persistent parameters: 10247936 in 260 params [2023-10-17 22:41:37,268] [INFO] [utils.py:802:see_memory_usage] DeepSpeedZeRoOffload initialize [end] [2023-10-17 22:41:37,268] [INFO] [utils.py:803:see_memory_usage] MA 0.31 GB Max_MA 12.99 GB CA 13.01 GB Max_CA 13 GB [2023-10-17 22:41:37,269] [INFO] [utils.py:810:see_memory_usage] CPU Virtual Memory: used = 199.51 GB, percent = 79.3% [2023-10-17 22:41:37,759] [INFO] [utils.py:802:see_memory_usage] Before creating fp16 partitions [2023-10-17 22:41:37,760] [INFO] [utils.py:803:see_memory_usage] MA 0.31 GB Max_MA 0.31 GB CA 13.01 GB Max_CA 13 GB [2023-10-17 22:41:37,760] [INFO] [utils.py:810:see_memory_usage] CPU Virtual Memory: used = 200.1 GB, percent = 79.6% [2023-10-17 22:41:38,377] [INFO] [utils.py:802:see_memory_usage] After creating fp16 partitions: 1 [2023-10-17 22:41:38,377] [INFO] [utils.py:803:see_memory_usage] MA 0.31 GB Max_MA 0.31 GB CA 13.01 GB Max_CA 13 GB [2023-10-17 22:41:38,377] [INFO] [utils.py:810:see_memory_usage] CPU Virtual Memory: used = 199.63 GB, percent = 79.4% [2023-10-17 22:41:38,946] [INFO] [utils.py:802:see_memory_usage] Before creating fp32 partitions [2023-10-17 22:41:38,947] [INFO] [utils.py:803:see_memory_usage] MA 0.31 GB Max_MA 0.31 GB CA 13.01 GB Max_CA 13 GB [2023-10-17 22:41:38,947] [INFO] [utils.py:810:see_memory_usage] CPU Virtual Memory: used = 200.27 GB, percent = 79.6% [2023-10-17 22:41:39,533] [INFO] [utils.py:802:see_memory_usage] After creating fp32 partitions [2023-10-17 22:41:39,533] [INFO] [utils.py:803:see_memory_usage] MA 0.31 GB Max_MA 0.31 GB CA 13.01 GB Max_CA 13 GB [2023-10-17 22:41:39,533] [INFO] [utils.py:810:see_memory_usage] CPU Virtual Memory: used = 200.19 GB, percent = 79.6% [2023-10-17 22:41:40,012] [INFO] [utils.py:802:see_memory_usage] Before initializing optimizer states [2023-10-17 22:41:40,012] [INFO] [utils.py:803:see_memory_usage] MA 0.31 GB Max_MA 0.31 GB CA 13.01 GB Max_CA 13 GB [2023-10-17 22:41:40,013] [INFO] [utils.py:810:see_memory_usage] CPU Virtual Memory: used = 200.68 GB, percent = 79.8% [2023-10-17 22:41:40,636] [INFO] [utils.py:802:see_memory_usage] After initializing optimizer states [2023-10-17 22:41:40,637] [INFO] [utils.py:803:see_memory_usage] MA 0.31 GB Max_MA 0.31 GB CA 13.01 GB Max_CA 13 GB [2023-10-17 22:41:40,653] [INFO] [utils.py:810:see_memory_usage] CPU Virtual Memory: used = 200.93 GB, percent = 79.9% [2023-10-17 22:41:40,653] [INFO] [stage3.py:459:_setup_for_real_optimizer] optimizer state initialized [2023-10-17 22:41:41,334] [INFO] [utils.py:802:see_memory_usage] After initializing ZeRO optimizer [2023-10-17 22:41:41,335] [INFO] [utils.py:803:see_memory_usage] MA 0.41 GB Max_MA 0.41 GB CA 13.1 GB Max_CA 13 GB [2023-10-17 22:41:41,335] [INFO] [utils.py:810:see_memory_usage] CPU Virtual Memory: used = 201.63 GB, percent = 80.2% [2023-10-17 22:41:41,335] [INFO] [logging.py:96:log_dist] [Rank 0] DeepSpeed Final Optimizer = adamw [2023-10-17 22:41:41,335] [INFO] [logging.py:96:log_dist] [Rank 0] DeepSpeed using configured LR scheduler = WarmupDecayLR [2023-10-17 22:41:41,335] [INFO] [logging.py:96:log_dist] [Rank 0] DeepSpeed LR Scheduler = <deepspeed.runtime.lr_schedules.WarmupDecayLR object at 0x7fb1e7f0ed50> [2023-10-17 22:41:41,335] [INFO] [logging.py:96:log_dist] [Rank 0] step=0, skipped=0, lr=[2e-05], mom=[[0.9, 0.95]] [2023-10-17 22:41:41,337] [INFO] [config.py:968:print] DeepSpeedEngine configuration: [2023-10-17 22:41:41,338] [INFO] [config.py:972:print] activation_checkpointing_config { "partition_activations": true, "contiguous_memory_optimization": true, "cpu_checkpointing": true, "number_checkpoints": null, "synchronize_checkpoint_boundary": false, "profile": false } [2023-10-17 22:41:41,338] [INFO] [config.py:972:print] aio_config ................... {'block_size': 1048576, 'queue_depth': 8, 'thread_count': 1, 'single_submit': False, 'overlap_events': True} [2023-10-17 22:41:41,338] [INFO] [config.py:972:print] amp_enabled .................. False [2023-10-17 22:41:41,338] [INFO] [config.py:972:print] amp_params ................... False [2023-10-17 22:41:41,338] [INFO] [config.py:972:print] autotuning_config ............ { "enabled": false, "start_step": null, "end_step": null, "metric_path": null, "arg_mappings": null, "metric": "throughput", "model_info": null, "results_dir": "autotuning_results", "exps_dir": "autotuning_exps", "overwrite": true, "fast": true, "start_profile_step": 3, "end_profile_step": 5, "tuner_type": "gridsearch", "tuner_early_stopping": 5, "tuner_num_trials": 50, "model_info_path": null, "mp_size": 1, "max_train_batch_size": null, "min_train_batch_size": 1, "max_train_micro_batch_size_per_gpu": 1.024000e+03, "min_train_micro_batch_size_per_gpu": 1, "num_tuning_micro_batch_sizes": 3 } [2023-10-17 22:41:41,338] [INFO] [config.py:972:print] bfloat16_enabled ............. True [2023-10-17 22:41:41,338] [INFO] [config.py:972:print] checkpoint_parallel_write_pipeline False [2023-10-17 22:41:41,338] [INFO] [config.py:972:print] checkpoint_tag_validation_enabled True [2023-10-17 22:41:41,338] [INFO] [config.py:972:print] checkpoint_tag_validation_fail False [2023-10-17 22:41:41,338] [INFO] [config.py:972:print] comms_config ................. <deepspeed.comm.config.DeepSpeedCommsConfig object at 0x7fb547229a90> [2023-10-17 22:41:41,338] [INFO] [config.py:972:print] communication_data_type ...... None [2023-10-17 22:41:41,338] [INFO] [config.py:972:print] compression_config ........... {'weight_quantization': {'shared_parameters': {'enabled': False, 'quantizer_kernel': False, 'schedule_offset': 0, 'quantize_groups': 1, 'quantize_verbose': False, 'quantization_type': 'symmetric', 'quantize_weight_in_forward': False, 'rounding': 'nearest', 'fp16_mixed_quantize': False, 'quantize_change_ratio': 0.001}, 'different_groups': {}}, 'activation_quantization': {'shared_parameters': {'enabled': False, 'quantization_type': 'symmetric', 'range_calibration': 'dynamic', 'schedule_offset': 1000}, 'different_groups': {}}, 'sparse_pruning': {'shared_parameters': {'enabled': False, 'method': 'l1', 'schedule_offset': 1000}, 'different_groups': {}}, 'row_pruning': {'shared_parameters': {'enabled': False, 'method': 'l1', 'schedule_offset': 1000}, 'different_groups': {}}, 'head_pruning': {'shared_parameters': {'enabled': False, 'method': 'topk', 'schedule_offset': 1000}, 'different_groups': {}}, 'channel_pruning': {'shared_parameters': {'enabled': False, 'method': 'l1', 'schedule_offset': 1000}, 'different_groups': {}}, 'layer_reduction': {'enabled': False}} [2023-10-17 22:41:41,338] [INFO] [config.py:972:print] curriculum_enabled_legacy .... False [2023-10-17 22:41:41,338] [INFO] [config.py:972:print] curriculum_params_legacy ..... False [2023-10-17 22:41:41,338] [INFO] [config.py:972:print] data_efficiency_config ....... {'enabled': False, 'seed': 1234, 'data_sampling': {'enabled': False, 'num_epochs': 1000, 'num_workers': 0, 'curriculum_learning': {'enabled': False}}, 'data_routing': {'enabled': False, 'random_ltd': {'enabled': False, 'layer_token_lr_schedule': {'enabled': False}}}} [2023-10-17 22:41:41,338] [INFO] [config.py:972:print] data_efficiency_enabled ...... False [2023-10-17 22:41:41,338] [INFO] [config.py:972:print] dataloader_drop_last ......... False [2023-10-17 22:41:41,338] [INFO] [config.py:972:print] disable_allgather ............ False [2023-10-17 22:41:41,338] [INFO] [config.py:972:print] dump_state ................... False [2023-10-17 22:41:41,338] [INFO] [config.py:972:print] dynamic_loss_scale_args ...... None [2023-10-17 22:41:41,338] [INFO] [config.py:972:print] eigenvalue_enabled ........... False [2023-10-17 22:41:41,338] [INFO] [config.py:972:print] eigenvalue_gas_boundary_resolution 1 [2023-10-17 22:41:41,338] [INFO] [config.py:972:print] eigenvalue_layer_name ........ bert.encoder.layer [2023-10-17 22:41:41,338] [INFO] [config.py:972:print] eigenvalue_layer_num ......... 0 [2023-10-17 22:41:41,338] [INFO] [config.py:972:print] eigenvalue_max_iter .......... 100 [2023-10-17 22:41:41,338] [INFO] [config.py:972:print] eigenvalue_stability ......... 1e-06 [2023-10-17 22:41:41,338] [INFO] [config.py:972:print] eigenvalue_tol ............... 0.01 [2023-10-17 22:41:41,338] [INFO] [config.py:972:print] eigenvalue_verbose ........... False [2023-10-17 22:41:41,338] [INFO] [config.py:972:print] elasticity_enabled ........... False [2023-10-17 22:41:41,338] [INFO] [config.py:972:print] flops_profiler_config ........ { "enabled": false, "recompute_fwd_factor": 0.0, "profile_step": 1, "module_depth": -1, "top_modules": 1, "detailed": true, "output_file": null } [2023-10-17 22:41:41,338] [INFO] [config.py:972:print] fp16_auto_cast ............... None [2023-10-17 22:41:41,338] [INFO] [config.py:972:print] fp16_enabled ................. False [2023-10-17 22:41:41,338] [INFO] [config.py:972:print] fp16_master_weights_and_gradients False [2023-10-17 22:41:41,338] [INFO] [config.py:972:print] global_rank .................. 0 [2023-10-17 22:41:41,338] [INFO] [config.py:972:print] grad_accum_dtype ............. bf16 [2023-10-17 22:41:41,338] [INFO] [config.py:972:print] gradient_accumulation_steps .. 8 [2023-10-17 22:41:41,338] [INFO] [config.py:972:print] gradient_clipping ............ 1.0 [2023-10-17 22:41:41,338] [INFO] [config.py:972:print] gradient_predivide_factor .... 1.0 [2023-10-17 22:41:41,338] [INFO] [config.py:972:print] hybrid_engine ................ enabled=False max_out_tokens=512 inference_tp_size=1 release_inference_cache=False pin_parameters=True tp_gather_partition_size=8 [2023-10-17 22:41:41,338] [INFO] [config.py:972:print] initial_dynamic_scale ........ 1 [2023-10-17 22:41:41,338] [INFO] [config.py:972:print] load_universal_checkpoint .... False [2023-10-17 22:41:41,338] [INFO] [config.py:972:print] loss_scale ................... 1.0 [2023-10-17 22:41:41,339] [INFO] [config.py:972:print] memory_breakdown ............. False [2023-10-17 22:41:41,339] [INFO] [config.py:972:print] mics_hierarchial_params_gather False [2023-10-17 22:41:41,339] [INFO] [config.py:972:print] mics_shard_size .............. -1 [2023-10-17 22:41:41,339] [INFO] [config.py:972:print] monitor_config ............... tensorboard=TensorBoardConfig(enabled=False, output_path='', job_name='DeepSpeedJobName') wandb=WandbConfig(enabled=False, group=None, team=None, project='deepspeed') csv_monitor=CSVConfig(enabled=False, output_path='', job_name='DeepSpeedJobName') enabled=False [2023-10-17 22:41:41,339] [INFO] [config.py:972:print] nebula_config ................ { "enabled": false, "persistent_storage_path": null, "persistent_time_interval": 100, "num_of_version_in_retention": 2, "enable_nebula_load": true, "load_path": null } [2023-10-17 22:41:41,339] [INFO] [config.py:972:print] optimizer_legacy_fusion ...... False [2023-10-17 22:41:41,339] [INFO] [config.py:972:print] optimizer_name ............... adamw [2023-10-17 22:41:41,339] [INFO] [config.py:972:print] optimizer_params ............. {'lr': 2e-05, 'betas': [0.9, 0.95], 'eps': 1e-08, 'weight_decay': 0.0001} [2023-10-17 22:41:41,339] [INFO] [config.py:972:print] pipeline ..................... {'stages': 'auto', 'partition': 'best', 'seed_layers': False, 'activation_checkpoint_interval': 0} [2023-10-17 22:41:41,339] [INFO] [config.py:972:print] pld_enabled .................. False [2023-10-17 22:41:41,339] [INFO] [config.py:972:print] pld_params ................... False [2023-10-17 22:41:41,339] [INFO] [config.py:972:print] prescale_gradients ........... False [2023-10-17 22:41:41,339] [INFO] [config.py:972:print] scheduler_name ............... WarmupDecayLR [2023-10-17 22:41:41,339] [INFO] [config.py:972:print] scheduler_params ............. {'warmup_min_lr': 0, 'warmup_max_lr': 2e-05, 'warmup_num_steps': 1000, 'total_num_steps': 10000} [2023-10-17 22:41:41,339] [INFO] [config.py:972:print] sparse_attention ............. None [2023-10-17 22:41:41,339] [INFO] [config.py:972:print] sparse_gradients_enabled ..... False [2023-10-17 22:41:41,339] [INFO] [config.py:972:print] steps_per_print .............. 10 [2023-10-17 22:41:41,339] [INFO] [config.py:972:print] train_batch_size ............. 8 [2023-10-17 22:41:41,339] [INFO] [config.py:972:print] train_micro_batch_size_per_gpu 1 [2023-10-17 22:41:41,339] [INFO] [config.py:972:print] use_node_local_storage ....... False [2023-10-17 22:41:41,339] [INFO] [config.py:972:print] wall_clock_breakdown ......... False [2023-10-17 22:41:41,339] [INFO] [config.py:972:print] weight_quantization_config ... None [2023-10-17 22:41:41,339] [INFO] [config.py:972:print] world_size ................... 1 [2023-10-17 22:41:41,339] [INFO] [config.py:972:print] zero_allow_untested_optimizer False [2023-10-17 22:41:41,339] [INFO] [config.py:972:print] zero_config .................. stage=3 contiguous_gradients=True reduce_scatter=True reduce_bucket_size=50000000 allgather_partitions=True allgather_bucket_size=50000000 overlap_comm=False load_from_fp32_weights=True elastic_checkpoint=False offload_param=DeepSpeedZeroOffloadParamConfig(device='cpu', nvme_path=None, buffer_count=5, buffer_size=100,000,000, max_in_cpu=1,000,000,000, pin_memory=False) offload_optimizer=DeepSpeedZeroOffloadOptimizerConfig(device='cpu', nvme_path=None, buffer_count=4, pin_memory=False, pipeline=False, pipeline_read=False, pipeline_write=False, fast_init=False) sub_group_size=1,000,000,000 cpu_offload_param=None cpu_offload_use_pin_memory=None cpu_offload=None prefetch_bucket_size=50,000,000 param_persistence_threshold=100,000 model_persistence_threshold=sys.maxsize max_live_parameters=1,000,000,000 max_reuse_distance=1,000,000,000 gather_16bit_weights_on_model_save=False stage3_gather_fp16_weights_on_model_save=False ignore_unused_parameters=True legacy_stage1=False round_robin_gradients=False zero_hpz_partition_size=1 zero_quantized_weights=False zero_quantized_nontrainable_weights=False zero_quantized_gradients=False mics_shard_size=-1 mics_hierarchical_params_gather=False memory_efficient_linear=True pipeline_loading_checkpoint=False override_module_apply=True [2023-10-17 22:41:41,339] [INFO] [config.py:972:print] zero_enabled ................. True [2023-10-17 22:41:41,339] [INFO] [config.py:972:print] zero_force_ds_cpu_optimizer .. True [2023-10-17 22:41:41,339] [INFO] [config.py:972:print] zero_optimization_stage ...... 3 [2023-10-17 22:41:41,339] [INFO] [config.py:958:print_user_config] json = { "train_micro_batch_size_per_gpu": 1, "gradient_accumulation_steps": 8, "gradient_clipping": 1.0, "steps_per_print": 10, "optimizer": { "type": "AdamW", "params": { "lr": 2e-05, "betas": [0.9, 0.95], "eps": 1e-08, "weight_decay": 0.0001 } }, "scheduler": { "type": "WarmupDecayLR", "params": { "warmup_min_lr": 0, "warmup_max_lr": 2e-05, "warmup_num_steps": 1000, "total_num_steps": 1.000000e+04 } }, "bf16": { "enabled": true }, "zero_optimization": { "stage": 3, "offload_param": { "device": "cpu", "pin_memory": false }, "offload_optimizer": { "device": "cpu", "pin_memory": false }, "allgather_bucket_size": 5.000000e+07, "reduce_bucket_size": 5.000000e+07, "allgather_partitions": true, "reduce_scatter": true, "overlap_comm": false, "contiguous_gradients": true }, "activation_checkpointing": { "partition_activations": true, "cpu_checkpointing": true, "contiguous_memory_optimization": true, "number_checkpoints": null, "synchronize_checkpoint_boundary": false, "profile": false }, "data_types": { "grad_accum_dtype": "bf16" } } Traceback (most recent call last): File "/data/user/llama2/src/7_train/train.py", line 449, in <module> trainer.train_train() File "/data/user/llama2/src/7_train/train.py", line 408, in train_train self.train_epoch(epoch) File "/data/user/llama2/src/7_train/train.py", line 437, in train_epoch self.model_engine.backward(loss) File "/home/user/miniconda3/envs/llama2/lib/python3.11/site-packages/deepspeed/utils/nvtx.py", line 15, in wrapped_fn ret_val = func(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^ File "/home/user/miniconda3/envs/llama2/lib/python3.11/site-packages/deepspeed/runtime/engine.py", line 1929, in backward self.optimizer.backward(loss, retain_graph=retain_graph) File "/home/user/miniconda3/envs/llama2/lib/python3.11/site-packages/deepspeed/utils/nvtx.py", line 15, in wrapped_fn ret_val = func(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^ File "/home/user/miniconda3/envs/llama2/lib/python3.11/site-packages/deepspeed/runtime/zero/stage3.py", line 2091, in backward self.loss_scaler.backward(loss.float(), retain_graph=retain_graph) File "/home/user/miniconda3/envs/llama2/lib/python3.11/site-packages/deepspeed/runtime/fp16/loss_scaler.py", line 63, in backward scaled_loss.backward(retain_graph=retain_graph) File "/home/user/miniconda3/envs/llama2/lib/python3.11/site-packages/torch/_tensor.py", line 492, in backward torch.autograd.backward( File "/home/user/miniconda3/envs/llama2/lib/python3.11/site-packages/torch/autograd/__init__.py", line 251, in backward Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass RuntimeError: Trying to backward through the graph a second time (or directly access saved tensors after they have already been freed). Saved intermediate values of the graph are freed when you call .backward() or autograd.grad(). Specify retain_graph=True if you need to backward through the graph a second time or if you need to access saved tensors after calling backward. [2023-10-17 22:42:02,884] [INFO] [launch.py:315:sigkill_handler] Killing subprocess 230633 [2023-10-17 22:42:02,885] [ERROR] [launch.py:321:sigkill_handler] ['/home/user/miniconda3/envs/llama2/bin/python', '-u', 'train.py', '--local_rank=0'] exits with return code = 1I would appreciate any help.
I should submit the issue to https://github.com/microsoft/DeepSpeed Apologies for the error submission.
Describe the bug When I am training my model, everything goes well in the first batch, but an error occurs in the second batch:
RuntimeError: Trying to backward through the graph a second time (or directly access saved tensors after they have already been freed). Saved intermediate values of the graph are freed when you call .backward() or autograd.grad(). Specify retain_graph=True if you need to backward through the graph a second time or if you need to access saved tensors after calling backward.To Reproduce I am fine-tuning llama2 using my own implementation of LoRA. Each lora linear in my llama2 has different LoRA rank. Each linear in my model is replaced to
My dataset is
Collate function to pad tokens of a batch to same length
Initialize model and deepspeed
Deepspeed config
Main code of training
Expected behavior Fix the error
Trying to backward through the graph a second timeds_report output
Screenshots No Screenshots
System info (please complete the following information): OS: Ubuntu 20.04.4 GPU: 1x RTX 3090ti Interconnects: No Python version: 3.11.4
Launcher context
PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:64 deepspeed --include localhost:0 train.pyDocker context No docker
Additional context Complete deepspeed output In Reproduce, I simplified some of the code, so the error traceback is slightly different from Reproduce.
I would appreciate any help.