Comments (5)
Ricardokevins
commented on July 30, 2024
对应的err文件
:job_id:03000000
[2024-05-24 18:57:09,526] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect)
:actor_name:CriticModelRayActor
[2024-05-24 18:58:07,087] [INFO] [comm.py:637:init_distributed] cdb=None
[2024-05-24 18:58:07,088] [INFO] [comm.py:668:init_distributed] Initializing TorchBackend in DeepSpeed with backend nccl
LLMForSequenceRegression(
(model): LlamaModel(
(embed_tokens): Embedding(128256, 4096)
(layers): ModuleList(
(0-31): 32 x LlamaDecoderLayer(
(self_attn): LlamaFlashAttention2(
(q_proj): Linear(in_features=4096, out_features=4096, bias=False)
(k_proj): Linear(in_features=4096, out_features=1024, bias=False)
(v_proj): Linear(in_features=4096, out_features=1024, bias=False)
(o_proj): Linear(in_features=4096, out_features=4096, bias=False)
(rotary_emb): LlamaRotaryEmbedding()
)
(mlp): LlamaMLP(
(gate_proj): Linear(in_features=4096, out_features=14336, bias=False)
(up_proj): Linear(in_features=4096, out_features=14336, bias=False)
(down_proj): Linear(in_features=14336, out_features=4096, bias=False)
(act_fn): SiLU()
)
(input_layernorm): LlamaRMSNorm()
(post_attention_layernorm): LlamaRMSNorm()
)
)
(norm): LlamaRMSNorm()
)
(value_head): Linear(in_features=4096, out_features=1, bias=False)
)
reward normalization status: True
mean: tensor([0.], dtype=torch.bfloat16), std tensor([1.], dtype=torch.bfloat16)
Time to load cpu_adam op: 2.4042906761169434 seconds
[2024-05-24 18:58:16,843] [INFO] [logging.py:96:log_dist] [Rank 0] DeepSpeed info: version=0.13.5, git-hash=unknown, git-branch=unknown
[2024-05-24 18:58:16,843] [INFO] [comm.py:662:init_distributed] Distributed backend already initialized
Adam Optimizer #0 is created with AVX512 arithmetic capability.
Config: alpha=0.000009, betas=(0.900000, 0.950000), weight_decay=0.000000, adam_w=1
n136-112-040:375881:375881 [0] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth0
n136-112-040:375881:375881 [0] NCCL INFO Bootstrap : Using eth0:10.136.112.40<0>
n136-112-040:375881:375881 [0] NCCL INFO NET/Plugin : dlerror=libnccl-net.so: cannot open shared object file: No such file or directory No plugin found (libnccl-net.so), using internal implementation
n136-112-040:375881:375881 [0] NCCL INFO cudaDriverVersion 12010
NCCL version 2.20.5+cuda12.4
n136-112-040:375881:377674 [0] NCCL INFO NCCL_IB_DISABLE set by environment to 0.
n136-112-040:375881:377674 [0] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth0
n136-112-040:375881:377674 [0] NCCL INFO NCCL_IB_HCA set to mlx5
n136-112-040:375881:377674 [0] NCCL INFO NET/IB : No device found.
n136-112-040:375881:377674 [0] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth0
n136-112-040:375881:377674 [0] NCCL INFO NET/Socket : Using [0]eth0:10.136.112.40<0>
n136-112-040:375881:377674 [0] NCCL INFO Using non-device net plugin version 0
n136-112-040:375881:377674 [0] NCCL INFO Using network Socket
n136-112-040:375881:377674 [0] NCCL INFO comm 0x1bbd32a0 rank 0 nranks 2 cudaDev 0 nvmlDev 2 busId 4a000 commId 0xb015bed65ddba90d - Init START
n136-112-040:375881:377674 [0] NCCL INFO Setting affinity for GPU 2 to ffffffff,00000000,ffffffff
n136-112-040:375881:377674 [0] NCCL INFO comm 0x1bbd32a0 rank 0 nRanks 2 nNodes 1 localRanks 2 localRank 0 MNNVL 0
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n136-112-040:375881:377674 [0] NCCL INFO P2P Chunksize set to 524288
n136-112-040:375881:377674 [0] NCCL INFO Channel 00/0 : 0[2] -> 1[3] via P2P/CUMEM/read
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n136-112-040:375881:377674 [0] NCCL INFO Channel 10/0 : 0[2] -> 1[3] via P2[2024-05-24 18:58:21,492] [INFO] [logging.py:96:log_dist] [Rank 0] DeepSpeed Flops Profiler Enabled: False
[2024-05-24 18:58:21,493] [INFO] [logging.py:96:log_dist] [Rank 0] Using client Optimizer as basic optimizer
[2024-05-24 18:58:21,493] [INFO] [logging.py:96:log_dist] [Rank 0] Removing param_group that has no 'params' in the basic Optimizer
[2024-05-24 18:58:21,504] [INFO] [logging.py:96:log_dist] [Rank 0] DeepSpeed Basic Optimizer = DeepSpeedCPUAdam
[2024-05-24 18:58:21,504] [INFO] [utils.py:56:is_zero_supported_optimizer] Checking ZeRO support for optimizer=DeepSpeedCPUAdam type=<class 'deepspeed.ops.adam.cpu_adam.DeepSpeedCPUAdam'>
[2024-05-24 18:58:21,504] [INFO] [logging.py:96:log_dist] [Rank 0] Creating torch.bfloat16 ZeRO stage 2 optimizer
[2024-05-24 18:58:21,504] [INFO] [stage_1_and_2.py:149:init] Reduce bucket size 500,000,000
[2024-05-24 18:58:21,504] [INFO] [stage_1_and_2.py:150:init] Allgather bucket size 500,000,000
[2024-05-24 18:58:21,504] [INFO] [stage_1_and_2.py:151:init] CPU Offload: True
[2024-05-24 18:58:21,504] [INFO] [stage_1_and_2.py:152:init] Round robin gradient partitioning: False
[2024-05-24 18:58:40,366] [INFO] [utils.py:800:see_memory_usage] Before initializing optimizer states
[2024-05-24 18:58:40,367] [INFO] [utils.py:801:see_memory_usage] MA 15.08 GB Max_MA 15.08 GB CA 15.59 GB Max_CA 16 GB
[2024-05-24 18:58:40,367] [INFO] [utils.py:808:see_memory_usage] CPU Virtual Memory: used = 222.94 GB, percent = 11.1%
[2024-05-24 18:58:47,530] [INFO] [utils.py:800:see_memory_usage] After initializing optimizer states
[2024-05-24 18:58:47,530] [INFO] [utils.py:801:see_memory_usage] MA 15.08 GB Max_MA 15.08 GB CA 15.59 GB Max_CA 16 GB
[2024-05-24 18:58:47,530] [INFO] [utils.py:808:see_memory_usage] CPU Virtual Memory: used = 245.8 GB, percent = 12.2%
[2024-05-24 18:58:47,530] [INFO] [stage_1_and_2.py:539:init] optimizer state initialized
[2024-05-24 18:58:47,639] [INFO] [utils.py:800:see_memory_usage] After initializing ZeRO optimizer
[2024-05-24 18:58:47,639] [INFO] [utils.py:801:see_memory_usage] MA 15.08 GB Max_MA 15.08 GB CA 15.59 GB Max_CA 16 GB
[2024-05-24 18:58:47,639] [INFO] [utils.py:808:see_memory_usage] CPU Virtual Memory: used = 245.78 GB, percent = 12.2%
[2024-05-24 18:58:47,643] [INFO] [logging.py:96:log_dist] [Rank 0] DeepSpeed Final Optimizer = DeepSpeedCPUAdam
[2024-05-24 18:58:47,643] [INFO] [logging.py:96:log_dist] [Rank 0] DeepSpeed using client LR scheduler
[2024-05-24 18:58:47,643] [INFO] [logging.py:96:log_dist] [Rank 0] DeepSpeed LR Scheduler = <torch.optim.lr_scheduler.LambdaLR object at 0x7fa8334f8ed0>
[2024-05-24 18:58:47,643] [INFO] [logging.py:96:log_dist] [Rank 0] step=0, skipped=0, lr=[0.0, 0.0], mom=[(0.9, 0.95), (0.9, 0.95)]
[2024-05-24 18:58:47,644] [INFO] [config.py:996:print] DeepSpeedEngine configuration:
[2024-05-24 18:58:47,644] [INFO] [config.py:1000:print] activation_checkpointing_config {
"partition_activations": false,
"contiguous_memory_optimization": false,
"cpu_checkpointing": false,
"number_checkpoints": null,
"synchronize_checkpoint_boundary": false,
"profile": false
}
[2024-05-24 18:58:47,644] [INFO] [config.py:1000:print] aio_config ................... {'block_size': 1048576, 'queue_depth': 8, 'thread_count': 1, 'single_submit': False, 'overlap_events': True}
[2024-05-24 18:58:47,644] [INFO] [config.py:1000:print] amp_enabled .................. False
[2024-05-24 18:58:47,644] [INFO] [config.py:1000:print] amp_params ................... False
[2024-05-24 18:58:47,644] [INFO] [config.py:1000: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
}
[2024-05-24 18:58:47,644] [INFO] [config.py:1000:print] bfloat16_enabled ............. True
[2024-05-24 18:58:47,644] [INFO] [config.py:1000:print] bfloat16_immediate_grad_update False
[2024-05-24 18:58:47,644] [INFO] [config.py:1000:print] checkpoint_parallel_write_pipeline False
[2024-05-24 18:58:47,644] [INFO] [config.py:1000:print] checkpoint_tag_validation_enabled True
[2024-05-24 18:58:47,644] [INFO] [config.py:1000:print] checkpoint_tag_validation_fail False
[2024-05-24 18:58:47,644] [INFO] [config.py:1000:print] comms_config ................. <deepspeed.comm.config.DeepSpeedCommsConfig object at 0x7fa832d61a50>
[2024-05-24 18:58:47,644] [INFO] [config.py:1000:print] communication_data_type ...... None
[2024-05-24 18:58:47,644] [INFO] [config.py:1000:print] compile_config ............... enabled=False backend='inductor' kwargs={}
[2024-05-24 18:58:47,644] [INFO] [config.py:1000: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}}
[2024-05-24 18:58:47,644] [INFO] [config.py:1000:print] curriculum_enabled_legacy .... False
[2024-05-24 18:58:47,644] [INFO] [config.py:1000:print] curriculum_params_legacy ..... False
[2024-05-24 18:58:47,644] [INFO] [config.py:1000: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}}}}
[2024-05-24 18:58:47,644] [INFO] [config.py:1000:print] data_efficiency_enabled ...... False
[2024-05-24 18:58:47,645] [INFO] [config.py:1000:print] dataloader_drop_last ......... False
[2024-05-24 18:58:47,645] [INFO] [config.py:1000:print] disable_allgather ............ False
[2024-05-24 18:58:47,645] [INFO] [config.py:1000:print] dump_state ................... False
[2024-05-24 18:58:47,645] [INFO] [config.py:1000:print] dynamic_loss_scale_args ...... None
[2024-05-24 18:58:47,645] [INFO] [config.py:1000:print] eigenvalue_enabled ........... False
[2024-05-24 18:58:47,645] [INFO] [config.py:1000:print] eigenvalue_gas_boundary_resolution 1
[2024-05-24 18:58:47,645] [INFO] [config.py:1000:print] eigenvalue_layer_name ........ bert.encoder.layer
[2024-05-24 18:58:47,645] [INFO] [config.py:1000:print] eigenvalue_layer_num ......... 0
[2024-05-24 18:58:47,645] [INFO] [config.py:1000:print] eigenvalue_max_iter .......... 100
[2024-05-24 18:58:47,645] [INFO] [config.py:1000:print] eigenvalue_stability ......... 1e-06
[2024-05-24 18:58:47,645] [INFO] [config.py:1000:print] eigenvalue_tol ............... 0.01
[2024-05-24 18:58:47,645] [INFO] [config.py:1000:print] eigenvalue_verbose ........... False
[2024-05-24 18:58:47,645] [INFO] [config.py:1000:print] elasticity_enabled ........... False
[2024-05-24 18:58:47,645] [INFO] [config.py:1000: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
}
[2024-05-24 18:58:47,645] [INFO] [config.py:1000:print] fp16_auto_cast ............... None
[2024-05-24 18:58:47,645] [INFO] [config.py:1000:print] fp16_enabled ................. False
[2024-05-24 18:58:47,645] [INFO] [config.py:1000:print] fp16_master_weights_and_gradients False
[2024-05-24 18:58:47,645] [INFO] [config.py:1000:print] global_rank .................. 0
[2024-05-24 18:58:47,645] [INFO] [config.py:1000:print] grad_accum_dtype ............. bf16
[2024-05-24 18:58:47,645] [INFO] [config.py:1000:print] gradient_accumulation_steps .. 16
[2024-05-24 18:58:47,645] [INFO] [config.py:1000:print] gradient_clipping ............ 1.0
[2024-05-24 18:58:47,645] [INFO] [config.py:1000:print] gradient_predivide_factor .... 1.0
[2024-05-24 18:58:47,645] [INFO] [config.py:1000:print] graph_harvesting ............. False
[2024-05-24 18:58:47,645] [INFO] [config.py:1000: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
[2024-05-24 18:58:47,645] [INFO] [config.py:1000:print] initial_dynamic_scale ........ 1
[2024-05-24 18:58:47,645] [INFO] [config.py:1000:print] load_universal_checkpoint .... False
[2024-05-24 18:58:47,645] [INFO] [config.py:1000:print] loss_scale ................... 1.0
[2024-05-24 18:58:47,645] [INFO] [config.py:1000:print] memory_breakdown ............. False
[2024-05-24 18:58:47,645] [INFO] [config.py:1000:print] mics_hierarchial_params_gather False
[2024-05-24 18:58:47,645] [INFO] [config.py:1000:print] mics_shard_size .............. -1
[2024-05-24 18:58:47,645] [INFO] [config.py:1000: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
[2024-05-24 18:58:47,645] [INFO] [config.py:1000: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
}
[2024-05-24 18:58:47,645] [INFO] [config.py:1000:print] optimizer_legacy_fusion ...... False
[2024-05-24 18:58:47,645] [INFO] [config.py:1000:print] optimizer_name ............... None
[2024-05-24 18:58:47,645] [INFO] [config.py:1000:print] optimizer_params ............. None
[2024-05-24 18:58:47,645] [INFO] [config.py:1000:print] pipeline ..................... {'stages': 'auto', 'partition': 'best', 'seed_layers': False, 'activation_checkpoint_interval': 0, 'pipe_partitioned': True, 'grad_partitioned': True}
[2024-05-24 18:58:47,645] [INFO] [config.py:1000:print] pld_enabled .................. False
[2024-05-24 18:58:47,645] [INFO] [config.py:1000:print] pld_params ................... False
[2024-05-24 18:58:47,645] [INFO] [config.py:1000:print] prescale_gradients ........... False
[2024-05-24 18:58:47,645] [INFO] [config.py:1000:print] scheduler_name ............... None
[2024-05-24 18:58:47,645] [INFO] [config.py:1000:print] scheduler_params ............. None
[2024-05-24 18:58:47,645] [INFO] [config.py:1000:print] seq_parallel_communication_data_type torch.float32
[2024-05-24 18:58:47,645] [INFO] [config.py:1000:print] sparse_attention ............. None
[2024-05-24 18:58:47,645] [INFO] [config.py:1000:print] sparse_gradients_enabled ..... False
[2024-05-24 18:58:47,645] [INFO] [config.py:1000:print] steps_per_print .............. 100
[2024-05-24 18:58:47,645] [INFO] [config.py:1000:print] train_batch_size ............. 64
[2024-05-24 18:58:47,645] [INFO] [config.py:1000:print] train_micro_batch_size_per_gpu 2
[2024-05-24 18:58:47,645] [INFO] [config.py:1000:print] use_data_before_expert_parallel_ False
[2024-05-24 18:58:47,645] [INFO] [config.py:1000:print] use_node_local_storage ....... False
[2024-05-24 18:58:47,645] [INFO] [config.py:1000:print] wall_clock_breakdown ......... False
[2024-05-24 18:58:47,646] [INFO] [config.py:1000:print] weight_quantization_config ... None
[2024-05-24 18:58:47,646] [INFO] [config.py:1000:print] world_size ................... 2
[2024-05-24 18:58:47,646] [INFO] [config.py:1000:print] zero_allow_untested_optimizer False
[2024-05-24 18:58:47,646] [INFO] [config.py:1000:print] zero_config .................. stage=2 contiguous_gradients=True reduce_scatter=True reduce_bucket_size=500,000,000 use_multi_rank_bucket_allreduce=True allgather_partitions=True allgather_bucket_size=500,000,000 overlap_comm=False load_from_fp32_weights=True elastic_checkpoint=False offload_param=DeepSpeedZeroOffloadParamConfig(device='none', 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=True, pipeline=False, pipeline_read=False, pipeline_write=False, fast_init=False, ratio=1.0) 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
[2024-05-24 18:58:47,646] [INFO] [config.py:1000:print] zero_enabled ................. True
[2024-05-24 18:58:47,646] [INFO] [config.py:1000:print] zero_force_ds_cpu_optimizer .. True
[2024-05-24 18:58:47,646] [INFO] [config.py:1000:print] zero_optimization_stage ...... 2
[2024-05-24 18:58:47,646] [INFO] [config.py:986:print_user_config] json = {
"steps_per_print": 100,
"zero_optimization": {
"stage": 2,
"offload_param": {
"device": "none"
},
"offload_optimizer": {
"device": "cpu",
"pin_memory": true
},
"sub_group_size": "auto",
"stage3_max_live_parameters": "auto",
"stage3_max_reuse_distance": "auto",
"stage3_param_persistence_threshold": "auto",
"stage3_prefetch_bucket_size": "auto",
"reduce_bucket_size": "auto",
"zero_hpz_partition_size": 1,
"zero_quantized_weights": false,
"zero_quantized_gradients": false
},
"bf16": {
"enabled": true
},
"gradient_clipping": 1.0,
"prescale_gradients": false,
"wall_clock_breakdown": false,
"data_types": {
"grad_accum_dtype": "bf16"
},
"train_micro_batch_size_per_gpu": 2,
"train_batch_size": 64
}
Generates critic values. ===== I am alive
P/CUMEM/read
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n136-112-040:375881:377674 [0] NCCL INFO comm 0x1bbd32a0 rank 0 nranks 2 cudaDev 0 nvmlDev 2 busId 4a000 commId 0xb015bed65ddba90d - Init COMPLETE
Generates critic values. ===== I am alive
Generates critic values. ===== I am alive
Generates critic values. ===== I am alive
Generates critic values. ===== I am alive
Generates critic values. ===== I am alive
Generates critic values. ===== I am alive
Generates critic values. ===== I am alive
Generates critic values. ===== I am alive
Generates critic values. ===== I am alive
Generates critic values. ===== I am alive
Generates critic values. ===== I am alive
Generates critic values. ===== I am alive
Generates critic values. ===== I am alive
Generates critic values. ===== I am alive
Generates critic values. ===== I am alive
Generates critic values. ===== I am alive
Generates critic values. ===== I am alive
Generates critic values. ===== I am alive
Generates critic values. ===== I am alive
Generates critic values. ===== I am alive
Generates critic values. ===== I am alive
Generates critic values. ===== I am alive
Generates critic values. ===== I am alive
Generates critic values. ===== I am alive
Generates critic values. ===== I am alive
Generates critic values. ===== I am alive
Generates critic values. ===== I am alive
Generates critic values. ===== I am alive
Generates critic values. ===== I am alive
Generates critic values. ===== I am alive
Generates critic values. ===== I am alive
I am progressing 0 !~
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n136-112-040:375881:382814 [0] NCCL INFO 24 coll channels, 0 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer
n136-112-040:375881:382814 [0] NCCL INFO comm 0x26252b80 rank 0 nranks 2 cudaDev 0 nvmlDev 2 busId 4a000 commId 0xe84413f7a9d26087 - Init COMPLETE
I am progressing 1 !~
I am progressing 2 !~
I am progressing 3 !~
I am progressing 4 !~
I am progressing 5 !~
I am progressing 6 !~
I am progressing 7 !~
I am progressing 8 !~
I am progressing 9 !~
I am progressing 10 !~
I am progressing 11 !~
I am progressing 12 !~
I am progressing 13 !~
I am progressing 14 !~
I am progressing 15 !~
和
:job_id:03000000
/home/tiger/.local/lib/python3.11/site-packages/transformers/deepspeed.py:23: FutureWarning: transformers.deepspeed module is deprecated and will be removed in a future version. Please import deepspeed modules directly from transformers.integrations
warnings.warn(
:actor_name:CriticModelRayActor
The argument trust_remote_code is to be used with Auto classes. It has no effect here and is ignored.
You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with model.to('cuda').
Loading checkpoint shards: 0%| | 0/4 [00:00<?, ?it/s]
Loading checkpoint shards: 25%|██▌ | 1/4 [00:00<00:01, 1.54it/s]
Loading checkpoint shards: 50%|█████ | 2/4 [00:01<00:01, 1.34it/s]
Loading checkpoint shards: 75%|███████▌ | 3/4 [00:02<00:00, 1.21it/s]
Loading checkpoint shards: 100%|██████████| 4/4 [00:02<00:00, 1.69it/s]
Loading checkpoint shards: 100%|██████████| 4/4 [00:02<00:00, 1.53it/s]
Some weights of LLMForSequenceRegression were not initialized from the model checkpoint at /mnt/bn/shesjlq20t/HDFS/Trained/Llama3-8b-chat-rm-v14 and are newly initialized: ['value_head.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
Using /home/tiger/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...
Loading extension module cpu_adam...
libibverbs: Warning: couldn't open config directory '/etc/libibverbs.d'.
Train epoch [1/1]: 0%| | 0/64 [00:00<?, ?it/s]
Train epoch [1/1]: 0%| | 0/64 [00:10<?, ?it/s, critic_loss=0.0367, values=0.42]
Train epoch [1/1]: 2%|▏ | 1/64 [00:10<10:37, 10.12s/it, critic_loss=0.0367, values=0.42]
Train epoch [1/1]: 2%|▏ | 1/64 [00:12<10:37, 10.12s/it, critic_loss=0.0226, values=0.271]
Train epoch [1/1]: 3%|▎ | 2/64 [00:12<05:28, 5.30s/it, critic_loss=0.0226, values=0.271]
Train epoch [1/1]: 3%|▎ | 2/64 [00:14<05:28, 5.30s/it, critic_loss=0.0202, values=0.19]
Train epoch [1/1]: 5%|▍ | 3/64 [00:14<04:02, 3.98s/it, critic_loss=0.0202, values=0.19]
Train epoch [1/1]: 5%|▍ | 3/64 [00:16<04:02, 3.98s/it, critic_loss=0.0462, values=0.646]
Train epoch [1/1]: 6%|▋ | 4/64 [00:16<03:10, 3.17s/it, critic_loss=0.0462, values=0.646]
Train epoch [1/1]: 6%|▋ | 4/64 [00:18<03:10, 3.17s/it, critic_loss=0.0225, values=0.54]
Train epoch [1/1]: 8%|▊ | 5/64 [00:18<02:42, 2.75s/it, critic_loss=0.0225, values=0.54]
Train epoch [1/1]: 8%|▊ | 5/64 [00:20<02:42, 2.75s/it, critic_loss=0.021, values=0.117]
Train epoch [1/1]: 9%|▉ | 6/64 [00:20<02:20, 2.42s/it, critic_loss=0.021, values=0.117]
Train epoch [1/1]: 9%|▉ | 6/64 [00:21<02:20, 2.42s/it, critic_loss=0.0237, values=0.508]
Train epoch [1/1]: 11%|█ | 7/64 [00:21<02:05, 2.21s/it, critic_loss=0.0237, values=0.508]
Train epoch [1/1]: 11%|█ | 7/64 [00:23<02:05, 2.21s/it, critic_loss=0.0565, values=0.0332]
Train epoch [1/1]: 12%|█▎ | 8/64 [00:23<01:57, 2.09s/it, critic_loss=0.0565, values=0.0332]
Train epoch [1/1]: 12%|█▎ | 8/64 [00:25<01:57, 2.09s/it, critic_loss=0.0381, values=0.412]
Train epoch [1/1]: 14%|█▍ | 9/64 [00:25<01:48, 1.98s/it, critic_loss=0.0381, values=0.412]
Train epoch [1/1]: 14%|█▍ | 9/64 [00:27<01:48, 1.98s/it, critic_loss=0.0519, values=0.582]
Train epoch [1/1]: 16%|█▌ | 10/64 [00:27<01:45, 1.96s/it, critic_loss=0.0519, values=0.582]
Train epoch [1/1]: 16%|█▌ | 10/64 [00:29<01:45, 1.96s/it, critic_loss=0.0439, values=0.0381]
Train epoch [1/1]: 17%|█▋ | 11/64 [00:29<01:44, 1.98s/it, critic_loss=0.0439, values=0.0381]
Train epoch [1/1]: 17%|█▋ | 11/64 [00:31<01:44, 1.98s/it, critic_loss=0.0211, values=0.313]
Train epoch [1/1]: 19%|█▉ | 12/64 [00:31<01:40, 1.93s/it, critic_loss=0.0211, values=0.313]
Train epoch [1/1]: 19%|█▉ | 12/64 [00:32<01:40, 1.93s/it, critic_loss=0.041, values=0.951]
Train epoch [1/1]: 20%|██ | 13/64 [00:32<01:32, 1.82s/it, critic_loss=0.041, values=0.951]
Train epoch [1/1]: 20%|██ | 13/64 [00:34<01:32, 1.82s/it, critic_loss=0.0165, values=-0.578]
Train epoch [1/1]: 22%|██▏ | 14/64 [00:34<01:34, 1.89s/it, critic_loss=0.0165, values=-0.578]
Train epoch [1/1]: 22%|██▏ | 14/64 [00:36<01:34, 1.89s/it, critic_loss=0.0243, values=0.105]
Train epoch [1/1]: 23%|██▎ | 15/64 [00:36<01:31, 1.87s/it, critic_loss=0.0243, values=0.105]
from openrlhf.
Ricardokevins
commented on July 30, 2024
Generates critic values. ===== I am alive 和 I am progressing 15 !~是我加上的调试语句，希望可以看出哪里有问题（但是失败了
from openrlhf.
Ricardokevins
commented on July 30, 2024
我用的机器内存蛮大的，有2T
下面是启动脚本
set -x
# ray start --head --node-ip-address 0.0.0.0 --num-gpus 8
# if you want to launch ray on more nodes, use
# ray start --address {MASTER-NODE-ADDRESS}:6379 --num-gpus 8
# ray stop
# ps aux | grep '/usr/bin/python3' | grep -v grep | awk '{print $2}' | xargs kill
WANDB_PROJECT=${project} WANDB_NAME=${expr} ray job submit --address="http://127.0.0.1:8265" \
--runtime-env-json='{"working_dir": "xxxxx/OpenRLHF-main", "pip": "xxxxxxxOpenRLHF-main/requirements.txt"}' \
-- python3 examples/train_ppo_ray.py \
--ref_num_nodes 1 \
--ref_num_gpus_per_node 1 \
--reward_num_nodes 1 \
--reward_num_gpus_per_node 1 \
--critic_num_nodes 1 \
--critic_num_gpus_per_node 2 \
--actor_num_nodes 1 \
--actor_num_gpus_per_node 2 \
--vllm_num_engines 2 \
--vllm_tensor_parallel_size 1 \
--use_wandb HelloWorldHelloWorldHelloWorldHelloWorld \
--wandb_project ${project} \
--wandb_run_name ${expr} \
--save_path ./7b_llama \
--micro_train_batch_size 2 \
--train_batch_size 64 \
--micro_rollout_batch_size 4 \
--rollout_batch_size 256 \
--max_epochs 1 \
--grad_accum_dtype bf16 \
--prompt_max_len 1024 \
--generate_max_len 1024 \
--zero_stage 2 \
--bf16 \
--actor_learning_rate 5e-7 \
--critic_learning_rate 9e-6 \
--init_kl_coef 0.01 \
--max_samples 10000 \
--normalize_reward \
--actor_init_on_gpu \
--adam_offload \
--flash_attn \
--gradient_checkpointing
主要是critic model被kill的没有报错，感觉很奇怪，没有什么思路
from openrlhf.
Ricardokevins
commented on July 30, 2024
好像用Lora来训练就暂时没有报错。不过还是希望可以全量训练。
--lora_rank 16 \
--lora_alpha 32 \
--lora_dropout 0.05 \
我注意到一个奇怪的现象，有没有可能是因为critic model在两张卡share参数的时候显存分配问题？如GPU 4,5
from openrlhf.
hijkzzz
commented on July 30, 2024
- 检查是不是用的我们提供的 docker image https://github.com/OpenLLMAI/OpenRLHF/tree/main/dockerfile 或者类似兼容的
- 可以尝试下：
git pull （升级了ray 减少了内存使用）
ray job submit --address="http://127.0.0.1:8265" \
--runtime-env-json='{"working_dir": "/openrlhf", "pip": "/openrlhf/requirements.txt"}' \
-- python3 examples/train_ppo_ray.py \
--ref_num_nodes 1 \
--ref_num_gpus_per_node 2 \
--reward_num_nodes 1 \
--reward_num_gpus_per_node 2 \
--critic_num_nodes 1 \
--critic_num_gpus_per_node 2 \
--actor_num_nodes 1 \
--actor_num_gpus_per_node 2 \
--vllm_num_engines 2 \
--vllm_tensor_parallel_size 2 \
--colocate_critic_reward \
--colocate_actor_ref \
--ref_reward_offload \
--pretrain meta-llama/Meta-Llama-3-8B-Instruct \
--reward_pretrain meta-llama/Meta-Llama-3-8B-Instruct \
--save_path /openrlhf/examples/test_scripts/ckpt/llama_ray \
--micro_train_batch_size 4 \
--train_batch_size 128 \
--micro_rollout_batch_size 16 \
--rollout_batch_size 1024 \
--max_epochs 1 \
--prompt_max_len 1024 \
--generate_max_len 1024 \
--zero_stage 3 \
--bf16 \
--actor_learning_rate 5e-7 \
--critic_learning_rate 9e-6 \
--init_kl_coef 0.01 \
--prompt_data Open-Orca/OpenOrca \
--prompt_data_probs 1.0 \
--max_samples 50000 \
--normalize_reward \
--adam_offload \
--flash_attn \
--gradient_checkpointing
from openrlhf.
Related Issues (20)
- Failed to update weights to vLLM HOT 3
- run train_ppo_llama_ray.sh error
- Moving model between GPU and CPU HOT 3
- train_ppo_llama_ray_70b.sh run two H800 machine error HOT 1
- ray多节点训练下deepspeed zero3的切分还是按照 node数*8卡来切分吗？ HOT 1
- train_ppo_llama_ray.sh run two H800 machine error HOT 3
- mixtral 8*7B的最佳训练参数，推理参数可以提供一个吗? HOT 1
- [Question] Is multi-nodes stage 3 model loading supported? HOT 2
- 训练启动时，如何指定gpu的数量? HOT 1
- /openrlhf must be an existing directory or a zip package HOT 1
- 多卡并行无法model.generate HOT 2
- Generate function for distributional training HOT 2
- [rank3]: RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:3 and cpu! HOT 3
- qwen2 sft后的模型使用kto训练loss nan HOT 3
- Could you give an example of testing deepspeed-chat time? HOT 7
- qwen2 72B PPO OOM HOT 5
- PPO加载完模型后卡在bundle_reservation_check_func这里 HOT 1
- How much memory(RAM) is required to train a 70B Llama2 model with two 80G A800 nodes? HOT 7
- Qwen2 ppo HOT 1
- vLLM related: model's max seq len (8192) is larger than the maximum number of tokens that can be stored in KV cache (6048). HOT 2
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from openrlhf.