Time-LLM: RuntimeError: expected scalar type Float but found BFloat16

I am trying to run the ETTm1 example, but despite a plethora of efforts, I keep getting:

[2024-02-07 17:07:11,875] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2024-02-07 17:07:12,281] [INFO] [logging.py:96:log_dist] [Rank -1] DeepSpeed info: version=0.13.1, git-hash=unknown, git-branch=unknown
[2024-02-07 17:07:12,282] [INFO] [comm.py:637:init_distributed] cdb=None
[2024-02-07 17:07:12,282] [INFO] [comm.py:652:init_distributed] Not using the DeepSpeed or dist launchers, attempting to detect MPI environment...
[2024-02-07 17:07:12,293] [INFO] [comm.py:702:mpi_discovery] Discovered MPI settings of world_rank=0, local_rank=0, world_size=1, master_addr=172.19.2.2, master_port=29500
[2024-02-07 17:07:12,293] [INFO] [comm.py:668:init_distributed] Initializing TorchBackend in DeepSpeed with backend nccl
[2024-02-07 17:07:13,600] [INFO] [logging.py:96:log_dist] [Rank 0] DeepSpeed Flops Profiler Enabled: False
[2024-02-07 17:07:13,601] [INFO] [logging.py:96:log_dist] [Rank 0] Using client Optimizer as basic optimizer
[2024-02-07 17:07:13,601] [INFO] [logging.py:96:log_dist] [Rank 0] Removing param_group that has no 'params' in the basic Optimizer
[2024-02-07 17:07:13,602] [INFO] [logging.py:96:log_dist] [Rank 0] DeepSpeed Basic Optimizer = Adam
[2024-02-07 17:07:13,602] [INFO] [utils.py:56:is_zero_supported_optimizer] Checking ZeRO support for optimizer=Adam type=<class 'torch.optim.adam.Adam'>
[2024-02-07 17:07:13,603] [INFO] [logging.py:96:log_dist] [Rank 0] Creating torch.bfloat16 ZeRO stage 2 optimizer
[2024-02-07 17:07:13,603] [INFO] [stage_1_and_2.py:143:__init__] Reduce bucket size 200000000
[2024-02-07 17:07:13,603] [INFO] [stage_1_and_2.py:144:__init__] Allgather bucket size 200000000
[2024-02-07 17:07:13,603] [INFO] [stage_1_and_2.py:145:__init__] CPU Offload: False
[2024-02-07 17:07:13,603] [INFO] [stage_1_and_2.py:146:__init__] Round robin gradient partitioning: False
[2024-02-07 17:07:13,759] [INFO] [utils.py:791:see_memory_usage] Before initializing optimizer states
[2024-02-07 17:07:13,760] [INFO] [utils.py:792:see_memory_usage] MA 0.0 GB         Max_MA 0.0 GB         CA 0.0 GB         Max_CA 0 GB 
[2024-02-07 17:07:13,761] [INFO] [utils.py:799:see_memory_usage] CPU Virtual Memory:  used = 2.27 GB, percent = 7.2%
[2024-02-07 17:07:13,980] [INFO] [utils.py:791:see_memory_usage] After initializing optimizer states
[2024-02-07 17:07:13,981] [INFO] [utils.py:792:see_memory_usage] MA 0.0 GB         Max_MA 0.0 GB         CA 0.0 GB         Max_CA 0 GB 
[2024-02-07 17:07:13,981] [INFO] [utils.py:799:see_memory_usage] CPU Virtual Memory:  used = 2.32 GB, percent = 7.4%
[2024-02-07 17:07:13,981] [INFO] [stage_1_and_2.py:533:__init__] optimizer state initialized
[2024-02-07 17:07:14,103] [INFO] [utils.py:791:see_memory_usage] After initializing ZeRO optimizer
[2024-02-07 17:07:14,104] [INFO] [utils.py:792:see_memory_usage] MA 0.0 GB         Max_MA 0.0 GB         CA 0.0 GB         Max_CA 0 GB 
[2024-02-07 17:07:14,105] [INFO] [utils.py:799:see_memory_usage] CPU Virtual Memory:  used = 2.32 GB, percent = 7.4%
[2024-02-07 17:07:14,107] [INFO] [logging.py:96:log_dist] [Rank 0] DeepSpeed Final Optimizer = Adam
[2024-02-07 17:07:14,107] [INFO] [logging.py:96:log_dist] [Rank 0] DeepSpeed using client LR scheduler
[2024-02-07 17:07:14,107] [INFO] [logging.py:96:log_dist] [Rank 0] DeepSpeed LR Scheduler = None
[2024-02-07 17:07:14,107] [INFO] [logging.py:96:log_dist] [Rank 0] step=0, skipped=0, lr=[3.9999999999999996e-05], mom=[(0.95, 0.999)]
[2024-02-07 17:07:14,108] [INFO] [config.py:984:print] DeepSpeedEngine configuration:
[2024-02-07 17:07:14,108] [INFO] [config.py:988:print]   activation_checkpointing_config  {
    "partition_activations": false, 
    "contiguous_memory_optimization": false, 
    "cpu_checkpointing": false, 
    "number_checkpoints": null, 
    "synchronize_checkpoint_boundary": false, 
    "profile": false
}
[2024-02-07 17:07:14,108] [INFO] [config.py:988:print]   aio_config ................... {'block_size': 1048576, 'queue_depth': 8, 'thread_count': 1, 'single_submit': False, 'overlap_events': True}
[2024-02-07 17:07:14,108] [INFO] [config.py:988:print]   amp_enabled .................. False
[2024-02-07 17:07:14,108] [INFO] [config.py:988:print]   amp_params ................... False
[2024-02-07 17:07:14,109] [INFO] [config.py:988: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
}
[2024-02-07 17:07:14,109] [INFO] [config.py:988:print]   bfloat16_enabled ............. True
[2024-02-07 17:07:14,109] [INFO] [config.py:988:print]   checkpoint_parallel_write_pipeline  False
[2024-02-07 17:07:14,109] [INFO] [config.py:988:print]   checkpoint_tag_validation_enabled  True
[2024-02-07 17:07:14,109] [INFO] [config.py:988:print]   checkpoint_tag_validation_fail  False
[2024-02-07 17:07:14,109] [INFO] [config.py:988:print]   comms_config ................. <deepspeed.comm.config.DeepSpeedCommsConfig object at 0x7985856dae60>
[2024-02-07 17:07:14,109] [INFO] [config.py:988:print]   communication_data_type ...... None
[2024-02-07 17:07:14,109] [INFO] [config.py:988: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}}
[2024-02-07 17:07:14,109] [INFO] [config.py:988:print]   curriculum_enabled_legacy .... False
[2024-02-07 17:07:14,109] [INFO] [config.py:988:print]   curriculum_params_legacy ..... False
[2024-02-07 17:07:14,109] [INFO] [config.py:988: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}}}}
[2024-02-07 17:07:14,109] [INFO] [config.py:988:print]   data_efficiency_enabled ...... False
[2024-02-07 17:07:14,109] [INFO] [config.py:988:print]   dataloader_drop_last ......... False
[2024-02-07 17:07:14,109] [INFO] [config.py:988:print]   disable_allgather ............ False
[2024-02-07 17:07:14,109] [INFO] [config.py:988:print]   dump_state ................... False
[2024-02-07 17:07:14,109] [INFO] [config.py:988:print]   dynamic_loss_scale_args ...... None
[2024-02-07 17:07:14,110] [INFO] [config.py:988:print]   eigenvalue_enabled ........... False
[2024-02-07 17:07:14,110] [INFO] [config.py:988:print]   eigenvalue_gas_boundary_resolution  1
[2024-02-07 17:07:14,110] [INFO] [config.py:988:print]   eigenvalue_layer_name ........ bert.encoder.layer
[2024-02-07 17:07:14,110] [INFO] [config.py:988:print]   eigenvalue_layer_num ......... 0
[2024-02-07 17:07:14,110] [INFO] [config.py:988:print]   eigenvalue_max_iter .......... 100
[2024-02-07 17:07:14,110] [INFO] [config.py:988:print]   eigenvalue_stability ......... 1e-06
[2024-02-07 17:07:14,110] [INFO] [config.py:988:print]   eigenvalue_tol ............... 0.01
[2024-02-07 17:07:14,110] [INFO] [config.py:988:print]   eigenvalue_verbose ........... False
[2024-02-07 17:07:14,110] [INFO] [config.py:988:print]   elasticity_enabled ........... False
[2024-02-07 17:07:14,110] [INFO] [config.py:988: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
}
[2024-02-07 17:07:14,110] [INFO] [config.py:988:print]   fp16_auto_cast ............... None
[2024-02-07 17:07:14,110] [INFO] [config.py:988:print]   fp16_enabled ................. False
[2024-02-07 17:07:14,110] [INFO] [config.py:988:print]   fp16_master_weights_and_gradients  False
[2024-02-07 17:07:14,110] [INFO] [config.py:988:print]   global_rank .................. 0
[2024-02-07 17:07:14,110] [INFO] [config.py:988:print]   grad_accum_dtype ............. None
[2024-02-07 17:07:14,110] [INFO] [config.py:988:print]   gradient_accumulation_steps .. 1
[2024-02-07 17:07:14,110] [INFO] [config.py:988:print]   gradient_clipping ............ 0.0
[2024-02-07 17:07:14,110] [INFO] [config.py:988:print]   gradient_predivide_factor .... 1.0
[2024-02-07 17:07:14,110] [INFO] [config.py:988:print]   graph_harvesting ............. False
[2024-02-07 17:07:14,111] [INFO] [config.py:988: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
[2024-02-07 17:07:14,111] [INFO] [config.py:988:print]   initial_dynamic_scale ........ 1
[2024-02-07 17:07:14,111] [INFO] [config.py:988:print]   load_universal_checkpoint .... False
[2024-02-07 17:07:14,111] [INFO] [config.py:988:print]   loss_scale ................... 1.0
[2024-02-07 17:07:14,111] [INFO] [config.py:988:print]   memory_breakdown ............. False
[2024-02-07 17:07:14,111] [INFO] [config.py:988:print]   mics_hierarchial_params_gather  False
[2024-02-07 17:07:14,111] [INFO] [config.py:988:print]   mics_shard_size .............. -1
[2024-02-07 17:07:14,111] [INFO] [config.py:988: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
[2024-02-07 17:07:14,111] [INFO] [config.py:988: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
}
[2024-02-07 17:07:14,111] [INFO] [config.py:988:print]   optimizer_legacy_fusion ...... False
[2024-02-07 17:07:14,111] [INFO] [config.py:988:print]   optimizer_name ............... None
[2024-02-07 17:07:14,111] [INFO] [config.py:988:print]   optimizer_params ............. None
[2024-02-07 17:07:14,111] [INFO] [config.py:988:print]   pipeline ..................... {'stages': 'auto', 'partition': 'best', 'seed_layers': False, 'activation_checkpoint_interval': 0, 'pipe_partitioned': True, 'grad_partitioned': True}
[2024-02-07 17:07:14,111] [INFO] [config.py:988:print]   pld_enabled .................. False
[2024-02-07 17:07:14,111] [INFO] [config.py:988:print]   pld_params ................... False
[2024-02-07 17:07:14,111] [INFO] [config.py:988:print]   prescale_gradients ........... False
[2024-02-07 17:07:14,112] [INFO] [config.py:988:print]   scheduler_name ............... None
[2024-02-07 17:07:14,112] [INFO] [config.py:988:print]   scheduler_params ............. None
[2024-02-07 17:07:14,112] [INFO] [config.py:988:print]   seq_parallel_communication_data_type  torch.float32
[2024-02-07 17:07:14,112] [INFO] [config.py:988:print]   sparse_attention ............. None
[2024-02-07 17:07:14,112] [INFO] [config.py:988:print]   sparse_gradients_enabled ..... False
[2024-02-07 17:07:14,112] [INFO] [config.py:988:print]   steps_per_print .............. inf
[2024-02-07 17:07:14,112] [INFO] [config.py:988:print]   train_batch_size ............. 24
[2024-02-07 17:07:14,112] [INFO] [config.py:988:print]   train_micro_batch_size_per_gpu  24
[2024-02-07 17:07:14,112] [INFO] [config.py:988:print]   use_data_before_expert_parallel_  False
[2024-02-07 17:07:14,112] [INFO] [config.py:988:print]   use_node_local_storage ....... False
[2024-02-07 17:07:14,112] [INFO] [config.py:988:print]   wall_clock_breakdown ......... False
[2024-02-07 17:07:14,112] [INFO] [config.py:988:print]   weight_quantization_config ... None
[2024-02-07 17:07:14,112] [INFO] [config.py:988:print]   world_size ................... 1
[2024-02-07 17:07:14,112] [INFO] [config.py:988:print]   zero_allow_untested_optimizer  True
[2024-02-07 17:07:14,112] [INFO] [config.py:988:print]   zero_config .................. stage=2 contiguous_gradients=True reduce_scatter=True reduce_bucket_size=200000000 use_multi_rank_bucket_allreduce=True allgather_partitions=True allgather_bucket_size=200000000 overlap_comm=True load_from_fp32_weights=True elastic_checkpoint=False offload_param=None offload_optimizer=None sub_group_size=1000000000 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
[2024-02-07 17:07:14,112] [INFO] [config.py:988:print]   zero_enabled ................. True
[2024-02-07 17:07:14,112] [INFO] [config.py:988:print]   zero_force_ds_cpu_optimizer .. True
[2024-02-07 17:07:14,112] [INFO] [config.py:988:print]   zero_optimization_stage ...... 2
[2024-02-07 17:07:14,113] [INFO] [config.py:974:print_user_config]   json = {
    "bf16": {
        "enabled": true, 
        "auto_cast": true
    }, 
    "zero_optimization": {
        "stage": 2, 
        "allgather_partitions": true, 
        "allgather_bucket_size": 2.000000e+08, 
        "overlap_comm": true, 
        "reduce_scatter": true, 
        "reduce_bucket_size": 2.000000e+08, 
        "contiguous_gradients": true, 
        "sub_group_size": 1.000000e+09
    }, 
    "gradient_accumulation_steps": 1, 
    "train_batch_size": 24, 
    "train_micro_batch_size_per_gpu": 24, 
    "steps_per_print": inf, 
    "wall_clock_breakdown": false, 
    "fp16": {
        "enabled": false
    }, 
    "zero_allow_untested_optimizer": true
}
0it [00:00, ?it/s]
Traceback (most recent call last):
  File "/kaggle/working/Time-LLM/run_main.py", line 208, in <module>
    outputs = model(batch_x, batch_x_mark, dec_inp, batch_y_mark)
  File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl
    return forward_call(*args, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/deepspeed/utils/nvtx.py", line 15, in wrapped_fn
    ret_val = func(*args, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/deepspeed/runtime/engine.py", line 1842, in forward
    loss = self.module(*inputs, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl
    return forward_call(*args, **kwargs)
  File "/kaggle/working/Time-LLM/models/Autoformer.py", line 146, in forward
    dec_out = self.forecast(x_enc, x_mark_enc, x_dec, x_mark_dec)
  File "/kaggle/working/Time-LLM/models/Autoformer.py", line 102, in forecast
    enc_out = self.enc_embedding(x_enc, x_mark_enc)
  File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl
    return forward_call(*args, **kwargs)
  File "/kaggle/working/Time-LLM/layers/Embed.py", line 145, in forward
    x = self.value_embedding(x) + self.temporal_embedding(x_mark)
  File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl
    return forward_call(*args, **kwargs)
  File "/kaggle/working/Time-LLM/layers/Embed.py", line 42, in forward
    x = self.tokenConv(x.permute(0, 2, 1)).transpose(1, 2)
  File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl
    return forward_call(*args, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/conv.py", line 310, in forward
    return self._conv_forward(input, self.weight, self.bias)
  File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/conv.py", line 303, in _conv_forward
    return F.conv1d(F.pad(input, self._reversed_padding_repeated_twice, mode=self.padding_mode),
RuntimeError: expected scalar type Float but found BFloat16

About this issue

Original URL
State: closed
Created 5 months ago
Comments: 17 (6 by maintainers)

Most upvoted comments

@KimMeen I’m PhD student in generative AI and thanks for the code… most clean and understandle code!!

aliper96 on Feb 8, 2024

@aliper96 I think I’m close, but I get the following error, something with the paths maybe? Check it live in Kaggle here:

---------------------------------------------------------------------------
HFValidationError                         Traceback (most recent call last)
File /opt/conda/lib/python3.10/site-packages/transformers/utils/hub.py:385, in cached_file(path_or_repo_id, filename, cache_dir, force_download, resume_download, proxies, token, revision, local_files_only, subfolder, repo_type, user_agent, _raise_exceptions_for_missing_entries, _raise_exceptions_for_connection_errors, _commit_hash, **deprecated_kwargs)
    383 try:
    384     # Load from URL or cache if already cached
--> 385     resolved_file = hf_hub_download(
    386         path_or_repo_id,
    387         filename,
    388         subfolder=None if len(subfolder) == 0 else subfolder,
    389         repo_type=repo_type,
    390         revision=revision,
    391         cache_dir=cache_dir,
    392         user_agent=user_agent,
    393         force_download=force_download,
    394         proxies=proxies,
    395         resume_download=resume_download,
    396         token=token,
    397         local_files_only=local_files_only,
    398     )
    399 except GatedRepoError as e:

File /opt/conda/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py:110, in validate_hf_hub_args.<locals>._inner_fn(*args, **kwargs)
    109 if arg_name in ["repo_id", "from_id", "to_id"]:
--> 110     validate_repo_id(arg_value)
    112 elif arg_name == "token" and arg_value is not None:

File /opt/conda/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py:158, in validate_repo_id(repo_id)
    157 if repo_id.count("/") > 1:
--> 158     raise HFValidationError(
    159         "Repo id must be in the form 'repo_name' or 'namespace/repo_name':"
    160         f" '{repo_id}'. Use `repo_type` argument if needed."
    161     )
    163 if not REPO_ID_REGEX.match(repo_id):

HFValidationError: Repo id must be in the form 'repo_name' or 'namespace/repo_name': '/mnt/alps/modelhub/pretrained_model/LLaMA/7B_hf/'. Use `repo_type` argument if needed.

The above exception was the direct cause of the following exception:

OSError                                   Traceback (most recent call last)
Cell In[22], line 30
     28     model = DLinear.Model(args).float()
     29 else:
---> 30     model = TimeLLM.Model(args).float()
     32 model = model.to(torch.bfloat16)
     35 path = os.path.join(args.checkpoints,
     36                     setting + '-' + args.model_comment)  # unique checkpoint saving path

File /kaggle/working/Time-LLM/models/TimeLLM.py:44, in Model.__init__(self, configs, patch_len, stride)
     41 self.patch_len = configs.patch_len
     42 self.stride = configs.stride
---> 44 self.llama_config = LlamaConfig.from_pretrained('/mnt/alps/modelhub/pretrained_model/LLaMA/7B_hf/')
     45 # self.llama_config = LlamaConfig.from_pretrained('huggyllama/llama-7b')
     46 self.llama_config.num_hidden_layers = configs.llm_layers

File /opt/conda/lib/python3.10/site-packages/transformers/configuration_utils.py:605, in PretrainedConfig.from_pretrained(cls, pretrained_model_name_or_path, cache_dir, force_download, local_files_only, token, revision, **kwargs)
    601 kwargs["revision"] = revision
    603 cls._set_token_in_kwargs(kwargs, token)
--> 605 config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
    606 if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
    607     logger.warning(
    608         f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
    609         f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
    610     )

File /opt/conda/lib/python3.10/site-packages/transformers/configuration_utils.py:634, in PretrainedConfig.get_config_dict(cls, pretrained_model_name_or_path, **kwargs)
    632 original_kwargs = copy.deepcopy(kwargs)
    633 # Get config dict associated with the base config file
--> 634 config_dict, kwargs = cls._get_config_dict(pretrained_model_name_or_path, **kwargs)
    635 if "_commit_hash" in config_dict:
    636     original_kwargs["_commit_hash"] = config_dict["_commit_hash"]

File /opt/conda/lib/python3.10/site-packages/transformers/configuration_utils.py:689, in PretrainedConfig._get_config_dict(cls, pretrained_model_name_or_path, **kwargs)
    685 configuration_file = kwargs.pop("_configuration_file", CONFIG_NAME)
    687 try:
    688     # Load from local folder or from cache or download from model Hub and cache
--> 689     resolved_config_file = cached_file(
    690         pretrained_model_name_or_path,
    691         configuration_file,
    692         cache_dir=cache_dir,
    693         force_download=force_download,
    694         proxies=proxies,
    695         resume_download=resume_download,
    696         local_files_only=local_files_only,
    697         token=token,
    698         user_agent=user_agent,
    699         revision=revision,
    700         subfolder=subfolder,
    701         _commit_hash=commit_hash,
    702     )
    703     commit_hash = extract_commit_hash(resolved_config_file, commit_hash)
    704 except EnvironmentError:
    705     # Raise any environment error raise by `cached_file`. It will have a helpful error message adapted to
    706     # the original exception.

File /opt/conda/lib/python3.10/site-packages/transformers/utils/hub.py:450, in cached_file(path_or_repo_id, filename, cache_dir, force_download, resume_download, proxies, token, revision, local_files_only, subfolder, repo_type, user_agent, _raise_exceptions_for_missing_entries, _raise_exceptions_for_connection_errors, _commit_hash, **deprecated_kwargs)
    448     raise EnvironmentError(f"There was a specific connection error when trying to load {path_or_repo_id}:\n{err}")
    449 except HFValidationError as e:
--> 450     raise EnvironmentError(
    451         f"Incorrect path_or_model_id: '{path_or_repo_id}'. Please provide either the path to a local folder or the repo_id of a model on the Hub."
    452     ) from e
    453 return resolved_file

OSError: Incorrect path_or_model_id: '/mnt/alps/modelhub/pretrained_model/LLaMA/7B_hf/'. Please provide either the path to a local folder or the repo_id of a model on the Hub.

@gsamaras Simply use 'huggyllama/llama-7b' instead of '/mnt/alps/modelhub/pretrained_model/LLaMA/7B_hf/' in TimeLLM.py will solve this issue.

KimMeen on Feb 8, 2024