replace SLURM_JOB_ID with SHA

llmfoundry/data/finetuning/dataloader.py

@@ -2,6 +2,8 @@
 # SPDX-License-Identifier: Apache-2.0
 import logging
 import os
+import random
+import hashlib
 from typing import Any, Dict, Optional, Tuple, Union
 
 import torch
@@ -532,10 +534,11 @@ def _download_remote_hf_dataset(remote_path: str, split: str) -> str:
 
         # Since we don't know exactly what the extension will be, since it is one of a list
         # use a signal file to wait for instead of the desired file
-        slurm_job_id = int(os.getenv('SLURM_JOB_ID', -1))
+        random_number = random.randint(0, 999999)
+        sha_signature = hashlib.sha256(str(random_number).encode()).hexdigest()
         signal_file_path = os.path.join(
             finetune_dir,
-            f'.node_{dist.get_node_rank()}_slurm_job_id{slurm_job_id}_local_rank0_completed',
+            f'.sha_{sha_signature}_completed',
         )
         if dist.get_local_rank() == 0:
             try:

llmfoundry/data/finetuning/tasks.py

@@ -34,6 +34,8 @@ def preprocessing_fn(example: Dict) -> Dict[str, str]:
 import importlib
 import logging
 import os
+import random
+import hashlib
 import warnings
 from collections.abc import Mapping
 from functools import partial
@@ -814,8 +816,9 @@ def build_from_hf(
         Returns:
             Dataset: The tokenized dataset.
         """
-        slurm_job_id = int(os.getenv('SLURM_JOB_ID', -1))
-        signal_file_path = f'.node_{dist.get_node_rank()}_slurm_job_id{slurm_job_id}_local_rank0_data_prep_completed'
+        random_number = random.randint(0, 999999)
+        sha_signature = hashlib.sha256(str(random_number).encode()).hexdigest()
+        signal_file_path = f'.sha_{sha_signature}_data_prep_completed'
 
         # Non local rank 0 ranks will wait here for local rank 0 to finish the data processing.
         # Once local rank 0 is done, the datasets are all cached on disk, and all other ranks

llmfoundry/models/hf/hf_causal_lm.py

@@ -5,6 +5,8 @@
 
 import logging
 import os
+import random
+import hashlib
 import warnings
 from typing import (
     TYPE_CHECKING,
@@ -339,8 +341,9 @@ def _autoset_attn_implementation_monkeypatch(
                 f'init_device="{init_device}" must be either "cpu" or "meta".',
             )
 
-        slurm_job_id = int(os.getenv('SLURM_JOB_ID', -1))
-        signal_file_path = f'.node_{dist.get_node_rank()}_local_rank0_slurm_job_id{slurm_job_id}_completed'
+        random_number = random.randint(0, 999999)
+        sha_signature = hashlib.sha256(str(random_number).encode()).hexdigest() 
+        signal_file_path = f'.sha_{sha_signature}_completed'
         if dist.get_local_rank() == 0:
             with open(signal_file_path, 'wb') as f:
                 f.write(b'local_rank0_completed_download')

llmfoundry/utils/builders.py

@@ -5,6 +5,8 @@
 import functools
 import logging
 import os
+import random
+import hashlib
 import re
 from collections import OrderedDict
 from typing import (
@@ -453,8 +455,9 @@ def build_tokenizer(
     os.environ['TRANSFORMERS_NO_ADVISORY_WARNINGS'] = '1'
     os.environ['TOKENIZERS_PARALLELISM'] = 'false'
 
-    slurm_job_id = int(os.getenv('SLURM_JOB_ID', -1))
-    signal_file_path = f'.node_{dist.get_node_rank()}_local_rank0_slurm_job_id{slurm_job_id}_completed_tokenizer_setup'
+    random_number = random.randint(0, 999999)
+    sha_signature = hashlib.sha256(str(random_number).encode()).hexdigest() 
+    signal_file_path = f'.sha_{sha_signature}_completed_tokenizer_setup'
 
     if dist.is_available() and dist.is_initialized(
     ) and dist.get_world_size() > 1: