replace zero_only with simpler if statement (#2592)

.github/workflows/multi-gpu-e2e.yml

@@ -9,6 +9,7 @@ on:
       - 'pyproject.toml'
       - '.github/workflows/multi-gpu-e2e.yml'
       - 'src/axolotl/core/trainers/mixins/sequence_parallel.py'
+      - 'src/axolotl/utils/distributed.py'
   workflow_dispatch:
   schedule:
     - cron: '0 0 * * 1,4'  # Runs at 00:00 UTC every monday & thursday

.github/workflows/tests.yml

@@ -27,6 +27,9 @@ concurrency:
   group: ${{ github.workflow }}-${{ github.ref }}
   cancel-in-progress: ${{ github.ref != 'refs/heads/main' }}
 
+env:
+  TRANSFORMERS_IS_CI: "yes"
+
 jobs:
   pre-commit:
     name: pre-commit

src/axolotl/integrations/cut_cross_entropy/__init__.py

@@ -25,7 +25,7 @@ import torch
 
 from axolotl.integrations.base import BasePlugin
 from axolotl.utils import get_pytorch_version
-from axolotl.utils.distributed import zero_only
+from axolotl.utils.distributed import is_main_process
 
 from .args import CutCrossEntropyArgs  # pylint: disable=unused-import. # noqa: F401
 
@@ -76,7 +76,7 @@ class CutCrossEntropyPlugin(BasePlugin):
                 cce_patch,
             )
 
-            with zero_only():
+            if is_main_process(use_environ=True):
                 LOG.info(
                     f"Applying Cut Cross Entropy to model type: {cfg.model_config_type}"
                 )

src/axolotl/integrations/liger/__init__.py

@@ -23,8 +23,8 @@ import logging
 import sys
 
 from axolotl.integrations.base import BasePlugin
+from axolotl.utils.distributed import is_main_process
 
-from ...utils.distributed import zero_only
 from .args import LigerArgs  # pylint: disable=unused-import. # noqa: F401
 from .utils import patch_with_compile_disable
 
@@ -85,7 +85,7 @@ class LigerPlugin(BasePlugin):
                 kwargs["geglu"] = cfg.liger_glu_activation
             elif "swiglu" in liger_fn_sig.parameters:
                 kwargs["swiglu"] = cfg.liger_glu_activation
-            with zero_only():
+            if is_main_process(use_environ=True):
                 LOG.info(
                     f"Applying LIGER to {cfg.model_config_type} with kwargs: {kwargs}"
                 )

src/axolotl/utils/distributed.py

@@ -69,17 +69,27 @@ def barrier():
         dist.barrier()
 
 
-def is_main_process():
+def is_main_process(use_environ=False):
     """
     Check if the current process is the main process. If not in distributed mode,
     always return `True`.
+
+    Args:
+    - use_environ (bool, optional): Use environment variable to determine main process.
+
+    Returns:
+    - bool: `True` if the current process is the main process, `False` otherwise.
     """
+    if use_environ:
+        return os.environ.get("LOCAL_RANK", "0") == "0"
     if not is_distributed():
         return True
     return dist.get_rank() == 0
 
 
-def is_local_main_process():
+def is_local_main_process(use_environ=False):
+    if use_environ:
+        return os.environ.get("LOCAL_RANK", "0") == "0"
     return PartialState().is_local_main_process
 
 
@@ -99,17 +109,6 @@ def cleanup_distributed():
         torch.distributed.destroy_process_group()
 
 
-@contextmanager
-def zero_only():
-    """
-    Context manager that only runs the enclosed block on the main rank.
-    """
-    if is_main_process():
-        yield
-    else:
-        yield None
-
-
 @contextmanager
 def zero_first(is_main):
     """

src/axolotl/utils/models.py

@@ -68,7 +68,7 @@ from axolotl.utils.distributed import (
     get_device_count,
     get_device_type,
     is_local_main_process,
-    zero_only,
+    is_main_process,
 )
 from axolotl.utils.gradient_checkpointing import hf_grad_checkpoint_offload_wrapper
 from axolotl.utils.lora_embeddings import get_linear_embedding_layers
@@ -437,7 +437,7 @@ def load_tokenizer(cfg):
             {"additional_special_tokens": additional_special_tokens}
         )
 
-    with zero_only():
+    if is_main_process(use_environ=True):
         LOG.debug(f"EOS: {tokenizer.eos_token_id} / {tokenizer.eos_token}")
         LOG.debug(f"BOS: {tokenizer.bos_token_id} / {tokenizer.bos_token}")
         LOG.debug(f"PAD: {tokenizer.pad_token_id} / {tokenizer.pad_token}")