Merge branch 'master' into uly-hf

deepspeed/inference/v2/checkpoint/huggingface_engine.py

@@ -15,13 +15,13 @@
 
 class HuggingFaceCheckpointEngine(CheckpointEngineBase):
 
-    def __init__(self, model_name_or_path: str, auth_token: str = None) -> None:
+    def __init__(self, model_name_or_path: str, auth_token: str = None, **hf_kwargs) -> None:
         super().__init__()
         from transformers import AutoConfig, GenerationConfig
 
         self.model_name_or_path = model_name_or_path
         self.auth_token = auth_token
-        self.model_config = AutoConfig.from_pretrained(self.model_name_or_path)
+        self.model_config = AutoConfig.from_pretrained(self.model_name_or_path, **hf_kwargs)
         # Define this property here so we can use it in the model implementation
         if not hasattr(self.model_config, "max_seq_length"):
             if hasattr(self.model_config, "max_position_embeddings"):

deepspeed/runtime/zero/linear.py

@@ -16,6 +16,7 @@
 #when implemented outside of torch.autograd.Function
 
 import math
+import functools
 
 import torch
 from torch import Tensor
@@ -33,8 +34,14 @@ def print_rank_0(message, debug=False, force=False):
 
 
 try:
-    autocast_custom_fwd = get_accelerator().amp().custom_fwd
-    autocast_custom_bwd = get_accelerator().amp().custom_bwd
+    # Fix `torch.[device].amp.custom_fwd/bwd` FutureWarning in torch 2.4
+    if hasattr(torch, 'amp') and hasattr(torch.amp, 'custom_fwd') and hasattr(torch.amp, 'custom_bwd'):
+        autocast_custom_fwd = functools.partial(torch.amp.custom_fwd, device_type=get_accelerator().device_name())
+        autocast_custom_bwd = functools.partial(torch.amp.custom_bwd, device_type=get_accelerator().device_name())
+    else:
+        # original implementation
+        autocast_custom_fwd = get_accelerator().amp().custom_fwd
+        autocast_custom_bwd = get_accelerator().amp().custom_bwd
 except (ImportError, AttributeError) as exp:
     autocast_custom_fwd = noop_decorator
     autocast_custom_bwd = noop_decorator