[Misc] remove peft as dependency for prompt models

vllm/config.py

@@ -1558,14 +1558,6 @@ class PromptAdapterConfig:
     prompt_adapter_dtype: Optional[torch.dtype] = None
 
     def __post_init__(self):
-        library_name = 'peft'
-        try:
-            __import__(library_name)
-        except ImportError as e:
-            raise ImportError(
-                f"'{library_name}' is not installed for prompt adapter support."
-                f"Please install it using 'pip install {library_name}'."
-            ) from e
 
         if self.max_prompt_adapters < 1:
             raise ValueError(f"max_prompt_adapters "

vllm/prompt_adapter/models.py

@@ -14,6 +14,7 @@
 from vllm.prompt_adapter.layers import (
     VocabParallelEmbeddingWithPromptAdapter)  # yapf: disable
 from vllm.prompt_adapter.layers import PromptAdapterMapping
+from vllm.prompt_adapter.utils import load_peft_weights
 
 logger = logging.getLogger(__name__)
 
@@ -90,7 +91,6 @@ def from_local_checkpoint(
         config: PromptAdapterConfig,
         device: str = "cuda",
     ) -> "PromptAdapterModel":
-        from peft.utils import load_peft_weights
 
         if num_virtual_tokens > config.max_prompt_adapter_token:
             raise ValueError(

vllm/prompt_adapter/utils.py

@@ -0,0 +1,93 @@
+# code borrowed from: https://github.com/huggingface/peft/blob/v0.12.0/src/peft/utils/save_and_load.py#L420
+
+import os
+from typing import Optional
+
+import torch
+from huggingface_hub import file_exists, hf_hub_download
+from huggingface_hub.utils import EntryNotFoundError
+from safetensors.torch import load_file as safe_load_file
+
+WEIGHTS_NAME = "adapter_model.bin"
+SAFETENSORS_WEIGHTS_NAME = "adapter_model.safetensors"
+
+
+# Get current device name based on available devices
+def infer_device() -> str:
+    if torch.cuda.is_available():
+        return "cuda"
+    return "cpu"
+
+
+def load_peft_weights(model_id: str,
+                      device: Optional[str] = None,
+                      **hf_hub_download_kwargs) -> dict:
+    r"""
+    A helper method to load the PEFT weights from the HuggingFace Hub or locally
+
+    Args:
+        model_id (`str`):
+            The local path to the adapter weights or the name of the adapter to
+            load from the HuggingFace Hub.
+        device (`str`):
+            The device to load the weights onto.
+        hf_hub_download_kwargs (`dict`):
+            Additional arguments to pass to the `hf_hub_download` method when 
+            loading from the HuggingFace Hub.
+    """
+    path = (os.path.join(model_id, hf_hub_download_kwargs["subfolder"])
+            if hf_hub_download_kwargs.get("subfolder", None) is not None else
+            model_id)
+
+    if device is None:
+        device = infer_device()
+
+    if os.path.exists(os.path.join(path, SAFETENSORS_WEIGHTS_NAME)):
+        filename = os.path.join(path, SAFETENSORS_WEIGHTS_NAME)
+        use_safetensors = True
+    elif os.path.exists(os.path.join(path, WEIGHTS_NAME)):
+        filename = os.path.join(path, WEIGHTS_NAME)
+        use_safetensors = False
+    else:
+        token = hf_hub_download_kwargs.get("token", None)
+        if token is None:
+            token = hf_hub_download_kwargs.get("use_auth_token", None)
+
+        hub_filename = (os.path.join(hf_hub_download_kwargs["subfolder"],
+                                     SAFETENSORS_WEIGHTS_NAME)
+                        if hf_hub_download_kwargs.get("subfolder", None)
+                        is not None else SAFETENSORS_WEIGHTS_NAME)
+        has_remote_safetensors_file = file_exists(
+            repo_id=model_id,
+            filename=hub_filename,
+            revision=hf_hub_download_kwargs.get("revision", None),
+            repo_type=hf_hub_download_kwargs.get("repo_type", None),
+            token=token,
+        )
+        use_safetensors = has_remote_safetensors_file
+
+        if has_remote_safetensors_file:
+            # Priority 1: load safetensors weights
+            filename = hf_hub_download(
+                model_id,
+                SAFETENSORS_WEIGHTS_NAME,
+                **hf_hub_download_kwargs,
+            )
+        else:
+            try:
+                filename = hf_hub_download(model_id, WEIGHTS_NAME,
+                                           **hf_hub_download_kwargs)
+            except EntryNotFoundError:
+                raise ValueError(  # noqa: B904
+                    f"Can't find weights for {model_id} in {model_id} or \
+                    in the Hugging Face Hub. "
+                    f"Please check that the file {WEIGHTS_NAME} or \
+                    {SAFETENSORS_WEIGHTS_NAME} is present at {model_id}.")
+
+    if use_safetensors:
+        adapters_weights = safe_load_file(filename, device=device)
+    else:
+        adapters_weights = torch.load(filename,
+                                      map_location=torch.device(device))
+
+    return adapters_weights