ENH: Add support for LoRA hotswapping

docs/source/en/peft.md

@@ -151,3 +151,45 @@ model.enable_adapters()
 # disable all adapters
 model.disable_adapters()
 ```
+
+## Hotswapping adapters
+
+A common use case when serving multiple adapters is to load one adapter first, generate output, load another adapter, generate more outputs, load another adapter, etc. This can be inefficient, since each time a new adapter is loaded, new memory is reserved; moreover, if the model is compiled with `torch.compile`, it needs to be re-compiled each time a new adapter is used. When switching frequently, the compilation time may never be amortized.
+
+To better support this common workflow, you can "hotswap" a LoRA adapter, to avoid accumulating memory and, in some cases, recompilation. It requires an adapter to already be loaded, and the new adapter weights are swapped in-place for the existing adapter. Note that other PEFT methods are not supported yet, only LoRA.
+
+Pass `hotswap=True` when loading a LoRA adapter to enable this feature. It is important to indicate the name of the existing adapter (`"default"` is the default adapter name) to be swapped.
+
+```python
+model = AutoModel.from_pretrained(...)
+# load adapter 1 as normal
+model.load_adapter(file_name_adapter_1)
+# generate outputs with adapter 1
+...
+# now hotswap the 2nd adapter
+model.load_adapter(file_name_adapter_2, hotswap=True, adapter_name="default")
+# generate outputs with adapter 2
+```
+
+For compiled models, it is often necessary to call [`~integrations.peft.PeftAdapterMixin.enable_peft_hotswap`] to avoid recompilation. Call this method _before_ loading the first adapter, while `torch.compile` should be called _after_ loading the first adapter.
+
+```python
+model = AutoModel.from_pretrained(...)
+max_rank = ...  # the highest rank among all LoRAs that you want to load
+# call *before* compiling and loading the LoRA adapter
+model.enable_peft_hotswap(target_rank=max_rank)
+model.load_adapter(file_name_1, adapter_name="default")
+# optionally compile the model now
+model = torch.compile(model, ...)
+output_1 = model(...)
+# now you can hotswap the 2nd adapter, use the same name as for the 1st
+model.load_adapter(file_name_2, adapter_name="default", hotswap=True)
+output_2 = model(...)
+```
+
+The `target_rank=max_rank` argument is important for setting the maximum rank among all LoRA adapters that will be loaded. If you have one adapter with rank 8 and another with rank 16, pass `target_rank=16`. You should use a higher value if in doubt. By default, this value is 128.
+
+However, there can be situations where recompilation is unavoidable. For example, if the hotswapped adapter targets more layers than the initial adapter, then recompilation is triggered. Try to load the adapter that targets the most layers first. Refer to the PEFT docs on [hotswapping](https://huggingface.co/docs/peft/main/en/package_reference/hotswap#peft.utils.hotswap.hotswap_adapter) for more details about the limitations of this feature.
+
+> [!Tip]
+> Move your code inside the `with torch._dynamo.config.patch(error_on_recompile=True)` context manager to detect if a model was recompiled. If you detect recompilation despite following all the steps above, please open an issue with [PEFT](https://github.com/huggingface/peft/issues) with a reproducible example.

src/transformers/integrations/peft.py

@@ -15,7 +15,7 @@
 import importlib
 import inspect
 import re
-from typing import Any, Optional, Union
+from typing import Any, Literal, Optional, Union
 
 from packaging import version
 
@@ -83,6 +83,7 @@ class PeftAdapterMixin:
     """
 
     _hf_peft_config_loaded = False
+    _prepare_peft_hotswap_kwargs: Optional[dict] = None
 
     def load_adapter(
         self,
@@ -98,6 +99,7 @@ def load_adapter(
         adapter_state_dict: Optional[dict[str, "torch.Tensor"]] = None,
         low_cpu_mem_usage: bool = False,
         is_trainable: bool = False,
+        hotswap: bool = False,
         adapter_kwargs: Optional[dict[str, Any]] = None,
     ) -> None:
         """
@@ -153,12 +155,52 @@ def load_adapter(
             is_trainable (`bool`, *optional*, defaults to `False`):
                 Whether the adapter should be trainable or not. If `False`, the adapter will be frozen and can only be
                 used for inference.
+            hotswap : (`bool`, *optional*, defaults to `False`)
+                Whether to substitute an existing (LoRA) adapter with the newly loaded adapter in-place. This means
+                that, instead of loading an additional adapter, this will take the existing adapter weights and replace
+                them with the weights of the new adapter. This can be faster and more memory efficient. However, the
+                main advantage of hotswapping is that when the model is compiled with torch.compile, loading the new
+                adapter does not require recompilation of the model. When using hotswapping, the passed `adapter_name`
+                should be the name of an already loaded adapter.
+
+                If the new adapter and the old adapter have different ranks and/or LoRA alphas (i.e. scaling), you need
+                to call an additional method before loading the adapter:
+
+                ```py
+                model = AutoModel.from_pretrained(...)
+                max_rank = ...  # the highest rank among all LoRAs that you want to load
+                # call *before* compiling and loading the LoRA adapter
+                model.enable_peft_hotswap(target_rank=max_rank)
+                model.load_adapter(file_name_1, adapter_name="default")
+                # optionally compile the model now
+                model = torch.compile(model, ...)
+                output_1 = model(...)
+                # now you can hotswap the 2nd adapter, use the same name as for the 1st
+                model.load_adapter(file_name_2, adapter_name="default", hotswap=True)
+                output_2 = model(...)
+                ```
+
+                Note that hotswapping comes with a couple of limitations documented here:
+                https://huggingface.co/docs/peft/main/en/package_reference/hotswap
             adapter_kwargs (`dict[str, Any]`, *optional*):
                 Additional keyword arguments passed along to the `from_pretrained` method of the adapter config and
                 `find_adapter_config_file` method.
         """
+        from peft import PeftType
+
         check_peft_version(min_version=MIN_PEFT_VERSION)
 
+        if hotswap:
+            min_version_hotswap = "0.15.0"
+            if version.parse(importlib.metadata.version("peft")) < version.parse(min_version_hotswap):
+                raise ValueError(f"To hotswap the adapter, you need PEFT >= v{min_version_hotswap}.")
+            if (not self._hf_peft_config_loaded) or (adapter_name not in self.peft_config):
+                raise ValueError(
+                    "To hotswap an adapter, there must already be an existing adapter with the same adapter name."
+                )
+            if any(conf.peft_type != PeftType.LORA for conf in self.peft_config.values()):
+                raise ValueError("Hotswapping is currently only supported for LoRA, please set `hotswap=False`.")
+
         # peft only supports low_cpu_mem_usage starting from v0.13.0
         peft_load_kwargs = {}
         key_mapping = adapter_kwargs.pop("key_mapping", None) if adapter_kwargs is not None else None
@@ -181,8 +223,12 @@ def load_adapter(
         from peft import PeftConfig, inject_adapter_in_model, load_peft_weights
         from peft.utils import set_peft_model_state_dict
 
-        if self._hf_peft_config_loaded and adapter_name in self.peft_config:
+        if self._hf_peft_config_loaded and (not hotswap) and (adapter_name in self.peft_config):
             raise ValueError(f"Adapter with name {adapter_name} already exists. Please use a different name.")
+        elif hotswap and ((not self._hf_peft_config_loaded) or (adapter_name not in self.peft_config)):
+            raise ValueError(
+                "To hotswap an adapter, there must already be an existing adapter with the same adapter name."
+            )
 
         if peft_model_id is None and (adapter_state_dict is None and peft_config is None):
             raise ValueError(
@@ -231,8 +277,12 @@ def load_adapter(
             )
             peft_config.inference_mode = not is_trainable
 
-        # Create and add fresh new adapters into the model.
-        inject_adapter_in_model(peft_config, self, adapter_name, **peft_load_kwargs)
+        if peft_config.peft_type != PeftType.LORA:
+            raise ValueError("Hotswapping is currently only supported for LoRA, please set `hotswap=False`.")
+
+        if not hotswap:
+            # Create and add fresh new adapters into the model, unless the weights are hotswapped
+            inject_adapter_in_model(peft_config, self, adapter_name, **peft_load_kwargs)
 
         if not self._hf_peft_config_loaded:
             self._hf_peft_config_loaded = True
@@ -255,12 +305,47 @@ def load_adapter(
                     # Early exit of the loop
                     if n_replace > 0:
                         break
+
+            # For hotswapping, we need the adapter name to be present in the state dict keys
+            if hotswap:
+                if key.endswith("lora_A.weight") or key.endswith("lora_B.weight"):
+                    new_key = new_key[: -len(".weight")] + f".{adapter_name}.weight"
+                elif key.endswith("lora_B.bias"):  # lora_bias=True option
+                    new_key = new_key[: -len(".bias")] + f".{adapter_name}.bias"
             processed_adapter_state_dict[new_key] = value
 
         # Load state dict
-        incompatible_keys = set_peft_model_state_dict(
-            self, processed_adapter_state_dict, adapter_name, **peft_load_kwargs
-        )
+        if not hotswap:
+            incompatible_keys = set_peft_model_state_dict(
+                self, processed_adapter_state_dict, adapter_name, **peft_load_kwargs
+            )
+
+            if self._prepare_peft_hotswap_kwargs is not None:
+                # For hotswapping of compiled models or adapters with different ranks.
+                # If the user called enable_peft_hotswap, we need to ensure it is called:
+                # - after the first adapter was loaded
+                # - before the model is compiled and the 2nd adapter is being hotswapped in
+                # Therefore, it needs to be called here
+                from peft.utils.hotswap import prepare_model_for_compiled_hotswap
+
+                prepare_model_for_compiled_hotswap(self, config=peft_config, **self._prepare_peft_hotswap_kwargs)
+                # We only want to call prepare_model_for_compiled_hotswap once
+                self._prepare_peft_hotswap_kwargs = None
+        else:
+            from peft.utils.hotswap import check_hotswap_configs_compatible, hotswap_adapter_from_state_dict
+
+            check_hotswap_configs_compatible(self.peft_config[adapter_name], peft_config)
+            try:
+                hotswap_adapter_from_state_dict(
+                    model=self,
+                    state_dict=processed_adapter_state_dict,
+                    adapter_name=adapter_name,
+                    config=peft_config,
+                )
+            except Exception as e:
+                logger.error(f"Hotswapping {adapter_name} was unsucessful with the following error: \n{e}")
+                raise
+            incompatible_keys = None
 
         if incompatible_keys is not None:
             err_msg = ""
@@ -302,6 +387,46 @@ def load_adapter(
                 offload_index=offload_index,
             )
 
+    def enable_peft_hotswap(
+        self, target_rank: int = 128, check_compiled: Literal["error", "warn", "ignore"] = "error"
+    ) -> None:
+        """Enables the possibility to hotswap PEFT adapters with different ranks, or, if the model is compiled, without
+        triggering recompilation.
+
+        Right now, hotswapping is only supported for LoRA.
+
+        Calling this method is only required when hotswapping adapters and if the model is compiled or if the ranks of
+        the loaded adapters differ. If the ranks are all identical and the model is not compiled, hotswapping works
+        without calling this method first.
+
+        Args:
+            target_rank (`int`, *optional*, defaults to `128`):
+                The highest rank among all the adapters that will be loaded.
+            check_compiled (`str`, *optional*, defaults to `"error"`):
+                How to handle the case when the model is already compiled, which should generally be avoided. The
+                options are:
+                  - "error" (default): raise an error
+                  - "warn": issue a warning
+                  - "ignore": do nothing
+        """
+        min_version_hotswap = "0.15.0"
+        if version.parse(importlib.metadata.version("peft")) < version.parse(min_version_hotswap):
+            raise ValueError(f"To hotswap the adapter, you need PEFT >= v{min_version_hotswap}.")
+
+        if getattr(self, "peft_config", {}):
+            if check_compiled == "error":
+                raise RuntimeError("Call `enable_peft_hotswap` before loading the first adapter.")
+            elif check_compiled == "warn":
+                logger.warning(
+                    "It is recommended to call `enable_peft_hotswap` before loading the first adapter to avoid recompilation."
+                )
+            elif check_compiled != "ignore":
+                raise ValueError(
+                    f"check_compiles should be one of 'error', 'warn', or 'ignore', got '{check_compiled}' instead."
+                )
+
+        self._prepare_peft_hotswap_kwargs = {"target_rank": target_rank, "check_compiled": check_compiled}
+
     def add_adapter(self, adapter_config, adapter_name: Optional[str] = None) -> None:
         r"""
         If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT