ENH BOFT don't save boft_P buffer (#2050)

The buffer does not need to be part of the checkpoint, by making it
non-persistent, the file size can be greatly reduced.

src/peft/tuners/boft/layer.py

@@ -337,7 +337,7 @@ class BOFTLayer(BaseTunerLayer):
             perm_mat = self.perm2mat(perm)
             P[i] = perm_mat
 
-        self.register_buffer("boft_P", P)
+        self.register_buffer("boft_P", P, persistent=False)
 
         self.boft_R[adapter_name] = nn.Parameter(
             torch.zeros(boft_n_butterfly_factor + 1, boft_block_num, boft_block_size, boft_block_size)
@@ -771,7 +771,7 @@ class Conv2d(nn.Module, BOFTLayer):
             perm_mat = self.perm2mat(perm)
             P[i] = perm_mat
 
-        self.register_buffer("boft_P", P)
+        self.register_buffer("boft_P", P, persistent=False)
 
         self.boft_R[adapter_name] = nn.Parameter(
             torch.zeros(boft_n_butterfly_factor + 1, boft_block_num, boft_block_size, boft_block_size)

tests/test_boft.py

@@ -0,0 +1,84 @@
+# Copyright 2024-present the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import torch
+from safetensors.torch import load_file
+from transformers import AutoModelForCausalLM
+
+from peft import BOFTConfig, PeftModel, get_peft_model
+from peft.utils import infer_device
+
+
+class TestBoft:
+    device = infer_device()
+
+    def test_boft_state_dict(self, tmp_path):
+        # see #2050
+        # ensure that the boft_P buffer is not stored in the checkpoint file and is not necessary to load the model
+        # correctly
+        torch.manual_seed(0)
+
+        inputs = torch.arange(10).view(-1, 1).to(self.device)
+        model_id = "hf-internal-testing/tiny-random-OPTForCausalLM"
+        model = AutoModelForCausalLM.from_pretrained(model_id).to(self.device)
+        model.eval()
+        output_base = model(inputs).logits
+
+        config = BOFTConfig(init_weights=False)
+        model = get_peft_model(model, config)
+        model.eval()
+        output_peft = model(inputs).logits
+
+        atol, rtol = 1e-5, 1e-8
+        # sanity check: loading boft changed the output
+        assert not torch.allclose(output_base, output_peft, atol=atol, rtol=rtol)
+
+        model.save_pretrained(tmp_path)
+        del model
+
+        # check that the boft_P buffer is not present
+        state_dict = load_file(tmp_path / "adapter_model.safetensors")
+        assert not any("boft_P" in key for key in state_dict)
+
+        # sanity check: the model still produces the same output after loading
+        model = AutoModelForCausalLM.from_pretrained(model_id).to(self.device)
+        model = PeftModel.from_pretrained(model, tmp_path)
+        output_loaded = model(inputs).logits
+        assert torch.allclose(output_peft, output_loaded, atol=atol, rtol=rtol)
+
+    def test_boft_old_checkpoint_including_boft_P(self, tmp_path):
+        # see #2050
+        # This test exists to ensure that after the boft_P buffer was made non-persistent, old checkpoints can still be
+        # loaded successfully.
+        torch.manual_seed(0)
+
+        inputs = torch.arange(10).view(-1, 1).to(self.device)
+        model_id = "hf-internal-testing/tiny-random-OPTForCausalLM"
+        model = AutoModelForCausalLM.from_pretrained(model_id).to(self.device)
+
+        # first create the expected output
+        config = BOFTConfig(init_weights=False)
+        model = get_peft_model(model, config)
+        model.eval()
+        output_peft = model(inputs).logits
+        del model
+
+        model = AutoModelForCausalLM.from_pretrained(model_id).to(self.device)
+        # checkpoint from before the PR whose state_dict still contains boft_P
+        hub_id = "peft-internal-testing/boft-tiny-opt-peft-v0.12"
+        model = PeftModel.from_pretrained(model, hub_id)
+        output_old = model(inputs).logits
+
+        atol, rtol = 1e-5, 1e-8
+        assert torch.allclose(output_peft, output_old, atol=atol, rtol=rtol)