refactor neft patch to be more re-usable similar to trl's impl (#796)

gitbook/README.md

@@ -1,2 +1 @@
 # Page
-

src/axolotl/monkeypatch/neft_embeddings.py

@@ -0,0 +1,65 @@
+"""
+patches implemented through the trainer hooks to enable NEFT/noisy embeddings per https://arxiv.org/abs/2310.05914
+"""
+import torch
+from peft import PeftModel
+from transformers import PreTrainedModel
+
+
+def patch_neft(alpha, model):
+    embeddings = None
+    if isinstance(model, PreTrainedModel):
+        embeddings = model.get_input_embeddings()
+    if isinstance(model, PeftModel):
+        embeddings = model.base_model.get_input_embeddings()
+    if not embeddings:
+        raise ValueError(f"unhandled model class for neft: {model.__class__.__name__}")
+    embeddings.noisy_embedding_alpha = alpha
+    old_forward = embeddings.forward
+
+    # This hack seems to be needed to properly use a custom forward pass
+    # all credits to: https://discuss.pytorch.org/t/how-can-i-replace-the-forward-method-of-a-predefined-torchvision-model-with-my-customized-forward-function/54224/11
+    bound_method = neft_forward.__get__(  # pylint: disable=no-value-for-parameter
+        embeddings, embeddings.__class__
+    )
+    setattr(embeddings, "forward", bound_method)
+
+    embeddings._old_forward = old_forward  # pylint: disable=protected-access
+    return model
+
+
+def unpatch_neft(model):
+    embeddings = None
+    if isinstance(model, PreTrainedModel):
+        embeddings = model.get_input_embeddings()
+    if isinstance(model, PeftModel):
+        embeddings = model.base_model.get_input_embeddings()
+    if not embeddings:
+        raise ValueError(f"unhandled model class for neft: {model.__class__.__name__}")
+    if hasattr(embeddings, "_old_forward"):
+        embeddings.forward = embeddings._old_forward  # pylint: disable=protected-access
+        del embeddings._old_forward  # pylint: disable=protected-access
+        del embeddings.noisy_embedding_alpha
+
+
+def neft_forward(self, inputs: torch.Tensor):
+    embeddings = self._old_forward(inputs)  # pylint: disable=protected-access
+
+    if self.training:
+        dims = torch.tensor(embeddings.size(1) * embeddings.size(2))
+        mag_norm = self.noisy_embedding_alpha / torch.sqrt(dims)
+        embeddings = embeddings + torch.zeros_like(embeddings).uniform_(
+            -mag_norm, mag_norm
+        )
+
+    return embeddings
+
+
+def pretrain_hook(cfg, trainer):
+    if cfg.noisy_embedding_alpha:
+        trainer.model = patch_neft(cfg.noisy_embedding_alpha, trainer.model)
+
+
+def post_train_hook(cfg, trainer):
+    if cfg.noisy_embedding_alpha:
+        unpatch_neft(trainer.model)

src/axolotl/train.py

@@ -16,6 +16,7 @@
 
 from axolotl.common.cli import TrainerCliArgs
 from axolotl.logging_config import configure_logging
+from axolotl.monkeypatch import neft_embeddings
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.models import load_model, load_tokenizer
 from axolotl.utils.trainer import setup_trainer
@@ -107,13 +108,15 @@ def terminate_handler(_, __, model):
     if cfg.group_by_length:
         LOG.info("hang tight... sorting dataset for group_by_length")
 
+    pretrain_hooks(cfg, trainer)
     if cfg.flash_optimum:
         with torch.backends.cuda.sdp_kernel(
             enable_flash=True, enable_math=True, enable_mem_efficient=True
         ):
             trainer.train(resume_from_checkpoint=resume_from_checkpoint)
     else:
         trainer.train(resume_from_checkpoint=resume_from_checkpoint)
+    post_train_hooks(cfg, trainer)
 
     LOG.info(f"Training Completed!!! Saving pre-trained model to {cfg.output_dir}")
 
@@ -163,3 +166,23 @@ def terminate_handler(_, __, model):
         trainer.create_model_card(model_name=cfg.output_dir.lstrip("./"))
 
     return model, tokenizer
+
+
+def pretrain_hooks(cfg, trainer):
+    """
+    Run hooks right before kicking off the training
+    :param cfg:
+    :param trainer:
+    :return:
+    """
+    neft_embeddings.pretrain_hook(cfg, trainer)
+
+
+def post_train_hooks(cfg, trainer):
+    """
+    Run hooks right after training completes
+    :param cfg:
+    :param trainer:
+    :return:
+    """
+    neft_embeddings.post_train_hook(cfg, trainer)

src/axolotl/utils/models.py

@@ -180,26 +180,6 @@ def load_model(
         LOG.info("patching with flash attention")
         replace_mistral_attn_with_flash_attn(packed=cfg.sample_packing)
 
-    if cfg.is_llama_derived_model and cfg.noisy_embedding_alpha:
-        from axolotl.monkeypatch.llama_embeddings_hijack import (
-            replace_llama_embeddings_with_uniform_distribution,
-        )
-
-        LOG.info("patching with noisy embeddings")
-        replace_llama_embeddings_with_uniform_distribution(
-            noise_alpha=cfg.noisy_embedding_alpha
-        )
-
-    if cfg.is_mistral_derived_model and cfg.noisy_embedding_alpha:
-        from axolotl.monkeypatch.mistral_embeddings_hijack import (
-            replace_mistral_embeddings_with_uniform_distribution,
-        )
-
-        LOG.info("patching with noisy embeddings")
-        replace_mistral_embeddings_with_uniform_distribution(
-            noise_alpha=cfg.noisy_embedding_alpha
-        )
-
     if cfg.is_llama_derived_model and cfg.xpos_rope:
         from axolotl.monkeypatch.xpos_rope_llama_monkey_patch import (
             replace_llama_rope_with_xpos_rope,