add noisy embedding

README.md

@@ -672,6 +672,11 @@ adam_epsilon:
 # Gradient clipping max norm
 max_grad_norm:
 
+# Augmentation techniques
+# NEFT https://arxiv.org/abs/2310.05914, set this to a number (paper default is 5) to add noise to embeddings
+# currently only supported on Llama and Mistral
+noisy_embedding_alpha:
+
 # Whether to bettertransformers
 flash_optimum:
 # Whether to use xformers attention patch https://github.com/facebookresearch/xformers:

src/axolotl/monkeypatch/llama_embeddings_hijack.py

@@ -0,0 +1,40 @@
+"""
+patch to add noisy embeddings per https://arxiv.org/abs/2310.05914
+"""
+
+import torch
+import transformers.models.llama.modeling_llama
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+
+def replace_llama_embeddings_with_uniform_distribution(noise_alpha=5):
+    # pylint: disable=duplicate-code
+    def noised_embed(orig_embed, noise_alpha, model):
+        def new_func(input_ids):
+            # during training, we add noise to the embedding
+            # during generation, we don't add noise to the embedding
+            if model.training:
+                embed_init = orig_embed(input_ids)
+                dims = torch.tensor(embed_init.size(1) * embed_init.size(2))
+                mag_norm = noise_alpha / torch.sqrt(dims)
+                return embed_init + torch.zeros_like(embed_init).uniform_(
+                    -mag_norm, mag_norm
+                )
+            return orig_embed(input_ids)
+
+        return new_func
+
+    def post_init(orig_post_init):
+        def new_func(self):
+            orig_post_init(self)
+            self.embed_tokens.forward = noised_embed(
+                self.embed_tokens.forward, noise_alpha, self
+            )
+
+        return new_func
+
+    transformers.models.llama.modeling_llama.LlamaModel.post_init = post_init(
+        transformers.models.llama.modeling_llama.LlamaModel.post_init
+    )

src/axolotl/monkeypatch/mistral_embeddings_hijack.py

@@ -0,0 +1,40 @@
+"""
+patch to add noisy embeddings per https://arxiv.org/abs/2310.05914
+"""
+
+import torch
+import transformers.models.mistral.modeling_mistral
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+
+def replace_mistral_embeddings_with_uniform_distribution(noise_alpha=5):
+    # pylint: disable=duplicate-code
+    def noised_embed(orig_embed, noise_alpha, model):
+        def new_func(input_ids):
+            # during training, we add noise to the embedding
+            # during generation, we don't add noise to the embedding
+            if model.training:
+                embed_init = orig_embed(input_ids)
+                dims = torch.tensor(embed_init.size(1) * embed_init.size(2))
+                mag_norm = noise_alpha / torch.sqrt(dims)
+                return embed_init + torch.zeros_like(embed_init).uniform_(
+                    -mag_norm, mag_norm
+                )
+            return orig_embed(input_ids)
+
+        return new_func
+
+    def post_init(orig_post_init):
+        def new_func(self):
+            orig_post_init(self)
+            self.embed_tokens.forward = noised_embed(
+                self.embed_tokens.forward, noise_alpha, self
+            )
+
+        return new_func
+
+    transformers.models.mistral.modeling_mistral.MistralModel.post_init = post_init(
+        transformers.models.mistral.modeling_mistral.MistralModel.post_init
+    )

src/axolotl/utils/models.py

@@ -180,6 +180,26 @@ 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,