when carrying out iterative spin, need to update reference model with…

… policy after each iteration

self_rewarding_lm_pytorch/self_rewarding_lm_pytorch.py

@@ -30,7 +30,10 @@
     adam_optimizer_with_linear_decay
 )
 
-from self_rewarding_lm_pytorch.spin import SPINTrainer
+from self_rewarding_lm_pytorch.spin import (
+    SPIN,
+    SPINTrainer
+)
 
 from einops import rearrange, repeat
 
@@ -635,6 +638,7 @@ def __init__(
         accelerate_kwargs: dict = dict(),
         sft_trainer_kwargs: dict = dict(),
         spin_trainer_kwargs: dict = dict(),
+        spin_kwargs: dict = dict(),
         dpo_trainer_kwargs: dict = dict(),
     ):
         super().__init__()
@@ -677,10 +681,20 @@ def __init__(
 
         assert len(self.spin_trainers) == 0 or exists(train_sft_dataset)
 
+        self.spin = None
+
+        if num_spin_cycles > 0:
+            self.spin = SPIN(
+                model,
+                pad_id = pad_id,
+                λ = spin_λ,
+                **spin_kwargs
+            )
+
         for _ in range(num_spin_cycles):
 
             spin_trainer = SPINTrainer(
-                model,
+                self.spin,
                 accelerator = self.accelerator,
                 train_sft_dataset = train_sft_dataset,
                 valid_sft_dataset = valid_sft_dataset,
@@ -790,6 +804,8 @@ def forward(
 
             spin_trainer()
 
+            self.spin.update_reference_model_with_policy()
+
             self.save(f'spin.{spin_cycle}.ckpt.pt', overwrite = overwrite_checkpoints)
 
 

self_rewarding_lm_pytorch/spin.py

@@ -1,7 +1,7 @@
 from pathlib import Path
 
 from beartype import beartype
-from beartype.typing import Optional, Callable
+from beartype.typing import Optional, Callable, Union
 from torchtyping import TensorType
 
 import torch
@@ -140,7 +140,7 @@ def forward(
 class SPINTrainer(Module):
     def __init__(
         self,
-        model: Module,
+        model: Union[Module, SPIN],
         *,
         train_sft_dataset: Dataset,
         max_seq_len: int,
@@ -169,13 +169,15 @@ def __init__(
         if not exists(self.accelerator):
             self.accelerator = Accelerator(**accelerate_kwargs)
 
-        self.model = SPIN(
-            model,
-            λ = spin_λ,
-            pad_id = pad_id,
-            ref_model_ema_decay = ref_model_ema_decay
-        )
+        if not isinstance(model, SPIN):
+            model = SPIN(
+                model,
+                λ = spin_λ,
+                pad_id = pad_id,
+                ref_model_ema_decay = ref_model_ema_decay
+            )
 
+        self.model = model
         self.epochs = epochs
         self.train_dataloader = DataLoader(train_sft_dataset, batch_size = batch_size, shuffle = True, drop_last = True)
 

setup.py

@@ -3,7 +3,7 @@
 setup(
   name = 'self-rewarding-lm-pytorch',
   packages = find_packages(exclude=[]),
-  version = '0.0.36',
+  version = '0.0.37',
   license='MIT',
   description = 'Self Rewarding LM - Pytorch',
   author = 'Phil Wang',