Add kd coefficient scheduler

src/axolotl/core/trainer_builder.py

@@ -751,8 +751,12 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
 
         if self.cfg.kd_ce_alpha is not None:
             training_arguments_kwargs["kd_ce_alpha"] = self.cfg.kd_ce_alpha
+            if self.cfg.kd_ce_alpha_end is not None:
+                training_arguments_kwargs["kd_ce_alpha_end"] = self.cfg.kd_ce_alpha_end
         if self.cfg.kd_alpha is not None:
             training_arguments_kwargs["kd_alpha"] = self.cfg.kd_alpha
+            if self.cfg.kd_alpha_end is not None:
+                training_arguments_kwargs["kd_alpha_end"] = self.cfg.kd_alpha_end
         if self.cfg.kd_temperature is not None:
             training_arguments_kwargs["kd_temperature"] = self.cfg.kd_temperature
         if self.cfg.kd_zscore_base_temp is not None:

src/axolotl/integrations/kd/__init__.py

@@ -34,3 +34,12 @@ class KDPlugin(BasePlugin):
 
             return AxolotlKDTrainer
         return None
+
+    def add_callbacks_post_trainer(self, cfg, trainer):
+        callbacks = []
+        if cfg.kd_trainer:
+            from .callbacks import KDAlphaSchedulerCallback
+
+            callbacks.append(KDAlphaSchedulerCallback())
+
+        return callbacks

src/axolotl/integrations/kd/args.py

@@ -30,6 +30,8 @@ class KDArgs(BaseModel):
         float
     ] = None  # loss coefficient for cross-entropy loss during KD
     kd_alpha: Optional[float] = None  # loss coefficient for KD loss
+    kd_ce_alpha_end: Optional[float] = None  # end value for kd_ce_alpha
+    kd_alpha_end: Optional[float] = None  # end value for kd_alpha
     kd_temperature: Optional[float] = None  # temperature for sampling during KD
     kd_zscore_base_temp: Optional[float] = None  # base temperature for zscore scaling
     kd_top_k_before_softmax: Optional[

src/axolotl/integrations/kd/callbacks.py

@@ -0,0 +1,28 @@
+from transformers import TrainerCallback
+
+
+class KDAlphaSchedulerCallback(TrainerCallback):
+    """Callback to for scheduling KD alpha during training."""
+
+    def on_epoch_begin(
+        self, args, state, control, **kwargs  # pylint: disable=unused-argument
+    ):
+        if int(state.epoch) == 0:
+            state.kd_alpha = args.kd_alpha
+            state.kd_ce_alpha = args.kd_ce_alpha
+        elif int(state.epoch) == state.num_train_epochs - 1:
+            if args.kd_alpha_end is not None:
+                control.kd_alpha = args.kd_alpha_end
+            if args.kd_ce_alpha_end is not None:
+                control.kd_ce_alpha = args.kd_ce_alpha_end
+        else:
+            epoch_steps = state.num_train_epochs - 1
+            scale = int(state.epoch) / epoch_steps
+            if args.kd_alpha_end is not None:
+                control.kd_alpha = (
+                    args.kd_alpha + (args.kd_alpha_end - args.kd_alpha) * scale
+                )
+            if args.kd_ce_alpha_end is not None:
+                control.kd_ce_alpha = (
+                    args.kd_ce_alpha + (args.kd_ce_alpha_end - args.kd_ce_alpha) * scale
+                )

src/axolotl/integrations/kd/chat_template.py

@@ -62,10 +62,16 @@ class ChatTemplateStrategyWithKD(ChatTemplateStrategy):
         Transform logprobs to target format for KD training
         """
 
-        logprobs = sample.pop(self.logprobs_field)
+        if "target_logprobs" in sample.keys() and "target_token_ids" in sample.keys():
+            logprobs = sample.pop("target_logprobs")
+            token_ids = sample.pop("target_token_ids")
+        else:
+            logprobs = sample.pop(self.logprobs_field)
+            token_ids = [None] * len(logprobs)
+
         target_seq_len = len(logprobs)
         input_seq_len = len(sample["input_ids"])
-        input_padding_len = input_seq_len - target_seq_len
+        target_padding_len = input_seq_len - target_seq_len
         # get non-zero top-k (prune None logprobs from vllm data step)
         top_k_vals = [
             len(logprobs[i])
@@ -82,11 +88,11 @@ class ChatTemplateStrategyWithKD(ChatTemplateStrategy):
         target_token_ids = []
         target_mask = []
 
-        if input_padding_len < 0:
+        if target_padding_len < 0:
             # logprobs is longer than target_seq_len,
             # so we need to slice from the left/beginning of logprobs
             logprobs = logprobs[:-input_seq_len]
-            input_padding_len = 0
+            target_padding_len = 0
             # target_seq_len = input_seq_len
 
         # truncate the second dimension of the logprobs to top_k
@@ -98,33 +104,37 @@ class ChatTemplateStrategyWithKD(ChatTemplateStrategy):
         # for causal models, if we start the range at 1, then we don't need to shift in the trainer
         # otherwise, we need to shift in the trainer
         shift = 0
-        for _ in range(shift, input_padding_len):
+        for _ in range(shift, target_padding_len):
             target_logprobs.append([-float("inf")] * top_k)
             target_token_ids.append(list(range(top_k)))
             target_mask.append([0] * top_k)
 
-        for position in range(input_padding_len, input_seq_len):
+        for position in range(target_padding_len, input_seq_len):
             if sample["labels"][position] == -100:
                 target_mask.append([0] * top_k)
             else:
                 target_mask.append([1] * top_k)
 
-        for _, token_pos_logprobs in enumerate(logprobs):
+        for token_pos_logprobs, token_pos_token_ids in zip(logprobs, token_ids):
             # Initialize collections for logprobs and token_ids
             position_logprobs = []
             position_token_ids = []
 
             # Process each token probability entry
-            for entry in token_pos_logprobs:
-                # Extract logprob value
-                logprob = entry["logprob"]
+            if token_pos_token_ids is None:
+                for entry in token_pos_logprobs:
+                    # Extract logprob value
+                    logprob = entry["logprob"]
 
-                # Parse token_id from the "token_id:###" format
-                token_id = int(entry["token"].split(":")[1])
+                    # Parse token_id from the "token_id:###" format
+                    token_id = int(entry["token"].split(":")[1])
 
-                # Append to our collections
-                position_logprobs.append(logprob)
-                position_token_ids.append(token_id)
+                    # Append to our collections
+                    position_logprobs.append(logprob)
+                    position_token_ids.append(token_id)
+            else:
+                position_logprobs = token_pos_logprobs
+                position_token_ids = token_pos_token_ids
 
             # Convert to a tensor for easier manipulation
             position_logprobs_tensor = torch.tensor(
@@ -143,6 +153,7 @@ class ChatTemplateStrategyWithKD(ChatTemplateStrategy):
                 teacher_probs_t2 = teacher_probs_t1**exponent
             else:
                 teacher_probs_t2 = teacher_probs_t1
+
             # Re-normalize
             teacher_probs_t2 = teacher_probs_t2 / teacher_probs_t2.sum(
                 dim=0, keepdim=True

src/axolotl/integrations/kd/trainer.py

@@ -16,17 +16,35 @@
 KD trainer
 """
 
+from transformers import TrainerControl
+
 from axolotl.core.trainers.base import AxolotlTrainer
 
 from .topk_logprob.forward_kl import loss as topk_kd_loss
 from .topk_logprob.forward_kl import topk_kd_loss_with_zscore
 
 
+class AxolotlKDTrainerControl(TrainerControl):
+    kd_alpha: float = 1.0
+    kd_ce_alpha: float = 0.0
+
+    def state(self) -> dict:
+        state_val = super().state()
+        state_val["args"]["kd_alpha"] = self.kd_alpha
+        state_val["args"]["kd_ce_alpha"] = self.kd_ce_alpha
+
+
 class AxolotlKDTrainer(AxolotlTrainer):
     """
     Custom trainer subclass for Knowledge Distillation (KD)
     """
 
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.kd_alpha = self.args.kd_alpha
+        self.kd_ce_alpha = self.args.kd_ce_alpha
+        self.control = AxolotlKDTrainerControl()
+
     def _set_signature_columns_if_needed(self):
         super()._set_signature_columns_if_needed()
         columns_to_add = []
@@ -95,9 +113,8 @@ class AxolotlKDTrainer(AxolotlTrainer):
                 top_k_before_softmax=1 if self.args.kd_top_k_before_softmax else 0,
             )
 
-        if self.args.kd_ce_alpha > 0:
-            kd_alpha = self.args.kd_alpha
-            loss = self.args.kd_ce_alpha * outputs["loss"] + kd_alpha * loss_kd
+        if self.kd_ce_alpha > 0:
+            loss = self.kd_ce_alpha * outputs["loss"] + self.kd_alpha * loss_kd
         else:
             loss = loss_kd
         # Save past state if it exists

src/axolotl/utils/callbacks/__init__.py

@@ -813,6 +813,15 @@ class SaveAxolotlConfigtoWandBCallback(TrainerCallback):
                 )
             except (FileNotFoundError, ConnectionError) as err:
                 LOG.warning(f"Error while saving Axolotl config to WandB: {err}")
+            # TODO if using deepspeed and it's a file, save deepspeed config too
+            if args.deepspeed and os.path.isfile(args.deepspeed):
+                LOG.info(f"DeepSpeed config has been saved to the WandB run.")
+                artifact = wandb.Artifact(
+                    f"deepspeed-{wandb.run.id}", type="deepspeed-config"
+                )
+                artifact.add_file(args.deepspeed)
+                wandb.log_artifact(artifact)
+                wandb.save(args.deepspeed)
         return control
 
 

src/axolotl/utils/collators/batching.py

@@ -173,10 +173,16 @@ class V2BatchSamplerDataCollatorForSeq2Seq(DataCollatorForSeq2Seq):
                     ]
                     out_features[i][feature] = np.concatenate(arrays)
                 else:
-                    arrays = [
-                        np.array(item[feature]) for item in features_ if feature in item
-                    ]
-                    out_features[i][feature] = np.concatenate(arrays)
+                    try:
+                        arrays = [
+                            np.array(item[feature])
+                            for item in features_
+                            if feature in item
+                        ]
+                        if arrays[0].dtype != "object":
+                            out_features[i][feature] = np.concatenate(arrays)
+                    except ValueError:
+                        pass
         return super().__call__(out_features, return_tensors=return_tensors)
 
 

tests/core/test_trainer_builder.py

@@ -25,8 +25,8 @@ def fixture_cfg():
             "optimizer": "adamw_torch_fused",
             "sequence_len": 2048,
             "rl": True,
-            "adam_beta1": 0.998,
-            "adam_beta2": 0.9,
+            "adam_beta1": 0.91,
+            "adam_beta2": 0.998,
             "adam_epsilon": 0.00001,
             "dataloader_num_workers": 1,
             "dataloader_pin_memory": True,
@@ -60,8 +60,8 @@ class TestHFRLTrainerBuilder:
     def test_build_training_arguments(self, cfg, model, tokenizer):
         builder = HFRLTrainerBuilder(cfg, model, tokenizer)
         training_arguments = builder.build_training_arguments(100)
-        assert training_arguments.adam_beta1 == 0.998
-        assert training_arguments.adam_beta2 == 0.9
+        assert training_arguments.adam_beta1 == 0.91
+        assert training_arguments.adam_beta2 == 0.998
         assert training_arguments.adam_epsilon == 0.00001
         assert training_arguments.dataloader_num_workers == 1
         assert training_arguments.dataloader_pin_memory is True