fix: num_items_in_batch wrong type in kd trainer loss

src/axolotl/integrations/kd/trainer.py

@@ -74,6 +74,9 @@ class AxolotlKDTrainer(AxolotlTrainer):
         target_token_ids_for_loss = target_token_ids[..., 1:, :].contiguous()
         target_mask_for_loss = target_mask[..., 1:, :].contiguous()
 
+        if num_items_in_batch is None:
+            num_items_in_batch = -1
+
         if self.args.kd_zscore_base_temp:
             loss_kd = topk_kd_loss_with_zscore(
                 shift_logits,

src/axolotl/monkeypatch/utils.py

@@ -16,24 +16,15 @@ from transformers.utils import is_torch_bf16_gpu_available
 
 @torch.jit.script
 def get_max_seqlen_in_batch(attention_mask: torch.Tensor) -> torch.Tensor:
-    # Keep max_num as a tensor instead of extracting to Python int
-    max_num = torch.max(attention_mask)
-
-    # Create a range tensor for comparison
-    range_tensor = torch.arange(
-        1, max_num + 1, device=attention_mask.device, dtype=attention_mask.dtype
-    )
-
-    # Vectorized approach - compare attention_mask with each value in range
-    mask = attention_mask.unsqueeze(-1) == range_tensor.unsqueeze(0).unsqueeze(0)
-
-    # Sum along sequence dimension to get counts
-    counts = mask.sum(dim=1).to(dtype=torch.int32)
-
-    # Flatten and filter non-zero values
+    max_num = int(torch.max(attention_mask).item())
+    batch_size, _ = attention_mask.shape
+    counts = torch.zeros((batch_size, max_num), dtype=torch.int32)
+    for i in range(1, max_num + 1):
+        mask = attention_mask == i
+        counts[:, i - 1] = torch.sum(mask, dim=-1).to(dtype=torch.int32)
     result = counts.flatten()
-    nonzero_mask = result != 0
-    return result[nonzero_mask]
+    nonzero_indices = torch.nonzero(result).squeeze(-1)
+    return result[nonzero_indices]
 
 
 @torch.jit.script

src/axolotl/train.py

@@ -521,11 +521,6 @@ def train(
     """
     print_axolotl_text_art()
 
-    if cfg.activation_memory_budget is not None:
-        torch._functorch.config.activation_memory_budget = (  # pylint: disable=protected-access
-            cfg.activation_memory_budget
-        )
-
     # Setup model, tokenizer, (causal or RLHF) trainer, etc.
     (
         trainer,

src/axolotl/utils/schemas/config.py

@@ -182,7 +182,6 @@ class AxolotlInputConfig(
         default=False
     )
     gradient_checkpointing_kwargs: dict[str, Any] | None = None
-    activation_memory_budget: float | None = None
 
     unfrozen_parameters: list[str] | None = None
 
@@ -1080,19 +1079,6 @@ class AxolotlInputConfig(
             )
         return data
 
-    @model_validator(mode="before")
-    @classmethod
-    def check_activation_memory_budget_w_compile(cls, data):
-        if data.get("activation_memory_budget") is not None and not data.get(
-            "torch_compile"
-        ):
-            LOG.warning(
-                "activation_memory_budget is enabled, but torch_compile is not set. "
-                "Automatically setting torch_compile to true."
-            )
-            data["torch_compile"] = True
-        return data
-
     @model_validator(mode="before")
     @classmethod
     def check_npu_config(cls, data):