fix graph break for compile

src/axolotl/monkeypatch/utils.py

@@ -16,15 +16,24 @@ from transformers.utils import is_torch_bf16_gpu_available
 
 @torch.jit.script
 def get_max_seqlen_in_batch(attention_mask: torch.Tensor) -> torch.Tensor:
-    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)
+    # 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
     result = counts.flatten()
-    nonzero_indices = torch.nonzero(result).squeeze(-1)
-    return result[nonzero_indices]
+    nonzero_mask = result != 0
+    return result[nonzero_mask]
 
 
 @torch.jit.script

src/axolotl/train.py

@@ -521,6 +521,11 @@ 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/data/sft.py

@@ -53,7 +53,7 @@ from axolotl.utils.data.utils import (
     retry_on_request_exceptions,
 )
 from axolotl.utils.dict import DictDefault
-from axolotl.utils.distributed import is_local_main_process
+from axolotl.utils.distributed import is_local_main_process, zero_first
 from axolotl.utils.trainer import (
     calculate_total_num_steps,
     process_datasets_for_packing,
@@ -66,31 +66,32 @@ LOG = logging.getLogger(__name__)
 def prepare_dataset(cfg, tokenizer, processor=None, preprocess_iterable=None):
     prompters = []
     if not cfg.pretraining_dataset:
-        if cfg.test_datasets:
-            train_dataset, _, prompters = load_prepare_datasets(
-                tokenizer,
-                cfg,
-                DEFAULT_DATASET_PREPARED_PATH,
-                split="train",
-                processor=processor,
-                preprocess_iterable=preprocess_iterable,
-            )
-            _, eval_dataset, _ = load_prepare_datasets(
-                tokenizer,
-                cfg,
-                DEFAULT_DATASET_PREPARED_PATH,
-                split="test",
-                processor=processor,
-                preprocess_iterable=preprocess_iterable,
-            )
-        else:
-            train_dataset, eval_dataset, prompters = load_prepare_datasets(
-                tokenizer,
-                cfg,
-                DEFAULT_DATASET_PREPARED_PATH,
-                processor=processor,
-                preprocess_iterable=preprocess_iterable,
-            )
+        with zero_first(is_local_main_process()):
+            if cfg.test_datasets:
+                train_dataset, _, prompters = load_prepare_datasets(
+                    tokenizer,
+                    cfg,
+                    DEFAULT_DATASET_PREPARED_PATH,
+                    split="train",
+                    processor=processor,
+                    preprocess_iterable=preprocess_iterable,
+                )
+                _, eval_dataset, _ = load_prepare_datasets(
+                    tokenizer,
+                    cfg,
+                    DEFAULT_DATASET_PREPARED_PATH,
+                    split="test",
+                    processor=processor,
+                    preprocess_iterable=preprocess_iterable,
+                )
+            else:
+                train_dataset, eval_dataset, prompters = load_prepare_datasets(
+                    tokenizer,
+                    cfg,
+                    DEFAULT_DATASET_PREPARED_PATH,
+                    processor=processor,
+                    preprocess_iterable=preprocess_iterable,
+                )
     else:
         # Load streaming dataset if pretraining_dataset is given
         path = cfg.pretraining_dataset
@@ -271,7 +272,7 @@ def load_tokenized_prepared_datasets(
         LOG.info("Loading raw datasets...")
         if not cfg.is_preprocess:
             LOG.warning(
-                "Processing datasets during training can lead to VRAM instability. Please use `axolotl preprocess` to prepare your dataset."
+                "Processing datasets during training can lead to VRAM instability. Please pre-process your dataset."
             )
 
         if cfg.seed:

src/axolotl/utils/schemas/config.py

@@ -182,6 +182,7 @@ class AxolotlInputConfig(
         default=False
     )
     gradient_checkpointing_kwargs: dict[str, Any] | None = None
+    activation_memory_budget: float | None = None
 
     unfrozen_parameters: list[str] | None = None
 
@@ -1079,6 +1080,19 @@ 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):