fix conditional for None values

src/axolotl/__init__.py

@@ -4,4 +4,4 @@ import pkgutil
 
 __path__ = pkgutil.extend_path(__path__, __name__)  # Make this a namespace package
 
-__version__ = "0.12.2"
+__version__ = "0.13.0.dev"

src/axolotl/cli/args.py

@@ -40,12 +40,6 @@ class VllmServeCliArgs:
         default=None,
         metadata={"help": "Number of tensor parallel workers to use."},
     )
-    data_parallel_size: Optional[int] = field(
-        default=None,
-        metadata={
-            "help": "Number of data parallel workers to use for vLLM serving. This controls how many model replicas are used for parallel inference."
-        },
-    )
     host: Optional[str] = field(
         default=None,  # nosec B104
         metadata={"help": "Host address to run the server on."},

src/axolotl/core/builders/causal.py

@@ -424,7 +424,7 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
     ):
         if training_args.pretraining:
             if (
-                self.cfg.pretraining_sample_concatenation is False
+                not self.cfg.pretraining_sample_concatenation
                 or self.cfg.micro_batch_size > 1
             ):
                 return DataCollatorForSeq2Seq(self.tokenizer, **kwargs)

src/axolotl/core/trainers/base.py

@@ -272,6 +272,20 @@ class AxolotlTrainer(
                     num_workers=self.args.dataloader_num_workers,
                     rank=self.args.process_index,
                 )
+
+        if (
+            self.args.accelerator_config is not None
+            and self.args.accelerator_config.split_batches
+            and self.args.accelerator_config.dispatch_batches
+        ):
+            if self.args.sample_packing and self.args.pretraining:
+                if not self.args.eval_sample_packing and not is_training:
+                    dataloader_params["batch_size"] *= self.accelerator.num_processes
+                else:
+                    dataloader_params["batch_size"] = self.accelerator.num_processes
+            elif not self.args.sample_packing and self.args.pretraining:
+                dataloader_params["batch_size"] *= self.accelerator.num_processes
+
         if self.args.sample_packing and (
             (is_training and not self.args.pretraining)
             or (not is_training and self.args.eval_sample_packing is not False)

src/axolotl/utils/data/sft.py

@@ -28,7 +28,7 @@ from axolotl.utils.data.shared import (
 )
 from axolotl.utils.data.utils import (
     deduplicate_and_log_datasets,
-    handle_long_seq_in_dataset,
+    drop_long_seq_in_dataset,
     retry_on_request_exceptions,
 )
 from axolotl.utils.data.wrappers import get_dataset_wrapper
@@ -339,9 +339,9 @@ def _load_raw_datasets(
 
     if not cfg.skip_prepare_dataset:
         if split == "test" and cfg.eval_sequence_len:
-            dataset = handle_long_seq_in_dataset(dataset, cfg.eval_sequence_len, cfg)
+            dataset = drop_long_seq_in_dataset(dataset, cfg.eval_sequence_len, cfg)
         else:
-            dataset = handle_long_seq_in_dataset(dataset, cfg.sequence_len, cfg)
+            dataset = drop_long_seq_in_dataset(dataset, cfg.sequence_len, cfg)
         if cfg.sample_packing:
             dataset, _ = process_datasets_for_packing(cfg, dataset, None)
 

src/axolotl/utils/data/utils.py

@@ -148,36 +148,7 @@ def deduplicate_and_log_datasets(
     return dataset, other_dataset
 
 
-def truncate_long_seq(sample, sequence_len=2048, min_sequence_len=2):
-    """
-    Truncate samples whose sequence length is too long (> sequence_len)
-    or drop those too short (< min_sequence_len).
-    """
-    min_sequence_len = min_sequence_len or 2
-
-    input_ids = sample["input_ids"]
-    results = []
-
-    # Batched (input_ids is a list of lists)
-    for i, seq in enumerate(input_ids):
-        length = len(seq)
-        if length < min_sequence_len:
-            results.append(False)
-        elif length > sequence_len:
-            sample["input_ids"][i] = seq[:sequence_len]
-            if "attention_mask" in sample:
-                sample["attention_mask"][i] = sample["attention_mask"][i][:sequence_len]
-            if "labels" in sample:
-                sample["labels"][i] = sample["labels"][i][:sequence_len]
-            if "position_ids" in sample:
-                sample["position_ids"][i] = sample["position_ids"][i][:sequence_len]
-            results.append(True)
-        else:
-            results.append(True)
-    return results
-
-
-def handle_long_seq_in_dataset(
+def drop_long_seq_in_dataset(
     dataset: Dataset, sequence_len: int, cfg: DictDefault
 ) -> Dataset:
     """Remove sequences longer than configured maximum from dataset.
@@ -221,21 +192,8 @@ def handle_long_seq_in_dataset(
     if filter_map_kwargs:
         drop_long_kwargs["desc"] = f"Dropping Long Sequences (>{sequence_len})"
 
-    excess_length_strategy = (cfg.excess_length_strategy or "drop").lower()
-    if excess_length_strategy == "truncate":
-        process_fn = functools.partial(
-            truncate_long_seq,
-            sequence_len=sequence_len,
-            min_sequence_len=cfg.min_sample_len,
-        )
-        drop_long_kwargs["desc"] = (
-            f"Truncating/Filtering Sequences (target_len={sequence_len})"
-        )
-    else:
-        process_fn = drop_long
-
     dataset = dataset.filter(
-        process_fn,
+        drop_long,
         batched=True,
         **filter_map_kwargs,
         **drop_long_kwargs,
@@ -243,11 +201,6 @@ def handle_long_seq_in_dataset(
     if prior_len:
         dropped = prior_len - len(dataset)
         if dropped:
-            action = (
-                "truncated/filtered"
-                if excess_length_strategy == "truncate"
-                else "dropped"
-            )
-            LOG.warning(f"{action.title()} {dropped} samples from dataset")
+            LOG.warning(f"Dropped {dropped} long samples from dataset")
 
     return dataset

src/axolotl/utils/schemas/config.py

@@ -414,12 +414,6 @@ class AxolotlInputConfig(
             "description": "The maximum length of an input to train with, this should typically be less than 2048 as most models have a token/context limit of 2048"
         },
     )
-    excess_length_strategy: Literal["drop", "truncate"] | None = Field(
-        default=None,
-        json_schema_extra={
-            "description": "What to do when a tokenized row exceeds sequence_len. 'drop' removes the row; 'truncate' slices tensors to sequence_len. Defaults to 'drop' for backward compatibility."
-        },
-    )
     eval_sequence_len: int | None = Field(
         default=None,
         json_schema_extra={

tests/test_packed_batch_sampler.py

@@ -8,7 +8,7 @@ from transformers import AutoTokenizer
 from axolotl.datasets import TokenizedPromptDataset
 from axolotl.prompt_strategies.completion import load
 from axolotl.utils.collators import V2BatchSamplerDataCollatorForSeq2Seq
-from axolotl.utils.data.utils import handle_long_seq_in_dataset
+from axolotl.utils.data.utils import drop_long_seq_in_dataset
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.samplers import MultipackBatchSampler, get_dataset_lengths
 
@@ -70,7 +70,7 @@ class TestBatchedSamplerPacking:
         )
         train_dataset = concatenate_datasets([dataset_wrapper])
 
-        train_dataset = handle_long_seq_in_dataset(train_dataset, cfg.sequence_len, cfg)
+        train_dataset = drop_long_seq_in_dataset(train_dataset, cfg.sequence_len, cfg)
 
         lengths = get_dataset_lengths(train_dataset)
         batch_sampler = MultipackBatchSampler(