lint

src/axolotl/utils/data/rl.py

@@ -120,13 +120,21 @@ def drop_long_rl_seq(
             # Truncate the chosen and rejected responses if needed
             if len_chosen > max_response_len:
                 # Tokenize, truncate, and decode
-                chosen_tokens = tokenizer(chosen, add_special_tokens=False)["input_ids"][:max_response_len]
-                sample["chosen"] = tokenizer.decode(chosen_tokens, skip_special_tokens=True)
+                chosen_tokens = tokenizer(chosen, add_special_tokens=False)[
+                    "input_ids"
+                ][:max_response_len]
+                sample["chosen"] = tokenizer.decode(
+                    chosen_tokens, skip_special_tokens=True
+                )
 
             if len_rejected > max_response_len:
                 # Tokenize, truncate, and decode
-                rejected_tokens = tokenizer(rejected, add_special_tokens=False)["input_ids"][:max_response_len]
-                sample["rejected"] = tokenizer.decode(rejected_tokens, skip_special_tokens=True)
+                rejected_tokens = tokenizer(rejected, add_special_tokens=False)[
+                    "input_ids"
+                ][:max_response_len]
+                sample["rejected"] = tokenizer.decode(
+                    rejected_tokens, skip_special_tokens=True
+                )
 
             return sample
 
@@ -159,8 +167,12 @@ def drop_long_rl_seq(
             # Truncate the completion if needed
             if len_completion > max_completion_len:
                 # Tokenize, truncate, and decode
-                completion_tokens = tokenizer(completion, add_special_tokens=False)["input_ids"][:max_completion_len]
-                sample["completion"] = tokenizer.decode(completion_tokens, skip_special_tokens=True)
+                completion_tokens = tokenizer(completion, add_special_tokens=False)[
+                    "input_ids"
+                ][:max_completion_len]
+                sample["completion"] = tokenizer.decode(
+                    completion_tokens, skip_special_tokens=True
+                )
 
             return sample
 
@@ -245,7 +257,9 @@ def load_prepare_preference_datasets(cfg):
                         load_from_cache_file=not cfg.is_preprocess,
                         desc="Truncating Long Sequences",
                     )
-                    LOG.info(f"Truncated long samples in dataset index {i} to {cfg.sequence_len} tokens")
+                    LOG.info(
+                        f"Truncated long samples in dataset index {i} to {cfg.sequence_len} tokens"
+                    )
 
         combined_datasets = concatenate_datasets(split_datasets)
         combined_datasets = combined_datasets.shuffle(seed=cfg.seed)

src/axolotl/utils/schemas/config.py

@@ -188,7 +188,9 @@ class AxolotlInputConfig(
     sequence_len: int = Field(default=512)
     excess_token_handling: Literal["drop", "truncate"] = Field(
         default="drop",
-        json_schema_extra={"description": "how to handle tokens exceeding max sequence length - drop the sample or truncate"},
+        json_schema_extra={
+            "description": "how to handle tokens exceeding max sequence length - drop the sample or truncate"
+        },
     )
     min_sample_len: int | None = None
     max_prompt_len: int = Field(

src/axolotl/utils/trainer.py

@@ -235,7 +235,9 @@ def drop_long_seq(sample, sequence_len=2048, min_sequence_len=2):
     return results
 
 
-def truncate_or_drop_long_seq(sample, sequence_len=2048, min_sequence_len=2, handling="drop"):
+def truncate_or_drop_long_seq(
+    sample, sequence_len=2048, min_sequence_len=2, handling="drop"
+):
     """
     Either drop or truncate samples whose sequence length is either too long (> sequence_len)
     or too short (< min_sequence_len).
@@ -292,7 +294,9 @@ def truncate_or_drop_long_seq(sample, sequence_len=2048, min_sequence_len=2, han
             return sample
 
         # For drop mode or if the sample doesn't exceed max length
-        return min_sequence_len <= length <= sequence_len if handling == "drop" else sample
+        return (
+            min_sequence_len <= length <= sequence_len if handling == "drop" else sample
+        )
 
     # Batched (input_ids is a list of lists)
     if handling == "drop":
@@ -316,7 +320,9 @@ def truncate_or_drop_long_seq(sample, sequence_len=2048, min_sequence_len=2, han
 
                 # Also truncate attention_mask if present
                 if "attention_mask" in sample:
-                    sample["attention_mask"][i] = sample["attention_mask"][i][:sequence_len]
+                    sample["attention_mask"][i] = sample["attention_mask"][i][
+                        :sequence_len
+                    ]
 
                 # Also truncate labels if present
                 if "labels" in sample:
@@ -468,7 +474,11 @@ def process_datasets_for_packing(cfg, train_dataset, eval_dataset):
 
 
 def process_pretraining_datasets_for_packing(
-    train_dataset, sequence_len, skip_position_ids=True, drop_attention_mask=False, handling="drop"
+    train_dataset,
+    sequence_len,
+    skip_position_ids=True,
+    drop_attention_mask=False,
+    handling="drop",
 ):
     drop_long_fn = partial(drop_long_seq, sequence_len=sequence_len)
 
@@ -480,7 +490,9 @@ def process_pretraining_datasets_for_packing(
             load_from_cache_file=False,
         )
     else:
-        truncate_fn = partial(truncate_or_drop_long_seq, sequence_len=sequence_len, handling=handling)
+        truncate_fn = partial(
+            truncate_or_drop_long_seq, sequence_len=sequence_len, handling=handling
+        )
         train_dataset = train_dataset.map(
             truncate_fn,
             desc="Truncating Long Sequences",