fix: drop long seq even if not sample packing (#2211)

* fix: drop long seq even if not sample packing

* fix: logging import

* fix: cfg passed being none

* fix: try to fix logging

* fix: refactor call to not use accelerate log

* fix: try to fix circular import issue

* fix: don't drop when skip prepare

* chore: remove duplicate line

* fix: update warning to mention that sequences will be trimmed

* fix: do not drop seq if input_ids don't exist

* fix: increase RM unittest sequence length to reduce trim warnings

* fix: solve conflicts

* fix: default min_seq_len in case of None

src/axolotl/prompt_strategies/bradley_terry/chat_template.py

@@ -47,7 +47,7 @@ def _tokenize_single_prompt(self, prompt):
 
         if len(chosen_tokenized["input_ids"]) > max_length:
             LOG.warning(
-                f"Chosen sequence exceeds max sequence length: {len(chosen_tokenized['input_ids'])}",
+                f"To-be-trimmed chosen sequence exceeds max sequence length: {len(chosen_tokenized['input_ids'])}",
             )
 
             chosen_tokenized["input_ids"] = chosen_tokenized["input_ids"][:max_length]
@@ -70,7 +70,7 @@ def _tokenize_single_prompt(self, prompt):
 
         if len(rejected_tokenized["input_ids"]) > max_length:
             LOG.warning(
-                f"Rejected sequence exceeds max sequence length: {len(rejected_tokenized['input_ids'])}",
+                f"To-be-trimmed rejected sequence exceeds max sequence length: {len(rejected_tokenized['input_ids'])}",
             )
 
             rejected_tokenized["input_ids"] = rejected_tokenized["input_ids"][

src/axolotl/utils/data/sft.py

@@ -46,6 +46,7 @@
 from axolotl.utils.data.shared import load_dataset_w_config
 from axolotl.utils.data.utils import (
     deduplicate_and_log_datasets,
+    drop_long_seq_in_dataset,
     md5,
     retry_on_request_exceptions,
 )
@@ -56,7 +57,7 @@
     process_datasets_for_packing,
 )
 
-LOG = logging.getLogger("axolotl")
+LOG = logging.getLogger(__name__)
 
 
 @retry_on_request_exceptions(max_retries=3, delay=5)
@@ -339,8 +340,11 @@ def for_d_in_datasets(dataset_configs):
             else:
                 LOG.debug("NOT shuffling merged datasets")
 
-        if cfg.sample_packing and not cfg.skip_prepare_dataset:
-            dataset, _ = process_datasets_for_packing(cfg, dataset, None)
+        if not cfg.skip_prepare_dataset:
+            dataset = drop_long_seq_in_dataset(dataset, cfg)
+
+            if cfg.sample_packing:
+                dataset, _ = process_datasets_for_packing(cfg, dataset, None)
 
         if cfg.local_rank == 0 and not cfg.skip_prepare_dataset:
             LOG.info(f"Saving merged prepared dataset to disk... {prepared_ds_path}")

src/axolotl/utils/data/utils.py

@@ -1,15 +1,21 @@
 """data handling helpers"""
+
 import functools
 import hashlib
 import logging
 import time
 from enum import Enum
 
 import huggingface_hub
+import numpy as np
 import requests
-from datasets import Dataset
+from datasets import Dataset, IterableDataset
+
+from axolotl.utils.dict import DictDefault
+from axolotl.utils.samplers.utils import get_dataset_lengths
+from axolotl.utils.trainer import drop_long_seq
 
-LOG = logging.getLogger("axolotl")
+LOG = logging.getLogger(__name__)
 
 
 class RetryStrategy(Enum):
@@ -150,3 +156,53 @@ def deduplicate_and_log_datasets(
         )
 
     return train_dataset, eval_dataset, dataset
+
+
+def drop_long_seq_in_dataset(dataset: Dataset, cfg: DictDefault):
+    if "input_ids" not in dataset.column_names:
+        LOG.warning(
+            "Dataset does not contain 'input_ids' column. Skip drop long seq. This is expected for RewardModeling."
+        )
+        return dataset
+
+    drop_long = functools.partial(
+        drop_long_seq,
+        sequence_len=cfg.sequence_len,
+        min_sequence_len=cfg.min_sample_len,
+    )
+
+    try:
+        min_input_len = np.min(get_dataset_lengths(dataset))
+        LOG.debug(f"min_input_len: {min_input_len}")
+        max_input_len = np.max(get_dataset_lengths(dataset))
+        LOG.debug(f"max_input_len: {max_input_len}")
+    except AttributeError:
+        pass
+
+    try:
+        prior_len = len(dataset)
+    except TypeError:
+        # handle iterable datasets case
+        prior_len = None
+
+    filter_map_kwargs = {}
+    if not isinstance(dataset, IterableDataset):
+        filter_map_kwargs["num_proc"] = cfg.dataset_processes
+        filter_map_kwargs["load_from_cache_file"] = not cfg.is_preprocess
+
+    drop_long_kwargs = {}
+    if filter_map_kwargs:
+        drop_long_kwargs["desc"] = "Dropping Long Sequences"
+
+    dataset = dataset.filter(
+        drop_long,
+        batched=True,
+        **filter_map_kwargs,
+        **drop_long_kwargs,
+    )
+    if prior_len:
+        dropped = prior_len - len(dataset)
+        if dropped:
+            LOG.warning(f"Dropped {dropped} long samples from dataset")
+
+    return dataset

src/axolotl/utils/samplers/utils.py

@@ -13,5 +13,4 @@ def get_dataset_lengths(dataset):
     else:
         input_ids = dataset.data.column("input_ids")
         lengths = np.vectorize(len)(np.array(input_ids, dtype=object))
-        return lengths
     return lengths

src/axolotl/utils/trainer.py

@@ -1,4 +1,5 @@
 """Module containing the Trainer class and related functions"""
+
 import json
 import math
 import os
@@ -210,6 +211,8 @@ def drop_long_seq(sample, sequence_len=2048, min_sequence_len=2):
 
     Works for both single-example (list[int]) or batched (list[list[int]]).
     """
+    min_sequence_len = min_sequence_len or 2
+
     input_ids = sample["input_ids"]
 
     # Edge case: if input_ids is empty
@@ -232,20 +235,6 @@ def drop_long_seq(sample, sequence_len=2048, min_sequence_len=2):
 
 
 def process_datasets_for_packing(cfg, train_dataset, eval_dataset):
-    drop_long = partial(
-        drop_long_seq,
-        sequence_len=cfg.sequence_len,
-        min_sequence_len=cfg.min_sample_len or 2,
-    )
-
-    try:
-        min_input_len = np.min(get_dataset_lengths(train_dataset))
-        LOG.debug(f"min_input_len: {min_input_len}", main_process_only=True)
-        max_input_len = np.max(get_dataset_lengths(train_dataset))
-        LOG.debug(f"max_input_len: {max_input_len}", main_process_only=True)
-    except AttributeError:
-        pass
-
     if cfg.model_config_type == "mamba":
         LOG.info("dropping attention_mask column")
         train_dataset = train_dataset.remove_columns("attention_mask")
@@ -259,46 +248,6 @@ def process_datasets_for_packing(cfg, train_dataset, eval_dataset):
         if eval_dataset and "token_type_ids" in eval_dataset.column_names:
             eval_dataset = eval_dataset.remove_columns("token_type_ids")
 
-    filter_map_kwargs = {}
-    if not isinstance(train_dataset, IterableDataset):
-        filter_map_kwargs["num_proc"] = cfg.dataset_processes
-        filter_map_kwargs["load_from_cache_file"] = not cfg.is_preprocess
-
-    try:
-        prior_len = len(train_dataset)
-    except TypeError:
-        # handle iterable datasets case
-        prior_len = None
-    drop_long_kwargs = {}
-    if filter_map_kwargs:
-        drop_long_kwargs["desc"] = "Dropping Long Sequences"
-    train_dataset = train_dataset.filter(
-        drop_long,
-        batched=True,
-        **filter_map_kwargs,
-        **drop_long_kwargs,
-    )
-    if prior_len:
-        dropped = prior_len - len(train_dataset)
-        if dropped:
-            LOG.warning(f"Dropped {dropped} long samples from train dataset")
-
-    if eval_dataset:
-        try:
-            prior_len = len(eval_dataset)
-        except TypeError:
-            # handle iterable datasets case
-            prior_len = None
-        eval_dataset = eval_dataset.filter(
-            drop_long,
-            **filter_map_kwargs,
-            **drop_long_kwargs,
-        )
-        if prior_len:
-            dropped = prior_len - len(eval_dataset)
-            if dropped:
-                LOG.warning(f"Dropped {dropped} long samples from eval dataset")
-
     def drop_no_trainable_tokens(sample):
         """
         Drop samples if all labels are -100 (i.e., zero trainable tokens).
@@ -325,6 +274,11 @@ def drop_no_trainable_tokens(sample):
     except TypeError:
         # handle iterable datasets case
         prior_len = None
+    filter_map_kwargs = {}
+    if not isinstance(train_dataset, IterableDataset):
+        filter_map_kwargs["num_proc"] = cfg.dataset_processes
+        filter_map_kwargs["load_from_cache_file"] = not cfg.is_preprocess
+
     drop_long_kwargs = {}
     if filter_map_kwargs:
         drop_long_kwargs["desc"] = "Drop Samples with Zero Trainable Tokens"

tests/e2e/test_reward_model_smollm2.py

@@ -33,7 +33,7 @@ def test_rm_lora(self, temp_dir):
                 "num_labels": 1,
                 "chat_template": "alpaca",
                 "reward_model": True,
-                "sequence_len": 1024,
+                "sequence_len": 2048,
                 "pad_to_sequence_len": True,
                 "adapter": "lora",
                 "lora_r": 8,