Deprecate max packed sequence len

README.md

@@ -639,10 +639,6 @@ sequence_len: 2048
 # Pad inputs so each step uses constant sized buffers
 # This will reduce memory fragmentation and may prevent OOMs, by re-using memory more efficiently
 pad_to_sequence_len:
-# Max sequence length to concatenate training samples together up to
-# Inspired by StackLLaMA. see https://huggingface.co/blog/stackllama#supervised-fine-tuning
-# FutureWarning: This will soon be DEPRECATED
-max_packed_sequence_len: 1024
 # Use efficient multi-packing with block diagonal attention and per sequence position_ids. Recommend set to 'true'
 sample_packing:
 # Set to 'false' if getting errors during eval with sample_packing on.

src/axolotl/utils/config.py

@@ -186,18 +186,6 @@ def validate_config(cfg):
             raise ValueError(
                 "bf16 requested, but AMP is not supported on this GPU. Requires Ampere series or above."
             )
-    if cfg.max_packed_sequence_len and cfg.sample_packing:
-        raise ValueError(
-            "please set only one of max_packed_sequence_len (deprecated soon) or sample_packing"
-        )
-    if cfg.max_packed_sequence_len:
-        LOG.warning(
-            str(
-                PendingDeprecationWarning(
-                    "max_packed_sequence_len will be deprecated in favor of sample_packing"
-                )
-            )
-        )
 
     if cfg.sample_packing and not cfg.pad_to_sequence_len:
         LOG.warning(

src/axolotl/utils/data.py

@@ -19,7 +19,7 @@
 from transformers import PreTrainedTokenizerBase
 
 from axolotl.common.const import DEFAULT_DATASET_PREPARED_PATH
-from axolotl.datasets import ConstantLengthDataset, TokenizedPromptDataset
+from axolotl.datasets import TokenizedPromptDataset
 from axolotl.prompt_strategies import load
 from axolotl.prompt_tokenizers import (
     AlpacaMultipleChoicePromptTokenizingStrategy,
@@ -74,6 +74,11 @@ def prepare_dataset(cfg, tokenizer):
         if isinstance(cfg.pretraining_dataset, dict):
             path = cfg.pretraining_dataset["path"]
             name = cfg.pretraining_dataset["name"]
+        elif isinstance(cfg.pretraining_dataset, list) and isinstance(
+            cfg.pretraining_dataset[0], dict
+        ):
+            path = cfg.pretraining_dataset[0]["path"]
+            name = cfg.pretraining_dataset[0]["name"]
 
         train_dataset = load_pretraining_dataset(
             path,
@@ -88,11 +93,6 @@ def prepare_dataset(cfg, tokenizer):
         eval_dataset = None
         return train_dataset, eval_dataset, cfg.max_steps, prompters
 
-    with zero_first(is_main_process()):
-        train_dataset, eval_dataset = process_datasets_for_packing(
-            cfg, train_dataset, eval_dataset, tokenizer
-        )
-
     if eval_dataset and cfg.sample_packing and cfg.eval_sample_packing is not False:
         total_eval_steps = calculate_total_num_steps(cfg, eval_dataset, update=False)
         if total_eval_steps == 0:
@@ -382,6 +382,9 @@ def for_d_in_datasets(dataset_configs):
         if len(datasets) > 1:
             LOG.info("shuffle merged datasets")
             dataset = dataset.shuffle(seed=seed)
+
+        dataset, _ = process_datasets_for_packing(cfg, dataset, None, tokenizer)
+
         if cfg.local_rank == 0:
             LOG.info(f"Saving merged prepared dataset to disk... {prepared_ds_path}")
             dataset.save_to_disk(prepared_ds_path)
@@ -419,119 +422,9 @@ def load_prepare_datasets(
     cfg,
     default_dataset_prepared_path,
 ) -> Tuple[Dataset, Dataset, List[Prompter]]:
-    max_packed_sequence_len = (
-        cfg.max_packed_sequence_len if cfg.max_packed_sequence_len else cfg.sequence_len
+    dataset, prompters = load_tokenized_prepared_datasets(
+        tokenizer, cfg, default_dataset_prepared_path
     )
-    max_packed_sequence_len = min(
-        max_packed_sequence_len, cfg.sequence_len
-    )  # make sure we don't accidentally set it larger than sequence_len
-
-    tokenizer_name = tokenizer.__class__.__name__
-    prompters: List[Prompter] = []
-    if cfg.max_packed_sequence_len is not None:
-        # see if we can go ahead and load the stacked dataset
-        seed = f"@{str(cfg.seed)}" if cfg.seed else ""
-        ds_hash = str(
-            md5(
-                (
-                    str(cfg.sequence_len)
-                    + "@"
-                    + str(max_packed_sequence_len)
-                    + seed
-                    + "|".join(
-                        sorted([f"{d.path}:{d.type}:{d.shards}" for d in cfg.datasets])
-                    )
-                    + "|"
-                    + tokenizer_name
-                )
-            )
-        )
-        prepared_ds_path = (
-            Path(cfg.dataset_prepared_path) / ds_hash
-            if cfg.dataset_prepared_path
-            else Path(default_dataset_prepared_path) / ds_hash
-        )
-
-        dataset = None
-        use_auth_token = cfg.hf_use_auth_token
-        try:
-            if cfg.push_dataset_to_hub:
-                LOG.info(
-                    f"Checking for packed prepared dataset from hub... {cfg.push_dataset_to_hub}/{ds_hash}"
-                )
-                dataset = load_dataset(
-                    f"{cfg.push_dataset_to_hub}/{ds_hash}",
-                    token=use_auth_token,
-                )
-                dataset = dataset["train"]
-        except Exception:  # pylint: disable=broad-except # nosec
-            pass
-
-        if dataset:
-            ...
-        elif (
-            cfg.dataset_prepared_path
-            and any(prepared_ds_path.glob("*"))
-            and not cfg.is_preprocess
-        ):
-            LOG.info(
-                f"Loading prepared packed dataset from disk at {prepared_ds_path}..."
-            )
-            dataset = load_from_disk(str(prepared_ds_path))
-            LOG.info("Prepared packed dataset loaded from disk...")
-            if cfg.push_dataset_to_hub:
-                LOG.info(
-                    f"Saving packed prepared dataset with push_to_hub... {cfg.push_dataset_to_hub}/{ds_hash}"
-                )
-                dataset.push_to_hub(
-                    f"{cfg.push_dataset_to_hub}/{ds_hash}", private=True
-                )
-        else:
-            dataset, prompters = load_tokenized_prepared_datasets(
-                tokenizer, cfg, default_dataset_prepared_path
-            )
-
-            if cfg.seed:
-                dataset = dataset.shuffle(seed=cfg.seed)
-
-            constant_len_dataset = ConstantLengthDataset(
-                tokenizer,
-                [dataset],
-                seq_length=max_packed_sequence_len,
-            )
-            LOG.info(f"packing master dataset to len: {cfg.max_packed_sequence_len}")
-            dataset = Dataset.from_list(list(constant_len_dataset))
-
-            # filter out bad data
-            # TODO convert to dataset.filter(...)
-            dataset = Dataset.from_list(
-                [
-                    d
-                    for d in dataset
-                    if len(d["input_ids"]) <= cfg.sequence_len
-                    and len(d["input_ids"]) > 0
-                    and len(d["input_ids"]) == len(d["attention_mask"])
-                    and len(d["input_ids"]) == len(d["labels"])
-                ]
-            )
-
-            if cfg.local_rank == 0:
-                LOG.info(
-                    f"Saving packed prepared dataset to disk... {prepared_ds_path}"
-                )
-                dataset.save_to_disk(prepared_ds_path)
-                if cfg.push_dataset_to_hub:
-                    LOG.info(
-                        f"Saving packed prepared dataset with push_to_hub... {cfg.push_dataset_to_hub}/{ds_hash}"
-                    )
-                    dataset.push_to_hub(
-                        f"{cfg.push_dataset_to_hub}/{ds_hash}",
-                        private=True,
-                    )
-    else:
-        dataset, prompters = load_tokenized_prepared_datasets(
-            tokenizer, cfg, default_dataset_prepared_path
-        )
 
     if cfg.dataset_shard_num and cfg.dataset_shard_idx is not None:
         LOG.info(
@@ -872,6 +765,7 @@ def load_pretraining_dataset(path, tokenizer, cfg, name=None, max_tokens=2048, s
     dataset = dataset.map(
         encode,
         batched=True,
+        batch_size=10_000,
         input_columns="text",
         # remove all the existing columns after mapping since they end up having
         # a different length than the encoded/tokenized column

src/axolotl/utils/models.py

@@ -301,11 +301,7 @@ def load_model(
         LOG.info("patching with flash attention")
         replace_mixtral_attn_with_multipack_flash_attn()
 
-    if (
-        cfg.is_llama_derived_model
-        and (cfg.max_packed_sequence_len or cfg.sample_packing)
-        and not inference
-    ):
+    if cfg.is_llama_derived_model and cfg.sample_packing and not inference:
         from axolotl.monkeypatch.llama_expand_mask import hijack_expand_mask
 
         LOG.info("patching _expand_mask")

src/axolotl/utils/trainer.py

@@ -81,6 +81,15 @@ def trainer_weighted_loss(model_output, labels, shift_labels=True):
     return weighted_cross_entropy(logits, labels, weights)
 
 
+@contextmanager
+def disable_datasets_caching():
+    try:
+        set_caching_enabled(False)
+        yield
+    finally:
+        set_caching_enabled(True)
+
+
 def add_position_ids(sample):
     sample_len = len(sample["input_ids"])
     sample["position_ids"] = torch.arange(len(sample["input_ids"]))
@@ -97,15 +106,6 @@ def drop_long_seq(sample, sequence_len=2048):
     return len(sample["input_ids"]) <= sequence_len and len(sample["input_ids"]) > 0
 
 
-@contextmanager
-def disable_datasets_caching():
-    try:
-        set_caching_enabled(False)
-        yield
-    finally:
-        set_caching_enabled(True)
-
-
 def process_datasets_for_packing(cfg, train_dataset, eval_dataset, tokenizer):
     drop_long = partial(drop_long_seq, sequence_len=cfg.sequence_len)
     with zero_first(is_main_process()):
@@ -226,8 +226,7 @@ def calculate_total_num_steps(cfg, train_dataset, update=True):
                 sampler=RandomSampler(train_dataset),
                 batch_size=cfg.micro_batch_size,
                 drop_last=True,
-                batch_max_len=cfg.micro_batch_size
-                * (cfg.max_packed_sequence_len or cfg.sequence_len),
+                batch_max_len=cfg.micro_batch_size * cfg.sequence_len,
                 lengths=get_dataset_lengths(train_dataset),
             )
 

tests/test_validation.py

@@ -325,19 +325,6 @@ def test_adamw_hyperparams(self):
         validate_config(cfg)
 
     def test_packing(self):
-        cfg = DictDefault(
-            {
-                "max_packed_sequence_len": 2048,
-            }
-        )
-        with self._caplog.at_level(logging.WARNING):
-            validate_config(cfg)
-            assert any(
-                "max_packed_sequence_len will be deprecated in favor of sample_packing"
-                in record.message
-                for record in self._caplog.records
-            )
-
         cfg = DictDefault(
             {
                 "sample_packing": True,
@@ -352,16 +339,6 @@ def test_packing(self):
                 for record in self._caplog.records
             )
 
-        cfg = DictDefault(
-            {
-                "max_packed_sequence_len": 2048,
-                "sample_packing": True,
-            }
-        )
-        regex_exp = r".*set only one of max_packed_sequence_len \(deprecated soon\) or sample_packing.*"
-        with pytest.raises(ValueError, match=regex_exp):
-            validate_config(cfg)
-
     @pytest.mark.skipif(
         is_torch_bf16_gpu_available(),
         reason="test should only run on gpus w/o bf16 support",