add sequential sample packing

Author: DreamGenX

examples/llama-3/lora-1b-sample-packing-sequentially.yml

@@ -0,0 +1,80 @@
+base_model: meta-llama/Llama-3.2-1B
+# optionally might have model_type or tokenizer_type
+model_type: LlamaForCausalLM
+tokenizer_type: AutoTokenizer
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
+
+load_in_8bit: true
+load_in_4bit: false
+strict: false
+
+datasets:
+  - path: mhenrichsen/alpaca_2k_test
+    type: alpaca
+  - path: mhenrichsen/alpaca_2k_test
+    type: alpaca
+dataset_prepared_path:
+val_set_size: 0.0
+output_dir: ./outputs/lora-out
+
+test_value: true
+
+sequence_len: 4096
+sample_packing: true
+sample_packing_sequentially: true
+curriculum_sampling: true
+eval_sample_packing: false
+pad_to_sequence_len: true
+
+adapter: lora
+lora_model_dir:
+lora_r: 32
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_linear: true
+lora_fan_in_fan_out:
+lora_modules_to_save:
+  - embed_tokens
+  - lm_head
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 4
+micro_batch_size: 2
+num_epochs: 4
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+train_on_inputs: false
+group_by_length: false
+bf16: auto
+fp16:
+tf32: false
+
+gradient_checkpointing: true
+early_stopping_patience:
+resume_from_checkpoint:
+local_rank:
+logging_steps: 1
+xformers_attention:
+flash_attention: true
+s2_attention:
+
+warmup_steps: 10
+evals_per_epoch: 4
+eval_table_size:
+eval_max_new_tokens: 128
+saves_per_epoch: 1
+debug:
+deepspeed:
+weight_decay: 0.0
+fsdp:
+fsdp_config:
+special_tokens:
+  pad_token: <|end_of_text|>

src/axolotl/core/trainers/base.py

@@ -419,6 +419,7 @@ def _get_train_sampler(self) -> Optional[torch.utils.data.Sampler]:
                 batch_size=batch_size,
                 group_size=self.args.sample_packing_group_size,
                 bin_size=self.args.sample_packing_bin_size,
+                sequential=self.args.sample_packing_sequentially,
                 drop_last=True,
             )
         if self.args.curriculum_sampling:

src/axolotl/core/training_args.py

@@ -32,6 +32,10 @@ class AxolotlTrainingMixins:
         default=False,
         metadata={"help": "Use sample packing for efficient training."},
     )
+    sample_packing_sequentially: bool = field(
+        default=False,
+        metadata={"help": "Use next-fit sample packing that preserves the order of samples coming from the sampler. Use in combination with curriculum_sampling for fully sequential packing."},
+    )
     multipack_real_batches: bool = field(
         default=False,
         metadata={"help": "Use real batches for efficient training."},

src/axolotl/utils/config/models/input/v0_4_1/__init__.py

@@ -795,6 +795,7 @@ class AxolotlInputConfig(
     sample_packing: Optional[bool] = None
     sample_packing_group_size: Optional[int] = 100_000
     sample_packing_bin_size: Optional[int] = 200
+    sample_packing_sequentially: Optional[bool] = None
     eval_sample_packing: Optional[bool] = None
     pad_to_sequence_len: Optional[bool] = None
     curriculum_sampling: Optional[bool] = None

src/axolotl/utils/samplers/multipack.py

@@ -8,7 +8,7 @@
 
 import numba
 import numpy as np
-from torch.utils.data import BatchSampler, Sampler
+from torch.utils.data import BatchSampler, Sampler, SequentialSampler
 
 from axolotl.utils.distributed import reduce_and_broadcast
 
@@ -103,6 +103,55 @@ def allocate(
     return result, s, len(result) * c * n
 
 
+@numba.njit
+def allocate_sequentially(lengths: np.ndarray, rank: int, c: int, n: int):
+    """
+    Sequential allocator that preserves example order
+
+    Parameters:
+    - lengths: The lengths of all examples
+    - rank: The current rank (for distributed training)
+    - c: The capacity of each bin (maximum sequence length)
+    - n: Number of ranks
+
+    Returns:
+    - result: List of batches for the current rank
+    - total_used: Number of actual example tokens
+    - total_slots: Maximum theoretical number of example tokens (number of bins * bin capacity)
+    """
+    result = []
+    total_used = 0
+
+    # First, do sequential packing into bins
+    all_bins = []
+    current_bin = [0 for i in range(0)] # numba hint
+    remaining_capacity = c
+
+    for idx, size in enumerate(lengths):
+        if size <= remaining_capacity:
+            # Example fits in current bin
+            current_bin.append(idx)
+            remaining_capacity -= size
+            total_used += size
+        else:
+            # Example doesn't fit, start a new bin
+            if current_bin:  # Add non-empty bin to all_bins
+                all_bins.append(current_bin)
+            current_bin = [idx]
+            remaining_capacity = c - size
+            total_used += size
+
+    # Add the last bin if not empty
+    if current_bin:
+        all_bins.append(current_bin)
+
+    # Assign bins to ranks - each rank gets every n-th bin
+    for bin_idx in range(rank, len(all_bins), n):
+        result.append(all_bins[bin_idx])
+
+    return result, total_used, len(all_bins) * c
+
+
 class MultipackBatchSampler(BatchSampler):
     """
     Batch Sampler class for multipack
@@ -117,13 +166,15 @@ def __init__(
         packing_efficiency_estimate: float = 1.0,
         drop_last: bool = False,
         num_count_samples: int = 16,
+        sequential: bool = False,
         **kwargs,
     ):
         super().__init__(sampler, batch_size, drop_last)
         self.batch_size = batch_size
         self.batch_max_len = batch_max_len
         self.lengths: np.ndarray = lengths
         self.packing_efficiency_estimate = packing_efficiency_estimate or 1.0
+        self.sequential = sequential
 
         assert isinstance(self.lengths, np.ndarray)
 
@@ -138,6 +189,10 @@ def __init__(
         # the minimum packed dataset length across all ranks determined by a gather/broadcast
         self.len_across_ranks = None
 
+        if self.sequential and not isinstance(sampler, SequentialSampler):
+            LOG.warn("using sequential sample packing with non-sequential sampler, did you want to also enable curriculum_sampling?")
+
+
     def set_epoch(self, epoch: int):
         self.epoch = epoch
 
@@ -147,13 +202,23 @@ def generate_batches(self, set_stats=False):
         lengths = self.lengths[indices]
         lengths_cumsum = np.cumsum(lengths)
 
-        batches, total_used, total_slots = allocate(
-            lengths=lengths,
-            lengths_cumsum=lengths_cumsum,
-            rank=0,
-            c=self.batch_max_len,
-            n=1,
-        )
+        if self.sequential:
+            LOG.debug("using sequential sample packing algorithm")
+            batches, total_used, total_slots = allocate_sequentially(
+                lengths=lengths,
+                rank=0,
+                c=self.batch_max_len,
+                n=1,
+            )
+        else:
+            LOG.debug("using non-sequential sample packing algorithm")
+            batches, total_used, total_slots = allocate(
+                lengths=lengths,
+                lengths_cumsum=lengths_cumsum,
+                rank=0,
+                c=self.batch_max_len,
+                n=1,
+            )
 
         batches = [
             [

src/axolotl/utils/trainer.py

@@ -13,7 +13,7 @@
 import torch.cuda
 from accelerate.logging import get_logger
 from datasets import IterableDataset, disable_caching, enable_caching
-from torch.utils.data import DataLoader, RandomSampler
+from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
 from transformers.utils import is_torch_bf16_gpu_available
 
 from axolotl.core.trainer_builder import HFCausalTrainerBuilder, HFRLTrainerBuilder
@@ -455,13 +455,18 @@ def calculate_total_num_steps(cfg, train_dataset, update=True):
             else:
                 sampler_batch_size = cfg.micro_batch_size
                 batch_max_len = cfg.sequence_len
+            if cfg.curriculum_sampling:
+                sampler = SequentialSampler(train_dataset)
+            else:
+                sampler = RandomSampler(train_dataset)
             sampler = MultipackBatchSampler(
-                sampler=RandomSampler(train_dataset),
+                sampler=sampler,
                 lengths=get_dataset_lengths(train_dataset),
                 batch_size=sampler_batch_size,
                 batch_max_len=batch_max_len,
                 group_size=cfg.sample_packing_group_size,
                 bin_size=cfg.sample_packing_bin_size,
+                sequential=cfg.sample_packing_sequentially,
                 drop_last=True,
             )
 

tests/test_packed_batch_sampler.py

@@ -34,7 +34,8 @@ class TestBatchedSamplerPacking:
         ],
     )
     @pytest.mark.parametrize("max_seq_length", [4096, 512])
-    def test_packing(self, batch_size, num_workers, tokenizer, max_seq_length):
+    @pytest.mark.parametrize("sequential", [True, False])
+    def test_packing(self, batch_size, num_workers, tokenizer, max_seq_length, sequential):
         import axolotl.monkeypatch.data.batch_dataset_fetcher  # pylint: disable=unused-import  # noqa: F401
 
         dataset = load_dataset(
@@ -70,6 +71,7 @@ def test_packing(self, batch_size, num_workers, tokenizer, max_seq_length):
             batch_max_len=max_seq_length,
             group_size=100000,
             bin_size=200,
+            sequential=sequential
         )
 
         loader = DataLoader(