use DataCollatorWithFlattening when not sample packing (#2167)

docs/config.qmd

@@ -245,6 +245,9 @@ sample_packing_group_size: 100000
 # The number of samples which can be packed into one sequence. Increase if using a large sequence_len with many short samples.
 sample_packing_bin_size: 200
 
+# Use batch flattening for speedups when not using sample_packing
+batch_flattening:
+
 # Passed through to transformers when loading the model when launched without accelerate
 # Use `sequential` when training w/ model parallelism to limit memory
 device_map:

src/axolotl/core/trainer_builder.py

@@ -28,6 +28,7 @@ from torch import nn
 from torch.optim.lr_scheduler import OneCycleLR
 from torch.utils.data import BatchSampler, DataLoader, RandomSampler, SequentialSampler
 from transformers import (
+    DataCollatorWithFlattening,
     EarlyStoppingCallback,
     Trainer,
     TrainerCallback,
@@ -1989,9 +1990,11 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
                 V2BatchSamplerDataCollatorForSeq2Seq,
                 BatchSamplerDataCollatorForSeq2Seq,
                 DataCollatorForSeq2Seq,
+                DataCollatorWithFlattening,
                 RewardDataCollatorWithPadding,
             ]
         ]
+        collator_args = [self.tokenizer]
         if self.cfg.reward_model:
             collator = RewardDataCollatorWithPadding
             if "max_length" in kwargs:
@@ -2011,12 +2014,18 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
                 collator = MultiModalChatDataCollator
                 kwargs["processor"] = self.processor
                 kwargs["chat_template"] = training_args.chat_template
+            elif self.cfg.batch_flattening:
+                collator = DataCollatorWithFlattening
+                collator_args.pop(0)
+                kwargs.pop("pad_to_multiple_of", None)
+                kwargs.pop("padding", None)
             else:
                 collator = DataCollatorForSeq2Seq
 
+        kwargs["return_tensors"] = "pt"
+
         return collator(
-            self.tokenizer,
-            return_tensors="pt",
+            *collator_args,
             **kwargs,
         )
 

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

@@ -696,6 +696,8 @@ class AxolotlInputConfig(
     curriculum_sampling: Optional[bool] = None
     multipack_real_batches: Optional[bool] = None
 
+    batch_flattening: Optional[Union[Literal["auto"], bool]] = None
+
     # for PoSE context length extension
     use_pose: Optional[bool] = None
     pose_split_on_token_ids: Optional[List[int]] = None
@@ -924,6 +926,30 @@ class AxolotlInputConfig(
 
         return data
 
+    @model_validator(mode="before")
+    @classmethod
+    def check_batch_flattening_fa(cls, data):
+        if data.get("batch_flattening"):
+            batch_flattening_auto = data.get("batch_flattening") == "auto"
+            if not data.get("flash_attention") and not batch_flattening_auto:
+                raise ValueError("batch_flattening requires flash attention")
+            if data.get("sample_packing") and not batch_flattening_auto:
+                raise ValueError("batch_flattening not compatible with sample_packing")
+            if data.get("micro_batch_size") == 1 and not batch_flattening_auto:
+                LOG.warning("batch_flattening has no effect with micro_batch_size == 1")
+
+            if (
+                batch_flattening_auto
+                and data.get("flash_attention")
+                and not data.get("sample_packing")
+                and data.get("micro_batch_size") > 1
+            ):
+                data["batch_flattening"] = True
+            elif batch_flattening_auto:
+                data["batch_flattening"] = False
+
+        return data
+
     @model_validator(mode="before")
     @classmethod
     def check_sample_packing_w_rl(cls, data):

tests/e2e/test_llama.py

@@ -104,3 +104,42 @@ class TestLlama:
 
         train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
         assert (Path(temp_dir) / "model.safetensors").exists()
+
+    def test_batch_flattening(self, temp_dir):
+        # pylint: disable=duplicate-code
+        cfg = DictDefault(
+            {
+                "base_model": "HuggingFaceTB/SmolLM2-135M",
+                "trust_remote_code": True,
+                "sequence_len": 512,
+                "val_set_size": 0.01,
+                "special_tokens": {
+                    "pad_token": "<|endoftext|>",
+                },
+                "datasets": [
+                    {
+                        "path": "mhenrichsen/alpaca_2k_test",
+                        "type": "alpaca",
+                    },
+                ],
+                "num_epochs": 1,
+                "max_steps": 5,
+                "micro_batch_size": 4,
+                "gradient_accumulation_steps": 1,
+                "output_dir": temp_dir,
+                "learning_rate": 0.00001,
+                "optimizer": "adamw_8bit",
+                "lr_scheduler": "cosine",
+                "flash_attention": True,
+                "sample_packing": False,
+                "batch_flattening": True,
+                "bf16": True,
+                "save_safetensors": True,
+            }
+        )
+        normalize_config(cfg)
+        cli_args = TrainerCliArgs()
+        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
+
+        train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
+        assert (Path(temp_dir) / "model.safetensors").exists()

tests/patched/test_validation.py

@@ -1236,6 +1236,76 @@ class TestTorchCompileValidation(BaseValidation):
         assert updated_cfg.torch_compile is False
 
 
+class TestSampleOptimConfigValidation(BaseValidation):
+    """
+    test configurations for sample optimizations like batch flattening
+    """
+
+    def test_batch_flattening_auto_enables(self, minimal_cfg):
+        cfg = (
+            DictDefault(
+                {
+                    "flash_attention": True,
+                    "sample_packing": None,
+                    "micro_batch_size": 2,
+                    "batch_flattening": "auto",
+                }
+            )
+            | minimal_cfg
+        )
+
+        new_cfg = validate_config(cfg)
+        assert new_cfg["batch_flattening"] is True
+
+    def test_batch_flattening_auto_no_fa(self, minimal_cfg):
+        cfg = (
+            DictDefault(
+                {
+                    "flash_attention": False,
+                    "sample_packing": None,
+                    "micro_batch_size": 2,
+                    "batch_flattening": "auto",
+                }
+            )
+            | minimal_cfg
+        )
+
+        new_cfg = validate_config(cfg)
+        assert new_cfg["batch_flattening"] is False
+
+    def test_batch_flattening_auto_mbsz_1(self, minimal_cfg):
+        cfg = (
+            DictDefault(
+                {
+                    "flash_attention": True,
+                    "sample_packing": None,
+                    "micro_batch_size": 1,
+                    "batch_flattening": "auto",
+                }
+            )
+            | minimal_cfg
+        )
+
+        new_cfg = validate_config(cfg)
+        assert new_cfg["batch_flattening"] is False
+
+    def test_batch_flattening_auto_packing(self, minimal_cfg):
+        cfg = (
+            DictDefault(
+                {
+                    "flash_attention": True,
+                    "sample_packing": True,
+                    "micro_batch_size": 2,
+                    "batch_flattening": "auto",
+                }
+            )
+            | minimal_cfg
+        )
+
+        new_cfg = validate_config(cfg)
+        assert new_cfg["batch_flattening"] is False
+
+
 class TestValidationCheckModelConfig(BaseValidation):
     """
     Test the validation for the config when the model config is available