fix

cicd/cicd.sh

@@ -6,6 +6,5 @@ python -c "import torch; assert '$PYTORCH_VERSION' in torch.__version__"
 pytest -v --durations=10 -n8 --ignore=tests/e2e/ --ignore=tests/patched/ /workspace/axolotl/tests/
 # pytest -v --durations=10 -n8 --dist loadfile /workspace/axolotl/tests/patched/
 pytest -v --durations=10 /workspace/axolotl/tests/e2e/patched/
-pytest -v --durations=10 -n1 /workspace/axolotl/tests/e2e/solo/
 pytest -v --durations=10 /workspace/axolotl/tests/e2e/integrations/
-pytest -v --durations=10 --ignore=tests/e2e/solo/ --ignore=tests/e2e/patched/ --ignore=tests/e2e/multigpu/ --ignore=tests/e2e/integrations/ /workspace/axolotl/tests/e2e/
+pytest -v --durations=10 --ignore=tests/e2e/patched/ --ignore=tests/e2e/multigpu/ --ignore=tests/e2e/integrations/ /workspace/axolotl/tests/e2e/

docs/config.qmd

@@ -244,8 +244,6 @@ total_num_tokens:
 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
-# whether to concatenate samples during pretraining
-pretraining_sample_concatenation:
 
 # Use batch flattening for speedups when not using sample_packing
 batch_flattening:

requirements.txt

@@ -13,9 +13,9 @@ liger-kernel==0.5.2
 packaging==23.2
 
 peft==0.14.0
-transformers @ git+https://github.com/huggingface/transformers.git@mueller-trainer-refactor
+transformers==4.47.1
 tokenizers>=0.21.0
-accelerate==1.3.0
+accelerate==1.2.1
 datasets==3.2.0
 deepspeed==0.16.1
 trl==0.13.0

src/axolotl/common/datasets.py

@@ -11,7 +11,7 @@ from datasets import Dataset
 import axolotl.monkeypatch.data.batch_dataset_fetcher  # pylint: disable=unused-import  # noqa: F401
 from axolotl.cli.args import PreprocessCliArgs, TrainerCliArgs
 from axolotl.utils.data import prepare_dataset
-from axolotl.utils.data.rl import load_prepare_preference_datasets
+from axolotl.utils.data.rl import load_prepare_dpo_datasets
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.models import load_processor, load_tokenizer
 from axolotl.utils.tokenization import check_dataset_labels
@@ -103,9 +103,9 @@ def load_preference_datasets(
     cli_args: Union[PreprocessCliArgs, TrainerCliArgs],
 ) -> TrainDatasetMeta:
     """
-    Loads one or more training or evaluation datasets for RL training using paired
+    Loads one or more training or evaluation datasets for DPO training, calling
-    preference data, calling `axolotl.utils.data.rl.load_prepare_preference_datasets`.
+    `axolotl.utils.data.rl.load_prepare_dpo_datasets`. Optionally, logs out debug
-    Optionally, logs out debug information.
+    information.
 
     Args:
         cfg: Dictionary mapping `axolotl` config keys to values.
@@ -115,7 +115,7 @@ def load_preference_datasets(
         Dataclass with fields for training and evaluation datasets and the computed
         `total_num_steps`.
     """
-    train_dataset, eval_dataset = load_prepare_preference_datasets(cfg)
+    train_dataset, eval_dataset = load_prepare_dpo_datasets(cfg)
     total_num_steps = int(
         math.ceil(len(train_dataset) * cfg.num_epochs / cfg.batch_size)
     )

src/axolotl/core/trainer_builder.py

@@ -1877,8 +1877,6 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         self, training_args: AxolotlTrainingArguments, is_eval=False, **kwargs
     ):
         if training_args.pretraining:
-            if self.cfg.pretraining_sample_concatenation is False:
-                return DataCollatorForSeq2Seq(self.tokenizer, **kwargs)
             return None
 
         if self.cfg.model_config_type == "mamba":

src/axolotl/monkeypatch/trainer_grad_accum.py

@@ -14,85 +14,15 @@ LOG = logging.getLogger("axolotl.monkeypatch.trainer_grad_accum")
 
 ORIGINAL_CONTEXT_CODE = """
     with self.compute_loss_context_manager():
-        if self.model_accepts_loss_kwargs:
-            loss = self.compute_loss(model, inputs)
-        else:
         loss = self.compute_loss(model, inputs, num_items_in_batch=num_items_in_batch)
-
-    del inputs
-    if (
-        self.args.torch_empty_cache_steps is not None
-        and self.state.global_step % self.args.torch_empty_cache_steps == 0
-    ):
-        if is_torch_xpu_available():
-            torch.xpu.empty_cache()
-        elif is_torch_mlu_available():
-            torch.mlu.empty_cache()
-        elif is_torch_musa_available():
-            torch.musa.empty_cache()
-        elif is_torch_npu_available():
-            torch.npu.empty_cache()
-        elif is_torch_mps_available(min_version="2.0"):
-            torch.mps.empty_cache()
-        else:
-            torch.cuda.empty_cache()
-
-    kwargs = {}
-
-    # For LOMO optimizers you need to explicitly use the learnign rate
-    if self.args.optim in [OptimizerNames.LOMO, OptimizerNames.ADALOMO]:
-        kwargs["learning_rate"] = self._get_learning_rate()
-
-    if self.args.n_gpu > 1:
-        loss = loss.mean()  # mean() to average on multi-gpu parallel training
-
-    if self.use_apex:
-        with amp.scale_loss(loss, self.optimizer) as scaled_loss:
-            scaled_loss.backward()
-    else:
-        # Finally we need to normalize the loss for reporting
-        if num_items_in_batch is None:
-            loss = loss / self.args.gradient_accumulation_steps
 """
 
 PATCHED_CONTEXT_CODE = """
     with self.compute_loss_context_manager():
+        if self.model_accepts_loss_kwargs:
             loss = self.compute_loss(model, inputs, num_items_in_batch=num_items_in_batch)
-
-    del inputs
-    if (
-        self.args.torch_empty_cache_steps is not None
-        and self.state.global_step % self.args.torch_empty_cache_steps == 0
-    ):
-        if is_torch_xpu_available():
-            torch.xpu.empty_cache()
-        elif is_torch_mlu_available():
-            torch.mlu.empty_cache()
-        elif is_torch_musa_available():
-            torch.musa.empty_cache()
-        elif is_torch_npu_available():
-            torch.npu.empty_cache()
-        elif is_torch_mps_available(min_version="2.0"):
-            torch.mps.empty_cache()
         else:
-            torch.cuda.empty_cache()
+            loss = self.compute_loss(model, inputs)
-
-    kwargs = {}
-
-    # For LOMO optimizers you need to explicitly use the learnign rate
-    if self.args.optim in [OptimizerNames.LOMO, OptimizerNames.ADALOMO]:
-        kwargs["learning_rate"] = self._get_learning_rate()
-
-    if self.args.n_gpu > 1:
-        loss = loss.mean()  # mean() to average on multi-gpu parallel training
-
-    if self.use_apex:
-        with amp.scale_loss(loss, self.optimizer) as scaled_loss:
-            scaled_loss.backward()
-    else:
-        # Finally we need to normalize the loss for reporting
-        if not self.model_accepts_loss_kwargs and self.compute_loss_func is None:
-            loss = loss / self.args.gradient_accumulation_steps
 """
 
 ORIGINAL_LLAMA_FCLM_CODE = """

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

@@ -706,12 +706,6 @@ class AxolotlInputConfig(
     pad_to_sequence_len: Optional[bool] = None
     curriculum_sampling: Optional[bool] = None
     multipack_real_batches: Optional[bool] = None
-    pretraining_sample_concatenation: Optional[bool] = Field(
-        default=None,
-        json_schema_extra={
-            "description": "whether to soft pack/concatenate samples during pretraining",
-        },
-    )
 
     batch_flattening: Optional[Union[Literal["auto"], bool]] = None
 

src/axolotl/utils/data/__init__.py

@@ -5,7 +5,7 @@ from axolotl.utils.data.pretraining import (  # noqa: F401
     encode_pretraining,
     wrap_pretraining_dataset,
 )
-from axolotl.utils.data.rl import load_prepare_preference_datasets  # noqa: F401
+from axolotl.utils.data.rl import load_prepare_dpo_datasets  # noqa: F401
 from axolotl.utils.data.sft import (  # noqa: F401
     get_dataset_wrapper,
     load_prepare_datasets,

src/axolotl/utils/data/pretraining.py

@@ -18,14 +18,10 @@ LOG = logging.getLogger("axolotl")
 
 
 def encode_pretraining(
-    tokenizer: PreTrainedTokenizerBase,
+    tokenizer: PreTrainedTokenizerBase, max_tokens: int, examples: Dict[str, List]
-    max_tokens: int,
-    examples: Dict[str, List],
-    text_column: str = "text",
-    concatenate: bool = True,
 ) -> Dict[str, List]:
     res = tokenizer(
-        examples[text_column],
+        examples["text"],
         truncation=True,
         max_length=max_tokens - 2,
         add_special_tokens=True,
@@ -34,13 +30,6 @@ def encode_pretraining(
     input_ids = [torch.tensor(seq) for seq in res["input_ids"]]
     targets = [torch.tensor(seq) for seq in res["input_ids"]]
     attention_mask = [torch.tensor(seq) for seq in res["attention_mask"]]
-    if not concatenate:
-        return {
-            "input_ids": [seq.tolist() for seq in input_ids],
-            "labels": [seq.tolist() for seq in targets],
-            "attention_mask": [seq.tolist() for seq in attention_mask],
-        }
-
     new_input_ids = []
     new_labels = []
     new_attention_mask = []
@@ -207,13 +196,7 @@ def wrap_pretraining_dataset(
         # set this to 1 so downstream data_loader doesn't try to increase the batch again
         cfg.micro_batch_size = 1
     else:
-        encode = functools.partial(
+        encode = functools.partial(encode_pretraining, tokenizer, max_tokens)
-            encode_pretraining,
-            tokenizer,
-            max_tokens,
-            text_column=cfg.pretraining_dataset[0].text_column or "text",
-            concatenate=cfg.pretraining_sample_concatenation is True,
-        )
 
     if cfg.shuffle_merged_datasets:
         dataset = dataset.shuffle(seed=seed, buffer_size=buffer_size)

src/axolotl/utils/data/rl.py

@@ -115,7 +115,7 @@ def drop_long_rl_seq(
     raise ValueError("Unknown RL type")
 
 
-def load_prepare_preference_datasets(cfg):
+def load_prepare_dpo_datasets(cfg):
     def load_split(dataset_cfgs, _cfg):
         split_datasets: List[Any] = []
         for i, ds_cfg in enumerate(dataset_cfgs):

src/axolotl/utils/models.py

@@ -386,15 +386,16 @@ class ModelLoader:
         if self.cfg.flash_attention:
             self.patch_attention()
 
-        # if self.cfg.model_config_type == "llama":
+        if self.cfg.model_config_type == "llama":
-        #     from axolotl.monkeypatch.trainer_grad_accum import (  # patch_forward_for_ga,
+            from axolotl.monkeypatch.trainer_grad_accum import (
-        #         patch_flash_attention_forward,
+                patch_flash_attention_forward,
-        #         patch_training_step_for_ga,
+                patch_forward_for_ga,
-        #     )
+                patch_training_step_for_ga,
-        #
+            )
-        #     patch_flash_attention_forward()
+
-        #     # patch_forward_for_ga()
+            patch_flash_attention_forward()
-        #     patch_training_step_for_ga()
+            patch_forward_for_ga()
+            patch_training_step_for_ga()
 
         if self.cfg.sample_packing and self.cfg.s2_attention:
             raise ValueError(
@@ -1056,7 +1057,7 @@ class ModelLoader:
         )
         if (
             hasattr(self.model, "get_input_embeddings")
-            and self.model.get_input_embeddings().num_embeddings != embeddings_len
+            and self.model.get_input_embeddings().num_embeddings < embeddings_len
         ):
             resize_kwargs = {}
             if self.cfg.mean_resizing_embeddings is not None:

tests/e2e/patched/test_mixtral_samplepack.py

@@ -102,5 +102,9 @@ class TestMixtral(unittest.TestCase):
         cli_args = TrainerCliArgs()
         dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
 
-        train(cfg=cfg, dataset_meta=dataset_meta)
+        model, _ = train(cfg=cfg, dataset_meta=dataset_meta)
+        assert (
+            "MixtralFlashAttention2"
+            in model.model.layers[0].self_attn.__class__.__name__
+        )
         check_model_output_exists(temp_dir, cfg)

tests/e2e/patched/test_model_patches.py

@@ -49,7 +49,12 @@ class TestModelPatches(unittest.TestCase):
         )
         normalize_config(cfg)
         tokenizer = load_tokenizer(cfg)
-        load_model(cfg, tokenizer, inference=False)
+        model, _ = load_model(cfg, tokenizer, inference=False)
+
+        assert (
+            "MixtralFlashAttention2"
+            in model.model.layers[0].self_attn.__class__.__name__
+        )
 
     @with_temp_dir
     def test_mistral_multipack(self, temp_dir):

tests/e2e/patched/test_unsloth_integration.py

@@ -3,6 +3,8 @@ import unittest
 
 import pytest
 
+from axolotl.monkeypatch.unsloth_ import check_self_attn_is_patchable
+
 
 @pytest.mark.skip(
     reason="Unsloth integration will be broken going into latest transformers"
@@ -11,8 +13,6 @@ class TestUnslothIntegration(unittest.TestCase):
     """Unsloth monkeypatch integration tests."""
 
     def test_is_self_attn_patchable(self):
-        from axolotl.monkeypatch.unsloth_ import check_self_attn_is_patchable
-
         # ensures the current version of transformers has loss code that matches our patching code
         self.assertTrue(
             check_self_attn_is_patchable(),

tests/e2e/test_llama_pretrain.py

@@ -4,8 +4,7 @@ E2E tests for llama pretrain
 
 import logging
 import os
-
+import unittest
-import pytest
 
 from axolotl.cli.args import TrainerCliArgs
 from axolotl.common.datasets import load_datasets
@@ -13,22 +12,19 @@ from axolotl.train import train
 from axolotl.utils.config import normalize_config
 from axolotl.utils.dict import DictDefault
 
-from .utils import check_model_output_exists
+from .utils import check_model_output_exists, with_temp_dir
 
 LOG = logging.getLogger("axolotl.tests.e2e")
 os.environ["WANDB_DISABLED"] = "true"
 
 
-class TestPretrainLlama:
+class TestPretrainLlama(unittest.TestCase):
     """
     Test case for Llama models w pretraining
     """
 
-    @pytest.mark.parametrize(
+    @with_temp_dir
-        "sample_packing",
+    def test_pretrain_w_sample_packing(self, temp_dir):
-        [True, False],
-    )
-    def test_pretrain(self, temp_dir, sample_packing):
         # pylint: disable=duplicate-code
         cfg = DictDefault(
             {
@@ -36,7 +32,7 @@ class TestPretrainLlama:
                 "tokenizer_type": "LlamaTokenizer",
                 "flash_attention": True,
                 "sequence_len": 1024,
-                "sample_packing": sample_packing,
+                "sample_packing": True,
                 "special_tokens": {
                     "unk_token": "<unk>",
                     "bos_token": "<s>",

tests/e2e/test_relora_llama.py

@@ -13,7 +13,7 @@ from axolotl.train import train
 from axolotl.utils.config import normalize_config
 from axolotl.utils.dict import DictDefault
 
-from ..utils import check_model_output_exists, check_tensorboard, with_temp_dir
+from .utils import check_model_output_exists, check_tensorboard, with_temp_dir
 
 LOG = logging.getLogger("axolotl.tests.e2e")
 os.environ["WANDB_DISABLED"] = "true"

tests/patched/test_llama_trainer_ga.py

@@ -1,8 +1,6 @@
 """"Test module for checking whether the Hugging Face Transformers is working as expected."""
 import unittest
 
-import pytest
-
 from axolotl.monkeypatch.trainer_grad_accum import (
     check_forward_is_patchable,
     check_training_step_is_patchable,
@@ -12,7 +10,6 @@ from axolotl.monkeypatch.trainer_grad_accum import (
 class TestTrainerGAIntegration(unittest.TestCase):
     """llama monkeypatch integration tests."""
 
-    @pytest.mark.skip("may not be needed for latest transformers version")
     def test_train_step_patchable(self):
         # ensures the current version of transformers has loss code that matches our patching code
         self.assertTrue(
@@ -20,7 +17,6 @@ class TestTrainerGAIntegration(unittest.TestCase):
             "HF transformers Trainer.training_step has changed and isn't patchable",
         )
 
-    @pytest.mark.skip("may not be needed for latest transformers version")
     def test_model_forward_patchable(self):
         # ensures the current version of transformers has loss code that matches our patching code
         self.assertTrue(

tests/test_datasets.py

@@ -17,7 +17,7 @@ from huggingface_hub import snapshot_download
 from transformers import AutoTokenizer
 
 from axolotl.utils.data import load_tokenized_prepared_datasets
-from axolotl.utils.data.rl import load_prepare_preference_datasets
+from axolotl.utils.data.rl import load_prepare_dpo_datasets
 from axolotl.utils.dict import DictDefault
 
 
@@ -280,7 +280,7 @@ class TestDatasetPreparation(unittest.TestCase):
             }
         )
 
-        train_dataset, _ = load_prepare_preference_datasets(cfg)
+        train_dataset, _ = load_prepare_dpo_datasets(cfg)
 
         assert len(train_dataset) == 1800
         assert "conversation" in train_dataset.features
@@ -329,7 +329,7 @@ class TestDatasetPreparation(unittest.TestCase):
             }
         )
 
-        train_dataset, _ = load_prepare_preference_datasets(cfg)
+        train_dataset, _ = load_prepare_dpo_datasets(cfg)
 
         assert len(train_dataset) == 1800
         assert "conversation" in train_dataset.features

tests/test_exact_deduplication.py

@@ -12,7 +12,7 @@ from datasets import Dataset
 from transformers import AutoTokenizer
 
 from axolotl.utils.data import prepare_dataset
-from axolotl.utils.data.rl import load_prepare_preference_datasets
+from axolotl.utils.data.rl import load_prepare_dpo_datasets
 from axolotl.utils.data.utils import deduplicate_and_log_datasets
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.models import load_processor, load_tokenizer
@@ -236,7 +236,7 @@ class TestDeduplicateRLDataset(unittest.TestCase):
         """Verify that loading with deduplication removes duplicates."""
 
         # Load the dataset using the deduplication setting
-        train_dataset, _ = load_prepare_preference_datasets(self.cfg)
+        train_dataset, _ = load_prepare_dpo_datasets(self.cfg)
 
         # Verify that the dataset has been deduplicated
         assert len(train_dataset) == 1800, "Dataset was not properly deduplicated"
@@ -245,7 +245,7 @@ class TestDeduplicateRLDataset(unittest.TestCase):
         """Verify that loading without deduplication retains duplicates."""
         self.cfg.dataset_exact_deduplication = False
         # Load the dataset without deduplication
-        train_dataset, _ = load_prepare_preference_datasets(self.cfg)
+        train_dataset, _ = load_prepare_dpo_datasets(self.cfg)
 
         # Verify that the dataset retains duplicates
         assert (