wip

* bumping TRL version

* apply upstream fixes to our custom fix

---------

Co-authored-by: Wing Lian <wing@axolotl.ai>

.github/workflows/multi-gpu-e2e.yml

@@ -4,6 +4,10 @@ on:
   pull_request:
     paths:
       - 'tests/e2e/multigpu/*.py'
+      - 'requirements.txt'
+      - 'setup.py'
+      - 'pyproject.toml'
+      - '.github/workflows/multi-gpu-e2e.yml'
   workflow_dispatch:
   schedule:
     - cron: '0 0 * * 1,4'  # Runs at 00:00 UTC every monday & thursday

cicd/multigpu.py

@@ -37,15 +37,11 @@ temp_dir = tempfile.mkdtemp()
 with open(pathlib.Path(temp_dir) / "Dockerfile", "w", encoding="utf-8") as f:
     f.write(dockerfile_contents)
 
-cicd_image = (
+cicd_image = Image.from_dockerfile(
-    Image.from_dockerfile(
+    pathlib.Path(temp_dir) / "Dockerfile",
-        pathlib.Path(temp_dir) / "Dockerfile",
+    force_build=True,
-        force_build=True,
+    gpu="A10G",
-        gpu="A10G",
+).env(df_args)
-    )
-    .env(df_args)
-    .pip_install("fastapi==0.110.0", "pydantic==2.6.3")
-)
 
 app = App("Axolotl CI/CD", secrets=[])
 

docs/config.qmd

@@ -407,7 +407,10 @@ save_total_limit: # Checkpoints saved at a time
 max_steps:
 
 # bool of whether to include tokens trainer per second in the training metrics. This iterates over the entire dataset once, so it takes some time.
-include_tokens_per_second:
+include_tokens_per_second: # Optional[bool]
+
+# whether to find batch size that fits in memory. Passed to underlying transformers Trainer
+auto_find_batch_size: # Optional[bool]
 
 eval_table_size: # Approximate number of predictions sent to wandb depending on batch size. Enabled above 0. Default is 0
 eval_max_new_tokens: # Total number of tokens generated for predictions sent to wandb. Default is 128

docs/lora_optims.qmd

@@ -12,6 +12,7 @@ to leverage operator fusion and tensor re-use in order to improve speed and redu
 memory usage during the forward and backward passes of these calculations.
 
 We currently support several common model architectures, including (but not limited to):
+
 - `llama`
 - `mistral`
 - `qwen2`
@@ -82,7 +83,7 @@ lora_o_kernel: true
 ## Requirements
 
 - One or more NVIDIA or AMD GPUs (in order to use the Triton kernels)
-    - AMD can be used with experimental Triton support by setting the environment variable `TORCH_ROCM_AOTRITON_ENABLE_EXPERIMENTAL=1`
+    - Note: Set `TORCH_ROCM_AOTRITON_ENABLE_EXPERIMENTAL=1` to enable [memory-efficient attention on AMD GPUs](https://github.com/ROCm/aotriton/issues/16#issuecomment-2346675491)
 - Targeted LoRA adapters cannot use Dropout
     - This may limit model expressivity / cause overfitting
 - Targeted LoRA adapters cannot have bias terms

requirements.txt

@@ -13,12 +13,12 @@ liger-kernel==0.5.2
 packaging==23.2
 
 peft==0.14.0
-transformers==4.48.3
+transformers==4.49.0
 tokenizers>=0.21.0
 accelerate==1.3.0
 datasets==3.2.0
 deepspeed==0.16.1
-trl==0.15.0
+trl==0.15.1
 
 optimum==1.16.2
 hf_transfer

src/axolotl/__init__.py

@@ -4,4 +4,4 @@ import pkgutil
 
 __path__ = pkgutil.extend_path(__path__, __name__)  # Make this a namespace package
 
-__version__ = "0.7.0"
+__version__ = "0.8.0.dev0"

src/axolotl/cli/cloud/modal_.py

@@ -123,8 +123,6 @@ class ModalCloud(Cloud):
         if env := self.get_env():
             image = image.env(env)
 
-        image = image.pip_install("fastapi==0.110.0", "pydantic==2.6.3")
-
         return image
 
     def get_secrets(self):

src/axolotl/core/trainer_builder.py

@@ -59,6 +59,7 @@ from axolotl.core.training_args import (
     AxolotlTrainingArguments,
 )
 from axolotl.integrations.base import PluginManager
+from axolotl.monkeypatch.attention.sequence_parallel import USPRingAttnType, get_extract_fn
 from axolotl.monkeypatch.multipack import SUPPORTED_MULTIPACK_MODEL_TYPES
 from axolotl.monkeypatch.relora import ReLoRACallback
 from axolotl.utils import is_comet_available, is_mlflow_available
@@ -746,6 +747,11 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             # A100 is best at 64, while others at 8. Let's use the larger so we don't have to check
             # https://docs.nvidia.com/deeplearning/performance/dl-performance-matrix-multiplication/index.html
             data_collator_kwargs["pad_to_multiple_of"] = 64
+        if self.cfg.sp_ulysses_degree:
+            data_collator_kwargs["sp_extract_fn"] = get_extract_fn(
+                USPRingAttnType.ZIGZAG,
+                sp_ulysses_degree=self.cfg.sp_ulysses_degree
+            )
 
         if self.cfg.reward_model:
             data_collator_kwargs["max_length"] = self.cfg.sequence_len

src/axolotl/core/trainers/grpo/trainer.py

@@ -78,7 +78,6 @@ class AxolotlGRPOTrainer(SchedulerMixin, GRPOTrainer):
             if is_peft_model(unwrapped_model):
                 unwrapped_model.merge_adapter()
                 state_dict = unwrapped_model.state_dict()
-                unwrapped_model.unmerge_adapter()
                 # Remove base_model and base_layer prefixes
                 state_dict = {
                     k.removeprefix("base_model.model.")
@@ -100,8 +99,10 @@ class AxolotlGRPOTrainer(SchedulerMixin, GRPOTrainer):
                 }
             else:
                 state_dict = unwrapped_model.state_dict()
-        if self.accelerator.is_main_process:
+            if self.accelerator.is_main_process:
-            llm_model = (
+                llm_model = (
-                self.llm.llm_engine.model_executor.driver_worker.model_runner.model
+                    self.llm.llm_engine.model_executor.driver_worker.model_runner.model
-            )
+                )
-            llm_model.load_weights(state_dict.items())
+                llm_model.load_weights(state_dict.items())
+            if is_peft_model(unwrapped_model):
+                unwrapped_model.unmerge_adapter()

src/axolotl/core/training_args.py

@@ -206,6 +206,16 @@ class AxolotlTrainingMixins:
         },
     )
 
+    sp_ulysses_degree: Optional[int] = field(
+        default=None,
+        metadata={"help": "Ulysses parallelism for hybrid sequence parallel long context attn"},
+    )
+
+    sp_ring_degree: Optional[int] = field(
+        default=None,
+        metadata={"help": "Ring attention parallelism for sequence parallel long context attn"},
+    )
+
 
 @dataclass
 class AxolotlTrainingArguments(AxolotlTrainingMixins, TrainingArguments):

src/axolotl/monkeypatch/attention/sequence_parallel/__init__.py

@@ -0,0 +1,45 @@
+from enum import Enum
+from functools import partial
+
+from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS
+from yunchang import set_seq_parallel_pg, EXTRACT_FUNC_DICT
+
+from axolotl.utils.distributed import get_world_size, get_rank
+
+
+class USPRingAttnType(Enum):
+    BASIC = "basic"
+    ZIGZAG = "zigzag"
+    STRIPE = "stripe"
+
+def apply_usp_attn_patch(ring_impl_type: USPRingAttnType):
+    from axolotl.monkeypatch.attention.sequence_parallel.usp import build_usp_fa_forward
+
+    fa_forward = build_usp_fa_forward(ring_impl_type)
+    ALL_ATTENTION_FUNCTIONS["flash_attention_2"] = fa_forward
+
+def get_extract_fn(ring_impl_type: USPRingAttnType, sp_ulysses_degree: int):
+    fn = EXTRACT_FUNC_DICT["basic"]
+    if ring_impl_type.value in EXTRACT_FUNC_DICT.keys():
+        fn = EXTRACT_FUNC_DICT[ring_impl_type.value]
+
+    # map bad key upstream
+    elif ring_impl_type == USPRingAttnType.STRIPE:
+        fn = EXTRACT_FUNC_DICT["strip"]
+
+    world_size = get_world_size()
+    rd = world_size // sp_ulysses_degree
+
+    return partial(fn, rank=get_rank(), world_size=world_size, rd=rd, ud=sp_ulysses_degree)
+
+def set_usp_parallel_group(sp_ulysses_degree):
+    """
+    setup distributed parallel group for USP attention
+    make sure this gets called before building any USP attention modules
+    :param sp_ulysses_degree:
+    :return:
+    """
+    world_size = get_world_size()
+    rank = get_rank()
+    sp_ring_degree = world_size // sp_ulysses_degree
+    set_seq_parallel_pg(sp_ulysses_degree, sp_ring_degree, rank, world_size)

src/axolotl/monkeypatch/attention/sequence_parallel/usp.py

@@ -0,0 +1,36 @@
+from enum import Enum
+from typing import Optional, Tuple, Callable
+
+import torch
+from yunchang import LongContextAttention
+
+from axolotl.monkeypatch.attention.sequence_parallel import USPRingAttnType
+
+
+def build_usp_fa_forward(ring_impl_type: USPRingAttnType) -> Callable:
+    usp_attn = LongContextAttention(ring_impl_type.value)
+
+    def flash_attention_forward(
+            module: torch.nn.Module,  # pylint: disable=unused-argument
+            query: torch.Tensor,
+            key: torch.Tensor,
+            value: torch.Tensor,
+            attention_mask: Optional[torch.Tensor],  # pylint: disable=unused-argument
+            dropout: float = 0.0,
+            scaling: Optional[float] = None,
+            sliding_window: Optional[int] = None,  # pylint: disable=unused-argument
+            softcap: Optional[float] = None,
+            **kwargs,  # pylint: disable=unused-argument
+    ) -> Tuple[torch.Tensor, None]:
+        attn_output = usp_attn(
+            query,
+            key,
+            value,
+            dropout_p=dropout,
+            softmax_scale=scaling,
+            causal=True,
+            softcap=softcap,
+        )
+        return attn_output, None
+
+    return flash_attention_forward

src/axolotl/monkeypatch/relora.py

@@ -127,6 +127,8 @@ class ReLoRACallback(TrainerCallback):
         optimizer: torch.optim.Optimizer,
         **_kwargs,
     ):
+        if not optimizer:
+            optimizer = state.optimizer
         if state.global_step > 0 and state.global_step % self.relora_steps == 0:
             checkpoint_folder = os.path.join(
                 args.output_dir,

src/axolotl/prompt_strategies/chat_template.py

@@ -272,8 +272,7 @@ class ChatTemplateStrategy(PromptTokenizingStrategy):
                 dict(zip(feature_names, row))
             )
             for key, val in tokenized_prompt.items():
-                for i in range(0, len(val), self.sequence_len):
+                res[key].append(val)
-                    res[key].append(val[i : i + self.sequence_len])
 
         # If there are no examples left, return an empty dictionary
         if not res:

src/axolotl/utils/collators/batching.py

@@ -3,7 +3,7 @@ DataCollator for axolotl to pad labels and position_ids for packed sequences
 """
 
 from dataclasses import dataclass
-from typing import Any, Optional, Union
+from typing import Any, Optional, Union, Callable
 
 import numpy as np
 from transformers import PreTrainedTokenizerBase
@@ -53,6 +53,7 @@ class DataCollatorForSeq2Seq:
     label_pad_token_id: int = -100
     position_pad_token_id: int = 0
     return_tensors: str = "pt"
+    sp_extract_fn: Optional[Callable] = None
 
     def __call__(self, features, return_tensors=None):
         labels = None
@@ -121,6 +122,10 @@ class DataCollatorForSeq2Seq:
 
         return features
 
+    def seq_parallel_split(self, features):
+        if self.sp_extract_fn:
+            pass
+        return features
 
 @dataclass
 class BatchSamplerDataCollatorForSeq2Seq(DataCollatorForSeq2Seq):

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

@@ -342,6 +342,7 @@ class LoraConfig(BaseModel):
     peft_use_dora: Optional[bool] = None
     peft_use_rslora: Optional[bool] = None
     peft_layer_replication: Optional[List[Tuple[int, int]]] = None
+    peft_init_lora_weights: Optional[Union[bool, str]] = None
 
     qlora_sharded_model_loading: Optional[bool] = Field(
         default=False,
@@ -831,6 +832,8 @@ class AxolotlInputConfig(
 
     eager_attention: Optional[bool] = None
 
+    sp_ulysses_degree: Optional[int] = None
+
     unsloth_cross_entropy_loss: Optional[bool] = None
     unsloth_lora_mlp: Optional[bool] = None
     unsloth_lora_qkv: Optional[bool] = None

src/axolotl/utils/data/utils.py

@@ -172,10 +172,11 @@ def drop_long_seq_in_dataset(dataset: Dataset, cfg: DictDefault):
     )
 
     try:
-        min_input_len = np.min(get_dataset_lengths(dataset))
+        ds_lengths = get_dataset_lengths(dataset, from_arrow=True)
-        LOG.debug(f"min_input_len: {min_input_len}")
+        min_input_len = np.min(ds_lengths)
-        max_input_len = np.max(get_dataset_lengths(dataset))
+        LOG.info(f"min_input_len: {min_input_len}")
-        LOG.debug(f"max_input_len: {max_input_len}")
+        max_input_len = np.max(ds_lengths)
+        LOG.info(f"max_input_len: {max_input_len}")
     except AttributeError:
         pass
 

src/axolotl/utils/distributed.py

@@ -86,6 +86,12 @@ def get_world_size():
     return int(os.getenv("WORLD_SIZE", "1"))
 
 
+def get_rank():
+    if not is_distributed():
+        return 0
+    return dist.get_rank()
+
+
 @contextmanager
 def zero_only():
     """

src/axolotl/utils/models.py

@@ -1321,6 +1321,8 @@ def load_lora(model, cfg, inference=False, config_only=False):
     if loftq_bits:
         lora_config_kwargs["loftq_config"] = LoftQConfig(loftq_bits=loftq_bits)
         lora_config_kwargs["init_lora_weights"] = "loftq"
+    if cfg.peft_init_lora_weights:
+        lora_config_kwargs["init_lora_weights"] = cfg.peft_init_lora_weights
     if cfg.peft_use_dora:
         lora_config_kwargs["use_dora"] = cfg.peft_use_dora
         LOG.info("Initializing LoRA weights using dora. This might take longer.")

src/axolotl/utils/samplers/utils.py

@@ -4,13 +4,17 @@ helper util to calculate dataset lengths
 import numpy as np
 
 
-def get_dataset_lengths(dataset):
+def get_dataset_lengths(dataset, from_arrow=False):
-    if "length" in dataset.data.column_names:
+    if "length" in dataset.column_names:
-        lengths = np.array(dataset.data.column("length"))
+        lengths = np.array(dataset["length"])
-    elif "position_ids" in dataset.data.column_names:
+    elif "position_ids" in dataset.column_names:
-        position_ids = dataset.data.column("position_ids")
+        position_ids = dataset["position_ids"]
         lengths = np.array([x[-1] + 1 for x in position_ids])
     else:
-        input_ids = dataset.data.column("input_ids")
+        if from_arrow:
-        lengths = np.vectorize(len)(np.array(input_ids, dtype=object))
+            input_ids = dataset.data.column("input_ids")
+            lengths = np.vectorize(len)(np.array(input_ids, dtype=object))
+        else:
+            input_ids = dataset["input_ids"]
+            lengths = np.array([len(seq) for seq in input_ids])
     return lengths

src/axolotl/utils/trainer.py

@@ -346,7 +346,7 @@ def process_datasets_for_packing(cfg, train_dataset, eval_dataset):
                     load_from_cache_file=not cfg.is_preprocess,
                     desc="Add position_id column (PoSE)",
                 )
-    elif cfg.sample_packing:
+    elif cfg.sample_packing or cfg.sp_ulysses_degree:
         drop_long_kwargs = {}
         if filter_map_kwargs:
             drop_long_kwargs["desc"] = "Add position_id column (Sample Packing)"

tests/prompt_strategies/conftest.py

@@ -125,6 +125,12 @@ def fixture_llama3_tokenizer():
     return tokenizer
 
 
+@pytest.fixture(name="smollm2_tokenizer", scope="session", autouse=True)
+def fixture_smollm2_tokenizer():
+    tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/SmolLM2-135M")
+    return tokenizer
+
+
 @pytest.fixture(name="mistralv03_tokenizer", scope="session", autouse=True)
 def fixture_mistralv03_tokenizer():
     tokenizer = AutoTokenizer.from_pretrained(

tests/prompt_strategies/test_dpo_chatml.py

@@ -0,0 +1,61 @@
+"""
+Tests for loading DPO preference datasets with chatml formatting
+"""
+import unittest
+
+import pytest
+
+from axolotl.prompt_strategies.dpo import load as load_dpo
+from axolotl.utils.data.rl import load_prepare_preference_datasets
+from axolotl.utils.dict import DictDefault
+
+
+@pytest.fixture(name="minimal_dpo_cfg")
+def fixture_cfg():
+    return DictDefault(
+        {
+            "base_model": "HuggingFaceTB/SmolLM2-135M",
+            "tokenizer_config": "HuggingFaceTB/SmolLM2-135M",
+            "rl": "dpo",
+            "learning_rate": 0.000001,
+            "micro_batch_size": 1,
+            "gradient_accumulation_steps": 1,
+            "special_tokens": {
+                "pad_token": "<|endoftext|>",
+            },
+            "sequence_len": 2048,
+        }
+    )
+
+
+class TestDPOChatml:
+    """
+    Test loading DPO preference datasets with chatml formatting
+    """
+
+    def test_default(self, minimal_dpo_cfg):
+        cfg = DictDefault(
+            {
+                "datasets": [
+                    {
+                        "path": "argilla/distilabel-intel-orca-dpo-pairs",
+                        "type": "chatml",
+                        "split": "train[:1%]",
+                    }
+                ]
+            }
+            | minimal_dpo_cfg
+        )
+
+        # test that dpo.load works
+        load_dpo("chatml", cfg)
+        # now actually load the datasets with the strategy
+        train_ds, _ = load_prepare_preference_datasets(cfg)
+        assert train_ds[0]["prompt"].startswith("<|im_start|>")
+        assert train_ds[0]["prompt"].endswith("<|im_start|>assistant\n")
+        assert "chosen" in train_ds[0]
+        assert "rejected" in train_ds[0]
+
+
+if __name__ == "__main__":
+    unittest.main()

tests/test_packed_batch_sampler.py

@@ -7,6 +7,7 @@ from transformers import AutoTokenizer
 from axolotl.datasets import TokenizedPromptDataset
 from axolotl.prompt_strategies.completion import load
 from axolotl.utils.collators import V2BatchSamplerDataCollatorForSeq2Seq
+from axolotl.utils.data.utils import drop_long_seq_in_dataset
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.samplers import MultipackBatchSampler, get_dataset_lengths
 
@@ -18,11 +19,6 @@ def fixture_tokenizer():
     return tokenizer
 
 
-@pytest.fixture(name="max_seq_length")
-def fixture_max_seq_length():
-    return 4096
-
-
 class TestBatchedSamplerPacking:
     """
     Test class for packing streaming dataset sequences
@@ -37,6 +33,7 @@ class TestBatchedSamplerPacking:
             (2, 2),
         ],
     )
+    @pytest.mark.parametrize("max_seq_length", [4096, 512])
     def test_packing(self, batch_size, num_workers, tokenizer, max_seq_length):
         import axolotl.monkeypatch.data.batch_dataset_fetcher  # pylint: disable=unused-import  # noqa: F401
 
@@ -62,6 +59,9 @@ class TestBatchedSamplerPacking:
             dataset,
         )
         train_dataset = concatenate_datasets([dataset_wrapper])
+
+        train_dataset = drop_long_seq_in_dataset(train_dataset, cfg)
+
         lengths = get_dataset_lengths(train_dataset)
         batch_sampler = MultipackBatchSampler(
             sampler=RandomSampler(train_dataset),
@@ -90,7 +90,7 @@ class TestBatchedSamplerPacking:
                 batch_idxs.extend(pack)
 
         for batch in loader:
-            assert len(batch["input_ids"]) <= batch_size * max_seq_length
+            assert batch["input_ids"].numel() <= batch_size * max_seq_length
             assert batch["input_ids"].shape[1] == max_seq_length
 
         original_idxs = set(range(len(train_dataset)))