add transformers module to cleanup

* allow flexibility in transformers version for FSDP

* more flexibility with dev versions of 4.47.0.dev0

* add patch for fsdp

* fix typo

* correct fn name

* stray character

* fix patch

* reset Trainer too

* also reset Trainer.training_step

* allow tests/patched to run more than one process on e2e runner

* skip tests/patched in e2e for now since it's run in regular pytest

cicd/cicd.sh

@@ -2,6 +2,6 @@
 set -e
 
 pytest -v --durations=10 -n8 --ignore=tests/e2e/ --ignore=tests/patched/ /workspace/axolotl/tests/
-pytest -v --durations=10 -n1 --dist loadfile /workspace/axolotl/tests/patched/
+pytest -v --durations=10 -n8 --dist loadfile /workspace/axolotl/tests/patched/
 pytest -v --durations=10 -n1 --dist loadfile /workspace/axolotl/tests/e2e/patched/ /workspace/axolotl/tests/e2e/integrations/
 pytest -v --durations=10 --ignore=tests/e2e/patched/ --ignore=tests/e2e/multigpu/ --ignore=tests/e2e/integrations/ /workspace/axolotl/tests/e2e/

docker/Dockerfile-base

@@ -16,7 +16,7 @@ ENV PYTHON_VERSION=$PYTHON_VERSION
 ENV TORCH_CUDA_ARCH_LIST=$TORCH_CUDA_ARCH_LIST
 
 RUN apt-get update \
-    && apt-get install -y wget git build-essential ninja-build git-lfs libaio-dev && rm -rf /var/lib/apt/lists/* \
+    && apt-get install -y wget git build-essential ninja-build git-lfs libaio-dev pkg-config && rm -rf /var/lib/apt/lists/* \
     && wget \
     https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh \
     && mkdir /root/.conda \

requirements.txt

@@ -1,7 +1,7 @@
 --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
 packaging==23.2
 peft==0.14.0
-transformers==4.47.0
+transformers>=4.46.3
 tokenizers>=0.20.1
 bitsandbytes==0.45.0
 accelerate==1.2.0
@@ -31,7 +31,7 @@ art
 gradio==3.50.2
 tensorboard
 python-dotenv==1.0.1
-autoawq==0.2.7.post2
+autoawq==0.2.7.post3
 triton>=2.3.0
 liger-kernel==0.4.2
 

src/axolotl/cli/main.py

@@ -5,6 +5,7 @@ from typing import Optional
 
 import click
 
+import axolotl
 from axolotl.cli.utils import (
     add_options_from_config,
     add_options_from_dataclass,
@@ -16,6 +17,7 @@ from axolotl.utils.config.models.input.v0_4_1 import AxolotlInputConfig
 
 
 @click.group()
+@click.version_option(version=axolotl.__version__, prog_name="axolotl")
 def cli():
     """Axolotl CLI - Train and fine-tune large language models"""
 

src/axolotl/core/trainer_builder.py

@@ -22,6 +22,7 @@ from typing import Any, Dict, List, Literal, Optional, Type, Union
 import torch
 import transformers
 from datasets import Dataset
+from packaging import version
 from peft.optimizers import create_loraplus_optimizer
 from torch import nn
 from torch.optim.lr_scheduler import OneCycleLR
@@ -973,7 +974,13 @@ class AxolotlTrainer(SchedulerMixin, Trainer):
         for key, metrics in self._stored_metrics[train_eval].items():
             logs[key] = torch.tensor(metrics).mean().item()
         del self._stored_metrics[train_eval]
-        return super().log(logs, start_time)
+
+        if version.parse(transformers.__version__) >= version.parse("4.47.0.dev0"):
+            try:
+                return super().log(logs, start_time)
+            except TypeError:
+                return super().log(logs)  # transformers<=4.46
+        return super().log(logs)  # transformers<=4.46
 
     def store_metrics(
         self, metrics: Dict[str, float], train_eval: Literal["train", "eval"] = "train"
@@ -1165,9 +1172,13 @@ class AxolotlDPOTrainer(SchedulerMixin, DPOTrainer):
         for key, metrics in self._stored_metrics[train_eval].items():
             logs[key] = torch.tensor(metrics).mean().item()
         del self._stored_metrics[train_eval]
-        return super(DPOTrainer, self).log(  # pylint: disable=bad-super-call
+
-            logs, start_time
+        if version.parse(transformers.__version__) >= version.parse("4.47.0.dev0"):
-        )
+            return super(DPOTrainer, self).log(  # pylint: disable=bad-super-call
+                logs, start_time
+            )
+        # transformers<=4.46
+        return super(DPOTrainer, self).log(logs)  # pylint: disable=bad-super-call
 
 
 class AxolotlORPOTrainer(SchedulerMixin, ORPOTrainer):
@@ -1185,9 +1196,13 @@ class AxolotlORPOTrainer(SchedulerMixin, ORPOTrainer):
         for key, metrics in self._stored_metrics[train_eval].items():
             logs[key] = torch.tensor(metrics).mean().item()
         del self._stored_metrics[train_eval]
-        return super(ORPOTrainer, self).log(  # pylint: disable=bad-super-call
+
-            logs, start_time
+        if version.parse(transformers.__version__) >= version.parse("4.47.0.dev0"):
-        )
+            return super(ORPOTrainer, self).log(  # pylint: disable=bad-super-call
+                logs, start_time
+            )
+        # transformers<=4.46
+        return super(ORPOTrainer, self).log(logs)  # pylint: disable=bad-super-call
 
 
 class AxolotlKTOTrainer(SchedulerMixin, KTOTrainer):
@@ -1232,9 +1247,13 @@ class AxolotlKTOTrainer(SchedulerMixin, KTOTrainer):
         for key, metrics in self._stored_metrics[train_eval].items():
             logs[f"{prefix}{key}"] = torch.Tensor(metrics).mean().item()
         del self._stored_metrics[train_eval]
-        return super(KTOTrainer, self).log(  # pylint: disable=bad-super-call
+
-            logs, start_time
+        if version.parse(transformers.__version__) >= version.parse("4.47.0.dev0"):
-        )
+            return super(KTOTrainer, self).log(  # pylint: disable=bad-super-call
+                logs, start_time
+            )
+        # transformers<=4.46
+        return super(KTOTrainer, self).log(logs)  # pylint: disable=bad-super-call
 
 
 class AxolotlCPOTrainer(SchedulerMixin, CPOTrainer):
@@ -1252,9 +1271,13 @@ class AxolotlCPOTrainer(SchedulerMixin, CPOTrainer):
         for key, metrics in self._stored_metrics[train_eval].items():
             logs[key] = torch.tensor(metrics).mean().item()
         del self._stored_metrics[train_eval]
-        return super(CPOTrainer, self).log(  # pylint: disable=bad-super-call
+
-            logs, start_time
+        if version.parse(transformers.__version__) >= version.parse("4.47.0.dev0"):
-        )
+            return super(CPOTrainer, self).log(  # pylint: disable=bad-super-call
+                logs, start_time
+            )
+        # transformers<=4.46
+        return super(CPOTrainer, self).log(logs)  # pylint: disable=bad-super-call
 
 
 class AxolotlRewardTrainer(SchedulerMixin, RewardTrainer):
@@ -1266,9 +1289,12 @@ class AxolotlRewardTrainer(SchedulerMixin, RewardTrainer):
 
     def log(self, logs: Dict[str, float], start_time: Optional[float] = None) -> None:
         # TODO remove once trl supports the updated to the Trainer.log method
-        return super(RewardTrainer, self).log(  # pylint: disable=bad-super-call
+        if version.parse(transformers.__version__) >= version.parse("4.47.0.dev0"):
-            logs, start_time
+            return super(RewardTrainer, self).log(  # pylint: disable=bad-super-call
-        )
+                logs, start_time
+            )
+        # transformers<=4.46
+        return super(RewardTrainer, self).log(logs)  # pylint: disable=bad-super-call
 
 
 class TrainerBuilderBase(abc.ABC):

src/axolotl/monkeypatch/trainer_fsdp_optim.py

@@ -0,0 +1,80 @@
+"""
+fix for FSDP optimizer save in trainer w 4.47.0
+"""
+import inspect
+import logging
+
+from transformers import Trainer
+
+from axolotl.monkeypatch.unsloth_ import detab_code
+
+LOG = logging.getLogger("axolotl.monkeypatch.trainer_fsdp_save")
+
+ORIGINAL_TRAINER_CODE = """
+
+    delay_optimizer_creation = is_sagemaker_mp_enabled() or self.is_fsdp_xla_enabled
+
+"""
+
+PATCHED_TRAINER_CODE = """
+
+    delay_optimizer_creation = is_sagemaker_mp_enabled() or self.is_fsdp_xla_enabled or self.is_fsdp_enabled
+
+"""
+
+
+def get_training_loop_code() -> str:
+    training_loop = inspect.getsource(
+        Trainer._inner_training_loop  # pylint: disable=protected-access
+    )
+    return training_loop
+
+
+def check_training_loop_is_patchable() -> bool:
+    training_loop = get_training_loop_code()
+    training_loop, _ = detab_code(training_loop)
+    return ORIGINAL_TRAINER_CODE in training_loop
+
+
+def patch_training_loop_for_fsdp():
+    """
+    monkeypatch for fixing the training loop for fsdp with optimizer save
+    """
+
+    try:
+        training_loop = get_training_loop_code()
+    except OSError:
+        return
+    Trainer._original_inner_training_loop = (  # pylint: disable=protected-access
+        training_loop
+    )
+    training_loop, _ = detab_code(training_loop)
+    if ORIGINAL_TRAINER_CODE not in training_loop:
+        return
+
+    training_loop = training_loop.replace(ORIGINAL_TRAINER_CODE, PATCHED_TRAINER_CODE)
+    training_loop = training_loop.replace(
+        "def _inner_training_loop(",
+        "def _fixed_inner_training_loop(",
+        1,
+    )
+
+    # load imports necessary
+    import transformers.trainer
+
+    items_to_import = []
+    for item in dir(transformers.trainer):
+        if item in training_loop:
+            items_to_import.append(item)
+
+    exec(  # pylint: disable=exec-used  # nosec B102
+        "from transformers.trainer import ("
+        + ", ".join(x for x in items_to_import)
+        + ")",
+        globals(),
+    )
+    exec(training_loop, globals())  # pylint: disable=exec-used  # nosec B102
+    LOG.info("patching _inner_training_loop for fsdp optimizer save")
+    Trainer._inner_training_loop = (  # pylint: disable=protected-access
+        _fixed_inner_training_loop  # pylint: disable=undefined-variable  # noqa: F821
+    )

src/axolotl/monkeypatch/trainer_grad_accum.py

@@ -3,14 +3,13 @@ fix for FSDP gradient accumulation
 see https://github.com/huggingface/transformers/pull/35128
 """
 import inspect
+import logging
 
-from accelerate.logging import get_logger
+from transformers import LlamaForCausalLM, Trainer
-from transformers import LlamaForCausalLM
-from transformers.trainer import Trainer
 
 from axolotl.monkeypatch.unsloth_ import detab_code
 
-LOG = get_logger("axolotl.monkeypatch.trainer_grad_accum")
+LOG = logging.getLogger("axolotl.monkeypatch.trainer_grad_accum")
 
 ORIGINAL_CONTEXT_CODE = """
     with self.compute_loss_context_manager():
@@ -67,7 +66,7 @@ PATCHED_LLAMA_FCLM_CODE = """
     return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
     # remove num_items_in_batch otherwise self.model attempts to pass it to flash_attention
-    num_items_in_batch = kwargs.pop("num_items_in_batch")
+    num_items_in_batch = kwargs.pop("num_items_in_batch", None)
 
     # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
     outputs = self.model(
@@ -111,12 +110,17 @@ def patch_training_step_for_ga():
     monkeypatch for fixing the training loop for gradient accumulation
     """
 
-    training_step = get_training_step_code()
+    try:
+        training_step = get_training_step_code()
+    except OSError:
+        return
     Trainer._original_training_step = training_step  # pylint: disable=protected-access
     training_step, _ = detab_code(training_step)
-    assert (
+    if ORIGINAL_CONTEXT_CODE not in training_step:
-        ORIGINAL_CONTEXT_CODE in training_step
+        return
-    ), "Original training_step code not found"
+    # assert (
+    #     ORIGINAL_CONTEXT_CODE in training_step
+    # ), "Original training_step code not found"
 
     training_step = training_step.replace(ORIGINAL_CONTEXT_CODE, PATCHED_CONTEXT_CODE)
     training_step = training_step.replace(
@@ -140,7 +144,7 @@ def patch_training_step_for_ga():
         globals(),
     )
     exec(training_step, globals())  # pylint: disable=exec-used  # nosec B102
-    LOG.info("patching training_step", main_process_only=True)
+    LOG.info("patching training_step")
     Trainer.training_step = (  # pylint: disable=protected-access
         _fixed_training_step  # pylint: disable=undefined-variable  # noqa: F821
     )
@@ -164,10 +168,15 @@ def patch_forward_for_ga():
     monkeypatch for fixing the training loop for gradient accumulation
     """
 
-    forward = get_model_forward_code()
+    try:
+        forward = get_model_forward_code()
+    except OSError:
+        return
     LlamaForCausalLM._original_forward = forward  # pylint: disable=protected-access
     forward, _ = detab_code(forward)
-    assert ORIGINAL_LLAMA_FCLM_CODE in forward, "Original forward code not found"
+    if ORIGINAL_LLAMA_FCLM_CODE not in forward:
+        return
+    # assert ORIGINAL_LLAMA_FCLM_CODE in forward, "Original forward code not found"
 
     forward = forward.replace(ORIGINAL_LLAMA_FCLM_CODE, PATCHED_LLAMA_FCLM_CODE)
     forward = forward.replace(
@@ -191,7 +200,7 @@ def patch_forward_for_ga():
         globals(),
     )
     exec(forward, globals())  # pylint: disable=exec-used  # nosec B102
-    LOG.info("patching forward", main_process_only=True)
+    LOG.info("patching forward")
     LlamaForCausalLM.forward = (  # pylint: disable=protected-access
         _fixed_forward  # pylint: disable=undefined-variable  # noqa: F821
     )

src/axolotl/monkeypatch/unsloth_.py

@@ -9,10 +9,7 @@ import torch
 from accelerate.logging import get_logger
 from peft import PeftModelForCausalLM
 from torch import nn
-from transformers.models.llama.modeling_llama import (
+from transformers.models.llama.modeling_llama import LlamaFlashAttention2
-    LlamaFlashAttention2,
-    LlamaForCausalLM,
-)
 
 LOG = get_logger("axolotl.monkeypatch.unsloth")
 
@@ -55,11 +52,6 @@ def original_apply_o(self, hidden_states):
     return attn_output
 
 
-def get_forward_code() -> str:
-    forward = inspect.getsource(LlamaForCausalLM.forward)
-    return forward
-
-
 def get_self_attn_code() -> str:
     forward = inspect.getsource(LlamaFlashAttention2.forward)
     return forward
@@ -102,12 +94,22 @@ def integrate_cross_entropy_loss_patch(model_type: str = "llama") -> None:
 
 
 def detab_code(code: str) -> Tuple[str, str]:
-    spaces = re.match(r"([\s\t]{1,})", code).group(0)
+    try:
-    code = re.sub(r"^" + spaces, "", code, flags=re.MULTILINE)
+        spaces = re.match(r"([\s\t]{1,})", code).group(0)
+        code = re.sub(r"^" + spaces, "", code, flags=re.MULTILINE)
+    except AttributeError:
+        return code, ""
     return code, spaces
 
 
+self_attn_lora_patched = False  # pylint: disable=invalid-name
+
+
 def patch_self_attn_lora():
+    global self_attn_lora_patched  # pylint: disable=global-statement
+    if self_attn_lora_patched:
+        # prevent patching multiple times
+        return
     self_attn_forward = get_self_attn_code()
     LlamaFlashAttention2._original_forward = (  # pylint: disable=protected-access
         self_attn_forward
@@ -139,6 +141,7 @@ def patch_self_attn_lora():
         globals(),
     )
     exec(self_attn_forward, globals())  # pylint: disable=exec-used  # nosec B102
+    self_attn_lora_patched = True
     LOG.info("patching unsloth attn lora", main_process_only=True)
     LlamaFlashAttention2.forward = (
         unsloth_attn_forward  # pylint: disable=undefined-variable  # noqa: F821

src/axolotl/utils/config/__init__.py

@@ -153,7 +153,7 @@ def normalize_config(cfg):
     cfg.is_llama_derived_model = (
         (
             hasattr(model_config, "model_type")
-            and model_config.model_type == ["llama", "mllama_text_model"]
+            and model_config.model_type in ["llama", "mllama_text_model"]
         )
         or cfg.is_llama_derived_model
         or "llama" in cfg.base_model.lower()

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

@@ -1432,20 +1432,6 @@ class AxolotlInputConfig(
                 )
         return data
 
-    @model_validator(mode="before")
-    @classmethod
-    def notify_qlora_unsloth(cls, data):
-        if (
-            data.get("unsloth_lora_mlp")
-            or data.get("unsloth_lora_qkv")
-            or data.get("unsloth_lora_o")
-        ):
-            LOG.info(
-                "Unsloth may not be well supported with the latest version of Transformers, "
-                "resulting in loss that is incorrect."
-            )
-        return data
-
     @model_validator(mode="before")
     @classmethod
     def check_torch_compile_deepspeed(cls, data):

src/axolotl/utils/models.py

@@ -380,6 +380,13 @@ class ModelLoader:
         plugin_manager = PluginManager.get_instance()
         plugin_manager.pre_model_load(self.cfg)
 
+        if self.cfg.fsdp:
+            from axolotl.monkeypatch.trainer_fsdp_optim import (
+                patch_training_loop_for_fsdp,
+            )
+
+            patch_training_loop_for_fsdp()
+
         if self.cfg.gradient_checkpointing == "unsloth":
             transformers.modeling_utils.checkpoint = hf_grad_checkpoint_unsloth_wrapper
 
@@ -406,10 +413,14 @@ class ModelLoader:
             and self.cfg.flash_attention
             and self.cfg.sample_packing
         ):
-            has_remote_code = (
+            if "auto_map" in self.model_config:
-                "auto_map" in self.model_config
+                try:
-                and "AutoModelForCausalLM" in self.model_config["auto_map"]
+                    auto_map_config = self.model_config["auto_map"]
-            )
+                except TypeError:
+                    auto_map_config = self.model_config.auto_map
+                has_remote_code = "AutoModelForCausalLM" in auto_map_config
+            else:
+                has_remote_code = False
             if has_remote_code and self.cfg.trust_remote_code is False:
                 # if explicitly set in the YAML, we should prefer that, for example if explicitly disabled
                 has_remote_code = self.cfg.trust_remote_code

tests/conftest.py

@@ -2,7 +2,9 @@
 shared pytest fixtures
 """
 import functools
+import importlib
 import shutil
+import sys
 import tempfile
 import time
 
@@ -113,3 +115,40 @@ def temp_dir():
     yield _temp_dir
     # Clean up the directory after the test
     shutil.rmtree(_temp_dir)
+
+
+@pytest.fixture(scope="function", autouse=True)
+def cleanup_monkeypatches():
+    from transformers import Trainer
+    from transformers.models.llama.modeling_llama import LlamaFlashAttention2
+
+    original_fa2_forward = LlamaFlashAttention2.forward
+    original_trainer_inner_training_loop = (
+        Trainer._inner_training_loop  # pylint: disable=protected-access
+    )
+    original_trainer_training_step = Trainer.training_step
+    # monkey patches can happen inside the tests
+    yield
+    # Reset LlamaFlashAttention2 forward
+    LlamaFlashAttention2.forward = original_fa2_forward
+    Trainer._inner_training_loop = (  # pylint: disable=protected-access
+        original_trainer_inner_training_loop
+    )
+    Trainer.training_step = original_trainer_training_step
+
+    # Reset other known monkeypatches
+    modules_to_reset: list[tuple[str, list[str]]] = [
+        ("transformers",),
+        ("transformers.models.llama.modeling_llama", ["LlamaFlashAttention2"]),
+        ("transformers.trainer", ["Trainer"]),
+        ("transformers.loss.loss_utils",),
+    ]
+    for module_name_tuple in modules_to_reset:
+        module_name = module_name_tuple[0]
+        module = importlib.import_module(module_name)
+        sys.modules[module_name] = module
+        importlib.reload(sys.modules[module_name])
+        if len(module_name_tuple) > 1:
+            module_globals = module_name_tuple[1]
+            for module_global in module_globals:
+                globals().pop(module_global, None)

tests/e2e/patched/test_unsloth_qlora.py

@@ -20,7 +20,6 @@ os.environ["WANDB_DISABLED"] = "true"
 
 
 # pylint: disable=duplicate-code
-@pytest.mark.skip(reason="latest unsloth doesn't work with latest transformers")
 class TestUnslothQLoRA:
     """
     Test class for Unsloth QLoRA Llama models
@@ -37,6 +36,9 @@ class TestUnslothQLoRA:
                 "sequence_len": 1024,
                 "sample_packing": sample_packing,
                 "flash_attention": True,
+                "unsloth_lora_mlp": True,
+                "unsloth_lora_qkv": True,
+                "unsloth_lora_o": True,
                 "load_in_4bit": True,
                 "adapter": "qlora",
                 "lora_r": 16,
@@ -83,6 +85,9 @@ class TestUnslothQLoRA:
             {
                 "base_model": "HuggingFaceTB/SmolLM2-135M",
                 "sequence_len": 1024,
+                "unsloth_lora_mlp": True,
+                "unsloth_lora_qkv": True,
+                "unsloth_lora_o": True,
                 "sample_packing": False,
                 "load_in_4bit": True,
                 "adapter": "qlora",
@@ -134,6 +139,9 @@ class TestUnslothQLoRA:
             {
                 "base_model": "HuggingFaceTB/SmolLM2-135M",
                 "sequence_len": 1024,
+                "unsloth_lora_mlp": True,
+                "unsloth_lora_qkv": True,
+                "unsloth_lora_o": True,
                 "sample_packing": False,
                 "load_in_4bit": True,
                 "adapter": "qlora",