Revert "checkpoint model on first step callback (#2906)"

This reverts commit 10ba1622f7.

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

@@ -33,6 +33,13 @@ jobs:
             axolotl_extras:
             num_gpus: 2
             nightly_build: "true"
+          - cuda: 126
+            cuda_version: 12.6.3
+            python_version: "3.11"
+            pytorch: 2.7.0
+            axolotl_extras: vllm
+            num_gpus: 2
+            nightly_build: "true"
           - cuda: 126
             cuda_version: 12.6.3
             python_version: "3.11"

.github/workflows/nightlies.yml

@@ -12,11 +12,16 @@ jobs:
       fail-fast: false
       matrix:
         include:
-          - cuda: 124
-            cuda_version: 12.4.1
+          - cuda: 126
+            cuda_version: 12.6.3
             python_version: "3.11"
             pytorch: 2.6.0
             axolotl_extras:
+          - cuda: 126
+            cuda_version: 12.6.3
+            python_version: "3.11"
+            pytorch: 2.7.1
+            axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:
       - name: Checkout
@@ -60,15 +65,15 @@ jobs:
     strategy:
       matrix:
         include:
-          - cuda: 124
-            cuda_version: 12.4.1
+          - cuda: 126
+            cuda_version: 12.6.3
             python_version: "3.11"
             pytorch: 2.6.0
             axolotl_extras:
           - cuda: 126
             cuda_version: 12.6.3
             python_version: "3.11"
-            pytorch: 2.6.0
+            pytorch: 2.7.1
             axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:

_quarto.yml

@@ -276,6 +276,7 @@ website:
             - docs/torchao.qmd
             - docs/custom_integrations.qmd
             - docs/sequence_parallelism.qmd
+            - docs/gradient_checkpointing.qmd
 
         - section: "Troubleshooting"
           contents:

docs/gradient_checkpointing.qmd

@@ -0,0 +1,29 @@
+---
+title: Gradient Checkpointing and Activation Offloading
+---
+
+Gradient checkpointing and activation offloading are techniques used to optimize the performance of deep learning
+models by reducing the memory footprint and improving computational efficiency.
+
+### Enabling Gradient Checkpointing
+
+```yaml
+gradient_checkpointing: true
+```
+
+### Enabling Activation Offloading
+
+```yaml
+gradient_checkpointing: true  # required for activation offloading
+activation_offloading: true
+```
+
+Activation offloading variants:
+
+The default `activation_offloading: true` offloads activations to CPU and uses CUDA streams
+to overlap the communications and computations when offloading.
+
+The `activation_offloading: legacy` naively offloads activations to CPU and without additional optimizations.
+
+For resource constrained environments with limited CPU memory, `activation_offloading: disk` offloads
+activations to disk instead of CPU RAM so that much larger context lengths can be trained with minimal memory.

requirements.txt

@@ -6,19 +6,19 @@ triton>=3.0.0
 mamba-ssm==1.2.0.post1
 xformers>=0.0.23.post1
 autoawq==0.2.7.post3
-liger-kernel==0.5.10
+liger-kernel==0.6.0
 # END section
 
 packaging==23.2
 
-huggingface_hub==0.32.2
-peft==0.15.2
-transformers==4.53.1
+huggingface_hub>=0.33.0
+peft==0.16.0
+transformers==4.53.2
 tokenizers>=0.21.1
 accelerate==1.8.1
-datasets==3.6.0
+datasets==4.0.0
 deepspeed>=0.17.0
-trl==0.18.2
+trl==0.19.1
 hf_xet==1.1.2
 
 optimum==1.16.2

setup.py

@@ -73,9 +73,9 @@ def parse_requirements(extras_require_map):
                 extras_require_map["vllm"] = ["vllm>=0.9.0"]
             elif (major, minor) >= (2, 6):
                 _install_requires.pop(_install_requires.index(xformers_version))
-                _install_requires.append(
-                    "xformers==0.0.29.post2"
-                )  # vllm needs post2 w torch 2.6
+                _install_requires.append("xformers==0.0.29.post3")
+                # since we only support 2.6.0+cu126
+                _dependency_links.append("https://download.pytorch.org/whl/cu126")
                 extras_require_map["vllm"] = ["vllm==0.8.5.post1"]
             elif (major, minor) >= (2, 5):
                 _install_requires.pop(_install_requires.index(xformers_version))
@@ -121,7 +121,7 @@ extras_require = {
         "yunchang==0.6.0",
     ],
     "deepspeed": [
-        "deepspeed==0.17.1",
+        "deepspeed==0.17.2",
         "deepspeed-kernels",
     ],
     "mamba-ssm": [

src/axolotl/cli/config.py

@@ -16,7 +16,6 @@ from transformers.utils import is_torch_bf16_gpu_available
 from axolotl.integrations.base import PluginManager
 from axolotl.utils.comet_ import setup_comet_env_vars
 from axolotl.utils.config import (
-    migrate_fsdp_config,
     normalize_cfg_datasets,
     normalize_config,
     validate_config,
@@ -227,7 +226,6 @@ def load_cfg(
         },
     )
 
-    migrate_fsdp_config(cfg)
     prepare_optim_env(cfg)
     prepare_opinionated_env(cfg)
     normalize_config(cfg)

src/axolotl/cli/preprocess.py

@@ -1,5 +1,6 @@
 """CLI to run preprocessing of a dataset."""
 
+import os
 import warnings
 from pathlib import Path
 from typing import Union
@@ -95,6 +96,7 @@ def do_cli(
         kwargs: Additional keyword arguments to override config file values.
     """
     # pylint: disable=duplicate-code
+    os.environ["AXOLOTL_IS_PREPROCESS"] = "1"
     parsed_cfg = load_cfg(config, **kwargs)
     parsed_cfg.is_preprocess = True
     parser = transformers.HfArgumentParser(PreprocessCliArgs)

src/axolotl/cli/vllm_serve.py

@@ -37,7 +37,6 @@ def do_vllm_serve(
     Returns:
         process_id: the process id of the started VLLM server
     """
-    patch_vllm_worker()
     cfg = load_cfg(config)
     model = cfg.base_model
 
@@ -47,6 +46,9 @@ def do_vllm_serve(
     tensor_parallel_size = (
         cli_args.get("tensor_parallel_size") or cfg.vllm.tensor_parallel_size
     )
+    data_parallel_size = (
+        cli_args.get("data_parallel_size") or cfg.vllm.data_parallel_size
+    )
     host = cli_args.get("host") or cfg.vllm.host
     port = cli_args.get("port") or cfg.vllm.port
     gpu_memory_utilization = (
@@ -68,6 +70,7 @@ def do_vllm_serve(
     vllm_script_args = AxolotlScriptArguments(
         model=model,
         tensor_parallel_size=tensor_parallel_size,
+        data_parallel_size=data_parallel_size,
         host=host,
         port=port,
         gpu_memory_utilization=gpu_memory_utilization,

src/axolotl/core/builders/base.py

@@ -112,13 +112,6 @@ class TrainerBuilderBase(abc.ABC):
             plugin_manager.add_callbacks_pre_trainer(cfg=self.cfg, model=self.model)
         )
 
-        if self.cfg.profiler_steps:
-            callbacks.append(
-                PytorchProfilerCallback(
-                    steps_to_profile=self.cfg.profiler_steps,
-                )
-            )
-
         if self.cfg.gc_steps:
             callbacks.append(GCCallback(gc_steps=self.cfg.gc_steps))
 
@@ -145,6 +138,14 @@ class TrainerBuilderBase(abc.ABC):
 
         callbacks.append(GPUStatsCallback(cfg=self.cfg))
 
+        if self.cfg.profiler_steps:
+            callbacks.append(
+                PytorchProfilerCallback(
+                    steps_to_profile=self.cfg.profiler_steps,
+                    profiler_steps_start=self.cfg.profiler_steps_start,
+                )
+            )
+
         return callbacks
 
     def get_post_trainer_create_callbacks(self, trainer):
@@ -418,6 +419,9 @@ class TrainerBuilderBase(abc.ABC):
             torch._dynamo.config.suppress_errors = (  # pylint: disable=protected-access
                 True
             )
+            torch._dynamo.config.accumulated_cache_size_limit = (  # pylint: disable=protected-access
+                256
+            )
             training_args_kwargs["torch_compile"] = self.cfg.torch_compile
             if self.cfg.torch_compile_backend:
                 training_args_kwargs["torch_compile_backend"] = (
@@ -426,8 +430,16 @@ class TrainerBuilderBase(abc.ABC):
             if self.cfg.torch_compile_mode:
                 training_args_kwargs["torch_compile_mode"] = self.cfg.torch_compile_mode
 
+    def _configure_accelerator_config(self, training_args_kwargs: dict):
+        if self.cfg.accelerator_config:
+            training_args_kwargs["accelerator_config"] = self.cfg.accelerator_config
+
     def _configure_gradient_checkpointing(self, training_args_kwargs: dict):
-        if self.cfg.gradient_checkpointing:
+        if self.cfg.activation_offloading is True:
+            # don't use the HF gradient checkpointing, manually wrap
+            training_args_kwargs["gradient_checkpointing"] = False
+            training_args_kwargs["activation_offloading"] = True
+        elif self.cfg.gradient_checkpointing:
             training_args_kwargs["gradient_checkpointing"] = (
                 self.cfg.gradient_checkpointing
             )
@@ -510,5 +522,6 @@ class TrainerBuilderBase(abc.ABC):
         self._configure_scheduler(training_args_kwargs)
         self._configure_optimizer(training_args_kwargs, trainer_kwargs)
         self._configure_torch_compile(training_args_kwargs)
+        self._configure_accelerator_config(training_args_kwargs)
 
         return training_args_kwargs, trainer_kwargs

src/axolotl/core/builders/causal.py

@@ -310,11 +310,6 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
                 self.cfg.neftune_noise_alpha
             )
 
-        if self.cfg.accelerator_config:
-            training_arguments_kwargs["accelerator_config"] = (
-                self.cfg.accelerator_config
-            )
-
         if self.cfg.image_size:
             training_arguments_kwargs["image_size"] = self.cfg.image_size
         if self.cfg.image_resize_algorithm:

src/axolotl/core/trainers/base.py

@@ -25,6 +25,7 @@ from trl.trainer.utils import pad_to_length
 from typing_extensions import override
 
 from axolotl.core.trainers.mixins import (
+    ActivationOffloadingMixin,
     CheckpointSaveMixin,
     OptimizerMixin,
     PackingMixin,
@@ -48,6 +49,7 @@ class AxolotlTrainer(
     OptimizerMixin,
     RngLoaderMixin,
     CheckpointSaveMixin,
+    ActivationOffloadingMixin,
     Trainer,
 ):
     """Extend the base Trainer for axolotl helpers"""
@@ -75,18 +77,6 @@ class AxolotlTrainer(
         if self.args.orpo_alpha:
             self.loss_fct = torch.nn.CrossEntropyLoss(reduction="none")
 
-    def _wrap_model(self, model, training=True, dataloader=None):
-        if self.args.torch_compile:
-            torch._dynamo.config.accumulated_cache_size_limit = (  # pylint: disable=protected-access
-                256
-            )
-            model = torch.compile(
-                model,
-                backend=self.args.torch_compile_backend,
-                mode=self.args.torch_compile_mode,
-            )
-        return super()._wrap_model(model, training=training, dataloader=dataloader)
-
     def _create_multipack_sampler(
         self, base_sampler: Sampler, dataset: Dataset
     ) -> MultipackBatchSampler:

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

@@ -14,6 +14,7 @@ from axolotl.core.trainers.grpo.trainer import (
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.logging import get_logger
 from axolotl.utils.schemas.trl import TRLConfig
+from axolotl.utils.schemas.vllm import VllmConfig
 
 LOG = get_logger(__name__)
 
@@ -41,9 +42,18 @@ class GRPOStrategy:
             return grpo_args_kwargs
 
         trl: TRLConfig = cfg.trl  # type: ignore
+        vllm_cfg: VllmConfig = cfg.vllm  # type: ignore
 
         if trl.use_vllm:
             grpo_args_kwargs["use_vllm"] = trl.use_vllm
+            grpo_args_kwargs["vllm_mode"] = trl.vllm_mode
+            if trl.vllm_mode == "colocate":
+                grpo_args_kwargs["vllm_gpu_memory_utilization"] = (
+                    vllm_cfg.gpu_memory_utilization
+                )
+                grpo_args_kwargs["vllm_tensor_parallel_size"] = (
+                    vllm_cfg.tensor_parallel_size
+                )
             grpo_args_kwargs["vllm_server_host"] = trl.vllm_server_host or trl.vllm.host  # type: ignore[attr-defined]
             grpo_args_kwargs["vllm_server_port"] = trl.vllm_server_port or trl.vllm.port  # type: ignore[attr-defined]
             if trl.vllm_server_timeout:

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

@@ -59,42 +59,6 @@ class AxolotlGRPOTrainer(
 
     _tag_names = ["trl", "grpo", "axolotl"]
 
-    def get_train_dataloader(self):
-        if self.train_dataset is None:
-            raise ValueError("Trainer: training requires a train_dataset.")
-
-        train_dataset = self.train_dataset
-        data_collator = self.data_collator
-        if isinstance(train_dataset, datasets.Dataset):
-            train_dataset = self._remove_unused_columns(
-                train_dataset, description="training"
-            )
-        else:
-            data_collator = self._get_collator_with_removed_columns(
-                data_collator, description="training"
-            )
-
-        dataloader_params = {
-            "batch_size": self._train_batch_size
-            * self.args.steps_per_generation,  # < this is the change
-            "collate_fn": data_collator,
-            "num_workers": self.args.dataloader_num_workers,
-            "pin_memory": self.args.dataloader_pin_memory,
-            "persistent_workers": self.args.dataloader_persistent_workers,
-        }
-
-        if not isinstance(train_dataset, torch.utils.data.IterableDataset):
-            dataloader_params["sampler"] = self._get_train_sampler()
-            dataloader_params["drop_last"] = self.args.dataloader_drop_last
-            dataloader_params["worker_init_fn"] = partial(
-                seed_worker,
-                num_workers=self.args.dataloader_num_workers,
-                rank=self.args.process_index,
-            )
-            dataloader_params["prefetch_factor"] = self.args.dataloader_prefetch_factor
-
-        return self.accelerator.prepare(DataLoader(train_dataset, **dataloader_params))
-
 
 class AxolotlGRPOSequenceParallelTrainer(AxolotlGRPOTrainer):
     """Extend the base GRPOTrainer for sequence parallelism handling"""
@@ -252,7 +216,11 @@ class AxolotlGRPOSequenceParallelTrainer(AxolotlGRPOTrainer):
                 dataloader_params["drop_last"] = self.args.dataloader_drop_last
 
             if not is_eval:
-                dataloader_params["worker_init_fn"] = seed_worker
+                dataloader_params["worker_init_fn"] = partial(
+                    seed_worker,
+                    num_workers=self.args.dataloader_num_workers,
+                    rank=self.args.process_index,
+                )
 
         # Create the dataloader
         dataloader = DataLoader(dataset, **dataloader_params)

src/axolotl/core/trainers/mixins/__init__.py

@@ -3,6 +3,7 @@
 # pylint: disable=unused-import
 # flake8: noqa
 
+from .activation_checkpointing import ActivationOffloadingMixin
 from .checkpoints import CheckpointSaveMixin
 from .optimizer import OptimizerMixin
 from .packing import PackingMixin

src/axolotl/core/trainers/mixins/activation_checkpointing.py

@@ -0,0 +1,37 @@
+"""
+Trainer mixin for activation checkpointing w offloading
+"""
+
+import contextlib
+
+from torch import nn
+from torch.distributed.algorithms._checkpoint.checkpoint_wrapper import (
+    apply_activation_checkpointing,
+)
+from torch.distributed.fsdp.wrap import ModuleWrapPolicy
+from transformers import GradientCheckpointingLayer, Trainer
+from trl.models.activation_offloading import get_act_offloading_ctx_manager
+
+
+class ActivationOffloadingMixin(Trainer):
+    """
+    Trainer mixin class for activation checkpointing w offloading
+    """
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        if self.args.activation_offloading:
+            self.activation_offload_context = get_act_offloading_ctx_manager(
+                self.model, use_streams=True
+            )
+        else:
+            self.activation_offload_context = contextlib.nullcontext()
+
+    def training_step(self, *args, **kwargs):
+        with self.activation_offload_context:
+            return super().training_step(*args, **kwargs)
+
+
+def ac_wrap_hf_model(model: nn.Module, **kwargs):
+    auto_wrap_policy = ModuleWrapPolicy(set((GradientCheckpointingLayer,)))
+    apply_activation_checkpointing(model, auto_wrap_policy=auto_wrap_policy, **kwargs)

src/axolotl/core/training_args_base.py

@@ -217,6 +217,11 @@ class AxolotlTrainingMixins:
         },
     )
 
+    activation_offloading: bool | None = field(
+        default=None,
+        metadata={"help": "Use activation offloading with CUDA streams for training."},
+    )
+
     # multi-modal section
 
     image_size: int | tuple[int, int] | None = field(

src/axolotl/integrations/kd/kernels/models.py

@@ -6,15 +6,21 @@ from typing import Optional, Union, Unpack
 
 import torch
 from transformers import Cache
-from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
 from transformers.modeling_outputs import CausalLMOutputWithPast
-from transformers.utils import LossKwargs
 
+try:
+    from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
+    from transformers.utils import LossKwargs
 
-class KwargsForCausalLM(FlashAttentionKwargs, LossKwargs):
-    """
-    placeholder kwargs for hf model classes
-    """
+    class TransformersKwargs(FlashAttentionKwargs, LossKwargs):
+        """
+        placeholder kwargs for hf model classes
+        """
+
+except ImportError:
+    from transformers.utils.generic import (  # type: ignore[no-redef]
+        TransformersKwargs,
+    )
 
 
 def kldiv_forward_llama_like(
@@ -33,7 +39,7 @@ def kldiv_forward_llama_like(
     output_hidden_states: Optional[bool] = None,
     cache_position: Optional[torch.LongTensor] = None,
     logits_to_keep: Union[int, torch.Tensor] = 0,  # pylint: disable=unused-argument
-    **kwargs: Unpack[KwargsForCausalLM],  # type: ignore[misc]
+    **kwargs: Unpack[TransformersKwargs],  # type: ignore[misc]
 ) -> CausalLMOutputWithPast:
     # pylint: disable=duplicate-code
     output_attentions = (

src/axolotl/loaders/model.py

@@ -198,12 +198,22 @@ class ModelLoader:
         ):
             self.model = self.model.merge_and_unload()
 
+        self._apply_activation_checkpointing()
         self._resize_token_embeddings()
         self._adjust_model_config()
         self._configure_embedding_dtypes()
         self._configure_qat()
         log_gpu_memory_usage(LOG, "Memory usage after model load", 0)
 
+    def _apply_activation_checkpointing(self):
+        if self.cfg.activation_offloading is True:
+            from axolotl.core.trainers.mixins.activation_checkpointing import (
+                ac_wrap_hf_model,
+            )
+
+            # ^^ importing this at the module level breaks plugins
+            ac_wrap_hf_model(self.model)
+
     def _resize_token_embeddings(self):
         """Resize token embeddings if needed."""
         embeddings_len = (

src/axolotl/loaders/patch_manager.py

@@ -7,7 +7,6 @@ import importlib.util
 from functools import cached_property
 
 import addict
-import torch
 import transformers
 from transformers import PretrainedConfig, PreTrainedModel
 
@@ -168,28 +167,19 @@ class PatchManager:
 
     def _apply_gradient_checkpointing_patches(self):
         """Apply patches for gradient checkpointing."""
-        if self.cfg.gradient_checkpointing in ["unsloth", "offload"]:
+        if (
+            self.cfg.gradient_checkpointing
+            and self.cfg.activation_offloading == "legacy"
+        ):
             from axolotl.monkeypatch.gradient_checkpointing import (
-                CheckpointFunctionWithCPUOffload,
                 hf_grad_checkpoint_offload_wrapper,
             )
 
-            if (
-                self.cfg.gradient_checkpointing_kwargs
-                and "use_reentrant" in self.cfg.gradient_checkpointing_kwargs
-                and self.cfg.gradient_checkpointing_kwargs["use_reentrant"] is False
-            ):
-                transformers.modeling_utils.checkpoint = (
-                    hf_grad_checkpoint_offload_wrapper
-                )
-            else:
-                transformers.modeling_utils.checkpoint.CheckpointFunction = (
-                    CheckpointFunctionWithCPUOffload
-                )
-                torch.utils.checkpoint.CheckpointFunction = (
-                    CheckpointFunctionWithCPUOffload
-                )
-        if self.cfg.gradient_checkpointing == "offload_disk":
+            transformers.modeling_utils.checkpoint = hf_grad_checkpoint_offload_wrapper
+        elif (
+            self.cfg.gradient_checkpointing
+            and self.cfg.activation_offloading == "offload_disk"
+        ):
             from axolotl.monkeypatch.gradient_checkpointing import (
                 hf_grad_checkpoint_disk_offload_wrapper,
             )

src/axolotl/monkeypatch/gradient_checkpointing/__init__.py

@@ -6,7 +6,6 @@ from functools import partial
 from packaging import version
 
 from axolotl.monkeypatch.gradient_checkpointing.offload_cpu import (  # noqa: F401
-    CheckpointFunctionWithCPUOffload,
     CPU_Offloaded_Gradient_Checkpointer,
 )
 from axolotl.monkeypatch.gradient_checkpointing.offload_disk import (

src/axolotl/monkeypatch/gradient_checkpointing/offload_cpu.py

@@ -14,18 +14,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import contextlib
 import inspect
 
 import torch
 from packaging import version
 from torch.utils.checkpoint import (
-    _get_autocast_kwargs,
-    _get_device_module,
-    _infer_device_type,
-    check_backward_validity,
-    detach_variable,
-    get_device_states,
     set_device_states,
 )
 
@@ -76,153 +69,3 @@ class CPU_Offloaded_Gradient_Checkpointer(  # pylint: disable=invalid-name
         ) + (
             None,
         ) * len(ctx.args)
-
-
-# Copyright 2025 Snowflake Inc.
-# SPDX-License-Identifier: Apache-2.0
-# https://github.com/snowflakedb/ArcticTraining/blob/main/arctic_training/monkey_patches.py
-class CheckpointFunctionWithCPUOffload(torch.autograd.Function):
-    """
-    This is a torch/utils/checkpoint.py CheckpointFunction monkey patch that offloads the first tensor to cpu during forward and back to cuda during backward. This allows significant memory savings when using a very long seqlen. e.g. for llama 8b at 100k it's 24GB saved per gpu: `((100_000*4096)*2*32/2**30)`
-    In the case of a very long seqlen 100k+ the copying to/from cpu overhead is not big, because dense quadratic attention compute will dominate.
-    """
-
-    @staticmethod
-    def forward(ctx, run_function, preserve_rng_state, *args):
-        check_backward_validity(args)
-        ctx.run_function = run_function
-        ctx.preserve_rng_state = preserve_rng_state
-        # Accommodates the (remote) possibility that autocast is enabled for cpu AND gpu.
-        ctx.device_type = _infer_device_type(*args)
-        ctx.device_autocast_kwargs, ctx.cpu_autocast_kwargs = _get_autocast_kwargs(
-            ctx.device_type
-        )
-        if preserve_rng_state:
-            ctx.fwd_cpu_state = torch.get_rng_state()
-            # Don't eagerly initialize the cuda context by accident.
-            # (If the user intends that the context is initialized later, within their
-            # run_function, we SHOULD actually stash the cuda state here.  Unfortunately,
-            # we have no way to anticipate this will happen before we run the function.)
-            ctx.had_device_in_fwd = False
-            device_module = _get_device_module(ctx.device_type)
-            if getattr(device_module, "_initialized", False):
-                ctx.had_device_in_fwd = True
-                ctx.fwd_devices, ctx.fwd_device_states = get_device_states(*args)
-
-        # Save non-tensor inputs in ctx, keep a placeholder None for tensors
-        # to be filled out during the backward.
-        ctx.inputs = []
-        ctx.tensor_indices = []
-        tensor_inputs = []
-        # x = None
-        for i, arg in enumerate(args):
-            if torch.is_tensor(arg):
-                # cpu-offload
-                # we don't want the 2nd tensor - usually it's a shared 4D attn mask which is huge [seq,seq]
-                # upstream could accept a list of arg indices to offload
-                if i == 0:
-                    # print(f"{arg.shape=}")
-                    ctx.x_device = arg.device
-                    ctx.x_requires_grad = arg.requires_grad
-                    t = arg.detach().cpu()
-                else:
-                    t = arg
-                tensor_inputs.append(t)
-                ctx.tensor_indices.append(i)
-                ctx.inputs.append(None)
-            else:
-                ctx.inputs.append(arg)
-
-        ctx.save_for_backward(*tensor_inputs)
-
-        with torch.no_grad():
-            outputs = run_function(*args)
-
-        return outputs
-
-    @staticmethod
-    def backward(ctx, *args):
-        if (
-            not torch.autograd._is_checkpoint_valid()  # pylint: disable=protected-access
-        ):
-            raise RuntimeError(
-                "When use_reentrant=True, torch.utils.checkpoint is incompatible"
-                " with .grad() or passing an `inputs` parameter to .backward()."
-                " To resolve this error, you can either set use_reentrant=False,"
-                " or call .backward() without passing the `inputs` argument."
-            )
-        # Copy the list to avoid modifying original list.
-        inputs = list(ctx.inputs)
-        tensor_indices = ctx.tensor_indices
-        tensors = ctx.saved_tensors
-
-        # Fill in inputs with appropriate saved tensors.
-        for i, idx in enumerate(tensor_indices):
-            if i == 0:
-                t = (
-                    tensors[i]
-                    .to(ctx.x_device)
-                    .detach()
-                    .requires_grad_(ctx.x_requires_grad)
-                )
-            else:
-                t = tensors[i]
-            inputs[idx] = t
-
-        # Stash the surrounding rng state, and mimic the state that was
-        # present at this time during forward.  Restore the surrounding state
-        # when we're done.
-        rng_devices = []
-        if ctx.preserve_rng_state and ctx.had_device_in_fwd:
-            rng_devices = ctx.fwd_devices
-        with torch.random.fork_rng(
-            devices=rng_devices,
-            enabled=ctx.preserve_rng_state,
-            device_type=ctx.device_type,
-        ):
-            if ctx.preserve_rng_state:
-                torch.set_rng_state(ctx.fwd_cpu_state)
-                if ctx.had_device_in_fwd:
-                    if has_device_type:
-                        # newer pytorch (as early as 2.7)
-                        set_device_states(
-                            ctx.fwd_devices,
-                            ctx.fwd_device_states,
-                            device_type=ctx.device_type,
-                        )
-                    else:
-                        # older pytorch (at least 2.4)
-                        set_device_states(ctx.fwd_devices, ctx.fwd_device_states)
-            detached_inputs = detach_variable(tuple(inputs))
-
-            device_autocast_ctx = (
-                torch.amp.autocast(
-                    device_type=ctx.device_type, **ctx.device_autocast_kwargs
-                )
-                if torch.amp.is_autocast_available(ctx.device_type)
-                else contextlib.nullcontext()
-            )
-            with torch.enable_grad(), device_autocast_ctx, torch.amp.autocast("cpu", **ctx.cpu_autocast_kwargs):  # type: ignore[attr-defined]
-                outputs = ctx.run_function(*detached_inputs)
-
-        if isinstance(outputs, torch.Tensor):
-            outputs = (outputs,)
-
-        # run backward() with only tensor that requires grad
-        outputs_with_grad = []
-        args_with_grad = []
-        for i in range(len(outputs)):  # pylint: disable=consider-using-enumerate
-            if torch.is_tensor(outputs[i]) and outputs[i].requires_grad:
-                outputs_with_grad.append(outputs[i])
-                args_with_grad.append(args[i])
-        if len(outputs_with_grad) == 0:
-            raise RuntimeError(
-                "none of output has requires_grad=True, this checkpoint() is not necessary"
-            )
-        torch.autograd.backward(outputs_with_grad, args_with_grad)
-        grads = tuple(
-            inp.grad if isinstance(inp, torch.Tensor) else None
-            for inp in detached_inputs
-        )
-
-        return (None, None) + grads

src/axolotl/prompt_strategies/chat_template.py

@@ -379,6 +379,22 @@ class ChatTemplateStrategy(PromptTokenizingStrategy):
         Public method that can handle either a single prompt or a batch of prompts.
         """
 
+        def _remove_none_values(obj):
+            """
+            Remove null from a dictionary-like obj or list.
+            These can appear due to Dataset loading causing schema merge.
+            See https://github.com/axolotl-ai-cloud/axolotl/pull/2909
+            """
+            if hasattr(obj, "items"):
+                return {
+                    k: _remove_none_values(v) for k, v in obj.items() if v is not None
+                }
+            if isinstance(obj, list):
+                return [_remove_none_values(elem) for elem in obj]
+            return obj
+
+        prompt = _remove_none_values(prompt)
+
         if not self.is_prompt_batched(prompt) or not self.supports_batched:
             return self._tokenize_single_prompt(prompt)
 

src/axolotl/train.py

@@ -224,6 +224,9 @@ def execute_training(
         #     torch.set_default_dtype(torch.bfloat16)
         trainer.train(resume_from_checkpoint=resume_from_checkpoint)
 
+        plugin_manager = PluginManager.get_instance()
+        plugin_manager.post_train(cfg, trainer.model)
+
 
 def save_trained_model(
     cfg: DictDefault,
@@ -510,6 +513,9 @@ def setup_model_and_trainer(cfg: DictDefault, dataset_meta: TrainDatasetMeta) ->
         peft_config=peft_config,
     )
 
+    plugin_manager = PluginManager.get_instance()
+    plugin_manager.post_trainer_create(cfg, trainer)
+
     return (
         trainer,
         model,
@@ -541,9 +547,6 @@ def train(
         processor,
     ) = setup_model_and_trainer(cfg, dataset_meta)
 
-    plugin_manager = PluginManager.get_instance()
-    plugin_manager.post_trainer_create(cfg, trainer)
-
     # Handle untrained tokens if configured
     safe_serialization = cfg.save_safetensors is True
     train_dataset = dataset_meta.train_dataset
@@ -566,6 +569,4 @@ def train(
     if not cfg.use_ray:
         cleanup_distributed()
 
-    plugin_manager.post_train(cfg, model)
-
     return model, tokenizer, trainer

src/axolotl/utils/callbacks/__init__.py

@@ -841,21 +841,35 @@ class SaveAxolotlConfigtoWandBCallback(TrainerCallback):
 class GCCallback(TrainerCallback):
     """Callback to garbage collect torch cache"""
 
-    def __init__(self, gc_steps=None):
-        self.gc_steps = gc_steps
+    def __init__(self, gc_steps: int | None = -1):
+        self.gc_steps: int = gc_steps or -1
+        self.next_gc_on_begin_step: int = -1
+
+    def _gc(self):
+        torch.cuda.empty_cache()
+        gc.collect()
+
+    def on_step_begin(
+        self, args, state, control, **kwargs  # pylint: disable=unused-argument
+    ):
+        if self.next_gc_on_begin_step == state.global_step:
+            self._gc()
 
     def on_step_end(
         self, args, state, control, **kwargs  # pylint: disable=unused-argument
     ):
-        if self.gc_steps > 0 and state.global_step % self.gc_steps == 0:
-            torch.cuda.empty_cache()
-            gc.collect()
+        if control.should_evaluate:
+            # automatically GC before evals so the eval memory spike from the CEL doesn't OOM the trainer
+            self._gc()
+            # also GC on the start of the next step after the eval
+            self.next_gc_on_begin_step = state.global_step + 1
+        elif self.gc_steps > 0 and state.global_step % self.gc_steps == 0:
+            self._gc()
 
     def on_epoch_end(
         self, args, state, control, **kwargs  # pylint: disable=unused-argument
     ):
-        torch.cuda.empty_cache()
-        gc.collect()
+        self._gc()
 
 
 def colab_inference_post_train_callback(trainer: Trainer):

src/axolotl/utils/callbacks/profiler.py

@@ -19,9 +19,27 @@ class PytorchProfilerCallback(TrainerCallback):
     PyTorch Profiler callback to create snapshots of GPU memory usage at specified steps.
     """
 
-    def __init__(self, steps_to_profile: int = 5):
-        self.steps_to_profile = steps_to_profile
-        if self.steps_to_profile:
+    def __init__(self, steps_to_profile: int = 5, profiler_steps_start: int = 0):
+        # steps are 0 indexed, so to start at 0-th step, we start at beginning of first step,
+        # and finish at end of last step, so 5 steps_to_profile is steps [0, 1, 2, 3, 4]
+        self.profiler_steps_end = profiler_steps_start + steps_to_profile - 1
+        if profiler_steps_start == 0:
+            # start recording memory allocations before everything is allocated, because if we start
+            # at the beginning of step 0, we won't have any memory allocations in the traces
+            torch.cuda.memory._record_memory_history(  # pylint: disable=protected-access
+                enabled="all"
+            )
+            profiler_steps_start = -1
+        self.profiler_steps_start = profiler_steps_start
+
+    def on_step_begin(  # pylint: disable=unused-argument
+        self,
+        args: TrainingArguments,  # pylint: disable=unused-argument
+        state: TrainerState,
+        control: TrainerControl,  # pylint: disable=unused-argument
+        **kwargs,  # pylint: disable=unused-argument
+    ):
+        if state.global_step == self.profiler_steps_start:
             torch.cuda.memory._record_memory_history(  # pylint: disable=protected-access
                 enabled="all"
             )
@@ -33,7 +51,28 @@ class PytorchProfilerCallback(TrainerCallback):
         control: TrainerControl,  # pylint: disable=unused-argument
         **kwargs,  # pylint: disable=unused-argument
     ):
-        if state.global_step == self.steps_to_profile:
+        if state.global_step == self.profiler_steps_end:
+            snapshot = torch.cuda.memory._snapshot()  # pylint: disable=protected-access
+            with open(Path(args.output_dir) / "snapshot.pickle", "wb") as fout:
+                dump(snapshot, fout)
+
+            # tell CUDA to stop recording memory allocations now
+            torch.cuda.memory._record_memory_history(  # pylint: disable=protected-access
+                enabled=None
+            )
+
+    def on_train_end(  # pylint: disable=unused-argument
+        self,
+        args: TrainingArguments,  # pylint: disable=unused-argument
+        state: TrainerState,
+        control: TrainerControl,  # pylint: disable=unused-argument
+        **kwargs,  # pylint: disable=unused-argument
+    ):
+        # make sure to record if we happen to have more steps than steps to profile
+        if (
+            state.global_step >= self.profiler_steps_start
+            and state.global_step < self.profiler_steps_end
+        ):
             snapshot = torch.cuda.memory._snapshot()  # pylint: disable=protected-access
             with open(Path(args.output_dir) / "snapshot.pickle", "wb") as fout:
                 dump(snapshot, fout)

src/axolotl/utils/config/__init__.py

@@ -314,16 +314,3 @@ def prepare_plugins(cfg):
         plugin_manager = PluginManager.get_instance()
         for plugin_name in cfg["plugins"]:
             plugin_manager.register(plugin_name)
-
-
-# TODO @SalmanMohammadi remove this function in 0.12
-def migrate_fsdp_config(cfg):
-    if cfg.get("fsdp_config"):
-        fsdp_config_keys = cfg.fsdp_config.keys()
-        if "fsdp_version" in fsdp_config_keys:
-            cfg.fsdp_version = cfg.fsdp_config.pop("fsdp_version")
-
-        for key in list(fsdp_config_keys):
-            if key.startswith("fsdp_") and key != "fsdp_version":
-                cfg.fsdp_config[key.replace("fsdp_", "")] = cfg.fsdp_config[key]
-                del cfg.fsdp_config[key]

src/axolotl/utils/mistral_tokenizer.py

@@ -497,3 +497,131 @@ class HFMistralTokenizer:
         return [
             self._mistral.instruct_tokenizer.tokenizer.id_to_piece(id) for id in ids
         ]
+
+    def __call__(
+        self,
+        text: str | list[str],
+        add_special_tokens: bool = True,
+        padding: bool | str = False,
+        truncation: bool = False,
+        max_length: int | None = None,
+        return_tensors: str | None = None,
+        **kwargs,
+    ) -> dict[str, list[int] | np.ndarray | Tensor]:
+        """
+        Tokenize text and return a dictionary with input_ids and attention_mask.
+
+        Args:
+            text: Input text string or list of strings to tokenize.
+            add_special_tokens: Whether to add special tokens (BOS/EOS).
+            padding: Whether to pad sequences. Can be True, False, "longest", or "max_length".
+            truncation: Whether to truncate sequences to max_length.
+            max_length: Maximum sequence length for truncation/padding.
+            return_tensors: Return format ("pt" for PyTorch, "np" for NumPy, None for lists).
+
+        Returns:
+            Dictionary with "input_ids" and "attention_mask" keys.
+        """
+        # if kwargs passed, raise error
+        if kwargs:
+            raise ValueError(
+                f"Unsupported kwargs: {kwargs}. Please create an issue on GitHub."
+            )
+
+        # `np` can work with inhomogeneous shapes but let's not support it until needed.
+        if (
+            isinstance(text, list)
+            and len(text) > 1
+            and return_tensors in ("pt", "np")
+            and padding is False
+            and truncation is False
+        ):
+            raise ValueError(
+                "return_tensors='pt' or 'np' requires padding or truncation."
+            )
+
+        # Handle single string input
+        if isinstance(text, str):
+            text = [text]
+
+        # Encode all texts
+        # TODO: figure out how to parallelize this
+        batch_input_ids = []
+        for single_text in text:
+            input_ids = self.encode(single_text, add_special_tokens=add_special_tokens)
+
+            # Handle truncation
+            if truncation and max_length is not None and len(input_ids) > max_length:
+                input_ids = input_ids[:max_length]
+
+            batch_input_ids.append(input_ids)
+
+        # Create attention masks (1 for real tokens, 0 for padding)
+        attention_masks = [[1] * len(input_ids) for input_ids in batch_input_ids]
+
+        # Handle padding
+        if padding in (True, "longest"):
+            # Pad to longest sequence in batch
+            max_len = max(len(input_ids) for input_ids in batch_input_ids)
+
+            for i, input_ids in enumerate(batch_input_ids):
+                pad_length = max_len - len(input_ids)
+                if pad_length > 0:
+                    if self.padding_side == "right":
+                        batch_input_ids[i] = (
+                            input_ids + [self.pad_token_id] * pad_length
+                        )
+                        attention_masks[i] = attention_masks[i] + [0] * pad_length
+                    else:  # left padding
+                        batch_input_ids[i] = [
+                            self.pad_token_id
+                        ] * pad_length + input_ids
+                        attention_masks[i] = [0] * pad_length + attention_masks[i]
+
+        elif padding == "max_length":
+            if max_length is None:
+                raise ValueError(
+                    "max_length must be specified when padding='max_length'"
+                )
+
+            for i, input_ids in enumerate(batch_input_ids):
+                pad_length = max_length - len(input_ids)
+                if pad_length > 0:
+                    if self.padding_side == "right":
+                        batch_input_ids[i] = (
+                            input_ids + [self.pad_token_id] * pad_length
+                        )
+                        attention_masks[i] = attention_masks[i] + [0] * pad_length
+                    else:  # left padding
+                        batch_input_ids[i] = [
+                            self.pad_token_id
+                        ] * pad_length + input_ids
+                        attention_masks[i] = [0] * pad_length + attention_masks[i]
+
+        # Prepare result
+        result = {}
+
+        # Handle return tensor format
+        if return_tensors == "pt":
+            import torch
+
+            result["input_ids"] = torch.tensor(batch_input_ids, dtype=torch.long)
+            result["attention_mask"] = torch.tensor(attention_masks, dtype=torch.long)
+        elif return_tensors == "np":
+            result["input_ids"] = np.array(batch_input_ids, dtype=np.int64)
+            result["attention_mask"] = np.array(attention_masks, dtype=np.int64)
+        elif return_tensors is None:
+            result["input_ids"] = batch_input_ids
+            result["attention_mask"] = attention_masks
+        else:
+            raise ValueError(
+                f"Unsupported return_tensors='{return_tensors}'. "
+                "Only 'pt' and 'np' are supported."
+            )
+
+        # If single input, return single sequences (not batched)
+        if len(text) == 1 and return_tensors is None:
+            result["input_ids"] = result["input_ids"][0]
+            result["attention_mask"] = result["attention_mask"][0]
+
+        return result

src/axolotl/utils/schemas/config.py

@@ -320,7 +320,12 @@ class AxolotlInputConfig(
         },
     )
 
-    gc_steps: int | None = None
+    gc_steps: int | None = Field(
+        default=None,
+        json_schema_extra={
+            "description": "Run garbage collection every `gc_steps` steps. -1 will run on epoch end and before evaluations. Default is 0 (disabled)."
+        },
+    )
 
     bf16: Literal["auto"] | bool | None = Field(
         default="auto",
@@ -360,6 +365,12 @@ class AxolotlInputConfig(
             "description": "Additional kwargs to pass to the trainer for gradient checkpointing"
         },
     )
+    activation_offloading: Literal["legacy", "disk"] | bool | None = Field(
+        default=False,
+        json_schema_extra={
+            "description": "Whether to offload activations. Available options are: true, false, 'legacy', 'disk'."
+        },
+    )
 
     unfrozen_parameters: list[str] | None = None
 
@@ -573,6 +584,12 @@ class AxolotlInputConfig(
             "description": "Deepspeed config path. e.g., deepspeed_configs/zero3.json"
         },
     )
+    deepcompile: bool | None = Field(
+        default=None,
+        json_schema_extra={
+            "description": "Whether to use deepcompile for faster training with deepspeed"
+        },
+    )
     fsdp: list[str] | None = Field(
         default=None,
         json_schema_extra={"description": "FSDP configuration"},
@@ -618,7 +635,12 @@ class AxolotlInputConfig(
             "description": "One of 'varlen_llama3', 'batch_ring', 'batch_zigzag', 'batch_stripe'. Defaults to 'varlen_llama3' in the sample packing case, and 'batch_ring' in the non-sample packing case."
         },
     )
-
+    tensor_parallel_size: int | None = Field(
+        default=None,
+        json_schema_extra={
+            "description": "Number of tensor parallel processes in TP group. Only supported with DeepSpeed AutoTP."
+        },
+    )
     special_tokens: SpecialTokensConfig | None = Field(
         default=None,
         json_schema_extra={
@@ -730,6 +752,12 @@ class AxolotlInputConfig(
             "description": "Enable the pytorch profiler to capture the first N steps of training to the output_dir. see https://pytorch.org/blog/understanding-gpu-memory-1/ for more information. Snapshots can be visualized @ https://pytorch.org/memory_viz"
         },
     )
+    profiler_steps_start: int | None = Field(
+        default=0,
+        json_schema_extra={
+            "description": "Which step to start the profiler at. Useful for only capturing a few steps mid-run."
+        },
+    )
     include_tokens_per_second: bool | None = Field(
         default=None,
         json_schema_extra={
@@ -1143,72 +1171,6 @@ class AxolotlConfigWCapabilities(AxolotlInputConfig):
 
         return data
 
-    @model_validator(mode="before")
-    @classmethod
-    def check_fsdp_version(cls, data):
-        fsdp_config = data.get("fsdp_config", {})
-        if fsdp_config and str(data.get("fsdp_version")) != "2":
-            LOG.info(
-                "FSDP1 will be deprecated in an upcoming release of Axolotl."
-                "We recommend that you use FSDP version 2 for better performance and compatibility. "
-                "Please see this link for more details: https://docs.axolotl.ai/docs/multi-gpu.html#sec-fsdp "
-                "For more details on migrating your config. "
-            )
-        return data
-
-    @model_validator(mode="before")
-    @classmethod
-    def check_fsdp2_base_model_quant_ram_efficient_loading(cls, data):
-        fsdp_config = data.get("fsdp_config")
-        if fsdp_config and data.get("fsdp_version") == 2:
-            if fsdp_config.get("cpu_ram_efficient_loading") and (
-                data.get("load_in_8bit") or data.get("load_in_4bit")
-            ):
-                raise ValueError(
-                    "FSDP2 does not support load_in_8bit or load_in_4bit with cpu_ram_efficient_loading. Please do one of the following: use DeepSpeed, "
-                    "set fsdp_version to 1, or disable cpu_ram_efficient_loading."
-                )
-        return data
-
-    @model_validator(mode="before")
-    @classmethod
-    def check_fsdp2_base_model_quant_dpo(cls, data):
-        if data.get("fsdp_version") == 2 and data.get("rl") in [
-            RLType.DPO,
-            RLType.KTO,
-            RLType.ORPO,
-            RLType.IPO,
-        ]:
-            if data.get("load_in_8bit") or data.get("load_in_4bit"):
-                raise ValueError(
-                    "FSDP2 does not support load_in_8bit or load_in_4bit with DPO. Please use DeepSpeed or set `fsdp_version` to 1."
-                )
-
-        return data
-
-    @model_validator(mode="before")
-    @classmethod
-    def check_fsdp_version_in_fsdp_config(cls, data):
-        if fsdp_config := data.get("fsdp_config"):
-            if fsdp_config.get("fsdp_version"):
-                LOG.warning(
-                    "Configuring `fsdp_version` in `fsdp_config` is deprecated. "
-                    "Please configure `fsdp_version` as a top-level field."
-                )
-        return data
-
-    @model_validator(mode="before")
-    @classmethod
-    def check_fsdp_config_kwargs_prefix(cls, data):
-        if fsdp_config := data.get("fsdp_config"):
-            for key, _ in fsdp_config.items():
-                if key.startswith("fsdp_"):
-                    LOG.warning_once(
-                        "Configuring FSDP fields with the `fsdp_` prefix is deprecated. "
-                        "Please omit the `fsdp_` prefix from the any fields in `fsdp_config`."
-                    )
-        return data
-
     @model_validator(mode="before")
     @classmethod
     def default_dataloader_opts(cls, data):

src/axolotl/utils/schemas/trl.py

@@ -1,5 +1,7 @@
 """Pydantic models for TRL trainer configuration"""
 
+from typing import Literal
+
 from pydantic import BaseModel, Field
 
 
@@ -27,6 +29,12 @@ class TRLConfig(BaseModel):
         default=False,
         json_schema_extra={"description": "Whether to use VLLM for RL training."},
     )
+    vllm_mode: Literal["server", "colocate"] | None = Field(
+        default=None,
+        json_schema_extra={
+            "description": "VLLM mode to use, one of 'server' or 'colocate'"
+        },
+    )
     vllm_server_host: str | None = Field(
         default="0.0.0.0",  # nosec B104
         json_schema_extra={"description": "Host of the vLLM server to connect to."},

src/axolotl/utils/schemas/validation.py

@@ -1,8 +1,11 @@
 """Module with validation methods for config pydantic model."""
 
-# pylint: disable=too-many-lines
+# pylint: disable=too-many-boolean-expressions
 
+import json
 import logging
+import tempfile
+from pathlib import Path
 
 from pydantic import (
     field_validator,
@@ -12,6 +15,8 @@ from transformers.utils.import_utils import is_torch_npu_available
 
 from axolotl.utils.schemas.enums import ChatTemplate, RingAttnFunc, RLType
 
+# pylint: disable=too-many-lines
+
 LOG = logging.getLogger(__name__)
 
 SUPPORTED_METRICS = {"sacrebleu", "comet", "ter", "chrf", "perplexity"}
@@ -748,43 +753,181 @@ class OptimizationValidationMixin:
 
     @model_validator(mode="before")
     @classmethod
-    def check_fsdp_offload_w_8bit_optimizer(cls, data):
-        if (
-            data.get("fsdp")
-            and "8bit" in data.get("optimizer", "")
-            and data.get("fsdp_config")
-            and data["fsdp_config"].get("fsdp_offload_params")
-            and str(data["fsdp_config"].get("fsdp_version")) != "2"
-        ):
-            raise ValueError(
-                f"FSDP Offload not compatible with {data.get('optimizer')}"
+    def check_fsdp_version(cls, data):
+        fsdp_config = data.get("fsdp_config", {})
+        if fsdp_config and str(data.get("fsdp_version")) != "2":
+            LOG.info(
+                "FSDP1 will be deprecated in an upcoming release of Axolotl."
+                "We recommend that you use FSDP version 2 for better performance and compatibility. "
+                "Please see this link for more details: https://docs.axolotl.ai/docs/multi-gpu.html#sec-fsdp "
+                "For more details on migrating your config. "
             )
-        if (
-            data.get("fsdp")
-            and "8bit" in data.get("optimizer", "")
-            and data.get("fsdp_config")
-            and str(data["fsdp_config"].get("fsdp_version")) == "2"
-        ):
-            if data.get("optimizer", "") in ["adamw_8bit", "adamw_bnb_8bit"]:
-                # CUDA ops errors with bnb 8bit optimizer + FSDP2
+        return data
+
+    @model_validator(mode="after")
+    def check_fsdp2_base_model_quant_ram_efficient_loading(self):
+        fsdp_config = self.fsdp_config if hasattr(self, "fsdp_config") else None
+        fsdp_version = self.fsdp_version if hasattr(self, "fsdp_version") else None
+        load_in_8bit = self.load_in_8bit if hasattr(self, "load_in_8bit") else None
+        load_in_4bit = self.load_in_4bit if hasattr(self, "load_in_4bit") else None
+        if fsdp_config and fsdp_version == 2:
+            if fsdp_config.get("cpu_ram_efficient_loading") and (
+                load_in_8bit or load_in_4bit
+            ):
                 raise ValueError(
-                    f"FSDP2 not compatible with {data.get('optimizer')}, use `adamw_torch_8bit` instead"
+                    "FSDP2 does not support load_in_8bit or load_in_4bit with cpu_ram_efficient_loading. Please do one of the following: use DeepSpeed, "
+                    "set fsdp_version to 1, or disable cpu_ram_efficient_loading."
+                )
+        return self
+
+    @model_validator(mode="before")
+    @classmethod
+    def check_fsdp2_base_model_quant_rl(cls, data):
+        if data.get("fsdp_version") == 2 and data.get("rl") in [
+            RLType.DPO,
+            RLType.KTO,
+            RLType.ORPO,
+            RLType.IPO,
+        ]:
+            if data.get("load_in_8bit") or data.get("load_in_4bit"):
+                raise ValueError(
+                    f"FSDP2 does not support load_in_8bit or load_in_4bit with {data.get('rl')}. Please use DeepSpeed or set `fsdp_version` to 1."
                 )
 
         return data
 
     @model_validator(mode="before")
     @classmethod
-    def check_fsdp_sharded_state_dict_w_safetensors(cls, data):
+    def check_fsdp_version_in_fsdp_config(cls, data):
+        if data.get("fsdp_config"):
+            if data.get("fsdp_config", {}).get("fsdp_version"):
+                LOG.warning(
+                    "Configuring `fsdp_version` in `fsdp_config` is deprecated. "
+                    "Please configure `fsdp_version` as a top-level field."
+                )
+                data["fsdp_version"] = data.get("fsdp_config").pop("fsdp_version")
+        return data
+
+    @model_validator(mode="before")
+    @classmethod
+    def check_fsdp_config_kwargs_prefix(cls, data):
+        if fsdp_config := data.get("fsdp_config"):
+            should_fix = False
+            for key, _ in fsdp_config.items():
+                if key.startswith("fsdp_"):
+                    should_fix = True
+                    LOG.warning_once(
+                        "Configuring FSDP fields with the `fsdp_` prefix is deprecated. "
+                        "Please omit the `fsdp_` prefix from the any fields in `fsdp_config`."
+                    )
+            if should_fix:
+                update_fsdp_config = {}
+                for key, value in fsdp_config.items():
+                    if key.startswith("fsdp_") and key != "fsdp_version":
+                        update_fsdp_config[key.replace("fsdp_", "")] = value
+                    else:
+                        update_fsdp_config[key] = value
+                data["fsdp_config"] = update_fsdp_config
+        return data
+
+    @model_validator(mode="after")
+    def check_fsdp_offload_w_8bit_optimizer(self):
         if (
-            data.get("fsdp_config")
-            and data.get("save_safetensors")
-            and data.get("fsdp_config")
-            and data["fsdp_config"].get("fsdp_state_dict_type") == "SHARDED_STATE_DICT"
+            hasattr(self, "fsdp_config")
+            and self.fsdp_config
+            and self.optimizer
+            and "8bit" in self.optimizer.value
+            and self.fsdp_config["offload_params"]
+            and str(self.fsdp_version) != "2"
+        ):
+            raise ValueError(
+                f"FSDP Offload not compatible with {str(self.optimizer.value)}"
+            )
+        return self
+
+    @model_validator(mode="after")
+    def check_fsdp2_w_8bit_optimizer(self):
+        if (
+            hasattr(self, "fsdp_config")
+            and self.fsdp_config
+            and self.optimizer
+            and "8bit" in self.optimizer.value
+            and str(self.fsdp_version) == "2"
+        ):
+            if self.optimizer in ["adamw_8bit", "adamw_bnb_8bit"]:
+                # CUDA ops errors with bnb 8bit optimizer + FSDP2
+                raise ValueError(
+                    f"FSDP2 not compatible with {self.optimizer.value}, use `adamw_torch_8bit` instead"
+                )
+
+        return self
+
+    @model_validator(mode="after")
+    def check_fsdp_sharded_state_dict_w_safetensors(self):
+        if (
+            hasattr(self, "fsdp_config")
+            and self.fsdp_config
+            and hasattr(self, "save_safetensors")
+            and self.save_safetensors
+            and self.fsdp_config.get("state_dict_type", "") == "SHARDED_STATE_DICT"
+            and str(getattr(self, "fsdp_version", "1")) != "2"
         ):
             raise ValueError(
                 "FSDP SHARDED_STATE_DICT not compatible with save_safetensors"
             )
+        return self
+
+    @model_validator(mode="before")
+    @classmethod
+    def check_tensor_parallel_size_update_ds_json(cls, data):
+        tensor_parallel_size = data.get("tensor_parallel_size")
+        if tensor_parallel_size is not None and tensor_parallel_size > 1:
+            if not data.get("deepspeed"):
+                raise ValueError(
+                    "Tensor parallelism (TP) is only supported with DeepSpeed"
+                )
+            with open(data.get("deepspeed"), "r", encoding="utf-8") as ds_fin:
+                ds_config = json.load(ds_fin)
+                should_save = False
+                if "tensor_parallel" not in ds_config:
+                    ds_config["tensor_parallel"] = {"autotp_size": tensor_parallel_size}
+                    should_save = True
+                if (
+                    "gather_16bit_weights_on_model_save"
+                    not in ds_config["zero_optimization"]
+                ):
+                    ds_config["zero_optimization"][
+                        "gather_16bit_weights_on_model_save"
+                    ] = True
+                    should_save = True
+                if should_save:
+                    temp_dir = tempfile.mkdtemp()
+                    with open(
+                        Path(temp_dir) / "autotp_ds.json", "w", encoding="utf-8"
+                    ) as ds_fout:
+                        json.dump(ds_config, ds_fout, indent=4)
+                    data["deepspeed"] = str(Path(temp_dir) / "autotp_ds.json")
+
+        return data
+
+    @model_validator(mode="before")
+    @classmethod
+    def check_deepcompile(cls, data):
+        deepcompile = data.get("deepcompile")
+        if deepcompile:
+            if not data.get("deepspeed"):
+                raise ValueError("DeepCompile is only supported with DeepSpeed")
+            with open(data.get("deepspeed"), "r", encoding="utf-8") as ds_fin:
+                ds_config = json.load(ds_fin)
+                if "compile" not in ds_config:
+                    ds_config["compile"] = {"deepcompile": True}
+                    temp_dir = tempfile.mkdtemp()
+                    with open(
+                        Path(temp_dir) / "deepcompile_ds.json", "w", encoding="utf-8"
+                    ) as ds_fout:
+                        json.dump(ds_config, ds_fout, indent=4)
+                    data["deepspeed"] = str(Path(temp_dir) / "deepcompile_ds.json")
+
         return data
 
 
@@ -932,6 +1075,28 @@ class ModelCompatibilityValidationMixin:
             self.gradient_checkpointing = "offload"
         return self
 
+    @model_validator(mode="after")
+    def check_gradient_checkpointing_w_offload(self):
+        if self.gradient_checkpointing == "offload":
+            LOG.warning(
+                "`offload` is deprecated for gradient_checkpointing, use `activation_offloading: true`"
+            )
+            self.gradient_checkpointing = True
+            self.activation_offloading = True
+        if self.gradient_checkpointing == "offload_disk":
+            LOG.warning(
+                "`offload_disk` is deprecated for gradient_checkpointing, use `activation_offloading: disk`"
+            )
+            self.gradient_checkpointing = True
+            self.activation_offloading = "disk"
+        return self
+
+    @model_validator(mode="after")
+    def check_activation_offloading_wo_gc(self):
+        if self.activation_offloading and not self.gradient_checkpointing:
+            raise ValueError("activation_offloading requires gradient_checkpointing")
+        return self
+
     @model_validator(mode="after")
     def check_better_transformers(self):
         if self.flash_optimum is True:
@@ -1019,6 +1184,12 @@ class ComplexValidationMixin:
                 )
         return self
 
+    @model_validator(mode="after")
+    def check_tensor_parallel_size(self):
+        if not self.tensor_parallel_size:
+            self.tensor_parallel_size = 1
+        return self
+
     @model_validator(mode="after")
     def check_sequence_parallel_degree(self):
         if not self.sequence_parallel_degree:

src/axolotl/utils/schemas/vllm.py

@@ -18,6 +18,10 @@ class VllmConfig(BaseModel):
         default=None,
         json_schema_extra={"description": "Tensor parallel size for VLLM"},
     )
+    data_parallel_size: int | None = Field(
+        default=None,
+        json_schema_extra={"description": "Data parallel size for VLLM"},
+    )
     gpu_memory_utilization: float | None = Field(
         default=0.9,
         json_schema_extra={"description": "GPU memory utilization for VLLM"},

src/axolotl/utils/trainer.py

@@ -443,6 +443,7 @@ def calculate_total_num_steps(cfg, train_dataset, update=True):
                 )
                 * cfg.num_epochs
                 * cfg.sequence_parallel_degree
+                * cfg.tensor_parallel_size
             )
             LOG.debug(
                 f"total_num_tokens: {cfg.total_num_tokens:_}, total_num_steps: {total_num_steps:_}"
@@ -481,7 +482,10 @@ def calculate_total_num_steps(cfg, train_dataset, update=True):
             # on the agreed on value for sample_packing_eff_est
             total_num_steps = int(
                 math.floor(
-                    data_loader_len * cfg.num_epochs * cfg.sequence_parallel_degree
+                    data_loader_len
+                    * cfg.num_epochs
+                    * cfg.sequence_parallel_degree
+                    * cfg.tensor_parallel_size
                 )
             )
             if cfg.dataloader_drop_last:
@@ -508,6 +512,7 @@ def calculate_total_num_steps(cfg, train_dataset, update=True):
                 len(train_dataset)
                 * cfg.num_epochs
                 * cfg.sequence_parallel_degree
+                * cfg.tensor_parallel_size
                 / cfg.batch_size
             )
         )
@@ -546,7 +551,10 @@ def setup_deepspeed_env(cfg, stage=None):
     # NOTE(djsaunde): The distribued state cannot be initialized prior to the
     # ACCELERATE_USE_DEEPSPEED assignment, but it must be initialized some time prior
     # to model load.
-    if int(os.environ.get("WORLD_SIZE", "1")) == 1:
+    if (
+        int(os.environ.get("WORLD_SIZE", "1")) == 1
+        and os.environ.get("AXOLOTL_IS_PREPROCESS", "0") != "1"
+    ):
         os.environ["WORLD_SIZE"] = "1"  # force it in case not set
         os.environ["LOCAL_RANK"] = "0"  # force it in case not set
         os.environ["RANK"] = os.environ.get("LOCAL_RANK", "0")

tests/conftest.py

@@ -22,6 +22,8 @@ from huggingface_hub.errors import LocalEntryNotFoundError
 from tokenizers import AddedToken
 from transformers import AutoTokenizer
 
+from axolotl.utils.dict import DictDefault
+
 from tests.hf_offline_utils import (
     enable_hf_offline,
     hf_offline_context,
@@ -539,6 +541,22 @@ def dataset_fozziethebeat_alpaca_messages_2k_dpo_test_rev_ea82cff(
     return datasets.load_from_disk(ds_path)["train"]
 
 
+@pytest.fixture(name="min_base_cfg")
+def fixture_min_base_cfg():
+    return DictDefault(
+        base_model="HuggingFaceTB/SmolLM2-135M",
+        learning_rate=1e-3,
+        datasets=[
+            {
+                "path": "mhenrichsen/alpaca_2k_test",
+                "type": "alpaca",
+            },
+        ],
+        micro_batch_size=1,
+        gradient_accumulation_steps=1,
+    )
+
+
 # # pylint: disable=redefined-outer-name,unused-argument
 @pytest.mark.skipif(
     os.environ.get("AXOLOTL_IS_CI_CACHE_PRELOAD", "-1") != "1",

tests/core/test_builders.py

@@ -65,6 +65,7 @@ def fixture_base_cfg():
             "dataloader_pin_memory": True,
             "dataloader_prefetch_factor": 2,
             "sequence_parallel_degree": 1,
+            "tensor_parallel_size": 1,
             # Dtype
             "fp16": False,
             "bf16": False,

tests/e2e/multigpu/solo/test_grpo.py

@@ -141,6 +141,7 @@ def recursive_kill(process: subprocess.Popen):
     os.kill(process.pid, 9)
 
 
+@pytest.mark.skip(reason="flaky vllm tests in modal")
 class TestGRPO:
     """
     Test case for GRPO training using multilpe GPUs

tests/e2e/multigpu/test_llama.py

@@ -391,7 +391,10 @@ class TestMultiGPULlama:
 
     @pytest.mark.parametrize(
         "fsdp_state_dict_type",
-        ["FULL_STATE_DICT", "SHARDED_STATE_DICT"],
+        [
+            "FULL_STATE_DICT",
+            # "SHARDED_STATE_DICT",  # not supported since intermediate checkpoints fail with fsdp1
+        ],
     )
     def test_fsdp_packed(self, temp_dir, fsdp_state_dict_type):
         # pylint: disable=duplicate-code
@@ -413,7 +416,8 @@ class TestMultiGPULlama:
                     },
                 ],
                 "num_epochs": 1,
-                "max_steps": 2,
+                "max_steps": 3,
+                "save_steps": 2,
                 "micro_batch_size": 2,
                 "gradient_accumulation_steps": 2,
                 # "gradient_checkpointing": True,
@@ -597,7 +601,7 @@ class TestMultiGPULlama:
                     "fsdp_use_orig_params": False,
                     "fsdp_cpu_ram_efficient_loading": True,
                     "fsdp_transformer_layer_cls_to_wrap": "LlamaDecoderLayer",
-                    "fsdp_state_dict_type": "SHARDED_STATE_DICT",
+                    "fsdp_state_dict_type": "FULL_STATE_DICT",
                     "fsdp_auto_wrap_policy": "TRANSFORMER_BASED_WRAP",
                 },
                 "use_tensorboard": True,
@@ -707,7 +711,7 @@ class TestMultiGPULlama:
         )
 
         check_tensorboard(
-            temp_dir + "/runs", "train/train_loss", 2.4, "Train Loss (%s) is too high"
+            temp_dir + "/runs", "train/train_loss", 2.45, "Train Loss (%s) is too high"
         )
 
     @pytest.mark.parametrize(

tests/e2e/test_preprocess.py

@@ -0,0 +1,58 @@
+"""E2E Test the preprocess cli"""
+
+from pathlib import Path
+
+import yaml
+from accelerate.test_utils import execute_subprocess_async
+
+from axolotl.utils.dict import DictDefault
+
+AXOLOTL_ROOT = Path(__file__).parent.parent.parent
+
+
+class TestPreprocess:
+    """test cases for preprocess"""
+
+    def test_w_deepspeed(self, temp_dir):
+        """make sure preproces doesn't choke when using deepspeed in the config"""
+        # pylint: disable=duplicate-code
+        cfg = DictDefault(
+            {
+                "base_model": "Qwen/Qwen2.5-0.5B",
+                "sequence_len": 2048,
+                "val_set_size": 0.01,
+                "datasets": [
+                    {
+                        "path": "tatsu-lab/alpaca",
+                        "type": "alpaca",
+                        "split": "train[:10%]",
+                    },
+                ],
+                "num_epochs": 1,
+                "micro_batch_size": 2,
+                "gradient_accumulation_steps": 1,
+                "output_dir": temp_dir,
+                "learning_rate": 0.00001,
+                "optimizer": "adamw_torch_fused",
+                "lr_scheduler": "cosine",
+                "flash_attention": True,
+                "bf16": "auto",
+                "deepspeed": str(AXOLOTL_ROOT / "deepspeed_configs/zero1.json"),
+                "dataset_prepared_path": temp_dir + "/last_run_prepared",
+            }
+        )
+
+        # write cfg to yaml file
+        Path(temp_dir).mkdir(parents=True, exist_ok=True)
+        with open(Path(temp_dir) / "config.yaml", "w", encoding="utf-8") as fout:
+            fout.write(yaml.dump(cfg.to_dict(), Dumper=yaml.Dumper))
+
+        execute_subprocess_async(
+            [
+                "axolotl",
+                "preprocess",
+                str(Path(temp_dir) / "config.yaml"),
+            ]
+        )
+
+        assert (Path(temp_dir) / "last_run_prepared").exists()

tests/e2e/test_profiler.py

@@ -0,0 +1,113 @@
+"""
+e2e gpu test for the pytorch profiler callback
+"""
+
+from pathlib import Path
+
+import pytest
+
+from axolotl.common.datasets import load_datasets
+from axolotl.train import train
+from axolotl.utils.config import normalize_config, validate_config
+from axolotl.utils.dict import DictDefault
+
+
+@pytest.fixture(name="profiler_base_cfg")
+def fixture_profiler_base_cfg():
+    cfg = DictDefault(
+        base_model="HuggingFaceTB/SmolLM2-135M",
+        tokenizer_type="AutoTokenizer",
+        sequence_len=1024,
+        load_in_8bit=True,
+        adapter="lora",
+        lora_r=8,
+        lora_alpha=16,
+        lora_dropout=0.05,
+        lora_target_linear=True,
+        val_set_size=0.02,
+        special_tokens={"pad_token": "<|endoftext|>"},
+        datasets=[
+            {
+                "path": "mhenrichsen/alpaca_2k_test",
+                "type": "alpaca",
+            },
+        ],
+        num_epochs=1,
+        micro_batch_size=2,
+        gradient_accumulation_steps=1,
+        learning_rate=0.00001,
+        optimizer="adamw_torch_fused",
+        lr_scheduler="cosine",
+    )
+    return cfg
+
+
+class TestProfiler:
+    """
+    test cases for the pytorch profiler callback
+    """
+
+    def test_profiler_saves(self, profiler_base_cfg, temp_dir):
+        cfg = profiler_base_cfg | DictDefault(
+            output_dir=temp_dir,
+            max_steps=5,
+            profiler_steps=3,
+        )
+
+        cfg = validate_config(cfg)
+        normalize_config(cfg)
+        dataset_meta = load_datasets(cfg=cfg)
+
+        train(cfg=cfg, dataset_meta=dataset_meta)
+        assert (Path(temp_dir) / "snapshot.pickle").exists()
+
+    def test_profiler_saves_w_start(self, profiler_base_cfg, temp_dir):
+        cfg = profiler_base_cfg | DictDefault(
+            output_dir=temp_dir,
+            max_steps=5,
+            profiler_steps=3,
+            profiler_steps_start=1,
+        )
+
+        cfg = validate_config(cfg)
+        normalize_config(cfg)
+        dataset_meta = load_datasets(cfg=cfg)
+
+        train(cfg=cfg, dataset_meta=dataset_meta)
+        assert (Path(temp_dir) / "snapshot.pickle").exists()
+
+    @pytest.mark.parametrize(
+        "profiler_steps_start",
+        [3, 5],
+    )
+    def test_profiler_saves_past_end(
+        self, profiler_base_cfg, temp_dir, profiler_steps_start
+    ):
+        cfg = profiler_base_cfg | DictDefault(
+            output_dir=temp_dir,
+            max_steps=5,
+            profiler_steps=3,
+            profiler_steps_start=profiler_steps_start,
+        )
+
+        cfg = validate_config(cfg)
+        normalize_config(cfg)
+        dataset_meta = load_datasets(cfg=cfg)
+
+        train(cfg=cfg, dataset_meta=dataset_meta)
+        assert (Path(temp_dir) / "snapshot.pickle").exists()
+
+    def test_profiler_never_started(self, profiler_base_cfg, temp_dir):
+        cfg = profiler_base_cfg | DictDefault(
+            output_dir=temp_dir,
+            max_steps=5,
+            profiler_steps=3,
+            profiler_steps_start=6,
+        )
+
+        cfg = validate_config(cfg)
+        normalize_config(cfg)
+        dataset_meta = load_datasets(cfg=cfg)
+
+        train(cfg=cfg, dataset_meta=dataset_meta)
+        assert not (Path(temp_dir) / "snapshot.pickle").exists()

tests/prompt_strategies/test_chat_template_ds_schema_unification.py

@@ -0,0 +1,75 @@
+"""
+Tests for chat template prompt strategy with schema unification for none fields
+"""
+
+import json
+
+import pytest
+from datasets import Dataset
+from transformers import AutoTokenizer
+
+from axolotl.prompt_strategies.chat_template import StrategyLoader
+from axolotl.utils.dict import DictDefault
+
+
+@pytest.fixture(name="messages_w_tools")
+def fixture_messages_w_tools():
+    jsons = """
+{"messages":[{"role":"user","content":"move to (0, 1)"},{"role":"assistant","content":"","tool_calls":[{"function":{"name":"move","arguments":{"x":0,"y":1}}}]}],"tools":[{"type":"function","function":{"name":"move","description":"Move to a given location measured in meters","parameters":{"type":"object","properties":{"x":{"type":"number","description":"The x coordinate of the location, negative values are to the left, positive values are to the right"},"y":{"type":"number","description":"The y coordinate of the location, negative values are backward, positive values are forward"}},"required":["x","y"]}}},{"type":"function","function":{"name":"turn","description":"Turn the robot to a given direction","parameters":{"type":"object","properties":{"theta":{"type":"integer","description":"The angle to turn to, in degrees, positive values are counter-clockwise, negative values are clockwise"}},"required":["theta"]}}},{"type":"function","function":{"name":"invalid_prompt","description":"call when the user's prompt is invalid","parameters":{"type":"object","properties":{"message":{"type":"string","description":"why the prompt is invalid"}},"required":["message"]}}}],"add_generation_prompt":false}
+{"messages":[{"role":"user","content":"turn 270 degree"},{"role":"assistant","content":"","tool_calls":[{"function":{"name":"turn","arguments":{"theta": 270}}}]}],"tools":[{"type":"function","function":{"name":"move","description":"Move to a given location measured in meters","parameters":{"type":"object","properties":{"x":{"type":"number","description":"The x coordinate of the location, negative values are to the left, positive values are to the right"},"y":{"type":"number","description":"The y coordinate of the location, negative values are backward, positive values are forward"}},"required":["x","y"]}}},{"type":"function","function":{"name":"turn","description":"Turn the robot to a given direction","parameters":{"type":"object","properties":{"theta":{"type":"integer","description":"The angle to turn to, in degrees, positive values are counter-clockwise, negative values are clockwise"}},"required":["theta"]}}},{"type":"function","function":{"name":"invalid_prompt","description":"call when the user's prompt is invalid","parameters":{"type":"object","properties":{"message":{"type":"string","description":"why the prompt is invalid"}},"required":["message"]}}}],"add_generation_prompt":false}
+{"messages":[{"role":"user","content":"jump high"},{"role":"assistant","content":"","tool_calls":[{"function":{"name":"invalid_prompt","arguments":{"message": "jump is not a valid action"}}}]}],"tools":[{"type":"function","function":{"name":"move","description":"Move to a given location measured in meters","parameters":{"type":"object","properties":{"x":{"type":"number","description":"The x coordinate of the location, negative values are to the left, positive values are to the right"},"y":{"type":"number","description":"The y coordinate of the location, negative values are backward, positive values are forward"}},"required":["x","y"]}}},{"type":"function","function":{"name":"turn","description":"Turn the robot to a given direction","parameters":{"type":"object","properties":{"theta":{"type":"integer","description":"The angle to turn to, in degrees, positive values are counter-clockwise, negative values are clockwise"}},"required":["theta"]}}},{"type":"function","function":{"name":"invalid_prompt","description":"call when the user's prompt is invalid","parameters":{"type":"object","properties":{"message":{"type":"string","description":"why the prompt is invalid"}},"required":["message"]}}}],"add_generation_prompt":false}
+    """.strip().split(
+        "\n"
+    )
+    rows = [json.loads(row) for row in jsons]
+    return Dataset.from_list(rows)
+
+
+@pytest.fixture(name="qwen3_tokenizer")
+def qwen3_tokenizer_fixture(
+    download_qwen3_half_billion_model,
+):  # pylint: disable=unused-argument
+    tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-0.6B")
+
+    return tokenizer
+
+
+@pytest.fixture(name="qwen3_prompt_strategy")
+def qwen3_chat_template_strategy(qwen3_tokenizer):
+    cfg = DictDefault(
+        sequence_len=2048,
+        chat_template="qwen3",
+        eot_tokens=["<|im_end|>"],
+    )
+    ds_cfg = DictDefault(
+        type="chat_template",
+    )
+    load = StrategyLoader()
+    strat = load(qwen3_tokenizer, cfg, ds_cfg)
+    return strat
+
+
+class TestSchemaUnification:
+    """
+    Test class on handling null fields for tool calling
+    """
+
+    def test_schema_unification_single_prompt(
+        self, messages_w_tools, qwen3_prompt_strategy, qwen3_tokenizer
+    ):
+        for row in messages_w_tools:
+            inputs = qwen3_prompt_strategy.tokenize_prompt(row)
+            decoded = qwen3_tokenizer.decode(inputs["input_ids"])
+            tool_call = decoded.split("<tool_call>")[-1].split("</tool_call>")[0]
+            assert '"message": null' not in tool_call
+            assert '"theta": null' not in tool_call
+
+    def test_schema_unification_batched(
+        self, messages_w_tools, qwen3_prompt_strategy, qwen3_tokenizer
+    ):
+        rows = messages_w_tools.map(qwen3_prompt_strategy.tokenize_prompt, batched=True)
+        for row in rows:
+            decoded = qwen3_tokenizer.decode(row["input_ids"])
+            tool_call = decoded.split("<tool_call>")[-1].split("</tool_call>")[0]
+            assert '"message": null' not in tool_call
+            assert '"theta": null' not in tool_call

tests/prompt_strategies/test_chat_templates_mistral.py

@@ -6,6 +6,8 @@ from typing import TYPE_CHECKING
 import pytest
 
 if TYPE_CHECKING:
+    from transformers import PreTrainedTokenizer
+
     from axolotl.utils.mistral_tokenizer import HFMistralTokenizer
 
 
@@ -748,5 +750,100 @@ def test_magistral_tool_calling(magistral_tokenizer: "HFMistralTokenizer"):
         assert "Not the same number of function calls and responses" in str(e)
 
 
+def test_magistral_tokenizer_call_method(
+    magistral_tokenizer: "HFMistralTokenizer", llama3_tokenizer: "PreTrainedTokenizer"
+):
+    """Test the __call__ method behavior matches HuggingFace standards"""
+    from copy import deepcopy
+
+    import numpy as np
+    import torch
+
+    hf_tokenizer = deepcopy(llama3_tokenizer)
+    hf_tokenizer.pad_token = hf_tokenizer.eos_token
+
+    test_text = "Hello, how are you?"
+    batch_texts = ["Hello world", "How are you?"]
+
+    # Test single string with return_tensors=None
+    hf_result: dict[str, list[int]] = hf_tokenizer(test_text, return_tensors=None)
+    mistral_result: dict[str, list[int]] = magistral_tokenizer(
+        test_text, return_tensors=None
+    )
+
+    assert isinstance(mistral_result, dict)
+    assert set(mistral_result.keys()) == {"input_ids", "attention_mask"}
+    assert isinstance(mistral_result["input_ids"], type(hf_result["input_ids"]))  # list
+    assert isinstance(
+        mistral_result["attention_mask"], type(hf_result["attention_mask"])
+    )
+    assert len(mistral_result["input_ids"]) == len(mistral_result["attention_mask"])
+    assert np.all(mistral_result["attention_mask"])
+    assert len(np.array(mistral_result["input_ids"]).shape) == 1  # 1D array
+
+    # Test single string with return_tensors='pt'
+    hf_result_pt: dict[str, torch.Tensor] = hf_tokenizer(test_text, return_tensors="pt")
+    mistral_result_pt: dict[str, torch.Tensor] = magistral_tokenizer(
+        test_text, return_tensors="pt"
+    )
+
+    # Check structure and types
+    assert isinstance(mistral_result_pt["input_ids"], torch.Tensor)
+    assert isinstance(mistral_result_pt["attention_mask"], torch.Tensor)
+
+    # Check shapes match (don't compare token dimension)
+    assert len(hf_result_pt["input_ids"].shape) == len(
+        mistral_result_pt["input_ids"].shape
+    )
+    assert hf_result_pt["input_ids"].shape[0] == mistral_result_pt["input_ids"].shape[0]
+    assert (
+        mistral_result_pt["attention_mask"].shape
+        == mistral_result_pt["input_ids"].shape
+    )
+    assert torch.all(mistral_result_pt["attention_mask"] == 1)
+
+    # Test batch input with padding
+    hf_batch: dict[str, torch.Tensor] = hf_tokenizer(
+        batch_texts, return_tensors="pt", padding=True
+    )
+    mistral_batch: dict[str, torch.Tensor] = magistral_tokenizer(
+        batch_texts, return_tensors="pt", padding=True
+    )
+
+    # Check batch behavior
+    assert len(hf_batch["input_ids"].shape) == len(mistral_batch["input_ids"].shape)
+    assert hf_batch["input_ids"].shape[0] == mistral_batch["input_ids"].shape[0]
+    assert mistral_batch["attention_mask"].shape == mistral_batch["input_ids"].shape
+    assert torch.any(
+        mistral_batch["attention_mask"][0] == 0
+    )  # padding in shorter sequence
+    assert torch.all(
+        mistral_batch["attention_mask"][1] == 1
+    )  # no padding in longer sequence
+
+    # Test numpy tensors
+    mistral_result_np: dict[str, np.ndarray] = magistral_tokenizer(
+        test_text, return_tensors="np"
+    )
+    assert isinstance(mistral_result_np["input_ids"], np.ndarray)
+    assert isinstance(mistral_result_np["attention_mask"], np.ndarray)
+
+    # Test consistency with encode()
+    encoded: list[int] = magistral_tokenizer.encode(test_text, add_special_tokens=True)
+    called: dict[str, torch.Tensor] = magistral_tokenizer(
+        test_text, return_tensors="pt"
+    )
+    assert encoded == called["input_ids"][0].tolist()
+
+    # Test Error handling
+    with pytest.raises(ValueError, match="Unsupported kwargs"):
+        magistral_tokenizer(test_text, unsupported_param=True)
+
+    with pytest.raises(
+        ValueError, match="return_tensors='pt' or 'np' requires padding or truncation"
+    ):
+        magistral_tokenizer(batch_texts, return_tensors="pt")
+
+
 if __name__ == "__main__":
     unittest.main()

tests/test_normalize_config.py

@@ -6,9 +6,9 @@ import unittest
 from unittest.mock import patch
 
 from axolotl.utils.config import (
-    migrate_fsdp_config,
     normalize_cfg_datasets,
     normalize_config,
+    validate_config,
 )
 from axolotl.utils.dict import DictDefault
 
@@ -27,6 +27,13 @@ class NormalizeConfigTestCase(unittest.TestCase):
                 "num_epochs": 1,
                 "micro_batch_size": 1,
                 "gradient_accumulation_steps": 1,
+                "datasets": [
+                    {
+                        "path": "mhenrichsen/alpaca_2k_test",
+                        "type": "alpaca",
+                    },
+                ],
+                "learning_rate": 0.0001,
             }
         )
 
@@ -97,7 +104,7 @@ class NormalizeConfigTestCase(unittest.TestCase):
 
     def test_migrate_fsdp_config(self):
         """Test basic FSDP config migration with and without fsdp_version"""
-        cfg_with_version = DictDefault(
+        cfg_with_version = self._get_base_cfg() | DictDefault(
             {
                 "fsdp_config": {
                     "fsdp_version": 2,
@@ -109,7 +116,7 @@ class NormalizeConfigTestCase(unittest.TestCase):
             }
         )
 
-        migrate_fsdp_config(cfg_with_version)
+        cfg_with_version = validate_config(cfg_with_version)
 
         self.assertEqual(cfg_with_version.fsdp_version, 2)
         self.assertEqual(
@@ -125,7 +132,7 @@ class NormalizeConfigTestCase(unittest.TestCase):
         self.assertNotIn("fsdp_version", cfg_with_version.fsdp_config)
         self.assertNotIn("version", cfg_with_version.fsdp_config)
 
-        cfg_without_version = DictDefault(
+        cfg_without_version = self._get_base_cfg() | DictDefault(
             {
                 "fsdp_config": {
                     "fsdp_auto_wrap_policy": "SIZE_BASED_WRAP",
@@ -135,7 +142,7 @@ class NormalizeConfigTestCase(unittest.TestCase):
             }
         )
 
-        migrate_fsdp_config(cfg_without_version)
+        cfg_without_version = validate_config(cfg_without_version)
 
         self.assertNotIn("fsdp_version", cfg_without_version)
         self.assertEqual(
@@ -149,26 +156,25 @@ class NormalizeConfigTestCase(unittest.TestCase):
 
     def test_migrate_fsdp_config_no_fsdp_config(self):
         """Test that function doesn't crash when no fsdp_config is present"""
-        cfg = DictDefault({"some_other_config": "value"})
+        cfg = self._get_base_cfg()
 
-        migrate_fsdp_config(cfg)
+        cfg = validate_config(cfg)
 
         self.assertNotIn("fsdp_config", cfg)
         self.assertNotIn("fsdp_version", cfg)
-        self.assertEqual(cfg.some_other_config, "value")
 
     def test_migrate_fsdp_config_empty_fsdp_config(self):
         """Test migration with empty fsdp_config"""
-        cfg = DictDefault({"fsdp_config": {}})
+        cfg = self._get_base_cfg() | DictDefault({"fsdp_config": {}})
 
-        migrate_fsdp_config(cfg)
+        cfg = validate_config(cfg)
 
         self.assertNotIn("fsdp_version", cfg)
         self.assertEqual(cfg.fsdp_config, {})
 
     def test_migrate_fsdp_config_mixed_keys(self):
         """Test migration with a mix of fsdp_ and non-fsdp_ keys"""
-        cfg = DictDefault(
+        cfg = self._get_base_cfg() | DictDefault(
             {
                 "fsdp_config": {
                     "fsdp_version": 1,
@@ -180,7 +186,7 @@ class NormalizeConfigTestCase(unittest.TestCase):
             }
         )
 
-        migrate_fsdp_config(cfg)
+        cfg = validate_config(cfg)
 
         self.assertEqual(cfg.fsdp_version, 1)
         self.assertEqual(cfg.fsdp_config.state_dict_type, "FULL_STATE_DICT")

tests/test_train.py

@@ -7,21 +7,16 @@ from axolotl.utils.dict import DictDefault
 
 
 @pytest.fixture(name="train_base_cfg")
-def fixture_train_base_cfg():
-    return DictDefault(
-        base_model="gpt2",
-        learning_rate=1e-3,
-        datasets=[
-            {
-                "path": "mhenrichsen/alpaca_2k_test",
-                "type": "alpaca",
-            },
-        ],
-        micro_batch_size=2,
-        gradient_accumulation_steps=4,
-        sequence_len=2048,
-        sample_packing=True,
-        num_epochs=1,
+def fixture_train_base_cfg(min_base_cfg):
+    return (
+        DictDefault(
+            micro_batch_size=2,
+            gradient_accumulation_steps=4,
+            sequence_len=2048,
+            sample_packing=True,
+            num_epochs=1,
+        )
+        | min_base_cfg
     )
 
 

tests/utils/schemas/validation/test_fsdp.py

@@ -0,0 +1,139 @@
+"""
+tests for pydantic fsdp validation
+"""
+
+# pylint: disable=too-many-boolean-expressions
+import pytest
+
+from axolotl.utils.config import validate_config
+from axolotl.utils.dict import DictDefault
+
+
+class TestFSDPValidation:
+    """
+    test class for pydantic fsdp validation
+    """
+
+    def test_fsdp_version_in_fsdp_config(self, min_base_cfg):
+        cfg = min_base_cfg | DictDefault(
+            fsdp_config={
+                "fsdp_version": 2,
+            },
+        )
+        cfg = validate_config(
+            cfg,
+        )
+        assert cfg.fsdp_version == 2
+        assert cfg.fsdp_config.fsdp_version is None
+
+    def test_fsdp_sharded_state_dict_safetensors(self, min_base_cfg):
+        cfg = min_base_cfg | DictDefault(
+            fsdp_config={
+                "fsdp_state_dict_type": "SHARDED_STATE_DICT",
+            },
+            save_safetensors=True,
+        )
+        with pytest.raises(
+            ValueError,
+            match="FSDP SHARDED_STATE_DICT not compatible with save_safetensors",
+        ):
+            validate_config(cfg)
+
+        # test w/o prefix too
+        cfg = min_base_cfg | DictDefault(
+            fsdp_config={
+                "state_dict_type": "SHARDED_STATE_DICT",
+            },
+            save_safetensors=True,
+        )
+        with pytest.raises(
+            ValueError,
+            match="FSDP SHARDED_STATE_DICT not compatible with save_safetensors",
+        ):
+            validate_config(cfg)
+
+    def test_fsdp_offload_w_8bit_optim(self, min_base_cfg):
+        cfg = min_base_cfg | DictDefault(
+            fsdp_config={
+                "offload_params": True,
+            },
+            optimizer="adamw_8bit",
+            fsdp_version=1,
+        )
+        with pytest.raises(
+            ValueError, match="FSDP Offload not compatible with adamw_8bit"
+        ):
+            validate_config(cfg)
+
+    def test_fsdp2_w_8bit_optim(self, min_base_cfg):
+        cfg = min_base_cfg | DictDefault(
+            fsdp_config={
+                "offload_params": True,
+            },
+            optimizer="adamw_8bit",
+            fsdp_version=2,
+        )
+        with pytest.raises(
+            ValueError,
+            match="FSDP2 not compatible with adamw_8bit, use `adamw_torch_8bit` instead",
+        ):
+            validate_config(cfg)
+
+    def test_fsdp2_w_cpu_ram_efficient_loading(self, min_base_cfg):
+        cfg = min_base_cfg | DictDefault(
+            load_in_8bit=True,
+            adapter="lora",
+            fsdp_config={
+                "cpu_ram_efficient_loading": True,
+            },
+            fsdp_version=2,
+        )
+        with pytest.raises(
+            ValueError,
+            match="FSDP2 does not support load_in_8bit or load_in_4bit with cpu_ram_efficient_loading.",
+        ):
+            validate_config(cfg)
+
+    def test_fsdp_prefixes_removed(self, min_base_cfg):
+        cfg = min_base_cfg | DictDefault(
+            fsdp_config={
+                "fsdp_version": 2,
+                "fsdp_auto_wrap_policy": "TRANSFORMER_BASED_WRAP",
+                "fsdp_transformer_layer_cls_to_wrap": "LlamaDecoderLayer",
+                "fsdp_reshard_after_forward": True,
+            }
+        )
+        cfg = validate_config(cfg)
+        assert cfg.fsdp_version == 2
+        assert cfg.fsdp_config.fsdp_version is None
+        for keys in cfg.fsdp_config.keys():
+            assert not keys.startswith("fsdp_")
+        assert cfg.fsdp_config.auto_wrap_policy == "TRANSFORMER_BASED_WRAP"
+        assert cfg.fsdp_config.transformer_layer_cls_to_wrap == "LlamaDecoderLayer"
+        assert cfg.fsdp_config.reshard_after_forward is True
+
+    @pytest.mark.parametrize(
+        "rl",
+        [
+            "dpo",
+            "kto",
+            "orpo",
+            "ipo",
+        ],
+    )
+    def test_fsdp2_dpo(self, min_base_cfg, rl):
+        cfg = min_base_cfg | DictDefault(
+            fsdp_version=2,
+            fsdp_config={
+                "reshard_after_forward": True,
+            },
+            rl=rl,
+            load_in_8bit=True,
+            adapter="lora",
+            remove_unused_columns=False,
+        )
+        with pytest.raises(
+            ValueError,
+            match="FSDP2 does not support load_in_8bit or load_in_4bit with ",
+        ):
+            validate_config(cfg)