slow fsdp1 test

docs/cli.qmd

@@ -210,8 +210,6 @@ axolotl lm-eval config.yml
 Configuration options:
 
 ```yaml
-lm_eval_model: # model to evaluate (local or hf path)
-
 # List of tasks to evaluate
 lm_eval_tasks:
   - arc_challenge
@@ -220,7 +218,7 @@ lm_eval_batch_size: # Batch size for evaluation
 output_dir: # Directory to save evaluation results
 ```
 
-See [LM Eval Harness integration docs](https://docs.axolotl.ai/docs/custom_integrations.html#language-model-evaluation-harness-lm-eval) for full configuration details.
+See [LM Eval Harness](https://github.com/EleutherAI/lm-evaluation-harness) for more details.
 
 ### delinearize-llama4
 

requirements.txt

@@ -2,10 +2,10 @@
 
 # START section of dependencies that don't install on Darwin/MacOS
 bitsandbytes==0.49.1
-triton>=3.0.0
+triton>=3.4.0
 mamba-ssm==1.2.0.post1
 xformers>=0.0.23.post1
-liger-kernel==0.6.4
+liger-kernel==0.6.5
 # END section
 
 packaging==26.0

src/axolotl/integrations/cut_cross_entropy/__init__.py

@@ -104,7 +104,7 @@ class CutCrossEntropyPlugin(BasePlugin):
 
     def patch_llama_like(
         self,
-        model_type_to_patch: str,
+        model_type: str,
     ) -> None:
         """
         Generic patch for model architectures with causal lm similar to llama
@@ -112,10 +112,7 @@ class CutCrossEntropyPlugin(BasePlugin):
         from cut_cross_entropy.transformers.patch import PATCH_FNS
 
         def patch_generic(
-            maybe_model,
-            patch_options,
-            remote_model_id: str | None,
-            model_type: str,
+            maybe_model, patch_options, model_type: str, remote_model_id: str | None
         ):
             import cut_cross_entropy.transformers.llama
             from cut_cross_entropy.transformers.llama import cce_forward
@@ -139,13 +136,11 @@ class CutCrossEntropyPlugin(BasePlugin):
                     f"Error: {str(e)}"
                 ) from e
 
-        if model_type_to_patch not in PATCH_FNS:
+        if model_type not in PATCH_FNS:
             LOG.warning_once(
-                "Setting up generic cce patch for model type: %s", model_type_to_patch
+                "Setting up generic cce patch for model type: %s", model_type
             )
             LOG.warning_once(
-                f"Generic Cut Cross Entropy + {model_type_to_patch} support is experimental and may not work as expected."
-            )
-            PATCH_FNS[model_type_to_patch] = partial(
-                patch_generic, model_type=model_type_to_patch
+                f"Generic Cut Cross Entropy + {model_type} support is experimental and may not work as expected."
             )
+            PATCH_FNS[model_type] = partial(patch_generic, model_type=model_type)

src/axolotl/integrations/kernels/README.md

@@ -1,44 +0,0 @@
-# Kernels Integration
-
-MoE (Mixture of Experts) kernels speed up training for MoE layers and reduce VRAM costs. In transformers v5, `batched_mm` and `grouped_mm` were integrated as built-in options via the `experts_implementation` config kwarg:
-
-```python
-class ExpertsInterface(GeneralInterface):
-    _global_mapping = {
-        "batched_mm": batched_mm_experts_forward,
-        "grouped_mm": grouped_mm_experts_forward,
-    }
-```
-
-In our custom integration, we add support for **ScatterMoE**, which is even more efficient and faster than `grouped_mm`.
-
-## Usage
-
-Add the following to your axolotl YAML config:
-
-```yaml
-plugins:
-  - axolotl.integrations.kernels.KernelsPlugin
-
-use_kernels: true
-use_scattermoe: true
-```
-
-**Important:** Setting `experts_implementation` is incompatible with `use_scattermoe`.
-
-## How It Works
-
-The `KernelsPlugin` runs before model loading and:
-
-1. Registers the ScatterMoE kernel from the [`axolotl-ai-co/scattermoe`](https://huggingface.co/axolotl-ai-co/scattermoe) Hub repo.
-2. Patches the model's `SparseMoeBlock` forward method with the optimized ScatterMoE implementation.
-
-This works for any MoE model in transformers that uses a `SparseMoeBlock` class (Mixtral, Qwen2-MoE, OLMoE, etc.).
-
-## Limitations
-
-ScatterMoE uses a softmax -> topk routing, so results may be different for some model arch as baseline (GPT-OSS, GLM_MOE_DSA).
-
-## Note on MegaBlocks
-
-We tested [MegaBlocks](https://huggingface.co/kernels-community/megablocks) but were unable to ensure numerical accuracy, so we did not integrate it. It was also incompatible with many newer model architectures in transformers.

src/axolotl/integrations/lm_eval/README.md

@@ -6,12 +6,6 @@ See https://github.com/EleutherAI/lm-evaluation-harness
 
 ## Usage
 
-There are two ways to use the LM Eval integration:
-
-### 1. Post-Training Evaluation
-
-When training with the plugin enabled, evaluation runs automatically after training completes:
-
 ```yaml
 plugins:
   - axolotl.integrations.lm_eval.LMEvalPlugin
@@ -22,50 +16,9 @@ lm_eval_tasks:
   - arc_easy
 
 lm_eval_batch_size: # Batch size for evaluation
-
-# Directory to save evaluation results.
-# The final model is loaded from this directory
-# unless specified otherwise (see below)
-output_dir:
+output_dir: # Directory to save evaluation results
 ```
 
-Run training as usual:
-```bash
-axolotl train config.yml
-```
-
-### 2. Standalone CLI Evaluation
-
-Evaluate any model directly without training:
-
-```yaml
-lm_eval_model: meta-llama/Llama-2-7b-hf
-
-plugins:
-  - axolotl.integrations.lm_eval.LMEvalPlugin
-
-lm_eval_tasks:
-  - gsm8k
-  - hellaswag
-  - arc_easy
-
-lm_eval_batch_size: 8
-output_dir: ./outputs
-```
-
-Run evaluation:
-```bash
-axolotl lm-eval config.yml
-```
-
-## Model Selection Priority
-
-The model to evaluate is selected in the following priority order:
-
-1. **`lm_eval_model`** - Explicit model path or HuggingFace repo (highest priority)
-2. **`hub_model_id`** - Trained model pushed to HuggingFace Hub
-3. **`output_dir`** - Local checkpoint directory containing trained model weights
-
 ## Citation
 
 ```bib

src/axolotl/integrations/lm_eval/__init__.py

@@ -5,7 +5,7 @@ Module for the Plugin for LM Eval Harness
 import subprocess  # nosec
 
 from axolotl.integrations.base import BasePlugin
-from axolotl.integrations.lm_eval.cli import build_lm_eval_command, get_model_path
+from axolotl.integrations.lm_eval.cli import build_lm_eval_command
 
 from .args import LMEvalArgs as LMEvalArgs
 
@@ -29,7 +29,7 @@ class LMEvalPlugin(BasePlugin):
                 wandb_project=cfg.wandb_project,
                 wandb_entity=cfg.wandb_entity,
                 wandb_name=cfg.wandb_name,
-                model=get_model_path(cfg),
+                model=cfg.lm_eval_model or cfg.hub_model_id,
             ):
                 subprocess.run(  # nosec
                     lm_eval_args,

src/axolotl/integrations/lm_eval/cli.py

@@ -13,21 +13,6 @@ import yaml
 from axolotl.utils.dict import DictDefault
 
 
-def get_model_path(cfg: DictDefault) -> str | None:
-    """
-    Determine which model path to use for evaluation.
-
-    Priority order (highest to lowest):
-    1. lm_eval_model - Explicit model path override
-    2. hub_model_id - Model pushed to HuggingFace Hub
-    3. None - Falls back to output_dir in build_lm_eval_command
-
-    Returns:
-        Model path string or None to use output_dir fallback
-    """
-    return cfg.lm_eval_model or cfg.hub_model_id or None
-
-
 def build_lm_eval_command(
     tasks: list[str],
     bfloat16=True,
@@ -123,7 +108,7 @@ def lm_eval(config: str, cloud: Optional[str] = None):
             wandb_project=cfg.wandb_project,
             wandb_entity=cfg.wandb_entity,
             wandb_name=cfg.wandb_name,
-            model=get_model_path(cfg),
+            model=cfg.lm_eval_model or cfg.hub_model_id,
             revision=cfg.revision,
             apply_chat_template=cfg.apply_chat_template,
             fewshot_as_multiturn=cfg.fewshot_as_multiturn,

src/axolotl/kernels/lora.py

@@ -15,7 +15,7 @@ from torch import nn
 from torch.distributed.tensor import DTensor
 
 from .geglu import geglu_backward, geglu_forward
-from .quantize import dequantize_weight
+from .quantize import dequantize
 from .swiglu import swiglu_backward, swiglu_forward
 from .utils import torch_amp_custom_bwd, torch_amp_custom_fwd
 
@@ -46,12 +46,6 @@ def get_lora_parameters(
     W = base_layer.weight
     b = base_layer.bias
 
-    # Unwrap DTensor if FSDP2 left the weight wrapped -- DTensor does not proxy
-    # attribute access to the underlying tensor subclass, so torchao methods like
-    # .dequantize() or .get_original_weight() would not be visible.
-    if isinstance(W, DTensor):
-        W = W.full_tensor()
-
     if not hasattr(proj, "disable_adapters") or proj.disable_adapters or proj.merged:
         quant_state = getattr(W, "quant_state", None)
         return W, b, quant_state, None, None, None
@@ -92,7 +86,6 @@ def matmul_lora(
     B: torch.Tensor | None,
     s: float | None,
     out: torch.Tensor | None = None,
-    transpose: bool = True,
 ) -> torch.Tensor:
     """
     Efficient fused matmul + LoRA computation.
@@ -105,15 +98,12 @@ def matmul_lora(
         B: LoRA B matrix [out_features, rank]
         s: LoRA scaling factor
         out: Optional output tensor for inplace operations
-        transpose: If True (default), transpose W before matmul (forward path).
-            Set to False for backward paths where W is already in the correct layout.
 
     Returns:
         Result of X @ W + X @ A @ B
     """
     dtype = X.dtype
-    is_quantized = W_quant is not None or type(W) is not torch.Tensor
-    W = dequantize_weight(W, W_quant, transpose=transpose)
+    W = dequantize(W.t(), W_quant)
 
     reshape = False
     if X.dim() == 3:
@@ -122,7 +112,7 @@ def matmul_lora(
         reshape = True
 
     out = torch.matmul(X, W, out=out)
-    if is_quantized:
+    if W_quant is not None:
         del W
 
     if A is not None:
@@ -302,16 +292,15 @@ class LoRA_MLP(torch.autograd.Function):
         up = up.view(-1, up.shape[-1])
         dtype = X.dtype
 
-        # Down projection (backward: no transpose needed, W is already [out, in])
+        # Down projection
         grad_down = matmul_lora(
             grad_output,
-            down_weight,
+            down_weight.t(),
             None,
             down_quant,
             down_B,
             down_A,
             down_scale,
-            transpose=False,
         )
 
         # Activation backward
@@ -343,7 +332,7 @@ class LoRA_MLP(torch.autograd.Function):
 
         if dX is not None:
             # Up projection gradients
-            up_weight = dequantize_weight(up_weight, up_quant, transpose=True)
+            up_weight = dequantize(up_weight.t(), up_quant)
             if ctx.inplace:
                 dX = torch.matmul(grad_up, up_weight.t(), out=X)
             else:
@@ -355,7 +344,7 @@ class LoRA_MLP(torch.autograd.Function):
                 dX += grad_up @ up_B.to(dtype).t() @ (up_scale * up_A.to(dtype).t())
 
             # Gate projection gradients
-            gate_weight = dequantize_weight(gate_weight, gate_quant)
+            gate_weight = dequantize(gate_weight, gate_quant)
             dX += grad_gate @ gate_weight
             del gate_weight
 
@@ -642,7 +631,7 @@ class LoRA_QKV(torch.autograd.Function):
         out_buffer = X if ctx.inplace else None
 
         # Q path
-        q_weight_t = dequantize_weight(q_weight, q_quant)
+        q_weight_t = dequantize(q_weight, q_quant)
         grad_X = torch.mm(q_grad, q_weight_t, out=out_buffer)
         del q_weight
         del q_weight_t
@@ -650,7 +639,7 @@ class LoRA_QKV(torch.autograd.Function):
             grad_X.addmm_(q_grad, torch.mm(B_q_scaled, A_q_scaled))
 
         # K path
-        k_weight_t = dequantize_weight(k_weight, k_quant)
+        k_weight_t = dequantize(k_weight, k_quant)
         grad_X.addmm_(k_grad, k_weight_t)
         del k_weight
         del k_weight_t
@@ -658,7 +647,7 @@ class LoRA_QKV(torch.autograd.Function):
             grad_X.addmm_(k_grad, torch.mm(B_k_scaled, A_k_scaled))
 
         # V path
-        v_weight_t = dequantize_weight(v_weight, v_quant)
+        v_weight_t = dequantize(v_weight, v_quant)
         grad_X.addmm_(v_grad, v_weight_t)
         del v_weight
         del v_weight_t
@@ -821,7 +810,7 @@ class LoRA_O(torch.autograd.Function):
         d_B = s * A @ dY_X
 
         # Get derivative for dX
-        W = dequantize_weight(W, W_quant, transpose=True)
+        W = dequantize(W.t(), W_quant)
         dX = dY @ W.t()
         del W
 

src/axolotl/kernels/quantize.py

@@ -146,43 +146,3 @@ def dequantize(
     # Handle transposed data
     is_transposed: bool = W.shape[0] == 1
     return out.t() if is_transposed else out
-
-
-def dequantize_weight(
-    W: torch.Tensor,
-    quant_state: QuantState | list | None = None,
-    transpose: bool = False,
-) -> torch.Tensor:
-    """Unified dequantization for both torchao and bnb quantized weights.
-
-    For torchao tensor subclasses (AffineQuantizedTensor, NF4Tensor), dequantizes
-    using the appropriate instance method. For bnb Params4bit, delegates to the
-    optimized CUDA kernel in ``dequantize``.
-
-    Args:
-        W: Quantized weight tensor ``[out_features, in_features]``.
-        quant_state: bnb ``QuantState`` (None for torchao / unquantized).
-        transpose: If True, return ``[in_features, out_features]``.
-
-    Returns:
-        Dequantized float tensor, optionally transposed.
-    """
-    # torchao path: tensor subclass with embedded quantization state
-    if quant_state is None and type(W) is not torch.Tensor:
-        result = None
-        # NF4Tensor (check first — NF4Tensor.dequantize is a static method)
-        if hasattr(W, "get_original_weight"):
-            result = W.get_original_weight()
-        else:
-            # AffineQuantizedTensor (INT4, etc.)
-            try:
-                result = W.dequantize()
-            except (TypeError, RuntimeError):
-                pass
-        if result is not None:
-            return result.t() if transpose else result
-
-    # bnb path: transpose input before the CUDA kernel (existing convention)
-    if transpose:
-        return dequantize(W.t(), quant_state)
-    return dequantize(W, quant_state)

src/axolotl/loaders/adapter.py

@@ -23,7 +23,6 @@ from axolotl.loaders.utils import get_linear_embedding_layers
 from axolotl.telemetry.errors import send_errors
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.logging import get_logger
-from axolotl.utils.schemas.enums import TorchAOQuantDType
 
 LOG = get_logger(__name__)
 
@@ -135,13 +134,11 @@ def load_lora(
 
     rank = int(os.environ.get("LOCAL_RANK", 0))
 
-    is_torchao = cfg.peft and cfg.peft.backend == "torchao"
     if (
         cfg.fsdp_config
         and cfg.adapter
         and cfg.fsdp_config.cpu_ram_efficient_loading
         and rank != 0
-        and not is_torchao
     ):
         setup_quantized_meta_for_peft(model)
 
@@ -149,15 +146,6 @@ def load_lora(
     if cfg.peft_autocast_adapter_dtype is not None:
         model_kwargs["autocast_adapter_dtype"] = cfg.peft_autocast_adapter_dtype
 
-    # Patch PEFT's torchao dispatch before any model creation/loading.
-    # Must happen before both get_peft_model and PeftModel.from_pretrained,
-    # as both trigger LoRA layer dispatch that would fail for INT4/NF4 weights.
-    # INT8 is natively supported by PEFT's TorchaoLoraLinear, so skip the patch.
-    if is_torchao and cfg.peft.weight_dtype != TorchAOQuantDType.int8:
-        from axolotl.monkeypatch.peft.utils import patch_peft_torchao_dispatch
-
-        patch_peft_torchao_dispatch()
-
     if cfg.lora_model_dir:
         LOG.debug("Loading pretrained PEFT - LoRA")
         if cfg.lora_on_cpu:
@@ -184,7 +172,6 @@ def load_lora(
         and cfg.adapter
         and cfg.fsdp_config.cpu_ram_efficient_loading
         and rank != 0
-        and not is_torchao
     ):
         setup_quantized_peft_meta_for_training(model)
 

src/axolotl/loaders/model.py

@@ -158,15 +158,6 @@ class ModelLoader:
         """Property that determines if FSDP with QLoRA is enabled."""
         return self.is_fsdp_enabled and self.cfg.adapter == "qlora"
 
-    @property
-    def is_torchao_qlora(self):
-        """Property that determines if torchao backend is used for QLoRA."""
-        return (
-            self.cfg.adapter == "qlora"
-            and self.cfg.peft
-            and self.cfg.peft.backend == "torchao"
-        )
-
     @send_errors
     def load(self) -> tuple[PreTrainedModel | PeftModelForCausalLM, PeftConfig | None]:
         """Load and prepare the model with all configurations and patches.
@@ -500,9 +491,8 @@ class ModelLoader:
 
         # FSDP requires control over device placement, so don't set device_map when FSDP is enabled
         if self.is_fsdp_enabled:
-            # For QLoRA + FSDP with bnb, we still need to set device_map for proper initialization
-            # torchao tensors work natively with FSDP2, no device_map override needed
-            if self.is_qlora_and_fsdp_enabled and not self.is_torchao_qlora:
+            # For QLoRA + FSDP, we still need to set device_map to "auto" for proper initialization
+            if self.is_qlora_and_fsdp_enabled:
                 self.model_kwargs["device_map"] = {
                     "": int(os.environ.get("LOCAL_RANK", 0))
                 }
@@ -571,44 +561,6 @@ class ModelLoader:
                 self.model_kwargs["quantization_config"] = BitsAndBytesConfig(
                     **self.model_config.quantization_config
                 )
-        elif (
-            self.cfg.adapter == "qlora"
-            and self.cfg.peft
-            and self.cfg.peft.backend == "torchao"
-            and not self.cfg.merge_lora
-        ):
-            from transformers import TorchAoConfig
-
-            from axolotl.utils.schemas.enums import TorchAOQuantDType
-
-            weight_dtype = self.cfg.peft.weight_dtype
-            if weight_dtype == TorchAOQuantDType.int4:
-                group_size = self.cfg.peft.group_size or 128
-                self.model_kwargs["quantization_config"] = TorchAoConfig(
-                    quant_type="int4_weight_only",
-                    group_size=group_size,
-                )
-            elif weight_dtype == TorchAOQuantDType.int8:
-                group_size = self.cfg.peft.group_size or 128
-                self.model_kwargs["quantization_config"] = TorchAoConfig(
-                    quant_type="int8_weight_only",
-                    group_size=group_size,
-                )
-            elif weight_dtype == TorchAOQuantDType.nf4:
-                from torchao.dtypes._nf4tensor_api import NF4WeightOnlyConfig
-
-                block_size = self.cfg.peft.group_size or 64
-                self.model_kwargs["quantization_config"] = TorchAoConfig(
-                    quant_type=NF4WeightOnlyConfig(
-                        block_size=block_size,
-                        scaler_block_size=256,
-                    ),
-                )
-            else:
-                raise ValueError(
-                    f"Unsupported torchao weight_dtype for QLoRA: {weight_dtype}. "
-                    "Supported: int4, int8, nf4"
-                )
         elif self.cfg.adapter == "qlora" and self.cfg.load_in_4bit:
             bnb_config = {
                 "load_in_4bit": True,
@@ -908,10 +860,6 @@ class ModelLoader:
             # Make sure everything is in the same dtype
             skip_prepare_model_for_kbit_training = True
 
-        # torchao quantized models don't use Params4bit and don't need kbit preparation
-        if self.is_torchao_qlora:
-            skip_prepare_model_for_kbit_training = True
-
         if (
             not skip_prepare_model_for_kbit_training
             and self.cfg.adapter in ["lora", "qlora"]

src/axolotl/loaders/patch_manager.py

@@ -348,12 +348,10 @@ class PatchManager:
 
     def _apply_fsdp2_bnb_patches(self):
         """Apply FSDP2 BNB patches."""
-        is_torchao = self.cfg.peft and self.cfg.peft.backend == "torchao"
         if (
             self.cfg.fsdp_config
             and str(self.cfg.fsdp_version) == "2"
             and self.cfg.adapter == "qlora"
-            and not is_torchao
         ):
             from axolotl.monkeypatch.fsdp2_qlora import (
                 apply_init_sharded_param_patch,

src/axolotl/monkeypatch/peft/utils.py

@@ -78,30 +78,3 @@ def patch_peft_prep_code():
     axolotl.loaders.model.prepare_model_for_kbit_training = (
         fixed_prepare_model_for_kbit_training
     )
-
-
-def patch_peft_torchao_dispatch():
-    """Skip PEFT's TorchaoLoraLinear for non-INT8 torchao weights.
-
-    PEFT's dispatch_torchao() matches AffineQuantizedTensor but then errors in
-    _check_dtype_supported() because it only allows INT8. Our LoRA kernels handle
-    dequantization explicitly, so we bypass PEFT's torchao dispatch entirely and
-    let it fall back to standard Linear LoRA layers.
-    """
-    try:
-        from peft.tuners.lora import torchao as peft_torchao
-    except ImportError:
-        LOG.warning("Could not import peft.tuners.lora.torchao for patching")
-        return
-
-    if getattr(peft_torchao, "_axolotl_patched", False):
-        return
-
-    def patched_dispatch(target, adapter_name, lora_config, **kwargs):
-        # Return None so PEFT falls back to standard Linear LoRA layers.
-        # Our LoRA kernels handle torchao dequantization explicitly.
-        return None
-
-    peft_torchao.dispatch_torchao = patched_dispatch
-    peft_torchao._axolotl_patched = True
-    LOG.info("Patched PEFT dispatch_torchao to skip TorchaoLoraLinear")

src/axolotl/utils/schemas/enums.py

@@ -8,7 +8,6 @@ import torch
 class TorchAOQuantDType(Enum):
     int4 = torch.int4
     int8 = torch.int8
-    nf4 = "nf4"
     float8_e4m3fn = torch.float8_e4m3fn
     nvfp4 = "nvfp4"
 
@@ -17,8 +16,6 @@ class TorchAOQuantDType(Enum):
             return TorchAOQuantDType.int4
         if str == "int8":
             return TorchAOQuantDType.int8
-        if str == "nf4":
-            return TorchAOQuantDType.nf4
         if str in ["float8_e4m3fn", "fp8", "float8"]:
             return TorchAOQuantDType.float8_e4m3fn
         if str == "nvfp4":

src/axolotl/utils/schemas/peft.py

@@ -1,12 +1,9 @@
 """Pydantic models for PEFT-related configuration"""
 
-from typing import Any, Literal
+from typing import Any
 
 from pydantic import BaseModel, Field, field_validator, model_validator
 
-from axolotl.utils.schemas.enums import TorchAOQuantDType
-from axolotl.utils.schemas.quantization import validate_ao_dtype
-
 
 class LoftQConfig(BaseModel):
     """LoftQ configuration subset"""
@@ -18,7 +15,7 @@ class LoftQConfig(BaseModel):
 
 
 class PeftConfig(BaseModel):
-    """PEFT configuration subset"""
+    """peftq configuration subset"""
 
     loftq_config: LoftQConfig | None = Field(
         default=None,
@@ -26,29 +23,6 @@ class PeftConfig(BaseModel):
             "description": "Configuration options for loftq initialization for LoRA"
         },
     )
-    backend: Literal["bnb", "torchao"] | None = Field(
-        default=None,
-        json_schema_extra={
-            "description": "Quantization backend for QLoRA. 'bnb' for bitsandbytes (default), 'torchao' for torchao."
-        },
-    )
-    weight_dtype: TorchAOQuantDType | None = Field(
-        default=None,
-        json_schema_extra={
-            "description": "Weight quantization dtype (int4, int8, or nf4). Also used with bnb backend to auto-configure quantization."
-        },
-    )
-    group_size: int | None = Field(
-        default=None,
-        json_schema_extra={
-            "description": "Group size for quantization. Defaults to 128 for int4, 64 for nf4."
-        },
-    )
-
-    @field_validator("weight_dtype", mode="before")
-    @classmethod
-    def validate_weight_dtype(cls, v):
-        return validate_ao_dtype(v)
 
 
 class LoraConfig(BaseModel):
@@ -182,56 +156,6 @@ class LoraConfig(BaseModel):
 
     merge_lora: bool | None = None
 
-    @model_validator(mode="before")
-    @classmethod
-    def auto_detect_qlora(cls, data):
-        """Auto-set adapter type and quantization flags from peft config.
-
-        When peft.backend and peft.weight_dtype are set, this infers the correct
-        adapter type and internal flags (load_in_4bit, load_in_8bit) so users
-        don't need to set them manually.
-        """
-        peft = data.get("peft")
-        if not isinstance(peft, dict):
-            return data
-
-        backend = peft.get("backend")
-        weight_dtype = peft.get("weight_dtype")
-
-        # Validate: weight_dtype requires backend
-        if weight_dtype and not backend:
-            raise ValueError(
-                "peft.backend is required when peft.weight_dtype is set. "
-                "Use 'torchao' or 'bnb'."
-            )
-
-        if not weight_dtype:
-            return data
-
-        adapter = data.get("adapter")
-
-        if backend == "torchao":
-            # torchao: any quantized weight_dtype means qlora
-            if adapter == "lora":
-                data["adapter"] = "qlora"
-
-        elif backend == "bnb":
-            if weight_dtype == "nf4":
-                # bnb nf4 = qlora with load_in_4bit
-                if adapter == "lora":
-                    data["adapter"] = "qlora"
-                data.setdefault("load_in_4bit", True)
-            elif weight_dtype == "int8":
-                # bnb int8 = lora with load_in_8bit
-                data.setdefault("load_in_8bit", True)
-            else:
-                raise ValueError(
-                    f"peft.weight_dtype '{weight_dtype}' is not supported with bnb backend. "
-                    "Supported: nf4, int8."
-                )
-
-        return data
-
     @model_validator(mode="before")
     @classmethod
     def validate_adapter(cls, data):
@@ -249,8 +173,6 @@ class LoraConfig(BaseModel):
     @model_validator(mode="after")
     def validate_qlora(self):
         if self.adapter == "qlora":
-            is_torchao = self.peft and self.peft.backend == "torchao"
-
             if self.merge_lora:
                 # can't merge qlora if loaded in 8bit or 4bit
                 if self.load_in_8bit:
@@ -262,20 +184,7 @@ class LoraConfig(BaseModel):
                 if self.load_in_4bit:
                     raise ValueError("Can't merge qlora if loaded in 4bit")
 
-            elif is_torchao:
-                # torchao backend: validate torchao-specific requirements
-                if self.load_in_4bit or self.load_in_8bit:
-                    raise ValueError(
-                        "load_in_4bit/load_in_8bit are for bitsandbytes. "
-                        "With peft.backend: torchao, quantization is handled by torchao."
-                    )
-                if not self.peft.weight_dtype:
-                    raise ValueError(
-                        "peft.weight_dtype is required when peft.backend is 'torchao'"
-                    )
-
             else:
-                # Default bnb path
                 if self.load_in_8bit:
                     raise ValueError("Can't load qlora in 8bit")
 

src/axolotl/utils/schemas/quantization.py

@@ -16,8 +16,6 @@ def validate_ao_dtype(v: Any) -> TorchAOQuantDType | None:
         return TorchAOQuantDType.int4
     if v == "int8":
         return TorchAOQuantDType.int8
-    if v == "nf4":
-        return TorchAOQuantDType.nf4
     if v in ["float8_e4m3fn", "fp8", "float8"]:
         return TorchAOQuantDType.float8_e4m3fn
     if v == "nvfp4":

tests/e2e/kernels/test_quantize.py

@@ -3,7 +3,7 @@
 import torch
 from bitsandbytes.functional import QuantState
 
-from axolotl.kernels.quantize import dequantize, dequantize_weight
+from axolotl.kernels.quantize import dequantize
 
 
 def test_dequantize_null_state():
@@ -100,18 +100,3 @@ def test_dequantize_output_tensor():
 
     result = dequantize(W, quant_state, out=out)
     assert result is out
-
-
-def test_dequantize_weight_plain_tensor():
-    """Test that dequantize_weight passes through unquantized tensors unchanged"""
-    W = torch.randn(32, 64)
-    result = dequantize_weight(W, quant_state=None, transpose=False)
-    assert torch.equal(result, W)
-
-
-def test_dequantize_weight_plain_tensor_transpose():
-    """Test that dequantize_weight transposes unquantized tensors"""
-    W = torch.randn(32, 64)
-    result = dequantize_weight(W, quant_state=None, transpose=True)
-    assert result.shape == (64, 32)
-    assert torch.equal(result, W.t())

tests/e2e/multigpu/test_fsdp1.py

@@ -186,6 +186,7 @@ class TestFSDP1:
 
         verify_training_success(temp_dir)
 
+    @pytest.mark.skip(reason="slow test, deprecate fsdp1 asap")
     def test_dpo_fft(self, temp_dir):
         cfg = DictDefault(
             {

tests/utils/lora/test_config_validation_lora.py

@@ -3,14 +3,6 @@ import pytest
 from axolotl.utils.config import validate_config
 from axolotl.utils.dict import DictDefault
 
-BASE_CFG = {
-    "datasets": [{"path": "dummy_dataset", "type": "alpaca"}],
-    "micro_batch_size": 1,
-    "gradient_accumulation_steps": 1,
-    "learning_rate": 1e-5,
-    "base_model": "dummy_model",
-}
-
 
 class TestLoRAConfigValidation:
     """Test suite for LoRA/QLoRA configuration validation"""
@@ -157,195 +149,3 @@ class TestLoRAConfigValidation:
         result = validate_config(valid_config)
         assert result["lora_qkv_kernel"] is True
         assert result["trust_remote_code"] is None
-
-
-class TestTorchaoQLoRAConfigValidation:
-    """Test suite for torchao QLoRA auto-detection and validation"""
-
-    # --- Auto-detection: torchao ---
-
-    @pytest.mark.parametrize("weight_dtype", ["int4", "int8", "nf4"])
-    def test_torchao_auto_detect_from_lora(self, weight_dtype):
-        """adapter: lora + peft.backend: torchao auto-upgrades to qlora"""
-        cfg = DictDefault(
-            {
-                "adapter": "lora",
-                "peft": {"backend": "torchao", "weight_dtype": weight_dtype},
-                **BASE_CFG,
-            }
-        )
-        result = validate_config(cfg)
-        assert result["adapter"] == "qlora"
-        assert result["peft"]["backend"] == "torchao"
-
-    def test_torchao_explicit_qlora(self):
-        """adapter: qlora + peft.backend: torchao works directly"""
-        cfg = DictDefault(
-            {
-                "adapter": "qlora",
-                "peft": {"backend": "torchao", "weight_dtype": "int4"},
-                **BASE_CFG,
-            }
-        )
-        result = validate_config(cfg)
-        assert result["adapter"] == "qlora"
-
-    # --- Auto-detection: bnb ---
-
-    def test_bnb_nf4_auto_detect_from_lora(self):
-        """adapter: lora + peft.backend: bnb + weight_dtype: nf4 → qlora + load_in_4bit"""
-        cfg = DictDefault(
-            {
-                "adapter": "lora",
-                "peft": {"backend": "bnb", "weight_dtype": "nf4"},
-                **BASE_CFG,
-            }
-        )
-        result = validate_config(cfg)
-        assert result["adapter"] == "qlora"
-        assert result["load_in_4bit"] is True
-
-    def test_bnb_int8_auto_detect_from_lora(self):
-        """adapter: lora + peft.backend: bnb + weight_dtype: int8 → lora + load_in_8bit"""
-        cfg = DictDefault(
-            {
-                "adapter": "lora",
-                "peft": {"backend": "bnb", "weight_dtype": "int8"},
-                **BASE_CFG,
-            }
-        )
-        result = validate_config(cfg)
-        assert result["adapter"] == "lora"
-        assert result["load_in_8bit"] is True
-
-    def test_bnb_nf4_explicit_qlora_auto_sets_load_in_4bit(self):
-        """adapter: qlora + peft.backend: bnb + weight_dtype: nf4 auto-sets load_in_4bit"""
-        cfg = DictDefault(
-            {
-                "adapter": "qlora",
-                "peft": {"backend": "bnb", "weight_dtype": "nf4"},
-                **BASE_CFG,
-            }
-        )
-        result = validate_config(cfg)
-        assert result["adapter"] == "qlora"
-        assert result["load_in_4bit"] is True
-
-    # --- Backward compat ---
-
-    def test_old_style_qlora_unchanged(self):
-        """Old-style adapter: qlora + load_in_4bit: true still works"""
-        cfg = DictDefault(
-            {
-                "adapter": "qlora",
-                "load_in_4bit": True,
-                **BASE_CFG,
-            }
-        )
-        result = validate_config(cfg)
-        assert result["adapter"] == "qlora"
-        assert result["load_in_4bit"] is True
-
-    def test_old_style_lora_8bit_unchanged(self):
-        """Old-style adapter: lora + load_in_8bit: true still works"""
-        cfg = DictDefault(
-            {
-                "adapter": "lora",
-                "load_in_8bit": True,
-                **BASE_CFG,
-            }
-        )
-        result = validate_config(cfg)
-        assert result["adapter"] == "lora"
-        assert result["load_in_8bit"] is True
-
-    def test_plain_lora_unchanged(self):
-        """adapter: lora without peft block stays as lora"""
-        cfg = DictDefault(
-            {
-                "adapter": "lora",
-                **BASE_CFG,
-            }
-        )
-        result = validate_config(cfg)
-        assert result["adapter"] == "lora"
-
-    # --- Validation errors ---
-
-    def test_torchao_with_load_in_4bit_errors(self):
-        """peft.backend: torchao + load_in_4bit is a conflict"""
-        cfg = DictDefault(
-            {
-                "adapter": "qlora",
-                "load_in_4bit": True,
-                "peft": {"backend": "torchao", "weight_dtype": "int4"},
-                **BASE_CFG,
-            }
-        )
-        with pytest.raises(ValueError, match="load_in_4bit.*bitsandbytes"):
-            validate_config(cfg)
-
-    def test_torchao_with_load_in_8bit_errors(self):
-        """peft.backend: torchao + load_in_8bit is a conflict"""
-        cfg = DictDefault(
-            {
-                "adapter": "qlora",
-                "load_in_8bit": True,
-                "peft": {"backend": "torchao", "weight_dtype": "int4"},
-                **BASE_CFG,
-            }
-        )
-        with pytest.raises(ValueError, match="load_in_4bit.*bitsandbytes"):
-            validate_config(cfg)
-
-    def test_torchao_without_weight_dtype_errors(self):
-        """peft.backend: torchao without weight_dtype errors"""
-        cfg = DictDefault(
-            {
-                "adapter": "qlora",
-                "peft": {"backend": "torchao"},
-                **BASE_CFG,
-            }
-        )
-        with pytest.raises(ValueError, match="peft.weight_dtype is required"):
-            validate_config(cfg)
-
-    def test_weight_dtype_without_backend_errors(self):
-        """peft.weight_dtype without peft.backend errors"""
-        cfg = DictDefault(
-            {
-                "adapter": "lora",
-                "peft": {"weight_dtype": "int4"},
-                **BASE_CFG,
-            }
-        )
-        with pytest.raises(ValueError, match="peft.backend is required"):
-            validate_config(cfg)
-
-    def test_bnb_unsupported_weight_dtype_errors(self):
-        """peft.backend: bnb + unsupported weight_dtype errors"""
-        cfg = DictDefault(
-            {
-                "adapter": "lora",
-                "peft": {"backend": "bnb", "weight_dtype": "int4"},
-                **BASE_CFG,
-            }
-        )
-        with pytest.raises(ValueError, match="not supported with bnb"):
-            validate_config(cfg)
-
-    # --- Redundant flags don't conflict ---
-
-    def test_bnb_nf4_with_explicit_load_in_4bit(self):
-        """peft.backend: bnb + weight_dtype: nf4 + load_in_4bit: true is fine (redundant)"""
-        cfg = DictDefault(
-            {
-                "adapter": "lora",
-                "load_in_4bit": True,
-                "peft": {"backend": "bnb", "weight_dtype": "nf4"},
-                **BASE_CFG,
-            }
-        )
-        result = validate_config(cfg)
-        assert result["adapter"] == "qlora"
-        assert result["load_in_4bit"] is True