add kernels for gpt oss models (#3020)

* add kernels for gpt oss models

* add support for gpt-oss

* typo incorrect package

* fix: layout for configs and added wandb/epochs

* add gptoss example w offload and set moe leaf for z3

* add support for Mxfp4Config from yaml

* update yaml to use official model

* fix lora and don't allow triton to go above 3.3.1

* fix lr and tweak vram use

* fix range for triton since pinned wasn't compatible with toch 2.6.0

* update cce with gpt oss patches

---------

Co-authored-by: NanoCode012 <nano@axolotl.ai>

examples/colab-notebooks/colab-axolotl-example.ipynb

@@ -40,7 +40,7 @@
         "%%capture\n",
         "# This step can take ~5-10 minutes to install dependencies\n",
         "!pip install --no-build-isolation axolotl[flash-attn]>=0.9.1\n",
-        "!pip install \"cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@cbd58e0\""
+        "!pip install \"cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@48b5169\""
       ]
     },
     {

examples/gpt-oss/README.md

@@ -0,0 +1,9 @@
+# OpenAI's GPT-OSS
+
+GPT-OSS is a 20 billion parameter MoE model trained by OpenAI, released in August 2025.
+
+- 20B Full Parameter SFT can be trained on 8x48GB GPUs (peak reserved memory @ ~36GiB/GPU) - [YAML](./gpt-oss-20b-fft-fsdp2.yaml)
+- 20B LoRA SFT (all linear layers, and experts in last two layers) can be trained a single GPU (peak reserved memory @ ~47GiB)
+  - removing the experts from `lora_target_parameters` will allow the model to fit around ~44GiB of VRAM
+  - [YAML](./gpt-oss-20b-sft-lora-singlegpu.yaml)
+- 20B Full Parameter SFT with FSDP2 offloading can be trained on 2x24GB GPUs (peak reserved memory @ ~21GiB/GPU) - [YAML](./gpt-oss-20b-fft-fsdp2-offload.yaml)

examples/gpt-oss/gpt-oss-20b-fft-fsdp2-offload.yaml

@@ -0,0 +1,62 @@
+base_model: openai/gpt-oss-20b
+use_kernels: true
+model_quantization_config: Mxfp4Config
+model_quantization_config_kwargs:
+  dequantize: true
+
+plugins:
+  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
+
+experimental_skip_move_to_device: true  # prevent OOM by NOT putting model to GPU before sharding
+
+datasets:
+  - path: winglian/pirate-ultrachat-10k
+    type: chat_template
+    split: train
+
+dataset_prepared_path: last_run_prepared
+val_set_size: 0
+output_dir: ./outputs/gpt-oss-out/
+
+sequence_len: 4096
+sample_packing: true
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 2
+micro_batch_size: 1
+num_epochs: 1
+
+optimizer: adamw_torch_fused  # 8bit optimizers do not work with FSDP2 offload
+lr_scheduler: constant_with_warmup
+learning_rate: 2e-5
+
+bf16: true
+tf32: true
+
+flash_attention: true
+attn_implementation: kernels-community/vllm-flash-attn3
+
+gradient_checkpointing: true
+activation_offloading: true
+
+logging_steps: 1
+saves_per_epoch: 1
+
+warmup_ratio: 0.1
+
+special_tokens:
+eot_tokens:
+  - "<|end|>"
+
+fsdp_version: 2
+fsdp_config:
+  offload_params: true
+  state_dict_type: SHARDED_STATE_DICT
+  auto_wrap_policy: TRANSFORMER_BASED_WRAP
+  transformer_layer_cls_to_wrap: GptOssDecoderLayer
+  reshard_after_forward: true

examples/gpt-oss/gpt-oss-20b-fft-fsdp2.yaml

@@ -0,0 +1,62 @@
+base_model: openai/gpt-oss-20b
+use_kernels: true
+model_quantization_config: Mxfp4Config
+model_quantization_config_kwargs:
+  dequantize: true
+
+plugins:
+  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
+
+experimental_skip_move_to_device: true  # prevent OOM by NOT putting model to GPU before sharding
+
+datasets:
+  - path: winglian/pirate-ultrachat-10k
+    type: chat_template
+    split: train
+
+dataset_prepared_path: last_run_prepared
+val_set_size: 0
+output_dir: ./outputs/gpt-oss-out/
+
+sequence_len: 4096
+sample_packing: true
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 2
+micro_batch_size: 1
+num_epochs: 1
+
+optimizer: adamw_torch_8bit
+lr_scheduler: constant_with_warmup
+learning_rate: 2e-5
+
+bf16: true
+tf32: true
+
+flash_attention: true
+attn_implementation: kernels-community/vllm-flash-attn3
+
+gradient_checkpointing: true
+activation_offloading: true
+
+logging_steps: 1
+saves_per_epoch: 1
+
+warmup_ratio: 0.1
+
+special_tokens:
+eot_tokens:
+  - "<|end|>"
+
+fsdp_version: 2
+fsdp_config:
+  offload_params: false
+  state_dict_type: SHARDED_STATE_DICT
+  auto_wrap_policy: TRANSFORMER_BASED_WRAP
+  transformer_layer_cls_to_wrap: GptOssDecoderLayer
+  reshard_after_forward: true

examples/gpt-oss/gpt-oss-20b-sft-lora-singlegpu.yaml

@@ -0,0 +1,64 @@
+base_model: openai/gpt-oss-20b
+use_kernels: true
+model_quantization_config: Mxfp4Config
+model_quantization_config_kwargs:
+  dequantize: true
+
+plugins:
+  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
+
+experimental_skip_move_to_device: true  # prevent OOM by not putting model to GPU before sharding
+
+datasets:
+  - path: winglian/pirate-ultrachat-10k
+    type: chat_template
+    split: train
+
+dataset_prepared_path: last_run_prepared
+val_set_size: 0
+output_dir: ./outputs/gpt-oss-out/
+
+sequence_len: 4096
+sample_packing: true
+
+adapter: lora
+lora_r: 8
+lora_alpha: 16
+lora_dropout: 0.0  # dropout not supported when using LoRA over expert parameters
+lora_target_linear: true
+lora_target_parameters:  # target the experts in the last two layers
+  - "22._checkpoint_wrapped_module.mlp.experts.gate_up_proj"
+  - "22._checkpoint_wrapped_module.mlp.experts.down_proj"
+  - "23._checkpoint_wrapped_module.mlp.experts.gate_up_proj"
+  - "23._checkpoint_wrapped_module.mlp.experts.down_proj"
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 8
+micro_batch_size: 1
+num_epochs: 1
+
+optimizer: adamw_torch_8bit
+lr_scheduler: constant_with_warmup
+learning_rate: 2e-4
+
+bf16: true
+tf32: true
+
+flash_attention: true
+attn_implementation: kernels-community/vllm-flash-attn3
+
+gradient_checkpointing: true
+activation_offloading: true
+
+logging_steps: 1
+saves_per_epoch: 1
+warmup_ratio: 0.1
+
+special_tokens:
+eot_tokens:
+  - "<|end|>"

requirements.txt

@@ -2,7 +2,8 @@
 
 # START section of dependencies that don't install on Darwin/MacOS
 bitsandbytes==0.46.1
-triton>=3.0.0
+# triton 3.4.0 is not compatible with CCE
+triton>=3.0.0,<3.4.0
 mamba-ssm==1.2.0.post1
 xformers>=0.0.23.post1
 autoawq==0.2.7.post3
@@ -20,6 +21,7 @@ datasets==4.0.0
 deepspeed>=0.17.0
 trl==0.20.0
 hf_xet==1.1.5
+kernels==0.9.0
 
 optimum==1.16.2
 hf_transfer

scripts/cutcrossentropy_install.py

@@ -29,5 +29,5 @@ UV_PREFIX = "uv " if USE_UV else ""
 
 print(
     UNINSTALL_PREFIX
-    + f'{UV_PREFIX}pip install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@cbd58e0"'
+    + f'{UV_PREFIX}pip install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@48b5169"'
 )

src/axolotl/common/architectures.py

@@ -13,4 +13,5 @@ MOE_ARCH_BLOCK = {
     "qwen2_moe": "Qwen2MoeSparseMoeBlock",
     "qwen3_moe": "Qwen3MoeSparseMoeBlock",
     "deepseek_v2": "DeepseekV2MoE",
+    "gpt_oss": "GptOssExperts",
 }

src/axolotl/core/trainers/base.py

@@ -567,9 +567,9 @@ class AxolotlTrainer(
             # Add memory usage
             try:
                 active, allocated, reserved = get_gpu_memory_usage()
-                logs["memory/max_memory_active(gib)"] = round(active, 2)
-                logs["memory/max_memory_allocated(gib)"] = round(allocated, 2)
-                logs["memory/device_memory_reserved(gib)"] = round(reserved, 2)
+                logs["memory/max_mem_active(gib)"] = round(active, 2)
+                logs["memory/max_mem_allocated(gib)"] = round(allocated, 2)
+                logs["memory/device_mem_reserved(gib)"] = round(reserved, 2)
             except (ValueError, TypeError, FileNotFoundError):
                 pass
 

src/axolotl/integrations/cut_cross_entropy/README.md

@@ -19,7 +19,7 @@ python scripts/cutcrossentropy_install.py | sh
 
 - If you are installing from pip
 ```bash
-pip3 uninstall -y cut-cross-entropy && pip3 install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@cbd58e0"
+pip3 uninstall -y cut-cross-entropy && pip3 install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@48b5169"
 ```
 
 ## Usage

src/axolotl/integrations/cut_cross_entropy/__init__.py

@@ -34,7 +34,7 @@ LOG = get_logger(__name__)
 
 _CCE_INSTALL_MESSAGE = (
     "Please install Axolotl's fork of cut_cross_entropy with transformers support using "
-    '`pip install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@cbd58e0"`'
+    '`pip install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@48b5169"`'
 )
 
 

src/axolotl/loaders/model.py

@@ -202,6 +202,8 @@ class ModelLoader:
         self._set_device_map_config()
         if self.cfg.revision_of_model:
             self.model_kwargs["revision"] = self.cfg.revision_of_model
+        if self.cfg.use_kernels:
+            self.model_kwargs["use_kernels"] = self.cfg.use_kernels
         self._set_quantization_config()
         self._set_attention_config()
 
@@ -565,8 +567,17 @@ class ModelLoader:
 
     def _set_quantization_config(self):
         """Set up quantization config (bitsandbytes, awq, gptq, etc.)"""
-        self.model_kwargs["load_in_8bit"] = self.cfg.load_in_8bit
-        self.model_kwargs["load_in_4bit"] = self.cfg.load_in_4bit
+
+        if self.cfg.model_quantization_config == "Mxfp4Config":
+            from transformers import Mxfp4Config
+
+            mxfp4_kwargs = {}
+            if self.cfg.model_quantization_config_kwargs:
+                mxfp4_kwargs = self.cfg.model_quantization_config_kwargs
+            self.model_kwargs["quantization_config"] = Mxfp4Config(**mxfp4_kwargs)
+        else:
+            self.model_kwargs["load_in_8bit"] = self.cfg.load_in_8bit
+            self.model_kwargs["load_in_4bit"] = self.cfg.load_in_4bit
 
         if self.cfg.gptq:
             if not hasattr(self.model_config, "quantization_config"):
@@ -648,7 +659,9 @@ class ModelLoader:
 
     def _set_attention_config(self):
         """Sample packing uses custom FA2 patch"""
-        if self.cfg.flex_attention:
+        if self.cfg.attn_implementation:
+            self.model_kwargs["attn_implementation"] = self.cfg.attn_implementation
+        elif self.cfg.flex_attention:
             self.model_kwargs["attn_implementation"] = "flex_attention"
             self.model_config._attn_implementation = (  # pylint: disable=protected-access
                 "flex_attention"

src/axolotl/utils/schemas/config.py

@@ -544,6 +544,13 @@ class AxolotlInputConfig(
 
     eager_attention: bool | None = None
 
+    attn_implementation: str | None = Field(
+        default=None,
+        json_schema_extra={
+            "description": "Specify a custom attention implementation, used mostly for kernels."
+        },
+    )
+
     unsloth_cross_entropy_loss: bool | None = None
     unsloth_lora_mlp: bool | None = None
     unsloth_lora_qkv: bool | None = None

src/axolotl/utils/schemas/model.py

@@ -1,5 +1,7 @@
 """Pydantic models for model input / output, etc. configuration"""
 
+from typing import Any, Literal
+
 from pydantic import BaseModel, Field, field_validator
 
 from axolotl.utils.logging import get_logger
@@ -70,6 +72,20 @@ class ModelInputConfig(BaseModel):
         },
     )
 
+    use_kernels: bool | None = Field(
+        default=None,
+        json_schema_extra={"description": "Use custom kernels, e.g. MegaBlocks."},
+    )
+
+    model_quantization_config: Literal["Mxfp4Config"] | None = Field(
+        default=None,
+        json_schema_extra={"description": "Model loading quantization config"},
+    )
+    model_quantization_config_kwargs: dict[str, Any] | None = Field(
+        default=None,
+        json_schema_extra={"description": "kwargs for model quantization config"},
+    )
+
     @field_validator("trust_remote_code")
     @classmethod
     def hint_trust_remote_code(cls, trust_remote_code):

src/axolotl/utils/schemas/validation.py

@@ -972,6 +972,16 @@ class SystemValidationMixin:
             raise ValueError("deepspeed and fsdp cannot be used together.")
         return data
 
+    @model_validator(mode="before")
+    @classmethod
+    def check_model_quantization_config_vs_bnb(cls, data):
+        if data.get("model_quantization_config"):
+            if data.get("load_in_8bit") or data.get("load_in_4bit"):
+                raise ValueError(
+                    "model_quantization_config and load_in_8bit or load_in_4bit cannot be used together."
+                )
+        return data
+
     @model_validator(mode="before")
     @classmethod
     def check_npu_config(cls, data):