add: support mxfp4 axo (#3375)

* mxfp4 axo

* import lint

* test for qat mxfp4

* config for mxfp4

* add qat:

* pass base config

* MXFakeQuantizeConfig

* lint

* tune config so it fits in 32GB VRAM

---------

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

src/axolotl/utils/quantization.py

@@ -5,6 +5,7 @@ Utilities for quantization including QAT and PTQ using torchao.
 import torch
 from packaging import version
 from torchao.core.config import AOBaseConfig
+from torchao.prototype.qat import MXFakeQuantizeConfig
 from torchao.quantization import quantize_
 from torchao.quantization.qat import (
     QATConfig,
@@ -40,6 +41,13 @@ if version.parse(torch.__version__) >= version.parse("2.8.0"):
     except:
         pass
 
+    try:
+        from torchao.prototype.qat import MXFakeQuantizeConfig
+
+        quantization_config_to_str[MXFakeQuantizeConfig] = "mxfp4"
+    except ImportError:
+        pass
+
 
 def get_quantization_config(
     weight_dtype: TorchAOQuantDType,
@@ -109,6 +117,19 @@ def get_quantization_config(
         if group_size is not None and group_size != 16:
             raise ValueError("NVFP4 quantization must use a group_size of 16")
         return NVFP4InferenceConfig()
+
+    if weight_dtype == TorchAOQuantDType.mxfp4:
+        from torchao.prototype.qat import MXFakeQuantizeConfig
+
+        # MXFP4 uses block_size=32 by default (vs NVFP4's 16)
+        block_size = group_size if group_size is not None else 32
+        if block_size != 32:
+            raise ValueError(
+                "MXFP4 quantization must use a block_size (group_size) of 32"
+            )
+
+        return MXFakeQuantizeConfig(dtype=torch.float4_e2m1fn_x2, block_size=block_size)
+
     raise ValueError(
         f"Invalid activation/weight dtype combination: {activation_dtype}/{weight_dtype}"
     )
@@ -179,7 +200,13 @@ def prepare_model_for_qat(
         activation_dtype=activation_dtype,
         group_size=group_size,
     )
-    qat_config = QATConfig(base_config)
+    if isinstance(base_config, MXFakeQuantizeConfig):
+        qat_config = QATConfig(
+            activation_config=base_config,
+            weight_config=base_config,
+        )
+    else:
+        qat_config = QATConfig(base_config)
     quantize_(model, qat_config)
     if quantize_embedding:
         # activation fake quantization is not supported for embedding layers
@@ -188,7 +215,12 @@ def prepare_model_for_qat(
             activation_dtype=None,
             group_size=group_size,
         )
-        embedding_qat_config = QATConfig(embedding_base_config)
+        if isinstance(embedding_base_config, MXFakeQuantizeConfig):
+            embedding_qat_config = QATConfig(
+                weight_config=embedding_base_config,
+            )
+        else:
+            embedding_qat_config = QATConfig(embedding_base_config)
         quantize_(
             model,
             embedding_qat_config,

src/axolotl/utils/schemas/enums.py

@@ -10,6 +10,7 @@ class TorchAOQuantDType(Enum):
     int8 = torch.int8
     float8_e4m3fn = torch.float8_e4m3fn
     nvfp4 = "nvfp4"
+    mxfp4 = "mxfp4"
 
     def from_string(str):
         if str == "int4":
@@ -20,6 +21,8 @@ class TorchAOQuantDType(Enum):
             return TorchAOQuantDType.float8_e4m3fn
         if str == "nvfp4":
             return TorchAOQuantDType.nvfp4
+        if str == "mxfp4":
+            return TorchAOQuantDType.mxfp4
 
 
 class RLType(str, Enum):

src/axolotl/utils/schemas/quantization.py

@@ -20,6 +20,9 @@ def validate_ao_dtype(v: Any) -> TorchAOQuantDType | None:
         return TorchAOQuantDType.float8_e4m3fn
     if v == "nvfp4":
         return TorchAOQuantDType.nvfp4
+    if v == "mxfp4":
+        return TorchAOQuantDType.mxfp4
+
     raise ValueError(
         f"Invalid dtype: '{v}'. Must be one of: {[e.name for e in TorchAOQuantDType] + ['fp8', 'float8']}"
     )