5/x mx cleanup: rename mx inf config to MXDynamicActivationMXWeightConfig

benchmarks/float8/float8_inference_roofline.py

@@ -39,7 +39,7 @@
 
 import torchao
 from torchao.prototype.mx_formats.inference_workflow import (
-    MXFPInferenceConfig,
+    MXDynamicActivationMXWeightConfig,
     NVFP4InferenceConfig,
     NVFP4MMConfig,
 )
@@ -433,13 +433,13 @@ def run(
                     kernel_preference=KernelPreference.TORCH,
                 )
             elif recipe_name == "mxfp8_cublas":
-                config = MXFPInferenceConfig(
+                config = MXDynamicActivationMXWeightConfig(
                     activation_dtype=torch.float8_e4m3fn,
                     weight_dtype=torch.float8_e4m3fn,
                     kernel_preference=KernelPreference.AUTO,
                 )
             elif recipe_name == "mxfp4_cutlass":
-                config = MXFPInferenceConfig(
+                config = MXDynamicActivationMXWeightConfig(
                     activation_dtype=torch.float4_e2m1fn_x2,
                     weight_dtype=torch.float4_e2m1fn_x2,
                     kernel_preference=KernelPreference.AUTO,

test/integration/test_vllm.py

@@ -41,7 +41,7 @@
 from transformers import AutoModelForCausalLM, AutoTokenizer, TorchAoConfig
 from vllm import LLM, SamplingParams
 
-from torchao.prototype.mx_formats import MXFPInferenceConfig
+from torchao.prototype.mx_formats import MXDynamicActivationMXWeightConfig
 from torchao.quantization.granularity import PerRow, PerTensor
 from torchao.quantization.quant_api import (
     CutlassInt4PackedLayout,
@@ -70,7 +70,7 @@ def get_tests() -> List[TorchAoConfig]:
             Int8DynamicActivationInt4WeightConfig(layout=CutlassInt4PackedLayout())
         )
     ]
-    SM100_TESTS = [TorchAoConfig(MXFPInferenceConfig())]
+    SM100_TESTS = [TorchAoConfig(MXDynamicActivationMXWeightConfig())]
 
     # Check CUDA availability first
     if not torch.cuda.is_available():

test/prototype/mx_formats/test_inference_workflow.py

@@ -13,7 +13,7 @@
 from torch.profiler import ProfilerActivity, profile
 
 from torchao.prototype.mx_formats.inference_workflow import (
-    MXFPInferenceConfig,
+    MXDynamicActivationMXWeightConfig,
     NVFP4InferenceConfig,
     NVFP4MMConfig,
 )
@@ -106,7 +106,7 @@ def test_inference_workflow_mx(
         kernel_choice = KernelPreference.EMULATED
     else:
         kernel_choice = KernelPreference.AUTO
-    config = MXFPInferenceConfig(
+    config = MXDynamicActivationMXWeightConfig(
         activation_dtype=elem_dtype,
         weight_dtype=elem_dtype,
         kernel_preference=kernel_choice,
@@ -247,7 +247,7 @@ class VLLMIntegrationTestCase(TorchAOIntegrationTestCase):
         reason="torch.compile requires PyTorch 2.8+",
     )
     def test_slice_and_copy_similar_to_vllm(self):
-        config = MXFPInferenceConfig(
+        config = MXDynamicActivationMXWeightConfig(
             activation_dtype=torch.float8_e4m3fn,
             weight_dtype=torch.float8_e4m3fn,
             kernel_preference=KernelPreference.EMULATED,
@@ -260,7 +260,7 @@ def test_slice_and_copy_similar_to_vllm(self):
         reason="torch.compile requires PyTorch 2.8+",
     )
     def test_narrow_similar_to_vllm(self):
-        config = MXFPInferenceConfig(
+        config = MXDynamicActivationMXWeightConfig(
             activation_dtype=torch.float8_e4m3fn,
             weight_dtype=torch.float8_e4m3fn,
             kernel_preference=KernelPreference.EMULATED,

test/prototype/mx_formats/test_mx_serialization.py

@@ -13,7 +13,7 @@
 import torch.nn as nn
 
 from torchao.prototype.mx_formats.inference_workflow import (
-    MXFPInferenceConfig,
+    MXDynamicActivationMXWeightConfig,
     NVFP4InferenceConfig,
     NVFP4MMConfig,
 )
@@ -41,7 +41,7 @@ def test_serialization(recipe_name):
     fname = None
     with tempfile.NamedTemporaryFile(delete=False, mode="w") as f:
         if recipe_name == "mxfp8":
-            config = MXFPInferenceConfig(
+            config = MXDynamicActivationMXWeightConfig(
                 activation_dtype=torch.float8_e4m3fn,
                 weight_dtype=torch.float8_e4m3fn,
                 kernel_preference=KernelPreference.EMULATED,

torchao/prototype/mx_formats/README.md

@@ -108,7 +108,7 @@ import torch.nn as nn
 from torchao.quantization import quantize_
 import torchao.prototype.mx_formats
 from torchao.prototype.mx_formats.inference_workflow import (
-    MXFPInferenceConfig,
+    MXDynamicActivationMXWeightConfig,
     NVFP4InferenceConfig,
     NVFP4MMConfig,
 )
@@ -120,7 +120,7 @@ x = torch.randn(128, 32, device="cuda", dtype=torch.bfloat16)
 # mxfp8
 
 m_mxfp8 = copy.deepcopy(m)
-config = MXFPInferenceConfig(
+config = MXDynamicActivationMXWeightConfig(
     activation_dtype=torch.float8_e4m3fn,
     weight_dtype=torch.float8_e4m3fn,
     kernel_preference=KernelPreference.AUTO,
@@ -132,7 +132,7 @@ y_mxfp8 = m_mxfp8(x)
 # mxfp4
 
 m_mxfp4 = copy.deepcopy(m)
-config = MXFPInferenceConfig(
+config = MXDynamicActivationMXWeightConfig(
     activation_dtype=torch.float4_e2m1fn_x2,
     weight_dtype=torch.float4_e2m1fn_x2,
     kernel_preference=KernelPreference.AUTO,

torchao/prototype/mx_formats/__init__.py

@@ -5,7 +5,7 @@
 
 # Note: Prototype and subject to change
 from torchao.prototype.mx_formats.inference_workflow import (
-    MXFPInferenceConfig,
+    MXDynamicActivationMXWeightConfig,
     NVFP4InferenceConfig,
     NVFP4MMConfig,
 )
@@ -17,7 +17,7 @@
 __all__ = [
     "MXLinearConfig",
     "MXLinearRecipeName",
-    "MXFPInferenceConfig",
+    "MXDynamicActivationMXWeightConfig",
     "NVFP4InferenceConfig",
     "NVFP4MMConfig",
 ]

torchao/prototype/mx_formats/inference_workflow.py

@@ -36,39 +36,16 @@
 )
 
 
-# TODO The naming for these configs is a little weird, rename before moving to public API
-# Note: This API is extra prototype and will change in the future
 @dataclass
-class MXFPInferenceConfig(AOBaseConfig):
+class MXDynamicActivationMXWeightConfig(AOBaseConfig):
     """
     MX Format Inference Quantization
 
     This module provides support for running inference with float8 quantization using MX formats.
-    The quantization flow works as follows:
-
-    1. Weight Quantization:
-    - In _mx_inference_linear_transform(), the module's weight is converted to an MXTensor
-    - The weight is quantized to the specified dtype (float8_e4m3fn by default)
-    - This happens when quantize_() is called with an MXFPInferenceConfig
-
-    2. Activation Quantization:
-    - A callable (_input_activation_quant_func_mxfp) is defined that will quantize
-        activations during inference to the same dtype
-    - This function is passed to to_linear_activation_quantized() along with the
-        already-quantized weight
-
-    3. Runtime Flow:
-    - When the quantized module is called, the input goes through the LinearActivationQuantizedTensor
-    - The input (activation) is quantized just-in-time using the provided function
-    - The MX quantized activation and MX weight are used together in F.linear
 
     Requirements:
     - NVIDIA SM100+ hardware (Blackwell or newer) is required for execution
     - PyTorch 2.5+ for proper serialization support
-
-    See also:
-    - LinearActivationQuantizedTensor in torchao.quantization.quant_api
-    - MXTensor in torchao.prototype.mx_formats.mx_tensor
     """
 
     block_size: int = 32
@@ -95,9 +72,9 @@ def _linear_extra_repr(self):
     return f"in_features={self.weight.shape[1]}, out_features={self.weight.shape[0]}, weight={_quantization_type(self.weight)}"
 
 
-@register_quantize_module_handler(MXFPInferenceConfig)
+@register_quantize_module_handler(MXDynamicActivationMXWeightConfig)
 def _mx_inference_linear_transform(
-    module: torch.nn.Module, config: MXFPInferenceConfig
+    module: torch.nn.Module, config: MXDynamicActivationMXWeightConfig
 ):
     weight = module.weight