Test cases

Author: jainapurva

test/integration/test_integration.py

@@ -72,7 +72,7 @@
     AQInt8WeightOnlyQuantizedLinearWeight2,
     AQInt8WeightOnlyQuantizedLinearWeight3,
     AutoQuantizableLinearWeight,
-
+    AQFloat8WeightOnlyQuantizedLinearWeight,
 )
 from torch.ao.quantization.quantize_fx import convert_to_reference_fx, prepare_fx
 import os
@@ -98,6 +98,7 @@
 COMMON_DTYPES = [torch.float32, torch.float16, torch.bfloat16]
 
 COMMON_DEVICE_DTYPE = list(itertools.product(COMMON_DEVICES, COMMON_DTYPES)).copy()
+is_H100 = torch.cuda.is_available() and torch.cuda.get_device_capability() >= (9, 0)
 
 def _int8wo_api(mod):
     if TORCH_VERSION_AT_LEAST_2_4:
@@ -744,6 +745,14 @@ def test_aq_int8_weight_only_quant_3_subclass(self, device, dtype):
             AQInt8WeightOnlyQuantizedLinearWeight3.from_float, device, 35, test_dtype=dtype
         )
 
+    @parameterized.expand(COMMON_DEVICE_DTYPE)
+    @unittest.skipIf(not TORCH_VERSION_AT_LEAST_2_5, "autoquant+aqt needs newer pytorch")
+    @unittest.skipIf(not is_H100, "Need H100 to run")
+    def test_aq_float8_weight_only_quant_subclass(self, device, dtype):
+        self._test_lin_weight_subclass_impl(
+            AQFloat8WeightOnlyQuantizedLinearWeight.from_float, device, 30, test_dtype=dtype
+        )
+
     @parameterized.expand(COMMON_DEVICE_DTYPE)
     @unittest.skipIf(not TORCH_VERSION_AT_LEAST_2_3, "int4 requires torch nightly.")
     # @unittest.skipIf(TORCH_VERSION_AT_LEAST_2_5, "int4 skipping 2.5+ for now")

torchao/quantization/autoquant.py

@@ -501,7 +501,6 @@ def from_float(cls, weight):
     # AQInt8WeightOnlyQuantizedLinearWeight3,
     # TODO this gets picked in places where it makes perf worse, why?
     AQInt8DynamicallyQuantizedLinearWeight,
-    AQFloat8WeightOnlyQuantizedLinearWeight,
 ]
 
 DEFAULT_INT4_AUTOQUANT_CLASS_LIST = [
@@ -510,6 +509,11 @@ def from_float(cls, weight):
     AQInt4G64WeightOnlyQuantizedLinearWeight
 ]
 
+OTHER_AUTOQUANT_CLASS_LIST = [
+    AQFloat8WeightOnlyQuantizedLinearWeight,
+]
+
+
 def _change_linears_to_autoquantizable(model, **kwargs):
     """
     Converts all linear weight tensors to the