Generalize FakeQuantizer beyond intx

docs/source/api_ref_qat.rst

@@ -28,7 +28,8 @@ Custom QAT APIs
     IntxFakeQuantizeConfig
     FakeQuantizedLinear
     FakeQuantizedEmbedding
-    FakeQuantizer
+    FakeQuantizerBase
+    IntxFakeQuantizer
     linear.enable_linear_fake_quant
     linear.disable_linear_fake_quant
 

test/quantization/test_qat.py

@@ -46,7 +46,7 @@
     IntxFakeQuantizeConfig,
 )
 from torchao.quantization.qat.fake_quantizer import (
-    FakeQuantizer,
+    IntxFakeQuantizer,
     _Float8RowwiseActivationFakeQuantizer,
 )
 from torchao.quantization.qat.linear import (
@@ -1466,10 +1466,10 @@ def test_fake_quantize_config_torch_intx(self):
     )
     def test_fake_quantizer_repr(self):
         """
-        Test that `repr(FakeQuantizer(config))` exposes useful config details.
+        Test that `repr(IntxFakeQuantizer(config))` exposes useful config details.
         """
         config = IntxFakeQuantizeConfig(torch.int4, group_size=128)
-        fake_quantizer = FakeQuantizer(config)
+        fake_quantizer = IntxFakeQuantizer(config)
         fake_quantizer_repr = repr(fake_quantizer)
         self.assertTrue("dtype=torch.int4" in fake_quantizer_repr)
         self.assertTrue("group_size=128" in fake_quantizer_repr)
@@ -1500,15 +1500,15 @@ def test_qat_linear_bias(self):
     def test_fake_quantize_per_token_vs_convert(self, dtype: torch.dtype):
         """
         Test that the following produce the exact same numerics:
-          1. FakeQuantizer with asymmetric per_token config
+          1. IntxFakeQuantizer with asymmetric per_token config
           2. torchao.quantization.utils.per_token_dynamic_quant
         """
         from torchao.quantization.utils import per_token_dynamic_quant
 
         torch.manual_seed(self.SEED)
         x = torch.randn(1, 235, 2048).to(dtype)
         config = IntxFakeQuantizeConfig(torch.int8, "per_token", is_symmetric=False)
-        fake_quantizer = FakeQuantizer(config)
+        fake_quantizer = IntxFakeQuantizer(config)
         fake_quantizer_out = fake_quantizer(x)
         baseline_out = per_token_dynamic_quant(x)
         torch.testing.assert_close(fake_quantizer_out, baseline_out, atol=0, rtol=0)
@@ -1580,7 +1580,7 @@ def test_fake_quantize_config_eps(self):
             is_symmetric=False,
             eps=eps,
         )
-        fake_quantizer = FakeQuantizer(config)
+        fake_quantizer = IntxFakeQuantizer(config)
         actual_out = fake_quantizer(x)
         torch.testing.assert_close(expected_out, actual_out, atol=0, rtol=0)
 
@@ -1638,7 +1638,7 @@ def test_qat_8da4w_eps(self):
     )
     def test_fake_quantizer_range_learning(self):
         """
-        Test that range learning requires `FakeQuantizer`s to be initialized correctly.
+        Test that range learning requires `IntxFakeQuantizer`s to be initialized correctly.
         """
         config = IntxFakeQuantizeConfig(
             torch.int8,
@@ -1648,7 +1648,7 @@ def test_fake_quantizer_range_learning(self):
             scale_precision=torch.float32,
             zero_point_precision=torch.float32,
         )
-        fake_quantizer = FakeQuantizer(config)
+        fake_quantizer = IntxFakeQuantizer(config)
         example_inputs = (torch.randn(2, 3),)
 
         # Not initialized, should fail
@@ -1770,7 +1770,7 @@ def test_qat_fp8a4w_quantizer(self):
             self.assertIsInstance(
                 linear.activation_fake_quantizer, _Float8RowwiseActivationFakeQuantizer
             )
-            self.assertIsInstance(linear.weight_fake_quantizer, FakeQuantizer)
+            self.assertIsInstance(linear.weight_fake_quantizer, IntxFakeQuantizer)
         prev_weight = copy.deepcopy(m.linear1.weight)
 
         # Simulate training
@@ -1854,6 +1854,7 @@ def test_qat_api_deprecation(self):
         """
         from torchao.quantization.qat import (
             FakeQuantizeConfig,
+            FakeQuantizer,
             from_intx_quantization_aware_training,
             intx_quantization_aware_training,
         )
@@ -1868,6 +1869,7 @@ def test_qat_api_deprecation(self):
             intx_quantization_aware_training: (),
             from_intx_quantization_aware_training: (),
             FakeQuantizeConfig: (torch.int8, "per_channel"),
+            FakeQuantizer: (IntxFakeQuantizeConfig(torch.int8, "per_channel"),),
         }
 
         with warnings.catch_warnings(record=True) as _warnings:

torchao/quantization/prototype/qat/fake_quantizer.py

@@ -1,5 +1,5 @@
 from torchao.quantization.qat.fake_quantizer import (
-    FakeQuantizer,
+    IntxFakeQuantizer as FakeQuantizer,
 )
 
 __all__ = [

torchao/quantization/qat/__init__.py

@@ -17,7 +17,11 @@
     FakeQuantizeConfigBase,
     IntxFakeQuantizeConfig,
 )
-from .fake_quantizer import FakeQuantizer
+from .fake_quantizer import (
+    FakeQuantizer,
+    FakeQuantizerBase,
+    IntxFakeQuantizer,
+)
 from .linear import (
     FakeQuantizedLinear,
     Float8ActInt4WeightQATQuantizer,
@@ -29,8 +33,9 @@
     "QATConfig",
     "QATStep",
     "FakeQuantizeConfigBase",
+    "FakeQuantizerBase",
     "IntxFakeQuantizeConfig",
-    "FakeQuantizer",
+    "IntxFakeQuantizer",
     "FakeQuantizedLinear",
     "FakeQuantizedEmbedding",
     # Prototype
@@ -42,6 +47,7 @@
     "Int4WeightOnlyQATQuantizer",
     "Int8DynActInt4WeightQATQuantizer",
     # for BC
+    "FakeQuantizer",
     "FakeQuantizeConfig",
     "from_intx_quantization_aware_training",
     "FromIntXQuantizationAwareTrainingConfig",

torchao/quantization/qat/api.py

@@ -382,14 +382,14 @@ def initialize_fake_quantizers(
 ) -> None:
     """
     (Prototype) Initialize the scales and zero points on all
-    :class:`~torchao.quantization.qat.fake_quantizer.FakeQuantizer`
+    :class:`~torchao.quantization.qat.fake_quantizer.IntxFakeQuantizerBase`
     in the model based on the provided example inputs.
     """
     # avoid circular dependencies
-    from torchao.quantization.qat.fake_quantizer import FakeQuantizer
+    from torchao.quantization.qat.fake_quantizer import IntxFakeQuantizer
 
     def _set_initialized(m: torch.nn.Module):
-        if isinstance(m, FakeQuantizer):
+        if isinstance(m, IntxFakeQuantizer):
             m._initialized = True
 
     model.apply(_set_initialized)

torchao/quantization/qat/embedding.py

@@ -17,7 +17,7 @@
     FakeQuantizeConfigBase,
     IntxFakeQuantizeConfig,
 )
-from .fake_quantizer import FakeQuantizer
+from .fake_quantizer import FakeQuantizerBase
 from .utils import (
     _get_qmin_qmax,
 )
@@ -66,7 +66,7 @@ def __init__(
             **kwargs,
         )
         if weight_config is not None:
-            self.weight_fake_quantizer = FakeQuantizer(weight_config)
+            self.weight_fake_quantizer = FakeQuantizerBase.from_config(weight_config)
         else:
             self.weight_fake_quantizer = None
 

torchao/quantization/qat/fake_quantizer.py

@@ -34,15 +34,37 @@
     _fake_quantize_per_channel_group,
     _fake_quantize_per_token,
     _Float8RowwiseFakeQuantize,
+    _log_deprecation_warning,
 )
 
 
-class FakeQuantizer(torch.nn.Module):
+class FakeQuantizerBase(torch.nn.Module):
     """
     Generic module for applying fake quantization to a tensor, as specified in the config.
     """
 
-    def __init__(self, config: FakeQuantizeConfigBase):
+    config: FakeQuantizeConfigBase
+
+    def __repr__(self) -> str:
+        """
+        Return a human readable representation of this `FakeQuantizer` with config details.
+        """
+        return "FakeQuantizer(%s)" % self.config
+
+    @staticmethod
+    def from_config(config: FakeQuantizeConfigBase) -> "FakeQuantizerBase":
+        if isinstance(config, IntxFakeQuantizeConfig):
+            return IntxFakeQuantizer(config)
+        else:
+            raise ValueError(f"Unknown config type: {config}")
+
+
+class IntxFakeQuantizer(FakeQuantizerBase):
+    """
+    Generic module for applying integer fake quantization to a tensor, as specified in the config.
+    """
+
+    def __init__(self, config: IntxFakeQuantizeConfig):
         super().__init__()
         self.config = config
         self.enabled = True
@@ -62,9 +84,6 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
         if not self.enabled:
             return x
 
-        if not isinstance(self.config, IntxFakeQuantizeConfig):
-            raise ValueError("Only IntxFakeQuantizeConfig is supported currently")
-
         if (
             self.config.range_learning
             and not self._initialized
@@ -186,13 +205,19 @@ def _maybe_update_qparams_for_range_learning(self) -> None:
         self.scale = torch.nn.Parameter(scale, requires_grad=True)
         self.zero_point = torch.nn.Parameter(zero_point, requires_grad=True)
 
-    def __repr__(self) -> str:
-        """
-        Return a human readable representation of this `FakeQuantizer` with config details.
-        """
-        return "FakeQuantizer(%s)" % self.config
+
+# For BC
+class FakeQuantizer(IntxFakeQuantizer):
+    """
+    (Deprecated) Please use :class:`~torchao.quantization.qat.IntxFakeQuantizer` instead.
+    """
+
+    def __init__(self, config: FakeQuantizeConfigBase):
+        super().__init__(config)
+        _log_deprecation_warning(self)
 
 
+# TODO: make this a FakeQuantizerBase
 class _Float8RowwiseActivationFakeQuantizer(torch.nn.Module):
     """
     Simple fake quantizer for float8 rowwise fake quantization, intended for activations only.

torchao/quantization/qat/linear.py

@@ -32,7 +32,7 @@
     IntxFakeQuantizeConfig,
 )
 from .fake_quantizer import (
-    FakeQuantizer,
+    FakeQuantizerBase,
     _Float8RowwiseActivationFakeQuantizer,
 )
 from .utils import (
@@ -84,7 +84,9 @@ def __init__(
         )
         # initialize activation fake quantizer
         if activation_config is not None:
-            self.activation_fake_quantizer = FakeQuantizer(activation_config)
+            self.activation_fake_quantizer = FakeQuantizerBase.from_config(
+                activation_config
+            )
         else:
             self.activation_fake_quantizer = None
 
@@ -97,7 +99,7 @@ def __init__(
                         "in_features (%s) %% group_size (%s) must be == 0"
                         % (in_features, group_size)
                     )
-            self.weight_fake_quantizer = FakeQuantizer(weight_config)
+            self.weight_fake_quantizer = FakeQuantizerBase.from_config(weight_config)
         else:
             self.weight_fake_quantizer = None