[do not land] testing if moving this breaks my PRs

test/quantization/test_qat.py

@@ -17,13 +17,13 @@
 from torch.ao.quantization.fx._decomposed import quantized_decomposed_lib  # noqa: F401
 
 from torchao import quantize_
-from torchao.quantization.GPTQ import _replace_linear_8da4w, _replace_linear_int4
 from torchao.quantization.granularity import (
     PerAxis,
     PerGroup,
     PerRow,
     PerToken,
 )
+from torchao.quantization.MOVED_GPTQ import _replace_linear_8da4w, _replace_linear_int4
 from torchao.quantization.qat.api import (
     ComposableQATQuantizer,
     FakeQuantizeConfig,
@@ -284,7 +284,7 @@ def _set_ptq_weight(
         Set the weight to the quantized version of the given fp32 weights,
         for making linear outputs comparable with QAT.
         """
-        from torchao.quantization.GPTQ import (
+        from torchao.quantization.MOVED_GPTQ import (
             Int8DynActInt4WeightLinear,
             WeightOnlyInt4Linear,
         )
@@ -332,7 +332,7 @@ def _set_ptq_weight(
         not TORCH_VERSION_AT_LEAST_2_4, "skipping when torch version is 2.4 or lower"
     )
     def test_qat_8da4w_linear(self):
-        from torchao.quantization.GPTQ import Int8DynActInt4WeightLinear
+        from torchao.quantization.MOVED_GPTQ import Int8DynActInt4WeightLinear
         from torchao.quantization.qat.linear import Int8DynActInt4WeightQATLinear
 
         group_size = 128
@@ -365,7 +365,7 @@ def test_qat_8da4w_linear(self):
         not TORCH_VERSION_AT_LEAST_2_4, "skipping when torch version is 2.4 or lower"
     )
     def test_qat_8da4w_quantizer(self):
-        from torchao.quantization.GPTQ import Int8DynActInt4WeightQuantizer
+        from torchao.quantization.MOVED_GPTQ import Int8DynActInt4WeightQuantizer
         from torchao.quantization.qat import Int8DynActInt4WeightQATQuantizer
 
         group_size = 16
@@ -683,7 +683,7 @@ def test_qat_4w_primitives(self):
     )
     @unittest.skipIf(not _CUDA_IS_AVAILABLE, "skipping when cuda is not available")
     def test_qat_4w_linear(self):
-        from torchao.quantization.GPTQ import WeightOnlyInt4Linear
+        from torchao.quantization.MOVED_GPTQ import WeightOnlyInt4Linear
         from torchao.quantization.qat.linear import Int4WeightOnlyQATLinear
 
         group_size = 128
@@ -730,7 +730,7 @@ def test_qat_4w_quantizer_gradients(self):
     )
     @unittest.skipIf(not _CUDA_IS_AVAILABLE, "skipping when cuda is not available")
     def test_qat_4w_quantizer(self):
-        from torchao.quantization.GPTQ import Int4WeightOnlyQuantizer
+        from torchao.quantization.MOVED_GPTQ import Int4WeightOnlyQuantizer
         from torchao.quantization.qat import Int4WeightOnlyQATQuantizer
 
         group_size = 32

test/quantization/test_quant_api.py

@@ -310,7 +310,7 @@ def api(model):
         not TORCH_VERSION_AT_LEAST_2_3, "skipping when torch verion is 2.3 or lower"
     )
     def test_8da4w_quantizer(self):
-        from torchao.quantization.GPTQ import Int8DynActInt4WeightLinear
+        from torchao.quantization.MOVED_GPTQ import Int8DynActInt4WeightLinear
         from torchao.quantization.quant_api import Int8DynActInt4WeightQuantizer
 
         quantizer = Int8DynActInt4WeightQuantizer(groupsize=32)
@@ -325,7 +325,7 @@ def test_8da4w_quantizer(self):
         not TORCH_VERSION_AT_LEAST_2_3, "skipping when torch verion is 2.3 or lower"
     )
     def test_8da4w_quantizer_linear_bias(self):
-        from torchao.quantization.GPTQ import Int8DynActInt4WeightLinear
+        from torchao.quantization.MOVED_GPTQ import Int8DynActInt4WeightLinear
         from torchao.quantization.quant_api import Int8DynActInt4WeightQuantizer
 
         quantizer = Int8DynActInt4WeightQuantizer(groupsize=32)
@@ -343,7 +343,7 @@ def test_8da4w_quantizer_linear_bias(self):
     @unittest.skip("skipping until we get checkpoints for gpt-fast")
     def test_8da4w_gptq_quantizer(self):
         from torchao._models._eval import InputRecorder, TransformerEvalWrapper
-        from torchao.quantization.GPTQ import Int8DynActInt4WeightGPTQQuantizer
+        from torchao.quantization.MOVED_GPTQ import Int8DynActInt4WeightGPTQQuantizer
 
         # should be similar to TorchCompileDynamicQuantizer
         precision = torch.bfloat16
@@ -519,7 +519,7 @@ def test_gptq_quantizer_int4_weight_only(self):
     @unittest.skip("skipping until we get checkpoints for gpt-fast")
     def test_quantizer_int4_weight_only(self):
         from torchao._models._eval import TransformerEvalWrapper
-        from torchao.quantization.GPTQ import Int4WeightOnlyQuantizer
+        from torchao.quantization.MOVED_GPTQ import Int4WeightOnlyQuantizer
 
         precision = torch.bfloat16
         device = "cuda"
@@ -648,7 +648,7 @@ def test_quantized_tensor_subclass_8da4w(self, mapping_type):
         )
 
         # reference
-        from torchao.quantization.GPTQ import Int8DynActInt4WeightLinear
+        from torchao.quantization.MOVED_GPTQ import Int8DynActInt4WeightLinear
         from torchao.quantization.quant_api import Int8DynActInt4WeightQuantizer
 
         quantizer = Int8DynActInt4WeightQuantizer(

torchao/quantization/__init__.py

@@ -13,13 +13,6 @@
     OTHER_AUTOQUANT_CLASS_LIST,
     autoquant,
 )
-from .GPTQ import (
-    Int4WeightOnlyGPTQQuantizer,
-    Int4WeightOnlyQuantizer,
-    Int8DynActInt4WeightGPTQQuantizer,
-    Int8DynActInt4WeightLinear,
-    Int8DynActInt4WeightQuantizer,
-)
 from .granularity import (
     PerAxis,
     PerGroup,
@@ -34,6 +27,13 @@
 from .linear_activation_scale import (
     to_weight_tensor_with_linear_activation_scale_metadata,
 )
+from .MOVED_GPTQ import (
+    Int4WeightOnlyGPTQQuantizer,
+    Int4WeightOnlyQuantizer,
+    Int8DynActInt4WeightGPTQQuantizer,
+    Int8DynActInt4WeightLinear,
+    Int8DynActInt4WeightQuantizer,
+)
 from .observer import (
     AffineQuantizedMinMaxObserver,
     AffineQuantizedObserverBase,

torchao/quantization/qat/linear.py

@@ -10,15 +10,15 @@
 import torch.nn.functional as F
 
 from torchao.dtypes.utils import is_device
-from torchao.quantization.GPTQ import (
+from torchao.quantization.granularity import PerGroup
+from torchao.quantization.MOVED_GPTQ import (
     Int8DynActInt4WeightLinear,
     WeightOnlyInt4Linear,
     _check_linear_int4_k,
     _replace_linear_8da4w,
     _replace_linear_int4,
     groupwise_affine_quantize_tensor,
 )
-from torchao.quantization.granularity import PerGroup
 from torchao.quantization.quant_primitives import (
     TorchAODType,
     ZeroPointDomain,

torchao/quantization/quant_api.py

@@ -83,12 +83,6 @@
 )
 
 from .autoquant import AutoQuantizableLinearWeight, autoquant
-from .GPTQ import (
-    Int4WeightOnlyGPTQQuantizer,
-    Int4WeightOnlyQuantizer,
-    Int8DynActInt4WeightGPTQQuantizer,
-    Int8DynActInt4WeightQuantizer,
-)
 from .granularity import (
     Granularity,
     PerAxis,
@@ -100,6 +94,12 @@
     LinearActivationQuantizedTensor,
     to_linear_activation_quantized,
 )
+from .MOVED_GPTQ import (
+    Int4WeightOnlyGPTQQuantizer,
+    Int4WeightOnlyQuantizer,
+    Int8DynActInt4WeightGPTQQuantizer,
+    Int8DynActInt4WeightQuantizer,
+)
 from .qat import (
     intx_quantization_aware_training,
 )