[BE] Convert quant_primitives methods private

docs/source/api_ref_quantization.rst

@@ -63,14 +63,8 @@ Quantization Primitives
 
     choose_qparams_affine
     choose_qparams_affine_with_min_max
-    choose_qparams_affine_floatx
     quantize_affine
-    quantize_affine_floatx
     dequantize_affine
-    dequantize_affine_floatx
-    choose_qparams_and_quantize_affine_hqq
-    fake_quantize_affine
-    fake_quantize_affine_cachemask
     safe_int_mm
     int_scaled_matmul
     MappingType

test/dtypes/test_affine_quantized_float.py

@@ -42,10 +42,10 @@
 )
 from torchao.quantization.quant_primitives import (
     MappingType,
+    _choose_qparams_affine_float8,
+    _dequantize_affine_float8,
+    _quantize_affine_float8,
     choose_qparams_affine,
-    choose_qparams_affine_float8,
-    dequantize_affine_float8,
-    quantize_affine_float8,
 )
 from torchao.utils import (
     is_sm_at_least_89,
@@ -358,21 +358,21 @@ def test_mm_float8dq_per_row(
     @common_utils.parametrize("output_dtype", [torch.float32, torch.bfloat16])
     @common_utils.parametrize("block_size", [None, (1, 32), (2, 16), (4, 8)])
     def test_dequantize_affine_float8(self, float8_dtype, output_dtype, block_size):
-        """Test dequantize_affine_float8 with various configurations"""
+        """Test _dequantize_affine_float8 with various configurations"""
 
         device = "cuda"
         input_tensor = torch.randn(8, 64, device=device, dtype=torch.float32)
 
         # Choose quantization parameters
-        scale = choose_qparams_affine_float8(
+        scale = _choose_qparams_affine_float8(
             input_tensor, float8_dtype=float8_dtype, block_size=block_size
         )
 
         # Quantize
-        quantized = quantize_affine_float8(input_tensor, scale, float8_dtype)
+        quantized = _quantize_affine_float8(input_tensor, scale, float8_dtype)
 
         # Dequantize
-        dequantized = dequantize_affine_float8(quantized, scale, output_dtype)
+        dequantized = _dequantize_affine_float8(quantized, scale, output_dtype)
 
         # Verify output properties
         self.assertEqual(dequantized.dtype, output_dtype)
@@ -395,7 +395,7 @@ def test_dequantize_affine_float8_scale_broadcasting(self):
         block_size = (2, 16)  # 2x2 blocks in first dim, 2x16 blocks in second dim
 
         # Choose quantization parameters
-        scale = choose_qparams_affine_float8(
+        scale = _choose_qparams_affine_float8(
             input_tensor, float8_dtype=torch.float8_e4m3fn, block_size=block_size
         )
 
@@ -407,10 +407,10 @@ def test_dequantize_affine_float8_scale_broadcasting(self):
         self.assertEqual(scale.shape, expected_scale_shape)
 
         # Quantize
-        quantized = quantize_affine_float8(input_tensor, scale, torch.float8_e4m3fn)
+        quantized = _quantize_affine_float8(input_tensor, scale, torch.float8_e4m3fn)
 
         # Dequantize
-        dequantized = dequantize_affine_float8(quantized, scale, torch.float32)
+        dequantized = _dequantize_affine_float8(quantized, scale, torch.float32)
 
         # Verify shapes match
         self.assertEqual(dequantized.shape, input_tensor.shape)

test/dtypes/test_floatx.py

@@ -91,13 +91,13 @@ def test_from_scaled_tc_floatx_compile(self, ebits, mbits, device):
     @parametrize("ebits,mbits", _Floatx_DTYPES)
     def test_to_copy_device(self, ebits, mbits):
         from torchao.quantization.quant_primitives import (
-            choose_qparams_affine_floatx,
-            quantize_affine_floatx,
+            _choose_qparams_affine_floatx,
+            _quantize_affine_floatx,
         )
 
         x = torch.randn(256, 64)
-        scale = choose_qparams_affine_floatx(x, ebits, mbits)
-        x = quantize_affine_floatx(x, scale, ebits, mbits)
+        scale = _choose_qparams_affine_floatx(x, ebits, mbits)
+        x = _quantize_affine_floatx(x, scale, ebits, mbits)
         _layout = FloatxTensorCoreLayout(ebits, mbits)
         floatx_tensor_impl = FloatxTensorCoreAQTTensorImpl.from_plain(
             x, scale, None, _layout

test/prototype/test_gguf_quant.py

@@ -13,7 +13,7 @@
     GGUFWeightOnlyConfig,
 )
 from torchao.quantization import quantize_
-from torchao.quantization.quant_primitives import choose_qparams_gguf
+from torchao.quantization.quant_primitives import _choose_qparams_gguf
 from torchao.quantization.utils import compute_error
 
 
@@ -31,7 +31,7 @@ def test_choose_qparams_gguf(self):
             super_block_min_scale,
             quantized_block_scale,
             quantized_block_min,
-        ) = choose_qparams_gguf(self.input, self.block_size, self.dtype)
+        ) = _choose_qparams_gguf(self.input, self.block_size, self.dtype)
 
         assert super_block_scale_scale.shape, (2, 8)
         assert super_block_min_scale.shape, (2, 8)

test/quantization/test_marlin_qqq.py

@@ -21,7 +21,7 @@
 )
 from torchao.quantization.quant_primitives import (
     MappingType,
-    choose_qparams_and_quantize_affine_qqq,
+    _choose_qparams_and_quantize_affine_qqq,
 )
 from torchao.testing.utils import skip_if_rocm
 from torchao.utils import TORCH_VERSION_AT_LEAST_2_5
@@ -102,7 +102,7 @@ def test_pack_unpack_equivalence(self):
 
         for group_size in [-1, 128]:
             # Quantize weights
-            q_w, s_group, s_channel, _ = choose_qparams_and_quantize_affine_qqq(
+            q_w, s_group, s_channel, _ = _choose_qparams_and_quantize_affine_qqq(
                 w, num_bits, group_size
             )
 

test/quantization/test_qat.py

@@ -64,9 +64,9 @@
     MappingType,
     TorchAODType,
     ZeroPointDomain,
+    _fake_quantize_affine,
     choose_qparams_affine,
     dequantize_affine,
-    fake_quantize_affine,
     quantize_affine,
 )
 from torchao.quantization.unified import (
@@ -637,7 +637,7 @@ def test_qat_4w_primitives(self):
             group_size,
             scales_precision,
         )
-        w_fq = fake_quantize_affine(
+        w_fq = _fake_quantize_affine(
             weight,
             block_size,
             scales,

test/quantization/test_quant_primitives.py

@@ -13,11 +13,11 @@
 from torchao.quantization.quant_primitives import (
     MappingType,
     ZeroPointDomain,
+    _choose_qparams_affine_tinygemm,
+    _fake_quantize_affine,
+    _fake_quantize_affine_cachemask,
     choose_qparams_affine,
-    choose_qparams_affine_tinygemm,
     dequantize_affine,
-    fake_quantize_affine,
-    fake_quantize_affine_cachemask,
     quantize_affine,
 )
 
@@ -672,7 +672,7 @@ def test_get_groupwise_affine_qparams(self):
                 zero_point_domain=zero_point_domain,
             )
             if zero_point_domain == ZeroPointDomain.FLOAT:
-                scale, zero_point = choose_qparams_affine_tinygemm(
+                scale, zero_point = _choose_qparams_affine_tinygemm(
                     input,
                     mapping_type,
                     block_size,
@@ -780,7 +780,7 @@ def test_fake_quantize_affine(self):
         dequantized = dequantize_affine(
             quantized, block_size, scale, zero_point, dtype, quant_min, quant_max
         )
-        fake_quantized = fake_quantize_affine(
+        fake_quantized = _fake_quantize_affine(
             input, block_size, scale, zero_point, dtype, quant_min, quant_max
         )
         torch.testing.assert_close(dequantized, fake_quantized)
@@ -816,7 +816,7 @@ def test_fake_quantize_affine_cachemask(self):
         dequantized = dequantize_affine(
             quantized, block_size, scale, zero_point, dtype, quant_min, quant_max
         )
-        (fake_quantized, mask) = fake_quantize_affine_cachemask(
+        (fake_quantized, mask) = _fake_quantize_affine_cachemask(
             input,
             block_size,
             scale,

test/test_ops.py

@@ -23,7 +23,9 @@
     marlin_qqq_workspace,
     pack_to_marlin_qqq,
 )
-from torchao.quantization.quant_primitives import choose_qparams_and_quantize_affine_qqq
+from torchao.quantization.quant_primitives import (
+    _choose_qparams_and_quantize_affine_qqq,
+)
 from torchao.sparsity.marlin import inject_24, marlin_24_workspace, pack_to_marlin_24
 from torchao.utils import (
     TORCH_VERSION_AT_LEAST_2_5,
@@ -713,7 +715,7 @@ def test_marlin_qqq(batch_size, k_chunk, n_chunk, num_bits, group_size, mnk_fact
     )
 
     # Quantize weights
-    q_w, s_group, s_channel, w_ref = choose_qparams_and_quantize_affine_qqq(
+    q_w, s_group, s_channel, w_ref = _choose_qparams_and_quantize_affine_qqq(
         b_weight, num_bits, group_size
     )
     q_w = q_w.t()

torchao/dtypes/affine_quantized_tensor.py

@@ -18,22 +18,22 @@
     FP8_TYPES,
     MappingType,
     ZeroPointDomain,
+    _choose_qparams_affine_dont_preserve_zero,
+    _choose_qparams_affine_float8,
+    _choose_qparams_affine_floatx,
+    _choose_qparams_affine_tinygemm,
+    _choose_qparams_and_quantize_affine_hqq,
+    _dequantize_affine_float8,
+    _dequantize_affine_floatx,
+    _dequantize_affine_no_zero_point,
+    _dequantize_affine_tinygemm,
+    _quantize_affine_float8,
+    _quantize_affine_floatx,
+    _quantize_affine_no_zero_point,
+    _quantize_affine_tinygemm,
     choose_qparams_affine,
-    choose_qparams_affine_dont_preserve_zero,
-    choose_qparams_affine_float8,
-    choose_qparams_affine_floatx,
-    choose_qparams_affine_tinygemm,
-    choose_qparams_and_quantize_affine_hqq,
     dequantize_affine,
-    dequantize_affine_float8,
-    dequantize_affine_floatx,
-    dequantize_affine_no_zero_point,
-    dequantize_affine_tinygemm,
     quantize_affine,
-    quantize_affine_float8,
-    quantize_affine_floatx,
-    quantize_affine_no_zero_point,
-    quantize_affine_tinygemm,
 )
 from torchao.utils import (
     TORCH_VERSION_AT_LEAST_2_5,
@@ -142,7 +142,7 @@ def dequantize(self, output_dtype: Optional[torch.dtype] = None) -> torch.Tensor
 
         if isinstance(self._layout, FloatxTensorCoreLayout):
             int_data, scale = self.tensor_impl.get_plain()
-            return dequantize_affine_floatx(
+            return _dequantize_affine_floatx(
                 int_data,
                 scale,
                 self._layout.ebits,
@@ -151,11 +151,11 @@ def dequantize(self, output_dtype: Optional[torch.dtype] = None) -> torch.Tensor
             )
         elif isinstance(self._layout, Float8Layout):
             data, scale, _ = self.tensor_impl.get_plain()
-            return dequantize_affine_float8(data, scale, output_dtype)
+            return _dequantize_affine_float8(data, scale, output_dtype)
         else:
             data, scale, zero_point = self.tensor_impl.get_plain()
             if self.zero_point_domain == ZeroPointDomain.FLOAT:
-                dq = dequantize_affine_tinygemm(
+                dq = _dequantize_affine_tinygemm(
                     data,
                     self.block_size,
                     scale,
@@ -166,7 +166,7 @@ def dequantize(self, output_dtype: Optional[torch.dtype] = None) -> torch.Tensor
                     output_dtype=output_dtype,
                 )
             elif self.zero_point_domain == ZeroPointDomain.NONE:
-                dq = dequantize_affine_no_zero_point(
+                dq = _dequantize_affine_no_zero_point(
                     data,
                     self.block_size,
                     scale,
@@ -270,7 +270,7 @@ def from_hp_to_intx(
             from torchao.dtypes import Int4CPULayout
             from torchao.dtypes.uintx import TensorCoreTiledLayout
 
-            data, scale, zero_point, _ = choose_qparams_and_quantize_affine_hqq(
+            data, scale, zero_point, _ = _choose_qparams_and_quantize_affine_hqq(
                 input_float,
                 nbits=nbits,
                 group_size=group_size,
@@ -291,7 +291,7 @@ def from_hp_to_intx(
             data = data.to(target_dtype)
         else:
             if zero_point_domain == ZeroPointDomain.FLOAT and not preserve_zero:
-                scale, zero_point = choose_qparams_affine_tinygemm(
+                scale, zero_point = _choose_qparams_affine_tinygemm(
                     input_float,
                     mapping_type,
                     block_size,
@@ -303,7 +303,7 @@ def from_hp_to_intx(
                     zero_point_dtype,
                 )
             elif zero_point_domain == ZeroPointDomain.INT and not preserve_zero:
-                scale, zero_point = choose_qparams_affine_dont_preserve_zero(
+                scale, zero_point = _choose_qparams_affine_dont_preserve_zero(
                     input_float,
                     mapping_type,
                     block_size,
@@ -329,7 +329,7 @@ def from_hp_to_intx(
             # choose_qparams_affine is a custom op that does support returning optional Tensors. We thus set the zero_point to None if its domain is None
             if zero_point_domain == ZeroPointDomain.NONE:
                 zero_point = None
-                data = quantize_affine_no_zero_point(
+                data = _quantize_affine_no_zero_point(
                     input_float,
                     block_size,
                     scale,
@@ -339,7 +339,7 @@ def from_hp_to_intx(
                     quant_max,
                 )
             elif zero_point_domain == ZeroPointDomain.FLOAT:
-                data = quantize_affine_tinygemm(
+                data = _quantize_affine_tinygemm(
                     input_float,
                     block_size,
                     scale,
@@ -400,7 +400,7 @@ def from_hp_to_intx_static(
 
         if zero_point_domain == ZeroPointDomain.NONE:
             zero_point = None
-            int_data = quantize_affine_no_zero_point(
+            int_data = _quantize_affine_no_zero_point(
                 input_float,
                 block_size,
                 scale,
@@ -410,7 +410,7 @@ def from_hp_to_intx_static(
                 quant_max,
             )
         elif zero_point_domain == ZeroPointDomain.FLOAT:
-            int_data = quantize_affine_tinygemm(
+            int_data = _quantize_affine_tinygemm(
                 input_float,
                 block_size,
                 scale,
@@ -462,10 +462,10 @@ def from_hp_to_floatx(
         if target_dtype in FP8_TYPES:
             original_shape = input_float.shape
             input_float = _layout.pre_process(input_float)
-            scale = choose_qparams_affine_float8(
+            scale = _choose_qparams_affine_float8(
                 input_float, float8_dtype=target_dtype, block_size=block_size
             )
-            data = quantize_affine_float8(input_float, scale, target_dtype)
+            data = _quantize_affine_float8(input_float, scale, target_dtype)
             data, scale, zero_point = _layout.post_process(
                 data, scale, None, block_size
             )
@@ -499,7 +499,7 @@ def from_hp_to_floatx_static(
                 input_float, scale, ZeroPointDomain.NONE, block_size
             )
 
-            data = quantize_affine_float8(
+            data = _quantize_affine_float8(
                 input_float,
                 scale,
                 target_dtype,
@@ -545,8 +545,8 @@ def from_hp_to_fpx(
 
         ebits, mbits = _layout.ebits, _layout.mbits
         # Note: these ops are hardcoded to have per axis quantization (axis=1) right now
-        scale = choose_qparams_affine_floatx(input_float, ebits, mbits)
-        floatx_unpacked = quantize_affine_floatx(input_float, scale, ebits, mbits)
+        scale = _choose_qparams_affine_floatx(input_float, ebits, mbits)
+        floatx_unpacked = _quantize_affine_floatx(input_float, scale, ebits, mbits)
         floatx_packed, scale, _ = _layout.post_process(
             floatx_unpacked, scale, None, block_size
         )