Remove old change_linear_weights_to_* APIs

benchmarks/benchmark_aq.py

@@ -75,11 +75,13 @@ def _ref_change_linear_weights_to_int8_dqtensors(model, filter_fn=None, **kwargs
     """
     from torchao.quantization.quant_api import (
         _get_subclass_inserter,
-        _in_features_greater_than_16,
         _is_linear,
     )
     from torchao.quantization.subclass import Int8DynamicallyQuantizedLinearWeight
 
+    def _in_features_greater_than_16(mod, *args):
+        return hasattr(mod, "in_features") and mod.in_features > 16
+
     if filter_fn is None:
         filter_fn = lambda *args: _is_linear(*args) and _in_features_greater_than_16(
             *args

test/integration/test_integration.py

@@ -40,7 +40,6 @@
 from torchao.quantization.quant_api import (
     Float8DynamicActivationFloat8WeightConfig,
     _replace_with_custom_fn_if_matches_filter,
-    change_linear_weights_to_int8_dqtensors,
     int4_weight_only,
     int8_dynamic_activation_int4_weight,
     int8_dynamic_activation_int8_weight,
@@ -1829,11 +1828,6 @@ class TestAOTI(unittest.TestCase):
         list(itertools.product(TENSOR_SUBCLASS_APIS, COMMON_DEVICES, COMMON_DTYPES)),
     )
     def test_aoti(self, api, test_device, test_dtype):
-        if api is change_linear_weights_to_int8_dqtensors and test_device == "cuda":
-            self.skipTest(
-                f"{api} in {test_device} is not support for aoti compilation yet"
-            )
-
         if (
             test_device == "cuda"
             and torch.cuda.is_available()

test/quantization/test_quant_api.py

@@ -146,32 +146,6 @@ def forward(self, x):
         return x
 
 
-def _ref_change_linear_weights_to_int8_dqtensors(model, filter_fn=None, **kwargs):
-    """
-    The deprecated implementation for int8 dynamic quant API, used as a reference for
-    numerics and performance
-    """
-    from torchao.quantization.quant_api import (
-        _get_subclass_inserter,
-        _in_features_greater_than_16,
-        _is_linear,
-    )
-    from torchao.quantization.subclass import Int8DynamicallyQuantizedLinearWeight
-
-    if filter_fn is None:
-        filter_fn = lambda *args: _is_linear(*args) and _in_features_greater_than_16(
-            *args
-        )
-
-    _replace_with_custom_fn_if_matches_filter(
-        model,
-        _get_subclass_inserter(
-            Int8DynamicallyQuantizedLinearWeight, enable_parametrization=False, **kwargs
-        ),
-        filter_fn,
-    )
-
-
 def _get_ref_change_linear_weights_to_woqtensors(deprecated_tenosr_subclass):
     def _ref_change_linear_weights_to_woqtensors(model, filter_fn=None, **kwargs):
         """

torchao/quantization/README.md

@@ -125,44 +125,29 @@ be applied individually. While there are a large variety of quantization apis, t
 #### A16W4 WeightOnly Quantization
 
 ```python
-# for torch 2.4+
 from torchao.quantization import quantize_, Int4WeightOnlyConfig
 group_size = 32
 
 # you can enable [hqq](https://github.com/mobiusml/hqq/tree/master) quantization which is expected to improves accuracy through
 # use_hqq flag for `Int4WeightOnlyConfig` quantization
 use_hqq = False
 quantize_(model, Int4WeightOnlyConfig(group_size=group_size, use_hqq=use_hqq))
-
-# for torch 2.2.2 and 2.3
-from torchao.quantization.quant_api import change_linear_weights_to_int4_woqtensors
-change_linear_weights_to_int4_woqtensors(model)
 ```
 
 Note: The quantization error incurred by applying int4 quantization to your model can be fairly significant, so using external techniques like GPTQ may be necessary to obtain a usable model.
 
 #### A16W8 Int8 WeightOnly Quantization
 
 ```python
-# for torch 2.4+
 from torchao.quantization import quantize_, Int8WeightOnlyConfig
 quantize_(model, Int8WeightOnlyConfig())
-
-# for torch 2.2.2 and 2.3
-from torchao.quantization.quant_api import change_linear_weights_to_int8_woqtensors
-change_linear_weights_to_int8_woqtensors(model)
 ```
 
 #### A8W8 Int8 Dynamic Quantization
 
 ```python
-# for torch 2.4+
 from torchao.quantization import quantize_, Int8DynamicActivationInt8WeightConfig
 quantize_(model, Int8DynamicActivationInt8WeightConfig())
-
-# for torch 2.2.2 and 2.3
-from torchao.quantization.quant_api import change_linear_weights_to_int8_dqtensors
-change_linear_weights_to_int8_dqtensors(model)
 ```
 
 ### A16W8 Float8 WeightOnly Quantization

torchao/quantization/quant_api.py

@@ -120,9 +120,6 @@
     ZeroPointDomain,
 )
 from .subclass import (
-    Int4WeightOnlyQuantizedLinearWeight,
-    Int8DynamicallyQuantizedLinearWeight,
-    Int8WeightOnlyQuantizedLinearWeight,
     QuantizedLinearWeightBase,
 )
 from .unified import Quantizer, TwoStepQuantizer
@@ -172,109 +169,6 @@
 }
 
 
-######
-# TO BE DEPRECATED START
-######
-def _in_features_greater_than_16(mod, *args):
-    return hasattr(mod, "in_features") and mod.in_features > 16
-
-
-# TODO: delete
-def change_linear_weights_to_int8_dqtensors(model, filter_fn=None, **kwargs):
-    """
-    Converts all linear weight tensors to the `Int8DynamicallyQuantizedLinearWeight`
-    Tensor subclass, effectively applying the same form of quantization
-    as apply_dynamic_quant while not modifying the linear modules.
-    """
-    raise ImportError(
-        "This API is deprecated for pytorch 2.4+, please checkout quantization/README.md for most up to date APIs"
-    )
-
-    if filter_fn is None:
-        filter_fn = lambda *args: _is_linear(*args) and _in_features_greater_than_16(
-            *args
-        )
-
-    _replace_with_custom_fn_if_matches_filter(
-        model,
-        _get_subclass_inserter(
-            Int8DynamicallyQuantizedLinearWeight, enable_parametrization=False, **kwargs
-        ),
-        filter_fn,
-    )
-
-
-# TODO: delete
-def change_linear_weights_to_int8_woqtensors(model, filter_fn=None, **kwargs):
-    """
-    Converts all linear weight tensors to the
-    `Int8WeightOnlyQuantizedLinearWeight` tensor subclass,
-    effectively applying the same form of quantization
-    as apply_weight_only_int8_quant while not modifying the linear modules.
-    """
-    raise ImportError(
-        "This API is deprecated for pytorch 2.4+, please checkout quantization/README.md for most up to date APIs"
-    )
-
-    _replace_with_custom_fn_if_matches_filter(
-        model,
-        _get_subclass_inserter(
-            Int8WeightOnlyQuantizedLinearWeight, enable_parametrization=False, **kwargs
-        ),
-        _is_linear if filter_fn is None else filter_fn,
-    )
-
-
-# TODO: delete
-def change_linear_weights_to_int4_woqtensors(
-    model,
-    groupsize=128,
-    inner_k_tiles=8,
-    filter_fn=None,
-    zero_point_domain=ZeroPointDomain.FLOAT,
-    preserve_zero=False,
-):
-    """
-    Converts all linear weight tensors to the
-    `Int4WeightOnlyQuantizedLinearWeight` tensor subclass,
-    effectively applying the same form of quantization
-    as apply_dynamic_quant while not modifying the linear modules.
-    Args:
-        `groupsize`: parameter for quantization, controls the granularity of quantization, smaller
-         size is more fine grained, choices are [256, 128, 64, 32]
-        `inner_k_tiles`: parameter for int4 mm kernel, choices are [8, 4, 2]
-        `filter_fn`: function that takes a nn.Module instance and fully qualified name of the module, \
-            returns True if we want to run `config` on
-        `zero_point_domain`: data type of zeros points, choices are [ZeroPointDomain.FLOAT, \
-            ZeroPointDomain.INT, ZeroPointDomain.NONE]
-        `preserve_zero`: whether to preserve zero, default is False
-    """
-    raise ImportError(
-        "This API is deprecated for pytorch 2.4+, please checkout quantization/README.md for most up to date APIs"
-    )
-
-    if filter_fn is None:
-        filter_fn = _is_linear
-
-    _replace_with_custom_fn_if_matches_filter(
-        model,
-        _get_subclass_inserter(
-            Int4WeightOnlyQuantizedLinearWeight,
-            enable_parametrization=False,
-            groupsize=groupsize,
-            inner_k_tiles=inner_k_tiles,
-            zero_point_domain=zero_point_domain,
-            preserve_zero=preserve_zero,
-        ),
-        filter_fn,
-    )
-
-
-########
-# TO BE DEPRECATED END
-########
-
-
 def _replace_with_custom_fn_if_matches_filter(
     model,
     replacement_fn,