Remove old change_linear_weights_to_* APIs (#2721)

* Deprecate old TORCH_VERSION variables

**Summary:** This commit deprecates the following variables:

```
TORCH_VERSION_AT_LEAST_2_5
TORCH_VERSION_AT_LEAST_2_4
TORCH_VERSION_AT_LEAST_2_3
TORCH_VERSION_AT_LEAST_2_2
TORCH_VERSION_AFTER_2_5
TORCH_VERSION_AFTER_2_4
TORCH_VERSION_AFTER_2_3
TORCH_VERSION_AFTER_2_2
```

As of this commit, the latest released version of PyTorch is 2.8,
which means we can drop support for 2.5 and before since we only
support 3 of the latest releases.

The next commit will remove usages of all of these variables
from within torchao.

**Test Plan:**
```
python test/test_utils.py -k torch_version_deprecation
```

[ghstack-poisoned]

* Update on "Deprecate old TORCH_VERSION variables"


**Summary:** This commit deprecates the following variables:

```
TORCH_VERSION_AT_LEAST_2_5
TORCH_VERSION_AT_LEAST_2_4
TORCH_VERSION_AT_LEAST_2_3
TORCH_VERSION_AT_LEAST_2_2
TORCH_VERSION_AFTER_2_5
TORCH_VERSION_AFTER_2_4
TORCH_VERSION_AFTER_2_3
TORCH_VERSION_AFTER_2_2
```

As of this commit, the latest released version of PyTorch is 2.8,
which means we can drop support for 2.5 and before since we only
support 3 of the latest releases.

The next commit will remove usages of all of these variables
from within torchao.

**Test Plan:**
```
python test/test_utils.py -k torch_version_deprecation
```

[ghstack-poisoned]

* Drop support for PyTorch 2.5 and before

**Summary:** We gate on the PyTorch version throughout the repo.
Recently PyTorch 2.8 was released, so the oldest PyTorch version
we need to support is 2.6. After this commit, we assume the user
is running PyTorch 2.6+, and remove all references to the following
variables, which are deprecated.

```
TORCH_VERSION_AT_LEAST_2_6
TORCH_VERSION_AT_LEAST_2_5
TORCH_VERSION_AT_LEAST_2_4
TORCH_VERSION_AT_LEAST_2_3
TORCH_VERSION_AT_LEAST_2_2
TORCH_VERSION_AFTER_2_5
TORCH_VERSION_AFTER_2_4
TORCH_VERSION_AFTER_2_3
TORCH_VERSION_AFTER_2_2
```

**Test Plan:** CI

[ghstack-poisoned]

* Remove old `change_linear_weights_to_*` APIs

**Summary:** This commit removes these super old quantization
APIs that aren't even accessible by the user:

```
change_linear_weights_to_int8_dqtensors
change_linear_weights_to_int8_woqtensors
change_linear_weights_to_int4_woqtensors
```

**Test Plan:** CI

[ghstack-poisoned]

* Update base for Update on "Remove old `change_linear_weights_to_*` APIs"


**Summary:** This commit removes these super old quantization
APIs that aren't even accessible by the user:

```
change_linear_weights_to_int8_dqtensors
change_linear_weights_to_int8_woqtensors
change_linear_weights_to_int4_woqtensors
```

**Test Plan:** CI

[ghstack-poisoned]

* Update base for Update on "Remove old `change_linear_weights_to_*` APIs"


**Summary:** This commit removes these super old quantization
APIs that aren't even accessible by the user:

```
change_linear_weights_to_int8_dqtensors
change_linear_weights_to_int8_woqtensors
change_linear_weights_to_int4_woqtensors
```

**Test Plan:** CI

[ghstack-poisoned]

* Update base for Update on "Remove old `change_linear_weights_to_*` APIs"


**Summary:** This commit removes these super old quantization
APIs that aren't even accessible by the user:

```
change_linear_weights_to_int8_dqtensors
change_linear_weights_to_int8_woqtensors
change_linear_weights_to_int4_woqtensors
```

**Test Plan:** CI

[ghstack-poisoned]

* Update base for Update on "Remove old `change_linear_weights_to_*` APIs"


**Summary:** This commit removes these super old quantization
APIs that aren't even accessible by the user:

```
change_linear_weights_to_int8_dqtensors
change_linear_weights_to_int8_woqtensors
change_linear_weights_to_int4_woqtensors
```

**BC-breaking notes:**

Before:
```
change_linear_weights_to_int8_dqtensors(model)
change_linear_weights_to_int8_woqtensors(model)
change_linear_weights_to_int4_woqtensors(model)
```

After:
```
quantize_(model, Int8WeightOnlyConfig())
quantize_(model, Int4WeightOnlyConfig())
```

**Test Plan:** CI

[ghstack-poisoned]

* Update base for Update on "Remove old `change_linear_weights_to_*` APIs"


**Summary:** This commit removes these super old quantization
APIs that aren't even accessible by the user:

```
change_linear_weights_to_int8_dqtensors
change_linear_weights_to_int8_woqtensors
change_linear_weights_to_int4_woqtensors
```

**Test Plan:** CI

[ghstack-poisoned]

* Update base for Update on "Remove old `change_linear_weights_to_*` APIs"


**Summary:** This commit removes these super old quantization
APIs that aren't even accessible by the user:

```
change_linear_weights_to_int8_dqtensors
change_linear_weights_to_int8_woqtensors
change_linear_weights_to_int4_woqtensors
```

**Test Plan:** CI

[ghstack-poisoned]

* Update base for Update on "Remove old `change_linear_weights_to_*` APIs"


**Summary:** This commit removes these super old quantization
APIs that aren't even accessible by the user:

```
change_linear_weights_to_int8_dqtensors
change_linear_weights_to_int8_woqtensors
change_linear_weights_to_int4_woqtensors
```

**Test Plan:** CI

[ghstack-poisoned]

* Update base for Update on "Remove old `change_linear_weights_to_*` APIs"


**Summary:** This commit removes these super old quantization
APIs that aren't even accessible by the user:

```
change_linear_weights_to_int8_dqtensors
change_linear_weights_to_int8_woqtensors
change_linear_weights_to_int4_woqtensors
```

**Test Plan:** CI

[ghstack-poisoned]

* Update base for Update on "Remove old `change_linear_weights_to_*` APIs"


**Summary:** This commit removes these super old quantization
APIs that aren't even accessible by the user:

```
change_linear_weights_to_int8_dqtensors
change_linear_weights_to_int8_woqtensors
change_linear_weights_to_int4_woqtensors
```

**Test Plan:** CI

[ghstack-poisoned]

Author: andrewor14

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,
@@ -1852,11 +1851,6 @@ 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,