Revert "Remove config functions like int4_weight_only (#3145)"

This reverts commit 53a66f8.

Author: andrewor14

README.md

@@ -261,7 +261,7 @@ Our framework makes it straightforward to add tensor parallel support to your cu
 
 We've added support for authoring and releasing [custom ops](./torchao/csrc/) that do not graph break with `torch.compile()`. We have a few examples you can follow
 
-1. [fp6](torchao/dtypes/floatx/README.md) for 2x faster inference over fp16 with an easy to use API `quantize_(model, FPXWeightOnlyConfig(3, 2))`
+1. [fp6](torchao/dtypes/floatx/README.md) for 2x faster inference over fp16 with an easy to use API `quantize_(model, fpx_weight_only(3, 2))`
 2. [2:4 Sparse Marlin GEMM](https://github.com/pytorch/ao/pull/733) 2x speedups for FP16xINT4 kernels even at batch sizes up to 256
 3. [int4 tinygemm unpacker](https://github.com/pytorch/ao/pull/415) which makes it easier to switch quantized backends for inference
 

test/quantization/test_quant_api.py

@@ -10,6 +10,7 @@
 import gc
 import tempfile
 import unittest
+import warnings
 from pathlib import Path
 
 import torch
@@ -786,6 +787,56 @@ def test_int4wo_cuda_serialization(self):
             # load state_dict in cuda
             model.load_state_dict(sd, assign=True)
 
+    def test_config_deprecation(self):
+        """
+        Test that old config functions like `int4_weight_only` trigger deprecation warnings.
+        """
+        from torchao.quantization import (
+            float8_dynamic_activation_float8_weight,
+            float8_static_activation_float8_weight,
+            float8_weight_only,
+            fpx_weight_only,
+            gemlite_uintx_weight_only,
+            int4_dynamic_activation_int4_weight,
+            int4_weight_only,
+            int8_dynamic_activation_int4_weight,
+            int8_dynamic_activation_int8_weight,
+            int8_weight_only,
+            uintx_weight_only,
+        )
+
+        # Reset deprecation warning state, otherwise we won't log warnings here
+        warnings.resetwarnings()
+
+        # Map from deprecated API to the args needed to instantiate it
+        deprecated_apis_to_args = {
+            float8_dynamic_activation_float8_weight: (),
+            float8_static_activation_float8_weight: (torch.randn(3)),
+            float8_weight_only: (),
+            fpx_weight_only: (3, 2),
+            gemlite_uintx_weight_only: (),
+            int4_dynamic_activation_int4_weight: (),
+            int4_weight_only: (),
+            int8_dynamic_activation_int4_weight: (),
+            int8_dynamic_activation_int8_weight: (),
+            int8_weight_only: (),
+            uintx_weight_only: (torch.uint4,),
+        }
+
+        with warnings.catch_warnings(record=True) as _warnings:
+            # Call each deprecated API twice
+            for cls, args in deprecated_apis_to_args.items():
+                cls(*args)
+                cls(*args)
+
+            # Each call should trigger the warning only once
+            self.assertEqual(len(_warnings), len(deprecated_apis_to_args))
+            for w in _warnings:
+                self.assertIn(
+                    "is deprecated and will be removed in a future release",
+                    str(w.message),
+                )
+
 
 common_utils.instantiate_parametrized_tests(TestQuantFlow)
 

torchao/quantization/__init__.py

@@ -64,9 +64,21 @@
     PlainLayout,
     TensorCoreTiledLayout,
     UIntXWeightOnlyConfig,
+    float8_dynamic_activation_float8_weight,
+    float8_static_activation_float8_weight,
+    float8_weight_only,
+    fpx_weight_only,
+    gemlite_uintx_weight_only,
+    int4_dynamic_activation_int4_weight,
+    int4_weight_only,
+    int8_dynamic_activation_int4_weight,
+    int8_dynamic_activation_int8_semi_sparse_weight,
+    int8_dynamic_activation_int8_weight,
+    int8_weight_only,
     intx_quantization_aware_training,
     quantize_,
     swap_conv2d_1x1_to_linear,
+    uintx_weight_only,
 )
 from .quant_primitives import (
     MappingType,
@@ -117,7 +129,19 @@
     "ALL_AUTOQUANT_CLASS_LIST",
     # top level API - manual
     "quantize_",
+    "int4_dynamic_activation_int4_weight",
+    "int8_dynamic_activation_int4_weight",
+    "int8_dynamic_activation_int8_weight",
+    "int8_dynamic_activation_int8_semi_sparse_weight",
+    "int4_weight_only",
+    "int8_weight_only",
     "intx_quantization_aware_training",
+    "float8_weight_only",
+    "float8_dynamic_activation_float8_weight",
+    "float8_static_activation_float8_weight",
+    "uintx_weight_only",
+    "fpx_weight_only",
+    "gemlite_uintx_weight_only",
     "swap_conv2d_1x1_to_linear",
     "Int4DynamicActivationInt4WeightConfig",
     "Int8DynamicActivationInt4WeightConfig",

torchao/quantization/quant_api.py

@@ -96,6 +96,7 @@
     to_weight_tensor_with_linear_activation_quantization_metadata,
 )
 from torchao.utils import (
+    _ConfigDeprecationWrapper,
     is_MI300,
     is_sm_at_least_89,
     is_sm_at_least_90,
@@ -144,7 +145,18 @@
     "autoquant",
     "_get_subclass_inserter",
     "quantize_",
+    "int8_dynamic_activation_int4_weight",
+    "int8_dynamic_activation_int8_weight",
+    "int8_dynamic_activation_int8_semi_sparse_weight",
+    "int4_weight_only",
+    "int8_weight_only",
     "intx_quantization_aware_training",
+    "float8_weight_only",
+    "uintx_weight_only",
+    "fpx_weight_only",
+    "gemlite_uintx_weight_only",
+    "float8_dynamic_activation_float8_weight",
+    "float8_static_activation_float8_weight",
     "Int8DynActInt4WeightQuantizer",
     "Float8DynamicActivationFloat8SemiSparseWeightConfig",
     "ModuleFqnToConfig",
@@ -491,7 +503,7 @@ def quantize_(
         # Int8DynamicActivationInt8WeightConfig (optimized with int8 mm op and torch.compile)
         # Int4WeightOnlyConfig (optimized with int4 tinygemm kernel and torch.compile)
         # Int8WeightOnlyConfig (optimized with int8 mm op and torch.compile
-        from torchao.quantization.quant_api import Int4WeightOnlyConfig
+        from torchao.quantization.quant_api import int4_weight_only
 
         m = nn.Sequential(nn.Linear(32, 1024), nn.Linear(1024, 32))
         quantize_(m, Int4WeightOnlyConfig(group_size=32, version=1))
@@ -613,6 +625,12 @@ def __post_init__(self):
         )
 
 
+# for BC
+int8_dynamic_activation_int4_weight = _ConfigDeprecationWrapper(
+    "int8_dynamic_activation_int4_weight", Int8DynamicActivationInt4WeightConfig
+)
+
+
 @register_quantize_module_handler(Int8DynamicActivationInt4WeightConfig)
 def _int8_dynamic_activation_int4_weight_transform(
     module: torch.nn.Module,
@@ -978,6 +996,12 @@ def __post_init__(self):
         )
 
 
+# for bc
+int4_dynamic_activation_int4_weight = _ConfigDeprecationWrapper(
+    "int4_dynamic_activation_int4_weight", Int4DynamicActivationInt4WeightConfig
+)
+
+
 @register_quantize_module_handler(Int4DynamicActivationInt4WeightConfig)
 def _int4_dynamic_activation_int4_weight_transform(
     module: torch.nn.Module, config: Int4DynamicActivationInt4WeightConfig
@@ -1035,6 +1059,12 @@ def __post_init__(self):
         )
 
 
+# for BC
+gemlite_uintx_weight_only = _ConfigDeprecationWrapper(
+    "gemlite_uintx_weight_only", GemliteUIntXWeightOnlyConfig
+)
+
+
 @register_quantize_module_handler(GemliteUIntXWeightOnlyConfig)
 def _gemlite_uintx_weight_only_transform(
     module: torch.nn.Module, config: GemliteUIntXWeightOnlyConfig
@@ -1112,6 +1142,11 @@ def __post_init__(self):
         torch._C._log_api_usage_once("torchao.quantization.Int4WeightOnlyConfig")
 
 
+# for BC
+# TODO maybe change other callsites
+int4_weight_only = _ConfigDeprecationWrapper("int4_weight_only", Int4WeightOnlyConfig)
+
+
 def _int4_weight_only_quantize_tensor(weight, config):
     # TODO(future PR): perhaps move this logic to a different file, to keep the API
     # file clean of implementation details
@@ -1323,6 +1358,10 @@ def __post_init__(self):
         torch._C._log_api_usage_once("torchao.quantization.Int8WeightOnlyConfig")
 
 
+# for BC
+int8_weight_only = _ConfigDeprecationWrapper("int8_weight_only", Int8WeightOnlyConfig)
+
+
 def _int8_weight_only_quantize_tensor(weight, config):
     mapping_type = MappingType.SYMMETRIC
     target_dtype = torch.int8
@@ -1480,6 +1519,12 @@ def __post_init__(self):
         )
 
 
+# for BC
+int8_dynamic_activation_int8_weight = _ConfigDeprecationWrapper(
+    "int8_dynamic_activation_int8_weight", Int8DynamicActivationInt8WeightConfig
+)
+
+
 def _int8_dynamic_activation_int8_weight_quantize_tensor(weight, config):
     layout = config.layout
     act_mapping_type = config.act_mapping_type
@@ -1585,6 +1630,12 @@ def __post_init__(self):
         torch._C._log_api_usage_once("torchao.quantization.Float8WeightOnlyConfig")
 
 
+# for BC
+float8_weight_only = _ConfigDeprecationWrapper(
+    "float8_weight_only", Float8WeightOnlyConfig
+)
+
+
 def _float8_weight_only_quant_tensor(weight, config):
     if config.version == 1:
         warnings.warn(
@@ -1743,6 +1794,12 @@ def __post_init__(self):
         self.granularity = [activation_granularity, weight_granularity]
 
 
+# for bc
+float8_dynamic_activation_float8_weight = _ConfigDeprecationWrapper(
+    "float8_dynamic_activation_float8_weight", Float8DynamicActivationFloat8WeightConfig
+)
+
+
 def _float8_dynamic_activation_float8_weight_quantize_tensor(weight, config):
     activation_dtype = config.activation_dtype
     weight_dtype = config.weight_dtype
@@ -1918,6 +1975,12 @@ def __post_init__(self):
         )
 
 
+# for bc
+float8_static_activation_float8_weight = _ConfigDeprecationWrapper(
+    "float8_static_activation_float8_weight", Float8StaticActivationFloat8WeightConfig
+)
+
+
 @register_quantize_module_handler(Float8StaticActivationFloat8WeightConfig)
 def _float8_static_activation_float8_weight_transform(
     module: torch.nn.Module, config: Float8StaticActivationFloat8WeightConfig
@@ -2000,6 +2063,12 @@ def __post_init__(self):
         torch._C._log_api_usage_once("torchao.quantization.UIntXWeightOnlyConfig")
 
 
+# for BC
+uintx_weight_only = _ConfigDeprecationWrapper(
+    "uintx_weight_only", UIntXWeightOnlyConfig
+)
+
+
 @register_quantize_module_handler(UIntXWeightOnlyConfig)
 def _uintx_weight_only_transform(
     module: torch.nn.Module, config: UIntXWeightOnlyConfig
@@ -2278,6 +2347,10 @@ def __post_init__(self):
         torch._C._log_api_usage_once("torchao.quantization.FPXWeightOnlyConfig")
 
 
+# for BC
+fpx_weight_only = _ConfigDeprecationWrapper("fpx_weight_only", FPXWeightOnlyConfig)
+
+
 @register_quantize_module_handler(FPXWeightOnlyConfig)
 def _fpx_weight_only_transform(
     module: torch.nn.Module, config: FPXWeightOnlyConfig

torchao/utils.py

@@ -12,7 +12,7 @@
 from functools import reduce
 from importlib.metadata import version
 from math import gcd
-from typing import Any, Callable, Optional
+from typing import Any, Callable, Optional, Type
 
 import torch
 import torch.nn.utils.parametrize as parametrize
@@ -433,6 +433,25 @@ def __eq__(self, other):
 TORCH_VERSION_AFTER_2_2 = _deprecated_torch_version_after("2.2.0.dev")
 
 
+class _ConfigDeprecationWrapper:
+    """
+    A deprecation wrapper that directs users from a deprecated "config function"
+    (e.g. `int4_weight_only`) to the replacement config class.
+    """
+
+    def __init__(self, deprecated_name: str, config_cls: Type):
+        self.deprecated_name = deprecated_name
+        self.config_cls = config_cls
+
+    def __call__(self, *args, **kwargs):
+        warnings.warn(
+            f"`{self.deprecated_name}` is deprecated and will be removed in a future release. "
+            f"Please use `{self.config_cls.__name__}` instead. Example usage:\n"
+            f"    quantize_(model, {self.config_cls.__name__}(...))"
+        )
+        return self.config_cls(*args, **kwargs)
+
+
 """
 Helper function for implementing aten op or torch function dispatch
 and dispatching to these implementations.