Factor out the specific configurations to helper functions

Summary:
int4wo, int8wo, int8dyn, 8da4w are specific configurations for quantize function, we factor that out in the PR so they are easy to use

Test Plan:
python test/quantization/test_quant_api.py

Reviewers:

Subscribers:

Tasks:

Tags:

test/quantization/test_quant_api.py

@@ -37,6 +37,10 @@
     Quantizer,
     TwoStepQuantizer,
     quantize,
+    get_apply_8da4w_quant,
+    get_apply_int4wo_quant,
+    get_apply_int8wo_quant,
+    get_apply_int8dyn_quant,
 )
 from torchao.quantization.utils import (
     TORCH_VERSION_AFTER_2_3,
@@ -416,42 +420,11 @@ def test_eval_wrapper(self):
     # TODO: move to a separate test file
     @unittest.skipIf(not TORCH_VERSION_AFTER_2_4, "Test only enabled for 2.4+")
     def test_quantized_tensor_subclass_8da4w(self):
-        # weight settings
         groupsize = 32
-        mapping_type = MappingType.SYMMETRIC
-        block_size = (1, groupsize)
-        target_dtype = torch.int8
-        eps = torch.finfo(torch.float32).eps
-        quant_min = -8
-        quant_max = 7
-
-        # TODO: make a general helper function?
-        # input settings
-        def get_per_token_block_size(x):
-            block_size = []
-            for i in range(len(x.shape)-1):
-                block_size.append(1)
-            block_size.append(x.shape[-1])
-            return block_size
-
-        # input settings
-        input_mapping_type = MappingType.ASYMMETRIC
-        input_target_dtype = torch.int8
-        input_quant_func = lambda x: to_aq(x, input_mapping_type, get_per_token_block_size(x), input_target_dtype)
-
         m = ToyLinearModel().eval()
         m_copy = copy.deepcopy(m)
         example_inputs = m.example_inputs()
-
-        def apply_weight_quant(weight):
-            return to_aq(weight, mapping_type, block_size, target_dtype, quant_min, quant_max, eps)
-
-        def apply_act_quant(weight):
-            return to_laq(weight, input_quant_func)
-
-        # note: order is important
-        m = quantize(m, apply_weight_quant)
-        m = quantize(m, apply_act_quant)
+        m = quantize(m, get_apply_8da4w_quant(groupsize=groupsize))
 
         assert isinstance(m.linear1.weight, LinearActQuantizedTensor)
         assert isinstance(m.linear2.weight, LinearActQuantizedTensor)
@@ -474,27 +447,13 @@ def apply_act_quant(weight):
     @unittest.skipIf(not TORCH_VERSION_AFTER_2_4, "Test only enabled for 2.4+")
     @unittest.skipIf(not torch.cuda.is_available(), "Need CUDA available")
     def test_quantized_tensor_subclass_int4(self):
-        # weight settings
-        groupsize = 32
-        mapping_type = MappingType.ASYMMETRIC
-        block_size = (1, groupsize)
-        target_dtype = torch.int32
-        quant_min = 0
-        quant_max = 15
-        eps = 1e-6
-        preserve_zero = False
-        zero_point_dtype = torch.bfloat16
-        zero_point_domain = ZeroPointDomain.FLOAT
-
         # use 1024 so that we don't need padding
         m = ToyLinearModel(1024, 1024, 1024).eval().to(torch.bfloat16).to("cuda")
         m_copy = copy.deepcopy(m)
         example_inputs = tuple(map(lambda x: x.to(torch.bfloat16).to("cuda"), m.example_inputs()))
 
-        def apply_weight_quant(weight):
-            return to_aq(weight, mapping_type, block_size, target_dtype, quant_min, quant_max, eps, zero_point_dtype=zero_point_dtype, preserve_zero=preserve_zero, zero_point_domain=zero_point_domain)
-
-        m = quantize(m, apply_weight_quant)
+        groupsize = 32
+        m = quantize(m, get_apply_int4wo_quant(groupsize=groupsize))
         assert isinstance(m.linear1.weight, AffineQuantizedTensor)
         assert isinstance(m.linear2.weight, AffineQuantizedTensor)
 
@@ -511,21 +470,11 @@ def apply_weight_quant(weight):
     @unittest.skipIf(not TORCH_VERSION_AFTER_2_4, "Test only enabled for 2.4+")
     @unittest.skipIf(not torch.cuda.is_available(), "Need CUDA available")
     def test_quantized_tensor_subclass_int8(self):
-        # weight settings
-        mapping_type = MappingType.SYMMETRIC
-        target_dtype = torch.int8
-        eps = torch.finfo(torch.float32).eps
-        zero_point_dtype = torch.int64
-
         m = ToyLinearModel().eval().to(torch.bfloat16)
         m_copy = copy.deepcopy(m)
         example_inputs = tuple(map(lambda x: x.to(torch.bfloat16), m.example_inputs()))
 
-        def apply_weight_quant(weight):
-            block_size = (1, weight.shape[1])
-            return to_aq(weight, mapping_type, block_size, target_dtype, eps=eps, zero_point_dtype=zero_point_dtype)
-
-        m = quantize(m, apply_weight_quant)
+        m = quantize(m, get_apply_int8wo_quant())
 
         assert isinstance(m.linear1.weight, AffineQuantizedTensor)
         assert isinstance(m.linear2.weight, AffineQuantizedTensor)
@@ -543,43 +492,12 @@ def apply_weight_quant(weight):
     @unittest.skipIf(not TORCH_VERSION_AFTER_2_4, "Test only enabled for 2.4+")
     @unittest.skipIf(not torch.cuda.is_available(), "Need CUDA available")
     def test_quantized_tensor_subclass_int8_dyn_quant(self):
-        # weight settings
-        mapping_type = MappingType.SYMMETRIC
-        def get_weight_block_size(x):
-            return (1, x.shape[1])
-        target_dtype = torch.int8
-        eps = torch.finfo(torch.float32).eps
-        zero_point_dtype = torch.int64
-
-        # input settings
-        def get_per_token_block_size(x):
-            block_size = list(x.shape)
-            for i in range(len(block_size)-1):
-                block_size[i] = 1
-            return block_size
-
-        input_mapping_type = MappingType.SYMMETRIC
-        input_target_dtype = torch.int8
-        input_eps = 1e-5
-        input_quant_min = -127
-        input_quant_max = 127
-        input_quant_func = lambda x: to_aq(x, input_mapping_type, get_per_token_block_size(x), input_target_dtype, eps=input_eps, quant_min=input_quant_min, quant_max=input_quant_max, scale_dtype=torch.float32 if x.dtype == torch.float16 else None)
-
         # use 1024 so that we don't need padding
         m = ToyLinearModel(1024, 1024, 1024).eval().to(torch.bfloat16).to("cuda")
         m_copy = copy.deepcopy(m)
         # setting batch_size to 20 to be compatible with the kernel
         example_inputs = tuple(map(lambda x: x.to(torch.bfloat16).to("cuda"), m.example_inputs(batch_size=20)))
-
-        def apply_weight_quant(weight):
-            block_size = get_weight_block_size(weight)
-            return to_aq(weight, mapping_type, block_size, target_dtype, eps=eps, zero_point_dtype=zero_point_dtype)
-
-        def apply_act_quant(weight):
-            return to_laq(weight, input_quant_func)
-
-        m = quantize(m, apply_weight_quant)
-        m = quantize(m, apply_act_quant)
+        m = quantize(m, get_apply_int8dyn_quant())
 
         assert isinstance(m.linear1.weight, LinearActQuantizedTensor)
         assert isinstance(m.linear2.weight, LinearActQuantizedTensor)

torchao/quantization/quant_api.py

@@ -32,6 +32,12 @@
     Int8DynamicallyQuantizedLinearWeight,
     Int8WeightOnlyQuantizedLinearWeight,
     QuantizedLinearWeightBase,
+    to_laq,
+)
+
+from .quant_primitives import (
+    MappingType,
+    ZeroPointDomain,
 )
 from .weight_only import WeightOnlyInt8QuantLinear
 from .unified import Quantizer, TwoStepQuantizer
@@ -56,6 +62,10 @@
     "quantize",
     "autoquant",
     "_get_subclass_inserter",
+    "get_apply_8da4w_quant",
+    "get_apply_int4wo_quant",
+    "get_apply_int8wo_quant",
+    "get_apply_int8dyn_quant",
 ]
 
 if TORCH_VERSION_AFTER_2_3:
@@ -287,3 +297,103 @@ def filter_fn(module, fqn):
         _is_linear if filter_fn is None else filter_fn,
     )
     return model
+
+def get_apply_8da4w_quant(groupsize=32):
+
+    def apply_8da4w_quant(weight):
+        # avoid circular dep
+        from torchao.dtypes.aqt import to_aq
+
+        # weight settings
+        mapping_type = MappingType.SYMMETRIC
+        block_size = (1, groupsize)
+        target_dtype = torch.int8
+        eps = torch.finfo(torch.float32).eps
+        quant_min = -8
+        quant_max = 7
+
+        # TODO: make a general helper function?
+        # input settings
+        def get_per_token_block_size(x):
+            block_size = []
+            for i in range(len(x.shape)-1):
+                block_size.append(1)
+            block_size.append(x.shape[-1])
+            return block_size
+
+        # input settings
+        input_mapping_type = MappingType.ASYMMETRIC
+        input_target_dtype = torch.int8
+        input_quant_func = lambda x: to_aq(x, input_mapping_type, get_per_token_block_size(x), input_target_dtype)
+
+        weight = to_aq(weight, mapping_type, block_size, target_dtype, quant_min, quant_max, eps)
+        weight = to_laq(weight, input_quant_func)
+        return weight
+
+    return apply_8da4w_quant
+
+
+def get_apply_int4wo_quant(groupsize=32):
+    def apply_int4wo_quant(weight):
+        # avoid circular dep
+        from torchao.dtypes.aqt import to_aq
+
+        groupsize = 32
+        mapping_type = MappingType.ASYMMETRIC
+        block_size = (1, groupsize)
+        target_dtype = torch.int32
+        quant_min = 0
+        quant_max = 15
+        eps = 1e-6
+        preserve_zero = False
+        zero_point_dtype = torch.bfloat16
+        zero_point_domain = ZeroPointDomain.FLOAT
+        return to_aq(weight, mapping_type, block_size, target_dtype, quant_min, quant_max, eps, zero_point_dtype=zero_point_dtype, preserve_zero=preserve_zero, zero_point_domain=zero_point_domain)
+
+    return apply_int4wo_quant
+
+
+def get_apply_int8wo_quant():
+    def apply_int8wo_quant(weight):
+        # avoid circular dep
+        from torchao.dtypes.aqt import to_aq
+
+        mapping_type = MappingType.SYMMETRIC
+        target_dtype = torch.int8
+        eps = torch.finfo(torch.float32).eps
+        zero_point_dtype = torch.int64
+        block_size = (1, weight.shape[1])
+        return to_aq(weight, mapping_type, block_size, target_dtype, eps=eps, zero_point_dtype=zero_point_dtype)
+    return apply_int8wo_quant
+
+def get_apply_int8dyn_quant():
+    def apply_int8dyn_quant(weight):
+        # avoid circular dep
+        from torchao.dtypes.aqt import to_aq
+        # weight settings
+        mapping_type = MappingType.SYMMETRIC
+        def get_weight_block_size(x):
+            return (1, x.shape[1])
+        target_dtype = torch.int8
+        eps = torch.finfo(torch.float32).eps
+        zero_point_dtype = torch.int64
+
+        # input settings
+        def get_per_token_block_size(x):
+            block_size = list(x.shape)
+            for i in range(len(block_size)-1):
+                block_size[i] = 1
+            return block_size
+
+        input_mapping_type = MappingType.SYMMETRIC
+        input_target_dtype = torch.int8
+        input_eps = 1e-5
+        input_quant_min = -127
+        input_quant_max = 127
+        input_quant_func = lambda x: to_aq(x, input_mapping_type, get_per_token_block_size(x), input_target_dtype, eps=input_eps, quant_min=input_quant_min, quant_max=input_quant_max, scale_dtype=torch.float32 if x.dtype == torch.float16 else None)
+
+        block_size = get_weight_block_size(weight)
+        weight = to_aq(weight, mapping_type, block_size, target_dtype, eps=eps, zero_point_dtype=zero_point_dtype)
+        weight = to_laq(weight, input_quant_func)
+        return weight
+    return apply_int8dyn_quant