Remove input_quant_func from AffineQuantizedTensor subclass

test/quantization/test_quant_api.py

@@ -395,7 +395,10 @@ 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):
-        from torchao.quantization.subclass import AffineQuantizedTensor
+        from torchao.quantization.subclass import (
+            AffineQuantizedTensor,
+            LinearActQuantizedTensor,
+        )
         from torchao.quantization.quant_primitives import MappingType
         import copy
 
@@ -409,6 +412,7 @@ def test_quantized_tensor_subclass_8da4w(self):
         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):
@@ -421,13 +425,18 @@ def get_per_token_block_size(x):
         input_target_dtype = torch.int8
         input_quant_func = lambda x: AffineQuantizedTensor.from_float(x, input_mapping_type, get_per_token_block_size(x), input_target_dtype)
 
+        def dynamic_quant(linear):
+            # note: order is important
+            linear.weight = torch.nn.Parameter(AffineQuantizedTensor.from_float(linear.weight, mapping_type, block_size, target_dtype, quant_min, quant_max, eps), requires_grad=False)
+            linear.weight = torch.nn.Parameter(LinearActQuantizedTensor.from_float(linear.weight, input_quant_func), requires_grad=False)
+
         m = ToyLinearModel().eval()
         m_copy = copy.deepcopy(m)
         example_inputs = m.example_inputs()
-        m.linear1.weight = torch.nn.Parameter(AffineQuantizedTensor.from_float(m.linear1.weight, mapping_type, block_size, target_dtype, quant_min, quant_max, eps, input_quant_func=input_quant_func), requires_grad=False)
-        m.linear2.weight = torch.nn.Parameter(AffineQuantizedTensor.from_float(m.linear2.weight, mapping_type, block_size, target_dtype, quant_min, quant_max, eps, input_quant_func=input_quant_func), requires_grad=False)
-        assert isinstance(m.linear1.weight, AffineQuantizedTensor)
-        assert isinstance(m.linear2.weight, AffineQuantizedTensor)
+        dynamic_quant(m.linear1)
+        dynamic_quant(m.linear2)
+        assert isinstance(m.linear1.weight, LinearActQuantizedTensor)
+        assert isinstance(m.linear2.weight, LinearActQuantizedTensor)
 
         # reference
         from torchao.quantization.quant_api import Int8DynActInt4WeightQuantizer
@@ -461,9 +470,6 @@ def test_quantized_tensor_subclass_int4(self):
         preserve_zero = False
         zero_point_dtype = torch.bfloat16
 
-        # weight only quantization
-        input_quant_func = 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)
@@ -475,7 +481,6 @@ def to_quantized(weight):
                 zero_point_dtype=zero_point_dtype,
                 preserve_zero=preserve_zero,
                 zero_point_domain=ZeroPointDomain.FLOAT,
-                input_quant_func=input_quant_func,
             )
 
         m.linear1.weight = torch.nn.Parameter(to_quantized(m.linear1.weight), requires_grad=False)
@@ -506,16 +511,13 @@ def test_quantized_tensor_subclass_int8(self):
         eps = torch.finfo(torch.float32).eps
         zero_point_dtype = torch.int64
 
-        # weight only quantization
-        input_quant_func = None
-
         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 to_quantized(weight):
             block_size = (1, weight.shape[1])
-            return AffineQuantizedTensor.from_float(weight, mapping_type, block_size, target_dtype, eps=eps, zero_point_dtype=zero_point_dtype, input_quant_func=input_quant_func)
+            return AffineQuantizedTensor.from_float(weight, mapping_type, block_size, target_dtype, eps=eps, zero_point_dtype=zero_point_dtype)
 
         m.linear1.weight = torch.nn.Parameter(to_quantized(m.linear1.weight), requires_grad=False)
         m.linear2.weight = torch.nn.Parameter(to_quantized(m.linear2.weight), requires_grad=False)
@@ -532,5 +534,63 @@ def to_quantized(weight):
         torch.testing.assert_close(res, ref, rtol=0.00001, atol=1e-2)
 
 
+    @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):
+        from torchao.quantization.subclass import AffineQuantizedTensor
+        from torchao.quantization.subclass import LinearActQuantizedTensor
+        from torchao.quantization.quant_primitives import MappingType
+        from torchao.quantization.quant_primitives import ZeroPointDomain
+        import copy
+
+        # 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: AffineQuantizedTensor.from_float(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.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 dynamic_quant(linear):
+            # note: order is important
+            linear.weight = torch.nn.Parameter(AffineQuantizedTensor.from_float(linear.weight, mapping_type, get_weight_block_size(linear.weight), target_dtype, eps=eps, zero_point_dtype=zero_point_dtype), requires_grad=False)
+            linear.weight = torch.nn.Parameter(LinearActQuantizedTensor.from_float(linear.weight, input_quant_func), requires_grad=False)
+
+        dynamic_quant(m.linear1)
+        dynamic_quant(m.linear2)
+        assert isinstance(m.linear1.weight, LinearActQuantizedTensor)
+        assert isinstance(m.linear2.weight, LinearActQuantizedTensor)
+        assert isinstance(m.linear1.weight.original_weight_tensor, AffineQuantizedTensor)
+        assert isinstance(m.linear2.weight.original_weight_tensor, AffineQuantizedTensor)
+
+        # reference
+        from torchao.quantization.quant_api import change_linear_weights_to_int8_dqtensors
+        change_linear_weights_to_int8_dqtensors(m_copy)
+
+        res = m(*example_inputs)
+        ref = m_copy(*example_inputs)
+
+        self.assertTrue(torch.equal(res, ref))
+
+
 if __name__ == "__main__":
     unittest.main()