Add symmetric quantization with no clipping error in the tensor subcl…

…ass based API

test/quantization/test_quant_api.py

@@ -526,6 +526,31 @@ def test_quantized_tensor_subclass_8da4w(self):
         self.assertTrue(torch.equal(res, ref))
 
     @unittest.skipIf(not TORCH_VERSION_AT_LEAST_2_4, "Test only enabled for 2.4+")
+    def test_quantized_tensor_subclass_8da4w_no_clipping_err(self):
+        group_size = 32
+        m = ToyLinearModel().eval()
+        m_copy = copy.deepcopy(m)
+        example_inputs = m.example_inputs()
+        quantize_(m, int8_dynamic_activation_int4_weight(group_size=group_size, mapping_type=MappingType.SYMMETRIC_NO_CLIPPING_ERR))
+
+        assert isinstance(m.linear1.weight, LinearActivationQuantizedTensor)
+        assert isinstance(m.linear2.weight, LinearActivationQuantizedTensor)
+        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 Int8DynActInt4WeightQuantizer
+        from torchao.quantization.GPTQ import Int8DynActInt4WeightLinear
+
+        quantizer = Int8DynActInt4WeightQuantizer(groupsize=group_size, mapping_type=MappingType.SYMMETRIC_NO_CLIPPING_ERR)
+        m_copy = quantizer.quantize(m_copy)
+        assert isinstance(m_copy.linear1, Int8DynActInt4WeightLinear)
+        assert isinstance(m_copy.linear2, Int8DynActInt4WeightLinear)
+
+        res = m(*example_inputs)
+        ref = m_copy(*example_inputs)
+        self.assertTrue(torch.equal(res, ref))
+
     # @unittest.skipIf(TORCH_VERSION_AT_LEAST_2_5, "Test currently doesn't work for 2.5+")
     @unittest.skipIf(not torch.cuda.is_available(), "Need CUDA available")
     def test_quantized_tensor_subclass_int4(self):

torchao/quantization/quant_api.py

@@ -473,14 +473,13 @@ def _int8_asymm_per_token_quant(x: torch.Tensor) -> torch.Tensor:
     target_dtype = torch.int8
     return to_affine_quantized_intx(x, mapping_type, _get_per_token_block_size(x), target_dtype)
 
-def apply_int8_dynamic_activation_int4_weight_quant(weight, group_size=32):
+def apply_int8_dynamic_activation_int4_weight_quant(weight, group_size=32, mapping_type=MappingType.SYMMETRIC):
     """This is defined here instead of local function to support serialization
     """
     if weight.shape[-1] % group_size != 0:
         return weight
 
     # weight settings
-    mapping_type = MappingType.SYMMETRIC
     block_size = (1, group_size)
     target_dtype = torch.int8
     eps = torch.finfo(torch.float32).eps
@@ -494,7 +493,7 @@ def apply_int8_dynamic_activation_int4_weight_quant(weight, group_size=32):
     weight = to_linear_activation_quantized(weight, input_quant_func)
     return weight
 
-def int8_dynamic_activation_int4_weight(group_size=32):
+def int8_dynamic_activation_int4_weight(group_size=32, mapping_type=MappingType.SYMMETRIC):
     """Applies int8 dynamic per token asymmetric activation quantization and int4 per group weight symmetric quantization to linear
     This is used to produce a model for executorch backend, but currently executorch did not
     support lowering for the quantized model from this flow yet
@@ -503,7 +502,7 @@ def int8_dynamic_activation_int4_weight(group_size=32):
         `group_size`: parameter for quantization, controls the granularity of quantization, smaller
          size is more fine grained
     """
-    return _get_linear_subclass_inserter(apply_int8_dynamic_activation_int4_weight_quant, group_size=group_size)
+    return _get_linear_subclass_inserter(apply_int8_dynamic_activation_int4_weight_quant, group_size=group_size, mapping_type=mapping_type)
 
 
 def int4_weight_only(group_size=128, layout_type=TensorCoreTiledLayoutType(inner_k_tiles=8), use_hqq=False):