fix(mge/core): fix dtype promotion issue for quantized dtype

GitOrigin-RevId: 9d09c8fa6f

imperative/python/megengine/core/tensor/utils.py

@@ -14,7 +14,7 @@ import numpy as np
 from ..ops import builtin
 from ..ops.special import Const
 from ..tensor.core import OpBase, TensorBase, TensorWrapperBase, apply
-from .dtype import is_equal
+from .dtype import is_equal, is_quantize
 
 
 def dtype_promotion(inputs):
@@ -122,7 +122,7 @@ def convert_single_value(v, inputs, *, dtype=None, device=None):
     tensors = [i for i in inputs if isinstance(i, (TensorBase, TensorWrapperBase))]
     assert len(tensors) > 0
     if isinstance(v, (TensorWrapperBase, TensorBase)):
-        v = astype(v, dtype)
+        v = astype(v, v.dtype if is_quantize(v.dtype) else dtype)
     else:
         (v,) = Const(v, dtype=dtype, device=device)(*tensors)
     return v

imperative/python/megengine/module/module.py

@@ -12,7 +12,6 @@ from typing import Any, Callable, Iterable, Optional, Set, Tuple, Union
 
 import numpy as np
 
-from ..core.tensor.dtype import is_quantize
 from ..core.tensor.utils import make_shape_tuple
 from ..logger import get_logger
 from ..tensor import Parameter, Tensor
@@ -529,11 +528,7 @@ class Module(metaclass=ABCMeta):
             ), "param `{}` shape mismatch, should be {}, get {}".format(
                 k, var.shape, to_be_load.shape
             )
-            # For quantized dtype, the initialized dtype
-            # scale/zero_points maybe invalid, use pretrained dtype instead.
-            if is_quantize(to_be_load.dtype) and is_quantize(var.dtype):
-                var = var.astype(to_be_load.dtype)
-            var._reset(to_be_load)
+            var._reset(type(var)(to_be_load, dtype=to_be_load.dtype, device=var.device))
             loaded.append(k)
 
         return set(loaded), set(skipped)

imperative/python/test/unit/functional/test_elemwise.py

@@ -10,6 +10,7 @@ import numpy as np
 
 import megengine.functional as F
 from megengine import tensor
+from megengine.core.tensor import dtype
 from megengine.functional.elemwise import _elwise
 
 
@@ -150,3 +151,18 @@ def test_logical_oprs():
     np.testing.assert_equal(x & y, F.logical_and(xx, yy).numpy())
     np.testing.assert_equal(x | y, F.logical_or(xx, yy).numpy())
     np.testing.assert_equal(x ^ y, F.logical_xor(xx, yy).numpy())
+
+
+def test_qadd():
+    inp_scale = 0.5
+    outp_scale = 0.2
+    x = np.arange(6).reshape(2, 3).astype("float32")
+    y = np.arange(6).reshape(2, 3).astype("float32")
+    x = tensor(x, dtype=dtype.qint8(inp_scale))
+    y = tensor(y, dtype=dtype.qint8(inp_scale))
+    result_mge = F.elemwise._elemwise_multi_type(
+        x, y, mode="QADD", dtype=dtype.qint8(outp_scale)
+    )
+    result_mge = result_mge.astype("float32").numpy()
+    result_expect = x.astype("float32").numpy() + y.astype("float32").numpy()
+    np.testing.assert_almost_equal(result_mge, result_expect, decimal=6)