test(mge/fakequant): use fixed input for lsq test to temperarily avoid precision error

GitOrigin-RevId: e91c71874eb16e8535497ec7c5840d08cf1a3d2b

imperative/python/test/unit/quantization/test_fake_quant.py

@@ -191,19 +191,34 @@ class LSQ_numpy:
 
 
 def test_lsq():
-    def preprocess(scale, eps):
-        scale = np.array([0]) if scale < eps else scale - eps
-        return np.abs(scale) + eps
-
     g = []
 
     def cb(grad):
         g.append(grad)
 
-    x = np.random.randint(-128, 128, size=(1, 2, 3, 4)).astype("float32")
-    s = np.random.rand(1)
+    # FIXME: use random number when LSQ is fixed
+    # x = np.random.randint(-128, 128, size=(1, 2, 3, 4)).astype("float32")
+    # s = np.random.rand(1)
+    x = np.array(
+        [
+            [
+                [
+                    [4.0, 38.0, -121.0, 38.0],
+                    [15.0, -115.0, -112.0, 24.0],
+                    [23.0, -65.0, 109.0, -115.0],
+                ],
+                [
+                    [-66.0, -90.0, -45.0, -101.0],
+                    [68.0, -98.0, 108.0, -79.0],
+                    [54.0, 63.0, -10.0, -50.0],
+                ],
+            ]
+        ],
+        dtype="float32",
+    )
+    s = np.array([0.02918224], dtype="float32")
     eps = np.array([1e-5], dtype="float32")
-    s = preprocess(s, eps)
+    s = np.abs(s) if np.abs(s) > eps else eps
     zero_point = np.array([1.0], dtype="float32")
     grad_s = np.array([2.0], dtype="float32")