diff --git a/benchmark/torchscripts/CountSketch.pt b/benchmark/torchscripts/CountSketch.pt
new file mode 100644
index 00000000..71459484
Binary files /dev/null and b/benchmark/torchscripts/CountSketch.pt differ
diff --git a/benchmark/torchscripts/CountSketch.py b/benchmark/torchscripts/CountSketch.py
new file mode 100644
index 00000000..84685928
--- /dev/null
+++ b/benchmark/torchscripts/CountSketch.py
@@ -0,0 +1,55 @@
+import torch
+import time
+import os
+import math
+
+
+@torch.jit.script
+def CountSketch(A: torch.Tensor, B: torch.Tensor, s: int):
+    m1, n = A.shape
+    n, m2 = B.shape
+    # initialize sketch matrices
+    Ca = torch.zeros((m1, s))
+    Cb = torch.zeros((s, m2))
+
+    L = torch.randint(s, (n, ))
+    G = torch.randint(2, (n, ))
+
+    # modify the random column with random sign
+    for i in range(0, n):
+        if G[i] == 1:
+            Ca[:, L[i]] += A[:, i]
+            Cb[L[i], :] += B[i, :]
+        else:
+            Ca[:, L[i]] -= A[:, i]
+            Cb[L[i], :] -= B[i, :]
+
+    return torch.matmul(Ca, Cb)
+
+
+def main():
+    width = 1000
+    A = torch.rand(10000, width)
+    B = torch.rand(width, 5000)
+
+    t = time.time()
+
+    aResult = CountSketch(A, B, 500)
+    print("approximate: " + str(time.time() - t) + "s")
+
+    print(aResult)
+
+    # exact result
+    t = time.time()
+    eResult = torch.matmul(A, B)
+    print("\nExact: " + str(time.time() - t) + "s")
+
+    print(eResult)
+
+    print("\nerror: " + str(torch.norm(aResult - eResult, p='fro').item()))
+
+    CountSketch_script = CountSketch.save("CountSketch.pt")
+
+
+if __name__ == '__main__':
+    main()