From 8290eabafb22a8a54249165ce09dd31ff1f31c7c Mon Sep 17 00:00:00 2001
From: Neo Chien <cchung100m@cs.ccu.edu.tw>
Date: Sat, 22 Feb 2020 05:03:41 +0800
Subject: [PATCH] [TEST][FLAKY]
 topi/tests/python/test_topi_sort.py::test_argsort (#4891)

* [TEST][FLAKY] topi/tests/python/test_topi_sort.py::test_argsort

* upadate test function of argsort like topk

* Shuffle index and get data from shuffled index

* Replace the random.uniform with np.arange
---
 topi/tests/python/test_topi_sort.py | 38 +++++++++++++++++++++++------
 1 file changed, 31 insertions(+), 7 deletions(-)

diff --git a/topi/tests/python/test_topi_sort.py b/topi/tests/python/test_topi_sort.py
index c084a7c431b6..0ad4e987d17d 100644
--- a/topi/tests/python/test_topi_sort.py
+++ b/topi/tests/python/test_topi_sort.py
@@ -21,11 +21,26 @@
 import topi
 import topi.testing
 
-def test_argsort():
+
+def verify_argsort(axis, is_ascend):
     dshape = (20, 100)
-    data = tvm.placeholder(dshape, name="data", dtype="float32")
-    np_data = np.random.rand(dshape[0], dshape[1]).astype(data.dtype)
-    np_result = np.argsort(-np_data)
+    data_dtype = "float32"
+    data = tvm.placeholder(dshape, name="data", dtype=data_dtype)
+
+    perm = np.arange(dshape[0] * dshape[1], dtype=data_dtype)
+    np.random.shuffle(perm)
+    np_data = perm.reshape(dshape)
+
+    if is_ascend:
+        np_indices = np.argsort(np_data, axis=axis)
+    else:
+        np_indices = np.argsort(-np_data, axis=axis)
+
+    if axis == 0:
+        np_indices = np_indices[:dshape[axis], :]
+    else:
+        np_indices = np_indices[:, :dshape[axis]]
+
     def check_device(device):
         ctx = tvm.context(device, 0)
         if not ctx.exist:
@@ -33,18 +48,19 @@ def check_device(device):
             return
         print("Running on target: %s" % device)
         with tvm.target.create(device):
-            out = topi.argsort(data, axis=-1, is_ascend=False)
+            out = topi.argsort(data, axis=axis, is_ascend=is_ascend)
             s = topi.generic.schedule_argsort(out)
 
         tvm_data = tvm.nd.array(np_data, ctx)
-        tvm_out = tvm.nd.array(np.zeros(dshape, dtype="float32"), ctx)
+        tvm_out = tvm.nd.array(np.zeros(dshape, dtype=data_dtype), ctx)
         f = tvm.build(s, [data, out], device)
         f(tvm_data, tvm_out)
-        tvm.testing.assert_allclose(tvm_out.asnumpy(), np_result.astype("float32"), rtol=1e0)
+        tvm.testing.assert_allclose(tvm_out.asnumpy(), np_indices.astype(data_dtype), rtol=1e0)
 
     for device in ['llvm', 'cuda', 'opencl']:
         check_device(device)
 
+
 def verify_topk(k, axis, ret_type, is_ascend, dtype):
     shape = (20, 100)
     data_dtype = "float32"
@@ -95,6 +111,14 @@ def check_device(device):
     for device in ['llvm', 'cuda', 'opencl']:
         check_device(device)
 
+
+def test_argsort():
+    np.random.seed(0)
+    for axis in [0, -1, 1]:
+        verify_argsort(axis, True)
+        verify_argsort(axis, False)
+
+
 def test_topk():
     np.random.seed(0)
     for k in [0, 1, 5]: