sxjscience commented on a change in pull request #7230:
URL: https://github.com/apache/tvm/pull/7230#discussion_r556152281
##########
File path: tests/python/frontend/mxnet/test_forward.py
##########
@@ -1935,6 +1935,29 @@ def verify(data_shape, axis, use_length, length):
verify((2, 3, 4), 2, True, np.array([[3, 4, 2], [1, 2,
1]]).astype("int32"))
+@pytest.mark.parametrize(
+ "data_shape, row_sel, col",
+ [
+ ((5, 7), (0, 1, 2, 3, 4,), 2),
+ ],
+)
+@pytest.mark.parametrize("dtype", ["float64", "float32"])
+@tvm.testing.parametrize_targets
+@pytest.mark.parametrize("kind", ["graph", "vm", "debug"])
+def test_forward_npi_advanced_indexing_multiple(data_shape, row_sel, col,
dtype, target, ctx, kind):
+ data_np = np.random.uniform(size=data_shape).astype(dtype)
+ data = mx.sym.var("data")
+ ref_res = mx.np.array(data_np)[row_sel, col]
+
+ # TODO need to add the proper symbol operator
+ mx_sym = mx.sym.np.(data.as_np_ndarray()[row_sel, col])
Review comment:
Also, I think you will need to install the nightly version of MXNet 2.0.
You may follow the guide in https://github.com/dmlc/gluon-nlp.
```bash
# Install the version with CUDA 10.1
python3 -m pip install -U --pre "mxnet-cu101>=2.0.0b20201206" -f
https://dist.mxnet.io/python
# Install the version with CUDA 10.2
python3 -m pip install -U --pre "mxnet-cu102>=2.0.0b20201206" -f
https://dist.mxnet.io/python
# Install the version with CUDA 11
python3 -m pip install -U --pre "mxnet-cu110>=2.0.0b20201206" -f
https://dist.mxnet.io/python
# Install the cpu-only version
python3 -m pip install -U --pre "mxnet>=2.0.0b20201206" -f
https://dist.mxnet.io/python
```
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