fix a typo mistake

Author: taomiao

python/tvm/relay/frontend/pytorch.py

@@ -2680,7 +2680,7 @@ def nonzero(self, inputs, input_types, is_numpy_style=False):
         return ret
 
     def nonzero_numpy(self, inputs, input_types):
-        return self.nonzero(inputs, input_types, is_numpy_style=False)
+        return self.nonzero(inputs, input_types, is_numpy_style=True)
 
     def scatter(self, inputs, input_types):
         assert len(inputs) == 4 or len(inputs) == 5, (

tests/python/frontend/pytorch/test_nonzero_numpy.py

@@ -0,0 +1,49 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+# pylint: disable=import-self, too-many-lines, len-as-condition, no-else-return, unused-variable, too-many-nested-blocks
+# pylint: disable=consider-iterating-dictionary, invalid-name, unused-argument, unused-variable, broad-except
+# pylint: disable=import-outside-toplevel, simplifiable-if-expression, cell-var-from-loop, unnecessary-lambda
+# pylint: disable=missing-function-docstring, redefined-builtin, use-implicit-booleaness-not-comparison
+"""Tests to ensure nonzero_numpy are correctly"""
+from torch import nn
+import torch
+import tvm
+
+
+class NonZeroModule(nn.Module):
+    """Module that performs nonzero"""
+
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, x, mask):
+        mask_index = torch.nonzero(mask, as_tuple=True)
+        x[mask_index] = torch.ones_like(x[mask_index])
+        return x
+
+def test_pytorch_nonzero():
+    model = NonZeroModule()
+    x = torch.zeros((2, 10), dtype=torch.float32)
+    mask = torch.randint(0, 2, (2, 10)).bool()
+    with torch.no_grad():
+        traced_torch_model = torch.jit.trace(model, (x, mask))
+    import_input = [("input0", (2, 10)), ("input1", (2, 10))]
+    relay_model_ir, relay_model_params = tvm.relay.frontend.from_pytorch(
+        traced_torch_model, import_input
+    )
+
+