Add tests for non-contiguous tensors for Python backend (triton-infer…

…ence-server#3017)

* Add tests for non-contiguous tensors for Python backend

* Review edits

qa/L0_backend_python/env/test.sh

@@ -25,7 +25,6 @@
 # (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
 # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 
-CLIENT_PY=./lifecycle_test.py
 CLIENT_LOG="./client.log"
 source ../common.sh
 source ../../common/util.sh

qa/L0_backend_python/python_test.py

@@ -1,6 +1,6 @@
 #!/usr/bin/python
 
-# Copyright (c) 2019-2021, NVIDIA CORPORATION. All rights reserved.
+# Copyright 2019-2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 #
 # Redistribution and use in source and binary forms, with or without
 # modification, are permitted provided that the following conditions
@@ -27,6 +27,7 @@
 # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 
 import sys
+
 sys.path.append("../common")
 
 import unittest
@@ -40,7 +41,6 @@
 
 
 class PythonTest(tu.TestResultCollector):
-
     def _infer_help(self, model_name, shape, data_type):
         with httpclient.InferenceServerClient("localhost:8000") as client:
             input_data_0 = np.array(np.random.randn(*shape), dtype=data_type)
@@ -257,6 +257,30 @@ def test_string(self):
                 self.assertTrue(output0 is not None)
                 self.assertTrue(output0[0] == input_data.astype(np.bytes_))
 
+    def test_non_contiguous(self):
+        model_name = 'non_contiguous'
+        shape = [2, 64, 84, 32, 55]
+        new_shape = [64, 2, 32, 55, 84]
+        shape_reorder = [1, 0, 4, 2, 3]
+        with httpclient.InferenceServerClient("localhost:8000") as client:
+            input_numpy = np.random.rand(*shape)
+            input_numpy = input_numpy.astype(np.float32)
+            inputs = [
+                httpclient.InferInput("INPUT0", shape,
+                                      np_to_triton_dtype(input_numpy.dtype))
+            ]
+            inputs[0].set_data_from_numpy(input_numpy)
+            result = client.infer(model_name, inputs)
+            output0 = input_numpy.reshape(new_shape)
+
+            # Transpose the tensor to create a non-contiguous tensor.
+            output1 = input_numpy.T
+            output2 = np.transpose(input_numpy, shape_reorder)
+
+            self.assertTrue(np.all(output0 == result.as_numpy('OUTPUT0')))
+            self.assertTrue(np.all(output1 == result.as_numpy('OUTPUT1')))
+            self.assertTrue(np.all(output2 == result.as_numpy('OUTPUT2')))
+
 
 if __name__ == '__main__':
     unittest.main()

qa/L0_backend_python/test.sh

@@ -1,5 +1,5 @@
 #!/bin/bash
-# Copyright (c) 2020-2021, NVIDIA CORPORATION. All rights reserved.
+# Copyright 2020-2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 #
 # Redistribution and use in source and binary forms, with or without
 # modification, are permitted provided that the following conditions
@@ -27,7 +27,7 @@
 
 CLIENT_PY=./python_test.py
 CLIENT_LOG="./client.log"
-EXPECTED_NUM_TESTS="7"
+EXPECTED_NUM_TESTS="8"
 SERVER=/opt/tritonserver/bin/tritonserver
 BASE_SERVER_ARGS="--model-repository=`pwd`/models --log-verbose=1"
 PYTHON_BACKEND_BRANCH=$PYTHON_BACKEND_REPO_TAG
@@ -78,6 +78,10 @@ mkdir -p models/init_args/1/
 cp ../python_models/init_args/model.py ./models/init_args/1/
 cp ../python_models/init_args/config.pbtxt ./models/init_args/
 
+mkdir -p models/non_contiguous/1/
+cp ../python_models/non_contiguous/model.py ./models/non_contiguous/1/
+cp ../python_models/non_contiguous/config.pbtxt ./models/non_contiguous/config.pbtxt
+
 # Unicode Characters
 mkdir -p models/string/1/
 cp ../python_models/string/model.py ./models/string/1/

qa/python_models/non_contiguous/config.pbtxt

@@ -0,0 +1,55 @@
+# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions
+# are met:
+#  * Redistributions of source code must retain the above copyright
+#    notice, this list of conditions and the following disclaimer.
+#  * Redistributions in binary form must reproduce the above copyright
+#    notice, this list of conditions and the following disclaimer in the
+#    documentation and/or other materials provided with the distribution.
+#  * Neither the name of NVIDIA CORPORATION nor the names of its
+#    contributors may be used to endorse or promote products derived
+#    from this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
+# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+# PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE COPYRIGHT OWNER OR
+# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
+# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
+# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
+# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+name: "non_contiguous"
+backend: "python"
+
+input [
+  {
+    name: "INPUT0"
+    data_type: TYPE_FP32
+    dims: [ -1, -1, -1, -1, -1 ]
+  }
+]
+output [
+  {
+    name: "OUTPUT0"
+    data_type: TYPE_FP32
+    dims: [ -1, -1, -1, -1, -1 ]
+  },
+  {
+    name: "OUTPUT1"
+    data_type: TYPE_FP32
+    dims: [ -1, -1, -1, -1, -1 ]
+  },
+  {
+    name: "OUTPUT2"
+    data_type: TYPE_FP32
+    dims: [ -1, -1, -1, -1, -1 ]
+  }
+]
+
+instance_group [{ kind: KIND_CPU }]

qa/python_models/non_contiguous/model.py

@@ -0,0 +1,51 @@
+# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions
+# are met:
+#  * Redistributions of source code must retain the above copyright
+#    notice, this list of conditions and the following disclaimer.
+#  * Redistributions in binary form must reproduce the above copyright
+#    notice, this list of conditions and the following disclaimer in the
+#    documentation and/or other materials provided with the distribution.
+#  * Neither the name of NVIDIA CORPORATION nor the names of its
+#    contributors may be used to endorse or promote products derived
+#    from this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
+# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+# PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE COPYRIGHT OWNER OR
+# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
+# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
+# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
+# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+import numpy as np
+import sys
+
+sys.path.append('../../')
+import triton_python_backend_utils as pb_utils
+
+
+class TritonPythonModel:
+    def execute(self, requests):
+        responses = []
+        new_shape = [64, 2, 32, 55, 84]
+        shape_reorder = [1, 0, 4, 2, 3]
+        for request in requests:
+            input_tensor = pb_utils.get_input_tensor_by_name(request, "INPUT0")
+            input_numpy = input_tensor.as_numpy()
+            input_shape = input_numpy.shape
+            output0 = pb_utils.Tensor("OUTPUT0",
+                                      input_numpy.reshape(new_shape))
+            # Transpose the tensor to create a non-contiguous tensor.
+            output1 = pb_utils.Tensor("OUTPUT1", input_numpy.T)
+            output2 = pb_utils.Tensor("OUTPUT2",
+                                      np.transpose(input_numpy, shape_reorder))
+            responses.append(
+                pb_utils.InferenceResponse([output0, output1, output2]))
+        return responses