Add metrics model namespacing label test

qa/L0_metrics/metrics_config_test.py

@@ -1,5 +1,5 @@
 #!/usr/bin/python
-# Copyright 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# Copyright 2023-2024, 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
@@ -138,6 +138,16 @@ def test_inf_summaries_exist_with_cache(self):
         for metric in bad_patterns:
             self.assertNotIn(metric, metrics)
 
+    def test_model_namespacing(self):
+        metrics = self._get_metrics()
+        expected_namespaces = [
+            "/opt/tritonserver/qa/L0_metrics/model_namespacing_repos/addsub_repo",
+            "/opt/tritonserver/qa/L0_metrics/model_namespacing_repos/subadd_repo",
+        ]
+        for namespace in expected_namespaces:
+            label = 'namespace="' + namespace + '"'
+            self.assertIn(label, metrics)
+
 
 if __name__ == "__main__":
     unittest.main()

qa/L0_metrics/model_namespacing_repos/addsub_repo/addsub_ensemble/config.pbtxt

@@ -0,0 +1,83 @@
+# Copyright 2024, 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.
+
+platform: "ensemble"
+max_batch_size: 0
+version_policy: { all { } }
+
+input [
+  {
+    name: "INPUT0"
+    data_type: TYPE_INT32
+    dims: [ 16 ]
+  }
+]
+input [
+  {
+    name: "INPUT1"
+    data_type: TYPE_INT32
+    dims: [ 16 ]
+  }
+]
+output [
+  {
+    name: "OUTPUT0"
+    data_type: TYPE_INT32
+    dims: [ 16 ]
+  }
+]
+output [
+  {
+    name: "OUTPUT1"
+    data_type: TYPE_INT32
+    dims: [ 16 ]
+  }
+]
+
+ensemble_scheduling {
+  step [
+    {
+      model_name: "composing_model"
+      model_version: -1
+      input_map {
+        key: "INPUT0"
+        value: "INPUT0"
+      }
+      input_map {
+        key: "INPUT1"
+        value: "INPUT1"
+      }
+      output_map {
+        key: "OUTPUT0"
+        value: "OUTPUT0"
+      }
+      output_map {
+        key: "OUTPUT1"
+        value: "OUTPUT1"
+      }
+    }
+  ]
+}

qa/L0_metrics/model_namespacing_repos/addsub_repo/composing_model/1/model.py

@@ -0,0 +1,121 @@
+# Copyright 2024, 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 json
+
+import numpy as np
+import triton_python_backend_utils as pb_utils
+
+
+class TritonPythonModel:
+    # Use auto complete feature to ship config.pbtxt along with the Python
+    # model definition
+    @staticmethod
+    def auto_complete_config(auto_complete_model_config):
+        # Only use packaged config if config is not explicitly provided
+        config = auto_complete_model_config.as_dict()
+        if (len(config["input"]) != 0) or (len(config["output"]) != 0):
+            return auto_complete_model_config
+
+        auto_complete_model_config.add_input(
+            {
+                "name": "INPUT0",
+                "data_type": "TYPE_INT32",
+                "dims": [
+                    16,
+                ],
+            }
+        )
+        auto_complete_model_config.add_input(
+            {
+                "name": "INPUT1",
+                "data_type": "TYPE_INT32",
+                "dims": [
+                    16,
+                ],
+            }
+        )
+        auto_complete_model_config.add_output(
+            {
+                "name": "OUTPUT0",
+                "data_type": "TYPE_INT32",
+                "dims": [
+                    16,
+                ],
+            }
+        )
+        auto_complete_model_config.add_output(
+            {
+                "name": "OUTPUT1",
+                "data_type": "TYPE_INT32",
+                "dims": [
+                    16,
+                ],
+            }
+        )
+        return auto_complete_model_config
+
+    def initialize(self, args):
+        self.model_config = model_config = json.loads(args["model_config"])
+
+        output0_config = pb_utils.get_output_config_by_name(model_config, "OUTPUT0")
+        output1_config = pb_utils.get_output_config_by_name(model_config, "OUTPUT1")
+
+        self.output0_dtype = pb_utils.triton_string_to_numpy(
+            output0_config["data_type"]
+        )
+        self.output1_dtype = pb_utils.triton_string_to_numpy(
+            output1_config["data_type"]
+        )
+
+    def execute(self, requests):
+        """This function is called on inference request."""
+
+        responses = []
+        for request in requests:
+            in_0 = pb_utils.get_input_tensor_by_name(request, "INPUT0")
+            in_1 = pb_utils.get_input_tensor_by_name(request, "INPUT1")
+            responses.append(pb_utils.InferenceResponse(self.addsub(in_0, in_1)))
+        return responses
+
+    def addsub(self, in_0, in_1):
+        if (
+            in_0.as_numpy().dtype.type is np.bytes_
+            or in_0.as_numpy().dtype == np.object_
+        ):
+            out_0, out_1 = (
+                in_0.as_numpy().astype(np.int32) + in_1.as_numpy().astype(np.int32),
+                in_0.as_numpy().astype(np.int32) - in_1.as_numpy().astype(np.int32),
+            )
+        else:
+            out_0, out_1 = (
+                in_0.as_numpy() + in_1.as_numpy(),
+                in_0.as_numpy() - in_1.as_numpy(),
+            )
+
+        out_tensor_0 = pb_utils.Tensor("OUTPUT0", out_0.astype(self.output0_dtype))
+        out_tensor_1 = pb_utils.Tensor("OUTPUT1", out_1.astype(self.output1_dtype))
+        return [out_tensor_0, out_tensor_1]

qa/L0_metrics/model_namespacing_repos/subadd_repo/composing_model/1/model.py

@@ -0,0 +1,121 @@
+# Copyright 2024, 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 json
+
+import numpy as np
+import triton_python_backend_utils as pb_utils
+
+
+class TritonPythonModel:
+    # Use auto complete feature to ship config.pbtxt along with the Python
+    # model definition
+    @staticmethod
+    def auto_complete_config(auto_complete_model_config):
+        # Only use packaged config if config is not explicitly provided
+        config = auto_complete_model_config.as_dict()
+        if (len(config["input"]) != 0) or (len(config["output"]) != 0):
+            return auto_complete_model_config
+
+        auto_complete_model_config.add_input(
+            {
+                "name": "INPUT0",
+                "data_type": "TYPE_INT32",
+                "dims": [
+                    16,
+                ],
+            }
+        )
+        auto_complete_model_config.add_input(
+            {
+                "name": "INPUT1",
+                "data_type": "TYPE_INT32",
+                "dims": [
+                    16,
+                ],
+            }
+        )
+        auto_complete_model_config.add_output(
+            {
+                "name": "OUTPUT0",
+                "data_type": "TYPE_INT32",
+                "dims": [
+                    16,
+                ],
+            }
+        )
+        auto_complete_model_config.add_output(
+            {
+                "name": "OUTPUT1",
+                "data_type": "TYPE_INT32",
+                "dims": [
+                    16,
+                ],
+            }
+        )
+        return auto_complete_model_config
+
+    def initialize(self, args):
+        self.model_config = model_config = json.loads(args["model_config"])
+
+        output0_config = pb_utils.get_output_config_by_name(model_config, "OUTPUT0")
+        output1_config = pb_utils.get_output_config_by_name(model_config, "OUTPUT1")
+
+        self.output0_dtype = pb_utils.triton_string_to_numpy(
+            output0_config["data_type"]
+        )
+        self.output1_dtype = pb_utils.triton_string_to_numpy(
+            output1_config["data_type"]
+        )
+
+    def execute(self, requests):
+        """This function is called on inference request."""
+
+        responses = []
+        for request in requests:
+            in_0 = pb_utils.get_input_tensor_by_name(request, "INPUT0")
+            in_1 = pb_utils.get_input_tensor_by_name(request, "INPUT1")
+            responses.append(pb_utils.InferenceResponse(self.subadd(in_0, in_1)))
+        return responses
+
+    def subadd(self, in_0, in_1):
+        if (
+            in_0.as_numpy().dtype.type is np.bytes_
+            or in_0.as_numpy().dtype == np.object_
+        ):
+            out_0, out_1 = (
+                in_0.as_numpy().astype(np.int32) - in_1.as_numpy().astype(np.int32),
+                in_0.as_numpy().astype(np.int32) + in_1.as_numpy().astype(np.int32),
+            )
+        else:
+            out_0, out_1 = (
+                in_0.as_numpy() - in_1.as_numpy(),
+                in_0.as_numpy() + in_1.as_numpy(),
+            )
+
+        out_tensor_0 = pb_utils.Tensor("OUTPUT0", out_0.astype(self.output0_dtype))
+        out_tensor_1 = pb_utils.Tensor("OUTPUT1", out_1.astype(self.output1_dtype))
+        return [out_tensor_0, out_tensor_1]