Test preserve_ordering for oldest strategy sequence batcher

qa/L0_sequence_batcher/sequence_batcher_test.py

@@ -31,10 +31,12 @@
 sys.path.append("../common")
 
 import os
+import random
 import threading
 import time
 import unittest
 from builtins import str
+from functools import partial
 
 import numpy as np
 import sequence_util as su
@@ -3432,5 +3434,185 @@ def test_send_request_after_timeout(self):
             raise last_err
 
 
+class SequenceBatcherPreserveOrderingTest(su.SequenceBatcherTestUtil):
+    def setUp(self):
+        super().setUp()
+        # By default, find tritonserver on "localhost", but can be overridden
+        # with TRITONSERVER_IPADDR envvar
+        self.server_address_ = (
+            os.environ.get("TRITONSERVER_IPADDR", "localhost") + ":8001"
+        )
+
+        # Prepare input and expected output based on the model and
+        # the infer sequence sent for testing. If the test is to be extended
+        # for different sequence and model, then proper grouping should be added
+        self.model_name_ = "sequence_py"
+        self.tensor_data_ = np.ones(shape=[1, 1], dtype=np.int32)
+        self.inputs_ = [grpcclient.InferInput("INPUT0", [1, 1], "INT32")]
+        self.inputs_[0].set_data_from_numpy(self.tensor_data_)
+        self.triton_client = grpcclient.InferenceServerClient(self.server_address_)
+
+        # Atomic request ID for multi-threaded inference
+        self.request_id_lock = threading.Lock()
+        self.request_id = 1
+
+    def send_sequence(self, seq_id, seq_id_map, req_id_map):
+        if seq_id not in seq_id_map:
+            seq_id_map[seq_id] = []
+
+        start, middle, end = (True, False), (False, False), (False, True)
+        # Send sequence with 1 start, 1 middle, and 1 end request
+        seq_flags = [start, middle, end]
+        for start_flag, end_flag in seq_flags:
+            # Introduce random sleep to better interweave requests from different sequences
+            time.sleep(random.uniform(0.0, 1.0))
+
+            # Serialize sending requests to ensure ordered request IDs
+            with self.request_id_lock:
+                req_id = self.request_id
+                self.request_id += 1
+
+                # Store metadata to validate results later
+                req_id_map[req_id] = seq_id
+                seq_id_map[seq_id].append(req_id)
+
+                self.triton_client.async_stream_infer(
+                    self.model_name_,
+                    self.inputs_,
+                    sequence_id=seq_id,
+                    sequence_start=start_flag,
+                    sequence_end=end_flag,
+                    timeout=None,
+                    request_id=str(req_id),
+                )
+
+    def _test_sequence_ordering(self, preserve_ordering, decoupled):
+        # 1. Send a few grpc streaming sequence requests to the model.
+        # 2. With grpc streaming, the model should receive the requests in
+        #    the same order they are sent from client, and the client should
+        #    receive the responses in the same order sent back by the
+        #    model/server. With sequence scheduler, the requests for each sequence should be routed to the same model
+        #    instance, and no two requests from the same sequence should
+        #    get batched together.
+        # 3. With preserve_ordering=False, we may get the responses back in a different
+        #    order than the requests, but with grpc streaming we should still expect responses for each sequence to be ordered.
+        # 4. Assert that the sequence values are ordered, and that the response IDs per sequence are ordered
+        class SequenceResult:
+            def __init__(self, seq_id, result, request_id):
+                self.seq_id = seq_id
+                self.result = result
+                self.request_id = int(request_id)
+
+        def full_callback(sequence_dict, sequence_list, result, error):
+            # We expect no model errors for this test
+            if error:
+                self.assertTrue(False, error)
+
+            # Gather all the necessary metadata for validation
+            request_id = int(result.get_response().id)
+            sequence_id = request_id_map[request_id]
+            # Overall list of results in the order received, regardless of sequence ID
+            sequence_list.append(SequenceResult(sequence_id, result, request_id))
+            # Ordered results organized by their seq IDs
+            sequence_dict[sequence_id].append(result)
+
+        # Store ordered list in which responses are received by client
+        sequence_list = []
+        # Store mapping of sequence ID to response results
+        sequence_dict = {}
+        # Store mapping of sequence ID to request IDs and vice versa
+        sequence_id_map = {}
+        request_id_map = {}
+
+        # Start stream
+        seq_callback = partial(full_callback, sequence_dict, sequence_list)
+        self.triton_client.start_stream(callback=seq_callback)
+
+        # Send N sequences concurrently
+        threads = []
+        num_sequences = 10
+        for i in range(num_sequences):
+            # Sequence IDs are 1-indexed
+            sequence_id = i + 1
+            # Add a result list and callback for each sequence
+            sequence_dict[sequence_id] = []
+            threads.append(
+                threading.Thread(
+                    target=self.send_sequence,
+                    args=(sequence_id, sequence_id_map, request_id_map),
+                )
+            )
+
+        # Start all sequence threads
+        for t in threads:
+            t.start()
+
+        # Wait for threads to return
+        for t in threads:
+            t.join()
+
+        # Block until all requests are completed
+        self.triton_client.stop_stream()
+
+        # Make sure some inferences occurred and metadata was collected
+        self.assertGreater(len(sequence_dict), 0)
+        self.assertGreater(len(sequence_list), 0)
+
+        # Validate model results are sorted per sequence ID (model specific logic)
+        print(f"=== {preserve_ordering=} {decoupled=} ===")
+        print("Outputs per Sequence:")
+        for seq_id, sequence in sequence_dict.items():
+            seq_outputs = [
+                result.as_numpy("OUTPUT0").flatten().tolist() for result in sequence
+            ]
+            print(f"{seq_id}: {seq_outputs}")
+            self.assertEqual(seq_outputs, sorted(seq_outputs))
+
+        # Validate request/response IDs for each response in a sequence is sorted
+        # This should be true regardless of preserve_ordering or not
+        print("Request IDs per Sequence:")
+        for seq_id in sequence_id_map:
+            per_seq_request_ids = sequence_id_map[seq_id]
+            print(f"{seq_id}: {per_seq_request_ids}")
+            self.assertEqual(per_seq_request_ids, sorted(per_seq_request_ids))
+
+        # Validate results are sorted in request order if preserve_ordering is True
+        if preserve_ordering:
+            request_ids = [s.request_id for s in sequence_list]
+            print(f"Request IDs overall:\n{request_ids}")
+            sequence_ids = [s.seq_id for s in sequence_list]
+            print(f"Sequence IDs overall:\n{sequence_ids}")
+            self.assertEqual(request_ids, sorted(request_ids))
+
+        # Assert some dynamic batching of requests was done
+        stats = self.triton_client.get_inference_statistics(
+            model_name=self.model_name_, headers={}, as_json=True
+        )
+        model_stats = stats["model_stats"][0]
+        self.assertEqual(model_stats["name"], self.model_name_)
+        self.assertLess(
+            int(model_stats["execution_count"]), int(model_stats["inference_count"])
+        )
+
+    def test_sequence_with_preserve_ordering(self):
+        self.model_name_ = "seqpy_preserve_ordering_nondecoupled"
+        self._test_sequence_ordering(preserve_ordering=True, decoupled=False)
+
+    def test_sequence_without_preserve_ordering(self):
+        self.model_name_ = "seqpy_no_preserve_ordering_nondecoupled"
+        self._test_sequence_ordering(preserve_ordering=False, decoupled=False)
+
+    # FIXME [DLIS-5280]: This may fail for decoupled models if writes to GRPC
+    # stream are done out of order in server, so disable test for now.
+    # def test_sequence_with_preserve_ordering_decoupled(self):
+    #    self.model_name_ = "seqpy_preserve_ordering_decoupled"
+    #    self._test_sequence_ordering(preserve_ordering=True, decoupled=True)
+
+    # FIXME [DLIS-5280]
+    # def test_sequence_without_preserve_ordering_decoupled(self):
+    #    self.model_name_ = "seqpy_no_preserve_ordering_decoupled"
+    #    self._test_sequence_ordering(preserve_ordering=False, decoupled=True)
+
+
 if __name__ == "__main__":
     unittest.main()

qa/L0_sequence_batcher/test.sh

@@ -800,6 +800,82 @@ if [ "$TEST_SYSTEM_SHARED_MEMORY" -ne 1 ] && [ "$TEST_CUDA_SHARED_MEMORY" -ne 1
     set -e
 fi
 
+### Start Preserve Ordering Tests ###
+
+# Test preserve ordering true/false and decoupled/non-decoupled
+TEST_CASE=SequenceBatcherPreserveOrderingTest
+MODEL_PATH=preserve_ordering_models
+BASE_MODEL="../python_models/sequence_py"
+rm -r ${MODEL_PATH}
+
+# FIXME [DLIS-5280]: This may fail for decoupled models if writes to GRPC
+# stream are done out of order in server, so decoupled tests are disabled.
+MODES="decoupled nondecoupled"
+for mode in $MODES; do
+    NO_PRESERVE="${MODEL_PATH}/seqpy_no_preserve_ordering_${mode}"
+    mkdir -p ${NO_PRESERVE}/1
+    cp ${BASE_MODEL}/config.pbtxt ${NO_PRESERVE}
+    cp ${BASE_MODEL}/model.py ${NO_PRESERVE}/1
+
+    PRESERVE="${MODEL_PATH}/seqpy_preserve_ordering_${mode}"
+    cp -r ${NO_PRESERVE} ${PRESERVE}
+    sed -i "s/^preserve_ordering: False/preserve_ordering: True/" ${PRESERVE}/config.pbtxt
+
+    if [ ${mode} == "decoupled" ]; then
+      echo -e "\nmodel_transaction_policy { decoupled: true }" >> ${NO_PRESERVE}/config.pbtxt
+      echo -e "\nmodel_transaction_policy { decoupled: true }" >> ${PRESERVE}/config.pbtxt
+    fi
+done
+
+SERVER_ARGS="--model-repository=$MODELDIR/$MODEL_PATH ${SERVER_ARGS_EXTRA}"
+SERVER_LOG="./$TEST_CASE.$MODEL_PATH.server.log"
+
+if [ "$TEST_VALGRIND" -eq 1 ]; then
+    LEAKCHECK_LOG="./$i.$MODEL_PATH.valgrind.log"
+    LEAKCHECK_ARGS="$LEAKCHECK_ARGS_BASE --log-file=$LEAKCHECK_LOG"
+    run_server_leakcheck
+else
+    run_server
+fi
+
+if [ "$SERVER_PID" == "0" ]; then
+    echo -e "\n***\n*** Failed to start $SERVER\n***"
+    cat $SERVER_LOG
+    exit 1
+fi
+
+echo "Test: $TEST_CASE, repository $MODEL_PATH" >>$CLIENT_LOG
+
+set +e
+python3 $BATCHER_TEST $TEST_CASE >>$CLIENT_LOG 2>&1
+if [ $? -ne 0 ]; then
+    echo -e "\n***\n*** Test $TEST_CASE Failed\n***" >>$CLIENT_LOG
+    echo -e "\n***\n*** Test $TEST_CASE Failed\n***"
+    RET=1
+else
+    # 2 for preserve_ordering = True/False, 2 for decoupled = True/False
+    check_test_results $TEST_RESULT_FILE 4
+    if [ $? -ne 0 ]; then
+        cat $CLIENT_LOG
+        echo -e "\n***\n*** Test Result Verification Failed\n***"
+        RET=1
+    fi
+fi
+set -e
+
+kill_server
+
+set +e
+if [ "$TEST_VALGRIND" -eq 1 ]; then
+    python3 ../common/check_valgrind_log.py -f $LEAKCHECK_LOG
+    if [ $? -ne 0 ]; then
+        RET=1
+    fi
+fi
+set -e
+
+### End Preserve Ordering Tests ###
+
 if [ $RET -eq 0 ]; then
     echo -e "\n***\n*** Test Passed\n***"
 else

qa/python_models/sequence_py/config.pbtxt

@@ -0,0 +1,53 @@
+# Copyright (c) 2023, 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.
+
+backend: "python"
+max_batch_size: 4
+
+input [
+  {
+    name: "INPUT0"
+    data_type: TYPE_INT32
+    dims: [ 1 ]
+
+  }
+]
+output [
+  {
+    name: "OUTPUT0"
+    data_type: TYPE_INT32
+    dims: [ 1 ]
+  }
+]
+
+sequence_batching {
+  oldest {
+    max_candidate_sequences: 4
+    max_queue_delay_microseconds: 1000000
+    preserve_ordering: False
+  }
+  max_sequence_idle_microseconds: 10000000
+}