Fixed an issue in updating realized dims

onnxruntime/core/providers/nuphar/runtime/compute_ctx.h

@@ -178,18 +178,18 @@ class KernelComputeCtx {
   }
 
   // UpdateRealizedDims is used to sync realize dim
-  // Note insert_exclusive_axis is introduced to adjusted shape.
+  // Note insert_inclusive_axis is introduced to adjusted shape.
   // It is commonly used in Scan or other subgraphs
   // when Tensors' shapes in a subgraph are sliced from the main grahp.
-  // Using the sliced axis as insert_exclusive_axis can find the correct shape dim in the main graph
+  // Using the sliced axis as insert_inclusive_axis can find the correct shape dim in the main graph
   inline void UpdateRealizedDims(
       const std::vector<std::pair<size_t, std::string>>& symbols,
       std::vector<int64_t>& realized_output_shape,
-      size_t insert_exclusive_axis = 65535 /*minimal maximum of size_t*/) {
+      size_t insert_inclusive_axis = 65535 /*minimal maximum of size_t*/) {
     for (const auto& s_pair : symbols) {
       size_t dim = s_pair.first;
       size_t adjusted_dim = dim;
-      if (dim > insert_exclusive_axis) {
+      if (dim >= insert_inclusive_axis) {
         adjusted_dim = dim + 1;
       }
 

onnxruntime/test/python/onnxruntime_test_python_nuphar.py

@@ -7,7 +7,7 @@
 from onnx import numpy_helper
 import onnxruntime as onnxrt
 import os
-from onnxruntime.nuphar.rnn_benchmark import perf_test
+from onnxruntime.nuphar.rnn_benchmark import perf_test, generate_model
 from pathlib import Path
 import shutil
 import sys
@@ -131,6 +131,51 @@ def test_rnn_benchmark(self):
                                                 min_duration_seconds=1)
 
 
+    def test_batch_scan(self):
+        input_dim = 3
+        hidden_dim = 5
+        bidirectional = False
+        layers = 3
+
+        lstm_model_name = 'test_batch_rnn_lstm.onnx'
+        # create an LSTM model for generating baseline data
+        generate_model('lstm', input_dim, hidden_dim, bidirectional, layers, lstm_model_name, batch_one=False, has_seq_len=True)
+
+        seq_len = 8
+        batch_size = 2
+        # prepare input
+        data_input = (np.random.rand(seq_len, batch_size, input_dim) * 2 - 1).astype(np.float32)
+        data_seq_len = np.random.randint(1, seq_len, size=(batch_size,), dtype=np.int32)
+
+        # run lstm as baseline
+        sess = onnxrt.InferenceSession(lstm_model_name)
+        first_lstm_data_output = sess.run([], {'input':data_input[:,0:1,:], 'seq_len':data_seq_len[0:1]})
+
+        lstm_data_output = []
+        lstm_data_output = first_lstm_data_output
+
+        for b in range(1, batch_size):
+            lstm_data_output = lstm_data_output + sess.run([], {'input':data_input[:,b:(b+1),:], 'seq_len':data_seq_len[b:(b+1)]})
+        lstm_data_output = np.concatenate(lstm_data_output, axis=1)
+
+        # generate a batch scan model
+        scan_model_name = 'test_batch_rnn_scan.onnx'
+        subprocess.run([sys.executable, '-m', 'onnxruntime.nuphar.model_editor', '--input', lstm_model_name, '--output', scan_model_name, '--mode', 'to_scan'], check=True)
+
+        # run scan_batch with batch size 1
+        sess = onnxrt.InferenceSession(scan_model_name)
+        scan_batch_data_output = sess.run([], {'input':data_input[:,0:1,:], 'seq_len':data_seq_len[0:1]})
+        assert np.allclose(first_lstm_data_output, scan_batch_data_output)
+
+        # run scan_batch with batch size 2
+        scan_batch_data_output = sess.run([], {'input':data_input, 'seq_len':data_seq_len})
+        assert np.allclose(lstm_data_output, scan_batch_data_output)
+
+        # run scan_batch with batch size 1 again
+        scan_batch_data_output = sess.run([], {'input':data_input[:,0:1,:], 'seq_len':data_seq_len[0:1]})
+        assert np.allclose(first_lstm_data_output, scan_batch_data_output)
+
+
     def test_symbolic_shape_infer(self):
         cwd = os.getcwd()
         test_model_dir = os.path.join(cwd, '..', 'models')