[Audio] Fix extra step in Euler sampler for flow matching inference

nemo/collections/audio/parts/submodules/flow.py

@@ -234,7 +234,7 @@ def forward(
         if state_length is not None:
             state = mask_sequence_tensor(state, state_length)
 
-        for t in time_steps:
+        for t in time_steps[:-1]:
             time = t * torch.ones(state.shape[0], device=state.device)
 
             if estimator_condition is None:

tests/collections/audio/test_audio_flowmatching.py

@@ -0,0 +1,54 @@
+# Copyright (c) 2025, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed 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.
+from dataclasses import dataclass
+
+import pytest
+import torch
+
+from nemo.collections.audio.parts.submodules.flow import ConditionalFlowMatchingEulerSampler
+
+NUM_STEPS = [1, 5, 10, 20, 100]
+
+
+@pytest.mark.parametrize("num_steps", NUM_STEPS)
+def test_euler_sampler_nfe(num_steps):
+    """
+    For this specific solver the number of steps should be equal to the number of function (estimator) evaluations
+    """
+
+    class IdentityEstimator(torch.nn.Module):
+        def forward(self, input, input_length, condition):
+            return input, input_length
+
+    @dataclass
+    class ForwardCounterHook:
+        counter: int = 0
+
+        def __call__(self, *args, **kwargs):
+            self.counter += 1
+
+    estimator = IdentityEstimator()
+    counter_hook = ForwardCounterHook()
+    estimator.register_forward_hook(counter_hook)
+
+    sampler = ConditionalFlowMatchingEulerSampler(estimator=estimator, num_steps=num_steps)
+
+    b, c, d, l = 2, 3, 4, 5
+    lengths = [5, 3]
+    init_state = torch.randn(b, c, d, l)
+    init_state_length = torch.LongTensor(lengths)
+
+    sampler.forward(state=init_state, estimator_condition=None, state_length=init_state_length)
+
+    assert counter_hook.counter == sampler.num_steps