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93 lines
3.5 KiB
Python
93 lines
3.5 KiB
Python
# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from dataclasses import dataclass
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import pytest
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import torch
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from nemo.collections.audio.parts.submodules.flow import ConditionalFlowMatchingEulerSampler, OptimalTransportFlow
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NUM_STEPS = [1, 5, 10, 20, 100]
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TIMES_MIN = [0.0, 1e-8, 1e-2, 0.1, 0.25, 0.4]
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TIMES_MAX = [0.5, 0.7, 0.99, 1.0 - 1e-8, 1.0]
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@pytest.mark.parametrize("num_steps", NUM_STEPS)
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@pytest.mark.parametrize("estimator_target", ['conditional_vector_field', 'data'])
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def test_euler_sampler_nfe(num_steps, estimator_target):
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"""
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For this specific solver the number of steps should be equal to the number of function (estimator) evaluations
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"""
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class IdentityEstimator(torch.nn.Module):
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def forward(self, input, input_length, condition):
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return input, input_length
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@dataclass
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class ForwardCounterHook:
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counter: int = 0
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def __call__(self, *args, **kwargs):
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self.counter += 1
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estimator = IdentityEstimator()
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counter_hook = ForwardCounterHook()
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estimator.register_forward_hook(counter_hook)
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flow = OptimalTransportFlow()
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sampler = ConditionalFlowMatchingEulerSampler(
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estimator=estimator, num_steps=num_steps, estimator_target=estimator_target, flow=flow
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)
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b, c, d, l = 2, 3, 4, 5
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lengths = [5, 3]
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init_state = torch.randn(b, c, d, l)
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init_state_length = torch.LongTensor(lengths)
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sampler.forward(state=init_state, estimator_condition=None, state_length=init_state_length)
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assert counter_hook.counter == sampler.num_steps
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@pytest.mark.parametrize('time_min', TIMES_MIN)
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@pytest.mark.parametrize('time_max', TIMES_MAX)
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def test_time_generation_bounds_optimal_transport(time_min, time_max):
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"""
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This test uses a flow with certain time_min and time_max parameters to generate timepoints and checks if timepoints belong in [time_min, time_max] interval.
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"""
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rng = torch.Generator(device='cpu')
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rng.manual_seed(0)
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flow = OptimalTransportFlow(time_min=time_min, time_max=time_max)
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time = flow.generate_time(batch_size=1_000, rng=rng)
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assert torch.all(time >= time_min).item()
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assert torch.all(time <= time_max).item()
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@pytest.mark.parametrize('time_min', TIMES_MIN)
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@pytest.mark.parametrize('time_max', TIMES_MAX)
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def test_time_generation_bounds_optimal_transport_negative_examples(time_min, time_max):
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"""
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This test uses a flow with certain time_min and time_max parameters, widens them, generates timepoints and checks if timepoints belong in [time_min, time_max] interval.
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Since we widen the interval when initializing the flow, we expect that after taking enough samples some of them will be outside intended interval.
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"""
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rng = torch.Generator(device='cpu')
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rng.manual_seed(0)
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flow = OptimalTransportFlow(time_min=time_min - 0.1, time_max=time_max + 0.1)
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time = flow.generate_time(batch_size=1_000, rng=rng)
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assert not torch.all(time >= time_min).item()
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assert not torch.all(time <= time_max).item()
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