File size: 6,138 Bytes
90884df | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 | from __future__ import annotations
import subprocess
import sys
from pathlib import Path
import pytest
import torch
from torch import nn
from torch.nn import functional as F
from music3lab.codec.flow_encoder import (
ContinuousFlowEncoder,
audio_metrics,
latent_normalized_mse,
learning_rate,
load_flow_encoder_config,
prior_mean_latents,
)
from music3lab.codec.runner import (
FileRecord,
TeacherDatasetManifest,
TeacherSplitRecord,
)
from music3lab.vocoder import FlowVocoderLatents
ROOT = Path(__file__).resolve().parents[1]
CONFIG = ROOT / "configs" / "flow-encoder-v1.yaml"
class TinyVocoder(nn.Module):
def __init__(self) -> None:
super().__init__()
self.gain = nn.Parameter(torch.ones(2), requires_grad=False)
def forward(self, latents: FlowVocoderLatents) -> torch.Tensor:
value = F.interpolate(
latents.tensor[:, :2].float(),
size=44032,
mode="linear",
align_corners=False,
)
return value * self.gain.view(1, 2, 1)
def test_frozen_config_has_exact_geometry_and_disjoint_64_16_16_seeds() -> None:
config = load_flow_encoder_config(CONFIG).config
seeds = config.teacher.split_seeds()
assert {key: len(value) for key, value in seeds.items()} == {
"train": 64,
"validation": 16,
"heldout": 16,
}
assert len({item for values in seeds.values() for item in values}) == 96
assert config.capability.endswith("not_native_rvq")
assert config.model.input_samples == 44032
assert config.model.latent_frames == 86
def test_encoder_exact_geometry_is_deterministic_and_has_gradients() -> None:
loaded = load_flow_encoder_config(CONFIG).config
torch.manual_seed(9)
encoder = ContinuousFlowEncoder(
loaded.model, latent_mean=loaded.loss.latent_mean
)
audio = torch.randn(2, 2, 44032)
first = encoder(audio)
second = encoder(audio)
assert first.shape == (2, 128, 86)
assert torch.equal(first, second)
target = torch.randn_like(first)
latent_normalized_mse(first, target, loaded.loss).mean().backward()
assert any(
parameter.grad is not None and parameter.grad.abs().sum() > 0
for parameter in encoder.parameters()
)
def test_prior_mean_baseline_and_schedule_are_frozen() -> None:
config = load_flow_encoder_config(CONFIG).config
prior = prior_mean_latents(3, config.model, config.loss)
assert prior.shape == (3, 128, 86)
assert float(prior.mean()) == pytest.approx(config.loss.latent_mean)
assert learning_rate(0, config.training) == pytest.approx(
config.training.maximum_learning_rate
)
assert learning_rate(config.training.steps, config.training) == pytest.approx(
config.training.minimum_learning_rate
)
def test_frozen_decoder_has_no_weight_grad_while_encoder_input_does() -> None:
decoder = TinyVocoder()
latents = torch.randn(2, 128, 86, requires_grad=True)
audio = decoder(FlowVocoderLatents(latents))
audio.square().mean().backward()
assert latents.grad is not None and latents.grad.abs().sum() > 0
assert all(parameter.grad is None for parameter in decoder.parameters())
def test_audio_metrics_order_exact_before_distorted() -> None:
target = torch.randn(2, 2, 44032)
noise = 0.1 * torch.randn(2, 2, 44032)
exact = audio_metrics(target, target)
distorted = audio_metrics(target + noise, target)
assert exact.mae == 0
assert exact.correlation > distorted.correlation
assert exact.unscaled_snr_db > distorted.unscaled_snr_db
def test_cli_help_is_cpu_safe() -> None:
result = subprocess.run(
[
sys.executable,
"-B",
str(ROOT / "scripts" / "run_flow_encoder_pilot.py"),
"--help",
],
check=True,
text=True,
capture_output=True,
env={
"PATH": __import__("os").environ["PATH"],
"PYTHONPATH": str(ROOT / "src"),
"CUDA_VISIBLE_DEVICES": "-1",
"PYTHONDONTWRITEBYTECODE": "1",
},
)
normalized = " ".join(result.stdout.split())
assert "does not produce native RVQ tokens" in normalized
def test_teacher_manifest_canonicalizes_nested_split_models() -> None:
config = load_flow_encoder_config(CONFIG)
counts = {"train": 64, "validation": 16, "heldout": 16}
starts = {"train": 1000, "validation": 2000, "heldout": 3000}
splits = {
name: TeacherSplitRecord(
count=count,
seeds=tuple(range(starts[name], starts[name] + count)),
file=FileRecord(
path=f"{name}.safetensors",
sha256="1" * 64,
size=1,
),
audio_shape=(count, 2, 44032),
latent_shape=(count, 128, 86),
audio_content_sha256="2" * 64,
latent_content_sha256="3" * 64,
)
for name, count in counts.items()
}
manifest = TeacherDatasetManifest.create(
schema_version="music3lab.flow-encoder-teachers.v1",
capability="continuous_flow_vocoder_latent_teacher_pairs_not_native_rvq",
config_file_sha256=config.file_sha256,
config_semantic_digest=config.semantic_digest,
model_revision=config.config.model_revision,
base_id=config.config.expected_base_id,
base_manifest_file_sha256="4" * 64,
diffusers_revision=config.config.diffusers_revision,
prompt_sha256="5" * 64,
lyrics_sha256="6" * 64,
persistent_pipeline_load_count=1,
replay_exact_count=96,
producer_project_git_commit="7" * 40,
publication_project_git_commit="8" * 40,
recovered_from_complete_quarantine=True,
generation_seconds=None,
peak_cuda_allocated_bytes=None,
splits=splits,
)
assert (
TeacherDatasetManifest.model_validate_json(
__import__(
"music3lab.codec.flow_encoder",
fromlist=["canonical_json_bytes"],
).canonical_json_bytes(manifest)
)
== manifest
)
|