[Refactor] fish_speech: vendor DAC codec modules, drop external fish-speech dependency (#4871)

Signed-off-by: Yueqian Lin <linyueqian@outlook.com>

tests/model_executor/models/fish_speech/test_dac_modules.py

@@ -0,0 +1,110 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""CPU tests for the vendored DAC codec modules (dac_modules).
+
+Guards the checkpoint contract: the module tree built by build_dac_codec()
+must keep the exact parameter/buffer names and shapes that codec.pth was
+saved with (verified against the upstream fish-speech 0.1.0 package), and
+the encode/decode surfaces used by dac_encoder.py / fish_speech_dac_decoder.py
+must keep their shapes.
+"""
+
+import pytest
+import torch
+
+pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
+
+# Spot checks covering every submodule family and both weight-norm styles.
+# Derived from the upstream fish-speech 0.1.0 state dict (541 entries total);
+# legacy weight_norm (weight_g/weight_v) comes from the descript quantizer
+# projections, parametrizations.weight.original0/1 from the causal convs.
+EXPECTED_KEY_SAMPLES = [
+    # encoder: first conv + deepest encoder-block transformer
+    "encoder.block.0.conv.parametrizations.weight.original0",
+    "encoder.block.4.block.5.layers.3.attention.wqkv.weight",
+    "encoder.block.4.block.5.freqs_cis",
+    "encoder.block.1.block.0.block.0.alpha",
+    # quantizer: semantic + residual VQ (legacy weight_norm keys)
+    "quantizer.semantic_quantizer.quantizers.0.codebook.weight",
+    "quantizer.quantizer.quantizers.8.in_proj.weight_g",
+    "quantizer.quantizer.quantizers.8.in_proj.weight_v",
+    # quantizer pre/post transformers + down/upsample convnext
+    "quantizer.pre_module.layers.7.feed_forward.w1.weight",
+    "quantizer.post_module.norm.weight",
+    "quantizer.downsample.1.1.dwconv.conv.weight",
+    "quantizer.upsample.0.0.conv.weight",
+    # decoder: transposed conv + final conv
+    "decoder.model.1.block.1.conv.parametrizations.weight.original1",
+    "decoder.model.6.conv.parametrizations.weight.original0",
+]
+
+NUM_STATE_DICT_ENTRIES = 541
+DAC_NUM_CODEBOOKS = 10  # 1 semantic + 9 residual
+
+
+@pytest.fixture(scope="module")
+def codec():
+    from vllm_omni.model_executor.models.fish_speech.dac_utils import build_dac_codec
+
+    model = build_dac_codec()
+    model.eval()
+    return model
+
+
+def test_state_dict_contract(codec):
+    sd = codec.state_dict()
+    assert len(sd) == NUM_STATE_DICT_ENTRIES
+    for key in EXPECTED_KEY_SAMPLES:
+        assert key in sd, f"checkpoint-contract key missing: {key}"
+
+
+def test_no_fish_speech_import_required(codec):
+    import sys
+
+    assert "fish_speech" not in sys.modules, "vendored codec must not import the fish-speech package"
+
+
+@torch.no_grad()
+def test_encode_shapes(codec):
+    frame_length = codec.frame_length  # 2048 samples per code frame
+    wav = torch.randn(1, 1, frame_length * 3 + 100)
+    lengths = torch.tensor([wav.shape[-1]], dtype=torch.long)
+    codes, code_lengths = codec.encode(wav, lengths)
+    assert codes.shape[0] == 1
+    assert codes.shape[1] == DAC_NUM_CODEBOOKS
+    assert codes.shape[2] == 4  # ceil(3.x frames)
+    assert code_lengths.tolist() == [4]
+    assert codes.dtype in (torch.int64, torch.int32)
+
+
+@torch.no_grad()
+def test_decode_shapes(codec):
+    frames = 8
+    codes = torch.randint(0, 1024, (1, DAC_NUM_CODEBOOKS, frames), dtype=torch.long)
+    feature_lengths = torch.tensor([frames], dtype=torch.long)
+    wav, audio_lengths = codec.decode(codes, feature_lengths)
+    assert wav.shape[0] == 1 and wav.shape[1] == 1
+    assert audio_lengths.tolist() == [frames * codec.frame_length]
+    assert wav.shape[-1] >= audio_lengths[0] - codec.frame_length
+
+
+@torch.no_grad()
+def test_decoder_runner_mutations(codec):
+    """The stage-1 runner prunes encode-only parts and bakes weight norm;
+    decode must still work afterwards (mirrors fish_speech_dac_decoder)."""
+    from torch.nn.utils.parametrize import remove_parametrizations
+
+    codec.encoder = None
+    codec.quantizer.pre_module = None
+    codec.quantizer.downsample = None
+    for module in codec.modules():
+        parametrizations = getattr(module, "parametrizations", None)
+        if not parametrizations:
+            continue
+        for name in list(parametrizations.keys()):
+            remove_parametrizations(module, name, leave_parametrized=True)
+
+    codes = torch.randint(0, 1024, (2, DAC_NUM_CODEBOOKS, 5), dtype=torch.long)
+    wav, audio_lengths = codec.decode(codes, torch.tensor([5, 3], dtype=torch.long))
+    assert wav.shape[0] == 2
+    assert audio_lengths.tolist() == [5 * codec.frame_length, 3 * codec.frame_length]