[Refactor][CI] Reorganize test tree, and move slow e2e to weekly (#4951)

Signed-off-by: wangyu <410167048@qq.com>

tests/model_executor/models/moss_tts/test_codec_cudagraph.py

@@ -1,14 +1,15 @@
 """Tests for MossTTSCUDAGraphCodecWrapper numerical equivalence.
 
 Verifies that CUDA Graph-accelerated decoding produces results equivalent
-to eager mode, with special attention to the two-argument _decode interface
-(codes [NQ, 1, T] + lengths [1]) and the NQ-first input convention.
+to eager mode, with special attention to the two-argument _decode
+interface (codes [NQ, 1, T] + lengths [1]) and the NQ-first input convention.
 """
 
 import pytest
 import torch
 import torch.nn as nn
 
+from tests.helpers.mark import hardware_marks
 from vllm_omni.model_executor.models.moss_tts.audio_tokenizer import (
     MossAudioTokenizerDecoderOutput,
 )
@@ -16,8 +17,10 @@ from vllm_omni.model_executor.models.moss_tts.moss_codec_cudagraph import (
     MossTTSCUDAGraphCodecWrapper,
 )
 
-pytestmark = [pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA required")]
-
+pytestmark = [
+    pytest.mark.core_model,
+    *hardware_marks(res={"cuda": "L4"}, num_cards=1),
+]
 DEVICE = torch.device("cuda:0")
 NUM_QUANTIZERS = 8
 DOWNSAMPLE_RATE = 4  # synthetic; real checkpoint uses 1920
@@ -31,8 +34,9 @@ DOWNSAMPLE_RATE = 4  # synthetic; real checkpoint uses 1920
 class SyntheticCodecModel(nn.Module):
     """Minimal stand-in for MossAudioTokenizerModel.
 
-    Exposes the same two-argument _decode(codes, lengths) interface and
-    returns a MossAudioTokenizerDecoderOutput, mirroring the real model.
+    Exposes the same two-argument ``_decode(codes, lengths)`` interface
+    used by ``MossTTSCUDAGraphCodecWrapper`` and returns a
+    MossAudioTokenizerDecoderOutput.
 
     Input:
         codes:   [NQ, B, T]  long  — RVQ codes

tests/model_executor/models/moss_tts/test_per_request_codes.py

@@ -23,6 +23,9 @@ import functools
 
 import pytest
 
+pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
+
+
 torch = pytest.importorskip("torch")
 
 N_VQ = 4