[Model] Minimize CosyVoice3 streaming regression tests

Signed-off-by: Sy03 <1370724210@qq.com>

tests/model_executor/models/cosyvoice3/test_cosyvoice3_full_batch.py

@@ -108,26 +108,6 @@ def test_packed_stream_noise_growth_preserves_prefix_and_global_rng():
     assert model._stream_position_noise(0, like).shape == (1, 80, 0)
 
 
-@hardware_test(res={"cuda": "H100"}, num_cards=1)
-def test_packed_stream_launch_uses_query_bound(monkeypatch):
-    """A long KV prefix must not inflate the chunk-causal query launch grid."""
-    from vllm_omni.model_executor.models.cosyvoice3.code2wav_core import packed_dit
-
-    bound = []
-
-    def forward(estimator, x, *args, max_seqlen_q, **kwargs):
-        bound.append(max_seqlen_q)
-        return x
-
-    monkeypatch.setattr(packed_dit, "forward_packed_tensor_geometry", forward)
-    estimator = object.__new__(packed_dit.PackedDiT)
-    rows = packed_dit.pack_rows([50], torch.device("cpu"))
-    attention = packed_dit.RaggedRowAttention(rows, heads=16, head_dim=64, chunk_size=50, width=4096)
-    hidden = torch.empty(1, 50, 80)
-    assert estimator.forward_full(hidden, None, None, None, None, None, attention) is hidden
-    assert bound == [50]
-
-
 @hardware_test(res={"cuda": "H100"}, num_cards=1)
 @torch.inference_mode()
 def test_packed_stream_attention_matches_chunk_causal_reference():

tests/model_executor/models/cosyvoice3/test_cosyvoice3_hift_graph.py

@@ -20,31 +20,22 @@ def test_hift_graph_preserves_current_and_previous_request(finalize):
     hift.f0_predictor.remove_weight_norm()
     hift.enable_decode_graphs()
     generator = torch.Generator(device="cuda").manual_seed(17)
-    first = None
-    first_saved = None
+    saved: list[tuple[torch.Tensor, torch.Tensor]] = []
     for _ in range(5):
         mel = torch.randn(1, 80, 30, device="cuda", generator=generator)
         source = torch.randn(1, 1, 30 * 480, device="cuda", generator=generator)
         expected = hift._decode_eager(mel, source, finalize=finalize)
         actual = hift.decode(mel, source, finalize=finalize)
         torch.testing.assert_close(actual, expected, rtol=1e-5, atol=1e-6)
-        if first is None and hift._decode_graphs.graphs:
-            first, first_saved = actual, actual.clone()
-        elif first is not None:
-            assert torch.equal(first, first_saved)
+        for previous, snapshot in saved:
+            assert torch.equal(previous, snapshot)
+        if hift._decode_graphs.graphs:
+            saved.append((actual, actual.clone()))
     assert len(hift._decode_graphs.graphs) == 1
-
-
-@hardware_test(res={"cuda": "L4"}, num_cards=1)
-@torch.inference_mode()
-def test_hift_graph_capacity_keeps_eager_fallback():
-    hift = _make_hift().cuda().eval()
-    hift.remove_weight_norm()
-    hift.enable_decode_graphs()
-    hift._decode_graphs.max_graphs = 0
-    mel = torch.randn(1, 80, 30, device="cuda")
-    source = torch.randn(1, 1, 30 * 480, device="cuda")
-    actual = hift.decode(mel, source, finalize=True)
-    expected = hift._decode_eager(mel, source, finalize=True)
+    hift._decode_graphs.max_graphs = 1
+    mel = torch.randn(1, 80, 31, device="cuda", generator=generator)
+    source = torch.randn(1, 1, 31 * 480, device="cuda", generator=generator)
+    actual = hift.decode(mel, source, finalize=finalize)
+    expected = hift._decode_eager(mel, source, finalize=finalize)
     assert torch.equal(actual, expected)
-    assert not hift._decode_graphs.graphs
+    assert len(hift._decode_graphs.graphs) == 1