[CI][ROCm] Shorten the blocking CosyVoice test path (#7748)

Signed-off-by: andyluo7 <andy.luo@amd.com>

tests/model_executor/models/cosyvoice3/test_cosyvoice3_components.py

@@ -260,6 +260,37 @@ class TestDiTAttention:
         assert attention.to_k.out_features == 512
         assert attention.to_v.out_features == 512
 
+    def test_embedded_dit_honors_diffusion_attention_backend(self, monkeypatch):
+        """The LLM-hosted code2wav DiT must consume the diffusion env override."""
+        from vllm_omni.diffusion.config import get_current_diffusion_config_or_none
+        from vllm_omni.model_executor.models.cosyvoice3.cosyvoice3_code2wav import _build_dit_estimator
+
+        monkeypatch.setenv("DIFFUSION_ATTENTION_BACKEND", "TORCH_SDPA")
+
+        estimator = _build_dit_estimator(
+            {
+                "dim": 32,
+                "depth": 1,
+                "heads": 2,
+                "dim_head": 16,
+                "dropout": 0.0,
+                "ff_mult": 2,
+                "mel_dim": 8,
+                "mu_dim": 8,
+                "spk_dim": 4,
+                "out_channels": 8,
+                "static_chunk_size": 4,
+                "num_decoding_left_chunks": 1,
+            }
+        )
+
+        attention = estimator.transformer_blocks[0].attn.attn
+        assert attention.backend_pref == "TORCH_SDPA"
+        assert attention.backend_explicit is True
+        assert attention.attn_backend is not None
+        assert attention.attn_backend.get_name() == "SDPA"
+        assert get_current_diffusion_config_or_none() is None
+
     @pytest.mark.core_model
     @pytest.mark.cpu
     def test_forward_passes_2d_mask_without_casting_qkv(self):

tests/model_executor/models/cosyvoice3/test_cosyvoice3_incremental_hift.py

@@ -39,7 +39,10 @@ CHUNK_LEN = 24
 TOTAL_MEL = 96
 SPM = 480  # samples per mel frame for the small test HiFT (8*5*3*4), matching the real model
 
-LONG_TOTAL_MEL = 400  # long enough that the 64-frame window actually truncates
+WINDOW_LEN = 64
+# Six chunks exercise three updates after history exceeds the bounded window,
+# without spending minutes recomputing an unnecessarily long synthetic signal.
+LONG_TOTAL_MEL = 144
 PCM16_LSB = 1.0 / 32767.0  # smallest representable PCM16 difference
 
 # Worst observed deviation is 1.97e-7 (~2x float32 eps); atol=1e-6 keeps 5x headroom.
@@ -189,16 +192,19 @@ def test_incremental_hift_bounded_window_is_close(config):
 def test_incremental_hift_matches_streaming_reference_within_tolerance(config):
     """Windowed output matches the full-cumulative reference within float32 rounding.
 
-    The load-bearing correctness test: 400 mel frames far exceeds the 64-frame
-    window, so truncation genuinely exercises the phase/noise carry. Float64
-    agrees to ~1e-16, confirming the residual here is rounding, not truncation.
+    The load-bearing correctness test crosses the 64-frame window repeatedly,
+    so truncation genuinely exercises the phase/noise carry. Float64 agrees to
+    ~1e-16, confirming the residual here is rounding, not truncation.
     """
     hift = _make_hift(config)
-    model = _make_model(hift, window_len=64)  # the real _hift_window_len
+    model = _make_model(hift, window_len=WINDOW_LEN)  # the real _hift_window_len
     chunks = _chunks(total_mel=LONG_TOTAL_MEL)
 
     trim = int(hift.f0_predictor.condnet[0].causal_padding)
-    assert LONG_TOTAL_MEL > 64 + trim + CHUNK_LEN  # window must actually truncate
+    truncating_steps = sum(
+        history_len > WINDOW_LEN + trim for history_len in range(CHUNK_LEN, LONG_TOTAL_MEL, CHUNK_LEN)
+    )
+    assert truncating_steps >= 3  # exercise repeated truncation, not only the boundary
 
     full = _full_reference(model, chunks)
     incr = _incremental(model, chunks)