[CI] Reducing mithril-h100-pool in ready&merge CI, optimize ready CI duration (#4354)

Signed-off-by: [Your Name] <your.email@example.com>
Signed-off-by: wangyu <410167048@qq.com>
Signed-off-by: wangyu <53896905+yenuo26@users.noreply.github.com>
Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>
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Co-authored-by: Hongsheng Liu <liuhongsheng4@huawei.com>

tests/model_executor/models/step_audio2/test_step_audio2_thinker.py

@@ -0,0 +1,151 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Unit tests for Step-Audio2 thinker processor, encoder, and token handling."""
+
+import numpy as np
+import pytest
+import torch
+
+pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
+
+
+def create_dummy_audio(sample_rate: int = 16000, duration_sec: float = 1.0) -> tuple[np.ndarray, int]:
+    """Create a dummy audio signal (sine wave) for testing."""
+    t = np.linspace(0, duration_sec, int(sample_rate * duration_sec), dtype=np.float32)
+    audio = 0.5 * np.sin(2 * np.pi * 440 * t)
+    return audio, sample_rate
+
+
+def test_processor_loads_correctly():
+    """Test that Step-Audio2 processor can be imported and initialized."""
+    from vllm_omni.model_executor.models.step_audio2.step_audio2_thinker import (
+        StepAudio2MultiModalProcessor,
+        StepAudio2ProcessingInfo,
+        StepAudio2Processor,
+    )
+
+    assert StepAudio2MultiModalProcessor is not None
+    assert StepAudio2ProcessingInfo is not None
+    assert StepAudio2Processor is not None
+
+
+def test_audio_preprocessing():
+    """Test that audio preprocessing produces correct tensor shapes."""
+    from vllm_omni.model_executor.models.step_audio2.step_audio2_thinker import (
+        log_mel_spectrogram,
+        padding_mels,
+    )
+
+    audio, _sample_rate = create_dummy_audio(sample_rate=16000, duration_sec=1.0)
+
+    mel = log_mel_spectrogram(audio)
+
+    assert mel.ndim == 2, f"Expected 2D tensor, got {mel.ndim}D"
+    assert mel.shape[0] == 128, f"Expected 128 mel bins, got {mel.shape[0]}"
+
+    mels = [mel, mel]
+    padded_mels, _mel_lens = padding_mels(mels)
+
+    assert padded_mels.ndim == 3, f"Expected 3D tensor, got {padded_mels.ndim}D"
+    assert padded_mels.shape[0] == 2, f"Expected batch size 2, got {padded_mels.shape[0]}"
+    assert padded_mels.shape[1] == 128, f"Expected 128 mel bins, got {padded_mels.shape[1]}"
+
+
+def test_feature_length_calculation():
+    """Test that audio feature length calculation is correct."""
+    from vllm_omni.model_executor.models.step_audio2.step_audio2_thinker import (
+        calculate_audio_feature_length,
+    )
+
+    assert calculate_audio_feature_length(1000) == 125
+
+    assert calculate_audio_feature_length(100) > 0
+
+    assert calculate_audio_feature_length(1) >= 1
+
+
+def test_token_config_constants():
+    """Test that token configuration constants are set correctly."""
+    from vllm_omni.model_executor.models.step_audio2.step_audio2_constants import (
+        DEFAULT_TOKEN_CONFIG,
+        STEP_AUDIO2_AUDIO_END,
+        STEP_AUDIO2_AUDIO_PATCH_TOKEN_ID,
+        STEP_AUDIO2_AUDIO_START,
+        STEP_AUDIO2_AUDIO_VOCAB_SIZE,
+        STEP_AUDIO2_TEXT_MAX,
+    )
+
+    assert STEP_AUDIO2_TEXT_MAX < STEP_AUDIO2_AUDIO_START, "Text tokens should come before audio tokens"
+    assert STEP_AUDIO2_AUDIO_END == STEP_AUDIO2_AUDIO_START + STEP_AUDIO2_AUDIO_VOCAB_SIZE - 1
+    assert STEP_AUDIO2_AUDIO_PATCH_TOKEN_ID > STEP_AUDIO2_TEXT_MAX
+    assert STEP_AUDIO2_AUDIO_PATCH_TOKEN_ID < STEP_AUDIO2_AUDIO_START
+
+    assert DEFAULT_TOKEN_CONFIG.text_max == STEP_AUDIO2_TEXT_MAX
+    assert DEFAULT_TOKEN_CONFIG.audio_start == STEP_AUDIO2_AUDIO_START
+
+
+def test_mm_field_config_structure():
+    """Test that multimodal field config is properly structured for vLLM batching."""
+    from vllm.multimodal.inputs import MultiModalFieldConfig
+
+    audio_lens = torch.tensor([100, 200, 150], dtype=torch.int32)
+
+    field_config = MultiModalFieldConfig.flat_from_sizes("audio", audio_lens)
+
+    assert field_config is not None
+
+
+def test_audio_encoder_output_shape():
+    """Test that audio encoder produces correct output shapes."""
+    from vllm_omni.model_executor.models.step_audio2.step_audio2_thinker import (
+        Adaptor,
+        AudioEncoder,
+    )
+
+    encoder = AudioEncoder(n_mels=128, n_ctx=1500, n_state=512, n_head=8, n_layer=6)
+
+    batch_size = 2
+    n_mels = 128
+    time_steps = 400
+    x = torch.randn(batch_size, n_mels, time_steps)
+    x_len = torch.tensor([time_steps, time_steps // 2], dtype=torch.int32)
+
+    encoded, _encoded_lens = encoder(x, x_len)
+
+    assert encoded.ndim == 3, f"Expected 3D tensor, got {encoded.ndim}D"
+    assert encoded.shape[0] == batch_size
+    assert encoded.shape[2] == 512
+
+    adapter = Adaptor(n_state=512, n_hidden=4096, kernel_size=3, stride=2)
+    adapted = adapter(encoded)
+
+    assert adapted.ndim == 3
+    assert adapted.shape[0] == batch_size
+    assert adapted.shape[2] == 4096
+
+
+def test_token_separation():
+    """Test that token separation works correctly."""
+    from vllm_omni.model_executor.models.step_audio2.step_audio2_thinker import (
+        StepAudio2ThinkerForConditionalGeneration,
+    )
+
+    token_ids = [100, 200, 151700, 300, 151800, 400]
+
+    text_tokens, audio_tokens = StepAudio2ThinkerForConditionalGeneration.separate_tokens(token_ids)
+
+    assert text_tokens == [100, 200, 300, 400]
+    assert audio_tokens == [4, 104]
+
+
+def test_has_audio_output():
+    """Test detection of audio tokens in output."""
+    from vllm_omni.model_executor.models.step_audio2.step_audio2_thinker import (
+        StepAudio2ThinkerForConditionalGeneration,
+    )
+
+    text_only = [100, 200, 300]
+    assert not StepAudio2ThinkerForConditionalGeneration.has_audio_output(text_only)
+
+    with_audio = [100, 200, 151700, 300]
+    assert StepAudio2ThinkerForConditionalGeneration.has_audio_output(with_audio)