[Model] Add GLM-TTS text-to-speech model support

tests/e2e/offline_inference/test_glm_tts_model.py

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+import os
+import sys
+from pathlib import Path
+
+import numpy as np
+import pytest
+import torch
+
+from vllm_omni.outputs import OmniRequestOutput
+
+# ruff: noqa: E402
+REPO_ROOT = Path(__file__).resolve().parents[2]
+if str(REPO_ROOT) not in sys.path:
+    sys.path.insert(0, str(REPO_ROOT))
+
+from vllm_omni import Omni
+
+os.environ["VLLM_TEST_CLEAN_GPU_MEMORY"] = "1"
+
+# GLM-TTS model from HuggingFace
+# Note: For CI testing, a smaller random-weights model could be created
+models = ["zai-org/GLM-TTS"]
+
+
+@pytest.mark.skip(reason="GLM-TTS model requires full download; enable when model available locally")
+@pytest.mark.parametrize("model_name", models)
+def test_glm_tts_model(model_name: str):
+    """Test GLM-TTS text-to-speech generation."""
+    m = Omni(model=model_name)
+
+    # GLM-TTS parameters
+    audio_duration_s = 5.0  # 5 second audio
+    sample_rate = 22050  # GLM-TTS default sample rate
+
+    # Generate speech tokens placeholder (in production, LLM generates these)
+    # For testing, we provide mock tokens
+    speech_tokens = torch.randint(0, 10000, (1, 100), dtype=torch.long)
+    speaker_embedding = torch.randn(1, 192)
+
+    outputs = m.generate(
+        "Hello, this is a test of GLM-TTS text to speech synthesis.",
+        num_inference_steps=8,  # Minimal steps for speed
+        guidance_scale=1.0,
+        generator=torch.Generator("cuda").manual_seed(42),
+        num_outputs_per_prompt=1,
+        extra={
+            "audio_duration_s": audio_duration_s,
+            "speech_tokens": speech_tokens,
+            "speaker_embedding": speaker_embedding,
+        },
+    )
+
+    # Verify output structure
+    assert outputs is not None
+    first_output = outputs[0]
+    assert hasattr(first_output, "request_output") and first_output.request_output
+
+    req_out = first_output.request_output[0]
+    assert isinstance(req_out, OmniRequestOutput)
+    assert hasattr(req_out, "images") and len(req_out.images) >= 1
+
+    # For TTS, the "images" field contains audio numpy arrays
+    audio = req_out.images[0]
+    assert isinstance(audio, np.ndarray)
+    # audio shape: (batch, channels, samples) or (channels, samples)
+    assert audio.ndim >= 2
+
+
+@pytest.mark.skip(reason="Unit test for pipeline components")
+def test_glm_tts_dit_model():
+    """Test GLM-TTS DiT model forward pass."""
+    from vllm_omni.diffusion.models.glm_tts.glm_tts_dit import GLMTTSDiTModel
+
+    # Create model with default config
+    model = GLMTTSDiTModel(
+        hidden_size=256,  # Small for testing
+        num_attention_heads=4,
+        num_hidden_layers=2,
+        head_dim=64,
+        mel_dim=80,
+        speech_token_dim=128,
+        speech_token_vocab_size=1000,
+        speaker_embed_dim=64,
+    ).cuda()
+
+    # Test forward pass
+    batch_size = 2
+    seq_len = 100
+    token_len = 50
+
+    noisy_mel = torch.randn(batch_size, seq_len, 80).cuda()
+    timestep = torch.rand(batch_size).cuda()
+    speech_tokens = torch.randint(0, 1000, (batch_size, token_len)).cuda()
+    speaker_embedding = torch.randn(batch_size, 64).cuda()
+
+    output = model(
+        noisy_mel=noisy_mel,
+        timestep=timestep,
+        speech_tokens=speech_tokens,
+        speaker_embedding=speaker_embedding,
+    )
+
+    assert output.shape == (batch_size, seq_len, 80)
+
+
+@pytest.mark.skip(reason="Unit test for flow sampler")
+def test_glm_tts_flow_sampler():
+    """Test GLM-TTS flow matching sampler."""
+    from vllm_omni.diffusion.models.glm_tts.glm_tts_dit import GLMTTSDiTModel
+    from vllm_omni.diffusion.models.glm_tts.pipeline_glm_tts import GLMTTSFlowSampler
+
+    # Create small model for testing
+    model = GLMTTSDiTModel(
+        hidden_size=256,
+        num_attention_heads=4,
+        num_hidden_layers=2,
+        head_dim=64,
+        mel_dim=80,
+        speech_token_dim=128,
+        speech_token_vocab_size=1000,
+        speaker_embed_dim=64,
+    ).cuda()
+
+    sampler = GLMTTSFlowSampler(num_steps=4, cfg_scale=1.0)
+
+    batch_size = 1
+    seq_len = 50
+    token_len = 25
+
+    speech_tokens = torch.randint(0, 1000, (batch_size, token_len)).cuda()
+    speaker_embedding = torch.randn(batch_size, 64).cuda()
+
+    mel = sampler.sample(
+        model=model,
+        shape=(batch_size, seq_len, 80),
+        speech_tokens=speech_tokens,
+        speaker_embedding=speaker_embedding,
+        device=torch.device("cuda"),
+        dtype=torch.float32,
+    )
+
+    assert mel.shape == (batch_size, seq_len, 80)

vllm_omni/diffusion/models/glm_tts/__init__.py

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+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+"""GLM-TTS model support for vLLM-Omni."""
+
+from vllm_omni.diffusion.models.glm_tts.pipeline_glm_tts import (
+    GLMTTSPipeline,
+    get_glm_tts_post_process_func,
+)
+from vllm_omni.diffusion.models.glm_tts.glm_tts_dit import (
+    GLMTTSDiTModel,
+)
+
+__all__ = [
+    "GLMTTSDiTModel",
+    "GLMTTSPipeline",
+    "get_glm_tts_post_process_func",
+]