scott
8de67c8bd9
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Build ROCm Image / build (push) Failing after 11s
ROCm 7.2 + PyTorch 2.11.0 has a bug where PyTorch passes workspace=0 to MIOpen convolutions, forcing fallback to the slow GemmFwdRest solver. This caused s3gen.inference to take 15-22s instead of <5s, making synthesis 3-4x slower than real-time audio playback. ROCm 6.1 allocates workspace correctly so MIOpen picks fast GEMM solvers without needing torch.compile workarounds. Changes: - Base image: rocm/dev-ubuntu-22.04:7.2 → 6.1 - torch 2.11.0 → 2.5.1 (rocm6.1 wheel index) - Add pytorch_triton_rocm==3.1.0 - transformers 5.2.0 → 4.46.3, safetensors 0.5.3 → 0.4.0 - s3tokenizer unpinned → 0.3.0 - resemble-perth==1.0.1 directly (v1.0.1 is pip-installable; drop stub) - Drop Dockerfile perth_stub steps - Drop torch.compile and timing patches from engine.py (not needed) - Drop multi-pass warmup from main.py (torch JIT warmup not needed) - Drop ROCm 7.2-specific env vars from docker-compose.yml Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
92 lines
2.3 KiB
Python
92 lines
2.3 KiB
Python
import logging
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import torch
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from typing import Optional, Tuple
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logger = logging.getLogger(__name__)
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chatterbox_model = None
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_sample_rate = 24000
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_is_turbo = False
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def _test_cuda() -> bool:
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try:
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if torch.cuda.is_available():
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torch.zeros(1).cuda()
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return True
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except Exception:
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pass
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return False
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def detect_device() -> str:
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return "cuda" if _test_cuda() else "cpu"
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def load_model() -> bool:
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global chatterbox_model, _sample_rate, _is_turbo
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from config import get_model_repo_id, get_device_override
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device = get_device_override() or detect_device()
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repo_id = get_model_repo_id()
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logger.info(f"Loading model '{repo_id}' on device '{device}'")
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try:
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if "turbo" in repo_id.lower():
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from chatterbox.tts_turbo import ChatterboxTurboTTS
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chatterbox_model = ChatterboxTurboTTS.from_pretrained(device)
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_is_turbo = True
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else:
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from chatterbox.tts import ChatterboxTTS
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chatterbox_model = ChatterboxTTS.from_pretrained(device)
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_is_turbo = False
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_sample_rate = 24000
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logger.info("Model loaded successfully")
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return True
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except Exception:
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logger.exception("Failed to load model")
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return False
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def get_sample_rate() -> int:
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return _sample_rate
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def synthesize(
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text: str,
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audio_prompt_path: Optional[str] = None,
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exaggeration: float = 0.5,
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cfg_weight: float = 0.5,
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temperature: float = 0.8,
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seed: int = 0,
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) -> Tuple[torch.Tensor, int]:
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if chatterbox_model is None:
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raise RuntimeError("Model not loaded. Call load_model() first.")
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if seed > 0:
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torch.manual_seed(seed)
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if torch.cuda.is_available():
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torch.cuda.manual_seed_all(seed)
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kwargs: dict = {}
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if audio_prompt_path:
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kwargs["audio_prompt_path"] = audio_prompt_path
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if _is_turbo:
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kwargs["temperature"] = temperature
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else:
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kwargs["exaggeration"] = exaggeration
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kwargs["cfg_weight"] = cfg_weight
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with torch.inference_mode():
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wav = chatterbox_model.generate(text=text, **kwargs)
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if torch.cuda.is_available():
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torch.cuda.synchronize()
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torch.cuda.empty_cache()
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return wav, _sample_rate
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