fix: mask all stop_token_ids in sampling_ids + multi-GPU device support

[lonrencn]

Summary

Two bug fixes for CosyVoice3 inference:

1. sampling_ids() only masks speech_token_size (sos) during ignore_eos=True, not eos or other stop tokens — on certain transformers versions, the LLM can predict EOS with high confidence as the first token, and the unmasked EOS leaks through the ignore_eos window, producing garbage output.

2. Hardcoded torch.device('cuda') defaults to cuda:0 — on multi-GPU systems, torch.cuda.set_device() has no effect and models load on the wrong GPU.

Changes

cosyvoice/llm/llm.py — sampling_ids fix

CosyVoice3LM.stop_token_ids = [speech_token_size + i for i in range(200)] covers sos(6561), eos(6562), task_id(6563), fill_token(6564), etc. The original code only masked index speech_token_size (sos=6561), leaving eos(6562) unmasked during the minimum-length window.

This is usually harmless because a well-functioning LLM doesn't predict EOS early. But with slight numerical differences across transformers versions (e.g., SDPA mask handling changed between 4.x and 5.x), the logits can shift enough (~0.012) to flip EOS from #2 to #1, causing the model to output garbage.

The fix uses hasattr to maintain backward compatibility with CosyVoice1/2.

cosyvoice/cli/model.py + cosyvoice/cli/frontend.py — multi-GPU fix

torch.device('cuda') always defaults to cuda:0, ignoring torch.cuda.set_device(). Changed to torch.device(f'cuda:{torch.cuda.current_device()}') in all model classes.

Testing

Verified with Fun-CosyVoice3-0.5B-2512:

• Chinese: "你好世界" → ASR: "你好，世界。" ✅
• English: "Hello, this is a test." → ASR correct ✅
• Environment: Python 3.11, torch 2.11+cu128, transformers 5.3.0