关于 Cosyvoice2 在 H200 GPU 上启用 fp16 性能表现的疑问与探讨 （Inquiry Regarding Cosyvoice2 fp16 Performance on H200 GPUs）

[zhouzhouwen]

尊敬的 Cosyvoice2 团队及开源社区的朋友们：

大家好！

首先，衷心感谢各位为 Cosyvoice2 项目付出的巨大努力和贡献。

我在使用 Cosyvoice2 的过程中，特别关注了 fp16 这一启动选项。根据理解，该选项旨在通过将模型切换到半精度浮点（fp16）模式来加速推理过程。我推测这可能是为了更好地利用像 H200 这样对 fp16 运算有深度优化的高端 GPU 的能力，因为据了解，这类 GPU 在 fp16 上的性能优势显著，尽管其 fp32 性能有时可能不及 RTX 4090 等消费级显卡。

然而，在我实际于 H200 GPU 上进行的测试中，遇到了一些预期之外的情况。具体测试结果如下：

启用 fp16 选项：针对 2000 条数据样本进行推理，总耗时为 7328.24 秒。 禁用 fp16 选项（使用默认设置，推测为 fp32）：使用完全相同的 2000 条数据样本进行推理，总耗时为 6629.10 秒。 从结果来看，在我的 H200 环境下，启用 fp16 似乎并未带来预期的性能提升，反而比默认设置耗时更长。

这让我产生一个疑问：为了充分发挥 fp16 在 H200 等硬件上的加速潜力，是否需要同时配合启用 JIT (Just-In-Time compilation) 或 TRT (TensorRT) 等进一步的优化选项？或者说，是否存在其他配置因素或潜在原因，导致了当前 fp16 模式下性能未达预期的现象？

提出这个问题，希望能得到官方团队或社区内有相关经验的朋友们的指点和解答。我相信厘清这一点，对于所有希望在高性能硬件上优化 Cosyvoice2 推理效率的用户都将非常有帮助。

再次感谢大家的宝贵时间和专业知识！

此致

敬礼

Dear Cosyvoice2 Team and Members of the Open Source Community,

Greetings!

First and foremost, I would like to express my sincere gratitude for your tremendous efforts and contributions to the Cosyvoice2 project.

While working with Cosyvoice2, I've paid close attention to the fp16 startup option. My understanding is that this option is intended to accelerate the inference process by setting the AI model to use half-precision floating-point (fp16) calculations. I initially hypothesized that this might be particularly targeted at leveraging the capabilities of high-end GPUs like the H200, which feature significant optimizations for fp16 operations – even though their fp32 performance might sometimes trail behind consumer cards like the RTX 4090.

However, during my actual tests conducted on an H200 GPU, I encountered some unexpected results. Here is a summary of my findings:

With fp16 enabled: Processing 2000 data samples took a total of 7328.24 seconds. With fp16 disabled (using default settings, presumably fp32): Processing the exact same 2000 data samples took a total of 6629.10 seconds. Based on these results, it appears that enabling fp16 on my H200 setup did not yield the anticipated performance improvement; instead, it was slower than the default configuration.

This leads me to a question: To fully unlock the potential acceleration benefits of fp16 on hardware like the H200, is it necessary to concurrently enable further optimization options such as JIT (Just-In-Time compilation) or TRT (TensorRT)? Alternatively, could there be other configuration factors or underlying reasons that might explain why the fp16 mode did not perform as expected in my tests?

I am raising this query in the hope of receiving guidance and clarification from the official team or experienced members within the community. I believe understanding this behavior would be highly beneficial for all users aiming to optimize Cosyvoice2 inference efficiency on high-performance hardware.

Thank you very much for your time and expertise.

Best regards,

[lovewater]

我在RTX5070ti上测得开启TRT后速度进步明显。最新版本TRT必须搭配流式输出使用，非常奇怪但也只有忍了。

FP16选项开和关，在5070ti上速度相差不大，但fp16更快。

最后：消费级显卡从2080系开始就支持fp16了这不是什么新鲜玩意儿。

[github-actions[bot]]

This issue is stale because it has been open for 30 days with no activity.