[Experimental] Add ROCm/HIP support (100% vibe‑coded, for testing purposes)

[IIIIIllllIIIIIlllll]

Test Summary – ROCm/HIP Backend (Qwen3-TTS)
GPU：AMD Radeon Graphics (gfx1151, Strix Halo), 120 GiB VRAM

ROCm：7.2.4

Build：cmake -S . -B build_hip -DENGINE_ENABLE_HIP=ON -DGPU_TARGETS=gfx1151 -DCMAKE_C_COMPILER="$(hipconfig -l)/clang" -DCMAKE_CXX_COMPILER="$(hipconfig -l)/clang++" -DCMAKE_BUILD_TYPE=Release

Model：Qwen3-TTS-12Hz-1.7B-CustomVoice

Input：

{
    "model": "qwen3-tts",
    "input": "Hey, you. You’re finally awake. You were trying to cross the border, right? Walked right into that Imperial ambush, same as us, and that thief over there.",
    "options": {
      "speaker": "Vivian"
    }
  }

Generation time：9.8 s → RTF = 0.89× (faster than real‑time)

Output：
11 s audio file
test-rocm.wav

Detection：ggml_cuda_init correctly identifies ROCm device, backend_type() returns Hip, memory queries accurate

About this PR:
I noticed that llama.cpp already has ROCm support via its ggml-hip backend, and the key insight is that HIP maps almost all CUDA APIs to HIP equivalents at compile time through a vendor header. Since audio.cpp reuses GGML's CUDA backend code, I thought: “If it works for llama.cpp, there’s no reason it shouldn't work for audio.cpp – there might be tricky bugs, but at least I should prove the concept.”

To get started, I took the ROCm‑related CMakeLists.txt from llama.cpp and fed it to DeepSeek V4 PRO (an LLM), asking it to adapt the build logic for this codebase. The result was a clean CMake configuration that successfully compiled and ran on Linux with the HIP backend enabled. This is therefore a 100% vibe‑coded proof‑of‑concept – it works, but please treat it as a reference/test implementation rather than a fully polished production feature.

I'm not English speaker, so please forgive any unclear phrasing. I'm happy to clarify or adjust anything.

[0xShug0]

@IIIIIllllIIIIIlllll Sorry I missed your new updates. Great work!

I've seen many Reddit users asking about running these models on ROCm, so your PR is really important for closing a major gap. I'm curious, though, can't the Python implementations run on ROCm?

The assumption that “if it works for llama.cpp, it should work for audio.cpp” is generally correct. However, the audio-model ecosystem is much messier, while LLM architectures are relatively standardized. Some GGML operations used by audio models may be supported on CUDA but unavailable on Vulkan, Metal, or other backends. In some cases, I have to modify GGML to add the missing backend support. These issues often only appear at runtime.

[0xShug0]

@IIIIIllllIIIIIlllll I called for help on reddit and hopefully more people can help with testing the model behaviors on ROCm.

[IIIIIllllIIIIIlllll]

@IIIIIllllIIIIIlllll Sorry I missed your new updates. Great work!

I've seen many Reddit users asking about running these models on ROCm, so your PR is really important for closing a major gap. I'm curious, though, can't the Python implementations run on ROCm?

The assumption that “if it works for llama.cpp, it should work for audio.cpp” is generally correct. However, the audio-model ecosystem is much messier, while LLM architectures are relatively standardized. Some GGML operations used by audio models may be supported on CUDA but unavailable on Vulkan, Metal, or other backends. In some cases, I have to modify GGML to add the missing backend support. These issues often only appear at runtime.

AMD GPUs have many compatibility issues and are not as simple and easy to use as CUDA.

Therefore, your project is of great value to AMD users.

[0xShug0]

@IIIIIllllIIIIIlllll Just to let you know, I’m currently upgrading the vendored GGML from v0.12 to upstream v0.16. It brings a decent performance boost for some models and better support for other backends.

Another user asked about ROCm support today. Looking forward to your update and please keep me posted!

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Today I gave the popular Kimi K3 model a try, asking it to help me continue optimizing the ROCm backend. The process was painfully slow and tormented me for quite a while. Anyway, the ROCm backend can now be compiled on Windows — my 780M iGPU can run Index-TT2 without issues, and everything works fine on Linux as well. More testing… to be continued tomorrow.