diff --git a/README.md b/README.md index 1dbfbc3..3235f15 100644 --- a/README.md +++ b/README.md @@ -29,10 +29,10 @@ Embeddings (`Xenova/all-MiniLM-L6-v2`) run on CPU via ONNX so the GPU's ``` 3. **Node 22+** and **Docker**. -> Building vLLM 0.19.0 against ROCm 7.2 for RDNA3 is non-trivial. The full -> bare-metal install — including `--no-build-isolation`, `amdsmi`, GEMM -> tuning, and the RDNA3 limitations — lives in **[vllm-setup.md](vllm-setup.md)**. -> Follow it once before continuing. +> Building vLLM v0.21.0 against ROCm 7.2 for RDNA3 is non-trivial. The full +> bare-metal install — including `--no-build-isolation`, `amdsmi`, the C++20 +> compiler requirement, GEMM tuning, and the RDNA3 limitations — lives in +> **[vllm-setup.md](vllm-setup.md)**. 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"integrity": "sha512-O/PgfnpT1xKSDeQYSCfRI5Gy3hPf91mKVDuYLUHZJMiDFptvP41MSnWofm8dnCm0256ZNfZIM7DSzuSMAFnjHA==", - "license": "ISC", - "peer": true, - "peerDependencies": { - "zod": "^3.25.28 || ^4" - } } } } diff --git a/package.json b/package.json index d588a16..038581f 100644 --- a/package.json +++ b/package.json @@ -34,17 +34,21 @@ }, "homepage": "https://github.com/teleoric/rag-poc#readme", "dependencies": { - "@langchain/community": "^1.1.27", + "@huggingface/transformers": "^4.2.0", "@langchain/core": "^1.1.39", "@langchain/openai": "^1.4.2", "@langchain/qdrant": "^1.0.1", "@langchain/textsplitters": "^1.0.1", - "@xenova/transformers": "^2.17.2", - "uuid": "^13.0.0" + "uuid": "^13.0.2" }, "devDependencies": { "@types/node": "^25.5.2", "tsx": "^4.21.0", "typescript": "^6.0.2" + }, + "overrides": { + "uuid": "^13.0.2", + "langsmith": "^0.6.0", + "ws": "^8.20.1" } } diff --git a/src/ragPipeline.ts b/src/ragPipeline.ts index c189b73..7d2e88b 100644 --- a/src/ragPipeline.ts +++ b/src/ragPipeline.ts @@ -3,7 +3,7 @@ import { QdrantVectorStore } from "@langchain/qdrant"; import { QdrantClient } from "@qdrant/js-client-rest"; import { Embeddings } from "@langchain/core/embeddings"; import { Document } from "@langchain/core/documents"; -import { pipeline, type FeatureExtractionPipeline } from "@xenova/transformers"; +import { pipeline, type FeatureExtractionPipeline } from "@huggingface/transformers"; import { v5 as uuidv5 } from "uuid"; import { fileURLToPath } from "node:url"; import { argv, env, exit } from "node:process"; diff --git a/vllm-setup.md b/vllm-setup.md index f28d2c4..ccd1560 100644 --- a/vllm-setup.md +++ b/vllm-setup.md @@ -1,6 +1,11 @@ -# vLLM 0.19.0 on ROCm 7.2 — RDNA3 (gfx1100) Setup Guide +# vLLM 0.21.0 on ROCm 7.2 — RDNA3 (gfx1100) Setup Guide -> Tested with vLLM v0.19.0, ROCm 7.2, Ubuntu, Radeon RX 7900 XT (gfx1100) +> Tested with vLLM v0.21.0, ROCm 7.2.2, Ubuntu, Radeon RX 7900 XT (gfx1100). +> +> v0.21.0 vs the previous v0.19.0 procedure: requires **C++20** (gcc ≥ 10), +> **PyTorch ≥ 2.11**, and **HuggingFace `transformers` ≥ 5** — all resolved +> automatically by the install steps below on Ubuntu 22.04 / 24.04. The RDNA3 +> workarounds (`BUILD_FA=0`, `--enforce-eager`, ROCM_ATTN backend) are unchanged. --- @@ -15,13 +20,14 @@ - Ubuntu 22.04 or 24.04 LTS - Linux kernel 6.10+ recommended for gfx1100 stability -- ROCm 7.2 installed -- Python 3.12 +- ROCm 7.2.2 installed (v0.21.0 references 7.2.2 upstream; 7.2.0/.1 may work but isn't tested) +- Python 3.12 (3.14 also supported by v0.21.0) +- C++20-capable compiler: gcc ≥ 10 (Ubuntu 22.04 default is gcc-11, 24.04 is gcc-13 — both fine) ### Verify ROCm 7.2 Installation ```bash -cat /opt/rocm/.info/version # Must show 7.2.x +cat /opt/rocm/.info/version # Must show 7.2.x (7.2.2 recommended) rocminfo | grep gfx # Must show gfx1100 rocm-smi --showproductname # Must show your GPU ls /dev/kfd /dev/dri/render* # Device nodes must exist @@ -49,14 +55,19 @@ source ~/vllm-env/bin/activate ## 2. Install PyTorch for ROCm 7.2 -ROCm 7.2 requires nightly PyTorch wheels: +vLLM v0.21.0 requires **PyTorch ≥ 2.11**. ROCm 7.2 still requires nightly +PyTorch wheels: ```bash pip install --pre torch torchvision torchaudio \ - --index-url https://download.pytorch.org/whl/nightly/rocm6.4 + --index-url https://download.pytorch.org/whl/nightly/rocm7.2 ``` -> ROCm 7.x wheels are published under the `rocm6.4` nightly index as of this writing. If a `rocm7.2` index becomes available, use that instead. +> The nightly `rocm7.2` index is the right target for ROCm 7.2.x hosts. The +> older `rocm6.4` nightly index will install wheels whose embedded HIP API +> doesn't match `/opt/rocm` 7.2 — that produces an `undefined symbol: +> _ZN3c10*` linker error at runtime when vLLM's C++ extensions try to load. +> See troubleshooting if you previously installed from `rocm6.4`. ### Verify PyTorch ROCm @@ -83,7 +94,7 @@ print('Device name:', torch.cuda.get_device_name(0)) ## 3. Install amdsmi -vLLM 0.19.0 uses `amdsmi` for ROCm platform detection. It ships with ROCm 7.2 but must be installed into the venv: +vLLM v0.21.0 uses `amdsmi` for ROCm platform detection (same pattern as v0.19.0). It ships with ROCm 7.2 but must be installed into the venv: ```bash cp -r /opt/rocm/share/amd_smi /tmp/amd_smi @@ -114,17 +125,24 @@ pip install numpy "setuptools>=75.0" setuptools-scm wheel cmake ninja pip install huggingface_hub ``` -> `setuptools>=75.0` is required because vLLM 0.19.0 uses PEP 639 SPDX license strings in `pyproject.toml` that older setuptools versions reject. +> `setuptools>=75.0` is required because vLLM uses PEP 639 SPDX license strings +> in `pyproject.toml` that older setuptools versions reject. +> +> **C++20 note (v0.21.0):** vLLM v0.21.0 requires a C++20-capable compiler. +> Verify with `gcc --version` — version ≥ 10 is fine. Ubuntu 22.04 default +> (gcc-11) and 24.04 default (gcc-13) both satisfy this. If you're on an older +> distribution, `sudo apt install g++-11` then export `CXX=g++-11` before the +> build. --- -## 5. Clone and Build vLLM 0.19.0 +## 5. Clone and Build vLLM 0.21.0 ```bash cd ~/projects # or your preferred directory git clone https://github.com/vllm-project/vllm.git cd vllm -git checkout v0.19.0 +git checkout v0.21.0 ``` ### Set Build Environment Variables @@ -135,15 +153,45 @@ export HIP_PATH=/opt/rocm export PYTORCH_ROCM_ARCH="gfx1100" export BUILD_FA=0 # CK FlashAttention does NOT support RDNA3 export MAX_JOBS=4 # Limit build parallelism if RAM-constrained +export CMAKE_ARGS="-DHIP_FOUND=TRUE" # see note below ``` +> **`CMAKE_ARGS="-DHIP_FOUND=TRUE"` is required with the `rocm7.2` nightly +> torch.** Torch's bundled `LoadHIP.cmake` detects HIP correctly and prints +> every ROCm library version, but it no longer exports the `HIP_FOUND` global +> variable that vLLM's `CMakeLists.txt` checks. Without this override the +> build dies with `CMake Error at CMakeLists.txt:151 ... Can't find CUDA or +> HIP installation` even though HIP is clearly present in the configure log. + ### Build ```bash pip install --no-build-isolation -e . ``` -> **`--no-build-isolation` is critical.** Without it, pip creates an isolated build environment that pulls a CUDA torch, causing the build to fail looking for `/opt/rocm/bin/nvcc`. +> **`--no-build-isolation` is critical.** Without it, pip creates an isolated +> build environment that pulls a CUDA torch, causing the build to fail looking +> for `/opt/rocm/bin/nvcc`. + +### Verify the C++ extensions actually loaded + +The build succeeding is not the same as the runtime extensions loading. Run: + +```bash +python -c "import vllm._C, vllm._rocm_C; print('vllm._C OK')" +``` + +This must print `vllm._C OK` with **no preceding ImportError warnings**. If +you see `undefined symbol: _ZN3c10*`, the compiled `.so` files don't match +your installed torch — almost always because torch was bumped after vLLM was +built. Clean and rebuild: + +```bash +cd ~/projects/vllm +rm -rf build/ .deps/ vllm/_C*.so vllm/_rocm_C*.so vllm.egg-info +find . -name CMakeCache.txt -delete +pip install --no-build-isolation -e . --force-reinstall --no-deps +``` --- @@ -161,6 +209,7 @@ export BUILD_FA=0 export MAX_JOBS=4 export TORCH_ROCM_AOTRITON_ENABLE_EXPERIMENTAL=1 export PYTORCH_TUNABLEOP_ENABLED=1 +# CMAKE_ARGS only matters at build time; harmless to leave out of runtime env EOF ``` @@ -183,6 +232,8 @@ huggingface-cli login ## 8. Smoke Test +### Stage 1 — Tiny model (build sanity) + ```bash python -c " from vllm import LLM, SamplingParams @@ -192,13 +243,44 @@ print(out[0].outputs[0].text) " ``` -**Expected log lines confirming ROCm detection:** +**Expected log lines confirming ROCm detection** (the prefix changed slightly +in v0.21.0 because of the new TURBOQUANT backend probe): ``` -Using ROCM_ATTN backend out of potential backends: ['ROCM_ATTN', 'TRITON_ATTN'] +Found incompatible backend(s) [TURBOQUANT] with AttentionType.DECODER. +Overriding with ROCM_ATTN out of potential backends: ['ROCM_ATTN', 'TRITON_ATTN']. +``` + +> **opt-125m passing is necessary but not sufficient.** Its model code path +> does not exercise the activation kernels registered by `vllm._C`. If the +> compiled extensions are stale or ABI-broken, opt-125m can still load and +> generate via Python fallbacks. Always follow with Stage 2. + +### Stage 2 — Llama-architecture model (exercises `_C` kernels) + +This is the real validation. Llama uses `SiluAndMul` from `vllm._C`; if the +extensions didn't load, this fails at model construction with +`AttributeError: '_OpNamespace' '_C' object has no attribute 'silu_and_mul'`. + +```bash +HF_TOKEN=$HF_TOKEN python -c " +from vllm import LLM +llm = LLM(model='meta-llama/Llama-3.1-8B-Instruct', + enforce_eager=True, gpu_memory_utilization=0.90, max_model_len=4096) +print(llm.generate('The architectural difference between').outputs[0].text) +" ``` -> The `EngineCore died unexpectedly` message at shutdown is a known benign race condition in one-shot `LLM.generate()` usage. It does not indicate a real failure. +Expect the engine to log `Initializing a V1 LLM engine (v0.21.0)`, load +weights in ~15-25 s, and produce text. First request also emits three +`jit_monitor` warnings about Triton kernel JIT compilation — those are +**normal first-run behavior** (latency spike on request 1 only; subsequent +requests hit the JIT cache). + +> The `EngineCore died unexpectedly` message at shutdown is a known benign +> race condition in one-shot `LLM.generate()` usage. It does not indicate a +> real failure. Same applies to +> `ProcessGroupNCCL.cpp:1554: destroy_process_group() was not called`. --- @@ -238,7 +320,7 @@ curl http://localhost:8000/v1/chat/completions \ ## 10. Attention Backend -vLLM 0.19.0 on RDNA3 uses `ROCM_ATTN` by default. To verify: +vLLM v0.21.0 on RDNA3 uses `ROCM_ATTN` by default (unchanged from v0.19.0 — CK FlashAttention still targets CDNA only). To verify: ```bash vllm serve facebook/opt-125m --dtype float16 --enforce-eager 2>&1 | grep -i "attn\|backend\|attention" @@ -246,9 +328,9 @@ vllm serve facebook/opt-125m --dtype float16 --enforce-eager 2>&1 | grep -i "att Expected: `Using ROCM_ATTN backend out of potential backends: ['ROCM_ATTN', 'TRITON_ATTN']` -### vLLM 0.19.0 Environment Variable Notes +### vLLM Environment Variable Notes (v0.19.0 → v0.21.0) -The following env vars are **not recognized** by vLLM 0.19.0 and will produce warnings: +The following env vars are **not recognized** by vLLM v0.19.0+ and will produce warnings: - `VLLM_PLATFORM` — platform detection is handled via `amdsmi`, not env vars - `VLLM_USE_TRITON_FLASH_ATTN` — attention backend selection is automatic in the ROCm attention selector @@ -335,7 +417,7 @@ print(len(amdsmi.amdsmi_get_processor_handles()), 'GPUs') amdsmi.amdsmi_shut_down() " -# vLLM 0.19.0 platform detection +# vLLM platform detection python -c " from vllm.platforms import current_platform print('device_type:', repr(current_platform.device_type)) @@ -358,8 +440,14 @@ print('is_rocm:', current_platform.is_rocm()) | `Cannot update time stamp of directory 'amdsmi.egg-info'` | Building amdsmi from read-only ROCm dir | Copy to `/tmp` first, then `pip install .` | | `RuntimeError: operator torchvision::nms does not exist` | torchvision version mismatch | `pip uninstall torchvision -y` and reinstall from same ROCm nightly index | | `EngineCore died unexpectedly` on shutdown | Benign race condition in one-shot usage | Ignore — not a real error | -| `Unknown vLLM environment variable: VLLM_PLATFORM` | Env var not used in v0.19.0 | Remove from environment; detection uses amdsmi | -| `Unknown vLLM environment variable: VLLM_USE_TRITON_FLASH_ATTN` | Env var not used in v0.19.0 | Remove from environment; backend selection is automatic | +| `Unknown vLLM environment variable: VLLM_PLATFORM` | Env var not used in v0.19.0+ | Remove from environment; detection uses amdsmi | +| `Unknown vLLM environment variable: VLLM_USE_TRITON_FLASH_ATTN` | Env var not used in v0.19.0+ | Remove from environment; backend selection is automatic | +| Build error mentioning `-std=c++20` or `requires C++20` | v0.21.0 needs C++20 compiler | `sudo apt install g++-11` and `export CXX=g++-11` before `pip install -e .` | +| `CMake Error at CMakeLists.txt:151 ... Can't find CUDA or HIP installation` (configure log clearly shows HIP detected) | rocm7.2 nightly torch's `LoadHIP.cmake` doesn't export the `HIP_FOUND` global vLLM's check relies on | `export CMAKE_ARGS="-DHIP_FOUND=TRUE"` before `pip install --no-build-isolation -e .` | +| `ImportError: ... vllm/_C.abi3.so: undefined symbol: _ZN3c103hip*` at runtime (often surfacing as `AttributeError: '_OpNamespace' '_C' object has no attribute 'silu_and_mul'` when loading a Llama-architecture model) | The compiled C++ extensions were linked against a different torch ABI than the one currently installed — usually because torch was upgraded after vLLM was built, or vLLM source was bumped without rebuilding `.so` files | Clean and rebuild: `rm -rf build/ .deps/ vllm/_C*.so vllm/_rocm_C*.so vllm.egg-info && find . -name CMakeCache.txt -delete && pip install --no-build-isolation -e . --force-reinstall --no-deps`. Then re-run the §5 _C smoke. | +| `Failed to import from vllm._C with ImportError` in opt-125m smoke but generation completes anyway | C extensions are broken but opt-125m's code path uses Python fallbacks | Don't trust this as a passing build. Run the Llama smoke (§8 Stage 2) — it will fail with `AttributeError: silu_and_mul`. Fix per the row above. | +| `Triton kernel JIT compilation during inference` warnings on first request | New v0.21.0 `jit_monitor` informational feature; Triton kernels are JIT-compiled on first shape/config | Harmless. Latency spike on request 1 only; subsequent requests hit the JIT cache. Extend warmup if cold-start latency matters in production. | +| `Cannot use ROCm custom paged attention kernel, falling back to Triton implementation` | The hand-written HIP paged-attention kernel is CDNA-only; gfx1100 uses the Triton-compiled fallback **inside** ROCM_ATTN | Expected on RDNA3. Not a fallback to a different backend — just a fallback for one kernel within ROCM_ATTN. | ---