Features integration without fp8 #7
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The ROCM stack with PyTorch supports a wide set of gfx architectures. This can be displayed by printing PYTORCH_ROCM_ARCH env. In the absence of PYTORCH_ROCM_ARCH pytorch uses theoutput from rocm_agent_enumerator to choose what to compile for. vllm supports a subset of these, (gfx908, gfx90a,...) Due to a need to potentially support multiple architectures at once (ex. docker image) it's important to make sure vllm is compiled with them all unless specified otherwise. We now gather either the PYTORCH_ROCM_ARCH env or rocm_agent_enumerator output and cross reference with ROCM_SUPPORTED_ARCHS from vllm to generate a list of arches to build for.
This is a bit of a hack. But Ray seems to have some serious perf degradation when running multi gpu latency benchmarks. Allow distributed to be used when Ray is disabled, and make sure we connect via env ranking instead of tcp/port based.
…ster execution Also add reporting functionality for easy display
Co-authored-by: Vinayak Gokhale <vinayak.gokhale@amd.com>
Added Flag for controlling triton vs default flow. More small changes to dockerfile
shajrawi
reviewed
Mar 22, 2024
…den by --worker-use-ray
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one more small ask: Can we have a markdown doc under docs/ with all our flags we can configure / turn on and off? Most importantly: Tunable ops and running with it enabled (but also which attention implementation to use, ray..etc) |
shajrawi
approved these changes
Mar 22, 2024
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Putting together (similar to #5 except without fp8 kv_cache):
Custom ops and kernels from private v0.2.7_dllehr
triton attention kernel from jpvillam/v0.3.3_triton
Option to run multi GPU using torchrun instead or ray