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hf_text-generation-inference/docs/source/installation_inferentia.md

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MI300 compatibility (#1764) Adds support for AMD Instinct MI300 in TGI. Most changes are: * Support PyTorch TunableOp to pick the GEMM/GEMV kernels for decoding https://github.com/pytorch/pytorch/tree/main/aten/src/ATen/cuda/tunable. TunableOp is disabled by default, and can be enabled with `PYTORCH_TUNABLEOP_ENABLED=1`. * Update ROCm dockerfile to PyTorch 2.3 (actually patched with changes from https://github.com/pytorch/pytorch/pull/124362) * Support SILU & Linear custom kernels contributed by AMD * Update vLLM paged attention to https://github.com/fxmarty/rocm-vllm/, branching out of a much more recent commit https://github.com/ROCm/vllm/commit/3489ce7936c5de588916ae3047c44c23c0b0c308 * Support FA2 Triton kernel as recommended by AMD. Can be used by specifying `ROCM_USE_FLASH_ATTN_V2_TRITON=1`. * Update dockerfile to ROCm 6.1 By default, TunableOp tuning results are saved in `/data` (e.g. `/data/tunableop_meta-llama-Llama-2-70b-chat-hf_tp1_rank0.csv`) in order to avoid to have to rerun the tuning at each `docker run`. Example: ``` Validator,PT_VERSION,2.3.0 Validator,ROCM_VERSION,6.1.0.0-82-5fabb4c Validator,HIPBLASLT_VERSION,0.7.0-1549b021 Validator,GCN_ARCH_NAME,gfx942:sramecc+:xnack- Validator,ROCBLAS_VERSION,4.1.0-cefa4a9b-dirty GemmTunableOp_Half_TN,tn_8192_7_28672,Gemm_Rocblas_45475,0.132098 GemmTunableOp_Half_TN,tn_10240_4_8192,Gemm_Rocblas_45546,0.0484431 GemmTunableOp_Half_TN,tn_32000_6_8192,Default,0.149546 GemmTunableOp_Half_TN,tn_32000_3_8192,Gemm_Rocblas_45520,0.147119 GemmTunableOp_Half_TN,tn_8192_3_28672,Gemm_Rocblas_45475,0.132645 GemmTunableOp_Half_TN,tn_10240_3_8192,Gemm_Rocblas_45546,0.0482971 GemmTunableOp_Half_TN,tn_57344_5_8192,Gemm_Rocblas_45520,0.255694 GemmTunableOp_Half_TN,tn_10240_7_8192,Gemm_Rocblas_45517,0.0482522 GemmTunableOp_Half_TN,tn_8192_3_8192,Gemm_Rocblas_45546,0.0444671 GemmTunableOp_Half_TN,tn_8192_5_8192,Gemm_Rocblas_45546,0.0445834 GemmTunableOp_Half_TN,tn_57344_7_8192,Gemm_Rocblas_45520,0.25622 GemmTunableOp_Half_TN,tn_8192_2_28672,Gemm_Rocblas_45475,0.132122 GemmTunableOp_Half_TN,tn_8192_4_8192,Gemm_Rocblas_45517,0.0453191 GemmTunableOp_Half_TN,tn_10240_5_8192,Gemm_Rocblas_45517,0.0482514 GemmTunableOp_Half_TN,tn_8192_5_28672,Gemm_Rocblas_45542,0.133914 GemmTunableOp_Half_TN,tn_8192_2_8192,Gemm_Rocblas_45517,0.0446516 GemmTunableOp_Half_TN,tn_8192_1_28672,Gemm_Hipblaslt_TN_10814,0.131953 GemmTunableOp_Half_TN,tn_10240_2_8192,Gemm_Rocblas_45546,0.0481043 GemmTunableOp_Half_TN,tn_32000_4_8192,Gemm_Rocblas_45520,0.147497 GemmTunableOp_Half_TN,tn_8192_6_28672,Gemm_Rocblas_45529,0.134895 GemmTunableOp_Half_TN,tn_57344_2_8192,Gemm_Rocblas_45520,0.254716 GemmTunableOp_Half_TN,tn_57344_4_8192,Gemm_Rocblas_45520,0.255731 GemmTunableOp_Half_TN,tn_10240_6_8192,Gemm_Rocblas_45517,0.0484816 GemmTunableOp_Half_TN,tn_57344_3_8192,Gemm_Rocblas_45520,0.254701 GemmTunableOp_Half_TN,tn_8192_4_28672,Gemm_Rocblas_45475,0.132159 GemmTunableOp_Half_TN,tn_32000_2_8192,Default,0.147524 GemmTunableOp_Half_TN,tn_32000_5_8192,Default,0.147074 GemmTunableOp_Half_TN,tn_8192_6_8192,Gemm_Rocblas_45546,0.0454045 GemmTunableOp_Half_TN,tn_57344_6_8192,Gemm_Rocblas_45520,0.255582 GemmTunableOp_Half_TN,tn_32000_7_8192,Default,0.146705 GemmTunableOp_Half_TN,tn_8192_7_8192,Gemm_Rocblas_45546,0.0445489 ``` --------- Co-authored-by: Mohit Sharma <mohit21sharma.ms@gmail.com>
2024-05-17 07:30:47 -06:00
# Using TGI with Inferentia
Check out this [guide](https://github.com/huggingface/optimum-neuron/tree/main/text-generation-inference) on how to serve models with TGI on Inferentia2.