Package: rwkv-paddle
The RWKV Language Model Inference on PaddlePaddlehttps://github.com/HighCWu/rwkv-paddlehttps://github.com/BlinkDL/ChatRWKVhttps://github.com/BlinkDL/RWKV-LMPS: Some strategies are not supported on PaddlePaddle. The best supported strategies are 'cuda fp16' and 'cpu fp32'.PS: PaddlePaddle version should be greater than 2.4.0.importos# set these before import RWKVos.environ['RWKV_JIT_ON']='0'# RWKV JIT Mode is not supported on paddlepaddle nowos.environ["RWKV_CUDA_ON"]='0'# '1' to compile CUDA kernel (10x faster), requires c++ compiler & cuda libraries########################################################################################################## Use '/' in model path, instead of '\'. Use ctx4096 models if you need long ctx.## fp16 = good for GPU (!!! DOES NOT support CPU !!!)# fp32 = good for CPU# bf16 = worse accuracy, supports CPU# xxxi8 (example: fp16i8, fp32i8) = xxx with int8 quantization to save 50% VRAM/RAM, slower, slightly less accuracy## We consider [ln_out+head] to be an extra layer, so L12-D768 (169M) has "13" layers, L24-D2048 (1.5B) has "25" layers, etc.# Strategy Examples: (device = cpu/cuda/cuda:0/cuda:1/...)# 'cpu fp32' = all layers cpu fp32# 'cuda fp16' = all layers cuda fp16# 'cuda fp16i8' = all layers cuda fp16 with int8 quantization# 'cuda fp16i8 *10 -> cpu fp32' = first 10 layers cuda fp16i8, then cpu fp32 (increase 10 for better speed)# 'cuda:0 fp16 *10 -> cuda:1 fp16 *8 -> cpu fp32' = first 10 layers cuda:0 fp16, then 8 layers cuda:1 fp16, then cpu fp32## Basic Strategy Guide: (fp16i8 works for any GPU)# 100% VRAM = 'cuda fp16'                   # all layers cuda fp16#  98% VRAM = 'cuda fp16i8 *1 -> cuda fp16' # first 1 layer  cuda fp16i8, then cuda fp16#  96% VRAM = 'cuda fp16i8 *2 -> cuda fp16' # first 2 layers cuda fp16i8, then cuda fp16#  94% VRAM = 'cuda fp16i8 *3 -> cuda fp16' # first 3 layers cuda fp16i8, then cuda fp16#  ...#  50% VRAM = 'cuda fp16i8'                 # all layers cuda fp16i8#  48% VRAM = 'cuda fp16i8 -> cpu fp32 *1'  # most layers cuda fp16i8, last 1 layer  cpu fp32#  46% VRAM = 'cuda fp16i8 -> cpu fp32 *2'  # most layers cuda fp16i8, last 2 layers cpu fp32#  44% VRAM = 'cuda fp16i8 -> cpu fp32 *3'  # most layers cuda fp16i8, last 3 layers cpu fp32#  ...#   0% VRAM = 'cpu fp32'                    # all layers cpu fp32## Use '+' for STREAM mode, which can save VRAM too, and it is sometimes faster# 'cuda fp16i8 *10+' = first 10 layers cuda fp16i8, then fp16i8 stream the rest to it (increase 10 for better speed)## Extreme STREAM: 3G VRAM is enough to run RWKV 14B (slow. will be faster in future)# 'cuda fp16i8 *0+ -> cpu fp32 *1' = stream all layers cuda fp16i8, last 1 layer [ln_out+head] cpu fp32## ########################################################################################################fromrwkv_paddle.modelimportRWKVfromrwkv_paddle.utilsimportPIPELINE,PIPELINE_ARGS# download models: https://huggingface.co/BlinkDLmodel=RWKV(model='/fsx/BlinkDL/HF-MODEL/rwkv-4-pile-169m/RWKV-4-Pile-169M-20220807-8023',strategy='cpu fp32')pipeline=PIPELINE(model,"20B_tokenizer.json")# 20B_tokenizer.json is in https://github.com/HighCWu/rwkv-paddlectx="\nIn a shocking finding, scientist discovered a herd of dragons living in a remote, previously unexplored valley, in Tibet. Even more surprising to the researchers was the fact that the dragons spoke perfect Chinese."print(ctx,end='')defmy_print(s):print(s,end='',flush=True)# For alpha_frequency and alpha_presence, see "Frequency and presence penalties":# https://platform.openai.com/docs/api-reference/parameter-detailsargs=PIPELINE_ARGS(temperature=1.0,top_p=0.7,top_k=100,# top_k = 0 then ignorealpha_frequency=0.25,alpha_presence=0.25,token_ban=[0],# ban the generation of some tokenstoken_stop=[],# stop generation whenever you see any token herechunk_len=256)# split input into chunks to save VRAM (shorter -> slower)pipeline.generate(ctx,token_count=200,args=args,callback=my_print)print('\n')out,state=model.forward([187,510,1563,310,247],None)print(out.detach().cpu().numpy())# get logitsout,state=model.forward([187,510],None)out,state=model.forward([1563],state)# RNN has state (use deepcopy to clone states)out,state=model.forward([310,247],state)print(out.detach().cpu().numpy())# same result as aboveprint('\n')