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C++ example

C++ example support automatic identification model and tokenizer which is implemented by SentencePiece, excluding Opt model which tokenizer is a hard code.

Step 1: Build binary file

Please refer to Build from source to build C++ example binary which is built with xFasterTransformer library and under build directory named example.

Step 2: Prepare models

Please refer to Prepare model

Step 3: Run binary

# Recommend preloading `libiomp5.so` to get a better performance.
# or LD_PRELOAD=libiomp5.so manually, `libiomp5.so` file will be in `3rdparty/mkl/lib` directory after build xFasterTransformer.
export $(python -c 'import xfastertransformer as xft; print(xft.get_env())')

# run single instance like
./example -m ${MODEL_PATH} -t ${TOKEN_PATH}

# run multi-instance like
OMP_NUM_THREADS=48 mpirun \
  -n 1 numactl -N 0 -m 0 ./example -m ${MODEL_PATH} -t ${TOKEN_PATH} : \
  -n 1 numactl -N 1 -m 1 ./example -m ${MODEL_PATH} -t ${TOKEN_PATH}

More parameter options settings:

  • -?, -h, --help Help information
  • -m, --model directory path of xft format model.
  • -t, --token path of tokenizer file(name like tokenizer.model), invalid for Opt and Qwen model.
  • -i, --input input prompt, invalid for Opt and Qwen model. Default use Once upon a time, there existed a little girl who liked to have adventures.
  • -d, --dtype data type, default fp16, should be one of ["fp16", "bf16", "int8", "w8a8", "int4", "nf4", "bf16_fp16", "bf16_int8", "bf16_w8a8", "bf16_int4", "bf16_nf4", "w8a8_int8", "w8a8_int4", "w8a8_nf4"]
  • -l, --input_len input token size. Input token ids will ben expand to this size if it greater than input prompt's size.
  • -n, --num_beams number of beam size, default 1.
  • -b, --batch_size batch size, default 1. If greater than 1, input prompt will be duplicated this times.
  • --output_len max tokens can generate excluded input, default 100.
  • --loop number of loop, default 1.
  • --no_stream disable streaming output.
  • --do_sample use sampling.
  • --prefix_len shared prefix tokens num.
  • --topK number of highest probability tokens to keep for top-k-filtering.
  • --temperature value used to modulate the next token probabilities.
  • --topP retain minimal tokens above topP threshold.