ipex-llm/python/llm/dev/benchmark/all-in-one/config.yaml
hxsz1997 b6234eb4e2
Add task in allinone (#11226)
* add task

* update prompt

* modify typos

* add more cases in summarize

* Make the summarize & QA prompt preprocessing as a util function
2024-06-06 17:22:40 +08:00

42 lines
3.1 KiB
YAML

repo_id:
# - 'THUDM/chatglm2-6b'
- 'meta-llama/Llama-2-7b-chat-hf'
# - 'liuhaotian/llava-v1.5-7b' # requires a LLAVA_REPO_DIR env variables pointing to the llava dir; added only for gpu win related test_api now
local_model_hub: 'path to your local model hub'
warm_up: 1 # must set >=2 when run "pipeline_parallel_gpu" test_api
num_trials: 3
num_beams: 1 # default to greedy search
low_bit: 'sym_int4' # default to use 'sym_int4' (i.e. symmetric int4)
batch_size: 1 # default to 1
in_out_pairs:
- '32-32'
- '1024-128'
test_api:
- "transformer_int4_fp16_gpu" # on Intel GPU, transformer-like API, (qtype=int4), (dtype=fp16)
# - "transformer_int4_fp16_gpu_win" # on Intel GPU for Windows, transformer-like API, (qtype=int4), (dtype=fp16)
# - "transformer_int4_gpu" # on Intel GPU, transformer-like API, (qtype=int4), (dtype=fp32)
# - "transformer_int4_gpu_win" # on Intel GPU for Windows, transformer-like API, (qtype=int4), (dtype=fp32)
# - "transformer_int4_loadlowbit_gpu_win" # on Intel GPU for Windows, transformer-like API, (qtype=int4), use load_low_bit API. Please make sure you have used the save.py to save the converted low bit model
# - "bigdl_fp16_gpu" # on Intel GPU, use ipex-llm transformers API, (dtype=fp16), (qtype=fp16)
# - "optimize_model_gpu" # on Intel GPU, can optimize any pytorch models include transformer model
# - "deepspeed_optimize_model_gpu" # on Intel GPU, deepspeed autotp inference
# - "pipeline_parallel_gpu" # on Intel GPU, pipeline parallel inference
# - "speculative_gpu" # on Intel GPU, inference with self-speculative decoding
# - "transformer_int4" # on Intel CPU, transformer-like API, (qtype=int4)
# - "native_int4" # on Intel CPU
# - "optimize_model" # on Intel CPU, can optimize any pytorch models include transformer model
# - "pytorch_autocast_bf16" # on Intel CPU
# - "transformer_autocast_bf16" # on Intel CPU
# - "bigdl_ipex_bf16" # on Intel CPU, (qtype=bf16)
# - "bigdl_ipex_int4" # on Intel CPU, (qtype=int4)
# - "bigdl_ipex_int8" # on Intel CPU, (qtype=int8)
# - "speculative_cpu" # on Intel CPU, inference with self-speculative decoding
# - "deepspeed_transformer_int4_cpu" # on Intel CPU, deepspeed autotp inference
# - "transformer_int4_fp16_lookahead_gpu" # on Intel GPU, transformer-like API, with lookahead, (qtype=int4), (dtype=fp16)
cpu_embedding: False # whether put embedding to CPU
streaming: False # whether output in streaming way (only available now for gpu win related test_api)
use_fp16_torch_dtype: True # whether use fp16 for non-linear layer (only available now for "pipeline_parallel_gpu" test_api)
n_gpu: 2 # number of GPUs to use (only available now for "pipeline_parallel_gpu" test_api)
lookahead: 3
max_matching_ngram_size: 2
task: 'continuation' # when test_api is "transformer_int4_fp16_lookahead_gpu", task could be 'QA', 'continuation' or 'summarize'