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add transpose_value_cache for NPU benchmark (#12092)

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* add `transpose_value_cache`

* update

* update
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Ch1y0q 2024-09-19 18:45:05 +08:00 committed by GitHub
parent f7fb3c896c
commit 9650bf616a
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2 changed files with 12 additions and 7 deletions
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1
python/llm/dev/benchmark/all-in-one/config.yaml
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@ -41,3 +41,4 @@ streaming: False # whether output in streaming way (only available now for gpu w
optimize_model: False # whether apply further optimization on NPU (only available now for transformers_int4_npu_win test_api)
use_fp16_torch_dtype: True # whether use fp16 for non-linear layer (only available now for "pipeline_parallel_gpu" test_api)
task: 'continuation' # task can be 'continuation', 'QA' and 'summarize'
transpose_value_cache: True # whether apply transposed v_cache optimization on NPU (only available now for transformers_int4_npu_win test_api)

18
python/llm/dev/benchmark/all-in-one/run.py
View file

@ -136,7 +136,7 @@ def preprocess_prompt(tokenizer, in_len, task):
input_ids = tokenizer.encode(input_str, return_tensors="pt")
return input_ids
def run_model(repo_id, test_api, in_out_pairs, local_model_hub=None, warm_up=1, num_trials=3, num_beams=1, low_bit='sym_int4', cpu_embedding=False, batch_size=1, streaming=False, use_fp16_torch_dtype=False, lookahead=False, task='continuation', optimize_model=False):
def run_model(repo_id, test_api, in_out_pairs, local_model_hub=None, warm_up=1, num_trials=3, num_beams=1, low_bit='sym_int4', cpu_embedding=False, batch_size=1, streaming=False, use_fp16_torch_dtype=False, lookahead=False, task='continuation', optimize_model=False, transpose_value_cache=True):
# TODO: make a parameter
result= {}
if test_api == 'transformer_int4':
@ -188,7 +188,7 @@ def run_model(repo_id, test_api, in_out_pairs, local_model_hub=None, warm_up=1,
elif test_api == 'pipeline_parallel_gpu':
result = run_pipeline_parallel_gpu(repo_id, local_model_hub, in_out_pairs, warm_up, num_trials, num_beams, low_bit, batch_size, cpu_embedding, fp16=use_fp16_torch_dtype)
elif test_api == 'transformers_int4_npu_win':
result = transformers_int4_npu_win(repo_id, local_model_hub, in_out_pairs, warm_up, num_trials, num_beams, low_bit, batch_size, optimize_model)
result = transformers_int4_npu_win(repo_id, local_model_hub, in_out_pairs, warm_up, num_trials, num_beams, low_bit, batch_size, optimize_model, transpose_value_cache)
else:
invalidInputError(False, "Unknown test_api " + test_api + ", please check your config.yaml.")
@ -604,7 +604,8 @@ def transformers_int4_npu_win(repo_id,
num_beams,
low_bit,
batch_size,
optimize_model):
optimize_model,
transpose_value_cache):
from ipex_llm.transformers.npu_model import AutoModel, AutoModelForCausalLM
from transformers import AutoTokenizer, LlamaTokenizer
@ -616,17 +617,17 @@ def transformers_int4_npu_win(repo_id,
st = time.perf_counter()
if repo_id in CHATGLM_IDS:
model = AutoModel.from_pretrained(model_path, load_in_low_bit=low_bit, trust_remote_code=True,
optimize_model=optimize_model, max_output_len=max_output_len, max_prompt_len=int(in_out_len[0]), transpose_value_cache=True,
optimize_model=optimize_model, max_output_len=max_output_len, max_prompt_len=int(in_out_len[0]), transpose_value_cache=transpose_value_cache,
torch_dtype=torch.float16, attn_implementation="eager").eval()
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
elif repo_id in LLAMA_IDS:
model = AutoModelForCausalLM.from_pretrained(model_path, load_in_low_bit=low_bit, trust_remote_code=True, torch_dtype=torch.float16,
optimize_model=optimize_model, max_output_len=max_output_len, max_prompt_len=int(in_out_len[0]), transpose_value_cache=True,
optimize_model=optimize_model, max_output_len=max_output_len, max_prompt_len=int(in_out_len[0]), transpose_value_cache=transpose_value_cache,
use_cache=True, attn_implementation="eager").eval()
tokenizer = LlamaTokenizer.from_pretrained(model_path, trust_remote_code=True)
else:
model = AutoModelForCausalLM.from_pretrained(model_path, load_in_low_bit=low_bit, trust_remote_code=True, torch_dtype=torch.float16,
optimize_model=optimize_model, max_output_len=max_output_len, max_prompt_len=int(in_out_len[0]), transpose_value_cache=True,
optimize_model=optimize_model, max_output_len=max_output_len, max_prompt_len=int(in_out_len[0]), transpose_value_cache=transpose_value_cache,
use_cache=True, attn_implementation="eager").eval()
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
end = time.perf_counter()
@ -2033,6 +2034,9 @@ if __name__ == '__main__':
if 'optimize_model' in conf:
optimize_model = conf['optimize_model']
lookahead = False
transpose_value_cache = True
if 'transpose_value_cache' in conf:
transpose_value_cache = conf['transpose_value_cache']
import pandas as pd
for api in conf.test_api:
@ -2058,7 +2062,7 @@ if __name__ == '__main__':
if task in ['QA', 'summarize'] and conf['num_beams'] == 1 and batch_size == 1:
lookahead = True
run_model(model, api, in_out_pairs, conf['local_model_hub'], conf['warm_up'], conf['num_trials'], conf['num_beams'],
conf['low_bit'], conf['cpu_embedding'], batch_size, streaming, use_fp16_torch_dtype, lookahead, task, optimize_model)
conf['low_bit'], conf['cpu_embedding'], batch_size, streaming, use_fp16_torch_dtype, lookahead, task, optimize_model, transpose_value_cache)
df = pd.DataFrame(results, columns=['model', '1st token avg latency (ms)', '2+ avg latency (ms/token)', 'encoder time (ms)',
'input/output tokens', 'batch_size', 'actual input/output tokens', 'num_beams', 'low_bit', 'cpu_embedding',
'model loading time (s)', 'peak mem (GB)', 'streaming', 'use_fp16_torch_dtype'])