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LLM: fix llama tokenizer for all-in-one benchmark (#9129)

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* fix tokenizer for gpu benchmark

* fix ipex fp16

* meet code review

* fix
This commit is contained in:
Ruonan Wang 2023-10-11 13:39:39 +08:00 committed by GitHub
parent 2ad67a18b1
commit 1c8d5da362
1 changed files with 34 additions and 20 deletions
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54
python/llm/dev/benchmark/all-in-one/run.py
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@ -30,6 +30,11 @@ sys.path.append(benchmark_util_path)
from benchmark_util import BenchmarkWrapper
from bigdl.llm.utils.common.log4Error import invalidInputError
LLAMA_IDS = ['meta-llama/Llama-2-7b-chat-hf','meta-llama/Llama-2-13b-chat-hf',
'meta-llama/Llama-2-70b-chat-hf','decapoda-research/llama-7b-hf',
'decapoda-research/llama-65b-hf','lmsys/vicuna-7b-v1.5',
'lmsys/vicuna-13b-v1.3','project-baize/merged-baize-30b']
results = []
@ -122,16 +127,7 @@ def run_transformer_int4(repo_id,
if repo_id in ['THUDM/chatglm-6b', 'THUDM/chatglm2-6b']:
model = AutoModel.from_pretrained(model_path, load_in_4bit=True, trust_remote_code=True, torch_dtype='auto')
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
elif repo_id in ['meta-llama/Llama-2-70b-chat-hf']:
# Can be removed when issue https://github.com/analytics-zoo/nano/issues/563 is resolved.
model = AutoModelForCausalLM.from_pretrained(model_path, load_in_4bit=True,
trust_remote_code=True, optimize_model=False)
# Need to use LlamaTokenizer, reason please refer to issue: https://github.com/intel-analytics/BigDL/issues/8944
tokenizer = LlamaTokenizer.from_pretrained(model_path, trust_remote_code=True)
elif repo_id in ['meta-llama/Llama-2-7b-chat-hf','meta-llama/Llama-2-13b-chat-hf',
'meta-llama/Llama-2-70b-chat-hf','decapoda-research/llama-7b-hf',
'decapoda-research/llama-65b-hf','lmsys/vicuna-7b-v1.5',
'lmsys/vicuna-13b-v1.3','project-baize/merged-baize-30b']:
elif repo_id in LLAMA_IDS:
model = AutoModelForCausalLM.from_pretrained(model_path, load_in_4bit=True, trust_remote_code=True)
tokenizer = LlamaTokenizer.from_pretrained(model_path, trust_remote_code=True)
else:
@ -179,10 +175,7 @@ def run_pytorch_autocast_bf16(repo_id,
if repo_id in ['THUDM/chatglm-6b', 'THUDM/chatglm2-6b']:
# TODO: need verify chatglm family run bf16.
invalidInputError(False, "Currently pytorch do not support bfloat16 on cpu for chatglm models.")
elif repo_id in ['meta-llama/Llama-2-7b-chat-hf','meta-llama/Llama-2-13b-chat-hf',
'meta-llama/Llama-2-70b-chat-hf','decapoda-research/llama-7b-hf',
'decapoda-research/llama-65b-hf','lmsys/vicuna-7b-v1.5',
'lmsys/vicuna-13b-v1.3','project-baize/merged-baize-30b']:
elif repo_id in LLAMA_IDS:
model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True, torch_dtype=torch.bfloat16)
# Need to use LlamaTokenizer, reason please refer to issue: https://github.com/intel-analytics/BigDL/issues/8944
tokenizer = LlamaTokenizer.from_pretrained(model_path, trust_remote_code=True)
@ -224,7 +217,7 @@ def run_optimize_model(repo_id,
in_out_pairs,
warm_up,
num_trials):
from transformers import AutoModel, AutoModelForCausalLM, AutoTokenizer
from transformers import AutoModel, AutoModelForCausalLM, AutoTokenizer, LlamaTokenizer
from bigdl.llm import optimize_model
model_path = get_model_path(repo_id, local_model_hub)
@ -235,6 +228,11 @@ def run_optimize_model(repo_id,
model = AutoModel.from_pretrained(model_path, torch_dtype='auto', low_cpu_mem_usage=True, trust_remote_code=True)
model = optimize_model(model)
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
elif repo_id in LLAMA_IDS:
model = AutoModelForCausalLM.from_pretrained(model_path, load_in_4bit=True, trust_remote_code=True,
use_cache=True, low_cpu_mem_usage=True)
model = optimize_model(model)
tokenizer = LlamaTokenizer.from_pretrained(model_path, trust_remote_code=True)
else:
model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype='auto', low_cpu_mem_usage=True)
model = optimize_model(model)
@ -276,17 +274,22 @@ def run_transformer_int4_gpu(repo_id,
warm_up,
num_trials):
from bigdl.llm.transformers import AutoModel, AutoModelForCausalLM
from transformers import AutoTokenizer, GPTJForCausalLM
from transformers import AutoTokenizer, GPTJForCausalLM, LlamaTokenizer
import intel_extension_for_pytorch as ipex
model_path = get_model_path(repo_id, local_model_hub)
# Load model in 4 bit,
# which convert the relevant layers in the model into INT4 format
st = time.perf_counter()
if repo_id in ['THUDM/chatglm-6b', 'THUDM/chatglm2-6b']:
model = AutoModel.from_pretrained(model_path, load_in_4bit=True, optimize_model=True, trust_remote_code=True,
use_cache=True)
model = AutoModel.from_pretrained(model_path, load_in_4bit=True, optimize_model=True,
trust_remote_code=True, use_cache=True)
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
model = model.to('xpu')
elif repo_id in LLAMA_IDS:
model = AutoModelForCausalLM.from_pretrained(model_path, load_in_4bit=True, trust_remote_code=True,
use_cache=True)
tokenizer = LlamaTokenizer.from_pretrained(model_path, trust_remote_code=True)
model = model.to('xpu')
else:
model = AutoModelForCausalLM.from_pretrained(model_path, optimize_model=True, load_in_4bit=True,
trust_remote_code=True, use_cache=True)
@ -334,7 +337,7 @@ def run_optimize_model_gpu(repo_id,
in_out_pairs,
warm_up,
num_trials):
from transformers import AutoModel, AutoModelForCausalLM, AutoTokenizer, GPTJForCausalLM
from transformers import AutoModel, AutoModelForCausalLM, AutoTokenizer, GPTJForCausalLM, LlamaTokenizer
from bigdl.llm import optimize_model
import intel_extension_for_pytorch as ipex
model_path = get_model_path(repo_id, local_model_hub)
@ -347,6 +350,12 @@ def run_optimize_model_gpu(repo_id,
model = optimize_model(model)
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
model = model.to('xpu')
elif repo_id in LLAMA_IDS:
model = AutoModelForCausalLM.from_pretrained(model_path, load_in_4bit=True, trust_remote_code=True,
use_cache=True, low_cpu_mem_usage=True)
model = optimize_model(model)
tokenizer = LlamaTokenizer.from_pretrained(model_path, trust_remote_code=True)
model = model.to('xpu')
else:
model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype='auto', low_cpu_mem_usage=True,
trust_remote_code=True, use_cache=True)
@ -396,7 +405,7 @@ def run_ipex_fp16_gpu(repo_id,
warm_up,
num_trials):
from transformers import AutoModel, AutoModelForCausalLM
from transformers import AutoTokenizer, GPTJForCausalLM
from transformers import AutoTokenizer, GPTJForCausalLM, LlamaTokenizer
import intel_extension_for_pytorch as ipex
model_path = get_model_path(repo_id, local_model_hub)
st = time.perf_counter()
@ -404,6 +413,11 @@ def run_ipex_fp16_gpu(repo_id,
model = AutoModel.from_pretrained(model_path, trust_remote_code=True, use_cache=True)
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
model = model.half().to('xpu')
elif repo_id in LLAMA_IDS:
model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True,
use_cache=True)
tokenizer = LlamaTokenizer.from_pretrained(model_path, trust_remote_code=True)
model = model.half().to('xpu')
else:
model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True, use_cache=True)
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)