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[LLM] all-on-one update: memory optimize and streaming output (#10302)

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* Memory saving for continous in-out pair run and add support for streaming output on MTL iGPU

* Small fix

* Small fix

* Add things back
This commit is contained in:
Yuwen Hu 2024-03-01 18:02:30 +08:00 committed by GitHub
parent 367b1db4f7
commit 27d9a14989
3 changed files with 45 additions and 18 deletions
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1
python/llm/dev/benchmark/all-in-one/README.md
View file

@ -50,6 +50,7 @@ test_api:
# - "transformer_int4_gpu_win" # on Intel GPU for Windows
# - "transformer_int4_loadlowbit_gpu_win" # on Intel GPU for Windows using load_low_bit API. Please make sure you have used the save.py to save the converted low bit model
cpu_embedding: False # whether put embedding to CPU (only avaiable now for gpu win related test_api)
streaming: False # whether output in streaming way (only avaiable now for gpu win related test_api)
```

4
python/llm/dev/benchmark/all-in-one/config.yaml
View file

@ -23,5 +23,7 @@ test_api:
# - "transformer_int4_gpu" # on Intel GPU
# - "optimize_model_gpu" # on Intel GPU
# - "deepspeed_transformer_int4_cpu" # on Intel SPR Server
# - "transformer_int4_gpu_win" # on Intel GPU for Windows (catch GPU peak memory)
# - "transformer_int4_gpu_win" # on Intel GPU for Windows
# - "transformer_int4_loadlowbit_gpu_win" # on Intel GPU for Windows using load_low_bit API. Please make sure you have used the save.py to save the converted low bit model
cpu_embedding: False # whether put embedding to CPU (only avaiable now for gpu win related test_api)
streaming: False # whether output in streaming way (only avaiable now for gpu win related test_api)

58
python/llm/dev/benchmark/all-in-one/run.py
View file

@ -63,7 +63,7 @@ def run_model_in_thread(model, in_out, tokenizer, result, warm_up, num_beams, in
result[in_out].append([model.first_cost, model.rest_cost_mean, model.encoder_time,
actual_in_len, actual_out_len, load_time, model.peak_memory])
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):
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):
# TODO: make a parameter
result= {}
if test_api == 'transformer_int4':
@ -85,11 +85,11 @@ def run_model(repo_id, test_api, in_out_pairs, local_model_hub=None, warm_up=1,
elif test_api == 'deepspeed_transformer_int4_cpu':
result = run_deepspeed_transformer_int4_cpu(repo_id, local_model_hub, in_out_pairs, warm_up, num_trials, num_beams, low_bit, batch_size)
elif test_api == 'transformer_int4_gpu_win':
result = run_transformer_int4_gpu_win(repo_id, local_model_hub, in_out_pairs, warm_up, num_trials, num_beams, low_bit, cpu_embedding, batch_size)
result = run_transformer_int4_gpu_win(repo_id, local_model_hub, in_out_pairs, warm_up, num_trials, num_beams, low_bit, cpu_embedding, batch_size, streaming)
elif test_api == 'transformer_int4_loadlowbit_gpu_win':
# drop the results of the first time for better performance
run_transformer_int4_loadlowbit_gpu_win(repo_id, local_model_hub, in_out_pairs, warm_up, num_trials, num_beams, low_bit, cpu_embedding, batch_size)
result = run_transformer_int4_loadlowbit_gpu_win(repo_id, local_model_hub, in_out_pairs, warm_up, num_trials, num_beams, low_bit, cpu_embedding, batch_size)
run_transformer_int4_loadlowbit_gpu_win(repo_id, local_model_hub, in_out_pairs, warm_up, num_trials, num_beams, low_bit, cpu_embedding, batch_size, streaming)
result = run_transformer_int4_loadlowbit_gpu_win(repo_id, local_model_hub, in_out_pairs, warm_up, num_trials, num_beams, low_bit, cpu_embedding, batch_size, streaming)
elif test_api == 'transformer_autocast_bf16':
result = run_transformer_autocast_bf16(repo_id, local_model_hub, in_out_pairs, warm_up, num_trials, num_beams, batch_size)
@ -107,7 +107,9 @@ def run_model(repo_id, test_api, in_out_pairs, local_model_hub=None, warm_up=1,
low_bit,
cpu_embedding if 'win' in test_api else 'N/A',
round(result[in_out_pair][-1][5], 2),
result[in_out_pair][-1][6] if 'int4_gpu' in test_api or 'int4_loadlowbit_gpu' in test_api else 'N/A']) # currently only peak mem for transformer_int4_gpu is caught here
result[in_out_pair][-1][6] if 'int4_gpu' in test_api or 'int4_loadlowbit_gpu' in test_api else 'N/A',
streaming if 'win' in test_api else 'N/A'],
)
def get_model_path(repo_id, local_model_hub):
@ -800,9 +802,10 @@ def run_transformer_int4_gpu_win(repo_id,
num_beams,
low_bit,
cpu_embedding,
batch_size):
batch_size,
streaming):
from bigdl.llm.transformers import AutoModel, AutoModelForCausalLM
from transformers import AutoTokenizer, GPTJForCausalLM, LlamaTokenizer
from transformers import AutoTokenizer, GPTJForCausalLM, LlamaTokenizer, TextStreamer
import intel_extension_for_pytorch as ipex
model_path = get_model_path(repo_id, local_model_hub)
# Load model in 4 bit,
@ -839,6 +842,7 @@ def run_transformer_int4_gpu_win(repo_id,
print(">> loading of model costs {}s and {}GB".format(load_time, torch.xpu.memory.memory_reserved()/(1024**3)))
model = BenchmarkWrapper(model)
streamer = TextStreamer(tokenizer, skip_prompt=True)
result = {}
with torch.inference_mode():
@ -865,14 +869,19 @@ def run_transformer_int4_gpu_win(repo_id,
result[in_out] = []
for i in range(num_trials + warm_up):
st = time.perf_counter()
output_ids = model.generate(input_ids, do_sample=False, max_new_tokens=out_len,
num_beams=num_beams)
if streaming:
output_ids = model.generate(input_ids, do_sample=False, max_new_tokens=out_len,
num_beams=num_beams, streamer=streamer)
else:
output_ids = model.generate(input_ids, do_sample=False, max_new_tokens=out_len,
num_beams=num_beams)
torch.xpu.synchronize()
end = time.perf_counter()
output_ids = output_ids.cpu()
print("model generate cost: " + str(end - st))
output = tokenizer.batch_decode(output_ids)
print(output[0])
if not streaming:
print(output[0])
actual_out_len = output_ids.shape[1] - actual_in_len
if i >= warm_up:
result[in_out].append([model.first_cost, model.rest_cost_mean, model.encoder_time,
@ -881,6 +890,8 @@ def run_transformer_int4_gpu_win(repo_id,
except RuntimeError:
traceback.print_exc()
pass
torch.xpu.synchronize()
torch.xpu.empty_cache()
model.to('cpu')
torch.xpu.synchronize()
torch.xpu.empty_cache()
@ -897,9 +908,10 @@ def run_transformer_int4_loadlowbit_gpu_win(repo_id,
num_beams,
low_bit,
cpu_embedding,
batch_size):
batch_size,
streaming):
from bigdl.llm.transformers import AutoModel, AutoModelForCausalLM
from transformers import AutoTokenizer, GPTJForCausalLM, LlamaTokenizer
from transformers import AutoTokenizer, GPTJForCausalLM, LlamaTokenizer, TextStreamer
import intel_extension_for_pytorch as ipex
model_path = get_model_path(repo_id, local_model_hub)
# Load BigDL-LLM optimized low bit model
@ -935,6 +947,7 @@ def run_transformer_int4_loadlowbit_gpu_win(repo_id,
print(">> loading of model costs {}s and {}GB".format(load_time, torch.xpu.memory.memory_reserved()/(1024**3)))
model = BenchmarkWrapper(model)
streamer = TextStreamer(tokenizer, skip_prompt=True)
result = {}
with torch.inference_mode():
@ -961,14 +974,19 @@ def run_transformer_int4_loadlowbit_gpu_win(repo_id,
result[in_out] = []
for i in range(num_trials + warm_up):
st = time.perf_counter()
output_ids = model.generate(input_ids, do_sample=False, max_new_tokens=out_len,
num_beams=num_beams)
if streaming:
output_ids = model.generate(input_ids, do_sample=False, max_new_tokens=out_len,
num_beams=num_beams, streamer=streamer)
else:
output_ids = model.generate(input_ids, do_sample=False, max_new_tokens=out_len,
num_beams=num_beams)
torch.xpu.synchronize()
end = time.perf_counter()
output_ids = output_ids.cpu()
print("model generate cost: " + str(end - st))
output = tokenizer.batch_decode(output_ids)
print(output[0])
if not streaming:
print(output[0])
actual_out_len = output_ids.shape[1] - actual_in_len
if i >= warm_up:
result[in_out].append([model.first_cost, model.rest_cost_mean, model.encoder_time,
@ -977,6 +995,8 @@ def run_transformer_int4_loadlowbit_gpu_win(repo_id,
except RuntimeError:
traceback.print_exc()
pass
torch.xpu.synchronize()
torch.xpu.empty_cache()
model.to('cpu')
torch.xpu.synchronize()
torch.xpu.empty_cache()
@ -1059,6 +1079,10 @@ if __name__ == '__main__':
today = date.today()
if 'exclude' in conf:
excludes = conf['exclude']
streaming = False
if 'streaming' in conf:
streaming = conf['streaming']
import pandas as pd
for api in conf.test_api:
@ -1073,9 +1097,9 @@ if __name__ == '__main__':
if model_id_input in excludes or model_id_input_batch_size in excludes:
in_out_pairs.remove(in_out)
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'], conf['batch_size'])
conf['low_bit'], conf['cpu_embedding'], conf['batch_size'], streaming)
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)'])
'model loading time (s)', 'peak mem (GB)', 'streaming'])
df.to_csv(csv_name)
results = []