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[LLM] Add model loading time record for all-in-one benchmark (#10201)

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* Add model loading time record in csv for all-in-one benchmark

* Small fix

* Small fix to number after .
This commit is contained in:
Yuwen Hu 2024-02-22 13:57:18 +08:00 committed by GitHub
parent 60e11b6739
commit 21de2613ce
2 changed files with 43 additions and 30 deletions
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71
python/llm/dev/benchmark/all-in-one/run.py
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@ -46,7 +46,7 @@ LLAVA_IDS = ['liuhaotian/llava-v1.5-7b']
results = []
excludes = []
def run_model_in_thread(model, in_out, tokenizer, result, warm_up, num_beams, input_ids, out_len, actual_in_len, num_trials):
def run_model_in_thread(model, in_out, tokenizer, result, warm_up, num_beams, input_ids, out_len, actual_in_len, num_trials, load_time):
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,
@ -61,7 +61,7 @@ def run_model_in_thread(model, in_out, tokenizer, result, warm_up, num_beams, in
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,
actual_in_len, actual_out_len, model.peak_memory])
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):
# TODO: make a parameter
@ -106,7 +106,8 @@ def run_model(repo_id, test_api, in_out_pairs, local_model_hub=None, warm_up=1,
num_beams,
low_bit,
cpu_embedding if 'win' in test_api else 'N/A',
result[in_out_pair][-1][5] 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
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
def get_model_path(repo_id, local_model_hub):
@ -186,7 +187,8 @@ def run_transformer_int4(repo_id,
use_cache=True).eval()
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
end = time.perf_counter()
print(">> loading of model costs {}s".format(end - st))
load_time = end - st
print(">> loading of model costs {}s".format(load_time))
model = BenchmarkWrapper(model)
@ -223,7 +225,7 @@ def run_transformer_int4(repo_id,
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,
actual_in_len, actual_out_len])
actual_in_len, actual_out_len, load_time])
return result
def run_pytorch_autocast_bf16(repo_id,
@ -251,7 +253,8 @@ def run_pytorch_autocast_bf16(repo_id,
use_cache=True)
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
end = time.perf_counter()
print(">> loading of model costs {}s".format(end - st))
load_time = end - st
print(">> loading of model costs {}s".format(load_time))
model = BenchmarkWrapper(model)
result = {}
@ -288,7 +291,7 @@ def run_pytorch_autocast_bf16(repo_id,
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,
actual_in_len, actual_out_len])
actual_in_len, actual_out_len, load_time])
return result
def run_optimize_model(repo_id,
@ -320,7 +323,8 @@ def run_optimize_model(repo_id,
model = optimize_model(model, low_bit=low_bit)
tokenizer = AutoTokenizer.from_pretrained(model_path)
end = time.perf_counter()
print(">> loading of model costs {}s".format(end - st))
load_time = end - st
print(">> loading of model costs {}s".format(load_time))
model = BenchmarkWrapper(model)
@ -357,7 +361,7 @@ def run_optimize_model(repo_id,
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,
actual_in_len, actual_out_len])
actual_in_len, actual_out_len, load_time])
return result
@ -407,7 +411,8 @@ def run_transformer_int4_gpu(repo_id,
# For gpt-j model family, this optimization can provide a better performance.
model = ipex.optimize(model.eval(), inplace=True)
end = time.perf_counter()
print(">> loading of model costs {}s and {}GB".format(end - st, torch.xpu.memory.memory_reserved()/(1024**3)))
load_time = end - st
print(">> loading of model costs {}s and {}GB".format(load_time, torch.xpu.memory.memory_reserved()/(1024**3)))
model = BenchmarkWrapper(model)
@ -435,7 +440,7 @@ def run_transformer_int4_gpu(repo_id,
input_ids = tokenizer(input_list, return_tensors="pt").input_ids.to('xpu')
actual_in_len = input_ids.shape[1]
result[in_out] = []
thread = threading.Thread(target=run_model_in_thread, args=(model, in_out, tokenizer, result, warm_up, num_beams, input_ids, out_len, actual_in_len, num_trials))
thread = threading.Thread(target=run_model_in_thread, args=(model, in_out, tokenizer, result, warm_up, num_beams, input_ids, out_len, actual_in_len, num_trials, load_time))
thread.start()
thread.join()
@ -445,13 +450,14 @@ def run_transformer_int4_gpu(repo_id,
encoder_time = round(np.mean(result[in_out], axis=0)[2]*1000.0, 2)
input_output_tokens = in_out
actual_input_output_tokens = f'{int(np.mean(result[in_out], axis=0)[3])}' + f'-{int(np.mean(result[in_out], axis=0)[4])}'
peak_mem = result[in_out][-1][5]
load_time = round(result[in_out][-1][5], 2)
peak_mem = result[in_out][-1][6]
with open(csv_name, mode='a', newline='') as file:
csv_writer = csv.writer(file)
file.seek(0, os.SEEK_END)
if file.tell() == 0:
csv_writer.writerow(["","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","peak mem (GB)"])
csv_writer.writerow(['', repo_id, first_token_latency, rest_token_latency, encoder_time, input_output_tokens, batch_size, actual_input_output_tokens, num_beams, low_bit, '', peak_mem])
csv_writer.writerow(["","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)"])
csv_writer.writerow(['', repo_id, first_token_latency, rest_token_latency, encoder_time, input_output_tokens, batch_size, actual_input_output_tokens, num_beams, low_bit, '', load_time, peak_mem])
model.to('cpu')
torch.xpu.synchronize()
@ -497,7 +503,8 @@ def run_optimize_model_gpu(repo_id,
# For gpt-j model family, this optimization can provide a better performance.
model = ipex.optimize(model.eval(), inplace=True)
end = time.perf_counter()
print(">> loading of model costs {}s".format(end - st))
load_time = end - st
print(">> loading of model costs {}s".format(load_time))
model = BenchmarkWrapper(model)
@ -536,7 +543,7 @@ def run_optimize_model_gpu(repo_id,
print(output[0])
if i >= warm_up:
result[in_out].append([model.first_cost, model.rest_cost_mean, model.encoder_time,
actual_in_len, actual_out_len])
actual_in_len, actual_out_len, load_time])
del model
torch.xpu.empty_cache()
return result
@ -571,7 +578,8 @@ def run_ipex_fp16_gpu(repo_id,
# For gpt-j model family, this optimization can provide a better performance.
model = ipex.optimize(model.eval(), inplace=True)
end = time.perf_counter()
print(">> loading of model costs {}s".format(end - st))
load_time = end - st
print(">> loading of model costs {}s".format(load_time))
model = BenchmarkWrapper(model)
@ -610,7 +618,7 @@ def run_ipex_fp16_gpu(repo_id,
print(output[0])
if i >= warm_up:
result[in_out].append([model.first_cost, model.rest_cost_mean, model.encoder_time,
actual_in_len, actual_out_len])
actual_in_len, actual_out_len, load_time])
del model
torch.xpu.empty_cache()
return result
@ -648,7 +656,8 @@ def run_bigdl_fp16_gpu(repo_id,
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
model = model.to('xpu')
end = time.perf_counter()
print(">> loading of model costs {}s".format(end - st))
load_time = end - st
print(">> loading of model costs {}s".format(load_time))
model = BenchmarkWrapper(model)
@ -687,7 +696,7 @@ def run_bigdl_fp16_gpu(repo_id,
print(output[0])
if i >= warm_up:
result[in_out].append([model.first_cost, model.rest_cost_mean, model.encoder_time,
actual_in_len, actual_out_len])
actual_in_len, actual_out_len, load_time])
del model
torch.xpu.empty_cache()
return result
@ -739,7 +748,8 @@ def run_deepspeed_transformer_int4_cpu(repo_id,
model = model.to(f'cpu:{local_rank}')
end = time.perf_counter()
print(">> loading of model costs {}s".format(end - st))
load_time = end - st
print(">> loading of model costs {}s".format(load_time))
model = BenchmarkWrapper(model)
@ -778,7 +788,7 @@ def run_deepspeed_transformer_int4_cpu(repo_id,
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,
actual_in_len, actual_out_len])
actual_in_len, actual_out_len, load_time])
return result
@ -825,7 +835,8 @@ def run_transformer_int4_gpu_win(repo_id,
# For gpt-j model family, this optimization can provide a better performance.
model = ipex.optimize(model.eval(), inplace=True)
end = time.perf_counter()
print(">> loading of model costs {}s and {}GB".format(end - st, torch.xpu.memory.memory_reserved()/(1024**3)))
load_time = end - st
print(">> loading of model costs {}s and {}GB".format(load_time, torch.xpu.memory.memory_reserved()/(1024**3)))
model = BenchmarkWrapper(model)
@ -865,7 +876,7 @@ def run_transformer_int4_gpu_win(repo_id,
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,
actual_in_len, actual_out_len, model.peak_memory])
actual_in_len, actual_out_len, load_time, model.peak_memory])
# torch.xpu.empty_cache() # this may make first token slower
except RuntimeError:
traceback.print_exc()
@ -920,7 +931,8 @@ def run_transformer_int4_loadlowbit_gpu_win(repo_id,
# For gpt-j model family, this optimization can provide a better performance.
model = ipex.optimize(model.eval(), inplace=True)
end = time.perf_counter()
print(">> loading of model costs {}s and {}GB".format(end - st, torch.xpu.memory.memory_reserved()/(1024**3)))
load_time = end - st
print(">> loading of model costs {}s and {}GB".format(load_time, torch.xpu.memory.memory_reserved()/(1024**3)))
model = BenchmarkWrapper(model)
@ -960,7 +972,7 @@ def run_transformer_int4_loadlowbit_gpu_win(repo_id,
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,
actual_in_len, actual_out_len, model.peak_memory])
actual_in_len, actual_out_len, load_time, model.peak_memory])
# torch.xpu.empty_cache() # this may make first token slower
except RuntimeError:
traceback.print_exc()
@ -1000,7 +1012,8 @@ def run_transformer_autocast_bf16( repo_id,
use_cache=True).eval()
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
end = time.perf_counter()
print(">> loading of model costs {}s".format(end - st))
load_time = end - st
print(">> loading of model costs {}s".format(load_time))
model = BenchmarkWrapper(model)
@ -1037,7 +1050,7 @@ def run_transformer_autocast_bf16( repo_id,
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,
actual_in_len, actual_out_len])
actual_in_len, actual_out_len, load_time])
return result
if __name__ == '__main__':
@ -1063,6 +1076,6 @@ if __name__ == '__main__':
conf['low_bit'], conf['cpu_embedding'], conf['batch_size'])
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',
'peak mem (GB)'])
'model loading time (s)', 'peak mem (GB)'])
df.to_csv(csv_name)
results = []

2
python/llm/test/benchmark/csv_to_html.py
View file

@ -162,7 +162,7 @@ def main():
subset2=['best diff1(%)','best diff2(%)']
columns={'1st token avg latency (ms)': '{:.2f}', '2+ avg latency (ms/token)': '{:.2f}', 'last1': '{:.2f}', 'diff1(%)': '{:.2f}',
'last2': '{:.2f}', 'diff2(%)': '{:.2f}', 'encoder time (ms)': '{:.2f}', 'peak mem (GB)': '{:.2f}',
'best 1': '{:.2f}', 'best diff1(%)': '{:.2f}', 'best 2': '{:.2f}', 'best diff2(%)': '{:.2f}'}
'best 1': '{:.2f}', 'best diff1(%)': '{:.2f}', 'best 2': '{:.2f}', 'best diff2(%)': '{:.2f}', 'model loading time (s)': '{:.2f}'}
styled_df = latest_csv.style.format(columns).applymap(lambda val: highlight_vals(val, max=3.0, color1='red', color2='green'), subset=subset1)
styled_df = styled_df.applymap(lambda val: highlight_vals(val, max=3.0, color1='yellow'), subset=subset2)