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Fix nightly perf (#10899)

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* Fix nightly perf by adding default value in benchmark for use_fp16_torch_dtype

* further fixes
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Yuwen Hu 2024-04-28 09:39:29 +08:00 committed by GitHub
parent ea035f5e15
commit ddfdaec137
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GPG key ID: B5690EEEBB952194
1 changed files with 6 additions and 3 deletions
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9
python/llm/dev/benchmark/all-in-one/run.py
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@ -127,7 +127,7 @@ def run_model(repo_id, test_api, in_out_pairs, local_model_hub=None, warm_up=1,
round(result[in_out_pair][-1][5], 2), round(result[in_out_pair][-1][5], 2),
result[in_out_pair][-1][6] if any(keyword in test_api for keyword in ['int4_gpu', 'int4_fp16_gpu_win', 'int4_loadlowbit_gpu', 'fp16_gpu', 'deepspeed_optimize_model_gpu']) else 'N/A', result[in_out_pair][-1][6] if any(keyword in test_api for keyword in ['int4_gpu', 'int4_fp16_gpu_win', 'int4_loadlowbit_gpu', 'fp16_gpu', 'deepspeed_optimize_model_gpu']) else 'N/A',
streaming if 'win' in test_api else 'N/A', streaming if 'win' in test_api else 'N/A',
use_fp16_torch_dtype], use_fp16_torch_dtype if 'pipeline_parallel_gpu' in test_api else 'N/A'],
) )
@ -1805,7 +1805,10 @@ if __name__ == '__main__':
streaming = False streaming = False
if 'streaming' in conf: if 'streaming' in conf:
streaming = conf['streaming'] streaming = conf['streaming']
if 'use_fp16_torch_dtype' in conf:
use_fp16_torch_dtype = conf['use_fp16_torch_dtype']
if 'n_gpu' in conf:
n_gpu = conf['n_gpu']
import pandas as pd import pandas as pd
for api in conf.test_api: for api in conf.test_api:
@ -1820,7 +1823,7 @@ if __name__ == '__main__':
if model_id_input in excludes or model_id_input_batch_size in excludes: if model_id_input in excludes or model_id_input_batch_size in excludes:
in_out_pairs.remove(in_out) 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'], 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'], streaming, conf['use_fp16_torch_dtype'], conf['n_gpu']) conf['low_bit'], conf['cpu_embedding'], conf['batch_size'], streaming, use_fp16_torch_dtype, n_gpu)
df = pd.DataFrame(results, columns=['model', '1st token avg latency (ms)', '2+ avg latency (ms/token)', 'encoder time (ms)', 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', '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']) 'model loading time (s)', 'peak mem (GB)', 'streaming', 'use_fp16_torch_dtype'])