LLM: ensure the result of daily arc perf test (#10016)
* ensure the result of daily arc perf test * small fix * small fix * small fix * small fix * small fix * small fix * small fix * small fix * small fix * small fix * concat more csvs * small fix * revert some files
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WeiguangHan
GitHub
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9724939499
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d2d3f6b091
3 changed files with 42 additions and 12 deletions
23
.github/workflows/llm_performance_tests.yml
vendored
23
.github/workflows/llm_performance_tests.yml
vendored
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@ -84,7 +84,7 @@ jobs:
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source /opt/intel/oneapi/setvars.sh
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bash python/llm/test/run-llm-install-tests.sh
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- name: Test on xpu
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- name: Test on xpu(transformers==4.31.0)
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shell: bash
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run: |
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date_for_test_version=$(date -d yesterday +%Y-%m-%d)
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@ -100,12 +100,25 @@ jobs:
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# change csv name
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sed -i 's/{today}/{today}_test1/g' run.py
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python run.py
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- name: Test on xpu(transformers==4.34.0)
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shell: bash
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run: |
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source /opt/intel/oneapi/setvars.sh
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export USE_XETLA=OFF
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export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
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# upgrade transformers for model Mistral-7B-v0.1
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python -m pip install transformers==4.34.0
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cp ../../../test/benchmark/arc-perf-transformers-434.yaml ./config.yaml
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cp python/llm/test/benchmark/arc-perf-transformers-434.yaml python/llm/dev/benchmark/all-in-one/config.yaml
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cd python/llm/dev/benchmark/all-in-one
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# change csv name
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sed -i 's/test1/test2/g' run.py
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python run.py
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- name: Concat csv and generate html
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shell: bash
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run: |
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cd python/llm/dev/benchmark/all-in-one
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python ../../../test/benchmark/concat_csv.py
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for file in *.csv; do
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if [[ $file != *test* ]]; then
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@ -115,7 +128,11 @@ jobs:
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python -m pip install pandas==1.5.3
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cd ../../../test/benchmark
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python csv_to_html.py -f $CSV_SAVE_PATH
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cd ../../dev/benchmark/all-in-one/
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- name: Check and upload results to ftp
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shell: bash
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run: |
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cd python/llm/dev/benchmark/all-in-one
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python ../../../test/benchmark/check_results.py -c test1 -y ../../../test/benchmark/arc-perf-test.yaml
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python ../../../test/benchmark/check_results.py -c test2 -y ../../../test/benchmark/arc-perf-transformers-434.yaml
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find . -name "*test*.csv" -delete
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@ -21,6 +21,7 @@ import time
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import gc
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import traceback
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import threading
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import csv
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import numpy as np
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from datetime import date
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@ -432,6 +433,21 @@ def run_transformer_int4_gpu(repo_id,
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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))
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thread.start()
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thread.join()
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if result[in_out]:
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first_token_latency = round(np.mean(result[in_out], axis=0)[0]*1000.0, 2)
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rest_token_latency = round(np.mean(result[in_out], axis=0)[1]*1000.0, 2)
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encoder_time = round(np.mean(result[in_out], axis=0)[2]*1000.0, 2)
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input_output_tokens = in_out
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actual_input_output_tokens = f'{int(np.mean(result[in_out], axis=0)[3])}' + f'-{int(np.mean(result[in_out], axis=0)[4])}'
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peak_mem = result[in_out][-1][5]
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with open(csv_name, mode='a', newline='') as file:
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csv_writer = csv.writer(file)
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file.seek(0, os.SEEK_END)
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if file.tell() == 0:
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csv_writer.writerow(["","model","1st token avg latency (ms)","2+ avg latency (ms/token)","encoder time (ms)","input/output tokens","actual input/output tokens","num_beams","low_bit","cpu_embedding","peak mem (GB)"])
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csv_writer.writerow(['', repo_id, first_token_latency, rest_token_latency, encoder_time, input_output_tokens, actual_input_output_tokens, num_beams, low_bit, '', peak_mem])
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model.to('cpu')
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torch.xpu.synchronize()
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torch.xpu.empty_cache()
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@ -439,7 +455,6 @@ def run_transformer_int4_gpu(repo_id,
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gc.collect()
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return result
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def run_optimize_model_gpu(repo_id,
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local_model_hub,
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in_out_pairs,
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@ -933,6 +948,8 @@ if __name__ == '__main__':
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import pandas as pd
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for api in conf.test_api:
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global csv_name
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csv_name = f'{current_dir}/{api}-results-{today}.csv'
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for model in conf.repo_id:
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in_out_pairs = conf['in_out_pairs'].copy()
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if excludes:
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@ -943,8 +960,7 @@ if __name__ == '__main__':
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run_model(model, api, in_out_pairs, conf['local_model_hub'], conf['warm_up'], conf['num_trials'], conf['num_beams'],
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conf['low_bit'], conf['cpu_embedding'], conf['batch_size'])
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df = pd.DataFrame(results, columns=['model', '1st token avg latency (ms)', '2+ avg latency (ms/token)', 'encoder time (ms)',
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'input/output tokens', 'actual input/output tokens', 'num_beams', 'low_bit', 'cpu_embedding',
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'input/output tokens', 'actual input/output tokens', 'num_beams', 'low_bit', 'cpu_embedding',
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'peak mem (GB)'])
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df.to_csv(f'{current_dir}/{api}-results-{today}.csv')
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df.to_csv(csv_name)
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results = []
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@ -34,12 +34,9 @@ def main():
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csv_files.append(file_path)
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csv_files.sort()
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df1 = pd.read_csv(csv_files[0], index_col=0)
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df2 = pd.read_csv(csv_files[1], index_col=0)
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merged_df = pd.concat([df1, df2], ignore_index=True)
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merged_df = pd.concat([pd.read_csv(file, index_col=0) for file in csv_files], ignore_index=True)
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merged_df.reset_index(drop=True, inplace=True)
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merged_csv=csv_files[0].replace("_test1", "")
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merged_csv = csv_files[0].replace("_test1", "").replace("_test2", "")
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merged_df.to_csv(merged_csv)
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if __name__ == "__main__":
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