LLM: add batch_size to the csv and html (#10080)
* LLM: add batch_size to the csv and html * small fix
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1 changed files with 4 additions and 3 deletions
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@ -96,6 +96,7 @@ def run_model(repo_id, test_api, in_out_pairs, local_model_hub=None, warm_up=1,
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round(np.mean(result[in_out_pair], axis=0)[1]*1000.0, 2),
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round(np.mean(result[in_out_pair], axis=0)[2]*1000.0, 2),
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in_out_pair,
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batch_size,
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f'{int(np.mean(result[in_out_pair], axis=0)[3])}' +
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f'-{int(np.mean(result[in_out_pair], axis=0)[4])}',
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num_beams,
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@ -445,8 +446,8 @@ def run_transformer_int4_gpu(repo_id,
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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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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)"])
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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])
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model.to('cpu')
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torch.xpu.synchronize()
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@ -960,7 +961,7 @@ 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', 'batch_size', 'actual input/output tokens', 'num_beams', 'low_bit', 'cpu_embedding',
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'peak mem (GB)'])
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df.to_csv(csv_name)
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results = []
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