kernel_pm_acc.py

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# Copyright (c) 2021, The Regents of the University of California, Davis
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import argparse
from analysis.inference import infer

if __name__ == '__main__':
    parser = argparse.ArgumentParser('Get performance model error for ops.')
    parser.add_argument('--op-type', type=str, default='all')
    parser.add_argument('--backward', action='store_true', default=False)
    parser.add_argument('--emb-heuristic', action='store_true', default=False)
    parser.add_argument("--emb-data-path-suffix", type=str, default='fbgemm_dlrm_datasets_test')
    args = parser.parse_args()

    if args.op_type == 'all':
        op_list = ['embedding_lookup', 'fully_connected', \
            'conv2d', 'conv1d', 'concat', 'memcpy', \
            'transpose', 'bn', 'tril', \
            'all_to_all', 'all_reduce'
        ]
        pass_list = ['forward', 'backward']
    else:
        op_list = [args.op_type]
        pass_list = ['backward' if args.backward else 'forward']

    for op_type in op_list:
        for p in pass_list:
            if (op_type == 'fully_connected' or \
                    op_type == 'transpose' or \
                    op_type == 'concat' or \
                    op_type == 'memcpy' or \
                    op_type == 'all_to_all' or \
                    op_type == 'all_reduce') and \
                        p == 'backward': # No backward for these ops
                continue
            if op_type == 'embedding_lookup' and args.emb_heuristic:
                for big in [False, True]:
                    for hit_rate_estimation in [False, True]:
                        for fbgemm in [False, True]:
                            infer(op_type, p=='backward', big=big, hit_rate_estimation=hit_rate_estimation, fbgemm=fbgemm)
            else:
                infer(
                    op_type,
                    backward=(p=='backward'),
                    emb_use_mlp=True,
                    suffix=args.emb_data_path_suffix,
                )