Better GA initialization
This commit is contained in:
Philippe Tillet
@@ -32,12 +32,12 @@ TYPES = { 'vector-axpy': {'template':atd.VectorAxpyTemplate,
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'perf-measure': 'GFLOP/s'} }
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def do_tuning(args, devices):
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device = devices[args.device]
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def do_tuning(args):
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device = args['device']
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dname = misc_tools.sanitize_string(device.name)
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if args.update:
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json_out = json.load(open(args.update, 'r'))
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if os.path.isfile(args['json-file']):
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json_out = json.load(open(args['json-file'], 'r'))
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else:
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json_out = {}
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json_out["version"] = "1.0"
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@@ -45,16 +45,16 @@ def do_tuning(args, devices):
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def map_to_list(T, x):
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return list(map(T, x if isinstance(x, list) else [x]))
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if(args.method=='simple'):
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default_tuning_sizes = {'vector-axpy': [args.blas1_size], 'reduction': [args.blas1_size],
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'matrix-axpy' : args.blas2_size, 'row-wise-reduction' : args.blas2_size,
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'matrix-product': args.blas3_size}
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if(args['method']=='simple'):
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default_tuning_sizes = {'vector-axpy': args['blas1-sizes'], 'reduction': args['blas1-sizes'],
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'matrix-axpy' : args['blas2-sizes'], 'row-wise-reduction' : args['blas2-sizes'],
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'matrix-product': args['blas3-sizes']}
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for operation in ['vector-axpy', 'reduction', 'matrix-axpy', 'row-wise-reduction', 'matrix-product']:
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for datatype in [vcl.float32, vcl.float64]:
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if any(x in args.exclude_operations.split(',') for x in [operation, operation + '-' + datatype.__name__]):
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if any(x in args['exclude-operations'] for x in [operation, operation + '-' + datatype.__name__]):
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continue
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ctx = cl.Context([device])
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@@ -100,25 +100,25 @@ def do_tuning(args, devices):
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json_out[full_operation][datatype.__name__] = {}
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D = json_out[full_operation][datatype.__name__]
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if args.method == 'simple':
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if args['method'] == 'simple':
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print default_tuning_sizes[operation]
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profiles = [execution_handler(map(int,default_tuning_sizes[operation]))]
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else:
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def compute_perf(x, t):
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return TYPES[operation]['perf-index']([datatype().itemsize, x, t])
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profiles_generator = log_space_gen_product(a, b, args.sample_size, dimsample)
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profiles_generator = log_space_gen_product(a, b, args['sample-size'], dimsample)
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profiles = dataset.sample_profiles(execution_handler, profiles_generator)
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if args.build_model:
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if args['build-model']:
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dataset_generator = log_space_gen_product(a, b, 1000, dimsample)
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X, Y, profiles = dataset.sample_dataset(os.path.join(full_operation,datatype.__name__), profiles, execution_handler, dataset_generator)
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clf = train_model(X, Y, profiles, TYPES[operation]['perf-measure'])
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D['predictor'] = [{'children_left': e.tree_.children_left.tolist(),
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'children_right': e.tree_.children_right.tolist(),
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'threshold': e.tree_.threshold.astype('float32').tolist(),
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'feature': e.tree_.feature.astype('float32').tolist(),
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'value': e.tree_.value[:,:,0].astype('float32').tolist()} for e in clf.estimators_]
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if args.viennacl_src_path:
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misc_tools.update_viennacl_headers(args.viennacl_src_path,device,datatype,operation,additional_parameters,profiles[0])
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'threshold': e.tree_.threshold.astype('float64').tolist(),
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'feature': e.tree_.feature.astype('float64').tolist(),
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'value': e.tree_.value[:,:,0].astype('float64').tolist()} for e in clf.estimators_]
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if args['viennacl-src-path']:
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misc_tools.update_viennacl_headers(args['viennacl-src-path'],device,datatype,operation,additional_parameters,profiles[0])
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D['profiles'] = [map(int, x) for x in profiles]
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@@ -146,7 +146,7 @@ def do_tuning(args, devices):
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tune(execution_handler, 100, 4000, 2, ())
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#Row-wise reduction
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if operation=='row-wise-reduction':
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for A_trans in args.gemv_layouts.split(','):
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for A_trans in args['gemv-layouts']:
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def execution_handler(sizes, fname=os.devnull, parameters=None):
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A = vcl.Matrix(sizes if A_trans=='N' else sizes[::-1], context=ctx, dtype=datatype, layout=vcl.COL_MAJOR)
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x = vcl.Vector(sizes[1], context=ctx, dtype=datatype)
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@@ -156,21 +156,21 @@ def do_tuning(args, devices):
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tune(execution_handler, 100, 4000, 2, (A_trans,))
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#Matrix Product
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if operation=='matrix-product':
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for layout in args.gemm_layouts.split(','):
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for L in args['gemm-layouts']:
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A_trans = L[0]
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B_trans = L[1]
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def execution_handler(sizes, fname=os.devnull, parameters=None):
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A_trans = layout[0]
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B_trans = layout[1]
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A = vcl.Matrix((sizes[0], sizes[2]) if A_trans=='N' else (sizes[2],sizes[0]), context=ctx, dtype=datatype, layout=vcl.COL_MAJOR);
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B = vcl.Matrix((sizes[2], sizes[1]) if B_trans=='N' else (sizes[1],sizes[2]), context=ctx, dtype=datatype, layout=vcl.COL_MAJOR);
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A = vcl.Matrix((sizes[0], sizes[2]) if A_trans=='N' else (sizes[2],sizes[0]), context=ctx, dtype=datatype, layout=vcl.COL_MAJOR)
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B = vcl.Matrix((sizes[2], sizes[1]) if B_trans=='N' else (sizes[1],sizes[2]), context=ctx, dtype=datatype, layout=vcl.COL_MAJOR)
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LHS = A if A_trans=='N' else A.T
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RHS = B if B_trans=='N' else B.T
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alpha = vcl.HostScalar(1.0, context=ctx, dtype = datatype)
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beta = vcl.HostScalar(1.0, context=ctx, dtype = datatype)
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C = vcl.Matrix((sizes[0], sizes[1]), context=ctx, dtype = datatype, layout=vcl.COL_MAJOR)
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return execute(device, vcl.Assign(C,LHS*RHS*alpha + C*beta),(A_trans, B_trans), sizes, fname, parameters)
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tune(execution_handler, 100, 4000, 3,(layout[0], layout[1]))
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return execute(device, vcl.Assign(C,LHS*RHS*alpha + C*beta),(A_trans,B_trans), sizes, fname, parameters)
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tune(execution_handler, 100, 2000, 3,(A_trans,B_trans))
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json.dump(json_out, open(dname + '.json','w'))
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json.dump(json_out, open(args['json-file'],'w'))
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@@ -178,40 +178,37 @@ def do_tuning(args, devices):
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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subparsers = parser.add_subparsers(dest='action')
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print_devices_parser = subparsers.add_parser('list-devices', help='list the devices available')
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tune_parser = subparsers.add_parser('tune', help='tune using a specific configuration file')
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tune_parser.add_argument("--device", default=0, type=int)
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tune_parser.add_argument("--exclude-operations", default = '', type=str)
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tune_parser.add_argument("--gemm-layouts", default='NN,NT,TN,TT', type=str)
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tune_parser.add_argument("--gemv-layouts", default='N,T', type=str)
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tune_parser.add_argument("--update", default='', type=str)
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tune_parser.add_argument("--viennacl-src-path", default='', type=str)
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tune_subparsers = tune_parser.add_subparsers(dest='method')
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simple_parser = tune_subparsers.add_parser('simple', help = 'Tune each operation for unique sizes')
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simple_parser.add_argument("--blas1-size", default = 10e6, type=int)
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simple_parser.add_argument("--blas2-size", nargs=2, default=[2560,2560], type=int)
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simple_parser.add_argument("--blas3-size", nargs=3, default=[1536,1536,1536],type=int)
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full_parser = tune_subparsers.add_parser('full', help = 'Tune each operation for randomly chosen sizes')
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full_parser.add_argument("--build-model", default=False, type=bool)
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full_parser.add_argument("--sample-size", default=30, type=int)
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args = parser.parse_args()
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devices = [d for platform in cl.get_platforms() for d in platform.get_devices()]
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if(args.action=='list-devices'):
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print("----------------")
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print("Devices available:")
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print("----------------")
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for (i, d) in enumerate(devices):
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print 'Device', i, '|', cl.device_type.to_string(d.type), '|', d.name, 'on', d.platform.name
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print("----------------")
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else:
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print("------")
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print("Auto-tuning")
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print("------")
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do_tuning(args, devices)
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print("----------------")
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print("Devices available:")
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print("----------------")
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for (i, d) in enumerate(devices):
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print 'Device', i, '|', cl.device_type.to_string(d.type), '|', d.name, 'on', d.platform.name
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print("----------------")
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args = {}
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def add_input(help, default):
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return raw_input(help + "[" + default + "] : ") or default
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args['device'] = devices[int(add_input('Device to tune for','0'))]
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args['exclude-operations'] = add_input('Operations to exclude','vector-axpy,matrix-axpy,reduction,row-wise-reduction,matrix-product-float64').split(',')
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if not 'matrix-product' in args['exclude-operations']:
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args['gemm-layouts'] = add_input('GEMM Layouts', 'NN,NT,TN,TT').split(',')
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if not 'row-wise-reduction' in args['exclude-operations']:
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args['gemv-layouts'] = add_input('GEMV Layouts', 'N,T').split(',')
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args['json-file'] = add_input('JSON File', misc_tools.sanitize_string(args['device'].name) + '.json')
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args['method'] = add_input('Tuning type', 'simple')
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if args['method'] == 'simple':
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args['blas1-sizes'] = [int(float(add_input('BLAS1 size', '10e6')))]
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args['blas2-sizes'] = map(int, add_input('BLAS2 sizes (M,N)', '2560,2560').split(','))
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args['blas3-sizes'] = map(int, add_input('BLAS3 sizes (M,N,K)', '1024,1024,1024').split(','))
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args['build-model'] = True
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args['sample-size'] = 30
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args['viennacl-src-path'] = ''
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print("------")
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print("Auto-tuning")
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print("------")
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do_tuning(args)
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