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How to use run_benchmark method in lisa

Best Python code snippet using lisa_python

benchmark_supervised.py

Source:benchmark_supervised.py

2from benchmarks.utils.pyg_train_supervised import cross_validation_with_val_set3from benchmarks.models.supervised.gcn_models import GCN_2, PyG_GCN4from benchmarks.models.supervised.gin_models import GIN_2, PyG_GIN, GIN0_2, PyG_GIN05from benchmarks.models.supervised.sage_models import GraphSAGE_2, PyG_GraphSAGE6def run_benchmark(7    name,8    model_nn,9    num_layers,10    num_hidden_units,11    folds=10,12    batch_size=128,13    lr=0.01,14    weight_decay=0,15    lr_decay_factor=0.5,16    lr_decay_step_size=50,17    num_epochs=100,18    debug=False,19    **kwargs20):21    dataset = get_TU_dataset(name)22    model = model_nn(dataset, num_layers, num_hidden_units, **kwargs)23    return cross_validation_with_val_set(24        dataset,25        model,26        folds=folds,27        epochs=num_epochs,28        batch_size=batch_size,29        lr=lr,30        lr_decay_factor=lr_decay_factor,31        lr_decay_step_size=lr_decay_step_size,32        weight_decay=weight_decay,33        debug=debug,34    )35def run_pytorch_benchmarks_gcn(name, num_layers, num_hidden_units):36    run_benchmark(name, GCN_2, num_layers, num_hidden_units)37    run_benchmark(name, PyG_GCN, num_layers, num_hidden_units)38def run_pytorch_benchmarks_gin_0(name, num_layers, num_hidden_units):39    run_benchmark(name, GIN0_2, num_layers, num_hidden_units)40    run_benchmark(name, PyG_GIN0, num_layers, num_hidden_units)41def run_pytorch_benchmarks_gin(name, num_layers, num_hidden_units):42    run_benchmark(name, GIN_2, num_layers, num_hidden_units)43    run_benchmark(name, PyG_GIN, num_layers, num_hidden_units)44def run_pytorch_benchmarks_sage(name, num_layers, num_hidden_units):45    run_benchmark(name, GraphSAGE_2, num_layers, num_hidden_units)46    run_benchmark(name, PyG_GraphSAGE, num_layers, num_hidden_units)47if __name__ == "__main__":...

xxt.py

Source:xxt.py

...27    for i in nb.prange(n):28        xi = x[i]  # [m]29        out[i] = x @ xi30    return out31def run_benchmark(fn: tp.Callable, state):32    x = get_x(seed, n, m, dtype)33    # warmup34    fn(x)35    while state:36        fn(x)37@benchmark.register38def numpy_impl(state):39    def f(x):40        return x @ x.T41    run_benchmark(f, state)42@benchmark.register43def xxt_v0_impl(state):44    run_benchmark(xxt_v0, state)45@benchmark.register46def xxt_v1_impl(state):47    run_benchmark(xxt_v1, state)48@benchmark.register49def xxt_v2_impl(state):50    run_benchmark(xxt_v2, state)51if __name__ == "__main__":...

compare.py

Source:compare.py

1#!/usr/bin/env python2# coding: utf-83import timeit4def run_benchmark(M, N, K, n):5	count = 506	setup_string = 'import numpy as np; a = np.random.random_sample(({0}, {2})); b = np.random.random_sample(({2}, {1}))'.format(M, N, K)7	#print(setup_string)8	elapsed = timeit.timeit(9		setup=setup_string,10		stmt='np.dot(a, b)',11		number=count)12	average_millis = (elapsed / count) * 100013	# 1.11 ops/sec  Â±0.22%  n = 7 Âµ = 904ms14	# 235 ops/sec  Â±2.86%  n = 59 Âµ = 4ms15	print("ok {3} {0}x{2} . {2}x{1}".format(M, N, K, n))16	print("# {0:.2f} ops/sec n = {1} Âµ = {2:.2f}ms".format(count / elapsed, count, average_millis))17print("TAP version 13")18run_benchmark( 128,  128,  128, 1)19run_benchmark( 128,  128,  256, 2)20run_benchmark( 256,  256,  256, 3)21run_benchmark( 512,  512,  256, 4)22run_benchmark( 256,  256,  512, 5)23run_benchmark( 512,  512,  512, 6)24run_benchmark(1024, 1024,  512, 7)25run_benchmark(1024, 1024,  512, 8)26run_benchmark(1024, 1024, 1024, 9)27run_benchmark(2048, 2048, 2048, 10)28print("\n1..9")29print("# tests 9")30print("# pass  9")...

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