examples/model_selection/TRAILS-Database-Native-Model-Selection/internal/ml/model_selection/src/eva_engine/phase1/algo/singa_ms/cnn_ms/autograd/resnet_dist.py

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# to you under the Apache License, Version 2.0 (the
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#   http://www.apache.org/licenses/LICENSE-2.0
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# the code is modified from
# https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py

from singa import autograd
from singa import tensor
from singa import device
from singa import opt

import numpy as np
from tqdm import trange

if __name__ == "__main__":
    sgd = opt.SGD(lr=0.1, momentum=0.9, weight_decay=1e-5)
    sgd = opt.DistOpt(sgd)

    if (sgd.global_rank == 0):
        print("Start intialization...........", flush=True)

    dev = device.create_cuda_gpu_on(sgd.local_rank)

    from resnet import resnet50
    model = resnet50()

    niters = 100
    batch_size = 32
    IMG_SIZE = 224

    tx = tensor.Tensor((batch_size, 3, IMG_SIZE, IMG_SIZE), dev)
    ty = tensor.Tensor((batch_size,), dev, tensor.int32)
    autograd.training = True
    x = np.random.randn(batch_size, 3, IMG_SIZE, IMG_SIZE).astype(np.float32)
    y = np.random.randint(0, 1000, batch_size, dtype=np.int32)
    tx.copy_from_numpy(x)
    ty.copy_from_numpy(y)

    import time

    dev.Sync()
    start = time.time()
    fd = 0
    softmax = 0
    update = 0
    with trange(niters) as t:
        for _ in t:
            dev.Sync()
            tick = time.time()
            x = model(tx)
            dev.Sync()
            fd += time.time() - tick
            tick = time.time()
            loss = autograd.softmax_cross_entropy(x, ty)
            dev.Sync()
            softmax += time.time() - tick
            sgd.backward_and_update(loss)

    dev.Sync()
    end = time.time()
    throughput = float(sgd.world_size * niters * batch_size) / (end - start)
    titer = (end - start) / float(niters)
    tforward = float(fd) / float(niters)
    tsoftmax = float(softmax) / float(niters)
    tbackward = titer - tforward - tsoftmax

    if (sgd.global_rank == 0):
        print("\nThroughput = {} per second".format(throughput), flush=True)
        print("Total={}, forward={}, softmax={}, backward={}".format(
            titer, tforward, tsoftmax, tbackward),
              flush=True)