benchmarks/fastrnns/runner.py

from collections import namedtuple
from functools import partial
import torch
import torchvision.models as cnn

from .factory import (dropoutlstm_creator, imagenet_cnn_creator,
                      layernorm_pytorch_lstm_creator, lnlstm_creator,
                      lstm_creator, lstm_multilayer_creator,
                      lstm_premul_bias_creator, lstm_premul_creator,
                      lstm_simple_creator, pytorch_lstm_creator,
                      varlen_lstm_creator, varlen_pytorch_lstm_creator)


class DisableCuDNN():
    def __enter__(self):
        self.saved = torch.backends.cudnn.enabled
        torch.backends.cudnn.enabled = False

    def __exit__(self, *args, **kwargs):
        torch.backends.cudnn.enabled = self.saved


class DummyContext():
    def __enter__(self):
        pass

    def __exit__(self, *args, **kwargs):
        pass


class AssertNoJIT():
    def __enter__(self):
        import os
        enabled = os.environ.get('PYTORCH_JIT', 1)
        assert not enabled

    def __exit__(self, *args, **kwargs):
        pass


RNNRunner = namedtuple('RNNRunner', [
    'name', 'creator', 'context',
])


def get_nn_runners(*names):
    return [nn_runners[name] for name in names]


nn_runners = {
    'cudnn': RNNRunner('cudnn', pytorch_lstm_creator, DummyContext),
    'cudnn_dropout': RNNRunner('cudnn_dropout', partial(pytorch_lstm_creator, dropout=0.4), DummyContext),
    'cudnn_layernorm': RNNRunner('cudnn_layernorm', layernorm_pytorch_lstm_creator, DummyContext),
    'vl_cudnn': RNNRunner('vl_cudnn', varlen_pytorch_lstm_creator, DummyContext),
    'vl_jit': RNNRunner('vl_jit', partial(varlen_lstm_creator, script=True), DummyContext),
    'vl_py': RNNRunner('vl_py', varlen_lstm_creator, DummyContext),
    'aten': RNNRunner('aten', pytorch_lstm_creator, DisableCuDNN),
    'jit': RNNRunner('jit', lstm_creator, DummyContext),
    'jit_premul': RNNRunner('jit_premul', lstm_premul_creator, DummyContext),
    'jit_premul_bias': RNNRunner('jit_premul_bias', lstm_premul_bias_creator, DummyContext),
    'jit_simple': RNNRunner('jit_simple', lstm_simple_creator, DummyContext),
    'jit_multilayer': RNNRunner('jit_multilayer', lstm_multilayer_creator, DummyContext),
    'jit_layernorm': RNNRunner('jit_layernorm', lnlstm_creator, DummyContext),
    'jit_layernorm_decom': RNNRunner('jit_layernorm_decom',
                                     partial(lnlstm_creator, decompose_layernorm=True),
                                     DummyContext),
    'jit_dropout': RNNRunner('jit_dropout', dropoutlstm_creator, DummyContext),
    'py': RNNRunner('py', partial(lstm_creator, script=False), DummyContext),
    'resnet18': RNNRunner('resnet18', imagenet_cnn_creator(cnn.resnet18, jit=False), DummyContext),
    'resnet18_jit': RNNRunner('resnet18_jit', imagenet_cnn_creator(cnn.resnet18), DummyContext),
    'resnet50': RNNRunner('resnet50', imagenet_cnn_creator(cnn.resnet50, jit=False), DummyContext),
    'resnet50_jit': RNNRunner('resnet50_jit', imagenet_cnn_creator(cnn.resnet50), DummyContext),
}