Performance tests.

4 лет назад · 13940671cc
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--- a/experiments/decagon_run_effcat/decagon_run_effcat.py
+++ b/experiments/decagon_run_effcat/decagon_run_effcat.py
@@ -0,0 +1,131 @@
 #!/usr/bin/env python3

 from icosagon.data import Data
 from icosagon.trainprep import TrainValTest, \
    prepare_training
 from icosagon.model import Model
 from icosagon.trainloop import TrainLoop
 import os
 import pandas as pd
 from bisect import bisect_left
 import torch
 import sys


 def index(a, x):
    i = bisect_left(a, x)
    if i != len(a) and a[i] == x:
        return i
    raise ValueError


 def load_data(dev):
    path = '/pstore/data/data_science/ref/decagon'
    df_combo = pd.read_csv(os.path.join(path, 'bio-decagon-combo.csv'))
    df_effcat = pd.read_csv(os.path.join(path, 'bio-decagon-effectcategories.csv'))
    df_mono = pd.read_csv(os.path.join(path, 'bio-decagon-mono.csv'))
    df_ppi = pd.read_csv(os.path.join(path, 'bio-decagon-ppi.csv'))
    df_tgtall = pd.read_csv(os.path.join(path, 'bio-decagon-targets-all.csv'))
    df_tgt = pd.read_csv(os.path.join(path, 'bio-decagon-targets.csv'))
    lst = [ 'df_combo', 'df_effcat', 'df_mono', 'df_ppi', 'df_tgtall', 'df_tgt' ]
    for nam in lst:
        print(f'len({nam}): {len(locals()[nam])}')
        print(f'{nam}.columns: {locals()[nam].columns}')

    genes = set()
    genes = genes.union(df_ppi['Gene 1']).union(df_ppi['Gene 2']) \
        .union(df_tgtall['Gene']).union(df_tgt['Gene'])
    genes = sorted(genes)
    print('len(genes):', len(genes))

    drugs = set()
    drugs = drugs.union(df_combo['STITCH 1']).union(df_combo['STITCH 2']) \
        .union(df_mono['STITCH']).union(df_tgtall['STITCH']).union(df_tgt['STITCH'])
    drugs = sorted(drugs)
    print('len(drugs):', len(drugs))

    data = Data()
    data.add_node_type('Gene', len(genes))
    data.add_node_type('Drug', len(drugs))

    print('Preparing PPI...')
    print('Indexing rows...')
    rows = [index(genes, g) for g in df_ppi['Gene 1']]
    print('Indexing cols...')
    cols = [index(genes, g) for g in df_ppi['Gene 2']]
    indices = list(zip(rows, cols))
    indices = torch.tensor(indices).transpose(0, 1)
    values = torch.ones(len(rows))
    print('indices.shape:', indices.shape, 'values.shape:', values.shape)
    adj_mat = torch.sparse_coo_tensor(indices, values, size=(len(genes),) * 2,
        device=dev)
    adj_mat = (adj_mat + adj_mat.transpose(0, 1)) / 2
    print('adj_mat created')
    fam = data.add_relation_family('PPI', 0, 0, True)
    rel = fam.add_relation_type('PPI', adj_mat)
    print('OK')

    print('Preparing Drug-Gene (Target) edges...')
    rows = [index(drugs, d) for d in df_tgtall['STITCH']]
    cols = [index(genes, g) for g in df_tgtall['Gene']]
    indices = list(zip(rows, cols))
    indices = torch.tensor(indices).transpose(0, 1)
    values = torch.ones(len(rows))
    adj_mat = torch.sparse_coo_tensor(indices, values, size=(len(drugs), len(genes)),
        device=dev)
    fam = data.add_relation_family('Drug-Gene (Target)', 1, 0, True)
    rel = fam.add_relation_type('Drug-Gene (Target)', adj_mat)
    print('OK')

    df_combo_effcat = df_combo.merge(df_effcat, left_on='Polypharmacy Side Effect', right_on='Side Effect')
    disease_classes = []

    print('Preparing Drug-Drug (Side Effect) edges...')
    fam = data.add_relation_family('Drug-Drug (Side Effect)', 1, 1, True)
    print('# of side effects:', len(df_combo), 'unique:', len(df_combo['Polypharmacy Side Effect'].unique()))
    for discls, df in df_combo_effcat.groupby('Disease Class'):
        disease_classes.append(discls)
        sys.stdout.write('.') # print(eff, '...')
        sys.stdout.flush()
        rows = [index(drugs, d) for d in df['STITCH 1']]
        cols = [index(drugs, d) for d in df['STITCH 2']]
        indices = list(zip(rows, cols))
        indices = torch.tensor(indices).transpose(0, 1)
        values = torch.ones(len(rows))
        adj_mat = torch.sparse_coo_tensor(indices, values, size=(len(drugs), len(drugs)),
            device=dev)
        adj_mat = (adj_mat + adj_mat.transpose(0, 1)) / 2
        rel = fam.add_relation_type(df['Polypharmacy Side Effect'], adj_mat)
    print()
    print('len(disease_classes):', len(disease_classes))
    print('OK')

    return data


 def _wrap(obj, method_name):
    orig_fn = getattr(obj, method_name)
    def fn(*args, **kwargs):
        print(f'{method_name}() :: ENTER')
        res = orig_fn(*args, **kwargs)
        print(f'{method_name}() :: EXIT')
        return res
    setattr(obj, method_name, fn)


 def main():
    dev = torch.device('cuda:0')
    data = load_data(dev)
    prep_d = prepare_training(data, TrainValTest(.8, .1, .1))
    _wrap(Model, 'build')
    model = Model(prep_d)
    model = model.to(dev)
    # model = torch.nn.DataParallel(model, ['cuda:0', 'cuda:1'])
    _wrap(TrainLoop, 'build')
    _wrap(TrainLoop, 'run_epoch')
    loop = TrainLoop(model, batch_size=512, shuffle=True)
    loop.run_epoch()


 if __name__ == '__main__':
    main()
--- a/src/icosagon/compile.py
+++ b/src/icosagon/compile.py
@@ -0,0 +1,28 @@
 #
 # The goal of this module is to make Icosagon more efficient.
 # It takes the nice Icosagon model architecture and tries to
 # formulate it in terms of batch matrix multiplications instead
 # of using Python for loops.
 #

 from .weights import init_glorot
 from .input
 import torch


 class EncodeLayer(object):
    def __init__(self, num_relation_types, input_dim, output_dim):
        weights = [ init_glorot(input_dim, output_dim) \
            for _ in range(num_relation_types) ]
        weights = torch.cat(weights)


 class Compiler(object):
    def __init__(self, data: Data, layer_dimensions: List[int] = [32, 64]) -> None:
        self.data = data
        self.layer_dimensions = layer_dimensions
        self.build()

    def build(self) -> None:
        for fam in data.relation_families:
            init_glorot(in_channels, out_channels)
--- a/tests/icosagon/test_trainloop.py
+++ b/tests/icosagon/test_trainloop.py
@@ -79,3 +79,36 @@ def test_timing_01():
    for _ in range(1300):
        _ = torch.sparse.mm(adj_mat, rep)
    print('Elapsed:', time.time() - t)


 def test_timing_02():
    adj_mat = (torch.rand(2000, 2000) < .001).to(torch.float32)
    adj_mat_batch = [adj_mat.view(1, 2000, 2000)] * 1300
    adj_mat_batch = torch.cat(adj_mat_batch)
    rep = torch.eye(2000).requires_grad_(True)
    t = time.time()
    res = torch.matmul(adj_mat_batch, rep)
    print('Elapsed:', time.time() - t)
    print('res.shape:', res.shape)


 def test_timing_03():
    adj_mat = (torch.rand(2000, 2000) < .001).to(torch.float32)
    adj_mat_batch = [adj_mat.view(1, 2000, 2000).to_sparse()] * 1300
    adj_mat_batch = torch.cat(adj_mat_batch)
    rep = torch.eye(2000).requires_grad_(True)
    rep_batch = [rep.view(1, 2000, 2000)] * 1300
    rep_batch = torch.cat(rep_batch)
    t = time.time()
    with pytest.raises(RuntimeError):
        _ = torch.bmm(adj_mat_batch, rep)
    print('Elapsed:', time.time() - t)


 def test_timing_04():
    adj_mat = (torch.rand(2000, 2000) < .0001).to(torch.float32).to_sparse()
    rep = torch.eye(2000).requires_grad_(True)
    t = time.time()
    for _ in range(1300):
        _ = torch.sparse.mm(adj_mat, rep)
    print('Elapsed:', time.time() - t)