/tests/compute/test_sparse.py

from dgl.ops import gspmm, gsddmm, edge_softmax, segment_reduce
from test_utils.graph_cases import get_cases
from test_utils import parametrize_idtype
import dgl
import random
import pytest, unittest
import networkx as nx
import backend as F
import numpy as np 
import torch

random.seed(42)
np.random.seed(42)

udf_msg = {
    'add': lambda edges: {'m': edges.src['x'] + edges.data['w']},
    'sub': lambda edges: {'m': edges.src['x'] - edges.data['w']},
    'mul': lambda edges: {'m': edges.src['x'] * edges.data['w']},
    'div': lambda edges: {'m': edges.src['x'] / edges.data['w']},
    'copy_lhs': lambda edges: {'m': edges.src['x']},
    'copy_rhs': lambda edges: {'m': edges.data['w']}
}

def select(target, src, edge, dst):
    if target == 'u':
        return src
    elif target == 'v':
        return dst
    elif target == 'e':
        return edge

def binary_op(msg, x, y):
    if msg == 'add':
        return x + y
    elif msg == 'sub':
        return x - y
    elif msg == 'mul':
        return x * y
    elif msg == 'div':
        return x / y
    elif msg == 'dot':
        return F.sum(x * y, -1, keepdims=True)
    elif msg == 'copy_lhs':
        return x
    elif msg == 'copy_rhs':
        return y

def edge_func(lhs_target, rhs_target, msg):
    def foo(edges):
        return {
            'm': binary_op(
                msg,
                select(lhs_target, edges.src, edges.data, edges.dst)['x'],
                select(rhs_target, edges.src, edges.data, edges.dst)['y']
            )
        }
    return foo

udf_apply_edges = {
    lhs_target + '_' + msg + '_' + rhs_target: edge_func(lhs_target, rhs_target, msg)
    for lhs_target in ['u', 'v', 'e']
    for rhs_target in ['u', 'v', 'e']
    for msg in ['add', 'sub', 'mul', 'div', 'dot', 'copy_lhs', 'copy_rhs']
}

udf_reduce = {
    'sum': lambda nodes: {'v': F.sum(nodes.mailbox['m'], 1)},
    'min': lambda nodes: {'v': F.min(nodes.mailbox['m'], 1)},
    'max': lambda nodes: {'v': F.max(nodes.mailbox['m'], 1)}
}

graphs = [
#    dgl.rand_graph(30, 0),
    dgl.rand_graph(30, 100),
    dgl.rand_bipartite('_U', '_E', '_V', 30, 40, 300)
]

spmm_shapes = [
    ((1, 2, 1, 3, 1), (4, 1, 3, 1, 1)),
    ((3, 3), (1, 3)),
    ((1,), (3,)),
    ((3,), (1,)),
    ((1,), (1,)),
    ((), ())
]

sddmm_shapes = [
    ((1, 2, 1, 3, 1), (4, 1, 3, 1, 1)),
    ((5, 3, 1, 7), (1, 3, 7, 7)),
    ((1, 3, 3), (4, 1, 3)),
    ((3,), (3,)),
    ((1,), (1,))
]

edge_softmax_shapes = [
    (1,), (1, 3), (3, 4, 5)
]

@pytest.mark.parametrize('g', graphs)
@pytest.mark.parametrize('shp', spmm_shapes)
@pytest.mark.parametrize('msg', ['add', 'sub', 'mul', 'div', 'copy_lhs', 'copy_rhs'])
@pytest.mark.parametrize('reducer', ['sum', 'min', 'max'])
@parametrize_idtype
def test_spmm(idtype, g, shp, msg, reducer):
    g = g.astype(idtype).to(F.ctx())
    print(g)
    print(g.idtype)

    hu = F.tensor(np.random.rand(*((g.number_of_src_nodes(),) + shp[0])) + 1)
    he = F.tensor(np.random.rand(*((g.number_of_edges(),) + shp[1])) + 1)
    print('u shape: {}, e shape: {}'.format(F.shape(hu), F.shape(he)))

    g.srcdata['x'] = F.attach_grad(F.clone(hu))
    g.edata['w'] = F.attach_grad(F.clone(he))
    print('SpMM(message func: {}, reduce func: {})'.format(msg, reducer))

    u = F.attach_grad(F.clone(hu))
    e = F.attach_grad(F.clone(he))
    with F.record_grad():
        v = gspmm(g, msg, reducer, u, e)
        if reducer in ['max', 'min']:
            v = F.replace_inf_with_zero(v)
        if g.number_of_edges() > 0:
            F.backward(F.reduce_sum(v))
            if msg != 'copy_rhs':
                grad_u = F.grad(u)
            if msg != 'copy_lhs':
                grad_e = F.grad(e)

    with F.record_grad():
        g.update_all(udf_msg[msg], udf_reduce[reducer])
        if g.number_of_edges() > 0:
            v1 = g.dstdata['v']
            assert F.allclose(v, v1)
            print('forward passed')

            F.backward(F.reduce_sum(v1))
            if msg != 'copy_rhs':
                if reducer in ['min', 'max']: # there might be some numerical errors
                    rate = F.reduce_sum(F.abs(F.grad(g.srcdata['x']) - grad_u)) /\
                           F.reduce_sum(F.abs(grad_u))
                    assert F.as_scalar(rate) < 1e-2, rate
                else:
                    assert F.allclose(F.grad(g.srcdata['x']), grad_u)
            if msg != 'copy_lhs':
                if reducer in ['min', 'max']:
                    rate = F.reduce_sum(F.abs(F.grad(g.edata['w']) - grad_e)) /\
                           F.reduce_sum(F.abs(grad_e))
                    assert F.as_scalar(rate) < 1e-2, rate
                else:
                    assert F.allclose(F.grad(g.edata['w']), grad_e)
            print('backward passed')

    g.srcdata.pop('x')
    g.edata.pop('w')
    if 'v' in g.dstdata: g.dstdata.pop('v')

@pytest.mark.parametrize('g', graphs)
@pytest.mark.parametrize('shp', sddmm_shapes)
@pytest.mark.parametrize('lhs_target', ['u', 'v', 'e'])
@pytest.mark.parametrize('rhs_target', ['u', 'v', 'e'])
@pytest.mark.parametrize('msg', ['add', 'sub', 'mul', 'div', 'dot', 'copy_lhs', 'copy_rhs'])
@parametrize_idtype
def test_sddmm(g, shp, lhs_target, rhs_target, msg, idtype):
    if lhs_target == rhs_target:
        return
    g = g.astype(idtype).to(F.ctx())
    if dgl.backend.backend_name == 'mxnet' and g.number_of_edges() == 0:
        pytest.skip()   # mxnet do not support zero shape tensor
    print(g)
    print(g.idtype)

    len_lhs = select(
        lhs_target,
        g.number_of_src_nodes(),
        g.number_of_edges(),
        g.number_of_dst_nodes())
    lhs_shp = (len_lhs,) + shp[0]
    len_rhs = select(
        rhs_target,
        g.number_of_src_nodes(),
        g.number_of_edges(),
        g.number_of_dst_nodes())
    rhs_shp = (len_rhs,) + shp[1]
    feat_lhs = F.tensor(np.random.rand(*lhs_shp) + 1)
    feat_rhs = F.tensor(np.random.rand(*rhs_shp) + 1)
    print('lhs shape: {}, rhs shape: {}'.format(F.shape(feat_lhs), F.shape(feat_rhs)))

    lhs_frame = select(
        lhs_target,
        g.srcdata,
        g.edata,
        g.dstdata)
    rhs_frame = select(
        rhs_target,
        g.srcdata,
        g.edata,
        g.dstdata)
    lhs_frame['x'] = F.attach_grad(F.clone(feat_lhs))
    rhs_frame['y'] = F.attach_grad(F.clone(feat_rhs))
    msg_func = lhs_target + '_' + msg + '_' + rhs_target
    print('SDDMM(message func: {})'.format(msg_func))

    lhs = F.attach_grad(F.clone(feat_lhs))
    rhs = F.attach_grad(F.clone(feat_rhs))
    with F.record_grad():
        e = gsddmm(g, msg, lhs, rhs, lhs_target=lhs_target, rhs_target=rhs_target)
        F.backward(F.reduce_sum(e))
        grad_lhs = F.grad(lhs)
        grad_rhs = F.grad(rhs)

    with F.record_grad():
        g.apply_edges(udf_apply_edges[msg_func])
        if g.number_of_edges() > 0:
            e1 = g.edata['m']
            assert F.allclose(e, e1)
            print('forward passed')

            F.backward(F.reduce_sum(e1))
            if msg != 'copy_rhs':
                assert F.allclose(F.grad(lhs_frame['x']), grad_lhs)
            if msg != 'copy_lhs':
                assert F.allclose(F.grad(rhs_frame['y']), grad_rhs)
            print('backward passed')

    lhs_frame.pop('x')
    rhs_frame.pop('y')
    if 'm' in g.edata: g.edata.pop('m')

@pytest.mark.parametrize('g', get_cases(['clique']))
@pytest.mark.parametrize('norm_by', ['src', 'dst'])
@pytest.mark.parametrize('shp', edge_softmax_shapes)
@parametrize_idtype
def test_edge_softmax(g, norm_by, shp, idtype):
    g = g.astype(idtype).to(F.ctx())
    edata = F.tensor(np.random.rand(g.number_of_edges(), *shp))
    e1 = F.attach_grad(F.clone(edata))

    with F.record_grad():
        score1 = edge_softmax(g, e1, norm_by=norm_by)
        F.backward(F.reduce_sum(score1))
        grad_edata = F.grad(e1)

    with F.record_grad():
        e2 = F.attach_grad(F.clone(edata))
        e2_2d = F.reshape(
            e2, (g.number_of_src_nodes(), g.number_of_dst_nodes(), *e2.shape[1:]))
        if norm_by == 'src':
            score2 = F.softmax(e2_2d, 1)
            score2 = F.reshape(score2, (-1, *e2.shape[1:]))
        if norm_by == 'dst':
            score2 = F.softmax(e2_2d, 0)
            score2 = F.reshape(score2, (-1, *e2.shape[1:]))
        assert F.allclose(score1, score2)
        print('forward passed')

        F.backward(F.reduce_sum(score2))
        assert F.allclose(F.grad(e2), grad_edata)
        print('backward passed')

@pytest.mark.parametrize('reducer', ['sum', 'max', 'min', 'mean'])
def test_segment_reduce(reducer):
    ctx = F.ctx()
    value = F.tensor(np.random.rand(10, 5))
    v1 = F.attach_grad(F.clone(value))
    v2 = F.attach_grad(F.clone(value))
    seglen = F.tensor([2, 3, 0, 4, 1, 0, 0])
    u = F.copy_to(F.arange(0, F.shape(value)[0], F.int32), ctx)
    v = F.repeat(F.copy_to(F.arange(0, len(seglen), F.int32), ctx),
                 seglen, dim=0)

    num_nodes = {'_U': len(u), '_V': len(seglen)}
    g = dgl.convert.heterograph({('_U', '_E', '_V'): (u, v)}, num_nodes_dict=num_nodes)
    with F.record_grad():
        rst1 = gspmm(g, 'copy_lhs', reducer, v1, None)
        if reducer in ['max', 'min']:
            rst1 = F.replace_inf_with_zero(rst1)
        F.backward(F.reduce_sum(rst1))
        grad1 = F.grad(v1)

    with F.record_grad():
        rst2 = segment_reduce(seglen, v2, reducer=reducer)
        F.backward(F.reduce_sum(rst2))
        assert F.allclose(rst1, rst2)
        print('forward passed')

        grad2 = F.grad(v2)
        assert F.allclose(grad1, grad2)
        print('backward passed')

@unittest.skipIf(dgl.backend.backend_name != 'pytorch', reason='Only support PyTorch for now')
@parametrize_idtype
@pytest.mark.parametrize('feat_size', [1, 8, 16, 64, 256])
@pytest.mark.parametrize('dtype,tol', [(torch.float16,1e-2),(torch.float32,3e-3),(torch.float64,1e-4)])
def test_segment_mm(idtype, feat_size, dtype, tol):
    if F._default_context_str == 'cpu' and dtype == torch.float16:
        pytest.skip("fp16 support for CPU linalg functions has been removed in PyTorch.")
    dev = F.ctx()
    # input
    a = torch.tensor(np.random.rand(100, feat_size)).to(dev).to(dtype)
    a.requires_grad_()
    b = torch.tensor(np.random.rand(10, feat_size, feat_size + 1)).to(dev).to(dtype)
    b.requires_grad_()
    seglen_a = torch.tensor([10, 15, 8, 0, 1, 9, 18, 24, 15, 0])
    dc = torch.tensor(np.random.rand(100, feat_size + 1)).to(dev).to(dtype)
    # compute
    c = dgl.ops.segment_mm(a, b, seglen_a)
    c.backward(dc)
    da = a.grad.clone()
    db = b.grad.clone()
    # ground truth
    c_t = []
    off = 0
    for i, l in enumerate(seglen_a):
        c_t.append(a[off:off+l] @ b[i])
        off += l
    c_t = torch.cat(c_t).to(dtype)
    a.grad.zero_()
    b.grad.zero_()
    c_t.backward(dc)
    da_t = a.grad
    db_t = b.grad

    assert torch.allclose(c, c_t, atol=tol, rtol=tol)
    assert torch.allclose(da, da_t, atol=tol, rtol=tol)
    assert torch.allclose(db, db_t, atol=tol, rtol=tol)

@unittest.skipIf(dgl.backend.backend_name != 'pytorch', reason='Only support PyTorch for now')
@parametrize_idtype
@pytest.mark.parametrize('feat_size', [1, 8, 16, 64, 256])
def test_gather_mm_idx_b(idtype, feat_size):
    import torch
    dev = F.ctx()
    # input
    a = torch.tensor(np.random.rand(100, feat_size)).to(dev)
    a.requires_grad_()
    b = torch.tensor(np.random.rand(10, feat_size, feat_size + 1)).to(dev)
    b.requires_grad_()
    idx = torch.tensor(np.random.randint(0, 10, 100)).to(dev).long()
    dc = torch.tensor(np.random.rand(100, feat_size + 1)).to(dev)
    # compute
    c = dgl.ops.gather_mm(a, b, idx_b=idx)
    c.backward(dc)
    da = a.grad.clone()
    db = b.grad.clone()
    # ground truth
    c_t = torch.bmm(a.unsqueeze(1), b[idx]).squeeze(1)
    a.grad.zero_()
    b.grad.zero_()
    c_t.backward(dc)
    da_t = a.grad
    db_t = b.grad

    assert torch.allclose(c, c_t, atol=1e-4, rtol=1e-4)
    assert torch.allclose(da, da_t, atol=1e-4, rtol=1e-4)
    assert torch.allclose(db, db_t, atol=1e-4, rtol=1e-4)

@unittest.skipIf(dgl.backend.backend_name != 'pytorch', reason='Only support PyTorch for now')
@parametrize_idtype
@pytest.mark.parametrize('feat_size', [1, 8, 16, 64, 256])
def _test_gather_mm_idx_a(idtype, feat_size):
    # TODO(minjie): currently disabled due to bugs in the CUDA kernel. Need to fix it later.
    import torch
    dev = F.ctx()
    # input
    a = torch.tensor(np.random.rand(10, feat_size)).to(dev)
    a.requires_grad_()
    b = torch.tensor(np.random.rand(100, feat_size, feat_size + 1)).to(dev)
    b.requires_grad_()
    idx = torch.tensor(np.random.randint(0, 10, 100)).to(dev)
    dc = torch.tensor(np.random.rand(100, feat_size + 1)).to(dev)
    # compute
    c = dgl.ops.gather_mm(a, b, idx_a=idx)
    c.backward(dc)
    da = a.grad.clone()
    db = b.grad.clone()
    # ground truth
    c_t = torch.bmm(a[idx].unsqueeze(1), b).squeeze(1)
    a.grad.zero_()
    b.grad.zero_()
    c_t.backward(dc)
    da_t = a.grad
    db_t = b.grad

    assert torch.allclose(c, c_t, atol=1e-4, rtol=1e-4)
    assert torch.allclose(da, da_t, atol=1e-4, rtol=1e-4)
    assert torch.allclose(db, db_t, atol=1e-4, rtol=1e-4)

@unittest.skipIf(dgl.backend.backend_name != 'pytorch', reason='Only support PyTorch for now')
@unittest.skipIf(F._default_context_str == 'gpu', reason="Libxsmm only fit in CPU.")
def test_use_libxsmm_switch():
    import torch
    g = dgl.graph(([0, 0, 0, 1, 1, 2], [0, 1, 2, 1, 2, 2]))
    x = torch.ones(3, 2, requires_grad=True)
    y = torch.arange(1, 13).float().view(6, 2).requires_grad_()

    assert dgl.is_libxsmm_enabled()
    dgl.ops.u_mul_e_sum(g, x, y)
    dgl.use_libxsmm(False)
    assert ~dgl.is_libxsmm_enabled()
    dgl.ops.u_mul_e_sum(g, x, y)
    dgl.use_libxsmm(True)
    assert dgl.is_libxsmm_enabled()
    dgl.ops.u_mul_e_sum(g, x, y)