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dmlc--dgl
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0.8.x
T
nv-dlasalle 1425150459 Fix test naming (#4023) 
Co-authored-by: Mufei Li <mufeili1996@gmail.com>
2022-05-23 17:21:06 +08:00

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8.9 KiB
Python
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import numpy as np
import scipy.sparse as ssp
import pytest
import dgl
from test_utils import parametrize_idtype
import backend as F
def _random_simple_graph(idtype, dtype, ctx, M, N, max_nnz, srctype, dsttype, etype):
src = np.random.randint(0, M, (max_nnz,))
dst = np.random.randint(0, N, (max_nnz,))
val = np.random.randn(max_nnz)
a = ssp.csr_matrix((val, (src, dst)), shape=(M, N))
a.sum_duplicates()
a = a.tocoo()
# shuffle edges
perm = np.random.permutation(a.nnz)
row = a.row[perm]
col = a.col[perm]
val = a.data[perm]
a = ssp.csr_matrix((val, (row, col)), shape=(M, N))
A = dgl.heterograph(
{(srctype, etype, dsttype): (
F.copy_to(F.tensor(row, dtype=idtype), ctx),
F.copy_to(F.tensor(col, dtype=idtype), ctx))},
num_nodes_dict={srctype: a.shape[0], dsttype: a.shape[1]})
A.edata['w'] = F.copy_to(F.tensor(val, dtype=dtype), ctx)
return a, A
@parametrize_idtype
@pytest.mark.parametrize('dtype', [F.float32, F.float64])
def test_csrmm(idtype, dtype):
a, A = _random_simple_graph(idtype, dtype, F.ctx(), 500, 600, 9000, 'A', 'B', 'AB')
b, B = _random_simple_graph(idtype, dtype, F.ctx(), 600, 700, 9000, 'B', 'C', 'BC')
C, C_weights = dgl.sparse._csrmm(A._graph, A.edata['w'], B._graph, B.edata['w'], 2)
C_adj = C.adjacency_matrix_scipy(0, False, 'csr')
C_adj.data = F.asnumpy(C_weights)
C_adj = F.tensor(C_adj.todense(), dtype=dtype)
c = F.tensor((a * b).todense(), dtype=dtype)
assert F.allclose(C_adj, c)
@parametrize_idtype
@pytest.mark.parametrize('dtype', [F.float32, F.float64])
@pytest.mark.parametrize('num_vtypes', [1, 2])
def test_csrmm_backward(idtype, dtype, num_vtypes):
a, A = _random_simple_graph(idtype, dtype, F.ctx(), 3, 4, 6, 'A', 'B', 'AB')
b, B = _random_simple_graph(idtype, dtype, F.ctx(), 4, 3, 6, 'B', 'A' if num_vtypes == 1 else 'C', 'BA')
A_row, A_col = A.edges(order='eid')
B_row, B_col = B.edges(order='eid')
A_row = F.asnumpy(A_row)
A_col = F.asnumpy(A_col)
B_row = F.asnumpy(B_row)
B_col = F.asnumpy(B_col)
a_dense = F.attach_grad(F.tensor(a.todense(), dtype=dtype))
b_dense = F.attach_grad(F.tensor(b.todense(), dtype=dtype))
A.edata['w'] = F.attach_grad(A.edata['w'])
B.edata['w'] = F.attach_grad(B.edata['w'])
with F.record_grad():
C = dgl.adj_product_graph(A, B, 'w')
assert len(C.ntypes) == num_vtypes
assert len(C.etypes) == 1
C_dense = np.zeros((3, 3))
C_row, C_col = C.edges(order='eid')
C_row = F.asnumpy(C_row)
C_col = F.asnumpy(C_col)
C_dense[C_row, C_col] = F.asnumpy(C.edata['w'])
c_dense = F.matmul(a_dense, b_dense)
assert np.allclose(C_dense, F.asnumpy(c_dense), rtol=1e-4, atol=1e-4)
F.backward(F.reduce_sum(C.edata['w']) + F.reduce_sum(c_dense))
a_dense_grad = F.asnumpy(F.grad(a_dense))[A_row, A_col]
b_dense_grad = F.asnumpy(F.grad(b_dense))[B_row, B_col]
A_spspmm_grad = F.asnumpy(F.grad(A.edata['w']))
B_spspmm_grad = F.asnumpy(F.grad(B.edata['w']))
assert np.allclose(a_dense_grad, A_spspmm_grad, rtol=1e-4, atol=1e-4)
assert np.allclose(b_dense_grad, B_spspmm_grad, rtol=1e-4, atol=1e-4)
@parametrize_idtype
@pytest.mark.parametrize('dtype', [F.float32, F.float64])
def test_csrsum(idtype, dtype):
a, A = _random_simple_graph(idtype, dtype, F.ctx(), 500, 600, 9000, 'A', 'B', 'AB')
b, B = _random_simple_graph(idtype, dtype, F.ctx(), 500, 600, 9000, 'A', 'B', 'AB')
C, C_weights = dgl.sparse._csrsum([A._graph, B._graph], [A.edata['w'], B.edata['w']])
C_adj = C.adjacency_matrix_scipy(0, False, 'csr')
C_adj.data = F.asnumpy(C_weights)
C_adj = F.tensor(C_adj.todense(), dtype=dtype)
c = F.tensor((a + b).todense(), dtype=dtype)
assert F.allclose(C_adj, c)
@parametrize_idtype
@pytest.mark.parametrize('dtype', [F.float32, F.float64])
@pytest.mark.parametrize('nelems', [1, 2])
def test_csrsum_backward(idtype, dtype, nelems):
a, A = _random_simple_graph(idtype, dtype, F.ctx(), 3, 4, 6, 'A', 'B', 'AB')
b, B = _random_simple_graph(idtype, dtype, F.ctx(), 3, 4, 6, 'A', 'B', 'AB')
A_row, A_col = A.edges(order='eid')
B_row, B_col = B.edges(order='eid')
A_row = F.asnumpy(A_row)
A_col = F.asnumpy(A_col)
B_row = F.asnumpy(B_row)
B_col = F.asnumpy(B_col)
a_dense = F.attach_grad(F.tensor(a.todense(), dtype=dtype))
b_dense = F.attach_grad(F.tensor(b.todense(), dtype=dtype))
A.edata['w'] = F.attach_grad(A.edata['w'])
B.edata['w'] = F.attach_grad(B.edata['w'])
with F.record_grad():
if nelems == 2:
# Test for two element case
C = dgl.adj_sum_graph([A, B], 'w')
assert C.canonical_etypes == A.canonical_etypes
C_dense = np.zeros((3, 4))
C_row, C_col = C.edges(order='eid')
C_row = F.asnumpy(C_row)
C_col = F.asnumpy(C_col)
C_dense[C_row, C_col] = F.asnumpy(C.edata['w'])
c_dense = a_dense + b_dense
assert np.allclose(C_dense, F.asnumpy(c_dense), rtol=1e-4, atol=1e-4)
F.backward(F.reduce_sum(C.edata['w']) + F.reduce_sum(c_dense))
a_dense_grad = F.asnumpy(F.grad(a_dense))[A_row, A_col]
b_dense_grad = F.asnumpy(F.grad(b_dense))[B_row, B_col]
A_spspmm_grad = F.asnumpy(F.grad(A.edata['w']))
B_spspmm_grad = F.asnumpy(F.grad(B.edata['w']))
assert np.allclose(a_dense_grad, A_spspmm_grad, rtol=1e-4, atol=1e-4)
assert np.allclose(b_dense_grad, B_spspmm_grad, rtol=1e-4, atol=1e-4)
elif nelems == 1:
# Test for single element case
C = dgl.adj_sum_graph([A], 'w')
assert C.canonical_etypes == A.canonical_etypes
C_dense = np.zeros((3, 4))
C_row, C_col = C.edges(order='eid')
C_row = F.asnumpy(C_row)
C_col = F.asnumpy(C_col)
C_dense[C_row, C_col] = F.asnumpy(C.edata['w'])
c_dense = a_dense
assert np.allclose(C_dense, F.asnumpy(c_dense), rtol=1e-4, atol=1e-4)
F.backward(F.reduce_sum(C.edata['w']) + F.reduce_sum(c_dense))
a_dense_grad = F.asnumpy(F.grad(a_dense))[A_row, A_col]
A_spspmm_grad = F.asnumpy(F.grad(A.edata['w']))
assert np.allclose(a_dense_grad, A_spspmm_grad, rtol=1e-4, atol=1e-4)
@parametrize_idtype
@pytest.mark.parametrize('dtype', [F.float32, F.float64])
@pytest.mark.parametrize('A_nnz', [9000, 0])
@pytest.mark.parametrize('B_nnz', [9000, 0])
def test_csrmask(idtype, dtype, A_nnz, B_nnz):
a, A = _random_simple_graph(idtype, dtype, F.ctx(), 500, 600, A_nnz, 'A', 'B', 'AB')
b, B = _random_simple_graph(idtype, dtype, F.ctx(), 500, 600, B_nnz, 'A', 'B', 'AB')
C = dgl.sparse._csrmask(A._graph, A.edata['w'], B._graph)
B_row, B_col = B.edges(order='eid')
B_row = F.asnumpy(B_row)
B_col = F.asnumpy(B_col)
c = F.tensor(a.todense()[B_row, B_col], dtype)
assert F.allclose(C, c)
@parametrize_idtype
@pytest.mark.parametrize('dtype', [F.float32, F.float64])
def test_csrmask_backward(idtype, dtype):
a, A = _random_simple_graph(idtype, dtype, F.ctx(), 3, 4, 6, 'A', 'B', 'AB')
b, B = _random_simple_graph(idtype, dtype, F.ctx(), 3, 4, 6, 'A', 'B', 'AB')
A_row, A_col = A.edges(order='eid')
B_row, B_col = B.edges(order='eid')
A_row = F.asnumpy(A_row)
A_col = F.asnumpy(A_col)
B_row = F.asnumpy(B_row)
B_col = F.asnumpy(B_col)
a_dense = F.attach_grad(F.tensor(a.todense(), dtype=dtype))
A.edata['w'] = F.attach_grad(A.edata['w'])
with F.record_grad():
# Test for two element case
C1 = F.csrmask(A._graph, A.edata['w'], B._graph)
if dgl.backend.backend_name == 'tensorflow':
import tensorflow as tf
C2 = tf.gather_nd(a_dense, tf.stack([B_row, B_col], 1))
else:
C2 = a_dense[B_row, B_col]
assert F.allclose(C1, C2, rtol=1e-4, atol=1e-4)
F.backward(F.reduce_sum(C1) + F.reduce_sum(C2))
a_dense_grad = F.asnumpy(F.grad(a_dense))[A_row, A_col]
A_spspmm_grad = F.asnumpy(F.grad(A.edata['w']))
assert np.allclose(a_dense_grad, A_spspmm_grad, rtol=1e-4, atol=1e-4)
if __name__ == '__main__':
test_csrmm(F.int32, F.float32)
test_csrmm(F.int64, F.float32)
test_csrsum(F.int32, F.float32)
test_csrsum(F.int64, F.float32)
test_csrmask(F.int32, F.float32, 9000, 9000)
test_csrmask(F.int64, F.float32, 9000, 0)
test_csrmask(F.int32, F.float32, 0, 9000)
test_csrmask(F.int64, F.float32, 0, 0)
test_csrmm_backward(F.int32, F.float32, 1)
test_csrmm_backward(F.int64, F.float32, 1)
test_csrmm_backward(F.int32, F.float32, 2)
test_csrmm_backward(F.int64, F.float32, 2)
test_csrsum_backward(F.int32, F.float32, 1)
test_csrsum_backward(F.int64, F.float32, 1)
test_csrsum_backward(F.int32, F.float32, 2)
test_csrsum_backward(F.int64, F.float32, 2)
test_csrmask_backward(F.int32, F.float32)
test_csrmask_backward(F.int64, F.float32)
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