mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-07 12:21:27 +01:00
032d6b0643
Test Plan: revert-hammer
Differential Revision:
D28112689 (1416e57465)
Original commit changeset: f825cd4bce40
fbshipit-source-id: 421fc590797ac5fab6a55ac6f213361fbba7cd5b
281 lines
13 KiB
Python
281 lines
13 KiB
Python
import torch
|
|
import warnings
|
|
import unittest
|
|
import random
|
|
from torch.testing._internal.common_utils import \
|
|
(IS_MACOS, IS_WINDOWS, TestCase, run_tests, load_tests, coalescedonoff)
|
|
from torch.testing._internal.common_device_type import \
|
|
(instantiate_device_type_tests, dtypes, onlyCPU)
|
|
|
|
# load_tests from torch.testing._internal.common_utils is used to automatically filter tests for
|
|
# sharding on sandcastle. This line silences flake warnings
|
|
load_tests = load_tests
|
|
|
|
class TestSparseCSR(TestCase):
|
|
|
|
@onlyCPU
|
|
def test_csr_layout(self, _):
|
|
self.assertEqual(str(torch.sparse_csr), 'torch.sparse_csr')
|
|
self.assertEqual(type(torch.sparse_csr), torch.layout)
|
|
|
|
@onlyCPU
|
|
@dtypes(torch.double)
|
|
def test_sparse_csr_constructor_shape_inference(self, device, dtype):
|
|
crow_indices = [0, 2, 4]
|
|
col_indices = [0, 1, 0, 1]
|
|
values = [1, 2, 3, 4]
|
|
sparse = torch.sparse_csr_tensor(torch.tensor(crow_indices, dtype=torch.int64),
|
|
torch.tensor(col_indices, dtype=torch.int64),
|
|
torch.tensor(values), dtype=dtype, device=device)
|
|
self.assertEqual(torch.tensor(crow_indices, dtype=torch.int64), sparse.crow_indices())
|
|
self.assertEqual((len(crow_indices) - 1, max(col_indices) + 1), sparse.shape)
|
|
self.assertEqual(dtype, sparse.dtype)
|
|
self.assertEqual(torch.device(device), sparse.device)
|
|
|
|
@onlyCPU
|
|
@dtypes(*torch.testing.get_all_dtypes(include_bool=False, include_half=False,
|
|
include_bfloat16=False, include_complex=False))
|
|
def test_sparse_csr_constructor(self, device, dtype):
|
|
crow_indices = [0, 2, 4]
|
|
col_indices = [0, 1, 0, 1]
|
|
values = [1, 2, 3, 4]
|
|
for index_dtype in [torch.int32, torch.int64]:
|
|
sparse = torch.sparse_csr_tensor(torch.tensor(crow_indices, dtype=index_dtype),
|
|
torch.tensor(col_indices, dtype=index_dtype),
|
|
torch.tensor(values),
|
|
size=(2, 10),
|
|
dtype=dtype,
|
|
device=device)
|
|
self.assertEqual((2, 10), sparse.shape)
|
|
self.assertEqual(torch.tensor(crow_indices, dtype=index_dtype), sparse.crow_indices())
|
|
self.assertEqual(torch.tensor(col_indices, dtype=index_dtype), sparse.col_indices())
|
|
self.assertEqual(torch.tensor(values, dtype=dtype), sparse.values())
|
|
|
|
with self.assertRaises(RuntimeError):
|
|
torch.sparse_csr_tensor(crow_indices, torch.tensor(col_indices), values, size=(2, 10))
|
|
|
|
@onlyCPU
|
|
@dtypes(torch.double)
|
|
def test_factory_size_check(self, device, dtype):
|
|
crow_indices = [0, 2, 4]
|
|
col_indices = [0, 1, 0, 1]
|
|
values = [1, 2, 3, 4]
|
|
size = (2, 10)
|
|
torch.sparse_csr_tensor(torch.tensor(crow_indices), torch.tensor(col_indices), torch.tensor(values), size,
|
|
dtype=dtype, device=device)
|
|
|
|
with self.assertRaisesRegex(RuntimeError,
|
|
r"crow_indices\.numel\(\) must be size\(0\) \+ 1, but got: 3"):
|
|
torch.sparse_csr_tensor(torch.tensor(crow_indices), torch.tensor(col_indices), torch.tensor(values), (1, 1),
|
|
dtype=dtype, device=device)
|
|
|
|
with self.assertRaisesRegex(RuntimeError, "0th value of crow_indices must be 0"):
|
|
torch.sparse_csr_tensor(torch.tensor([-1, -1, -1]), torch.tensor(col_indices), torch.tensor(values), size,
|
|
dtype=dtype, device=device)
|
|
|
|
with self.assertRaisesRegex(RuntimeError, "last value of crow_indices should be less than length of col_indices."):
|
|
torch.sparse_csr_tensor(torch.tensor(crow_indices), torch.tensor([0, 0, 0]), torch.tensor(values), size,
|
|
dtype=dtype, device=device)
|
|
|
|
with self.assertRaisesRegex(RuntimeError,
|
|
r"col_indices and values must have equal sizes, " +
|
|
r"but got col_indices\.size\(0\): 4, values\.size\(0\): 5"):
|
|
torch.sparse_csr_tensor(torch.tensor(crow_indices), torch.tensor(col_indices), torch.tensor([0, 0, 0, 0, 0]),
|
|
size, dtype=dtype, device=device)
|
|
|
|
@onlyCPU
|
|
@unittest.skip("see: https://github.com/pytorch/pytorch/issues/58762")
|
|
def test_sparse_csr_print(self, device):
|
|
orig_maxDiff = self.maxDiff
|
|
self.maxDiff = None
|
|
shape_nnz = [
|
|
((10, 10), 10),
|
|
((100, 10), 10),
|
|
((1000, 10), 10)
|
|
]
|
|
printed = []
|
|
for shape, nnz in shape_nnz:
|
|
values_shape = torch.Size((nnz,))
|
|
col_indices_shape = torch.Size((nnz,))
|
|
crow_indices_shape = torch.Size((shape[0] + 1,))
|
|
printed.append("# shape: {}".format(torch.Size(shape)))
|
|
printed.append("# nnz: {}".format(nnz))
|
|
printed.append("# crow_indices shape: {}".format(crow_indices_shape))
|
|
printed.append("# col_indices shape: {}".format(col_indices_shape))
|
|
printed.append("# values_shape: {}".format(values_shape))
|
|
for index_dtype in [torch.int32, torch.int64]:
|
|
for dtype in torch.testing.floating_types():
|
|
printed.append("########## {}/{} ##########".format(dtype, index_dtype))
|
|
x = self.genSparseCSRTensor(shape, nnz, device=device, dtype=torch.float32, index_dtype=torch.int64)
|
|
printed.append("# sparse tensor")
|
|
printed.append(str(x))
|
|
printed.append("# _crow_indices")
|
|
printed.append(str(x.crow_indices()))
|
|
printed.append("# _col_indices")
|
|
printed.append(str(x.col_indices()))
|
|
printed.append("# _values")
|
|
printed.append(str(x.values()))
|
|
printed.append('')
|
|
printed.append('')
|
|
self.assertExpected('\n'.join(printed))
|
|
self.maxDiff = orig_maxDiff
|
|
|
|
@onlyCPU
|
|
def test_sparse_csr_from_dense(self, device):
|
|
dense = torch.tensor([[4, 5, 0], [0, 0, 0], [1, 0, 0]], device=device)
|
|
sparse = dense.to_sparse_csr()
|
|
self.assertEqual(torch.tensor([0, 2, 2, 3], dtype=torch.int64), sparse.crow_indices())
|
|
self.assertEqual(torch.tensor([0, 1, 0], dtype=torch.int64), sparse.col_indices())
|
|
self.assertEqual(torch.tensor([4, 5, 1]), sparse.values())
|
|
|
|
dense = torch.tensor([[0, 0, 0], [0, 0, 1], [1, 0, 0]], device=device)
|
|
sparse = dense.to_sparse_csr()
|
|
self.assertEqual(torch.tensor([0, 0, 1, 2], dtype=torch.int64), sparse.crow_indices())
|
|
self.assertEqual(torch.tensor([2, 0], dtype=torch.int64), sparse.col_indices())
|
|
self.assertEqual(torch.tensor([1, 1]), sparse.values())
|
|
|
|
dense = torch.tensor([[2, 2, 2], [2, 2, 2], [2, 2, 2]], device=device)
|
|
sparse = dense.to_sparse_csr()
|
|
self.assertEqual(torch.tensor([0, 3, 6, 9], dtype=torch.int64), sparse.crow_indices())
|
|
self.assertEqual(torch.tensor([0, 1, 2] * 3, dtype=torch.int64), sparse.col_indices())
|
|
self.assertEqual(torch.tensor([2] * 9), sparse.values())
|
|
|
|
@onlyCPU
|
|
@dtypes(torch.double)
|
|
def test_dense_convert(self, device, dtype):
|
|
size = (5, 5)
|
|
dense = torch.randn(size, dtype=dtype, device=device)
|
|
sparse = dense.to_sparse_csr()
|
|
self.assertEqual(sparse.to_dense(), dense)
|
|
|
|
size = (4, 6)
|
|
dense = torch.randn(size, dtype=dtype, device=device)
|
|
sparse = dense.to_sparse_csr()
|
|
self.assertEqual(sparse.to_dense(), dense)
|
|
|
|
crow_indices = torch.tensor([0, 3, 5])
|
|
col_indices = torch.tensor([0, 1, 2, 0, 1])
|
|
values = torch.tensor([1, 2, 1, 3, 4], dtype=dtype)
|
|
csr = torch.sparse_csr_tensor(crow_indices, col_indices,
|
|
values, dtype=dtype, device=device)
|
|
dense = torch.tensor([[1, 2, 1], [3, 4, 0]], dtype=dtype, device=device)
|
|
self.assertEqual(csr.to_dense(), dense)
|
|
|
|
@coalescedonoff
|
|
@onlyCPU
|
|
@dtypes(torch.double)
|
|
def test_coo_to_csr_convert(self, device, dtype, coalesced):
|
|
size = (5, 5)
|
|
sparse_dim = 2
|
|
nnz = 10
|
|
sparse_coo, _, _ = self.genSparseTensor(size, sparse_dim, nnz, coalesced, device, dtype)
|
|
sparse_csr = sparse_coo.to_sparse_csr()
|
|
|
|
self.assertTrue(sparse_csr.is_sparse_csr)
|
|
self.assertEqual(sparse_csr.to_dense(), sparse_coo.to_dense())
|
|
|
|
vec = torch.randn((5, 1), dtype=dtype, device=device)
|
|
coo_product = sparse_coo.matmul(vec)
|
|
csr_product = sparse_csr.matmul(vec)
|
|
|
|
self.assertEqual(coo_product, csr_product)
|
|
|
|
vec = torch.randn((100, 1), dtype=dtype, device=device)
|
|
index = torch.tensor([
|
|
[1, 0, 35, 14, 39, 6, 71, 66, 40, 27],
|
|
[92, 31, 62, 50, 22, 65, 89, 74, 56, 34],
|
|
], dtype=torch.int32)
|
|
values = torch.tensor([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], dtype=dtype, device=device)
|
|
coo = torch.sparse_coo_tensor(index, values, torch.Size([100, 100]), dtype=dtype, device=device)
|
|
csr = coo.to_sparse_csr()
|
|
|
|
self.assertEqual(coo.matmul(vec), csr.matmul(vec))
|
|
|
|
@onlyCPU
|
|
@unittest.skipIf(IS_MACOS or IS_WINDOWS, "MKL doesn't work on windows or mac")
|
|
@dtypes(torch.float, torch.double)
|
|
def test_mkl_matvec_warnings(self, device, dtype):
|
|
if torch.has_mkl:
|
|
for index_dtype in [torch.int32, torch.int64]:
|
|
sp = torch.sparse_csr_tensor(torch.tensor([0, 2, 4]),
|
|
torch.tensor([0, 1, 0, 1]),
|
|
torch.tensor([1, 2, 3, 4], dtype=dtype, device=device))
|
|
vec = torch.randn((2, 1), dtype=dtype, device=device)
|
|
with warnings.catch_warnings(record=True) as w:
|
|
sp.matmul(vec)
|
|
self.assertEqual(len(w), 2)
|
|
self.assertIn("Pytorch is compiled with MKL LP64 and will convert crow_indices to int32",
|
|
str(w[0].message))
|
|
self.assertIn("Pytorch is compiled with MKL LP64 and will convert col_indices to int32",
|
|
str(w[1].message))
|
|
|
|
@dtypes(torch.double)
|
|
def test_dense_convert_error(self, device, dtype):
|
|
size = (4, 2, 4)
|
|
dense = torch.randn(size, dtype=dtype, device=device)
|
|
|
|
with self.assertRaisesRegex(RuntimeError, "Only 2D"):
|
|
sparse = dense.to_sparse_csr()
|
|
|
|
@onlyCPU
|
|
@dtypes(torch.float, torch.double)
|
|
def test_csr_matvec(self, device, dtype):
|
|
side = 100
|
|
for index_dtype in [torch.int32, torch.int64]:
|
|
csr = self.genSparseCSRTensor((side, side), 1000, device=device, dtype=dtype, index_dtype=index_dtype)
|
|
vec = torch.randn(side, dtype=dtype, device=device)
|
|
|
|
res = csr.matmul(vec)
|
|
expected = csr.to_dense().matmul(vec)
|
|
|
|
self.assertEqual(res, expected)
|
|
|
|
bad_vec = torch.randn(side + 10, dtype=dtype, device=device)
|
|
with self.assertRaisesRegex(RuntimeError, "mv: expected"):
|
|
csr.matmul(bad_vec)
|
|
|
|
@onlyCPU
|
|
@dtypes(torch.double)
|
|
def test_mm(self, device, dtype):
|
|
def test_shape(di, dj, dk, nnz):
|
|
x = self.genSparseCSRTensor((di, dj), nnz, device=device, dtype=dtype, index_dtype=torch.int32)
|
|
t = torch.randn(di, dk, dtype=dtype, device=device)
|
|
y = torch.randn(dj, dk, dtype=dtype, device=device)
|
|
alpha = random.random()
|
|
beta = random.random()
|
|
|
|
# res = beta * t + alpha * (x @ y)
|
|
res = torch.addmm(t, x, y, beta=beta, alpha=alpha)
|
|
expected = torch.addmm(t, x.to_dense(), y, beta=beta, alpha=alpha)
|
|
self.assertEqual(res, expected)
|
|
|
|
res = torch.addmm(t, x, y)
|
|
expected = torch.addmm(t, x.to_dense(), y)
|
|
self.assertEqual(res, expected)
|
|
|
|
res = torch.mm(x, y)
|
|
expected = torch.mm(x.to_dense(), y)
|
|
self.assertEqual(res, expected)
|
|
|
|
for i in range(2, 5):
|
|
for j in range(2, 8):
|
|
for k in range(2, 8):
|
|
test_shape(i, j, k, i * j // 2)
|
|
|
|
@onlyCPU
|
|
@dtypes(*torch.testing.floating_types())
|
|
def test_coo_csr_conversion(self, device, dtype):
|
|
size = (5, 5)
|
|
dense = torch.randn(size, dtype=dtype, device=device)
|
|
coo_sparse = dense.to_sparse()
|
|
csr_sparse = coo_sparse.to_sparse_csr()
|
|
|
|
self.assertEqual(csr_sparse.to_dense(), dense)
|
|
|
|
|
|
# e.g., TestSparseCSRCPU and TestSparseCSRCUDA
|
|
instantiate_device_type_tests(TestSparseCSR, globals())
|
|
|
|
if __name__ == '__main__':
|
|
run_tests()
|