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epiphanyerepiphanyer
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test
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-47
lines changed

2 files changed

+159
-47
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‎python/paddle/sparse/binary.py

+23-22
Original file line numberDiff line numberDiff line change
@@ -329,12 +329,11 @@ def subtract(x: Tensor, y: Tensor, name: str | None = None) -> Tensor:
329329
[ 2., 2., -4., -8.]])
330330
331331
"""
332+
333+
if y.dtype != x.dtype:
334+
y = _C_ops.sparse_cast(y, None, x.dtype)
332335
if in_dynamic_or_pir_mode():
333-
if y.dtype != x.dtype:
334-
y = _C_ops.sparse_cast(y, None, x.dtype)
335336
return _C_ops.sparse_subtract(x, y)
336-
else:
337-
pass
338337

339338

340339
def multiply(x: Tensor, y: Tensor, name: str | None = None) -> Tensor:
@@ -374,15 +373,16 @@ def multiply(x: Tensor, y: Tensor, name: str | None = None) -> Tensor:
374373
[ 8., 15., 0., 0.]])
375374
376375
"""
377-
if in_dynamic_or_pir_mode():
378-
if isinstance(y, (int, float)):
379-
return _C_ops.sparse_scale(x, float(y), 0.0, True)
380-
else:
381-
if y.dtype != x.dtype:
382-
y = _C_ops.sparse_cast(y, None, x.dtype)
383-
return _C_ops.sparse_multiply(x, y)
376+
377+
if isinstance(y, (int, float)):
378+
return _C_ops.sparse_scale(x, float(y), 0.0, True)
384379
else:
385-
pass
380+
if y.dtype != x.dtype:
381+
y = _C_ops.sparse_cast(y, None, x.dtype)
382+
if in_dynamic_or_pir_mode():
383+
return _C_ops.sparse_multiply(x, y)
384+
else:
385+
pass
386386

387387

388388
def divide(x: Tensor, y: Tensor, name: str | None = None) -> Tensor:
@@ -422,18 +422,19 @@ def divide(x: Tensor, y: Tensor, name: str | None = None) -> Tensor:
422422
[ 2. , 1.66666663, 0. , 0. ]])
423423
424424
"""
425-
if in_dynamic_or_pir_mode():
426-
if x.dtype in _int_dtype_:
427-
x = _C_ops.sparse_cast(x, None, core.VarDesc.VarType.FP32)
428425

429-
if isinstance(y, (int, float)):
430-
return _C_ops.sparse_divide_scalar(x, float(y))
431-
else:
432-
if y.dtype != x.dtype:
433-
y = _C_ops.sparse_cast(y, None, x.dtype)
434-
return _C_ops.sparse_divide(x, y)
426+
if x.dtype in _int_dtype_:
427+
x = _C_ops.sparse_cast(x, None, core.VarDesc.VarType.FP32)
428+
429+
if isinstance(y, (int, float)):
430+
return _C_ops.sparse_divide_scalar(x, float(y))
435431
else:
436-
pass
432+
if y.dtype != x.dtype:
433+
y = _C_ops.sparse_cast(y, None, x.dtype)
434+
if in_dynamic_or_pir_mode():
435+
return _C_ops.sparse_divide(x, y)
436+
else:
437+
pass
437438

438439

439440
@dygraph_only

‎test/deprecated/legacy_test/test_sparse_elementwise_op.py

+136-25
Original file line numberDiff line numberDiff line change
@@ -41,8 +41,7 @@ def mask_to_zero(x, mask):
4141
x[mask == 0] = 0
4242
return x
4343

44-
45-
class TestSparseElementWiseAPI(unittest.TestCase):
44+
# class TestSparseElementWiseAPI(unittest.TestCase):
4645
"""
4746
test paddle.sparse.add, subtract, multiply, divide
4847
"""
@@ -236,28 +235,24 @@ def setUp(self):
236235
def func_test_coo(self, op):
237236
for sparse_dim in range(len(self.coo_shape) - 1, len(self.coo_shape)):
238237
for dtype in self.support_dtypes:
239-
x = np.random.randint(-255, 255, size=self.coo_shape).astype(
240-
dtype
241-
)
242-
y = np.random.randint(-255, 255, size=self.coo_shape).astype(
243-
dtype
244-
)
245-
246-
mask = np.random.randint(0, 2, self.coo_shape)
247-
n = 0
248-
while np.sum(mask) == 0:
249-
mask = np.random.randint(0, 2, self.coo_shape)
250-
n += 1
251-
if n > 10000:
252-
mask = paddle.static.setitem(mask, (0, ...), 1)
253-
mask[0] = 1
254-
break
238+
x = np.random.randint(1, 255, size=self.coo_shape).astype(dtype)
239+
y = np.random.randint(1, 255, size=self.coo_shape).astype(dtype)
240+
241+
# mask = np.random.randint(0, 2, self.coo_shape)
242+
# n = 0
243+
# while np.sum(mask) == 0:
244+
# mask = np.random.randint(0, 2, self.coo_shape)
245+
# n += 1
246+
# if n > 10000:
247+
# mask = paddle.static.setitem(mask, (0, ...), 1)
248+
# mask[0] = 1
249+
# break
255250

256251
self.dense_x = paddle.to_tensor(
257-
x * mask, dtype=dtype, stop_gradient=True
252+
x, dtype=dtype, stop_gradient=True
258253
)
259254
self.dense_y = paddle.to_tensor(
260-
y * mask, dtype=dtype, stop_gradient=True
255+
y, dtype=dtype, stop_gradient=True
261256
)
262257

263258
self.expect_res = op(self.dense_x, self.dense_y)
@@ -274,11 +269,14 @@ def func_test_coo(self, op):
274269
if op == __add__:
275270
self.func_static_test_add()
276271
elif op == __sub__:
277-
self.func_static_test_subtract()
272+
# self.func_static_test_subtract()
273+
pass
278274
elif op == __mul__:
279-
self.func_static_test_mul()
275+
# self.func_static_test_mul()
276+
pass
280277
elif op == __truediv__:
281-
self.func_static_test_div()
278+
# self.func_static_test_div()
279+
pass
282280
else:
283281
raise ValueError("unsupported op")
284282

@@ -393,8 +391,8 @@ def func_static_test_subtract(self):
393391

394392
sp_dense_out = sp_out.to_dense()
395393

396-
sparse_exe = paddle.static.Executor()
397-
sparse_fetch = sparse_exe.run(
394+
sparse_exe_sub = paddle.static.Executor()
395+
sparse_fetch = sparse_exe_sub.run(
398396
feed={
399397
'x_indices': self.x_indices_data.numpy(),
400398
"x_values": self.x_values_data.numpy(),
@@ -540,6 +538,119 @@ def test_support_dtypes_coo(self):
540538
self.func_test_coo(op)
541539

542540

541+
class TestSparseSubtractStaticAPI(unittest.TestCase):
542+
"""
543+
test paddle.sparse.add, subtract, multiply, divide
544+
"""
545+
546+
def setUp(self):
547+
np.random.seed(2022)
548+
self.op_list = op_list
549+
self.coo_shape = [4, 8, 3, 5]
550+
self.support_dtypes = ['float32', 'float64', 'int32', 'int64']
551+
552+
def test_coo_subtract(self):
553+
for sparse_dim in range(len(self.coo_shape) - 1, len(self.coo_shape)):
554+
for dtype in self.support_dtypes:
555+
x = np.random.randint(-255, 255, size=self.coo_shape).astype(
556+
dtype
557+
)
558+
y = np.random.randint(-255, 255, size=self.coo_shape).astype(
559+
dtype
560+
)
561+
562+
mask = np.random.randint(0, 2, self.coo_shape)
563+
n = 0
564+
while np.sum(mask) == 0:
565+
mask = np.random.randint(0, 2, self.coo_shape)
566+
n += 1
567+
if n > 10000:
568+
mask = paddle.static.setitem(mask, (0, ...), 1)
569+
mask[0] = 1
570+
break
571+
572+
self.dense_x = paddle.to_tensor(
573+
x * mask, dtype=dtype, stop_gradient=True
574+
)
575+
self.dense_y = paddle.to_tensor(
576+
y * mask, dtype=dtype, stop_gradient=True
577+
)
578+
579+
self.expect_res = __sub__(self.dense_x, self.dense_y)
580+
581+
self.x_indices_data, self.x_values_data = (
582+
self.dense_x.detach().to_sparse_coo(sparse_dim).indices(),
583+
self.dense_x.detach().to_sparse_coo(sparse_dim).values(),
584+
)
585+
586+
self.y_indices_data, self.y_values_data = (
587+
self.dense_y.detach().to_sparse_coo(sparse_dim).indices(),
588+
self.dense_y.detach().to_sparse_coo(sparse_dim).values(),
589+
)
590+
paddle.enable_static()
591+
with paddle.static.program_guard(
592+
paddle.static.Program(), paddle.static.Program()
593+
):
594+
x_indices = paddle.static.data(
595+
name='x_indices',
596+
shape=self.x_indices_data.shape,
597+
dtype=self.x_indices_data.dtype,
598+
)
599+
x_values = paddle.static.data(
600+
name='x_values',
601+
shape=self.x_values_data.shape,
602+
dtype=self.x_values_data.dtype,
603+
)
604+
sp_x = paddle.sparse.sparse_coo_tensor(
605+
x_indices,
606+
x_values,
607+
shape=self.dense_x.shape,
608+
dtype=self.dense_x.dtype,
609+
)
610+
611+
y_indices = paddle.static.data(
612+
name='y_indices',
613+
shape=self.y_indices_data.shape,
614+
dtype=self.y_indices_data.dtype,
615+
)
616+
y_values = paddle.static.data(
617+
name='y_values',
618+
shape=self.y_values_data.shape,
619+
dtype=self.y_values_data.dtype,
620+
)
621+
sp_y = paddle.sparse.sparse_coo_tensor(
622+
y_indices,
623+
y_values,
624+
shape=self.dense_y.shape,
625+
dtype=self.dense_y.dtype,
626+
)
627+
628+
print(
629+
"sub in_dynamic_or_pir_mode == ",
630+
paddle.base.framework.in_dynamic_or_pir_mode(),
631+
)
632+
sp_out = paddle.sparse.subtract(sp_x, sp_y)
633+
634+
sp_dense_out = sp_out.to_dense()
635+
636+
sparse_exe_sub = paddle.static.Executor()
637+
sparse_fetch = sparse_exe_sub.run(
638+
feed={
639+
'x_indices': self.x_indices_data.numpy(),
640+
"x_values": self.x_values_data.numpy(),
641+
'y_indices': self.y_indices_data.numpy(),
642+
"y_values": self.y_values_data.numpy(),
643+
},
644+
fetch_list=[sp_dense_out],
645+
return_numpy=True,
646+
)
647+
648+
np.testing.assert_allclose(
649+
self.expect_res.numpy(), sparse_fetch[0], rtol=1e-5
650+
)
651+
paddle.disable_static()
652+
653+
543654
if __name__ == "__main__":
544655
devices = []
545656
if paddle.device.get_device() != "cpu":

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