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zhangkaihuo
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[cherry-pick]Fix english documents of sparse api (#47496)
Fix english documents of sparse api
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‎python/paddle/sparse/binary.py

Lines changed: 57 additions & 66 deletions
Original file line numberDiff line numberDiff line change
@@ -33,13 +33,13 @@
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@dygraph_only
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def matmul(x, y, name=None):
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"""
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Note:
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Note:
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This API is only supported from ``CUDA 11.0`` .
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Applies matrix multiplication of two Tensors.
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Applies matrix multiplication of two Tensors.
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The supported input/output Tensor layout are as follows:
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Note:
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x[SparseCsrTensor] @ y[SparseCsrTensor] -> out[SparseCsrTensor]
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x[SparseCsrTensor] @ y[DenseTensor] -> out[DenseTensor]
@@ -49,14 +49,14 @@ def matmul(x, y, name=None):
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It supports backward propagation.
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Dimensions `x` and `y` must be >= 2D. Automatic broadcasting of Tensor is not supported.
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the shape of `x` should be `[*, M, K]` , and the shape of `y` should be `[*, K, N]` , where `*`
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the shape of `x` should be `[*, M, K]` , and the shape of `y` should be `[*, K, N]` , where `*`
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is zero or more batch dimensions.
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Args:
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x (Tensor): The input tensor. It can be SparseCooTensor/SparseCsrTensor. The data type can be float32 or float64.
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y (Tensor): The input tensor. It can be SparseCooTensor/SparseCsrTensor/DenseTensor. The data type can be float32 or float64.
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name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
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Returns:
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Tensor: Its layout is determined by that of `x` and `y` .
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@@ -72,9 +72,9 @@ def matmul(x, y, name=None):
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cols = [1, 2, 0]
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values = [1., 2., 3.]
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csr = paddle.sparse.sparse_csr_tensor(crows, cols, values, [3, 3])
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# Tensor(shape=[3, 3], dtype=paddle.float32, place=Place(gpu:0), stop_gradient=True,
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# crows=[0, 1, 2, 3],
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# cols=[1, 2, 0],
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# Tensor(shape=[3, 3], dtype=paddle.float32, place=Place(gpu:0), stop_gradient=True,
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# crows=[0, 1, 2, 3],
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# cols=[1, 2, 0],
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# values=[1., 2., 3.])
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dense = paddle.ones([3, 2])
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out = paddle.sparse.matmul(csr, dense)
@@ -87,9 +87,9 @@ def matmul(x, y, name=None):
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indices = [[0, 1, 2], [1, 2, 0]]
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values = [1., 2., 3.]
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coo = paddle.sparse.sparse_coo_tensor(indices, values, [3, 3])
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# Tensor(shape=[3, 3], dtype=paddle.float32, place=Place(gpu:0), stop_gradient=True,
90+
# Tensor(shape=[3, 3], dtype=paddle.float32, place=Place(gpu:0), stop_gradient=True,
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# indices=[[0, 1, 2],
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# [1, 2, 0]],
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# [1, 2, 0]],
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# values=[1., 2., 3.])
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dense = paddle.ones([3, 2])
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out = paddle.sparse.matmul(coo, dense)
@@ -104,13 +104,13 @@ def matmul(x, y, name=None):
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@dygraph_only
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def masked_matmul(x, y, mask, name=None):
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"""
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Note:
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Note:
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This API is only supported from ``CUDA 11.3`` .
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Applies matrix multiplication of two Dense Tensors.
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Applies matrix multiplication of two Dense Tensors.
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The supported input/output Tensor layout are as follows:
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Note:
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x[DenseTensor] @ y[DenseTensor] * mask[SparseCooTensor] -> out[SparseCooTensor]
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x[DenseTensor] @ y[DenseTensor] * mask[SparseCsrTensor] -> out[SparseCsrTensor]
@@ -153,9 +153,9 @@ def masked_matmul(x, y, mask, name=None):
153153
y = paddle.rand([5, 4])
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out = paddle.sparse.masked_matmul(x, y, mask)
156-
# Tensor(shape=[3, 4], dtype=paddle.float32, place=Place(gpu:0), stop_gradient=True,
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# crows=[0, 2, 3, 5],
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# cols=[1, 3, 2, 0, 1],
156+
# Tensor(shape=[3, 4], dtype=paddle.float32, place=Place(gpu:0), stop_gradient=True,
157+
# crows=[0, 2, 3, 5],
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# cols=[1, 3, 2, 0, 1],
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# values=[0.98986477, 0.97800624, 1.14591956, 0.68561077, 0.94714981])
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"""
@@ -165,11 +165,11 @@ def masked_matmul(x, y, mask, name=None):
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@dygraph_only
166166
def mv(x, vec, name=None):
167167
"""
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Note:
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Note:
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This API is only supported from ``CUDA 11.0`` .
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Applies matrix-vector product of Sparse Matrix 'x' and Dense vector 'vec' .
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Applies matrix-vector product of Sparse Matrix 'x' and Dense vector 'vec' .
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The supported input/output Tensor layout are as follows:
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Note:
@@ -178,39 +178,39 @@ def mv(x, vec, name=None):
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It supports backward propagation.
180180
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The shape of `x` should be `[M, N]` , and the shape of `y` should be `[N]` ,
181+
The shape of `x` should be `[M, N]` , and the shape of `y` should be `[N]` ,
182182
and the shape of `out` will be `[M]` .
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Args:
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x (Tensor): The input 2D tensor. It must be SparseCooTensor/SparseCsrTensor. The data type can be float32 or float64.
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y (Tensor): The input 1D tensor. It must be DenseTensor vector. The data type can be float32 or float64.
187187
name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
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Returns:
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Tensor: 1D Tensor.
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Examples:
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.. code-block:: python
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# required: gpu
197197
import paddle
198-
from paddle.fluid.framework import _test_eager_guard
198+
from paddle.fluid.framework import _test_eager_guard
199199
paddle.seed(100)
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# csr @ dense -> dense
202-
with _test_eager_guard():
202+
with _test_eager_guard():
203203
crows = [0, 2, 3, 5]
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cols = [1, 3, 2, 0, 1]
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values = [1., 2., 3., 4., 5.]
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dense_shape = [3, 4]
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csr = paddle.sparse.sparse_csr_tensor(crows, cols, values, dense_shape)
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# Tensor(shape=[3, 4], dtype=paddle.float32, place=Place(gpu:0), stop_gradient=True,
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# crows=[0, 2, 3, 5],
210-
# cols=[1, 3, 2, 0, 1],
208+
# Tensor(shape=[3, 4], dtype=paddle.float32, place=Place(gpu:0), stop_gradient=True,
209+
# crows=[0, 2, 3, 5],
210+
# cols=[1, 3, 2, 0, 1],
211211
# values=[1., 2., 3., 4., 5.])
212212
vec = paddle.randn([4])
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out = paddle.sparse.mv(csr, vec)
215215
# Tensor(shape=[3], dtype=float32, place=Place(gpu:0), stop_gradient=True,
216216
# [-3.85499096, -2.42975140, -1.75087738])
@@ -241,17 +241,15 @@ def add(x, y, name=None):
241241
.. code-block:: python
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import paddle
244-
from paddle.fluid.framework import _test_eager_guard
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paddle.device.set_device("cpu")
247246
248-
with _test_eager_guard():
249-
x = paddle.to_tensor([[0, -1, 0, 2], [0, 0, -3, 0], [4, 5, 0, 0]], 'float32')
250-
y = paddle.to_tensor([[0, 0, 0, -2], [0, 2, -3, 0], [2, 3, 4, 8]], 'float32')
251-
sparse_x = x.to_sparse_csr()
252-
sparse_y = y.to_sparse_csr()
253-
sparse_z = paddle.sparse.add(sparse_x, sparse_y)
254-
print(sparse_z.to_dense())
247+
x = paddle.to_tensor([[0, -1, 0, 2], [0, 0, -3, 0], [4, 5, 0, 0]], 'float32')
248+
y = paddle.to_tensor([[0, 0, 0, -2], [0, 2, -3, 0], [2, 3, 4, 8]], 'float32')
249+
sparse_x = x.to_sparse_csr()
250+
sparse_y = y.to_sparse_csr()
251+
sparse_z = paddle.sparse.add(sparse_x, sparse_y)
252+
print(sparse_z.to_dense())
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# [[ 0., -1., 0., 0.],
257255
# [ 0., 2., -6., 0.],
@@ -268,10 +266,9 @@ def add(x, y, name=None):
268266
inputs = {'x': x, 'y': y}
269267
helper = LayerHelper(op_type)
270268
out = helper.create_sparse_variable_for_type_inference(x.dtype)
271-
helper.append_op(type=op_type,
272-
inputs=inputs,
273-
outputs={'out': out},
274-
attrs={})
269+
helper.append_op(
270+
type=op_type, inputs=inputs, outputs={'out': out}, attrs={}
271+
)
275272
return out
276273

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@@ -298,17 +295,15 @@ def subtract(x, y, name=None):
298295
.. code-block:: python
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import paddle
301-
from paddle.fluid.framework import _test_eager_guard
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paddle.device.set_device("cpu")
304300
305-
with _test_eager_guard():
306-
x = paddle.to_tensor([[0, -1, 0, 2], [0, 0, -3, 0], [4, 5, 0, 0]], 'float32')
307-
y = paddle.to_tensor([[0, 0, 0, -2], [0, 2, -3, 0], [2, 3, 4, 8]], 'float32')
308-
sparse_x = x.to_sparse_csr()
309-
sparse_y = y.to_sparse_csr()
310-
sparse_z = paddle.sparse.subtract(sparse_x, sparse_y)
311-
print(sparse_z.to_dense())
301+
x = paddle.to_tensor([[0, -1, 0, 2], [0, 0, -3, 0], [4, 5, 0, 0]], 'float32')
302+
y = paddle.to_tensor([[0, 0, 0, -2], [0, 2, -3, 0], [2, 3, 4, 8]], 'float32')
303+
sparse_x = x.to_sparse_csr()
304+
sparse_y = y.to_sparse_csr()
305+
sparse_z = paddle.sparse.subtract(sparse_x, sparse_y)
306+
print(sparse_z.to_dense())
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# [[ 0., -1., 0., 4.],
314309
# [ 0., -2., 0., 0.],
@@ -343,17 +338,15 @@ def multiply(x, y, name=None):
343338
.. code-block:: python
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import paddle
346-
from paddle.fluid.framework import _test_eager_guard
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paddle.device.set_device("cpu")
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350-
with _test_eager_guard():
351-
x = paddle.to_tensor([[0, -1, 0, 2], [0, 0, -3, 0], [4, 5, 0, 0]], 'float32')
352-
y = paddle.to_tensor([[0, 0, 0, -2], [0, 2, -3, 0], [2, 3, 4, 8]], 'float32')
353-
sparse_x = x.to_sparse_csr()
354-
sparse_y = y.to_sparse_csr()
355-
sparse_z = paddle.sparse.multiply(sparse_x, sparse_y)
356-
print(sparse_z.to_dense())
344+
x = paddle.to_tensor([[0, -1, 0, 2], [0, 0, -3, 0], [4, 5, 0, 0]], 'float32')
345+
y = paddle.to_tensor([[0, 0, 0, -2], [0, 2, -3, 0], [2, 3, 4, 8]], 'float32')
346+
sparse_x = x.to_sparse_csr()
347+
sparse_y = y.to_sparse_csr()
348+
sparse_z = paddle.sparse.multiply(sparse_x, sparse_y)
349+
print(sparse_z.to_dense())
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# [[ 0., 0., 0., -4.],
359352
# [ 0., 0., 9., 0.],
@@ -391,17 +384,15 @@ def divide(x, y, name=None):
391384
.. code-block:: python
392385
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import paddle
394-
from paddle.fluid.framework import _test_eager_guard
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paddle.device.set_device("cpu")
397389
398-
with _test_eager_guard():
399-
x = paddle.to_tensor([[0, -1, 0, 2], [0, 0, -3, 0], [4, 5, 0, 0]], 'float32')
400-
y = paddle.to_tensor([[0, 0, 0, -2], [0, 2, -3, 0], [2, 3, 4, 8]], 'float32')
401-
sparse_x = x.to_sparse_csr()
402-
sparse_y = y.to_sparse_csr()
403-
sparse_z = paddle.sparse.divide(sparse_x, sparse_y)
404-
print(sparse_z.to_dense())
390+
x = paddle.to_tensor([[0, -1, 0, 2], [0, 0, -3, 0], [4, 5, 0, 0]], 'float32')
391+
y = paddle.to_tensor([[0, 0, 0, -2], [0, 2, -3, 0], [2, 3, 4, 8]], 'float32')
392+
sparse_x = x.to_sparse_csr()
393+
sparse_y = y.to_sparse_csr()
394+
sparse_z = paddle.sparse.divide(sparse_x, sparse_y)
395+
print(sparse_z.to_dense())
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# [[ nan , -inf. , nan , -1. ],
407398
# [ nan , 0. , 1. , nan ],

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