Skip to content

Fix test and doc #44735

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 2 commits into from
Aug 1, 2022
Merged

Fix test and doc #44735

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
dbe5edb
fix test and doc
Jul 29, 2022
a255521
fix test
Aug 1, 2022
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
131 changes: 67 additions & 64 deletions python/paddle/fluid/tests/unittests/test_sparse_norm_op.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -18,87 +18,90 @@
import paddle
from paddle.incubate.sparse import nn
import paddle.fluid as fluid
from paddle.fluid.framework import _test_eager_guard
import copy


class TestSparseBatchNorm(unittest.TestCase):

def test(self):
fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": True})
with _test_eager_guard():
paddle.seed(0)
channels = 4
shape = [2, 3, 6, 6, channels]
#there is no zero in dense_x
dense_x = paddle.randn(shape)
dense_x.stop_gradient = False

batch_norm = paddle.nn.BatchNorm3D(channels, data_format="NDHWC")
dense_y = batch_norm(dense_x)
dense_y.backward(dense_y)

sparse_dim = 4
dense_x2 = copy.deepcopy(dense_x)
dense_x2.stop_gradient = False
sparse_x = dense_x2.to_sparse_coo(sparse_dim)
sparse_batch_norm = paddle.incubate.sparse.nn.BatchNorm(channels)
# set same params
sparse_batch_norm._mean.set_value(batch_norm._mean)
sparse_batch_norm._variance.set_value(batch_norm._variance)
sparse_batch_norm.weight.set_value(batch_norm.weight)

sparse_y = sparse_batch_norm(sparse_x)
# compare the result with dense batch_norm
assert np.allclose(dense_y.flatten().numpy(),
sparse_y.values().flatten().numpy(),
atol=1e-5,
rtol=1e-5)

# test backward
sparse_y.backward(sparse_y)
assert np.allclose(dense_x.grad.flatten().numpy(),
sparse_x.grad.values().flatten().numpy(),
atol=1e-5,
rtol=1e-5)
paddle.seed(0)
channels = 4
shape = [2, 3, 6, 6, channels]
#there is no zero in dense_x
dense_x = paddle.randn(shape)
dense_x.stop_gradient = False

batch_norm = paddle.nn.BatchNorm3D(channels, data_format="NDHWC")
dense_y = batch_norm(dense_x)
dense_y.backward(dense_y)

sparse_dim = 4
dense_x2 = copy.deepcopy(dense_x)
dense_x2.stop_gradient = False
sparse_x = dense_x2.to_sparse_coo(sparse_dim)
sparse_batch_norm = paddle.incubate.sparse.nn.BatchNorm(channels)
# set same params
sparse_batch_norm._mean.set_value(batch_norm._mean)
sparse_batch_norm._variance.set_value(batch_norm._variance)
sparse_batch_norm.weight.set_value(batch_norm.weight)

sparse_y = sparse_batch_norm(sparse_x)
# compare the result with dense batch_norm
assert np.allclose(dense_y.flatten().numpy(),
sparse_y.values().flatten().numpy(),
atol=1e-5,
rtol=1e-5)

# test backward
sparse_y.backward(sparse_y)
assert np.allclose(dense_x.grad.flatten().numpy(),
sparse_x.grad.values().flatten().numpy(),
atol=1e-5,
rtol=1e-5)
fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": False})

def test_error_layout(self):
with _test_eager_guard():
with self.assertRaises(ValueError):
shape = [2, 3, 6, 6, 3]
x = paddle.randn(shape)
sparse_x = x.to_sparse_coo(4)
sparse_batch_norm = paddle.incubate.sparse.nn.BatchNorm(
3, data_format='NCDHW')
sparse_batch_norm(sparse_x)
with self.assertRaises(ValueError):
shape = [2, 3, 6, 6, 3]
x = paddle.randn(shape)
sparse_x = x.to_sparse_coo(4)
sparse_batch_norm = paddle.incubate.sparse.nn.BatchNorm(
3, data_format='NCDHW')
sparse_batch_norm(sparse_x)

def test2(self):
with _test_eager_guard():
paddle.seed(123)
channels = 3
x_data = paddle.randn((1, 6, 6, 6, channels)).astype('float32')
dense_x = paddle.to_tensor(x_data)
sparse_x = dense_x.to_sparse_coo(4)
batch_norm = paddle.incubate.sparse.nn.BatchNorm(channels)
batch_norm_out = batch_norm(sparse_x)
print(batch_norm_out.shape)
# [1, 6, 6, 6, 3]
paddle.seed(123)
channels = 3
x_data = paddle.randn((1, 6, 6, 6, channels)).astype('float32')
dense_x = paddle.to_tensor(x_data)
sparse_x = dense_x.to_sparse_coo(4)
batch_norm = paddle.incubate.sparse.nn.BatchNorm(channels)
batch_norm_out = batch_norm(sparse_x)
dense_bn = paddle.nn.BatchNorm1D(channels)
dense_x = dense_x.reshape((-1, dense_x.shape[-1]))
dense_out = dense_bn(dense_x)
assert np.allclose(dense_out.numpy(), batch_norm_out.values().numpy())
# [1, 6, 6, 6, 3]


class TestSyncBatchNorm(unittest.TestCase):

def test_sync_batch_norm(self):
with _test_eager_guard():
x = np.array([[[[0.3, 0.4], [0.3, 0.07]],
[[0.83, 0.37], [0.18, 0.93]]]]).astype('float32')
x = paddle.to_tensor(x)
x = x.to_sparse_coo(len(x.shape) - 1)

if paddle.is_compiled_with_cuda():
sync_batch_norm = nn.SyncBatchNorm(2)
hidden1 = sync_batch_norm(x)
print(hidden1)
x = np.array([[[[0.3, 0.4], [0.3, 0.07]],
[[0.83, 0.37], [0.18, 0.93]]]]).astype('float32')
x = paddle.to_tensor(x)
sparse_x = x.to_sparse_coo(len(x.shape) - 1)

if paddle.is_compiled_with_cuda():
sparse_sync_bn = nn.SyncBatchNorm(2)
sparse_hidden = sparse_sync_bn(sparse_x)

dense_sync_bn = paddle.nn.SyncBatchNorm(2)
x = x.reshape((-1, x.shape[-1]))
dense_hidden = dense_sync_bn(x)
assert np.allclose(sparse_hidden.values().numpy(),
dense_hidden.numpy())

def test_convert(self):
base_model = paddle.nn.Sequential(nn.Conv3D(3, 5, 3), nn.BatchNorm(5),
Expand Down
6 changes: 4 additions & 2 deletions python/paddle/incubate/sparse/nn/layer/norm.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -229,6 +229,7 @@ class SyncBatchNorm(paddle.nn.SyncBatchNorm):

Shapes:
input: Tensor that the dimension from 2 to 5.

output: Tensor with the same shape as input.

Examples:
Expand Down Expand Up @@ -278,7 +279,7 @@ def forward(self, x):

@classmethod
def convert_sync_batchnorm(cls, layer):
"""
r"""
Helper function to convert :class: `paddle.incubate.sparse.nn.BatchNorm` layers in the model to :class: `paddle.incubate.sparse.nn.SyncBatchNorm` layers.

Parameters:
Expand All @@ -290,13 +291,14 @@ def convert_sync_batchnorm(cls, layer):
Examples:

.. code-block:: python

import paddle
import paddle.incubate.sparse.nn as nn

model = paddle.nn.Sequential(nn.Conv3D(3, 5, 3), nn.BatchNorm(5))
sync_model = nn.SyncBatchNorm.convert_sync_batchnorm(model)

"""

layer_output = layer
if isinstance(layer, _BatchNormBase):
if layer._weight_attr != None and not isinstance(
Expand Down