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[PIR]Migrate Sparse API subm_conv2d and subm_conv3d N0.4 #66866

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Aug 1, 2024
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[PIR]Migrate Sparse API subm_conv2d and subm_conv3d N0.4 #66866

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ca9c422
update softmax unittest
epiphanyer Jul 31, 2024
80fd664
update subm_conv2d and subm_conv3d unittest
epiphanyer Jul 31, 2024
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196 changes: 196 additions & 0 deletions test/legacy_test/test_sparse_conv_op.py
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Original file line number Diff line number Diff line change
Expand Up @@ -18,8 +18,10 @@
import numpy as np

import paddle
import paddle.device
from paddle import sparse
from paddle.base import core
from paddle.base.framework import in_pir_mode

logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(message)s', level=logging.INFO
Expand Down Expand Up @@ -579,5 +581,199 @@ def test2D_cpu(self):
paddle.disable_static()


devices = []
if paddle.device.get_device() != "cpu":
devices.append(paddle.device.get_device())
else:
devices.append('cpu')


class TestSparseSubmConvStatic(unittest.TestCase):
'''
test subm_conv2d and subm_conv3d in static graph in pir mode.
compare the results of subm_conv2d in static graph and dynamic graph, use the result in dynamic graph as the correct answer.
'''

def check_result_subm_conv2d(self, x_shape, weight_shape):
'''
x_shape: the shape of input tensor x, [N, H, W, C]
weight_shape: the shape of conv kernel, [kH, kW, C/g, M]
compare the output of paddle.sparse.nn.functional.subm_conv2d in static graph and dynamic graph.
'''
for device in devices:
paddle.device.set_device(device)
x = paddle.rand(x_shape, dtype='float32')
weight = paddle.randn(weight_shape, dtype='float32')
x_indices_data, x_values_data = (
x.detach().to_sparse_coo(sparse_dim=len(x_shape)).indices(),
x.detach().to_sparse_coo(sparse_dim=len(x_shape)).values(),
)
w_indices_data, w_values_data = (
weight.detach()
.to_sparse_coo(sparse_dim=len(weight_shape))
.indices(),
weight.detach()
.to_sparse_coo(sparse_dim=len(weight_shape))
.values(),
)
x.stop_gradient = False
weight.stop_gradient = False

dynamic_out = paddle.sparse.nn.functional.subm_conv2d(x, weight)
dynamic_out_dense = dynamic_out.to_dense()

paddle.enable_static()
with paddle.static.program_guard(
paddle.static.Program(), paddle.static.Program()
):
x_indices = paddle.static.data(
name="x_indices",
shape=x_indices_data.shape,
dtype=x_indices_data.dtype,
)
x_values = paddle.static.data(
name="x_values",
shape=x_values_data.shape,
dtype=x_values_data.dtype,
)
w_indices = paddle.static.data(
name="w_indices",
shape=w_indices_data.shape,
dtype=w_indices_data.dtype,
)
w_values = paddle.static.data(
name="w_values",
shape=w_values_data.shape,
dtype=w_values_data.dtype,
)

static_x = paddle.sparse.sparse_coo_tensor(
x_indices,
x_values,
shape=x_shape,
dtype=x.dtype,
)
static_w = paddle.sparse.sparse_coo_tensor(
w_indices,
w_values,
shape=weight_shape,
dtype=weight.dtype,
)
static_out = paddle.sparse.nn.functional.subm_conv2d(
static_x, static_w
)
static_dense_out = static_out.to_dense()

st_exe = paddle.static.Executor()
st_fetch = st_exe.run(
feed={
"x_indices": x_indices_data.numpy(),
"x_values": x_values_data.numpy(),
"w_indices": w_indices_data.numpy(),
"w_values": w_values_data.numpy(),
},
fetch_list=[static_dense_out],
return_numpy=True,
)
np.testing.assert_allclose(
dynamic_out_dense.numpy(), st_fetch[0], rtol=1e-05
)
paddle.disable_static()

def check_result_subm_conv3d(self, x_shape, weight_shape):
'''
x_shape: the shape of input tensor x, [N, D, H, W, C]
weight_shape: the shape of conv kernel, [kD, kH, kW, C/g, M]
compare the output of paddle.sparse.nn.functional.subm_conv3d in static graph and dynamic graph.
'''
for device in devices:
paddle.device.set_device(device)
x = paddle.rand(x_shape, dtype='float32')
weight = paddle.randn(weight_shape, dtype='float32')
x_indices_data, x_values_data = (
x.detach().to_sparse_coo(sparse_dim=len(x_shape)).indices(),
x.detach().to_sparse_coo(sparse_dim=len(x_shape)).values(),
)
w_indices_data, w_values_data = (
weight.detach()
.to_sparse_coo(sparse_dim=len(weight_shape))
.indices(),
weight.detach()
.to_sparse_coo(sparse_dim=len(weight_shape))
.values(),
)
x.stop_gradient = False
weight.stop_gradient = False

dynamic_out = paddle.sparse.nn.functional.subm_conv3d(x, weight)
dynamic_out_dense = dynamic_out.to_dense()

paddle.enable_static()
with paddle.static.program_guard(
paddle.static.Program(), paddle.static.Program()
):
x_indices = paddle.static.data(
name="x_indices",
shape=x_indices_data.shape,
dtype=x_indices_data.dtype,
)
x_values = paddle.static.data(
name="x_values",
shape=x_values_data.shape,
dtype=x_values_data.dtype,
)
w_indices = paddle.static.data(
name="w_indices",
shape=w_indices_data.shape,
dtype=w_indices_data.dtype,
)
w_values = paddle.static.data(
name="w_values",
shape=w_values_data.shape,
dtype=w_values_data.dtype,
)

static_x = paddle.sparse.sparse_coo_tensor(
x_indices,
x_values,
shape=x_shape,
dtype=x.dtype,
)
static_w = paddle.sparse.sparse_coo_tensor(
w_indices,
w_values,
shape=weight_shape,
dtype=weight.dtype,
)
static_out = paddle.sparse.nn.functional.subm_conv3d(
static_x, static_w
)
static_dense_out = static_out.to_dense()

st_exe = paddle.static.Executor()
st_fetch = st_exe.run(
feed={
"x_indices": x_indices_data.numpy(),
"x_values": x_values_data.numpy(),
"w_indices": w_indices_data.numpy(),
"w_values": w_values_data.numpy(),
},
fetch_list=[static_dense_out],
return_numpy=True,
)
np.testing.assert_allclose(
dynamic_out_dense.numpy(), st_fetch[0], rtol=1e-05
)
paddle.disable_static()

def test_subm_conv2d(self):
if in_pir_mode():
self.check_result_subm_conv2d([1, 3, 4, 1], [3, 3, 1, 1])

def test_subm_conv3d(self):
if in_pir_mode():
self.check_result_subm_conv3d([1, 1, 3, 4, 1], [1, 3, 3, 1, 1])


if __name__ == "__main__":
unittest.main()
80 changes: 80 additions & 0 deletions test/legacy_test/test_sparse_fused_attention_op.py
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Original file line number Diff line number Diff line change
Expand Up @@ -21,7 +21,9 @@
import numpy as np

import paddle
import paddle.sparse
from paddle.base import core
from paddle.base.framework import in_pir_mode


def get_cuda_version():
Expand Down Expand Up @@ -177,5 +179,83 @@ def setUp(self):
self.use_mask = True


devices = []
if paddle.device.get_device() != "cpu":
devices.append(paddle.device.get_device())
else:
devices.append('cpu')


class TestSparseSoftmaxStaticAPI(unittest.TestCase):
'''
Test the API paddle.sparse.nn.functional.softmax on some sparse tensors in pir mode in static graph.
'''

def check_result_coo(self, x_shape):
'''
x_shape: a tensor shape,
generate a sparse tensor with shape "x_shape" and compute the output of paddle.sparse.nn.functional.softmax.
compare the output of paddle.sparse.nn.functional.softmax and the output of paddle.nn.functional.Softmax.
'''
for device in devices:
paddle.device.set_device(device)
x = paddle.rand(x_shape, dtype='float32')
indices_data, values_data = (
x.detach().to_sparse_coo(sparse_dim=len(x_shape)).indices(),
x.detach().to_sparse_coo(sparse_dim=len(x_shape)).values(),
)

x.stop_gradient = False
out = paddle.nn.functional.softmax(x)

paddle.enable_static()
with paddle.static.program_guard(
paddle.static.Program(), paddle.staic.Program()
):
indices = paddle.static.data(
name="indices",
shape=indices_data.shape,
dtype=indices_data.dtype,
)
values = paddle.static.data(
name="values",
shape=values_data.shape,
dtype=values_data.dtype,
)

sp_x = paddle.sparse.sparse_coo_tensor(
indices,
values,
shape=x.shape,
dtype=x.dtype,
)
sp_out = paddle.sparse.nn.functional.softmax(sp_x)
sp_dense_out = sp_out.to_dense()

sp_exe = paddle.static.Executor()
sp_fetch = sp_exe.run(
feed={
"indices": indices_data.numpy(),
"values": values_data.numpy(),
},
fetch_list=[sp_dense_out],
return_numpy=True,
)
np.testing.assert_allclose(out.numpy(), sp_fetch[0], rtol=1e-05)
paddle.disable_static()

def test_softmax_2d(self):
if in_pir_mode():
self.check_result_coo([3, 4])

def test_softmax_3d(self):
if in_pir_mode():
self.check_result_coo([3, 4, 5])

def test_softmax_4d(self):
if in_pir_mode():
self.check_result_coo([3, 4, 5, 6])


if __name__ == '__main__':
unittest.main()