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[Cleanup][B-12]Replace to_variable #61533

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Feb 3, 2024
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[Cleanup][B-12]Replace to_variable #61533

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Fix to_variable
  • Loading branch information
@co63oc
co63oc committed Feb 3, 2024
commit 78b034cc0cf77b4fb758abda8d71058e19f1d2f1
4 changes: 2 additions & 2 deletions test/legacy_test/test_imperative_trace_non_persistable_inputs.py
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Expand Up @@ -25,7 +25,7 @@ class SimpleFCLayer(paddle.nn.Layer):
def __init__(self, feature_size, batch_size, fc_size):
super().__init__()
self._linear = paddle.nn.Linear(feature_size, fc_size)
self._offset = base.dygraph.to_variable(
self._offset = paddle.to_tensor(
np.random.random((batch_size, fc_size)).astype('float32')
)

Expand Down Expand Up @@ -55,7 +55,7 @@ def test_main(self):
}

for _ in range(10):
in_x = base.dygraph.to_variable(
in_x = paddle.to_tensor(
np.random.random((batch_size, feature_size)).astype(
'float32'
)
Expand Down
22 changes: 11 additions & 11 deletions test/legacy_test/test_imperative_transformer_sorted_gradient.py
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Expand Up @@ -21,7 +21,7 @@
import paddle.nn.functional as F
from paddle import base
from paddle.base import core
from paddle.base.dygraph import guard, to_variable
from paddle.base.dygraph import guard
from paddle.nn import Layer, Linear

np.set_printoptions(suppress=True)
Expand Down Expand Up @@ -178,18 +178,18 @@ def create_data(is_static=False):
]
else:
enc_inputs = [
to_variable(src_word_np, name='src_word'),
to_variable(src_pos_np, name='src_pos'),
to_variable(src_slf_attn_bias_np, name='src_slf_attn_bias'),
paddle.to_tensor(src_word_np),
paddle.to_tensor(src_pos_np),
paddle.to_tensor(src_slf_attn_bias_np),
]
dec_inputs = [
to_variable(trg_word_np, name='trg_word'),
to_variable(trg_pos_np, name='trg_pos'),
to_variable(trg_slf_attn_bias_np, name='trg_slf_attn_bias'),
to_variable(trg_src_attn_bias_np, name='trg_src_attn_bias'),
paddle.to_tensor(trg_word_np),
paddle.to_tensor(trg_pos_np),
paddle.to_tensor(trg_slf_attn_bias_np),
paddle.to_tensor(trg_src_attn_bias_np),
]
label = to_variable(lbl_word_np, name='lbl_word')
weight = to_variable(lbl_weight_np, name='lbl_weight')
label = paddle.to_tensor(lbl_word_np)
weight = paddle.to_tensor(lbl_weight_np)
return enc_inputs, dec_inputs, label, weight


Expand Down Expand Up @@ -680,7 +680,7 @@ def __init__(
)

# use in dygraph_mode to fit different length batch
# self._pos_emb._w = to_variable(
# self._pos_emb._w = paddle.to_tensor(
# position_encoding_init(self._src_max_len, self._src_emb_dim))

def forward(self, src_word, src_pos):
Expand Down
2 changes: 1 addition & 1 deletion test/legacy_test/test_imperative_triple_grad.py
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Expand Up @@ -39,7 +39,7 @@ def __impl__(*args, **kwargs):
def random_var(size, low=-1, high=1, dtype='float32'):
np.random.seed(2021)
x_np = np.random.uniform(low=low, high=high, size=size).astype(dtype)
return base.dygraph.to_variable(x_np)
return paddle.to_tensor(x_np)


class TestDygraphTripleGradMatmul(TestCase):
Expand Down
4 changes: 2 additions & 2 deletions test/legacy_test/test_imperative_using_non_zero_gpu.py
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Expand Up @@ -18,13 +18,13 @@

import paddle
from paddle import base
from paddle.base.dygraph import guard, to_variable
from paddle.base.dygraph import guard


class TestImperativeUsingNonZeroGpu(unittest.TestCase):
def run_main(self, np_arr, place):
with guard(place):
var = to_variable(np_arr)
var = paddle.to_tensor(np_arr)
np.testing.assert_array_equal(np_arr, var.numpy())

def test_non_zero_gpu(self):
Expand Down
8 changes: 4 additions & 4 deletions test/legacy_test/test_index_select_op.py
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Expand Up @@ -237,8 +237,8 @@ def test_dygraph_api(self):
self.input_data()
# case 1:
with base.dygraph.guard():
x = base.dygraph.to_variable(self.data_x)
index = base.dygraph.to_variable(self.data_index)
x = paddle.to_tensor(self.data_x)
index = paddle.to_tensor(self.data_index)
z = paddle.index_select(x, index)
np_z = z.numpy()
expect_out = np.array(
Expand All @@ -248,8 +248,8 @@ def test_dygraph_api(self):

# case 2:
with base.dygraph.guard():
x = base.dygraph.to_variable(self.data_x)
index = base.dygraph.to_variable(self.data_index)
x = paddle.to_tensor(self.data_x)
index = paddle.to_tensor(self.data_index)
z = paddle.index_select(x, index, axis=1)
np_z = z.numpy()
expect_out = np.array(
Expand Down
5 changes: 2 additions & 3 deletions test/legacy_test/test_instance_norm_op.py
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Expand Up @@ -22,7 +22,6 @@
import paddle
from paddle import base, nn
from paddle.base import Program, core, program_guard
from paddle.base.dygraph import to_variable


def _reference_instance_norm_naive(x, scale, bias, epsilon, mean, var):
Expand Down Expand Up @@ -929,7 +928,7 @@ def test_norm(self):
instance_norm = paddle.nn.InstanceNorm2D(
5, weight_attr=False, bias_attr=False
)
outputs = instance_norm(to_variable(inputs))
outputs = instance_norm(paddle.to_tensor(inputs))
np.testing.assert_allclose(
outputs.numpy(), out_np, rtol=1e-05, atol=1e-06
)
Expand Down Expand Up @@ -963,7 +962,7 @@ def test_norm(self):
instance_norm = paddle.nn.InstanceNorm2D(
3, weight_attr=True, bias_attr=True
)
outputs = instance_norm(to_variable(inputs))
outputs = instance_norm(paddle.to_tensor(inputs))
np.testing.assert_allclose(
outputs.numpy(), out_np, rtol=1e-05, atol=1e-06
)
Expand Down
10 changes: 5 additions & 5 deletions test/legacy_test/test_instance_norm_op_v2.py
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Expand Up @@ -91,17 +91,17 @@ def test_error(self):
def error1d():
x_data_4 = np.random.random(size=(2, 1, 3, 3)).astype('float32')
instance_norm1d = paddle.nn.InstanceNorm1D(1)
instance_norm1d(base.dygraph.to_variable(x_data_4))
instance_norm1d(paddle.to_tensor(x_data_4))

def error2d():
x_data_3 = np.random.random(size=(2, 1, 3)).astype('float32')
instance_norm2d = paddle.nn.InstanceNorm2D(1)
instance_norm2d(base.dygraph.to_variable(x_data_3))
instance_norm2d(paddle.to_tensor(x_data_3))

def error3d():
x_data_4 = np.random.random(size=(2, 1, 3, 3)).astype('float32')
instance_norm3d = paddle.nn.InstanceNorm3D(1)
instance_norm3d(base.dygraph.to_variable(x_data_4))
instance_norm3d(paddle.to_tensor(x_data_4))

def weight_bias_false():
x_data_4 = np.random.random(size=(2, 1, 3, 3)).astype('float32')
Expand All @@ -127,13 +127,13 @@ def test_dygraph(self):
def compute_v1(x):
with base.dygraph.guard(p):
bn = paddle.nn.InstanceNorm2D(shape[1])
y = bn(base.dygraph.to_variable(x))
y = bn(paddle.to_tensor(x))
return y.numpy()

def compute_v2(x):
with base.dygraph.guard(p):
bn = paddle.nn.InstanceNorm2D(shape[1])
y = bn(base.dygraph.to_variable(x))
y = bn(paddle.to_tensor(x))
return y.numpy()

x = np.random.randn(*shape).astype("float32")
Expand Down
4 changes: 2 additions & 2 deletions test/legacy_test/test_inverse_op.py
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Expand Up @@ -129,7 +129,7 @@ def test_dygraph(self):
for place in self.places:
with base.dygraph.guard(place):
input_np = np.random.random([4, 4]).astype("float64")
input = base.dygraph.to_variable(input_np)
input = paddle.to_tensor(input_np)
result = paddle.inverse(input)
np.testing.assert_allclose(
result.numpy(), np.linalg.inv(input_np), rtol=1e-05
Expand Down Expand Up @@ -200,7 +200,7 @@ def test_dygraph(self):
for place in self.places:
with base.dygraph.guard(place):
input_np = np.ones([4, 4]).astype("float64")
input = base.dygraph.to_variable(input_np)
input = paddle.to_tensor(input_np)
try:
result = paddle.inverse(input)
except RuntimeError as ex:
Expand Down
8 changes: 4 additions & 4 deletions test/xpu/test_index_select_op_xpu.py
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Expand Up @@ -132,8 +132,8 @@ def test_dygraph_api(self):
self.input_data()
# case 1:
with base.dygraph.guard():
x = base.dygraph.to_variable(self.data_x)
index = base.dygraph.to_variable(self.data_index)
x = paddle.to_tensor(self.data_x)
index = paddle.to_tensor(self.data_index)
z = paddle.index_select(x, index)
np_z = z.numpy()
expect_out = np.array(
Expand All @@ -143,8 +143,8 @@ def test_dygraph_api(self):

# case 2:
with base.dygraph.guard():
x = base.dygraph.to_variable(self.data_x)
index = base.dygraph.to_variable(self.data_index)
x = paddle.to_tensor(self.data_x)
index = paddle.to_tensor(self.data_index)
z = paddle.index_select(x, index, axis=1)
np_z = z.numpy()
expect_out = np.array(
Expand Down
2 changes: 1 addition & 1 deletion test/xpu/test_inverse_op_xpu.py
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Expand Up @@ -103,7 +103,7 @@ def test_dygraph(self):
for place in self.places:
with base.dygraph.guard(place):
input_np = np.ones([4, 4]).astype("float32")
input = base.dygraph.to_variable(input_np)
input = paddle.to_tensor(input_np)
with self.assertRaises(OSError):
result = paddle.inverse(input)

Expand Down