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[Cleanup][B-10]Replace to_variable #61531

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Feb 3, 2024
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[Cleanup][B-10]Replace to_variable #61531

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12 changes: 6 additions & 6 deletions test/legacy_test/test_imperative_hook_for_layer.py
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Expand Up @@ -17,9 +17,9 @@
import numpy as np
from test_imperative_lod_tensor_to_selected_rows import SimpleNet

import paddle
from paddle import base
from paddle.base import core
from paddle.base.dygraph import base as imperative_base

call_forward_post_hook = False
call_forward_pre_hook = False
Expand Down Expand Up @@ -78,9 +78,9 @@ def test_forward_hook_return_value(self):
)
y_data = y_data.reshape((-1, 1))

input = imperative_base.to_variable(input_word)
input1 = imperative_base.to_variable(input_word1)
y = imperative_base.to_variable(y_data)
input = paddle.to_tensor(input_word)
input1 = paddle.to_tensor(input_word1)
y = paddle.to_tensor(y_data)

simplenet = SimpleNet(
hidden_size=20,
Expand Down Expand Up @@ -161,8 +161,8 @@ def test_forward_hook(self):
)
y_data = y_data.reshape((-1, 1))

input = imperative_base.to_variable(input_word)
y = imperative_base.to_variable(y_data)
input = paddle.to_tensor(input_word)
y = paddle.to_tensor(y_data)

simplenet = SimpleNet(
hidden_size=20,
Expand Down
3 changes: 1 addition & 2 deletions test/legacy_test/test_imperative_layer_trainable.py
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Expand Up @@ -18,15 +18,14 @@

import paddle
from paddle import base
from paddle.base import dygraph


class TestImperativeLayerTrainable(unittest.TestCase):
def test_set_trainable(self):
with base.dygraph.guard():
label = np.random.uniform(-1, 1, [10, 10]).astype(np.float32)

label = dygraph.to_variable(label)
label = paddle.to_tensor(label)

linear = paddle.nn.Linear(10, 10)
y = linear(label)
Expand Down
5 changes: 2 additions & 3 deletions test/legacy_test/test_imperative_lod_tensor_to_selected_rows.py
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Expand Up @@ -21,7 +21,6 @@
import paddle
from paddle import base
from paddle.base import core
from paddle.base.dygraph.base import to_variable


class SimpleNet(paddle.nn.Layer):
Expand Down Expand Up @@ -133,8 +132,8 @@ def simple_net_float32(self, is_sparse, dtype):
x_data = x_data.reshape((-1, num_steps))
y_data = y_data.reshape((-1, 1))

x = to_variable(x_data)
y = to_variable(y_data)
x = paddle.to_tensor(x_data)
y = paddle.to_tensor(y_data)
outs = simple_net(x, y)
dy_loss = outs
if i == 0:
Expand Down
5 changes: 2 additions & 3 deletions test/legacy_test/test_imperative_mnist_sorted_gradient.py
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Expand Up @@ -21,7 +21,6 @@
import paddle
from paddle import base
from paddle.base import core
from paddle.base.dygraph.base import to_variable


class TestImperativeMnistSortGradient(unittest.TestCase):
Expand Down Expand Up @@ -55,8 +54,8 @@ def test_mnist_sort_gradient_float32(self):
.reshape(128, 1)
)

img2 = to_variable(dy_x_data2)
label2 = to_variable(y_data2)
img2 = paddle.to_tensor(dy_x_data2)
label2 = paddle.to_tensor(y_data2)
label2.stop_gradient = True

cost2 = mnist2(img2)
Expand Down
2 changes: 1 addition & 1 deletion test/legacy_test/test_imperative_named_members.py
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Expand Up @@ -91,7 +91,7 @@ def __init__(self):
self.linear2 = paddle.nn.Linear(5, 5)
self.conv2d = paddle.nn.Conv2D(3, 2, 3)
self.embedding = paddle.nn.Embedding(128, 16)
self.h_0 = base.dygraph.to_variable(
self.h_0 = paddle.to_tensor(
np.zeros([10, 10]).astype('float32')
)
self.weight = self.create_parameter(
Expand Down
9 changes: 4 additions & 5 deletions test/legacy_test/test_imperative_ocr_attention_model.py
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Expand Up @@ -20,7 +20,6 @@
import paddle
from paddle import base
from paddle.base import core
from paddle.base.dygraph.base import to_variable
from paddle.nn import BatchNorm, Linear


Expand Down Expand Up @@ -210,7 +209,7 @@ def __init__(
h_0 = np.zeros(
(Config.batch_size, rnn_hidden_size), dtype="float32"
)
h_0 = to_variable(h_0)
h_0 = paddle.to_tensor(h_0)
else:
h_0 = paddle.tensor.fill_constant(
shape=[Config.batch_size, rnn_hidden_size],
Expand Down Expand Up @@ -464,10 +463,10 @@ def run_dygraph():
dy_param_init_value[param.name] = param.numpy()
for epoch in range(epoch_num):
for batch_id in range(batch_num):
label_in = to_variable(label_in_np)
label_out = to_variable(label_out_np)
label_in = paddle.to_tensor(label_in_np)
label_out = paddle.to_tensor(label_out_np)
label_out.stop_gradient = True
img = to_variable(image_np)
img = paddle.to_tensor(image_np)
dy_prediction = ocr_attention(img, label_in)
label_out = paddle.reshape(label_out, [-1, 1])
dy_prediction = paddle.reshape(
Expand Down
8 changes: 4 additions & 4 deletions test/legacy_test/test_imperative_optimizer.py
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Expand Up @@ -234,7 +234,7 @@ def test_constant_lr(self):

linear = paddle.nn.Linear(10, 10)

a = base.dygraph.to_variable(a)
a = paddle.to_tensor(a)

b = linear(a)

Expand All @@ -258,7 +258,7 @@ def test_lr_decay(self):

linear = paddle.nn.Linear(10, 10)

a = base.dygraph.to_variable(a)
a = paddle.to_tensor(a)

b = linear(a)

Expand Down Expand Up @@ -289,7 +289,7 @@ def test_lr_decay_natural_exp(self):

linear = paddle.nn.Linear(10, 10)

a = base.dygraph.to_variable(a)
a = paddle.to_tensor(a)

b = linear(a)

Expand Down Expand Up @@ -324,7 +324,7 @@ def test_set_lr(self):

linear = paddle.nn.Linear(10, 10)

a = base.dygraph.to_variable(a)
a = paddle.to_tensor(a)

b = linear(a)

Expand Down
12 changes: 6 additions & 6 deletions test/legacy_test/test_imperative_optimizer_v2.py
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Expand Up @@ -533,7 +533,7 @@ def test_constant_lr(self):

linear = paddle.nn.Linear(10, 10)

a = base.dygraph.to_variable(a)
a = paddle.to_tensor(a)

b = linear(a)

Expand All @@ -557,7 +557,7 @@ def test_lr_decay(self):

linear = paddle.nn.Linear(10, 10)

a = base.dygraph.to_variable(a)
a = paddle.to_tensor(a)

b = linear(a)

Expand Down Expand Up @@ -585,7 +585,7 @@ def test_lr_scheduler_natural_exp(self):
a = np.random.uniform(-0.1, 0.1, [10, 10]).astype("float32")

linear = paddle.nn.Linear(10, 10)
a = base.dygraph.to_variable(a)
a = paddle.to_tensor(a)
b = linear(a)

loss = paddle.mean(b)
Expand All @@ -611,7 +611,7 @@ def test_set_lr(self):

linear = paddle.nn.Linear(10, 10)

a = base.dygraph.to_variable(a)
a = paddle.to_tensor(a)

b = linear(a)

Expand Down Expand Up @@ -647,7 +647,7 @@ def test_set_lr_scheduler(self):

linear = paddle.nn.Linear(10, 10)

a = base.dygraph.to_variable(a)
a = paddle.to_tensor(a)

b = linear(a)

Expand Down Expand Up @@ -879,7 +879,7 @@ def test_parameter_list(self):
)

in_np = np.random.uniform(-0.1, 0.1, [10, 10]).astype("float32")
in_data = base.dygraph.to_variable(in_np)
in_data = paddle.to_tensor(in_np)

y = linear_1(in_data)
y = linear_2(y)
Expand Down
13 changes: 8 additions & 5 deletions test/legacy_test/test_imperative_parallel_coalesce_split.py
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Expand Up @@ -21,7 +21,6 @@
import paddle.nn.functional as F
from paddle import base
from paddle.base import core
from paddle.base.dygraph.base import to_variable


class MyLayer(paddle.nn.Layer):
Expand Down Expand Up @@ -49,10 +48,14 @@ def test_coalesce_split(self):

# test variables prepare
vars = []
vars.append(to_variable(np.random.random([2, 3]).astype("float32")))
vars.append(to_variable(np.random.random([4, 9]).astype("float32")))
vars.append(
to_variable(np.random.random([10, 1]).astype("float32"))
paddle.to_tensor(np.random.random([2, 3]).astype("float32"))
)
vars.append(
paddle.to_tensor(np.random.random([4, 9]).astype("float32"))
)
vars.append(
paddle.to_tensor(np.random.random([10, 1]).astype("float32"))
)
var_groups = OrderedDict()
var_groups.setdefault(0, vars)
Expand Down Expand Up @@ -81,7 +84,7 @@ def test_reshape_inplace(self):
ori_shape = [2, 25]
new_shape = [5, 10]
x_data = np.random.random(ori_shape).astype("float32")
x = to_variable(x_data)
x = paddle.to_tensor(x_data)
_reshape_inplace(x, new_shape)
self.assertEqual(x.shape, new_shape)

Expand Down
2 changes: 1 addition & 1 deletion test/legacy_test/test_imperative_partitial_backward.py
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Expand Up @@ -24,7 +24,7 @@ class TestImperativePartitialBackward(unittest.TestCase):
def test_partitial_backward(self):
with base.dygraph.guard():
x = np.random.randn(2, 4, 5).astype("float32")
x = base.dygraph.to_variable(x)
x = paddle.to_tensor(x)
linear1 = paddle.nn.Linear(5, 10)
linear2 = paddle.nn.Linear(5, 10)

Expand Down