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未验证 提交 1f93de31 编写于 作者: 姜 姜永久 提交者： GitHub
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rm unittest eager guard tests part20 sparse_mv2split (#48879)

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python/paddle/fluid/tests/unittests/test_sparse_mv_op.py
浏览文件 @ 1f93de31
......@@ -19,7 +19,6 @@ import unittest
import numpy as np
import paddle
from paddle.fluid.framework import _test_eager_guard
paddle.seed(100)
......@@ -43,7 +42,6 @@ def get_cuda_version():
class TestCsrMv(unittest.TestCase):
# x: csr-matrix, y: dense-vec, out: dense-vec
def test_mv(self):
with _test_eager_guard():
paddle.set_default_dtype('float64')
origin_x = paddle.rand([64, 32])
mask = paddle.randint(0, 2, [64, 32])
......@@ -84,7 +82,6 @@ class TestCsrMv(unittest.TestCase):
class TestCooMv(unittest.TestCase):
# x: csr-matrix, y: dense-vec, out: dense-vec
def test_mv(self):
with _test_eager_guard():
paddle.set_default_dtype('float64')
origin_x = paddle.rand([64, 32])
mask = paddle.randint(0, 2, [64, 32])
......
python/paddle/fluid/tests/unittests/test_sparse_pooling_op.py
浏览文件 @ 1f93de31
......@@ -18,7 +18,6 @@ import unittest
import numpy as np
import paddle
from paddle.fluid.framework import _test_eager_guard
class TestMaxPool3DFunc(unittest.TestCase):
......@@ -42,7 +41,6 @@ class TestMaxPool3DFunc(unittest.TestCase):
self.setPadding()
def test(self):
with _test_eager_guard():
self.setUp()
self.dense_x.stop_gradient = False
sparse_x = self.dense_x.to_sparse_coo(4)
......@@ -102,7 +100,6 @@ class TestInput(TestMaxPool3DFunc):
class TestMaxPool3DAPI(unittest.TestCase):
def test(self):
with _test_eager_guard():
dense_x = paddle.randn((2, 3, 6, 6, 3))
sparse_x = dense_x.to_sparse_coo(4)
max_pool3d = paddle.sparse.nn.MaxPool3D(
......
python/paddle/fluid/tests/unittests/test_sparse_softmax_op.py
浏览文件 @ 1f93de31
......@@ -18,14 +18,12 @@ import numpy as np
import scipy.sparse as sp
import paddle
from paddle.fluid.framework import _test_eager_guard
np.random.seed(2022)
class TestCsrSoftmax(unittest.TestCase):
def test_softmax2d(self):
with _test_eager_guard():
mask = np.random.rand(16, 128) < 0.5
np_x = np.random.rand(16, 128) * mask
np_csr = sp.csr_matrix(np_x)
......@@ -73,12 +71,9 @@ class TestCsrSoftmax(unittest.TestCase):
np.testing.assert_allclose(
csr.grad.cols().numpy(), np_csr.indices, rtol=1e-05
)
np.testing.assert_allclose(
csr.grad.values().numpy(), dx, rtol=1e-05
)
np.testing.assert_allclose(csr.grad.values().numpy(), dx, rtol=1e-05)
def test_softmax3d(self):
with _test_eager_guard():
batchNum = 16
mask = np.random.rand(batchNum, 16, 128) < 0.5
np_x = np.random.rand(batchNum, 16, 128) * mask
......@@ -126,9 +121,7 @@ class TestCsrSoftmax(unittest.TestCase):
batch_offset += np_csr.nnz
np.testing.assert_allclose(
csr.grad.values().numpy(), dx, rtol=1e-05
)
np.testing.assert_allclose(csr.grad.values().numpy(), dx, rtol=1e-05)
if __name__ == "__main__":
......
python/paddle/fluid/tests/unittests/test_sparse_transpose_op.py
浏览文件 @ 1f93de31
......@@ -17,13 +17,11 @@ import unittest
import numpy as np
import paddle
from paddle.fluid.framework import _test_eager_guard
class TestTranspose(unittest.TestCase):
# x: sparse, out: sparse
def check_result(self, x_shape, dims, format):
with _test_eager_guard():
mask = paddle.randint(0, 2, x_shape).astype("float32")
# "+ 1" to make sure that all zero elements in "origin_x" is caused by multiplying by "mask",
# or the backward checks may fail.
......
python/paddle/fluid/tests/unittests/test_sparse_utils_op.py
浏览文件 @ 1f93de31
......@@ -19,14 +19,12 @@ import numpy as np
import paddle
import paddle.fluid as fluid
import paddle.fluid.core as core
from paddle.fluid.framework import _test_eager_guard
devices = ['cpu', 'gpu']
class TestSparseCreate(unittest.TestCase):
def test_create_coo_by_tensor(self):
with _test_eager_guard():
indices = [[0, 0, 1, 2, 2], [1, 3, 2, 0, 1]]
values = [1, 2, 3, 4, 5]
dense_shape = [3, 4]
......@@ -40,7 +38,6 @@ class TestSparseCreate(unittest.TestCase):
assert np.array_equal(values, coo.values().numpy())
def test_create_coo_by_np(self):
with _test_eager_guard():
indices = [[0, 1, 2], [1, 2, 0]]
values = [1.0, 2.0, 3.0]
dense_shape = [3, 3]
......@@ -50,7 +47,6 @@ class TestSparseCreate(unittest.TestCase):
assert np.array_equal(values, coo.values().numpy())
def test_create_csr_by_tensor(self):
with _test_eager_guard():
crows = [0, 2, 3, 5]
cols = [1, 3, 2, 0, 1]
values = [1, 2, 3, 4, 5]
......@@ -68,14 +64,11 @@ class TestSparseCreate(unittest.TestCase):
)
def test_create_csr_by_np(self):
with _test_eager_guard():
crows = [0, 2, 3, 5]
cols = [1, 3, 2, 0, 1]
values = [1, 2, 3, 4, 5]
dense_shape = [3, 4]
csr = paddle.sparse.sparse_csr_tensor(
crows, cols, values, dense_shape
)
csr = paddle.sparse.sparse_csr_tensor(crows, cols, values, dense_shape)
# test the to_string.py
assert np.array_equal(5, csr.nnz())
assert np.array_equal(crows, csr.crows().numpy())
......@@ -83,7 +76,6 @@ class TestSparseCreate(unittest.TestCase):
assert np.array_equal(values, csr.values().numpy())
def test_place(self):
with _test_eager_guard():
place = core.CPUPlace()
indices = [[0, 1], [0, 1]]
values = [1.0, 2.0]
......@@ -107,7 +99,6 @@ class TestSparseCreate(unittest.TestCase):
assert csr.values().place.is_cpu_place()
def test_dtype(self):
with _test_eager_guard():
indices = [[0, 1], [0, 1]]
values = [1.0, 2.0]
dense_shape = [2, 2]
......@@ -127,7 +118,6 @@ class TestSparseCreate(unittest.TestCase):
assert csr.dtype == paddle.float16
def test_create_coo_no_shape(self):
with _test_eager_guard():
indices = [[0, 1], [0, 1]]
values = [1.0, 2.0]
indices = paddle.to_tensor(indices, dtype='int32')
......@@ -138,7 +128,6 @@ class TestSparseCreate(unittest.TestCase):
class TestSparseConvert(unittest.TestCase):
def test_to_sparse_coo(self):
with _test_eager_guard():
x = [[0, 1, 0, 2], [0, 0, 3, 0], [4, 5, 0, 0]]
indices = [[0, 0, 1, 2, 2], [1, 3, 2, 0, 1]]
values = [1.0, 2.0, 3.0, 4.0, 5.0]
......@@ -156,13 +145,10 @@ class TestSparseConvert(unittest.TestCase):
stop_gradient=True,
)
out.backward(out_grad)
assert np.array_equal(
dense_x.grad.numpy(), out_grad.to_dense().numpy()
)
assert np.array_equal(dense_x.grad.numpy(), out_grad.to_dense().numpy())
def test_coo_to_dense(self):
fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": True})
with _test_eager_guard():
indices = [[0, 0, 1, 2, 2], [1, 3, 2, 0, 1]]
values = [1.0, 2.0, 3.0, 4.0, 5.0]
indices_dtypes = ['int32', 'int64']
......@@ -202,7 +188,6 @@ class TestSparseConvert(unittest.TestCase):
fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": False})
def test_to_sparse_csr(self):
with _test_eager_guard():
x = [[0, 1, 0, 2], [0, 0, 3, 0], [4, 5, 0, 0]]
crows = [0, 2, 3, 5]
cols = [1, 3, 2, 0, 1]
......@@ -218,7 +203,6 @@ class TestSparseConvert(unittest.TestCase):
def test_coo_values_grad(self):
fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": True})
with _test_eager_guard():
indices = [[0, 0, 1, 2, 2], [1, 3, 2, 0, 1]]
values = [1.0, 2.0, 3.0, 4.0, 5.0]
sparse_x = paddle.sparse.sparse_coo_tensor(
......@@ -260,7 +244,6 @@ class TestSparseConvert(unittest.TestCase):
fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": False})
def test_sparse_coo_tensor_grad(self):
with _test_eager_guard():
for device in devices:
if device == 'cpu' or (
device == 'gpu' and paddle.is_compiled_with_cuda()
......@@ -284,9 +267,7 @@ class TestSparseConvert(unittest.TestCase):
)
sparse_x.backward(sparse_out_grad)
correct_values_grad = [0, 3]
assert np.array_equal(
correct_values_grad, values.grad.numpy()
)
assert np.array_equal(correct_values_grad, values.grad.numpy())
# test the non-zero values is a vector
values = [[1, 1], [2, 2]]
......@@ -303,12 +284,9 @@ class TestSparseConvert(unittest.TestCase):
)
sparse_x.backward(sparse_out_grad)
correct_values_grad = [[0, 0], [3, 3]]
assert np.array_equal(
correct_values_grad, values.grad.numpy()
)
assert np.array_equal(correct_values_grad, values.grad.numpy())
def test_sparse_coo_tensor_sorted(self):
with _test_eager_guard():
for device in devices:
if device == 'cpu' or (
device == 'gpu' and paddle.is_compiled_with_cuda()
......@@ -326,9 +304,7 @@ class TestSparseConvert(unittest.TestCase):
assert np.array_equal(
indices_sorted, sparse_x.indices().numpy()
)
assert np.array_equal(
values_sorted, sparse_x.values().numpy()
)
assert np.array_equal(values_sorted, sparse_x.values().numpy())
# test the non-zero values is a vector
values = [[1.0, 1.0], [2.0, 2.0], [3.0, 3.0]]
......@@ -339,13 +315,9 @@ class TestSparseConvert(unittest.TestCase):
assert np.array_equal(
indices_sorted, sparse_x.indices().numpy()
)
assert np.array_equal(
values_sorted, sparse_x.values().numpy()
)
assert np.array_equal(values_sorted, sparse_x.values().numpy())
def test_batch_csr(self):
with _test_eager_guard():
def verify(dense_x):
sparse_x = dense_x.to_sparse_csr()
out = sparse_x.to_dense()
......@@ -383,7 +355,6 @@ class TestSparseConvert(unittest.TestCase):
class TestCooError(unittest.TestCase):
def test_small_shape(self):
with _test_eager_guard():
with self.assertRaises(ValueError):
indices = [[2, 3], [0, 2]]
values = [1, 2]
......@@ -394,7 +365,6 @@ class TestCooError(unittest.TestCase):
)
def test_same_nnz(self):
with _test_eager_guard():
with self.assertRaises(ValueError):
# 2. test the nnz of indices must same as nnz of values
indices = [[1, 2], [1, 0]]
......@@ -402,7 +372,6 @@ class TestCooError(unittest.TestCase):
sparse_x = paddle.sparse.sparse_coo_tensor(indices, values)
def test_same_dimensions(self):
with _test_eager_guard():
with self.assertRaises(ValueError):
indices = [[1, 2], [1, 0]]
values = [1, 2, 3]
......@@ -412,7 +381,6 @@ class TestCooError(unittest.TestCase):
)
def test_indices_dtype(self):
with _test_eager_guard():
with self.assertRaises(TypeError):
indices = [[1.0, 2.0], [0, 1]]
values = [1, 2]
......@@ -421,7 +389,6 @@ class TestCooError(unittest.TestCase):
class TestCsrError(unittest.TestCase):
def test_dimension1(self):
with _test_eager_guard():
with self.assertRaises(ValueError):
crows = [0, 1, 2, 3]
cols = [0, 1, 2]
......@@ -432,7 +399,6 @@ class TestCsrError(unittest.TestCase):
)
def test_dimension2(self):
with _test_eager_guard():
with self.assertRaises(ValueError):
crows = [0, 1, 2, 3]
cols = [0, 1, 2]
......@@ -443,7 +409,6 @@ class TestCsrError(unittest.TestCase):
)
def test_same_shape1(self):
with _test_eager_guard():
with self.assertRaises(ValueError):
crows = [0, 1, 2, 3]
cols = [0, 1, 2, 3]
......@@ -454,7 +419,6 @@ class TestCsrError(unittest.TestCase):
)
def test_same_shape2(self):
with _test_eager_guard():
with self.assertRaises(ValueError):
crows = [0, 1, 2, 3]
cols = [0, 1, 2, 3]
......@@ -465,7 +429,6 @@ class TestCsrError(unittest.TestCase):
)
def test_same_shape3(self):
with _test_eager_guard():
with self.assertRaises(ValueError):
crows = [0, 1, 2, 3, 0, 1, 2]
cols = [0, 1, 2, 3, 0, 1, 2]
......@@ -476,7 +439,6 @@ class TestCsrError(unittest.TestCase):
)
def test_crows_first_value(self):
with _test_eager_guard():
with self.assertRaises(ValueError):
crows = [1, 1, 2, 3]
cols = [0, 1, 2]
......@@ -487,7 +449,6 @@ class TestCsrError(unittest.TestCase):
)
def test_dtype(self):
with _test_eager_guard():
with self.assertRaises(TypeError):
crows = [0, 1, 2, 3.0]
cols = [0, 1, 2]
......
python/paddle/fluid/tests/unittests/test_split_op.py
浏览文件 @ 1f93de31
......@@ -20,7 +20,6 @@ from op_test import OpTest, convert_float_to_uint16
import paddle
import paddle.fluid as fluid
from paddle.fluid import Program, core, program_guard
from paddle.fluid.framework import _test_eager_guard
class TestSplitOp(OpTest):
......@@ -453,7 +452,6 @@ class API_TestDygraphFluidSplit(unittest.TestCase):
x1_out = x1.numpy()
x2_out = x2.numpy()
ex_x0, ex_x1, ex_x2 = np.split(input_1, 3, axis=1)
with _test_eager_guard():
# input is a variable which shape is [4, 6, 6]
input = paddle.to_tensor(input_1)
input.stop_gradient = False
......@@ -465,9 +463,7 @@ class API_TestDygraphFluidSplit(unittest.TestCase):
loss.backward()
manul_grad = np.zeros_like(input_1)
manul_grad[:, :2, :] = 1
np.testing.assert_allclose(
input.gradient(), manul_grad, rtol=1e-05
)
np.testing.assert_allclose(input.gradient(), manul_grad, rtol=1e-05)
np.testing.assert_allclose(ex_x0, eager_x0_out, rtol=1e-05)
np.testing.assert_allclose(ex_x1, eager_x1_out, rtol=1e-05)
np.testing.assert_allclose(ex_x2, eager_x2_out, rtol=1e-05)
......@@ -486,7 +482,6 @@ class API_TestDygraphFluidSplit(unittest.TestCase):
x1_out = x1.numpy()
x2_out = x2.numpy()
ex_x0, ex_x1, ex_x2 = np.split(input_1, 3, axis=1)
with _test_eager_guard():
# input is a variable which shape is [4, 6, 6]
input = paddle.to_tensor(input_1)
input.stop_gradient = False
......@@ -498,9 +493,7 @@ class API_TestDygraphFluidSplit(unittest.TestCase):
loss.backward()
manul_grad = np.zeros_like(input_1)
manul_grad[:, :2, :] = 1
np.testing.assert_allclose(
input.gradient(), manul_grad, rtol=1e-05
)
np.testing.assert_allclose(input.gradient(), manul_grad, rtol=1e-05)
np.testing.assert_allclose(ex_x0, eager_x0_out, rtol=1e-05)
np.testing.assert_allclose(ex_x1, eager_x1_out, rtol=1e-05)
np.testing.assert_allclose(ex_x2, eager_x2_out, rtol=1e-05)
......@@ -522,7 +515,6 @@ class API_TestDygraphSplit(unittest.TestCase):
x2_out = x2.numpy()
ex_x0, ex_x1, ex_x2 = np.split(input_1, 3, axis=1)
with _test_eager_guard():
# input is a variable which shape is [4, 6, 6]
input = paddle.to_tensor(input_1)
input.stop_gradient = False
......@@ -534,9 +526,7 @@ class API_TestDygraphSplit(unittest.TestCase):
loss.backward()
manul_grad = np.zeros_like(input_1)
manul_grad[:, :2, :] = 1
np.testing.assert_allclose(
input.gradient(), manul_grad, rtol=1e-05
)
np.testing.assert_allclose(input.gradient(), manul_grad, rtol=1e-05)
np.testing.assert_allclose(ex_x0, eager_x0_out, rtol=1e-05)
np.testing.assert_allclose(ex_x1, eager_x1_out, rtol=1e-05)
np.testing.assert_allclose(ex_x2, eager_x2_out, rtol=1e-05)
......@@ -570,7 +560,6 @@ class API_TestDygraphSplit(unittest.TestCase):
out_dy_np = out_dy.numpy()
ex_out = np.split(input_1, [6], axis=1)
ex_out = ex_out[0]
with _test_eager_guard():
input = paddle.to_tensor(input_1)
out_eager = paddle.split(input, [6], axis=1)
out_eager = out_eager[0]
......@@ -612,7 +601,7 @@ class API_TestDygraphSplit(unittest.TestCase):
np.testing.assert_allclose(ex_x1, x1_out, rtol=1e-05)
np.testing.assert_allclose(ex_x2, x2_out, rtol=1e-05)
def func_negative_one_section(self):
def test_negative_one_section(self):
with fluid.dygraph.guard():
input_1 = np.random.random([4, 6, 6]).astype("int32")
# input is a variable which shape is [4, 6, 6]
......@@ -622,11 +611,6 @@ class API_TestDygraphSplit(unittest.TestCase):
x0_out = x0[0].numpy()
np.testing.assert_array_equal(x0_out, input.numpy())
def test_negative_one_section(self):
with _test_eager_guard():
self.func_negative_one_section()
self.func_negative_one_section()
class API_TestEmptySplit(unittest.TestCase):
def test_axis_input_empty_section(self):
......
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