rm unittest eager guard tests part20 sparse_mv2split (PaddlePaddle#48879

)
lxsbupt · Dec 15, 2022 · 0706407 · 0706407
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diff --git a/python/paddle/fluid/tests/unittests/test_sparse_mv_op.py b/python/paddle/fluid/tests/unittests/test_sparse_mv_op.py
@@ -19,7 +19,6 @@
 import numpy as np
 
 import paddle
-from paddle.fluid.framework import _test_eager_guard
 
 paddle.seed(100)
 
@@ -43,38 +42,37 @@ 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])
-            origin_x = origin_x * mask
-            origin_vec = paddle.rand([32])
-
-            dense_x = origin_x.detach()
-            dense_x.stop_gradient = False
-            dense_vec = origin_vec.detach()
-            dense_vec.stop_gradient = False
-            dense_out = paddle.mv(dense_x, dense_vec)
-            dense_out.backward()
-
-            sp_x = origin_x.detach().to_sparse_csr()
-            sp_x.stop_gradient = False
-            sp_vec = origin_vec.detach()
-            sp_vec.stop_gradient = False
-            sp_out = paddle.sparse.mv(sp_x, sp_vec)
-            sp_out.backward()
-
-            np.testing.assert_allclose(
-                sp_out.numpy(), dense_out.numpy(), rtol=1e-05
-            )
-            np.testing.assert_allclose(
-                sp_x.grad.to_dense().numpy(),
-                (dense_x.grad * mask).numpy(),
-                rtol=1e-05,
-            )
-            np.testing.assert_allclose(
-                sp_vec.grad.numpy(), dense_vec.grad.numpy(), rtol=1e-05
-            )
+        paddle.set_default_dtype('float64')
+        origin_x = paddle.rand([64, 32])
+        mask = paddle.randint(0, 2, [64, 32])
+        origin_x = origin_x * mask
+        origin_vec = paddle.rand([32])
+
+        dense_x = origin_x.detach()
+        dense_x.stop_gradient = False
+        dense_vec = origin_vec.detach()
+        dense_vec.stop_gradient = False
+        dense_out = paddle.mv(dense_x, dense_vec)
+        dense_out.backward()
+
+        sp_x = origin_x.detach().to_sparse_csr()
+        sp_x.stop_gradient = False
+        sp_vec = origin_vec.detach()
+        sp_vec.stop_gradient = False
+        sp_out = paddle.sparse.mv(sp_x, sp_vec)
+        sp_out.backward()
+
+        np.testing.assert_allclose(
+            sp_out.numpy(), dense_out.numpy(), rtol=1e-05
+        )
+        np.testing.assert_allclose(
+            sp_x.grad.to_dense().numpy(),
+            (dense_x.grad * mask).numpy(),
+            rtol=1e-05,
+        )
+        np.testing.assert_allclose(
+            sp_vec.grad.numpy(), dense_vec.grad.numpy(), rtol=1e-05
+        )
 
 
 @unittest.skipIf(
@@ -84,38 +82,37 @@ def test_mv(self):
 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])
-            origin_x = origin_x * mask
-            origin_vec = paddle.rand([32])
-
-            dense_x = origin_x.detach()
-            dense_x.stop_gradient = False
-            dense_vec = origin_vec.detach()
-            dense_vec.stop_gradient = False
-            dense_out = paddle.mv(dense_x, dense_vec)
-            dense_out.backward()
-
-            sp_x = origin_x.detach().to_sparse_coo(sparse_dim=2)
-            sp_x.stop_gradient = False
-            sp_vec = origin_vec.detach()
-            sp_vec.stop_gradient = False
-            sp_out = paddle.sparse.mv(sp_x, sp_vec)
-            sp_out.backward()
-
-            np.testing.assert_allclose(
-                sp_out.numpy(), dense_out.numpy(), rtol=1e-05
-            )
-            np.testing.assert_allclose(
-                sp_x.grad.to_dense().numpy(),
-                (dense_x.grad * mask).numpy(),
-                rtol=1e-05,
-            )
-            np.testing.assert_allclose(
-                sp_vec.grad.numpy(), dense_vec.grad.numpy(), rtol=1e-05
-            )
+        paddle.set_default_dtype('float64')
+        origin_x = paddle.rand([64, 32])
+        mask = paddle.randint(0, 2, [64, 32])
+        origin_x = origin_x * mask
+        origin_vec = paddle.rand([32])
+
+        dense_x = origin_x.detach()
+        dense_x.stop_gradient = False
+        dense_vec = origin_vec.detach()
+        dense_vec.stop_gradient = False
+        dense_out = paddle.mv(dense_x, dense_vec)
+        dense_out.backward()
+
+        sp_x = origin_x.detach().to_sparse_coo(sparse_dim=2)
+        sp_x.stop_gradient = False
+        sp_vec = origin_vec.detach()
+        sp_vec.stop_gradient = False
+        sp_out = paddle.sparse.mv(sp_x, sp_vec)
+        sp_out.backward()
+
+        np.testing.assert_allclose(
+            sp_out.numpy(), dense_out.numpy(), rtol=1e-05
+        )
+        np.testing.assert_allclose(
+            sp_x.grad.to_dense().numpy(),
+            (dense_x.grad * mask).numpy(),
+            rtol=1e-05,
+        )
+        np.testing.assert_allclose(
+            sp_vec.grad.numpy(), dense_vec.grad.numpy(), rtol=1e-05
+        )
 
 
 if __name__ == "__main__":

diff --git a/python/paddle/fluid/tests/unittests/test_sparse_pooling_op.py b/python/paddle/fluid/tests/unittests/test_sparse_pooling_op.py
@@ -18,7 +18,6 @@
 import numpy as np
 
 import paddle
-from paddle.fluid.framework import _test_eager_guard
 
 
 class TestMaxPool3DFunc(unittest.TestCase):
@@ -42,32 +41,31 @@ def setUp(self):
         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)
-            sparse_out = paddle.sparse.nn.functional.max_pool3d(
-                sparse_x,
-                self.kernel_sizes,
-                stride=self.strides,
-                padding=self.paddings,
-            )
-            out = sparse_out.to_dense()
-            out.backward(out)
-
-            dense_x = copy.deepcopy(self.dense_x)
-            dense_out = paddle.nn.functional.max_pool3d(
-                dense_x,
-                self.kernel_sizes,
-                stride=self.strides,
-                padding=self.paddings,
-                data_format='NDHWC',
-            )
-            dense_out.backward(dense_out)
-
-            # compare with dense
-            assert np.allclose(dense_out.numpy(), out.numpy())
-            assert np.allclose(dense_x.grad.numpy(), self.dense_x.grad.numpy())
+        self.setUp()
+        self.dense_x.stop_gradient = False
+        sparse_x = self.dense_x.to_sparse_coo(4)
+        sparse_out = paddle.sparse.nn.functional.max_pool3d(
+            sparse_x,
+            self.kernel_sizes,
+            stride=self.strides,
+            padding=self.paddings,
+        )
+        out = sparse_out.to_dense()
+        out.backward(out)
+
+        dense_x = copy.deepcopy(self.dense_x)
+        dense_out = paddle.nn.functional.max_pool3d(
+            dense_x,
+            self.kernel_sizes,
+            stride=self.strides,
+            padding=self.paddings,
+            data_format='NDHWC',
+        )
+        dense_out.backward(dense_out)
+
+        # compare with dense
+        assert np.allclose(dense_out.numpy(), out.numpy())
+        assert np.allclose(dense_x.grad.numpy(), self.dense_x.grad.numpy())
 
 
 class TestStride(TestMaxPool3DFunc):
@@ -102,19 +100,18 @@ def setInput(self):
 
 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(
-                kernel_size=3, data_format='NDHWC'
-            )
-            out = max_pool3d(sparse_x)
-            out = out.to_dense()
-
-            dense_out = paddle.nn.functional.max_pool3d(
-                dense_x, 3, data_format='NDHWC'
-            )
-            assert np.allclose(dense_out.numpy(), out.numpy())
+        dense_x = paddle.randn((2, 3, 6, 6, 3))
+        sparse_x = dense_x.to_sparse_coo(4)
+        max_pool3d = paddle.sparse.nn.MaxPool3D(
+            kernel_size=3, data_format='NDHWC'
+        )
+        out = max_pool3d(sparse_x)
+        out = out.to_dense()
+
+        dense_out = paddle.nn.functional.max_pool3d(
+            dense_x, 3, data_format='NDHWC'
+        )
+        assert np.allclose(dense_out.numpy(), out.numpy())
 
 
 if __name__ == "__main__":