rm unittest eager guard tests part20 sparse_mv2split (#48879)

python/paddle/fluid/tests/unittests/test_sparse_mv_op.py

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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):