add and open 0D test pnorm and cond (#53616)

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

@@ -2478,51 +2478,156 @@ class TestSundryAPI(unittest.TestCase):
         np.testing.assert_allclose(out, np.array(12))
         self.assertEqual(x.grad.shape, [2, 2])
 
-    def test_cond(self):
-        pass
-        # def assert_shape(out):
-        #     self.assertEqual(out.shape, [])
+    def test_linalg_norm(self):
+        # 1D input, p = fro ,axis = None, using reduceInferMeta
+        x_1 = paddle.arange(24, dtype="float32") - 12
+        x_1.stop_gradient = False
+        out_1 = paddle.linalg.norm(x_1)
+        out_1.retain_grads()
+        out_1.backward()
+
+        self.assertEqual(out_1.shape, [])
+        self.assertTrue(x_1.grad.shape, [24])
+
+        # 1D input, p = 1 ,axis = None,
+        # using p_nrom, as_vector = True
+        x_2 = paddle.arange(24, dtype="float32") - 12
+        x_2.stop_gradient = False
+        out_2 = paddle.linalg.norm(x_2, p=1)
+        out_2.retain_grads()
+        out_2.backward()
+
+        self.assertEqual(out_2.shape, [])
+        self.assertEqual(x_2.grad.shape, [24])
+
+        # 1D input, p = 1 ,axis = 0,
+        # using p_nrom, as_vector = False
+        x_2_p = paddle.arange(24, dtype="float32") - 12
+        x_2_p.stop_gradient = False
+        out_2_p = paddle.linalg.norm(x_2_p, p=1, axis=0)
+        out_2_p.retain_grads()
+        out_2_p.backward()
+
+        self.assertEqual(out_2_p.shape, [])
+        self.assertEqual(x_2_p.grad.shape, [24])
+
+        # 1D input, p = fro ,axis = 0,
+        # using p_nrom, as_vector = False
+        x_2_fro = paddle.arange(24, dtype="float32") - 12
+        x_2_fro.stop_gradient = False
+        out_2_fro = paddle.linalg.norm(x_2_fro, p="fro", axis=0)
+        out_2_fro.retain_grads()
+        out_2_fro.backward()
+
+        self.assertEqual(out_2_fro.shape, [])
+        self.assertEqual(x_2_fro.grad.shape, [24])
+
+        # 2D input, p = 1, axis = [0, 1]
+        # using p_matrix_norm ,depends on paddle.sum
+        x_3 = paddle.arange(24, dtype="float32").reshape([4, 6])
+        x_3.stop_gradient = False
+        out_3 = paddle.linalg.norm(x_3, p=1, axis=[0, 1])
+        out_3.retain_grads()
+        out_3.backward()
+        self.assertEqual(out_3.shape, [])
+        self.assertEqual(x_3.grad.shape, [4, 6])
+
+        # 2D input, p = 1, axis = None
+        # using p_matrix_norm, depends on paddle.sum
+        x_4 = paddle.arange(24, dtype="float32").reshape([4, 6])
+        x_4.stop_gradient = False
+        out_4 = paddle.linalg.norm(x_4)
+        out_4.retain_grads()
+        out_4.backward()
+        self.assertEqual(out_4.shape, [])
+        self.assertEqual(x_4.grad.shape, [4, 6])
+
+        # 2D input, p = inf, axis = [0, 1]
+        # using p_matrix_norm, depends on paddle.sum
+        x_5 = paddle.arange(24, dtype="float32").reshape([4, 6])
+        x_5.stop_gradient = False
+        out_5 = paddle.linalg.norm(x_5, p=2, axis=[0, 1])
+        out_5.retain_grads()
+        out_5.backward()
+
+        self.assertEqual(out_5.shape, [])
+        self.assertEqual(x_5.grad.shape, [4, 6])
+
+        # 2D input, p = -inf, axis = [0, 1]
+        x_6 = paddle.arange(24, dtype="float32").reshape([4, 6])
+        x_6.stop_gradient = False
+        out_6 = paddle.linalg.norm(x_6, p=-float("inf"), axis=[0, 1])
+        out_6.retain_grads()
+        out_6.backward()
+
+        self.assertEqual(out_6.shape, [])
+        self.assertEqual(x_6.grad.shape, [4, 6])
+
+    def test_linalg_cond(self):
+        def assert_shape(out):
+            self.assertEqual(out.shape, [])
 
-        # x = paddle.to_tensor([[1.0, 0, -1], [0, 1, 0], [1, 0, 1]])
-        # x.stop_gradient = False
-        # p = 2 : use paddle.sum, paddle.max, paddle.min
-        # out = paddle.linalg.cond(x)
-        # assert_shape(out)
+        x1 = paddle.to_tensor([[1.0, 0, -1], [0, 1, 0], [1, 0, 1]])
+        x1.stop_gradient = False
+        # p = 2 : use paddle.sum
+        out = paddle.linalg.cond(x1)
+        out.backward()
+        assert_shape(out)
+        self.assertEqual(x1.grad.shape, [3, 3])
 
         # p = fro : use paddle.sum
-        # out_fro = paddle.linalg.cond(x, p='fro')
-        # assert_shape(out_fro)
-
-        # p = nuc : use paddle.sum, paddle.max, paddle.min
-        # out_nuc = paddle.linalg.cond(x, p='nuc')
-        # assert_shape(out_nuc)
-
-        # p in (-1, 1) : use paddle.sum, paddle.max, paddle.min
-        # out_1 = paddle.linalg.cond(x, p=1)
-        # assert_shape(out_1)
-        # out_minus_1 = paddle.linalg.cond(x, p=-1)
-        # assert_shape(out_minus_1)
-
-        # p in (-2, 2) :use paddle.max, paddle.min
-        # out_2 = paddle.linalg.cond(x, p=2)
-        # assert_shape(out_2)
-        # out_minus_2 = paddle.linalg.cond(x, p=-2)
-        # assert_shape(out_minus_2)
-
-        # p in (-inf, inf):use paddle.sum, paddle.max, paddle.min
-        # out_inf = paddle.linalg.cond(x, p=float("inf"))
-        # assert_shape(out_inf)
-        # out_minus_inf = paddle.linalg.cond(x, p=-float("inf"))
-        # assert_shape(out_minus_inf)
-        # out_minus_inf.backward()
-        # self.assertTrue(x.grad.shape, [3, 3])
-
-        # a = paddle.randn([2, 4, 4])
-        # a.stop_gradient = False
-        # a_cond_fro = paddle.linalg.cond(a, p='fro')
-        # a_cond_fro.backward()
-        # self.assertEqual(len(a_cond_fro.shape), 1)
-        # self.assertEqual(a.grad.shape, [2, 4, 4])
+        x2 = paddle.to_tensor([[1.0, 0, -1], [0, 1, 0], [1, 0, 1]])
+        x2.stop_gradient = False
+        out_fro = paddle.linalg.cond(x2, p='fro')
+        out_fro.backward()
+        assert_shape(out_fro)
+        self.assertEqual(x2.grad.shape, [3, 3])
+
+        # p = nuc : use paddle.sum
+        x3 = paddle.to_tensor([[1.0, 0, -1], [0, 1, 0], [1, 0, 1]])
+        x3.stop_gradient = False
+        out_nuc = paddle.linalg.cond(x3, p='nuc')
+        out_nuc.backward()
+        assert_shape(out_nuc)
+        self.assertEqual(x3.grad.shape, [3, 3])
+
+        # p in (-1, 1) : use paddle.sum
+        x4 = paddle.to_tensor([[1.0, 0, -1], [0, 1, 0], [1, 0, 1]])
+        x4.stop_gradient = False
+        out_1 = paddle.linalg.cond(x4, p=1)
+        out_1.backward()
+        assert_shape(out_1)
+        self.assertEqual(x4.grad.shape, [3, 3])
+
+        x5 = paddle.to_tensor([[1.0, 0, -1], [0, 1, 0], [1, 0, 1]])
+        x5.stop_gradient = False
+        out_minus_1 = paddle.linalg.cond(x5, p=-1)
+        out_minus_1.backward()
+        assert_shape(out_minus_1)
+        self.assertEqual(x5.grad.shape, [3, 3])
+
+        # p in (-2, 2)  depends on paddle.sum
+        x6 = paddle.to_tensor([[1.0, 0, -1], [0, 1, 0], [1, 0, 1]])
+        x6.stop_gradient = False
+        out_2 = paddle.linalg.cond(x6, p=2)
+        out_2.backward()
+        assert_shape(out_2)
+        self.assertEqual(x6.grad.shape, [3, 3])
+
+        # p in (-inf, inf):use paddle.sum
+        x8 = paddle.to_tensor([[1.0, 0, -1], [0, 1, 0], [1, 0, 1]])
+        x8.stop_gradient = False
+        out_inf = paddle.linalg.cond(x8, p=float("inf"))
+        out_inf.backward()
+        assert_shape(out_inf)
+        self.assertEqual(x8.grad.shape, [3, 3])
+
+        a = paddle.randn([2, 4, 4])
+        a.stop_gradient = False
+        a_cond_fro = paddle.linalg.cond(a, p='fro')
+        a_cond_fro.backward()
+        self.assertEqual(len(a_cond_fro.shape), 1)
+        self.assertEqual(a.grad.shape, [2, 4, 4])
 
     def test_cov(self):
         xt = paddle.randn((3, 4))
@@ -4549,20 +4654,197 @@ class TestSundryAPIStatic(unittest.TestCase):
         np.testing.assert_allclose(res[0], np.array(12))
 
     @prog_scope()
-    def test_cond(self):
-        pass
-        # use paddle.sum, paddle.max, paddle.min
-        # x = paddle.to_tensor([[1.0, 0, -1], [0, 1, 0], [1, 0, 1]])
-        # x.stop_gradient = False
-        # out = paddle.linalg.cond(x)
-        # paddle.static.append_backward(out)
-
-        # prog = paddle.static.default_main_program()
-        # res = self.exe.run(prog, fetch_list=[out, x.grad_name])
-
-        # self.assertTrue(res[0].shape, ())
-        # self.assertTrue(res[1].shape, (3, 3))
-        # np.testing.assert_allclose(out, np.array(1.41421342))
+    def test_linalg_norm(self):
+        # 1D input, p = fro ,axis = None, using reduceInferMeta
+        x_1 = paddle.arange(24, dtype="float32") - 12
+        x_1.stop_gradient = False
+        out_1 = paddle.linalg.norm(x_1)
+        paddle.static.append_backward(out_1)
+
+        prog = paddle.static.default_main_program()
+
+        res = self.exe.run(prog, fetch_list=[out_1, x_1.grad_name])
+        self.assertEqual(res[0].shape, ())
+        self.assertEqual(res[1].shape, (24,))
+
+        # 1D input, p = 1 ,axis = None,
+        # using p_nrom, as_vector = True
+        x_2 = paddle.arange(24, dtype="float32") - 12
+        x_2.stop_gradient = False
+        out_2 = paddle.linalg.norm(x_2, p=1)
+        paddle.static.append_backward(out_2.sum())
+
+        prog = paddle.static.default_main_program()
+        res = self.exe.run(prog, fetch_list=[out_2, x_2.grad_name])
+        self.assertEqual(res[0].shape, ())
+        self.assertEqual(res[1].shape, (24,))
+
+        # 1D input, p = 1 ,axis = 0,
+        # using p_nrom, as_vector = False
+        x_2_p = paddle.arange(24, dtype="float32") - 12
+        x_2_p.stop_gradient = False
+        out_2_p = paddle.linalg.norm(x_2_p, p=1, axis=0)
+        paddle.static.append_backward(out_2_p.sum())
+
+        prog = paddle.static.default_main_program()
+        res = self.exe.run(prog, fetch_list=[out_2_p, x_2_p.grad_name])
+        self.assertEqual(res[0].shape, ())
+        self.assertEqual(res[1].shape, (24,))
+
+        # 1D input, p = fro ,axis = 0,
+        # using p_nrom, as_vector = False
+        x_2_fro = paddle.arange(24, dtype="float32") - 12
+        x_2_fro.stop_gradient = False
+        out_2_fro = paddle.linalg.norm(x_2_fro, p="fro", axis=0)
+        paddle.static.append_backward(out_2_fro.sum())
+
+        prog = paddle.static.default_main_program()
+        res = self.exe.run(prog, fetch_list=[out_2_fro, x_2_fro.grad_name])
+        self.assertEqual(res[0].shape, ())
+        self.assertEqual(res[1].shape, (24,))
+
+        # 2D input, p = 1, axis = [0, 1]
+        # using p_matrix_norm ,depends on  paddle.sum
+        x_3 = paddle.arange(24, dtype="float32").reshape([4, 6])
+        x_3.stop_gradient = False
+        out_3 = paddle.linalg.norm(x_3, p=1, axis=[0, 1])
+        paddle.static.append_backward(out_3.sum())
+
+        prog = paddle.static.default_main_program()
+        res = self.exe.run(prog, fetch_list=[out_3, x_3.grad_name])
+        self.assertEqual(res[0].shape, ())
+        self.assertEqual(res[1].shape, (4, 6))
+
+        # 2D input, p = 1, axis = None
+        # using p_matrix_norm, depends on paddle.sum
+        x_4 = paddle.arange(24, dtype="float32").reshape([4, 6])
+        x_4.stop_gradient = False
+        out_4 = paddle.linalg.norm(x_4)
+        paddle.static.append_backward(out_4.sum())
+
+        prog = paddle.static.default_main_program()
+        res = self.exe.run(prog, fetch_list=[out_4, x_4.grad_name])
+
+        self.assertEqual(res[0].shape, ())
+        self.assertEqual(res[1].shape, (4, 6))
+
+        # 2D input, p = inf, axis = None
+        x_5 = paddle.arange(24, dtype="float32").reshape([4, 6])
+        x_5.stop_gradient = False
+        out_5 = paddle.linalg.norm(x_5)
+        paddle.static.append_backward(out_5.sum())
+        prog = paddle.static.default_main_program()
+        res = self.exe.run(prog, fetch_list=[out_5, x_5.grad_name])
+
+        self.assertEqual(res[0].shape, ())
+        self.assertEqual(res[1].shape, (4, 6))
+
+        # 2D input, p = -inf, axis = [0, 1]
+        x_6 = paddle.arange(24, dtype="float32").reshape([4, 6])
+        x_6.stop_gradient = False
+        out_6 = paddle.linalg.norm(x_6, p=-float("inf"), axis=[0, 1])
+        paddle.static.append_backward(out_6.sum())
+        prog = paddle.static.default_main_program()
+        res = self.exe.run(prog, fetch_list=[out_6, x_6.grad_name])
+
+        self.assertEqual(res[0].shape, ())
+        self.assertEqual(res[1].shape, (4, 6))
+
+    @prog_scope()
+    def test_linalg_cond(self):
+        # use paddle.sum
+        x = paddle.to_tensor([[1.0, 0, -1], [0, 1, 0], [1, 0, 1]])
+        x.stop_gradient = False
+        out = paddle.linalg.cond(x)
+        paddle.static.append_backward(out)
+
+        prog = paddle.static.default_main_program()
+        res = self.exe.run(prog, fetch_list=[out, x.grad_name])
+        self.assertEqual(res[0].shape, ())
+        self.assertEqual(res[1].shape, (3, 3))
+
+        # p = fro : use paddle.sum
+        x2 = paddle.to_tensor([[1.0, 0, -1], [0, 1, 0], [1, 0, 1]])
+        x2.stop_gradient = False
+        out_fro = paddle.linalg.cond(x2, p='fro')
+        paddle.static.append_backward(out_fro)
+
+        prog = paddle.static.default_main_program()
+        res = self.exe.run(prog, fetch_list=[out_fro, x.grad_name])
+
+        self.assertEqual(res[0].shape, ())
+        self.assertEqual(res[1].shape, (3, 3))
+
+        # p = nuc : use paddle.sum
+        x3 = paddle.to_tensor([[1.0, 0, -1], [0, 1, 0], [1, 0, 1]])
+        x3.stop_gradient = False
+        out_nuc = paddle.linalg.cond(x3, p='nuc')
+        paddle.static.append_backward(out_nuc)
+
+        prog = paddle.static.default_main_program()
+        res = self.exe.run(prog, fetch_list=[out_nuc, x.grad_name])
+
+        self.assertEqual(res[0].shape, ())
+        self.assertEqual(res[1].shape, (3, 3))
+
+        # p in (-1, 1) : use paddle.sum
+        x4 = paddle.to_tensor([[1.0, 0, -1], [0, 1, 0], [1, 0, 1]])
+        x4.stop_gradient = False
+        out_1 = paddle.linalg.cond(x4, p=1)
+        paddle.static.append_backward(out_1)
+
+        prog = paddle.static.default_main_program()
+        res = self.exe.run(prog, fetch_list=[out_1, x.grad_name])
+
+        self.assertEqual(res[0].shape, ())
+        self.assertEqual(res[1].shape, (3, 3))
+
+        x5 = paddle.to_tensor([[1.0, 0, -1], [0, 1, 0], [1, 0, 1]])
+        x5.stop_gradient = False
+        out_minus_1 = paddle.linalg.cond(x5, p=-1)
+        paddle.static.append_backward(out_minus_1)
+
+        prog = paddle.static.default_main_program()
+        res = self.exe.run(prog, fetch_list=[out_minus_1, x.grad_name])
+
+        self.assertEqual(res[0].shape, ())
+        self.assertEqual(res[1].shape, (3, 3))
+
+        # p in (-2, 2)  depends on paddle.sum
+        x6 = paddle.to_tensor([[1.0, 0, -1], [0, 1, 0], [1, 0, 1]])
+        x6.stop_gradient = False
+        out_2 = paddle.linalg.cond(x6, p=2)
+        paddle.static.append_backward(out_2)
+
+        prog = paddle.static.default_main_program()
+        res = self.exe.run(prog, fetch_list=[out_2, x.grad_name])
+
+        self.assertEqual(res[0].shape, ())
+        self.assertEqual(res[1].shape, (3, 3))
+
+        # p in (-inf, inf):use paddle.sum
+        x8 = paddle.to_tensor([[1.0, 0, -1], [0, 1, 0], [1, 0, 1]])
+        x8.stop_gradient = False
+        out_inf = paddle.linalg.cond(x8, p=float("inf"))
+        paddle.static.append_backward(out_inf)
+
+        prog = paddle.static.default_main_program()
+        res = self.exe.run(prog, fetch_list=[out_inf, x.grad_name])
+
+        self.assertEqual(res[0].shape, ())
+        self.assertEqual(res[1].shape, (3, 3))
+
+        # depends on paddle.sum
+        a = paddle.randn([2, 4, 4])
+        a.stop_gradient = False
+        a_cond_fro = paddle.linalg.cond(a, p='fro')
+        paddle.static.append_backward(a_cond_fro.sum())
+
+        prog = paddle.static.default_main_program()
+        res = self.exe.run(prog, fetch_list=[a_cond_fro, a.grad_name])
+
+        self.assertEqual(res[0].shape, (2,))
+        self.assertEqual(res[1].shape, (2, 4, 4))
 
     @prog_scope()
     def test_cov(self):