chore: import upstream snapshot with attribution

test/legacy_test/test_stack_op.py

@@ -0,0 +1,621 @@
+# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+
+import numpy as np
+from op_test import (
+    OpTest,
+    convert_float_to_uint16,
+    get_device_place,
+    is_custom_device,
+)
+
+import paddle
+from paddle import base
+
+paddle.enable_static()
+
+
+class TestStackOpBase(OpTest):
+    def initDefaultParameters(self):
+        self.num_inputs = 4
+        self.input_dim = (5, 6, 7)
+        self.axis = 0
+        self.dtype = 'float64'
+
+    def initParameters(self):
+        pass
+
+    def get_x_names(self):
+        x_names = []
+        for i in range(self.num_inputs):
+            x_names.append(f'x{i}')
+        return x_names
+
+    def setUp(self):
+        self.initDefaultParameters()
+        self.initParameters()
+        self.op_type = 'stack'
+        self.prim_op_type = "comp"
+        self.python_api = paddle.stack
+        self.public_python_api = paddle.stack
+        self.x = []
+        for i in range(self.num_inputs):
+            self.x.append(
+                np.random.random(size=self.input_dim).astype(self.dtype)
+            )
+
+        tmp = []
+        x_names = self.get_x_names()
+        for i in range(self.num_inputs):
+            tmp.append((x_names[i], self.x[i]))
+
+        self.inputs = {'X': tmp}
+        self.outputs = {'Y': np.stack(self.x, axis=self.axis)}
+        self.attrs = {'axis': self.axis}
+
+    def test_check_output(self):
+        self.check_output(check_prim=True, check_pir=True, check_prim_pir=True)
+
+    def test_check_grad(self):
+        self.check_grad(
+            self.get_x_names(),
+            'Y',
+            check_prim=True,
+            check_pir=True,
+            check_prim_pir=True,
+        )
+
+
+class TestStackOp1(TestStackOpBase):
+    def initParameters(self):
+        self.num_inputs = 8
+
+
+class TestStackOp2(TestStackOpBase):
+    def initParameters(self):
+        self.num_inputs = 10
+
+
+class TestStackOp3(TestStackOpBase):
+    def initParameters(self):
+        self.axis = -1
+
+
+class TestStackOp4(TestStackOpBase):
+    def initParameters(self):
+        self.axis = -4
+
+
+class TestStackOp5(TestStackOpBase):
+    def initParameters(self):
+        self.axis = 1
+
+
+class TestStackOp6(TestStackOpBase):
+    def initParameters(self):
+        self.axis = 3
+
+
+class TestStackOp_ZeroDim(TestStackOpBase):
+    def initParameters(self):
+        self.input_dim = ()
+        self.enable_cinn = False
+
+
+class TestStackFP16Op(TestStackOpBase):
+    def initParameters(self):
+        self.dtype = np.float16
+
+
+class TestStackFP16Op1(TestStackOpBase):
+    def initParameters(self):
+        self.dtype = np.float16
+        self.num_inputs = 8
+
+
+class TestStackFP16Op2(TestStackOpBase):
+    def initParameters(self):
+        self.dtype = np.float16
+        self.num_inputs = 10
+
+
+class TestStackFP16Op3(TestStackOpBase):
+    def initParameters(self):
+        self.dtype = np.float16
+        self.axis = -1
+
+
+class TestStackFP16Op4(TestStackOpBase):
+    def initParameters(self):
+        self.dtype = np.float16
+        self.axis = -4
+
+
+class TestStackFP16Op5(TestStackOpBase):
+    def initParameters(self):
+        self.dtype = np.float16
+        self.axis = 1
+
+
+class TestStackFP16Op6(TestStackOpBase):
+    def initParameters(self):
+        self.dtype = np.float16
+        self.axis = 3
+
+
+class TestStackBF16Op(OpTest):
+    def initDefaultParameters(self):
+        self.num_inputs = 4
+        self.input_dim = (5, 6, 7)
+        self.axis = 0
+        self.dtype = np.uint16
+
+    def initParameters(self):
+        pass
+
+    def get_x_names(self):
+        x_names = []
+        for i in range(self.num_inputs):
+            x_names.append(f'x{i}')
+        return x_names
+
+    def setUp(self):
+        self.initDefaultParameters()
+        self.initParameters()
+        self.op_type = 'stack'
+        self.prim_op_type = "comp"
+        self.python_api = paddle.stack
+        self.public_python_api = paddle.stack
+        self.x = []
+        for i in range(self.num_inputs):
+            self.x.append(
+                np.random.random(size=self.input_dim).astype(np.float32)
+            )
+
+        out = np.stack(self.x, axis=self.axis)
+
+        tmp = []
+        x_names = self.get_x_names()
+        for i in range(self.num_inputs):
+            tmp.append((x_names[i], convert_float_to_uint16(self.x[i])))
+
+        self.inputs = {'X': tmp}
+        self.outputs = {'Y': convert_float_to_uint16(out)}
+        self.attrs = {'axis': self.axis}
+
+    def test_check_output(self):
+        self.check_output(check_prim=True, check_pir=True, check_prim_pir=True)
+
+    def test_check_grad(self):
+        self.check_grad(
+            self.get_x_names(),
+            'Y',
+            check_prim=True,
+            check_pir=True,
+            check_prim_pir=True,
+        )
+
+
+class TestStackAPIWithDenseTensorArray(unittest.TestCase):
+    """
+    Test stack api when the input(x) is a DenseTensorArray.
+    """
+
+    def setUp(self):
+        self.axis = 1
+        self.iter_num = 3
+        self.input_shape = [2, 3]
+        self.x = np.random.random(self.input_shape).astype("float32")
+        self.place = (
+            get_device_place()
+            if (base.is_compiled_with_cuda() or is_custom_device())
+            else base.CPUPlace()
+        )
+
+    def test_case(self):
+        self.program = paddle.static.Program()
+        with paddle.static.program_guard(self.program):
+            input = paddle.assign(self.x)
+            tensor_array = paddle.tensor.create_array(dtype='float32')
+            zero = paddle.tensor.fill_constant(
+                shape=[1], value=0, dtype="int64"
+            )
+
+            for i in range(self.iter_num):
+                paddle.tensor.array_write(input, zero + i, tensor_array)
+
+            self.out_var = paddle.stack(tensor_array, axis=self.axis)
+        self.assertTrue(self.out_var.shape[self.axis] == -1)
+        exe = base.Executor(self.place)
+        res = exe.run(self.program, fetch_list=self.out_var)
+        np.testing.assert_array_equal(
+            res[0], np.stack([self.x] * self.iter_num, axis=self.axis)
+        )
+
+
+class TestTensorStackAPIWithDenseTensorArray(unittest.TestCase):
+    """
+    Test stack api when the input(x) is a DenseTensorArray.
+    """
+
+    def setUp(self):
+        self.axis = 1
+        self.iter_num = 3
+        self.input_shape = [2, 3]
+        self.x = np.random.random(self.input_shape).astype("float32")
+        self.place = (
+            get_device_place()
+            if (base.is_compiled_with_cuda() or is_custom_device())
+            else base.CPUPlace()
+        )
+
+    def test_case(self):
+        self.program = paddle.static.Program()
+        with paddle.static.program_guard(self.program):
+            input = paddle.assign(self.x)
+            tensor_array = paddle.tensor.create_array(dtype='float32')
+            zero = paddle.tensor.fill_constant(
+                shape=[1], value=0, dtype="int64"
+            )
+
+            for i in range(self.iter_num):
+                paddle.tensor.array_write(input, zero + i, tensor_array)
+
+            self.out_var = paddle.stack(tensor_array, axis=self.axis)
+        self.assertTrue(self.out_var.shape[self.axis] == -1)
+        exe = base.Executor(self.place)
+        res = exe.run(self.program, fetch_list=self.out_var)
+        np.testing.assert_array_equal(
+            res[0], np.stack([self.x] * self.iter_num, axis=self.axis)
+        )
+
+
+class API_test(unittest.TestCase):
+    def test_out(self):
+        with paddle.static.program_guard(
+            paddle.static.Program(), paddle.static.Program()
+        ):
+            data1 = paddle.static.data('data1', shape=[1, 2], dtype='float64')
+            data2 = paddle.static.data('data2', shape=[1, 2], dtype='float64')
+            data3 = paddle.static.data('data3', shape=[1, 2], dtype='float64')
+            result_stack = paddle.stack([data1, data2, data3], axis=0)
+            place = base.CPUPlace()
+            exe = base.Executor(place)
+            input1 = np.random.random([1, 2]).astype('float64')
+            input2 = np.random.random([1, 2]).astype('float64')
+            input3 = np.random.random([1, 2]).astype('float64')
+            (result,) = exe.run(
+                feed={"data1": input1, "data2": input2, "data3": input3},
+                fetch_list=[result_stack],
+            )
+            expected_result = np.stack([input1, input2, input3], axis=0)
+            np.testing.assert_allclose(expected_result, result, rtol=1e-05)
+
+    def test_single_tensor_error(self):
+        with paddle.static.program_guard(
+            paddle.static.Program(), paddle.static.Program()
+        ):
+            x = paddle.rand([2, 3])
+            self.assertRaises(TypeError, paddle.stack, x)
+
+
+class API_DygraphTest(unittest.TestCase):
+    def test_out(self):
+        data1 = np.array([[1.0, 2.0]])
+        data2 = np.array([[3.0, 4.0]])
+        data3 = np.array([[5.0, 6.0]])
+        with base.dygraph.guard():
+            x1 = paddle.to_tensor(data1)
+            x2 = paddle.to_tensor(data2)
+            x3 = paddle.to_tensor(data3)
+            result = paddle.stack([x1, x2, x3])
+            result_np = result.numpy()
+        expected_result = np.stack([data1, data2, data3])
+        np.testing.assert_allclose(expected_result, result_np, rtol=1e-05)
+
+        with base.dygraph.guard():
+            y1 = paddle.to_tensor(data1)
+            result = paddle.stack([y1], axis=0)
+            result_np_2 = result.numpy()
+        expected_result_2 = np.stack([data1], axis=0)
+        np.testing.assert_allclose(expected_result_2, result_np_2, rtol=1e-05)
+
+    def test_single_tensor_error(self):
+        with base.dygraph.guard():
+            x = paddle.to_tensor([1, 2, 3])
+            self.assertRaisesRegex(
+                ValueError,
+                r"\(InvalidArgument\) stack\(\): argument 'x' \(position 0\) must be list of Tensors",
+                paddle.stack,
+                x,
+            )
+
+
+class TestStackOpWithNegativeShape(unittest.TestCase):
+    def test_out(self):
+        main_prg, startup_prg = paddle.static.Program(), paddle.static.Program()
+        with paddle.static.program_guard(main_prg, startup_prg):
+            b = paddle.static.data(name='b', shape=[-1], dtype='int64')
+            e = paddle.static.data(name='e', shape=[3], dtype='int64')
+            k = paddle.stack([b, e], axis=0)
+            exe = paddle.static.Executor()
+            exe.run(startup_prg)
+            out = exe.run(
+                main_prg,
+                feed={
+                    'b': np.ones(
+                        [
+                            3,
+                        ]
+                    ).astype("int64"),
+                    'e': np.zeros(
+                        [
+                            3,
+                        ]
+                    ).astype("int64"),
+                },
+                fetch_list=[k],
+            )
+        np.testing.assert_allclose(
+            out[0], np.array([[1, 1, 1], [0, 0, 0]]), rtol=1e-05
+        )
+
+
+class TestStackAPI_ZeroDim(unittest.TestCase):
+    def test_dygraph(self):
+        paddle.disable_static()
+
+        x1 = paddle.rand([])
+        x2 = paddle.rand([])
+        x1.stop_gradient = False
+        x2.stop_gradient = False
+        out = paddle.stack([x1, x2])
+        out.retain_grads()
+        out.backward()
+
+        self.assertEqual(out.shape, [2])
+        self.assertEqual(x1.grad.shape, [])
+        self.assertEqual(x2.grad.shape, [])
+        self.assertEqual(out.grad.shape, [2])
+
+        paddle.enable_static()
+
+
+class TestStackListOfSingleTensor(unittest.TestCase):
+    def setUp(self):
+        paddle.disable_static()
+        paddle.seed(2022)
+        self.x = [paddle.randn((4, 2, 6), dtype="float32")]
+        self.x[0].stop_gradient = False
+
+    def test_list_single_tensor(self):
+        expect = paddle.stack(self.x)
+        paddle.base.core._set_prim_all_enabled(True)
+        st_model = paddle.jit.to_static(
+            paddle.stack,
+            backend=None,
+            full_graph=True,
+        )
+        actual = st_model(self.x)
+        np.testing.assert_allclose(expect, actual)
+        paddle.enable_static()
+
+
+class TestPrimStackGrad(unittest.TestCase):
+    def setUp(self):
+        paddle.disable_static()
+        paddle.seed(2022)
+        self.x = [paddle.randn((4, 2, 6), dtype="float32") for _ in range(3)]
+        for i in range(len(self.x)):
+            self.x[i].stop_gradient = False
+
+    def test_stack_double_grad(self):
+        paddle.base.core.set_prim_eager_enabled(True)
+        z = paddle.stack(self.x)
+        z = paddle.tanh(z)
+        grads_out = paddle.grad(z, self.x[1], create_graph=True)
+        ggrads_out = paddle.grad(grads_out, self.x[1], create_graph=True)[0]
+
+        zz = paddle.tanh(self.x[1])
+        grads_expected = paddle.grad(zz, self.x[1], create_graph=True)
+        ggrads_expected = paddle.grad(
+            grads_expected, self.x[1], create_graph=False
+        )[0]
+
+        np.testing.assert_allclose(ggrads_out, ggrads_expected)
+        paddle.enable_static()
+        paddle.base.core.set_prim_eager_enabled(False)
+
+    def test_stack_triple_grad(self):
+        paddle.base.core.set_prim_eager_enabled(True)
+        z = paddle.stack(self.x)
+        z = paddle.tanh(z)
+        grads_out = paddle.grad(z, self.x[1], create_graph=True)
+        ggrads_out = paddle.grad(grads_out, self.x[1], create_graph=True)
+        gggrads_out = paddle.grad(ggrads_out, self.x[1], create_graph=False)[0]
+
+        zz = paddle.tanh(self.x[1])
+        grads_expected = paddle.grad(zz, self.x[1], create_graph=True)
+        ggrads_expected = paddle.grad(
+            grads_expected, self.x[1], create_graph=True
+        )
+        gggrads_expected = paddle.grad(
+            ggrads_expected, self.x[1], create_graph=True
+        )[0]
+
+        np.testing.assert_allclose(gggrads_out, gggrads_expected)
+        paddle.enable_static()
+        paddle.base.core.set_prim_eager_enabled(False)
+
+
+class TestStackAPI_ZeroSizedTensor(unittest.TestCase):
+    def test_dygraph_cpu(self):
+        place = base.CPUPlace()
+        paddle.disable_static(place)
+
+        x1 = paddle.ones([1, 0])
+        x2 = paddle.ones([1, 0])
+        x1.stop_gradient = False
+        x2.stop_gradient = False
+        out = paddle.stack([x1, x2])
+        out.retain_grads()
+        out.backward()
+
+        np.testing.assert_equal(out.shape, [2, 1, 0])
+        np.testing.assert_equal(x1.grad.shape, [1, 0])
+        np.testing.assert_equal(x2.grad.shape, [1, 0])
+        np.testing.assert_equal(out, np.ones([2, 1, 0]))
+
+        paddle.enable_static()
+
+    def test_dygraph_gpu(self):
+        if base.is_compiled_with_cuda() or is_custom_device():
+            place = get_device_place()
+            paddle.disable_static(place)
+
+            x1 = paddle.ones([1, 0])
+            x2 = paddle.ones([1, 0])
+            x1.stop_gradient = False
+            x2.stop_gradient = False
+            out = paddle.stack([x1, x2])
+            out.retain_grads()
+            out.backward()
+
+            np.testing.assert_equal(out.shape, [2, 1, 0])
+            np.testing.assert_equal(x1.grad.shape, [1, 0])
+            np.testing.assert_equal(x2.grad.shape, [1, 0])
+            np.testing.assert_equal(out, np.ones([2, 1, 0]))
+
+            paddle.enable_static()
+
+    def test_static_cpu(self):
+        paddle.enable_static()
+        place = base.CPUPlace()
+        exe = base.Executor(place)
+        with paddle.static.program_guard(
+            paddle.static.Program(), paddle.static.Program()
+        ):
+            data1 = paddle.static.data('data1', shape=[0, 2], dtype='float64')
+            data2 = paddle.static.data('data2', shape=[0, 2], dtype='float64')
+            data3 = paddle.static.data('data3', shape=[0, 2], dtype='float64')
+            result_stack = paddle.stack([data1, data2, data3], axis=0)
+            input1 = np.ones([0, 2]).astype('float64')
+            input2 = np.ones([0, 2]).astype('float64')
+            input3 = np.ones([0, 2]).astype('float64')
+            (result,) = exe.run(
+                feed={"data1": input1, "data2": input2, "data3": input3},
+                fetch_list=[result_stack],
+            )
+            expected_result = np.stack([input1, input2, input3], axis=0)
+            np.testing.assert_equal(expected_result, result)
+
+    def test_static_gpu(self):
+        if base.is_compiled_with_cuda() or is_custom_device():
+            paddle.enable_static()
+            place = get_device_place()
+            exe = base.Executor(place)
+            with paddle.static.program_guard(
+                paddle.static.Program(), paddle.static.Program()
+            ):
+                data1 = paddle.static.data(
+                    'data1', shape=[0, 2], dtype='float64'
+                )
+                data2 = paddle.static.data(
+                    'data2', shape=[0, 2], dtype='float64'
+                )
+                data3 = paddle.static.data(
+                    'data3', shape=[0, 2], dtype='float64'
+                )
+                result_stack = paddle.stack([data1, data2, data3], axis=0)
+                input1 = np.ones([0, 2]).astype('float64')
+                input2 = np.ones([0, 2]).astype('float64')
+                input3 = np.ones([0, 2]).astype('float64')
+                (result,) = exe.run(
+                    feed={"data1": input1, "data2": input2, "data3": input3},
+                    fetch_list=[result_stack],
+                )
+                expected_result = np.stack([input1, input2, input3], axis=0)
+                np.testing.assert_equal(expected_result, result)
+
+
+class TestStackOutAndParamDecorator(unittest.TestCase):
+    def setUp(self):
+        paddle.disable_static()
+        self.inputs_np = [
+            np.random.rand(2, 3).astype(np.float32) for _ in range(3)
+        ]
+        self.test_types = [
+            "decorator_tensors",
+            "decorator_dim",
+            "decorator_both",
+            "out",
+            "out_decorator",
+        ]
+
+    def do_test(self, test_type):
+        inputs = [
+            paddle.to_tensor(x, stop_gradient=False) for x in self.inputs_np
+        ]
+
+        if test_type == 'raw':
+            result = paddle.stack(inputs, axis=1)
+            result.mean().backward()
+            grads = [x.grad for x in inputs]
+            return result, grads
+        elif test_type == 'decorator_tensors':
+            result = paddle.stack(tensors=inputs, axis=1)
+            result.mean().backward()
+            grads = [x.grad for x in inputs]
+            return result, grads
+        elif test_type == 'decorator_dim':
+            result = paddle.stack(inputs, dim=1)
+            result.mean().backward()
+            grads = [x.grad for x in inputs]
+            return result, grads
+        elif test_type == 'decorator_both':
+            result = paddle.stack(tensors=inputs, dim=1)
+            result.mean().backward()
+            grads = [x.grad for x in inputs]
+            return result, grads
+        elif test_type == 'out':
+            out = paddle.empty((2, 3, 3), dtype='float32')
+            out.stop_gradient = False
+            paddle.stack(inputs, axis=1, out=out)
+            out.mean().backward()
+            grads = [x.grad for x in inputs]
+            return out, grads
+        elif test_type == 'out_decorator':
+            out = paddle.empty((2, 3, 3), dtype='float32')
+            out.stop_gradient = False
+            paddle.stack(tensors=inputs, dim=1, out=out)
+            out.mean().backward()
+            grads = [x.grad for x in inputs]
+            return out, grads
+        else:
+            raise ValueError(f"Unknown test type: {test_type}")
+
+    def test_all(self):
+        out_std, grads_std = self.do_test('raw')
+        for test_type in self.test_types:
+            out, grads = self.do_test(test_type)
+            np.testing.assert_allclose(out.numpy(), out_std.numpy(), rtol=1e-20)
+            for g, g_std in zip(grads, grads_std):
+                np.testing.assert_allclose(g.numpy(), g_std.numpy(), rtol=1e-20)
+        paddle.enable_static()
+
+
+if __name__ == '__main__':
+    unittest.main()