[NPU] add tril_triu (#34097)

* [NPU] add tril_triu

* [NPU] delete debug codes

* [NPU] add more test cases, and api test

* [NPU] optimize codes style

paddle/fluid/operators/tril_triu_op_npu.cc

+/* Copyright (c) 2021 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 Licnse. */
+
+#include "paddle/fluid/operators/tril_triu_op.h"
+#include "paddle/fluid/operators/npu_op_runner.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename DeviceContext, typename T>
+class TrilTriuNPUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* x = ctx.Input<Tensor>("X");
+    auto* out = ctx.Output<Tensor>("Out");
+    int diagonal = ctx.Attr<int>("diagonal");
+    bool lower = ctx.Attr<bool>("lower");
+
+    out->mutable_data<T>(ctx.GetPlace());
+
+    std::string op_type = lower ? "Tril" : "Triu";
+
+    framework::NPUAttributeMap attr_input = {{"diagonal", diagonal}};
+
+    auto stream =
+        ctx.template device_context<paddle::platform::NPUDeviceContext>()
+            .stream();
+
+    const auto& runner = NpuOpRunner(op_type, {*x}, {*out}, attr_input);
+    runner.Run(stream);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+namespace plat = paddle::platform;
+REGISTER_OP_NPU_KERNEL(
+    tril_triu, ops::TrilTriuNPUKernel<plat::NPUDeviceContext, float>,
+    ops::TrilTriuNPUKernel<plat::NPUDeviceContext, plat::float16>);

python/paddle/fluid/tests/unittests/npu/test_tril_triu_op_npu.py

+#   Copyright (c) 2021 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.
+
+from __future__ import print_function
+
+import unittest
+import numpy as np
+from paddle.fluid.tests.unittests.op_test import OpTest
+import paddle
+import paddle.fluid as fluid
+import paddle.tensor as tensor
+from paddle.fluid.framework import Program, program_guard
+
+paddle.enable_static()
+
+
+class TestNPUTrilTriu(OpTest):
+    """ the base class of other op testcases
+    """
+
+    def setUp(self):
+        self.op_type = "tril_triu"
+        self.set_npu()
+        self.init_dtype()
+        self.initTestCase()
+
+        self.real_np_op = getattr(np, self.real_op_type)
+
+        self.inputs = {'X': self.X}
+        self.attrs = {
+            'diagonal': self.diagonal,
+            'lower': True if self.real_op_type == 'tril' else False,
+        }
+        self.outputs = {
+            'Out': self.real_np_op(self.X, self.diagonal)
+            if self.diagonal else self.real_np_op(self.X)
+        }
+
+    def test_check_output(self):
+        self.check_output_with_place(self.place)
+
+    def set_npu(self):
+        self.__class__.use_npu = True
+        self.place = paddle.NPUPlace(0)
+
+    def init_dtype(self):
+        self.dtype = np.float32
+
+    def initTestCase(self):
+        self.real_op_type = np.random.choice(['triu', 'tril'])
+        self.diagonal = None
+        self.X = np.arange(1, 101, dtype=self.dtype).reshape([10, -1])
+
+
+def case_generator(op_type, Xshape, diagonal, expected):
+    """
+    Generate testcases with the params shape of X, diagonal and op_type.
+    If arg `expercted` is 'success', it will register an Optest case and expect to pass.
+    Otherwise, it will register an API case and check the expect failure.
+    """
+    cls_name = "{0}_{1}_shape_{2}_diag_{3}".format(expected, op_type, Xshape,
+                                                   diagonal)
+    errmsg = {
+        "diagonal: TypeError":
+        "diagonal in {} must be a python Int".format(op_type),
+        "input: ValueError":
+        "x shape in {} must be at least 2-D".format(op_type),
+    }
+
+    class FailureCase(unittest.TestCase):
+        def test_failure(self):
+            paddle.enable_static()
+
+            data = fluid.data(shape=Xshape, dtype='float32', name=cls_name)
+            with self.assertRaisesRegexp(
+                    eval(expected.split(':')[-1]), errmsg[expected]):
+                getattr(tensor, op_type)(x=data, diagonal=diagonal)
+
+    class SuccessCase(TestNPUTrilTriu):
+        def initTestCase(self):
+            paddle.enable_static()
+
+            self.real_op_type = op_type
+            self.diagonal = diagonal
+            self.X = np.random.random(Xshape).astype("float32")
+
+    CLASS = locals()['SuccessCase' if expected == "success" else 'FailureCase']
+    CLASS.__name__ = cls_name
+    globals()[cls_name] = CLASS
+
+
+### NOTE: meaningful diagonal is [1 - min(H, W), max(H, W) -1]
+### test the diagonal just at the border, upper/lower the border, 
+###     negative/positive integer within range and a zero
+cases = {
+    'success': {
+        (2, 2, 3, 4, 5): [-100, -3, -1, 0, 2, 4, 100],  # normal shape
+        (10, 10, 1, 1): [-100, -1, 0, 1, 100],  # small size of matrix
+    },
+    'diagonal: TypeError': {
+        (20, 20): [
+            '2020',
+            [20],
+            {
+                20: 20
+            },
+            (20, 20),
+            20.20,
+        ],  # str, list, dict, tuple, float
+    },
+    'input: ValueError': {
+        (2020, ): [None],
+    },
+}
+for _op_type in ['tril', 'triu']:
+    for _expected, _params in cases.items():
+        for _Xshape, _diaglist in _params.items():
+            list(
+                map(lambda _diagonal: case_generator(_op_type, _Xshape, _diagonal, _expected),
+                    _diaglist))
+
+
+class TestTrilTriuOpAPI(unittest.TestCase):
+    """ test case by using API and has -1 dimension 
+    """
+
+    def test_api(self):
+        paddle.enable_static()
+
+        dtypes = ['float16', 'float32']
+        for dtype in dtypes:
+            prog = Program()
+            startup_prog = Program()
+            with program_guard(prog, startup_prog):
+                data = np.random.random([1, 9, 9, 4]).astype(dtype)
+                x = fluid.data(shape=[1, 9, -1, 4], dtype=dtype, name='x')
+                tril_out, triu_out = tensor.tril(x), tensor.triu(x)
+
+                place = fluid.NPUPlace(0)
+                exe = fluid.Executor(place)
+                tril_out, triu_out = exe.run(
+                    fluid.default_main_program(),
+                    feed={"x": data},
+                    fetch_list=[tril_out, triu_out], )
+                self.assertTrue(np.allclose(tril_out, np.tril(data)))
+                self.assertTrue(np.allclose(triu_out, np.triu(data)))
+
+    def test_api_with_dygraph(self):
+        paddle.disable_static(fluid.NPUPlace(0))
+
+        dtypes = ['float16', 'float32']
+        for dtype in dtypes:
+            with fluid.dygraph.guard():
+                data = np.random.random([1, 9, 9, 4]).astype(dtype)
+                x = fluid.dygraph.to_variable(data)
+                tril_out, triu_out = tensor.tril(x).numpy(), tensor.triu(
+                    x).numpy()
+                self.assertTrue(np.allclose(tril_out, np.tril(data)))
+                self.assertTrue(np.allclose(triu_out, np.triu(data)))
+
+    def test_fluid_api(self):
+        paddle.enable_static()
+
+        dtypes = ['float16', 'float32']
+        for dtype in dtypes:
+            prog = Program()
+            startup_prog = Program()
+            with program_guard(prog, startup_prog):
+                data = np.random.random([1, 9, 9, 4]).astype(dtype)
+                x = fluid.data(shape=[1, 9, -1, 4], dtype=dtype, name='x')
+                triu_out = fluid.layers.triu(x)
+
+                place = fluid.NPUPlace(0)
+                exe = fluid.Executor(place)
+                triu_out = exe.run(fluid.default_main_program(),
+                                   feed={"x": data},
+                                   fetch_list=[triu_out])
+
+
+if __name__ == '__main__':
+    unittest.main()