[NPU] add cumsum (#34188)

* [NPU] add cumsum

* [NPU] delete debug codes

* [NPU] add attr flatten and unittests, and api tests

* [NPU] delete comment codes

* [NPU] add attr flatten and axis exclusive check

* [NPU] delete skipIf

paddle/fluid/operators/cumsum_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/framework/tensor.h"
+#include "paddle/fluid/operators/cum_op.h"
+#include "paddle/fluid/operators/npu_op_runner.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+
+template <typename DeviceContext, typename T>
+class CumSumNPUKernel : 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 axis = ctx.Attr<int>("axis");
+    bool exclusive = ctx.Attr<bool>("exclusive");
+    bool reverse = ctx.Attr<bool>("reverse");
+
+    out->mutable_data<T>(ctx.GetPlace());
+
+    framework::NPUAttributeMap attr_input = {
+        {"axis", axis}, {"exclusive", exclusive}, {"reverse", reverse}};
+
+    auto stream =
+        ctx.template device_context<paddle::platform::NPUDeviceContext>()
+            .stream();
+
+    bool flatten = ctx.Attr<bool>("flatten");
+    if (flatten) {
+      PADDLE_ENFORCE_EQ(
+          axis, -1,
+          platform::errors::InvalidArgument(
+              "when flatten is true, attr axis must be default %d, but got %d",
+              -1, axis));
+
+      Tensor new_x(x->type());
+      new_x.ShareDataWith(*x);
+
+      new_x.Resize(framework::make_ddim({x->numel()}));
+
+      const auto& runner = NpuOpRunner("CumsumD", {new_x}, {*out}, attr_input);
+      runner.Run(stream);
+    } else {
+      const auto& runner = NpuOpRunner("CumsumD", {*x}, {*out}, attr_input);
+      runner.Run(stream);
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+namespace plat = paddle::platform;
+REGISTER_OP_NPU_KERNEL(
+    cumsum, ops::CumSumNPUKernel<plat::NPUDeviceContext, int>,
+    ops::CumSumNPUKernel<plat::NPUDeviceContext, float>,
+    ops::CumSumNPUKernel<plat::NPUDeviceContext, plat::float16>);

python/paddle/fluid/tests/unittests/npu/test_cumsum_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.core as core
+import paddle.fluid as fluid
+from paddle.fluid import compiler, Program, program_guard
+
+paddle.enable_static()
+
+
+class TestCumsumOp(unittest.TestCase):
+    def run_cases(self):
+        data_np = np.arange(12).reshape(3, 4)
+        data = paddle.to_tensor(data_np)
+
+        y = paddle.cumsum(data)
+        z = np.cumsum(data_np)
+        self.assertTrue(np.array_equal(z, y.numpy()))
+
+        y = paddle.cumsum(data, axis=0)
+        z = np.cumsum(data_np, axis=0)
+        self.assertTrue(np.array_equal(z, y.numpy()))
+
+        y = paddle.cumsum(data, axis=-1)
+        z = np.cumsum(data_np, axis=-1)
+        self.assertTrue(np.array_equal(z, y.numpy()))
+
+        y = paddle.cumsum(data, dtype='float32')
+        self.assertTrue(y.dtype == core.VarDesc.VarType.FP32)
+
+        y = paddle.cumsum(data, dtype=np.int32)
+        self.assertTrue(y.dtype == core.VarDesc.VarType.INT32)
+
+        y = paddle.cumsum(data, axis=-2)
+        z = np.cumsum(data_np, axis=-2)
+        self.assertTrue(np.array_equal(z, y.numpy()))
+
+    def run_static(self, use_npu=False):
+        with fluid.program_guard(fluid.Program()):
+            data_np = np.random.random((100, 100)).astype(np.float32)
+            x = paddle.static.data('X', [100, 100])
+            y = paddle.cumsum(x)
+            y2 = paddle.cumsum(x, axis=0)
+            y3 = paddle.cumsum(x, axis=-1)
+            y4 = paddle.cumsum(x, dtype='float32')
+            y5 = paddle.cumsum(x, dtype=np.int32)
+            y6 = paddle.cumsum(x, axis=-2)
+
+            place = fluid.NPUPlace(0) if use_npu else fluid.CPUPlace()
+            exe = fluid.Executor(place)
+            exe.run(fluid.default_startup_program())
+            out = exe.run(feed={'X': data_np},
+                          fetch_list=[
+                              y.name, y2.name, y3.name, y4.name, y5.name,
+                              y6.name
+                          ])
+
+            z = np.cumsum(data_np)
+            self.assertTrue(np.allclose(z, out[0]))
+            z = np.cumsum(data_np, axis=0)
+            self.assertTrue(np.allclose(z, out[1]))
+            z = np.cumsum(data_np, axis=-1)
+            self.assertTrue(np.allclose(z, out[2]))
+            self.assertTrue(out[3].dtype == np.float32)
+            self.assertTrue(out[4].dtype == np.int32)
+            z = np.cumsum(data_np, axis=-2)
+            self.assertTrue(np.allclose(z, out[5]))
+
+    def test_npu(self):
+        # Now, npu tests need setting paddle.enable_static()
+
+        self.run_static(use_npu=True)
+
+    def test_name(self):
+        with fluid.program_guard(fluid.Program()):
+            x = paddle.static.data('x', [3, 4])
+            y = paddle.cumsum(x, name='out')
+            self.assertTrue('out' in y.name)
+
+
+class TestNPUCumSumOp1(OpTest):
+    def setUp(self):
+        self.op_type = "cumsum"
+        self.set_npu()
+        self.init_dtype()
+        self.init_testcase()
+
+    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 init_testcase(self):
+        self.attrs = {'axis': 2}
+        self.inputs = {'X': np.random.random((5, 6, 10)).astype(self.dtype)}
+        self.outputs = {'Out': self.inputs['X'].cumsum(axis=2)}
+
+
+class TestNPUCumSumOp2(TestNPUCumSumOp1):
+    def init_testcase(self):
+        self.attrs = {'axis': -1, 'reverse': True}
+        self.inputs = {'X': np.random.random((5, 6, 10)).astype(self.dtype)}
+        self.outputs = {
+            'Out': np.flip(
+                np.flip(
+                    self.inputs['X'], axis=2).cumsum(axis=2), axis=2)
+        }
+
+
+class TestNPUCumSumOp3(TestNPUCumSumOp1):
+    def init_testcase(self):
+        self.attrs = {'axis': 1}
+        self.inputs = {'X': np.random.random((5, 6, 10)).astype(self.dtype)}
+        self.outputs = {'Out': self.inputs['X'].cumsum(axis=1)}
+
+
+class TestNPUCumSumOp4(TestNPUCumSumOp1):
+    def init_testcase(self):
+        self.attrs = {'axis': 0}
+        self.inputs = {'X': np.random.random((5, 6, 10)).astype(self.dtype)}
+        self.outputs = {'Out': self.inputs['X'].cumsum(axis=0)}
+
+
+class TestNPUCumSumOp5(TestNPUCumSumOp1):
+    def init_testcase(self):
+        self.inputs = {'X': np.random.random((5, 20)).astype(self.dtype)}
+        self.outputs = {'Out': self.inputs['X'].cumsum(axis=1)}
+
+
+class TestNPUCumSumOp7(TestNPUCumSumOp1):
+    def init_testcase(self):
+        self.inputs = {'X': np.random.random((100)).astype(self.dtype)}
+        self.outputs = {'Out': self.inputs['X'].cumsum(axis=0)}
+
+
+class TestNPUCumSumExclusive1(TestNPUCumSumOp1):
+    def init_testcase(self):
+        self.attrs = {'axis': 2, "exclusive": True}
+        a = np.random.random((4, 5, 65)).astype(self.dtype)
+        self.inputs = {'X': a}
+        self.outputs = {
+            'Out': np.concatenate(
+                (np.zeros(
+                    (4, 5, 1), dtype=self.dtype), a[:, :, :-1].cumsum(axis=2)),
+                axis=2)
+        }
+
+
+class TestNPUCumSumExclusive2(TestNPUCumSumOp1):
+    def init_testcase(self):
+        self.attrs = {'axis': 2, "exclusive": True}
+        a = np.random.random((1, 1, 888)).astype(self.dtype)
+        self.inputs = {'X': a}
+        self.outputs = {
+            'Out': np.concatenate(
+                (np.zeros(
+                    (1, 1, 1), dtype=self.dtype), a[:, :, :-1].cumsum(axis=2)),
+                axis=2)
+        }
+
+
+class TestNPUCumSumExclusive3(TestNPUCumSumOp1):
+    def init_testcase(self):
+        self.attrs = {'axis': 2, "exclusive": True}
+        a = np.random.random((4, 5, 888)).astype(self.dtype)
+        self.inputs = {'X': a}
+        self.outputs = {
+            'Out': np.concatenate(
+                (np.zeros(
+                    (4, 5, 1), dtype=self.dtype), a[:, :, :-1].cumsum(axis=2)),
+                axis=2)
+        }
+
+
+class TestNPUCumSumExclusive4(TestNPUCumSumOp1):
+    def init_testcase(self):
+        self.attrs = {'axis': 2, "exclusive": True}
+        a = np.random.random((1, 1, 3049)).astype(self.dtype)
+        self.inputs = {'X': a}
+        self.outputs = {
+            'Out': np.concatenate(
+                (np.zeros(
+                    (1, 1, 1), dtype=self.dtype), a[:, :, :-1].cumsum(axis=2)),
+                axis=2)
+        }
+
+
+class TestNPUCumSumExclusive5(TestNPUCumSumOp1):
+    def init_testcase(self):
+        self.attrs = {'axis': 2, "exclusive": True}
+        a = np.random.random((4, 5, 3096)).astype(self.dtype)
+        self.inputs = {'X': a}
+        self.outputs = {
+            'Out': np.concatenate(
+                (np.zeros(
+                    (4, 5, 1), dtype=self.dtype), a[:, :, :-1].cumsum(axis=2)),
+                axis=2)
+        }
+
+
+class TestNPUCumSumReverseExclusive(TestNPUCumSumOp1):
+    def init_testcase(self):
+        self.attrs = {'axis': 2, 'reverse': True, "exclusive": True}
+        a = np.random.random((4, 5, 6)).astype(self.dtype)
+        self.inputs = {'X': a}
+        a = np.flip(a, axis=2)
+        self.outputs = {
+            'Out': np.concatenate(
+                (np.flip(
+                    a[:, :, :-1].cumsum(axis=2), axis=2), np.zeros(
+                        (4, 5, 1), dtype=self.dtype)),
+                axis=2)
+        }
+
+
+class TestNPUCumSumWithFlatten1(TestNPUCumSumOp1):
+    def init_testcase(self):
+        self.attrs = {'flatten': True}
+        self.inputs = {'X': np.random.random((5, 6)).astype(self.dtype)}
+        self.outputs = {'Out': self.inputs['X'].cumsum()}
+
+
+class TestNPUCumSumWithFlatten2(TestNPUCumSumOp1):
+    def init_testcase(self):
+        self.attrs = {'flatten': True}
+        self.inputs = {'X': np.random.random((5, 6, 10)).astype(self.dtype)}
+        self.outputs = {'Out': self.inputs['X'].cumsum()}
+
+
+if __name__ == '__main__':
+    unittest.main()