NPU add elementwise_mod

paddle/fluid/operators/elementwise/elementwise_mod_op_npu.cc

@@ -0,0 +1,77 @@
+/* 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. */
+
+#include "paddle/fluid/operators/elementwise/elementwise_mod_op.h"
+#include "paddle/fluid/operators/elementwise/elementwise_npu.h"
+#include "paddle/fluid/operators/npu_op_runner.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+
+template <typename DeviceContext, typename T>
+class ElementwiseModNPUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto& dev_ctx =
+        ctx.template device_context<paddle::platform::NPUDeviceContext>();
+    auto* x = ctx.Input<Tensor>("X");
+    auto* y = ctx.Input<Tensor>("Y");
+    auto* out = ctx.Output<Tensor>("Out");
+    int axis = ctx.Attr<int>("axis");
+
+    auto x_dims = x->dims();
+    auto y_dims = y->dims();
+
+    axis = (axis == -1 ? std::abs(x_dims.size() - y_dims.size()) : axis);
+
+    bool direct_compute = false;
+    if (x_dims.size() >= y_dims.size()) {
+      direct_compute =
+          y_dims == framework::slice_ddim(x_dims, axis, x_dims.size());
+    } else {
+      direct_compute =
+          x_dims == framework::slice_ddim(y_dims, axis, y_dims.size());
+    }
+
+    Tensor transformed_x, transformed_y;
+    if (direct_compute) {
+      transformed_x.ShareDataWith(*x);
+      transformed_y.ShareDataWith(*y);
+    } else {
+      NpuElementWiseOpBroadcast<T>(dev_ctx, x, y, axis, &transformed_x,
+                                   &transformed_y);
+    }
+    out->mutable_data<T>(ctx.GetPlace());
+    const auto& runner =
+        NpuOpRunner("FloorMod", {transformed_x, transformed_y}, {*out}, {});
+    auto stream = dev_ctx.stream();
+    runner.Run(stream);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+
+REGISTER_OP_NPU_KERNEL(
+    elementwise_mod,
+    ops::ElementwiseModNPUKernel<paddle::platform::NPUDeviceContext, float>,
+    ops::ElementwiseModNPUKernel<paddle::platform::NPUDeviceContext, double>,
+    ops::ElementwiseModNPUKernel<paddle::platform::NPUDeviceContext, int>,
+    ops::ElementwiseModNPUKernel<paddle::platform::NPUDeviceContext, int64_t>,
+    ops::ElementwiseModNPUKernel<paddle::platform::NPUDeviceContext,
+                                 paddle::platform::float16>);

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

@@ -0,0 +1,182 @@
+#  Copyright (c) 2019 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 numpy as np
+import unittest
+
+import sys
+sys.path.append("..")
+from op_test import OpTest
+
+import paddle
+import paddle.fluid as fluid
+
+import random
+
+paddle.enable_static()
+
+
+class TestElementwiseModOp(OpTest):
+    def setUp(self):
+        self.set_npu()
+        self.place = paddle.NPUPlace(0)
+        self.op_type = "elementwise_mod"
+        self.axis = -1
+        self.init_dtype()
+        self.init_input_output()
+        self.init_kernel_type()
+        self.init_axis()
+
+        self.inputs = {
+            'X': OpTest.np_dtype_to_fluid_dtype(self.x),
+            'Y': OpTest.np_dtype_to_fluid_dtype(self.y)
+        }
+        self.attrs = {'axis': self.axis, 'use_mkldnn': self.use_mkldnn}
+        self.outputs = {'Out': self.out}
+
+    def init_kernel_type(self):
+        self.use_mkldnn = False
+
+    def init_dtype(self):
+        self.dtype = np.int32
+
+    def init_axis(self):
+        pass
+
+    def set_npu(self):
+        self.__class__.use_npu = True
+
+    def init_input_output(self):
+        self.x = np.random.uniform(0, 10000, [10, 10]).astype(self.dtype)
+        self.y = np.random.uniform(0, 1000, [10, 10]).astype(self.dtype)
+        self.out = np.mod(self.x, self.y)
+
+    def test_check_output(self):
+        self.check_output_with_place(self.place)
+
+
+class TestElementwiseModOpInt64(TestElementwiseModOp):
+    def init_dtype(self):
+        self.dtype = np.int64
+
+
+class TestElementwiseModOp_scalar(TestElementwiseModOp):
+    def init_input_output(self):
+        scale_x = random.randint(0, 100000000)
+        scale_y = random.randint(1, 100000000)
+        self.x = (np.random.rand(2, 3, 4) * scale_x).astype(self.dtype)
+        self.y = (np.random.rand(1) * scale_y + 1).astype(self.dtype)
+        self.out = np.mod(self.x, self.y)
+
+
+class TestElementwiseModOpFloat(TestElementwiseModOp):
+    def init_dtype(self):
+        self.dtype = np.float32
+
+    def init_input_output(self):
+        self.x = np.random.uniform(-1000, 1000, [10, 10]).astype(self.dtype)
+        self.y = np.random.uniform(-100, 100, [10, 10]).astype(self.dtype)
+        self.out = np.fmod(self.y + np.fmod(self.x, self.y), self.y)
+
+    def test_check_output(self):
+        self.check_output_with_place(self.place, atol=1e-4)
+
+
+class TestElementwiseModOpDouble(TestElementwiseModOpFloat):
+    def init_dtype(self):
+        self.dtype = np.float64
+
+    def test_check_output(self):
+        self.check_output_with_place(self.place)
+
+
+class TestElementwiseModOpFP16(TestElementwiseModOpFloat):
+    def init_dtype(self):
+        self.dtype = np.float16
+
+    def test_check_output(self):
+        self.check_output_with_place(self.place, atol=1e-1)
+
+
+class TestElementwiseModOp_broadcast_0(TestElementwiseModOp):
+    def init_input_output(self):
+        self.x = np.random.rand(100, 2, 3).astype(self.dtype)
+        self.y = np.random.rand(100).astype(self.dtype)
+        self.out = np.mod(self.x, self.y.reshape(100, 1, 1))
+
+    def init_axis(self):
+        self.axis = 0
+
+
+class TestElementwiseModOp_broadcast_1(TestElementwiseModOp):
+    def init_input_output(self):
+        self.x = np.random.rand(2, 100, 3).astype(self.dtype)
+        self.y = np.random.rand(100).astype(self.dtype)
+        self.out = np.mod(self.x, self.y.reshape(1, 100, 1))
+
+    def init_axis(self):
+        self.axis = 1
+
+
+class TestElementwiseModOp_broadcast_2(TestElementwiseModOp):
+    def init_input_output(self):
+        self.x = np.random.rand(2, 3, 100).astype(self.dtype)
+        self.y = np.random.rand(100).astype(self.dtype)
+        self.out = np.mod(self.x, self.y.reshape(1, 1, 100))
+
+    def init_axis(self):
+        self.axis = 2
+
+
+class TestRemainderOp(unittest.TestCase):
+    def test_name(self):
+        paddle.set_device('npu:0')
+        with fluid.program_guard(fluid.Program()):
+            x = fluid.data(name="x", shape=[2, 3], dtype="int64")
+            y = fluid.data(name='y', shape=[2, 3], dtype='int64')
+            y_1 = paddle.remainder(x, y, name='div_res')
+            self.assertEqual(('div_res' in y_1.name), True)
+
+    def test_dygraph(self):
+        paddle.set_device('npu:0')
+        with fluid.dygraph.guard():
+            np_x = np.array([2, 3, 8, 7]).astype('int64')
+            np_y = np.array([1, 5, 3, 3]).astype('int64')
+            x = paddle.to_tensor(np_x)
+            y = paddle.to_tensor(np_y)
+            z = paddle.remainder(x, y)
+            np_z = z.numpy()
+            z_expected = np.array([0, 3, 2, 1])
+            self.assertEqual((np_z == z_expected).all(), True)
+
+            np_x = np.array([-3.3, 11.5, -2, 3.5])
+            np_y = np.array([-1.2, 2., 3.3, -2.3])
+            x = paddle.to_tensor(np_x)
+            y = paddle.to_tensor(np_y)
+            z = x % y
+            z_expected = np.array([-0.9, 1.5, 1.3, -1.1])
+            self.assertEqual(np.allclose(z_expected, z.numpy()), True)
+
+            np_x = np.array([-3, 11, -2, 3])
+            np_y = np.array([-1, 2, 3, -2])
+            x = paddle.to_tensor(np_x, dtype="int64")
+            y = paddle.to_tensor(np_y, dtype="int64")
+            z = x % y
+            z_expected = np.array([0, 1, 1, -1])
+            self.assertEqual(np.allclose(z_expected, z.numpy()), True)
+
+
+if __name__ == '__main__':
+    unittest.main()