diff --git a/paddle/fluid/operators/optimizers/sgd_op_npu.cc b/paddle/fluid/operators/optimizers/sgd_op_npu.cc new file mode 100644 index 0000000000000000000000000000000000000000..b7aaff5d45791807bd5dd53d755749ea239e402a --- /dev/null +++ b/paddle/fluid/operators/optimizers/sgd_op_npu.cc @@ -0,0 +1,62 @@ +/* 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 +#include + +#include "paddle/fluid/operators/npu_op_runner.h" +#include "paddle/fluid/operators/optimizers/sgd_op.h" + +namespace paddle { +namespace operators { + +template +class SGDNPUKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& ctx) const override { + auto* learning_rate = ctx.Input("LearningRate"); + auto* param_var = ctx.Input("Param"); + auto* grad_var = ctx.Input("Grad"); + auto* param_out = ctx.Output("ParamOut"); + + param_out->mutable_data(ctx.GetPlace()); + + auto runner = + NpuOpRunner("ApplyGradientDescent", + {*param_var, *learning_rate, *grad_var}, {*param_out}, {}); + + auto stream = + ctx.template device_context() + .stream(); + runner.Run(stream); + + // NOTE(zhiqiu): ApplyGradientDescent updates params inplace, so + // if param and param_out is not same, we need to do copy. + if (param_out->data() != param_var->data()) { + ctx.template device_context().Wait(); + framework::TensorCopySync(*param_var, ctx.GetPlace(), param_out); + } + } +}; + +} // namespace operators +} // namespace paddle + +namespace ops = paddle::operators; + +REGISTER_OP_NPU_KERNEL( + sgd, ops::SGDNPUKernel, + ops::SGDNPUKernel, + ops::SGDNPUKernel); diff --git a/python/paddle/fluid/tests/unittests/npu/test_sgd_op_npu.py b/python/paddle/fluid/tests/unittests/npu/test_sgd_op_npu.py new file mode 100644 index 0000000000000000000000000000000000000000..af0dea4776d23fdebe26f68b5c84c7d3d07d2940 --- /dev/null +++ b/python/paddle/fluid/tests/unittests/npu/test_sgd_op_npu.py @@ -0,0 +1,119 @@ +# 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. + +import numpy as np +import unittest +import sys +sys.path.append("..") +from op_test import OpTest +import paddle +import paddle.fluid as fluid + +paddle.enable_static() +SEED = 2021 + + +@unittest.skipIf(not paddle.is_compiled_with_npu(), + "core is not compiled with NPU") +class TestSGD(OpTest): + def setUp(self): + self.set_npu() + self.place = paddle.NPUPlace(0) + self.op_type = "sgd" + self.conf() + w = np.random.random((self.h, self.w)).astype("float32") + g = np.random.random((self.h, self.w)).astype("float32") + lr = np.array([0.1]).astype("float32") + + self.inputs = {'Param': w, 'Grad': g, 'LearningRate': lr} + self.outputs = {'ParamOut': w - lr * g} + + def set_npu(self): + self.__class__.use_npu = True + + def init_dtype(self): + self.dtype = np.float32 + + def conf(self): + self.h = 12 + self.w = 15 + + def test_check_output(self): + self.check_output_with_place(self.place, check_dygraph=False) + + +@unittest.skipIf(not paddle.is_compiled_with_npu(), + "core is not compiled with NPU") +class TestNet(unittest.TestCase): + def _test(self, run_npu=True): + main_prog = paddle.static.Program() + startup_prog = paddle.static.Program() + main_prog.random_seed = SEED + startup_prog.random_seed = SEED + np.random.seed(SEED) + + a_np = np.random.random(size=(32, 32)).astype('float32') + b_np = np.random.random(size=(32, 32)).astype('float32') + label_np = np.random.randint(2, size=(32, 1)).astype('int64') + + with paddle.static.program_guard(main_prog, startup_prog): + a = paddle.static.data(name="a", shape=[32, 32], dtype='float32') + b = paddle.static.data(name="b", shape=[32, 32], dtype='float32') + label = paddle.static.data( + name="label", shape=[32, 1], dtype='int64') + + sum = paddle.add(a, b) + z = paddle.pow(sum, 2.0) + + fc_1 = fluid.layers.fc(input=z, size=128) + prediction = fluid.layers.fc(input=fc_1, size=2, act='softmax') + + cost = fluid.layers.cross_entropy(input=prediction, label=label) + loss = fluid.layers.reduce_mean(cost) + sgd = fluid.optimizer.SGD(learning_rate=0.01) + sgd.minimize(loss) + + if run_npu: + place = paddle.NPUPlace(0) + else: + place = paddle.CPUPlace() + + exe = paddle.static.Executor(place) + exe.run(startup_prog) + + print("Start run on {}".format(place)) + for epoch in range(100): + + pred_res, loss_res = exe.run( + main_prog, + feed={"a": a_np, + "b": b_np, + "label": label_np}, + fetch_list=[prediction, loss]) + if epoch % 10 == 0: + print("Epoch {} | Prediction[0]: {}, Loss: {}".format( + epoch, pred_res[0], loss_res)) + + return pred_res, loss_res + + def test_npu(self): + cpu_pred, cpu_loss = self._test(False) + npu_pred, npu_loss = self._test(True) + + self.assertTrue(np.allclose(npu_pred, cpu_pred)) + self.assertTrue(np.allclose(npu_loss, cpu_loss)) + + +if __name__ == '__main__': + unittest.main()