diff --git a/paddle/fluid/operators/optimizers/adam_op_npu.cc b/paddle/fluid/operators/optimizers/adam_op_npu.cc new file mode 100644 index 0000000000000000000000000000000000000000..134544c2f65bc397acc3cb6451990e6cee3b0990 --- /dev/null +++ b/paddle/fluid/operators/optimizers/adam_op_npu.cc @@ -0,0 +1,161 @@ +/* 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/adam_op.h" + +namespace paddle { +namespace operators { + +using Tensor = framework::Tensor; +using LoDTensor = framework::LoDTensor; + +template +class AdamNPUKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& ctx) const override { + const auto* param_var = ctx.InputVar("Param"); + PADDLE_ENFORCE_EQ(param_var->IsType(), true, + platform::errors::InvalidArgument( + "The Var(%s)'s type should be LoDTensor, " + "but the received is %s", + ctx.InputNames("Param").front(), + framework::ToTypeName(param_var->Type()))); + T epsilon = static_cast(ctx.Attr("epsilon")); + auto* param = ctx.Input("Param"); + auto* grad_var = ctx.InputVar("Grad"); + PADDLE_ENFORCE_EQ(grad_var->IsType(), true, + platform::errors::InvalidArgument( + "The Grad(%s)'s type should be LoDTensor, " + "but the received is %s", + ctx.InputNames("Grad").front(), + framework::ToTypeName(param_var->Type()))); + auto* grad = ctx.Input("Grad"); + auto* mom1 = ctx.Input("Moment1"); + auto* mom2 = ctx.Input("Moment2"); + auto* lr = ctx.Input("LearningRate"); + + auto* beta1_pow = ctx.Input("Beta1Pow"); + auto* beta2_pow = ctx.Input("Beta2Pow"); + + auto* param_out = ctx.Output("ParamOut"); + auto* mom1_out = ctx.Output("Moment1Out"); + auto* mom2_out = ctx.Output("Moment2Out"); + auto* beta1_pow_out = ctx.Output("Beta1PowOut"); + auto* beta2_pow_out = ctx.Output("Beta2PowOut"); + + param_out->mutable_data(ctx.GetPlace()); + mom1_out->mutable_data(ctx.GetPlace()); + mom2_out->mutable_data(ctx.GetPlace()); + beta1_pow_out->mutable_data(ctx.GetPlace()); + beta2_pow_out->mutable_data(ctx.GetPlace()); + + T beta1 = static_cast(ctx.Attr("beta1")); + if (ctx.HasInput("Beta1Tensor")) { + auto* beta1_tensor = ctx.Input("Beta1Tensor"); + PADDLE_ENFORCE_EQ(beta1_tensor->numel(), 1, + platform::errors::InvalidArgument( + "Input(Beta1Tensor) size must be 1, but get %d", + beta1_tensor->numel())); + beta1 = static_cast(GetAttrFromTensor(beta1_tensor)); + } + T beta2 = static_cast(ctx.Attr("beta2")); + if (ctx.HasInput("Beta2Tensor")) { + auto* beta2_tensor = ctx.Input("Beta2Tensor"); + PADDLE_ENFORCE_EQ(beta2_tensor->numel(), 1, + platform::errors::InvalidArgument( + "Input(Beta2Tensor) size must be 1, but get %d", + beta2_tensor->numel())); + beta2 = static_cast(GetAttrFromTensor(beta2_tensor)); + } + VLOG(3) << "beta1_pow.numel() : " << beta1_pow->numel() + << "beta2_pow.numel() : " << beta2_pow->numel(); + VLOG(3) << "param.numel(): " << param->numel(); + + PADDLE_ENFORCE_EQ(beta1_pow_out->numel(), 1, + platform::errors::InvalidArgument( + "beta1 pow output size should be 1, but received " + "value is:%d.", + beta1_pow_out->numel())); + + PADDLE_ENFORCE_EQ(beta2_pow_out->numel(), 1, + platform::errors::InvalidArgument( + "beta2 pow output size should be 1, but received " + "value is:%d.", + beta2_pow_out->numel())); + + // reshape + Tensor beta1_tensor(framework::proto::VarType::FP32); + beta1_tensor.mutable_data({1}, ctx.GetPlace()); + TensorFromVector(std::vector{beta1}, ctx.device_context(), + &beta1_tensor); + Tensor beta2_tensor(framework::proto::VarType::FP32); + beta2_tensor.mutable_data({1}, ctx.GetPlace()); + TensorFromVector(std::vector{beta2}, ctx.device_context(), + &beta2_tensor); + + Tensor epsilon_tensor(framework::proto::VarType::FP32); + epsilon_tensor.mutable_data({1}, ctx.GetPlace()); + TensorFromVector(std::vector{epsilon}, ctx.device_context(), + &epsilon_tensor); + auto stream = + ctx.template device_context() + .stream(); + auto runner = + NpuOpRunner("ApplyAdamD", + { + *param, *mom1, *mom2, *beta1_pow, *beta2_pow, *lr, + beta1_tensor, beta2_tensor, epsilon_tensor, *grad, + }, + { + *param_out, *mom1_out, *mom2_out, + }, + {}); + runner.Run(stream); + + // NOTE(zhiqiu): ApplyAdamD updates params inplace, so + // if param and param_out is not same, we need to do copy. + if (param_out->data() != param->data()) { + ctx.template device_context().Wait(); + framework::TensorCopySync(*param, ctx.GetPlace(), param_out); + } + if (mom1_out->data() != mom1->data()) { + ctx.template device_context().Wait(); + framework::TensorCopySync(*mom1, ctx.GetPlace(), mom1_out); + } + if (mom2_out->data() != mom2->data()) { + ctx.template device_context().Wait(); + framework::TensorCopySync(*mom2, ctx.GetPlace(), mom2_out); + } + auto runner_m1 = + NpuOpRunner("Mul", {*beta1_pow, beta1_tensor}, {*beta1_pow_out}, {}); + runner_m1.Run(stream); + auto runner_m2 = + NpuOpRunner("Mul", {*beta2_pow, beta2_tensor}, {*beta2_pow_out}, {}); + runner_m2.Run(stream); + } +}; + +} // namespace operators +} // namespace paddle + +namespace ops = paddle::operators; + +REGISTER_OP_NPU_KERNEL( + adam, ops::AdamNPUKernel, + ops::AdamNPUKernel); diff --git a/python/paddle/fluid/tests/unittests/npu/test_adam_op_npu.py b/python/paddle/fluid/tests/unittests/npu/test_adam_op_npu.py new file mode 100644 index 0000000000000000000000000000000000000000..ebf041388eeab9707ff9143de3002b11c7c6a94d --- /dev/null +++ b/python/paddle/fluid/tests/unittests/npu/test_adam_op_npu.py @@ -0,0 +1,148 @@ +# 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 +from test_adam_op import adam_step + +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 = "adam" + param = np.random.uniform(-1, 1, (102, 105)).astype("float32") + grad = np.random.uniform(-1, 1, (102, 105)).astype("float32") + moment1 = np.random.uniform(-1, 1, (102, 105)).astype("float32") + # The second moment is positive + moment2 = np.random.random((102, 105)).astype("float32") + + learning_rate = 0.004 + beta1 = 0.78 + beta2 = 0.836 + epsilon = 1e-4 + beta1_pow = beta1**10 + beta2_pow = beta2**10 + + self.inputs = { + 'Param': param, + 'Grad': grad, + 'Moment1': moment1, + 'Moment2': moment2, + 'LearningRate': np.array([learning_rate]).astype("float32"), + 'Beta1Pow': np.array([beta1_pow]).astype("float32"), + 'Beta2Pow': np.array([beta2_pow]).astype("float32") + } + + self.attrs = {'epsilon': epsilon, 'beta1': beta1, 'beta2': beta2} + + param_out, moment1_out, \ + moment2_out = adam_step(self.inputs, self.attrs) + + self.outputs = { + 'Moment1Out': moment1_out, + 'Moment2Out': moment2_out, + 'ParamOut': param_out, + 'Beta1PowOut': np.array([beta1_pow]).astype("float32") * beta1, + 'Beta2PowOut': np.array([beta2_pow]).astype("float32") * beta2 + } + + def set_npu(self): + self.__class__.use_npu = True + + def init_dtype(self): + self.dtype = np.float32 + + def test_check_output(self): + self.check_output_with_place(self.place, atol=1e-5, check_dygraph=False) + + +''' +# TODO(zhiqiu): The following test may let 0-3 card down. +# we need to analyze it and open it. + +@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) + adam = fluid.optimizer.Adam(learning_rate=0.01) + adam.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()