From 116800378a67077d65660e0699d4d96264a488bd Mon Sep 17 00:00:00 2001 From: Abhinav Arora Date: Thu, 12 Oct 2017 13:36:31 -0700 Subject: [PATCH] Adding the Adam Optimizer operator (#4733) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * add adam op moment1_out = beta1 * moment1 + (1 − beta1) * grad moment2_out = beta2 * moment2 + (1 − beta2) * grad * grad moment1_hat = moment1_out / (1 - beta1^t) moment2_hat = moment2_out / (1 - beta2^t) param_out = param - learning_rate * moment1_hat / (sqrt(moment2_hat) + epsilon) * fix moment 2 * Adding the Adam optimization operator * Adding more tests for Adam op --- paddle/operators/adam_op.cc | 144 ++++++++++++++ paddle/operators/adam_op.cu | 20 ++ paddle/operators/adam_op.h | 82 ++++++++ .../paddle/v2/framework/tests/test_adam_op.py | 186 ++++++++++++++++++ 4 files changed, 432 insertions(+) create mode 100644 paddle/operators/adam_op.cc create mode 100644 paddle/operators/adam_op.cu create mode 100644 paddle/operators/adam_op.h create mode 100644 python/paddle/v2/framework/tests/test_adam_op.py diff --git a/paddle/operators/adam_op.cc b/paddle/operators/adam_op.cc new file mode 100644 index 0000000000..293b37b775 --- /dev/null +++ b/paddle/operators/adam_op.cc @@ -0,0 +1,144 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +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/operators/adam_op.h" + +namespace paddle { +namespace operators { + +class AdamOp : public framework::OperatorWithKernel { + public: + using framework::OperatorWithKernel::OperatorWithKernel; + + protected: + void InferShape(framework::InferShapeContext *ctx) const override { + PADDLE_ENFORCE(ctx->HasInput("Param"), + "Input(Param) of AdamOp should not be null."); + PADDLE_ENFORCE(ctx->HasInput("Grad"), + "Input(Grad) of AdamOp should not be null."); + PADDLE_ENFORCE(ctx->HasInput("Moment1"), + "Input(Moment1) of AdamOp should not be null."); + PADDLE_ENFORCE(ctx->HasInput("Moment2"), + "Input(Moment2) of AdamOp should not be null."); + PADDLE_ENFORCE(ctx->HasInput("LearningRate"), + "Input(LearningRate) of AdamOp should not be null."); + PADDLE_ENFORCE(ctx->HasInput("Beta1Pow"), + "Input(Beta1Pow) of AdamOp should not be null."); + PADDLE_ENFORCE(ctx->HasInput("Beta2Pow"), + "Input(Beta2Pow) of AdamOp should not be null."); + + PADDLE_ENFORCE(ctx->HasOutput("ParamOut"), + "Output(ParamOut) of AdamOp should not be null."); + PADDLE_ENFORCE(ctx->HasOutput("Moment1Out"), + "Output(Moment1Out) of AdamOp should not be null."); + PADDLE_ENFORCE(ctx->HasOutput("Moment2Out"), + "Output(Moment2Out) of AdamOp should not be null."); + PADDLE_ENFORCE(ctx->HasOutput("Beta1PowOut"), + "Output(Beta1PowOut) of AdamOp should not be null."); + PADDLE_ENFORCE(ctx->HasOutput("Beta2PowOut"), + "Output(Beta2PowOut) of AdamOp should not be null."); + + auto lr_dims = ctx->GetInputDim("LearningRate"); + PADDLE_ENFORCE_EQ(framework::product(lr_dims), 1, + "Learning rate should have 1 dimension"); + auto beta1_pow_dims = ctx->GetInputDim("Beta1Pow"); + PADDLE_ENFORCE_EQ(framework::product(beta1_pow_dims), 1, + "Beta1 power accumulator should have 1 dimension"); + auto beta2_pow_dims = ctx->GetInputDim("Beta2Pow"); + PADDLE_ENFORCE_EQ(framework::product(beta1_pow_dims), 1, + "Beta1 power accumulator should have 1 dimension"); + + auto param_dims = ctx->GetInputDim("Param"); + PADDLE_ENFORCE_EQ( + param_dims, ctx->GetInputDim("Grad"), + "Param and Grad input of AdamOp should have same dimension"); + PADDLE_ENFORCE_EQ( + param_dims, ctx->GetInputDim("Moment1"), + "Param and Moment input of AdamOp should have same dimension"); + PADDLE_ENFORCE_EQ( + param_dims, ctx->GetInputDim("Moment2"), + "Param and InfNorm input of AdamOp should have same dimension"); + + ctx->SetOutputDim("ParamOut", param_dims); + ctx->SetOutputDim("Moment1Out", param_dims); + ctx->SetOutputDim("Moment2Out", param_dims); + ctx->SetOutputDim("Beta1PowOut", beta1_pow_dims); + ctx->SetOutputDim("Beta2PowOut", beta2_pow_dims); + } +}; + +class AdamOpMaker : public framework::OpProtoAndCheckerMaker { + public: + AdamOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker) + : OpProtoAndCheckerMaker(proto, op_checker) { + AddInput("Param", "(Tensor) Input parameter"); + AddInput("Grad", "(Tensor) Input gradient"); + AddInput("LearningRate", "(Tensor) Learning rate"); + AddInput("Moment1", "(Tensor) Input first moment"); + AddInput("Moment2", "(Tensor) Input second moment"); + AddInput("Beta1Pow", "(Tensor) Input beta1 power accumulator"); + AddInput("Beta2Pow", "(Tensor) Input beta2 power accumulator"); + + AddOutput("ParamOut", "(Tensor) Output parameter"); + AddOutput("Moment1Out", "(Tensor) Output first moment"); + AddOutput("Moment2Out", "(Tensor) Output second moment"); + AddOutput("Beta1PowOut", "(Tensor) Output beta1 power accumulator"); + AddOutput("Beta2PowOut", "(Tensor) Output beta2 power accumulator"); + + AddAttr("beta1", + "(float, default 0.9) " + "Exponential decay rate for the " + "first moment estimates.") + .SetDefault(0.9f); + AddAttr("beta2", + "(float, default 0.999) " + "exponential decay rate for the " + "second moment estimates.") + .SetDefault(0.999f); + AddAttr("epsilon", + "(float, default 1.0e-8) " + "Constant for numerical stability") + .SetDefault(1.0e-8f); + + AddComment(R"DOC( +Adam Updates Operator. + +This implements the Adam optimizer from Section 2 of the Adam +paper[1]. Adam is a first-order gradient-based optimization +method based on adaptive estimates of lower-order moments. + +Adam updates: + +moment1_out = beta1 * moment1 + (1 − beta1) * grad +moment2_out = beta2 * moment2 + (1 − beta2) * grad * grad +beta1_pow_out = beta1_pow * beta1 +beta2_pow_out = beta2_pow * beta2 +learning_rate_t = learning_rate_t * + sqrt(1 - beta2_pow_out) / (1 - beta1_pow_out) +param_out = param - learning_rate_t * moment1/ (sqrt(moment2) + epsilon) + +References: + [1] Adam: A Method for Stochastic Optimization + (https://arxiv.org/abs/1412.6980) + +)DOC"); + } +}; +} // namespace operators +} // namespace paddle + +namespace ops = paddle::operators; +REGISTER_OP_WITHOUT_GRADIENT(adam, ops::AdamOp, ops::AdamOpMaker); +REGISTER_OP_CPU_KERNEL(adam, + ops::AdamOpKernel); diff --git a/paddle/operators/adam_op.cu b/paddle/operators/adam_op.cu new file mode 100644 index 0000000000..a3def912e5 --- /dev/null +++ b/paddle/operators/adam_op.cu @@ -0,0 +1,20 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + + 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. */ + +#define EIGEN_USE_GPU +#include "paddle/operators/adam_op.h" + +namespace ops = paddle::operators; +REGISTER_OP_GPU_KERNEL(adam, + ops::AdamOpKernel); diff --git a/paddle/operators/adam_op.h b/paddle/operators/adam_op.h new file mode 100644 index 0000000000..789c2f14b3 --- /dev/null +++ b/paddle/operators/adam_op.h @@ -0,0 +1,82 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +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. */ + +#pragma once +#include "paddle/framework/eigen.h" +#include "paddle/framework/op_registry.h" + +namespace paddle { +namespace operators { + +template +class AdamOpKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& ctx) const override { + auto param_out_tensor = ctx.Output("ParamOut"); + auto moment1_out_tensor = ctx.Output("Moment1Out"); + auto moment2_out_tensor = ctx.Output("Moment2Out"); + auto beta1_pow_out_tensor = ctx.Output("Beta1PowOut"); + auto beta2_pow_out_tensor = ctx.Output("Beta2PowOut"); + + param_out_tensor->mutable_data(ctx.GetPlace()); + moment1_out_tensor->mutable_data(ctx.GetPlace()); + moment2_out_tensor->mutable_data(ctx.GetPlace()); + beta1_pow_out_tensor->mutable_data(ctx.GetPlace()); + beta2_pow_out_tensor->mutable_data(ctx.GetPlace()); + + float beta1 = ctx.Attr("beta1"); + float beta2 = ctx.Attr("beta2"); + float epsilon = ctx.Attr("epsilon"); + + auto param = framework::EigenVector::Flatten( + *ctx.Input("Param")); + auto grad = framework::EigenVector::Flatten( + *ctx.Input("Grad")); + auto moment1 = framework::EigenVector::Flatten( + *ctx.Input("Moment1")); + auto moment2 = framework::EigenVector::Flatten( + *ctx.Input("Moment2")); + auto lr = framework::EigenVector::Flatten( + *ctx.Input("LearningRate")); + auto beta1_pow = framework::EigenVector::Flatten( + *ctx.Input("Beta1Pow")); + auto beta2_pow = framework::EigenVector::Flatten( + *ctx.Input("Beta2Pow")); + auto param_out = framework::EigenVector::Flatten(*param_out_tensor); + auto moment1_out = framework::EigenVector::Flatten(*moment1_out_tensor); + auto moment2_out = framework::EigenVector::Flatten(*moment2_out_tensor); + auto beta1_pow_out = + framework::EigenVector::Flatten(*beta1_pow_out_tensor); + auto beta2_pow_out = + framework::EigenVector::Flatten(*beta2_pow_out_tensor); + auto place = ctx.GetEigenDevice(); + + moment1_out.device(place) = beta1 * moment1 + (1 - beta1) * grad; + moment2_out.device(place) = beta2 * moment2 + (1 - beta2) * grad.square(); + beta1_pow_out.device(place) = beta1_pow * beta1; + beta2_pow_out.device(place) = beta2_pow * beta2; + // All of these are tensors of 1 element + auto lr_t = lr * (1 - beta2_pow_out).sqrt() / (1 - beta1_pow_out); + // Eigen does not support automatic broadcast + // Get dimensions of moment vector to broadcast lr_t + Eigen::DSizes m_dsize(moment1_out_tensor->numel()); + param_out.device(place) = + param - + lr_t.broadcast(m_dsize) * + (moment1_out / (moment2_out.sqrt() + epsilon)); + } +}; + +} // namespace operators +} // namespace paddle diff --git a/python/paddle/v2/framework/tests/test_adam_op.py b/python/paddle/v2/framework/tests/test_adam_op.py new file mode 100644 index 0000000000..ff6faafa6e --- /dev/null +++ b/python/paddle/v2/framework/tests/test_adam_op.py @@ -0,0 +1,186 @@ +import unittest +import numpy as np +from op_test import OpTest + + +class TestAdamOp1(OpTest): + def setUp(self): + '''Test Adam Op with supplied attributes + ''' + 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, beta1_pow_out, \ + beta2_pow_out = adam_step(self.inputs, self.attrs) + + self.outputs = { + 'Moment1Out': moment1_out, + 'Moment2Out': moment2_out, + 'Beta1PowOut': beta1_pow_out, + 'Beta2PowOut': beta2_pow_out, + 'ParamOut': param_out + } + + def test_check_output(self): + self.check_output() + + +class TestAdamOp2(OpTest): + def setUp(self): + '''Test Adam Op with supplied attributes + ''' + 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 = 0.001 + beta1 = 0.9 + beta2 = 0.999 + epsilon = 1e-8 + 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") + } + + attributes = {'epsilon': epsilon, 'beta1': beta1, 'beta2': beta2} + + param_out, moment1_out, moment2_out, beta1_pow_out, \ + beta2_pow_out = adam_step(self.inputs, attributes) + + self.outputs = { + 'Moment1Out': moment1_out, + 'Moment2Out': moment2_out, + 'Beta1PowOut': beta1_pow_out, + 'Beta2PowOut': beta2_pow_out, + 'ParamOut': param_out + } + + def test_check_output(self): + self.check_output() + + +class TestAdamOpMultipleSteps(OpTest): + def setUp(self): + '''Test Adam Operator with supplied attributes + ''' + self.op_type = "adam" + self.num_steps = 10 + + 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 = 0.001 + beta1 = 0.9 + beta2 = 0.999 + epsilon = 1e-8 + 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} + + def test_check_output(self): + for _ in range(self.num_steps): + param_out, moment1_out, moment2_out, beta1_pow_out, \ + beta2_pow_out = adam_step(self.inputs, self.attrs) + + self.outputs = { + 'Moment1Out': moment1_out, + 'Moment2Out': moment2_out, + 'Beta1PowOut': beta1_pow_out, + 'Beta2PowOut': beta2_pow_out, + 'ParamOut': param_out + } + + # Verify output for this step + self.check_output() + + # Output of this step becomes input for next step + self.inputs['Param'] = param_out + self.inputs['Moment1'] = moment1_out + self.inputs['Moment2'] = moment2_out + self.inputs['Beta1Pow'] = beta1_pow_out + self.inputs['Beta2Pow'] = beta2_pow_out + + # Randomize gradient for next step + self.inputs['Grad'] = np.random.uniform( + -1, 1, (102, 105)).astype("float32") + + +def adam_step(inputs, attributes): + ''' + Simulate one step of the adam optimizer + :param inputs: dict of inputs + :param attributes: dict of attributes + :return tuple: tuple of output param, moment1, moment2, + beta1 power accumulator and beta2 power accumulator + ''' + param = inputs['Param'] + grad = inputs['Grad'] + moment1 = inputs['Moment1'] + moment2 = inputs['Moment2'] + lr = inputs['LearningRate'] + beta1_pow = inputs['Beta1Pow'] + beta2_pow = inputs['Beta2Pow'] + + beta1 = attributes['beta1'] + beta2 = attributes['beta2'] + epsilon = attributes['epsilon'] + + moment1_out = beta1 * moment1 + (1 - beta1) * grad + moment2_out = beta2 * moment2 + (1 - beta2) * np.square(grad) + beta1_pow_out = beta1_pow * beta1 + beta2_pow_out = beta2_pow * beta2 + lr_t = lr * np.sqrt(1 - beta2_pow_out) / (1 - beta1_pow_out) + param_out = param - lr_t * (moment1_out / (np.sqrt(moment2_out) + epsilon)) + return param_out, moment1_out, moment2_out, beta1_pow_out, beta2_pow_out + + +if __name__ == "__main__": + unittest.main() -- GitLab