Dong Zhihong
7 years ago
commit
16a39d24f3
@ -0,0 +1,86 @@
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package pserver
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import (
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"bytes"
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"encoding/binary"
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"fmt"
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"testing"
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"time"
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"github.com/stretchr/testify/assert"
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)
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const testDir = "./test_data"
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type myKV struct {
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m map[string][]byte
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}
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func (m *myKV) GetKey(key string, timeout time.Duration) ([]byte, error) {
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if m.m == nil {
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m.m = make(map[string][]byte)
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}
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return m.m[key], nil
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}
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func (m *myKV) PutKey(key string, value []byte, timeout time.Duration, withLease bool) error {
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if m.m == nil {
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m.m = make(map[string][]byte)
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}
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m.m[key] = value
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return nil
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}
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func TestCheckpoint(t *testing.T) {
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kv := &myKV{}
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s, err := NewService(0, time.Hour, testDir, kv, nil)
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assert.Nil(t, err)
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err = s.checkpoint()
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assert.Nil(t, err)
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_, err = LoadCheckpoint(kv, 0)
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assert.Nil(t, err)
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}
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func float32ToByte(f float32) []byte {
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var buf bytes.Buffer
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err := binary.Write(&buf, binary.LittleEndian, f)
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if err != nil {
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fmt.Println("binary.Write failed:", err)
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}
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return buf.Bytes()
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}
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func TestCheckpointWithData(t *testing.T) {
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kv := &myKV{}
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s, err := NewService(0, time.Hour, testDir, kv, nil)
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assert.Nil(t, err)
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var content []byte
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for i := 0; i < 50000; i++ {
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content = append(content, float32ToByte(float32(i))...)
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}
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p1 := Parameter{Name: "p1", ElementType: 1, Content: content}
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err = s.InitParam(ParameterWithConfig{Param: p1}, nil)
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assert.Nil(t, err)
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err = s.FinishInitParams(0, nil)
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assert.Nil(t, err)
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var p2 Parameter
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err = s.GetParam(p1.Name, &p2)
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assert.Nil(t, err)
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assert.Equal(t, p1, p2)
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err = s.checkpoint()
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assert.Nil(t, err)
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cp, err := LoadCheckpoint(kv, 0)
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assert.Nil(t, err)
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s1, err := NewService(0, time.Hour, testDir, kv, cp)
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assert.Nil(t, err)
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var p3 Parameter
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err = s1.GetParam(p1.Name, &p3)
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assert.Nil(t, err)
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assert.Equal(t, p1, p3)
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}
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@ -1,39 +0,0 @@
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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syntax = "proto2";
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option optimize_for = LITE_RUNTIME;
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package paddle.framework;
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import "framework.proto";
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/**
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* This file contains necessary information for model, checkpoint.
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* etc.
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*/
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message LoDInfo { repeated int64 level = 1; }
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/**
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* Save the LoDTensorDesc information through LoDTensorProto, its data memory
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* is copyed to c buffer immediately. See model_format.md for details.
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*/
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message LoDTensorProto {
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optional DataType data_type = 1;
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repeated int64 dims = 2; // [UNK, 640, 480] is saved as [-1, 640, 480]
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repeated LoDInfo levels = 3;
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optional int32 lod_level = 4 [ default = 0 ];
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optional int32 version = 5;
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}
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File diff suppressed because it is too large
Load Diff
@ -0,0 +1,138 @@
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/* Copyright (c) 2017 PaddlePaddle Authors. All Rights Reserve.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#pragma once
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#include "MKLDNNLayer.h"
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#include "mkldnn.hpp"
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namespace paddle {
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typedef mkldnn::batch_normalization_forward bn_fwd;
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typedef mkldnn::batch_normalization_backward bn_bwd;
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/**
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* @brief A subclass of MKLDNNLayer BatchNorm layer.
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*
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* The config file api is mkldnn_batch_norm
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*/
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class MKLDNNBatchNormLayer : public MKLDNNLayer {
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protected:
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// save forward primitive_desc, which can be used backward
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std::shared_ptr<bn_fwd::primitive_desc> fwdPD_;
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// Epsilon value used in the batch normalization formula.
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static const real EPS;
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// weight and bias in paddle
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std::unique_ptr<Weight> weight_;
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std::unique_ptr<Weight> biases_;
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// mkldnn use a large buffer store both scale and shift
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// which are weight and bias in paddle corresponding.
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MatrixPtr valueScaleShift_;
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MatrixPtr gradScaleShift_;
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// Moving average of mean.
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std::unique_ptr<Weight> movingMean_;
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// Moving average of variance.
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std::unique_ptr<Weight> movingVar_;
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// if useGlobalStats_ is true, will use the loaded mean and variance.
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// otherwise, calculate mean and variance in every mini-batch.
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bool useGlobalStats_;
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// used in MKLDNN primitive desc
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unsigned flags_;
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// use to compute moving mean and variance.
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real movingAvgFraction_;
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// whether the weight has been init
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bool hasInitedWgt_;
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// local mean and variance
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// when useGlobalStats_ they are loaded from moving mean and variance
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// when do not useGlobalStats_ they are calculated from this mini-batch
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MKLDNNMatrixPtr mean_;
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MKLDNNMatrixPtr var_;
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public:
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explicit MKLDNNBatchNormLayer(const LayerConfig& config)
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: MKLDNNLayer(config), useGlobalStats_(true), hasInitedWgt_(false) {}
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~MKLDNNBatchNormLayer() {}
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bool init(const LayerMap& layerMap,
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const ParameterMap& parameterMap) override;
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void forward(PassType passType) override;
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void reshape(
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int& bs, int& ic, int& ih, int& iw, int oc, int& oh, int& ow) override;
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void resetFwd(std::vector<mkldnn::primitive>& pipeline,
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MKLDNNMatrixPtr& in,
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MKLDNNMatrixPtr& wgt,
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MKLDNNMatrixPtr& bias,
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MKLDNNMatrixPtr& out) override;
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void resetBwd(std::vector<mkldnn::primitive>& pipeline,
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MKLDNNMatrixPtr& in,
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MKLDNNMatrixPtr& wgt,
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MKLDNNMatrixPtr& bias,
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MKLDNNMatrixPtr& out) override;
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void updateWeights(const UpdateCallback& callback) override;
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void convertWeightsFromPaddle() override;
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protected:
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void initWeight();
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/**
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* cal moving mean and variance.
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* moving = moving * AvgFraction + local * (1 - AvgFraction)
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*/
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void calMovingMeanAndVar();
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/**
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* Forward functions: reset buffers(input, weight, output),
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* reset primitive descriptor,
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* reset pipeline.
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*/
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void resetFwdBuffers(MKLDNNMatrixPtr& in,
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MKLDNNMatrixPtr& wgt,
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MKLDNNMatrixPtr& out);
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void resetFwdPD(std::shared_ptr<bn_fwd::primitive_desc>& pd,
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MKLDNNMatrixPtr in,
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MKLDNNMatrixPtr wgt,
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MKLDNNMatrixPtr out);
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void resetFwdPipeline(std::vector<mkldnn::primitive>& pipeline,
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std::shared_ptr<bn_fwd::primitive_desc>& pd,
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MKLDNNMatrixPtr& in,
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MKLDNNMatrixPtr& wgt,
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MKLDNNMatrixPtr& out);
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/**
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* Backward functions: reset buffers(input, weight, output),
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* reset primitive descriptor,
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* reset pipeline.
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*/
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void resetBwdBuffers(MKLDNNMatrixPtr& in,
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MKLDNNMatrixPtr& wgt,
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MKLDNNMatrixPtr& out);
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void resetBwdPD(std::shared_ptr<bn_bwd::primitive_desc>& pd,
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MKLDNNMatrixPtr& in,
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MKLDNNMatrixPtr& wgt,
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MKLDNNMatrixPtr& out);
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void resetBwdPipeline(std::vector<mkldnn::primitive>& pipeline,
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std::shared_ptr<bn_bwd::primitive_desc>& pd,
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MKLDNNMatrixPtr& in,
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MKLDNNMatrixPtr& wgt,
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MKLDNNMatrixPtr& out);
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};
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} // namespace paddle
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