Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into cmake_go_vendor

doc/design/simple_op_design.md

+## Interaction between C++ and Python
+
+Users employ API in Python to describe their own network, however, the network construction actually happens in C++. so Protobuf is introduced to send the message between Python and C++. 
+
+The Interaction between Python and C++ can be simplified as two steps:
+
+1. C++ tells Python how many Ops there are, and what parameter do users need to offer to initialize a new Op. Python then builds API for each Op at compile time.
+
+2. Users invoke APIs built by Python and provide necessary parameters. These parameters will be sent to C++ fo finish Op construction task.
+
+### Message form C++ to Python
+
+We define a Protobuf message class `OpProto` to hold message needed in the first step. What should an `OpProto` contain? This question is equivalent to “What message do we need to offer, to build a Python API which is legal and user oriented and can use to describe a whole Op.”
+
+Following message are necessary:
+
+1. Op's name, and its simple comment.
+2. Input and output variable number; each variable's name, type, and comment.
+3. Op's attributes; each attribute includes name, type, comment, **default value** and **value range**.
+
+So `OpProto` can be defined as follows:
+
+```proto
+enum AttrType {
+	INT = 1;
+	FLOAT = 2;
+	STRING = 3;
+	INTS = 4;
+	FLOATS = 5;
+	STRINGS = 6;
+};
+
+message AttrValue {
+	AttrType type = 1;
+	optional int iv = 2;
+	optional float fv = 3;
+	optional string sv = 4;
+	repeated int ivs = 5;
+	repeated float fvs = 6;
+	repeated string svs = 7;
+};
+
+message AttrProto {
+	required string name = 1;
+	required string comment = 2;
+	required AttrType type = 3;
+};
+
+message VarProto {
+	required string name = 1;
+	required string comment = 2;
+};
+
+message OpProto {
+	repeated VarProto inputs = 1;
+	repeated VarProto outputs = 2;
+	repeated AttrProto attrs = 3;
+	required string type = 4;
+	required string comment = 5;
+};
+```
+
+To generate Python code automatically:
+
+```python 
+def create_python_ops_creatation_functions():
+	op_protos = paddle.framework.OpRegistry.get_all_op_proto()
+	for type_name in op_protos:
+		op_proto = op_protos[type_name]
+		def __impl__(**kwargs):  # User must use key word args in Paddle API
+			inputs = [kwargs.get(ipt.name, "") for ipt in op_proto.inputs]
+			outputs = [kwargs.get(opt.name, "") for opt in op_proto.outputs]
+			attrs = [cast_to_op_attr(attr, kwargs.get(attr.name, None)) for attr in op_proto.attrs]
+			opdesc = （input, outputs, type_name, attrs）
+			return paddle.framework.OpRegistry.CreateOp(opdesc)
+		__impl__.__doc__ = create_doc_string(op_proto)
+		globals()[type_name] = __impl__
+
+create_python_ops_creatation_functions()
+```
+
+### Message from Python to C++
+
+To hold message needed in the above second step, we define Protobuf message class `OpDesc`. It is used to hold user-specified parameters in Op describing.
+
+```proto
+message OpDesc {
+	required string type = 1;	
+	repeated string inputs = 2;
+	repeated string outputs = 3;
+	map<string, AttrValue> attrs = 4;
+};
+```
+
+## OpProto Register
+
+Every Op has its own `OpProto`. For using convenience, we need to register them and record all their messages. For each `Op` class, we define a corresponding `OpMaker` class, in whose constructor we implement the `OpProto`'s building process. `OpMaker`'s constructor will be invoked by another function `OpRegistry::RegisterOp()`.
+
+```cpp
+class OpProtoMaker {
+public:
+	OpProtoMaker(OpProto* proto): proto_(proto) {}
+protected:
+	OpProto* proto_;
+	void AddInput(const std::string& name, const std::string& desc) {...}
+	void AddAttr(const std::string& name, const std::string& desc, TypeId type) {...}
+	void AddComment(const std::string& comment) { ... }
+};
+
+class OpRegistry {
+public:
+	using OpCreator = std::function<OperatorBase* (OpDesc& desc)>;
+	
+	template <typename OpType, typename OpMaker>
+	static void RegisterOp(const std::string& name) {
+		gCreators_[name] = [](const OpDesc& desc) {
+			return new OpType(desc);
+		};
+		OpProto& opProto = gProtos_[name];
+		OpMaker()(&opProto);
+	}
+
+	static map<string, OpCreator> gCreators_;
+	static map<string, OpProto> gProtos_;
+};
+
+template <typename OpType, typename OpMaker>
+class OpRegister {
+  public:
+    OpRegister(std::string type) {
+        OpRegistry::RegisterOp<OpType, OpMaker>(type);
+    }
+};
+
+#define REGISTER_OP(op_class, op_maker_class, type_name)         \
+    class op_class##Register {                                   \
+      private:                                                   \
+        const static OpRegister<#op_class, #op_maker_class> reg; \
+    };                                                           \
+    const Register op_class##Register::reg(#type_name);
+    
+class CosineOp {
+// ...
+}
+
+struct CosineOpProtoMaker : public OpProtoMaker {
+	CosineOpProtoMaker(OpProto* proto) : OpProtoMaker(proto) {
+		AddInput("input", "input of cosine op");
+		AddAttr("scale", "scale of cosine op", float).Default(1.0).LargerThan(0.0);
+		AddType("cos");
+		AddComment("This is cos op");
+	}
+}
+
+REGISTER_OP(CosineOp, CosineOpProtoMaker, cos);
+```
+
+In `REGISTER_OP(CosineOp, CosineOpProtoMaker, cos)`, we register not only `CosineOp` but also `CosineOpProto`. As fields of `CosineOpProto`, the default value and value range of `scale` are also registered here. 
+
+## Python API
+
+Python  APIs are divided into two types, high-level API and low-level API.
+
+### High-Level API
+
+High-level API is called by users directly, so it should keep its style consistent with existing V2 APIs.
+
+Here is a sample about how a define a fc layer:
+
+```python
+hd = fc_layer(input=data, size=56, with_bias=True, activation="sigmoid");
+```
+
+`hd` is the output of `fc_layer` and it's a `variable`. It can be further sent into other layers as input.
+
+The definition of `fc_layer()`:
+
+```python
+def fc_layer(input, size, with_bias, activation):
+	attr_map = {"size":size}
+	check_attrs(attr_map)
+	w = make_variable('w')
+	if with_bias:
+		b = make_variable('b')
+	else:
+		b = None
+	fc_output = make_variable('fc_output');
+	fc_op(input, w, b, fc_output, attr_map)
+	act_output = make_variable('sigmod_output');
+	if activation == "sigmod":
+		sigmod_op(fc_output, act_output);
+	elif:
+		# ...
+	return act_output;
+``` 
+
+### Low Leval API
+
+In above sample, `fc_op` and `sigmod_op` are low-level API. They build `OpDesc` and invoke corresponding C++ code.
+
+*TODO*

go/pserver/cclient/CMakeLists.txt

+cc_library(paddle_go_optimizer DEPS paddle_optimizer paddle_proto glog gflags protobuf)
 go_library(paddle_pserver_cclient STATIC)
-
-add_subdirectory(test)
+if(WITH_TESTING)
+  add_subdirectory(test)
+endif()

go/pserver/cclient/test/CMakeLists.txt

-
-cc_binary(main SRCS main.c DEPS paddle_pserver_cclient)
 cc_test(test_cclient SRCS test_cclient.c DEPS paddle_pserver_cclient)
+add_style_check_target(test_cclient test_cclient.c)

go/pserver/cclient/test/main.c

-#include <stdio.h>
-#include <stdlib.h>
-
-#include "libpaddle_pserver_cclient.h"
-
-// TODO(helin): Fix: gtest using cmake is not working, using this
-// hacky way for now.
-#define fail()                                          \
-  fprintf(stderr, "info: %s:%d: ", __FILE__, __LINE__); \
-  exit(-1);
-
-void sendGrads(paddle_pserver_client c) {
-  unsigned char grad_a[2000] = {2};
-  unsigned char grad_b[3000] = {3};
-  paddle_gradient grad1 = {
-      "param_a", PADDLE_ELEMENT_TYPE_FLOAT32, grad_a, 2000};
-  paddle_gradient grad2 = {
-      "param_b", PADDLE_ELEMENT_TYPE_FLOAT32, grad_b, 3000};
-  paddle_gradient* grads[2] = {&grad1, &grad2};
-  if (paddle_send_grads(c, grads, 2)) {
-    fail();
-  }
-}
-
-void getParams(paddle_pserver_client c) {
-  paddle_parameter param_a;
-  paddle_parameter param_b;
-  char name_a[] = "param_a";
-  char name_b[] = "param_b";
-  // Must pre-allocate the prameter content before calling paddle_get_params.
-  unsigned char content_a[2000] = {};
-  unsigned char content_b[3000] = {};
-  param_a.element_type = PADDLE_ELEMENT_TYPE_FLOAT32;
-  param_a.name = name_a;
-  param_a.content = content_a;
-  param_a.content_len = 2000;
-  param_b.element_type = PADDLE_ELEMENT_TYPE_FLOAT32;
-  param_b.name = name_b;
-  param_b.content = content_b;
-  param_b.content_len = 3000;
-
-  paddle_parameter* params[2] = {&param_a, &param_b};
-  if (paddle_get_params(c, params, 2)) {
-    fail();
-  }
-}
-
-int main() {
-  char addr[] = "localhost:3000";
-  paddle_pserver_client c = paddle_new_pserver_client(addr, 1);
-retry:
-  if (paddle_begin_init_params(c)) {
-    paddle_parameter param;
-    char name_a[] = "param_a";
-    char name_b[] = "param_b";
-    unsigned char content_a[2000] = {1};
-    unsigned char content_b[3000] = {0};
-    param.element_type = PADDLE_ELEMENT_TYPE_FLOAT32;
-    param.name = name_a;
-    param.content = content_a;
-    param.content_len = 2000;
-    int error = paddle_init_param(c, param, NULL, 0);
-    if (error != 0) {
-      goto retry;
-    }
-
-    param.element_type = PADDLE_ELEMENT_TYPE_FLOAT32;
-    param.name = name_b;
-    param.content = content_b;
-    param.content_len = 3000;
-    error = paddle_init_param(c, param, NULL, 0);
-    if (error != 0) {
-      goto retry;
-    }
-
-    error = paddle_finish_init_params(c);
-    if (error != 0) {
-      goto retry;
-    }
-  }
-
-  int i;
-  for (i = 0; i < 100; i++) {
-    sendGrads(c);
-    getParams(c);
-  }
-
-  if (paddle_save_model(c, "/tmp/")) {
-    fail();
-  }
-
-  return 0;
-}

go/pserver/cclient/test/test_cclient.c

@@ -3,113 +3,101 @@
 
 #include "libpaddle_pserver_cclient.h"
 
-typedef float real;
-
-void fail() {
-  // TODO(helin): fix: gtest using cmake is not working, using this
-  // hacky way for now.
-  printf("test failed.\n");
+// TODO(helin): Fix: gtest using cmake is not working, using this
+// hacky way for now.
+#define fail()                                          \
+  fprintf(stderr, "info: %s:%d: ", __FILE__, __LINE__); \
   exit(-1);
+
+void sendGrads(paddle_pserver_client c) {
+  unsigned char grad_a[2000] = {2};
+  unsigned char grad_b[3000] = {3};
+  paddle_gradient grad1 = {
+      "param_a", PADDLE_ELEMENT_TYPE_FLOAT32, grad_a, 2000};
+  paddle_gradient grad2 = {
+      "param_b", PADDLE_ELEMENT_TYPE_FLOAT32, grad_b, 3000};
+  paddle_gradient *grads[2] = {&grad1, &grad2};
+  if (paddle_send_grads(c, grads, 2)) {
+    fail();
+  }
 }
 
-void print_parameter(paddle_gradient* param) {
-  if (param == NULL) {
-    printf("param is NULL!!\n");
-  } else {
-    printf("==== parameter ====\n");
-    printf("name: %s\n", param->name);
-    printf("content_len: %d\n", param->content_len);
-    printf("content_type: %d\n", param->element_type);
-    int i;
-    for (i = 0; i < param->content_len / (int)sizeof(real); ++i) {
-      printf("%f ", ((float*)param->content)[i]);
-    }
-    printf("\n\n");
+void getParams(paddle_pserver_client c) {
+  paddle_parameter param_a;
+  paddle_parameter param_b;
+  char name_a[] = "param_a";
+  char name_b[] = "param_b";
+  // Must pre-allocate the prameter content before calling paddle_get_params.
+  unsigned char content_a[2000] = {};
+  unsigned char content_b[3000] = {};
+  param_a.element_type = PADDLE_ELEMENT_TYPE_FLOAT32;
+  param_a.name = name_a;
+  param_a.content = content_a;
+  param_a.content_len = 2000;
+  param_b.element_type = PADDLE_ELEMENT_TYPE_FLOAT32;
+  param_b.name = name_b;
+  param_b.content = content_b;
+  param_b.content_len = 3000;
+
+  paddle_parameter *params[2] = {&param_a, &param_b};
+  if (paddle_get_params(c, params, 2)) {
+    fail();
   }
 }
 
 int main() {
   char addr[] = "localhost:3000";
   paddle_pserver_client c = paddle_new_pserver_client(addr, 1);
-
-  char* names[] = {"param_a", "param_b"};
-
+  char *config_proto;
+  size_t config_proto_len = 0;
+  ssize_t nread;
+  FILE *fp = fopen("testdata/optimizer.pb.txt", "r");
+  if (!fp) {
+    fail();
+  }
+  while ((nread = getline(&config_proto, &config_proto_len, fp)) != -1) {
+    printf("%s", config_proto);
+  }
+  fclose(fp);
 retry:
-  printf("init parameter to pserver:\n");
-
-  real param_content1[] = {0.1, 0.2, 0.3};
-  real param_content2[] = {0.4, 0.5, 0.6};
-  paddle_parameter** params =
-      (paddle_parameter**)malloc(sizeof(paddle_parameter*) * 2);
-  params[0] = (paddle_parameter*)malloc(sizeof(paddle_parameter));
-  params[0]->name = names[0];
-  params[0]->content = (unsigned char*)param_content1;
-  params[0]->content_len = 3 * sizeof(real);
-  params[0]->element_type = PADDLE_ELEMENT_TYPE_FLOAT32;
-
-  params[1] = (paddle_parameter*)malloc(sizeof(paddle_parameter));
-  params[1]->name = names[1];
-  params[1]->content = (unsigned char*)param_content2;
-  params[1]->content_len = 3 * sizeof(real);
-  params[1]->element_type = PADDLE_ELEMENT_TYPE_INT32;
-
   if (paddle_begin_init_params(c)) {
-    if (paddle_init_param(c, *params[0], NULL, 0) != 0) {
+    paddle_parameter param;
+    char name_a[] = "param_a";
+    char name_b[] = "param_b";
+    unsigned char content_a[2000] = {1};
+    unsigned char content_b[3000] = {0};
+    param.element_type = PADDLE_ELEMENT_TYPE_FLOAT32;
+    param.name = name_a;
+    param.content = content_a;
+    param.content_len = 2000;
+    int error =
+        paddle_init_param(c, param, (void *)config_proto, config_proto_len);
+    if (error != 0) {
       goto retry;
     }
-    if (paddle_init_param(c, *params[1], NULL, 0) != 0) {
+
+    param.element_type = PADDLE_ELEMENT_TYPE_FLOAT32;
+    param.name = name_b;
+    param.content = content_b;
+    param.content_len = 3000;
+    error = paddle_init_param(c, param, (void *)config_proto, config_proto_len);
+    if (error != 0) {
       goto retry;
     }
-    if (paddle_finish_init_params(c) != 0) {
+
+    error = paddle_finish_init_params(c);
+    if (error != 0) {
       goto retry;
     }
-  } else {
-    fail();
-  }
-
-  printf("get inited parameters from pserver:\n");
-  // get parameters again by reusing the allocated parameter buffers.
-  if (paddle_get_params(c, params, 2) != 0) {
-    fail();
-  }
-  print_parameter(params[0]);
-  print_parameter(params[1]);
-
-  printf("send gradient to pserver:\n");
-  real gradient_content1[] = {0.01, 0.02, 0.03};
-  real gradinet_content2[] = {0.04, 0.05, 0.06};
-
-  paddle_gradient** grads =
-      (paddle_gradient**)malloc(sizeof(paddle_gradient*) * 2);
-  grads[0] = (paddle_gradient*)malloc(sizeof(paddle_gradient));
-  grads[0]->name = names[0];
-  grads[0]->content = (unsigned char*)gradient_content1;
-  grads[0]->content_len = 3 * sizeof(real);
-  grads[0]->element_type = PADDLE_ELEMENT_TYPE_FLOAT32;
-
-  grads[1] = (paddle_gradient*)malloc(sizeof(paddle_gradient));
-  grads[1]->name = names[1];
-  grads[1]->content = (unsigned char*)gradinet_content2;
-  grads[1]->content_len = 3 * sizeof(real);
-  grads[1]->element_type = PADDLE_ELEMENT_TYPE_INT32;
-
-  printf("print gradient sent to pserver:\n");
-  print_parameter(grads[0]);
-  print_parameter(grads[1]);
-
-  if (paddle_send_grads(c, grads, 2) != 0) {
-    fail();
   }
 
-  printf("get updated parameters from pserver:\n");
-  // get parameters again by reusing the allocated parameter buffers.
-  if (paddle_get_params(c, params, 2) != 0) {
-    fail();
+  int i;
+  for (i = 0; i < 100; i++) {
+    sendGrads(c);
+    getParams(c);
   }
-  print_parameter(params[0]);
-  print_parameter(params[1]);
 
-  if (paddle_save_model(c, "/tmp/") != 0) {
+  if (paddle_save_model(c, "/tmp/")) {
     fail();
   }
 

go/pserver/cclient/test/test_train.py

@@ -22,6 +22,8 @@ def main():
     # create optimizer
     optimizer = paddle.optimizer.Momentum(momentum=0)
 
+    #TODO(zhihong) : replace optimizer with new OptimizerConfig
+
     trainer = paddle.trainer.SGD(cost=cost,
                                  parameters=parameters,
                                  update_equation=optimizer,

go/pserver/client_test.go

 package pserver_test
 
 import (
+	"io/ioutil"
 	"net"
 	"net/http"
 	"net/rpc"
@@ -74,18 +75,22 @@ func TestClientFull(t *testing.T) {
 	}
 
 	const numParameter = 100
+	config, err := ioutil.ReadFile("./cclient/test/testdata/optimizer.pb.txt")
+	if err != nil {
+		t.Fatalf("read optimizer proto failed")
+	}
 	for i := 0; i < numParameter; i++ {
 		var p pserver.Parameter
 		p.Name = "p_" + strconv.Itoa(i)
 		p.ElementType = pserver.Float32
 		p.Content = make([]byte, (i+1)*100)
-		err := c.InitParam(pserver.ParameterWithConfig{Param: p})
+		err := c.InitParam(pserver.ParameterWithConfig{Param: p, Config: config})
 		if err != nil {
 			t.Fatal(err)
 		}
 	}
 
-	err := c.FinishInitParams()
+	err = c.FinishInitParams()
 	if err != nil {
 		t.Fatal(err)
 	}

go/pserver/optimizer.c

-#include <stdlib.h>
-
-#include "optimizer.h"
-
-typedef int (*update_func)(void*, void*, paddle_element_type, const void*, int);
-typedef void (*release_func)(void*);
-
-typedef struct paddle_optimizer {
-  update_func update;
-  release_func release;
-  void* optimizer;
-} paddle_optimizer;
-
-void paddle_release_optimizer(paddle_optimizer* o) {
-  o->release(o->optimizer);
-  free(o);
-}
-
-int paddle_update_parameter(paddle_optimizer* o,
-                            void* buffer,
-                            paddle_element_type element_type,
-                            const void* gradient,
-                            int num_bytes) {
-  return o->update(o->optimizer, buffer, element_type, gradient, num_bytes);
-}
-
-typedef struct { double learning_rate; } SGD_optimizer;
-
-int update_SGD(void* optimizer,
-               void* buffer,
-               paddle_element_type element_type,
-               const void* gradient,
-               int num_bytes) {
-  SGD_optimizer* o = (SGD_optimizer*)optimizer;
-  float* parameter = (float*)buffer;
-  float* grad = (float*)gradient;
-
-  int i;
-  for (i = 0; i < num_bytes / sizeof(float); ++i) {
-    parameter[i] -= o->learning_rate * grad[i];
-  }
-  return 0;
-}
-
-void release_SGD(void* optimizer) {
-  SGD_optimizer* o = (SGD_optimizer*)optimizer;
-  // nothing allocated on heap
-}
-
-paddle_optimizer* paddle_create_SGD_optimizer(double learning_rate) {
-  SGD_optimizer* impl = (SGD_optimizer*)malloc(sizeof(SGD_optimizer));
-  impl->learning_rate = learning_rate;
-  paddle_optimizer* opt = (paddle_optimizer*)malloc(sizeof(paddle_optimizer));
-  opt->update = update_SGD;
-  opt->release = release_SGD;
-  opt->optimizer = impl;
-  return opt;
-}

go/pserver/optimizer.go

 package pserver
 
-/*
-#include "optimizer.h"
-*/
+// #cgo CFLAGS: -I ../../
+// //FIXME: ldflags contain "build" path
+// #cgo LDFLAGS: ../../build/go/pserver/cclient/libpaddle_go_optimizer.a -lstdc++
+// #include "paddle/optimizer/optimizer.h"
+// #include <stdlib.h>
+// #include <string.h>
 import "C"
+
 import (
 	"fmt"
 	"unsafe"
-)
-
-type optimizerType int
 
-const (
-	sgd optimizerType = iota
+	log "github.com/sirupsen/logrus"
 )
 
 var nullPtr = unsafe.Pointer(uintptr(0))
 
 type optimizer struct {
-	opt *C.struct_paddle_optimizer
+	opt         *C.struct_paddle_optimizer
+	elementType ElementType
 }
 
-func newOptimizer(t optimizerType, learning_rate float64) *optimizer {
+func cArrayToSlice(p unsafe.Pointer, len int) []byte {
+	if p == nullPtr {
+		return nil
+	}
+
+	// create a Go clice backed by a C array, reference:
+	// https://github.com/golang/go/wiki/cgo#turning-c-arrays-into-go-slices
+	//
+	// Go garbage collector will not interact with this data, need
+	// to be freed properly.
+	return (*[1 << 30]byte)(p)[:len:len]
+}
+
+func newOptimizer(paramWithConfigs ParameterWithConfig) *optimizer {
 	o := &optimizer{}
-	o.opt = C.paddle_create_SGD_optimizer(C.double(learning_rate))
+	o.elementType = paramWithConfigs.Param.ElementType
+	p := paramWithConfigs.Param
+	c := paramWithConfigs.Config
+	log.WithFields(log.Fields{
+		"ElementType": p.ElementType,
+		"ParamSize":   len(p.Content),
+		"ConfigSize":  len(c),
+	}).Info("New Optimizer Created with config:")
+	var cbuffer unsafe.Pointer
+	cbuffer = C.malloc(C.size_t(len(p.Content)))
+	C.memcpy(cbuffer, unsafe.Pointer(&p.Content[0]), C.size_t(len(p.Content)))
+	o.opt = C.paddle_create_optimizer((*C.uchar)(&c[0]), C.int(len(c)),
+		C.paddle_element_type(p.ElementType), cbuffer, C.int(len(p.Content)/C.sizeof_float),
+		(*C.char)(nullPtr), 0)
 	return o
 }
 
-func (o *optimizer) UpdateParameter(p Parameter, g Gradient) error {
-	if len(p.Content) != len(g.Content) {
-		return fmt.Errorf("Name: %s, parameter and gradient length not match, parameter: %d, gradient: %d", p.Name, len(p.Content), len(g.Content))
-	}
+func (o *optimizer) GetWeights() []byte {
+	var buffer unsafe.Pointer
+	buffer_len := C.paddle_optimizer_get_weights(o.opt, &buffer)
+	return cArrayToSlice(buffer, int(buffer_len)*C.sizeof_float)
+}
 
-	if p.ElementType != g.ElementType {
-		return fmt.Errorf("Name: %s, parameter and gradient element type not match, parameter: %v, gradient: %v", p.Name, p.ElementType, g.ElementType)
+func (o *optimizer) UpdateParameter(g Gradient) error {
+	if o.elementType != g.ElementType {
+		return fmt.Errorf("Name: %s, parameter and gradient element type not match, parameter: %v, gradient: %v", g.Name, o.elementType, g.ElementType)
 	}
 
-	r := C.paddle_update_parameter(o.opt, unsafe.Pointer(&p.Content[0]), C.paddle_element_type(p.ElementType), unsafe.Pointer(&g.Content[0]), C.int(len(g.Content)))
+	r := C.paddle_update_parameter(o.opt, C.paddle_element_type(g.ElementType), unsafe.Pointer(&g.Content[0]), C.int(len(g.Content))/C.sizeof_float)
 	if r != 0 {
 		return fmt.Errorf("optimizer update returned error code: %d", r)
 	}

go/pserver/optimizer.h

-#ifndef PADDLE_PSERVER_OPTIMIZER_H
-#define PADDLE_PSERVER_OPTIMIZER_H
-
-typedef enum {
-  PADDLE_ELEMENT_TYPE_INT32 = 0,
-  PADDLE_ELEMENT_TYPE_UINT32 = 1,
-  PADDLE_ELEMENT_TYPE_INT64 = 2,
-  PADDLE_ELEMENT_TYPE_UINT64 = 3,
-  PADDLE_ELEMENT_TYPE_FLOAT32 = 4,
-  PADDLE_ELEMENT_TYPE_FLOAT64 = 5,
-} paddle_element_type;
-
-struct paddle_optimizer;
-struct paddle_optimizer* paddle_create_SGD_optimizer(double learning_rate);
-void paddle_release_optimizer(struct paddle_optimizer* o);
-int paddle_update_parameter(struct paddle_optimizer* o,
-                            void* buffer,
-                            paddle_element_type element_type,
-                            const void* gradient,
-                            int num_bytes);
-
-#endif /* PADDLE_PSERVER_OPTIMIZER_H */

go/pserver/optimizer_test.go

 package pserver
 
-import "testing"
+import (
+	"io/ioutil"
+	"testing"
+)
 
-func TestSGDCreateRelease(t *testing.T) {
-	o := newOptimizer(sgd, 1)
+func TestOptimizerCreateRelease(t *testing.T) {
+	p := Parameter{
+		Name:        "a",
+		ElementType: Int32,
+	}
+	p.Content = []byte{1, 3}
+	config, err := ioutil.ReadFile("./cclient/test/testdata/optimizer.pb.txt")
+	if err != nil {
+		t.Fatalf("read optimizer proto failed")
+	}
+	param := ParameterWithConfig{
+		Param:  p,
+		Config: config,
+	}
+	o := newOptimizer(param)
 	o.Cleanup()
 }

go/pserver/service.go

@@ -48,9 +48,8 @@ type Service struct {
 	initialized chan struct{}
 	idx         int
 
-	mu       sync.Mutex
-	opt      *optimizer
-	paramMap map[string]Parameter
+	mu     sync.Mutex
+	optMap map[string]*optimizer
 }
 
 // NewService creates a new service, will bypass etcd registration if no
@@ -58,9 +57,8 @@ type Service struct {
 func NewService(idx int) (*Service, error) {
 	s := &Service{
 		idx: idx,
-		opt: newOptimizer(sgd, 0.005),
 	}
-	s.paramMap = make(map[string]Parameter)
+  s.optMap = make(map[string]*optimizer)
 	s.initialized = make(chan struct{})
 	return s, nil
 }
@@ -81,7 +79,7 @@ func (s *Service) InitParam(paramWithConfigs ParameterWithConfig, dummy *int) er
 	// TODO(helin): check if paramWithConfigs.Param.Content is
 	// properly memory aligned, if not, make copy to a memory
 	// aligned region.
-	s.paramMap[paramWithConfigs.Param.Name] = paramWithConfigs.Param
+	s.optMap[paramWithConfigs.Param.Name] = newOptimizer(paramWithConfigs)
 	return nil
 }
 
@@ -110,12 +108,12 @@ func (s *Service) SendGrad(g Gradient, dummy *int) error {
 	s.mu.Lock()
 	defer s.mu.Unlock()
 
-	p, ok := s.paramMap[g.Name]
+	o, ok := s.optMap[g.Name]
 	if !ok {
 		return fmt.Errorf("parameter: %s does not exist", g.Name)
 	}
 
-	return s.opt.UpdateParameter(p, g)
+	return o.UpdateParameter(g)
 }
 
 // GetParam gets parameters from the parameter server.
@@ -124,7 +122,7 @@ func (s *Service) GetParam(name string, parameter *Parameter) error {
 	s.mu.Lock()
 	defer s.mu.Unlock()
 
-	p, ok := s.paramMap[name]
+	opt, ok := s.optMap[name]
 	if !ok {
 		return fmt.Errorf("parameter: %s does not exist", name)
 	}
@@ -136,7 +134,9 @@ func (s *Service) GetParam(name string, parameter *Parameter) error {
 	// nature. This race condition is allowed deliberately
 	// to save the program from making a copy of the
 	// paramter content.
-	*parameter = p
+	parameter.Name = name
+	parameter.ElementType = opt.elementType
+	parameter.Content = opt.GetWeights()
 	return nil
 }
 

go/pserver/service_test.go

 package pserver_test
 
 import (
+	"io/ioutil"
 	"reflect"
 	"sync"
 	"testing"
@@ -9,7 +10,7 @@ import (
 	"github.com/PaddlePaddle/Paddle/go/pserver"
 )
 
-func TestFull(t *testing.T) {
+func TestServiceFull(t *testing.T) {
 	s, err := pserver.NewService(0)
 	if err != nil {
 		t.Error(err)
@@ -18,7 +19,12 @@ func TestFull(t *testing.T) {
 	p.Name = "param_a"
 	p.Content = []byte{1, 0, 0, 0, 2, 0, 0, 0, 3, 0, 0, 0}
 	p.ElementType = pserver.Int32
-	err = s.InitParam(pserver.ParameterWithConfig{Param: p, Config: nil}, nil)
+	config, err := ioutil.ReadFile("./cclient/test/testdata/optimizer.pb.txt")
+	if err != nil {
+		t.Fatalf("read optimizer proto failed")
+	}
+
+	err = s.InitParam(pserver.ParameterWithConfig{Param: p, Config: config}, nil)
 	if err != nil {
 		t.FailNow()
 	}
@@ -27,7 +33,7 @@ func TestFull(t *testing.T) {
 	p1.Name = "param_b"
 	p1.Content = []byte{0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
 	p1.ElementType = pserver.Float32
-	err = s.InitParam(pserver.ParameterWithConfig{Param: p1, Config: nil}, nil)
+	err = s.InitParam(pserver.ParameterWithConfig{Param: p1, Config: config}, nil)
 	if err != nil {
 		t.FailNow()
 	}
@@ -48,6 +54,7 @@ func TestFull(t *testing.T) {
 	}
 
 	g1, g2 := pserver.Gradient(p1), pserver.Gradient(p)
+
 	err = s.SendGrad(g1, nil)
 	if err != nil {
 		t.FailNow()
@@ -142,7 +149,12 @@ func TestBlockUntilInitialized(t *testing.T) {
 	p.Name = "param_a"
 	p.Content = []byte{1, 0, 0, 0, 2, 0, 0, 0, 3, 0, 0, 0}
 	p.ElementType = pserver.Int32
-	err = s.InitParam(pserver.ParameterWithConfig{Param: p, Config: nil}, nil)
+	config, err := ioutil.ReadFile("./cclient/test/testdata/optimizer.pb.txt")
+	if err != nil {
+		t.Fatalf("read optimizer proto failed")
+	}
+	err = s.InitParam(pserver.ParameterWithConfig{Param: p, Config: config}, nil)
+
 	if err != nil {
 		t.FailNow()
 	}

paddle/framework/tensor.h

+/* 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
+
+namespace paddle {
+namespace framework {
+
+class Tensor {
+  using paddle::platform::Place;
+  using paddle::platform::get_place;
+
+ public:
+  template <typename T>
+  const T* data() const {
+    PADDLE_ASSERT(holder_ != nullptr,
+                  "Tensor::data must be called after Tensor::mutable_data");
+    return static_cast<const T*>(holder->Ptr());
+  }
+
+  template <typename T,  // must be POD types
+            typename = std::enable_if<std::is_pod<T>::value>::type>
+  T* mutable_data(DDim dims, Place place) {
+    if (holder_ == nullptr || holder_->Place() != place ||
+        holder_->Size() < dims.product() * sizeof(T)) {
+      holder_.reset(new PlaceholderImpl(place, dims.product() * sizeof(T)));
+    }
+    return static_cast<T*>(holder_->Ptr());
+  }
+
+  template <typename T,  // must be POD types
+            typename = std::enable_if<std::is_pod<T>::value>::type>
+  T* mutable_data(DDim dims) {
+    return mutable_data<T>(dims, paddle::platform::get_place());
+  }
+
+ private:
+  // Placeholder hides type T, so it doesn't appear as a template
+  // parameter of Variable.
+  struct Placeholder {
+    virtual ~Placeholder() {}
+    virtual void* Ptr() const = 0;
+    virtual Place Place() const = 0;
+    virtual size_t Size() const = 0;
+  };
+
+  template <typename T>
+  struct PlaceholderImpl : public Placeholder {
+    PlaceholderImpl(Place pl, size_t size)
+        : ptr_(paddle::memory::Alloc(pl, size), paddle::memory::Deleter(pl)),
+          place_(pl),
+          size_(size) {}
+
+    virtual void* Ptr() const { return static_cast<void*>(ptr_.get()); }
+    virtual size_t Size() const { return size_; }
+    virtual Place Place() const { return place_; }
+
+    std::unique_ptr<T, memory::Deleter> ptr_;
+    Place place_;  // record the place of ptr_.
+    size_t size_;  // size of the memory block.
+  };
+
+  std::unique_ptr<Placeholder> holder_;  // holds the memory block if allocated.
+};
+
+}  // namespace framework
+}  // namespace paddle

paddle/optimizer/CMakeLists.txt

@@ -12,6 +12,7 @@ set(OPITMIZER_SRCS
 add_library(paddle_optimizer STATIC ${OPITMIZER_SRCS})
 add_dependencies(paddle_optimizer paddle_proto ${external_project_dependencies})
 
+
 if(WITH_TESTING)
   add_simple_unittest(serialization_test)
   add_simple_unittest(parameter_optimizer_test)

python/paddle/trainer/config_parser.py

@@ -2082,10 +2082,10 @@ class MaxOutLayer(LayerBase):
 class RowConvLayer(LayerBase):
     def __init__(self, name, inputs, context_length, **xargs):
         super(RowConvLayer, self).__init__(
-            name, 'maxout', 0, inputs=inputs, **xargs)
+            name, 'row_conv', 0, inputs=inputs, **xargs)
         config_assert(
             len(self.inputs) == 1,
-            'TransLayer must have one and only one input')
+            'row convolution layer must have one and only one input.')
         input_layer = self.get_input_layer(0)
         row_conv_conf = self.config.inputs[0].row_conv_conf
         row_conv_conf.context_length = context_length

python/paddle/trainer_config_helpers/tests/configs/protostr/test_row_conv.protostr

@@ -7,7 +7,7 @@ layers {
 }
 layers {
   name: "__row_conv_layer_0__"
-  type: "maxout"
+  type: "row_conv"
   size: 2560
   active_type: "relu"
   inputs {

python/paddle/v2/dataset/flowers.py

@@ -30,6 +30,7 @@ http://www.robots.ox.ac.uk/~vgg/publications/papers/nilsback08.{pdf,ps.gz}.
 """
 import cPickle
 import itertools
+import functools
 from common import download
 import tarfile
 import scipy.io as scio
@@ -54,21 +55,26 @@ TEST_FLAG = 'trnid'
 VALID_FLAG = 'valid'
 
 
-def default_mapper(sample):
+def default_mapper(is_train, sample):
     '''
     map image bytes data to type needed by model input layer
     '''
     img, label = sample
     img = load_image_bytes(img)
-    img = simple_transform(img, 256, 224, True)
+    img = simple_transform(
+        img, 256, 224, is_train, mean=[103.94, 116.78, 123.68])
     return img.flatten().astype('float32'), label
 
 
+train_mapper = functools.partial(default_mapper, True)
+test_mapper = functools.partial(default_mapper, False)
+
+
 def reader_creator(data_file,
                    label_file,
                    setid_file,
                    dataset_name,
-                   mapper=default_mapper,
+                   mapper,
                    buffered_size=1024,
                    use_xmap=True):
     '''
@@ -118,7 +124,7 @@ def reader_creator(data_file,
         return map_readers(mapper, reader)
 
 
-def train(mapper=default_mapper, buffered_size=1024, use_xmap=True):
+def train(mapper=train_mapper, buffered_size=1024, use_xmap=True):
     '''
     Create flowers training set reader.
     It returns a reader, each sample in the reader is
@@ -141,7 +147,7 @@ def train(mapper=default_mapper, buffered_size=1024, use_xmap=True):
         buffered_size, use_xmap)
 
 
-def test(mapper=default_mapper, buffered_size=1024, use_xmap=True):
+def test(mapper=test_mapper, buffered_size=1024, use_xmap=True):
     '''
     Create flowers test set reader.
     It returns a reader, each sample in the reader is
@@ -164,7 +170,7 @@ def test(mapper=default_mapper, buffered_size=1024, use_xmap=True):
         buffered_size, use_xmap)
 
 
-def valid(mapper=default_mapper, buffered_size=1024, use_xmap=True):
+def valid(mapper=test_mapper, buffered_size=1024, use_xmap=True):
     '''
     Create flowers validation set reader.
     It returns a reader, each sample in the reader is

python/paddle/v2/image.py

@@ -262,7 +262,12 @@ def left_right_flip(im):
         return im[:, ::-1, :]
 
 
-def simple_transform(im, resize_size, crop_size, is_train, is_color=True):
+def simple_transform(im,
+                     resize_size,
+                     crop_size,
+                     is_train,
+                     is_color=True,
+                     mean=None):
     """
     Simply data argumentation for training. These operations include
     resizing, croping and flipping.
@@ -288,7 +293,19 @@ def simple_transform(im, resize_size, crop_size, is_train, is_color=True):
             im = left_right_flip(im)
     else:
         im = center_crop(im, crop_size)
-    im = to_chw(im)
+    if len(im.shape) == 3:
+        im = to_chw(im)
+
+    im = im.astype('float32')
+    if mean is not None:
+        mean = np.array(mean, dtype=np.float32)
+        # mean value, may be one value per channel 
+        if mean.ndim == 1:
+            mean = mean[:, np.newaxis, np.newaxis]
+        else:
+            # elementwise mean
+            assert len(mean.shape) == len(im)
+        im -= mean
 
     return im
 
@@ -297,7 +314,8 @@ def load_and_transform(filename,
                        resize_size,
                        crop_size,
                        is_train,
-                       is_color=True):
+                       is_color=True,
+                       mean=None):
     """
     Load image from the input file `filename` and transform image for
     data argumentation. Please refer to the `simple_transform` interface
@@ -318,5 +336,5 @@ def load_and_transform(filename,
     :type is_train: bool
     """
     im = load_image(filename)
-    im = simple_transform(im, resize_size, crop_size, is_train, is_color)
+    im = simple_transform(im, resize_size, crop_size, is_train, is_color, mean)
     return im

python/paddle/v2/optimizer.py

@@ -5,6 +5,8 @@ import paddle.trainer_config_helpers.optimizers as v1_optimizers
 """
 Optimizers(update equation) for SGD method.
 
+TODO(zhihong) : create new optimizer with proto config, add new optimizer here
+
 TODO(yuyang18): Complete comments.
 """