Merge pull request #145 from guru4elephant/fix_general_infer_name

fix general op reader problem, make fetch var changable during infere…

core/configure/proto/general_model_config.proto

@@ -25,7 +25,8 @@ message FeedVar {
 message FetchVar {
   optional string name = 1;
   optional string alias_name = 2;
-  repeated int32 shape = 3;
+  optional bool is_lod_tensor = 3 [ default = false ];
+  repeated int32 shape = 4;
 }
 message GeneralModelConfig {
   repeated FeedVar feed_var = 1;

core/general-client/src/general_model.cpp

@@ -108,7 +108,11 @@ std::vector<std::vector<float>> PredictorClient::predict(
   VLOG(2) << "fetch general model predictor done.";
   VLOG(2) << "float feed name size: " << float_feed_name.size();
   VLOG(2) << "int feed name size: " << int_feed_name.size();
+  VLOG(2) << "fetch name size: " << fetch_name.size();
   Request req;
+  for (auto & name : fetch_name) {
+    req.add_fetch_var_names(name);
+  }
   std::vector<Tensor *> tensor_vec;
   FeedInst *inst = req.add_insts();
   for (auto &name : float_feed_name) {

core/general-server/op/general_infer_op.cpp

@@ -30,8 +30,10 @@ namespace serving {
 using baidu::paddle_serving::predictor::MempoolWrapper;
 using baidu::paddle_serving::predictor::general_model::Tensor;
 using baidu::paddle_serving::predictor::general_model::Response;
+using baidu::paddle_serving::predictor::general_model::Request;
 using baidu::paddle_serving::predictor::general_model::FetchInst;
 using baidu::paddle_serving::predictor::InferManager;
+using baidu::paddle_serving::predictor::PaddleGeneralModelConfig;
 
 int GeneralInferOp::inference() {
   const GeneralReaderOutput *reader_out =
@@ -57,40 +59,65 @@ int GeneralInferOp::inference() {
     return -1;
   }
 
+  const Request *req = dynamic_cast<const Request *>(get_request_message());
+
+  VLOG(2) << "start to call load general model_conf op";
+  baidu::paddle_serving::predictor::Resource &resource =
+      baidu::paddle_serving::predictor::Resource::instance();
+  
+  VLOG(2) << "get resource pointer done.";
+  std::shared_ptr<PaddleGeneralModelConfig> model_config =
+      resource.get_general_model_config();
+
+  std::vector<int> fetch_index;
+  fetch_index.resize(req->fetch_var_names_size());
+  for (int i = 0; i < req->fetch_var_names_size(); ++i) {
+    fetch_index[i] =
+        model_config->_fetch_alias_name_to_index[req->fetch_var_names(i)];
+  }
+  
+  // response inst with only fetch_var_names
   Response *res = mutable_data<Response>();
 
   for (int i = 0; i < batch_size; ++i) {
     FetchInst *fetch_inst = res->add_insts();
-    for (int j = 0; j < out->size(); ++j) {
+    for (auto & idx : fetch_index) {
       Tensor *tensor = fetch_inst->add_tensor_array();
+      // currently only response float tensor or lod_tensor
       tensor->set_elem_type(1);
-      if (out->at(j).lod.size() == 1) {
+      if (model_config->_is_lod_fetch[idx]) {
+        VLOG(2) << "out[" << idx << " is lod_tensor";
         tensor->add_shape(-1);
       } else {
-        for (int k = 1; k < out->at(j).shape.size(); ++k) {
-          tensor->add_shape(out->at(j).shape[k]);
+        VLOG(2) << "out[" << idx << "] is tensor";
+        for (int k = 1; k < out->at(idx).shape.size(); ++k) {
+          VLOG(2) << "shape[" << k - 1 << "]: "
+                  << out->at(idx).shape[k];
+          tensor->add_shape(out->at(idx).shape[k]);
         }
       }
     }
   }
 
-  for (int i = 0; i < out->size(); ++i) {
-    float *data_ptr = static_cast<float *>(out->at(i).data.data());
+  int var_idx = 0;
+  for (auto & idx : fetch_index) {
+    float *data_ptr = static_cast<float *>(out->at(idx).data.data());
     int cap = 1;
-    for (int j = 1; j < out->at(i).shape.size(); ++j) {
-      cap *= out->at(i).shape[j];
+    for (int j = 1; j < out->at(idx).shape.size(); ++j) {
+      cap *= out->at(idx).shape[j];
     }
-    if (out->at(i).lod.size() == 1) {
+    if (model_config->_is_lod_fetch[idx]) {
       for (int j = 0; j < batch_size; ++j) {
-        for (int k = out->at(i).lod[0][j]; k < out->at(i).lod[0][j + 1]; k++) {
-          res->mutable_insts(j)->mutable_tensor_array(i)->add_data(
+        for (int k = out->at(idx).lod[0][j];
+             k < out->at(idx).lod[0][j + 1]; k++) {
+          res->mutable_insts(j)->mutable_tensor_array(var_idx)->add_data(
               reinterpret_cast<char *>(&(data_ptr[k])), sizeof(float));
         }
       }
     } else {
       for (int j = 0; j < batch_size; ++j) {
         for (int k = j * cap; k < (j + 1) * cap; ++k) {
-          res->mutable_insts(j)->mutable_tensor_array(i)->add_data(
+          res->mutable_insts(j)->mutable_tensor_array(var_idx)->add_data(
               reinterpret_cast<char *>(&(data_ptr[k])), sizeof(float));
         }
       }

core/general-server/op/general_reader_op.cpp

@@ -39,6 +39,9 @@ int conf_check(const Request *req,
     LOG(ERROR) << "feed var number not match.";
     return -1;
   }
+
+  VLOG(2) << "fetch var num in reader op: " << req->fetch_var_names_size();
+
   for (int i = 0; i < var_num; ++i) {
     if (model_config->_feed_type[i] !=
         req->insts(0).tensor_array(i).elem_type()) {
@@ -89,15 +92,15 @@ int GeneralReaderOp::inference() {
   VLOG(2) << "var num: " << var_num;
   // read config
 
-  LOG(INFO) << "start to call load general model_conf op";
+  VLOG(2) << "start to call load general model_conf op";
   baidu::paddle_serving::predictor::Resource &resource =
       baidu::paddle_serving::predictor::Resource::instance();
 
-  LOG(INFO) << "get resource pointer done.";
+  VLOG(2) << "get resource pointer done.";
   std::shared_ptr<PaddleGeneralModelConfig> model_config =
       resource.get_general_model_config();
 
-  LOG(INFO) << "print general model config done.";
+  VLOG(2) << "print general model config done.";
 
   // check
   res->reader_status = conf_check(req, model_config);
@@ -111,8 +114,8 @@ int GeneralReaderOp::inference() {
   elem_type.resize(var_num);
   elem_size.resize(var_num);
   capacity.resize(var_num);
-  paddle::PaddleTensor lod_tensor;
   for (int i = 0; i < var_num; ++i) {
+    paddle::PaddleTensor lod_tensor;
     elem_type[i] = req->insts(0).tensor_array(i).elem_type();
     VLOG(2) << "var[" << i << "] has elem type: " << elem_type[i];
     if (elem_type[i] == 0) {  // int64
@@ -138,11 +141,7 @@ int GeneralReaderOp::inference() {
       }
       VLOG(2) << "var[" << i << "] is tensor, capacity: " << capacity[i];
     }
-    if (i == 0) {
-      lod_tensor.name = "words";
-    } else {
-      lod_tensor.name = "label";
-    }
+    lod_tensor.name = model_config->_feed_name[i];
     in->push_back(lod_tensor);
   }
 

core/general-server/proto/general_model_service.proto

@@ -35,6 +35,7 @@ message FetchInst {
 
 message Request {
   repeated FeedInst insts = 1;
+  repeated string fetch_var_names = 2;
 };
 
 message Response {

core/predictor/framework/resource.cpp

@@ -189,14 +189,18 @@ int Resource::general_model_initialize(const std::string& path,
   VLOG(2) << "load general model config";
   VLOG(2) << "feed var num: " << feed_var_num;
   _config->_feed_name.resize(feed_var_num);
+  _config->_feed_alias_name.resize(feed_var_num);
   _config->_feed_type.resize(feed_var_num);
   _config->_is_lod_feed.resize(feed_var_num);
   _config->_capacity.resize(feed_var_num);
   _config->_feed_shape.resize(feed_var_num);
   for (int i = 0; i < feed_var_num; ++i) {
     _config->_feed_name[i] = model_config.feed_var(i).name();
+    _config->_feed_alias_name[i] = model_config.feed_var(i).alias_name();
     VLOG(2) << "feed var[" << i << "]: "
             << _config->_feed_name[i];
+    VLOG(2) << "feed var[" << i << "]: "
+            << _config->_feed_alias_name[i];
     _config->_feed_type[i] = model_config.feed_var(i).feed_type();
     VLOG(2) << "feed type[" << i << "]: "
             << _config->_feed_type[i];
@@ -219,15 +223,27 @@ int Resource::general_model_initialize(const std::string& path,
   }
 
   int fetch_var_num = model_config.fetch_var_size();
+  _config->_is_lod_fetch.resize(fetch_var_num);
   _config->_fetch_name.resize(fetch_var_num);
+  _config->_fetch_alias_name.resize(fetch_var_num);
   _config->_fetch_shape.resize(fetch_var_num);
   for (int i = 0; i < fetch_var_num; ++i) {
     _config->_fetch_name[i] = model_config.fetch_var(i).name();
+    _config->_fetch_alias_name[i] = model_config.fetch_var(i).alias_name();
+    _config->_fetch_name_to_index[_config->_fetch_name[i]] = i;
+    _config->_fetch_alias_name_to_index[_config->_fetch_alias_name[i]] = i;
+    if (model_config.fetch_var(i).is_lod_tensor()) {
+      VLOG(2) << "fetch var[" << i << "] is lod tensor";
+      _config->_fetch_shape[i] = {-1};
+      _config->_is_lod_fetch[i] = true;
+    } else {
+      _config->_is_lod_fetch[i] = false;
       for (int j = 0; j < model_config.fetch_var(i).shape_size(); ++j) {
         int dim = model_config.fetch_var(i).shape(j);
         _config->_fetch_shape[i].push_back(dim);
       }
     }
+  }
   return 0;
 }
 

core/predictor/framework/resource.h

@@ -16,6 +16,7 @@
 #include <memory>
 #include <string>
 #include <vector>
+#include <map>
 #include "core/cube/cube-api/include/cube_api.h"
 #include "core/kvdb/include/kvdb/paddle_rocksdb.h"
 #include "core/predictor/common/inner_common.h"
@@ -34,8 +35,10 @@ class PaddleGeneralModelConfig {
 
  public:
   std::vector<std::string> _feed_name;
+  std::vector<std::string> _feed_alias_name;
   std::vector<int> _feed_type;     // 0 int64, 1 float
   std::vector<bool> _is_lod_feed;  // true lod tensor
+  std::vector<bool> _is_lod_fetch;  // whether a fetch var is lod_tensor
   std::vector<int> _capacity;      //  capacity for each tensor
                                    /*
                                      feed_shape_ for feeded variable
@@ -45,7 +48,10 @@ class PaddleGeneralModelConfig {
   std::vector<std::vector<int>> _feed_shape;
 
   std::vector<std::string> _fetch_name;
+  std::vector<std::string> _fetch_alias_name;
   std::vector<std::vector<int>> _fetch_shape;
+  std::map<std::string, int> _fetch_name_to_index;
+  std::map<std::string, int> _fetch_alias_name_to_index;
 };
 
 class BaseRdDict;

core/sdk-cpp/proto/general_model_service.proto

@@ -35,6 +35,7 @@ message FetchInst {
 
 message Request {
   repeated FeedInst insts = 1;
+  repeated string fetch_var_names = 2;
 };
 
 message Response {

python/paddle_serving_client/io/__init__.py

@@ -41,7 +41,7 @@ def save_model(server_model_folder,
         feed_var = model_conf.FeedVar()
         feed_var.alias_name = key
         feed_var.name = feed_var_dict[key].name
-        feed_var.is_lod_tensor = feed_var_dict[key].lod_level == 1
+        feed_var.is_lod_tensor = feed_var_dict[key].lod_level >= 1
         if feed_var_dict[key].dtype == core.VarDesc.VarType.INT32 or \
            feed_var_dict[key].dtype == core.VarDesc.VarType.INT64:
             feed_var.feed_type = 0
@@ -61,7 +61,15 @@ def save_model(server_model_folder,
         fetch_var = model_conf.FetchVar()
         fetch_var.alias_name = key
         fetch_var.name = fetch_var_dict[key].name
-        fetch_var.shape.extend(fetch_var_dict[key].shape)
+        fetch_var.is_lod_tensor = fetch_var_dict[key].lod_level >= 1
+        if fetch_var.is_lod_tensor:
+            fetch_var.shape.extend([-1])
+        else:
+            tmp_shape = []
+            for v in fetch_var_dict[key].shape:
+                if v >= 0:
+                    tmp_shape.append(v)
+            fetch_var.shape.extend(tmp_shape)
         config.fetch_var.extend([fetch_var])
 
     cmd = "mkdir -p {}".format(client_config_folder)