succ run

core/configure/proto/multi_lang_general_model_service.proto

@@ -14,6 +14,10 @@
 
 syntax = "proto2";
 
+option java_multiple_files = true;
+option java_package = "io.paddle.serving.grpc";
+option java_outer_classname = "ServingProto";
+
 message Tensor {
   optional bytes data = 1;
   repeated int32 int_data = 2;

java/paddle-serving-sdk-java/src/main/java/io/paddle/serving/client/Client.java

@@ -18,7 +18,6 @@ import org.nd4j.linalg.factory.Nd4j;
 import io.paddle.serving.grpc.*;
 import io.paddle.serving.configure.*;
 
-
 public class Client {
     private ManagedChannel channel_;
     private MultiLangGeneralModelServiceGrpc.MultiLangGeneralModelServiceBlockingStub blockingStub_;
@@ -66,12 +65,13 @@ public class Client {
     }
 
     public Boolean connect(List<String> endpoints) {
-        // String target = "ipv4:" + String.join(",", endpoints);
+        // TODO
+        //String target = "ipv4:" + String.join(",", endpoints);
         String target = endpoints.get(0);
-        // TODO: max_receive_message_length and max_send_message_length
         try {
             channel_ = ManagedChannelBuilder.forTarget(target)
                 .defaultLoadBalancingPolicy("round_robin")
+                .maxInboundMessageSize(Integer.MAX_VALUE)
                 .usePlaintext()
                 .build();
             blockingStub_ = MultiLangGeneralModelServiceGrpc.newBlockingStub(channel_);
@@ -82,17 +82,13 @@ public class Client {
         }
         GetClientConfigRequest get_client_config_req = GetClientConfigRequest.newBuilder().build();
         GetClientConfigResponse resp;
-        System.out.println("try to call getClientConfig");
         try {
             resp = blockingStub_.getClientConfig(get_client_config_req);
         } catch (StatusRuntimeException e) {
             System.out.format("Get Client config failed: %s", e.toString());
             return false;
         }
-        System.out.println("succ call get client");
-        System.out.println(resp);
         String model_config_str = resp.getClientConfigStr();
-        System.out.println("model_config_str: " + model_config_str);
         _parseModelConfig(model_config_str);
         return true;
     }
@@ -112,6 +108,7 @@ public class Client {
         feedShapes_ = new HashMap<String, List<Integer>>();
         fetchTypes_ = new HashMap<String, Integer>();
         lodTensorSet_ = new HashSet<String>();
+        feedTensorLen_ = new HashMap<String, Integer>();
 
         List<FeedVar> feed_var_list = model_conf.getFeedVarList();
         for (FeedVar feed_var : feed_var_list) {
@@ -166,9 +163,6 @@ public class Client {
                 INDArray variable = feed_data.get(name);
                 long[] flattened_shape = {-1};
                 INDArray flattened_list = variable.reshape(flattened_shape);
-                for (Map.Entry<String, Integer> entry : feedTypes_.entrySet()) {
-                    System.out.println("Key = " + entry.getKey() + ", Value = " + entry.getValue());
-                }
                 int v_type = feedTypes_.get(name);
                 NdIndexIterator iter = new NdIndexIterator(flattened_list.shape());
                 if (v_type == 0) { // int64
@@ -184,15 +178,17 @@ public class Client {
                         tensor_builder.addFloatData(x);
                     }
                 } else if (v_type == 2) { // int32
-                    throw new IllegalArgumentException("error tensor value type.");
-                    //TODO
-                    /*
                     while (iter.hasNext()) {
                         long[] next_index = iter.next();
-                        int x = flattened_list.getInt(next_index);
-                        // TODO: long to int? 
+                        // the interface of INDArray is strange:
+                        // https://deeplearning4j.org/api/latest/org/nd4j/linalg/api/ndarray/INDArray.html
+                        int[] int_next_index = new int[next_index.length];
+                        for(int i = 0; i < next_index.length; i++) {
+                            int_next_index[i] = (int)next_index[i];
+                        }
+                        int x = flattened_list.getInt(int_next_index);
                         tensor_builder.addIntData(x);
-                    }*/
+                    }
                 } else {
                     throw new IllegalArgumentException("error tensor value type.");
                 }
@@ -204,7 +200,7 @@ public class Client {
         return req_builder.build();
     }
 
-    private HashMap<String, HashMap<String, INDArray>>
+    private Map<String, HashMap<String, INDArray>>
         _unpackInferenceResponse(
             InferenceResponse resp,
             Iterable<String> fetch,
@@ -213,7 +209,7 @@ public class Client {
                 resp, fetch, fetchTypes_, lodTensorSet_, need_variant_tag);
     }
 
-    private static HashMap<String, HashMap<String, INDArray>>
+    private static Map<String, HashMap<String, INDArray>>
         _staticUnpackInferenceResponse(
             InferenceResponse resp,
             Iterable<String> fetch,
@@ -227,6 +223,7 @@ public class Client {
         HashMap<String, HashMap<String, INDArray>> multi_result_map
             = new HashMap<String, HashMap<String, INDArray>>();
         for (ModelOutput model_result: resp.getOutputsList()) {
+            String engine_name = model_result.getEngineName();
             FetchInst inst = model_result.getInsts(0);
             HashMap<String, INDArray> result_map
                 = new HashMap<String, INDArray>();
@@ -270,25 +267,32 @@ public class Client {
                 }
                 index += 1;
             }
+            multi_result_map.put(engine_name, result_map);
         }
 
         // TODO: tag
         return multi_result_map;
     }
 
-    public Map<String, HashMap<String, INDArray>> predict(
+    public Map<String, INDArray> predict(
             HashMap<String, INDArray> feed,
             Iterable<String> fetch) {
         return predict(feed, fetch, false);
     }
-/*
+
+    public Map<String, HashMap<String, INDArray>> ensemble_predict(
+            HashMap<String, INDArray> feed,
+            Iterable<String> fetch) {
+        return ensemble_predict(feed, fetch, false);
+    }
+
     public PredictFuture async_predict(
-            Map<String, INDArray> feed,
+            HashMap<String, INDArray> feed,
             Iterable<String> fetch) {
         return async_predict(feed, fetch, false);
     }
-*/
-    public Map<String, HashMap<String, INDArray>> predict(
+
+    public Map<String, INDArray> predict(
             HashMap<String, INDArray> feed,
             Iterable<String> fetch,
             Boolean need_variant_tag) {
@@ -297,30 +301,69 @@ public class Client {
         feed_batch.add(feed);
         return predict(feed_batch, fetch, need_variant_tag);
     }
-/*
+    
+    public Map<String, HashMap<String, INDArray>> ensemble_predict(
+            HashMap<String, INDArray> feed,
+            Iterable<String> fetch,
+            Boolean need_variant_tag) {
+        List<HashMap<String, INDArray>> feed_batch
+            = new ArrayList<HashMap<String, INDArray>>();
+        feed_batch.add(feed);
+        return ensemble_predict(feed_batch, fetch, need_variant_tag);
+    }
+
     public PredictFuture async_predict(
-            Map<String, List<? extends Number>> feed,
+            HashMap<String, INDArray> feed,
             Iterable<String> fetch,
             Boolean need_variant_tag) {
-        List<? extends Map<String, List<? extends Number>>> feed_batch
-            = new ArrayList<? extends Map<String, List<? extends Number>>>();
+        List<HashMap<String, INDArray>> feed_batch
+            = new ArrayList<HashMap<String, INDArray>>();
         feed_batch.add(feed);
         return async_predict(feed_batch, fetch, need_variant_tag);
     }
-*/
-    public Map<String, HashMap<String, INDArray>> predict(
+
+    public Map<String, INDArray> predict(
             List<HashMap<String, INDArray>> feed_batch,
             Iterable<String> fetch) {
         return predict(feed_batch, fetch, false);
     }
-    /*
+    
+    public Map<String, HashMap<String, INDArray>> ensemble_predict(
+            List<HashMap<String, INDArray>> feed_batch,
+            Iterable<String> fetch) {
+        return ensemble_predict(feed_batch, fetch, false);
+    }
+
     public PredictFuture async_predict(
-            List<? extends Map<String, List<? extends Number>>> feed_batch,
+            List<HashMap<String, INDArray>> feed_batch,
             Iterable<String> fetch) {
         return async_predict(feed_batch, fetch, false);
     }
-*/
-    public Map<String, HashMap<String, INDArray>> predict(
+
+    public Map<String, INDArray> predict(
+            List<HashMap<String, INDArray>> feed_batch,
+            Iterable<String> fetch,
+            Boolean need_variant_tag) {
+        InferenceRequest req = _packInferenceRequest(feed_batch, fetch);
+        try {
+            InferenceResponse resp = blockingStub_.inference(req);
+            Map<String, HashMap<String, INDArray>> ensemble_result
+                = _unpackInferenceResponse(resp, fetch, need_variant_tag);
+            List<Map.Entry<String, HashMap<String, INDArray>>> list
+                = new ArrayList<Map.Entry<String, HashMap<String, INDArray>>>(
+                    ensemble_result.entrySet());
+            if (list.size() != 1) {
+                System.out.format("grpc failed: please use ensemble_predict impl.");
+                return null;
+            }
+            return list.get(0).getValue();
+        } catch (StatusRuntimeException e) {
+            System.out.format("grpc failed: %s", e.toString());
+            return null;
+        }
+    }
+
+    public Map<String, HashMap<String, INDArray>> ensemble_predict(
             List<HashMap<String, INDArray>> feed_batch,
             Iterable<String> fetch,
             Boolean need_variant_tag) {
@@ -331,31 +374,30 @@ public class Client {
                     resp, fetch, need_variant_tag);
         } catch (StatusRuntimeException e) {
             System.out.format("grpc failed: %s", e.toString());
-            return Collections.emptyMap();
+            return null;
         }
     }
-    /*
+
     public PredictFuture async_predict(
-            List<Map<String, List<? extends Number>>> feed_batch,
+            List<HashMap<String, INDArray>> feed_batch,
             Iterable<String> fetch,
             Boolean need_variant_tag) {
         InferenceRequest req = _packInferenceRequest(feed_batch, fetch);
         ListenableFuture<InferenceResponse> future = futureStub_.inference(req);
-        return new PredictFuture(
-                future, 
+        PredictFuture predict_future = new PredictFuture(future, 
             (InferenceResponse resp) -> {
                 return Client._staticUnpackInferenceResponse(
                 resp, fetch, fetchTypes_, lodTensorSet_, need_variant_tag);
-                });
             }
-    */
+        );
+        return predict_future;
+    }
 
     public static void main( String[] args ) {
         float[] data = {0.0137f, -0.1136f, 0.2553f, -0.0692f,
                     0.0582f, -0.0727f, -0.1583f, -0.0584f,
                     0.6283f, 0.4919f, 0.1856f, 0.0795f, -0.0332f};
         INDArray npdata = Nd4j.create(data);
-        System.out.println(npdata);
 
         Client client = new Client();
         List<String> endpoints = Arrays.asList("localhost:9393");
@@ -368,30 +410,58 @@ public class Client {
             = new HashMap<String, INDArray>() {{
                 put("x", npdata);
         }};
-        List<HashMap<String, INDArray>> feed_batch
-            = new ArrayList<HashMap<String, INDArray>>() {{
-                add(feed_data);
-        }};
         List<String> fetch = Arrays.asList("price");
+        Map<String, INDArray> fetch_map = client.predict(feed_data, fetch);
+        for (Map.Entry<String, INDArray> e : fetch_map.entrySet()) {
+            System.out.println("Key = " + e.getKey() + ", Value = " + e.getValue());
+        }
+        /*
         Map<String, HashMap<String, INDArray>> fetch_map
-            = client.predict(feed_batch, fetch);
-        System.out.println( "Hello World!" );
+            = client.predict(feed_data, fetch);
+        for (Map.Entry<String, HashMap<String, INDArray>> entry : fetch_map.entrySet()) {
+            System.out.println("Model = " + entry.getKey());
+            HashMap<String, INDArray> tt = entry.getValue();
+            for (Map.Entry<String, INDArray> e : tt.entrySet()) {
+                System.out.println("Key = " + e.getKey() + ", Value = " + e.getValue());
+            }
+        }
+        */
     }
 }
 
 class PredictFuture {
     private ListenableFuture<InferenceResponse> callFuture_;
     private Function<InferenceResponse, 
-                     Map<String, ? extends Map<String, List<? extends Number>>>> callBackFunc_;
+                     Map<String, HashMap<String, INDArray>>> callBackFunc_;
     
     PredictFuture(ListenableFuture<InferenceResponse> call_future,
             Function<InferenceResponse, 
-                    Map<String, ? extends Map<String, List<? extends Number>>>> call_back_func) {
+                     Map<String, HashMap<String, INDArray>>> call_back_func) {
         callFuture_ = call_future;
         callBackFunc_ = call_back_func;
     }
 
-    public Map<String, ? extends Map<String, List<? extends Number>>> get() throws Exception {
+    public Map<String, INDArray> get() throws Exception {
+        InferenceResponse resp = null;
+        try {
+            resp = callFuture_.get();
+        } catch (Exception e) {
+            System.out.format("grpc failed: %s", e.toString());
+            return null;
+        }
+        Map<String, HashMap<String, INDArray>> ensemble_result
+            = callBackFunc_.apply(resp);
+        List<Map.Entry<String, HashMap<String, INDArray>>> list
+            = new ArrayList<Map.Entry<String, HashMap<String, INDArray>>>(
+                    ensemble_result.entrySet());
+        if (list.size() != 1) {
+            System.out.format("grpc failed: please use get_ensemble impl.");
+            return null;
+        }
+        return list.get(0).getValue();
+    }
+
+    public Map<String, HashMap<String, INDArray>> get_ensemble() throws Exception {
         InferenceResponse resp = null;
         try {
             resp = callFuture_.get();

java/paddle-serving-sdk-java/src/main/proto/multi_lang_general_model_service.proto

@@ -18,8 +18,6 @@ option java_multiple_files = true;
 option java_package = "io.paddle.serving.grpc";
 option java_outer_classname = "ServingProto";
 
-package paddle.serving.grpc;
-
 message Tensor {
   optional bytes data = 1;
   repeated int32 int_data = 2;