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

Author: chengduoZH

benchmark/cluster/vgg16/vgg16_fluid.py

@@ -80,6 +80,8 @@ def str2bool(v):
     type=str,
     default="",
     help="Comma-separated list of hostname:port pairs")
+parser.add_argument(
+    "--profile", action='store_true', help="If set, profile a few steps.")
 
 # Flags for defining the tf.train.Server
 parser.add_argument(
@@ -183,8 +185,8 @@ def train_loop(exe, trainer_prog):
             start_time = time.time()
             num_samples = 0
             train_pass_acc.reset()
-            for batch_id, data in enumerate(train_reader()):
-                ts = time.time()
+
+            def run_step(batch_id, data):
                 img_data = np.array(
                     map(lambda x: x[0].reshape(data_shape), data)).astype(
                         "float32")
@@ -196,14 +198,28 @@ def train_loop(exe, trainer_prog):
                     feed={"pixel": img_data,
                           "label": y_data},
                     fetch_list=[avg_cost, batch_acc, batch_size])
+                return loss, acc, b_size
+
+            if args.profile and args.task_index == 0:
+                # warmup.
+                for batch_id, data in enumerate(train_reader()):
+                    if batch_id > 5: break
+                    run_step(batch_id, data)
+                with profiler.profiler('All', 'total', '/tmp/profile_vgg'):
+                    for batch_id, data in enumerate(train_reader()):
+                        if batch_id > 5: break
+                        run_step(batch_id, data)
+
+            for batch_id, data in enumerate(train_reader()):
+                ts = time.time()
+                loss, acc, b_size = run_step(batch_id, data)
                 iters += 1
                 num_samples += len(data)
                 train_pass_acc.add(value=acc, weight=b_size)
                 print(
-                    "Task:%d Pass = %d, Iters = %d, Loss = %f, Accuracy = %f, "
-                    "Speed = %.2f img/s " % (args.task_index, pass_id, iters,
-                                             loss, acc,
-                                             len(data) / (time.time() - ts))
+                    "Pass = %d, Iters = %d, Loss = %f, Accuracy = %f, "
+                    "Speed = %.2f img/s" % (pass_id, iters, loss, acc,
+                                            len(data) / (time.time() - ts))
                 )  # The accuracy is the accumulation of batches, but not the current batch.
 
             pass_elapsed = time.time() - start_time

contrib/float16/float16_inference_report.md renamed to contrib/float16/README.md

@@ -0,0 +1 @@
+../../v2/build_and_install/paddleci.png

doc/fluid/build_and_install/paddleci.png

@@ -125,12 +125,12 @@ Compile Time -> IR -> Runtime
 
 ## Operator/OpWithKernel/OpKernel
 
-![class_diagram](http://api.paddlepaddle.org/graphviz?dot=https://gist.githubusercontent.com/reyoung/53df507f6749762675dff3e7ce53372f/raw/49caf1fb70820fb4a6c217634317c9306f361f36/op_op_with_kern_class_diagram.dot)
+![class_diagram](https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/op_op_with_kern_class_diagram.dot)
 
 ---
 
 ## Operator
-![class_diagram](http://api.paddlepaddle.org/graphviz?dot=https://gist.githubusercontent.com/reyoung/53df507f6749762675dff3e7ce53372f/raw/dd598e8f1976f5759f58af5e5ef94738a6b2e661/op.dot)
+![class_diagram](https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/op.dot)
 
 * `Operator` is the fundamental building block of the user interface.
     * Operator stores input/output variable names and attributes.
@@ -141,7 +141,7 @@ Compile Time -> IR -> Runtime
 
 ## OpWithKernel/Kernel
 
-![class_diagram](http://api.paddlepaddle.org/graphviz?dot=https://gist.githubusercontent.com/reyoung/53df507f6749762675dff3e7ce53372f/raw/9d7f4eba185cf41c8e2fbfb40ae21890dbddcd39/op_with_kernel.dot)
+![class_diagram](https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/op_with_kernel.dot)
 
 * `OpWithKernel` inherits `Operator`.
 * `OpWithKernel` contains a Kernel map.

doc/fluid/design/motivation/refactorization.md

@@ -0,0 +1,4 @@
+digraph sample { 
+  graph [rankdir=TD]; node [shape=record];
+  op [label="{Operator| InferShape()=0\lRun()=0\l | map<string, string[]> inputs_\lmap<string, string[]> outputs_ \l AttributeMap attrs_\l}"]; 
+}

doc/fluid/images/op.dot

@@ -0,0 +1,38 @@
+digraph sample { 
+  graph [rankdir=TD]; node [shape=record];
+  op [label="{Operator| InferShape()=0\lRun()=0\l | map<string, string[]> inputs_\lmap<string, string[]> outputs_ \l AttributeMap attrs_\l}"]; 
+  op_with_kern [label="{OpWithKernel | InferShape()=0\lRun()\l | map<OpKernelKey,OpKernel>kernels_ }"]
+  op_kernel [label="{OpKernel | Compute()=0}"]
+  op_kernel_key [label="{OpKernelKey| Place place\n...}"]
+
+  op -> op_with_kern [dir=back, arrowtail=onormal]
+  op_with_kern -> op_kernel [arrowhead=vee, label="contains many"]
+
+  {
+    rank=same;
+    op_with_kern
+    op_kernel
+  }
+
+  op_kernel -> op_kernel_key [style=invis]
+
+  {
+    rank=same;
+    op_kernel
+    op_kernel_key
+  }
+
+  op_with_kern -> op_kernel_key [arrowhead=vee, label ="\nas map key"]
+
+  mul_op [label="MulOp"]
+  op_with_kern -> mul_op [dir=back, arrowtail=onormal]
+  mul_kernel [label="template <typename Place>\lclass MulOpKernel\l"]
+  op_kernel -> mul_kernel [dir=back, arrowtail=onormal]
+  mul_op -> mul_kernel [arrowhead=vee, label="register many"]
+  
+  {
+    rank=same;
+    mul_op;
+    mul_kernel;
+  }
+}

doc/fluid/images/op_op_with_kern_class_diagram.dot

@@ -0,0 +1,26 @@
+digraph sample { 
+  graph [rankdir=TD]; node [shape=record];
+  op [label="{Operator}"]; 
+  op_with_kern [label="{OpWithKernel | InferShape()=0\lRun()\l | map<OpKernelKey,OpKernel>kernels_ }"]
+  op_kernel [label="{OpKernel | Compute()=0}"]
+  op_kernel_key [label="{OpKernelKey| Place place\n...}"]
+
+  op -> op_with_kern [dir=back, arrowtail=onormal]
+  op_with_kern -> op_kernel [arrowhead=vee, label="contains many"]
+
+  {
+    rank=same;
+    op_with_kern
+    op_kernel
+  }
+
+  op_kernel -> op_kernel_key [style=invis]
+
+  {
+    rank=same;
+    op_kernel
+    op_kernel_key
+  }
+
+  op_with_kern -> op_kernel_key [arrowhead=vee, label ="\nas map key"]
+}

doc/fluid/images/op_with_kernel.dot

@@ -142,7 +142,7 @@ gated_unit
 -----------
 ..  autoclass:: paddle.v2.layer.gated_unit
     :noindex:
-    
+
 Recurrent Layer Group
 =====================
 
@@ -354,7 +354,7 @@ dropout
 --------
 ..  autoclass:: paddle.v2.layer.dropout
     :noindex:
-    
+
 dot_prod
 ---------
 .. autoclass:: paddle.v2.layer.dot_prod
@@ -460,6 +460,11 @@ multi_binary_label_cross_entropy_cost
 ..  autoclass:: paddle.v2.layer.multi_binary_label_cross_entropy_cost
     :noindex:
 
+classification_cost
+-------------------
+.. autoclass:: paddle.v2.layer.classification_cost
+   :noindex:
+
 huber_regression_cost
 -------------------------
 ..  autoclass:: paddle.v2.layer.huber_regression_cost
@@ -534,7 +539,7 @@ detection_output
 ----------------
 ..  autoclass:: paddle.v2.layer.detection_output
     :noindex:
-    
+
 Check Layer
 ============
 

doc/v2/api/config/layer.rst

@@ -41,7 +41,7 @@ Training docker image needs to package the paddle pserver and paddle trainer run
 - Generating the initialization arguments for `Paddle PServer` and `Paddle Training` processes.
 
 Since the paddlepaddle official docker image already has the runtimes we need, we'll take it as the base image and pack some additional scripts for the processes mentioned above to build our training image. for more detail, please find from the following link:
-- https://github.com/PaddlePaddle/Paddle/blob/develop/doc/howto/usage/cluster/src/k8s_train/Dockerfile
+- https://github.com/PaddlePaddle/Paddle/tree/develop/doc/v2/howto/cluster/multi_cluster/src/k8s_train/Dockerfile
 
 
 ```bash
@@ -62,7 +62,7 @@ represent the Docker Image which built in this step.
 ### Prepare Training Data
 
 We can download and split the training job by creating a Kubernetes Job, or custom your image
-by editing [k8s_train](./src/k8s_train/).
+by editing [k8s_train](https://github.com/PaddlePaddle/Paddle/tree/develop/doc/v2/howto/cluster/multi_cluster/src/k8s_train).
 
 Before creating a Job, we need to bind a [persistenVolumeClaim](https://kubernetes.io/docs/user-guide/persistent-volumes) by the different type of
 the different file system, the generated dataset would be saved on this volume.

doc/v2/howto/cluster/multi_cluster/k8s_distributed_en.md

@@ -348,8 +348,12 @@ void Executor::RunPreparedContext(ExecutorPrepareContext* ctx, Scope* scope,
       }
     }
   }
+  platform::DeviceContextPool::Instance().Get(place_)->Wait();
   if (create_vars && create_local_scope) {
     scope->DeleteScope(local_scope);
+  } else {
+    // Delete the local scopes created in operators.
+    scope->DropKids();
   }
   if (FLAGS_benchmark) {
     VLOG(2) << "-------------------------------------------------------";