Merge pull request #11632 from JiayiFeng/some_small_fixes

Some small fixes

paddle/fluid/operators/assign_value_op.cc

@@ -70,6 +70,7 @@ $$Out = values$$
 
 namespace ops = paddle::operators;
 
-REGISTER_OPERATOR(assign_value, ops::AssignValueOp, ops::AssignValueOpMaker);
+REGISTER_OPERATOR(assign_value, ops::AssignValueOp, ops::AssignValueOpMaker,
+                  paddle::framework::EmptyGradOpMaker);
 REGISTER_OP_CPU_KERNEL(assign_value, ops::AssignValueKernel<int>,
                        ops::AssignValueKernel<float>);

paddle/fluid/operators/random_crop_op.cc

@@ -37,6 +37,11 @@ class RandomCropOpMaker : public framework::OpProtoAndCheckerMaker {
     AddOutput("SeedOut", "The random seed after random cropping.")
         .AsIntermediate();
     AddAttr<std::vector<int>>("shape", "The shape of a cropped instance.");
+    AddAttr<int>("startup_seed",
+                 "If the input 'Seed' is not initialized, the 'startup_seed' "
+                 "will be used to replace it. Even so, the seed after random "
+                 "crop will also be outputed to the 'SeedOut'.")
+        .SetDefault(0);
     AddComment(R"DOC(
       This operator takes a batch of instance, and do random cropping on each instance.
       It means that cropping positions differs on each instance, which is determined
@@ -49,8 +54,6 @@ class RandomCropOpMaker : public framework::OpProtoAndCheckerMaker {
 class RandomCropOpInferShape : public framework::InferShapeBase {
  public:
   void operator()(framework::InferShapeContext* ctx) const override {
-    auto seed_dim = ctx->GetInputDim("Seed");
-    PADDLE_ENFORCE(seed_dim.size() == 1 && seed_dim[0] == 1);
     auto shape = ctx->Attrs().Get<std::vector<int>>("shape");
     auto x_dim = ctx->GetInputDim("X");
     PADDLE_ENFORCE_GT(x_dim.size(), static_cast<int64_t>(shape.size()));
@@ -62,7 +65,6 @@ class RandomCropOpInferShape : public framework::InferShapeBase {
       out_dim[x_i] = shape[shape_i];
     }
     ctx->SetOutputDim("Out", framework::make_ddim(out_dim));
-    ctx->SetOutputDim("SeedOut", framework::make_ddim({1}));
   }
 };
 

paddle/fluid/operators/random_crop_op.h

@@ -142,16 +142,22 @@ template <typename DeviceContext, typename T>
 class RandomCropKernel : public framework::OpKernel<T> {
  public:
   virtual void Compute(const framework::ExecutionContext& ctx) const {
-    auto& seed_tensor = detail::Ref(ctx.Input<framework::LoDTensor>("Seed"));
     int64_t seed = 0;
-    if (platform::is_cpu_place(seed_tensor.place())) {
-      seed = *seed_tensor.data<int64_t>();
+    auto& seed_tensor = detail::Ref(ctx.Input<framework::LoDTensor>("Seed"));
+    if (seed_tensor.IsInitialized()) {
+      if (platform::is_cpu_place(seed_tensor.place())) {
+        seed = *seed_tensor.data<int64_t>();
+      } else {
+        LOG(WARNING) << "It is slow to place seed in GPU memory. Please verify "
+                        "your program";
+        framework::LoDTensor cpu_seed;
+        framework::TensorCopySync(seed_tensor, platform::CPUPlace(), &cpu_seed);
+        seed = *cpu_seed.data<int64_t>();
+      }
     } else {
-      LOG(WARNING) << "It is slow to place seed in GPU memory. Please verify "
-                      "your program";
-      framework::LoDTensor cpu_seed;
-      framework::TensorCopySync(seed_tensor, platform::CPUPlace(), &cpu_seed);
-      seed = *cpu_seed.data<int64_t>();
+      VLOG(5) << "WARNING: The input 'Seed' is not initialized, use attribute "
+                 "'startup_seed' instead.";
+      seed = ctx.Attr<int>("startup_seed");
     }
     auto shape = ctx.Attr<std::vector<int>>("shape");
     auto& x = detail::Ref(ctx.Input<framework::LoDTensor>("X"));
@@ -171,7 +177,7 @@ class RandomCropKernel : public framework::OpKernel<T> {
     engine.discard(functor.prod_batchsize_dims_ *
                    (functor.rank_ - functor.num_batchsize_dims_));
     *ctx.Output<framework::LoDTensor>("SeedOut")->mutable_data<int64_t>(
-        platform::CPUPlace()) = engine();
+        framework::make_ddim({1}), platform::CPUPlace()) = engine();
   }
 };
 

paddle/fluid/operators/reader/create_custom_reader_op.cc

@@ -39,6 +39,7 @@ class CustomReader : public framework::DecoratedReader {
   const framework::ProgramDesc program_;
   int sub_block_id_;
   framework::Executor exe_;
+  framework::Scope scope_;
 
   std::vector<std::string> source_var_names_;
   std::vector<std::string> sink_var_names_;
@@ -158,23 +159,24 @@ void CustomReader::ReadNext(std::vector<framework::LoDTensor>* out) {
   // The scope for CustomReader's sub-block should be independent and shouldn't
   // be any other computation scope's child. Otherwise, data preprocessing and
   // compution cannot be concurrent.
-  framework::Scope scope;
+  framework::Scope* exe_scope = &scope_.NewScope();
   // 1. Copy LoDTensors from underlying reader's output to source variables.
   for (size_t i = 0; i < source_var_names_.size(); ++i) {
-    framework::Variable* var = scope.Var(source_var_names_[i]);
+    framework::Variable* var = exe_scope->Var(source_var_names_[i]);
     framework::LoDTensor* tensor = var->GetMutable<framework::LoDTensor>();
     tensor->ShareDataWith(underlying_outs[i]);
     tensor->set_lod(underlying_outs[i].lod());
   }
   // 2. Run the sub-block.
-  exe_.Run(program_, &scope, sub_block_id_, false, true);
+  exe_.Run(program_, exe_scope, sub_block_id_, false, true);
   // 3. Copy LoDTensors from sink variables to out.
   out->resize(sink_var_names_.size());
   for (size_t i = 0; i < sink_var_names_.size(); ++i) {
-    const auto& tensor = detail::Ref(scope.FindVar(sink_var_names_[i]))
+    const auto& tensor = detail::Ref(exe_scope->FindVar(sink_var_names_[i]))
                              .Get<framework::LoDTensor>();
     framework::TensorCopySync(tensor, platform::CPUPlace(), &(*out)[i]);
   }
+  scope_.DeleteScope(exe_scope);
 }
 
 }  // namespace reader

paddle/fluid/operators/reader/create_double_buffer_reader_op.cc

@@ -23,13 +23,13 @@ namespace reader {
 
 // 'Double buffer' means we shall maintain two batches of input data at the same
 // time. So the kCacheSize shoul be at least 2.
-static constexpr size_t kCacheSize = 3;
+static constexpr size_t kCacheSize = 5;
 // There will be two bacthes out of the channel during training:
 // 1. the one waiting to be sent to the channel
 // 2. the one just be received from the channel, which is also being used by
 // subsequent operators.
 // So the channel size should be kChacheSize - 2
-static constexpr size_t kChannelSize = 1;  // kCacheSize - 2
+static constexpr size_t kChannelSize = 3;  // kCacheSize - 2
 
 class DoubleBufferReader : public framework::DecoratedReader {
  public:

python/paddle/fluid/layers/io.py

@@ -110,7 +110,7 @@ class BlockGuardServ(BlockGuard):
 class ListenAndServ(object):
     """
     **ListenAndServ Layer**
-    
+
     ListenAndServ is used to create a rpc server bind and listen
     on specific TCP port, this server will run the sub-block when
     received variables from clients.
@@ -212,7 +212,7 @@ def Send(endpoints, send_vars, sync=True):
                    of send_vars to send
         send_vars (list): variables to send to server
         sync (bool): whether to wait the request finish
-    
+
     """
     assert (type(send_vars) == list)
 
@@ -469,10 +469,13 @@ def open_files(filenames,
        lod_levels(list): List of ints which declaring data lod_level.
        dtypes(list): List of strs which declaring data type.
        thread_num(int): The maximal concurrent prefetch thread number.
-       buffer_size(int): The size of prefetch buffer.
+       buffer_size(int|None): The size of prefetch buffer. If it is setted None, 
+            buffer size will be thread_num * 3.
+            Default: None
        pass_num(int): Number of passes to run.
        for_parallel(Bool): Set it as True if you are going to run 
             subsequent operators in parallel.
+            Default: True
 
     Returns:
        Variable: A Reader Variable via which we can get file data.
@@ -492,7 +495,7 @@ def open_files(filenames,
          image, label = fluid.layers.io.read_file(reader)
     """
     if buffer_size is None:
-        buffer_size = thread_num
+        buffer_size = thread_num * 3
     if isinstance(filenames, basestring):
         filenames = [filenames]
     dtypes = [convert_np_dtype_to_dtype_(dt) for dt in dtypes]

python/paddle/fluid/layers/nn.py

@@ -23,6 +23,7 @@ from layer_function_generator import autodoc, templatedoc
 from tensor import concat
 import utils
 import random
+from .. import unique_name
 
 __all__ = [
     'fc',
@@ -4896,34 +4897,26 @@ def random_crop(x, shape, seed=None):
         >>> cropped_img = fluid.layers.random_crop(img, shape=[3, 224, 224])
     """
     helper = LayerHelper("random_crop", **locals())
-    dtype = helper.input_dtype()
+    dtype = x.dtype
     out = helper.create_tmp_variable(dtype)
     if seed is None:
         seed = random.randint(-65536, 65535)
-
+    op_attrs = {"shape": shape}
     if isinstance(seed, int):
-        seed_value = seed
-        seed = helper.create_tmp_variable(dtype="int64")
-        helper.append_op(
-            type="fill_constant",
-            inputs={},
-            outputs={"Out": seed},
-            attrs={
-                "dtype": seed.dtype,
-                "shape": [1],
-                "value": float(seed_value),
-                "force_cpu": True
-            })
+        op_attrs["startup_seed"] = seed
+        seed = helper.create_variable(
+            name=unique_name.generate("random_crop_seed"),
+            dtype="int64",
+            persistable=True)
     elif not isinstance(seed, Variable):
         raise ValueError("'seed' must be a Variable or an int.")
-    seed_out = helper.create_tmp_variable(dtype="int64")
     helper.append_op(
         type="random_crop",
         inputs={"X": x,
                 "Seed": seed},
         outputs={"Out": out,
-                 "SeedOut": seed_out},
-        attrs={"shape": shape})
+                 "SeedOut": seed},
+        attrs=op_attrs)
     return out
 
 

python/paddle/fluid/layers/tensor.py

@@ -155,7 +155,7 @@ def cast(x, dtype):
 
     Examples:
         .. code-block:: python
-             
+
             data = fluid.layers.data(name='x', shape=[13], dtype='float32')
             result = fluid.layers.cast(x=data, dtype='float64')
     """
@@ -188,7 +188,7 @@ def concat(input, axis=0, name=None):
 
     Examples:
         .. code-block:: python
-        
+
            out = fluid.layers.concat(input=[Efirst, Esecond, Ethird, Efourth])
     """
     helper = LayerHelper('concat', **locals())
@@ -238,7 +238,7 @@ def sums(input, out=None):
     return out
 
 
-def assign(input, output):
+def assign(input, output=None):
     """
     **Assign**
 
@@ -246,7 +246,7 @@ def assign(input, output):
 
     Args:
         input(Variable|numpy.ndarray): The source variable
-        output(Variable): The destination variable
+        output(Variable|None): The destination variable
 
     Returns:
         Variable: The destination variable that was supplied as the *output*.
@@ -259,6 +259,8 @@ def assign(input, output):
           fluid.layers.assign(hidden, out)
     """
     helper = LayerHelper('assign', **locals())
+    if output is None:
+        output = helper.create_tmp_variable(dtype=input.dtype)
     if isinstance(input, Variable):
         helper.append_op(
             type='assign', inputs={'X': [input]}, outputs={'Out': [output]})