Feature/py executor test

paddle/framework/CMakeLists.txt

@@ -43,7 +43,7 @@ add_custom_command(TARGET framework_py_proto POST_BUILD
 cc_library(backward SRCS backward.cc DEPS net_op)
 cc_test(backward_test SRCS backward_test.cc DEPS backward recurrent_op device_context)
 
-cc_library(executor SRCS executor.cc DEPS op_registry device_context scope framework_proto backward)
+cc_library(executor SRCS executor.cc DEPS op_registry device_context scope framework_proto backward glog)
 
 cc_library(prune SRCS prune.cc DEPS framework_proto)
 cc_test(prune_test SRCS prune_test.cc DEPS op_info prune recurrent_op device_context)

paddle/framework/executor.cc

@@ -68,8 +68,10 @@ void Executor::Run(const ProgramDesc& pdesc, Scope* scope, int block_id) {
 
   for (auto& var : block.vars()) {
     if (var.persistable()) {
-      scope->Var(var.name());
+      auto* ptr = scope->Var(var.name());
+      VLOG(3) << "Create Variable " << var.name() << " global " << ptr;
     } else {
+      VLOG(3) << "Create Variable " << var.name() << " locally";
       local_scope.Var(var.name());
     }
   }

paddle/framework/feed_fetch_method.h

@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #pragma once
+#include "glog/logging.h"
 #include "paddle/framework/scope.h"
 #include "paddle/framework/variable.h"
 
@@ -24,6 +25,7 @@ void SetFeedVariable(const LoDTensor& input, const std::string& var_name,
                      size_t index) {
   // If var_name Variable is not found in GlobalScope, a new variable will
   // be created.
+  VLOG(10) << "Setting variable " << var_name;
   Variable* g_feed_value = GetGlobalScope().Var(var_name);
   auto& feed_inputs =
       *(g_feed_value->GetMutable<std::vector<paddle::framework::LoDTensor>>());
@@ -40,10 +42,14 @@ LoDTensor& GetFetchVariable(const std::string& var_name, size_t index) {
   // Since we want to fetch LodTensor from a variable, the variable must
   // be created alreadly.
   Variable* g_fetch_value = GetGlobalScope().FindVar(var_name);
+  VLOG(3) << "Fetched Variable" << g_fetch_value;
   auto& fetch_outputs =
-      *(g_fetch_value->GetMutable<std::vector<paddle::framework::LoDTensor>>());
+      g_fetch_value->Get<std::vector<paddle::framework::LoDTensor>>();
+  auto& tensor = fetch_outputs[index];
+  VLOG(3) << "Fetch " << var_name << " with index " << index
+          << " shape= " << tensor.dims();
   PADDLE_ENFORCE_LT(index, fetch_outputs.size());
-  return fetch_outputs[index];
+  return const_cast<LoDTensor&>(tensor);
 }
 
 }  // namespace framework

paddle/framework/framework.proto

@@ -112,6 +112,8 @@ message VarDesc {
   enum VarType {
     LOD_TENSOR = 1;
     SELECTED_ROWS = 2;
+    FEED_MINIBATCH = 3;
+    FETCH_LIST = 4;
   }
   required string name = 1;
   required VarType type = 2;

paddle/framework/op_registry.h

@@ -48,6 +48,7 @@ class Registrar {
 template <typename... ARGS>
 struct OperatorRegistrar : public Registrar {
   explicit OperatorRegistrar(const char* op_type) {
+    VLOG(3) << "Registering operator " << op_type;
     PADDLE_ENFORCE(!OpInfoMap::Instance().Has(op_type),
                    "'%s' is registered more than once.", op_type);
     static_assert(sizeof...(ARGS) != 0,

paddle/framework/program_desc_test.cc

@@ -80,4 +80,4 @@ TEST(ProgramDesc, copy_ctor) {
   // different and it is correct.
 }
 }  // namespace framework
-}  // namespace paddle
+}  // namespace paddle

paddle/framework/variable.h

@@ -25,7 +25,10 @@ class Variable {
  public:
   template <typename T>
   const T& Get() const {
-    PADDLE_ENFORCE(IsType<T>(), "Variable must be type %s", typeid(T).name());
+    PADDLE_ENFORCE(holder_ != nullptr, "Variable must hold some thing");
+    PADDLE_ENFORCE(IsType<T>(),
+                   "Variable must be type %s, the holding type is %s",
+                   typeid(T).name(), holder_->Type().name());
     return *static_cast<const T*>(holder_->Ptr());
   }
 

paddle/operators/feed_op.cc

@@ -28,6 +28,8 @@ class FeedOp : public framework::OperatorBase {
            const platform::DeviceContext &dev_ctx) const override {
     auto feed_var_name = Input("Input");
     auto *feed_var = scope.FindVar(feed_var_name);
+
+    VLOG(10) << "Feed var name " << feed_var_name;
     PADDLE_ENFORCE(feed_var != nullptr,
                    "Cannot find feed_var in scope, feed_var_name is %s",
                    feed_var_name);
@@ -48,10 +50,23 @@ class FeedOp : public framework::OperatorBase {
   }
 };
 
+class FeedOpInfoMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  FeedOpInfoMaker(framework::OpProto *proto,
+                  framework::OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("Input", "");
+    AddOutput("Out", "");
+    AddComment("");
+    AddAttr<int>("col", "");
+  }
+};
+
 }  // namespace operators
 }  // namespace paddle
 
 // We do not need to register OpInfoMaker,
 // since feed operator will not be used by end users directly
 REGISTER_OPERATOR(feed, paddle::operators::FeedOp,
-                  paddle::framework::EmptyGradOpMaker);
+                  paddle::framework::EmptyGradOpMaker,
+                  paddle::operators::FeedOpInfoMaker);

paddle/operators/fetch_op.cc

@@ -49,16 +49,34 @@ class FetchOp : public framework::OperatorBase {
     }
     auto &dst_item = fetch_list->at(col);
 
+    VLOG(3) << "Fetch " << fetch_var_name << ", shape=" << src_item.dims();
+
     // FIXME(yuyang18): Should we assume the fetch operator always generate
     // CPU outputs?
     dst_item.CopyFromTensor(src_item, platform::CPUPlace(), dev_ctx);
+
+    VLOG(3) << "Fetch out name = " << out_name
+            << " dest shape = " << dst_item.dims()
+            << " Fetched Var = " << out_var;
   }
 };
 
+class FetchOpInfoMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  FetchOpInfoMaker(framework::OpProto *proto,
+                   framework::OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("Input", "");
+    AddOutput("Out", "");
+    AddComment("");
+    AddAttr<int>("col", "");
+  }
+};
 }  // namespace operators
 }  // namespace paddle
 
 // We do not need to register OpInfoMaker,
 // since fetch operator will not be used by end users directly
 REGISTER_OPERATOR(fetch, paddle::operators::FetchOp,
-                  paddle::framework::EmptyGradOpMaker);
+                  paddle::framework::EmptyGradOpMaker,
+                  paddle::operators::FetchOpInfoMaker);

paddle/pybind/protobuf.cc

@@ -222,7 +222,9 @@ void BindVarDsec(py::module &m) {
 
   py::enum_<VarDesc::VarType>(var_desc, "VarType", "")
       .value("LOD_TENSOR", VarDesc::LOD_TENSOR)
-      .value("SELECTED_ROWS", VarDesc::SELECTED_ROWS);
+      .value("SELECTED_ROWS", VarDesc::SELECTED_ROWS)
+      .value("FEED_MINIBATCH", VarDesc::FEED_MINIBATCH)
+      .value("FETCH_LIST", VarDesc::FETCH_LIST);
 }
 
 void BindOpDesc(py::module &m) {

paddle/pybind/pybind.cc

@@ -111,6 +111,7 @@ PYBIND11_PLUGIN(core) {
              new (&instance) LoDTensor(new_lod);
 #endif
           })
+      .def("__init__", [](LoDTensor &instance) { new (&instance) LoDTensor(); })
       .def("set_lod",
            [](LoDTensor &self, const std::vector<std::vector<size_t>> &lod) {
 #ifndef PADDLE_WITH_CUDA
@@ -216,7 +217,8 @@ All parameter, weight, gradient are variables in Paddle.
       .def(py::init<>())
       .def("new_scope", [](Scope &self) -> Scope * { return &self.NewScope(); },
            py::return_value_policy::reference)
-      .def("drop_kids", &Scope::DropKids);
+      .def("drop_kids", &Scope::DropKids)
+      .def_static("global_scope", &GetGlobalScope);
 
   //! @note: Be careful! PyBind will return std::string as an unicode, not
   //! Python str. If you want a str object, you should cast them in Python.
@@ -264,6 +266,17 @@ All parameter, weight, gradient are variables in Paddle.
       .def(py::init<>())
       .def("__str__", string::to_string<const platform::CPUPlace &>);
 
+  py::class_<platform::Place>(m, "Place")
+      .def(py::init<>())
+      .def("set_place",
+           [](platform::Place &self, const platform::CPUPlace &cpu_place) {
+             self = cpu_place;
+           })
+      .def("set_place",
+           [](platform::Place &self, const platform::GPUPlace &gpu_place) {
+             self = gpu_place;
+           });
+
   py::class_<OperatorBase>(m, "Operator")
       .def_static("create",
                   [](py::bytes protobin) {
@@ -437,14 +450,15 @@ All parameter, weight, gradient are variables in Paddle.
   py::class_<framework::Executor>(m, "Executor")
       .def(py::init<std::vector<platform::Place> &>())
       .def("run",
-           [](Executor &self, const ProgramDesc &program_desc, int block_id) {
+           [](Executor &self, ProgramDescBind *program_bind, int block_id) {
              framework::Scope &global_scope = GetGlobalScope();
-             self.Run(program_desc, &global_scope, block_id);
+             self.Run(*program_bind->Proto(), &global_scope, block_id);
            });
 
   m.def("unique_integer", UniqueIntegerGenerator);
 
   m.def("is_compile_gpu", IsCompileGPU);
+  //! FIXME: it is no need to `set_xxx_float/double/int`
   m.def("set_feed_variable_float", framework::SetFeedVariable<float>);
   m.def("set_feed_variable_double", framework::SetFeedVariable<double>);
   m.def("set_feed_variable_int", framework::SetFeedVariable<int>);

python/paddle/v2/framework/executor.py

@@ -0,0 +1,59 @@
+import paddle.v2.framework.core as core
+from paddle.v2.framework.framework import Block, Program
+
+
+class Executor(object):
+    def __init__(self, places):
+        if not isinstance(places, list) and not isinstance(places, tuple):
+            places = [places]
+
+        act_places = []
+        for each in places:
+            p = core.Place()
+            p.set_place(each)
+            act_places.append(p)
+
+        self.executor = core.Executor(act_places)
+
+    def run(self,
+            program,
+            feed,
+            fetch_list,
+            feed_var_name='feed',
+            fetch_var_name='fetch'):
+        if not isinstance(program, Program):
+            raise TypeError()
+
+        program = program.clone()
+        global_block = program.global_block()
+        assert isinstance(global_block, Block)
+        feed_var = global_block.create_var(
+            name=feed_var_name,
+            type=core.VarDesc.VarType.FEED_MINIBATCH,
+            persistable=True)
+
+        for i, name in enumerate(feed):
+            out = global_block.var(name)
+            global_block.prepend_op(
+                'feed',
+                inputs={'Input': [feed_var]},
+                outputs={'Out': [out]},
+                attrs={'col': i})
+            # FIXME
+            core.set_feed_variable_float(feed[name], feed_var.name, i)
+
+        fetch_var = global_block.create_var(
+            name=fetch_var_name,
+            type=core.VarDesc.VarType.FETCH_LIST,
+            persistable=True)
+        for i, var in enumerate(fetch_list):
+            global_block.append_op(
+                type='fetch',
+                inputs={'Input': [var]},
+                outputs={'Out': [fetch_var]},
+                attrs={'col': i})
+
+        assert isinstance(global_block, Block)
+        self.executor.run(program.desc, 0)
+        for i, _ in enumerate(fetch_list):
+            yield core.get_fetch_variable(fetch_var_name, i)

python/paddle/v2/framework/framework.py

@@ -256,7 +256,8 @@ def __init__(self,
                     self.desc.set_block_attr(attr_name, attrs[attr_name].desc)
 
         self.desc.check_attrs()
-        self.desc.infer_shape(self.block.desc)
+        if type not in {'feed', 'fetch'}:
+            self.desc.infer_shape(self.block.desc)
 
     def __str__(self):
         protostr = self.desc.serialize_to_string()
@@ -323,9 +324,12 @@ def idx(self):
         return self.desc.id
 
     def var(self, name):
-        if name not in self.vars:
+        if not isinstance(name, basestring):
+            raise TypeError()
+        v = self.vars.get(name, None)
+        if v is None:
             raise ValueError("var %s not in this block" % name)
-        return self.vars[name]
+        return v
 
     def all_parameters(self):
         return {v for k, v in self.vars.iteritems() if isinstance(v, Parameter)}

python/paddle/v2/framework/layers.py

@@ -51,13 +51,15 @@ def fc(input,
     return helper.append_activation(pre_activation)
 
 
-def data(name,
-         shape,
-         data_type='float32',
-         type=core.VarDesc.VarType.LOD_TENSOR,
-         program=None):
+def data_layer(name,
+               shape,
+               data_type='float32',
+               type=core.VarDesc.VarType.LOD_TENSOR,
+               append_batch_size=True,
+               program=None):
     helper = LayerHelper('data', **locals())
-    shape = [-1] + shape  # append batch size as -1
+    if append_batch_size:
+        shape = [-1] + shape  # append batch size as -1
     return helper.create_global_variable(
         name=name, shape=shape, dtype=data_type, type=type)
 
@@ -112,6 +114,7 @@ def func(**kwargs):
 
 
 _create_op_func_('mean')
+_create_op_func_('mul')
 _create_op_func_('pool2d')
 
 

python/paddle/v2/framework/tests/test_executor_and_mul.py

@@ -0,0 +1,37 @@
+import unittest
+from paddle.v2.framework.layers import mul, data_layer
+import paddle.v2.framework.core as core
+from paddle.v2.framework.executor import Executor
+from paddle.v2.framework.framework import g_program
+import numpy
+
+
+class TestExecutor(unittest.TestCase):
+    def test_mul(self):
+        a = data_layer(name='a', shape=[784], data_type='float32')
+        b = data_layer(
+            name='b',
+            shape=[784, 100],
+            data_type='float32',
+            append_batch_size=False)
+        out = mul(x=a, y=b)
+        place = core.CPUPlace()
+        a_np = numpy.random.random((100, 784)).astype('float32')
+        tensor_a = core.LoDTensor()
+        tensor_a.set(a_np, place)
+        b_np = numpy.random.random((784, 100)).astype('float32')
+        tensor_b = core.LoDTensor()
+        tensor_b.set(b_np, place)
+        # del input_tensor
+        exe = Executor(place)
+        outs = list(
+            exe.run(g_program,
+                    feed={'a': tensor_a,
+                          'b': tensor_b},
+                    fetch_list=[out]))
+        out = numpy.array(outs[0])
+        self.assertEqual((100, 100), out.shape)
+
+
+if __name__ == '__main__':
+    unittest.main()