Merge pull request #15322 from velconia/imperative_resnet

Imperative Resnet

paddle/fluid/framework/operator.cc

@@ -19,8 +19,6 @@ limitations under the License. */
 #include <sstream>
 #include <string>
 #include <vector>
-#include "gflags/gflags.h"
-#include "glog/logging.h"
 #include "paddle/fluid/framework/data_transform.h"
 #include "paddle/fluid/framework/executor.h"
 #include "paddle/fluid/framework/lod_tensor.h"

paddle/fluid/imperative/CMakeLists.txt

 if(WITH_PYTHON)
-cc_library(layer SRCS layer.cc DEPS proto_desc operator)
-cc_library(tracer SRCS tracer.cc DEPS proto_desc)
+cc_library(layer SRCS layer.cc DEPS proto_desc operator device_context blas)
+cc_library(tracer SRCS tracer.cc DEPS proto_desc device_context)
 cc_library(engine SRCS engine.cc)
 endif()

paddle/fluid/imperative/layer.cc

@@ -13,6 +13,7 @@
 // limitations under the License.
 
 #include "paddle/fluid/imperative/layer.h"
+
 #include <deque>
 #include <limits>
 #include <map>
@@ -22,6 +23,9 @@
 #include "paddle/fluid/framework/lod_tensor.h"
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/framework/operator.h"
+#include "paddle/fluid/framework/tensor_util.h"
+#include "paddle/fluid/operators/math/blas.h"
+#include "paddle/fluid/platform/device_context.h"
 #include "paddle/fluid/string/printf.h"
 
 namespace paddle {
@@ -34,22 +38,66 @@ std::map<int, py::object> py_funcs_;
 
 using framework::Variable;
 
-void AddTo(Variable* src, Variable* dst) {
-  framework::LoDTensor* dst_tensor = dst->GetMutable<framework::LoDTensor>();
-  framework::LoDTensor* src_tensor = src->GetMutable<framework::LoDTensor>();
+namespace detail {
+
+template <typename T>
+class TensorAddToFunctor : public boost::static_visitor<> {
+ public:
+  TensorAddToFunctor(int64_t numel, const T* x, T* y)
+      : numel_(numel), x_(x), y_(y) {}
+
+  void operator()(const platform::CPUPlace& place) {
+    platform::CPUDeviceContext* ctx = dynamic_cast<platform::CPUDeviceContext*>(
+        platform::DeviceContextPool::Instance().Get(place));
+    auto blas = operators::math::GetBlas<platform::CPUDeviceContext, T>(*ctx);
+    blas.AXPY(numel_, 1., x_, y_);
+  }
+
+#ifdef PADDLE_WITH_CUDA
+  void operator()(const platform::CUDAPlace& place) {
+    platform::CUDADeviceContext* ctx =
+        dynamic_cast<platform::CUDADeviceContext*>(
+            platform::DeviceContextPool::Instance().Get(place));
+    auto blas = operators::math::GetBlas<platform::CUDADeviceContext, T>(*ctx);
+    blas.AXPY(numel_, 1., x_, y_);
+  }
+#else
+  void operator()(const platform::CUDAPlace& place) {
+    PADDLE_THROW("Do NOT support gradient merge in place %s", place);
+  }
+#endif
+
+  // there is NO blas in CUDAPinnedPlace
+  void operator()(const platform::CUDAPinnedPlace& place) {
+    PADDLE_THROW("Do NOT support gradient merge in place %s", place);
+  }
+
+ private:
+  int64_t numel_;
+  const T* x_;
+  T* y_;
+};
+
+}  // namespace detail
+
+void AddTo(Variable* src, Variable* dst, platform::Place place) {
+  framework::Tensor* dst_tensor = dst->GetMutable<framework::LoDTensor>();
+  framework::Tensor* src_tensor = src->GetMutable<framework::LoDTensor>();
+
   // FIXME(minqiyang): loss_grad op will pass a zero grad of label
   // ugly fix for it
   if (src_tensor->numel() == 0) {
     return;
   }
+
   PADDLE_ENFORCE(dst_tensor->numel() == src_tensor->numel(),
                  "dst_numel %lld vs. src_numel %lld", dst_tensor->numel(),
                  src_tensor->numel());
-  float* dst_data = dst_tensor->mutable_data<float>(platform::CPUPlace());
-  const float* src_data = src_tensor->data<float>();
-  for (int64_t i = 0; i < src_tensor->numel(); ++i) {
-    dst_data[i] += src_data[i];
-  }
+
+  detail::TensorAddToFunctor<float> func(
+      src_tensor->numel(), src_tensor->data<float>(),
+      dst_tensor->mutable_data<float>(place));
+  boost::apply_visitor(func, place);
 }
 
 class Autograd {
@@ -120,6 +168,36 @@ class Autograd {
   }
 };
 
+std::unique_ptr<VarBase> VarBase::NewVarBase(const platform::Place& dst_place,
+                                             const bool blocking) const {
+  PADDLE_ENFORCE(var_->IsInitialized(),
+                 "Variable must be initialized when getting numpy tensor");
+
+  std::unique_ptr<VarBase> new_var(new VarBase());
+  framework::LoDTensor* tensor =
+      new_var->var_->GetMutable<framework::LoDTensor>();
+  tensor->Resize(var_->Get<framework::LoDTensor>().dims());
+  tensor->set_lod(var_->Get<framework::LoDTensor>().lod());
+
+  if (blocking) {
+    platform::DeviceContext* dev_ctx =
+        platform::DeviceContextPool::Instance().Get(dst_place);
+
+    framework::TensorCopySync(var_->Get<framework::LoDTensor>(), dst_place,
+                              tensor);
+
+    dev_ctx->Wait();
+  } else {
+    framework::TensorCopy(var_->Get<framework::LoDTensor>(), dst_place, tensor);
+  }
+
+  if (platform::is_gpu_place(dst_place)) {
+    VLOG(3) << "copy tensor " << var_desc_->Name() << " from gpu";
+  }
+
+  return new_var;
+}
+
 framework::LoDTensor& VarBase::GradValue() {
   VLOG(3) << "get var grad " << var_desc_->Name();
   return *(grads_->var_->GetMutable<framework::LoDTensor>());
@@ -162,9 +240,8 @@ std::map<std::string, std::vector<VarBase*>> OpBase::ApplyGrad() {
     PADDLE_ENFORCE_NOT_NULL(op_kernel, "only support op with kernel");
 
     framework::Scope scope;
-    platform::CPUPlace place;
-    PreparedOp p = PreparedOp::Prepare(ctx, *op_kernel, place);
-    p.op.RuntimeInferShape(scope, place, ctx);
+    PreparedOp p = PreparedOp::Prepare(ctx, *op_kernel, place_);
+    p.op.RuntimeInferShape(scope, place_, ctx);
     p.func(framework::ExecutionContext(p.op, scope, *p.dev_ctx, p.ctx));
   }
 
@@ -176,7 +253,7 @@ std::map<std::string, std::vector<VarBase*>> OpBase::ApplyGrad() {
     for (size_t i = 0; i < outputs.size(); ++i) {
       framework::Variable* grad = outputs[i];
       framework::Variable* orig_grad = origin_outputs[i];
-      AddTo(grad, orig_grad);
+      AddTo(grad, orig_grad, place_);
       delete grad;
     }
   }
@@ -188,8 +265,10 @@ void VarBase::RunBackward() {
 
   VLOG(3) << "start backward";
   auto grads_t = grads_->var_->GetMutable<framework::LoDTensor>();
-  float* data = grads_t->mutable_data<float>(platform::CPUPlace());
-  std::fill(data, data + grads_t->numel(), 1.0);
+  operators::math::set_constant(
+      *(platform::DeviceContextPool::Instance().Get(
+          var_->GetMutable<framework::LoDTensor>()->place())),
+      grads_t, 1.0);
 
   PADDLE_ENFORCE(
       grads_ ==

paddle/fluid/imperative/layer.h

@@ -21,17 +21,21 @@
 #include <map>     // NOLINT
 #include <string>  // NOLINT
 #include <vector>  // NOLINT
+#include <memory>  // NOLINT
 
 #include "paddle/fluid/framework/op_desc.h"
 #include "paddle/fluid/framework/operator.h"
 #include "paddle/fluid/framework/var_desc.h"
 #include "paddle/fluid/platform/enforce.h"
+#include "paddle/fluid/platform/device_context.h"
 
 #include "paddle/fluid/imperative/type_defs.h"
 
 namespace paddle {
 namespace imperative {
 
+class VarBase;
+
 namespace py = ::pybind11;
 
 class PreparedOp {
@@ -81,6 +85,8 @@ class PreparedOp {
     return PreparedOp(op, ctx, kernel_iter->second, dev_ctx);
   }
 
+  inline platform::DeviceContext* GetDeviceContext() const { return dev_ctx; }
+
   const framework::OperatorBase& op;
   const framework::RuntimeContext& ctx;
   framework::OperatorWithKernel::OpKernelFunc func;
@@ -148,6 +154,9 @@ class VarBase {
 
   framework::LoDTensor& GradValue();
 
+  std::unique_ptr<VarBase> NewVarBase(const platform::Place& dst_place,
+                                      const bool blocking) const;
+
   inline std::string GradName() const {
     PADDLE_ENFORCE(
         var_desc_,
@@ -176,7 +185,8 @@ class OpBase {
       : op_desc_(nullptr),
         forward_id_(-1),
         grad_op_desc_(nullptr),
-        backward_id_(-1) {}
+        backward_id_(-1),
+        place_(platform::CPUPlace()) {}
 
   virtual ~OpBase() {
     if (grad_op_desc_) delete grad_op_desc_;
@@ -193,6 +203,8 @@ class OpBase {
   framework::OpDesc* grad_op_desc_;
   int backward_id_;
 
+  platform::Place place_;
+
   VarBasePtrMap input_vars_;
   VarBasePtrMap output_vars_;
   OpBasePtrMap pre_ops_;

paddle/fluid/imperative/tracer.cc

@@ -14,6 +14,10 @@
 
 #include "paddle/fluid/imperative/tracer.h"
 
+#include "paddle/fluid/operators/math/math_function.h"
+#include "paddle/fluid/platform/device_context.h"
+#include "paddle/fluid/platform/enforce.h"
+
 namespace paddle {
 namespace imperative {
 
@@ -31,16 +35,38 @@ void CreateGradOp(const framework::OpDesc& op_desc,
   *grad_op_desc = grad_op_descs[0].release();
 }
 
-void InitVar(framework::Variable* var, framework::Variable* grad_var) {
+void InitVar(framework::Variable* var, framework::Variable* grad_var,
+             platform::DeviceContext* dev_ctx) {
+  PADDLE_ENFORCE_NOT_NULL(dev_ctx,
+                          "Could not get valid device from forward op");
   auto& var_t = var->Get<framework::LoDTensor>();
-  float* data =
-      grad_var->GetMutable<framework::LoDTensor>()->mutable_data<float>(
-          var_t.dims(), platform::CPUPlace());
-  std::fill(data, data + var_t.numel(), 0.0);
+  grad_var->GetMutable<framework::LoDTensor>()->mutable_data<float>(
+      var_t.dims(), dev_ctx->GetPlace());
+  operators::math::set_constant(
+      *dev_ctx, grad_var->GetMutable<framework::LoDTensor>(), 0.0);
+}
+
+platform::Place GetExpectedPlace(platform::Place place, VarBasePtrMap inputs) {
+  platform::Place result = place;
+  for (auto it : inputs) {
+    for (VarBase* var : it.second) {
+      platform::Place tmp_place =
+          var->var_->Get<framework::LoDTensor>().place();
+      if (!platform::is_same_place(tmp_place, result)) {
+        PADDLE_THROW(
+            "Input variable should keep in the same place: %s, but get place: "
+            "%s of input %s instead",
+            result, tmp_place, it.first);
+      }
+    }
+  }
+
+  return result;
 }
 
 void Tracer::Trace(OpBase* op, const VarBasePtrMap& inputs,
                    const VarBasePtrMap& outputs, framework::BlockDesc* block,
+                   const platform::Place expected_place,
                    const bool stop_gradient) {
   std::map<std::string, VarBase*> vars;
 
@@ -105,10 +131,11 @@ void Tracer::Trace(OpBase* op, const VarBasePtrMap& inputs,
   PADDLE_ENFORCE_NOT_NULL(op_kernel, "only support op with kernel");
 
   framework::Scope scope;
-  platform::CPUPlace place;
-  PreparedOp p = PreparedOp::Prepare(ctx, *op_kernel, place);
-  p.op.RuntimeInferShape(scope, place, ctx);
-  p.func(framework::ExecutionContext(p.op, scope, *p.dev_ctx, p.ctx));
+  op->place_ = GetExpectedPlace(expected_place, inputs);
+  PreparedOp prepared_op = PreparedOp::Prepare(ctx, *op_kernel, op->place_);
+  prepared_op.op.RuntimeInferShape(scope, op->place_, ctx);
+  prepared_op.func(framework::ExecutionContext(
+      prepared_op.op, scope, *prepared_op.dev_ctx, prepared_op.ctx));
 
   if (!stop_gradient) {
     framework::OpDesc* grad_op_desc;
@@ -131,7 +158,8 @@ void Tracer::Trace(OpBase* op, const VarBasePtrMap& inputs,
         } else {
           VarBase* var = vars[var_it->second];
           if (!var->grads_->var_->IsInitialized()) {
-            InitVar(var->var_, var->grads_->var_);
+            InitVar(var->var_, var->grads_->var_,
+                    prepared_op.GetDeviceContext());
           }
           // Douts.
           grad_in_vars.push_back(var->grads_->var_);
@@ -144,10 +172,13 @@ void Tracer::Trace(OpBase* op, const VarBasePtrMap& inputs,
       for (const std::string& grad_outvar : it.second) {
         block->FindRecursiveOrCreateVar(grad_outvar);
         auto var_it = grad_to_var->find(grad_outvar);
-        PADDLE_ENFORCE(var_it != grad_to_var->end());
+        PADDLE_ENFORCE(var_it != grad_to_var->end(),
+                       "Could not found the grad op output var, should this "
+                       "operator %s's stop gradient be True",
+                       op_desc->Type());
         VarBase* var = vars[var_it->second];
         if (!var->grads_->var_->IsInitialized()) {
-          InitVar(var->var_, var->grads_->var_);
+          InitVar(var->var_, var->grads_->var_, prepared_op.GetDeviceContext());
         }
         grad_out_vars.push_back(var->grads_->var_);
       }
@@ -189,16 +220,23 @@ std::vector<VarBase*> Tracer::PyTrace(OpBase* op,
     for (VarBase* out : outputs) {
       grad_input_vars.push_back(out->var_);
     }
+
+    platform::CPUPlace place;
     for (VarBase* out : outputs) {
       grad_input_vars.push_back(out->grads_->var_);
       if (!grad_input_vars.back()->IsInitialized()) {
-        InitVar(out->var_, grad_input_vars.back());
+        // TODO(minqiyang): Add GPU support for PyLayer, only support CPU now
+        InitVar(out->var_, grad_input_vars.back(),
+                platform::DeviceContextPool::Instance().Get(place));
       }
     }
+
     for (const VarBase* inp : inputs) {
       grad_output_vars.push_back(inp->grads_->var_);
       if (!grad_output_vars.back()->IsInitialized()) {
-        InitVar(inp->var_, grad_output_vars.back());
+        // TODO(minqiyang): Add GPU support for PyLayer, only support CPU now
+        InitVar(inp->var_, grad_output_vars.back(),
+                platform::DeviceContextPool::Instance().Get(place));
       }
     }
   }

paddle/fluid/imperative/tracer.h

@@ -22,6 +22,7 @@
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/imperative/engine.h"
 #include "paddle/fluid/imperative/layer.h"
+#include "paddle/fluid/platform/place.h"
 
 namespace paddle {
 namespace imperative {
@@ -34,21 +35,25 @@ void CreateGradOp(const framework::OpDesc& op_desc,
 
 void InitVar(framework::Variable* var, framework::Variable* grad_var);
 
+platform::Place GetExpectedPlace(platform::Place place, VarBasePtrMap inputs);
+
 class Tracer {
  public:
   explicit Tracer(framework::BlockDesc* root_block) : root_block_(root_block) {}
 
   virtual ~Tracer() {}
 
-  void Trace(OpBase* op,
-             const std::map<std::string, std::vector<VarBase*>>& inputs,
-             const std::map<std::string, std::vector<VarBase*>>& outputs,
-             framework::BlockDesc* block, const bool stop_gradient = false);
+  void Trace(OpBase* op, const VarBasePtrMap& inputs,
+             const VarBasePtrMap& outputs, framework::BlockDesc* block,
+             const platform::Place expected_place,
+             const bool stop_gradient = false);
 
   std::vector<VarBase*> PyTrace(OpBase* op, const std::vector<VarBase*>& inputs,
                                 bool stop_gradient = false);
 
  private:
+  platform::Place GetPlace(const VarBasePtrMap& inputs);
+
   framework::BlockDesc* root_block_;
 };
 

paddle/fluid/platform/device_context.cc

@@ -30,8 +30,9 @@ platform::DeviceContext* DeviceContextPool::Get(const platform::Place& place) {
   auto it = device_contexts_.find(place);
   if (it == device_contexts_.end()) {
     PADDLE_THROW(
-        "'Place' is not supported, Please re-compile with WITH_GPU "
-        "option");
+        "Place %s is not supported, Please re-compile with WITH_GPU "
+        "option",
+        place);
   }
   return it->second.get().get();
 }

paddle/fluid/pybind/imperative.cc

@@ -15,18 +15,38 @@ limitations under the License. */
 #include "paddle/fluid/pybind/imperative.h"
 #include "paddle/fluid/framework/block_desc.h"
 #include "paddle/fluid/imperative/tracer.h"
+#include "paddle/fluid/imperative/type_defs.h"
 
 namespace paddle {
 namespace pybind {
 
 // Bind Methods
-void BindTracer(pybind11::module *m) {
+void BindTracer(pybind11::module* m) {
   pybind11::class_<imperative::Tracer>(*m, "Tracer", "")
       .def("__init__",
-           [](imperative::Tracer &self, framework::BlockDesc *root_block) {
+           [](imperative::Tracer& self, framework::BlockDesc* root_block) {
              new (&self) imperative::Tracer(root_block);
            })
-      .def("trace", &imperative::Tracer::Trace)
+      .def("trace",
+           [](imperative::Tracer& self, imperative::OpBase* op,
+              const imperative::VarBasePtrMap& inputs,
+              const imperative::VarBasePtrMap& outputs,
+              framework::BlockDesc* block,
+              const platform::CPUPlace expected_place,
+              const bool stop_gradient = false) {
+             self.Trace(op, inputs, outputs, block, expected_place,
+                        stop_gradient);
+           })
+      .def("trace",
+           [](imperative::Tracer& self, imperative::OpBase* op,
+              const imperative::VarBasePtrMap& inputs,
+              const imperative::VarBasePtrMap& outputs,
+              framework::BlockDesc* block,
+              const platform::CUDAPlace expected_place,
+              const bool stop_gradient = false) {
+             self.Trace(op, inputs, outputs, block, expected_place,
+                        stop_gradient);
+           })
       .def("py_trace", &imperative::Tracer::PyTrace,
            pybind11::return_value_policy::take_ownership);
 }

paddle/fluid/pybind/pybind.cc

@@ -138,6 +138,22 @@ PYBIND11_MODULE(core, m) {
       .def("_grad_ivar",
            [](const imperative::VarBase &self) { return self.grads_; },
            py::return_value_policy::reference)
+      .def("_copy_to",
+           [](const imperative::VarBase &self, const platform::CPUPlace &place,
+              bool blocking) {
+             std::unique_ptr<imperative::VarBase> new_var =
+                 self.NewVarBase(place, blocking);
+             return new_var.release();
+           },
+           py::return_value_policy::take_ownership)
+      .def("_copy_to",
+           [](const imperative::VarBase &self, const platform::CUDAPlace &place,
+              bool blocking) {
+             std::unique_ptr<imperative::VarBase> new_var =
+                 self.NewVarBase(place, blocking);
+             return new_var.release();
+           },
+           py::return_value_policy::take_ownership)
       .def("value", [](const imperative::VarBase &self) { return self.var_; },
            py::return_value_policy::reference)
       .def_property(

python/paddle/fluid/framework.py

@@ -70,6 +70,7 @@ ZERO_VAR_SUFFIX = core.kZeroVarSuffix()
 CONTROL_DEP_VAR_PREFIX = core.kControlDepVarName()
 
 _imperative_tracer_ = None
+_imperative_current_expected_place_ = None
 
 
 def _in_imperative_mode():
@@ -80,6 +81,10 @@ def _imperative_tracer():
     return _imperative_tracer_
 
 
+def _current_expected_place():
+    return _imperative_current_expected_place_
+
+
 class NameScope(object):
     def __init__(self, name="", parent=None):
         self._children = dict()
@@ -383,8 +388,8 @@ class Variable(object):
             self._ivar.stop_gradient = stop_gradient
 
     def _numpy(self):
-        tensor = self._ivar.value().get_tensor()
-        return np.array(tensor)
+        new_ivar = self._ivar._copy_to(core.CPUPlace(), True)
+        return np.array(new_ivar.value().get_tensor())
 
     def _backward(self):
         self._ivar._run_backward()
@@ -1311,6 +1316,7 @@ class Block(object):
     def _trace_op(self, op, stop_gradient=False):
         if _in_imperative_mode():
             _imperative_tracer().trace(op.iop, op.inputs, op.outputs, self.desc,
+                                       _imperative_current_expected_place_,
                                        stop_gradient)
 
     def _insert_op(self, index, *args, **kwargs):
@@ -2502,5 +2508,18 @@ def _imperative_guard(tracer):
     global _imperative_tracer_
     tmp_trace = _imperative_tracer_
     _imperative_tracer_ = tracer
+
     yield
+
     _imperative_tracer_ = tmp_trace
+
+
+@contextlib.contextmanager
+def _imperative_place_guard(place):
+    global _imperative_current_expected_place_
+    tmp_place = _imperative_current_expected_place_
+    _imperative_current_expected_place_ = place
+
+    yield
+
+    _imperative_current_expected_place_ = tmp_place

python/paddle/fluid/imperative/base.py

@@ -25,18 +25,28 @@ def enabled():
 
 
 @contextlib.contextmanager
-def guard():
+def guard(place=None):
     train = framework.Program()
     startup = framework.Program()
     tracer = core.Tracer(train.current_block().desc)
+
+    if place is None:
+        if core.is_compiled_with_cuda():
+            place = core.CUDAPlace(0)
+        else:
+            place = core.CPUPlace()
+
     with framework.program_guard(train, startup):
         with framework.unique_name.guard():
             with framework._imperative_guard(tracer):
-                yield
+                with framework._imperative_place_guard(place):
+                    yield
 
 
 def to_variable(value, block=None):
     if isinstance(value, np.ndarray):
+        assert enabled(), "to_variable could only be called in imperative mode"
+
         if not block:
             block = framework.default_main_program().current_block()
         py_var = framework.Variable(
@@ -47,9 +57,7 @@ def to_variable(value, block=None):
             dtype=value.dtype)
         var = py_var._ivar.value()
         tensor = var.get_tensor()
-        tensor.set(value, core.CPUPlace())
+        tensor.set(value, framework._current_expected_place())
         return py_var
     elif isinstance(value, framework.Variable):
         return value
-    else:
-        raise ValueError("Unsupported type %s" % type(value))

python/paddle/fluid/imperative/nn.py

@@ -27,6 +27,7 @@ __all__ = [
     'Conv2D',
     'Pool2D',
     'FC',
+    'BatchNorm',
 ]
 
 
@@ -55,7 +56,8 @@ class Conv2D(layers.Layer):
             param_attr=param_attr,
             bias_attr=bias_attr,
             dtype=dtype,
-            name=name)
+            name=name,
+            act=act)
 
         self._groups = groups
         self._stride = utils.convert_to_list(stride, 2, 'stride')
@@ -141,6 +143,7 @@ class Conv2D(layers.Layer):
             outputs={'Out': [pre_act]},
             attrs={'axis': 1})
 
+        # Currently, we don't support inplace in imperative mode
         return self._helper.append_activation(pre_act)
 
 
@@ -216,6 +219,7 @@ class FC(layers.Layer):
                  act=None,
                  name=None):
         super(FC, self).__init__()
+
         self._size = size
         self._num_flatten_dims = num_flatten_dims
         self._dtype = dtype
@@ -241,6 +245,16 @@ class FC(layers.Layer):
             dtype=self._dtype,
             is_bias=False)
 
+        if self._helper.bias_attr:
+            size = list([self._size])
+            self._b = self._helper.create_parameter(
+                attr=self._helper.bias_attr,
+                shape=size,
+                dtype=self._dtype,
+                is_bias=True)
+        else:
+            self._b = None
+
     def forward(self, input):
         tmp = self._helper.create_variable_for_type_inference(self._dtype)
         self._helper.append_op(
@@ -253,28 +267,155 @@ class FC(layers.Layer):
                 "y_num_col_dims": 1
             })
 
-        out = self._helper.create_variable_for_type_inference(self._dtype)
+        pre_bias = self._helper.create_variable_for_type_inference(self._dtype)
         self._helper.append_op(
             type="sum",
             inputs={"X": [tmp]},
-            outputs={"Out": out},
+            outputs={"Out": pre_bias},
             attrs={"use_mkldnn": False})
 
-        bias_attr = self._helper.bias_attr
-        if bias_attr:
-            # add bias
-            size = list(out.shape[1:])
-            if not self._built:
-                self._b = self._helper.create_parameter(
-                    attr=bias_attr, shape=size, dtype=out.dtype, is_bias=True)
-            bias_out = self._helper.create_variable_for_type_inference(
-                dtype=out.dtype)
+        if self._b:
+            pre_activation = self._helper.create_variable_for_type_inference(
+                dtype=self._dtype)
             self._helper.append_op(
                 type='elementwise_add',
-                inputs={'X': [out],
+                inputs={'X': [pre_bias],
                         'Y': [self._b]},
-                outputs={'Out': [bias_out]},
-                attrs={'axis': 1})
-            out = bias_out
-        # add activation
-        return self._helper.append_activation(out)
+                outputs={'Out': [pre_activation]},
+                attrs={'axis': self._num_flatten_dims})
+        else:
+            pre_activation = pre_bias
+        # Currently, we don't support inplace in imperative mode
+        return self._helper.append_activation(pre_activation)
+
+
+class BatchNorm(layers.Layer):
+    def __init__(self,
+                 num_channels,
+                 act=None,
+                 is_test=False,
+                 momentum=0.9,
+                 epsilon=1e-05,
+                 param_attr=None,
+                 bias_attr=None,
+                 dtype=core.VarDesc.VarType.FP32,
+                 data_layout='NCHW',
+                 in_place=False,
+                 name=None,
+                 moving_mean_name=None,
+                 moving_variance_name=None,
+                 do_model_average_for_mean_and_var=False,
+                 fuse_with_relu=False,
+                 use_global_stats=False):
+        super(BatchNorm, self).__init__()
+
+        assert bias_attr is not False, "bias_attr should not be False in batch_norm."
+
+        from ..layer_helper import LayerHelper
+        self._helper = LayerHelper(
+            'batch_norm',
+            param_attr=param_attr,
+            bias_attr=bias_attr,
+            name=name,
+            act=act)
+
+        if dtype == core.VarDesc.VarType.FP16:
+            self._dtype = core.VarDesc.VarType.FP32
+        else:
+            self._dtype = dtype
+
+        param_shape = [num_channels]
+
+        # create parameter
+        self._scale = self._helper.create_parameter(
+            attr=self._helper.param_attr,
+            shape=param_shape,
+            dtype=self._dtype,
+            default_initializer=Constant(1.0))
+
+        # TODO(minqiyang): change stop_gradient sign to trainable to align with static graph
+        #  # setting stop_gradient=True to reduce computation
+        #  if use_global_stats and self._helper.param_attr.learning_rate == 0.:
+        #  self._scale.stop_gradient = True
+
+        self._bias = self._helper.create_parameter(
+            attr=self._helper.bias_attr,
+            shape=param_shape,
+            dtype=self._dtype,
+            is_bias=True)
+        # TODO(minqiyang): change stop_gradient sign to trainable to align with static graph
+        #  # setting stop_gradient=True to reduce computation
+        #  if use_global_stats and self._helper.bias_attr.learning_rate == 0.:
+        #  self._bias.stop_gradient = True
+
+        self._mean = self._helper.create_parameter(
+            attr=ParamAttr(
+                name=moving_mean_name,
+                initializer=Constant(0.0),
+                trainable=False,
+                do_model_average=do_model_average_for_mean_and_var),
+            shape=param_shape,
+            dtype=self._dtype)
+        self._mean.stop_gradient = True
+
+        self._variance = self._helper.create_parameter(
+            attr=ParamAttr(
+                name=moving_variance_name,
+                initializer=Constant(1.0),
+                trainable=False,
+                do_model_average=do_model_average_for_mean_and_var),
+            shape=param_shape,
+            dtype=self._dtype)
+        self._variance.stop_gradient = True
+
+        self._in_place = in_place
+        self._momentum = momentum
+        self._epsilon = epsilon
+        self._is_test = is_test
+        self._fuse_with_relu = fuse_with_relu
+        self._use_global_stats = use_global_stats
+
+    def _build_once(self, input):
+        pass
+
+    def forward(self, input):
+        # create output
+        # mean and mean_out share the same memory
+        mean_out = self._mean
+        # variance and variance out share the same memory
+        variance_out = self._variance
+
+        saved_mean = self._helper.create_variable_for_type_inference(
+            dtype=self._dtype, stop_gradient=True)
+        saved_variance = self._helper.create_variable_for_type_inference(
+            dtype=self._dtype, stop_gradient=True)
+        batch_norm_out = input if self._in_place else self._helper.create_variable_for_type_inference(
+            self._dtype)
+
+        self._helper.append_op(
+            type="batch_norm",
+            inputs={
+                "X": input,
+                "Scale": self._scale,
+                "Bias": self._bias,
+                "Mean": self._mean,
+                "Variance": self._variance
+            },
+            outputs={
+                "Y": batch_norm_out,
+                "MeanOut": mean_out,
+                "VarianceOut": variance_out,
+                "SavedMean": saved_mean,
+                "SavedVariance": saved_variance
+            },
+            attrs={
+                "momentum": self._momentum,
+                "epsilon": self._epsilon,
+                "is_test": self._is_test,
+                "use_mkldnn": False,
+                "fuse_with_relu": self._fuse_with_relu,
+                "use_global_stats": self._use_global_stats
+            })
+
+        # Currently, we don't support inplace in imperative mode
+        return self._helper.append_activation(batch_norm_out)

python/paddle/fluid/layer_helper.py

@@ -435,7 +435,10 @@ class LayerHelper(object):
         act_type = act.pop('type')
         tmp = input_var
         # NOTE(dzhwinter): some activation support inplace compution.
-        if not core.IsInplace(act_type):
+        # NOTE(minqiyang): currently, we don't support inplace in imperative mode
+        if not imperative_base.enabled() and core.IsInplace(act_type):
+            tmp = input_var
+        else:
             tmp = self.create_variable_for_type_inference(dtype=input_var.dtype)
         self.append_op(
             type=act_type,

python/paddle/fluid/layers/learning_rate_scheduler.py

@@ -321,7 +321,7 @@ def append_LARS(params_grads, learning_rate, weight_decay):
         The decayed learning rate
     Examples:
         .. code-block:: python
-        
+
             learning_rate *= local_gw_ratio * sqrt(sumsq(param))
                         / (sqrt(sumsq(gradient))+ weight_decay * sqrt(sumsq(param)))
     """

python/paddle/fluid/layers/nn.py

@@ -2874,7 +2874,7 @@ def batch_norm(input,
         attr=helper.bias_attr, shape=param_shape, dtype=dtype, is_bias=True)
     # setting stop_gradient=True to reduce computation
     if use_global_stats and helper.bias_attr.learning_rate == 0.:
-        scale.stop_gradient = True
+        bias.stop_gradient = True
 
     mean = helper.create_parameter(
         attr=ParamAttr(
@@ -5856,7 +5856,8 @@ def autoincreased_step_counter(counter_name=None, begin=1, step=1):
             type='increment',
             inputs={'X': [counter]},
             outputs={'Out': [counter]},
-            attrs={'step': float(step)})
+            attrs={'step': float(step)},
+            stop_gradient=True)
         counter.stop_gradient = True
 
     return counter
@@ -9475,7 +9476,7 @@ def teacher_student_sigmoid_loss(input,
                                 by the previous operator.
         label (Variable|list):  the ground truth which is a 2-D tensor with
                                 shape [N x 1], where N is the batch size.
-        soft_max_up_bound  (float):  if input > soft_max_up_bound, will be bound 
+        soft_max_up_bound  (float):  if input > soft_max_up_bound, will be bound
         soft_max_lower_bound (float): if input < soft_max_lower_bound, will be bound
 
     Returns:

python/paddle/fluid/layers/tensor.py

@@ -382,7 +382,8 @@ def fill_constant(shape, dtype, value, force_cpu=False, out=None):
             'dtype': out.dtype,
             'value': float(value),
             'force_cpu': force_cpu or force_init_on_cpu()
-        })
+        },
+        stop_gradient=True)
     out.stop_gradient = True
     return out
 

python/paddle/fluid/optimizer.py

@@ -301,10 +301,10 @@ class Optimizer(object):
             no_grad_set (set|None): set of Variables should be ignored.
             callbacks (list|None): list of callables to run when appending backward
                 operator for one parameter.
-        
+
         Return:
             list: list of (param, grad) pair, grad is the output of backward.
-        
+
         Examples:
             See examples in `apply_gradients`.
         """
@@ -322,10 +322,10 @@ class Optimizer(object):
 
         Args:
             params_grads (list): list of (param, grad) pair to do optimization.
-        
+
         Returns:
             list: A list of operators appended to the current program.
-        
+
         Examples:
             .. code-block:: python
 
@@ -364,7 +364,7 @@ class Optimizer(object):
 
         This method combines interface `backward()` and
         `apply_gradients()` into one.
-        
+
         Args:
             loss (Variable): loss variable to run optimizations.
             startup_program (Program): startup_program for initializing parameters
@@ -381,18 +381,21 @@ class Optimizer(object):
         optimize_ops = []
         if imperative_base.enabled():
             if parameter_list is not None:
-                params_grads = parameter_list
+                parameters = parameter_list
             else:
                 parameters = program.global_block().all_parameters()
-                params_grads = []
-                for param in parameters:
-                    # create gradient variable
-                    grad_var = Variable(
-                        block=loss.block,
-                        name=param._ivar._grad_name(),
-                        stop_gradient=True,
-                        ivar=param._ivar._grad_ivar())
-                    params_grads.append((param, grad_var))
+
+            params_grads = []
+            for param in parameters:
+                if param.stop_gradient:
+                    continue
+                # create gradient variable
+                grad_var = Variable(
+                    block=loss.block,
+                    name=param._ivar._grad_name(),
+                    stop_gradient=True,
+                    ivar=param._ivar._grad_ivar())
+                params_grads.append((param, grad_var))
             with program_guard(program, startup_program):
                 optimize_ops = self._create_optimization_pass(params_grads)
         else:

python/paddle/fluid/tests/unittests/CMakeLists.txt

@@ -84,6 +84,7 @@ list(REMOVE_ITEM TEST_OPS test_parallel_executor_transformer)
 list(REMOVE_ITEM TEST_OPS test_image_classification_resnet)
 list(REMOVE_ITEM TEST_OPS test_bilinear_interp_op)
 list(REMOVE_ITEM TEST_OPS test_nearest_interp_op)
+list(REMOVE_ITEM TEST_OPS test_imperative_resnet)
 foreach(TEST_OP ${TEST_OPS})
     py_test_modules(${TEST_OP} MODULES ${TEST_OP})
 endforeach(TEST_OP)
@@ -91,6 +92,8 @@ py_test_modules(test_adam_op_multi_thread MODULES test_adam_op ENVS FLAGS_inner_
 py_test_modules(test_warpctc_op MODULES test_warpctc_op ENVS FLAGS_warpctc_dir=${WARPCTC_LIB_DIR} SERIAL)
 py_test_modules(test_bilinear_interp_op MODULES test_bilinear_interp_op SERIAL)
 py_test_modules(test_nearest_interp_op MODULES test_nearest_interp_op SERIAL)
+py_test_modules(test_imperative_resnet MODULES test_imperative_resnet ENVS
+  FLAGS_cudnn_deterministic=1)
 if(WITH_DISTRIBUTE)
     py_test_modules(test_dist_train MODULES test_dist_train SERIAL)
     set_tests_properties(test_listen_and_serv_op PROPERTIES TIMEOUT 20)

python/paddle/fluid/tests/unittests/test_imperative.py

@@ -133,7 +133,8 @@ class TestImperative(unittest.TestCase):
             x = fluid.layers.reduce_sum(fluid.layers.tanh(x1))
             param_grads = fluid.backward.append_backward(
                 x, parameter_list=[x1.name])[0]
-            exe = fluid.Executor(fluid.CPUPlace())
+            exe = fluid.Executor(fluid.CPUPlace(
+            ) if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0))
 
             static_out, static_grad = exe.run(
                 feed={inp.name: np_inp},
@@ -160,7 +161,8 @@ class TestImperative(unittest.TestCase):
             x = l(inp)[0]
             param_grads = fluid.backward.append_backward(
                 x, parameter_list=[l._x_for_debug.name])[0]
-            exe = fluid.Executor(fluid.CPUPlace())
+            exe = fluid.Executor(fluid.CPUPlace(
+            ) if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0))
 
             static_out, static_grad = exe.run(
                 feed={inp.name: np_inp},
@@ -186,7 +188,8 @@ class TestImperative(unittest.TestCase):
             out = mlp(inp)
             param_grads = fluid.backward.append_backward(
                 out, parameter_list=[mlp._fc1._w.name])[0]
-            exe = fluid.Executor(fluid.CPUPlace())
+            exe = fluid.Executor(fluid.CPUPlace(
+            ) if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0))
             exe.run(fluid.default_startup_program())
 
             static_out, static_grad = exe.run(

python/paddle/fluid/tests/unittests/test_imperative_gan.py

@@ -20,6 +20,7 @@ import sys
 
 import paddle
 import paddle.fluid as fluid
+import paddle.fluid.core as core
 from paddle.fluid.optimizer import SGDOptimizer
 from paddle.fluid.imperative.nn import Conv2D, Pool2D, FC
 from test_imperative_base import new_program_scope
@@ -58,7 +59,7 @@ class Generator(fluid.imperative.Layer):
 
 
 class TestImperativeMnist(unittest.TestCase):
-    def test_mnist_cpu_float32(self):
+    def test_gan_float32(self):
         seed = 90
 
         startup = fluid.Program()
@@ -115,7 +116,8 @@ class TestImperativeMnist(unittest.TestCase):
             sgd = SGDOptimizer(learning_rate=1e-3)
             sgd.minimize(g_loss)
 
-        exe = fluid.Executor(fluid.CPUPlace())
+        exe = fluid.Executor(fluid.CPUPlace() if not core.is_compiled_with_cuda(
+        ) else fluid.CUDAPlace(0))
         static_params = dict()
         with fluid.scope_guard(scope):
             img = np.ones([2, 1], np.float32)

python/paddle/fluid/tests/unittests/test_imperative_optimizer.py

@@ -145,7 +145,8 @@ class TestImperativeMnist(unittest.TestCase):
             fluid.default_startup_program().random_seed = seed
             fluid.default_main_program().random_seed = seed
 
-            exe = fluid.Executor(fluid.CPUPlace())
+            exe = fluid.Executor(fluid.CPUPlace(
+            ) if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0))
 
             mnist = MNIST()
             sgd = SGDOptimizer(learning_rate=1e-3)

python/paddle/fluid/tests/unittests/test_imperative_resnet.py

+# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import contextlib
+import unittest
+import numpy as np
+import six
+
+import paddle
+import paddle.fluid as fluid
+from paddle.fluid import core
+from paddle.fluid.layer_helper import LayerHelper
+from paddle.fluid.optimizer import SGDOptimizer
+from paddle.fluid.imperative.nn import Conv2D, Pool2D, BatchNorm, FC
+from paddle.fluid.imperative.base import to_variable
+from test_imperative_base import new_program_scope
+
+batch_size = 8
+train_parameters = {
+    "input_size": [3, 224, 224],
+    "input_mean": [0.485, 0.456, 0.406],
+    "input_std": [0.229, 0.224, 0.225],
+    "learning_strategy": {
+        "name": "piecewise_decay",
+        "batch_size": batch_size,
+        "epochs": [30, 60, 90],
+        "steps": [0.1, 0.01, 0.001, 0.0001]
+    },
+    "batch_size": batch_size,
+    "lr": 0.1,
+    "total_images": 1281164,
+}
+
+
+def optimizer_setting(params):
+    ls = params["learning_strategy"]
+    if ls["name"] == "piecewise_decay":
+        if "total_images" not in params:
+            total_images = 1281167
+        else:
+            total_images = params["total_images"]
+        batch_size = ls["batch_size"]
+        step = int(total_images / batch_size + 1)
+
+        bd = [step * e for e in ls["epochs"]]
+        base_lr = params["lr"]
+        lr = []
+        lr = [base_lr * (0.1**i) for i in range(len(bd) + 1)]
+        optimizer = fluid.optimizer.SGD(learning_rate=0.01)
+        # TODO(minqiyang): Add learning rate scheduler support to imperative mode
+        #  optimizer = fluid.optimizer.Momentum(
+    #  learning_rate=params["lr"],
+    #  learning_rate=fluid.layers.piecewise_decay(
+    #  boundaries=bd, values=lr),
+    #  momentum=0.9,
+    #  regularization=fluid.regularizer.L2Decay(1e-4))
+
+    return optimizer
+
+
+class ConvBNLayer(fluid.imperative.Layer):
+    def __init__(self,
+                 num_channels,
+                 num_filters,
+                 filter_size,
+                 stride=1,
+                 groups=1,
+                 act=None):
+        super(ConvBNLayer, self).__init__()
+
+        self._conv = Conv2D(
+            num_channels=num_channels,
+            num_filters=num_filters,
+            filter_size=filter_size,
+            stride=stride,
+            padding=(filter_size - 1) // 2,
+            groups=groups,
+            act=None,
+            bias_attr=None)
+
+        self._batch_norm = BatchNorm(num_filters, act=act)
+
+    def forward(self, inputs):
+        y = self._conv(inputs)
+        y = self._batch_norm(y)
+
+        return y
+
+
+class BottleneckBlock(fluid.imperative.Layer):
+    def __init__(self, num_channels, num_filters, stride, shortcut=True):
+        super(BottleneckBlock, self).__init__()
+
+        self.conv0 = ConvBNLayer(
+            num_channels=num_channels,
+            num_filters=num_filters,
+            filter_size=1,
+            act='relu')
+        self.conv1 = ConvBNLayer(
+            num_channels=num_filters,
+            num_filters=num_filters,
+            filter_size=3,
+            stride=stride,
+            act='relu')
+        self.conv2 = ConvBNLayer(
+            num_channels=num_filters,
+            num_filters=num_filters * 4,
+            filter_size=1,
+            act=None)
+
+        if not shortcut:
+            self.short = ConvBNLayer(
+                num_channels=num_channels,
+                num_filters=num_filters * 4,
+                filter_size=1,
+                stride=stride)
+
+        self.shortcut = shortcut
+
+        self._num_channels_out = num_filters * 4
+
+    def forward(self, inputs):
+        y = self.conv0(inputs)
+        conv1 = self.conv1(y)
+        conv2 = self.conv2(conv1)
+
+        if self.shortcut:
+            short = inputs
+        else:
+            short = self.short(inputs)
+
+        y = fluid.layers.elementwise_add(x=short, y=conv2)
+
+        layer_helper = LayerHelper('elementwise_add_activation', act='relu')
+        return layer_helper.append_activation(y)
+
+
+class ResNet(fluid.imperative.Layer):
+    def __init__(self, layers=50, class_dim=102):
+        super(ResNet, self).__init__()
+
+        self.layers = layers
+        supported_layers = [50, 101, 152]
+        assert layers in supported_layers, \
+            "supported layers are {} but input layer is {}".format(supported_layers, layers)
+
+        if layers == 50:
+            depth = [3, 4, 6, 3]
+        elif layers == 101:
+            depth = [3, 4, 23, 3]
+        elif layers == 152:
+            depth = [3, 8, 36, 3]
+        num_filters = [64, 128, 256, 512]
+
+        self.conv = ConvBNLayer(
+            num_channels=3, num_filters=64, filter_size=7, stride=2, act='relu')
+        self.pool2d_max = Pool2D(
+            pool_size=3, pool_stride=2, pool_padding=1, pool_type='max')
+
+        self.bottleneck_block_list = []
+        num_channels = 64
+        for block in range(len(depth)):
+            shortcut = False
+            for i in range(depth[block]):
+                bottleneck_block = BottleneckBlock(
+                    num_channels=num_channels,
+                    num_filters=num_filters[block],
+                    stride=2 if i == 0 and block != 0 else 1,
+                    shortcut=shortcut)
+                num_channels = bottleneck_block._num_channels_out
+                self.bottleneck_block_list.append(bottleneck_block)
+                shortcut = True
+
+        self.pool2d_avg = Pool2D(
+            pool_size=7, pool_type='avg', global_pooling=True)
+
+        import math
+        stdv = 1.0 / math.sqrt(2048 * 1.0)
+
+        self.out = FC(size=class_dim,
+                      act='softmax',
+                      param_attr=fluid.param_attr.ParamAttr(
+                          initializer=fluid.initializer.Uniform(-stdv, stdv)))
+
+    def forward(self, inputs):
+        y = self.conv(inputs)
+        y = self.pool2d_max(y)
+        for bottleneck_block in self.bottleneck_block_list:
+            y = bottleneck_block(y)
+        y = self.pool2d_avg(y)
+        y = self.out(y)
+        return y
+
+
+class TestImperativeResnet(unittest.TestCase):
+    def test_resnet_float32(self):
+        seed = 90
+
+        batch_size = train_parameters["batch_size"]
+        batch_num = 1
+        with fluid.imperative.guard():
+            fluid.default_startup_program().random_seed = seed
+            fluid.default_main_program().random_seed = seed
+
+            resnet = ResNet()
+            optimizer = optimizer_setting(train_parameters)
+            np.random.seed(seed)
+            import random
+            random.seed = seed
+            train_reader = paddle.batch(
+                paddle.dataset.flowers.train(use_xmap=False),
+                batch_size=batch_size)
+
+            dy_param_init_value = {}
+            for param in fluid.default_main_program().global_block(
+            ).all_parameters():
+                dy_param_init_value[param.name] = param._numpy()
+
+            for batch_id, data in enumerate(train_reader()):
+                if batch_id >= batch_num:
+                    break
+
+                dy_x_data = np.array(
+                    [x[0].reshape(3, 224, 224) for x in data]).astype('float32')
+                y_data = np.array([x[1] for x in data]).astype('int64').reshape(
+                    batch_size, 1)
+
+                img = to_variable(dy_x_data)
+                label = to_variable(y_data)
+                label._stop_gradient = True
+
+                out = resnet(img)
+                loss = fluid.layers.cross_entropy(input=out, label=label)
+                avg_loss = fluid.layers.mean(x=loss)
+
+                dy_out = avg_loss._numpy()
+
+                if batch_id == 0:
+                    for param in fluid.default_main_program().global_block(
+                    ).all_parameters():
+                        if param.name not in dy_param_init_value:
+                            dy_param_init_value[param.name] = param._numpy()
+
+                avg_loss._backward()
+
+                dy_grad_value = {}
+                for param in fluid.default_main_program().global_block(
+                ).all_parameters():
+                    if not param.stop_gradient:
+                        np_array = np.array(param._ivar._grad_ivar().value()
+                                            .get_tensor())
+                        dy_grad_value[param.name + core.grad_var_suffix(
+                        )] = np_array
+
+                optimizer.minimize(avg_loss)
+
+                dy_param_value = {}
+                for param in fluid.default_main_program().global_block(
+                ).all_parameters():
+                    dy_param_value[param.name] = param._numpy()
+
+        with new_program_scope():
+            fluid.default_startup_program().random_seed = seed
+            fluid.default_main_program().random_seed = seed
+
+            exe = fluid.Executor(fluid.CPUPlace(
+            ) if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0))
+
+            resnet = ResNet()
+            optimizer = optimizer_setting(train_parameters)
+
+            np.random.seed(seed)
+            import random
+            random.seed = seed
+            train_reader = paddle.batch(
+                paddle.dataset.flowers.train(use_xmap=False),
+                batch_size=batch_size)
+
+            img = fluid.layers.data(
+                name='pixel', shape=[3, 224, 224], dtype='float32')
+            label = fluid.layers.data(name='label', shape=[1], dtype='int64')
+            out = resnet(img)
+            loss = fluid.layers.cross_entropy(input=out, label=label)
+            avg_loss = fluid.layers.mean(x=loss)
+            optimizer.minimize(avg_loss)
+
+            # initialize params and fetch them
+            static_param_init_value = {}
+            static_param_name_list = []
+            static_grad_name_list = []
+            for param in fluid.default_startup_program().global_block(
+            ).all_parameters():
+                static_param_name_list.append(param.name)
+            for param in fluid.default_main_program().global_block(
+            ).all_parameters():
+                if not param.stop_gradient:
+                    static_grad_name_list.append(param.name +
+                                                 core.grad_var_suffix())
+
+            out = exe.run(fluid.default_startup_program(),
+                          fetch_list=static_param_name_list)
+
+            for i in range(len(static_param_name_list)):
+                static_param_init_value[static_param_name_list[i]] = out[i]
+
+            for batch_id, data in enumerate(train_reader()):
+                if batch_id >= batch_num:
+                    break
+
+                static_x_data = np.array(
+                    [x[0].reshape(3, 224, 224) for x in data]).astype('float32')
+                y_data = np.array([x[1] for x in data]).astype('int64').reshape(
+                    [batch_size, 1])
+
+                fetch_list = [avg_loss.name]
+                fetch_list.extend(static_param_name_list)
+                fetch_list.extend(static_grad_name_list)
+                out = exe.run(fluid.default_main_program(),
+                              feed={"pixel": static_x_data,
+                                    "label": y_data},
+                              fetch_list=fetch_list)
+
+                static_param_value = {}
+                static_grad_value = {}
+                static_out = out[0]
+                param_start_pos = 1
+                grad_start_pos = len(static_param_name_list) + param_start_pos
+                for i in range(param_start_pos,
+                               len(static_param_name_list) + param_start_pos):
+                    static_param_value[static_param_name_list[
+                        i - param_start_pos]] = out[i]
+                for i in range(grad_start_pos,
+                               len(static_grad_name_list) + grad_start_pos):
+                    static_grad_value[static_grad_name_list[
+                        i - grad_start_pos]] = out[i]
+
+        self.assertTrue(np.allclose(static_out, dy_out))
+
+        self.assertEqual(len(dy_param_init_value), len(static_param_init_value))
+        for key, value in six.iteritems(static_param_init_value):
+            self.assertTrue(np.allclose(value, dy_param_init_value[key]))
+            self.assertTrue(np.isfinite(value.all()))
+            self.assertFalse(np.isnan(value.any()))
+
+        self.assertEqual(len(dy_grad_value), len(static_grad_value))
+        for key, value in six.iteritems(static_grad_value):
+            self.assertTrue(np.allclose(value, dy_grad_value[key]))
+            self.assertTrue(np.isfinite(value.all()))
+            self.assertFalse(np.isnan(value.any()))
+
+        self.assertEqual(len(dy_param_value), len(static_param_value))
+        for key, value in six.iteritems(static_param_value):
+            self.assertTrue(np.allclose(value, dy_param_value[key]))
+            self.assertTrue(np.isfinite(value.all()))
+            self.assertFalse(np.isnan(value.any()))
+
+
+if __name__ == '__main__':
+    unittest.main()