Add single GPU support to imperative

paddle/fluid/imperative/CMakeLists.txt

-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)

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 {
@@ -31,22 +35,68 @@ 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, float>(*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, float>(*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 AddGradTo(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 {
@@ -158,7 +208,7 @@ 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;
+    platform::Place place = expected_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));
@@ -172,7 +222,7 @@ 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);
+      AddGradTo(grad, orig_grad, expected_place_);
       delete grad;
     }
   }
@@ -184,8 +234,10 @@ 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

@@ -26,12 +26,15 @@
 #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 +84,8 @@ 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;
@@ -159,7 +164,8 @@ class OpBase {
       : op_desc_(nullptr),
         forward_id_(-1),
         grad_op_desc_(nullptr),
-        backward_id_(-1) {}
+        backward_id_(-1),
+        expected_place_(platform::CPUPlace()) {}
 
   virtual ~OpBase() {
     if (grad_op_desc_) delete grad_op_desc_;
@@ -176,6 +182,8 @@ class OpBase {
   framework::OpDesc* grad_op_desc_;
   int backward_id_;
 
+  platform::Place expected_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>(), .0f);
+}
+
+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;
 
@@ -108,10 +134,12 @@ 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->expected_place_ = GetExpectedPlace(expected_place, inputs);
+  PreparedOp prepared_op =
+      PreparedOp::Prepare(ctx, *op_kernel, op->expected_place_);
+  prepared_op.op.RuntimeInferShape(scope, op->expected_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;
@@ -134,7 +162,8 @@ 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_);
@@ -147,10 +176,13 @@ 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_);
       }
@@ -193,16 +225,23 @@ 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);
 }

python/paddle/fluid/framework.py

@@ -66,6 +66,7 @@ ZERO_VAR_SUFFIX = core.kZeroVarSuffix()
 CONTROL_DEP_VAR_PREFIX = core.kControlDepVarName()
 
 _imperative_tracer_ = None
+_current_expected_place_ = None
 
 
 def _in_imperative_mode():
@@ -76,6 +77,10 @@ def _imperative_tracer():
     return _imperative_tracer_
 
 
+def _current_expected_place():
+    return _current_expected_place_
+
+
 class NameScope(object):
     def __init__(self, name="", parent=None):
         self._children = dict()
@@ -1299,7 +1304,7 @@ 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,
-                                       stop_gradient)
+                                       _current_expected_place_, stop_gradient)
 
     def _insert_op(self, index, *args, **kwargs):
         """
@@ -2312,9 +2317,16 @@ def _get_var(name, program=None):
 
 
 @contextlib.contextmanager
-def _imperative_guard(tracer):
+def _imperative_guard(tracer, place):
     global _imperative_tracer_
     tmp_trace = _imperative_tracer_
     _imperative_tracer_ = tracer
+
+    global _current_expected_place_
+    tmp_place = _current_expected_place_
+    _current_expected_place_ = place
+
     yield
+
     _imperative_tracer_ = tmp_trace
+    _current_expected_place_ = tmp_place

python/paddle/fluid/imperative/base.py

@@ -25,17 +25,28 @@ def enabled():
 
 
 @contextlib.contextmanager
-def guard():
+def guard(device=0):
     train = framework.Program()
     startup = framework.Program()
     tracer = core.Tracer(train.current_block().desc)
+
+    if device is None:
+        place = core.CPUPlace()
+    else:
+        if core.is_compiled_with_cuda():
+            place = core.CUDAPlace(device)
+        else:
+            place = core.CPUPlace()
+
     with framework.program_guard(train, startup):
         with framework.unique_name.guard():
-            with framework._imperative_guard(tracer):
+            with framework._imperative_guard(tracer, place):
                 yield
 
 
 def to_variable(value, block=None):
+    assert enabled(), "to_variable could only be called in imperative mode"
+
     if isinstance(value, np.ndarray):
         if not block:
             block = framework.default_main_program().current_block()
@@ -47,9 +58,7 @@ 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

@@ -252,15 +252,15 @@ 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})
 
         pre_activation = self._helper.append_bias_op(
-            pre_bias, dim_start=num_flatten_dims)
+            pre_bias, dim_start=self._num_flatten_dims)
         return self._helper.append_activation(pre_activation)
 
 
@@ -355,11 +355,11 @@ class BatchNorm(layers.Layer):
         variance_out = self._variance
 
         saved_mean = self._helper.create_variable_for_type_inference(
-            dtype=dtype, stop_gradient=True)
+            dtype=self._dtype, stop_gradient=True)
         saved_variance = self._helper.create_variable_for_type_inference(
-            dtype=dtype, stop_gradient=True)
+            dtype=self._dtype, stop_gradient=True)
         batch_norm_out = input if self._in_place else self._helper.create_variable_for_type_inference(
-            dtype)
+            self._dtype)
 
         self._helper.append_op(
             type="batch_norm",

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

@@ -5810,7 +5810,8 @@ 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

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 +381,19 @@ 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:
+                # 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

@@ -107,7 +107,6 @@ if(WITH_DISTRIBUTE)
 endif()
 py_test_modules(test_parallel_executor_crf MODULES test_parallel_executor_crf SERIAL)
 py_test_modules(test_parallel_executor_fetch_feed MODULES test_parallel_executor_fetch_feed SERIAL)
-set_tests_properties(test_parallel_executor_fetch_feed PROPERTIES TIMEOUT 150)
 py_test_modules(test_parallel_executor_transformer MODULES test_parallel_executor_transformer SERIAL)
 if(NOT APPLE)
     py_test_modules(test_image_classification_resnet MODULES test_image_classification_resnet SERIAL)

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

@@ -82,7 +82,7 @@ class MLP(fluid.imperative.Layer):
 
 class TestImperative(unittest.TestCase):
     def test_layer(self):
-        with fluid.imperative.guard():
+        with fluid.imperative.guard(device=None):
             cl = core.Layer()
             cl.forward([])
             l = fluid.imperative.Layer()
@@ -90,7 +90,7 @@ class TestImperative(unittest.TestCase):
 
     def test_pylayer_func_id(self):
 
-        with fluid.imperative.guard():
+        with fluid.imperative.guard(device=None):
 
             class PyLayer1(fluid.imperative.PyLayer):
                 def __init__(self):
@@ -130,7 +130,7 @@ class TestImperative(unittest.TestCase):
 
     def test_pylayer(self):
         np_inp = np.ones([2, 2], np.float32)
-        with fluid.imperative.guard():
+        with fluid.imperative.guard(device=None):
             my_py_layer = MyPyLayer()
             var_inp = fluid.imperative.base.to_variable(np_inp)
             outs = my_py_layer(var_inp)
@@ -158,7 +158,7 @@ class TestImperative(unittest.TestCase):
 
     def test_layer_in_out(self):
         np_inp = np.array([1.0, 2.0, -1.0], dtype=np.float32)
-        with fluid.imperative.guard():
+        with fluid.imperative.guard(device=None):
             var_inp = fluid.imperative.base.to_variable(np_inp)
             l = MyLayer()
             x = l(var_inp)[0]
@@ -185,7 +185,7 @@ class TestImperative(unittest.TestCase):
 
     def test_mlp(self):
         np_inp = np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32)
-        with fluid.imperative.guard():
+        with fluid.imperative.guard(device=None):
             var_inp = fluid.imperative.base.to_variable(np_inp)
             mlp = MLP()
             out = mlp(var_inp)

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

@@ -101,7 +101,7 @@ class TestImperativeMnist(unittest.TestCase):
     def test_mnist_cpu_float32(self):
         seed = 90
 
-        with fluid.imperative.guard():
+        with fluid.imperative.guard(device=None):
             fluid.default_startup_program().random_seed = seed
             fluid.default_main_program().random_seed = seed
 

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

@@ -34,7 +34,10 @@ train_parameters = {
         "batch_size": 256,
         "epochs": [30, 60, 90],
         "steps": [0.1, 0.01, 0.001, 0.0001]
-    }
+    },
+    "batch_size": 256,
+    "lr": 0.1,
+    "total_images": 1281164,
 }
 
 
@@ -52,24 +55,33 @@ def optimizer_setting(params):
         base_lr = params["lr"]
         lr = []
         lr = [base_lr * (0.1**i) for i in range(len(bd) + 1)]
-        optimizer = fluid.optimizer.Momentum(
-            learning_rate=fluid.layers.piecewise_decay(
-                boundaries=bd, values=lr),
-            momentum=0.9,
-            regularization=fluid.regularizer.L2Decay(1e-4))
+        optimizer = fluid.optimizer.SGD(learning_rate=params["lr"])
+        #  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_filters, filter_size, stride=1, groups=1, act=None):
+    def __init__(self,
+                 num_channels,
+                 num_filters,
+                 filter_size,
+                 stride=1,
+                 groups=1,
+                 act=None):
         super(ConvBNLayer, self).__init__()
 
         self._conv = Conv2D(
-            3,
-            num_filters,
-            filter_size,
-            stride, (filter_size - 1) // 2,
+            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)
@@ -84,36 +96,54 @@ class ConvBNLayer(fluid.imperative.Layer):
 
 
 class BottleneckBlock(fluid.imperative.Layer):
-    def __init__(self, num_filters, stride, shortcut=False):
+    def __init__(self, num_channels, num_filters, stride, shortcut=True):
         super(BottleneckBlock, self).__init__()
 
         self.conv0 = ConvBNLayer(
-            num_filters=num_filters, filter_size=1, act='relu')
+            num_channels=num_channels,
+            num_filters=num_filters,
+            filter_size=1,
+            act='relu')
         self.conv1 = ConvBNLayer(
-            num_filters=num_filters, filter_size=3, stride=stride, act='relu')
+            num_channels=num_filters,
+            num_filters=num_filters,
+            filter_size=3,
+            stride=stride,
+            act='relu')
         self.conv2 = ConvBNLayer(
-            num_filters=num_filters * 4, filter_size=1, act=None)
+            num_channels=num_filters,
+            num_filters=num_filters * 4,
+            filter_size=1,
+            act=None)
 
-        if shortcut:
+        if not shortcut:
             self.short = ConvBNLayer(
-                num_filters=num_filters * 4, filter_size=1, stride=stride)
+                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):
-        self.conv0()
-        self.conv1()
-        self.conv2()
+        y = self.conv0(inputs)
+        conv1 = self.conv1(y)
+        conv2 = self.conv2(conv1)
 
         if self.shortcut:
-            self.short()
+            short = inputs
+        else:
+            short = self.short(inputs)
 
-        return fluid.layers.elementwise_add(
-            x=self.short, y=self.conv2, act='relu')
+        return fluid.layers.elementwise_add(x=short, y=conv2, act='relu')
 
 
 class ResNet(fluid.imperative.Layer):
     def __init__(self, layers=50, class_dim=1000):
+        super(ResNet, self).__init__()
+
         self.layers = layers
         supported_layers = [50, 101, 152]
         assert layers in supported_layers, \
@@ -128,20 +158,23 @@ class ResNet(fluid.imperative.Layer):
         num_filters = [64, 128, 256, 512]
 
         self.conv = ConvBNLayer(
-            num_filters=64, filter_size=7, stride=2, act='relu')
+            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 = True
+            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 = False
+                shortcut = True
 
         self.pool2d_avg = Pool2D(
             pool_size=7, pool_type='avg', global_pooling=True)
@@ -160,12 +193,12 @@ class ResNet(fluid.imperative.Layer):
         for bottleneck_block in self.bottleneck_block_list:
             y = bottleneck_block(y)
         y = self.pool2d_avg(y)
-        y = self.out()
+        y = self.out(y)
         return y
 
 
 class TestImperativeResnet(unittest.TestCase):
-    def test_resnet_cpu_float32(self):
+    def test_resnet_gpu_float32(self):
         seed = 90
 
         with fluid.imperative.guard():
@@ -183,17 +216,17 @@ class TestImperativeResnet(unittest.TestCase):
                     break
 
                 x_data = np.array(
-                    [x[0].reshape(1, 28, 28) for x in data]).astype('float32')
+                    [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(
-                    128, 1)
+                    256, 1)
 
                 img = to_variable(x_data)
                 label = to_variable(y_data)
                 label._stop_gradient = True
 
-                cost = resnet(img)
+                out = resnet(img)
                 loss = fluid.layers.cross_entropy(input=out, label=label)
-                avg_loss = fluid.layers.mean(x=cost)
+                avg_loss = fluid.layers.mean(x=loss)
                 dy_out = avg_loss._numpy()
 
                 if batch_id == 0: