Add feed to ParallelExecutor

Author: panyx0718

doc/fluid/api/gen_doc.sh

@@ -142,6 +142,7 @@ class LoDTensor : public Tensor {
     return (lod_)[level].size() - 1;
   }
 
+  // Split LoDTensor and copy to each place specified in places.
   std::vector<LoDTensor> SplitLoDTensor(
       const std::vector<platform::Place> places) const;
 

paddle/fluid/framework/lod_tensor.h

@@ -150,13 +150,30 @@ void ParallelExecutor::BCastParamsToGPUs(
 #endif
 }
 
-void ParallelExecutor::Run(const std::vector<std::string> &fetch_tensors,
-                           const std::string &fetched_var_name) {
+void ParallelExecutor::Run(
+    const std::vector<std::string> &fetch_tensors,
+    const std::string &fetched_var_name,
+    const std::unordered_map<std::string, LoDTensor> &feed_tensors) {
   platform::RecordBlock b(0);
+  SplitTensorToPlaces(feed_tensors);
   auto fetch_data = member_->executor_->Run(fetch_tensors);
   *member_->global_scope_->Var(fetched_var_name)->GetMutable<FeedFetchList>() =
       fetch_data;
 }
 
+void ParallelExecutor::SplitTensorToPlaces(
+    const std::unordered_map<std::string, LoDTensor> &feed_tensors) {
+  for (auto it : feed_tensors) {
+    auto lod_tensors = it.second.SplitLoDTensor(member_->places_);
+    for (size_t j = 0; j < member_->places_.size(); ++j) {
+      // TODO(panxy0718): Do I need to delete this var?
+      member_->local_scopes_[j]
+          ->Var(it.first)
+          ->GetMutable<LoDTensor>()
+          ->ShareDataWith(lod_tensors[j]);
+    }
+  }
+}
+
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/framework/parallel_executor.cc

@@ -42,9 +42,13 @@ class ParallelExecutor {
                             bool allow_op_delay);
 
   void Run(const std::vector<std::string>& fetch_tensors,
-           const std::string& fetched_var_name = "fetched_var");
+           const std::string& fetched_var_name,
+           const std::unordered_map<std::string, LoDTensor>& feed_tensors);
 
  private:
+  void SplitTensorToPlaces(
+      const std::unordered_map<std::string, LoDTensor>& feed_tensors);
+
   ParallelExecutorPrivate* member_;
 
   void BCastParamsToGPUs(const ProgramDesc& startup_program) const;

paddle/fluid/framework/parallel_executor.h

@@ -26,25 +26,29 @@ def __init__(self,
                  use_cuda,
                  num_threads=None,
                  allow_op_delay=False):
-        places = []
+        self._places = []
+        self._act_places = []
         if use_cuda:
             for i in xrange(core.get_cuda_device_count()):
                 p = core.Place()
-                p.set_place(core.CUDAPlace(i))
-                places.append(p)
+                self._act_places.append(core.CUDAPlace(i))
+                p.set_place(self._act_places[-1])
+                self._places.append(p)
         else:
             for i in xrange(multiprocessing.cpu_count()):
                 p = core.Place()
-                p.set_place(core.CPUPlace())
-                places.append(p)
+                self._act_places.append(core.CPUPlace(i))
+                p.set_place(self._act_places[-1])
+                self._places.append(p)
+        assert self._places, "no place for execution"
 
         if num_threads is None:
             if use_cuda:
                 # Experiments on se-resnext shows that too many threads hurt
                 # performance. Worth tunning for other models in the future.
-                num_threads = len(places)
+                num_threads = len(self._places)
             else:
-                min(len(places) * 2, multiprocessing.cpu_count())
+                min(len(self._places) * 2, multiprocessing.cpu_count())
 
         startup = framework.default_startup_program()
         main = framework.default_main_program()
@@ -53,7 +57,7 @@ def __init__(self,
         self.executor = core.ParallelExecutor(
             num_threads,
             True if use_cuda else False,  # use_event
-            places,
+            self._places,
             set([
                 p.name for p in main.global_block().iter_parameters()
                 if not p.stop_gradient
@@ -65,8 +69,22 @@ def __init__(self,
             allow_op_delay)
         self.scope = scope
 
-    def run(self, fetch_list):
+    def run(self, fetch_list, feed_dict={}):
+        """
+        :param fetch_list: A list of variable names that will be fetched.
+        :param feed_dict: A dict mapping for feed variable name to LoDTensor
+          or numpy array.
+        :return: fetched value list.
+        """
+        feed_tensor_dict = {}
+        for i, feed_name in enumerate(feed_dict):
+            feed_tensor = feed_dict[feed_name]
+            if not isinstance(feed_tensor, core.LoDTensor):
+                feed_tensor = core.LoDTensor()
+                feed_tensor.set(feed_dict[feed_name], self._act_places[0])
+            feed_tensor_dict[feed_name] = feed_tensor
+
         fetch_var_name = '@FETCHED_VAR_NAME@'
-        self.executor.run(fetch_list, fetch_var_name)
+        self.executor.run(fetch_list, fetch_var_name, feed_tensor_dict)
         arr = self.scope.find_var(fetch_var_name).get_lod_tensor_array()
         return [arr[i] for i in range(len(arr))]

python/paddle/fluid/parallel_executor.py

@@ -21,13 +21,17 @@
 import paddle.dataset.wmt16 as wmt16
 
 
-def simple_fc_net():
-    reader = fluid.layers.open_recordio_file(
-        filename='./mnist.recordio',
-        shapes=[[-1, 784], [-1, 1]],
-        lod_levels=[0, 0],
-        dtypes=['float32', 'int64'])
-    img, label = fluid.layers.read_file(reader)
+def simple_fc_net(use_feed):
+    if use_feed:
+        img = fluid.layers.data(name='image', shape=[784], dtype='float32')
+        label = fluid.layers.data(name='label', shape=[1], dtype='int64')
+    else:
+        reader = fluid.layers.open_recordio_file(
+            filename='./mnist.recordio',
+            shapes=[[-1, 784], [-1, 1]],
+            lod_levels=[0, 0],
+            dtypes=['float32', 'int64'])
+        img, label = fluid.layers.read_file(reader)
     hidden = img
     for _ in xrange(4):
         hidden = fluid.layers.fc(
@@ -42,13 +46,18 @@ def simple_fc_net():
     return loss
 
 
-def fc_with_batchnorm():
-    reader = fluid.layers.open_recordio_file(
-        filename='./mnist.recordio',
-        shapes=[[-1, 784], [-1, 1]],
-        lod_levels=[0, 0],
-        dtypes=['float32', 'int64'])
-    img, label = fluid.layers.read_file(reader)
+def fc_with_batchnorm(use_feed):
+    if use_feed:
+        img = fluid.layers.data(name='image', shape=[784], dtype='float32')
+        label = fluid.layers.data(name='label', shape=[1], dtype='int64')
+    else:
+        reader = fluid.layers.open_recordio_file(
+            filename='./mnist.recordio',
+            shapes=[[-1, 784], [-1, 1]],
+            lod_levels=[0, 0],
+            dtypes=['float32', 'int64'])
+        img, label = fluid.layers.read_file(reader)
+
     hidden = img
     for _ in xrange(1):
         hidden = fluid.layers.fc(
@@ -135,7 +144,9 @@ def bottleneck_block(input, num_filters, stride, cardinality, reduction_ratio):
     return fluid.layers.elementwise_add(x=short, y=scale, act='relu')
 
 
-def SE_ResNeXt152Small(batch_size=2):
+def SE_ResNeXt152Small(batch_size=2, use_feed=False):
+    assert not use_feed, "SE_ResNeXt doesn't support feed yet"
+
     img = fluid.layers.fill_constant(
         shape=[batch_size, 3, 224, 224], dtype='float32', value=0.0)
     label = fluid.layers.fill_constant(
@@ -185,30 +196,28 @@ def check_network_convergence(self,
                                   memory_opt=True,
                                   iter=10,
                                   batch_size=None,
-                                  allow_op_delay=False):
+                                  allow_op_delay=False,
+                                  feed_dict={}):
         main = fluid.Program()
         startup = fluid.Program()
         with fluid.program_guard(main, startup):
-            loss = method()
+            loss = method(use_feed=len(feed_dict) > 0)
             adam = fluid.optimizer.Adam()
             adam.minimize(loss)
             if memory_opt:
                 fluid.memory_optimize(main)
 
-            exe = fluid.ParallelExecutor(
-                loss_name=loss.name,
-                use_cuda=True,
-                allow_op_delay=allow_op_delay)
+            exe = fluid.ParallelExecutor(loss_name=loss.name, use_cuda=True)
             if batch_size is not None:
                 batch_size *= fluid.core.get_cuda_device_count()
             begin = time.time()
-            first_loss, = exe.run([loss.name])
+            first_loss, = exe.run([loss.name], feed_dict=feed_dict)
             first_loss = numpy.array(first_loss)
 
             for i in xrange(iter):
-                exe.run([])
+                exe.run([], feed_dict=feed_dict)
 
-            last_loss, = exe.run([loss.name])
+            last_loss, = exe.run([loss.name], feed_dict=feed_dict)
             end = time.time()
 
             if batch_size is not None:
@@ -242,9 +251,19 @@ def test_simple_fc(self):
         self.check_network_convergence(simple_fc_net)
         self.check_network_convergence(simple_fc_net, allow_op_delay=True)
 
+        img = numpy.zeros(shape=[32, 784], dtype='float32')
+        label = numpy.ones(shape=[32, 1], dtype='int64')
+        self.check_network_convergence(
+            simple_fc_net, feed_dict={"image": img,
+                                      "label": label})
+
     def test_batchnorm_fc(self):
         self.check_network_convergence(fc_with_batchnorm)
-        self.check_network_convergence(fc_with_batchnorm, allow_op_delay=True)
+        img = numpy.zeros(shape=[32, 784], dtype='float32')
+        label = numpy.ones(shape=[32, 1], dtype='int64')
+        self.check_network_convergence(
+            fc_with_batchnorm, feed_dict={"image": img,
+                                          "label": label})
 
 
 class TestResnet(TestParallelExecutorBase):
@@ -400,7 +419,8 @@ def data_to_tensor(data_list, name_list, input_dict, place):
 import transformer_model
 
 
-def transformer():
+def transformer(use_feed):
+    assert not use_feed, "transfomer doesn't support feed yet"
     return transformer_model.transformer(
         ModelHyperParams.src_vocab_size + 1,
         ModelHyperParams.trg_vocab_size + 1, ModelHyperParams.max_length + 1,