Merge pull request #9942 from reyoung/feature/tuning_pe_trans

Feature/tuning pe trans

paddle/fluid/framework/parallel_executor.cc

@@ -155,13 +155,9 @@ void ParallelExecutor::BCastParamsToGPUs(
 #endif
 }
 
-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) {
+void ParallelExecutor::Run(const std::vector<std::string> &fetch_tensors,
+                           const std::string &fetched_var_name) {
   platform::RecordBlock b(0);
-  SplitTensorToPlaces(feed_tensors);
-
   // Create local scopes.
   for (auto &scope : member_->local_scopes_) {
     Scope &local_scope = scope->NewScope();
@@ -195,14 +191,28 @@ void ParallelExecutor::Run(
     auto &local_scope =
         *scope->Var(details::kLocalExecScopeName)->GetMutable<Scope *>();
     scope->DeleteScope(local_scope);
-    local_scope = nullptr;
   }
 }
 
-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_);
+void ParallelExecutor::FeedTensorsIntoLocalScopes(
+    const std::vector<std::unordered_map<std::string, LoDTensor>> &tensors) {
+  PADDLE_ENFORCE_EQ(member_->local_scopes_.size(), tensors.size());
+
+  for (size_t i = 0; i < tensors.size(); ++i) {
+    auto &map = tensors[i];
+    auto *scope = member_->local_scopes_[i];
+    for (auto &pair : map) {
+      auto *trg = scope->Var(pair.first)->GetMutable<LoDTensor>();
+      trg->ShareDataWith(pair.second);
+      trg->set_lod(pair.second.lod());
+    }
+  }
+}
+
+void ParallelExecutor::FeedAndSplitTensorIntoLocalScopes(
+    const std::unordered_map<std::string, LoDTensor> &tensors) {
+  for (auto pair : tensors) {
+    auto lod_tensors = pair.second.SplitLoDTensor(member_->places_);
     PADDLE_ENFORCE_EQ(
         member_->places_.size(), lod_tensors.size(),
         "The number of samples of current batch is less than the count of "
@@ -211,7 +221,7 @@ void ParallelExecutor::SplitTensorToPlaces(
     for (size_t j = 0; j < member_->places_.size(); ++j) {
       // TODO(panxy0718): Do I need to delete this var?
       auto t =
-          member_->local_scopes_[j]->Var(it.first)->GetMutable<LoDTensor>();
+          member_->local_scopes_[j]->Var(pair.first)->GetMutable<LoDTensor>();
       t->ShareDataWith(lod_tensors[j]);
       t->set_lod(lod_tensors[j].lod());
     }

paddle/fluid/framework/parallel_executor.h

@@ -44,16 +44,22 @@ class ParallelExecutor {
 
   std::vector<Scope*>& GetLocalScopes();
 
+  /**
+   * Feed tensors to local scopes. The size of tensors should be equal to the
+   * size of local scopes.
+   */
+  void FeedTensorsIntoLocalScopes(
+      const std::vector<std::unordered_map<std::string, LoDTensor>>& tensors);
+
+  void FeedAndSplitTensorIntoLocalScopes(
+      const std::unordered_map<std::string, LoDTensor>& tensors);
+
   void Run(const std::vector<std::string>& fetch_tensors,
-           const std::string& fetched_var_name,
-           const std::unordered_map<std::string, LoDTensor>& feed_tensors);
+           const std::string& fetched_var_name);
 
   void BCastParamsToGPUs(const std::unordered_set<std::string>& vars) const;
 
  private:
-  void SplitTensorToPlaces(
-      const std::unordered_map<std::string, LoDTensor>& feed_tensors);
-
   ParallelExecutorPrivate* member_;
 };
 

paddle/fluid/pybind/pybind.cc

@@ -505,11 +505,19 @@ All parameter, weight, gradient are variables in Paddle.
                                   scope, local_scopes, allow_op_delay);
            })
       .def("bcast_params", &ParallelExecutor::BCastParamsToGPUs)
+      // NOTE: even we return a vec<Scope*>* to Python use reference policy.
+      // We still cannot get local_scope from this vector, since the element
+      // of vec<Scope*> will be freed by Python GC. We can only return Scope*
+      // one by one and mark them as reference.
       .def("local_scopes",
            [](ParallelExecutor &self) -> std::vector<Scope *> * {
              return &self.GetLocalScopes();
            },
            py::return_value_policy::reference)
+      .def("feed_tensors_into_local_scopes",
+           &ParallelExecutor::FeedTensorsIntoLocalScopes)
+      .def("feed_and_split_tensor_into_local_scopes",
+           &ParallelExecutor::FeedAndSplitTensorIntoLocalScopes)
       .def("run", &ParallelExecutor::Run);
 
   BindRecordIOWriter(&m);

paddle/fluid/pybind/tensor_py.h

@@ -190,6 +190,11 @@ void PyCUDATensorSetFromArray(
       static_cast<const platform::CUDADeviceContext *>(pool.Get(place));
   paddle::platform::GpuMemcpyAsync(dst, array.data(), sizeof(T) * array.size(),
                                    cudaMemcpyHostToDevice, dev_ctx->stream());
+  // NOTE: For safety, here wait the copy complete.
+  // It because the CPU array.data() could be destroyed after this method.
+  // If we make this method async, it could be copied data from a memory buffer
+  // that has been freed.
+  dev_ctx->Wait();
 }
 
 template <>
@@ -216,6 +221,11 @@ void PyCUDATensorSetFromArray(
   paddle::platform::GpuMemcpyAsync(dst, array.data(),
                                    sizeof(uint16_t) * array.size(),
                                    cudaMemcpyHostToDevice, dev_ctx->stream());
+  // NOTE: For safety, here wait the copy complete.
+  // It because the CPU array.data() could be destroyed after this method.
+  // If we make this method async, it could be copied data from a memory buffer
+  // that has been freed.
+  dev_ctx->Wait();
 }
 
 template <typename T>

python/paddle/fluid/parallel_executor.py

@@ -16,6 +16,7 @@ import core
 import multiprocessing
 import framework
 import executor
+import sys
 
 __all__ = ['ParallelExecutor']
 
@@ -123,28 +124,93 @@ class ParallelExecutor(object):
             allow_op_delay)
         self.scope = scope
 
-    def run(self, fetch_list, feed_dict={}):
+    def run(self, fetch_list, feed=None, feed_dict=None):
         """
-        :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.
-        """
-        if not isinstance(feed_dict, dict):
-            raise TypeError("feed_dict should be a dict")
+        Run a parallel executor with fetch_list.
+
+        The feed parameter can be a dict or a list. If feed is a dict, the
+        feed data will be split into multiple devices. If feed is a list, we
+        assume the data has been splitted into multiple devices, the each
+        element in the list will be copied to each device directly.
+
+        For example, if the feed is a dict:
+        >>> exe = ParallelExecutor()
+        >>> # the image will be splitted into devices. If there is two devices
+        >>> # each device will process an image with shape (24, 1, 28, 28)
+        >>> exe.run(feed={'image': numpy.random.random(size=(48, 1, 28, 28))})
+
+        For example, if the feed is a list:
+        >>> exe = ParallelExecutor()
+        >>> # each device will process each element in the list.
+        >>> # the 1st device will process an image with shape (48, 1, 28, 28)
+        >>> # the 2nd device will process an image with shape (32, 1, 28, 28)
+        >>> #
+        >>> # you can use exe.device_count to get the device number.
+        >>> exe.run(feed=[{"image": numpy.random.random(size=(48, 1, 28, 28))},
+        >>>               {"image": numpy.random.random(size=(32, 1, 28, 28))},
+        >>>              ])
+
+
+        Args:
+            fetch_list(list): The fetched variable names
+            feed(list|dict|None): The feed variables. If the feed is a dict,
+                tensors in that dict will be splitted into each devices. If
+                the feed is a list, each element of the list will be copied
+                to each device.
+            feed_dict: Alias for feed parameter, for backward compatibility.
+                This parameter is deprecated.
 
-        feed_tensor_dict = {}
-        for i, feed_name in enumerate(feed_dict):
-            feed_tensor = feed_dict[feed_name]
+        Returns: fetched result list.
+
+        """
+        if feed is None:
+            feed = feed_dict
+            print >> sys.stderr, "`feed_dict` is deprecated. Please use `feed=`"
+
+        if isinstance(feed, dict):
+            feed_tensor_dict = dict()
+            for feed_name in feed:
+                feed_tensor = feed[feed_name]
                 if not isinstance(feed_tensor, core.LoDTensor):
                     feed_tensor = core.LoDTensor()
-                feed_tensor.set(feed_dict[feed_name], self._act_places[0])
+                    # always set to CPU place, since the tensor need to be splitted
+                    # it is fast in CPU
+                    feed_tensor.set(feed[feed_name], core.CPUPlace())
                 feed_tensor_dict[feed_name] = feed_tensor
 
+            self.executor.feed_and_split_tensor_into_local_scopes(
+                feed_tensor_dict)
+        elif isinstance(feed, list) or isinstance(feed, tuple):
+            if len(feed) != len(self._act_places):
+                raise ValueError(
+                    "Feed a list of tensor, the list should be the same size as places"
+                )
+
+            res = list()
+
+            for i, each in enumerate(feed):
+                if not isinstance(each, dict):
+                    raise TypeError(
+                        "Each element of feed list should be a dict")
+                res_dict = dict()
+                for feed_name in each:
+                    tensor = each[feed_name]
+                    if not isinstance(tensor, core.LoDTensor):
+                        tmp = core.LoDTensor()
+                        tmp.set(tensor, self._act_places[i])
+                        tensor = tmp
+                    res_dict[feed_name] = tensor
+                res.append(res_dict)
+            self.executor.feed_tensors_into_local_scopes(res)
+
         fetch_var_name = '@FETCHED_VAR_NAME@'
-        self.executor.run(fetch_list, fetch_var_name, feed_tensor_dict)
+        self.executor.run(fetch_list, fetch_var_name)
         arr = self.scope.find_var(fetch_var_name).get_lod_tensor_array()
         return [arr[i] for i in range(len(arr))]
 
     def bcast_params(self):
         self.executor.bcast_params(set(self.persistable_vars))
+
+    @property
+    def device_count(self):
+        return len(self._act_places)

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

@@ -203,12 +203,12 @@ class TestParallelExecutorBase(unittest.TestCase):
                                   iter=10,
                                   batch_size=None,
                                   allow_op_delay=False,
-                                  feed_dict={}):
+                                  feed_dict=None):
         main = fluid.Program()
         startup = fluid.Program()
         startup.random_seed = 1  # Fix random seed
         with fluid.program_guard(main, startup):
-            loss = method(use_feed=len(feed_dict) > 0)
+            loss = method(use_feed=feed_dict is not None)
             adam = fluid.optimizer.Adam()
             adam.minimize(loss)
             if memory_opt:
@@ -222,13 +222,13 @@ class TestParallelExecutorBase(unittest.TestCase):
             if batch_size is not None:
                 batch_size *= fluid.core.get_cuda_device_count()
             begin = time.time()
-            first_loss, = exe.run([loss.name], feed_dict=feed_dict)
+            first_loss, = exe.run([loss.name], feed=feed_dict)
             first_loss = numpy.array(first_loss)
 
             for i in xrange(iter):
-                exe.run([], feed_dict=feed_dict)
+                exe.run([], feed=feed_dict)
 
-            last_loss, = exe.run([loss.name], feed_dict=feed_dict)
+            last_loss, = exe.run([loss.name], feed=feed_dict)
             end = time.time()
 
             if batch_size is not None:
@@ -649,5 +649,5 @@ class TestCRFModel(unittest.TestCase):
             for i in xrange(10):
                 cur_batch = next(data)
                 print map(numpy.array,
-                          pe.run(feed_dict=feeder.feed(cur_batch),
+                          pe.run(feed=feeder.feed(cur_batch),
                                  fetch_list=[avg_cost.name]))[0]