Unified ParallelExecutor and Compiler (#15970)

* Unified ParallelExecutor and Compiler

paddle/fluid/framework/details/fast_threaded_ssa_graph_executor.cc

@@ -12,7 +12,9 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 #include "paddle/fluid/framework/details/fast_threaded_ssa_graph_executor.h"
+#include <memory>
 #include <string>
+#include <unordered_map>
 #include <vector>
 #include "paddle/fluid/framework/details/fetch_op_handle.h"
 #include "paddle/fluid/framework/details/multi_devices_helper.h"
@@ -55,7 +57,7 @@ FeedFetchList FastThreadedSSAGraphExecutor::Run(
   std::vector<FetchOpHandle *> fetch_ops;
 
   for (auto &fetch_var_name : fetch_tensors) {
-    for (auto &var_map : graph_->Get<details::GraphVars>("vars")) {
+    for (auto &var_map : graph_->Get<details::GraphVars>(details::kGraphVars)) {
       auto it = var_map.find(fetch_var_name);
       if (it != var_map.end()) {
         fetched_vars[fetch_var_name].push_back(*it->second.rbegin());

python/paddle/fluid/compiler.py

@@ -17,7 +17,6 @@ import os
 import six
 import sys
 from .. import compat as cpt
-from . import framework
 
 from . import core
 from . import framework
@@ -36,6 +35,30 @@ def _place_obj(place):
     return p
 
 
+def _is_pserver_mode(main_program):
+    main = main_program if main_program \
+        else default_main_program()
+    for op in main.global_block().ops:
+        if op.type in ["send", "recv"]:
+            return True
+    return False
+
+
+def get_available_places(use_cuda):
+    if use_cuda:
+        gpus_env = os.getenv("FLAGS_selected_gpus")
+        if gpus_env:
+            gpus = [int(s) for s in gpus_env.split(",")]
+        else:
+            gpus = [i for i in six.moves.range(core.get_cuda_device_count())]
+        places = [core.CUDAPlace(i) for i in gpus]
+    else:
+        cpu_num = int(os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
+        places = [core.CPUPlace() for _ in six.moves.range(cpu_num)]
+    assert places, "no place for execution"
+    return places
+
+
 class CompiledProgram(object):
     """
     Compiles to Graph for execution.
@@ -127,8 +150,7 @@ class CompiledProgram(object):
             self._exec_strategy = ExecutionStrategy()
         if self._build_strategy is None:
             self._build_strategy = BuildStrategy()
-        self._build_strategy.is_distribution = framework.is_pserver_mode(
-            self._program)
+        self._build_strategy.is_distribution = _is_pserver_mode(self._program)
         return self
 
     def with_inference_optimize(self, config):
@@ -153,9 +175,9 @@ class CompiledProgram(object):
     def _with_distributed(self):
         raise NotImplementedError()
 
-    def _compile_data_parallel(self):
+    def _compile_data_parallel(self, use_cuda=False, scope=None):
         if self._share_vars_from:
-            if self._scope:
+            if scope:
                 sys.stderr.write("share_vars_from is set, scope is ignored.\n")
             if not self._share_vars_from._is_data_parallel:
                 raise ValueError("share_vars_from is not data parallel. Cannot "
@@ -166,23 +188,11 @@ class CompiledProgram(object):
                     "var to share.")
             self._local_scopes = self._share_vars_from._executor.local_scopes()
         else:
+            assert scope is not None, ""
             self._local_scopes = []
 
-        self._exec_strategy.use_cuda = isinstance(self._place, core.CUDAPlace)
-        if self._exec_strategy.use_cuda:
-            gpus_env = os.getenv("FLAGS_selected_gpus")
-            if gpus_env:
-                gpus = [int(s) for s in gpus_env.split(",")]
-            else:
-                gpus = [
-                    i for i in six.moves.range(core.get_cuda_device_count())
-                ]
-            self._places = [core.CUDAPlace(i) for i in gpus]
-        else:
-            cpu_num = int(
-                os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
-            self._places = [core.CPUPlace() for _ in six.moves.range(cpu_num)]
-        assert self._places, "no place for execution"
+        self._exec_strategy.use_cuda = use_cuda
+        self._places = get_available_places(self._exec_strategy.use_cuda)
 
         if self._exec_strategy.num_threads == 0:
             if self._exec_strategy.use_cuda:
@@ -197,9 +207,11 @@ class CompiledProgram(object):
         # FIXME(dzhwinter): enable_inplace should be after memory_optimize
         # if turn on python memory optimize, turn off the inplace_pass.
         if self._build_strategy.memory_optimize is None:
-            self._build_strategy.memory_optimize = False if self._program and self._program._is_mem_optimized else True
+            self._build_strategy.memory_optimize = False \
+                if self._program and self._program._is_mem_optimized else True
         if self._build_strategy.enable_inplace is None:
-            self._build_strategy.enable_inplace = False if self._program and self._program._is_mem_optimized else True
+            self._build_strategy.enable_inplace = False \
+                if self._program and self._program._is_mem_optimized else True
 
         # TODO(wuyi): trainer endpoings should be passed in through
         # build_strategy, not program.xxx.
@@ -221,12 +233,12 @@ class CompiledProgram(object):
 
         places = list(map(_place_obj, self._places))
 
-        return core.ParallelExecutor(
-            places,
+        return core.ParallelExecutor(places,
                                      set(self._persistable_vars),
                                      cpt.to_text(self._loss_name)
-            if self._loss_name else six.u(''), self._scope, self._local_scopes,
-            self._exec_strategy, self._build_strategy, self._graph)
+                                     if self._loss_name else six.u(''), scope,
+                                     self._local_scopes, self._exec_strategy,
+                                     self._build_strategy, self._graph)
 
     def _compile_inference(self):
         return core.create_paddle_predictor(self._infer_config)
@@ -253,7 +265,9 @@ class CompiledProgram(object):
         self._scope = scope
         self._place = place
         if self._is_data_parallel:
-            self._executor = self._compile_data_parallel()
+            self._executor = self._compile_data_parallel(
+                use_cuda=isinstance(self._place, core.CUDAPlace),
+                scope=self._scope)
         elif self._is_inference:
             self._executor = self._compile_inference()
         else:

python/paddle/fluid/framework.py

@@ -87,15 +87,6 @@ def _current_expected_place():
     return _imperative_current_expected_place_
 
 
-def is_pserver_mode(main_program):
-    main = main_program if main_program \
-        else default_main_program()
-    for op in main.global_block().ops:
-        if op.type in ["send", "recv"]:
-            return True
-    return False
-
-
 class NameScope(object):
     def __init__(self, name="", parent=None):
         self._children = dict()

python/paddle/fluid/parallel_executor.py

@@ -13,15 +13,11 @@
 # limitations under the License.
 
 from __future__ import print_function
-import multiprocessing
 from . import core
 from . import framework
 from . import executor
-from .. import compat as cpt
-import warnings
+from . import compiler
 import sys
-import six
-import os
 
 __all__ = ['ParallelExecutor']
 
@@ -97,99 +93,27 @@ class ParallelExecutor(object):
             'Please use CompiledProgram and Executor. CompiledProgram '
             'is a central place for optimization and Executor is the '
             'unified executor. Example can be found in compiler.py.\n')
-        # step1: get places, the places are used in run too.
-        self._places = []
-        if use_cuda:
-            gpus_env = os.getenv("FLAGS_selected_gpus")
-            if gpus_env:
-                gpus = [int(s) for s in gpus_env.split(",")]
-            else:
-                gpus = [
-                    i for i in six.moves.range(core.get_cuda_device_count())
-                ]
-            self._places = [core.CUDAPlace(i) for i in gpus]
-        else:
-            cpu_num = int(
-                os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
-            self._places = [core.CPUPlace() for _ in six.moves.range(cpu_num)]
-        assert self._places, "no place for execution"
 
-        # step2: init exec_strategy
-        if exec_strategy is None:
-            exec_strategy = ExecutionStrategy()
-        exec_strategy.use_cuda = use_cuda
-        if exec_strategy.num_threads == 0:
-            if use_cuda:
-                # Experiments on se-resnext shows that too many threads hurt
-                # performance. Worth tunning for other models in the future.
-                exec_strategy.num_threads = len(self._places) * 4
-            else:
-                cpu_num = int(
-                    os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
-                exec_strategy.num_threads = cpu_num * 2
-
-        # step3: init build_strategy
         if build_strategy is None:
             build_strategy = BuildStrategy()
         build_strategy.num_trainers = num_trainers
         build_strategy.trainer_id = trainer_id
-        # FIXME(zcd): is_distribution_ is a temporary field, because in pserver mode,
-        # num_trainers is 1, so the current fields of build_strategy doesn't tell if
-        # it's distributed model.
-        build_strategy.is_distribution = framework.is_pserver_mode(
-            main_program) or num_trainers > 1
-
-        # step4: get main_program, scope, local_scopes
-        main = main_program if main_program \
-            else framework.default_main_program()
-        # FIXME(dzhwinter): enable_inplace should be after memory_optimize
-        # if turn on python memory optimize, turn off the inplace_pass.
-        if build_strategy.memory_optimize is None:
-            build_strategy.memory_optimize = False if main._is_mem_optimized else True
-        if build_strategy.enable_inplace is None:
-            build_strategy.enable_inplace = False if main._is_mem_optimized else True
-        scope = scope if scope is not None else executor.global_scope()
-
-        if share_vars_from and not isinstance(share_vars_from,
-                                              ParallelExecutor):
-            raise TypeError("share_vars_from must be ParallelExecutor.")
-
-        local_scopes = share_vars_from.executor.local_scopes()\
-            if share_vars_from else []
-
-        # step5: check trainers_endpoints, it is used for distribution.
-        trainers_endpoints = main._trainers_endpoints
-        if num_trainers > 1 and trainers_endpoints:
-            assert num_trainers == len(
-                trainers_endpoints), "num_trainers == len(endpoints)"
-            build_strategy.trainers_endpoints = trainers_endpoints
-
-        # step6: get persistable_vars, places. persistable_vars
-        # need be broadcast to other local_scope.
-        persistable_vars = set([
-            cpt.to_text(v.name) for v in [
-                var for var in main.list_vars()
-                if var.persistable and var.type != core.VarDesc.VarType.RAW
-            ]
-        ])
-
-        def place_obj(place):
-            p = core.Place()
-            p.set_place(place)
-            return p
-
-        places = list(map(place_obj, self._places))
 
-        # step7: init ParallelExecutor
-        # ParallelExecutor API will be deprecated, don't support parallel graph.
-        self._graph = core.Graph(main.desc)
+        self._places = compiler.get_available_places(use_cuda)
+        self._scope = scope if scope is not None else executor.global_scope()
 
-        self.executor = core.ParallelExecutor(
-            places, persistable_vars,
-            cpt.to_text(loss_name) if loss_name else six.u(''), scope,
-            local_scopes, exec_strategy, build_strategy, self._graph)
+        main_program = main_program if main_program is not None \
+            else framework.default_main_program()
 
-        self.scope = scope
+        self._compiled_program = compiler.CompiledProgram(main_program)
+        self._compiled_program.with_data_parallel(
+            loss_name=loss_name,
+            build_strategy=build_strategy,
+            exec_strategy=exec_strategy,
+            share_vars_from=share_vars_from)
+        self._place = core.CUDAPlace(0) if use_cuda else core.CPUPlace()
+        self._executor = executor.Executor(self._place)
+        self._compiled_program._compile(place=self._place, scope=self._scope)
 
     def run(self, fetch_list, feed=None, feed_dict=None, return_numpy=True):
         """
@@ -256,56 +180,11 @@ class ParallelExecutor(object):
                 loss = pe.run(feed=feeder.feed(cur_batch),
                               fetch_list=[avg_cost.name]))
         """
-        if feed is None and feed_dict is not None:
-            feed = feed_dict
-            print(
-                "`feed_dict` is deprecated. Please use `feed=`",
-                file=sys.stderr)
-
-        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()
-                    # 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._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._places[i])
-                        tensor = tmp
-                    res_dict[feed_name] = tensor
-                res.append(res_dict)
-            self.executor.feed_tensors_into_local_scopes(res)
-
-        fetch_var_name = 'fetch'
-        self.executor.run(fetch_list, fetch_var_name)
-        arr = self.scope.find_var(fetch_var_name).get_lod_tensor_array()
-
-        if return_numpy:
-            return executor.as_numpy(arr)
-
-        return [arr[i] for i in range(len(arr))]
+        return self._executor.run(program=self._compiled_program,
+                                  scope=self._scope,
+                                  feed=feed,
+                                  fetch_list=fetch_list,
+                                  return_numpy=return_numpy)
 
     @property
     def device_count(self):