Speeding up dygraph DataLoader with multiprocessing (#21762) (#22354)

* add multiprocess for dygraph data loader, test=develop

* polish code & add safe gurad, test=develop

* refactor dygraph dataloader & add signal handler, test=develop

* fix member initializer compile error on ci, test=develop

* fix member initializer compile error one more, test=develop

* remove useless config, test=develop

* skip windows incompatible problem, test=develop

* add unittest for coverage, test=coverage

* add more exception unittest case, test=develop

* deal with signal handler coverage, test=develop

* polish code & add signal handler tests, test=develop

* deal with coverage ci problem, test=develop

* split data loader test & coverage ci fix, test=develop

* remove test_imperative_data_loader_with_exception, test=develop

* remove singal process except test case, test=develop

* add exception tests again & remove sample list test, test=develop

* split normal and exception unittests to diff class, test=develop

* polish doc for use_multiprocess effect in static mode, test=develop

paddle/fluid/imperative/CMakeLists.txt

@@ -10,6 +10,7 @@ cc_library(engine SRCS engine.cc DEPS layer gradient_accumulator)
 cc_library(imperative_profiler SRCS profiler.cc)
 if(NOT WIN32)
     cc_library(nccl_context SRCS nccl_context.cc DEPS device_context)
+    cc_library(data_loader SRCS data_loader.cc DEPS enforce)
 endif(NOT WIN32)
 
 add_subdirectory(tests)

paddle/fluid/imperative/data_loader.cc

+// Copyright (c) 2019 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.
+
+#ifndef _WIN32
+
+#include "paddle/fluid/imperative/data_loader.h"
+
+#include <string.h>
+#include <sys/wait.h>
+
+#include <atomic>
+#include <csignal>
+#include <map>
+
+#include "paddle/fluid/platform/enforce.h"
+
+namespace paddle {
+namespace imperative {
+
+static std::map<int64_t, pid_t> load_process_pids;
+
+void SetLoadProcessPID(int64_t key, pid_t pid) {
+  VLOG(3) << "Dygraph Data Loader: set loader child process PID (" << key
+          << ", " << pid << ")";
+  load_process_pids[key] = pid;
+}
+
+void EraseLoadProcessPID(int64_t key) {
+  auto it = load_process_pids.find(key);
+  // Note: Can not find key also possible
+  if (it != load_process_pids.end()) {
+    VLOG(3) << "Dygraph Data Loader: erase loader child process PID (" << key
+            << ")";
+    load_process_pids.erase(it);
+  } else {
+    VLOG(3) << "Dygraph Data Loader: The dygrph loader (id: " << key
+            << ") you want erase does not exist.";
+  }
+}
+
+// sigaction doc: http://man7.org/linux/man-pages/man2/sigaction.2.html
+// sigemptyset doc: https://linux.die.net/man/3/sigemptyset
+// siginfo_t doc: https://www.mkssoftware.com/docs/man5/siginfo_t.5.asp
+// waitid doc: https://linux.die.net/man/2/waitid
+
+#define SIGNAL_HANDLE(SIGNAL)                   \
+  do {                                          \
+    struct sigaction sa;                        \
+    sa.sa_handler = SIG_DFL;                    \
+    sa.sa_flags = 0;                            \
+    if (sigemptyset(&sa.sa_mask) != 0 ||        \
+        sigaction(SIGNAL, &sa, nullptr) != 0) { \
+      _exit(EXIT_FAILURE);                      \
+    } else {                                    \
+      raise(SIGNAL);                            \
+    }                                           \
+  } while (0)
+
+#define REGISTER_SIGNAL_HANDLER(SIGNAL, HANDLER_NAME)             \
+  static void HANDLER_NAME(int sig, siginfo_t *info, void *ctx) { \
+    SIGNAL_HANDLE(SIGNAL);                                        \
+  }
+
+#define REGISTER_SPEC_SIGNAL_HANDLER(SIGNAL, HANDLER_NAME)        \
+  static void HANDLER_NAME(int sig, siginfo_t *info, void *ctx) { \
+    if (info->si_pid == getppid()) {                              \
+      _exit(EXIT_SUCCESS);                                        \
+    }                                                             \
+    SIGNAL_HANDLE(SIGNAL);                                        \
+  }
+
+REGISTER_SIGNAL_HANDLER(SIGSEGV, SIGSEGV_handler);
+REGISTER_SIGNAL_HANDLER(SIGBUS, SIGBUS_handler);
+REGISTER_SPEC_SIGNAL_HANDLER(SIGTERM, SIGTERM_handler);
+
+static inline void setSignalHandler(int signal,
+                                    void (*handler)(int, siginfo_t *, void *),
+                                    struct sigaction *old_sa_ptr) {
+  struct sigaction sa;
+  sa.sa_sigaction = handler;
+  sa.sa_flags = SA_RESTART | SA_SIGINFO | SA_NOCLDSTOP | SA_NODEFER;
+  if (sigemptyset(&sa.sa_mask) != 0 ||
+      sigaction(signal, &sa, old_sa_ptr) != 0) {
+    PADDLE_THROW(platform::errors::Fatal(
+        "An error occurred while setting handler for %s.", strsignal(signal)));
+  }
+}
+
+// Note: maybe need to add other signal handler
+void SetLoadProcessSignalHandler() {
+  setSignalHandler(SIGSEGV, &SIGSEGV_handler, nullptr);
+  setSignalHandler(SIGBUS, &SIGBUS_handler, nullptr);
+  setSignalHandler(SIGTERM, &SIGTERM_handler, nullptr);
+}
+
+void ThrowErrorIfLoadProcessFailed() {
+  int error;
+  pid_t process_pid;
+  siginfo_t infop;
+
+  for (auto &w : load_process_pids) {
+    process_pid = w.second;
+    // Use waitid rather than waitpid so that we can set NOWAIT, and that Python
+    // and other handlers can get whatever info they want about the child.
+    infop.si_pid = 0;
+    VLOG(3) << "Dygraph Data Loader: monitor loader child process "
+            << process_pid;
+    error = waitid(P_PID, process_pid, &infop, WEXITED | WNOHANG | WNOWAIT);
+    // ignore errors and case with no waitable child
+    if (error < 0 || infop.si_pid == 0) continue;
+    if (infop.si_code == CLD_EXITED &&
+        infop.si_status != EXIT_SUCCESS) {  // exit with error
+      PADDLE_THROW(platform::errors::Fatal(
+          "DataLoader process (pid %ld) exited unexpectedly with code %d. "
+          "Error detailed are lost due to multiprocessing. Rerunning with "
+          "DataLoader.from_generator(..., use_multiprocess=False) may give "
+          "better error trace.",
+          process_pid, infop.si_status));
+    } else if (infop.si_code == CLD_KILLED ||
+               infop.si_code == CLD_DUMPED) {  // killed by signal
+      PADDLE_THROW(platform::errors::Fatal(
+          "DataLoader process (pid %ld) exited is killed by signal: %s.",
+          process_pid, strsignal(infop.si_status)));
+    }
+  }
+}
+
+}  // namespace imperative
+}  // namespace paddle
+
+#endif

paddle/fluid/imperative/data_loader.h

+// Copyright (c) 2019 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.
+
+#pragma once
+
+#ifndef _WIN32
+
+#include <unistd.h>
+#include <cstdint>
+
+namespace paddle {
+namespace imperative {
+
+extern void SetLoadProcessPID(int64_t key, pid_t pid);
+extern void EraseLoadProcessPID(int64_t key);
+extern void SetLoadProcessSignalHandler();
+extern void ThrowErrorIfLoadProcessFailed();
+
+}  // namespace imperative
+}  // namespace paddle
+
+#endif

paddle/fluid/pybind/CMakeLists.txt

@@ -3,7 +3,7 @@ set(PYBIND_DEPS pybind python proto_desc memory executor fleet_wrapper box_wrapp
   analysis_predictor imperative_profiler imperative_flag save_load_util dlpack_tensor device_context)
 
 if(NOT WIN32)
-  set(PYBIND_DEPS ${PYBIND_DEPS} nccl_context)
+  set(PYBIND_DEPS ${PYBIND_DEPS} nccl_context data_loader)
 endif(NOT WIN32)
 
 if(WITH_PYTHON)

paddle/fluid/pybind/imperative.cc

@@ -25,6 +25,7 @@ limitations under the License. */
 #include <utility>
 #include <vector>
 #include "paddle/fluid/imperative/backward_strategy.h"
+#include "paddle/fluid/imperative/data_loader.h"
 #include "paddle/fluid/imperative/layer.h"
 #include "paddle/fluid/imperative/nccl_context.h"
 #include "paddle/fluid/imperative/profiler.h"
@@ -276,6 +277,19 @@ void BindImperative(py::module *m_ptr) {
           imperative::SetCurrentTracer(tracer);
         });
 
+#ifndef _WIN32
+  // Dygraph DataLoader signal handler
+  m.def("_set_process_pid", [](int64_t key, pid_t pid) {
+    imperative::SetLoadProcessPID(key, pid);
+  });
+  m.def("_erase_process_pid",
+        [](int64_t key) { imperative::EraseLoadProcessPID(key); });
+  m.def("_set_process_signal_handler",
+        []() { imperative::SetLoadProcessSignalHandler(); });
+  m.def("_throw_error_if_process_failed",
+        []() { imperative::ThrowErrorIfLoadProcessFailed(); });
+#endif
+
   py::class_<imperative::VarBase, std::shared_ptr<imperative::VarBase>>(
       m, "VarBase",
       R"DOC()DOC")

python/paddle/fluid/core.py

@@ -184,6 +184,11 @@ if avx_supported():
         from .core_avx import _save_dygraph_dict
         from .core_avx import _load_dygraph_dict
         from .core_avx import _create_loaded_parameter
+        if sys.platform != 'win32':
+            from .core_avx import _set_process_pid
+            from .core_avx import _erase_process_pid
+            from .core_avx import _set_process_signal_handler
+            from .core_avx import _throw_error_if_process_failed
     except Exception as e:
         if has_avx_core:
             raise e
@@ -220,6 +225,11 @@ if load_noavx:
         from .core_noavx import _save_dygraph_dict
         from .core_noavx import _load_dygraph_dict
         from .core_noavx import _create_loaded_parameter
+        if sys.platform != 'win32':
+            from .core_noavx import _set_process_pid
+            from .core_noavx import _erase_process_pid
+            from .core_noavx import _set_process_signal_handler
+            from .core_noavx import _throw_error_if_process_failed
     except Exception as e:
         if has_noavx_core:
             sys.stderr.write(

python/paddle/fluid/reader.py

@@ -12,10 +12,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from . import core, dygraph
+from . import core
 import sys
 import six
-import warnings
 import numpy as np
 import threading
 import paddle
@@ -27,6 +26,17 @@ from .unique_name import UniqueNameGenerator
 import logging
 from .dataset import DatasetBase, InMemoryDataset
 
+### Dygraph DataLoader configs ###
+import multiprocessing
+import signal
+# NOTE: queue has a different name in python2 and python3
+if sys.version_info[0] == 2:
+    import Queue as queue
+else:
+    import queue
+# NOTE: [ avoid hanging ] This value is used in getting data from another process
+MP_CHECK_TIMEOUT = 10
+
 __all__ = ['PyReader', 'DataLoader']
 
 data_loader_unique_name_generator = UniqueNameGenerator()
@@ -76,7 +86,8 @@ class DataLoader(object):
                        capacity=None,
                        use_double_buffer=True,
                        iterable=True,
-                       return_list=False):
+                       return_list=False,
+                       use_multiprocess=False):
         """
         Create a DataLoader object for loading data from Python generator. 
         Data would be prefetched using Python thread and be pushed
@@ -116,7 +127,12 @@ class DataLoader(object):
                 the name of each feeded variables. If return_list=True, the 
                 return value on each device would be a list(LoDTensor). It is
                 recommended to use return_list=False in static graph mode and
-                use return_list=True in dygraph mode.   
+                use return_list=True in dygraph mode.  
+            use_multiprocess (bool): whether to use multi-process to speed up
+                the data loading process in dygraph. Note: this parameter only
+                can be used in the dygraph mode. In the static graph mode,
+                whether this parameter is set or not has no effect.
+                The Default value is False.
 
         Returns:
             loader (DataLoader): the created DataLoader object.
@@ -254,8 +270,13 @@ class DataLoader(object):
                         assert label.shape == [BATCH_SIZE, 1]
                         assert relu.shape == [BATCH_SIZE, 784]
         """
-        return GeneratorLoader(feed_list, capacity, use_double_buffer, iterable,
-                               return_list)
+        if in_dygraph_mode():
+            return DygraphGeneratorLoader(feed_list, capacity,
+                                          use_double_buffer, iterable,
+                                          return_list, use_multiprocess)
+        else:
+            return GeneratorLoader(feed_list, capacity, use_double_buffer,
+                                   iterable, return_list)
 
     @staticmethod
     def from_dataset(dataset, places, drop_last=True):
@@ -295,6 +316,298 @@ class DataLoader(object):
         return DatasetLoader(dataset, places, drop_last)
 
 
+class DygraphGeneratorLoader(DataLoaderBase):
+    """
+    The GeneratorLoader of dygraph
+
+    The multiprocess dygraph GeneratorLoader's most functions are different from 
+    static graph GeneratorLoader, Separate implementation to keep code readable.
+    """
+
+    def __init__(self,
+                 feed_list=None,
+                 capacity=None,
+                 use_double_buffer=True,
+                 iterable=True,
+                 return_list=True,
+                 use_multiprocess=False):
+        self._batch_reader = None
+        self._places = None
+        self._feed_list = feed_list
+
+        if not capacity:
+            raise ValueError("Please give value to capacity.")
+        self._capacity = capacity
+        self._use_double_buffer = use_double_buffer
+
+        if not iterable:
+            logging.warning(
+                "Please NOTE: dygraph can support iterable mode only. Change to iterable mode."
+            )
+        self._iterable = True
+        if not return_list:
+            logging.warning(
+                "Please NOTE: dygraph can support return as list only. Change to return as list."
+            )
+        self._return_list = True
+
+        # NOTE: the multiprocessing in different platform is incompatible, we will solve it later
+        self._use_multiprocess = use_multiprocess
+        if self._use_multiprocess and (sys.platform == 'darwin' or
+                                       sys.platform == 'win32'):
+            logging.warning(
+                "NOTE: The multiprocess mode does not currently support MacOs and Windows."
+            )
+            self._use_multiprocess = False
+
+        if self._use_multiprocess:
+            # NOTE: the multiprocessing.Queue used to save loading data in self._process
+            self._data_queue = None
+            # NOTE: this process is used to load data asynchronously from self._batch_reader
+            self._process = None
+
+        # NOTE: the C++ LoDTensorBlockingQueue instance
+        self._blocking_queue = None
+        # NOTE: 1. In multiprocess mode, this thread is used to get next batch data from
+        # self._data_queue, then push it into self._blocking_queue; 2. In singleprocess
+        # mode, this thread is used to get next batch data from self._batch_reader, then 
+        # push it into self._blocking_queue
+        self._thread = None
+
+    @property
+    def queue(self):
+        return self._blocking_queue
+
+    @property
+    def iterable(self):
+        return self._iterable
+
+    def _wait_thread_ends(self):
+        thread = self._thread
+        if thread is not None:
+            self._blocking_queue.close()
+            thread.join()
+
+    def _wait_process_ends(self):
+        process = self._process
+        if process is not None:
+            self._data_queue.cancel_join_thread()
+            self._data_queue.close()
+            process.join()
+            # erase process id
+            core._erase_process_pid(id(self))
+
+    def _init_iterable(self):
+        self._wait_thread_ends()
+        if self._use_multiprocess:
+            self._wait_process_ends()
+        self._var_names = []
+        self._shapes = []
+        self._dtypes = []
+        self._need_check_feed = []
+        self._blocking_queue = core.init_lod_tensor_blocking_queue(
+            core.Variable(), self._capacity)
+        self._reader = core.create_py_reader(
+            self.queue, self._var_names, self._shapes, self._dtypes,
+            self._need_check_feed, self._places, self._use_double_buffer)
+
+    def _start(self):
+        if self._use_multiprocess:
+            # Set data_queue and process
+            self._data_queue = multiprocessing.Queue(self._capacity)
+            self._process = multiprocessing.Process(
+                target=self._reader_process_loop)
+            self._process.daemon = True
+            self._process.start()
+
+            # Set child process signal handler
+            # NOTE: [ avoiding hang ] 1. if the child process dies due to bus error/segfault
+            # or just hang, the main process will hang waiting for data, so here need to deal 
+            # with SIGSEGV and SIGBUS of child process; 2. if the main process end before child
+            # process, it shuts the all its daemonic children down with a SIGTERM (instead of 
+            # joining them without a timeout), so here nedd to deal with SIGTERM.
+            self._set_child_signal_handler()
+
+            # Set reader_thread
+            self._thread_done_event = threading.Event()
+            self._thread = threading.Thread(
+                target=self._reader_thread_loop_with_process)
+            self._thread.daemon = True
+            self._thread.start()
+        else:
+            self._thread = threading.Thread(target=self._reader_thread_loop)
+            self._thread.daemon = True
+            self._thread.start()
+
+    def _reset(self):
+        self._reader.reset()
+        self._wait_thread_ends()
+        if self._use_multiprocess:
+            self._wait_process_ends()
+
+    def __iter__(self):
+        assert self.iterable, "DataLoader is not iterable"
+        assert self._batch_reader is not None, \
+            "Data source of DataLoader has not set yet"
+
+        self._init_iterable()
+        self._start()
+        return self
+
+    def __next__(self):
+        try:
+            return self._reader.read_next_var_list()
+        except StopIteration:
+            self._reset()
+            six.reraise(*sys.exc_info())
+
+    @classmethod
+    def _check_input_array(cls, item):
+        arr = np.array(item)
+        if arr.dtype == np.object:
+            raise TypeError(
+                "\n\tFaild to convert input data to a regular ndarray :\n\t* Usually "
+                "this means the input data contains nested lists with different lengths. "
+                "\n\t* Check the reader function passed to 'decorate_batch_generator'"
+                " to locate the data causes this issue.\n\t* Please consider using "
+                "'fluid.create_lod_tensor' to convert it to a LoD-Tensor.")
+
+    def _set_child_signal_handler(self):
+        core._set_process_pid(id(self), self._process.pid)
+        current_handler = signal.getsignal(signal.SIGCHLD)
+        if not callable(current_handler):
+            current_handler = None
+
+        def __handler__(signum, frame):
+            core._throw_error_if_process_failed()
+            if current_handler is not None:
+                current_handler(signum, frame)
+
+        signal.signal(signal.SIGCHLD, __handler__)
+
+    def _reader_process_loop(self):
+        try:
+            # set signal handler
+            core._set_process_signal_handler()
+
+            for sample in self._batch_reader():
+                if sample is None:
+                    raise ValueError(
+                        "Sample in reader is None. Please check whether your dataset is valid."
+                    )
+                self._data_queue.put(sample)
+            self._data_queue.put(None)
+        except KeyboardInterrupt:
+            # NOTE: Main process will raise KeyboardInterrupt anyways, ignore it in child process
+            pass
+        except:
+            self._data_queue.cancel_join_thread()
+            self._data_queue.close()
+            six.reraise(*sys.exc_info())
+
+    def _reader_thread_loop_with_process(self):
+        while not self._thread_done_event.is_set():
+            try:
+                # NOTE: [ avoid hanging ] Even with carefully designed data dependencies 
+                # (i.e., a put() always corresponding to a get()), hanging on get() can 
+                # still happen when data in queue is corrupted (e.g., due to 
+                # Queue.cancel_join_thread or unexpected exit). So we set a timeout whenever 
+                # we try to get data from `data_queue`
+                sample = self._data_queue.get(timeout=MP_CHECK_TIMEOUT)
+            except queue.Empty:
+                self._thread_done_event.set()
+                logging.error("The reader has not read data for a long time.")
+
+            if not self._thread_done_event.is_set():
+                if sample is not None:
+                    try:
+                        array = core.LoDTensorArray()
+                        for item in sample:
+                            if not isinstance(item, core.LoDTensor):
+                                self._check_input_array(item)
+                                tmp = core.LoDTensor()
+                                tmp.set(item, core.CPUPlace())
+                                item = tmp
+                            array.append(item)
+                        if not self._blocking_queue.push(array):
+                            self._blocking_queue.close()
+                    except:
+                        self._thread_done_event.set()
+                        self._blocking_queue.kill()
+                        self._data_queue.close()
+                        logging.warning(
+                            "DygraphDataLoader reader thread raised an exception."
+                        )
+                        six.reraise(*sys.exc_info())
+                else:
+                    self._thread_done_event.set()
+                    self._blocking_queue.close()
+                    self._data_queue.close()
+            else:
+                self._blocking_queue.kill()
+                self._data_queue.close()
+
+    def _reader_thread_loop(self):
+        try:
+            for sample in self._batch_reader():
+                array = core.LoDTensorArray()
+                for item in sample:
+                    if not isinstance(item, core.LoDTensor):
+                        self._check_input_array(item)
+                        tmp = core.LoDTensor()
+                        tmp.set(item, core.CPUPlace())
+                        item = tmp
+
+                    array.append(item)
+
+                if not self._blocking_queue.push(array):
+                    break
+
+            self._blocking_queue.close()
+            self._thread = None
+        except Exception:
+            self._blocking_queue.kill()
+            self._thread = None
+            logging.warning(
+                "DygraphDataLoader reader thread raised an exception.")
+            six.reraise(*sys.exc_info())
+
+    def set_sample_generator(self,
+                             reader,
+                             batch_size,
+                             drop_last=True,
+                             places=None):
+        assert batch_size > 0, "batch_size must be larger than 0"
+        self.set_sample_list_generator(
+            paddle.batch(
+                reader, batch_size=batch_size, drop_last=drop_last),
+            places=places)
+        return self
+
+    def set_sample_list_generator(self, reader, places=None):
+        def __batch_reader_impl__():
+            for batch in reader():
+                slots = []
+                for items in batch:
+                    for i, item in enumerate(items):
+                        if len(slots) < len(items):
+                            slots.append([item])
+                        else:
+                            slots[i].append(item)
+                yield slots
+
+        self.set_batch_generator(__batch_reader_impl__, places)
+        return self
+
+    def set_batch_generator(self, reader, places=None):
+        self._batch_reader = reader
+        assert places is not None, "Places cannot be None when DataLoader is iterable"
+        self._places = _convert_places(places)
+        assert len(self._places) == 1, \
+            "Number of places must be 1 in dygraph mode"
+        return self
+
+
 class GeneratorLoader(DataLoaderBase):
     def __init__(self,
                  feed_list=None,
@@ -305,26 +618,14 @@ class GeneratorLoader(DataLoaderBase):
         self._tensor_reader = None
         self._places = None
         self._thread = None
+        self._queue = None
         self._feed_list = feed_list
         if not capacity:
             raise ValueError("Please give value to capacity.")
-        # force to use iterable mode under dygraph mode
-        if in_dygraph_mode():
-            if not iterable:
-                warnings.warn(
-                    "Please NOTE: dygraph can support iterable mode only. Change to iterable mode."
-                )
-            self._iterable = True
-            if not return_list:
-                warnings.warn(
-                    "Please NOTE: dygraph can support return as list only. Change to return as list."
-                )
-            self._return_list = True
-        else:
-            self._iterable = iterable
-            self._return_list = return_list
-            if not self._feed_list:
-                raise Exception("Feed list must be given under static mode.")
+        self._iterable = iterable
+        self._return_list = return_list
+        if not self._feed_list:
+            raise Exception("Feed list must be given under static mode.")
         self._use_double_buffer = use_double_buffer
         self._capacity = capacity
         if not self._iterable:
@@ -340,18 +641,12 @@ class GeneratorLoader(DataLoaderBase):
 
     def _init_iterable(self):
         self._wait_thread_ends()
-        if in_dygraph_mode():
-            self._var_names = []
-            self._shapes = []
-            self._dtypes = []
-            self._need_check_feed = []
-        else:
-            self._var_names = [v.name for v in self._feed_list]
-            self._shapes = [v.shape for v in self._feed_list]
-            self._dtypes = [v.dtype for v in self._feed_list]
-            self._need_check_feed = [
-                v.desc.need_check_feed() for v in self._feed_list
-            ]
+        self._var_names = [v.name for v in self._feed_list]
+        self._shapes = [v.shape for v in self._feed_list]
+        self._dtypes = [v.dtype for v in self._feed_list]
+        self._need_check_feed = [
+            v.desc.need_check_feed() for v in self._feed_list
+        ]
         self._queue = core.init_lod_tensor_blocking_queue(core.Variable(),
                                                           self._capacity)
         self._reader = core.create_py_reader(
@@ -442,27 +737,22 @@ class GeneratorLoader(DataLoaderBase):
 
     def __next__(self):
         try:
-            if in_dygraph_mode():
-                return self._reader.read_next_var_list()
+            if self._return_list:
+                return self._reader.read_next_list()
             else:
-                if self._return_list:
-                    return self._reader.read_next_list()
-                else:
-                    return self._reader.read_next()
+                return self._reader.read_next()
         except StopIteration:
             self._queue.close()
             self._reset()
             six.reraise(*sys.exc_info())
 
     def start(self):
-        if not in_dygraph_mode():
-            assert not self._iterable, "start() cannot be called when DataLoader is iterable"
-            self._start()
+        assert not self._iterable, "start() cannot be called when DataLoader is iterable"
+        self._start()
 
     def reset(self):
-        if not in_dygraph_mode():
-            assert not self._iterable, "reset() cannot be called when DataLoader is iterable"
-            self._reset()
+        assert not self._iterable, "reset() cannot be called when DataLoader is iterable"
+        self._reset()
 
     @classmethod
     def _check_input_array(cls, item):
@@ -518,56 +808,36 @@ class GeneratorLoader(DataLoaderBase):
                              drop_last=True,
                              places=None):
         assert batch_size > 0, "batch_size must be larger than 0"
-        if in_dygraph_mode():
+        has_lod = False
+        for f in self._feed_list:
+            if f.lod_level != 0:
+                has_lod = True
+                break
+
+        if has_lod:
             self.set_sample_list_generator(
                 paddle.batch(
                     reader, batch_size=batch_size, drop_last=drop_last),
                 places=places)
         else:
-            has_lod = False
-            for f in self._feed_list:
-                if f.lod_level != 0:
-                    has_lod = True
-                    break
-
-            if has_lod:
-                self.set_sample_list_generator(
-                    paddle.batch(
-                        reader, batch_size=batch_size, drop_last=drop_last),
-                    places=places)
-            else:
-                reader = BatchedTensorProvider(
-                    feed_list=self._feed_list,
-                    place=core.CPUPlace(),
-                    batch_size=batch_size,
-                    generator=reader,
-                    drop_last=drop_last)
-                self.set_batch_generator(reader, places=places)
+            reader = BatchedTensorProvider(
+                feed_list=self._feed_list,
+                place=core.CPUPlace(),
+                batch_size=batch_size,
+                generator=reader,
+                drop_last=drop_last)
+            self.set_batch_generator(reader, places=places)
         return self
 
     def set_sample_list_generator(self, reader, places=None):
-        if in_dygraph_mode():
-
-            def __tensor_reader_impl__():
-                for batch in reader():
-                    slots = []
-                    for items in batch:
-                        for i, item in enumerate(items):
-                            if len(slots) < len(items):
-                                slots.append([item])
-                            else:
-                                slots[i].append(item)
-                    yield slots
-        else:
-            with program_guard(Program(), Program()):
-                feeder = DataFeeder(
-                    feed_list=self._feed_list, place=core.CPUPlace())
-                paddle_reader = feeder.decorate_reader(
-                    reader, multi_devices=False)
+        with program_guard(Program(), Program()):
+            feeder = DataFeeder(
+                feed_list=self._feed_list, place=core.CPUPlace())
+            paddle_reader = feeder.decorate_reader(reader, multi_devices=False)
 
-            def __tensor_reader_impl__():
-                for slots in paddle_reader():
-                    yield [slots[var.name] for var in self._feed_list]
+        def __tensor_reader_impl__():
+            for slots in paddle_reader():
+                yield [slots[var.name] for var in self._feed_list]
 
         self.set_batch_generator(__tensor_reader_impl__, places)
         return self
@@ -577,9 +847,6 @@ class GeneratorLoader(DataLoaderBase):
         if self._iterable:
             assert places is not None, "Places cannot be None when DataLoader is iterable"
             self._places = _convert_places(places)
-            if in_dygraph_mode():
-                assert len(self._places) == 1, \
-                    "Number of places must be 1 in dygraph mode"
         else:
             if places is not None:
                 logging.info(

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

@@ -187,6 +187,9 @@ list(REMOVE_ITEM TEST_OPS test_fuse_bn_act_pass)
 if (APPLE OR WIN32)
   list(REMOVE_ITEM TEST_OPS test_dataset)
   list(REMOVE_ITEM TEST_OPS test_dataset_dataloader)
+  list(REMOVE_ITEM TEST_OPS test_imperative_data_loader)
+  list(REMOVE_ITEM TEST_OPS test_imperative_data_loader_process)
+  list(REMOVE_ITEM TEST_OPS test_imperative_signal_handler)
 endif()
 
 # Some ops need to check results when gc is enabled

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

+# Copyright (c) 2019 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 sys
+import unittest
+import numpy as np
+import paddle.fluid as fluid
+from paddle.fluid import core
+import paddle.compat as cpt
+
+
+def get_random_images_and_labels(image_shape, label_shape):
+    image = np.random.random(size=image_shape).astype('float32')
+    label = np.random.random(size=label_shape).astype('int64')
+    return image, label
+
+
+def sample_generator_creator(batch_size, batch_num):
+    def __reader__():
+        for _ in range(batch_num * batch_size):
+            image, label = get_random_images_and_labels([784], [1])
+            yield image, label
+
+    return __reader__
+
+
+def sample_list_generator_creator(batch_size, batch_num):
+    def __reader__():
+        for _ in range(batch_num):
+            sample_list = []
+            for _ in range(batch_size):
+                image, label = get_random_images_and_labels([784], [1])
+                sample_list.append([image, label])
+
+            yield sample_list
+
+    return __reader__
+
+
+def batch_generator_creator(batch_size, batch_num):
+    def __reader__():
+        for _ in range(batch_num):
+            batch_image, batch_label = get_random_images_and_labels(
+                [batch_size, 784], [batch_size, 1])
+            yield batch_image, batch_label
+
+    return __reader__
+
+
+class TestDygraphhDataLoader(unittest.TestCase):
+    def setUp(self):
+        self.batch_size = 8
+        self.batch_num = 4
+        self.epoch_num = 2
+        self.capacity = 2
+
+    def test_single_process_reader(self):
+        with fluid.dygraph.guard():
+            loader = fluid.io.DataLoader.from_generator(
+                capacity=self.capacity, iterable=False, use_multiprocess=False)
+            loader.set_sample_generator(
+                sample_generator_creator(self.batch_size, self.batch_num),
+                batch_size=self.batch_size,
+                places=fluid.CPUPlace())
+            for _ in range(self.epoch_num):
+                for image, label in loader():
+                    relu = fluid.layers.relu(image)
+                    self.assertEqual(image.shape, [self.batch_size, 784])
+                    self.assertEqual(label.shape, [self.batch_size, 1])
+                    self.assertEqual(relu.shape, [self.batch_size, 784])
+
+    def test_sample_genarator(self):
+        with fluid.dygraph.guard():
+            loader = fluid.io.DataLoader.from_generator(
+                capacity=self.capacity, use_multiprocess=True)
+            loader.set_sample_generator(
+                sample_generator_creator(self.batch_size, self.batch_num),
+                batch_size=self.batch_size,
+                places=fluid.CPUPlace())
+            for _ in range(self.epoch_num):
+                for image, label in loader():
+                    relu = fluid.layers.relu(image)
+                    self.assertEqual(image.shape, [self.batch_size, 784])
+                    self.assertEqual(label.shape, [self.batch_size, 1])
+                    self.assertEqual(relu.shape, [self.batch_size, 784])
+
+    def test_sample_list_generator(self):
+        with fluid.dygraph.guard():
+            loader = fluid.io.DataLoader.from_generator(
+                capacity=self.capacity, use_multiprocess=True)
+            loader.set_sample_list_generator(
+                sample_list_generator_creator(self.batch_size, self.batch_num),
+                places=fluid.CPUPlace())
+            for _ in range(self.epoch_num):
+                for image, label in loader():
+                    relu = fluid.layers.relu(image)
+                    self.assertEqual(image.shape, [self.batch_size, 784])
+                    self.assertEqual(label.shape, [self.batch_size, 1])
+                    self.assertEqual(relu.shape, [self.batch_size, 784])
+
+    def test_batch_genarator(self):
+        with fluid.dygraph.guard():
+            loader = fluid.io.DataLoader.from_generator(
+                capacity=self.capacity, use_multiprocess=True)
+            loader.set_batch_generator(
+                batch_generator_creator(self.batch_size, self.batch_num),
+                places=fluid.CPUPlace())
+            for _ in range(self.epoch_num):
+                for image, label in loader():
+                    relu = fluid.layers.relu(image)
+                    self.assertEqual(image.shape, [self.batch_size, 784])
+                    self.assertEqual(label.shape, [self.batch_size, 1])
+                    self.assertEqual(relu.shape, [self.batch_size, 784])
+
+
+class TestDygraphhDataLoaderWithException(unittest.TestCase):
+    def setUp(self):
+        self.batch_num = 4
+        self.capacity = 2
+
+    def test_not_capacity(self):
+        with fluid.dygraph.guard():
+            with self.assertRaisesRegexp(ValueError,
+                                         "Please give value to capacity."):
+                fluid.io.DataLoader.from_generator()
+
+    def test_single_process_with_thread_expection(self):
+        def error_sample_genarator(batch_num):
+            def __reader__():
+                for _ in range(batch_num):
+                    yield [[[1, 2], [1]]]
+
+            return __reader__
+
+        with fluid.dygraph.guard():
+            loader = fluid.io.DataLoader.from_generator(
+                capacity=self.capacity, iterable=False, use_multiprocess=False)
+            loader.set_batch_generator(
+                error_sample_genarator(self.batch_num), places=fluid.CPUPlace())
+            exception = None
+            try:
+                for _ in loader():
+                    print("test_single_process_with_thread_expection")
+            except core.EnforceNotMet as ex:
+                self.assertIn("Blocking queue is killed",
+                              cpt.get_exception_message(ex))
+                exception = ex
+            self.assertIsNotNone(exception)
+
+    def test_multi_process_with_thread_expection(self):
+        def error_sample_genarator(batch_num):
+            def __reader__():
+                for _ in range(batch_num):
+                    yield [[[1, 2], [1]]]
+
+            return __reader__
+
+        with fluid.dygraph.guard():
+            loader = fluid.io.DataLoader.from_generator(
+                capacity=self.capacity, use_multiprocess=True)
+            loader.set_batch_generator(
+                error_sample_genarator(self.batch_num), places=fluid.CPUPlace())
+            exception = None
+            try:
+                for _ in loader():
+                    print("test_multi_process_with_thread_expection")
+            except core.EnforceNotMet as ex:
+                self.assertIn("Blocking queue is killed",
+                              cpt.get_exception_message(ex))
+                exception = ex
+            self.assertIsNotNone(exception)
+
+
+if __name__ == '__main__':
+    unittest.main()

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

+# Copyright (c) 2019 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 sys
+import unittest
+import numpy as np
+import paddle.fluid as fluid
+
+if sys.version_info[0] == 2:
+    import Queue as queue
+else:
+    import queue
+
+
+def get_random_images_and_labels(image_shape, label_shape):
+    image = np.random.random(size=image_shape).astype('float32')
+    label = np.random.random(size=label_shape).astype('int64')
+    return image, label
+
+
+def batch_generator_creator(batch_size, batch_num):
+    def __reader__():
+        for _ in range(batch_num):
+            batch_image, batch_label = get_random_images_and_labels(
+                [batch_size, 784], [batch_size, 1])
+            yield batch_image, batch_label
+
+    return __reader__
+
+
+# NOTE: coverage CI can't cover child process code, so need these test.
+# Here test child process loop function in main process
+class TestDygraphhDataLoaderProcess(unittest.TestCase):
+    def setUp(self):
+        self.batch_size = 8
+        self.batch_num = 4
+        self.epoch_num = 2
+        self.capacity = 2
+
+    def test_reader_process_loop(self):
+        with fluid.dygraph.guard():
+            loader = fluid.io.DataLoader.from_generator(
+                capacity=self.batch_num + 1, use_multiprocess=True)
+            loader.set_batch_generator(
+                batch_generator_creator(self.batch_size, self.batch_num),
+                places=fluid.CPUPlace())
+            loader._data_queue = queue.Queue(self.batch_num + 1)
+            loader._reader_process_loop()
+            for _ in range(self.batch_num):
+                loader._data_queue.get(timeout=10)
+
+    def test_reader_process_loop_simple_none(self):
+        def none_sample_genarator(batch_num):
+            def __reader__():
+                for _ in range(batch_num):
+                    yield None
+
+            return __reader__
+
+        with fluid.dygraph.guard():
+            loader = fluid.io.DataLoader.from_generator(
+                capacity=self.batch_num + 1, use_multiprocess=True)
+            loader.set_batch_generator(
+                none_sample_genarator(self.batch_num), places=fluid.CPUPlace())
+            loader._data_queue = queue.Queue(self.batch_num + 1)
+            exception = None
+            try:
+                loader._reader_process_loop()
+            except AttributeError as ex:
+                exception = ex
+            self.assertIsNotNone(exception)
+
+
+if __name__ == '__main__':
+    unittest.main()

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

@@ -242,8 +242,6 @@ class TestDygraphResnet(unittest.TestCase):
             optimizer = optimizer_setting(
                 train_parameters, parameter_list=resnet.parameters())
             np.random.seed(seed)
-            import random
-            random.seed = seed
 
             batch_py_reader = fluid.io.PyReader(capacity=1)
             batch_py_reader.decorate_sample_list_generator(
@@ -330,8 +328,6 @@ class TestDygraphResnet(unittest.TestCase):
             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)

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

@@ -316,8 +316,6 @@ class TestImperativeResneXt(unittest.TestCase):
             optimizer = optimizer_setting(
                 train_parameters, parameter_list=se_resnext.parameters())
             np.random.seed(seed)
-            import random
-            random.seed = seed
 
             batch_py_reader = fluid.io.PyReader(capacity=1)
             batch_py_reader.decorate_sample_list_generator(
@@ -379,8 +377,6 @@ class TestImperativeResneXt(unittest.TestCase):
             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,

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

+# Copyright (c) 2019 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 os
+import sys
+import signal
+import unittest
+import multiprocessing
+import time
+
+import paddle.compat as cpt
+from paddle.fluid import core
+
+
+def set_child_signal_handler(self, child_pid):
+    core._set_process_pid(id(self), child_pid)
+    current_handler = signal.getsignal(signal.SIGCHLD)
+    if not callable(current_handler):
+        current_handler = None
+
+    def __handler__(signum, frame):
+        core._throw_error_if_process_failed()
+        if current_handler is not None:
+            current_handler(signum, frame)
+
+    signal.signal(signal.SIGCHLD, __handler__)
+
+
+class TestDygraphDataLoaderSingalHandler(unittest.TestCase):
+    def test_child_process_exit_will_error(self):
+        def __test_process__():
+            core._set_process_signal_handler()
+            sys.exit(1)
+
+        exception = None
+        try:
+            test_process = multiprocessing.Process(target=__test_process__)
+            test_process.start()
+
+            set_child_signal_handler(id(self), test_process.pid)
+            time.sleep(1)
+        except core.EnforceNotMet as ex:
+            self.assertIn("FatalError", cpt.get_exception_message(ex))
+            exception = ex
+
+        self.assertIsNotNone(exception)
+
+    def test_child_process_killed_by_sigsegv(self):
+        def __test_process__():
+            core._set_process_signal_handler()
+            os.kill(os.getpid(), signal.SIGSEGV)
+
+        exception = None
+        try:
+            test_process = multiprocessing.Process(target=__test_process__)
+            test_process.start()
+
+            set_child_signal_handler(id(self), test_process.pid)
+            time.sleep(1)
+        except core.EnforceNotMet as ex:
+            self.assertIn("FatalError", cpt.get_exception_message(ex))
+            exception = ex
+
+        self.assertIsNotNone(exception)
+
+    def test_child_process_killed_by_sigterm(self):
+        def __test_process__():
+            core._set_process_signal_handler()
+            time.sleep(10)
+
+        test_process = multiprocessing.Process(target=__test_process__)
+        test_process.daemon = True
+        test_process.start()
+
+        set_child_signal_handler(id(self), test_process.pid)
+        time.sleep(1)
+
+
+if __name__ == '__main__':
+    unittest.main()