Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into trt_dy_lib

test=develop

CMakeLists.txt

@@ -72,7 +72,7 @@ option(WITH_INFERENCE    "Compile fluid inference library"              ON)
 option(WITH_INFERENCE_API_TEST   "Test fluid inference high-level api interface"  OFF)
 option(WITH_SYSTEM_BLAS   "Use system blas library"           OFF)
 option(PY_VERSION       "Compile PaddlePaddle with python3 support"     ${PY_VERSION})
-option(WITH_FAST_MATH   "Make use of fast math library"                 OFF)
+option(WITH_FAST_MATH   "Make use of fast math library, might affect the precision to some extent" ON)
 
 # PY_VERSION
 if(NOT PY_VERSION)

paddle/fluid/API.spec

@@ -178,6 +178,7 @@ paddle.fluid.layers.batch ArgSpec(args=['reader', 'batch_size'], varargs=None, k
 paddle.fluid.layers.double_buffer ArgSpec(args=['reader', 'place', 'name'], varargs=None, keywords=None, defaults=(None, None))
 paddle.fluid.layers.random_data_generator ArgSpec(args=['low', 'high', 'shapes', 'lod_levels', 'for_parallel'], varargs=None, keywords=None, defaults=(True,))
 paddle.fluid.layers.py_reader ArgSpec(args=['capacity', 'shapes', 'dtypes', 'lod_levels', 'name', 'use_double_buffer'], varargs=None, keywords=None, defaults=(None, None, True))
+paddle.fluid.layers.create_py_reader_by_data ArgSpec(args=['capacity', 'feed_list', 'name', 'use_double_buffer'], varargs=None, keywords=None, defaults=(None, True))
 paddle.fluid.layers.Preprocessor.__init__ ArgSpec(args=['self', 'reader', 'name'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.Preprocessor.block ArgSpec(args=[], varargs='args', keywords='kwds', defaults=None)
 paddle.fluid.layers.Preprocessor.inputs ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)

paddle/fluid/CMakeLists.txt

@@ -12,6 +12,5 @@ endif(NOT WIN32)
 if(WITH_INFERENCE)
   # NOTE: please add subdirectory inference at last.
   add_subdirectory(inference)
+  add_subdirectory(train)
 endif()
-
-add_subdirectory(train)

paddle/fluid/inference/tests/api/analyzer_resnet50_tester.cc

@@ -27,9 +27,6 @@ void SetConfig(AnalysisConfig *cfg) {
   cfg->device = 0;
   cfg->enable_ir_optim = true;
   cfg->specify_input_name = true;
-#ifdef PADDLE_WITH_MKLDNN
-  cfg->_use_mkldnn = true;
-#endif
 }
 
 void SetInput(std::vector<std::vector<PaddleTensor>> *inputs) {

paddle/fluid/operators/cub_reduce.h

@@ -22,6 +22,7 @@
 
 #include <cub/cub.cuh>  // NOLINT
 #include "paddle/fluid/framework/tensor.h"
+#include "paddle/fluid/framework/tensor_util.h"
 
 namespace paddle {
 namespace operators {
@@ -293,7 +294,12 @@ void TensorReduce(const framework::Tensor& x, framework::Tensor* y,
   }
   auto x_data = x.data<Tx>();
   auto y_data = y->mutable_data<Ty>(x.place());
-  if (reduce_num == 1) return;
+  if (reduce_num == 1) {
+    auto out_dims = y->dims();
+    framework::TensorCopy(x, y->place(), y);
+    y->Resize(out_dims);
+    return;
+  }
 
 #define CUB_BLOCK_DIM_CASE(block_dim)                                    \
   case block_dim: {                                                      \

paddle/scripts/paddle_build.sh

@@ -600,7 +600,7 @@ EOF
     if [[ ${WITH_GPU} == "ON"  ]]; then
         NCCL_DEPS="apt-get install -y --allow-downgrades libnccl2=2.2.13-1+cuda${CUDA_MAJOR} libnccl-dev=2.2.13-1+cuda${CUDA_MAJOR} || true"
     else
-        NCCL_DEPS=""
+        NCCL_DEPS="true"
     fi
 
     if [[ ${WITH_FLUID_ONLY:-OFF} == "OFF" ]]; then

python/paddle/fluid/layers/io.py

@@ -30,7 +30,8 @@ from ..unique_name import generate as unique_name
 
 __all__ = [
     'data', 'open_files', 'read_file', 'shuffle', 'batch', 'double_buffer',
-    'random_data_generator', 'py_reader', 'Preprocessor', 'load'
+    'random_data_generator', 'py_reader', 'create_py_reader_by_data',
+    'Preprocessor', 'load'
 ]
 
 
@@ -470,6 +471,158 @@ def random_data_generator(low, high, shapes, lod_levels, for_parallel=True):
     return monkey_patch_reader_methods(main_prog_var)
 
 
+def _py_reader(capacity,
+               shapes,
+               dtypes,
+               lod_levels=None,
+               name=None,
+               use_double_buffer=True,
+               feed_list=None):
+
+    if feed_list is not None:
+        if not isinstance(feed_list, list):
+            raise TypeError("feed_list should be a list of Variable"
+                            " instead of " + str(type(feed_list)))
+        lod_levels = []
+        dtypes = []
+        shape_concat = []
+        ranks = []
+        shapes = []
+
+        for data in feed_list:
+            dtypes.append(data.dtype)
+            shape_concat.extend(data.shape)
+            ranks.append(len(data.shape))
+            shapes.append(data.shape)
+            lod_levels.append(data.lod_level)
+    else:
+        dtypes = [convert_np_dtype_to_dtype_(dt) for dt in dtypes]
+        shape_concat = []
+        ranks = []
+
+        for shape in shapes:
+            shape_concat.extend(shape)
+            ranks.append(len(shape))
+
+        if lod_levels is None:
+            lod_levels = [0] * len(shapes)
+
+    if name is None:
+        queue_name = unique_name('lod_tensor_blocking_queue')
+        reader_name = unique_name('create_py_reader')
+        double_buffer_name = unique_name('double_buffer')
+    else:
+        queue_name = "_".join([name, "queue"])
+        reader_name = "_".join([name, "reader"])
+        double_buffer_name = "_".join([name, "double_buffer"])
+
+    var = global_scope().var(queue_name)
+    feed_queue = core.init_lod_tensor_blocking_queue(var, capacity, shapes)
+
+    startup_blk = default_startup_program().current_block()
+    startup_var = startup_blk.create_var(name=reader_name)
+    startup_blk.append_op(
+        type='create_py_reader',
+        inputs={'blocking_queue': [queue_name]},
+        outputs={'Out': [startup_var]},
+        attrs={
+            'shape_concat': shape_concat,
+            'lod_levels': lod_levels,
+            'ranks': ranks
+        })
+
+    startup_var.desc.set_dtypes(dtypes)
+    startup_var.persistable = True
+
+    main_prog_var = _copy_reader_var_(default_main_program().current_block(),
+                                      startup_var)
+
+    reader = monkey_patch_reader_methods(main_prog_var)
+    if use_double_buffer:
+        double_buffer_reader = double_buffer(reader, name=double_buffer_name)
+        # we return a double buffer reader. However, the reset method comes from
+        # py_reader.
+        double_buffer_reader.reset = reader.reset
+        reader = double_buffer_reader
+
+    # monkey patch py_reader special methods
+    reader.queue = feed_queue
+    current_reset_method = reader.reset
+    reader.thread = None
+    reader.tensor_provider = None
+    reader.exited = False
+
+    def start_provide_thread(func):
+        def __provider_thread__():
+            for tensors in func():
+                array = core.LoDTensorArray()
+                for item in tensors:
+                    if not isinstance(item, core.LoDTensor):
+                        tmp = core.LoDTensor()
+                        tmp.set(item, core.CPUPlace())
+                        item = tmp
+
+                    array.append(item)
+
+                if reader.exited:
+                    break
+                feed_queue.push(array)
+                if reader.exited:
+                    break
+            feed_queue.close()
+
+        reader.thread = threading.Thread(target=__provider_thread__)
+        reader.thread.daemon = True
+        reader.thread.start()
+
+    def __set_tensor_provider__(func):
+        reader.tensor_provider = func
+
+    def __set_paddle_reader__(paddle_reader):
+        with program_guard(Program(), Program()):
+            actual_feed_list = feed_list
+            if actual_feed_list is None:
+                actual_feed_list = []
+                counter = 0
+                for dtype, shape, lod_level in zip(dtypes, shapes, lod_levels):
+                    name = str(counter)
+                    actual_feed_list.append(
+                        data(
+                            name=name,
+                            dtype=dtype,
+                            shape=shape,
+                            lod_level=lod_level))
+                    counter += 1
+
+            feeder = DataFeeder(
+                feed_list=actual_feed_list, place=core.CPUPlace())
+            paddle_reader = feeder.decorate_reader(
+                paddle_reader, multi_devices=False)
+
+        def __tensor_provider__():
+            for slots in paddle_reader():
+                yield [slots[str(idx)] for idx in six.moves.xrange(counter)]
+
+        __set_tensor_provider__(__tensor_provider__)
+
+    def __reset__():
+        current_reset_method()
+        if reader.thread is not None and reader.tensor_provider is not None:
+            reader.exited = True
+            reader.thread.join()
+            reader.exited = False
+
+    def __start__():
+        start_provide_thread(reader.tensor_provider)
+
+    reader.reset = __reset__
+    reader.decorate_tensor_provider = __set_tensor_provider__
+    reader.decorate_paddle_reader = __set_paddle_reader__
+    reader.start = __start__
+
+    return reader
+
+
 def py_reader(capacity,
               shapes,
               dtypes,
@@ -594,128 +747,72 @@ def py_reader(capacity,
         >>>     except fluid.core.EOFException:
         >>>         test_reader.reset()
     """
-    dtypes = [convert_np_dtype_to_dtype_(dt) for dt in dtypes]
-    shape_concat = []
-    ranks = []
-
-    for shape in shapes:
-        shape_concat.extend(shape)
-        ranks.append(len(shape))
-
-    if lod_levels is None:
-        lod_levels = [0] * len(shapes)
-
-    if name is None:
-        queue_name = unique_name('lod_tensor_blocking_queue')
-        reader_name = unique_name('create_py_reader')
-        double_buffer_name = unique_name('double_buffer')
-    else:
-        queue_name = "_".join([name, "queue"])
-        reader_name = "_".join([name, "reader"])
-        double_buffer_name = "_".join([name, "double_buffer"])
-
-    var = global_scope().var(queue_name)
-    feed_queue = core.init_lod_tensor_blocking_queue(var, capacity, shapes)
-
-    startup_blk = default_startup_program().current_block()
-    startup_var = startup_blk.create_var(name=reader_name)
-    startup_blk.append_op(
-        type='create_py_reader',
-        inputs={'blocking_queue': [queue_name]},
-        outputs={'Out': [startup_var]},
-        attrs={
-            'shape_concat': shape_concat,
-            'lod_levels': lod_levels,
-            'ranks': ranks
-        })
-
-    startup_var.desc.set_dtypes(dtypes)
-    startup_var.persistable = True
-
-    main_prog_var = _copy_reader_var_(default_main_program().current_block(),
-                                      startup_var)
-
-    reader = monkey_patch_reader_methods(main_prog_var)
-    if use_double_buffer:
-        double_buffer_reader = double_buffer(reader, name=double_buffer_name)
-        # we return a double buffer reader. However, the reset method comes from
-        # py_reader.
-        double_buffer_reader.reset = reader.reset
-        reader = double_buffer_reader
-
-    # monkey patch py_reader special methods
-    reader.queue = feed_queue
-    current_reset_method = reader.reset
-    reader.thread = None
-    reader.tensor_provider = None
-    reader.exited = False
-
-    def start_provide_thread(func):
-        def __provider_thread__():
-            for tensors in func():
-                array = core.LoDTensorArray()
-                for item in tensors:
-                    if not isinstance(item, core.LoDTensor):
-                        tmp = core.LoDTensor()
-                        tmp.set(item, core.CPUPlace())
-                        item = tmp
+    return _py_reader(
+        capacity=capacity,
+        shapes=shapes,
+        dtypes=dtypes,
+        lod_levels=lod_levels,
+        name=name,
+        use_double_buffer=use_double_buffer)
 
-                    array.append(item)
 
-                if reader.exited:
-                    break
-                feed_queue.push(array)
-                if reader.exited:
-                    break
-            feed_queue.close()
-
-        reader.thread = threading.Thread(target=__provider_thread__)
-        reader.thread.daemon = True
-        reader.thread.start()
-
-    def __set_tensor_provider__(func):
-        reader.tensor_provider = func
+def create_py_reader_by_data(capacity,
+                             feed_list,
+                             name=None,
+                             use_double_buffer=True):
+    """
+    Create a Python reader for data feeding in Python
 
-    def __set_paddle_reader__(paddle_reader):
-        with program_guard(Program(), Program()):
-            feed_list = []
-            counter = 0
-            for dtype, shape, lod_level in zip(dtypes, shapes, lod_levels):
-                name = str(counter)
-                feed_list.append(
-                    data(
-                        name=name,
-                        dtype=dtype,
-                        shape=shape,
-                        lod_level=lod_level))
-                counter += 1
-
-            feeder = DataFeeder(feed_list=feed_list, place=core.CPUPlace())
-            paddle_reader = feeder.decorate_reader(
-                paddle_reader, multi_devices=False)
+    This layer returns a Reader Variable.
 
-        def __tensor_provider__():
-            for slots in paddle_reader():
-                yield [slots[str(idx)] for idx in six.moves.xrange(counter)]
+    Works much like py_reader except that it's input is feed_list
+    instead of shapes, dtypes and lod_levels
 
-        __set_tensor_provider__(__tensor_provider__)
+    Args:
+       capacity(int): The buffer capacity maintained by :code:`py_reader`.
+       feed_list(list(Variable)): The data feed list.
+       name(basestring): The prefix Python queue name and Reader name. None will
+            be generated automatically.
+       use_double_buffer(bool): Whether use double buffer or not.
 
-    def __reset__():
-        current_reset_method()
-        if reader.thread is not None and reader.tensor_provider is not None:
-            reader.exited = True
-            reader.thread.join()
-            reader.exited = False
+    Returns:
+       Variable: A Reader from which we can get feeding data.
 
-    def __start__():
-        start_provide_thread(reader.tensor_provider)
+    Examples:
 
-    reader.reset = __reset__
-    reader.decorate_tensor_provider = __set_tensor_provider__
-    reader.decorate_paddle_reader = __set_paddle_reader__
-    reader.start = __start__
+        1. The basic usage of :code:`py_reader` is as follows:
 
-    return reader
+        >>> import paddle.fluid as fluid
+        >>> import paddle.dataset.mnist as mnist
+        >>>
+        >>> image = fluid.layers.data(name='image', shape=[3,224,224], dtypes='float32')
+        >>> label = fluid.layers.data(name='label', shape=[1], dtypes='int64')
+        >>> reader = fluid.layers.create_py_reader_by_data(capacity=64, feed_list=[image, label])
+        >>> reader.decorate_paddle_reader(
+        >>>     paddle.reader.shuffle(paddle.batch(mnist.train())
+        >>>
+        >>> img, label = fluid.layers.read_file(reader)
+        >>> loss = network(img, label) # some network definition
+        >>>
+        >>> fluid.Executor(fluid.CUDAPlace(0)).run(fluid.default_startup_program())
+        >>>
+        >>> exe = fluid.ParallelExecutor(use_cuda=True, loss_name=loss.name)
+        >>> for epoch_id in range(10):
+        >>>     reader.start()
+        >>>     try:
+        >>>         while True:
+        >>>             exe.run(fetch_list=[loss.name])
+        >>>     except fluid.core.EOFException:
+        >>>         reader.reset()
+    """
+    return _py_reader(
+        capacity=capacity,
+        shapes=None,
+        dtypes=None,
+        lod_levels=None,
+        name=name,
+        use_double_buffer=use_double_buffer,
+        feed_list=feed_list)
 
 
 def open_files(filenames,

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

@@ -53,13 +53,22 @@ def simple_fc_net(in_size,
                   hidden_sizes,
                   batch_size,
                   queue_capacity,
-                  use_double_buffer=False):
-    reader = fluid.layers.py_reader(
-        capacity=queue_capacity,
-        shapes=[[-1, in_size], [-1, 1]],
-        lod_levels=[0, 0],
-        dtypes=['float32', 'int64'],
-        use_double_buffer=False)
+                  use_double_buffer=False,
+                  use_feed_list=True):
+    if use_feed_list:
+        data = fluid.layers.data(name="data", dtype='float32', shape=[in_size])
+        label = fluid.layers.data(name='label', dtype='int64', shape=[1])
+        reader = fluid.layers.create_py_reader_by_data(
+            capacity=queue_capacity,
+            use_double_buffer=False,
+            feed_list=[data, label])
+    else:
+        reader = fluid.layers.py_reader(
+            capacity=queue_capacity,
+            shapes=[[-1, in_size], [-1, 1]],
+            lod_levels=[0, 0],
+            dtypes=['float32', 'int64'],
+            use_double_buffer=False)
     feed_queue = reader.queue
     reader = fluid.layers.batch(reader, batch_size=batch_size)
     if use_double_buffer:
@@ -100,14 +109,16 @@ class TestPyReaderUsingExecutor(unittest.TestCase):
                          if core.is_compiled_with_cuda() else [False]):
             for use_parallel_executor in [False, True]:
                 for use_double_buffer in [False, True]:
-                    print('Test Parameters:'),
-                    print({
-                        'use_cuda': use_cuda,
-                        'use_parallel_executor': use_parallel_executor,
-                        'use_double_buffer': use_double_buffer
-                    })
-                    self.main(use_cuda, use_parallel_executor,
-                              use_double_buffer)
+                    for use_feed_list in [False, True]:
+                        print('Test Parameters:'),
+                        print({
+                            'use_cuda': use_cuda,
+                            'use_parallel_executor': use_parallel_executor,
+                            'use_double_buffer': use_double_buffer,
+                            'use_feed_list': use_feed_list
+                        })
+                        self.main(use_cuda, use_parallel_executor,
+                                  use_double_buffer, use_feed_list)
 
     def random_reader(self):
         def reader():
@@ -143,12 +154,14 @@ class TestPyReaderUsingExecutor(unittest.TestCase):
     def main(self,
              use_cuda=True,
              use_parallel_executor=False,
-             use_double_buffer=False):
+             use_double_buffer=False,
+             use_feed_list=False):
         assert not use_cuda or use_cuda and core.is_compiled_with_cuda()
 
         self.use_cuda = use_cuda
         self.use_parallel_executor = use_parallel_executor
         self.use_double_buffer = use_double_buffer
+        self.use_feed_list = use_feed_list
 
         startup_program = fluid.Program()
         main_program = fluid.Program()
@@ -160,7 +173,8 @@ class TestPyReaderUsingExecutor(unittest.TestCase):
                 hidden_sizes=self.hidden_sizes,
                 batch_size=self.batch_size,
                 queue_capacity=self.queue_capacity,
-                use_double_buffer=self.use_double_buffer)
+                use_double_buffer=self.use_double_buffer,
+                use_feed_list=self.use_feed_list)
 
             place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
 

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

@@ -243,5 +243,87 @@ class TestKeepDimReduceSumMultiAxises(OpTest):
         self.check_grad(['X'], 'Out')
 
 
+class TestReduceSumWithDimOne(OpTest):
+    def setUp(self):
+        self.op_type = "reduce_sum"
+        self.inputs = {'X': np.random.random((10, 1, 1)).astype("float64")}
+        self.attrs = {'dim': [1, 2], 'keep_dim': True}
+        self.outputs = {
+            'Out': self.inputs['X'].sum(axis=tuple(self.attrs['dim']),
+                                        keepdims=True)
+        }
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(['X'], 'Out')
+
+
+class TestReduceSumWithNumelOne(OpTest):
+    def setUp(self):
+        self.op_type = "reduce_sum"
+        self.inputs = {'X': np.random.random((1, 1)).astype("float64")}
+        self.attrs = {'dim': [1], 'keep_dim': False}
+        self.outputs = {
+            'Out': self.inputs['X'].sum(axis=tuple(self.attrs['dim']),
+                                        keepdims=False)
+        }
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(['X'], 'Out')
+
+
+class TestReduceMeanWithDimOne(OpTest):
+    def setUp(self):
+        self.op_type = "reduce_mean"
+        self.inputs = {'X': np.random.random((10, 1, 1)).astype("float64")}
+        self.attrs = {'dim': [1], 'keep_dim': False}
+        self.outputs = {
+            'Out': self.inputs['X'].mean(
+                axis=tuple(self.attrs['dim']), keepdims=False)
+        }
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(['X'], 'Out')
+
+
+class TestReduceMeanWithNumelOne(OpTest):
+    def setUp(self):
+        self.op_type = "reduce_mean"
+        self.inputs = {'X': np.random.random((1, 1)).astype("float64")}
+        self.attrs = {'dim': [1], 'keep_dim': True}
+        self.outputs = {
+            'Out': self.inputs['X'].mean(
+                axis=tuple(self.attrs['dim']), keepdims=True)
+        }
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(['X'], 'Out')
+
+
+class TestReduceAll(OpTest):
+    def setUp(self):
+        self.op_type = "reduce_sum"
+        self.inputs = {'X': np.random.random((1, 1, 1)).astype("float64")}
+        self.attrs = {'reduce_all': True, 'keep_dim': False}
+        self.outputs = {'Out': self.inputs['X'].sum()}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(['X'], 'Out')
+
+
 if __name__ == '__main__':
     unittest.main()