A naive trainer implementation

python/paddle/fluid/__init__.py

@@ -21,8 +21,7 @@ import executor
 from executor import *
 
 import trainer
-from trainer import Trainer
-from trainer import Event
+from trainer import *
 
 import inferencer
 from inferencer import Inferencer

python/paddle/fluid/layers/io.py

@@ -50,8 +50,6 @@ def data(name,
        dtype(int|float): The type of data : float32, float_16, int etc
        type(VarType): The output type. By default it is LOD_TENSOR.
        lod_level(int): The LoD Level. 0 means the input data is not a sequence.
-       main_program(Program): Name of the main program that calls this
-       startup_program(Program): Name of the startup program
        stop_gradient(bool): A boolean that mentions whether gradient should flow.
 
     Returns:
@@ -74,13 +72,15 @@ def data(name,
     if append_batch_size:
         shape = [-1] + shape  # append batch size as -1
 
-    return helper.create_global_variable(
+    data_var = helper.create_global_variable(
         name=name,
         shape=shape,
         dtype=dtype,
         type=type,
         stop_gradient=stop_gradient,
         lod_level=lod_level)
+    data_var.is_data = True
+    return data_var
 
 
 class BlockGuardServ(BlockGuard):

python/paddle/fluid/optimizer.py

@@ -28,7 +28,8 @@ from contextlib import contextmanager
 __all__ = [
     'SGD', 'Momentum', 'Adagrad', 'Adam', 'Adamax', 'DecayedAdagrad',
     'SGDOptimizer', 'MomentumOptimizer', 'AdagradOptimizer', 'AdamOptimizer',
-    'AdamaxOptimizer', 'DecayedAdagradOptimizer', 'Adadelta', 'ModelAverage'
+    'AdamaxOptimizer', 'DecayedAdagradOptimizer', 'Adadelta', 'ModelAverage',
+    'Optimizer'
 ]
 
 

python/paddle/fluid/tests/book/word2vec/no_test_word2vec_new_api.py

@@ -79,9 +79,9 @@ def inference_network(is_sparse):
     return predict_word
 
 
-def train_network():
+def train_network(is_sparse):
     next_word = fluid.layers.data(name='nextw', shape=[1], dtype='int64')
-    predict_word = inference_network()
+    predict_word = inference_network(is_sparse)
     cost = fluid.layers.cross_entropy(input=predict_word, label=next_word)
     avg_cost = fluid.layers.mean(cost)
     return avg_cost
@@ -94,7 +94,8 @@ def train(use_cuda, is_sparse, save_path):
     place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
 
     def event_handler(event):
-        if isinstance(event, fluid.Event.END_EPOCH):
+        print type(event)
+        if isinstance(event, fluid.EndEpochEvent):
             avg_cost = trainer.test(reader=paddle.dataset.imikolov.test(
                 word_dict, N))
 
@@ -105,10 +106,11 @@ def train(use_cuda, is_sparse, save_path):
                 sys.exit("got NaN loss, training failed.")
 
     trainer = fluid.Trainer(
-        partial(inference_network, is_sparse),
+        partial(train_network, is_sparse),
         fluid.optimizer.SGD(learning_rate=0.001),
         place=place)
-    trainer.train(train_reader, 100, event_handler)
+    trainer.train(
+        reader=train_reader, num_epochs=100, event_handler=event_handler)
 
 
 def infer(use_cuda, save_path):

python/paddle/fluid/trainer.py

@@ -12,44 +12,200 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import core
+import framework
+import executor
+import data_feeder
+import contextlib
+
+# optimizer is same as the parameter of Trainer.__init__. Rename it to opt_module
+import optimizer as opt_module
+
 __all__ = [
-    'Event',
     'Trainer',
+    'BeginEpochEvent',
+    'EndEpochEvent',
+    'BeginStepEvent',
+    'EndStepEvent',
 ]
 
 
-class Event(object):
-    BEGIN_EPOCH = 0
-    END_EPOCH = 1
-    BEGIN_STEP = 2
-    END_STEP = 3
+class BeginEpochEvent(object):
+    def __init__(self, epoch_id):
+        self.epoch = epoch_id
+
+
+class EndEpochEvent(object):
+    def __init__(self, epoch_id):
+        self.epoch = epoch_id
 
-    def __init__(self):
-        self.step = 0
-        self.epoch = 0
-        self.type = Event.BEGIN_EPOCH
+
+class BeginStepEvent(object):
+    def __init__(self, epoch_id, step_id):
+        self.epoch = epoch_id
+        self.step = step_id
+
+
+class EndStepEvent(object):
+    def __init__(self, epoch_id, step_id):
+        self.epoch = epoch_id
+        self.step = step_id
 
 
 class Trainer(object):
+    """
+
+    Args:
+        network_func(callable): A function which will return loss. The loss must be a scaler.
+        optimizer(optimizer.Optimizer): The optimizer should be an instance of Optimizer
+        params:
+        place: The device place of this trainer.
+    """
+
     def __init__(self, network_func, optimizer, params=None, place=None):
         # 1. we need to generate a framework.Program by calling
         # network_func. Reference: fluid.program_guard in
         # test_word2vec.py
+        self.scope = self._get_scope_from_params(params)
+
+        self.startup_program = framework.Program()
+        self.train_program = framework.Program()
+
+        with framework.program_guard(self.train_program, self.startup_program):
+            loss = network_func()
+            if not isinstance(optimizer, opt_module.Optimizer):
+                raise TypeError(
+                    "The optimizer should be an instance of Optimizer")
+
+            optimizer.minimize(loss)
+
+        self.place = Trainer._check_and_get_place(place)
 
         # 2. move the default_main_program to self.program and run the
         # default_startup program on an empty core.Scope()
+        # Run startup program
+        if params is None:
+            exe = executor.Executor(place)
+            exe.run(self.startup_program, scope=self.scope)
 
         # 3. call self.params.add_vars with the initialized scope, it
         # will add the new vars of the initialized scope into
         # self.params.
-        self.network_func = network_func
-        self.optimizer = optimizer
-        self.params = params
-        self.place = place
+        # TODO(yuyang): This depends on parameters implementation.
+
         # TODO(helin): support distributed training
 
-    def train(self, reader, num_epochs, event_handler):
-        pass
+    def train(self,
+              num_epochs,
+              event_handler,
+              reader=None,
+              parallel=False,
+              feed_order=None):
+        """
+        Train the model.
+
+        Args:
+            num_epochs: The number of epoch. An epoch will process all data in reader
+            event_handler: The event handler. A function with type (ev:Event)->void
+            reader:
+            parallel: True if use multi-CPUs or multi-GPUs
+            feed_order: Feeding order of reader. None will following the defining
+                order in program
+
+        Returns:
+
+        """
+        if parallel:
+            raise NotImplementedError(
+                "Parallel Executor version of trainer is not implemented")
+
+        self._train_by_executor(num_epochs, event_handler, reader, feed_order)
 
     def test(self, reader):
         pass
+
+    def _get_scope_from_params(self, params):
+        """
+        Get Scope from parameter object.
+        Args:
+            params(Parameter|None): The parameter object instance. Could be None.
+
+        Returns: New scope if params is None. Or params.scope()
+        NOTE: This method is WIP. Not fully implemented.
+        """
+        if params is None:
+            return core.Scope()  # new scope when params is None
+        else:
+            raise NotImplementedError("Not implemented right now.")
+
+    @staticmethod
+    def _check_and_get_place(place):
+        """
+        Check the type of place or get the default place
+        Args:
+            place(None|core.CUDAPlace|core.CPUPlace): the place that trainer will be executed on.
+
+        Raises:
+            TypeError if the type mismatched.
+
+        Returns:
+            the original place if it is not None.
+            if fluid is compiled with CUDA, returns CUDAPlace(0) by default.
+            Otherwise returns CPUPlace by default.
+        """
+        if place is None:
+            if core.is_compiled_with_cuda():
+                return core.CUDAPlace(0)
+            else:
+                return core.CPUPlace()
+        else:
+            if not isinstance(place, core.CUDAPlace) and not isinstance(
+                    place, core.CPUPlace):
+                raise TypeError("Place should be either CUDAPlace or CPUPlace")
+            return place
+
+    @contextlib.contextmanager
+    def _prog_and_scope_guard(self):
+        with framework.program_guard(
+                main_program=self.train_program,
+                startup_program=self.startup_program):
+            with executor.scope_guard(self.scope):
+                yield
+
+    def _train_by_executor(self, num_epochs, event_handler, reader, feed_order):
+        """
+        Train by Executor and single device.
+
+        Args:
+            num_epochs:
+            event_handler:
+            reader:
+            feed_order:
+
+        Returns:
+
+        """
+        with self._prog_and_scope_guard():
+            exe = executor.Executor(self.place)
+            if feed_order is None:
+                feed_var_list = [
+                    var
+                    for var in self.train_program.global_block(
+                    ).vars.itervalues()
+                    if hasattr(var, 'is_data') and var.is_data
+                ]
+            else:
+                feed_var_list = [
+                    self.train_program.global_block().var(var_name)
+                    for var_name in feed_order
+                ]
+
+            feeder = data_feeder.DataFeeder(
+                feed_list=feed_var_list, place=self.place)
+            for epoch_id in range(num_epochs):
+                event_handler(BeginEpochEvent(epoch_id))
+                for step_id, data in enumerate(reader()):
+                    event_handler(BeginStepEvent(epoch_id, step_id))
+                    exe.run(feed=feeder.feed(data), fetch_list=[])
+                    event_handler(EndStepEvent(epoch_id, step_id))
+                event_handler(EndEpochEvent(epoch_id))