Init commit

python/paddle/v2/fluid/tests/test_parallel_op.py

 import unittest
-
-import paddle.v2.fluid.layers as layers
 import paddle.v2.fluid as fluid
-from paddle.v2.fluid.framework import Program
-from paddle.v2.fluid.executor import Executor
-from paddle.v2.fluid.backward import append_backward
-import numpy as np
-import paddle.v2.fluid.core as core
-
-
-class ParallelOpTest(unittest.TestCase):
-    def setUp(self):
-        x = layers.data(
-            shape=[-1, 30, 40],
-            dtype='float32',
-            name='x',
-            append_batch_size=False,
-            stop_gradient=False)
-
-        places = layers.get_places(device_count=4)
-        pd = layers.ParallelDo(places=places)
-
-        with pd.do():
-            data = pd.read_input(x)
-            hidden = layers.fc(input=data, size=7)
-            pd.write_output(hidden)
-        data = pd()
-        loss = layers.mean(x=data)
-        sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
-        sgd_optimizer.minimize(loss)
-
-        exe = fluid.Executor(fluid.CPUPlace())
-        exe.run(fluid.default_startup_program())
-        exe.run(fluid.default_main_program(),
-                feed={
-                    x.name: np.random.uniform(0.1, 0.6,
-                                              (20, 30, 40)).astype("float32")
-                })
-
-    def test_forward(self):
-        pass
+import numpy
+
+
+class BaseParallelForTest(unittest.TestCase):
+    def main(self, callback, feed, fetch):
+        cpu = fluid.CPUPlace()
+        result_cpu = self._main_impl_(
+            callback=callback,
+            feed=feed,
+            fetch=fetch,
+            place=cpu,
+            use_parallel=False)
+        print result_cpu
+
+    def _main_impl_(self, callback, feed, fetch, place, use_parallel=False):
+        main = fluid.Program()
+        startup = fluid.Program()
+        # Fix seed
+        main.random_seed = 10
+        startup.random_seed = 10
+
+        with fluid.program_guard(main, startup):
+            generator = callback()
+            # Automatically insert parallel do if use_parallel = True
+            if use_parallel:
+                places = fluid.layers.get_places()
+                pd = fluid.layers.ParallelDo(places)
+                data = next(generator)
+
+                if isinstance(data, fluid.Variable):
+                    data = [data]
+                with pd.do():
+                    ins = map(pd.read_input, data)
+                    if len(ins) == 1:
+                        ins = ins[0]
+                    generator.send(ins)  # patch input
+                    loss = next(generator)
+                    pd.write_output(loss)
+
+                loss = pd()
+            else:
+                data = next(generator)
+                generator.send(data)
+                loss = next(generator)
+
+            avg_loss = fluid.layers.mean(x=loss)
+            fluid.backward.append_backward(loss=avg_loss)
+
+        exe = fluid.Executor(place)
+        exe.run(startup)
+        return exe.run(main, feed=feed, fetch_list=fetch)
+
+
+class ParallelOpTest(BaseParallelForTest):
+    def test_simple_fc(self):
+        def __network__():
+            x = fluid.layers.data(shape=[784], dtype='float32', name='img')
+            x = yield x
+            hidden = fluid.layers.fc(input=x, size=200, param_attr='fc1.w')
+            loss = fluid.layers.mean(x=hidden)
+            yield loss
+
+        self.main(
+            callback=__network__,
+            feed={
+                'img': numpy.random.random(size=(128, 784)).astype('float32')
+            },
+            fetch='fc1.w@GRAD')
 
 
 if __name__ == '__main__':