check generated_op_

Author: chengduoZH

paddle/fluid/framework/details/nccl_all_reduce_op_handle.cc

@@ -36,7 +36,7 @@ void NCCLAllReduceOpHandle::RunImpl() {
     // Wait input done
     for (auto *in : inputs_) {
       auto &p = static_cast<VarHandle *>(in)->place_;
-      in->generated_op_->Wait(dev_ctxes_[p]);
+      if (in->generated_op_) in->generated_op_->Wait(dev_ctxes_[p]);
     }
 
     auto &var_name = static_cast<VarHandle *>(this->inputs_[0])->name_;

paddle/fluid/framework/details/send_op_handle.cc

@@ -32,7 +32,7 @@ void SendOpHandle::RunImpl() {
     if (in->DebugString() == "dummy") {  // HACK
       continue;
     }
-    in->generated_op_->Wait(dev_ctxes_[p]);
+    if (in->generated_op_) in->generated_op_->Wait(dev_ctxes_[p]);
   }
   auto &tmp_scope = local_scope_->FindVar(kLocalExecScopeName)->Get<Scope *>();
   // FIXME(wuyi): can not use RunAndRecordEvent here, for it will cause dead

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

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import numpy
+import numpy as np
 import unittest
 
 import paddle.fluid as fluid
@@ -243,7 +243,7 @@ def run_executor(exe, feed, fetch_list, program=None):
             begin = time.time()
             first_loss, = run_executor(
                 exe=exe, feed=feed_dict, fetch_list=[loss.name])
-            first_loss = numpy.array(first_loss)
+            first_loss = np.array(first_loss)
 
             for i in xrange(iter):
                 run_executor(exe=exe, feed=feed_dict, fetch_list=[])
@@ -256,7 +256,7 @@ def run_executor(exe, feed, fetch_list, program=None):
                 print "%.4f Instance per second" % (
                     (batch_size * iter + 2) / (end - begin))
 
-            last_loss = numpy.array(last_loss)
+            last_loss = np.array(last_loss)
 
             print first_loss, last_loss
             # self.assertGreater(first_loss[0], last_loss[0])
@@ -284,8 +284,8 @@ def check_simple_fc_convergence(self):
         self.check_network_convergence(simple_fc_net)
         self.check_network_convergence(simple_fc_net, allow_op_delay=True)
 
-        img = numpy.zeros(shape=[32, 784], dtype='float32')
-        label = numpy.ones(shape=[32, 1], dtype='int64')
+        img = np.zeros(shape=[32, 784], dtype='float32')
+        label = np.ones(shape=[32, 1], dtype='int64')
         self.check_network_convergence(
             simple_fc_net, feed_dict={"image": img,
                                       "label": label})
@@ -294,8 +294,8 @@ def test_simple_fc(self):
         self.check_simple_fc_convergence()
 
     def check_simple_fc_parallel_accuracy(self):
-        img = numpy.zeros(shape=[32, 784], dtype='float32')
-        label = numpy.ones(shape=[32, 1], dtype='int64')
+        img = np.zeros(shape=[32, 784], dtype='float32')
+        label = np.ones(shape=[32, 1], dtype='int64')
         single_first_loss, single_last_loss = self.check_network_convergence(
             method=simple_fc_net,
             seed=1000,
@@ -319,8 +319,8 @@ def test_simple_fc_parallel_accuracy(self):
 
     def check_batchnorm_fc_convergence(self):
         self.check_network_convergence(fc_with_batchnorm)
-        img = numpy.zeros(shape=[32, 784], dtype='float32')
-        label = numpy.ones(shape=[32, 1], dtype='int64')
+        img = np.zeros(shape=[32, 784], dtype='float32')
+        label = np.ones(shape=[32, 1], dtype='int64')
         self.check_network_convergence(
             fc_with_batchnorm, feed_dict={"image": img,
                                           "label": label})
@@ -404,9 +404,6 @@ class ModelHyperParams(object):
     dropout = 0.1
 
 
-import numpy as np
-
-
 def prepare_batch_input(insts, src_pad_idx, trg_pad_idx, n_head):
     """
     Pad the instances to the max sequence length in batch, and generate the
@@ -533,9 +530,8 @@ def check_network_convergence(self):
             opt.minimize(loss)
 
             batch_size = 32
-            image = numpy.random.normal(size=(batch_size,
-                                              784)).astype('float32')
-            label = numpy.random.randint(0, 10, (batch_size, 1), dtype="int64")
+            image = np.random.normal(size=(batch_size, 784)).astype('float32')
+            label = np.random.randint(0, 10, (batch_size, 1), dtype="int64")
 
             place = fluid.CUDAPlace(0)
             exe = fluid.Executor(place)
@@ -552,12 +548,12 @@ def check_network_convergence(self):
 
             for i in xrange(5):
                 test_loss, = test_exe.run([loss.name], feed=feed_dict)
-                test_loss = numpy.array(test_loss)
+                test_loss = np.array(test_loss)
 
                 train_loss, = train_exe.run([loss.name], feed=feed_dict)
-                train_loss = numpy.array(train_loss)
+                train_loss = np.array(train_loss)
                 self.assertTrue(
-                    numpy.allclose(
+                    np.allclose(
                         train_loss, test_loss, atol=1e-8),
                     "Train loss: " + str(train_loss) + "\n Test loss:" +
                     str(test_loss))
@@ -712,7 +708,7 @@ def check_network_convergence(self, is_sparse):
             data = train_data()
             for i in xrange(10):
                 cur_batch = next(data)
-                print map(numpy.array,
+                print map(np.array,
                           pe.run(feed=feeder.feed(cur_batch),
                                  fetch_list=[avg_cost.name]))[0]
 
@@ -723,7 +719,7 @@ def test_update_dense_parameter(self):
         self.check_network_convergence(is_sparse=False)
 
 
-# test fetch op
+# test fetch all the variables of global_block
 
 import paddle.dataset.flowers as flowers
 
@@ -763,7 +759,8 @@ def parallel_exe(self, train_inputs, seed):
             opt.minimize(loss)
 
             # TODO(zcd): I found that onece the memory optimizer is open,
-            # parallel_exe doesn't fetch some variable, such as conv2d_0.b_0@GRAD, conv2d_1.b_0@GRAD.
+            # parallel_exe doesn't fetch some variable, such as conv2d_0.b_0@GRAD,
+            # conv2d_1.b_0@GRAD. Those variables should not be pruned.
             # fluid.memory_optimize(main)
 
             place = fluid.CUDAPlace(0)
@@ -775,16 +772,15 @@ def parallel_exe(self, train_inputs, seed):
                 use_cuda=True, loss_name=loss.name, main_program=main)
 
             fetch_list = []
-            for data in train_inputs:
-                all_vars = main.global_block().vars
-                for k, v in all_vars.iteritems():
-                    if v.persistable and 'velocity' not in k:
-                        fetch_list.append(k)
+            all_vars = main.global_block().vars
+            for k, v in all_vars.iteritems():
+                if 'velocity' not in k:
+                    fetch_list.append(k)
 
+            for data in train_inputs:
                 ret = pe.run(fetch_list, feed=feeder.feed(data))
-                result = {}
                 for i in range(len(fetch_list)):
-                    result[fetch_list[i]] = np.sum(ret[i])
+                    print("%s - %s" % (fetch_list[i], np.sum(ret[i])))
 
     def test_update_sparse_parameter(self):
         tst_reader = paddle.batch(flowers.test(use_xmap=False), batch_size=16)