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check generated_op_

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testDrivenWord2Vec
chengduoZH 7 years ago
parent 2e5d44f102
commit aff8a26d71
3 changed files with 26 additions and 30 deletions
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2
paddle/fluid/framework/details/nccl_all_reduce_op_handle.cc
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@ -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_;

2
paddle/fluid/framework/details/send_op_handle.cc
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@ -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

52
python/paddle/fluid/tests/unittests/test_parallel_executor.py
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@ -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 @@ class TestParallelExecutorBase(unittest.TestCase):
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 @@ class TestParallelExecutorBase(unittest.TestCase):
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 @@ class TestMNIST(TestParallelExecutorBase):
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 @@ class TestMNIST(TestParallelExecutorBase):
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 @@ class TestMNIST(TestParallelExecutorBase):
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 @@ class ParallelExecutorTestingDuringTraining(unittest.TestCase):
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 @@ class ParallelExecutorTestingDuringTraining(unittest.TestCase):
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 @@ class TestCRFModel(unittest.TestCase):
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 @@ class TestCRFModel(unittest.TestCase):
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 @@ class TestFetchOp(unittest.TestCase):
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 @@ class TestFetchOp(unittest.TestCase):
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)

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