未验证 提交 8b1918f5 编写于 作者: C chengduo 提交者: GitHub

Merge pull request #10454 from chengduoZH/fix_fetchop

Fix fetch_op_handle
......@@ -49,7 +49,9 @@ void FetchOpHandle::RunImpl() {
platform::DeviceContextPool::Instance().Get(platform::CPUPlace());
for (auto *input : inputs_) {
auto *var = static_cast<VarHandle *>(input);
var->generated_op_->Wait(cpu_ctx);
if (var->generated_op_) {
var->generated_op_->Wait(cpu_ctx);
}
}
tensors_.resize(inputs_.size());
auto *var_handle = static_cast<VarHandle *>(inputs_[0]);
......
......@@ -36,7 +36,9 @@ 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_;
......
......@@ -32,7 +32,9 @@ 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
......
......@@ -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]
......@@ -721,3 +717,84 @@ class TestCRFModel(unittest.TestCase):
def test_update_dense_parameter(self):
self.check_network_convergence(is_sparse=False)
# test fetch all the variables of global_block
import paddle.dataset.flowers as flowers
import math
def Lenet(data, class_dim):
conv1 = fluid.layers.conv2d(data, 32, 5, 1, act=None)
bn1 = fluid.layers.batch_norm(conv1, act='relu')
pool1 = fluid.layers.pool2d(bn1, 2, 'max', 2)
conv2 = fluid.layers.conv2d(pool1, 50, 5, 1, act=None)
bn2 = fluid.layers.batch_norm(conv2, act='relu')
pool2 = fluid.layers.pool2d(bn2, 2, 'max', 2)
fc1 = fluid.layers.fc(pool2, size=500, act='relu')
fc2 = fluid.layers.fc(fc1, size=class_dim, act='softmax')
return fc2
class TestFetchOp(unittest.TestCase):
def parallel_exe(self, train_inputs, seed):
main = fluid.Program()
startup = fluid.Program()
startup.random_seed = seed
with fluid.program_guard(main, startup):
data = fluid.layers.data(
name='image', shape=[3, 224, 224], dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
out = Lenet(data, class_dim=102)
loss = fluid.layers.cross_entropy(input=out, label=label)
loss = fluid.layers.mean(loss)
opt = fluid.optimizer.Momentum(
learning_rate=0.1,
momentum=0.9,
regularization=fluid.regularizer.L2Decay(1e-4))
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. Those variables should not be pruned.
# fluid.memory_optimize(main)
place = fluid.CUDAPlace(0)
exe = fluid.Executor(place)
exe.run(startup)
feeder = fluid.DataFeeder(place=place, feed_list=[data, label])
pe = fluid.ParallelExecutor(
use_cuda=True, loss_name=loss.name, main_program=main)
fetch_list = []
all_vars = main.global_block().vars
for k, v in all_vars.iteritems():
if 'tmp' not in k and k[0] is not '_' or v.persistable:
fetch_list.append(k)
for data in train_inputs:
ret = pe.run(fetch_list, feed=feeder.feed(data))
for i in range(len(fetch_list)):
assert not math.isnan(np.sum(ret[i])) and \
not math.isinf(np.sum(ret[i]))
def test_update_sparse_parameter(self):
tst_reader = paddle.batch(flowers.test(use_xmap=False), batch_size=16)
tst_reader_iter = tst_reader()
iters = 3
train_inputs = []
for i in range(iters):
train_inputs.append(tst_reader_iter.next())
self.parallel_exe(train_inputs, seed=1)
if __name__ == '__main__':
unittest.main()
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