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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2018/05/11 09:31:30 UTC
[GitHub] marcoabreu closed pull request #10591: [MXNET-365] handle inplace
in mkldnn FallBackCompute
marcoabreu closed pull request #10591: [MXNET-365] handle inplace in mkldnn FallBackCompute
URL: https://github.com/apache/incubator-mxnet/pull/10591
This is a PR merged from a forked repository.
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diff --git a/Jenkinsfile b/Jenkinsfile
index d1dfd30f671..62ffb70e655 100644
--- a/Jenkinsfile
+++ b/Jenkinsfile
@@ -109,7 +109,7 @@ def python3_ut(docker_container_name) {
def python3_ut_mkldnn(docker_container_name) {
timeout(time: max_time, unit: 'MINUTES') {
- sh "ci/build.py --build --platform ${docker_container_name} /work/runtime_functions.sh unittest_ubuntu_python3_cpu_mkldnn"
+ sh "ci/build.py --platform ${docker_container_name} /work/runtime_functions.sh unittest_ubuntu_python3_cpu_mkldnn"
}
}
diff --git a/src/operator/nn/mkldnn/mkldnn_base.cc b/src/operator/nn/mkldnn/mkldnn_base.cc
index 2528d097d7f..61bcc5445e9 100644
--- a/src/operator/nn/mkldnn/mkldnn_base.cc
+++ b/src/operator/nn/mkldnn/mkldnn_base.cc
@@ -304,10 +304,15 @@ void FallBackCompute(FCompute fn, const nnvm::NodeAttrs &attrs,
std::vector<TBlob> out_blobs(outputs.size());
for (size_t i = 0; i < out_blobs.size(); i++) {
- if (req[i] == kWriteTo)
- const_cast<NDArray &>(outputs[i]).InvalidateMKLDNNData();
- CHECK(outputs[i].IsDefaultData());
- out_blobs[i] = outputs[i].data();
+ NDArray output = outputs[i];
+ // ensure output does not use mkldnn mem.
+ // for inplace, we already converted & copied input above.
+ if ((req[i] == kWriteTo) || (req[i] == kWriteInplace))
+ const_cast<NDArray &>(output).InvalidateMKLDNNData();
+ else if (req[i] == kAddTo)
+ output = outputs[i].Reorder2Default();
+ CHECK(output.IsDefaultData());
+ out_blobs[i] = output.data();
}
fn(attrs, ctx, in_blobs, req, out_blobs);
}
diff --git a/tests/python/mkl/test_mkldnn.py b/tests/python/mkl/test_mkldnn.py
index dc9e9141a35..2caf7af7eb4 100644
--- a/tests/python/mkl/test_mkldnn.py
+++ b/tests/python/mkl/test_mkldnn.py
@@ -18,57 +18,19 @@
"""
MKL-DNN related test cases
"""
-
-import mxnet as mx
+import sys
+import os
import numpy as np
-import sys,os,logging
+import mxnet as mx
+from mxnet.test_utils import assert_almost_equal
from mxnet import gluon
from mxnet.gluon import nn
curr_path = os.path.dirname(os.path.abspath(os.path.expanduser(__file__)))
sys.path.append(os.path.join(curr_path, '../unittest/'))
-from common import setup_module, with_seed
-from nose.tools import raises
-from mxnet.test_utils import assert_almost_equal
-
-
-def test_mkldnn_install():
- """
- This test will verify that MXNet is built/installed correctly when
- compiled with Intel MKL-DNN library. The method will try to import
- the mxnet module and see if the mkldnn library is mapped to this
- process's address space.
- """
- logging.basicConfig(level=logging.INFO)
-
- if not sys.platform.startswith('linux'):
- logging.info("Bypass mkldnn install test for non-Linux OS")
- return
-
- try:
- #pylint: disable=unused-variable
- import mxnet as mx
- except (ImportError, OSError) as e:
- assert 0, "Import mxnet error: %s. Please double check your build/" \
- "install steps or environment variable settings" % str(e)
-
- pid = os.getpid()
- rc = os.system("cat /proc/" + str(pid) +
- "/maps | grep libmkldnn > /dev/null")
-
- if rc == 0:
- logging.info("MXNet is built/installed correctly with MKL-DNN")
- else:
- assert 0, "MXNet is built/installed incorrectly with MKL-DNN, please " \
- "double check your build/install steps or environment " \
- "variable settings"
+from common import with_seed
def test_mkldnn_model():
- """
- This test will run a sample model for couple of iterations.
- """
-
- import mxnet as mx
model = os.path.join(os.path.dirname(os.path.realpath(__file__)), "data",
"test_mkldnn_test_mkldnn_model_model1.json")
shape = (32, 3, 300, 300)
@@ -96,17 +58,19 @@ def get_tensors(args, shapes, ctx):
except: # pylint: disable=bare-except
assert 0, "test_mkldnn_model exception in bind and execution"
-def test_mkldnn_engine_threading():
- """
- This test will trigger mkldnn engine on different thread of execution.
- The test will first kickoff simple model calculation, and then uses a
- gluon data iterator to trigger different thread context, and executes
- the model on this new thread.
- """
+def test_mkldnn_ndarray_slice():
+ ctx = mx.cpu()
+ net = gluon.nn.HybridSequential()
+ with net.name_scope():
+ net.add(gluon.nn.Conv2D(channels=32, kernel_size=3, activation=None))
+ net.collect_params().initialize(ctx=ctx)
+ x = mx.nd.array(np.ones([32, 3, 224, 224]), ctx)
+ y = net(x)
- import mxnet as mx
- from mxnet import gluon, nd
+ # trigger computation on ndarray slice
+ assert_almost_equal(y[0].asnumpy()[0, 0, 0], 0.3376348)
+def test_mkldnn_engine_threading():
net = gluon.nn.HybridSequential()
with net.name_scope():
net.add(gluon.nn.Conv2D(channels=32, kernel_size=3, activation=None))
@@ -121,47 +85,30 @@ def __getitem__(self, key):
X = (32, 3, 32, 32)
# trigger mkldnn execution thread
- y = net(nd.array(np.ones(X))).asnumpy()
+ y = net(mx.nd.array(np.ones(X))).asnumpy()
# Use Gluon dataloader to trigger different thread.
# below line triggers different execution thread
for _ in loader:
- y = net(nd.array(np.ones(X))).asnumpy()
- # output should have 0.3376348
- assert_almost_equal(y[0, 0, 0, 0], 0.3376348)
+ y = net(mx.nd.array(np.ones(X))).asnumpy()
+ # output should be 016711406 (non-mkldnn mode output)
+ assert_almost_equal(y[0, 0, 0, 0], 0.016711406)
break
-def test_mkldnn_ndarray_slice():
- """
- This test will trigger gluon computation on mkldnn with ndarray slice
- """
-
- import mxnet as mx
- from mxnet import gluon
- ctx = mx.cpu()
- net = gluon.nn.HybridSequential()
- with net.name_scope():
- net.add(gluon.nn.Conv2D(channels=32, kernel_size=3, activation=None))
- net.collect_params().initialize(ctx=ctx)
- x = mx.nd.array(np.ones([32, 3, 224, 224]), ctx)
- y = net(x)
-
- # trigger computation on ndarray slice
- assert_almost_equal(y[0].asnumpy()[0, 0, 0], 0.3376348)
@with_seed()
def test_reshape_before_conv():
- """
- This test will test gluon Conv2d computation on mkldnn with ndarray reshape
- """
class Net(gluon.HybridBlock):
+ """
+ test Net
+ """
def __init__(self, **kwargs):
super(Net, self).__init__(**kwargs)
with self.name_scope():
self.conv0 = nn.Conv2D(10, (3, 3))
self.conv1 = nn.Conv2D(5, (3, 3))
- def hybrid_forward(self, F, x):
+ def hybrid_forward(self, F, x, *args, **kwargs):
x_reshape = x.reshape((0, 0, 20, 5))
y = self.conv0(x_reshape)
y_reshape = y.reshape((0, 0, 9, 6))
@@ -185,17 +132,17 @@ def hybrid_forward(self, F, x):
@with_seed()
def test_slice_before_conv():
- """
- This test will test gluon Conv2d computation on mkldnn with ndarray slice
- """
class Net(gluon.HybridBlock):
+ """
+ test Net
+ """
def __init__(self, **kwargs):
super(Net, self).__init__(**kwargs)
with self.name_scope():
self.conv0 = nn.Conv2D(4, (3, 3))
self.conv1 = nn.Conv2D(4, (3, 3))
- def hybrid_forward(self, F, x):
+ def hybrid_forward(self, F, x, *args, **kwargs):
x_slice = x.slice(begin=(0, 0, 0, 0), end=(2, 4, 10, 10))
y = self.conv0(x_slice)
y_slice = y.slice(begin=(1, 0, 2, 2), end=(2, 1, 7, 7))
@@ -219,17 +166,17 @@ def hybrid_forward(self, F, x):
@with_seed()
def test_slice_reshape_before_conv():
- """
- This test will test gluon Conv2d computation on mkldnn with ndarray reshape and slice
- """
class Net(gluon.HybridBlock):
+ """
+ test Net
+ """
def __init__(self, **kwargs):
super(Net, self).__init__(**kwargs)
with self.name_scope():
self.conv0 = nn.Conv2D(4, (3, 3))
self.conv1 = nn.Conv2D(4, (3, 3))
- def hybrid_forward(self, F, x):
+ def hybrid_forward(self, F, x, *args, **kwargs):
x_slice = x.slice(begin=(0, 0, 0, 0), end=(2, 4, 8, 9))
y = self.conv0(x_slice)
y_reshape = y.reshape((0, 0, 14, 3))
@@ -251,5 +198,20 @@ def hybrid_forward(self, F, x):
mx.test_utils.assert_almost_equal(out1.asnumpy(), out2.asnumpy(), rtol=1e-5, atol=1e-6)
+def test_mkldnn_sum_inplace_with_cpu_layout():
+
+ x_shape = (32, 3, 224, 224)
+ x_npy = np.ones(x_shape)
+ y_shape = (32, 32, 222, 222)
+ y_npy = np.ones(y_shape)
+ x = mx.sym.Variable("x")
+ y = mx.sym.Variable("y")
+ z = mx.symbol.Convolution(data=x, num_filter=32, kernel=(3, 3))
+ z = mx.sym.add_n(z, y)
+ exe = z.simple_bind(ctx=mx.cpu(), x=x_shape, y=y_shape)
+ out = exe.forward(is_train=False, x=x_npy, y=y_npy)[0]
+ assert_almost_equal(out[0].asnumpy()[0, 0, 0], 1.0)
+
+
if __name__ == '__main__':
test_mkldnn_install()
diff --git a/tests/python/mkl/test_mkldnn_install.py b/tests/python/mkl/test_mkldnn_install.py
new file mode 100644
index 00000000000..c2f26df72f2
--- /dev/null
+++ b/tests/python/mkl/test_mkldnn_install.py
@@ -0,0 +1,56 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""
+MKL-DNN related test cases
+"""
+
+import sys
+import os
+import logging
+
+
+def test_mkldnn_install():
+ """
+ This test will verify that MXNet is built/installed correctly when
+ compiled with Intel MKL-DNN library. The method will try to import
+ the mxnet module and see if the mkldnn library is mapped to this
+ process's address space.
+ """
+ logging.basicConfig(level=logging.INFO)
+
+ if not sys.platform.startswith('linux'):
+ logging.info("Bypass mkldnn install test for non-Linux OS")
+ return
+
+ try:
+ #pylint: disable=unused-variable
+ import mxnet as mx
+ except (ImportError, OSError) as e:
+ assert 0, "Import mxnet error: %s. Please double check your build/" \
+ "install steps or environment variable settings" % str(e)
+
+ pid = os.getpid()
+ rc = os.system("cat /proc/" + str(pid) +
+ "/maps | grep libmkldnn > /dev/null")
+
+ if rc == 0:
+ logging.info("MXNet is built/installed correctly with MKL-DNN")
+ else:
+ assert 0, "MXNet is built/installed incorrectly with MKL-DNN, please " \
+ "double check your build/install steps or environment " \
+ "variable settings"
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