FM / mkl

/usr/local/lib64/python3.6/site-packages/caffe2/python/mkl/

/usr/local/lib64/python3.6/site-packages/caffe2/python/mkl

Name	Size	Mode	Actions
__pycache__/	-	0755	rm
mkl_concat_op_test.py	1173	0644	edit dl rm
mkl_conv_op_test.py	1684	0644	edit dl rm
mkl_copy_op_test.py	2149	0644	edit dl rm
mkl_elementwise_add_op_test.py	1201	0644	edit dl rm
mkl_elementwise_sum_op_test.py	1334	0644	edit dl rm
mkl_fc_op_test.py	927	0644	edit dl rm
mkl_fc_speed_test.py	3849	0644	edit dl rm
mkl_fill_op_test.py	1019	0644	edit dl rm
mkl_LRN_op_test.py	1160	0644	edit dl rm
mkl_LRN_speed_test.py	3182	0644	edit dl rm
mkl_pool_op_test.py	1332	0644	edit dl rm
mkl_pool_speed_test.py	4210	0644	edit dl rm
mkl_relu_op_test.py	988	0644	edit dl rm
mkl_sbn_op_test.py	3110	0644	edit dl rm
mkl_sbn_speed_test.py	4600	0644	edit dl rm
mkl_sigmoid_op_test.py	833	0644	edit dl rm
mkl_speed_test.py	3079	0644	edit dl rm
mkl_squeeze_op_test.py	968	0644	edit dl rm
rewrite_graph.py	8433	0644	edit dl rm
rewrite_graph_test.py	7889	0644	edit dl rm
__init__.py	0	0644	edit dl rm

Edit: /usr/local/lib64/python3.6/site-packages/caffe2/python/mkl/mkl_sbn_speed_test.py (4600B)

import unittest

import numpy as np
from caffe2.proto import caffe2_pb2
from caffe2.python import core, workspace, test_util

@unittest.skipIf(not workspace.C.has_mkldnn, "Skipping as we do not have mkldnn.")
class TestMKLBasic(test_util.TestCase):
    def testSpatialBNTestingSpeed(self):

input_channel = 10
        X = np.random.rand(1, input_channel, 100, 100).astype(np.float32) - 0.5
        scale = np.random.rand(input_channel).astype(np.float32) + 0.5
        bias = np.random.rand(input_channel).astype(np.float32) - 0.5
        mean = np.random.randn(input_channel).astype(np.float32)
        var = np.random.rand(input_channel).astype(np.float32) + 0.5

mkl_do = core.DeviceOption(caffe2_pb2.MKLDNN)
        # Makes sure that feed works.
        workspace.FeedBlob("X", X)
        workspace.FeedBlob("scale", scale)
        workspace.FeedBlob("bias", bias)
        workspace.FeedBlob("mean", mean)
        workspace.FeedBlob("var", var)
        workspace.FeedBlob("X_mkl", X, device_option=mkl_do)
        workspace.FeedBlob("scale_mkl", scale, device_option=mkl_do)
        workspace.FeedBlob("bias_mkl", bias, device_option=mkl_do)
        workspace.FeedBlob("mean_mkl", mean, device_option=mkl_do)
        workspace.FeedBlob("var_mkl", var, device_option=mkl_do)
        net = core.Net("test")
        # Makes sure that we can run relu.
        net.SpatialBN(["X", "scale", "bias","mean","var"], "Y", order="NCHW",
            is_test=True,
            epsilon=1e-5)
        net.SpatialBN(["X_mkl", "scale_mkl", "bias_mkl","mean_mkl","var_mkl"], "Y_mkl", order="NCHW",
            is_test=True,
            epsilon=1e-5, device_option=mkl_do)

workspace.CreateNet(net)
        workspace.RunNet(net)
        # makes sure that the results are good.
        np.testing.assert_allclose(
            workspace.FetchBlob("Y"),
            workspace.FetchBlob("Y_mkl"),
            atol=1e-2,
            rtol=1e-2)
        runtime = workspace.BenchmarkNet(net.Proto().name, 1, 100, True)

print("FC CPU runtime {}, MKL runtime {}.".format(runtime[1], runtime[2]))

def testSpatialBNTrainingSpeed(self):
        input_channel = 10
        X = np.random.rand(1, input_channel, 100, 100).astype(np.float32) - 0.5
        scale = np.random.rand(input_channel).astype(np.float32) + 0.5
        bias = np.random.rand(input_channel).astype(np.float32) - 0.5
        mean = np.random.randn(input_channel).astype(np.float32)
        var = np.random.rand(input_channel).astype(np.float32) + 0.5

#mean = np.zeros(input_channel)
        #var = np.zeros(input_channel)

mkl_do = core.DeviceOption(caffe2_pb2.MKLDNN)
        # Makes sure that feed works.
        workspace.FeedBlob("X", X)
        workspace.FeedBlob("scale", scale)
        workspace.FeedBlob("bias", bias)
        workspace.FeedBlob("mean", mean)
        workspace.FeedBlob("var", var)
        workspace.FeedBlob("X_mkl", X, device_option=mkl_do)
        workspace.FeedBlob("scale_mkl", scale, device_option=mkl_do)
        workspace.FeedBlob("bias_mkl", bias, device_option=mkl_do)
        workspace.FeedBlob("mean_mkl", mean, device_option=mkl_do)
        workspace.FeedBlob("var_mkl", var, device_option=mkl_do)
        net = core.Net("test")
        # Makes sure that we can run relu.
        net.SpatialBN(["X", "scale", "bias","mean", "var"],
            ["Y", "mean", "var", "saved_mean", "saved_var"],
            order="NCHW",
            is_test=False,
            epsilon=1e-5)
        net.SpatialBN(["X_mkl", "scale_mkl", "bias_mkl","mean_mkl","var_mkl"],
            ["Y_mkl", "mean_mkl", "var_mkl", "saved_mean_mkl", "saved_var_mkl"],
            order="NCHW",
            is_test=False,
            epsilon=1e-5,
            device_option=mkl_do)

workspace.CreateNet(net)
        workspace.RunNet(net)

# makes sure that the results are good.
        np.testing.assert_allclose(
            workspace.FetchBlob("Y"),
            workspace.FetchBlob("Y_mkl"),
            atol=1e-2,
            rtol=1e-2)
        np.testing.assert_allclose(
            workspace.FetchBlob("mean"),
            workspace.FetchBlob("mean_mkl"),
            atol=1e-2,
            rtol=1e-2)
        np.testing.assert_allclose(
            workspace.FetchBlob("var"),
            workspace.FetchBlob("var_mkl"),
            atol=1e-2,
            rtol=1e-2)

runtime = workspace.BenchmarkNet(net.Proto().name, 1, 100, True)

print("FC CPU runtime {}, MKL runtime {}.".format(runtime[1], runtime[2]))

if __name__ == '__main__':
    unittest.main()

import unittest import numpy as np from caffe2.proto import caffe2_pb2 from caffe2.python import core, workspace, test_util @unittest.skipIf(not workspace.C.has_mkldnn, "Skipping as we do not have mkldnn.") class TestMKLBasic(test_util.TestCase): def testSpatialBNTestingSpeed(self): input_channel = 10 X = np.random.rand(1, input_channel, 100, 100).astype(np.float32) - 0.5 scale = np.random.rand(input_channel).astype(np.float32) + 0.5 bias = np.random.rand(input_channel).astype(np.float32) - 0.5 mean = np.random.randn(input_channel).astype(np.float32) var = np.random.rand(input_channel).astype(np.float32) + 0.5 mkl_do = core.DeviceOption(caffe2_pb2.MKLDNN) # Makes sure that feed works. workspace.FeedBlob("X", X) workspace.FeedBlob("scale", scale) workspace.FeedBlob("bias", bias) workspace.FeedBlob("mean", mean) workspace.FeedBlob("var", var) workspace.FeedBlob("X_mkl", X, device_option=mkl_do) workspace.FeedBlob("scale_mkl", scale, device_option=mkl_do) workspace.FeedBlob("bias_mkl", bias, device_option=mkl_do) workspace.FeedBlob("mean_mkl", mean, device_option=mkl_do) workspace.FeedBlob("var_mkl", var, device_option=mkl_do) net = core.Net("test") # Makes sure that we can run relu. net.SpatialBN(["X", "scale", "bias","mean","var"], "Y", order="NCHW", is_test=True, epsilon=1e-5) net.SpatialBN(["X_mkl", "scale_mkl", "bias_mkl","mean_mkl","var_mkl"], "Y_mkl", order="NCHW", is_test=True, epsilon=1e-5, device_option=mkl_do) workspace.CreateNet(net) workspace.RunNet(net) # makes sure that the results are good. np.testing.assert_allclose( workspace.FetchBlob("Y"), workspace.FetchBlob("Y_mkl"), atol=1e-2, rtol=1e-2) runtime = workspace.BenchmarkNet(net.Proto().name, 1, 100, True) print("FC CPU runtime {}, MKL runtime {}.".format(runtime[1], runtime[2])) def testSpatialBNTrainingSpeed(self): input_channel = 10 X = np.random.rand(1, input_channel, 100, 100).astype(np.float32) - 0.5 scale = np.random.rand(input_channel).astype(np.float32) + 0.5 bias = np.random.rand(input_channel).astype(np.float32) - 0.5 mean = np.random.randn(input_channel).astype(np.float32) var = np.random.rand(input_channel).astype(np.float32) + 0.5 #mean = np.zeros(input_channel) #var = np.zeros(input_channel) mkl_do = core.DeviceOption(caffe2_pb2.MKLDNN) # Makes sure that feed works. workspace.FeedBlob("X", X) workspace.FeedBlob("scale", scale) workspace.FeedBlob("bias", bias) workspace.FeedBlob("mean", mean) workspace.FeedBlob("var", var) workspace.FeedBlob("X_mkl", X, device_option=mkl_do) workspace.FeedBlob("scale_mkl", scale, device_option=mkl_do) workspace.FeedBlob("bias_mkl", bias, device_option=mkl_do) workspace.FeedBlob("mean_mkl", mean, device_option=mkl_do) workspace.FeedBlob("var_mkl", var, device_option=mkl_do) net = core.Net("test") # Makes sure that we can run relu. net.SpatialBN(["X", "scale", "bias","mean", "var"], ["Y", "mean", "var", "saved_mean", "saved_var"], order="NCHW", is_test=False, epsilon=1e-5) net.SpatialBN(["X_mkl", "scale_mkl", "bias_mkl","mean_mkl","var_mkl"], ["Y_mkl", "mean_mkl", "var_mkl", "saved_mean_mkl", "saved_var_mkl"], order="NCHW", is_test=False, epsilon=1e-5, device_option=mkl_do) workspace.CreateNet(net) workspace.RunNet(net) # makes sure that the results are good. np.testing.assert_allclose( workspace.FetchBlob("Y"), workspace.FetchBlob("Y_mkl"), atol=1e-2, rtol=1e-2) np.testing.assert_allclose( workspace.FetchBlob("mean"), workspace.FetchBlob("mean_mkl"), atol=1e-2, rtol=1e-2) np.testing.assert_allclose( workspace.FetchBlob("var"), workspace.FetchBlob("var_mkl"), atol=1e-2, rtol=1e-2) runtime = workspace.BenchmarkNet(net.Proto().name, 1, 100, True) print("FC CPU runtime {}, MKL runtime {}.".format(runtime[1], runtime[2])) if __name__ == '__main__': unittest.main()