mirror of
https://github.com/saymrwulf/pytorch.git
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e3ea3e8c12
Summary: Closes https://github.com/caffe2/caffe2/pull/102 Differential Revision: D4448886 Pulled By: Yangqing fbshipit-source-id: 914d11cd79107895a9755154df3526fcf71a31ea
80 lines
3.2 KiB
Python
80 lines
3.2 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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import unittest
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import numpy as np
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from caffe2.proto import caffe2_pb2
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from caffe2.python import cnn, core, workspace, test_util
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@unittest.skipIf(not workspace.C.has_mkldnn, "Skipping as we do not have mkldnn.")
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class TestMKLBasic(test_util.TestCase):
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def testReLUSpeed(self):
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X = np.random.randn(128, 4096).astype(np.float32)
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mkl_do = core.DeviceOption(caffe2_pb2.MKLDNN)
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# Makes sure that feed works.
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workspace.FeedBlob("X", X)
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workspace.FeedBlob("X_mkl", X, device_option=mkl_do)
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net = core.Net("test")
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# Makes sure that we can run relu.
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net.Relu("X", "Y")
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net.Relu("X_mkl", "Y_mkl", device_option=mkl_do)
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workspace.CreateNet(net)
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workspace.RunNet(net)
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# makes sure that the results are good.
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np.testing.assert_allclose(
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workspace.FetchBlob("Y"),
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workspace.FetchBlob("Y_mkl"),
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atol=1e-10,
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rtol=1e-10)
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runtime = workspace.BenchmarkNet(net.Proto().name, 1, 100, True)
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# The returned runtime is the time of
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# [whole_net, cpu_op, mkl_op]
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# so we will assume that the MKL one runs faster than the CPU one.
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# Note(Yangqing): in fact, it seems that in optimized mode, this is
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# not always guaranteed - MKL runs slower than the Eigen vectorized
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# version, so I am turning this assertion off.
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#self.assertTrue(runtime[1] >= runtime[2])
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print("Relu CPU runtime {}, MKL runtime {}.".format(runtime[1], runtime[2]))
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def testConvSpeed(self):
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# We randomly select a shape to test the speed. Intentionally we
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# test a batch size of 1 since this may be the most frequent use
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# case for MKL during deployment time.
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X = np.random.rand(1, 256, 27, 27).astype(np.float32) - 0.5
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W = np.random.rand(192, 256, 3, 3).astype(np.float32) - 0.5
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b = np.random.rand(192).astype(np.float32) - 0.5
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mkl_do = core.DeviceOption(caffe2_pb2.MKLDNN)
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# Makes sure that feed works.
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workspace.FeedBlob("X", X)
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workspace.FeedBlob("W", W)
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workspace.FeedBlob("b", b)
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workspace.FeedBlob("X_mkl", X, device_option=mkl_do)
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workspace.FeedBlob("W_mkl", W, device_option=mkl_do)
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workspace.FeedBlob("b_mkl", b, device_option=mkl_do)
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net = core.Net("test")
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# Makes sure that we can run relu.
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net.Conv(["X", "W", "b"], "Y", pad=1, stride=1, kernel=3)
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net.Conv(["X_mkl", "W_mkl", "b_mkl"], "Y_mkl",
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pad=1, stride=1, kernel=3, device_option=mkl_do)
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workspace.CreateNet(net)
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workspace.RunNet(net)
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# makes sure that the results are good.
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np.testing.assert_allclose(
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workspace.FetchBlob("Y"),
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workspace.FetchBlob("Y_mkl"),
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atol=1e-2,
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rtol=1e-2)
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runtime = workspace.BenchmarkNet(net.Proto().name, 1, 100, True)
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print("Conv CPU runtime {}, MKL runtime {}.".format(runtime[1], runtime[2]))
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if __name__ == '__main__':
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unittest.main()
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