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Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/49980 From ``` ./python/libcst/libcst codemod remove_unused_imports.RemoveUnusedImportsWithGlean --no-format caffe2/ ``` Test Plan: Standard sandcastle tests Reviewed By: xush6528 Differential Revision: D25727359 fbshipit-source-id: c4f60005b10546423dc093d31d46deb418352286
107 lines
4.1 KiB
Python
107 lines
4.1 KiB
Python
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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 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 testMaxPoolingSpeed(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, 64, 224, 224).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.MaxPool("X", "Y", stride=2, kernel=3)
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net.MaxPool("X_mkl", "Y_mkl",
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stride=2, 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("Maxpooling CPU runtime {}, MKL runtime {}.".format(runtime[1], runtime[2]))
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def testAveragePoolingSpeed(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, 64, 224, 224).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.AveragePool("X", "Y", stride=2, kernel=3)
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net.AveragePool("X_mkl", "Y_mkl",
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stride=2, 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("Averagepooling CPU runtime {}, MKL runtime {}.".format(runtime[1], runtime[2]))
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def testConvReluMaxPoolSpeed(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, 3, 224, 224).astype(np.float32) - 0.5
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W = np.random.rand(64, 3, 11, 11).astype(np.float32) - 0.5
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b = np.random.rand(64).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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net.Conv(["X", "W", "b"], "C", pad=1, stride=1, kernel=11)
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net.Conv(["X_mkl", "W_mkl", "b_mkl"], "C_mkl",
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pad=1, stride=1, kernel=11, device_option=mkl_do)
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net.Relu("C", "R")
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net.Relu("C_mkl", "R_mkl", device_option=mkl_do)
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net.AveragePool("R", "Y", stride=2, kernel=3)
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net.AveragePool("R_mkl", "Y_mkl",
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stride=2, 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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if __name__ == '__main__':
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unittest.main()
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