mirror of
https://github.com/zebrajr/pytorch.git
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09c7771e9c
Summary: Action following https://github.com/pytorch/pytorch/issues/66232 Pull Request resolved: https://github.com/pytorch/pytorch/pull/66808 Reviewed By: mrshenli Differential Revision: D31761414 Pulled By: janeyx99 fbshipit-source-id: baf8c49ff9c4bcda7b0ea0f6aafd26380586e72d
112 lines
3.1 KiB
Python
112 lines
3.1 KiB
Python
# Owner(s): ["oncall: jit"]
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import os
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import sys
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import torch
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from torch import nn
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# Make the helper files in test/ importable
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pytorch_test_dir = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
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sys.path.append(pytorch_test_dir)
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from torch.testing._internal.jit_utils import JitTestCase
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if __name__ == '__main__':
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raise RuntimeError("This test file is not meant to be run directly, use:\n\n"
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"\tpython test/test_jit.py TESTNAME\n\n"
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"instead.")
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class Sequence(nn.Module):
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def __init__(self):
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super(Sequence, self).__init__()
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self.lstm1 = nn.LSTMCell(1, 51)
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self.lstm2 = nn.LSTMCell(51, 51)
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self.linear = nn.Linear(51, 1)
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def forward(self, input):
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outputs = []
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h_t = torch.zeros(input.size(0), 51)
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c_t = torch.zeros(input.size(0), 51)
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h_t2 = torch.zeros(input.size(0), 51)
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c_t2 = torch.zeros(input.size(0), 51)
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for input_t in input.split(1, dim=1):
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h_t, c_t = self.lstm1(input_t, (h_t, c_t))
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h_t2, c_t2 = self.lstm2(h_t, (h_t2, c_t2))
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output = self.linear(h_t2)
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outputs += [output]
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outputs = torch.cat(outputs, dim=1)
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return outputs
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class TestScriptProfile(JitTestCase):
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def test_basic(self):
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seq = torch.jit.script(Sequence())
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p = torch.jit._ScriptProfile()
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p.enable()
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seq(torch.rand((10, 100)))
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p.disable()
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self.assertNotEqual(p.dump_string(), "")
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def test_script(self):
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seq = Sequence()
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@torch.jit.script
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def fn():
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p = torch.jit._ScriptProfile()
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p.enable()
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_ = seq(torch.rand((10, 100)))
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p.disable()
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return p
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self.assertNotEqual(fn().dump_string(), "")
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def test_multi(self):
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seq = torch.jit.script(Sequence())
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profiles = [torch.jit._ScriptProfile() for _ in range(5)]
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for p in profiles:
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p.enable()
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last = None
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while len(profiles) > 0:
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seq(torch.rand((10, 10)))
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p = profiles.pop()
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p.disable()
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stats = p.dump_string()
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self.assertNotEqual(stats, "")
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if last:
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self.assertNotEqual(stats, last)
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last = stats
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def test_section(self):
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seq = Sequence()
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@torch.jit.script
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def fn():
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p = torch.jit._ScriptProfile()
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p.enable()
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_ = seq(torch.rand((10, 100)))
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p.disable()
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stats0 = p.dump_string()
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_ = seq(torch.rand((10, 10)))
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stats1 = p.dump_string()
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p.enable()
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_ = seq(torch.rand((10, 10)))
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p.disable()
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stats2 = p.dump_string()
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p.enable()
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return stats0, stats1, stats2
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s0, s1, s2 = fn()
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self.assertEqual(s0, s1)
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self.assertNotEqual(s1, s2)
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def test_empty(self):
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p = torch.jit._ScriptProfile()
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p.enable()
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p.disable()
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self.assertEqual(p.dump_string(), "")
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