mirror of
https://github.com/saymrwulf/pytorch.git
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ca0ac3a74b
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/72741 as titled. Context: This is useful in fast mitigating feature induced overfitting in the sense that we can do omni-transfer on a trained model and apply dropout with ratio = 1 on features resulting in overfitting. Directly removing the features would not be feasible on omni-transfer scenarios since the downstream FC sizes would change. Experimental records: https://fb.quip.com/npIkAgRc8jl9#temp:C:DWC050ceaba14424d23a78462c01 Doing dropout = 1 on selected features improves the eval NE over the next few hours (compared to v0 baseline) as is shown in the figures. Test Plan: ``` buck test caffe2/caffe2/python/operator_test:dropout_op_test ``` Reviewed By: ustctf Differential Revision: D34178732 fbshipit-source-id: 533feebe21bc582eefd756de397d5c7807c7438d (cherry picked from commit 5dabf9c484c0bc5410e3700e3010cdabb4bf903c)
108 lines
4.3 KiB
Python
108 lines
4.3 KiB
Python
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from hypothesis import assume, given, settings
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import hypothesis.strategies as st
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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
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import caffe2.python.hypothesis_test_util as hu
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import caffe2.python.serialized_test.serialized_test_util as serial
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class TestDropout(serial.SerializedTestCase):
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@serial.given(X=hu.tensor(),
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in_place=st.booleans(),
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ratio=st.floats(0, 0.999),
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engine=st.sampled_from(["", "CUDNN"]),
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**hu.gcs)
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def test_dropout_is_test(self, X, in_place, ratio, engine, gc, dc):
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"""Test with is_test=True for a deterministic reference impl."""
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# TODO(lukeyeager): enable this path when the GPU path is fixed
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if in_place:
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# Skip if trying in-place on GPU
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assume(not (gc.device_type in {caffe2_pb2.CUDA, caffe2_pb2.HIP} and engine == ''))
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# If in-place on CPU, don't compare with GPU
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dc = dc[:1]
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op = core.CreateOperator("Dropout", ["X"],
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["X" if in_place else "Y"],
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ratio=ratio, engine=engine, is_test=True)
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self.assertDeviceChecks(dc, op, [X], [0])
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# No sense in checking gradients for test phase
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def reference_dropout_test(x):
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return x, np.ones(x.shape, dtype=np.bool)
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self.assertReferenceChecks(
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gc, op, [X], reference_dropout_test,
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# The 'mask' output may be uninitialized
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outputs_to_check=[0])
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@given(X=hu.tensor(),
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in_place=st.booleans(),
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output_mask=st.booleans(),
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engine=st.sampled_from(["", "CUDNN"]),
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**hu.gcs)
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@settings(deadline=10000)
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def test_dropout_ratio0(self, X, in_place, output_mask, engine, gc, dc):
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"""Test with ratio=0 for a deterministic reference impl."""
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# TODO(lukeyeager): enable this path when the op is fixed
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if in_place:
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# Skip if trying in-place on GPU
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assume(gc.device_type not in {caffe2_pb2.CUDA, caffe2_pb2.HIP})
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# If in-place on CPU, don't compare with GPU
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dc = dc[:1]
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is_test = not output_mask
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op = core.CreateOperator("Dropout", ["X"],
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["X" if in_place else "Y"] +
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(["mask"] if output_mask else []),
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ratio=0.0, engine=engine,
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is_test=is_test)
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self.assertDeviceChecks(dc, op, [X], [0])
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if not is_test:
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self.assertGradientChecks(gc, op, [X], 0, [0])
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def reference_dropout_ratio0(x):
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return (x,) if is_test else (x, np.ones(x.shape, dtype=np.bool))
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self.assertReferenceChecks(
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gc, op, [X], reference_dropout_ratio0,
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# Don't check the mask with cuDNN because it's packed data
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outputs_to_check=None if engine != 'CUDNN' else [0])
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@given(X=hu.tensor(),
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in_place=st.booleans(),
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output_mask=st.booleans(),
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engine=st.sampled_from(["", "CUDNN"]),
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**hu.gcs)
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@settings(deadline=10000)
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def test_dropout_ratio1(self, X, in_place, output_mask, engine, gc, dc):
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"""Test with ratio=0 for a deterministic reference impl."""
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if in_place:
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# Skip if trying in-place on GPU
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assume(gc.device_type not in {caffe2_pb2.CUDA, caffe2_pb2.HIP})
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# If in-place on CPU, don't compare with GPU
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dc = dc[:1]
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is_test = not output_mask
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op = core.CreateOperator("Dropout", ["X"],
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["X" if in_place else "Y"] +
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(["mask"] if output_mask else []),
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ratio=1.0, engine=engine,
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is_test=is_test)
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self.assertDeviceChecks(dc, op, [X], [0])
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if not is_test:
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self.assertGradientChecks(gc, op, [X], 0, [0])
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def reference_dropout_ratio1(x):
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return (x,) if is_test else (np.zeros(x.shape, dtype=np.float), np.zeros(x.shape, dtype=np.bool))
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self.assertReferenceChecks(
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gc, op, [X], reference_dropout_ratio1,
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# Don't check the mask with cuDNN because it's packed data
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outputs_to_check=None if engine != 'CUDNN' else [0])
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