mirror of
https://github.com/saymrwulf/pytorch.git
synced 2026-05-15 21:00:47 +00:00
b311f255d8
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/75739 The tests are failing on platform010 and blocking the upgrade. Skip the tests given that Caffe2 on GPU is no longer supported. Test Plan: signals Reviewed By: ezyang Differential Revision: D35614159 Pull Request resolved: https://github.com/pytorch/pytorch/pull/75767 Approved by: https://github.com/ezyang
108 lines
4.3 KiB
Python
108 lines
4.3 KiB
Python
|
|
|
|
|
|
|
|
|
|
from hypothesis import assume, given, settings
|
|
import hypothesis.strategies as st
|
|
import numpy as np
|
|
|
|
from caffe2.proto import caffe2_pb2
|
|
from caffe2.python import core
|
|
import caffe2.python.hypothesis_test_util as hu
|
|
import caffe2.python.serialized_test.serialized_test_util as serial
|
|
|
|
|
|
class TestDropout(serial.SerializedTestCase):
|
|
|
|
@serial.given(X=hu.tensor(),
|
|
in_place=st.booleans(),
|
|
ratio=st.floats(0, 0.999),
|
|
engine=st.sampled_from(["", "CUDNN"]),
|
|
**hu.gcs_cpu_only)
|
|
def test_dropout_is_test(self, X, in_place, ratio, engine, gc, dc):
|
|
"""Test with is_test=True for a deterministic reference impl."""
|
|
# TODO(lukeyeager): enable this path when the GPU path is fixed
|
|
if in_place:
|
|
# Skip if trying in-place on GPU
|
|
assume(not (gc.device_type in {caffe2_pb2.CUDA, caffe2_pb2.HIP} and engine == ''))
|
|
# If in-place on CPU, don't compare with GPU
|
|
dc = dc[:1]
|
|
|
|
op = core.CreateOperator("Dropout", ["X"],
|
|
["X" if in_place else "Y"],
|
|
ratio=ratio, engine=engine, is_test=True)
|
|
|
|
self.assertDeviceChecks(dc, op, [X], [0])
|
|
# No sense in checking gradients for test phase
|
|
|
|
def reference_dropout_test(x):
|
|
return x, np.ones(x.shape, dtype=np.bool)
|
|
self.assertReferenceChecks(
|
|
gc, op, [X], reference_dropout_test,
|
|
# The 'mask' output may be uninitialized
|
|
outputs_to_check=[0])
|
|
|
|
@given(X=hu.tensor(),
|
|
in_place=st.booleans(),
|
|
output_mask=st.booleans(),
|
|
engine=st.sampled_from(["", "CUDNN"]),
|
|
**hu.gcs_cpu_only)
|
|
@settings(deadline=10000)
|
|
def test_dropout_ratio0(self, X, in_place, output_mask, engine, gc, dc):
|
|
"""Test with ratio=0 for a deterministic reference impl."""
|
|
# TODO(lukeyeager): enable this path when the op is fixed
|
|
if in_place:
|
|
# Skip if trying in-place on GPU
|
|
assume(gc.device_type not in {caffe2_pb2.CUDA, caffe2_pb2.HIP})
|
|
# If in-place on CPU, don't compare with GPU
|
|
dc = dc[:1]
|
|
is_test = not output_mask
|
|
op = core.CreateOperator("Dropout", ["X"],
|
|
["X" if in_place else "Y"] +
|
|
(["mask"] if output_mask else []),
|
|
ratio=0.0, engine=engine,
|
|
is_test=is_test)
|
|
|
|
self.assertDeviceChecks(dc, op, [X], [0])
|
|
if not is_test:
|
|
self.assertGradientChecks(gc, op, [X], 0, [0])
|
|
|
|
def reference_dropout_ratio0(x):
|
|
return (x,) if is_test else (x, np.ones(x.shape, dtype=np.bool))
|
|
self.assertReferenceChecks(
|
|
gc, op, [X], reference_dropout_ratio0,
|
|
# Don't check the mask with cuDNN because it's packed data
|
|
outputs_to_check=None if engine != 'CUDNN' else [0])
|
|
|
|
|
|
@given(X=hu.tensor(),
|
|
in_place=st.booleans(),
|
|
output_mask=st.booleans(),
|
|
engine=st.sampled_from(["", "CUDNN"]),
|
|
**hu.gcs_cpu_only)
|
|
@settings(deadline=10000)
|
|
def test_dropout_ratio1(self, X, in_place, output_mask, engine, gc, dc):
|
|
"""Test with ratio=0 for a deterministic reference impl."""
|
|
if in_place:
|
|
# Skip if trying in-place on GPU
|
|
assume(gc.device_type not in {caffe2_pb2.CUDA, caffe2_pb2.HIP})
|
|
# If in-place on CPU, don't compare with GPU
|
|
dc = dc[:1]
|
|
is_test = not output_mask
|
|
op = core.CreateOperator("Dropout", ["X"],
|
|
["X" if in_place else "Y"] +
|
|
(["mask"] if output_mask else []),
|
|
ratio=1.0, engine=engine,
|
|
is_test=is_test)
|
|
|
|
self.assertDeviceChecks(dc, op, [X], [0])
|
|
if not is_test:
|
|
self.assertGradientChecks(gc, op, [X], 0, [0])
|
|
|
|
def reference_dropout_ratio1(x):
|
|
return (x,) if is_test else (np.zeros(x.shape, dtype=np.float), np.zeros(x.shape, dtype=np.bool))
|
|
self.assertReferenceChecks(
|
|
gc, op, [X], reference_dropout_ratio1,
|
|
# Don't check the mask with cuDNN because it's packed data
|
|
outputs_to_check=None if engine != 'CUDNN' else [0])
|