move no_deadline to hypothesis_utils.py (#25598)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/25598

att

Test Plan:
CI

Imported from OSS

Differential Revision: D17192467

fbshipit-source-id: 9ee93b02cc293bb71ed114534d92eedda3ddee88

test/common_quantization.py

@@ -7,7 +7,6 @@ r"""Importing this file includes common utility methods and base clases for
 checking quantization api and properties of resulting modules.
 """
 
-import hypothesis
 import io
 import torch
 import torch.nn as nn
@@ -18,15 +17,6 @@ from torch.quantization import QuantWrapper, QuantStub, DeQuantStub, \
     default_qconfig, QConfig, default_observer, default_weight_observer, \
     default_qat_qconfig, propagate_qconfig, convert, DEFAULT_DYNAMIC_MODULE_MAPPING
 
-
-# Disable deadline testing if this version of hypthesis supports it, otherwise
-# just return the original function
-def no_deadline(fn):
-    try:
-        return hypothesis.settings(deadline=None)(fn)
-    except hypothesis.errors.InvalidArgument:
-        return fn
-
 def test_only_eval_fn(model, calib_data):
     r"""
     Default evaluation function takes a torch.utils.data.Dataset or a list of

test/hypothesis_utils.py

@@ -2,6 +2,7 @@ from collections import defaultdict
 import numpy as np
 import torch
 
+import hypothesis
 from hypothesis import assume
 from hypothesis import strategies as st
 from hypothesis.extra import numpy as stnp
@@ -250,3 +251,12 @@ def tensor_conv2d(draw,
     b = draw(tensor(shapes=(_out_channels,), elements=elements,
                     qparams=qparams[2]))
     return X, w, b, g
+
+
+# Disable deadline testing if this version of hypthesis supports it, otherwise
+# just return the original function
+def no_deadline(fn):
+    try:
+        return hypothesis.settings(deadline=None)(fn)
+    except hypothesis.errors.InvalidArgument:
+        return fn

test/test_fake_quant.py

@@ -6,7 +6,7 @@ import unittest
 from hypothesis import given
 from hypothesis import strategies as st
 import hypothesis_utils as hu
-from common_quantization import no_deadline
+from hypothesis_utils import no_deadline
 from common_utils import run_tests
 from torch.quantization import FakeQuantize
 

test/test_qat.py

@@ -8,10 +8,10 @@ from torch.nn import Conv2d, BatchNorm2d, ReLU
 from torch.nn._intrinsic.qat import ConvBn2d, ConvBnReLU2d
 from torch.quantization.QConfig import default_qat_qconfig
 from torch.utils.mkldnn import disable_mkldnn_conv
-from common_quantization import no_deadline
 from common_utils import TestCase, run_tests
 from hypothesis import given
 from hypothesis import strategies as st
+from hypothesis_utils import no_deadline
 from functools import reduce
 
 

test/test_quantized.py

@@ -9,10 +9,10 @@ from torch.nn.modules.utils import _pair
 from hypothesis import assume, given
 from hypothesis import strategies as st
 import hypothesis_utils as hu
+from hypothesis_utils import no_deadline
 
 from common_utils import TEST_WITH_UBSAN, TestCase, run_tests, IS_WINDOWS, IS_PPC
 from common_quantized import _quantize, _dequantize, _calculate_dynamic_qparams
-from common_quantization import no_deadline
 
 # Make sure we won't have overflows from vpmaddubsw instruction used in FBGEMM.
 # On the current Intel x86 architecture, we need to utilize vpmaddubsw instruction

test/test_quantized_nn_mods.py

@@ -7,10 +7,11 @@ from torch.nn.quantized.modules import Conv2d
 from torch.nn._intrinsic.quantized import ConvReLU2d
 import torch.quantization
 from common_utils import run_tests, tempfile
-from common_quantization import QuantizationTestCase, no_deadline, prepare_dynamic
+from common_quantization import QuantizationTestCase, prepare_dynamic
 from common_quantized import _calculate_dynamic_qparams
 from hypothesis import given
 from hypothesis import strategies as st
+from hypothesis_utils import no_deadline
 import unittest
 
 '''