[Operator Versioning][Test] Use hypothesis for better test input data and broader coverage (#70263)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/70263
Leverage the hypothesis library as it's more systematic way for testing. To write a test, it needs two parts:
1. A function that looks like a normal test in your test framework of choice but with some additional arguments
2. A given decorator that specifies how to provide those arguments.
ghstack-source-id: 146344955
Test Plan:
```
buck test mode/opt //caffe2/test:jit
python test/test_jit.py TestSaveLoadForOpVersion
```
Reviewed By: iseeyuan
Differential Revision: D33244389
fbshipit-source-id: c93d23f3d9575ebcb4e927a8caee42f4c3a6939d
diff --git a/test/jit/test_save_load_for_op_version.py b/test/jit/test_save_load_for_op_version.py
index d24ceaa..cb134c8 100644
--- a/test/jit/test_save_load_for_op_version.py
+++ b/test/jit/test_save_load_for_op_version.py
@@ -5,6 +5,8 @@
import os
import random
import sys
+import hypothesis.strategies as st
+from hypothesis import example, settings, given
import torch
@@ -100,6 +102,7 @@
node_count = sum(str(n).count(kind) for n in m.graph.nodes())
self.assertEqual(node_count, count)
+
"""
Tests that verify Torchscript remaps aten::div(_) from versions 0-3
to call either aten::true_divide(_), if an input is a float type,
@@ -109,7 +112,12 @@
div behavior has not yet been updated.
"""
- def test_versioned_div_tensor(self):
+ @settings(max_examples=10, deadline=200000) # A total of 10 examples will be generated
+ @given(
+ sample_input=st.tuples(st.integers(min_value=5, max_value=199), st.floats(min_value=5.0, max_value=199.0))
+ ) # Generate a pair (integer, float)
+ @example((2, 3, 2.0, 3.0)) # Ensure this example will be covered
+ def test_versioned_div_tensor(self, sample_input):
def historic_div(self, other):
if self.is_floating_point() or other.is_floating_point():
return self.true_divide(other)
@@ -142,8 +150,7 @@
current_mobile_module = self._save_load_mobile_module(MyModule)
self._verify_count("aten::div", current_module, 3)
- vals = (2., 3., 2, 3)
- for val_a, val_b in product(vals, vals):
+ for val_a, val_b in product(sample_input, sample_input):
a = torch.tensor((val_a,))
b = torch.tensor((val_b,))
@@ -162,7 +169,12 @@
_helper(current_module, torch.div)
_helper(current_mobile_module, torch.div)
- def test_versioned_div_tensor_inplace(self):
+ @settings(max_examples=10, deadline=200000) # A total of 10 examples will be generated
+ @given(
+ sample_input=st.tuples(st.integers(min_value=5, max_value=199), st.floats(min_value=5.0, max_value=199.0))
+ ) # Generate a pair (integer, float)
+ @example((2, 3, 2.0, 3.0)) # Ensure this example will be covered
+ def test_versioned_div_tensor_inplace(self, sample_input):
def historic_div_(self, other):
if self.is_floating_point() or other.is_floating_point():
return self.true_divide_(other)
@@ -190,8 +202,7 @@
current_mobile_module = self._save_load_mobile_module(MyModule)
self._verify_count("aten::div", current_module, 1)
- vals = (2., 3., 2, 3)
- for val_a, val_b in product(vals, vals):
+ for val_a, val_b in product(sample_input, sample_input):
a = torch.tensor((val_a,))
b = torch.tensor((val_b,))
@@ -212,7 +223,12 @@
_helper(current_module, torch.Tensor.div_)
_helper(current_mobile_module, torch.Tensor.div_)
- def test_versioned_div_tensor_out(self):
+ @settings(max_examples=10, deadline=200000) # A total of 10 examples will be generated
+ @given(
+ sample_input=st.tuples(st.integers(min_value=5, max_value=199), st.floats(min_value=5.0, max_value=199.0))
+ ) # Generate a pair (integer, float)
+ @example((2, 3, 2.0, 3.0)) # Ensure this example will be covered
+ def test_versioned_div_tensor_out(self, sample_input):
def historic_div_out(self, other, out):
if self.is_floating_point() or other.is_floating_point() or out.is_floating_point():
return torch.true_divide(self, other, out=out)
@@ -239,8 +255,7 @@
current_mobile_module = self._save_load_mobile_module(MyModule)
self._verify_count("aten::div", current_module, 1)
- vals = (2., 3., 2, 3)
- for val_a, val_b in product(vals, vals):
+ for val_a, val_b in product(sample_input, sample_input):
a = torch.tensor((val_a,))
b = torch.tensor((val_b,))
@@ -264,7 +279,12 @@
_helper(v3_mobile_module, historic_div_out)
_helper(current_mobile_module, torch.div)
- def test_versioned_div_scalar(self):
+ @settings(max_examples=10, deadline=200000) # A total of 10 examples will be generated
+ @given(
+ sample_input=st.tuples(st.integers(min_value=5, max_value=199), st.floats(min_value=5.0, max_value=199.0))
+ ) # Generate a pair (integer, float)
+ @example((2, 3, 2.0, 3.0)) # Ensure this example will be covered
+ def test_versioned_div_scalar(self, sample_input):
def historic_div_scalar_float(self, other: float):
return torch.true_divide(self, other)
@@ -309,8 +329,7 @@
for m in (current_module_float, current_module_int):
self._verify_count("aten::div", m, 1)
- vals = (2., 3., 2, 3)
- for val_a, val_b in product(vals, vals):
+ for val_a, val_b in product(sample_input, sample_input):
a = torch.tensor((val_a,))
b = val_b
@@ -334,7 +353,12 @@
_helper(v3_mobile_module_int, historic_div_scalar_int)
_helper(current_mobile_module_int, torch.div)
- def test_versioned_div_scalar_reciprocal(self):
+ @settings(max_examples=10, deadline=200000) # A total of 10 examples will be generated
+ @given(
+ sample_input=st.tuples(st.integers(min_value=5, max_value=199), st.floats(min_value=5.0, max_value=199.0))
+ ) # Generate a pair (integer, float)
+ @example((2, 3, 2.0, 3.0)) # Ensure this example will be covered
+ def test_versioned_div_scalar_reciprocal(self, sample_input):
def historic_div_scalar_float_reciprocal(self, other: float):
return other / self
@@ -381,8 +405,7 @@
current_mobile_module_float = self._save_load_mobile_module(MyModuleFloat)
current_mobile_module_int = self._save_load_mobile_module(MyModuleInt)
- vals = (2., 3., 2, 3)
- for val_a, val_b in product(vals, vals):
+ for val_a, val_b in product(sample_input, sample_input):
a = torch.tensor((val_a,))
b = val_b
@@ -415,7 +438,12 @@
_helper(v3_mobile_module_int, current_mobile_module_int)
_helper(current_mobile_module_int, torch.div)
- def test_versioned_div_scalar_inplace(self):
+ @settings(max_examples=10, deadline=200000) # A total of 10 examples will be generated
+ @given(
+ sample_input=st.tuples(st.integers(min_value=5, max_value=199), st.floats(min_value=5.0, max_value=199.0))
+ ) # Generate a pair (integer, float)
+ @example((2, 3, 2.0, 3.0)) # Ensure this example will be covered
+ def test_versioned_div_scalar_inplace(self, sample_input):
def historic_div_scalar_float_inplace(self, other: float):
return self.true_divide_(other)
@@ -468,8 +496,7 @@
for m in (current_module_float, current_module_int):
self._verify_count("aten::div", m, 1)
- vals = (2., 3., 2, 3)
- for val_a, val_b in product(vals, vals):
+ for val_a, val_b in product(sample_input, sample_input):
a = torch.tensor((val_a,))
b = val_b
