Merge pull request #81 from honno/more-tests

Make some type promotion tests standalone, dtype info tests

Author: honno

array_api_tests/test_creation_functions.py

@@ -1,8 +1,8 @@
 import math
-import pytest
 from itertools import count
 from typing import Iterator, NamedTuple, Union
 
+import pytest
 from hypothesis import assume, given, note
 from hypothesis import strategies as st
 
@@ -190,10 +190,7 @@ def test_arange(dtype, data):
             ), f"out[0]={out[0]}, but should be {_start} {f_func}"
 
 
-@given(
-    shape=hh.shapes(min_side=1),
-    data=st.data(),
-)
+@given(shape=hh.shapes(min_side=1), data=st.data())
 def test_asarray_scalars(shape, data):
     kw = data.draw(
         hh.kwargs(dtype=st.none() | xps.scalar_dtypes(), copy=st.none()), label="kw"
@@ -482,6 +479,29 @@ def test_linspace(num, dtype, endpoint, data):
         ah.assert_exactly_equal(out, expected)
 
 
+@given(
+    # The number and size of generated arrays is arbitrarily limited to prevent
+    # meshgrid() running out of memory.
+    dtypes=hh.mutually_promotable_dtypes(5, dtypes=dh.numeric_dtypes),
+    data=st.data(),
+)
+def test_meshgrid(dtypes, data):
+    arrays = []
+    shapes = data.draw(
+        hh.mutually_broadcastable_shapes(
+            len(dtypes), min_dims=1, max_dims=1, max_side=5
+        ),
+        label="shapes",
+    )
+    for i, (dtype, shape) in enumerate(zip(dtypes, shapes), 1):
+        x = data.draw(xps.arrays(dtype=dtype, shape=shape), label=f"x{i}")
+        arrays.append(x)
+    assert math.prod(x.size for x in arrays) <= hh.MAX_ARRAY_SIZE  # sanity check
+    out = xp.meshgrid(*arrays)
+    for i, x in enumerate(out):
+        ph.assert_dtype("meshgrid", dtypes, x.dtype, repr_name=f"out[{i}].dtype")
+
+
 def make_one(dtype: DataType) -> Scalar:
     if dtype is None or dh.is_float_dtype(dtype):
         return 1.0

array_api_tests/test_data_type_functions.py

@@ -0,0 +1,50 @@
+import pytest
+from hypothesis import given
+
+from . import _array_module as xp
+from . import dtype_helpers as dh
+from . import hypothesis_helpers as hh
+from . import pytest_helpers as ph
+from .typing import DataType
+
+
+def make_dtype_id(dtype: DataType) -> str:
+    return dh.dtype_to_name[dtype]
+
+
+@pytest.mark.parametrize("dtype", dh.float_dtypes, ids=make_dtype_id)
+def test_finfo(dtype):
+    out = xp.finfo(dtype)
+    f_func = f"[finfo({dh.dtype_to_name[dtype]})]"
+    for attr, stype in [
+        ("bits", int),
+        ("eps", float),
+        ("max", float),
+        ("min", float),
+        ("smallest_normal", float),
+    ]:
+        assert hasattr(out, attr), f"out has no attribute '{attr}' {f_func}"
+        value = getattr(out, attr)
+        assert isinstance(
+            value, stype
+        ), f"type(out.{attr})={type(value)!r}, but should be {stype.__name__} {f_func}"
+    # TODO: test values
+
+
+@pytest.mark.parametrize("dtype", dh.all_int_dtypes, ids=make_dtype_id)
+def test_iinfo(dtype):
+    out = xp.iinfo(dtype)
+    f_func = f"[iinfo({dh.dtype_to_name[dtype]})]"
+    for attr in ["bits", "max", "min"]:
+        assert hasattr(out, attr), f"out has no attribute '{attr}' {f_func}"
+        value = getattr(out, attr)
+        assert isinstance(
+            value, int
+        ), f"type(out.{attr})={type(value)!r}, but should be int {f_func}"
+    # TODO: test values
+
+
+@given(hh.mutually_promotable_dtypes(None))
+def test_result_type(dtypes):
+    out = xp.result_type(*dtypes)
+    ph.assert_dtype("result_type", dtypes, out, repr_name="out")

array_api_tests/test_linalg.py

@@ -16,7 +16,7 @@
 import pytest
 from hypothesis import assume, given
 from hypothesis.strategies import (booleans, composite, none, tuples, integers,
-                                   shared, sampled_from)
+                                   shared, sampled_from, data, just)
 
 from .array_helpers import assert_exactly_equal, asarray, equal, zero, infinity
 from .hypothesis_helpers import (xps, dtypes, shapes, kwargs, matrix_shapes,
@@ -33,6 +33,7 @@
 from .algos import broadcast_shapes
 
 from . import _array_module
+from . import _array_module as xp
 from ._array_module import linalg
 
 pytestmark = pytest.mark.ci
@@ -556,13 +557,20 @@ def test_svdvals(x):
 
 
 @given(
-    x1=xps.arrays(dtype=xps.floating_dtypes(), shape=shapes()),
-    x2=xps.arrays(dtype=xps.floating_dtypes(), shape=shapes()),
-    kw=kwargs(axes=todo)
+    dtypes=mutually_promotable_dtypes(dtypes=dh.numeric_dtypes),
+    shape=shapes(),
+    data=data(),
 )
-def test_tensordot(x1, x2, kw):
-    # res = _array_module.tensordot(x1, x2, **kw)
-    pass
+def test_tensordot(dtypes, shape, data):
+    # TODO: vary shapes, vary contracted axes, test different axes arguments
+    x1 = data.draw(xps.arrays(dtype=dtypes[0], shape=shape), label="x1")
+    x2 = data.draw(xps.arrays(dtype=dtypes[1], shape=shape), label="x2")
+
+    out = xp.tensordot(x1, x2, axes=len(shape))
+
+    ph.assert_dtype("tensordot", dtypes, out.dtype)
+    # TODO: assert shape and elements
+
 
 @pytest.mark.xp_extension('linalg')
 @given(
@@ -605,13 +613,21 @@ def true_trace(x_stack):
 
 
 @given(
-    x1=xps.arrays(dtype=xps.floating_dtypes(), shape=shapes()),
-    x2=xps.arrays(dtype=xps.floating_dtypes(), shape=shapes()),
-    kw=kwargs(axis=todo)
+    dtypes=mutually_promotable_dtypes(dtypes=dh.numeric_dtypes),
+    shape=shapes(),
+    data=data(),
 )
-def test_vecdot(x1, x2, kw):
-    # res = _array_module.vecdot(x1, x2, **kw)
-    pass
+def test_vecdot(dtypes, shape, data):
+    # TODO: vary shapes, test different axis arguments
+    x1 = data.draw(xps.arrays(dtype=dtypes[0], shape=shape), label="x1")
+    x2 = data.draw(xps.arrays(dtype=dtypes[1], shape=shape), label="x2")
+    kw = data.draw(kwargs(axis=just(-1)))
+
+    out = xp.vecdot(x1, x2, **kw)
+
+    ph.assert_dtype("vecdot", dtypes, out.dtype)
+    # TODO: assert shape and elements
+
 
 @pytest.mark.xp_extension('linalg')
 @given(

array_api_tests/test_type_promotion.py

@@ -16,16 +16,6 @@
 from .function_stubs import elementwise_functions
 from .typing import DataType, Param, ScalarType
 
-# TODO: move tests not covering elementwise funcs/ops into standalone tests
-# result_type, meshgrid, tensordor, vecdot
-
-
-@given(hh.mutually_promotable_dtypes(None))
-def test_result_type(dtypes):
-    out = xp.result_type(*dtypes)
-    ph.assert_dtype("result_type", dtypes, out, repr_name="out")
-
-
 bitwise_shift_funcs = [
     "bitwise_left_shift",
     "bitwise_right_shift",
@@ -133,37 +123,9 @@ def test_func_promotion(func_name, in_dtypes, out_dtype, data):
     promotion_params.append(p)
 
 
-@pytest.mark.parametrize("in_dtypes, out_dtype", promotion_params)
-@given(shapes=hh.mutually_broadcastable_shapes(3), data=st.data())
-def test_where(in_dtypes, out_dtype, shapes, data):
-    x1 = data.draw(xps.arrays(dtype=in_dtypes[0], shape=shapes[0]), label="x1")
-    x2 = data.draw(xps.arrays(dtype=in_dtypes[1], shape=shapes[1]), label="x2")
-    cond = data.draw(xps.arrays(dtype=xp.bool, shape=shapes[2]), label="condition")
-    out = xp.where(cond, x1, x2)
-    ph.assert_dtype("where", in_dtypes, out.dtype, out_dtype)
-
-
 numeric_promotion_params = promotion_params[1:]
 
 
-@pytest.mark.parametrize("in_dtypes, out_dtype", numeric_promotion_params)
-@given(shapes=hh.mutually_broadcastable_shapes(2, min_dims=2), data=st.data())
-def test_tensordot(in_dtypes, out_dtype, shapes, data):
-    x1 = data.draw(xps.arrays(dtype=in_dtypes[0], shape=shapes[0]), label="x1")
-    x2 = data.draw(xps.arrays(dtype=in_dtypes[1], shape=shapes[1]), label="x2")
-    out = xp.tensordot(x1, x2)
-    ph.assert_dtype("tensordot", in_dtypes, out.dtype, out_dtype)
-
-
-@pytest.mark.parametrize("in_dtypes, out_dtype", numeric_promotion_params)
-@given(shapes=hh.mutually_broadcastable_shapes(2, min_dims=1), data=st.data())
-def test_vecdot(in_dtypes, out_dtype, shapes, data):
-    x1 = data.draw(xps.arrays(dtype=in_dtypes[0], shape=shapes[0]), label="x1")
-    x2 = data.draw(xps.arrays(dtype=in_dtypes[1], shape=shapes[1]), label="x2")
-    out = xp.vecdot(x1, x2)
-    ph.assert_dtype("vecdot", in_dtypes, out.dtype, out_dtype)
-
-
 op_params: List[Param[str, str, Tuple[DataType, ...], DataType]] = []
 op_to_symbol = {**dh.unary_op_to_symbol, **dh.binary_op_to_symbol}
 for op, symbol in op_to_symbol.items():