ndarray dunders / binary ufuncs

.gitignore

@@ -1,8 +1,5 @@
-__pycache__/*
-autogen/__pycache__
-torch_np/__pycache__/*
-torch_np/tests/__pycache__/*
-torch_np/tests/numpy_tests/core/__pycache__/*
-torch_np/testing/__pycache__/*
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
 .coverage
 

autogen/gen_ufuncs_2.py

@@ -0,0 +1,213 @@
+from dump_namespace import grab_namespace, get_signature
+
+import numpy as np
+
+namespace = np
+
+dct = grab_namespace(namespace)
+
+
+# SKIP these (need special handling)
+skip = {np.frexp, np.modf,    # non-standard unary ufunc signatures
+        np.isnat,
+        np.invert,      # bitwise NOT operator
+        np.spacing,     # niche, does not have a direct equivalent
+}
+
+# np functions where torch names differ
+torch_names = {np.radians : "deg2rad",
+               np.degrees : "rad2deg",
+               np.conjugate : "conj_physical",
+               np.fabs : "absolute",       # FIXME: np.fabs raises form complex
+               np.rint : "round",
+               np.left_shift: "bitwise_left_shift",
+               np.right_shift: "bitwise_right_shift",
+}
+
+
+# np functions which do not have a torch equivalent
+default_stanza = "torch.{torch_name}(x, out=out)"
+
+stanzas = {np.cbrt : "torch.pow(x, 1/3, out=out)",
+
+           # XXX what on earth is np.positive
+           np.positive: "+x",
+
+           # these three do not have an out arg
+           np.isinf: "torch.isinf(x)",
+           np.isnan: "torch.isnan(x)",
+           np.isfinite: "torch.isfinite(x)",
+}
+
+
+# for these np functions, pytorch analog does not have the out= arg
+needs_out = {np.isinf, np.isnan, np.isfinite, np.positive}
+add_out_stanza = """
+    if out is not None:
+        out[...] = result
+"""
+
+
+header = """\
+# this file is autogenerated via gen_ufuncs.py
+# do not edit manually!
+
+import torch
+
+import _util
+from _ndarray import asarray_replacer
+
+"""
+
+test_header = header + """\
+import numpy as np
+import torch
+
+from _unary_ufuncs import *
+from testing import assert_allclose
+"""
+
+
+template = """ """
+
+test_template = """
+
+def test_{np_name}():
+    assert_allclose(np.{np_name}(0.5),
+                    {np_name}(0.5), atol=1e-14, check_dtype=False)
+
+"""
+
+
+###### UNARY UFUNCS ###################################
+
+_all_list = []
+main_text = header
+test_text = test_header
+
+_impl_list = []
+_ufunc_list = []
+
+for ufunc in dct['ufunc']:
+    if ufunc in skip:
+        continue
+
+    if ufunc.nin == 1:
+#        print(get_signature(ufunc))
+
+        torch_name = torch_names.get(ufunc)
+        if torch_name is None:
+            torch_name = ufunc.__name__
+
+#        print(ufunc.__name__, ' -- ', torch_name)   
+
+        _impl_stanza = "{np_name} = deco_unary_ufunc(torch.{torch_name})"
+        _impl_stanza = _impl_stanza.format(np_name=ufunc.__name__,
+                                     torch_name=torch_name,)
+        _impl_list.append(_impl_stanza)        
+
+        continue
+
+        torch_stanza = stanzas.get(ufunc)
+        if torch_stanza is None:
+            torch_stanza = default_stanza.format(torch_name=torch_name)
+
+        out_stanza= add_out_stanza if ufunc in needs_out else ""
+
+        main_text += template.format(np_name=ufunc.__name__,
+                                     torch_stanza=torch_stanza,
+                                     out_stanza=out_stanza)
+        test_text += test_template.format(np_name=ufunc.__name__)
+
+        _all_list.append(ufunc.__name__)
+
+
+print("\n".join(_impl_list))
+print("\n\n-----\n\n")
+
+'''
+main_text += "\n\n__all__ = %s" % _all_list
+
+
+with open("_unary_ufuncs.py", "w") as f:
+    f.write(main_text)
+
+with open("test_unary_ufuncs.py", "w") as f:
+    f.write(test_text)
+'''
+
+###### BINARY UFUNCS ###################################
+
+
+
+test_header = header + """\
+import numpy as np
+import torch
+
+from _binary_ufuncs import *
+from testing import assert_allclose
+"""
+
+
+template = """
+
+
+"""
+
+test_template = """
+
+def test_{np_name}():
+    assert_allclose(np.{np_name}(0.5, 0.6),
+                               {np_name}(0.5, 0.6), atol=1e-7, check_dtype=False)
+
+"""
+
+
+
+skip = {np.divmod,    # two outputs
+}
+
+
+torch_names = {np.power: "pow",
+               np.equal: "eq",
+}
+
+
+_all_list = []
+main_text = header
+test_text = test_header
+
+_impl_list = []
+_ufunc_list = []
+
+
+for ufunc in dct['ufunc']:
+
+    if ufunc in skip:
+        continue
+
+    if ufunc.nin == 2:
+ ##       print(get_signature(ufunc))
+
+        torch_name = torch_names.get(ufunc)
+        if torch_name is None:
+            torch_name = ufunc.__name__
+
+        _impl_stanza = "{np_name} = deco_binary_ufunc(torch.{torch_name})"
+        _impl_stanza = _impl_stanza.format(np_name=ufunc.__name__,
+                                     torch_name=torch_name,)
+        _impl_list.append(_impl_stanza)
+
+        _ufunc_stanza = "{np_name} = deco_ufunc_from_impl(_ufunc_impl.{np_name})"
+        _ufunc_stanza = _ufunc_stanza.format(np_name=ufunc.__name__)
+        _ufunc_list.append(_ufunc_stanza)
+
+
+print("\n".join(_impl_list))
+
+print("\n\n")
+print("\n".join(_ufunc_list))
+
+
+
+

torch_np/__init__.py

@@ -6,7 +6,7 @@
 from ._unary_ufuncs import *
 from ._binary_ufuncs import *
 from ._ndarray import can_cast, result_type, newaxis
-from ._util import AxisError
+from ._util import AxisError, UFuncTypeError
 from ._getlimits import iinfo, finfo
 from ._getlimits import errstate