pypy / pypy / module / micronumpy / interp_ufuncs.py

The default branch has multiple heads

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from pypy.interpreter.baseobjspace import Wrappable
from pypy.interpreter.error import OperationError, operationerrfmt
from pypy.interpreter.gateway import interp2app, unwrap_spec, NoneNotWrapped
from pypy.interpreter.typedef import TypeDef, GetSetProperty, interp_attrproperty
from pypy.module.micronumpy import interp_boxes, interp_dtype, support, loop
from pypy.rlib import jit
from pypy.rlib.rarithmetic import LONG_BIT
from pypy.tool.sourcetools import func_with_new_name


class W_Ufunc(Wrappable):
    _attrs_ = ["name", "promote_to_float", "promote_bools", "identity"]
    _immutable_fields_ = ["promote_to_float", "promote_bools", "name"]

    def __init__(self, name, promote_to_float, promote_bools, identity,
                 int_only):
        self.name = name
        self.promote_to_float = promote_to_float
        self.promote_bools = promote_bools

        self.identity = identity
        self.int_only = int_only

    def descr_repr(self, space):
        return space.wrap("<ufunc '%s'>" % self.name)

    def descr_get_identity(self, space):
        if self.identity is None:
            return space.w_None
        return self.identity

    def descr_call(self, space, __args__):
        from interp_numarray import BaseArray
        args_w, kwds_w = __args__.unpack()
        # it occurs to me that we don't support any datatypes that
        # require casting, change it later when we do
        kwds_w.pop('casting', None)
        w_subok = kwds_w.pop('subok', None)
        w_out = kwds_w.pop('out', space.w_None)
        # Setup a default value for out
        if space.is_w(w_out, space.w_None):
            out = None
        else:
            out = w_out
        if (w_subok is not None and space.is_true(w_subok)):
            raise OperationError(space.w_NotImplementedError,
                                 space.wrap("parameters unsupported"))
        if kwds_w or len(args_w) < self.argcount:
            raise OperationError(space.w_ValueError,
                space.wrap("invalid number of arguments")
            )
        elif (len(args_w) > self.argcount and out is not None) or \
             (len(args_w) > self.argcount + 1):
            raise OperationError(space.w_TypeError,
                space.wrap("invalid number of arguments")
            )
        # Override the default out value, if it has been provided in w_wargs
        if len(args_w) > self.argcount:
            out = args_w[-1]
        else:
            args_w = args_w[:] + [out]
        if out is not None and not isinstance(out, BaseArray):
            raise OperationError(space.w_TypeError, space.wrap(
                                            'output must be an array'))
        return self.call(space, args_w)

    @unwrap_spec(skipna=bool, keepdims=bool)
    def descr_reduce(self, space, w_obj, w_axis=NoneNotWrapped, w_dtype=None,
                     skipna=False, keepdims=False, w_out=None):
        """reduce(...)
        reduce(a, axis=0)

        Reduces `a`'s dimension by one, by applying ufunc along one axis.

        Let :math:`a.shape = (N_0, ..., N_i, ..., N_{M-1})`.  Then
        :math:`ufunc.reduce(a, axis=i)[k_0, ..,k_{i-1}, k_{i+1}, .., k_{M-1}]` =
        the result of iterating `j` over :math:`range(N_i)`, cumulatively applying
        ufunc to each :math:`a[k_0, ..,k_{i-1}, j, k_{i+1}, .., k_{M-1}]`.
        For a one-dimensional array, reduce produces results equivalent to:
        ::

         r = op.identity # op = ufunc
         for i in xrange(len(A)):
           r = op(r, A[i])
         return r

        For example, add.reduce() is equivalent to sum().

        Parameters
        ----------
        a : array_like
            The array to act on.
        axis : int, optional
            The axis along which to apply the reduction.

        Examples
        --------
        >>> np.multiply.reduce([2,3,5])
        30

        A multi-dimensional array example:

        >>> X = np.arange(8).reshape((2,2,2))
        >>> X
        array([[[0, 1],
                [2, 3]],
               [[4, 5],
                [6, 7]]])
        >>> np.add.reduce(X, 0)
        array([[ 4,  6],
               [ 8, 10]])
        >>> np.add.reduce(X) # confirm: default axis value is 0
        array([[ 4,  6],
               [ 8, 10]])
        >>> np.add.reduce(X, 1)
        array([[ 2,  4],
               [10, 12]])
        >>> np.add.reduce(X, 2)
        array([[ 1,  5],
               [ 9, 13]])
        """
        from pypy.module.micronumpy.interp_numarray import BaseArray
        if w_axis is None:
            axis = 0
        elif space.is_w(w_axis, space.w_None):
            axis = -1
        else:
            axis = space.int_w(w_axis)
        if space.is_w(w_out, space.w_None):
            out = None
        elif not isinstance(w_out, BaseArray):
            raise OperationError(space.w_TypeError, space.wrap(
                                                'output must be an array'))
        else:
            out = w_out
        return self.reduce(space, w_obj, False, False, axis, keepdims, out)

    def reduce(self, space, w_obj, multidim, promote_to_largest, axis,
               keepdims=False, out=None):
        from pypy.module.micronumpy.interp_numarray import convert_to_array, \
                                             Scalar, ReduceArray, W_NDimArray
        if self.argcount != 2:
            raise OperationError(space.w_ValueError, space.wrap("reduce only "
                "supported for binary functions"))
        assert isinstance(self, W_Ufunc2)
        obj = convert_to_array(space, w_obj)
        if axis >= len(obj.shape):
            raise OperationError(space.w_ValueError, space.wrap("axis(=%d) out of bounds" % axis))
        if isinstance(obj, Scalar):
            raise OperationError(space.w_TypeError, space.wrap("cannot reduce "
                "on a scalar"))
        size = obj.size
        if self.comparison_func:
            dtype = interp_dtype.get_dtype_cache(space).w_booldtype
        else:
            dtype = find_unaryop_result_dtype(
                space, obj.find_dtype(),
                promote_to_float=self.promote_to_float,
                promote_to_largest=promote_to_largest,
                promote_bools=True
            )
        shapelen = len(obj.shape)
        if self.identity is None and size == 0:
            raise operationerrfmt(space.w_ValueError, "zero-size array to "
                    "%s.reduce without identity", self.name)
        if shapelen > 1 and axis >= 0:
            if keepdims:
                shape = obj.shape[:axis] + [1] + obj.shape[axis + 1:]
            else:
                shape = obj.shape[:axis] + obj.shape[axis + 1:]
            if out:
                #Test for shape agreement
                if len(out.shape) > len(shape):
                    raise operationerrfmt(space.w_ValueError,
                        'output parameter for reduction operation %s' +
                        ' has too many dimensions', self.name)
                elif len(out.shape) < len(shape):
                    raise operationerrfmt(space.w_ValueError,
                        'output parameter for reduction operation %s' +
                        ' does not have enough dimensions', self.name)
                elif out.shape != shape:
                    raise operationerrfmt(space.w_ValueError,
                        'output parameter shape mismatch, expecting [%s]' +
                        ' , got [%s]',
                        ",".join([str(x) for x in shape]),
                        ",".join([str(x) for x in out.shape]),
                        )
                #Test for dtype agreement, perhaps create an itermediate
                #if out.dtype != dtype:
                #    raise OperationError(space.w_TypeError, space.wrap(
                #        "mismatched  dtypes"))
                return self.do_axis_reduce(obj, out.find_dtype(), axis, out)
            else:
                result = W_NDimArray(shape, dtype)
                return self.do_axis_reduce(obj, dtype, axis, result)
        if out:
            if len(out.shape)>0:
                raise operationerrfmt(space.w_ValueError, "output parameter "
                              "for reduction operation %s has too many"
                              " dimensions",self.name)
            arr = ReduceArray(self.func, self.name, self.identity, obj,
                                                            out.find_dtype())
            val = loop.compute(arr)
            assert isinstance(out, Scalar)
            out.value = val
        else:
            arr = ReduceArray(self.func, self.name, self.identity, obj, dtype)
            val = loop.compute(arr)
        return val

    def do_axis_reduce(self, obj, dtype, axis, result):
        from pypy.module.micronumpy.interp_numarray import AxisReduce
        arr = AxisReduce(self.func, self.name, self.identity, obj.shape, dtype,
                         result, obj, axis)
        loop.compute(arr)
        return arr.left

class W_Ufunc1(W_Ufunc):
    argcount = 1

    _immutable_fields_ = ["func", "name"]

    def __init__(self, func, name, promote_to_float=False, promote_bools=False,
        identity=None, bool_result=False, int_only=False):

        W_Ufunc.__init__(self, name, promote_to_float, promote_bools, identity,
                         int_only)
        self.func = func
        self.bool_result = bool_result

    def call(self, space, args_w):
        from pypy.module.micronumpy.interp_numarray import (Call1, BaseArray,
            convert_to_array, Scalar, shape_agreement)
        if len(args_w)<2:
            [w_obj] = args_w
            out = None
        else:
            [w_obj, out] = args_w
            if space.is_w(out, space.w_None):
                out = None
        w_obj = convert_to_array(space, w_obj)
        calc_dtype = find_unaryop_result_dtype(space,
                                  w_obj.find_dtype(),
                                  promote_to_float=self.promote_to_float,
                                  promote_bools=self.promote_bools)
        if out:
            if not isinstance(out, BaseArray):
                raise OperationError(space.w_TypeError, space.wrap(
                                                'output must be an array'))
            res_dtype = out.find_dtype()
        elif self.bool_result:
            res_dtype = interp_dtype.get_dtype_cache(space).w_booldtype
        else:
            res_dtype = calc_dtype
        if isinstance(w_obj, Scalar):
            arr = self.func(calc_dtype, w_obj.value.convert_to(calc_dtype))
            if isinstance(out,Scalar):
                out.value = arr
            elif isinstance(out, BaseArray):
                out.fill(space, arr)
            else:
                out = arr
            return space.wrap(out)
        if out:
            assert isinstance(out, BaseArray) # For translation
            broadcast_shape =  shape_agreement(space, w_obj.shape, out.shape)
            if not broadcast_shape or broadcast_shape != out.shape:
                raise operationerrfmt(space.w_ValueError,
                    'output parameter shape mismatch, could not broadcast [%s]' +
                    ' to [%s]',
                    ",".join([str(x) for x in w_obj.shape]),
                    ",".join([str(x) for x in out.shape]),
                    )
            w_res = Call1(self.func, self.name, out.shape, calc_dtype,
                                         res_dtype, w_obj, out)
            #Force it immediately
            w_res.get_concrete()
        else:
            w_res = Call1(self.func, self.name, w_obj.shape, calc_dtype,
                                         res_dtype, w_obj)
        w_obj.add_invalidates(w_res)
        return w_res


class W_Ufunc2(W_Ufunc):
    _immutable_fields_ = ["comparison_func", "func", "name", "int_only"]
    argcount = 2

    def __init__(self, func, name, promote_to_float=False, promote_bools=False,
        identity=None, comparison_func=False, int_only=False):

        W_Ufunc.__init__(self, name, promote_to_float, promote_bools, identity,
                         int_only)
        self.func = func
        self.comparison_func = comparison_func

    @jit.unroll_safe
    def call(self, space, args_w):
        from pypy.module.micronumpy.interp_numarray import (Call2,
            convert_to_array, Scalar, shape_agreement, BaseArray)
        if len(args_w) > 2:
            [w_lhs, w_rhs, w_out] = args_w
        else:
            [w_lhs, w_rhs] = args_w
            w_out = None
        w_lhs = convert_to_array(space, w_lhs)
        w_rhs = convert_to_array(space, w_rhs)
        if space.is_w(w_out, space.w_None) or w_out is None:
            out = None
            calc_dtype = find_binop_result_dtype(space,
                w_lhs.find_dtype(), w_rhs.find_dtype(),
                int_only=self.int_only,
                promote_to_float=self.promote_to_float,
                promote_bools=self.promote_bools,
            )
        elif not isinstance(w_out, BaseArray):
            raise OperationError(space.w_TypeError, space.wrap(
                    'output must be an array'))
        else:
            out = w_out
            calc_dtype = out.find_dtype()
        if self.comparison_func:
            res_dtype = interp_dtype.get_dtype_cache(space).w_booldtype
        else:
            res_dtype = calc_dtype
        if isinstance(w_lhs, Scalar) and isinstance(w_rhs, Scalar):
            arr = self.func(calc_dtype,
                w_lhs.value.convert_to(calc_dtype),
                w_rhs.value.convert_to(calc_dtype)
            )
            if isinstance(out,Scalar):
                out.value = arr
            elif isinstance(out, BaseArray):
                out.fill(space, arr)
            else:
                out = arr
            return space.wrap(out)
        new_shape = shape_agreement(space, w_lhs.shape, w_rhs.shape)
        # Test correctness of out.shape
        if out and out.shape != shape_agreement(space, new_shape, out.shape):
            raise operationerrfmt(space.w_ValueError,
                'output parameter shape mismatch, could not broadcast [%s]' +
                ' to [%s]',
                ",".join([str(x) for x in new_shape]),
                ",".join([str(x) for x in out.shape]),
                )
        w_res = Call2(self.func, self.name,
                      new_shape, calc_dtype,
                      res_dtype, w_lhs, w_rhs, out)
        w_lhs.add_invalidates(w_res)
        w_rhs.add_invalidates(w_res)
        if out:
            w_res.get_concrete()
        return w_res


W_Ufunc.typedef = TypeDef("ufunc",
    __module__ = "numpypy",

    __call__ = interp2app(W_Ufunc.descr_call),
    __repr__ = interp2app(W_Ufunc.descr_repr),

    identity = GetSetProperty(W_Ufunc.descr_get_identity),
    nin = interp_attrproperty("argcount", cls=W_Ufunc),

    reduce = interp2app(W_Ufunc.descr_reduce),
)


def find_binop_result_dtype(space, dt1, dt2, promote_to_float=False,
    promote_bools=False, int_only=False):
    # dt1.num should be <= dt2.num
    if dt1.num > dt2.num:
        dt1, dt2 = dt2, dt1
    if int_only and (not dt1.is_int_type() or not dt2.is_int_type()):
        raise OperationError(space.w_TypeError, space.wrap("Unsupported types"))
    # Some operations promote op(bool, bool) to return int8, rather than bool
    if promote_bools and (dt1.kind == dt2.kind == interp_dtype.BOOLLTR):
        return interp_dtype.get_dtype_cache(space).w_int8dtype
    if promote_to_float:
        return find_unaryop_result_dtype(space, dt2, promote_to_float=True)
    # If they're the same kind, choose the greater one.
    if dt1.kind == dt2.kind:
        return dt2

    # Everything promotes to float, and bool promotes to everything.
    if dt2.kind == interp_dtype.FLOATINGLTR or dt1.kind == interp_dtype.BOOLLTR:
        # Float32 + 8-bit int = Float64
        if dt2.num == 11 and dt1.itemtype.get_element_size() >= 4:
            return interp_dtype.get_dtype_cache(space).w_float64dtype
        return dt2

    # for now this means mixing signed and unsigned
    if dt2.kind == interp_dtype.SIGNEDLTR:
        # if dt2 has a greater number of bytes, then just go with it
        if dt1.itemtype.get_element_size() < dt2.itemtype.get_element_size():
            return dt2
        # we need to promote both dtypes
        dtypenum = dt2.num + 2
    else:
        # increase to the next signed type (or to float)
        dtypenum = dt2.num + 1
        # UInt64 + signed = Float64
        if dt2.num == 10:
            dtypenum += 1
    newdtype = interp_dtype.get_dtype_cache(space).builtin_dtypes[dtypenum]

    if (newdtype.itemtype.get_element_size() > dt2.itemtype.get_element_size() or
        newdtype.kind == interp_dtype.FLOATINGLTR):
        return newdtype
    else:
        # we only promoted to long on 32-bit or to longlong on 64-bit
        # this is really for dealing with the Long and Ulong dtypes
        if LONG_BIT == 32:
            dtypenum += 2
        else:
            dtypenum += 3
        return interp_dtype.get_dtype_cache(space).builtin_dtypes[dtypenum]


def find_unaryop_result_dtype(space, dt, promote_to_float=False,
    promote_bools=False, promote_to_largest=False):
    if promote_bools and (dt.kind == interp_dtype.BOOLLTR):
        return interp_dtype.get_dtype_cache(space).w_int8dtype
    if promote_to_float:
        if dt.kind == interp_dtype.FLOATINGLTR:
            return dt
        if dt.num >= 5:
            return interp_dtype.get_dtype_cache(space).w_float64dtype
        for bytes, dtype in interp_dtype.get_dtype_cache(space).float_dtypes_by_num_bytes:
            if (dtype.kind == interp_dtype.FLOATINGLTR and
                dtype.itemtype.get_element_size() > dt.itemtype.get_element_size()):
                return dtype
    if promote_to_largest:
        if dt.kind == interp_dtype.BOOLLTR or dt.kind == interp_dtype.SIGNEDLTR:
            return interp_dtype.get_dtype_cache(space).w_float64dtype
        elif dt.kind == interp_dtype.FLOATINGLTR:
            return interp_dtype.get_dtype_cache(space).w_float64dtype
        elif dt.kind == interp_dtype.UNSIGNEDLTR:
            return interp_dtype.get_dtype_cache(space).w_uint64dtype
        else:
            assert False
    return dt


def find_dtype_for_scalar(space, w_obj, current_guess=None):
    bool_dtype = interp_dtype.get_dtype_cache(space).w_booldtype
    long_dtype = interp_dtype.get_dtype_cache(space).w_longdtype
    int64_dtype = interp_dtype.get_dtype_cache(space).w_int64dtype

    if isinstance(w_obj, interp_boxes.W_GenericBox):
        dtype = w_obj.get_dtype(space)
        if current_guess is None:
            return dtype
        return find_binop_result_dtype(space, dtype, current_guess)

    if space.isinstance_w(w_obj, space.w_bool):
        if current_guess is None or current_guess is bool_dtype:
            return bool_dtype
        return current_guess
    elif space.isinstance_w(w_obj, space.w_int):
        if (current_guess is None or current_guess is bool_dtype or
            current_guess is long_dtype):
            return long_dtype
        return current_guess
    elif space.isinstance_w(w_obj, space.w_long):
        if (current_guess is None or current_guess is bool_dtype or
            current_guess is long_dtype or current_guess is int64_dtype):
            return int64_dtype
        return current_guess
    return interp_dtype.get_dtype_cache(space).w_float64dtype


def ufunc_dtype_caller(space, ufunc_name, op_name, argcount, comparison_func,
                       bool_result):
    dtype_cache = interp_dtype.get_dtype_cache(space)
    if argcount == 1:
        def impl(res_dtype, value):
            res = getattr(res_dtype.itemtype, op_name)(value)
            if bool_result:
                return dtype_cache.w_booldtype.box(res)
            return res
    elif argcount == 2:
        def impl(res_dtype, lvalue, rvalue):
            res = getattr(res_dtype.itemtype, op_name)(lvalue, rvalue)
            if comparison_func:
                return dtype_cache.w_booldtype.box(res)
            return res
    return func_with_new_name(impl, ufunc_name)

class UfuncState(object):
    def __init__(self, space):
        "NOT_RPYTHON"
        for ufunc_def in [
            ("add", "add", 2, {"identity": 0}),
            ("subtract", "sub", 2),
            ("multiply", "mul", 2, {"identity": 1}),
            ("bitwise_and", "bitwise_and", 2, {"identity": 1,
                                               "int_only": True}),
            ("bitwise_or", "bitwise_or", 2, {"identity": 0,
                                             "int_only": True}),
            ("bitwise_xor", "bitwise_xor", 2, {"int_only": True}),
            ("invert", "invert", 1, {"int_only": True}),
            ("floor_divide", "floordiv", 2, {"promote_bools": True}),
            ("divide", "div", 2, {"promote_bools": True}),
            ("true_divide", "div", 2, {"promote_to_float": True}),
            ("mod", "mod", 2, {"promote_bools": True}),
            ("power", "pow", 2, {"promote_bools": True}),
            ("left_shift", "lshift", 2, {"int_only": True}),
            ("right_shift", "rshift", 2, {"int_only": True}),

            ("equal", "eq", 2, {"comparison_func": True}),
            ("not_equal", "ne", 2, {"comparison_func": True}),
            ("less", "lt", 2, {"comparison_func": True}),
            ("less_equal", "le", 2, {"comparison_func": True}),
            ("greater", "gt", 2, {"comparison_func": True}),
            ("greater_equal", "ge", 2, {"comparison_func": True}),
            ("isnan", "isnan", 1, {"bool_result": True}),
            ("isinf", "isinf", 1, {"bool_result": True}),
            ("isneginf", "isneginf", 1, {"bool_result": True}),
            ("isposinf", "isposinf", 1, {"bool_result": True}),
            ("isfinite", "isfinite", 1, {"bool_result": True}),

            ('logical_and', 'logical_and', 2, {'comparison_func': True,
                                               'identity': 1}),
            ('logical_or', 'logical_or', 2, {'comparison_func': True,
                                             'identity': 0}),
            ('logical_xor', 'logical_xor', 2, {'comparison_func': True}),
            ('logical_not', 'logical_not', 1, {'bool_result': True}),

            ("maximum", "max", 2),
            ("minimum", "min", 2),

            ("copysign", "copysign", 2, {"promote_to_float": True}),

            ("positive", "pos", 1),
            ("negative", "neg", 1),
            ("absolute", "abs", 1),
            ("sign", "sign", 1, {"promote_bools": True}),
            ("signbit", "signbit", 1, {"bool_result": True}),
            ("reciprocal", "reciprocal", 1),

            ("fabs", "fabs", 1, {"promote_to_float": True}),
            ("fmod", "fmod", 2, {"promote_to_float": True}),
            ("floor", "floor", 1, {"promote_to_float": True}),
            ("ceil", "ceil", 1, {"promote_to_float": True}),
            ("exp", "exp", 1, {"promote_to_float": True}),
            ("exp2", "exp2", 1, {"promote_to_float": True}),
            ("expm1", "expm1", 1, {"promote_to_float": True}),

            ('sqrt', 'sqrt', 1, {'promote_to_float': True}),

            ("sin", "sin", 1, {"promote_to_float": True}),
            ("cos", "cos", 1, {"promote_to_float": True}),
            ("tan", "tan", 1, {"promote_to_float": True}),
            ("arcsin", "arcsin", 1, {"promote_to_float": True}),
            ("arccos", "arccos", 1, {"promote_to_float": True}),
            ("arctan", "arctan", 1, {"promote_to_float": True}),
            ("arctan2", "arctan2", 2, {"promote_to_float": True}),
            ("sinh", "sinh", 1, {"promote_to_float": True}),
            ("cosh", "cosh", 1, {"promote_to_float": True}),
            ("tanh", "tanh", 1, {"promote_to_float": True}),
            ("arcsinh", "arcsinh", 1, {"promote_to_float": True}),
            ("arccosh", "arccosh", 1, {"promote_to_float": True}),
            ("arctanh", "arctanh", 1, {"promote_to_float": True}),

            ("radians", "radians", 1, {"promote_to_float": True}),
            ("degrees", "degrees", 1, {"promote_to_float": True}),

            ("log", "log", 1, {"promote_to_float": True}),
            ("log2", "log2", 1, {"promote_to_float": True}),
            ("log10", "log10", 1, {"promote_to_float": True}),
            ("log1p", "log1p", 1, {"promote_to_float": True}),
            ("logaddexp", "logaddexp", 2, {"promote_to_float": True}),
            ("logaddexp2", "logaddexp2", 2, {"promote_to_float": True}),
        ]:
            self.add_ufunc(space, *ufunc_def)

    def add_ufunc(self, space, ufunc_name, op_name, argcount, extra_kwargs=None):
        if extra_kwargs is None:
            extra_kwargs = {}

        identity = extra_kwargs.get("identity")
        if identity is not None:
            identity = \
                 interp_dtype.get_dtype_cache(space).w_longdtype.box(identity)
        extra_kwargs["identity"] = identity

        func = ufunc_dtype_caller(space, ufunc_name, op_name, argcount,
            comparison_func=extra_kwargs.get("comparison_func", False),
            bool_result=extra_kwargs.get("bool_result", False),
        )
        if argcount == 1:
            ufunc = W_Ufunc1(func, ufunc_name, **extra_kwargs)
        elif argcount == 2:
            ufunc = W_Ufunc2(func, ufunc_name, **extra_kwargs)
        setattr(self, ufunc_name, ufunc)

def get(space):
    return space.fromcache(UfuncState)
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