import warnings import numpy as np import operator from numba.core import types, utils, config from numba.core.typing.templates import (AttributeTemplate, AbstractTemplate, CallableTemplate, Registry, signature) from numba.np.numpy_support import (ufunc_find_matching_loop, supported_ufunc_loop, as_dtype, from_dtype, as_dtype, resolve_output_type, carray, farray, _ufunc_loop_sig) from numba.core.errors import (TypingError, NumbaPerformanceWarning, NumbaTypeError, NumbaAssertionError) from numba import pndindex from numba.core.overload_glue import glue_typing registry = Registry() infer = registry.register infer_global = registry.register_global infer_getattr = registry.register_attr class Numpy_rules_ufunc(AbstractTemplate): @classmethod def _handle_inputs(cls, ufunc, args, kws): """ Process argument types to a given *ufunc*. Returns a (base types, explicit outputs, ndims, layout) tuple where: - `base types` is a tuple of scalar types for each input - `explicit outputs` is a tuple of explicit output types (arrays) - `ndims` is the number of dimensions of the loop and also of any outputs, explicit or implicit - `layout` is the layout for any implicit output to be allocated """ nin = ufunc.nin nout = ufunc.nout nargs = ufunc.nargs # preconditions assert nargs == nin + nout if len(args) < nin: msg = "ufunc '{0}': not enough arguments ({1} found, {2} required)" raise TypingError(msg=msg.format(ufunc.__name__, len(args), nin)) if len(args) > nargs: msg = "ufunc '{0}': too many arguments ({1} found, {2} maximum)" raise TypingError(msg=msg.format(ufunc.__name__, len(args), nargs)) args = [a.as_array if isinstance(a, types.ArrayCompatible) else a for a in args] arg_ndims = [a.ndim if isinstance(a, types.ArrayCompatible) else 0 for a in args] ndims = max(arg_ndims) # explicit outputs must be arrays (no explicit scalar return values supported) explicit_outputs = args[nin:] # all the explicit outputs must match the number max number of dimensions if not all(d == ndims for d in arg_ndims[nin:]): msg = "ufunc '{0}' called with unsuitable explicit output arrays." raise TypingError(msg=msg.format(ufunc.__name__)) if not all(isinstance(output, types.ArrayCompatible) for output in explicit_outputs): msg = "ufunc '{0}' called with an explicit output that is not an array" raise TypingError(msg=msg.format(ufunc.__name__)) if not all(output.mutable for output in explicit_outputs): msg = "ufunc '{0}' called with an explicit output that is read-only" raise TypingError(msg=msg.format(ufunc.__name__)) # find the kernel to use, based only in the input types (as does NumPy) base_types = [x.dtype if isinstance(x, types.ArrayCompatible) else x for x in args] # Figure out the output array layout, if needed. layout = None if ndims > 0 and (len(explicit_outputs) < ufunc.nout): layout = 'C' layouts = [x.layout if isinstance(x, types.ArrayCompatible) else '' for x in args] # Prefer C contig if any array is C contig. # Next, prefer F contig. # Defaults to C contig if not layouts are C/F. if 'C' not in layouts and 'F' in layouts: layout = 'F' return base_types, explicit_outputs, ndims, layout @property def ufunc(self): return self.key def generic(self, args, kws): # First, strip optional types, ufunc loops are typed on concrete types args = [x.type if isinstance(x, types.Optional) else x for x in args] ufunc = self.ufunc base_types, explicit_outputs, ndims, layout = self._handle_inputs( ufunc, args, kws) ufunc_loop = ufunc_find_matching_loop(ufunc, base_types) if ufunc_loop is None: raise TypingError("can't resolve ufunc {0} for types {1}".format(ufunc.__name__, args)) # check if all the types involved in the ufunc loop are supported in this mode if not supported_ufunc_loop(ufunc, ufunc_loop): msg = "ufunc '{0}' using the loop '{1}' not supported in this mode" raise TypingError(msg=msg.format(ufunc.__name__, ufunc_loop.ufunc_sig)) # if there is any explicit output type, check that it is valid explicit_outputs_np = [as_dtype(tp.dtype) for tp in explicit_outputs] # Numpy will happily use unsafe conversions (although it will actually warn) if not all (np.can_cast(fromty, toty, 'unsafe') for (fromty, toty) in zip(ufunc_loop.numpy_outputs, explicit_outputs_np)): msg = "ufunc '{0}' can't cast result to explicit result type" raise TypingError(msg=msg.format(ufunc.__name__)) # A valid loop was found that is compatible. The result of type inference should # be based on the explicit output types, and when not available with the type given # by the selected NumPy loop out = list(explicit_outputs) implicit_output_count = ufunc.nout - len(explicit_outputs) if implicit_output_count > 0: # XXX this is sometimes wrong for datetime64 and timedelta64, # as ufunc_find_matching_loop() doesn't do any type inference ret_tys = ufunc_loop.outputs[-implicit_output_count:] if ndims > 0: assert layout is not None # If either of the types involved in the ufunc operation have a # __array_ufunc__ method then invoke the first such one to # determine the output type of the ufunc. array_ufunc_type = None for a in args: if hasattr(a, "__array_ufunc__"): array_ufunc_type = a break output_type = types.Array if array_ufunc_type is not None: output_type = array_ufunc_type.__array_ufunc__(ufunc, "__call__", *args, **kws) if output_type is NotImplemented: msg = (f"unsupported use of ufunc {ufunc} on " f"{array_ufunc_type}") # raise TypeError here because # NumpyRulesArrayOperator.generic is capturing # TypingError raise NumbaTypeError(msg) elif not issubclass(output_type, types.Array): msg = (f"ufunc {ufunc} on {array_ufunc_type}" f"cannot return non-array {output_type}") # raise TypeError here because # NumpyRulesArrayOperator.generic is capturing # TypingError raise TypeError(msg) ret_tys = [output_type(dtype=ret_ty, ndim=ndims, layout=layout) for ret_ty in ret_tys] ret_tys = [resolve_output_type(self.context, args, ret_ty) for ret_ty in ret_tys] out.extend(ret_tys) return _ufunc_loop_sig(out, args) class NumpyRulesArrayOperator(Numpy_rules_ufunc): _op_map = { operator.add: "add", operator.sub: "subtract", operator.mul: "multiply", operator.truediv: "true_divide", operator.floordiv: "floor_divide", operator.mod: "remainder", operator.pow: "power", operator.lshift: "left_shift", operator.rshift: "right_shift", operator.and_: "bitwise_and", operator.or_: "bitwise_or", operator.xor: "bitwise_xor", operator.eq: "equal", operator.gt: "greater", operator.ge: "greater_equal", operator.lt: "less", operator.le: "less_equal", operator.ne: "not_equal", } @property def ufunc(self): return getattr(np, self._op_map[self.key]) @classmethod def install_operations(cls): for op, ufunc_name in cls._op_map.items(): infer_global(op)( type("NumpyRulesArrayOperator_" + ufunc_name, (cls,), dict(key=op)) ) def generic(self, args, kws): '''Overloads and calls base class generic() method, returning None if a TypingError occurred. Returning None for operators is important since operators are heavily overloaded, and by suppressing type errors, we allow type inference to check other possibilities before giving up (particularly user-defined operators). ''' try: sig = super(NumpyRulesArrayOperator, self).generic(args, kws) except TypingError: return None if sig is None: return None args = sig.args # Only accept at least one array argument, otherwise the operator # doesn't involve Numpy's ufunc machinery. if not any(isinstance(arg, types.ArrayCompatible) for arg in args): return None return sig _binop_map = NumpyRulesArrayOperator._op_map class NumpyRulesInplaceArrayOperator(NumpyRulesArrayOperator): _op_map = { operator.iadd: "add", operator.isub: "subtract", operator.imul: "multiply", operator.itruediv: "true_divide", operator.ifloordiv: "floor_divide", operator.imod: "remainder", operator.ipow: "power", operator.ilshift: "left_shift", operator.irshift: "right_shift", operator.iand: "bitwise_and", operator.ior: "bitwise_or", operator.ixor: "bitwise_xor", } def generic(self, args, kws): # Type the inplace operator as if an explicit output was passed, # to handle type resolution correctly. # (for example int8[:] += int16[:] should use an int8[:] output, # not int16[:]) lhs, rhs = args if not isinstance(lhs, types.ArrayCompatible): return args = args + (lhs,) sig = super(NumpyRulesInplaceArrayOperator, self).generic(args, kws) # Strip off the fake explicit output assert len(sig.args) == 3 real_sig = signature(sig.return_type, *sig.args[:2]) return real_sig class NumpyRulesUnaryArrayOperator(NumpyRulesArrayOperator): _op_map = { operator.pos: "positive", operator.neg: "negative", operator.invert: "invert", } def generic(self, args, kws): assert not kws if len(args) == 1 and isinstance(args[0], types.ArrayCompatible): return super(NumpyRulesUnaryArrayOperator, self).generic(args, kws) # list of unary ufuncs to register _math_operations = [ "add", "subtract", "multiply", "logaddexp", "logaddexp2", "true_divide", "floor_divide", "negative", "positive", "power", "float_power", "remainder", "fmod", "absolute", "rint", "sign", "conjugate", "exp", "exp2", "log", "log2", "log10", "expm1", "log1p", "sqrt", "square", "cbrt", "reciprocal", "divide", "mod", "divmod", "abs", "fabs" , "gcd", "lcm"] _trigonometric_functions = [ "sin", "cos", "tan", "arcsin", "arccos", "arctan", "arctan2", "hypot", "sinh", "cosh", "tanh", "arcsinh", "arccosh", "arctanh", "deg2rad", "rad2deg", "degrees", "radians" ] _bit_twiddling_functions = ["bitwise_and", "bitwise_or", "bitwise_xor", "invert", "left_shift", "right_shift", "bitwise_not" ] _comparison_functions = [ "greater", "greater_equal", "less", "less_equal", "not_equal", "equal", "logical_and", "logical_or", "logical_xor", "logical_not", "maximum", "minimum", "fmax", "fmin" ] _floating_functions = [ "isfinite", "isinf", "isnan", "signbit", "copysign", "nextafter", "modf", "ldexp", "frexp", "floor", "ceil", "trunc", "spacing" ] _logic_functions = [ "isnat" ] # This is a set of the ufuncs that are not yet supported by Lowering. In order # to trigger no-python mode we must not register them until their Lowering is # implemented. # # It also works as a nice TODO list for ufunc support :) _unsupported = set([ 'frexp', 'modf', ]) # A list of ufuncs that are in fact aliases of other ufuncs. They need to insert the # resolve method, but not register the ufunc itself _aliases = set(["bitwise_not", "mod", "abs"]) # In python3 np.divide is mapped to np.true_divide if np.divide == np.true_divide: _aliases.add("divide") def _numpy_ufunc(name): func = getattr(np, name) class typing_class(Numpy_rules_ufunc): key = func typing_class.__name__ = "resolve_{0}".format(name) if not name in _aliases: infer_global(func, types.Function(typing_class)) all_ufuncs = sum([_math_operations, _trigonometric_functions, _bit_twiddling_functions, _comparison_functions, _floating_functions, _logic_functions], []) supported_ufuncs = [x for x in all_ufuncs if x not in _unsupported] for func in supported_ufuncs: _numpy_ufunc(func) all_ufuncs = [getattr(np, name) for name in all_ufuncs] supported_ufuncs = [getattr(np, name) for name in supported_ufuncs] NumpyRulesUnaryArrayOperator.install_operations() NumpyRulesArrayOperator.install_operations() NumpyRulesInplaceArrayOperator.install_operations() supported_array_operators = set( NumpyRulesUnaryArrayOperator._op_map.keys() ).union( NumpyRulesArrayOperator._op_map.keys() ).union( NumpyRulesInplaceArrayOperator._op_map.keys() ) del _math_operations, _trigonometric_functions, _bit_twiddling_functions del _comparison_functions, _floating_functions, _unsupported del _aliases, _numpy_ufunc # ----------------------------------------------------------------------------- # Install global helpers for array methods. class Numpy_method_redirection(AbstractTemplate): """ A template redirecting a Numpy global function (e.g. np.sum) to an array method of the same name (e.g. ndarray.sum). """ # Arguments like *axis* can specialize on literals but also support # non-literals prefer_literal = True def generic(self, args, kws): pysig = None if kws: if self.method_name == 'sum': if 'axis' in kws and 'dtype' not in kws: def sum_stub(arr, axis): pass pysig = utils.pysignature(sum_stub) elif 'dtype' in kws and 'axis' not in kws: def sum_stub(arr, dtype): pass pysig = utils.pysignature(sum_stub) elif 'dtype' in kws and 'axis' in kws: def sum_stub(arr, axis, dtype): pass pysig = utils.pysignature(sum_stub) elif self.method_name == 'argsort': def argsort_stub(arr, kind='quicksort'): pass pysig = utils.pysignature(argsort_stub) else: fmt = "numba doesn't support kwarg for {}" raise TypingError(fmt.format(self.method_name)) arr = args[0] # This will return a BoundFunction meth_ty = self.context.resolve_getattr(arr, self.method_name) # Resolve arguments on the bound function meth_sig = self.context.resolve_function_type(meth_ty, args[1:], kws) if meth_sig is not None: return meth_sig.as_function().replace(pysig=pysig) # Function to glue attributes onto the numpy-esque object def _numpy_redirect(fname): numpy_function = getattr(np, fname) cls = type("Numpy_redirect_{0}".format(fname), (Numpy_method_redirection,), dict(key=numpy_function, method_name=fname)) infer_global(numpy_function, types.Function(cls)) for func in ['min', 'max', 'sum', 'prod', 'mean', 'var', 'std', 'cumsum', 'cumprod', 'argsort', 'nonzero', 'ravel']: _numpy_redirect(func) # ----------------------------------------------------------------------------- # Numpy scalar constructors # Register np.int8, etc. as converters to the equivalent Numba types np_types = set(getattr(np, str(nb_type)) for nb_type in types.number_domain) np_types.add(np.bool_) # Those may or may not be aliases (depending on the Numpy build / version) np_types.add(np.intc) np_types.add(np.intp) np_types.add(np.uintc) np_types.add(np.uintp) def register_number_classes(register_global): for np_type in np_types: nb_type = getattr(types, np_type.__name__) register_global(np_type, types.NumberClass(nb_type)) register_number_classes(infer_global) # ----------------------------------------------------------------------------- # Numpy array constructors def parse_shape(shape): """ Given a shape, return the number of dimensions. """ ndim = None if isinstance(shape, types.Integer): ndim = 1 elif isinstance(shape, (types.Tuple, types.UniTuple)): if all(isinstance(s, types.Integer) for s in shape): ndim = len(shape) return ndim def parse_dtype(dtype): """ Return the dtype of a type, if it is either a DtypeSpec (used for most dtypes) or a TypeRef (used for record types). """ if isinstance(dtype, types.DTypeSpec): return dtype.dtype elif isinstance(dtype, types.TypeRef): return dtype.instance_type elif isinstance(dtype, types.StringLiteral): dtstr = dtype.literal_value try: dt = np.dtype(dtstr) except TypeError: msg = f"Invalid NumPy dtype specified: '{dtstr}'" raise TypingError(msg) return from_dtype(dt) def _parse_nested_sequence(context, typ): """ Parse a (possibly 0d) nested sequence type. A (ndim, dtype) tuple is returned. Note the sequence may still be heterogeneous, as long as it converts to the given dtype. """ if isinstance(typ, (types.Buffer,)): raise TypingError("%r not allowed in a homogeneous sequence" % typ) elif isinstance(typ, (types.Sequence,)): n, dtype = _parse_nested_sequence(context, typ.dtype) return n + 1, dtype elif isinstance(typ, (types.BaseTuple,)): if typ.count == 0: # Mimick Numpy's behaviour return 1, types.float64 n, dtype = _parse_nested_sequence(context, typ[0]) dtypes = [dtype] for i in range(1, typ.count): _n, dtype = _parse_nested_sequence(context, typ[i]) if _n != n: raise TypingError("type %r does not have a regular shape" % (typ,)) dtypes.append(dtype) dtype = context.unify_types(*dtypes) if dtype is None: raise TypingError("cannot convert %r to a homogeneous type" % typ) return n + 1, dtype else: # Scalar type => check it's valid as a Numpy array dtype as_dtype(typ) return 0, typ @glue_typing(np.array) class NpArray(CallableTemplate): """ Typing template for np.array(). """ def generic(self): def typer(object, dtype=None): ndim, seq_dtype = _parse_nested_sequence(self.context, object) if dtype is None: dtype = seq_dtype else: dtype = parse_dtype(dtype) if dtype is None: return return types.Array(dtype, ndim, 'C') return typer @glue_typing(np.empty) @glue_typing(np.zeros) @glue_typing(np.ones) class NdConstructor(CallableTemplate): """ Typing template for np.empty(), .zeros(), .ones(). """ def generic(self): def typer(shape, dtype=None): if dtype is None: nb_dtype = types.double else: nb_dtype = parse_dtype(dtype) ndim = parse_shape(shape) if nb_dtype is not None and ndim is not None: return types.Array(dtype=nb_dtype, ndim=ndim, layout='C') return typer @glue_typing(np.empty_like) @glue_typing(np.zeros_like) @glue_typing(np.ones_like) class NdConstructorLike(CallableTemplate): """ Typing template for np.empty_like(), .zeros_like(), .ones_like(). """ def generic(self): """ np.empty_like(array) -> empty array of the same shape and layout np.empty_like(scalar) -> empty 0-d array of the scalar type """ def typer(arg, dtype=None): if dtype is not None: nb_dtype = parse_dtype(dtype) elif isinstance(arg, types.Array): nb_dtype = arg.dtype else: nb_dtype = arg if nb_dtype is not None: if isinstance(arg, types.Array): layout = arg.layout if arg.layout != 'A' else 'C' return arg.copy(dtype=nb_dtype, layout=layout, readonly=False) else: return types.Array(nb_dtype, 0, 'C') return typer @glue_typing(np.full) class NdFull(CallableTemplate): def generic(self): def typer(shape, fill_value, dtype=None): if dtype is None: nb_dtype = fill_value else: nb_dtype = parse_dtype(dtype) ndim = parse_shape(shape) if nb_dtype is not None and ndim is not None: return types.Array(dtype=nb_dtype, ndim=ndim, layout='C') return typer @glue_typing(np.full_like) class NdFullLike(CallableTemplate): def generic(self): """ np.full_like(array, val) -> array of the same shape and layout np.full_like(scalar, val) -> 0-d array of the scalar type """ def typer(arg, fill_value, dtype=None): if dtype is not None: nb_dtype = parse_dtype(dtype) elif isinstance(arg, types.Array): nb_dtype = arg.dtype else: nb_dtype = arg if nb_dtype is not None: if isinstance(arg, types.Array): return arg.copy(dtype=nb_dtype, readonly=False) else: return types.Array(dtype=nb_dtype, ndim=0, layout='C') return typer @glue_typing(np.identity) class NdIdentity(AbstractTemplate): def generic(self, args, kws): assert not kws n = args[0] if not isinstance(n, types.Integer): return if len(args) >= 2: nb_dtype = parse_dtype(args[1]) else: nb_dtype = types.float64 if nb_dtype is not None: return_type = types.Array(ndim=2, dtype=nb_dtype, layout='C') return signature(return_type, *args) def _infer_dtype_from_inputs(inputs): return dtype @glue_typing(np.linspace) class NdLinspace(AbstractTemplate): def generic(self, args, kws): assert not kws bounds = args[:2] if not all(isinstance(arg, types.Number) for arg in bounds): return if len(args) >= 3: num = args[2] if not isinstance(num, types.Integer): return if len(args) >= 4: # Not supporting the other arguments as it would require # keyword arguments for reasonable use. return if any(isinstance(arg, types.Complex) for arg in bounds): dtype = types.complex128 else: dtype = types.float64 return_type = types.Array(ndim=1, dtype=dtype, layout='C') return signature(return_type, *args) @glue_typing(np.frombuffer) class NdFromBuffer(CallableTemplate): def generic(self): def typer(buffer, dtype=None): if not isinstance(buffer, types.Buffer) or buffer.layout != 'C': return if dtype is None: nb_dtype = types.float64 else: nb_dtype = parse_dtype(dtype) if nb_dtype is not None: return types.Array(dtype=nb_dtype, ndim=1, layout='C', readonly=not buffer.mutable) return typer @glue_typing(np.sort) class NdSort(CallableTemplate): def generic(self): def typer(a): if isinstance(a, types.Array) and a.ndim == 1: return a return typer @glue_typing(np.asfortranarray) class AsFortranArray(CallableTemplate): def generic(self): def typer(a): if isinstance(a, types.Array): return a.copy(layout='F', ndim=max(a.ndim, 1)) return typer @glue_typing(np.ascontiguousarray) class AsContiguousArray(CallableTemplate): def generic(self): def typer(a): if isinstance(a, types.Array): return a.copy(layout='C', ndim=max(a.ndim, 1)) return typer @glue_typing(np.copy) class NdCopy(CallableTemplate): def generic(self): def typer(a): if isinstance(a, types.Array): layout = 'F' if a.layout == 'F' else 'C' return a.copy(layout=layout, readonly=False) return typer @glue_typing(np.expand_dims) class NdExpandDims(CallableTemplate): def generic(self): def typer(a, axis): if (not isinstance(a, types.Array) or not isinstance(axis, types.Integer)): return layout = a.layout if a.ndim <= 1 else 'A' return a.copy(ndim=a.ndim + 1, layout=layout) return typer class BaseAtLeastNdTemplate(AbstractTemplate): def generic(self, args, kws): assert not kws if not args or not all(isinstance(a, types.Array) for a in args): return rets = [self.convert_array(a) for a in args] if len(rets) > 1: retty = types.BaseTuple.from_types(rets) else: retty = rets[0] return signature(retty, *args) @glue_typing(np.atleast_1d) class NdAtLeast1d(BaseAtLeastNdTemplate): def convert_array(self, a): return a.copy(ndim=max(a.ndim, 1)) @glue_typing(np.atleast_2d) class NdAtLeast2d(BaseAtLeastNdTemplate): def convert_array(self, a): return a.copy(ndim=max(a.ndim, 2)) @glue_typing(np.atleast_3d) class NdAtLeast3d(BaseAtLeastNdTemplate): def convert_array(self, a): return a.copy(ndim=max(a.ndim, 3)) def _homogeneous_dims(context, func_name, arrays): ndim = arrays[0].ndim for a in arrays: if a.ndim != ndim: msg = (f"{func_name}(): all the input arrays must have same number " "of dimensions") raise NumbaTypeError(msg) return ndim def _sequence_of_arrays(context, func_name, arrays, dim_chooser=_homogeneous_dims): if (not isinstance(arrays, types.BaseTuple) or not len(arrays) or not all(isinstance(a, types.Array) for a in arrays)): raise TypeError("%s(): expecting a non-empty tuple of arrays, " "got %s" % (func_name, arrays)) ndim = dim_chooser(context, func_name, arrays) dtype = context.unify_types(*(a.dtype for a in arrays)) if dtype is None: raise TypeError("%s(): input arrays must have " "compatible dtypes" % func_name) return dtype, ndim def _choose_concatenation_layout(arrays): # Only create a F array if all input arrays have F layout. # This is a simplified version of Numpy's behaviour, # while Numpy's actually processes the input strides to # decide on optimal output strides # (see PyArray_CreateMultiSortedStridePerm()). return 'F' if all(a.layout == 'F' for a in arrays) else 'C' @glue_typing(np.concatenate) class NdConcatenate(CallableTemplate): def generic(self): def typer(arrays, axis=None): if axis is not None and not isinstance(axis, types.Integer): # Note Numpy allows axis=None, but it isn't documented: # https://github.com/numpy/numpy/issues/7968 return dtype, ndim = _sequence_of_arrays(self.context, "np.concatenate", arrays) if ndim == 0: raise TypeError("zero-dimensional arrays cannot be concatenated") layout = _choose_concatenation_layout(arrays) return types.Array(dtype, ndim, layout) return typer @glue_typing(np.stack) class NdStack(CallableTemplate): def generic(self): def typer(arrays, axis=None): if axis is not None and not isinstance(axis, types.Integer): # Note Numpy allows axis=None, but it isn't documented: # https://github.com/numpy/numpy/issues/7968 return dtype, ndim = _sequence_of_arrays(self.context, "np.stack", arrays) # This diverges from Numpy's behaviour, which simply inserts # a new stride at the requested axis (therefore can return # a 'A' array). layout = 'F' if all(a.layout == 'F' for a in arrays) else 'C' return types.Array(dtype, ndim + 1, layout) return typer class BaseStackTemplate(CallableTemplate): def generic(self): def typer(arrays): dtype, ndim = _sequence_of_arrays(self.context, self.func_name, arrays) ndim = max(ndim, self.ndim_min) layout = _choose_concatenation_layout(arrays) return types.Array(dtype, ndim, layout) return typer @glue_typing(np.hstack) class NdStack(BaseStackTemplate): func_name = "np.hstack" ndim_min = 1 @glue_typing(np.vstack) class NdStack(BaseStackTemplate): func_name = "np.vstack" ndim_min = 2 @glue_typing(np.dstack) class NdStack(BaseStackTemplate): func_name = "np.dstack" ndim_min = 3 def _column_stack_dims(context, func_name, arrays): # column_stack() allows stacking 1-d and 2-d arrays together for a in arrays: if a.ndim < 1 or a.ndim > 2: raise TypeError("np.column_stack() is only defined on " "1-d and 2-d arrays") return 2 @glue_typing(np.column_stack) class NdColumnStack(CallableTemplate): def generic(self): def typer(arrays): dtype, ndim = _sequence_of_arrays(self.context, "np.column_stack", arrays, dim_chooser=_column_stack_dims) layout = _choose_concatenation_layout(arrays) return types.Array(dtype, ndim, layout) return typer # ----------------------------------------------------------------------------- # Linear algebra class MatMulTyperMixin(object): def matmul_typer(self, a, b, out=None): """ Typer function for Numpy matrix multiplication. """ if not isinstance(a, types.Array) or not isinstance(b, types.Array): return if not all(x.ndim in (1, 2) for x in (a, b)): raise TypingError("%s only supported on 1-D and 2-D arrays" % (self.func_name, )) # Output dimensionality ndims = set([a.ndim, b.ndim]) if ndims == set([2]): # M * M out_ndim = 2 elif ndims == set([1, 2]): # M* V and V * M out_ndim = 1 elif ndims == set([1]): # V * V out_ndim = 0 if out is not None: if out_ndim == 0: raise TypeError("explicit output unsupported for vector * vector") elif out.ndim != out_ndim: raise TypeError("explicit output has incorrect dimensionality") if not isinstance(out, types.Array) or out.layout != 'C': raise TypeError("output must be a C-contiguous array") all_args = (a, b, out) else: all_args = (a, b) if not (config.DISABLE_PERFORMANCE_WARNINGS or all(x.layout in 'CF' for x in (a, b))): msg = ("%s is faster on contiguous arrays, called on %s" % (self.func_name, (a, b))) warnings.warn(NumbaPerformanceWarning(msg)) if not all(x.dtype == a.dtype for x in all_args): raise TypingError("%s arguments must all have " "the same dtype" % (self.func_name,)) if not isinstance(a.dtype, (types.Float, types.Complex)): raise TypingError("%s only supported on " "float and complex arrays" % (self.func_name,)) if out: return out elif out_ndim > 0: return types.Array(a.dtype, out_ndim, 'C') else: return a.dtype @glue_typing(np.dot) class Dot(MatMulTyperMixin, CallableTemplate): func_name = "np.dot()" def generic(self): def typer(a, b, out=None): # NOTE: np.dot() and the '@' operator have distinct semantics # for >2-D arrays, but we don't support them. return self.matmul_typer(a, b, out) return typer @glue_typing(np.vdot) class VDot(CallableTemplate): def generic(self): def typer(a, b): if not isinstance(a, types.Array) or not isinstance(b, types.Array): return if not all(x.ndim == 1 for x in (a, b)): raise TypingError("np.vdot() only supported on 1-D arrays") if not all(x.layout in 'CF' for x in (a, b)): warnings.warn("np.vdot() is faster on contiguous arrays, called on %s" % ((a, b),), NumbaPerformanceWarning) if not all(x.dtype == a.dtype for x in (a, b)): raise TypingError("np.vdot() arguments must all have " "the same dtype") if not isinstance(a.dtype, (types.Float, types.Complex)): raise TypingError("np.vdot() only supported on " "float and complex arrays") return a.dtype return typer @infer_global(operator.matmul) class MatMul(MatMulTyperMixin, AbstractTemplate): key = operator.matmul func_name = "'@'" def generic(self, args, kws): assert not kws restype = self.matmul_typer(*args) if restype is not None: return signature(restype, *args) def _check_linalg_matrix(a, func_name): if not isinstance(a, types.Array): return if not a.ndim == 2: raise TypingError("np.linalg.%s() only supported on 2-D arrays" % func_name) if not isinstance(a.dtype, (types.Float, types.Complex)): raise TypingError("np.linalg.%s() only supported on " "float and complex arrays" % func_name) # ----------------------------------------------------------------------------- # Miscellaneous functions @infer_global(np.ndenumerate) class NdEnumerate(AbstractTemplate): def generic(self, args, kws): assert not kws arr, = args if isinstance(arr, types.Array): enumerate_type = types.NumpyNdEnumerateType(arr) return signature(enumerate_type, *args) @infer_global(np.nditer) class NdIter(AbstractTemplate): def generic(self, args, kws): assert not kws if len(args) != 1: return arrays, = args if isinstance(arrays, types.BaseTuple): if not arrays: return arrays = list(arrays) else: arrays = [arrays] nditerty = types.NumpyNdIterType(arrays) return signature(nditerty, *args) @infer_global(pndindex) @infer_global(np.ndindex) class NdIndex(AbstractTemplate): def generic(self, args, kws): assert not kws # Either ndindex(shape) or ndindex(*shape) if len(args) == 1 and isinstance(args[0], types.BaseTuple): tup = args[0] if tup.count > 0 and not isinstance(tup, types.UniTuple): # Heterogeneous tuple return shape = list(tup) else: shape = args if all(isinstance(x, types.Integer) for x in shape): iterator_type = types.NumpyNdIndexType(len(shape)) return signature(iterator_type, *args) # We use the same typing key for np.round() and np.around() to # re-use the implementations automatically. @glue_typing(np.round) @glue_typing(np.around) class Round(AbstractTemplate): def generic(self, args, kws): assert not kws assert 1 <= len(args) <= 3 arg = args[0] if len(args) == 1: decimals = types.intp out = None else: decimals = args[1] if len(args) == 2: out = None else: out = args[2] supported_scalars = (types.Integer, types.Float, types.Complex) if isinstance(arg, supported_scalars): assert out is None return signature(arg, *args) if (isinstance(arg, types.Array) and isinstance(arg.dtype, supported_scalars) and isinstance(out, types.Array) and isinstance(out.dtype, supported_scalars) and out.ndim == arg.ndim): # arg can only be complex if out is complex too if (not isinstance(arg.dtype, types.Complex) or isinstance(out.dtype, types.Complex)): return signature(out, *args) @glue_typing(np.where) class Where(AbstractTemplate): def generic(self, args, kws): assert not kws if len(args) == 1: # 0-dim arrays return one result array ary = args[0] ndim = max(ary.ndim, 1) retty = types.UniTuple(types.Array(types.intp, 1, 'C'), ndim) return signature(retty, ary) elif len(args) == 3: cond, x, y = args retdty = from_dtype(np.promote_types( as_dtype(getattr(args[1], 'dtype', args[1])), as_dtype(getattr(args[2], 'dtype', args[2])))) if isinstance(cond, types.Array): # array where() if isinstance(x, types.Array) and isinstance(y, types.Array): if (cond.ndim == x.ndim == y.ndim): if x.layout == y.layout == cond.layout: retty = types.Array(retdty, x.ndim, x.layout) else: retty = types.Array(retdty, x.ndim, 'C') return signature(retty, *args) else: # x and y both scalar retty = types.Array(retdty, cond.ndim, cond.layout) return signature(retty, *args) else: # scalar where() if not isinstance(x, types.Array): retty = types.Array(retdty, 0, 'C') return signature(retty, *args) @glue_typing(np.sinc) class Sinc(AbstractTemplate): def generic(self, args, kws): assert not kws assert len(args) == 1 arg = args[0] supported_scalars = (types.Float, types.Complex) if (isinstance(arg, supported_scalars) or (isinstance(arg, types.Array) and isinstance(arg.dtype, supported_scalars))): return signature(arg, arg) @glue_typing(np.angle) class Angle(CallableTemplate): """ Typing template for np.angle() """ def generic(self): def typer(z, deg=False): if isinstance(z, types.Array): dtype = z.dtype else: dtype = z if isinstance(dtype, types.Complex): ret_dtype = dtype.underlying_float elif isinstance(dtype, types.Float): ret_dtype = dtype else: return if isinstance(z, types.Array): return z.copy(dtype=ret_dtype) else: return ret_dtype return typer @glue_typing(np.diag) class DiagCtor(CallableTemplate): """ Typing template for np.diag() """ def generic(self): def typer(ref, k=0): if isinstance(ref, types.Array): if ref.ndim == 1: rdim = 2 elif ref.ndim == 2: rdim = 1 else: return None if isinstance(k, (int, types.Integer)): return types.Array(ndim=rdim, dtype=ref.dtype, layout='C') return typer @glue_typing(np.take) class Take(AbstractTemplate): def generic(self, args, kws): if kws: raise NumbaAssertionError("kws not supported") if len(args) != 2: raise NumbaAssertionError("two arguments are required") arr, ind = args if isinstance(ind, types.Number): retty = arr.dtype elif isinstance(ind, types.Array): retty = types.Array(ndim=ind.ndim, dtype=arr.dtype, layout='C') elif isinstance(ind, types.List): retty = types.Array(ndim=1, dtype=arr.dtype, layout='C') elif isinstance(ind, types.BaseTuple): retty = types.Array(ndim=np.ndim(ind), dtype=arr.dtype, layout='C') else: return None return signature(retty, *args) # ----------------------------------------------------------------------------- # Numba helpers @glue_typing(carray) class NumbaCArray(CallableTemplate): layout = 'C' def generic(self): func_name = self.key.__name__ def typer(ptr, shape, dtype=types.none): if ptr is types.voidptr: ptr_dtype = None elif isinstance(ptr, types.CPointer): ptr_dtype = ptr.dtype else: raise NumbaTypeError("%s(): pointer argument expected, got '%s'" % (func_name, ptr)) if dtype is types.none: if ptr_dtype is None: raise NumbaTypeError("%s(): explicit dtype required for void* argument" % (func_name,)) dtype = ptr_dtype elif isinstance(dtype, types.DTypeSpec): dtype = dtype.dtype if ptr_dtype is not None and dtype != ptr_dtype: raise NumbaTypeError("%s(): mismatching dtype '%s' for pointer type '%s'" % (func_name, dtype, ptr)) else: raise NumbaTypeError("%s(): invalid dtype spec '%s'" % (func_name, dtype)) ndim = parse_shape(shape) if ndim is None: raise NumbaTypeError("%s(): invalid shape '%s'" % (func_name, shape)) return types.Array(dtype, ndim, self.layout) return typer @glue_typing(farray) class NumbaFArray(NumbaCArray): layout = 'F'