896 lines
37 KiB
Python
896 lines
37 KiB
Python
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from __future__ import absolute_import
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import copy
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from . import (ExprNodes, PyrexTypes, MemoryView,
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ParseTreeTransforms, StringEncoding, Errors)
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from .ExprNodes import CloneNode, ProxyNode, TupleNode
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from .Nodes import FuncDefNode, CFuncDefNode, StatListNode, DefNode
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from ..Utils import OrderedSet
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class FusedCFuncDefNode(StatListNode):
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"""
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This node replaces a function with fused arguments. It deep-copies the
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function for every permutation of fused types, and allocates a new local
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scope for it. It keeps track of the original function in self.node, and
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the entry of the original function in the symbol table is given the
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'fused_cfunction' attribute which points back to us.
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Then when a function lookup occurs (to e.g. call it), the call can be
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dispatched to the right function.
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node FuncDefNode the original function
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nodes [FuncDefNode] list of copies of node with different specific types
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py_func DefNode the fused python function subscriptable from
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Python space
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__signatures__ A DictNode mapping signature specialization strings
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to PyCFunction nodes
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resulting_fused_function PyCFunction for the fused DefNode that delegates
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to specializations
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fused_func_assignment Assignment of the fused function to the function name
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defaults_tuple TupleNode of defaults (letting PyCFunctionNode build
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defaults would result in many different tuples)
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specialized_pycfuncs List of synthesized pycfunction nodes for the
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specializations
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code_object CodeObjectNode shared by all specializations and the
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fused function
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fused_compound_types All fused (compound) types (e.g. floating[:])
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"""
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__signatures__ = None
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resulting_fused_function = None
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fused_func_assignment = None
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defaults_tuple = None
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decorators = None
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child_attrs = StatListNode.child_attrs + [
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'__signatures__', 'resulting_fused_function', 'fused_func_assignment']
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def __init__(self, node, env):
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super(FusedCFuncDefNode, self).__init__(node.pos)
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self.nodes = []
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self.node = node
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is_def = isinstance(self.node, DefNode)
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if is_def:
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# self.node.decorators = []
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self.copy_def(env)
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else:
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self.copy_cdef(env)
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# Perform some sanity checks. If anything fails, it's a bug
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for n in self.nodes:
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assert not n.entry.type.is_fused
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assert not n.local_scope.return_type.is_fused
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if node.return_type.is_fused:
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assert not n.return_type.is_fused
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if not is_def and n.cfunc_declarator.optional_arg_count:
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assert n.type.op_arg_struct
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node.entry.fused_cfunction = self
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# Copy the nodes as AnalyseDeclarationsTransform will prepend
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# self.py_func to self.stats, as we only want specialized
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# CFuncDefNodes in self.nodes
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self.stats = self.nodes[:]
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def copy_def(self, env):
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"""
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Create a copy of the original def or lambda function for specialized
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versions.
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"""
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fused_compound_types = PyrexTypes.unique(
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[arg.type for arg in self.node.args if arg.type.is_fused])
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fused_types = self._get_fused_base_types(fused_compound_types)
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permutations = PyrexTypes.get_all_specialized_permutations(fused_types)
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self.fused_compound_types = fused_compound_types
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if self.node.entry in env.pyfunc_entries:
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env.pyfunc_entries.remove(self.node.entry)
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for cname, fused_to_specific in permutations:
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copied_node = copy.deepcopy(self.node)
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# keep signature object identity for special casing in DefNode.analyse_declarations()
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copied_node.entry.signature = self.node.entry.signature
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self._specialize_function_args(copied_node.args, fused_to_specific)
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copied_node.return_type = self.node.return_type.specialize(
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fused_to_specific)
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copied_node.analyse_declarations(env)
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# copied_node.is_staticmethod = self.node.is_staticmethod
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# copied_node.is_classmethod = self.node.is_classmethod
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self.create_new_local_scope(copied_node, env, fused_to_specific)
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self.specialize_copied_def(copied_node, cname, self.node.entry,
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fused_to_specific, fused_compound_types)
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PyrexTypes.specialize_entry(copied_node.entry, cname)
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copied_node.entry.used = True
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env.entries[copied_node.entry.name] = copied_node.entry
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if not self.replace_fused_typechecks(copied_node):
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break
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self.orig_py_func = self.node
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self.py_func = self.make_fused_cpdef(self.node, env, is_def=True)
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def copy_cdef(self, env):
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"""
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Create a copy of the original c(p)def function for all specialized
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versions.
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"""
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permutations = self.node.type.get_all_specialized_permutations()
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# print 'Node %s has %d specializations:' % (self.node.entry.name,
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# len(permutations))
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# import pprint; pprint.pprint([d for cname, d in permutations])
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# Prevent copying of the python function
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self.orig_py_func = orig_py_func = self.node.py_func
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self.node.py_func = None
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if orig_py_func:
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env.pyfunc_entries.remove(orig_py_func.entry)
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fused_types = self.node.type.get_fused_types()
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self.fused_compound_types = fused_types
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new_cfunc_entries = []
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for cname, fused_to_specific in permutations:
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copied_node = copy.deepcopy(self.node)
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# Make the types in our CFuncType specific.
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type = copied_node.type.specialize(fused_to_specific)
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entry = copied_node.entry
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type.specialize_entry(entry, cname)
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# Reuse existing Entries (e.g. from .pxd files).
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for i, orig_entry in enumerate(env.cfunc_entries):
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if entry.cname == orig_entry.cname and type.same_as_resolved_type(orig_entry.type):
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copied_node.entry = env.cfunc_entries[i]
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if not copied_node.entry.func_cname:
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copied_node.entry.func_cname = entry.func_cname
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entry = copied_node.entry
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type = entry.type
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break
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else:
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new_cfunc_entries.append(entry)
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copied_node.type = type
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entry.type, type.entry = type, entry
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entry.used = (entry.used or
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self.node.entry.defined_in_pxd or
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env.is_c_class_scope or
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entry.is_cmethod)
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if self.node.cfunc_declarator.optional_arg_count:
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self.node.cfunc_declarator.declare_optional_arg_struct(
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type, env, fused_cname=cname)
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copied_node.return_type = type.return_type
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self.create_new_local_scope(copied_node, env, fused_to_specific)
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# Make the argument types in the CFuncDeclarator specific
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self._specialize_function_args(copied_node.cfunc_declarator.args,
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fused_to_specific)
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# If a cpdef, declare all specialized cpdefs (this
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# also calls analyse_declarations)
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copied_node.declare_cpdef_wrapper(env)
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if copied_node.py_func:
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env.pyfunc_entries.remove(copied_node.py_func.entry)
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self.specialize_copied_def(
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copied_node.py_func, cname, self.node.entry.as_variable,
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fused_to_specific, fused_types)
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if not self.replace_fused_typechecks(copied_node):
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break
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# replace old entry with new entries
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try:
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cindex = env.cfunc_entries.index(self.node.entry)
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except ValueError:
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env.cfunc_entries.extend(new_cfunc_entries)
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else:
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env.cfunc_entries[cindex:cindex+1] = new_cfunc_entries
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if orig_py_func:
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self.py_func = self.make_fused_cpdef(orig_py_func, env,
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is_def=False)
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else:
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self.py_func = orig_py_func
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def _get_fused_base_types(self, fused_compound_types):
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"""
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Get a list of unique basic fused types, from a list of
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(possibly) compound fused types.
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"""
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base_types = []
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seen = set()
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for fused_type in fused_compound_types:
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fused_type.get_fused_types(result=base_types, seen=seen)
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return base_types
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def _specialize_function_args(self, args, fused_to_specific):
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for arg in args:
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if arg.type.is_fused:
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arg.type = arg.type.specialize(fused_to_specific)
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if arg.type.is_memoryviewslice:
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arg.type.validate_memslice_dtype(arg.pos)
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def create_new_local_scope(self, node, env, f2s):
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"""
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Create a new local scope for the copied node and append it to
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self.nodes. A new local scope is needed because the arguments with the
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fused types are already in the local scope, and we need the specialized
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entries created after analyse_declarations on each specialized version
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of the (CFunc)DefNode.
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f2s is a dict mapping each fused type to its specialized version
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"""
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node.create_local_scope(env)
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node.local_scope.fused_to_specific = f2s
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# This is copied from the original function, set it to false to
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# stop recursion
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node.has_fused_arguments = False
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self.nodes.append(node)
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def specialize_copied_def(self, node, cname, py_entry, f2s, fused_compound_types):
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"""Specialize the copy of a DefNode given the copied node,
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the specialization cname and the original DefNode entry"""
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fused_types = self._get_fused_base_types(fused_compound_types)
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type_strings = [
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PyrexTypes.specialization_signature_string(fused_type, f2s)
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for fused_type in fused_types
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]
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node.specialized_signature_string = '|'.join(type_strings)
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node.entry.pymethdef_cname = PyrexTypes.get_fused_cname(
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cname, node.entry.pymethdef_cname)
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node.entry.doc = py_entry.doc
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node.entry.doc_cname = py_entry.doc_cname
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def replace_fused_typechecks(self, copied_node):
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"""
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Branch-prune fused type checks like
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if fused_t is int:
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...
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Returns whether an error was issued and whether we should stop in
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in order to prevent a flood of errors.
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"""
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num_errors = Errors.num_errors
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transform = ParseTreeTransforms.ReplaceFusedTypeChecks(
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copied_node.local_scope)
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transform(copied_node)
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if Errors.num_errors > num_errors:
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return False
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return True
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def _fused_instance_checks(self, normal_types, pyx_code, env):
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"""
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Generate Cython code for instance checks, matching an object to
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specialized types.
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"""
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for specialized_type in normal_types:
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# all_numeric = all_numeric and specialized_type.is_numeric
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pyx_code.context.update(
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py_type_name=specialized_type.py_type_name(),
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specialized_type_name=specialized_type.specialization_string,
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)
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pyx_code.put_chunk(
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u"""
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if isinstance(arg, {{py_type_name}}):
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dest_sig[{{dest_sig_idx}}] = '{{specialized_type_name}}'; break
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""")
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def _dtype_name(self, dtype):
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if dtype.is_typedef:
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return '___pyx_%s' % dtype
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return str(dtype).replace(' ', '_')
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def _dtype_type(self, dtype):
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if dtype.is_typedef:
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return self._dtype_name(dtype)
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return str(dtype)
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def _sizeof_dtype(self, dtype):
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if dtype.is_pyobject:
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return 'sizeof(void *)'
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else:
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return "sizeof(%s)" % self._dtype_type(dtype)
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def _buffer_check_numpy_dtype_setup_cases(self, pyx_code):
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"Setup some common cases to match dtypes against specializations"
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if pyx_code.indenter("if kind in b'iu':"):
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pyx_code.putln("pass")
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pyx_code.named_insertion_point("dtype_int")
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pyx_code.dedent()
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if pyx_code.indenter("elif kind == b'f':"):
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pyx_code.putln("pass")
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pyx_code.named_insertion_point("dtype_float")
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pyx_code.dedent()
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if pyx_code.indenter("elif kind == b'c':"):
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pyx_code.putln("pass")
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pyx_code.named_insertion_point("dtype_complex")
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pyx_code.dedent()
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if pyx_code.indenter("elif kind == b'O':"):
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pyx_code.putln("pass")
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pyx_code.named_insertion_point("dtype_object")
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pyx_code.dedent()
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match = "dest_sig[{{dest_sig_idx}}] = '{{specialized_type_name}}'"
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no_match = "dest_sig[{{dest_sig_idx}}] = None"
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def _buffer_check_numpy_dtype(self, pyx_code, specialized_buffer_types, pythran_types):
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"""
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Match a numpy dtype object to the individual specializations.
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"""
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self._buffer_check_numpy_dtype_setup_cases(pyx_code)
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for specialized_type in pythran_types+specialized_buffer_types:
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final_type = specialized_type
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if specialized_type.is_pythran_expr:
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specialized_type = specialized_type.org_buffer
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dtype = specialized_type.dtype
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pyx_code.context.update(
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itemsize_match=self._sizeof_dtype(dtype) + " == itemsize",
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signed_match="not (%s_is_signed ^ dtype_signed)" % self._dtype_name(dtype),
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dtype=dtype,
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|
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specialized_type_name=final_type.specialization_string)
|
||
|
|
|
||
|
|
dtypes = [
|
||
|
|
(dtype.is_int, pyx_code.dtype_int),
|
||
|
|
(dtype.is_float, pyx_code.dtype_float),
|
||
|
|
(dtype.is_complex, pyx_code.dtype_complex)
|
||
|
|
]
|
||
|
|
|
||
|
|
for dtype_category, codewriter in dtypes:
|
||
|
|
if dtype_category:
|
||
|
|
cond = '{{itemsize_match}} and (<Py_ssize_t>arg.ndim) == %d' % (
|
||
|
|
specialized_type.ndim,)
|
||
|
|
if dtype.is_int:
|
||
|
|
cond += ' and {{signed_match}}'
|
||
|
|
|
||
|
|
if final_type.is_pythran_expr:
|
||
|
|
cond += ' and arg_is_pythran_compatible'
|
||
|
|
|
||
|
|
if codewriter.indenter("if %s:" % cond):
|
||
|
|
#codewriter.putln("print 'buffer match found based on numpy dtype'")
|
||
|
|
codewriter.putln(self.match)
|
||
|
|
codewriter.putln("break")
|
||
|
|
codewriter.dedent()
|
||
|
|
|
||
|
|
def _buffer_parse_format_string_check(self, pyx_code, decl_code,
|
||
|
|
specialized_type, env):
|
||
|
|
"""
|
||
|
|
For each specialized type, try to coerce the object to a memoryview
|
||
|
|
slice of that type. This means obtaining a buffer and parsing the
|
||
|
|
format string.
|
||
|
|
TODO: separate buffer acquisition from format parsing
|
||
|
|
"""
|
||
|
|
dtype = specialized_type.dtype
|
||
|
|
if specialized_type.is_buffer:
|
||
|
|
axes = [('direct', 'strided')] * specialized_type.ndim
|
||
|
|
else:
|
||
|
|
axes = specialized_type.axes
|
||
|
|
|
||
|
|
memslice_type = PyrexTypes.MemoryViewSliceType(dtype, axes)
|
||
|
|
memslice_type.create_from_py_utility_code(env)
|
||
|
|
pyx_code.context.update(
|
||
|
|
coerce_from_py_func=memslice_type.from_py_function,
|
||
|
|
dtype=dtype)
|
||
|
|
decl_code.putln(
|
||
|
|
"{{memviewslice_cname}} {{coerce_from_py_func}}(object, int)")
|
||
|
|
|
||
|
|
pyx_code.context.update(
|
||
|
|
specialized_type_name=specialized_type.specialization_string,
|
||
|
|
sizeof_dtype=self._sizeof_dtype(dtype))
|
||
|
|
|
||
|
|
pyx_code.put_chunk(
|
||
|
|
u"""
|
||
|
|
# try {{dtype}}
|
||
|
|
if itemsize == -1 or itemsize == {{sizeof_dtype}}:
|
||
|
|
memslice = {{coerce_from_py_func}}(arg, 0)
|
||
|
|
if memslice.memview:
|
||
|
|
__PYX_XDEC_MEMVIEW(&memslice, 1)
|
||
|
|
# print 'found a match for the buffer through format parsing'
|
||
|
|
%s
|
||
|
|
break
|
||
|
|
else:
|
||
|
|
__pyx_PyErr_Clear()
|
||
|
|
""" % self.match)
|
||
|
|
|
||
|
|
def _buffer_checks(self, buffer_types, pythran_types, pyx_code, decl_code, env):
|
||
|
|
"""
|
||
|
|
Generate Cython code to match objects to buffer specializations.
|
||
|
|
First try to get a numpy dtype object and match it against the individual
|
||
|
|
specializations. If that fails, try naively to coerce the object
|
||
|
|
to each specialization, which obtains the buffer each time and tries
|
||
|
|
to match the format string.
|
||
|
|
"""
|
||
|
|
# The first thing to find a match in this loop breaks out of the loop
|
||
|
|
pyx_code.put_chunk(
|
||
|
|
u"""
|
||
|
|
""" + (u"arg_is_pythran_compatible = False" if pythran_types else u"") + u"""
|
||
|
|
if ndarray is not None:
|
||
|
|
if isinstance(arg, ndarray):
|
||
|
|
dtype = arg.dtype
|
||
|
|
""" + (u"arg_is_pythran_compatible = True" if pythran_types else u"") + u"""
|
||
|
|
elif __pyx_memoryview_check(arg):
|
||
|
|
arg_base = arg.base
|
||
|
|
if isinstance(arg_base, ndarray):
|
||
|
|
dtype = arg_base.dtype
|
||
|
|
else:
|
||
|
|
dtype = None
|
||
|
|
else:
|
||
|
|
dtype = None
|
||
|
|
|
||
|
|
itemsize = -1
|
||
|
|
if dtype is not None:
|
||
|
|
itemsize = dtype.itemsize
|
||
|
|
kind = ord(dtype.kind)
|
||
|
|
dtype_signed = kind == 'i'
|
||
|
|
""")
|
||
|
|
pyx_code.indent(2)
|
||
|
|
if pythran_types:
|
||
|
|
pyx_code.put_chunk(
|
||
|
|
u"""
|
||
|
|
# Pythran only supports the endianness of the current compiler
|
||
|
|
byteorder = dtype.byteorder
|
||
|
|
if byteorder == "<" and not __Pyx_Is_Little_Endian():
|
||
|
|
arg_is_pythran_compatible = False
|
||
|
|
elif byteorder == ">" and __Pyx_Is_Little_Endian():
|
||
|
|
arg_is_pythran_compatible = False
|
||
|
|
if arg_is_pythran_compatible:
|
||
|
|
cur_stride = itemsize
|
||
|
|
shape = arg.shape
|
||
|
|
strides = arg.strides
|
||
|
|
for i in range(arg.ndim-1, -1, -1):
|
||
|
|
if (<Py_ssize_t>strides[i]) != cur_stride:
|
||
|
|
arg_is_pythran_compatible = False
|
||
|
|
break
|
||
|
|
cur_stride *= <Py_ssize_t> shape[i]
|
||
|
|
else:
|
||
|
|
arg_is_pythran_compatible = not (arg.flags.f_contiguous and (<Py_ssize_t>arg.ndim) > 1)
|
||
|
|
""")
|
||
|
|
pyx_code.named_insertion_point("numpy_dtype_checks")
|
||
|
|
self._buffer_check_numpy_dtype(pyx_code, buffer_types, pythran_types)
|
||
|
|
pyx_code.dedent(2)
|
||
|
|
|
||
|
|
for specialized_type in buffer_types:
|
||
|
|
self._buffer_parse_format_string_check(
|
||
|
|
pyx_code, decl_code, specialized_type, env)
|
||
|
|
|
||
|
|
def _buffer_declarations(self, pyx_code, decl_code, all_buffer_types, pythran_types):
|
||
|
|
"""
|
||
|
|
If we have any buffer specializations, write out some variable
|
||
|
|
declarations and imports.
|
||
|
|
"""
|
||
|
|
decl_code.put_chunk(
|
||
|
|
u"""
|
||
|
|
ctypedef struct {{memviewslice_cname}}:
|
||
|
|
void *memview
|
||
|
|
|
||
|
|
void __PYX_XDEC_MEMVIEW({{memviewslice_cname}} *, int have_gil)
|
||
|
|
bint __pyx_memoryview_check(object)
|
||
|
|
""")
|
||
|
|
|
||
|
|
pyx_code.local_variable_declarations.put_chunk(
|
||
|
|
u"""
|
||
|
|
cdef {{memviewslice_cname}} memslice
|
||
|
|
cdef Py_ssize_t itemsize
|
||
|
|
cdef bint dtype_signed
|
||
|
|
cdef char kind
|
||
|
|
|
||
|
|
itemsize = -1
|
||
|
|
""")
|
||
|
|
|
||
|
|
if pythran_types:
|
||
|
|
pyx_code.local_variable_declarations.put_chunk(u"""
|
||
|
|
cdef bint arg_is_pythran_compatible
|
||
|
|
cdef Py_ssize_t cur_stride
|
||
|
|
""")
|
||
|
|
|
||
|
|
pyx_code.imports.put_chunk(
|
||
|
|
u"""
|
||
|
|
cdef type ndarray
|
||
|
|
ndarray = __Pyx_ImportNumPyArrayTypeIfAvailable()
|
||
|
|
""")
|
||
|
|
|
||
|
|
seen_typedefs = set()
|
||
|
|
seen_int_dtypes = set()
|
||
|
|
for buffer_type in all_buffer_types:
|
||
|
|
dtype = buffer_type.dtype
|
||
|
|
dtype_name = self._dtype_name(dtype)
|
||
|
|
if dtype.is_typedef:
|
||
|
|
if dtype_name not in seen_typedefs:
|
||
|
|
seen_typedefs.add(dtype_name)
|
||
|
|
decl_code.putln(
|
||
|
|
'ctypedef %s %s "%s"' % (dtype.resolve(), dtype_name,
|
||
|
|
dtype.empty_declaration_code()))
|
||
|
|
|
||
|
|
if buffer_type.dtype.is_int:
|
||
|
|
if str(dtype) not in seen_int_dtypes:
|
||
|
|
seen_int_dtypes.add(str(dtype))
|
||
|
|
pyx_code.context.update(dtype_name=dtype_name,
|
||
|
|
dtype_type=self._dtype_type(dtype))
|
||
|
|
pyx_code.local_variable_declarations.put_chunk(
|
||
|
|
u"""
|
||
|
|
cdef bint {{dtype_name}}_is_signed
|
||
|
|
{{dtype_name}}_is_signed = not (<{{dtype_type}}> -1 > 0)
|
||
|
|
""")
|
||
|
|
|
||
|
|
def _split_fused_types(self, arg):
|
||
|
|
"""
|
||
|
|
Specialize fused types and split into normal types and buffer types.
|
||
|
|
"""
|
||
|
|
specialized_types = PyrexTypes.get_specialized_types(arg.type)
|
||
|
|
|
||
|
|
# Prefer long over int, etc by sorting (see type classes in PyrexTypes.py)
|
||
|
|
specialized_types.sort()
|
||
|
|
|
||
|
|
seen_py_type_names = set()
|
||
|
|
normal_types, buffer_types, pythran_types = [], [], []
|
||
|
|
has_object_fallback = False
|
||
|
|
for specialized_type in specialized_types:
|
||
|
|
py_type_name = specialized_type.py_type_name()
|
||
|
|
if py_type_name:
|
||
|
|
if py_type_name in seen_py_type_names:
|
||
|
|
continue
|
||
|
|
seen_py_type_names.add(py_type_name)
|
||
|
|
if py_type_name == 'object':
|
||
|
|
has_object_fallback = True
|
||
|
|
else:
|
||
|
|
normal_types.append(specialized_type)
|
||
|
|
elif specialized_type.is_pythran_expr:
|
||
|
|
pythran_types.append(specialized_type)
|
||
|
|
elif specialized_type.is_buffer or specialized_type.is_memoryviewslice:
|
||
|
|
buffer_types.append(specialized_type)
|
||
|
|
|
||
|
|
return normal_types, buffer_types, pythran_types, has_object_fallback
|
||
|
|
|
||
|
|
def _unpack_argument(self, pyx_code):
|
||
|
|
pyx_code.put_chunk(
|
||
|
|
u"""
|
||
|
|
# PROCESSING ARGUMENT {{arg_tuple_idx}}
|
||
|
|
if {{arg_tuple_idx}} < len(<tuple>args):
|
||
|
|
arg = (<tuple>args)[{{arg_tuple_idx}}]
|
||
|
|
elif kwargs is not None and '{{arg.name}}' in <dict>kwargs:
|
||
|
|
arg = (<dict>kwargs)['{{arg.name}}']
|
||
|
|
else:
|
||
|
|
{{if arg.default}}
|
||
|
|
arg = (<tuple>defaults)[{{default_idx}}]
|
||
|
|
{{else}}
|
||
|
|
{{if arg_tuple_idx < min_positional_args}}
|
||
|
|
raise TypeError("Expected at least %d argument%s, got %d" % (
|
||
|
|
{{min_positional_args}}, {{'"s"' if min_positional_args != 1 else '""'}}, len(<tuple>args)))
|
||
|
|
{{else}}
|
||
|
|
raise TypeError("Missing keyword-only argument: '%s'" % "{{arg.default}}")
|
||
|
|
{{endif}}
|
||
|
|
{{endif}}
|
||
|
|
""")
|
||
|
|
|
||
|
|
def make_fused_cpdef(self, orig_py_func, env, is_def):
|
||
|
|
"""
|
||
|
|
This creates the function that is indexable from Python and does
|
||
|
|
runtime dispatch based on the argument types. The function gets the
|
||
|
|
arg tuple and kwargs dict (or None) and the defaults tuple
|
||
|
|
as arguments from the Binding Fused Function's tp_call.
|
||
|
|
"""
|
||
|
|
from . import TreeFragment, Code, UtilityCode
|
||
|
|
|
||
|
|
fused_types = self._get_fused_base_types([
|
||
|
|
arg.type for arg in self.node.args if arg.type.is_fused])
|
||
|
|
|
||
|
|
context = {
|
||
|
|
'memviewslice_cname': MemoryView.memviewslice_cname,
|
||
|
|
'func_args': self.node.args,
|
||
|
|
'n_fused': len(fused_types),
|
||
|
|
'min_positional_args':
|
||
|
|
self.node.num_required_args - self.node.num_required_kw_args
|
||
|
|
if is_def else
|
||
|
|
sum(1 for arg in self.node.args if arg.default is None),
|
||
|
|
'name': orig_py_func.entry.name,
|
||
|
|
}
|
||
|
|
|
||
|
|
pyx_code = Code.PyxCodeWriter(context=context)
|
||
|
|
decl_code = Code.PyxCodeWriter(context=context)
|
||
|
|
decl_code.put_chunk(
|
||
|
|
u"""
|
||
|
|
cdef extern from *:
|
||
|
|
void __pyx_PyErr_Clear "PyErr_Clear" ()
|
||
|
|
type __Pyx_ImportNumPyArrayTypeIfAvailable()
|
||
|
|
int __Pyx_Is_Little_Endian()
|
||
|
|
""")
|
||
|
|
decl_code.indent()
|
||
|
|
|
||
|
|
pyx_code.put_chunk(
|
||
|
|
u"""
|
||
|
|
def __pyx_fused_cpdef(signatures, args, kwargs, defaults):
|
||
|
|
# FIXME: use a typed signature - currently fails badly because
|
||
|
|
# default arguments inherit the types we specify here!
|
||
|
|
|
||
|
|
dest_sig = [None] * {{n_fused}}
|
||
|
|
|
||
|
|
if kwargs is not None and not kwargs:
|
||
|
|
kwargs = None
|
||
|
|
|
||
|
|
cdef Py_ssize_t i
|
||
|
|
|
||
|
|
# instance check body
|
||
|
|
""")
|
||
|
|
|
||
|
|
pyx_code.indent() # indent following code to function body
|
||
|
|
pyx_code.named_insertion_point("imports")
|
||
|
|
pyx_code.named_insertion_point("func_defs")
|
||
|
|
pyx_code.named_insertion_point("local_variable_declarations")
|
||
|
|
|
||
|
|
fused_index = 0
|
||
|
|
default_idx = 0
|
||
|
|
all_buffer_types = OrderedSet()
|
||
|
|
seen_fused_types = set()
|
||
|
|
for i, arg in enumerate(self.node.args):
|
||
|
|
if arg.type.is_fused:
|
||
|
|
arg_fused_types = arg.type.get_fused_types()
|
||
|
|
if len(arg_fused_types) > 1:
|
||
|
|
raise NotImplementedError("Determination of more than one fused base "
|
||
|
|
"type per argument is not implemented.")
|
||
|
|
fused_type = arg_fused_types[0]
|
||
|
|
|
||
|
|
if arg.type.is_fused and fused_type not in seen_fused_types:
|
||
|
|
seen_fused_types.add(fused_type)
|
||
|
|
|
||
|
|
context.update(
|
||
|
|
arg_tuple_idx=i,
|
||
|
|
arg=arg,
|
||
|
|
dest_sig_idx=fused_index,
|
||
|
|
default_idx=default_idx,
|
||
|
|
)
|
||
|
|
|
||
|
|
normal_types, buffer_types, pythran_types, has_object_fallback = self._split_fused_types(arg)
|
||
|
|
self._unpack_argument(pyx_code)
|
||
|
|
|
||
|
|
# 'unrolled' loop, first match breaks out of it
|
||
|
|
if pyx_code.indenter("while 1:"):
|
||
|
|
if normal_types:
|
||
|
|
self._fused_instance_checks(normal_types, pyx_code, env)
|
||
|
|
if buffer_types or pythran_types:
|
||
|
|
env.use_utility_code(Code.UtilityCode.load_cached("IsLittleEndian", "ModuleSetupCode.c"))
|
||
|
|
self._buffer_checks(buffer_types, pythran_types, pyx_code, decl_code, env)
|
||
|
|
if has_object_fallback:
|
||
|
|
pyx_code.context.update(specialized_type_name='object')
|
||
|
|
pyx_code.putln(self.match)
|
||
|
|
else:
|
||
|
|
pyx_code.putln(self.no_match)
|
||
|
|
pyx_code.putln("break")
|
||
|
|
pyx_code.dedent()
|
||
|
|
|
||
|
|
fused_index += 1
|
||
|
|
all_buffer_types.update(buffer_types)
|
||
|
|
all_buffer_types.update(ty.org_buffer for ty in pythran_types)
|
||
|
|
|
||
|
|
if arg.default:
|
||
|
|
default_idx += 1
|
||
|
|
|
||
|
|
if all_buffer_types:
|
||
|
|
self._buffer_declarations(pyx_code, decl_code, all_buffer_types, pythran_types)
|
||
|
|
env.use_utility_code(Code.UtilityCode.load_cached("Import", "ImportExport.c"))
|
||
|
|
env.use_utility_code(Code.UtilityCode.load_cached("ImportNumPyArray", "ImportExport.c"))
|
||
|
|
|
||
|
|
pyx_code.put_chunk(
|
||
|
|
u"""
|
||
|
|
candidates = []
|
||
|
|
for sig in <dict>signatures:
|
||
|
|
match_found = False
|
||
|
|
src_sig = sig.strip('()').split('|')
|
||
|
|
for i in range(len(dest_sig)):
|
||
|
|
dst_type = dest_sig[i]
|
||
|
|
if dst_type is not None:
|
||
|
|
if src_sig[i] == dst_type:
|
||
|
|
match_found = True
|
||
|
|
else:
|
||
|
|
match_found = False
|
||
|
|
break
|
||
|
|
|
||
|
|
if match_found:
|
||
|
|
candidates.append(sig)
|
||
|
|
|
||
|
|
if not candidates:
|
||
|
|
raise TypeError("No matching signature found")
|
||
|
|
elif len(candidates) > 1:
|
||
|
|
raise TypeError("Function call with ambiguous argument types")
|
||
|
|
else:
|
||
|
|
return (<dict>signatures)[candidates[0]]
|
||
|
|
""")
|
||
|
|
|
||
|
|
fragment_code = pyx_code.getvalue()
|
||
|
|
# print decl_code.getvalue()
|
||
|
|
# print fragment_code
|
||
|
|
from .Optimize import ConstantFolding
|
||
|
|
fragment = TreeFragment.TreeFragment(
|
||
|
|
fragment_code, level='module', pipeline=[ConstantFolding()])
|
||
|
|
ast = TreeFragment.SetPosTransform(self.node.pos)(fragment.root)
|
||
|
|
UtilityCode.declare_declarations_in_scope(
|
||
|
|
decl_code.getvalue(), env.global_scope())
|
||
|
|
ast.scope = env
|
||
|
|
# FIXME: for static methods of cdef classes, we build the wrong signature here: first arg becomes 'self'
|
||
|
|
ast.analyse_declarations(env)
|
||
|
|
py_func = ast.stats[-1] # the DefNode
|
||
|
|
self.fragment_scope = ast.scope
|
||
|
|
|
||
|
|
if isinstance(self.node, DefNode):
|
||
|
|
py_func.specialized_cpdefs = self.nodes[:]
|
||
|
|
else:
|
||
|
|
py_func.specialized_cpdefs = [n.py_func for n in self.nodes]
|
||
|
|
|
||
|
|
return py_func
|
||
|
|
|
||
|
|
def update_fused_defnode_entry(self, env):
|
||
|
|
copy_attributes = (
|
||
|
|
'name', 'pos', 'cname', 'func_cname', 'pyfunc_cname',
|
||
|
|
'pymethdef_cname', 'doc', 'doc_cname', 'is_member',
|
||
|
|
'scope'
|
||
|
|
)
|
||
|
|
|
||
|
|
entry = self.py_func.entry
|
||
|
|
|
||
|
|
for attr in copy_attributes:
|
||
|
|
setattr(entry, attr,
|
||
|
|
getattr(self.orig_py_func.entry, attr))
|
||
|
|
|
||
|
|
self.py_func.name = self.orig_py_func.name
|
||
|
|
self.py_func.doc = self.orig_py_func.doc
|
||
|
|
|
||
|
|
env.entries.pop('__pyx_fused_cpdef', None)
|
||
|
|
if isinstance(self.node, DefNode):
|
||
|
|
env.entries[entry.name] = entry
|
||
|
|
else:
|
||
|
|
env.entries[entry.name].as_variable = entry
|
||
|
|
|
||
|
|
env.pyfunc_entries.append(entry)
|
||
|
|
|
||
|
|
self.py_func.entry.fused_cfunction = self
|
||
|
|
for node in self.nodes:
|
||
|
|
if isinstance(self.node, DefNode):
|
||
|
|
node.fused_py_func = self.py_func
|
||
|
|
else:
|
||
|
|
node.py_func.fused_py_func = self.py_func
|
||
|
|
node.entry.as_variable = entry
|
||
|
|
|
||
|
|
self.synthesize_defnodes()
|
||
|
|
self.stats.append(self.__signatures__)
|
||
|
|
|
||
|
|
def analyse_expressions(self, env):
|
||
|
|
"""
|
||
|
|
Analyse the expressions. Take care to only evaluate default arguments
|
||
|
|
once and clone the result for all specializations
|
||
|
|
"""
|
||
|
|
for fused_compound_type in self.fused_compound_types:
|
||
|
|
for fused_type in fused_compound_type.get_fused_types():
|
||
|
|
for specialization_type in fused_type.types:
|
||
|
|
if specialization_type.is_complex:
|
||
|
|
specialization_type.create_declaration_utility_code(env)
|
||
|
|
|
||
|
|
if self.py_func:
|
||
|
|
self.__signatures__ = self.__signatures__.analyse_expressions(env)
|
||
|
|
self.py_func = self.py_func.analyse_expressions(env)
|
||
|
|
self.resulting_fused_function = self.resulting_fused_function.analyse_expressions(env)
|
||
|
|
self.fused_func_assignment = self.fused_func_assignment.analyse_expressions(env)
|
||
|
|
|
||
|
|
self.defaults = defaults = []
|
||
|
|
|
||
|
|
for arg in self.node.args:
|
||
|
|
if arg.default:
|
||
|
|
arg.default = arg.default.analyse_expressions(env)
|
||
|
|
defaults.append(ProxyNode(arg.default))
|
||
|
|
else:
|
||
|
|
defaults.append(None)
|
||
|
|
|
||
|
|
for i, stat in enumerate(self.stats):
|
||
|
|
stat = self.stats[i] = stat.analyse_expressions(env)
|
||
|
|
if isinstance(stat, FuncDefNode):
|
||
|
|
for arg, default in zip(stat.args, defaults):
|
||
|
|
if default is not None:
|
||
|
|
arg.default = CloneNode(default).coerce_to(arg.type, env)
|
||
|
|
|
||
|
|
if self.py_func:
|
||
|
|
args = [CloneNode(default) for default in defaults if default]
|
||
|
|
self.defaults_tuple = TupleNode(self.pos, args=args)
|
||
|
|
self.defaults_tuple = self.defaults_tuple.analyse_types(env, skip_children=True).coerce_to_pyobject(env)
|
||
|
|
self.defaults_tuple = ProxyNode(self.defaults_tuple)
|
||
|
|
self.code_object = ProxyNode(self.specialized_pycfuncs[0].code_object)
|
||
|
|
|
||
|
|
fused_func = self.resulting_fused_function.arg
|
||
|
|
fused_func.defaults_tuple = CloneNode(self.defaults_tuple)
|
||
|
|
fused_func.code_object = CloneNode(self.code_object)
|
||
|
|
|
||
|
|
for i, pycfunc in enumerate(self.specialized_pycfuncs):
|
||
|
|
pycfunc.code_object = CloneNode(self.code_object)
|
||
|
|
pycfunc = self.specialized_pycfuncs[i] = pycfunc.analyse_types(env)
|
||
|
|
pycfunc.defaults_tuple = CloneNode(self.defaults_tuple)
|
||
|
|
return self
|
||
|
|
|
||
|
|
def synthesize_defnodes(self):
|
||
|
|
"""
|
||
|
|
Create the __signatures__ dict of PyCFunctionNode specializations.
|
||
|
|
"""
|
||
|
|
if isinstance(self.nodes[0], CFuncDefNode):
|
||
|
|
nodes = [node.py_func for node in self.nodes]
|
||
|
|
else:
|
||
|
|
nodes = self.nodes
|
||
|
|
|
||
|
|
signatures = [StringEncoding.EncodedString(node.specialized_signature_string)
|
||
|
|
for node in nodes]
|
||
|
|
keys = [ExprNodes.StringNode(node.pos, value=sig)
|
||
|
|
for node, sig in zip(nodes, signatures)]
|
||
|
|
values = [ExprNodes.PyCFunctionNode.from_defnode(node, binding=True)
|
||
|
|
for node in nodes]
|
||
|
|
|
||
|
|
self.__signatures__ = ExprNodes.DictNode.from_pairs(self.pos, zip(keys, values))
|
||
|
|
|
||
|
|
self.specialized_pycfuncs = values
|
||
|
|
for pycfuncnode in values:
|
||
|
|
pycfuncnode.is_specialization = True
|
||
|
|
|
||
|
|
def generate_function_definitions(self, env, code):
|
||
|
|
if self.py_func:
|
||
|
|
self.py_func.pymethdef_required = True
|
||
|
|
self.fused_func_assignment.generate_function_definitions(env, code)
|
||
|
|
|
||
|
|
for stat in self.stats:
|
||
|
|
if isinstance(stat, FuncDefNode) and stat.entry.used:
|
||
|
|
code.mark_pos(stat.pos)
|
||
|
|
stat.generate_function_definitions(env, code)
|
||
|
|
|
||
|
|
def generate_execution_code(self, code):
|
||
|
|
# Note: all def function specialization are wrapped in PyCFunction
|
||
|
|
# nodes in the self.__signatures__ dictnode.
|
||
|
|
for default in self.defaults:
|
||
|
|
if default is not None:
|
||
|
|
default.generate_evaluation_code(code)
|
||
|
|
|
||
|
|
if self.py_func:
|
||
|
|
self.defaults_tuple.generate_evaluation_code(code)
|
||
|
|
self.code_object.generate_evaluation_code(code)
|
||
|
|
|
||
|
|
for stat in self.stats:
|
||
|
|
code.mark_pos(stat.pos)
|
||
|
|
if isinstance(stat, ExprNodes.ExprNode):
|
||
|
|
stat.generate_evaluation_code(code)
|
||
|
|
else:
|
||
|
|
stat.generate_execution_code(code)
|
||
|
|
|
||
|
|
if self.__signatures__:
|
||
|
|
self.resulting_fused_function.generate_evaluation_code(code)
|
||
|
|
|
||
|
|
code.putln(
|
||
|
|
"((__pyx_FusedFunctionObject *) %s)->__signatures__ = %s;" %
|
||
|
|
(self.resulting_fused_function.result(),
|
||
|
|
self.__signatures__.result()))
|
||
|
|
code.put_giveref(self.__signatures__.result())
|
||
|
|
|
||
|
|
self.fused_func_assignment.generate_execution_code(code)
|
||
|
|
|
||
|
|
# Dispose of results
|
||
|
|
self.resulting_fused_function.generate_disposal_code(code)
|
||
|
|
self.defaults_tuple.generate_disposal_code(code)
|
||
|
|
self.code_object.generate_disposal_code(code)
|
||
|
|
|
||
|
|
for default in self.defaults:
|
||
|
|
if default is not None:
|
||
|
|
default.generate_disposal_code(code)
|
||
|
|
|
||
|
|
def annotate(self, code):
|
||
|
|
for stat in self.stats:
|
||
|
|
stat.annotate(code)
|