""" This is the core of the vec. optimization. It combines dependency.py and schedule.py to rewrite a loop in vectorized form. See the rpython doc for more high level details. """ from __future__ import print_function import py import time from rpython.jit.metainterp.jitexc import NotAVectorizeableLoop, NotAProfitableLoop from rpython.jit.metainterp.compile import (CompileLoopVersionDescr, ResumeDescr) from rpython.jit.metainterp.history import (INT, FLOAT, VECTOR, ConstInt, ConstFloat, TargetToken, JitCellToken, AbstractFailDescr) from rpython.jit.metainterp.optimizeopt.optimizer import Optimizer, Optimization from rpython.jit.metainterp.optimizeopt.renamer import Renamer from rpython.jit.metainterp.optimizeopt.dependency import (DependencyGraph, MemoryRef, Node, IndexVar) from rpython.jit.metainterp.optimizeopt.version import LoopVersionInfo from rpython.jit.metainterp.optimizeopt.schedule import (VecScheduleState, SchedulerState, Scheduler, Pack, Pair, AccumPack, forwarded_vecinfo) from rpython.jit.metainterp.optimizeopt.guard import GuardStrengthenOpt from rpython.jit.metainterp.resoperation import (rop, ResOperation, GuardResOp, OpHelpers, VecOperation, VectorizationInfo) from rpython.rlib import listsort from rpython.rlib.objectmodel import we_are_translated from rpython.rlib.debug import debug_print, debug_start, debug_stop from rpython.rlib.jit import Counters from rpython.rtyper.lltypesystem.lloperation import llop from rpython.rtyper.lltypesystem import lltype, rffi from rpython.jit.backend.llsupport.symbolic import (WORD as INT_WORD, SIZEOF_FLOAT as FLOAT_WORD) def copy_resop(op): newop = op.copy() fwd = op.get_forwarded() if fwd is not None and isinstance(fwd, VectorizationInfo): newop.set_forwarded(fwd) return newop class VectorLoop(object): def __init__(self, label, oplist, jump): self.label = label self.inputargs = label.getarglist_copy() self.prefix = [] self.prefix_label = None assert self.label.getopnum() == rop.LABEL self.operations = oplist self.jump = jump assert self.jump.getopnum() == rop.JUMP self.align_operations = [] def setup_vectorization(self): for op in self.operations: op.set_forwarded(VectorizationInfo(op)) def teardown_vectorization(self): for op in self.operations: op.set_forwarded(None) def finaloplist(self, jitcell_token=None, reset_label_token=True, label=False): if jitcell_token: if reset_label_token: token = TargetToken(jitcell_token) token.original_jitcell_token = jitcell_token jitcell_token.target_tokens.append(token) self.label.setdescr(token) else: token = self.jump.getdescr() assert isinstance(token, TargetToken) if self.prefix_label: token = TargetToken(jitcell_token) token.original_jitcell_token = jitcell_token jitcell_token.target_tokens.append(token) self.prefix_label.setdescr(token) self.jump.setdescr(token) if reset_label_token: self.jump.setdescr(token) oplist = [] if self.prefix_label: oplist = self.prefix + [self.prefix_label] elif self.prefix: oplist = self.prefix if label: oplist = [self.label] + oplist if not label: for op in oplist: op.set_forwarded(None) self.jump.set_forwarded(None) ops = oplist + self.operations + [self.jump] return ops def clone(self): renamer = Renamer() label = copy_resop(self.label) prefix = [] for op in self.prefix: newop = copy_resop(op) renamer.rename(newop) if not newop.returns_void(): renamer.start_renaming(op, newop) prefix.append(newop) prefix_label = None if self.prefix_label: prefix_label = copy_resop(self.prefix_label) renamer.rename(prefix_label) oplist = [] for op in self.operations: newop = copy_resop(op) renamer.rename(newop) if not newop.returns_void(): renamer.start_renaming(op, newop) oplist.append(newop) jump = copy_resop(self.jump) renamer.rename(jump) loop = VectorLoop(copy_resop(self.label), oplist, jump) loop.prefix = prefix loop.prefix_label = prefix_label return loop def optimize_vector(trace, metainterp_sd, jitdriver_sd, warmstate, loop_info, loop_ops, jitcell_token=None): """ Enter the world of SIMD. Bails if it cannot transform the trace. """ user_code = not jitdriver_sd.vec and warmstate.vec_all e = len(loop_ops)-1 assert e > 0 assert rop.is_final(loop_ops[e].getopnum()) loop = VectorLoop(loop_info.label_op, loop_ops[:e], loop_ops[-1]) if user_code and user_loop_bail_fast_path(loop, warmstate): return loop_info, loop_ops # the original loop (output of optimize_unroll) info = LoopVersionInfo(loop_info) version = info.snapshot(loop) loop.setup_vectorization() try: debug_start("vec-opt-loop") metainterp_sd.logger_noopt.log_loop([], loop.finaloplist(label=True), -2, None, None, "pre vectorize") metainterp_sd.profiler.count(Counters.OPT_VECTORIZE_TRY) # start = time.clock() opt = VectorizingOptimizer(metainterp_sd, jitdriver_sd, warmstate.vec_cost) oplist = opt.run_optimization(metainterp_sd, info, loop, jitcell_token, user_code) end = time.clock() # metainterp_sd.profiler.count(Counters.OPT_VECTORIZED) metainterp_sd.logger_noopt.log_loop([], loop.finaloplist(label=True), -2, None, None, "post vectorize") nano = int((end-start)*10.0**9) debug_print("# vecopt factor: %d opcount: (%d -> %d) took %dns" % \ (opt.unroll_count+1, len(version.loop.operations), len(loop.operations), nano)) debug_stop("vec-opt-loop") # info.label_op = loop.label return info, oplist except NotAVectorizeableLoop: debug_stop("vec-opt-loop") # vectorization is not possible return loop_info, version.loop.finaloplist() except NotAProfitableLoop: debug_stop("vec-opt-loop") debug_print("failed to vectorize loop, cost model indicated it is not profitable") # cost model says to skip this loop return loop_info, version.loop.finaloplist() except Exception as e: debug_stop("vec-opt-loop") debug_print("failed to vectorize loop. THIS IS A FATAL ERROR!") if we_are_translated(): llop.debug_print_traceback(lltype.Void) else: raise finally: loop.teardown_vectorization() return loop_info, loop_ops def user_loop_bail_fast_path(loop, warmstate): """ In a fast path over the trace loop: try to prevent vecopt of spending time on a loop that will most probably fail. """ resop_count = 0 # the count of operations minus debug_merge_points vector_instr = 0 guard_count = 0 at_least_one_array_access = True for i,op in enumerate(loop.operations): if rop.is_jit_debug(op.opnum): continue if op.vector >= 0 and not rop.is_guard(op.opnum): vector_instr += 1 resop_count += 1 if op.is_primitive_array_access(): at_least_one_array_access = True if rop.is_call(op.opnum) or rop.is_call_assembler(op.opnum): return True if rop.is_guard(op.opnum): guard_count += 1 if not at_least_one_array_access: return True return False class VectorizingOptimizer(Optimizer): """ Try to unroll the loop and find instructions to group """ def __init__(self, metainterp_sd, jitdriver_sd, cost_threshold): Optimizer.__init__(self, metainterp_sd, jitdriver_sd) self.cpu = metainterp_sd.cpu self.vector_ext = self.cpu.vector_ext self.cost_threshold = cost_threshold self.packset = None self.unroll_count = 0 self.smallest_type_bytes = 0 self.orig_label_args = None def run_optimization(self, metainterp_sd, info, loop, jitcell_token, user_code): self.orig_label_args = loop.label.getarglist_copy() self.linear_find_smallest_type(loop) byte_count = self.smallest_type_bytes vsize = self.vector_ext.vec_size() # stop, there is no chance to vectorize this trace # we cannot optimize normal traces (if there is no label) if vsize == 0: debug_print("vector size is zero") raise NotAVectorizeableLoop if byte_count == 0: debug_print("could not find smallest type") raise NotAVectorizeableLoop if loop.label.getopnum() != rop.LABEL: debug_print("not a loop, can only vectorize loops") raise NotAVectorizeableLoop # find index guards and move to the earliest position graph = self.analyse_index_calculations(loop) if graph is not None: state = SchedulerState(metainterp_sd.cpu, graph) self.schedule(state) # reorder the trace # unroll self.unroll_count = self.get_unroll_count(vsize) align_unroll = self.unroll_count==1 and \ self.vector_ext.should_align_unroll self.unroll_loop_iterations(loop, self.unroll_count, align_unroll_once=align_unroll) # vectorize graph = DependencyGraph(loop) self.find_adjacent_memory_refs(graph) self.extend_packset() self.combine_packset() costmodel = GenericCostModel(self.cpu, self.cost_threshold) state = VecScheduleState(graph, self.packset, self.cpu, costmodel) self.schedule(state) if not state.profitable(): raise NotAProfitableLoop gso = GuardStrengthenOpt(graph.index_vars) gso.propagate_all_forward(info, loop, user_code) # re-schedule the trace -> removes many pure operations graph = DependencyGraph(loop) state = SchedulerState(self.cpu, graph) state.schedule() info.extra_before_label = loop.align_operations for op in loop.align_operations: op.set_forwarded(None) return loop.finaloplist(jitcell_token=jitcell_token, reset_label_token=False) def unroll_loop_iterations(self, loop, unroll_count, align_unroll_once=False): """ Unroll the loop `unroll_count` times. There can be an additional unroll step if alignment might benefit """ numops = len(loop.operations) renamer = Renamer() operations = loop.operations orig_jump_args = loop.jump.getarglist()[:] prohibit_opnums = (rop.GUARD_FUTURE_CONDITION, rop.GUARD_NOT_INVALIDATED, rop.DEBUG_MERGE_POINT) unrolled = [] if align_unroll_once: unroll_count += 1 # it is assumed that #label_args == #jump_args label_arg_count = len(orig_jump_args) label = loop.label jump = loop.jump new_label = loop.label for u in range(unroll_count): # fill the map with the renaming boxes. keys are boxes from the label for i in range(label_arg_count): la = label.getarg(i) ja = jump.getarg(i) ja = renamer.rename_box(ja) if la != ja: renamer.start_renaming(la, ja) # for i, op in enumerate(operations): if op.getopnum() in prohibit_opnums: continue # do not unroll this operation twice copied_op = copy_resop(op) if not copied_op.returns_void(): # every result assigns a new box, thus creates an entry # to the rename map. renamer.start_renaming(op, copied_op) # args = copied_op.getarglist() for a, arg in enumerate(args): value = renamer.rename_box(arg) copied_op.setarg(a, value) # not only the arguments, but also the fail args need # to be adjusted. rd_snapshot stores the live variables # that are needed to resume. if copied_op.is_guard(): self.copy_guard_descr(renamer, copied_op) # unrolled.append(copied_op) # if align_unroll_once and u == 0: descr = label.getdescr() args = label.getarglist()[:] new_label = ResOperation(rop.LABEL, args, descr) renamer.rename(new_label) # # the jump arguments have been changed # if label(iX) ... jump(i(X+1)) is called, at the next unrolled loop # must look like this: label(i(X+1)) ... jump(i(X+2)) args = loop.jump.getarglist() for i, arg in enumerate(args): value = renamer.rename_box(arg) loop.jump.setarg(i, value) # loop.label = new_label if align_unroll_once: loop.align_operations = operations loop.operations = unrolled else: loop.operations = operations + unrolled def copy_guard_descr(self, renamer, copied_op): descr = copied_op.getdescr() if descr: assert isinstance(descr, ResumeDescr) copied_op.setdescr(descr.clone()) failargs = renamer.rename_failargs(copied_op, clone=True) if not we_are_translated(): for arg in failargs: if arg is None: continue assert not arg.is_constant() copied_op.setfailargs(failargs) def linear_find_smallest_type(self, loop): # O(#operations) for i,op in enumerate(loop.operations): if op.is_primitive_array_access(): descr = op.getdescr() byte_count = descr.get_item_size_in_bytes() if self.smallest_type_bytes == 0 \ or byte_count < self.smallest_type_bytes: self.smallest_type_bytes = byte_count def get_unroll_count(self, simd_vec_reg_bytes): """ This is an estimated number of further unrolls """ # this optimization is not opaque, and needs info about the CPU byte_count = self.smallest_type_bytes if byte_count == 0: return 0 unroll_count = simd_vec_reg_bytes // byte_count return unroll_count-1 # it is already unrolled once def find_adjacent_memory_refs(self, graph): """ The pre pass already builds a hash of memory references and the operations. Since it is in SSA form there are no array indices. If there are two array accesses in the unrolled loop i0,i1 and i1 = int_add(i0,c), then i0 = i0 + 0, i1 = i0 + 1. They are represented as a linear combination: i*c/d + e, i is a variable, all others are integers that are calculated in reverse direction """ loop = graph.loop operations = loop.operations self.packset = PackSet(self.vector_ext.vec_size()) memory_refs = graph.memory_refs.items() # initialize the pack set for node_a,memref_a in memory_refs: for node_b,memref_b in memory_refs: if memref_a is memref_b: continue # instead of compare every possible combination and # exclue a_opidx == b_opidx only consider the ones # that point forward: if memref_a.is_adjacent_after(memref_b): pair = self.packset.can_be_packed(node_a, node_b, None, False) if pair: self.packset.add_pack(pair) def extend_packset(self): """ Follow dependency chains to find more candidates to put into pairs. """ pack_count = self.packset.pack_count() while True: i = 0 packs = self.packset.packs while i < len(packs): pack = packs[i] self.follow_def_uses(pack) i += 1 if pack_count == self.packset.pack_count(): pack_count = self.packset.pack_count() i = 0 while i < len(packs): pack = packs[i] self.follow_use_defs(pack) i += 1 if pack_count == self.packset.pack_count(): break pack_count = self.packset.pack_count() def follow_use_defs(self, pack): assert pack.numops() == 2 for ldep in pack.leftmost(True).depends(): for rdep in pack.rightmost(True).depends(): lnode = ldep.to rnode = rdep.to # only valid if left is in args of pack left left = lnode.getoperation() args = pack.leftmost().getarglist() if left is None or left not in args: continue isomorph = isomorphic(lnode.getoperation(), rnode.getoperation()) if isomorph and lnode.is_before(rnode): pair = self.packset.can_be_packed(lnode, rnode, pack, False) if pair: self.packset.add_pack(pair) def follow_def_uses(self, pack): assert pack.numops() == 2 for ldep in pack.leftmost(node=True).provides(): for rdep in pack.rightmost(node=True).provides(): lnode = ldep.to rnode = rdep.to left = pack.leftmost() args = lnode.getoperation().getarglist() if left is None or left not in args: continue isomorph = isomorphic(lnode.getoperation(), rnode.getoperation()) if isomorph and lnode.is_before(rnode): pair = self.packset.can_be_packed(lnode, rnode, pack, True) if pair: self.packset.add_pack(pair) def combine_packset(self): """ Combination is done iterating the packs that have a sorted op index of the first operation (= left). If a pack is marked as 'full', the next pack that is encountered having the full_pack.right == pack.left, the pack is removed. This is because the packs have intersecting edges. """ if len(self.packset.packs) == 0: debug_print("packset is empty") raise NotAVectorizeableLoop i = 0 j = 0 end_ij = len(self.packset.packs) while True: len_before = len(self.packset.packs) i = 0 while i < end_ij: while j < end_ij and i < end_ij: if i == j: # do not pack with itself! won't work... j += 1 continue pack1 = self.packset.packs[i] pack2 = self.packset.packs[j] if pack1.rightmost_match_leftmost(pack2): end_ij = self.packset.combine(i,j) else: # do not inc in rightmost_match_leftmost # this could miss some pack j += 1 i += 1 j = 0 if len_before == len(self.packset.packs): break self.packset.split_overloaded_packs(self.cpu.vector_ext) if not we_are_translated(): # some test cases check the accumulation variables self.packset.accum_vars = {} print("packs:") check = {} fail = False for pack in self.packset.packs: left = pack.operations[0] right = pack.operations[-1] if left in check or right in check: fail = True check[left] = None check[right] = None print(" ", pack) if fail: assert False def schedule(self, state): state.prepare() scheduler = Scheduler() scheduler.walk_and_emit(state) if not state.profitable(): return state.post_schedule() def analyse_index_calculations(self, loop): """ Tries to move guarding instructions an all the instructions that need to be computed for the guard to the loop header. This ensures that guards fail 'early' and relax dependencies. Without this step vectorization would not be possible! """ graph = DependencyGraph(loop) zero_deps = {} for node in graph.nodes: if node.depends_count() == 0: zero_deps[node] = 0 earlyexit = graph.imaginary_node("early exit") guards = graph.guards one_valid = False valid_guards = [] for guard_node in guards: modify_later = [] last_prev_node = None valid = True if guard_node in zero_deps: del zero_deps[guard_node] for prev_dep in guard_node.depends(): prev_node = prev_dep.to if prev_dep.is_failarg(): # remove this edge later. # 1) only because of failing, this dependency exists # 2) non pure operation points to this guard. # but if this guard only depends on pure operations, it can be checked # at an earlier position, the non pure op can execute later! modify_later.append(prev_node) else: for path in prev_node.iterate_paths(None, backwards=True, blacklist=True): if not path.is_always_pure(): valid = False else: if path.last() in zero_deps: del zero_deps[path.last()] if not valid: break if valid: # transformation is valid, modify the graph and execute # this guard earlier one_valid = True for node in modify_later: node.remove_edge_to(guard_node) # every edge that starts in the guard, the early exit # inherts the edge and guard then provides to early exit for dep in guard_node.provides()[:]: assert not dep.target_node().is_imaginary() earlyexit.edge_to(dep.target_node(), failarg=True) guard_node.remove_edge_to(dep.target_node()) valid_guards.append(guard_node) guard_node.edge_to(earlyexit) self.mark_guard(guard_node, loop) for node in zero_deps.keys(): assert not node.is_imaginary() earlyexit.edge_to(node) if one_valid: return graph return None def mark_guard(self, node, loop): """ Marks this guard as an early exit! """ op = node.getoperation() assert isinstance(op, GuardResOp) if op.getopnum() in (rop.GUARD_TRUE, rop.GUARD_FALSE): descr = CompileLoopVersionDescr() olddescr = op.getdescr() if olddescr: descr.copy_all_attributes_from(olddescr) op.setdescr(descr) arglistcopy = loop.label.getarglist_copy() if not we_are_translated(): for arg in arglistcopy: assert not arg.is_constant() op.setfailargs(arglistcopy) class CostModel(object): """ Utility to estimate the savings for the new trace loop. The main reaons to have this is of frequent unpack instructions, and the missing ability (by design) to detect not vectorizable loops. """ def __init__(self, cpu, threshold): self.threshold = threshold self.vec_reg_size = cpu.vector_ext.vec_size() self.savings = 0 def reset_savings(self): self.savings = 0 def record_cast_int(self, op): raise NotImplementedError def record_pack_savings(self, pack, times): raise NotImplementedError def record_vector_pack(self, box, index, count): raise NotImplementedError def record_vector_unpack(self, box, index, count): raise NotImplementedError def unpack_cost(self, op, index, count): raise NotImplementedError def savings_for_pack(self, pack, times): raise NotImplementedError def profitable(self): return self.savings >= 0 class GenericCostModel(CostModel): def record_pack_savings(self, pack, times): cost, benefit_factor = (1,1) node = pack.operations[0] op = node.getoperation() if op.getopnum() == rop.INT_SIGNEXT: cost, benefit_factor = self.cb_signext(pack) # self.savings += benefit_factor * times - cost def cb_signext(self, pack): left = pack.leftmost() if left.cast_to_bytesize() == left.cast_from_bytesize(): return 0, 0 # no benefit for this operation! needs many x86 instrs return 1,0 def record_cast_int(self, fromsize, tosize, count): # for each move there is 1 instruction if fromsize == 8 and tosize == 4 and count == 2: self.savings -= 1 else: self.savings += -count def record_vector_pack(self, src, index, count): vecinfo = forwarded_vecinfo(src) if vecinfo.datatype == FLOAT: if index == 1 and count == 1: self.savings -= 2 return self.savings -= count def record_vector_unpack(self, src, index, count): self.record_vector_pack(src, index, count) def isomorphic(l_op, r_op): """ Subject of definition, here it is equal operation. See limintations (vectorization.rst). """ if l_op.getopnum() == r_op.getopnum(): l_vecinfo = forwarded_vecinfo(l_op) r_vecinfo = forwarded_vecinfo(r_op) return l_vecinfo.bytesize == r_vecinfo.bytesize return False class PackSet(object): _attrs_ = ('packs', 'vec_reg_size') def __init__(self, vec_reg_size): self.packs = [] self.vec_reg_size = vec_reg_size def pack_count(self): return len(self.packs) def add_pack(self, pack): self.packs.append(pack) def can_be_packed(self, lnode, rnode, origin_pack, forward): """ Check to ensure that two nodes might be packed into a Pair. """ if isomorphic(lnode.getoperation(), rnode.getoperation()): # even if a guard depends on the previous it is able to lop = lnode.getoperation() independent = lnode.independent(rnode) if independent: if forward and origin_pack.is_accumulating(): # in this case the splitted accumulator must # be combined. This case is not supported debug_print("splitted accum must be flushed here (not supported)") raise NotAVectorizeableLoop # if self.contains_pair(lnode, rnode): return None # if origin_pack is None: op = lnode.getoperation() if rop.is_primitive_load(op.opnum): return Pair(lnode, rnode) else: return Pair(lnode, rnode) if self.profitable_pack(lnode, rnode, origin_pack, forward): return Pair(lnode, rnode) else: if self.contains_pair(lnode, rnode): return None if origin_pack is not None: return self.accumulates_pair(lnode, rnode, origin_pack) return None def contains_pair(self, lnode, rnode): for pack in self.packs: if pack.leftmost(node=True) is lnode or \ pack.rightmost(node=True) is rnode: return True return False def profitable_pack(self, lnode, rnode, origin_pack, forward): if self.prohibit_packing(origin_pack, origin_pack.leftmost(), lnode.getoperation(), forward): return False if self.prohibit_packing(origin_pack, origin_pack.rightmost(), rnode.getoperation(), forward): return False return True def prohibit_packing(self, pack, packed, inquestion, forward): """ Blocks the packing of some operations """ if inquestion.vector == -1: return True if packed.is_primitive_array_access(): if packed.getarg(1) is inquestion: return True if not forward and inquestion.getopnum() == rop.INT_SIGNEXT: # prohibit the packing of signext in backwards direction # the type cannot be determined! return True return False def combine(self, i, j): """ Combine two packs. It is assumed that the attribute self.packs is not iterated when calling this method. """ pkg_a = self.packs[i] pkg_b = self.packs[j] operations = pkg_a.operations for op in pkg_b.operations[1:]: operations.append(op) self.packs[i] = pkg_a.clone(operations) del self.packs[j] return len(self.packs) def accumulates_pair(self, lnode, rnode, origin_pack): # lnode and rnode are isomorphic and dependent assert isinstance(origin_pack, Pair) left = lnode.getoperation() opnum = left.getopnum() try: operator = AccumPack.SUPPORTED(opnum) except KeyError: pass else: right = rnode.getoperation() assert left.numargs() == 2 and not left.returns_void() scalar, index = self.getaccumulator_variable(left, right, origin_pack) if not scalar: return None # the dependency exists only because of the left? for dep in lnode.provides(): if dep.to is rnode: if not dep.because_of(scalar): # not quite ... this is not handlable return None # get the original variable scalar = left.getarg(index) # in either of the two cases the arguments are mixed, # which is not handled currently other_index = (index + 1) % 2 if left.getarg(other_index) is not origin_pack.leftmost(): return None if right.getarg(other_index) is not origin_pack.rightmost(): return None # this can be handled by accumulation size = INT_WORD if left.type == 'f': size = FLOAT_WORD l_vecinfo = forwarded_vecinfo(left) r_vecinfo = forwarded_vecinfo(right) if not (l_vecinfo.bytesize == r_vecinfo.bytesize and l_vecinfo.bytesize == size): # do not support if if the type size is smaller # than the cpu word size. # WHY? # to ensure accum is done on the right size, the dependencies # of leading/preceding signext/floatcast instructions needs to be # considered. => tree pattern matching problem. return None return AccumPack([lnode, rnode], operator, index) is_guard = left.is_guard() and left.getopnum() in (rop.GUARD_TRUE, rop.GUARD_FALSE) if is_guard: return AccumPack([lnode, rnode], 'g', 0) return None def getaccumulator_variable(self, left, right, origin_pack): for i, arg in enumerate(right.getarglist()): if arg is left: return arg, i return None, -1 def accumulate_prepare(self, state): vec_reg_size = state.vec_reg_size for pack in self.packs: if not pack.is_accumulating(): continue if pack.leftmost().is_guard(): # guard breaks dependencies, thus it is an accumulation pack continue for i,node in enumerate(pack.operations): op = node.getoperation() state.accumulation[op] = pack assert isinstance(pack, AccumPack) datatype = pack.getdatatype() bytesize = pack.getbytesize() count = vec_reg_size // bytesize signed = datatype == 'i' oplist = state.invariant_oplist # reset the box to zeros or ones if pack.reduce_init() == 0: vecop = OpHelpers.create_vec(datatype, bytesize, signed, count) oplist.append(vecop) opnum = rop.VEC_INT_XOR if datatype == FLOAT: # see PRECISION loss below raise NotImplementedError vecop = VecOperation(opnum, [vecop, vecop], vecop, count) oplist.append(vecop) elif pack.reduce_init() == 1: # PRECISION loss, because the numbers are accumulated (associative, commutative properties must hold) # you can end up a small number and a huge number that is finally multiplied. giving an # inprecision result, thus this is disabled now raise NotImplementedError # multiply is only supported by floats vecop = OpHelpers.create_vec_expand(ConstFloat(1.0), bytesize, signed, count) oplist.append(vecop) else: raise NotImplementedError("cannot handle %s" % pack.operator) # pack the scalar value args = [vecop, pack.getleftmostseed(), ConstInt(0), ConstInt(1)] vecop = OpHelpers.create_vec_pack(datatype, args, bytesize, signed, count) oplist.append(vecop) seed = pack.getleftmostseed() state.accumulation[seed] = pack # rename the variable with the box state.setvector_of_box(seed, 0, vecop) # prevent it from expansion state.renamer.start_renaming(seed, vecop) def split_overloaded_packs(self, vector_ext): newpacks = [] for i,pack in enumerate(self.packs): load = pack.pack_load(self.vec_reg_size) if load > Pack.FULL: pack.split(newpacks, self.vec_reg_size, vector_ext) continue if load < Pack.FULL: for op in pack.operations: op.priority = -100 pack.clear() self.packs[i] = None continue self.packs = [pack for pack in self.packs + newpacks if pack]