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8sa1-gcc/gcc/tree-vect-loop.c
Richard Biener d11bcbe166 tree-optimization/99947 - avoid v.safe_push (v[0]) 
This avoids (again) the C++ pitfall of pushing a reference to
sth being reallocated.

2021-04-07  Richard Biener  <rguenther@suse.de>

	PR tree-optimization/99947
	* tree-vect-loop.c (vectorizable_induction): Pre-allocate
	steps vector to avoid pushing elements from the reallocated
	vector.

	* gcc.dg/torture/pr99947.c: New testcase.
2021-04-07 10:04:20 +02:00

10071 lines
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/* Loop Vectorization
Copyright (C) 2003-2021 Free Software Foundation, Inc.
Contributed by Dorit Naishlos <dorit@il.ibm.com> and
Ira Rosen <irar@il.ibm.com>
This file is part of GCC.
GCC is free software; you can redistribute it and/or modify it under
the terms of the GNU General Public License as published by the Free
Software Foundation; either version 3, or (at your option) any later
version.
GCC is distributed in the hope that it will be useful, but WITHOUT ANY
WARRANTY; without even the implied warranty of MERCHANTABILITY or
FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License
for more details.
You should have received a copy of the GNU General Public License
along with GCC; see the file COPYING3. If not see
<http://www.gnu.org/licenses/>. */
#include "config.h"
#include "system.h"
#include "coretypes.h"
#include "backend.h"
#include "target.h"
#include "rtl.h"
#include "tree.h"
#include "gimple.h"
#include "cfghooks.h"
#include "tree-pass.h"
#include "ssa.h"
#include "optabs-tree.h"
#include "diagnostic-core.h"
#include "fold-const.h"
#include "stor-layout.h"
#include "cfganal.h"
#include "gimplify.h"
#include "gimple-iterator.h"
#include "gimplify-me.h"
#include "tree-ssa-loop-ivopts.h"
#include "tree-ssa-loop-manip.h"
#include "tree-ssa-loop-niter.h"
#include "tree-ssa-loop.h"
#include "cfgloop.h"
#include "tree-scalar-evolution.h"
#include "tree-vectorizer.h"
#include "gimple-fold.h"
#include "cgraph.h"
#include "tree-cfg.h"
#include "tree-if-conv.h"
#include "internal-fn.h"
#include "tree-vector-builder.h"
#include "vec-perm-indices.h"
#include "tree-eh.h"
/* Loop Vectorization Pass.
This pass tries to vectorize loops.
For example, the vectorizer transforms the following simple loop:
short a[N]; short b[N]; short c[N]; int i;
for (i=0; i<N; i++){
a[i] = b[i] + c[i];
}
as if it was manually vectorized by rewriting the source code into:
typedef int __attribute__((mode(V8HI))) v8hi;
short a[N]; short b[N]; short c[N]; int i;
v8hi *pa = (v8hi*)a, *pb = (v8hi*)b, *pc = (v8hi*)c;
v8hi va, vb, vc;
for (i=0; i<N/8; i++){
vb = pb[i];
vc = pc[i];
va = vb + vc;
pa[i] = va;
}
The main entry to this pass is vectorize_loops(), in which
the vectorizer applies a set of analyses on a given set of loops,
followed by the actual vectorization transformation for the loops that
had successfully passed the analysis phase.
Throughout this pass we make a distinction between two types of
data: scalars (which are represented by SSA_NAMES), and memory references
("data-refs"). These two types of data require different handling both
during analysis and transformation. The types of data-refs that the
vectorizer currently supports are ARRAY_REFS which base is an array DECL
(not a pointer), and INDIRECT_REFS through pointers; both array and pointer
accesses are required to have a simple (consecutive) access pattern.
Analysis phase:
===============
The driver for the analysis phase is vect_analyze_loop().
It applies a set of analyses, some of which rely on the scalar evolution
analyzer (scev) developed by Sebastian Pop.
During the analysis phase the vectorizer records some information
per stmt in a "stmt_vec_info" struct which is attached to each stmt in the
loop, as well as general information about the loop as a whole, which is
recorded in a "loop_vec_info" struct attached to each loop.
Transformation phase:
=====================
The loop transformation phase scans all the stmts in the loop, and
creates a vector stmt (or a sequence of stmts) for each scalar stmt S in
the loop that needs to be vectorized. It inserts the vector code sequence
just before the scalar stmt S, and records a pointer to the vector code
in STMT_VINFO_VEC_STMT (stmt_info) (stmt_info is the stmt_vec_info struct
attached to S). This pointer will be used for the vectorization of following
stmts which use the def of stmt S. Stmt S is removed if it writes to memory;
otherwise, we rely on dead code elimination for removing it.
For example, say stmt S1 was vectorized into stmt VS1:
VS1: vb = px[i];
S1: b = x[i]; STMT_VINFO_VEC_STMT (stmt_info (S1)) = VS1
S2: a = b;
To vectorize stmt S2, the vectorizer first finds the stmt that defines
the operand 'b' (S1), and gets the relevant vector def 'vb' from the
vector stmt VS1 pointed to by STMT_VINFO_VEC_STMT (stmt_info (S1)). The
resulting sequence would be:
VS1: vb = px[i];
S1: b = x[i]; STMT_VINFO_VEC_STMT (stmt_info (S1)) = VS1
VS2: va = vb;
S2: a = b; STMT_VINFO_VEC_STMT (stmt_info (S2)) = VS2
Operands that are not SSA_NAMEs, are data-refs that appear in
load/store operations (like 'x[i]' in S1), and are handled differently.
Target modeling:
=================
Currently the only target specific information that is used is the
size of the vector (in bytes) - "TARGET_VECTORIZE_UNITS_PER_SIMD_WORD".
Targets that can support different sizes of vectors, for now will need
to specify one value for "TARGET_VECTORIZE_UNITS_PER_SIMD_WORD". More
flexibility will be added in the future.
Since we only vectorize operations which vector form can be
expressed using existing tree codes, to verify that an operation is
supported, the vectorizer checks the relevant optab at the relevant
machine_mode (e.g, optab_handler (add_optab, V8HImode)). If
the value found is CODE_FOR_nothing, then there's no target support, and
we can't vectorize the stmt.
For additional information on this project see:
http://gcc.gnu.org/projects/tree-ssa/vectorization.html
*/
static void vect_estimate_min_profitable_iters (loop_vec_info, int *, int *);
static stmt_vec_info vect_is_simple_reduction (loop_vec_info, stmt_vec_info,
bool *, bool *);
/* Subroutine of vect_determine_vf_for_stmt that handles only one
statement. VECTYPE_MAYBE_SET_P is true if STMT_VINFO_VECTYPE
may already be set for general statements (not just data refs). */
static opt_result
vect_determine_vf_for_stmt_1 (vec_info *vinfo, stmt_vec_info stmt_info,
bool vectype_maybe_set_p,
poly_uint64 *vf)
{
gimple *stmt = stmt_info->stmt;
if ((!STMT_VINFO_RELEVANT_P (stmt_info)
&& !STMT_VINFO_LIVE_P (stmt_info))
|| gimple_clobber_p (stmt))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location, "skip.\n");
return opt_result::success ();
}
tree stmt_vectype, nunits_vectype;
opt_result res = vect_get_vector_types_for_stmt (vinfo, stmt_info,
&stmt_vectype,
&nunits_vectype);
if (!res)
return res;
if (stmt_vectype)
{
if (STMT_VINFO_VECTYPE (stmt_info))
/* The only case when a vectype had been already set is for stmts
that contain a data ref, or for "pattern-stmts" (stmts generated
by the vectorizer to represent/replace a certain idiom). */
gcc_assert ((STMT_VINFO_DATA_REF (stmt_info)
|| vectype_maybe_set_p)
&& STMT_VINFO_VECTYPE (stmt_info) == stmt_vectype);
else
STMT_VINFO_VECTYPE (stmt_info) = stmt_vectype;
}
if (nunits_vectype)
vect_update_max_nunits (vf, nunits_vectype);
return opt_result::success ();
}
/* Subroutine of vect_determine_vectorization_factor. Set the vector
types of STMT_INFO and all attached pattern statements and update
the vectorization factor VF accordingly. Return true on success
or false if something prevented vectorization. */
static opt_result
vect_determine_vf_for_stmt (vec_info *vinfo,
stmt_vec_info stmt_info, poly_uint64 *vf)
{
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location, "==> examining statement: %G",
stmt_info->stmt);
opt_result res = vect_determine_vf_for_stmt_1 (vinfo, stmt_info, false, vf);
if (!res)
return res;
if (STMT_VINFO_IN_PATTERN_P (stmt_info)
&& STMT_VINFO_RELATED_STMT (stmt_info))
{
gimple *pattern_def_seq = STMT_VINFO_PATTERN_DEF_SEQ (stmt_info);
stmt_info = STMT_VINFO_RELATED_STMT (stmt_info);
/* If a pattern statement has def stmts, analyze them too. */
for (gimple_stmt_iterator si = gsi_start (pattern_def_seq);
!gsi_end_p (si); gsi_next (&si))
{
stmt_vec_info def_stmt_info = vinfo->lookup_stmt (gsi_stmt (si));
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"==> examining pattern def stmt: %G",
def_stmt_info->stmt);
res = vect_determine_vf_for_stmt_1 (vinfo, def_stmt_info, true, vf);
if (!res)
return res;
}
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"==> examining pattern statement: %G",
stmt_info->stmt);
res = vect_determine_vf_for_stmt_1 (vinfo, stmt_info, true, vf);
if (!res)
return res;
}
return opt_result::success ();
}
/* Function vect_determine_vectorization_factor
Determine the vectorization factor (VF). VF is the number of data elements
that are operated upon in parallel in a single iteration of the vectorized
loop. For example, when vectorizing a loop that operates on 4byte elements,
on a target with vector size (VS) 16byte, the VF is set to 4, since 4
elements can fit in a single vector register.
We currently support vectorization of loops in which all types operated upon
are of the same size. Therefore this function currently sets VF according to
the size of the types operated upon, and fails if there are multiple sizes
in the loop.
VF is also the factor by which the loop iterations are strip-mined, e.g.:
original loop:
for (i=0; i<N; i++){
a[i] = b[i] + c[i];
}
vectorized loop:
for (i=0; i<N; i+=VF){
a[i:VF] = b[i:VF] + c[i:VF];
}
*/
static opt_result
vect_determine_vectorization_factor (loop_vec_info loop_vinfo)
{
class loop *loop = LOOP_VINFO_LOOP (loop_vinfo);
basic_block *bbs = LOOP_VINFO_BBS (loop_vinfo);
unsigned nbbs = loop->num_nodes;
poly_uint64 vectorization_factor = 1;
tree scalar_type = NULL_TREE;
gphi *phi;
tree vectype;
stmt_vec_info stmt_info;
unsigned i;
DUMP_VECT_SCOPE ("vect_determine_vectorization_factor");
for (i = 0; i < nbbs; i++)
{
basic_block bb = bbs[i];
for (gphi_iterator si = gsi_start_phis (bb); !gsi_end_p (si);
gsi_next (&si))
{
phi = si.phi ();
stmt_info = loop_vinfo->lookup_stmt (phi);
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location, "==> examining phi: %G",
phi);
gcc_assert (stmt_info);
if (STMT_VINFO_RELEVANT_P (stmt_info)
|| STMT_VINFO_LIVE_P (stmt_info))
{
gcc_assert (!STMT_VINFO_VECTYPE (stmt_info));
scalar_type = TREE_TYPE (PHI_RESULT (phi));
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"get vectype for scalar type: %T\n",
scalar_type);
vectype = get_vectype_for_scalar_type (loop_vinfo, scalar_type);
if (!vectype)
return opt_result::failure_at (phi,
"not vectorized: unsupported "
"data-type %T\n",
scalar_type);
STMT_VINFO_VECTYPE (stmt_info) = vectype;
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location, "vectype: %T\n",
vectype);
if (dump_enabled_p ())
{
dump_printf_loc (MSG_NOTE, vect_location, "nunits = ");
dump_dec (MSG_NOTE, TYPE_VECTOR_SUBPARTS (vectype));
dump_printf (MSG_NOTE, "\n");
}
vect_update_max_nunits (&vectorization_factor, vectype);
}
}
for (gimple_stmt_iterator si = gsi_start_bb (bb); !gsi_end_p (si);
gsi_next (&si))
{
if (is_gimple_debug (gsi_stmt (si)))
continue;
stmt_info = loop_vinfo->lookup_stmt (gsi_stmt (si));
opt_result res
= vect_determine_vf_for_stmt (loop_vinfo,
stmt_info, &vectorization_factor);
if (!res)
return res;
}
}
/* TODO: Analyze cost. Decide if worth while to vectorize. */
if (dump_enabled_p ())
{
dump_printf_loc (MSG_NOTE, vect_location, "vectorization factor = ");
dump_dec (MSG_NOTE, vectorization_factor);
dump_printf (MSG_NOTE, "\n");
}
if (known_le (vectorization_factor, 1U))
return opt_result::failure_at (vect_location,
"not vectorized: unsupported data-type\n");
LOOP_VINFO_VECT_FACTOR (loop_vinfo) = vectorization_factor;
return opt_result::success ();
}
/* Function vect_is_simple_iv_evolution.
FORNOW: A simple evolution of an induction variables in the loop is
considered a polynomial evolution. */
static bool
vect_is_simple_iv_evolution (unsigned loop_nb, tree access_fn, tree * init,
tree * step)
{
tree init_expr;
tree step_expr;
tree evolution_part = evolution_part_in_loop_num (access_fn, loop_nb);
basic_block bb;
/* When there is no evolution in this loop, the evolution function
is not "simple". */
if (evolution_part == NULL_TREE)
return false;
/* When the evolution is a polynomial of degree >= 2
the evolution function is not "simple". */
if (tree_is_chrec (evolution_part))
return false;
step_expr = evolution_part;
init_expr = unshare_expr (initial_condition_in_loop_num (access_fn, loop_nb));
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location, "step: %T, init: %T\n",
step_expr, init_expr);
*init = init_expr;
*step = step_expr;
if (TREE_CODE (step_expr) != INTEGER_CST
&& (TREE_CODE (step_expr) != SSA_NAME
|| ((bb = gimple_bb (SSA_NAME_DEF_STMT (step_expr)))
&& flow_bb_inside_loop_p (get_loop (cfun, loop_nb), bb))
|| (!INTEGRAL_TYPE_P (TREE_TYPE (step_expr))
&& (!SCALAR_FLOAT_TYPE_P (TREE_TYPE (step_expr))
|| !flag_associative_math)))
&& (TREE_CODE (step_expr) != REAL_CST
|| !flag_associative_math))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"step unknown.\n");
return false;
}
return true;
}
/* Return true if PHI, described by STMT_INFO, is the inner PHI in
what we are assuming is a double reduction. For example, given
a structure like this:
outer1:
x_1 = PHI <x_4(outer2), ...>;
...
inner:
x_2 = PHI <x_1(outer1), ...>;
...
x_3 = ...;
...
outer2:
x_4 = PHI <x_3(inner)>;
...
outer loop analysis would treat x_1 as a double reduction phi and
this function would then return true for x_2. */
static bool
vect_inner_phi_in_double_reduction_p (loop_vec_info loop_vinfo, gphi *phi)
{
use_operand_p use_p;
ssa_op_iter op_iter;
FOR_EACH_PHI_ARG (use_p, phi, op_iter, SSA_OP_USE)
if (stmt_vec_info def_info = loop_vinfo->lookup_def (USE_FROM_PTR (use_p)))
if (STMT_VINFO_DEF_TYPE (def_info) == vect_double_reduction_def)
return true;
return false;
}
/* Function vect_analyze_scalar_cycles_1.
Examine the cross iteration def-use cycles of scalar variables
in LOOP. LOOP_VINFO represents the loop that is now being
considered for vectorization (can be LOOP, or an outer-loop
enclosing LOOP). */
static void
vect_analyze_scalar_cycles_1 (loop_vec_info loop_vinfo, class loop *loop)
{
basic_block bb = loop->header;
tree init, step;
auto_vec<stmt_vec_info, 64> worklist;
gphi_iterator gsi;
bool double_reduc, reduc_chain;
DUMP_VECT_SCOPE ("vect_analyze_scalar_cycles");
/* First - identify all inductions. Reduction detection assumes that all the
inductions have been identified, therefore, this order must not be
changed. */
for (gsi = gsi_start_phis (bb); !gsi_end_p (gsi); gsi_next (&gsi))
{
gphi *phi = gsi.phi ();
tree access_fn = NULL;
tree def = PHI_RESULT (phi);
stmt_vec_info stmt_vinfo = loop_vinfo->lookup_stmt (phi);
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location, "Analyze phi: %G", phi);
/* Skip virtual phi's. The data dependences that are associated with
virtual defs/uses (i.e., memory accesses) are analyzed elsewhere. */
if (virtual_operand_p (def))
continue;
STMT_VINFO_DEF_TYPE (stmt_vinfo) = vect_unknown_def_type;
/* Analyze the evolution function. */
access_fn = analyze_scalar_evolution (loop, def);
if (access_fn)
{
STRIP_NOPS (access_fn);
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"Access function of PHI: %T\n", access_fn);
STMT_VINFO_LOOP_PHI_EVOLUTION_BASE_UNCHANGED (stmt_vinfo)
= initial_condition_in_loop_num (access_fn, loop->num);
STMT_VINFO_LOOP_PHI_EVOLUTION_PART (stmt_vinfo)
= evolution_part_in_loop_num (access_fn, loop->num);
}
if (!access_fn
|| vect_inner_phi_in_double_reduction_p (loop_vinfo, phi)
|| !vect_is_simple_iv_evolution (loop->num, access_fn, &init, &step)
|| (LOOP_VINFO_LOOP (loop_vinfo) != loop
&& TREE_CODE (step) != INTEGER_CST))
{
worklist.safe_push (stmt_vinfo);
continue;
}
gcc_assert (STMT_VINFO_LOOP_PHI_EVOLUTION_BASE_UNCHANGED (stmt_vinfo)
!= NULL_TREE);
gcc_assert (STMT_VINFO_LOOP_PHI_EVOLUTION_PART (stmt_vinfo) != NULL_TREE);
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location, "Detected induction.\n");
STMT_VINFO_DEF_TYPE (stmt_vinfo) = vect_induction_def;
}
/* Second - identify all reductions and nested cycles. */
while (worklist.length () > 0)
{
stmt_vec_info stmt_vinfo = worklist.pop ();
gphi *phi = as_a <gphi *> (stmt_vinfo->stmt);
tree def = PHI_RESULT (phi);
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location, "Analyze phi: %G", phi);
gcc_assert (!virtual_operand_p (def)
&& STMT_VINFO_DEF_TYPE (stmt_vinfo) == vect_unknown_def_type);
stmt_vec_info reduc_stmt_info
= vect_is_simple_reduction (loop_vinfo, stmt_vinfo, &double_reduc,
&reduc_chain);
if (reduc_stmt_info)
{
STMT_VINFO_REDUC_DEF (stmt_vinfo) = reduc_stmt_info;
STMT_VINFO_REDUC_DEF (reduc_stmt_info) = stmt_vinfo;
if (double_reduc)
{
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"Detected double reduction.\n");
STMT_VINFO_DEF_TYPE (stmt_vinfo) = vect_double_reduction_def;
STMT_VINFO_DEF_TYPE (reduc_stmt_info) = vect_double_reduction_def;
}
else
{
if (loop != LOOP_VINFO_LOOP (loop_vinfo))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"Detected vectorizable nested cycle.\n");
STMT_VINFO_DEF_TYPE (stmt_vinfo) = vect_nested_cycle;
}
else
{
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"Detected reduction.\n");
STMT_VINFO_DEF_TYPE (stmt_vinfo) = vect_reduction_def;
STMT_VINFO_DEF_TYPE (reduc_stmt_info) = vect_reduction_def;
/* Store the reduction cycles for possible vectorization in
loop-aware SLP if it was not detected as reduction
chain. */
if (! reduc_chain)
LOOP_VINFO_REDUCTIONS (loop_vinfo).safe_push
(reduc_stmt_info);
}
}
}
else
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"Unknown def-use cycle pattern.\n");
}
}
/* Function vect_analyze_scalar_cycles.
Examine the cross iteration def-use cycles of scalar variables, by
analyzing the loop-header PHIs of scalar variables. Classify each
cycle as one of the following: invariant, induction, reduction, unknown.
We do that for the loop represented by LOOP_VINFO, and also to its
inner-loop, if exists.
Examples for scalar cycles:
Example1: reduction:
loop1:
for (i=0; i<N; i++)
sum += a[i];
Example2: induction:
loop2:
for (i=0; i<N; i++)
a[i] = i; */
static void
vect_analyze_scalar_cycles (loop_vec_info loop_vinfo)
{
class loop *loop = LOOP_VINFO_LOOP (loop_vinfo);
vect_analyze_scalar_cycles_1 (loop_vinfo, loop);
/* When vectorizing an outer-loop, the inner-loop is executed sequentially.
Reductions in such inner-loop therefore have different properties than
the reductions in the nest that gets vectorized:
1. When vectorized, they are executed in the same order as in the original
scalar loop, so we can't change the order of computation when
vectorizing them.
2. FIXME: Inner-loop reductions can be used in the inner-loop, so the
current checks are too strict. */
if (loop->inner)
vect_analyze_scalar_cycles_1 (loop_vinfo, loop->inner);
}
/* Transfer group and reduction information from STMT_INFO to its
pattern stmt. */
static void
vect_fixup_reduc_chain (stmt_vec_info stmt_info)
{
stmt_vec_info firstp = STMT_VINFO_RELATED_STMT (stmt_info);
stmt_vec_info stmtp;
gcc_assert (!REDUC_GROUP_FIRST_ELEMENT (firstp)
&& REDUC_GROUP_FIRST_ELEMENT (stmt_info));
REDUC_GROUP_SIZE (firstp) = REDUC_GROUP_SIZE (stmt_info);
do
{
stmtp = STMT_VINFO_RELATED_STMT (stmt_info);
gcc_checking_assert (STMT_VINFO_DEF_TYPE (stmtp)
== STMT_VINFO_DEF_TYPE (stmt_info));
REDUC_GROUP_FIRST_ELEMENT (stmtp) = firstp;
stmt_info = REDUC_GROUP_NEXT_ELEMENT (stmt_info);
if (stmt_info)
REDUC_GROUP_NEXT_ELEMENT (stmtp)
= STMT_VINFO_RELATED_STMT (stmt_info);
}
while (stmt_info);
}
/* Fixup scalar cycles that now have their stmts detected as patterns. */
static void
vect_fixup_scalar_cycles_with_patterns (loop_vec_info loop_vinfo)
{
stmt_vec_info first;
unsigned i;
FOR_EACH_VEC_ELT (LOOP_VINFO_REDUCTION_CHAINS (loop_vinfo), i, first)
{
stmt_vec_info next = REDUC_GROUP_NEXT_ELEMENT (first);
while (next)
{
if ((STMT_VINFO_IN_PATTERN_P (next)
!= STMT_VINFO_IN_PATTERN_P (first))
|| STMT_VINFO_REDUC_IDX (vect_stmt_to_vectorize (next)) == -1)
break;
next = REDUC_GROUP_NEXT_ELEMENT (next);
}
/* If all reduction chain members are well-formed patterns adjust
the group to group the pattern stmts instead. */
if (! next
&& STMT_VINFO_REDUC_IDX (vect_stmt_to_vectorize (first)) != -1)
{
if (STMT_VINFO_IN_PATTERN_P (first))
{
vect_fixup_reduc_chain (first);
LOOP_VINFO_REDUCTION_CHAINS (loop_vinfo)[i]
= STMT_VINFO_RELATED_STMT (first);
}
}
/* If not all stmt in the chain are patterns or if we failed
to update STMT_VINFO_REDUC_IDX dissolve the chain and handle
it as regular reduction instead. */
else
{
stmt_vec_info vinfo = first;
stmt_vec_info last = NULL;
while (vinfo)
{
next = REDUC_GROUP_NEXT_ELEMENT (vinfo);
REDUC_GROUP_FIRST_ELEMENT (vinfo) = NULL;
REDUC_GROUP_NEXT_ELEMENT (vinfo) = NULL;
last = vinfo;
vinfo = next;
}
STMT_VINFO_DEF_TYPE (vect_stmt_to_vectorize (first))
= vect_internal_def;
loop_vinfo->reductions.safe_push (vect_stmt_to_vectorize (last));
LOOP_VINFO_REDUCTION_CHAINS (loop_vinfo).unordered_remove (i);
--i;
}
}
}
/* Function vect_get_loop_niters.
Determine how many iterations the loop is executed and place it
in NUMBER_OF_ITERATIONS. Place the number of latch iterations
in NUMBER_OF_ITERATIONSM1. Place the condition under which the
niter information holds in ASSUMPTIONS.
Return the loop exit condition. */
static gcond *
vect_get_loop_niters (class loop *loop, tree *assumptions,
tree *number_of_iterations, tree *number_of_iterationsm1)
{
edge exit = single_exit (loop);
class tree_niter_desc niter_desc;
tree niter_assumptions, niter, may_be_zero;
gcond *cond = get_loop_exit_condition (loop);
*assumptions = boolean_true_node;
*number_of_iterationsm1 = chrec_dont_know;
*number_of_iterations = chrec_dont_know;
DUMP_VECT_SCOPE ("get_loop_niters");
if (!exit)
return cond;
may_be_zero = NULL_TREE;
if (!number_of_iterations_exit_assumptions (loop, exit, &niter_desc, NULL)
|| chrec_contains_undetermined (niter_desc.niter))
return cond;
niter_assumptions = niter_desc.assumptions;
may_be_zero = niter_desc.may_be_zero;
niter = niter_desc.niter;
if (may_be_zero && integer_zerop (may_be_zero))
may_be_zero = NULL_TREE;
if (may_be_zero)
{
if (COMPARISON_CLASS_P (may_be_zero))
{
/* Try to combine may_be_zero with assumptions, this can simplify
computation of niter expression. */
if (niter_assumptions && !integer_nonzerop (niter_assumptions))
niter_assumptions = fold_build2 (TRUTH_AND_EXPR, boolean_type_node,
niter_assumptions,
fold_build1 (TRUTH_NOT_EXPR,
boolean_type_node,
may_be_zero));
else
niter = fold_build3 (COND_EXPR, TREE_TYPE (niter), may_be_zero,
build_int_cst (TREE_TYPE (niter), 0),
rewrite_to_non_trapping_overflow (niter));
may_be_zero = NULL_TREE;
}
else if (integer_nonzerop (may_be_zero))
{
*number_of_iterationsm1 = build_int_cst (TREE_TYPE (niter), 0);
*number_of_iterations = build_int_cst (TREE_TYPE (niter), 1);
return cond;
}
else
return cond;
}
*assumptions = niter_assumptions;
*number_of_iterationsm1 = niter;
/* We want the number of loop header executions which is the number
of latch executions plus one.
??? For UINT_MAX latch executions this number overflows to zero
for loops like do { n++; } while (n != 0); */
if (niter && !chrec_contains_undetermined (niter))
niter = fold_build2 (PLUS_EXPR, TREE_TYPE (niter), unshare_expr (niter),
build_int_cst (TREE_TYPE (niter), 1));
*number_of_iterations = niter;
return cond;
}
/* Function bb_in_loop_p
Used as predicate for dfs order traversal of the loop bbs. */
static bool
bb_in_loop_p (const_basic_block bb, const void *data)
{
const class loop *const loop = (const class loop *)data;
if (flow_bb_inside_loop_p (loop, bb))
return true;
return false;
}
/* Create and initialize a new loop_vec_info struct for LOOP_IN, as well as
stmt_vec_info structs for all the stmts in LOOP_IN. */
_loop_vec_info::_loop_vec_info (class loop *loop_in, vec_info_shared *shared)
: vec_info (vec_info::loop, init_cost (loop_in), shared),
loop (loop_in),
bbs (XCNEWVEC (basic_block, loop->num_nodes)),
num_itersm1 (NULL_TREE),
num_iters (NULL_TREE),
num_iters_unchanged (NULL_TREE),
num_iters_assumptions (NULL_TREE),
th (0),
versioning_threshold (0),
vectorization_factor (0),
max_vectorization_factor (0),
mask_skip_niters (NULL_TREE),
rgroup_compare_type (NULL_TREE),
simd_if_cond (NULL_TREE),
unaligned_dr (NULL),
peeling_for_alignment (0),
ptr_mask (0),
ivexpr_map (NULL),
scan_map (NULL),
slp_unrolling_factor (1),
single_scalar_iteration_cost (0),
vec_outside_cost (0),
vec_inside_cost (0),
vectorizable (false),
can_use_partial_vectors_p (param_vect_partial_vector_usage != 0),
using_partial_vectors_p (false),
epil_using_partial_vectors_p (false),
peeling_for_gaps (false),
peeling_for_niter (false),
no_data_dependencies (false),
has_mask_store (false),
scalar_loop_scaling (profile_probability::uninitialized ()),
scalar_loop (NULL),
orig_loop_info (NULL)
{
/* CHECKME: We want to visit all BBs before their successors (except for
latch blocks, for which this assertion wouldn't hold). In the simple
case of the loop forms we allow, a dfs order of the BBs would the same
as reversed postorder traversal, so we are safe. */
unsigned int nbbs = dfs_enumerate_from (loop->header, 0, bb_in_loop_p,
bbs, loop->num_nodes, loop);
gcc_assert (nbbs == loop->num_nodes);
for (unsigned int i = 0; i < nbbs; i++)
{
basic_block bb = bbs[i];
gimple_stmt_iterator si;
for (si = gsi_start_phis (bb); !gsi_end_p (si); gsi_next (&si))
{
gimple *phi = gsi_stmt (si);
gimple_set_uid (phi, 0);
add_stmt (phi);
}
for (si = gsi_start_bb (bb); !gsi_end_p (si); gsi_next (&si))
{
gimple *stmt = gsi_stmt (si);
gimple_set_uid (stmt, 0);
if (is_gimple_debug (stmt))
continue;
add_stmt (stmt);
/* If .GOMP_SIMD_LANE call for the current loop has 3 arguments, the
third argument is the #pragma omp simd if (x) condition, when 0,
loop shouldn't be vectorized, when non-zero constant, it should
be vectorized normally, otherwise versioned with vectorized loop
done if the condition is non-zero at runtime. */
if (loop_in->simduid
&& is_gimple_call (stmt)
&& gimple_call_internal_p (stmt)
&& gimple_call_internal_fn (stmt) == IFN_GOMP_SIMD_LANE
&& gimple_call_num_args (stmt) >= 3
&& TREE_CODE (gimple_call_arg (stmt, 0)) == SSA_NAME
&& (loop_in->simduid
== SSA_NAME_VAR (gimple_call_arg (stmt, 0))))
{
tree arg = gimple_call_arg (stmt, 2);
if (integer_zerop (arg) || TREE_CODE (arg) == SSA_NAME)
simd_if_cond = arg;
else
gcc_assert (integer_nonzerop (arg));
}
}
}
epilogue_vinfos.create (6);
}
/* Free all levels of rgroup CONTROLS. */
void
release_vec_loop_controls (vec<rgroup_controls> *controls)
{
rgroup_controls *rgc;
unsigned int i;
FOR_EACH_VEC_ELT (*controls, i, rgc)
rgc->controls.release ();
controls->release ();
}
/* Free all memory used by the _loop_vec_info, as well as all the
stmt_vec_info structs of all the stmts in the loop. */
_loop_vec_info::~_loop_vec_info ()
{
free (bbs);
release_vec_loop_controls (&masks);
release_vec_loop_controls (&lens);
delete ivexpr_map;
delete scan_map;
epilogue_vinfos.release ();
/* When we release an epiloge vinfo that we do not intend to use
avoid clearing AUX of the main loop which should continue to
point to the main loop vinfo since otherwise we'll leak that. */
if (loop->aux == this)
loop->aux = NULL;
}
/* Return an invariant or register for EXPR and emit necessary
computations in the LOOP_VINFO loop preheader. */
tree
cse_and_gimplify_to_preheader (loop_vec_info loop_vinfo, tree expr)
{
if (is_gimple_reg (expr)
|| is_gimple_min_invariant (expr))
return expr;
if (! loop_vinfo->ivexpr_map)
loop_vinfo->ivexpr_map = new hash_map<tree_operand_hash, tree>;
tree &cached = loop_vinfo->ivexpr_map->get_or_insert (expr);
if (! cached)
{
gimple_seq stmts = NULL;
cached = force_gimple_operand (unshare_expr (expr),
&stmts, true, NULL_TREE);
if (stmts)
{
edge e = loop_preheader_edge (LOOP_VINFO_LOOP (loop_vinfo));
gsi_insert_seq_on_edge_immediate (e, stmts);
}
}
return cached;
}
/* Return true if we can use CMP_TYPE as the comparison type to produce
all masks required to mask LOOP_VINFO. */
static bool
can_produce_all_loop_masks_p (loop_vec_info loop_vinfo, tree cmp_type)
{
rgroup_controls *rgm;
unsigned int i;
FOR_EACH_VEC_ELT (LOOP_VINFO_MASKS (loop_vinfo), i, rgm)
if (rgm->type != NULL_TREE
&& !direct_internal_fn_supported_p (IFN_WHILE_ULT,
cmp_type, rgm->type,
OPTIMIZE_FOR_SPEED))
return false;
return true;
}
/* Calculate the maximum number of scalars per iteration for every
rgroup in LOOP_VINFO. */
static unsigned int
vect_get_max_nscalars_per_iter (loop_vec_info loop_vinfo)
{
unsigned int res = 1;
unsigned int i;
rgroup_controls *rgm;
FOR_EACH_VEC_ELT (LOOP_VINFO_MASKS (loop_vinfo), i, rgm)
res = MAX (res, rgm->max_nscalars_per_iter);
return res;
}
/* Calculate the minimum precision necessary to represent:
MAX_NITERS * FACTOR
as an unsigned integer, where MAX_NITERS is the maximum number of
loop header iterations for the original scalar form of LOOP_VINFO. */
static unsigned
vect_min_prec_for_max_niters (loop_vec_info loop_vinfo, unsigned int factor)
{
class loop *loop = LOOP_VINFO_LOOP (loop_vinfo);
/* Get the maximum number of iterations that is representable
in the counter type. */
tree ni_type = TREE_TYPE (LOOP_VINFO_NITERSM1 (loop_vinfo));
widest_int max_ni = wi::to_widest (TYPE_MAX_VALUE (ni_type)) + 1;
/* Get a more refined estimate for the number of iterations. */
widest_int max_back_edges;
if (max_loop_iterations (loop, &max_back_edges))
max_ni = wi::smin (max_ni, max_back_edges + 1);
/* Work out how many bits we need to represent the limit. */
return wi::min_precision (max_ni * factor, UNSIGNED);
}
/* True if the loop needs peeling or partial vectors when vectorized. */
static bool
vect_need_peeling_or_partial_vectors_p (loop_vec_info loop_vinfo)
{
unsigned HOST_WIDE_INT const_vf;
HOST_WIDE_INT max_niter
= likely_max_stmt_executions_int (LOOP_VINFO_LOOP (loop_vinfo));
unsigned th = LOOP_VINFO_COST_MODEL_THRESHOLD (loop_vinfo);
if (!th && LOOP_VINFO_ORIG_LOOP_INFO (loop_vinfo))
th = LOOP_VINFO_COST_MODEL_THRESHOLD (LOOP_VINFO_ORIG_LOOP_INFO
(loop_vinfo));
if (LOOP_VINFO_NITERS_KNOWN_P (loop_vinfo)
&& LOOP_VINFO_PEELING_FOR_ALIGNMENT (loop_vinfo) >= 0)
{
/* Work out the (constant) number of iterations that need to be
peeled for reasons other than niters. */
unsigned int peel_niter = LOOP_VINFO_PEELING_FOR_ALIGNMENT (loop_vinfo);
if (LOOP_VINFO_PEELING_FOR_GAPS (loop_vinfo))
peel_niter += 1;
if (!multiple_p (LOOP_VINFO_INT_NITERS (loop_vinfo) - peel_niter,
LOOP_VINFO_VECT_FACTOR (loop_vinfo)))
return true;
}
else if (LOOP_VINFO_PEELING_FOR_ALIGNMENT (loop_vinfo)
/* ??? When peeling for gaps but not alignment, we could
try to check whether the (variable) niters is known to be
VF * N + 1. That's something of a niche case though. */
|| LOOP_VINFO_PEELING_FOR_GAPS (loop_vinfo)
|| !LOOP_VINFO_VECT_FACTOR (loop_vinfo).is_constant (&const_vf)
|| ((tree_ctz (LOOP_VINFO_NITERS (loop_vinfo))
< (unsigned) exact_log2 (const_vf))
/* In case of versioning, check if the maximum number of
iterations is greater than th. If they are identical,
the epilogue is unnecessary. */
&& (!LOOP_REQUIRES_VERSIONING (loop_vinfo)
|| ((unsigned HOST_WIDE_INT) max_niter
> (th / const_vf) * const_vf))))
return true;
return false;
}
/* Each statement in LOOP_VINFO can be masked where necessary. Check
whether we can actually generate the masks required. Return true if so,
storing the type of the scalar IV in LOOP_VINFO_RGROUP_COMPARE_TYPE. */
static bool
vect_verify_full_masking (loop_vec_info loop_vinfo)
{
unsigned int min_ni_width;
unsigned int max_nscalars_per_iter
= vect_get_max_nscalars_per_iter (loop_vinfo);
/* Use a normal loop if there are no statements that need masking.
This only happens in rare degenerate cases: it means that the loop
has no loads, no stores, and no live-out values. */
if (LOOP_VINFO_MASKS (loop_vinfo).is_empty ())
return false;
/* Work out how many bits we need to represent the limit. */
min_ni_width
= vect_min_prec_for_max_niters (loop_vinfo, max_nscalars_per_iter);
/* Find a scalar mode for which WHILE_ULT is supported. */
opt_scalar_int_mode cmp_mode_iter;
tree cmp_type = NULL_TREE;
tree iv_type = NULL_TREE;
widest_int iv_limit = vect_iv_limit_for_partial_vectors (loop_vinfo);
unsigned int iv_precision = UINT_MAX;
if (iv_limit != -1)
iv_precision = wi::min_precision (iv_limit * max_nscalars_per_iter,
UNSIGNED);
FOR_EACH_MODE_IN_CLASS (cmp_mode_iter, MODE_INT)
{
unsigned int cmp_bits = GET_MODE_BITSIZE (cmp_mode_iter.require ());
if (cmp_bits >= min_ni_width
&& targetm.scalar_mode_supported_p (cmp_mode_iter.require ()))
{
tree this_type = build_nonstandard_integer_type (cmp_bits, true);
if (this_type
&& can_produce_all_loop_masks_p (loop_vinfo, this_type))
{
/* Although we could stop as soon as we find a valid mode,
there are at least two reasons why that's not always the
best choice:
- An IV that's Pmode or wider is more likely to be reusable
in address calculations than an IV that's narrower than
Pmode.
- Doing the comparison in IV_PRECISION or wider allows
a natural 0-based IV, whereas using a narrower comparison
type requires mitigations against wrap-around.
Conversely, if the IV limit is variable, doing the comparison
in a wider type than the original type can introduce
unnecessary extensions, so picking the widest valid mode
is not always a good choice either.
Here we prefer the first IV type that's Pmode or wider,
and the first comparison type that's IV_PRECISION or wider.
(The comparison type must be no wider than the IV type,
to avoid extensions in the vector loop.)
??? We might want to try continuing beyond Pmode for ILP32
targets if CMP_BITS < IV_PRECISION. */
iv_type = this_type;
if (!cmp_type || iv_precision > TYPE_PRECISION (cmp_type))
cmp_type = this_type;
if (cmp_bits >= GET_MODE_BITSIZE (Pmode))
break;
}
}
}
if (!cmp_type)
return false;
LOOP_VINFO_RGROUP_COMPARE_TYPE (loop_vinfo) = cmp_type;
LOOP_VINFO_RGROUP_IV_TYPE (loop_vinfo) = iv_type;
return true;
}
/* Check whether we can use vector access with length based on precison
comparison. So far, to keep it simple, we only allow the case that the
precision of the target supported length is larger than the precision
required by loop niters. */
static bool
vect_verify_loop_lens (loop_vec_info loop_vinfo)
{
if (LOOP_VINFO_LENS (loop_vinfo).is_empty ())
return false;
unsigned int max_nitems_per_iter = 1;
unsigned int i;
rgroup_controls *rgl;
/* Find the maximum number of items per iteration for every rgroup. */
FOR_EACH_VEC_ELT (LOOP_VINFO_LENS (loop_vinfo), i, rgl)
{
unsigned nitems_per_iter = rgl->max_nscalars_per_iter * rgl->factor;
max_nitems_per_iter = MAX (max_nitems_per_iter, nitems_per_iter);
}
/* Work out how many bits we need to represent the length limit. */
unsigned int min_ni_prec
= vect_min_prec_for_max_niters (loop_vinfo, max_nitems_per_iter);
/* Now use the maximum of below precisions for one suitable IV type:
- the IV's natural precision
- the precision needed to hold: the maximum number of scalar
iterations multiplied by the scale factor (min_ni_prec above)
- the Pmode precision
If min_ni_prec is less than the precision of the current niters,
we perfer to still use the niters type. Prefer to use Pmode and
wider IV to avoid narrow conversions. */
unsigned int ni_prec
= TYPE_PRECISION (TREE_TYPE (LOOP_VINFO_NITERS (loop_vinfo)));
min_ni_prec = MAX (min_ni_prec, ni_prec);
min_ni_prec = MAX (min_ni_prec, GET_MODE_BITSIZE (Pmode));
tree iv_type = NULL_TREE;
opt_scalar_int_mode tmode_iter;
FOR_EACH_MODE_IN_CLASS (tmode_iter, MODE_INT)
{
scalar_mode tmode = tmode_iter.require ();
unsigned int tbits = GET_MODE_BITSIZE (tmode);
/* ??? Do we really want to construct one IV whose precision exceeds
BITS_PER_WORD? */
if (tbits > BITS_PER_WORD)
break;
/* Find the first available standard integral type. */
if (tbits >= min_ni_prec && targetm.scalar_mode_supported_p (tmode))
{
iv_type = build_nonstandard_integer_type (tbits, true);
break;
}
}
if (!iv_type)
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"can't vectorize with length-based partial vectors"
" because there is no suitable iv type.\n");
return false;
}
LOOP_VINFO_RGROUP_COMPARE_TYPE (loop_vinfo) = iv_type;
LOOP_VINFO_RGROUP_IV_TYPE (loop_vinfo) = iv_type;
return true;
}
/* Calculate the cost of one scalar iteration of the loop. */
static void
vect_compute_single_scalar_iteration_cost (loop_vec_info loop_vinfo)
{
class loop *loop = LOOP_VINFO_LOOP (loop_vinfo);
basic_block *bbs = LOOP_VINFO_BBS (loop_vinfo);
int nbbs = loop->num_nodes, factor;
int innerloop_iters, i;
DUMP_VECT_SCOPE ("vect_compute_single_scalar_iteration_cost");
/* Gather costs for statements in the scalar loop. */
/* FORNOW. */
innerloop_iters = 1;
if (loop->inner)
innerloop_iters = 50; /* FIXME */
for (i = 0; i < nbbs; i++)
{
gimple_stmt_iterator si;
basic_block bb = bbs[i];
if (bb->loop_father == loop->inner)
factor = innerloop_iters;
else
factor = 1;
for (si = gsi_start_bb (bb); !gsi_end_p (si); gsi_next (&si))
{
gimple *stmt = gsi_stmt (si);
stmt_vec_info stmt_info = loop_vinfo->lookup_stmt (stmt);
if (!is_gimple_assign (stmt) && !is_gimple_call (stmt))
continue;
/* Skip stmts that are not vectorized inside the loop. */
stmt_vec_info vstmt_info = vect_stmt_to_vectorize (stmt_info);
if (!STMT_VINFO_RELEVANT_P (vstmt_info)
&& (!STMT_VINFO_LIVE_P (vstmt_info)
|| !VECTORIZABLE_CYCLE_DEF
(STMT_VINFO_DEF_TYPE (vstmt_info))))
continue;
vect_cost_for_stmt kind;
if (STMT_VINFO_DATA_REF (stmt_info))
{
if (DR_IS_READ (STMT_VINFO_DATA_REF (stmt_info)))
kind = scalar_load;
else
kind = scalar_store;
}
else if (vect_nop_conversion_p (stmt_info))
continue;
else
kind = scalar_stmt;
record_stmt_cost (&LOOP_VINFO_SCALAR_ITERATION_COST (loop_vinfo),
factor, kind, stmt_info, 0, vect_prologue);
}
}
/* Now accumulate cost. */
void *target_cost_data = init_cost (loop);
stmt_info_for_cost *si;
int j;
FOR_EACH_VEC_ELT (LOOP_VINFO_SCALAR_ITERATION_COST (loop_vinfo),
j, si)
(void) add_stmt_cost (loop_vinfo, target_cost_data, si->count,
si->kind, si->stmt_info, si->vectype,
si->misalign, vect_body);
unsigned dummy, body_cost = 0;
finish_cost (target_cost_data, &dummy, &body_cost, &dummy);
destroy_cost_data (target_cost_data);
LOOP_VINFO_SINGLE_SCALAR_ITERATION_COST (loop_vinfo) = body_cost;
}
/* Function vect_analyze_loop_form_1.
Verify that certain CFG restrictions hold, including:
- the loop has a pre-header
- the loop has a single entry and exit
- the loop exit condition is simple enough
- the number of iterations can be analyzed, i.e, a countable loop. The
niter could be analyzed under some assumptions. */
opt_result
vect_analyze_loop_form_1 (class loop *loop, gcond **loop_cond,
tree *assumptions, tree *number_of_iterationsm1,
tree *number_of_iterations, gcond **inner_loop_cond)
{
DUMP_VECT_SCOPE ("vect_analyze_loop_form");
/* Different restrictions apply when we are considering an inner-most loop,
vs. an outer (nested) loop.
(FORNOW. May want to relax some of these restrictions in the future). */
if (!loop->inner)
{
/* Inner-most loop. We currently require that the number of BBs is
exactly 2 (the header and latch). Vectorizable inner-most loops
look like this:
(pre-header)
|
header <--------+
| | |
| +--> latch --+
|
(exit-bb) */
if (loop->num_nodes != 2)
return opt_result::failure_at (vect_location,
"not vectorized:"
" control flow in loop.\n");
if (empty_block_p (loop->header))
return opt_result::failure_at (vect_location,
"not vectorized: empty loop.\n");
}
else
{
class loop *innerloop = loop->inner;
edge entryedge;
/* Nested loop. We currently require that the loop is doubly-nested,
contains a single inner loop, and the number of BBs is exactly 5.
Vectorizable outer-loops look like this:
(pre-header)
|
header <---+
| |
inner-loop |
| |
tail ------+
|
(exit-bb)
The inner-loop has the properties expected of inner-most loops
as described above. */
if ((loop->inner)->inner || (loop->inner)->next)
return opt_result::failure_at (vect_location,
"not vectorized:"
" multiple nested loops.\n");
if (loop->num_nodes != 5)
return opt_result::failure_at (vect_location,
"not vectorized:"
" control flow in loop.\n");
entryedge = loop_preheader_edge (innerloop);
if (entryedge->src != loop->header
|| !single_exit (innerloop)
|| single_exit (innerloop)->dest != EDGE_PRED (loop->latch, 0)->src)
return opt_result::failure_at (vect_location,
"not vectorized:"
" unsupported outerloop form.\n");
/* Analyze the inner-loop. */
tree inner_niterm1, inner_niter, inner_assumptions;
opt_result res
= vect_analyze_loop_form_1 (loop->inner, inner_loop_cond,
&inner_assumptions, &inner_niterm1,
&inner_niter, NULL);
if (!res)
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"not vectorized: Bad inner loop.\n");
return res;
}
/* Don't support analyzing niter under assumptions for inner
loop. */
if (!integer_onep (inner_assumptions))
return opt_result::failure_at (vect_location,
"not vectorized: Bad inner loop.\n");
if (!expr_invariant_in_loop_p (loop, inner_niter))
return opt_result::failure_at (vect_location,
"not vectorized: inner-loop count not"
" invariant.\n");
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"Considering outer-loop vectorization.\n");
}
if (!single_exit (loop))
return opt_result::failure_at (vect_location,
"not vectorized: multiple exits.\n");
if (EDGE_COUNT (loop->header->preds) != 2)
return opt_result::failure_at (vect_location,
"not vectorized:"
" too many incoming edges.\n");
/* We assume that the loop exit condition is at the end of the loop. i.e,
that the loop is represented as a do-while (with a proper if-guard
before the loop if needed), where the loop header contains all the
executable statements, and the latch is empty. */
if (!empty_block_p (loop->latch)
|| !gimple_seq_empty_p (phi_nodes (loop->latch)))
return opt_result::failure_at (vect_location,
"not vectorized: latch block not empty.\n");
/* Make sure the exit is not abnormal. */
edge e = single_exit (loop);
if (e->flags & EDGE_ABNORMAL)
return opt_result::failure_at (vect_location,
"not vectorized:"
" abnormal loop exit edge.\n");
*loop_cond = vect_get_loop_niters (loop, assumptions, number_of_iterations,
number_of_iterationsm1);
if (!*loop_cond)
return opt_result::failure_at
(vect_location,
"not vectorized: complicated exit condition.\n");
if (integer_zerop (*assumptions)
|| !*number_of_iterations
|| chrec_contains_undetermined (*number_of_iterations))
return opt_result::failure_at
(*loop_cond,
"not vectorized: number of iterations cannot be computed.\n");
if (integer_zerop (*number_of_iterations))
return opt_result::failure_at
(*loop_cond,
"not vectorized: number of iterations = 0.\n");
return opt_result::success ();
}
/* Analyze LOOP form and return a loop_vec_info if it is of suitable form. */
opt_loop_vec_info
vect_analyze_loop_form (class loop *loop, vec_info_shared *shared)
{
tree assumptions, number_of_iterations, number_of_iterationsm1;
gcond *loop_cond, *inner_loop_cond = NULL;
opt_result res
= vect_analyze_loop_form_1 (loop, &loop_cond,
&assumptions, &number_of_iterationsm1,
&number_of_iterations, &inner_loop_cond);
if (!res)
return opt_loop_vec_info::propagate_failure (res);
loop_vec_info loop_vinfo = new _loop_vec_info (loop, shared);
LOOP_VINFO_NITERSM1 (loop_vinfo) = number_of_iterationsm1;
LOOP_VINFO_NITERS (loop_vinfo) = number_of_iterations;
LOOP_VINFO_NITERS_UNCHANGED (loop_vinfo) = number_of_iterations;
if (!integer_onep (assumptions))
{
/* We consider to vectorize this loop by versioning it under
some assumptions. In order to do this, we need to clear
existing information computed by scev and niter analyzer. */
scev_reset_htab ();
free_numbers_of_iterations_estimates (loop);
/* Also set flag for this loop so that following scev and niter
analysis are done under the assumptions. */
loop_constraint_set (loop, LOOP_C_FINITE);
/* Also record the assumptions for versioning. */
LOOP_VINFO_NITERS_ASSUMPTIONS (loop_vinfo) = assumptions;
}
if (!LOOP_VINFO_NITERS_KNOWN_P (loop_vinfo))
{
if (dump_enabled_p ())
{
dump_printf_loc (MSG_NOTE, vect_location,
"Symbolic number of iterations is ");
dump_generic_expr (MSG_NOTE, TDF_DETAILS, number_of_iterations);
dump_printf (MSG_NOTE, "\n");
}
}
stmt_vec_info loop_cond_info = loop_vinfo->lookup_stmt (loop_cond);
STMT_VINFO_TYPE (loop_cond_info) = loop_exit_ctrl_vec_info_type;
if (inner_loop_cond)
{
stmt_vec_info inner_loop_cond_info
= loop_vinfo->lookup_stmt (inner_loop_cond);
STMT_VINFO_TYPE (inner_loop_cond_info) = loop_exit_ctrl_vec_info_type;
}
gcc_assert (!loop->aux);
loop->aux = loop_vinfo;
return opt_loop_vec_info::success (loop_vinfo);
}
/* Scan the loop stmts and dependent on whether there are any (non-)SLP
statements update the vectorization factor. */
static void
vect_update_vf_for_slp (loop_vec_info loop_vinfo)
{
class loop *loop = LOOP_VINFO_LOOP (loop_vinfo);
basic_block *bbs = LOOP_VINFO_BBS (loop_vinfo);
int nbbs = loop->num_nodes;
poly_uint64 vectorization_factor;
int i;
DUMP_VECT_SCOPE ("vect_update_vf_for_slp");
vectorization_factor = LOOP_VINFO_VECT_FACTOR (loop_vinfo);
gcc_assert (known_ne (vectorization_factor, 0U));
/* If all the stmts in the loop can be SLPed, we perform only SLP, and
vectorization factor of the loop is the unrolling factor required by
the SLP instances. If that unrolling factor is 1, we say, that we
perform pure SLP on loop - cross iteration parallelism is not
exploited. */
bool only_slp_in_loop = true;
for (i = 0; i < nbbs; i++)
{
basic_block bb = bbs[i];
for (gphi_iterator si = gsi_start_phis (bb); !gsi_end_p (si);
gsi_next (&si))
{
stmt_vec_info stmt_info = loop_vinfo->lookup_stmt (si.phi ());
if (!stmt_info)
continue;
if ((STMT_VINFO_RELEVANT_P (stmt_info)
|| VECTORIZABLE_CYCLE_DEF (STMT_VINFO_DEF_TYPE (stmt_info)))
&& !PURE_SLP_STMT (stmt_info))
/* STMT needs both SLP and loop-based vectorization. */
only_slp_in_loop = false;
}
for (gimple_stmt_iterator si = gsi_start_bb (bb); !gsi_end_p (si);
gsi_next (&si))
{
if (is_gimple_debug (gsi_stmt (si)))
continue;
stmt_vec_info stmt_info = loop_vinfo->lookup_stmt (gsi_stmt (si));
stmt_info = vect_stmt_to_vectorize (stmt_info);
if ((STMT_VINFO_RELEVANT_P (stmt_info)
|| VECTORIZABLE_CYCLE_DEF (STMT_VINFO_DEF_TYPE (stmt_info)))
&& !PURE_SLP_STMT (stmt_info))
/* STMT needs both SLP and loop-based vectorization. */
only_slp_in_loop = false;
}
}
if (only_slp_in_loop)
{
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"Loop contains only SLP stmts\n");
vectorization_factor = LOOP_VINFO_SLP_UNROLLING_FACTOR (loop_vinfo);
}
else
{
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"Loop contains SLP and non-SLP stmts\n");
/* Both the vectorization factor and unroll factor have the form
GET_MODE_SIZE (loop_vinfo->vector_mode) * X for some rational X,
so they must have a common multiple. */
vectorization_factor
= force_common_multiple (vectorization_factor,
LOOP_VINFO_SLP_UNROLLING_FACTOR (loop_vinfo));
}
LOOP_VINFO_VECT_FACTOR (loop_vinfo) = vectorization_factor;
if (dump_enabled_p ())
{
dump_printf_loc (MSG_NOTE, vect_location,
"Updating vectorization factor to ");
dump_dec (MSG_NOTE, vectorization_factor);
dump_printf (MSG_NOTE, ".\n");
}
}
/* Return true if STMT_INFO describes a double reduction phi and if
the other phi in the reduction is also relevant for vectorization.
This rejects cases such as:
outer1:
x_1 = PHI <x_3(outer2), ...>;
...
inner:
x_2 = ...;
...
outer2:
x_3 = PHI <x_2(inner)>;
if nothing in x_2 or elsewhere makes x_1 relevant. */
static bool
vect_active_double_reduction_p (stmt_vec_info stmt_info)
{
if (STMT_VINFO_DEF_TYPE (stmt_info) != vect_double_reduction_def)
return false;
return STMT_VINFO_RELEVANT_P (STMT_VINFO_REDUC_DEF (stmt_info));
}
/* Function vect_analyze_loop_operations.
Scan the loop stmts and make sure they are all vectorizable. */
static opt_result
vect_analyze_loop_operations (loop_vec_info loop_vinfo)
{
class loop *loop = LOOP_VINFO_LOOP (loop_vinfo);
basic_block *bbs = LOOP_VINFO_BBS (loop_vinfo);
int nbbs = loop->num_nodes;
int i;
stmt_vec_info stmt_info;
bool need_to_vectorize = false;
bool ok;
DUMP_VECT_SCOPE ("vect_analyze_loop_operations");
auto_vec<stmt_info_for_cost> cost_vec;
for (i = 0; i < nbbs; i++)
{
basic_block bb = bbs[i];
for (gphi_iterator si = gsi_start_phis (bb); !gsi_end_p (si);
gsi_next (&si))
{
gphi *phi = si.phi ();
ok = true;
stmt_info = loop_vinfo->lookup_stmt (phi);
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location, "examining phi: %G", phi);
if (virtual_operand_p (gimple_phi_result (phi)))
continue;
/* Inner-loop loop-closed exit phi in outer-loop vectorization
(i.e., a phi in the tail of the outer-loop). */
if (! is_loop_header_bb_p (bb))
{
/* FORNOW: we currently don't support the case that these phis
are not used in the outerloop (unless it is double reduction,
i.e., this phi is vect_reduction_def), cause this case
requires to actually do something here. */
if (STMT_VINFO_LIVE_P (stmt_info)
&& !vect_active_double_reduction_p (stmt_info))
return opt_result::failure_at (phi,
"Unsupported loop-closed phi"
" in outer-loop.\n");
/* If PHI is used in the outer loop, we check that its operand
is defined in the inner loop. */
if (STMT_VINFO_RELEVANT_P (stmt_info))
{
tree phi_op;
if (gimple_phi_num_args (phi) != 1)
return opt_result::failure_at (phi, "unsupported phi");
phi_op = PHI_ARG_DEF (phi, 0);
stmt_vec_info op_def_info = loop_vinfo->lookup_def (phi_op);
if (!op_def_info)
return opt_result::failure_at (phi, "unsupported phi\n");
if (STMT_VINFO_RELEVANT (op_def_info) != vect_used_in_outer
&& (STMT_VINFO_RELEVANT (op_def_info)
!= vect_used_in_outer_by_reduction))
return opt_result::failure_at (phi, "unsupported phi\n");
if ((STMT_VINFO_DEF_TYPE (stmt_info) == vect_internal_def
|| (STMT_VINFO_DEF_TYPE (stmt_info)
== vect_double_reduction_def))
&& !vectorizable_lc_phi (loop_vinfo,
stmt_info, NULL, NULL))
return opt_result::failure_at (phi, "unsupported phi\n");
}
continue;
}
gcc_assert (stmt_info);
if ((STMT_VINFO_RELEVANT (stmt_info) == vect_used_in_scope
|| STMT_VINFO_LIVE_P (stmt_info))
&& STMT_VINFO_DEF_TYPE (stmt_info) != vect_induction_def)
/* A scalar-dependence cycle that we don't support. */
return opt_result::failure_at (phi,
"not vectorized:"
" scalar dependence cycle.\n");
if (STMT_VINFO_RELEVANT_P (stmt_info))
{
need_to_vectorize = true;
if (STMT_VINFO_DEF_TYPE (stmt_info) == vect_induction_def
&& ! PURE_SLP_STMT (stmt_info))
ok = vectorizable_induction (loop_vinfo,
stmt_info, NULL, NULL,
&cost_vec);
else if ((STMT_VINFO_DEF_TYPE (stmt_info) == vect_reduction_def
|| (STMT_VINFO_DEF_TYPE (stmt_info)
== vect_double_reduction_def)
|| STMT_VINFO_DEF_TYPE (stmt_info) == vect_nested_cycle)
&& ! PURE_SLP_STMT (stmt_info))
ok = vectorizable_reduction (loop_vinfo,
stmt_info, NULL, NULL, &cost_vec);
}
/* SLP PHIs are tested by vect_slp_analyze_node_operations. */
if (ok
&& STMT_VINFO_LIVE_P (stmt_info)
&& !PURE_SLP_STMT (stmt_info))
ok = vectorizable_live_operation (loop_vinfo,
stmt_info, NULL, NULL, NULL,
-1, false, &cost_vec);
if (!ok)
return opt_result::failure_at (phi,
"not vectorized: relevant phi not "
"supported: %G",
static_cast <gimple *> (phi));
}
for (gimple_stmt_iterator si = gsi_start_bb (bb); !gsi_end_p (si);
gsi_next (&si))
{
gimple *stmt = gsi_stmt (si);
if (!gimple_clobber_p (stmt)
&& !is_gimple_debug (stmt))
{
opt_result res
= vect_analyze_stmt (loop_vinfo,
loop_vinfo->lookup_stmt (stmt),
&need_to_vectorize,
NULL, NULL, &cost_vec);
if (!res)
return res;
}
}
} /* bbs */
add_stmt_costs (loop_vinfo, loop_vinfo->target_cost_data, &cost_vec);
/* All operations in the loop are either irrelevant (deal with loop
control, or dead), or only used outside the loop and can be moved
out of the loop (e.g. invariants, inductions). The loop can be
optimized away by scalar optimizations. We're better off not
touching this loop. */
if (!need_to_vectorize)
{
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"All the computation can be taken out of the loop.\n");
return opt_result::failure_at
(vect_location,
"not vectorized: redundant loop. no profit to vectorize.\n");
}
return opt_result::success ();
}
/* Return true if we know that the iteration count is smaller than the
vectorization factor. Return false if it isn't, or if we can't be sure
either way. */
static bool
vect_known_niters_smaller_than_vf (loop_vec_info loop_vinfo)
{
unsigned int assumed_vf = vect_vf_for_cost (loop_vinfo);
HOST_WIDE_INT max_niter;
if (LOOP_VINFO_NITERS_KNOWN_P (loop_vinfo))
max_niter = LOOP_VINFO_INT_NITERS (loop_vinfo);
else
max_niter = max_stmt_executions_int (LOOP_VINFO_LOOP (loop_vinfo));
if (max_niter != -1 && (unsigned HOST_WIDE_INT) max_niter < assumed_vf)
return true;
return false;
}
/* Analyze the cost of the loop described by LOOP_VINFO. Decide if it
is worthwhile to vectorize. Return 1 if definitely yes, 0 if
definitely no, or -1 if it's worth retrying. */
static int
vect_analyze_loop_costing (loop_vec_info loop_vinfo)
{
class loop *loop = LOOP_VINFO_LOOP (loop_vinfo);
unsigned int assumed_vf = vect_vf_for_cost (loop_vinfo);
/* Only loops that can handle partially-populated vectors can have iteration
counts less than the vectorization factor. */
if (!LOOP_VINFO_USING_PARTIAL_VECTORS_P (loop_vinfo))
{
if (vect_known_niters_smaller_than_vf (loop_vinfo))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"not vectorized: iteration count smaller than "
"vectorization factor.\n");
return 0;
}
}
/* If using the "very cheap" model. reject cases in which we'd keep
a copy of the scalar code (even if we might be able to vectorize it). */
if (flag_vect_cost_model == VECT_COST_MODEL_VERY_CHEAP
&& (LOOP_VINFO_PEELING_FOR_ALIGNMENT (loop_vinfo)
|| LOOP_VINFO_PEELING_FOR_GAPS (loop_vinfo)
|| LOOP_VINFO_PEELING_FOR_NITER (loop_vinfo)))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"some scalar iterations would need to be peeled\n");
return 0;
}
int min_profitable_iters, min_profitable_estimate;
vect_estimate_min_profitable_iters (loop_vinfo, &min_profitable_iters,
&min_profitable_estimate);
if (min_profitable_iters < 0)
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"not vectorized: vectorization not profitable.\n");
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"not vectorized: vector version will never be "
"profitable.\n");
return -1;
}
int min_scalar_loop_bound = (param_min_vect_loop_bound
* assumed_vf);
/* Use the cost model only if it is more conservative than user specified
threshold. */
unsigned int th = (unsigned) MAX (min_scalar_loop_bound,
min_profitable_iters);
LOOP_VINFO_COST_MODEL_THRESHOLD (loop_vinfo) = th;
if (LOOP_VINFO_NITERS_KNOWN_P (loop_vinfo)
&& LOOP_VINFO_INT_NITERS (loop_vinfo) < th)
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"not vectorized: vectorization not profitable.\n");
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"not vectorized: iteration count smaller than user "
"specified loop bound parameter or minimum profitable "
"iterations (whichever is more conservative).\n");
return 0;
}
/* The static profitablity threshold min_profitable_estimate includes
the cost of having to check at runtime whether the scalar loop
should be used instead. If it turns out that we don't need or want
such a check, the threshold we should use for the static estimate
is simply the point at which the vector loop becomes more profitable
than the scalar loop. */
if (min_profitable_estimate > min_profitable_iters
&& !LOOP_REQUIRES_VERSIONING (loop_vinfo)
&& !LOOP_VINFO_PEELING_FOR_NITER (loop_vinfo)
&& !LOOP_VINFO_PEELING_FOR_ALIGNMENT (loop_vinfo)
&& !vect_apply_runtime_profitability_check_p (loop_vinfo))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location, "no need for a runtime"
" choice between the scalar and vector loops\n");
min_profitable_estimate = min_profitable_iters;
}
/* If the vector loop needs multiple iterations to be beneficial then
things are probably too close to call, and the conservative thing
would be to stick with the scalar code. */
if (flag_vect_cost_model == VECT_COST_MODEL_VERY_CHEAP
&& min_profitable_estimate > (int) vect_vf_for_cost (loop_vinfo))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"one iteration of the vector loop would be"
" more expensive than the equivalent number of"
" iterations of the scalar loop\n");
return 0;
}
HOST_WIDE_INT estimated_niter;
/* If we are vectorizing an epilogue then we know the maximum number of
scalar iterations it will cover is at least one lower than the
vectorization factor of the main loop. */
if (LOOP_VINFO_EPILOGUE_P (loop_vinfo))
estimated_niter
= vect_vf_for_cost (LOOP_VINFO_ORIG_LOOP_INFO (loop_vinfo)) - 1;
else
{
estimated_niter = estimated_stmt_executions_int (loop);
if (estimated_niter == -1)
estimated_niter = likely_max_stmt_executions_int (loop);
}
if (estimated_niter != -1
&& ((unsigned HOST_WIDE_INT) estimated_niter
< MAX (th, (unsigned) min_profitable_estimate)))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"not vectorized: estimated iteration count too "
"small.\n");
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"not vectorized: estimated iteration count smaller "
"than specified loop bound parameter or minimum "
"profitable iterations (whichever is more "
"conservative).\n");
return -1;
}
return 1;
}
static opt_result
vect_get_datarefs_in_loop (loop_p loop, basic_block *bbs,
vec<data_reference_p> *datarefs,
unsigned int *n_stmts)
{
*n_stmts = 0;
for (unsigned i = 0; i < loop->num_nodes; i++)
for (gimple_stmt_iterator gsi = gsi_start_bb (bbs[i]);
!gsi_end_p (gsi); gsi_next (&gsi))
{
gimple *stmt = gsi_stmt (gsi);
if (is_gimple_debug (stmt))
continue;
++(*n_stmts);
opt_result res = vect_find_stmt_data_reference (loop, stmt, datarefs,
NULL, 0);
if (!res)
{
if (is_gimple_call (stmt) && loop->safelen)
{
tree fndecl = gimple_call_fndecl (stmt), op;
if (fndecl != NULL_TREE)
{
cgraph_node *node = cgraph_node::get (fndecl);
if (node != NULL && node->simd_clones != NULL)
{
unsigned int j, n = gimple_call_num_args (stmt);
for (j = 0; j < n; j++)
{
op = gimple_call_arg (stmt, j);
if (DECL_P (op)
|| (REFERENCE_CLASS_P (op)
&& get_base_address (op)))
break;
}
op = gimple_call_lhs (stmt);
/* Ignore #pragma omp declare simd functions
if they don't have data references in the
call stmt itself. */
if (j == n
&& !(op
&& (DECL_P (op)
|| (REFERENCE_CLASS_P (op)
&& get_base_address (op)))))
continue;
}
}
}
return res;
}
/* If dependence analysis will give up due to the limit on the
number of datarefs stop here and fail fatally. */
if (datarefs->length ()
> (unsigned)param_loop_max_datarefs_for_datadeps)
return opt_result::failure_at (stmt, "exceeded param "
"loop-max-datarefs-for-datadeps\n");
}
return opt_result::success ();
}
/* Look for SLP-only access groups and turn each individual access into its own
group. */
static void
vect_dissolve_slp_only_groups (loop_vec_info loop_vinfo)
{
unsigned int i;
struct data_reference *dr;
DUMP_VECT_SCOPE ("vect_dissolve_slp_only_groups");
vec<data_reference_p> datarefs = LOOP_VINFO_DATAREFS (loop_vinfo);
FOR_EACH_VEC_ELT (datarefs, i, dr)
{
gcc_assert (DR_REF (dr));
stmt_vec_info stmt_info = loop_vinfo->lookup_stmt (DR_STMT (dr));
/* Check if the load is a part of an interleaving chain. */
if (STMT_VINFO_GROUPED_ACCESS (stmt_info))
{
stmt_vec_info first_element = DR_GROUP_FIRST_ELEMENT (stmt_info);
unsigned int group_size = DR_GROUP_SIZE (first_element);
/* Check if SLP-only groups. */
if (!STMT_SLP_TYPE (stmt_info)
&& STMT_VINFO_SLP_VECT_ONLY (first_element))
{
/* Dissolve the group. */
STMT_VINFO_SLP_VECT_ONLY (first_element) = false;
stmt_vec_info vinfo = first_element;
while (vinfo)
{
stmt_vec_info next = DR_GROUP_NEXT_ELEMENT (vinfo);
DR_GROUP_FIRST_ELEMENT (vinfo) = vinfo;
DR_GROUP_NEXT_ELEMENT (vinfo) = NULL;
DR_GROUP_SIZE (vinfo) = 1;
if (STMT_VINFO_STRIDED_P (first_element))
DR_GROUP_GAP (vinfo) = 0;
else
DR_GROUP_GAP (vinfo) = group_size - 1;
vinfo = next;
}
}
}
}
}
/* Determine if operating on full vectors for LOOP_VINFO might leave
some scalar iterations still to do. If so, decide how we should
handle those scalar iterations. The possibilities are:
(1) Make LOOP_VINFO operate on partial vectors instead of full vectors.
In this case:
LOOP_VINFO_USING_PARTIAL_VECTORS_P == true
LOOP_VINFO_EPIL_USING_PARTIAL_VECTORS_P == false
LOOP_VINFO_PEELING_FOR_NITER == false
(2) Make LOOP_VINFO operate on full vectors and use an epilogue loop
to handle the remaining scalar iterations. In this case:
LOOP_VINFO_USING_PARTIAL_VECTORS_P == false
LOOP_VINFO_PEELING_FOR_NITER == true
There are two choices:
(2a) Consider vectorizing the epilogue loop at the same VF as the
main loop, but using partial vectors instead of full vectors.
In this case:
LOOP_VINFO_EPIL_USING_PARTIAL_VECTORS_P == true
(2b) Consider vectorizing the epilogue loop at lower VFs only.
In this case:
LOOP_VINFO_EPIL_USING_PARTIAL_VECTORS_P == false
When FOR_EPILOGUE_P is true, make this determination based on the
assumption that LOOP_VINFO is an epilogue loop, otherwise make it
based on the assumption that LOOP_VINFO is the main loop. The caller
has made sure that the number of iterations is set appropriately for
this value of FOR_EPILOGUE_P. */
opt_result
vect_determine_partial_vectors_and_peeling (loop_vec_info loop_vinfo,
bool for_epilogue_p)
{
/* Determine whether there would be any scalar iterations left over. */
bool need_peeling_or_partial_vectors_p
= vect_need_peeling_or_partial_vectors_p (loop_vinfo);
/* Decide whether to vectorize the loop with partial vectors. */
LOOP_VINFO_USING_PARTIAL_VECTORS_P (loop_vinfo) = false;
LOOP_VINFO_EPIL_USING_PARTIAL_VECTORS_P (loop_vinfo) = false;
if (LOOP_VINFO_CAN_USE_PARTIAL_VECTORS_P (loop_vinfo)
&& need_peeling_or_partial_vectors_p)
{
/* For partial-vector-usage=1, try to push the handling of partial
vectors to the epilogue, with the main loop continuing to operate
on full vectors.
??? We could then end up failing to use partial vectors if we
decide to peel iterations into a prologue, and if the main loop
then ends up processing fewer than VF iterations. */
if (param_vect_partial_vector_usage == 1
&& !LOOP_VINFO_EPILOGUE_P (loop_vinfo)
&& !vect_known_niters_smaller_than_vf (loop_vinfo))
LOOP_VINFO_EPIL_USING_PARTIAL_VECTORS_P (loop_vinfo) = true;
else
LOOP_VINFO_USING_PARTIAL_VECTORS_P (loop_vinfo) = true;
}
if (dump_enabled_p ())
{
if (LOOP_VINFO_USING_PARTIAL_VECTORS_P (loop_vinfo))
dump_printf_loc (MSG_NOTE, vect_location,
"operating on partial vectors%s.\n",
for_epilogue_p ? " for epilogue loop" : "");
else
dump_printf_loc (MSG_NOTE, vect_location,
"operating only on full vectors%s.\n",
for_epilogue_p ? " for epilogue loop" : "");
}
if (for_epilogue_p)
{
loop_vec_info orig_loop_vinfo = LOOP_VINFO_ORIG_LOOP_INFO (loop_vinfo);
gcc_assert (orig_loop_vinfo);
if (!LOOP_VINFO_USING_PARTIAL_VECTORS_P (loop_vinfo))
gcc_assert (known_lt (LOOP_VINFO_VECT_FACTOR (loop_vinfo),
LOOP_VINFO_VECT_FACTOR (orig_loop_vinfo)));
}
if (LOOP_VINFO_NITERS_KNOWN_P (loop_vinfo)
&& !LOOP_VINFO_USING_PARTIAL_VECTORS_P (loop_vinfo))
{
/* Check that the loop processes at least one full vector. */
poly_uint64 vf = LOOP_VINFO_VECT_FACTOR (loop_vinfo);
tree scalar_niters = LOOP_VINFO_NITERS (loop_vinfo);
if (known_lt (wi::to_widest (scalar_niters), vf))
return opt_result::failure_at (vect_location,
"loop does not have enough iterations"
" to support vectorization.\n");
/* If we need to peel an extra epilogue iteration to handle data
accesses with gaps, check that there are enough scalar iterations
available.
The check above is redundant with this one when peeling for gaps,
but the distinction is useful for diagnostics. */
tree scalar_nitersm1 = LOOP_VINFO_NITERSM1 (loop_vinfo);
if (LOOP_VINFO_PEELING_FOR_GAPS (loop_vinfo)
&& known_lt (wi::to_widest (scalar_nitersm1), vf))
return opt_result::failure_at (vect_location,
"loop does not have enough iterations"
" to support peeling for gaps.\n");
}
LOOP_VINFO_PEELING_FOR_NITER (loop_vinfo)
= (!LOOP_VINFO_USING_PARTIAL_VECTORS_P (loop_vinfo)
&& need_peeling_or_partial_vectors_p);
return opt_result::success ();
}
/* Function vect_analyze_loop_2.
Apply a set of analyses on LOOP, and create a loop_vec_info struct
for it. The different analyses will record information in the
loop_vec_info struct. */
static opt_result
vect_analyze_loop_2 (loop_vec_info loop_vinfo, bool &fatal, unsigned *n_stmts)
{
opt_result ok = opt_result::success ();
int res;
unsigned int max_vf = MAX_VECTORIZATION_FACTOR;
poly_uint64 min_vf = 2;
loop_vec_info orig_loop_vinfo = NULL;
/* If we are dealing with an epilogue then orig_loop_vinfo points to the
loop_vec_info of the first vectorized loop. */
if (LOOP_VINFO_EPILOGUE_P (loop_vinfo))
orig_loop_vinfo = LOOP_VINFO_ORIG_LOOP_INFO (loop_vinfo);
else
orig_loop_vinfo = loop_vinfo;
gcc_assert (orig_loop_vinfo);
/* The first group of checks is independent of the vector size. */
fatal = true;
if (LOOP_VINFO_SIMD_IF_COND (loop_vinfo)
&& integer_zerop (LOOP_VINFO_SIMD_IF_COND (loop_vinfo)))
return opt_result::failure_at (vect_location,
"not vectorized: simd if(0)\n");
/* Find all data references in the loop (which correspond to vdefs/vuses)
and analyze their evolution in the loop. */
loop_p loop = LOOP_VINFO_LOOP (loop_vinfo);
/* Gather the data references and count stmts in the loop. */
if (!LOOP_VINFO_DATAREFS (loop_vinfo).exists ())
{
opt_result res
= vect_get_datarefs_in_loop (loop, LOOP_VINFO_BBS (loop_vinfo),
&LOOP_VINFO_DATAREFS (loop_vinfo),
n_stmts);
if (!res)
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"not vectorized: loop contains function "
"calls or data references that cannot "
"be analyzed\n");
return res;
}
loop_vinfo->shared->save_datarefs ();
}
else
loop_vinfo->shared->check_datarefs ();
/* Analyze the data references and also adjust the minimal
vectorization factor according to the loads and stores. */
ok = vect_analyze_data_refs (loop_vinfo, &min_vf, &fatal);
if (!ok)
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"bad data references.\n");
return ok;
}
/* Classify all cross-iteration scalar data-flow cycles.
Cross-iteration cycles caused by virtual phis are analyzed separately. */
vect_analyze_scalar_cycles (loop_vinfo);
vect_pattern_recog (loop_vinfo);
vect_fixup_scalar_cycles_with_patterns (loop_vinfo);
/* Analyze the access patterns of the data-refs in the loop (consecutive,
complex, etc.). FORNOW: Only handle consecutive access pattern. */
ok = vect_analyze_data_ref_accesses (loop_vinfo, NULL);
if (!ok)
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"bad data access.\n");
return ok;
}
/* Data-flow analysis to detect stmts that do not need to be vectorized. */
ok = vect_mark_stmts_to_be_vectorized (loop_vinfo, &fatal);
if (!ok)
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"unexpected pattern.\n");
return ok;
}
/* While the rest of the analysis below depends on it in some way. */
fatal = false;
/* Analyze data dependences between the data-refs in the loop
and adjust the maximum vectorization factor according to
the dependences.
FORNOW: fail at the first data dependence that we encounter. */
ok = vect_analyze_data_ref_dependences (loop_vinfo, &max_vf);
if (!ok)
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"bad data dependence.\n");
return ok;
}
if (max_vf != MAX_VECTORIZATION_FACTOR
&& maybe_lt (max_vf, min_vf))
return opt_result::failure_at (vect_location, "bad data dependence.\n");
LOOP_VINFO_MAX_VECT_FACTOR (loop_vinfo) = max_vf;
ok = vect_determine_vectorization_factor (loop_vinfo);
if (!ok)
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"can't determine vectorization factor.\n");
return ok;
}
if (max_vf != MAX_VECTORIZATION_FACTOR
&& maybe_lt (max_vf, LOOP_VINFO_VECT_FACTOR (loop_vinfo)))
return opt_result::failure_at (vect_location, "bad data dependence.\n");
/* Compute the scalar iteration cost. */
vect_compute_single_scalar_iteration_cost (loop_vinfo);
poly_uint64 saved_vectorization_factor = LOOP_VINFO_VECT_FACTOR (loop_vinfo);
/* Check the SLP opportunities in the loop, analyze and build SLP trees. */
ok = vect_analyze_slp (loop_vinfo, *n_stmts);
if (!ok)
return ok;
/* If there are any SLP instances mark them as pure_slp. */
bool slp = vect_make_slp_decision (loop_vinfo);
if (slp)
{
/* Find stmts that need to be both vectorized and SLPed. */
vect_detect_hybrid_slp (loop_vinfo);
/* Update the vectorization factor based on the SLP decision. */
vect_update_vf_for_slp (loop_vinfo);
/* Optimize the SLP graph with the vectorization factor fixed. */
vect_optimize_slp (loop_vinfo);
/* Gather the loads reachable from the SLP graph entries. */
vect_gather_slp_loads (loop_vinfo);
}
bool saved_can_use_partial_vectors_p
= LOOP_VINFO_CAN_USE_PARTIAL_VECTORS_P (loop_vinfo);
/* We don't expect to have to roll back to anything other than an empty
set of rgroups. */
gcc_assert (LOOP_VINFO_MASKS (loop_vinfo).is_empty ());
/* This is the point where we can re-start analysis with SLP forced off. */
start_over:
/* Now the vectorization factor is final. */
poly_uint64 vectorization_factor = LOOP_VINFO_VECT_FACTOR (loop_vinfo);
gcc_assert (known_ne (vectorization_factor, 0U));
if (LOOP_VINFO_NITERS_KNOWN_P (loop_vinfo) && dump_enabled_p ())
{
dump_printf_loc (MSG_NOTE, vect_location,
"vectorization_factor = ");
dump_dec (MSG_NOTE, vectorization_factor);
dump_printf (MSG_NOTE, ", niters = %wd\n",
LOOP_VINFO_INT_NITERS (loop_vinfo));
}
/* Analyze the alignment of the data-refs in the loop.
Fail if a data reference is found that cannot be vectorized. */
ok = vect_analyze_data_refs_alignment (loop_vinfo);
if (!ok)
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"bad data alignment.\n");
return ok;
}
/* Prune the list of ddrs to be tested at run-time by versioning for alias.
It is important to call pruning after vect_analyze_data_ref_accesses,
since we use grouping information gathered by interleaving analysis. */
ok = vect_prune_runtime_alias_test_list (loop_vinfo);
if (!ok)
return ok;
/* Do not invoke vect_enhance_data_refs_alignment for epilogue
vectorization, since we do not want to add extra peeling or
add versioning for alignment. */
if (!LOOP_VINFO_EPILOGUE_P (loop_vinfo))
/* This pass will decide on using loop versioning and/or loop peeling in
order to enhance the alignment of data references in the loop. */
ok = vect_enhance_data_refs_alignment (loop_vinfo);
if (!ok)
return ok;
if (slp)
{
/* Analyze operations in the SLP instances. Note this may
remove unsupported SLP instances which makes the above
SLP kind detection invalid. */
unsigned old_size = LOOP_VINFO_SLP_INSTANCES (loop_vinfo).length ();
vect_slp_analyze_operations (loop_vinfo);
if (LOOP_VINFO_SLP_INSTANCES (loop_vinfo).length () != old_size)
{
ok = opt_result::failure_at (vect_location,
"unsupported SLP instances\n");
goto again;
}
/* Check whether any load in ALL SLP instances is possibly permuted. */
slp_tree load_node, slp_root;
unsigned i, x;
slp_instance instance;
bool can_use_lanes = true;
FOR_EACH_VEC_ELT (LOOP_VINFO_SLP_INSTANCES (loop_vinfo), x, instance)
{
slp_root = SLP_INSTANCE_TREE (instance);
int group_size = SLP_TREE_LANES (slp_root);
tree vectype = SLP_TREE_VECTYPE (slp_root);
bool loads_permuted = false;
FOR_EACH_VEC_ELT (SLP_INSTANCE_LOADS (instance), i, load_node)
{
if (!SLP_TREE_LOAD_PERMUTATION (load_node).exists ())
continue;
unsigned j;
stmt_vec_info load_info;
FOR_EACH_VEC_ELT (SLP_TREE_SCALAR_STMTS (load_node), j, load_info)
if (SLP_TREE_LOAD_PERMUTATION (load_node)[j] != j)
{
loads_permuted = true;
break;
}
}
/* If the loads and stores can be handled with load/store-lane
instructions record it and move on to the next instance. */
if (loads_permuted
&& SLP_INSTANCE_KIND (instance) == slp_inst_kind_store
&& vect_store_lanes_supported (vectype, group_size, false))
{
FOR_EACH_VEC_ELT (SLP_INSTANCE_LOADS (instance), i, load_node)
{
stmt_vec_info stmt_vinfo = DR_GROUP_FIRST_ELEMENT
(SLP_TREE_SCALAR_STMTS (load_node)[0]);
/* Use SLP for strided accesses (or if we can't
load-lanes). */
if (STMT_VINFO_STRIDED_P (stmt_vinfo)
|| ! vect_load_lanes_supported
(STMT_VINFO_VECTYPE (stmt_vinfo),
DR_GROUP_SIZE (stmt_vinfo), false))
break;
}
can_use_lanes
= can_use_lanes && i == SLP_INSTANCE_LOADS (instance).length ();
if (can_use_lanes && dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"SLP instance %p can use load/store-lanes\n",
instance);
}
else
{
can_use_lanes = false;
break;
}
}
/* If all SLP instances can use load/store-lanes abort SLP and try again
with SLP disabled. */
if (can_use_lanes)
{
ok = opt_result::failure_at (vect_location,
"Built SLP cancelled: can use "
"load/store-lanes\n");
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"Built SLP cancelled: all SLP instances support "
"load/store-lanes\n");
goto again;
}
}
/* Dissolve SLP-only groups. */
vect_dissolve_slp_only_groups (loop_vinfo);
/* Scan all the remaining operations in the loop that are not subject
to SLP and make sure they are vectorizable. */
ok = vect_analyze_loop_operations (loop_vinfo);
if (!ok)
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"bad operation or unsupported loop bound.\n");
return ok;
}
/* For now, we don't expect to mix both masking and length approaches for one
loop, disable it if both are recorded. */
if (LOOP_VINFO_CAN_USE_PARTIAL_VECTORS_P (loop_vinfo)
&& !LOOP_VINFO_MASKS (loop_vinfo).is_empty ()
&& !LOOP_VINFO_LENS (loop_vinfo).is_empty ())
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"can't vectorize a loop with partial vectors"
" because we don't expect to mix different"
" approaches with partial vectors for the"
" same loop.\n");
LOOP_VINFO_CAN_USE_PARTIAL_VECTORS_P (loop_vinfo) = false;
}
/* If we still have the option of using partial vectors,
check whether we can generate the necessary loop controls. */
if (LOOP_VINFO_CAN_USE_PARTIAL_VECTORS_P (loop_vinfo)
&& !vect_verify_full_masking (loop_vinfo)
&& !vect_verify_loop_lens (loop_vinfo))
LOOP_VINFO_CAN_USE_PARTIAL_VECTORS_P (loop_vinfo) = false;
/* If we're vectorizing an epilogue loop, the vectorized loop either needs
to be able to handle fewer than VF scalars, or needs to have a lower VF
than the main loop. */
if (LOOP_VINFO_EPILOGUE_P (loop_vinfo)
&& !LOOP_VINFO_CAN_USE_PARTIAL_VECTORS_P (loop_vinfo)
&& maybe_ge (LOOP_VINFO_VECT_FACTOR (loop_vinfo),
LOOP_VINFO_VECT_FACTOR (orig_loop_vinfo)))
return opt_result::failure_at (vect_location,
"Vectorization factor too high for"
" epilogue loop.\n");
/* Decide whether this loop_vinfo should use partial vectors or peeling,
assuming that the loop will be used as a main loop. We will redo
this analysis later if we instead decide to use the loop as an
epilogue loop. */
ok = vect_determine_partial_vectors_and_peeling (loop_vinfo, false);
if (!ok)
return ok;
/* Check the costings of the loop make vectorizing worthwhile. */
res = vect_analyze_loop_costing (loop_vinfo);
if (res < 0)
{
ok = opt_result::failure_at (vect_location,
"Loop costings may not be worthwhile.\n");
goto again;
}
if (!res)
return opt_result::failure_at (vect_location,
"Loop costings not worthwhile.\n");
/* If an epilogue loop is required make sure we can create one. */
if (LOOP_VINFO_PEELING_FOR_GAPS (loop_vinfo)
|| LOOP_VINFO_PEELING_FOR_NITER (loop_vinfo))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location, "epilog loop required\n");
if (!vect_can_advance_ivs_p (loop_vinfo)
|| !slpeel_can_duplicate_loop_p (LOOP_VINFO_LOOP (loop_vinfo),
single_exit (LOOP_VINFO_LOOP
(loop_vinfo))))
{
ok = opt_result::failure_at (vect_location,
"not vectorized: can't create required "
"epilog loop\n");
goto again;
}
}
/* During peeling, we need to check if number of loop iterations is
enough for both peeled prolog loop and vector loop. This check
can be merged along with threshold check of loop versioning, so
increase threshold for this case if necessary.
If we are analyzing an epilogue we still want to check what its
versioning threshold would be. If we decide to vectorize the epilogues we
will want to use the lowest versioning threshold of all epilogues and main
loop. This will enable us to enter a vectorized epilogue even when
versioning the loop. We can't simply check whether the epilogue requires
versioning though since we may have skipped some versioning checks when
analyzing the epilogue. For instance, checks for alias versioning will be
skipped when dealing with epilogues as we assume we already checked them
for the main loop. So instead we always check the 'orig_loop_vinfo'. */
if (LOOP_REQUIRES_VERSIONING (orig_loop_vinfo))
{
poly_uint64 niters_th = 0;
unsigned int th = LOOP_VINFO_COST_MODEL_THRESHOLD (loop_vinfo);
if (!vect_use_loop_mask_for_alignment_p (loop_vinfo))
{
/* Niters for peeled prolog loop. */
if (LOOP_VINFO_PEELING_FOR_ALIGNMENT (loop_vinfo) < 0)
{
dr_vec_info *dr_info = LOOP_VINFO_UNALIGNED_DR (loop_vinfo);
tree vectype = STMT_VINFO_VECTYPE (dr_info->stmt);
niters_th += TYPE_VECTOR_SUBPARTS (vectype) - 1;
}
else
niters_th += LOOP_VINFO_PEELING_FOR_ALIGNMENT (loop_vinfo);
}
/* Niters for at least one iteration of vectorized loop. */
if (!LOOP_VINFO_USING_PARTIAL_VECTORS_P (loop_vinfo))
niters_th += LOOP_VINFO_VECT_FACTOR (loop_vinfo);
/* One additional iteration because of peeling for gap. */
if (LOOP_VINFO_PEELING_FOR_GAPS (loop_vinfo))
niters_th += 1;
/* Use the same condition as vect_transform_loop to decide when to use
the cost to determine a versioning threshold. */
if (vect_apply_runtime_profitability_check_p (loop_vinfo)
&& ordered_p (th, niters_th))
niters_th = ordered_max (poly_uint64 (th), niters_th);
LOOP_VINFO_VERSIONING_THRESHOLD (loop_vinfo) = niters_th;
}
gcc_assert (known_eq (vectorization_factor,
LOOP_VINFO_VECT_FACTOR (loop_vinfo)));
/* Ok to vectorize! */
return opt_result::success ();
again:
/* Ensure that "ok" is false (with an opt_problem if dumping is enabled). */
gcc_assert (!ok);
/* Try again with SLP forced off but if we didn't do any SLP there is
no point in re-trying. */
if (!slp)
return ok;
/* If there are reduction chains re-trying will fail anyway. */
if (! LOOP_VINFO_REDUCTION_CHAINS (loop_vinfo).is_empty ())
return ok;
/* Likewise if the grouped loads or stores in the SLP cannot be handled
via interleaving or lane instructions. */
slp_instance instance;
slp_tree node;
unsigned i, j;
FOR_EACH_VEC_ELT (LOOP_VINFO_SLP_INSTANCES (loop_vinfo), i, instance)
{
stmt_vec_info vinfo;
vinfo = SLP_TREE_SCALAR_STMTS (SLP_INSTANCE_TREE (instance))[0];
if (! STMT_VINFO_GROUPED_ACCESS (vinfo))
continue;
vinfo = DR_GROUP_FIRST_ELEMENT (vinfo);
unsigned int size = DR_GROUP_SIZE (vinfo);
tree vectype = STMT_VINFO_VECTYPE (vinfo);
if (! vect_store_lanes_supported (vectype, size, false)
&& ! known_eq (TYPE_VECTOR_SUBPARTS (vectype), 1U)
&& ! vect_grouped_store_supported (vectype, size))
return opt_result::failure_at (vinfo->stmt,
"unsupported grouped store\n");
FOR_EACH_VEC_ELT (SLP_INSTANCE_LOADS (instance), j, node)
{
vinfo = SLP_TREE_SCALAR_STMTS (node)[0];
vinfo = DR_GROUP_FIRST_ELEMENT (vinfo);
bool single_element_p = !DR_GROUP_NEXT_ELEMENT (vinfo);
size = DR_GROUP_SIZE (vinfo);
vectype = STMT_VINFO_VECTYPE (vinfo);
if (! vect_load_lanes_supported (vectype, size, false)
&& ! vect_grouped_load_supported (vectype, single_element_p,
size))
return opt_result::failure_at (vinfo->stmt,
"unsupported grouped load\n");
}
}
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"re-trying with SLP disabled\n");
/* Roll back state appropriately. No SLP this time. */
slp = false;
/* Restore vectorization factor as it were without SLP. */
LOOP_VINFO_VECT_FACTOR (loop_vinfo) = saved_vectorization_factor;
/* Free the SLP instances. */
FOR_EACH_VEC_ELT (LOOP_VINFO_SLP_INSTANCES (loop_vinfo), j, instance)
vect_free_slp_instance (instance);
LOOP_VINFO_SLP_INSTANCES (loop_vinfo).release ();
/* Reset SLP type to loop_vect on all stmts. */
for (i = 0; i < LOOP_VINFO_LOOP (loop_vinfo)->num_nodes; ++i)
{
basic_block bb = LOOP_VINFO_BBS (loop_vinfo)[i];
for (gimple_stmt_iterator si = gsi_start_phis (bb);
!gsi_end_p (si); gsi_next (&si))
{
stmt_vec_info stmt_info = loop_vinfo->lookup_stmt (gsi_stmt (si));
STMT_SLP_TYPE (stmt_info) = loop_vect;
if (STMT_VINFO_DEF_TYPE (stmt_info) == vect_reduction_def
|| STMT_VINFO_DEF_TYPE (stmt_info) == vect_double_reduction_def)
{
/* vectorizable_reduction adjusts reduction stmt def-types,
restore them to that of the PHI. */
STMT_VINFO_DEF_TYPE (STMT_VINFO_REDUC_DEF (stmt_info))
= STMT_VINFO_DEF_TYPE (stmt_info);
STMT_VINFO_DEF_TYPE (vect_stmt_to_vectorize
(STMT_VINFO_REDUC_DEF (stmt_info)))
= STMT_VINFO_DEF_TYPE (stmt_info);
}
}
for (gimple_stmt_iterator si = gsi_start_bb (bb);
!gsi_end_p (si); gsi_next (&si))
{
if (is_gimple_debug (gsi_stmt (si)))
continue;
stmt_vec_info stmt_info = loop_vinfo->lookup_stmt (gsi_stmt (si));
STMT_SLP_TYPE (stmt_info) = loop_vect;
if (STMT_VINFO_IN_PATTERN_P (stmt_info))
{
stmt_vec_info pattern_stmt_info
= STMT_VINFO_RELATED_STMT (stmt_info);
if (STMT_VINFO_SLP_VECT_ONLY_PATTERN (pattern_stmt_info))
STMT_VINFO_IN_PATTERN_P (stmt_info) = false;
gimple *pattern_def_seq = STMT_VINFO_PATTERN_DEF_SEQ (stmt_info);
STMT_SLP_TYPE (pattern_stmt_info) = loop_vect;
for (gimple_stmt_iterator pi = gsi_start (pattern_def_seq);
!gsi_end_p (pi); gsi_next (&pi))
STMT_SLP_TYPE (loop_vinfo->lookup_stmt (gsi_stmt (pi)))
= loop_vect;
}
}
}
/* Free optimized alias test DDRS. */
LOOP_VINFO_LOWER_BOUNDS (loop_vinfo).truncate (0);
LOOP_VINFO_COMP_ALIAS_DDRS (loop_vinfo).release ();
LOOP_VINFO_CHECK_UNEQUAL_ADDRS (loop_vinfo).release ();
/* Reset target cost data. */
destroy_cost_data (LOOP_VINFO_TARGET_COST_DATA (loop_vinfo));
LOOP_VINFO_TARGET_COST_DATA (loop_vinfo)
= init_cost (LOOP_VINFO_LOOP (loop_vinfo));
/* Reset accumulated rgroup information. */
release_vec_loop_controls (&LOOP_VINFO_MASKS (loop_vinfo));
release_vec_loop_controls (&LOOP_VINFO_LENS (loop_vinfo));
/* Reset assorted flags. */
LOOP_VINFO_PEELING_FOR_NITER (loop_vinfo) = false;
LOOP_VINFO_PEELING_FOR_GAPS (loop_vinfo) = false;
LOOP_VINFO_COST_MODEL_THRESHOLD (loop_vinfo) = 0;
LOOP_VINFO_VERSIONING_THRESHOLD (loop_vinfo) = 0;
LOOP_VINFO_CAN_USE_PARTIAL_VECTORS_P (loop_vinfo)
= saved_can_use_partial_vectors_p;
goto start_over;
}
/* Return true if vectorizing a loop using NEW_LOOP_VINFO appears
to be better than vectorizing it using OLD_LOOP_VINFO. Assume that
OLD_LOOP_VINFO is better unless something specifically indicates
otherwise.
Note that this deliberately isn't a partial order. */
static bool
vect_better_loop_vinfo_p (loop_vec_info new_loop_vinfo,
loop_vec_info old_loop_vinfo)
{
struct loop *loop = LOOP_VINFO_LOOP (new_loop_vinfo);
gcc_assert (LOOP_VINFO_LOOP (old_loop_vinfo) == loop);
poly_int64 new_vf = LOOP_VINFO_VECT_FACTOR (new_loop_vinfo);
poly_int64 old_vf = LOOP_VINFO_VECT_FACTOR (old_loop_vinfo);
/* Always prefer a VF of loop->simdlen over any other VF. */
if (loop->simdlen)
{
bool new_simdlen_p = known_eq (new_vf, loop->simdlen);
bool old_simdlen_p = known_eq (old_vf, loop->simdlen);
if (new_simdlen_p != old_simdlen_p)
return new_simdlen_p;
}
/* Limit the VFs to what is likely to be the maximum number of iterations,
to handle cases in which at least one loop_vinfo is fully-masked. */
HOST_WIDE_INT estimated_max_niter = likely_max_stmt_executions_int (loop);
if (estimated_max_niter != -1)
{
if (known_le (estimated_max_niter, new_vf))
new_vf = estimated_max_niter;
if (known_le (estimated_max_niter, old_vf))
old_vf = estimated_max_niter;
}
/* Check whether the (fractional) cost per scalar iteration is lower
or higher: new_inside_cost / new_vf vs. old_inside_cost / old_vf. */
poly_int64 rel_new = new_loop_vinfo->vec_inside_cost * old_vf;
poly_int64 rel_old = old_loop_vinfo->vec_inside_cost * new_vf;
HOST_WIDE_INT est_rel_new_min
= estimated_poly_value (rel_new, POLY_VALUE_MIN);
HOST_WIDE_INT est_rel_new_max
= estimated_poly_value (rel_new, POLY_VALUE_MAX);
HOST_WIDE_INT est_rel_old_min
= estimated_poly_value (rel_old, POLY_VALUE_MIN);
HOST_WIDE_INT est_rel_old_max
= estimated_poly_value (rel_old, POLY_VALUE_MAX);
/* Check first if we can make out an unambigous total order from the minimum
and maximum estimates. */
if (est_rel_new_min < est_rel_old_min
&& est_rel_new_max < est_rel_old_max)
return true;
else if (est_rel_old_min < est_rel_new_min
&& est_rel_old_max < est_rel_new_max)
return false;
/* When old_loop_vinfo uses a variable vectorization factor,
we know that it has a lower cost for at least one runtime VF.
However, we don't know how likely that VF is.
One option would be to compare the costs for the estimated VFs.
The problem is that that can put too much pressure on the cost
model. E.g. if the estimated VF is also the lowest possible VF,
and if old_loop_vinfo is 1 unit worse than new_loop_vinfo
for the estimated VF, we'd then choose new_loop_vinfo even
though (a) new_loop_vinfo might not actually be better than
old_loop_vinfo for that VF and (b) it would be significantly
worse at larger VFs.
Here we go for a hacky compromise: pick new_loop_vinfo if it is
no more expensive than old_loop_vinfo even after doubling the
estimated old_loop_vinfo VF. For all but trivial loops, this
ensures that we only pick new_loop_vinfo if it is significantly
better than old_loop_vinfo at the estimated VF. */
if (est_rel_old_min != est_rel_new_min
|| est_rel_old_max != est_rel_new_max)
{
HOST_WIDE_INT est_rel_new_likely
= estimated_poly_value (rel_new, POLY_VALUE_LIKELY);
HOST_WIDE_INT est_rel_old_likely
= estimated_poly_value (rel_old, POLY_VALUE_LIKELY);
return est_rel_new_likely * 2 <= est_rel_old_likely;
}
/* If there's nothing to choose between the loop bodies, see whether
there's a difference in the prologue and epilogue costs. */
if (new_loop_vinfo->vec_outside_cost != old_loop_vinfo->vec_outside_cost)
return new_loop_vinfo->vec_outside_cost < old_loop_vinfo->vec_outside_cost;
return false;
}
/* Decide whether to replace OLD_LOOP_VINFO with NEW_LOOP_VINFO. Return
true if we should. */
static bool
vect_joust_loop_vinfos (loop_vec_info new_loop_vinfo,
loop_vec_info old_loop_vinfo)
{
if (!vect_better_loop_vinfo_p (new_loop_vinfo, old_loop_vinfo))
return false;
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"***** Preferring vector mode %s to vector mode %s\n",
GET_MODE_NAME (new_loop_vinfo->vector_mode),
GET_MODE_NAME (old_loop_vinfo->vector_mode));
return true;
}
/* If LOOP_VINFO is already a main loop, return it unmodified. Otherwise
try to reanalyze it as a main loop. Return the loop_vinfo on success
and null on failure. */
static loop_vec_info
vect_reanalyze_as_main_loop (loop_vec_info loop_vinfo, unsigned int *n_stmts)
{
if (!LOOP_VINFO_EPILOGUE_P (loop_vinfo))
return loop_vinfo;
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"***** Reanalyzing as a main loop with vector mode %s\n",
GET_MODE_NAME (loop_vinfo->vector_mode));
struct loop *loop = LOOP_VINFO_LOOP (loop_vinfo);
vec_info_shared *shared = loop_vinfo->shared;
opt_loop_vec_info main_loop_vinfo = vect_analyze_loop_form (loop, shared);
gcc_assert (main_loop_vinfo);
main_loop_vinfo->vector_mode = loop_vinfo->vector_mode;
bool fatal = false;
bool res = vect_analyze_loop_2 (main_loop_vinfo, fatal, n_stmts);
loop->aux = NULL;
if (!res)
{
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"***** Failed to analyze main loop with vector"
" mode %s\n",
GET_MODE_NAME (loop_vinfo->vector_mode));
delete main_loop_vinfo;
return NULL;
}
LOOP_VINFO_VECTORIZABLE_P (main_loop_vinfo) = 1;
return main_loop_vinfo;
}
/* Function vect_analyze_loop.
Apply a set of analyses on LOOP, and create a loop_vec_info struct
for it. The different analyses will record information in the
loop_vec_info struct. */
opt_loop_vec_info
vect_analyze_loop (class loop *loop, vec_info_shared *shared)
{
auto_vector_modes vector_modes;
/* Autodetect first vector size we try. */
unsigned int autovec_flags
= targetm.vectorize.autovectorize_vector_modes (&vector_modes,
loop->simdlen != 0);
unsigned int mode_i = 0;
DUMP_VECT_SCOPE ("analyze_loop_nest");
if (loop_outer (loop)
&& loop_vec_info_for_loop (loop_outer (loop))
&& LOOP_VINFO_VECTORIZABLE_P (loop_vec_info_for_loop (loop_outer (loop))))
return opt_loop_vec_info::failure_at (vect_location,
"outer-loop already vectorized.\n");
if (!find_loop_nest (loop, &shared->loop_nest))
return opt_loop_vec_info::failure_at
(vect_location,
"not vectorized: loop nest containing two or more consecutive inner"
" loops cannot be vectorized\n");
unsigned n_stmts = 0;
machine_mode autodetected_vector_mode = VOIDmode;
opt_loop_vec_info first_loop_vinfo = opt_loop_vec_info::success (NULL);
machine_mode next_vector_mode = VOIDmode;
poly_uint64 lowest_th = 0;
unsigned vectorized_loops = 0;
bool pick_lowest_cost_p = ((autovec_flags & VECT_COMPARE_COSTS)
&& !unlimited_cost_model (loop));
bool vect_epilogues = false;
opt_result res = opt_result::success ();
unsigned HOST_WIDE_INT simdlen = loop->simdlen;
while (1)
{
/* Check the CFG characteristics of the loop (nesting, entry/exit). */
opt_loop_vec_info loop_vinfo = vect_analyze_loop_form (loop, shared);
if (!loop_vinfo)
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"bad loop form.\n");
gcc_checking_assert (first_loop_vinfo == NULL);
return loop_vinfo;
}
loop_vinfo->vector_mode = next_vector_mode;
bool fatal = false;
/* When pick_lowest_cost_p is true, we should in principle iterate
over all the loop_vec_infos that LOOP_VINFO could replace and
try to vectorize LOOP_VINFO under the same conditions.
E.g. when trying to replace an epilogue loop, we should vectorize
LOOP_VINFO as an epilogue loop with the same VF limit. When trying
to replace the main loop, we should vectorize LOOP_VINFO as a main
loop too.
However, autovectorize_vector_modes is usually sorted as follows:
- Modes that naturally produce lower VFs usually follow modes that
naturally produce higher VFs.
- When modes naturally produce the same VF, maskable modes
usually follow unmaskable ones, so that the maskable mode
can be used to vectorize the epilogue of the unmaskable mode.
This order is preferred because it leads to the maximum
epilogue vectorization opportunities. Targets should only use
a different order if they want to make wide modes available while
disparaging them relative to earlier, smaller modes. The assumption
in that case is that the wider modes are more expensive in some
way that isn't reflected directly in the costs.
There should therefore be few interesting cases in which
LOOP_VINFO fails when treated as an epilogue loop, succeeds when
treated as a standalone loop, and ends up being genuinely cheaper
than FIRST_LOOP_VINFO. */
if (vect_epilogues)
LOOP_VINFO_ORIG_LOOP_INFO (loop_vinfo) = first_loop_vinfo;
res = vect_analyze_loop_2 (loop_vinfo, fatal, &n_stmts);
if (mode_i == 0)
autodetected_vector_mode = loop_vinfo->vector_mode;
if (dump_enabled_p ())
{
if (res)
dump_printf_loc (MSG_NOTE, vect_location,
"***** Analysis succeeded with vector mode %s\n",
GET_MODE_NAME (loop_vinfo->vector_mode));
else
dump_printf_loc (MSG_NOTE, vect_location,
"***** Analysis failed with vector mode %s\n",
GET_MODE_NAME (loop_vinfo->vector_mode));
}
loop->aux = NULL;
if (!fatal)
while (mode_i < vector_modes.length ()
&& vect_chooses_same_modes_p (loop_vinfo, vector_modes[mode_i]))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"***** The result for vector mode %s would"
" be the same\n",
GET_MODE_NAME (vector_modes[mode_i]));
mode_i += 1;
}
if (res)
{
LOOP_VINFO_VECTORIZABLE_P (loop_vinfo) = 1;
vectorized_loops++;
/* Once we hit the desired simdlen for the first time,
discard any previous attempts. */
if (simdlen
&& known_eq (LOOP_VINFO_VECT_FACTOR (loop_vinfo), simdlen))
{
delete first_loop_vinfo;
first_loop_vinfo = opt_loop_vec_info::success (NULL);
LOOP_VINFO_ORIG_LOOP_INFO (loop_vinfo) = NULL;
simdlen = 0;
}
else if (pick_lowest_cost_p && first_loop_vinfo)
{
/* Keep trying to roll back vectorization attempts while the
loop_vec_infos they produced were worse than this one. */
vec<loop_vec_info> &vinfos = first_loop_vinfo->epilogue_vinfos;
while (!vinfos.is_empty ()
&& vect_joust_loop_vinfos (loop_vinfo, vinfos.last ()))
{
gcc_assert (vect_epilogues);
delete vinfos.pop ();
}
if (vinfos.is_empty ()
&& vect_joust_loop_vinfos (loop_vinfo, first_loop_vinfo))
{
loop_vec_info main_loop_vinfo
= vect_reanalyze_as_main_loop (loop_vinfo, &n_stmts);
if (main_loop_vinfo == loop_vinfo)
{
delete first_loop_vinfo;
first_loop_vinfo = opt_loop_vec_info::success (NULL);
}
else if (main_loop_vinfo
&& vect_joust_loop_vinfos (main_loop_vinfo,
first_loop_vinfo))
{
delete first_loop_vinfo;
first_loop_vinfo = opt_loop_vec_info::success (NULL);
delete loop_vinfo;
loop_vinfo
= opt_loop_vec_info::success (main_loop_vinfo);
}
else
delete main_loop_vinfo;
}
}
if (first_loop_vinfo == NULL)
{
first_loop_vinfo = loop_vinfo;
lowest_th = LOOP_VINFO_VERSIONING_THRESHOLD (first_loop_vinfo);
}
else if (vect_epilogues
/* For now only allow one epilogue loop. */
&& first_loop_vinfo->epilogue_vinfos.is_empty ())
{
first_loop_vinfo->epilogue_vinfos.safe_push (loop_vinfo);
poly_uint64 th = LOOP_VINFO_VERSIONING_THRESHOLD (loop_vinfo);
gcc_assert (!LOOP_REQUIRES_VERSIONING (loop_vinfo)
|| maybe_ne (lowest_th, 0U));
/* Keep track of the known smallest versioning
threshold. */
if (ordered_p (lowest_th, th))
lowest_th = ordered_min (lowest_th, th);
}
else
{
delete loop_vinfo;
loop_vinfo = opt_loop_vec_info::success (NULL);
}
/* Only vectorize epilogues if PARAM_VECT_EPILOGUES_NOMASK is
enabled, SIMDUID is not set, it is the innermost loop and we have
either already found the loop's SIMDLEN or there was no SIMDLEN to
begin with.
TODO: Enable epilogue vectorization for loops with SIMDUID set. */
vect_epilogues = (!simdlen
&& loop->inner == NULL
&& param_vect_epilogues_nomask
&& LOOP_VINFO_PEELING_FOR_NITER (first_loop_vinfo)
&& !loop->simduid
/* For now only allow one epilogue loop, but allow
pick_lowest_cost_p to replace it. */
&& (first_loop_vinfo->epilogue_vinfos.is_empty ()
|| pick_lowest_cost_p));
/* Commit to first_loop_vinfo if we have no reason to try
alternatives. */
if (!simdlen && !vect_epilogues && !pick_lowest_cost_p)
break;
}
else
{
delete loop_vinfo;
loop_vinfo = opt_loop_vec_info::success (NULL);
if (fatal)
{
gcc_checking_assert (first_loop_vinfo == NULL);
break;
}
}
/* Handle the case that the original loop can use partial
vectorization, but want to only adopt it for the epilogue.
The retry should be in the same mode as original. */
if (vect_epilogues
&& loop_vinfo
&& LOOP_VINFO_EPIL_USING_PARTIAL_VECTORS_P (loop_vinfo))
{
gcc_assert (LOOP_VINFO_CAN_USE_PARTIAL_VECTORS_P (loop_vinfo)
&& !LOOP_VINFO_USING_PARTIAL_VECTORS_P (loop_vinfo));
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"***** Re-trying analysis with same vector mode"
" %s for epilogue with partial vectors.\n",
GET_MODE_NAME (loop_vinfo->vector_mode));
continue;
}
if (mode_i < vector_modes.length ()
&& VECTOR_MODE_P (autodetected_vector_mode)
&& (related_vector_mode (vector_modes[mode_i],
GET_MODE_INNER (autodetected_vector_mode))
== autodetected_vector_mode)
&& (related_vector_mode (autodetected_vector_mode,
GET_MODE_INNER (vector_modes[mode_i]))
== vector_modes[mode_i]))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"***** Skipping vector mode %s, which would"
" repeat the analysis for %s\n",
GET_MODE_NAME (vector_modes[mode_i]),
GET_MODE_NAME (autodetected_vector_mode));
mode_i += 1;
}
if (mode_i == vector_modes.length ()
|| autodetected_vector_mode == VOIDmode)
break;
/* Try the next biggest vector size. */
next_vector_mode = vector_modes[mode_i++];
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"***** Re-trying analysis with vector mode %s\n",
GET_MODE_NAME (next_vector_mode));
}
if (first_loop_vinfo)
{
loop->aux = (loop_vec_info) first_loop_vinfo;
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"***** Choosing vector mode %s\n",
GET_MODE_NAME (first_loop_vinfo->vector_mode));
LOOP_VINFO_VERSIONING_THRESHOLD (first_loop_vinfo) = lowest_th;
return first_loop_vinfo;
}
return opt_loop_vec_info::propagate_failure (res);
}
/* Return true if there is an in-order reduction function for CODE, storing
it in *REDUC_FN if so. */
static bool
fold_left_reduction_fn (tree_code code, internal_fn *reduc_fn)
{
switch (code)
{
case PLUS_EXPR:
*reduc_fn = IFN_FOLD_LEFT_PLUS;
return true;
default:
return false;
}
}
/* Function reduction_fn_for_scalar_code
Input:
CODE - tree_code of a reduction operations.
Output:
REDUC_FN - the corresponding internal function to be used to reduce the
vector of partial results into a single scalar result, or IFN_LAST
if the operation is a supported reduction operation, but does not have
such an internal function.
Return FALSE if CODE currently cannot be vectorized as reduction. */
static bool
reduction_fn_for_scalar_code (enum tree_code code, internal_fn *reduc_fn)
{
switch (code)
{
case MAX_EXPR:
*reduc_fn = IFN_REDUC_MAX;
return true;
case MIN_EXPR:
*reduc_fn = IFN_REDUC_MIN;
return true;
case PLUS_EXPR:
*reduc_fn = IFN_REDUC_PLUS;
return true;
case BIT_AND_EXPR:
*reduc_fn = IFN_REDUC_AND;
return true;
case BIT_IOR_EXPR:
*reduc_fn = IFN_REDUC_IOR;
return true;
case BIT_XOR_EXPR:
*reduc_fn = IFN_REDUC_XOR;
return true;
case MULT_EXPR:
case MINUS_EXPR:
*reduc_fn = IFN_LAST;
return true;
default:
return false;
}
}
/* If there is a neutral value X such that SLP reduction NODE would not
be affected by the introduction of additional X elements, return that X,
otherwise return null. CODE is the code of the reduction and VECTOR_TYPE
is the vector type that would hold element X. REDUC_CHAIN is true if
the SLP statements perform a single reduction, false if each statement
performs an independent reduction. */
static tree
neutral_op_for_slp_reduction (slp_tree slp_node, tree vector_type,
tree_code code, bool reduc_chain)
{
vec<stmt_vec_info> stmts = SLP_TREE_SCALAR_STMTS (slp_node);
stmt_vec_info stmt_vinfo = stmts[0];
tree scalar_type = TREE_TYPE (vector_type);
class loop *loop = gimple_bb (stmt_vinfo->stmt)->loop_father;
gcc_assert (loop);
switch (code)
{
case WIDEN_SUM_EXPR:
case DOT_PROD_EXPR:
case SAD_EXPR:
case PLUS_EXPR:
case MINUS_EXPR:
case BIT_IOR_EXPR:
case BIT_XOR_EXPR:
return build_zero_cst (scalar_type);
case MULT_EXPR:
return build_one_cst (scalar_type);
case BIT_AND_EXPR:
return build_all_ones_cst (scalar_type);
case MAX_EXPR:
case MIN_EXPR:
/* For MIN/MAX the initial values are neutral. A reduction chain
has only a single initial value, so that value is neutral for
all statements. */
if (reduc_chain)
return PHI_ARG_DEF_FROM_EDGE (stmt_vinfo->stmt,
loop_preheader_edge (loop));
return NULL_TREE;
default:
return NULL_TREE;
}
}
/* Error reporting helper for vect_is_simple_reduction below. GIMPLE statement
STMT is printed with a message MSG. */
static void
report_vect_op (dump_flags_t msg_type, gimple *stmt, const char *msg)
{
dump_printf_loc (msg_type, vect_location, "%s%G", msg, stmt);
}
/* Return true if we need an in-order reduction for operation CODE
on type TYPE. NEED_WRAPPING_INTEGRAL_OVERFLOW is true if integer
overflow must wrap. */
bool
needs_fold_left_reduction_p (tree type, tree_code code)
{
/* CHECKME: check for !flag_finite_math_only too? */
if (SCALAR_FLOAT_TYPE_P (type))
switch (code)
{
case MIN_EXPR:
case MAX_EXPR:
return false;
default:
return !flag_associative_math;
}
if (INTEGRAL_TYPE_P (type))
{
if (!operation_no_trapping_overflow (type, code))
return true;
return false;
}
if (SAT_FIXED_POINT_TYPE_P (type))
return true;
return false;
}
/* Return true if the reduction PHI in LOOP with latch arg LOOP_ARG and
has a handled computation expression. Store the main reduction
operation in *CODE. */
static bool
check_reduction_path (dump_user_location_t loc, loop_p loop, gphi *phi,
tree loop_arg, enum tree_code *code,
vec<std::pair<ssa_op_iter, use_operand_p> > &path)
{
auto_bitmap visited;
tree lookfor = PHI_RESULT (phi);
ssa_op_iter curri;
use_operand_p curr = op_iter_init_phiuse (&curri, phi, SSA_OP_USE);
while (USE_FROM_PTR (curr) != loop_arg)
curr = op_iter_next_use (&curri);
curri.i = curri.numops;
do
{
path.safe_push (std::make_pair (curri, curr));
tree use = USE_FROM_PTR (curr);
if (use == lookfor)
break;
gimple *def = SSA_NAME_DEF_STMT (use);
if (gimple_nop_p (def)
|| ! flow_bb_inside_loop_p (loop, gimple_bb (def)))
{
pop:
do
{
std::pair<ssa_op_iter, use_operand_p> x = path.pop ();
curri = x.first;
curr = x.second;
do
curr = op_iter_next_use (&curri);
/* Skip already visited or non-SSA operands (from iterating
over PHI args). */
while (curr != NULL_USE_OPERAND_P
&& (TREE_CODE (USE_FROM_PTR (curr)) != SSA_NAME
|| ! bitmap_set_bit (visited,
SSA_NAME_VERSION
(USE_FROM_PTR (curr)))));
}
while (curr == NULL_USE_OPERAND_P && ! path.is_empty ());
if (curr == NULL_USE_OPERAND_P)
break;
}
else
{
if (gimple_code (def) == GIMPLE_PHI)
curr = op_iter_init_phiuse (&curri, as_a <gphi *>(def), SSA_OP_USE);
else
curr = op_iter_init_use (&curri, def, SSA_OP_USE);
while (curr != NULL_USE_OPERAND_P
&& (TREE_CODE (USE_FROM_PTR (curr)) != SSA_NAME
|| ! bitmap_set_bit (visited,
SSA_NAME_VERSION
(USE_FROM_PTR (curr)))))
curr = op_iter_next_use (&curri);
if (curr == NULL_USE_OPERAND_P)
goto pop;
}
}
while (1);
if (dump_file && (dump_flags & TDF_DETAILS))
{
dump_printf_loc (MSG_NOTE, loc, "reduction path: ");
unsigned i;
std::pair<ssa_op_iter, use_operand_p> *x;
FOR_EACH_VEC_ELT (path, i, x)
dump_printf (MSG_NOTE, "%T ", USE_FROM_PTR (x->second));
dump_printf (MSG_NOTE, "\n");
}
/* Check whether the reduction path detected is valid. */
bool fail = path.length () == 0;
bool neg = false;
int sign = -1;
*code = ERROR_MARK;
for (unsigned i = 1; i < path.length (); ++i)
{
gimple *use_stmt = USE_STMT (path[i].second);
tree op = USE_FROM_PTR (path[i].second);
if (! is_gimple_assign (use_stmt)
/* The following make sure we can compute the operand index
easily plus it mostly disallows chaining via COND_EXPR condition
operands. */
|| (gimple_assign_rhs1_ptr (use_stmt) != path[i].second->use
&& (gimple_num_ops (use_stmt) <= 2
|| gimple_assign_rhs2_ptr (use_stmt) != path[i].second->use)
&& (gimple_num_ops (use_stmt) <= 3
|| gimple_assign_rhs3_ptr (use_stmt) != path[i].second->use)))
{
fail = true;
break;
}
tree_code use_code = gimple_assign_rhs_code (use_stmt);
if (use_code == MINUS_EXPR)
{
use_code = PLUS_EXPR;
/* Track whether we negate the reduction value each iteration. */
if (gimple_assign_rhs2 (use_stmt) == op)
neg = ! neg;
}
if (CONVERT_EXPR_CODE_P (use_code)
&& tree_nop_conversion_p (TREE_TYPE (gimple_assign_lhs (use_stmt)),
TREE_TYPE (gimple_assign_rhs1 (use_stmt))))
;
else if (*code == ERROR_MARK)
{
*code = use_code;
sign = TYPE_SIGN (TREE_TYPE (gimple_assign_lhs (use_stmt)));
}
else if (use_code != *code)
{
fail = true;
break;
}
else if ((use_code == MIN_EXPR
|| use_code == MAX_EXPR)
&& sign != TYPE_SIGN (TREE_TYPE (gimple_assign_lhs (use_stmt))))
{
fail = true;
break;
}
/* Check there's only a single stmt the op is used on. For the
not value-changing tail and the last stmt allow out-of-loop uses.
??? We could relax this and handle arbitrary live stmts by
forcing a scalar epilogue for example. */
imm_use_iterator imm_iter;
gimple *op_use_stmt;
unsigned cnt = 0;
FOR_EACH_IMM_USE_STMT (op_use_stmt, imm_iter, op)
if (!is_gimple_debug (op_use_stmt)
&& (*code != ERROR_MARK
|| flow_bb_inside_loop_p (loop, gimple_bb (op_use_stmt))))
{
/* We want to allow x + x but not x < 1 ? x : 2. */
if (is_gimple_assign (op_use_stmt)
&& gimple_assign_rhs_code (op_use_stmt) == COND_EXPR)
{
use_operand_p use_p;
FOR_EACH_IMM_USE_ON_STMT (use_p, imm_iter)
cnt++;
}
else
cnt++;
}
if (cnt != 1)
{
fail = true;
break;
}
}
return ! fail && ! neg && *code != ERROR_MARK;
}
bool
check_reduction_path (dump_user_location_t loc, loop_p loop, gphi *phi,
tree loop_arg, enum tree_code code)
{
auto_vec<std::pair<ssa_op_iter, use_operand_p> > path;
enum tree_code code_;
return (check_reduction_path (loc, loop, phi, loop_arg, &code_, path)
&& code_ == code);
}
/* Function vect_is_simple_reduction
(1) Detect a cross-iteration def-use cycle that represents a simple
reduction computation. We look for the following pattern:
loop_header:
a1 = phi < a0, a2 >
a3 = ...
a2 = operation (a3, a1)
or
a3 = ...
loop_header:
a1 = phi < a0, a2 >
a2 = operation (a3, a1)
such that:
1. operation is commutative and associative and it is safe to
change the order of the computation
2. no uses for a2 in the loop (a2 is used out of the loop)
3. no uses of a1 in the loop besides the reduction operation
4. no uses of a1 outside the loop.
Conditions 1,4 are tested here.
Conditions 2,3 are tested in vect_mark_stmts_to_be_vectorized.
(2) Detect a cross-iteration def-use cycle in nested loops, i.e.,
nested cycles.
(3) Detect cycles of phi nodes in outer-loop vectorization, i.e., double
reductions:
a1 = phi < a0, a2 >
inner loop (def of a3)
a2 = phi < a3 >
(4) Detect condition expressions, ie:
for (int i = 0; i < N; i++)
if (a[i] < val)
ret_val = a[i];
*/
static stmt_vec_info
vect_is_simple_reduction (loop_vec_info loop_info, stmt_vec_info phi_info,
bool *double_reduc, bool *reduc_chain_p)
{
gphi *phi = as_a <gphi *> (phi_info->stmt);
gimple *phi_use_stmt = NULL;
imm_use_iterator imm_iter;
use_operand_p use_p;
*double_reduc = false;
*reduc_chain_p = false;
STMT_VINFO_REDUC_TYPE (phi_info) = TREE_CODE_REDUCTION;
tree phi_name = PHI_RESULT (phi);
/* ??? If there are no uses of the PHI result the inner loop reduction
won't be detected as possibly double-reduction by vectorizable_reduction
because that tries to walk the PHI arg from the preheader edge which
can be constant. See PR60382. */
if (has_zero_uses (phi_name))
return NULL;
class loop *loop = (gimple_bb (phi))->loop_father;
unsigned nphi_def_loop_uses = 0;
FOR_EACH_IMM_USE_FAST (use_p, imm_iter, phi_name)
{
gimple *use_stmt = USE_STMT (use_p);
if (is_gimple_debug (use_stmt))
continue;
if (!flow_bb_inside_loop_p (loop, gimple_bb (use_stmt)))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"intermediate value used outside loop.\n");
return NULL;
}
nphi_def_loop_uses++;
phi_use_stmt = use_stmt;
}
tree latch_def = PHI_ARG_DEF_FROM_EDGE (phi, loop_latch_edge (loop));
if (TREE_CODE (latch_def) != SSA_NAME)
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"reduction: not ssa_name: %T\n", latch_def);
return NULL;
}
stmt_vec_info def_stmt_info = loop_info->lookup_def (latch_def);
if (!def_stmt_info
|| !flow_bb_inside_loop_p (loop, gimple_bb (def_stmt_info->stmt)))
return NULL;
bool nested_in_vect_loop
= flow_loop_nested_p (LOOP_VINFO_LOOP (loop_info), loop);
unsigned nlatch_def_loop_uses = 0;
auto_vec<gphi *, 3> lcphis;
bool inner_loop_of_double_reduc = false;
FOR_EACH_IMM_USE_FAST (use_p, imm_iter, latch_def)
{
gimple *use_stmt = USE_STMT (use_p);
if (is_gimple_debug (use_stmt))
continue;
if (flow_bb_inside_loop_p (loop, gimple_bb (use_stmt)))
nlatch_def_loop_uses++;
else
{
/* We can have more than one loop-closed PHI. */
lcphis.safe_push (as_a <gphi *> (use_stmt));
if (nested_in_vect_loop
&& (STMT_VINFO_DEF_TYPE (loop_info->lookup_stmt (use_stmt))
== vect_double_reduction_def))
inner_loop_of_double_reduc = true;
}
}
/* If we are vectorizing an inner reduction we are executing that
in the original order only in case we are not dealing with a
double reduction. */
if (nested_in_vect_loop && !inner_loop_of_double_reduc)
{
if (dump_enabled_p ())
report_vect_op (MSG_NOTE, def_stmt_info->stmt,
"detected nested cycle: ");
return def_stmt_info;
}
/* If this isn't a nested cycle or if the nested cycle reduction value
is used ouside of the inner loop we cannot handle uses of the reduction
value. */
if (nlatch_def_loop_uses > 1 || nphi_def_loop_uses > 1)
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"reduction used in loop.\n");
return NULL;
}
/* If DEF_STMT is a phi node itself, we expect it to have a single argument
defined in the inner loop. */
if (gphi *def_stmt = dyn_cast <gphi *> (def_stmt_info->stmt))
{
tree op1 = PHI_ARG_DEF (def_stmt, 0);
if (gimple_phi_num_args (def_stmt) != 1
|| TREE_CODE (op1) != SSA_NAME)
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"unsupported phi node definition.\n");
return NULL;
}
gimple *def1 = SSA_NAME_DEF_STMT (op1);
if (gimple_bb (def1)
&& flow_bb_inside_loop_p (loop, gimple_bb (def_stmt))
&& loop->inner
&& flow_bb_inside_loop_p (loop->inner, gimple_bb (def1))
&& is_gimple_assign (def1)
&& is_a <gphi *> (phi_use_stmt)
&& flow_bb_inside_loop_p (loop->inner, gimple_bb (phi_use_stmt)))
{
if (dump_enabled_p ())
report_vect_op (MSG_NOTE, def_stmt,
"detected double reduction: ");
*double_reduc = true;
return def_stmt_info;
}
return NULL;
}
/* Look for the expression computing latch_def from then loop PHI result. */
auto_vec<std::pair<ssa_op_iter, use_operand_p> > path;
enum tree_code code;
if (check_reduction_path (vect_location, loop, phi, latch_def, &code,
path))
{
STMT_VINFO_REDUC_CODE (phi_info) = code;
if (code == COND_EXPR && !nested_in_vect_loop)
STMT_VINFO_REDUC_TYPE (phi_info) = COND_REDUCTION;
/* Fill in STMT_VINFO_REDUC_IDX and gather stmts for an SLP
reduction chain for which the additional restriction is that
all operations in the chain are the same. */
auto_vec<stmt_vec_info, 8> reduc_chain;
unsigned i;
bool is_slp_reduc = !nested_in_vect_loop && code != COND_EXPR;
for (i = path.length () - 1; i >= 1; --i)
{
gimple *stmt = USE_STMT (path[i].second);
stmt_vec_info stmt_info = loop_info->lookup_stmt (stmt);
STMT_VINFO_REDUC_IDX (stmt_info)
= path[i].second->use - gimple_assign_rhs1_ptr (stmt);
enum tree_code stmt_code = gimple_assign_rhs_code (stmt);
bool leading_conversion = (CONVERT_EXPR_CODE_P (stmt_code)
&& (i == 1 || i == path.length () - 1));
if ((stmt_code != code && !leading_conversion)
/* We can only handle the final value in epilogue
generation for reduction chains. */
|| (i != 1 && !has_single_use (gimple_assign_lhs (stmt))))
is_slp_reduc = false;
/* For reduction chains we support a trailing/leading
conversions. We do not store those in the actual chain. */
if (leading_conversion)
continue;
reduc_chain.safe_push (stmt_info);
}
if (is_slp_reduc && reduc_chain.length () > 1)
{
for (unsigned i = 0; i < reduc_chain.length () - 1; ++i)
{
REDUC_GROUP_FIRST_ELEMENT (reduc_chain[i]) = reduc_chain[0];
REDUC_GROUP_NEXT_ELEMENT (reduc_chain[i]) = reduc_chain[i+1];
}
REDUC_GROUP_FIRST_ELEMENT (reduc_chain.last ()) = reduc_chain[0];
REDUC_GROUP_NEXT_ELEMENT (reduc_chain.last ()) = NULL;
/* Save the chain for further analysis in SLP detection. */
LOOP_VINFO_REDUCTION_CHAINS (loop_info).safe_push (reduc_chain[0]);
REDUC_GROUP_SIZE (reduc_chain[0]) = reduc_chain.length ();
*reduc_chain_p = true;
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"reduction: detected reduction chain\n");
}
else if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"reduction: detected reduction\n");
return def_stmt_info;
}
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"reduction: unknown pattern\n");
return NULL;
}
/* Estimate the number of peeled epilogue iterations for LOOP_VINFO.
PEEL_ITERS_PROLOGUE is the number of peeled prologue iterations,
or -1 if not known. */
static int
vect_get_peel_iters_epilogue (loop_vec_info loop_vinfo, int peel_iters_prologue)
{
int assumed_vf = vect_vf_for_cost (loop_vinfo);
if (!LOOP_VINFO_NITERS_KNOWN_P (loop_vinfo) || peel_iters_prologue == -1)
{
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"cost model: epilogue peel iters set to vf/2 "
"because loop iterations are unknown .\n");
return assumed_vf / 2;
}
else
{
int niters = LOOP_VINFO_INT_NITERS (loop_vinfo);
peel_iters_prologue = MIN (niters, peel_iters_prologue);
int peel_iters_epilogue = (niters - peel_iters_prologue) % assumed_vf;
/* If we need to peel for gaps, but no peeling is required, we have to
peel VF iterations. */
if (LOOP_VINFO_PEELING_FOR_GAPS (loop_vinfo) && !peel_iters_epilogue)
peel_iters_epilogue = assumed_vf;
return peel_iters_epilogue;
}
}
/* Calculate cost of peeling the loop PEEL_ITERS_PROLOGUE times. */
int
vect_get_known_peeling_cost (loop_vec_info loop_vinfo, int peel_iters_prologue,
int *peel_iters_epilogue,
stmt_vector_for_cost *scalar_cost_vec,
stmt_vector_for_cost *prologue_cost_vec,
stmt_vector_for_cost *epilogue_cost_vec)
{
int retval = 0;
*peel_iters_epilogue
= vect_get_peel_iters_epilogue (loop_vinfo, peel_iters_prologue);
if (!LOOP_VINFO_NITERS_KNOWN_P (loop_vinfo))
{
/* If peeled iterations are known but number of scalar loop
iterations are unknown, count a taken branch per peeled loop. */
if (peel_iters_prologue > 0)
retval = record_stmt_cost (prologue_cost_vec, 1, cond_branch_taken,
NULL, NULL_TREE, 0, vect_prologue);
if (*peel_iters_epilogue > 0)
retval += record_stmt_cost (epilogue_cost_vec, 1, cond_branch_taken,
NULL, NULL_TREE, 0, vect_epilogue);
}
stmt_info_for_cost *si;
int j;
if (peel_iters_prologue)
FOR_EACH_VEC_ELT (*scalar_cost_vec, j, si)
retval += record_stmt_cost (prologue_cost_vec,
si->count * peel_iters_prologue,
si->kind, si->stmt_info, si->misalign,
vect_prologue);
if (*peel_iters_epilogue)
FOR_EACH_VEC_ELT (*scalar_cost_vec, j, si)
retval += record_stmt_cost (epilogue_cost_vec,
si->count * *peel_iters_epilogue,
si->kind, si->stmt_info, si->misalign,
vect_epilogue);
return retval;
}
/* Function vect_estimate_min_profitable_iters
Return the number of iterations required for the vector version of the
loop to be profitable relative to the cost of the scalar version of the
loop.
*RET_MIN_PROFITABLE_NITERS is a cost model profitability threshold
of iterations for vectorization. -1 value means loop vectorization
is not profitable. This returned value may be used for dynamic
profitability check.
*RET_MIN_PROFITABLE_ESTIMATE is a profitability threshold to be used
for static check against estimated number of iterations. */
static void
vect_estimate_min_profitable_iters (loop_vec_info loop_vinfo,
int *ret_min_profitable_niters,
int *ret_min_profitable_estimate)
{
int min_profitable_iters;
int min_profitable_estimate;
int peel_iters_prologue;
int peel_iters_epilogue;
unsigned vec_inside_cost = 0;
int vec_outside_cost = 0;
unsigned vec_prologue_cost = 0;
unsigned vec_epilogue_cost = 0;
int scalar_single_iter_cost = 0;
int scalar_outside_cost = 0;
int assumed_vf = vect_vf_for_cost (loop_vinfo);
int npeel = LOOP_VINFO_PEELING_FOR_ALIGNMENT (loop_vinfo);
void *target_cost_data = LOOP_VINFO_TARGET_COST_DATA (loop_vinfo);
/* Cost model disabled. */
if (unlimited_cost_model (LOOP_VINFO_LOOP (loop_vinfo)))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location, "cost model disabled.\n");
*ret_min_profitable_niters = 0;
*ret_min_profitable_estimate = 0;
return;
}
/* Requires loop versioning tests to handle misalignment. */
if (LOOP_REQUIRES_VERSIONING_FOR_ALIGNMENT (loop_vinfo))
{
/* FIXME: Make cost depend on complexity of individual check. */
unsigned len = LOOP_VINFO_MAY_MISALIGN_STMTS (loop_vinfo).length ();
(void) add_stmt_cost (loop_vinfo, target_cost_data, len, vector_stmt,
NULL, NULL_TREE, 0, vect_prologue);
if (dump_enabled_p ())
dump_printf (MSG_NOTE,
"cost model: Adding cost of checks for loop "
"versioning to treat misalignment.\n");
}
/* Requires loop versioning with alias checks. */
if (LOOP_REQUIRES_VERSIONING_FOR_ALIAS (loop_vinfo))
{
/* FIXME: Make cost depend on complexity of individual check. */
unsigned len = LOOP_VINFO_COMP_ALIAS_DDRS (loop_vinfo).length ();
(void) add_stmt_cost (loop_vinfo, target_cost_data, len, vector_stmt,
NULL, NULL_TREE, 0, vect_prologue);
len = LOOP_VINFO_CHECK_UNEQUAL_ADDRS (loop_vinfo).length ();
if (len)
/* Count LEN - 1 ANDs and LEN comparisons. */
(void) add_stmt_cost (loop_vinfo, target_cost_data, len * 2 - 1,
scalar_stmt, NULL, NULL_TREE, 0, vect_prologue);
len = LOOP_VINFO_LOWER_BOUNDS (loop_vinfo).length ();
if (len)
{
/* Count LEN - 1 ANDs and LEN comparisons. */
unsigned int nstmts = len * 2 - 1;
/* +1 for each bias that needs adding. */
for (unsigned int i = 0; i < len; ++i)
if (!LOOP_VINFO_LOWER_BOUNDS (loop_vinfo)[i].unsigned_p)
nstmts += 1;
(void) add_stmt_cost (loop_vinfo, target_cost_data, nstmts,
scalar_stmt, NULL, NULL_TREE, 0, vect_prologue);
}
if (dump_enabled_p ())
dump_printf (MSG_NOTE,
"cost model: Adding cost of checks for loop "
"versioning aliasing.\n");
}
/* Requires loop versioning with niter checks. */
if (LOOP_REQUIRES_VERSIONING_FOR_NITERS (loop_vinfo))
{
/* FIXME: Make cost depend on complexity of individual check. */
(void) add_stmt_cost (loop_vinfo, target_cost_data, 1, vector_stmt,
NULL, NULL_TREE, 0, vect_prologue);
if (dump_enabled_p ())
dump_printf (MSG_NOTE,
"cost model: Adding cost of checks for loop "
"versioning niters.\n");
}
if (LOOP_REQUIRES_VERSIONING (loop_vinfo))
(void) add_stmt_cost (loop_vinfo, target_cost_data, 1, cond_branch_taken,
NULL, NULL_TREE, 0, vect_prologue);
/* Count statements in scalar loop. Using this as scalar cost for a single
iteration for now.
TODO: Add outer loop support.
TODO: Consider assigning different costs to different scalar
statements. */
scalar_single_iter_cost
= LOOP_VINFO_SINGLE_SCALAR_ITERATION_COST (loop_vinfo);
/* Add additional cost for the peeled instructions in prologue and epilogue
loop. (For fully-masked loops there will be no peeling.)
FORNOW: If we don't know the value of peel_iters for prologue or epilogue
at compile-time - we assume it's vf/2 (the worst would be vf-1).
TODO: Build an expression that represents peel_iters for prologue and
epilogue to be used in a run-time test. */
bool prologue_need_br_taken_cost = false;
bool prologue_need_br_not_taken_cost = false;
/* Calculate peel_iters_prologue. */
if (vect_use_loop_mask_for_alignment_p (loop_vinfo))
peel_iters_prologue = 0;
else if (npeel < 0)
{
peel_iters_prologue = assumed_vf / 2;
if (dump_enabled_p ())
dump_printf (MSG_NOTE, "cost model: "
"prologue peel iters set to vf/2.\n");
/* If peeled iterations are unknown, count a taken branch and a not taken
branch per peeled loop. Even if scalar loop iterations are known,
vector iterations are not known since peeled prologue iterations are
not known. Hence guards remain the same. */
prologue_need_br_taken_cost = true;
prologue_need_br_not_taken_cost = true;
}
else
{
peel_iters_prologue = npeel;
if (!LOOP_VINFO_NITERS_KNOWN_P (loop_vinfo) && peel_iters_prologue > 0)
/* If peeled iterations are known but number of scalar loop
iterations are unknown, count a taken branch per peeled loop. */
prologue_need_br_taken_cost = true;
}
bool epilogue_need_br_taken_cost = false;
bool epilogue_need_br_not_taken_cost = false;
/* Calculate peel_iters_epilogue. */
if (LOOP_VINFO_USING_PARTIAL_VECTORS_P (loop_vinfo))
/* We need to peel exactly one iteration for gaps. */
peel_iters_epilogue = LOOP_VINFO_PEELING_FOR_GAPS (loop_vinfo) ? 1 : 0;
else if (npeel < 0)
{
/* If peeling for alignment is unknown, loop bound of main loop
becomes unknown. */
peel_iters_epilogue = assumed_vf / 2;
if (dump_enabled_p ())
dump_printf (MSG_NOTE, "cost model: "
"epilogue peel iters set to vf/2 because "
"peeling for alignment is unknown.\n");
/* See the same reason above in peel_iters_prologue calculation. */
epilogue_need_br_taken_cost = true;
epilogue_need_br_not_taken_cost = true;
}
else
{
peel_iters_epilogue = vect_get_peel_iters_epilogue (loop_vinfo, npeel);
if (!LOOP_VINFO_NITERS_KNOWN_P (loop_vinfo) && peel_iters_epilogue > 0)
/* If peeled iterations are known but number of scalar loop
iterations are unknown, count a taken branch per peeled loop. */
epilogue_need_br_taken_cost = true;
}
stmt_info_for_cost *si;
int j;
/* Add costs associated with peel_iters_prologue. */
if (peel_iters_prologue)
FOR_EACH_VEC_ELT (LOOP_VINFO_SCALAR_ITERATION_COST (loop_vinfo), j, si)
{
(void) add_stmt_cost (loop_vinfo, target_cost_data,
si->count * peel_iters_prologue, si->kind,
si->stmt_info, si->vectype, si->misalign,
vect_prologue);
}
/* Add costs associated with peel_iters_epilogue. */
if (peel_iters_epilogue)
FOR_EACH_VEC_ELT (LOOP_VINFO_SCALAR_ITERATION_COST (loop_vinfo), j, si)
{
(void) add_stmt_cost (loop_vinfo, target_cost_data,
si->count * peel_iters_epilogue, si->kind,
si->stmt_info, si->vectype, si->misalign,
vect_epilogue);
}
/* Add possible cond_branch_taken/cond_branch_not_taken cost. */
if (prologue_need_br_taken_cost)
(void) add_stmt_cost (loop_vinfo, target_cost_data, 1, cond_branch_taken,
NULL, NULL_TREE, 0, vect_prologue);
if (prologue_need_br_not_taken_cost)
(void) add_stmt_cost (loop_vinfo, target_cost_data, 1,
cond_branch_not_taken, NULL, NULL_TREE, 0,
vect_prologue);
if (epilogue_need_br_taken_cost)
(void) add_stmt_cost (loop_vinfo, target_cost_data, 1, cond_branch_taken,
NULL, NULL_TREE, 0, vect_epilogue);
if (epilogue_need_br_not_taken_cost)
(void) add_stmt_cost (loop_vinfo, target_cost_data, 1,
cond_branch_not_taken, NULL, NULL_TREE, 0,
vect_epilogue);
/* Take care of special costs for rgroup controls of partial vectors. */
if (LOOP_VINFO_FULLY_MASKED_P (loop_vinfo))
{
/* Calculate how many masks we need to generate. */
unsigned int num_masks = 0;
rgroup_controls *rgm;
unsigned int num_vectors_m1;
FOR_EACH_VEC_ELT (LOOP_VINFO_MASKS (loop_vinfo), num_vectors_m1, rgm)
if (rgm->type)
num_masks += num_vectors_m1 + 1;
gcc_assert (num_masks > 0);
/* In the worst case, we need to generate each mask in the prologue
and in the loop body. One of the loop body mask instructions
replaces the comparison in the scalar loop, and since we don't
count the scalar comparison against the scalar body, we shouldn't
count that vector instruction against the vector body either.
Sometimes we can use unpacks instead of generating prologue
masks and sometimes the prologue mask will fold to a constant,
so the actual prologue cost might be smaller. However, it's
simpler and safer to use the worst-case cost; if this ends up
being the tie-breaker between vectorizing or not, then it's
probably better not to vectorize. */
(void) add_stmt_cost (loop_vinfo, target_cost_data, num_masks,
vector_stmt, NULL, NULL_TREE, 0, vect_prologue);
(void) add_stmt_cost (loop_vinfo, target_cost_data, num_masks - 1,
vector_stmt, NULL, NULL_TREE, 0, vect_body);
}
else if (LOOP_VINFO_FULLY_WITH_LENGTH_P (loop_vinfo))
{
/* Referring to the functions vect_set_loop_condition_partial_vectors
and vect_set_loop_controls_directly, we need to generate each
length in the prologue and in the loop body if required. Although
there are some possible optimizations, we consider the worst case
here. */
bool niters_known_p = LOOP_VINFO_NITERS_KNOWN_P (loop_vinfo);
bool need_iterate_p
= (!LOOP_VINFO_EPILOGUE_P (loop_vinfo)
&& !vect_known_niters_smaller_than_vf (loop_vinfo));
/* Calculate how many statements to be added. */
unsigned int prologue_stmts = 0;
unsigned int body_stmts = 0;
rgroup_controls *rgc;
unsigned int num_vectors_m1;
FOR_EACH_VEC_ELT (LOOP_VINFO_LENS (loop_vinfo), num_vectors_m1, rgc)
if (rgc->type)
{
/* May need one SHIFT for nitems_total computation. */
unsigned nitems = rgc->max_nscalars_per_iter * rgc->factor;
if (nitems != 1 && !niters_known_p)
prologue_stmts += 1;
/* May need one MAX and one MINUS for wrap around. */
if (vect_rgroup_iv_might_wrap_p (loop_vinfo, rgc))
prologue_stmts += 2;
/* Need one MAX and one MINUS for each batch limit excepting for
the 1st one. */
prologue_stmts += num_vectors_m1 * 2;
unsigned int num_vectors = num_vectors_m1 + 1;
/* Need to set up lengths in prologue, only one MIN required
for each since start index is zero. */
prologue_stmts += num_vectors;
/* Each may need two MINs and one MINUS to update lengths in body
for next iteration. */
if (need_iterate_p)
body_stmts += 3 * num_vectors;
}
(void) add_stmt_cost (loop_vinfo, target_cost_data, prologue_stmts,
scalar_stmt, NULL, NULL_TREE, 0, vect_prologue);
(void) add_stmt_cost (loop_vinfo, target_cost_data, body_stmts,
scalar_stmt, NULL, NULL_TREE, 0, vect_body);
}
/* FORNOW: The scalar outside cost is incremented in one of the
following ways:
1. The vectorizer checks for alignment and aliasing and generates
a condition that allows dynamic vectorization. A cost model
check is ANDED with the versioning condition. Hence scalar code
path now has the added cost of the versioning check.
if (cost > th & versioning_check)
jmp to vector code
Hence run-time scalar is incremented by not-taken branch cost.
2. The vectorizer then checks if a prologue is required. If the
cost model check was not done before during versioning, it has to
be done before the prologue check.
if (cost <= th)
prologue = scalar_iters
if (prologue == 0)
jmp to vector code
else
execute prologue
if (prologue == num_iters)
go to exit
Hence the run-time scalar cost is incremented by a taken branch,
plus a not-taken branch, plus a taken branch cost.
3. The vectorizer then checks if an epilogue is required. If the
cost model check was not done before during prologue check, it
has to be done with the epilogue check.
if (prologue == 0)
jmp to vector code
else
execute prologue
if (prologue == num_iters)
go to exit
vector code:
if ((cost <= th) | (scalar_iters-prologue-epilogue == 0))
jmp to epilogue
Hence the run-time scalar cost should be incremented by 2 taken
branches.
TODO: The back end may reorder the BBS's differently and reverse
conditions/branch directions. Change the estimates below to
something more reasonable. */
/* If the number of iterations is known and we do not do versioning, we can
decide whether to vectorize at compile time. Hence the scalar version
do not carry cost model guard costs. */
if (!LOOP_VINFO_NITERS_KNOWN_P (loop_vinfo)
|| LOOP_REQUIRES_VERSIONING (loop_vinfo))
{
/* Cost model check occurs at versioning. */
if (LOOP_REQUIRES_VERSIONING (loop_vinfo))
scalar_outside_cost += vect_get_stmt_cost (cond_branch_not_taken);
else
{
/* Cost model check occurs at prologue generation. */
if (LOOP_VINFO_PEELING_FOR_ALIGNMENT (loop_vinfo) < 0)
scalar_outside_cost += 2 * vect_get_stmt_cost (cond_branch_taken)
+ vect_get_stmt_cost (cond_branch_not_taken);
/* Cost model check occurs at epilogue generation. */
else
scalar_outside_cost += 2 * vect_get_stmt_cost (cond_branch_taken);
}
}
/* Complete the target-specific cost calculations. */
finish_cost (LOOP_VINFO_TARGET_COST_DATA (loop_vinfo), &vec_prologue_cost,
&vec_inside_cost, &vec_epilogue_cost);
vec_outside_cost = (int)(vec_prologue_cost + vec_epilogue_cost);
/* Stash the costs so that we can compare two loop_vec_infos. */
loop_vinfo->vec_inside_cost = vec_inside_cost;
loop_vinfo->vec_outside_cost = vec_outside_cost;
if (dump_enabled_p ())
{
dump_printf_loc (MSG_NOTE, vect_location, "Cost model analysis: \n");
dump_printf (MSG_NOTE, " Vector inside of loop cost: %d\n",
vec_inside_cost);
dump_printf (MSG_NOTE, " Vector prologue cost: %d\n",
vec_prologue_cost);
dump_printf (MSG_NOTE, " Vector epilogue cost: %d\n",
vec_epilogue_cost);
dump_printf (MSG_NOTE, " Scalar iteration cost: %d\n",
scalar_single_iter_cost);
dump_printf (MSG_NOTE, " Scalar outside cost: %d\n",
scalar_outside_cost);
dump_printf (MSG_NOTE, " Vector outside cost: %d\n",
vec_outside_cost);
dump_printf (MSG_NOTE, " prologue iterations: %d\n",
peel_iters_prologue);
dump_printf (MSG_NOTE, " epilogue iterations: %d\n",
peel_iters_epilogue);
}
/* Calculate number of iterations required to make the vector version
profitable, relative to the loop bodies only. The following condition
must hold true:
SIC * niters + SOC > VIC * ((niters - NPEEL) / VF) + VOC
where
SIC = scalar iteration cost, VIC = vector iteration cost,
VOC = vector outside cost, VF = vectorization factor,
NPEEL = prologue iterations + epilogue iterations,
SOC = scalar outside cost for run time cost model check. */
int saving_per_viter = (scalar_single_iter_cost * assumed_vf
- vec_inside_cost);
if (saving_per_viter <= 0)
{
if (LOOP_VINFO_LOOP (loop_vinfo)->force_vectorize)
warning_at (vect_location.get_location_t (), OPT_Wopenmp_simd,
"vectorization did not happen for a simd loop");
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"cost model: the vector iteration cost = %d "
"divided by the scalar iteration cost = %d "
"is greater or equal to the vectorization factor = %d"
".\n",
vec_inside_cost, scalar_single_iter_cost, assumed_vf);
*ret_min_profitable_niters = -1;
*ret_min_profitable_estimate = -1;
return;
}
/* ??? The "if" arm is written to handle all cases; see below for what
we would do for !LOOP_VINFO_USING_PARTIAL_VECTORS_P. */
if (LOOP_VINFO_USING_PARTIAL_VECTORS_P (loop_vinfo))
{
/* Rewriting the condition above in terms of the number of
vector iterations (vniters) rather than the number of
scalar iterations (niters) gives:
SIC * (vniters * VF + NPEEL) + SOC > VIC * vniters + VOC
<==> vniters * (SIC * VF - VIC) > VOC - SIC * NPEEL - SOC
For integer N, X and Y when X > 0:
N * X > Y <==> N >= (Y /[floor] X) + 1. */
int outside_overhead = (vec_outside_cost
- scalar_single_iter_cost * peel_iters_prologue
- scalar_single_iter_cost * peel_iters_epilogue
- scalar_outside_cost);
/* We're only interested in cases that require at least one
vector iteration. */
int min_vec_niters = 1;
if (outside_overhead > 0)
min_vec_niters = outside_overhead / saving_per_viter + 1;
if (dump_enabled_p ())
dump_printf (MSG_NOTE, " Minimum number of vector iterations: %d\n",
min_vec_niters);
if (LOOP_VINFO_USING_PARTIAL_VECTORS_P (loop_vinfo))
{
/* Now that we know the minimum number of vector iterations,
find the minimum niters for which the scalar cost is larger:
SIC * niters > VIC * vniters + VOC - SOC
We know that the minimum niters is no more than
vniters * VF + NPEEL, but it might be (and often is) less
than that if a partial vector iteration is cheaper than the
equivalent scalar code. */
int threshold = (vec_inside_cost * min_vec_niters
+ vec_outside_cost
- scalar_outside_cost);
if (threshold <= 0)
min_profitable_iters = 1;
else
min_profitable_iters = threshold / scalar_single_iter_cost + 1;
}
else
/* Convert the number of vector iterations into a number of
scalar iterations. */
min_profitable_iters = (min_vec_niters * assumed_vf
+ peel_iters_prologue
+ peel_iters_epilogue);
}
else
{
min_profitable_iters = ((vec_outside_cost - scalar_outside_cost)
* assumed_vf
- vec_inside_cost * peel_iters_prologue
- vec_inside_cost * peel_iters_epilogue);
if (min_profitable_iters <= 0)
min_profitable_iters = 0;
else
{
min_profitable_iters /= saving_per_viter;
if ((scalar_single_iter_cost * assumed_vf * min_profitable_iters)
<= (((int) vec_inside_cost * min_profitable_iters)
+ (((int) vec_outside_cost - scalar_outside_cost)
* assumed_vf)))
min_profitable_iters++;
}
}
if (dump_enabled_p ())
dump_printf (MSG_NOTE,
" Calculated minimum iters for profitability: %d\n",
min_profitable_iters);
if (!LOOP_VINFO_USING_PARTIAL_VECTORS_P (loop_vinfo)
&& min_profitable_iters < (assumed_vf + peel_iters_prologue))
/* We want the vectorized loop to execute at least once. */
min_profitable_iters = assumed_vf + peel_iters_prologue;
else if (min_profitable_iters < peel_iters_prologue)
/* For LOOP_VINFO_USING_PARTIAL_VECTORS_P, we need to ensure the
vectorized loop executes at least once. */
min_profitable_iters = peel_iters_prologue;
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
" Runtime profitability threshold = %d\n",
min_profitable_iters);
*ret_min_profitable_niters = min_profitable_iters;
/* Calculate number of iterations required to make the vector version
profitable, relative to the loop bodies only.
Non-vectorized variant is SIC * niters and it must win over vector
variant on the expected loop trip count. The following condition must hold true:
SIC * niters > VIC * ((niters - NPEEL) / VF) + VOC + SOC */
if (vec_outside_cost <= 0)
min_profitable_estimate = 0;
/* ??? This "else if" arm is written to handle all cases; see below for
what we would do for !LOOP_VINFO_USING_PARTIAL_VECTORS_P. */
else if (LOOP_VINFO_USING_PARTIAL_VECTORS_P (loop_vinfo))
{
/* This is a repeat of the code above, but with + SOC rather
than - SOC. */
int outside_overhead = (vec_outside_cost
- scalar_single_iter_cost * peel_iters_prologue
- scalar_single_iter_cost * peel_iters_epilogue
+ scalar_outside_cost);
int min_vec_niters = 1;
if (outside_overhead > 0)
min_vec_niters = outside_overhead / saving_per_viter + 1;
if (LOOP_VINFO_USING_PARTIAL_VECTORS_P (loop_vinfo))
{
int threshold = (vec_inside_cost * min_vec_niters
+ vec_outside_cost
+ scalar_outside_cost);
min_profitable_estimate = threshold / scalar_single_iter_cost + 1;
}
else
min_profitable_estimate = (min_vec_niters * assumed_vf
+ peel_iters_prologue
+ peel_iters_epilogue);
}
else
{
min_profitable_estimate = ((vec_outside_cost + scalar_outside_cost)
* assumed_vf
- vec_inside_cost * peel_iters_prologue
- vec_inside_cost * peel_iters_epilogue)
/ ((scalar_single_iter_cost * assumed_vf)
- vec_inside_cost);
}
min_profitable_estimate = MAX (min_profitable_estimate, min_profitable_iters);
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
" Static estimate profitability threshold = %d\n",
min_profitable_estimate);
*ret_min_profitable_estimate = min_profitable_estimate;
}
/* Writes into SEL a mask for a vec_perm, equivalent to a vec_shr by OFFSET
vector elements (not bits) for a vector with NELT elements. */
static void
calc_vec_perm_mask_for_shift (unsigned int offset, unsigned int nelt,
vec_perm_builder *sel)
{
/* The encoding is a single stepped pattern. Any wrap-around is handled
by vec_perm_indices. */
sel->new_vector (nelt, 1, 3);
for (unsigned int i = 0; i < 3; i++)
sel->quick_push (i + offset);
}
/* Checks whether the target supports whole-vector shifts for vectors of mode
MODE. This is the case if _either_ the platform handles vec_shr_optab, _or_
it supports vec_perm_const with masks for all necessary shift amounts. */
static bool
have_whole_vector_shift (machine_mode mode)
{
if (optab_handler (vec_shr_optab, mode) != CODE_FOR_nothing)
return true;
/* Variable-length vectors should be handled via the optab. */
unsigned int nelt;
if (!GET_MODE_NUNITS (mode).is_constant (&nelt))
return false;
vec_perm_builder sel;
vec_perm_indices indices;
for (unsigned int i = nelt / 2; i >= 1; i /= 2)
{
calc_vec_perm_mask_for_shift (i, nelt, &sel);
indices.new_vector (sel, 2, nelt);
if (!can_vec_perm_const_p (mode, indices, false))
return false;
}
return true;
}
/* TODO: Close dependency between vect_model_*_cost and vectorizable_*
functions. Design better to avoid maintenance issues. */
/* Function vect_model_reduction_cost.
Models cost for a reduction operation, including the vector ops
generated within the strip-mine loop in some cases, the initial
definition before the loop, and the epilogue code that must be generated. */
static void
vect_model_reduction_cost (loop_vec_info loop_vinfo,
stmt_vec_info stmt_info, internal_fn reduc_fn,
vect_reduction_type reduction_type,
int ncopies, stmt_vector_for_cost *cost_vec)
{
int prologue_cost = 0, epilogue_cost = 0, inside_cost = 0;
enum tree_code code;
optab optab;
tree vectype;
machine_mode mode;
class loop *loop = NULL;
if (loop_vinfo)
loop = LOOP_VINFO_LOOP (loop_vinfo);
/* Condition reductions generate two reductions in the loop. */
if (reduction_type == COND_REDUCTION)
ncopies *= 2;
vectype = STMT_VINFO_VECTYPE (stmt_info);
mode = TYPE_MODE (vectype);
stmt_vec_info orig_stmt_info = vect_orig_stmt (stmt_info);
code = gimple_assign_rhs_code (orig_stmt_info->stmt);
if (reduction_type == EXTRACT_LAST_REDUCTION)
/* No extra instructions are needed in the prologue. The loop body
operations are costed in vectorizable_condition. */
inside_cost = 0;
else if (reduction_type == FOLD_LEFT_REDUCTION)
{
/* No extra instructions needed in the prologue. */
prologue_cost = 0;
if (reduc_fn != IFN_LAST)
/* Count one reduction-like operation per vector. */
inside_cost = record_stmt_cost (cost_vec, ncopies, vec_to_scalar,
stmt_info, 0, vect_body);
else
{
/* Use NELEMENTS extracts and NELEMENTS scalar ops. */
unsigned int nelements = ncopies * vect_nunits_for_cost (vectype);
inside_cost = record_stmt_cost (cost_vec, nelements,
vec_to_scalar, stmt_info, 0,
vect_body);
inside_cost += record_stmt_cost (cost_vec, nelements,
scalar_stmt, stmt_info, 0,
vect_body);
}
}
else
{
/* Add in cost for initial definition.
For cond reduction we have four vectors: initial index, step,
initial result of the data reduction, initial value of the index
reduction. */
int prologue_stmts = reduction_type == COND_REDUCTION ? 4 : 1;
prologue_cost += record_stmt_cost (cost_vec, prologue_stmts,
scalar_to_vec, stmt_info, 0,
vect_prologue);
}
/* Determine cost of epilogue code.
We have a reduction operator that will reduce the vector in one statement.
Also requires scalar extract. */
if (!loop || !nested_in_vect_loop_p (loop, orig_stmt_info))
{
if (reduc_fn != IFN_LAST)
{
if (reduction_type == COND_REDUCTION)
{
/* An EQ stmt and an COND_EXPR stmt. */
epilogue_cost += record_stmt_cost (cost_vec, 2,
vector_stmt, stmt_info, 0,
vect_epilogue);
/* Reduction of the max index and a reduction of the found
values. */
epilogue_cost += record_stmt_cost (cost_vec, 2,
vec_to_scalar, stmt_info, 0,
vect_epilogue);
/* A broadcast of the max value. */
epilogue_cost += record_stmt_cost (cost_vec, 1,
scalar_to_vec, stmt_info, 0,
vect_epilogue);
}
else
{
epilogue_cost += record_stmt_cost (cost_vec, 1, vector_stmt,
stmt_info, 0, vect_epilogue);
epilogue_cost += record_stmt_cost (cost_vec, 1,
vec_to_scalar, stmt_info, 0,
vect_epilogue);
}
}
else if (reduction_type == COND_REDUCTION)
{
unsigned estimated_nunits = vect_nunits_for_cost (vectype);
/* Extraction of scalar elements. */
epilogue_cost += record_stmt_cost (cost_vec,
2 * estimated_nunits,
vec_to_scalar, stmt_info, 0,
vect_epilogue);
/* Scalar max reductions via COND_EXPR / MAX_EXPR. */
epilogue_cost += record_stmt_cost (cost_vec,
2 * estimated_nunits - 3,
scalar_stmt, stmt_info, 0,
vect_epilogue);
}
else if (reduction_type == EXTRACT_LAST_REDUCTION
|| reduction_type == FOLD_LEFT_REDUCTION)
/* No extra instructions need in the epilogue. */
;
else
{
int vec_size_in_bits = tree_to_uhwi (TYPE_SIZE (vectype));
tree bitsize =
TYPE_SIZE (TREE_TYPE (gimple_assign_lhs (orig_stmt_info->stmt)));
int element_bitsize = tree_to_uhwi (bitsize);
int nelements = vec_size_in_bits / element_bitsize;
if (code == COND_EXPR)
code = MAX_EXPR;
optab = optab_for_tree_code (code, vectype, optab_default);
/* We have a whole vector shift available. */
if (optab != unknown_optab
&& VECTOR_MODE_P (mode)
&& optab_handler (optab, mode) != CODE_FOR_nothing
&& have_whole_vector_shift (mode))
{
/* Final reduction via vector shifts and the reduction operator.
Also requires scalar extract. */
epilogue_cost += record_stmt_cost (cost_vec,
exact_log2 (nelements) * 2,
vector_stmt, stmt_info, 0,
vect_epilogue);
epilogue_cost += record_stmt_cost (cost_vec, 1,
vec_to_scalar, stmt_info, 0,
vect_epilogue);
}
else
/* Use extracts and reduction op for final reduction. For N
elements, we have N extracts and N-1 reduction ops. */
epilogue_cost += record_stmt_cost (cost_vec,
nelements + nelements - 1,
vector_stmt, stmt_info, 0,
vect_epilogue);
}
}
if (dump_enabled_p ())
dump_printf (MSG_NOTE,
"vect_model_reduction_cost: inside_cost = %d, "
"prologue_cost = %d, epilogue_cost = %d .\n", inside_cost,
prologue_cost, epilogue_cost);
}
/* Function get_initial_def_for_reduction
Input:
STMT_VINFO - a stmt that performs a reduction operation in the loop.
INIT_VAL - the initial value of the reduction variable
Output:
ADJUSTMENT_DEF - a tree that holds a value to be added to the final result
of the reduction (used for adjusting the epilog - see below).
Return a vector variable, initialized according to the operation that
STMT_VINFO performs. This vector will be used as the initial value
of the vector of partial results.
Option1 (adjust in epilog): Initialize the vector as follows:
add/bit or/xor: [0,0,...,0,0]
mult/bit and: [1,1,...,1,1]
min/max/cond_expr: [init_val,init_val,..,init_val,init_val]
and when necessary (e.g. add/mult case) let the caller know
that it needs to adjust the result by init_val.
Option2: Initialize the vector as follows:
add/bit or/xor: [init_val,0,0,...,0]
mult/bit and: [init_val,1,1,...,1]
min/max/cond_expr: [init_val,init_val,...,init_val]
and no adjustments are needed.
For example, for the following code:
s = init_val;
for (i=0;i<n;i++)
s = s + a[i];
STMT_VINFO is 's = s + a[i]', and the reduction variable is 's'.
For a vector of 4 units, we want to return either [0,0,0,init_val],
or [0,0,0,0] and let the caller know that it needs to adjust
the result at the end by 'init_val'.
FORNOW, we are using the 'adjust in epilog' scheme, because this way the
initialization vector is simpler (same element in all entries), if
ADJUSTMENT_DEF is not NULL, and Option2 otherwise.
A cost model should help decide between these two schemes. */
static tree
get_initial_def_for_reduction (loop_vec_info loop_vinfo,
stmt_vec_info stmt_vinfo,
enum tree_code code, tree init_val,
tree *adjustment_def)
{
class loop *loop = LOOP_VINFO_LOOP (loop_vinfo);
tree scalar_type = TREE_TYPE (init_val);
tree vectype = get_vectype_for_scalar_type (loop_vinfo, scalar_type);
tree def_for_init;
tree init_def;
REAL_VALUE_TYPE real_init_val = dconst0;
int int_init_val = 0;
gimple_seq stmts = NULL;
gcc_assert (vectype);
gcc_assert (POINTER_TYPE_P (scalar_type) || INTEGRAL_TYPE_P (scalar_type)
|| SCALAR_FLOAT_TYPE_P (scalar_type));
gcc_assert (nested_in_vect_loop_p (loop, stmt_vinfo)
|| loop == (gimple_bb (stmt_vinfo->stmt))->loop_father);
/* ADJUSTMENT_DEF is NULL when called from
vect_create_epilog_for_reduction to vectorize double reduction. */
if (adjustment_def)
*adjustment_def = NULL;
switch (code)
{
case WIDEN_SUM_EXPR:
case DOT_PROD_EXPR:
case SAD_EXPR:
case PLUS_EXPR:
case MINUS_EXPR:
case BIT_IOR_EXPR:
case BIT_XOR_EXPR:
case MULT_EXPR:
case BIT_AND_EXPR:
{
if (code == MULT_EXPR)
{
real_init_val = dconst1;
int_init_val = 1;
}
if (code == BIT_AND_EXPR)
int_init_val = -1;
if (SCALAR_FLOAT_TYPE_P (scalar_type))
def_for_init = build_real (scalar_type, real_init_val);
else
def_for_init = build_int_cst (scalar_type, int_init_val);
if (adjustment_def || operand_equal_p (def_for_init, init_val, 0))
{
/* Option1: the first element is '0' or '1' as well. */
if (!operand_equal_p (def_for_init, init_val, 0))
*adjustment_def = init_val;
init_def = gimple_build_vector_from_val (&stmts, vectype,
def_for_init);
}
else if (!TYPE_VECTOR_SUBPARTS (vectype).is_constant ())
{
/* Option2 (variable length): the first element is INIT_VAL. */
init_def = gimple_build_vector_from_val (&stmts, vectype,
def_for_init);
init_def = gimple_build (&stmts, CFN_VEC_SHL_INSERT,
vectype, init_def, init_val);
}
else
{
/* Option2: the first element is INIT_VAL. */
tree_vector_builder elts (vectype, 1, 2);
elts.quick_push (init_val);
elts.quick_push (def_for_init);
init_def = gimple_build_vector (&stmts, &elts);
}
}
break;
case MIN_EXPR:
case MAX_EXPR:
case COND_EXPR:
{
init_val = gimple_convert (&stmts, TREE_TYPE (vectype), init_val);
init_def = gimple_build_vector_from_val (&stmts, vectype, init_val);
}
break;
default:
gcc_unreachable ();
}
if (stmts)
gsi_insert_seq_on_edge_immediate (loop_preheader_edge (loop), stmts);
return init_def;
}
/* Get at the initial defs for the reduction PHIs in SLP_NODE.
NUMBER_OF_VECTORS is the number of vector defs to create.
If NEUTRAL_OP is nonnull, introducing extra elements of that
value will not change the result. */
static void
get_initial_defs_for_reduction (vec_info *vinfo,
slp_tree slp_node,
vec<tree> *vec_oprnds,
unsigned int number_of_vectors,
bool reduc_chain, tree neutral_op)
{
vec<stmt_vec_info> stmts = SLP_TREE_SCALAR_STMTS (slp_node);
stmt_vec_info stmt_vinfo = stmts[0];
unsigned HOST_WIDE_INT nunits;
unsigned j, number_of_places_left_in_vector;
tree vector_type;
unsigned int group_size = stmts.length ();
unsigned int i;
class loop *loop;
vector_type = STMT_VINFO_VECTYPE (stmt_vinfo);
gcc_assert (STMT_VINFO_DEF_TYPE (stmt_vinfo) == vect_reduction_def);
loop = (gimple_bb (stmt_vinfo->stmt))->loop_father;
gcc_assert (loop);
edge pe = loop_preheader_edge (loop);
gcc_assert (!reduc_chain || neutral_op);
/* NUMBER_OF_COPIES is the number of times we need to use the same values in
created vectors. It is greater than 1 if unrolling is performed.
For example, we have two scalar operands, s1 and s2 (e.g., group of
strided accesses of size two), while NUNITS is four (i.e., four scalars
of this type can be packed in a vector). The output vector will contain
two copies of each scalar operand: {s1, s2, s1, s2}. (NUMBER_OF_COPIES
will be 2).
If REDUC_GROUP_SIZE > NUNITS, the scalars will be split into several
vectors containing the operands.
For example, NUNITS is four as before, and the group size is 8
(s1, s2, ..., s8). We will create two vectors {s1, s2, s3, s4} and
{s5, s6, s7, s8}. */
if (!TYPE_VECTOR_SUBPARTS (vector_type).is_constant (&nunits))
nunits = group_size;
number_of_places_left_in_vector = nunits;
bool constant_p = true;
tree_vector_builder elts (vector_type, nunits, 1);
elts.quick_grow (nunits);
gimple_seq ctor_seq = NULL;
for (j = 0; j < nunits * number_of_vectors; ++j)
{
tree op;
i = j % group_size;
stmt_vinfo = stmts[i];
/* Get the def before the loop. In reduction chain we have only
one initial value. Else we have as many as PHIs in the group. */
if (reduc_chain)
op = j != 0 ? neutral_op : PHI_ARG_DEF_FROM_EDGE (stmt_vinfo->stmt, pe);
else if (((vec_oprnds->length () + 1) * nunits
- number_of_places_left_in_vector >= group_size)
&& neutral_op)
op = neutral_op;
else
op = PHI_ARG_DEF_FROM_EDGE (stmt_vinfo->stmt, pe);
/* Create 'vect_ = {op0,op1,...,opn}'. */
number_of_places_left_in_vector--;
elts[nunits - number_of_places_left_in_vector - 1] = op;
if (!CONSTANT_CLASS_P (op))
constant_p = false;
if (number_of_places_left_in_vector == 0)
{
tree init;
if (constant_p && !neutral_op
? multiple_p (TYPE_VECTOR_SUBPARTS (vector_type), nunits)
: known_eq (TYPE_VECTOR_SUBPARTS (vector_type), nunits))
/* Build the vector directly from ELTS. */
init = gimple_build_vector (&ctor_seq, &elts);
else if (neutral_op)
{
/* Build a vector of the neutral value and shift the
other elements into place. */
init = gimple_build_vector_from_val (&ctor_seq, vector_type,
neutral_op);
int k = nunits;
while (k > 0 && elts[k - 1] == neutral_op)
k -= 1;
while (k > 0)
{
k -= 1;
init = gimple_build (&ctor_seq, CFN_VEC_SHL_INSERT,
vector_type, init, elts[k]);
}
}
else
{
/* First time round, duplicate ELTS to fill the
required number of vectors. */
duplicate_and_interleave (vinfo, &ctor_seq, vector_type, elts,
number_of_vectors, *vec_oprnds);
break;
}
vec_oprnds->quick_push (init);
number_of_places_left_in_vector = nunits;
elts.new_vector (vector_type, nunits, 1);
elts.quick_grow (nunits);
constant_p = true;
}
}
if (ctor_seq != NULL)
gsi_insert_seq_on_edge_immediate (pe, ctor_seq);
}
/* For a statement STMT_INFO taking part in a reduction operation return
the stmt_vec_info the meta information is stored on. */
stmt_vec_info
info_for_reduction (vec_info *vinfo, stmt_vec_info stmt_info)
{
stmt_info = vect_orig_stmt (stmt_info);
gcc_assert (STMT_VINFO_REDUC_DEF (stmt_info));
if (!is_a <gphi *> (stmt_info->stmt)
|| !VECTORIZABLE_CYCLE_DEF (STMT_VINFO_DEF_TYPE (stmt_info)))
stmt_info = STMT_VINFO_REDUC_DEF (stmt_info);
gphi *phi = as_a <gphi *> (stmt_info->stmt);
if (STMT_VINFO_DEF_TYPE (stmt_info) == vect_double_reduction_def)
{
if (gimple_phi_num_args (phi) == 1)
stmt_info = STMT_VINFO_REDUC_DEF (stmt_info);
}
else if (STMT_VINFO_DEF_TYPE (stmt_info) == vect_nested_cycle)
{
edge pe = loop_preheader_edge (gimple_bb (phi)->loop_father);
stmt_vec_info info
= vinfo->lookup_def (PHI_ARG_DEF_FROM_EDGE (phi, pe));
if (info && STMT_VINFO_DEF_TYPE (info) == vect_double_reduction_def)
stmt_info = info;
}
return stmt_info;
}
/* Function vect_create_epilog_for_reduction
Create code at the loop-epilog to finalize the result of a reduction
computation.
STMT_INFO is the scalar reduction stmt that is being vectorized.
SLP_NODE is an SLP node containing a group of reduction statements. The
first one in this group is STMT_INFO.
SLP_NODE_INSTANCE is the SLP node instance containing SLP_NODE
REDUC_INDEX says which rhs operand of the STMT_INFO is the reduction phi
(counting from 0)
This function:
1. Completes the reduction def-use cycles.
2. "Reduces" each vector of partial results VECT_DEFS into a single result,
by calling the function specified by REDUC_FN if available, or by
other means (whole-vector shifts or a scalar loop).
The function also creates a new phi node at the loop exit to preserve
loop-closed form, as illustrated below.
The flow at the entry to this function:
loop:
vec_def = phi <vec_init, null> # REDUCTION_PHI
VECT_DEF = vector_stmt # vectorized form of STMT_INFO
s_loop = scalar_stmt # (scalar) STMT_INFO
loop_exit:
s_out0 = phi <s_loop> # (scalar) EXIT_PHI
use <s_out0>
use <s_out0>
The above is transformed by this function into:
loop:
vec_def = phi <vec_init, VECT_DEF> # REDUCTION_PHI
VECT_DEF = vector_stmt # vectorized form of STMT_INFO
s_loop = scalar_stmt # (scalar) STMT_INFO
loop_exit:
s_out0 = phi <s_loop> # (scalar) EXIT_PHI
v_out1 = phi <VECT_DEF> # NEW_EXIT_PHI
v_out2 = reduce <v_out1>
s_out3 = extract_field <v_out2, 0>
s_out4 = adjust_result <s_out3>
use <s_out4>
use <s_out4>
*/
static void
vect_create_epilog_for_reduction (loop_vec_info loop_vinfo,
stmt_vec_info stmt_info,
slp_tree slp_node,
slp_instance slp_node_instance)
{
stmt_vec_info reduc_info = info_for_reduction (loop_vinfo, stmt_info);
gcc_assert (reduc_info->is_reduc_info);
/* For double reductions we need to get at the inner loop reduction
stmt which has the meta info attached. Our stmt_info is that of the
loop-closed PHI of the inner loop which we remember as
def for the reduction PHI generation. */
bool double_reduc = false;
stmt_vec_info rdef_info = stmt_info;
if (STMT_VINFO_DEF_TYPE (stmt_info) == vect_double_reduction_def)
{
gcc_assert (!slp_node);
double_reduc = true;
stmt_info = loop_vinfo->lookup_def (gimple_phi_arg_def
(stmt_info->stmt, 0));
stmt_info = vect_stmt_to_vectorize (stmt_info);
}
gphi *reduc_def_stmt
= as_a <gphi *> (STMT_VINFO_REDUC_DEF (vect_orig_stmt (stmt_info))->stmt);
enum tree_code code = STMT_VINFO_REDUC_CODE (reduc_info);
internal_fn reduc_fn = STMT_VINFO_REDUC_FN (reduc_info);
tree vectype;
machine_mode mode;
class loop *loop = LOOP_VINFO_LOOP (loop_vinfo), *outer_loop = NULL;
basic_block exit_bb;
tree scalar_dest;
tree scalar_type;
gimple *new_phi = NULL, *phi;
gimple_stmt_iterator exit_gsi;
tree new_temp = NULL_TREE, new_name, new_scalar_dest;
gimple *epilog_stmt = NULL;
gimple *exit_phi;
tree bitsize;
tree def;
tree orig_name, scalar_result;
imm_use_iterator imm_iter, phi_imm_iter;
use_operand_p use_p, phi_use_p;
gimple *use_stmt;
bool nested_in_vect_loop = false;
auto_vec<gimple *> new_phis;
int j, i;
auto_vec<tree> scalar_results;
unsigned int group_size = 1, k;
auto_vec<gimple *> phis;
bool slp_reduc = false;
bool direct_slp_reduc;
tree new_phi_result;
tree induction_index = NULL_TREE;
if (slp_node)
group_size = SLP_TREE_LANES (slp_node);
if (nested_in_vect_loop_p (loop, stmt_info))
{
outer_loop = loop;
loop = loop->inner;
nested_in_vect_loop = true;
gcc_assert (!slp_node);
}
gcc_assert (!nested_in_vect_loop || double_reduc);
vectype = STMT_VINFO_REDUC_VECTYPE (reduc_info);
gcc_assert (vectype);
mode = TYPE_MODE (vectype);
tree initial_def = NULL;
tree induc_val = NULL_TREE;
tree adjustment_def = NULL;
if (slp_node)
;
else
{
/* Get at the scalar def before the loop, that defines the initial value
of the reduction variable. */
initial_def = PHI_ARG_DEF_FROM_EDGE (reduc_def_stmt,
loop_preheader_edge (loop));
/* Optimize: for induction condition reduction, if we can't use zero
for induc_val, use initial_def. */
if (STMT_VINFO_REDUC_TYPE (reduc_info) == INTEGER_INDUC_COND_REDUCTION)
induc_val = STMT_VINFO_VEC_INDUC_COND_INITIAL_VAL (reduc_info);
else if (double_reduc)
;
else if (nested_in_vect_loop)
;
else
adjustment_def = STMT_VINFO_REDUC_EPILOGUE_ADJUSTMENT (reduc_info);
}
unsigned vec_num;
int ncopies;
if (slp_node)
{
vec_num = SLP_TREE_VEC_STMTS (slp_node_instance->reduc_phis).length ();
ncopies = 1;
}
else
{
stmt_vec_info reduc_info = loop_vinfo->lookup_stmt (reduc_def_stmt);
vec_num = 1;
ncopies = STMT_VINFO_VEC_STMTS (reduc_info).length ();
}
/* For cond reductions we want to create a new vector (INDEX_COND_EXPR)
which is updated with the current index of the loop for every match of
the original loop's cond_expr (VEC_STMT). This results in a vector
containing the last time the condition passed for that vector lane.
The first match will be a 1 to allow 0 to be used for non-matching
indexes. If there are no matches at all then the vector will be all
zeroes.
PR92772: This algorithm is broken for architectures that support
masked vectors, but do not provide fold_extract_last. */
if (STMT_VINFO_REDUC_TYPE (reduc_info) == COND_REDUCTION)
{
auto_vec<std::pair<tree, bool>, 2> ccompares;
stmt_vec_info cond_info = STMT_VINFO_REDUC_DEF (reduc_info);
cond_info = vect_stmt_to_vectorize (cond_info);
while (cond_info != reduc_info)
{
if (gimple_assign_rhs_code (cond_info->stmt) == COND_EXPR)
{
gimple *vec_stmt = STMT_VINFO_VEC_STMTS (cond_info)[0];
gcc_assert (gimple_assign_rhs_code (vec_stmt) == VEC_COND_EXPR);
ccompares.safe_push
(std::make_pair (unshare_expr (gimple_assign_rhs1 (vec_stmt)),
STMT_VINFO_REDUC_IDX (cond_info) == 2));
}
cond_info
= loop_vinfo->lookup_def (gimple_op (cond_info->stmt,
1 + STMT_VINFO_REDUC_IDX
(cond_info)));
cond_info = vect_stmt_to_vectorize (cond_info);
}
gcc_assert (ccompares.length () != 0);
tree indx_before_incr, indx_after_incr;
poly_uint64 nunits_out = TYPE_VECTOR_SUBPARTS (vectype);
int scalar_precision
= GET_MODE_PRECISION (SCALAR_TYPE_MODE (TREE_TYPE (vectype)));
tree cr_index_scalar_type = make_unsigned_type (scalar_precision);
tree cr_index_vector_type = get_related_vectype_for_scalar_type
(TYPE_MODE (vectype), cr_index_scalar_type,
TYPE_VECTOR_SUBPARTS (vectype));
/* First we create a simple vector induction variable which starts
with the values {1,2,3,...} (SERIES_VECT) and increments by the
vector size (STEP). */
/* Create a {1,2,3,...} vector. */
tree series_vect = build_index_vector (cr_index_vector_type, 1, 1);
/* Create a vector of the step value. */
tree step = build_int_cst (cr_index_scalar_type, nunits_out);
tree vec_step = build_vector_from_val (cr_index_vector_type, step);
/* Create an induction variable. */
gimple_stmt_iterator incr_gsi;
bool insert_after;
standard_iv_increment_position (loop, &incr_gsi, &insert_after);
create_iv (series_vect, vec_step, NULL_TREE, loop, &incr_gsi,
insert_after, &indx_before_incr, &indx_after_incr);
/* Next create a new phi node vector (NEW_PHI_TREE) which starts
filled with zeros (VEC_ZERO). */
/* Create a vector of 0s. */
tree zero = build_zero_cst (cr_index_scalar_type);
tree vec_zero = build_vector_from_val (cr_index_vector_type, zero);
/* Create a vector phi node. */
tree new_phi_tree = make_ssa_name (cr_index_vector_type);
new_phi = create_phi_node (new_phi_tree, loop->header);
add_phi_arg (as_a <gphi *> (new_phi), vec_zero,
loop_preheader_edge (loop), UNKNOWN_LOCATION);
/* Now take the condition from the loops original cond_exprs
and produce a new cond_exprs (INDEX_COND_EXPR) which for
every match uses values from the induction variable
(INDEX_BEFORE_INCR) otherwise uses values from the phi node
(NEW_PHI_TREE).
Finally, we update the phi (NEW_PHI_TREE) to take the value of
the new cond_expr (INDEX_COND_EXPR). */
gimple_seq stmts = NULL;
for (int i = ccompares.length () - 1; i != -1; --i)
{
tree ccompare = ccompares[i].first;
if (ccompares[i].second)
new_phi_tree = gimple_build (&stmts, VEC_COND_EXPR,
cr_index_vector_type,
ccompare,
indx_before_incr, new_phi_tree);
else
new_phi_tree = gimple_build (&stmts, VEC_COND_EXPR,
cr_index_vector_type,
ccompare,
new_phi_tree, indx_before_incr);
}
gsi_insert_seq_before (&incr_gsi, stmts, GSI_SAME_STMT);
/* Update the phi with the vec cond. */
induction_index = new_phi_tree;
add_phi_arg (as_a <gphi *> (new_phi), induction_index,
loop_latch_edge (loop), UNKNOWN_LOCATION);
}
/* 2. Create epilog code.
The reduction epilog code operates across the elements of the vector
of partial results computed by the vectorized loop.
The reduction epilog code consists of:
step 1: compute the scalar result in a vector (v_out2)
step 2: extract the scalar result (s_out3) from the vector (v_out2)
step 3: adjust the scalar result (s_out3) if needed.
Step 1 can be accomplished using one the following three schemes:
(scheme 1) using reduc_fn, if available.
(scheme 2) using whole-vector shifts, if available.
(scheme 3) using a scalar loop. In this case steps 1+2 above are
combined.
The overall epilog code looks like this:
s_out0 = phi <s_loop> # original EXIT_PHI
v_out1 = phi <VECT_DEF> # NEW_EXIT_PHI
v_out2 = reduce <v_out1> # step 1
s_out3 = extract_field <v_out2, 0> # step 2
s_out4 = adjust_result <s_out3> # step 3
(step 3 is optional, and steps 1 and 2 may be combined).
Lastly, the uses of s_out0 are replaced by s_out4. */
/* 2.1 Create new loop-exit-phis to preserve loop-closed form:
v_out1 = phi <VECT_DEF>
Store them in NEW_PHIS. */
if (double_reduc)
loop = outer_loop;
exit_bb = single_exit (loop)->dest;
new_phis.create (slp_node ? vec_num : ncopies);
for (unsigned i = 0; i < vec_num; i++)
{
if (slp_node)
def = vect_get_slp_vect_def (slp_node, i);
else
def = gimple_get_lhs (STMT_VINFO_VEC_STMTS (rdef_info)[0]);
for (j = 0; j < ncopies; j++)
{
tree new_def = copy_ssa_name (def);
phi = create_phi_node (new_def, exit_bb);
if (j == 0)
new_phis.quick_push (phi);
else
{
def = gimple_get_lhs (STMT_VINFO_VEC_STMTS (rdef_info)[j]);
new_phis.quick_push (phi);
}
SET_PHI_ARG_DEF (phi, single_exit (loop)->dest_idx, def);
}
}
exit_gsi = gsi_after_labels (exit_bb);
/* 2.2 Get the relevant tree-code to use in the epilog for schemes 2,3
(i.e. when reduc_fn is not available) and in the final adjustment
code (if needed). Also get the original scalar reduction variable as
defined in the loop. In case STMT is a "pattern-stmt" (i.e. - it
represents a reduction pattern), the tree-code and scalar-def are
taken from the original stmt that the pattern-stmt (STMT) replaces.
Otherwise (it is a regular reduction) - the tree-code and scalar-def
are taken from STMT. */
stmt_vec_info orig_stmt_info = vect_orig_stmt (stmt_info);
if (orig_stmt_info != stmt_info)
{
/* Reduction pattern */
gcc_assert (STMT_VINFO_IN_PATTERN_P (orig_stmt_info));
gcc_assert (STMT_VINFO_RELATED_STMT (orig_stmt_info) == stmt_info);
}
scalar_dest = gimple_assign_lhs (orig_stmt_info->stmt);
scalar_type = TREE_TYPE (scalar_dest);
scalar_results.create (group_size);
new_scalar_dest = vect_create_destination_var (scalar_dest, NULL);
bitsize = TYPE_SIZE (scalar_type);
/* SLP reduction without reduction chain, e.g.,
# a1 = phi <a2, a0>
# b1 = phi <b2, b0>
a2 = operation (a1)
b2 = operation (b1) */
slp_reduc = (slp_node && !REDUC_GROUP_FIRST_ELEMENT (stmt_info));
/* True if we should implement SLP_REDUC using native reduction operations
instead of scalar operations. */
direct_slp_reduc = (reduc_fn != IFN_LAST
&& slp_reduc
&& !TYPE_VECTOR_SUBPARTS (vectype).is_constant ());
/* In case of reduction chain, e.g.,
# a1 = phi <a3, a0>
a2 = operation (a1)
a3 = operation (a2),
we may end up with more than one vector result. Here we reduce them to
one vector. */
if (REDUC_GROUP_FIRST_ELEMENT (stmt_info) || direct_slp_reduc)
{
gimple_seq stmts = NULL;
tree first_vect = PHI_RESULT (new_phis[0]);
first_vect = gimple_convert (&stmts, vectype, first_vect);
for (k = 1; k < new_phis.length (); k++)
{
gimple *next_phi = new_phis[k];
tree second_vect = PHI_RESULT (next_phi);
second_vect = gimple_convert (&stmts, vectype, second_vect);
first_vect = gimple_build (&stmts, code, vectype,
first_vect, second_vect);
}
gsi_insert_seq_before (&exit_gsi, stmts, GSI_SAME_STMT);
new_phi_result = first_vect;
new_phis.truncate (0);
new_phis.safe_push (SSA_NAME_DEF_STMT (first_vect));
}
/* Likewise if we couldn't use a single defuse cycle. */
else if (ncopies > 1)
{
gimple_seq stmts = NULL;
tree first_vect = PHI_RESULT (new_phis[0]);
first_vect = gimple_convert (&stmts, vectype, first_vect);
for (int k = 1; k < ncopies; ++k)
{
tree second_vect = PHI_RESULT (new_phis[k]);
second_vect = gimple_convert (&stmts, vectype, second_vect);
first_vect = gimple_build (&stmts, code, vectype,
first_vect, second_vect);
}
gsi_insert_seq_before (&exit_gsi, stmts, GSI_SAME_STMT);
new_phi_result = first_vect;
new_phis.truncate (0);
new_phis.safe_push (SSA_NAME_DEF_STMT (first_vect));
}
else
new_phi_result = PHI_RESULT (new_phis[0]);
if (STMT_VINFO_REDUC_TYPE (reduc_info) == COND_REDUCTION
&& reduc_fn != IFN_LAST)
{
/* For condition reductions, we have a vector (NEW_PHI_RESULT) containing
various data values where the condition matched and another vector
(INDUCTION_INDEX) containing all the indexes of those matches. We
need to extract the last matching index (which will be the index with
highest value) and use this to index into the data vector.
For the case where there were no matches, the data vector will contain
all default values and the index vector will be all zeros. */
/* Get various versions of the type of the vector of indexes. */
tree index_vec_type = TREE_TYPE (induction_index);
gcc_checking_assert (TYPE_UNSIGNED (index_vec_type));
tree index_scalar_type = TREE_TYPE (index_vec_type);
tree index_vec_cmp_type = truth_type_for (index_vec_type);
/* Get an unsigned integer version of the type of the data vector. */
int scalar_precision
= GET_MODE_PRECISION (SCALAR_TYPE_MODE (scalar_type));
tree scalar_type_unsigned = make_unsigned_type (scalar_precision);
tree vectype_unsigned = get_same_sized_vectype (scalar_type_unsigned,
vectype);
/* First we need to create a vector (ZERO_VEC) of zeros and another
vector (MAX_INDEX_VEC) filled with the last matching index, which we
can create using a MAX reduction and then expanding.
In the case where the loop never made any matches, the max index will
be zero. */
/* Vector of {0, 0, 0,...}. */
tree zero_vec = build_zero_cst (vectype);
gimple_seq stmts = NULL;
new_phi_result = gimple_convert (&stmts, vectype, new_phi_result);
gsi_insert_seq_before (&exit_gsi, stmts, GSI_SAME_STMT);
/* Find maximum value from the vector of found indexes. */
tree max_index = make_ssa_name (index_scalar_type);
gcall *max_index_stmt = gimple_build_call_internal (IFN_REDUC_MAX,
1, induction_index);
gimple_call_set_lhs (max_index_stmt, max_index);
gsi_insert_before (&exit_gsi, max_index_stmt, GSI_SAME_STMT);
/* Vector of {max_index, max_index, max_index,...}. */
tree max_index_vec = make_ssa_name (index_vec_type);
tree max_index_vec_rhs = build_vector_from_val (index_vec_type,
max_index);
gimple *max_index_vec_stmt = gimple_build_assign (max_index_vec,
max_index_vec_rhs);
gsi_insert_before (&exit_gsi, max_index_vec_stmt, GSI_SAME_STMT);
/* Next we compare the new vector (MAX_INDEX_VEC) full of max indexes
with the vector (INDUCTION_INDEX) of found indexes, choosing values
from the data vector (NEW_PHI_RESULT) for matches, 0 (ZERO_VEC)
otherwise. Only one value should match, resulting in a vector
(VEC_COND) with one data value and the rest zeros.
In the case where the loop never made any matches, every index will
match, resulting in a vector with all data values (which will all be
the default value). */
/* Compare the max index vector to the vector of found indexes to find
the position of the max value. */
tree vec_compare = make_ssa_name (index_vec_cmp_type);
gimple *vec_compare_stmt = gimple_build_assign (vec_compare, EQ_EXPR,
induction_index,
max_index_vec);
gsi_insert_before (&exit_gsi, vec_compare_stmt, GSI_SAME_STMT);
/* Use the compare to choose either values from the data vector or
zero. */
tree vec_cond = make_ssa_name (vectype);
gimple *vec_cond_stmt = gimple_build_assign (vec_cond, VEC_COND_EXPR,
vec_compare, new_phi_result,
zero_vec);
gsi_insert_before (&exit_gsi, vec_cond_stmt, GSI_SAME_STMT);
/* Finally we need to extract the data value from the vector (VEC_COND)
into a scalar (MATCHED_DATA_REDUC). Logically we want to do a OR
reduction, but because this doesn't exist, we can use a MAX reduction
instead. The data value might be signed or a float so we need to cast
it first.
In the case where the loop never made any matches, the data values are
all identical, and so will reduce down correctly. */
/* Make the matched data values unsigned. */
tree vec_cond_cast = make_ssa_name (vectype_unsigned);
tree vec_cond_cast_rhs = build1 (VIEW_CONVERT_EXPR, vectype_unsigned,
vec_cond);
gimple *vec_cond_cast_stmt = gimple_build_assign (vec_cond_cast,
VIEW_CONVERT_EXPR,
vec_cond_cast_rhs);
gsi_insert_before (&exit_gsi, vec_cond_cast_stmt, GSI_SAME_STMT);
/* Reduce down to a scalar value. */
tree data_reduc = make_ssa_name (scalar_type_unsigned);
gcall *data_reduc_stmt = gimple_build_call_internal (IFN_REDUC_MAX,
1, vec_cond_cast);
gimple_call_set_lhs (data_reduc_stmt, data_reduc);
gsi_insert_before (&exit_gsi, data_reduc_stmt, GSI_SAME_STMT);
/* Convert the reduced value back to the result type and set as the
result. */
stmts = NULL;
new_temp = gimple_build (&stmts, VIEW_CONVERT_EXPR, scalar_type,
data_reduc);
gsi_insert_seq_before (&exit_gsi, stmts, GSI_SAME_STMT);
scalar_results.safe_push (new_temp);
}
else if (STMT_VINFO_REDUC_TYPE (reduc_info) == COND_REDUCTION
&& reduc_fn == IFN_LAST)
{
/* Condition reduction without supported IFN_REDUC_MAX. Generate
idx = 0;
idx_val = induction_index[0];
val = data_reduc[0];
for (idx = 0, val = init, i = 0; i < nelts; ++i)
if (induction_index[i] > idx_val)
val = data_reduc[i], idx_val = induction_index[i];
return val; */
tree data_eltype = TREE_TYPE (TREE_TYPE (new_phi_result));
tree idx_eltype = TREE_TYPE (TREE_TYPE (induction_index));
unsigned HOST_WIDE_INT el_size = tree_to_uhwi (TYPE_SIZE (idx_eltype));
poly_uint64 nunits = TYPE_VECTOR_SUBPARTS (TREE_TYPE (induction_index));
/* Enforced by vectorizable_reduction, which ensures we have target
support before allowing a conditional reduction on variable-length
vectors. */
unsigned HOST_WIDE_INT v_size = el_size * nunits.to_constant ();
tree idx_val = NULL_TREE, val = NULL_TREE;
for (unsigned HOST_WIDE_INT off = 0; off < v_size; off += el_size)
{
tree old_idx_val = idx_val;
tree old_val = val;
idx_val = make_ssa_name (idx_eltype);
epilog_stmt = gimple_build_assign (idx_val, BIT_FIELD_REF,
build3 (BIT_FIELD_REF, idx_eltype,
induction_index,
bitsize_int (el_size),
bitsize_int (off)));
gsi_insert_before (&exit_gsi, epilog_stmt, GSI_SAME_STMT);
val = make_ssa_name (data_eltype);
epilog_stmt = gimple_build_assign (val, BIT_FIELD_REF,
build3 (BIT_FIELD_REF,
data_eltype,
new_phi_result,
bitsize_int (el_size),
bitsize_int (off)));
gsi_insert_before (&exit_gsi, epilog_stmt, GSI_SAME_STMT);
if (off != 0)
{
tree new_idx_val = idx_val;
if (off != v_size - el_size)
{
new_idx_val = make_ssa_name (idx_eltype);
epilog_stmt = gimple_build_assign (new_idx_val,
MAX_EXPR, idx_val,
old_idx_val);
gsi_insert_before (&exit_gsi, epilog_stmt, GSI_SAME_STMT);
}
tree new_val = make_ssa_name (data_eltype);
epilog_stmt = gimple_build_assign (new_val,
COND_EXPR,
build2 (GT_EXPR,
boolean_type_node,
idx_val,
old_idx_val),
val, old_val);
gsi_insert_before (&exit_gsi, epilog_stmt, GSI_SAME_STMT);
idx_val = new_idx_val;
val = new_val;
}
}
/* Convert the reduced value back to the result type and set as the
result. */
gimple_seq stmts = NULL;
val = gimple_convert (&stmts, scalar_type, val);
gsi_insert_seq_before (&exit_gsi, stmts, GSI_SAME_STMT);
scalar_results.safe_push (val);
}
/* 2.3 Create the reduction code, using one of the three schemes described
above. In SLP we simply need to extract all the elements from the
vector (without reducing them), so we use scalar shifts. */
else if (reduc_fn != IFN_LAST && !slp_reduc)
{
tree tmp;
tree vec_elem_type;
/* Case 1: Create:
v_out2 = reduc_expr <v_out1> */
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"Reduce using direct vector reduction.\n");
gimple_seq stmts = NULL;
new_phi_result = gimple_convert (&stmts, vectype, new_phi_result);
vec_elem_type = TREE_TYPE (TREE_TYPE (new_phi_result));
new_temp = gimple_build (&stmts, as_combined_fn (reduc_fn),
vec_elem_type, new_phi_result);
new_temp = gimple_convert (&stmts, scalar_type, new_temp);
gsi_insert_seq_before (&exit_gsi, stmts, GSI_SAME_STMT);
if ((STMT_VINFO_REDUC_TYPE (reduc_info) == INTEGER_INDUC_COND_REDUCTION)
&& induc_val)
{
/* Earlier we set the initial value to be a vector if induc_val
values. Check the result and if it is induc_val then replace
with the original initial value, unless induc_val is
the same as initial_def already. */
tree zcompare = build2 (EQ_EXPR, boolean_type_node, new_temp,
induc_val);
tmp = make_ssa_name (new_scalar_dest);
epilog_stmt = gimple_build_assign (tmp, COND_EXPR, zcompare,
initial_def, new_temp);
gsi_insert_before (&exit_gsi, epilog_stmt, GSI_SAME_STMT);
new_temp = tmp;
}
scalar_results.safe_push (new_temp);
}
else if (direct_slp_reduc)
{
/* Here we create one vector for each of the REDUC_GROUP_SIZE results,
with the elements for other SLP statements replaced with the
neutral value. We can then do a normal reduction on each vector. */
/* Enforced by vectorizable_reduction. */
gcc_assert (new_phis.length () == 1);
gcc_assert (pow2p_hwi (group_size));
slp_tree orig_phis_slp_node = slp_node_instance->reduc_phis;
vec<stmt_vec_info> orig_phis
= SLP_TREE_SCALAR_STMTS (orig_phis_slp_node);
gimple_seq seq = NULL;
/* Build a vector {0, 1, 2, ...}, with the same number of elements
and the same element size as VECTYPE. */
tree index = build_index_vector (vectype, 0, 1);
tree index_type = TREE_TYPE (index);
tree index_elt_type = TREE_TYPE (index_type);
tree mask_type = truth_type_for (index_type);
/* Create a vector that, for each element, identifies which of
the REDUC_GROUP_SIZE results should use it. */
tree index_mask = build_int_cst (index_elt_type, group_size - 1);
index = gimple_build (&seq, BIT_AND_EXPR, index_type, index,
build_vector_from_val (index_type, index_mask));
/* Get a neutral vector value. This is simply a splat of the neutral
scalar value if we have one, otherwise the initial scalar value
is itself a neutral value. */
tree vector_identity = NULL_TREE;
tree neutral_op = NULL_TREE;
if (slp_node)
{
stmt_vec_info first = REDUC_GROUP_FIRST_ELEMENT (stmt_info);
neutral_op
= neutral_op_for_slp_reduction (slp_node_instance->reduc_phis,
vectype, code, first != NULL);
}
if (neutral_op)
vector_identity = gimple_build_vector_from_val (&seq, vectype,
neutral_op);
for (unsigned int i = 0; i < group_size; ++i)
{
/* If there's no univeral neutral value, we can use the
initial scalar value from the original PHI. This is used
for MIN and MAX reduction, for example. */
if (!neutral_op)
{
tree scalar_value
= PHI_ARG_DEF_FROM_EDGE (orig_phis[i]->stmt,
loop_preheader_edge (loop));
scalar_value = gimple_convert (&seq, TREE_TYPE (vectype),
scalar_value);
vector_identity = gimple_build_vector_from_val (&seq, vectype,
scalar_value);
}
/* Calculate the equivalent of:
sel[j] = (index[j] == i);
which selects the elements of NEW_PHI_RESULT that should
be included in the result. */
tree compare_val = build_int_cst (index_elt_type, i);
compare_val = build_vector_from_val (index_type, compare_val);
tree sel = gimple_build (&seq, EQ_EXPR, mask_type,
index, compare_val);
/* Calculate the equivalent of:
vec = seq ? new_phi_result : vector_identity;
VEC is now suitable for a full vector reduction. */
tree vec = gimple_build (&seq, VEC_COND_EXPR, vectype,
sel, new_phi_result, vector_identity);
/* Do the reduction and convert it to the appropriate type. */
tree scalar = gimple_build (&seq, as_combined_fn (reduc_fn),
TREE_TYPE (vectype), vec);
scalar = gimple_convert (&seq, scalar_type, scalar);
scalar_results.safe_push (scalar);
}
gsi_insert_seq_before (&exit_gsi, seq, GSI_SAME_STMT);
}
else
{
bool reduce_with_shift;
tree vec_temp;
gcc_assert (slp_reduc || new_phis.length () == 1);
/* See if the target wants to do the final (shift) reduction
in a vector mode of smaller size and first reduce upper/lower
halves against each other. */
enum machine_mode mode1 = mode;
tree stype = TREE_TYPE (vectype);
unsigned nunits = TYPE_VECTOR_SUBPARTS (vectype).to_constant ();
unsigned nunits1 = nunits;
if ((mode1 = targetm.vectorize.split_reduction (mode)) != mode
&& new_phis.length () == 1)
{
nunits1 = GET_MODE_NUNITS (mode1).to_constant ();
/* For SLP reductions we have to make sure lanes match up, but
since we're doing individual element final reduction reducing
vector width here is even more important.
??? We can also separate lanes with permutes, for the common
case of power-of-two group-size odd/even extracts would work. */
if (slp_reduc && nunits != nunits1)
{
nunits1 = least_common_multiple (nunits1, group_size);
gcc_assert (exact_log2 (nunits1) != -1 && nunits1 <= nunits);
}
}
if (!slp_reduc
&& (mode1 = targetm.vectorize.split_reduction (mode)) != mode)
nunits1 = GET_MODE_NUNITS (mode1).to_constant ();
tree vectype1 = get_related_vectype_for_scalar_type (TYPE_MODE (vectype),
stype, nunits1);
reduce_with_shift = have_whole_vector_shift (mode1);
if (!VECTOR_MODE_P (mode1))
reduce_with_shift = false;
else
{
optab optab = optab_for_tree_code (code, vectype1, optab_default);
if (optab_handler (optab, mode1) == CODE_FOR_nothing)
reduce_with_shift = false;
}
/* First reduce the vector to the desired vector size we should
do shift reduction on by combining upper and lower halves. */
new_temp = new_phi_result;
while (nunits > nunits1)
{
nunits /= 2;
vectype1 = get_related_vectype_for_scalar_type (TYPE_MODE (vectype),
stype, nunits);
unsigned int bitsize = tree_to_uhwi (TYPE_SIZE (vectype1));
/* The target has to make sure we support lowpart/highpart
extraction, either via direct vector extract or through
an integer mode punning. */
tree dst1, dst2;
if (convert_optab_handler (vec_extract_optab,
TYPE_MODE (TREE_TYPE (new_temp)),
TYPE_MODE (vectype1))
!= CODE_FOR_nothing)
{
/* Extract sub-vectors directly once vec_extract becomes
a conversion optab. */
dst1 = make_ssa_name (vectype1);
epilog_stmt
= gimple_build_assign (dst1, BIT_FIELD_REF,
build3 (BIT_FIELD_REF, vectype1,
new_temp, TYPE_SIZE (vectype1),
bitsize_int (0)));
gsi_insert_before (&exit_gsi, epilog_stmt, GSI_SAME_STMT);
dst2 = make_ssa_name (vectype1);
epilog_stmt
= gimple_build_assign (dst2, BIT_FIELD_REF,
build3 (BIT_FIELD_REF, vectype1,
new_temp, TYPE_SIZE (vectype1),
bitsize_int (bitsize)));
gsi_insert_before (&exit_gsi, epilog_stmt, GSI_SAME_STMT);
}
else
{
/* Extract via punning to appropriately sized integer mode
vector. */
tree eltype = build_nonstandard_integer_type (bitsize, 1);
tree etype = build_vector_type (eltype, 2);
gcc_assert (convert_optab_handler (vec_extract_optab,
TYPE_MODE (etype),
TYPE_MODE (eltype))
!= CODE_FOR_nothing);
tree tem = make_ssa_name (etype);
epilog_stmt = gimple_build_assign (tem, VIEW_CONVERT_EXPR,
build1 (VIEW_CONVERT_EXPR,
etype, new_temp));
gsi_insert_before (&exit_gsi, epilog_stmt, GSI_SAME_STMT);
new_temp = tem;
tem = make_ssa_name (eltype);
epilog_stmt
= gimple_build_assign (tem, BIT_FIELD_REF,
build3 (BIT_FIELD_REF, eltype,
new_temp, TYPE_SIZE (eltype),
bitsize_int (0)));
gsi_insert_before (&exit_gsi, epilog_stmt, GSI_SAME_STMT);
dst1 = make_ssa_name (vectype1);
epilog_stmt = gimple_build_assign (dst1, VIEW_CONVERT_EXPR,
build1 (VIEW_CONVERT_EXPR,
vectype1, tem));
gsi_insert_before (&exit_gsi, epilog_stmt, GSI_SAME_STMT);
tem = make_ssa_name (eltype);
epilog_stmt
= gimple_build_assign (tem, BIT_FIELD_REF,
build3 (BIT_FIELD_REF, eltype,
new_temp, TYPE_SIZE (eltype),
bitsize_int (bitsize)));
gsi_insert_before (&exit_gsi, epilog_stmt, GSI_SAME_STMT);
dst2 = make_ssa_name (vectype1);
epilog_stmt = gimple_build_assign (dst2, VIEW_CONVERT_EXPR,
build1 (VIEW_CONVERT_EXPR,
vectype1, tem));
gsi_insert_before (&exit_gsi, epilog_stmt, GSI_SAME_STMT);
}
new_temp = make_ssa_name (vectype1);
epilog_stmt = gimple_build_assign (new_temp, code, dst1, dst2);
gsi_insert_before (&exit_gsi, epilog_stmt, GSI_SAME_STMT);
new_phis[0] = epilog_stmt;
}
if (reduce_with_shift && !slp_reduc)
{
int element_bitsize = tree_to_uhwi (bitsize);
/* Enforced by vectorizable_reduction, which disallows SLP reductions
for variable-length vectors and also requires direct target support
for loop reductions. */
int vec_size_in_bits = tree_to_uhwi (TYPE_SIZE (vectype1));
int nelements = vec_size_in_bits / element_bitsize;
vec_perm_builder sel;
vec_perm_indices indices;
int elt_offset;
tree zero_vec = build_zero_cst (vectype1);
/* Case 2: Create:
for (offset = nelements/2; offset >= 1; offset/=2)
{
Create: va' = vec_shift <va, offset>
Create: va = vop <va, va'>
} */
tree rhs;
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"Reduce using vector shifts\n");
gimple_seq stmts = NULL;
new_temp = gimple_convert (&stmts, vectype1, new_temp);
for (elt_offset = nelements / 2;
elt_offset >= 1;
elt_offset /= 2)
{
calc_vec_perm_mask_for_shift (elt_offset, nelements, &sel);
indices.new_vector (sel, 2, nelements);
tree mask = vect_gen_perm_mask_any (vectype1, indices);
new_name = gimple_build (&stmts, VEC_PERM_EXPR, vectype1,
new_temp, zero_vec, mask);
new_temp = gimple_build (&stmts, code,
vectype1, new_name, new_temp);
}
gsi_insert_seq_before (&exit_gsi, stmts, GSI_SAME_STMT);
/* 2.4 Extract the final scalar result. Create:
s_out3 = extract_field <v_out2, bitpos> */
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"extract scalar result\n");
rhs = build3 (BIT_FIELD_REF, scalar_type, new_temp,
bitsize, bitsize_zero_node);
epilog_stmt = gimple_build_assign (new_scalar_dest, rhs);
new_temp = make_ssa_name (new_scalar_dest, epilog_stmt);
gimple_assign_set_lhs (epilog_stmt, new_temp);
gsi_insert_before (&exit_gsi, epilog_stmt, GSI_SAME_STMT);
scalar_results.safe_push (new_temp);
}
else
{
/* Case 3: Create:
s = extract_field <v_out2, 0>
for (offset = element_size;
offset < vector_size;
offset += element_size;)
{
Create: s' = extract_field <v_out2, offset>
Create: s = op <s, s'> // For non SLP cases
} */
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"Reduce using scalar code.\n");
int vec_size_in_bits = tree_to_uhwi (TYPE_SIZE (vectype1));
int element_bitsize = tree_to_uhwi (bitsize);
tree compute_type = TREE_TYPE (vectype);
gimple_seq stmts = NULL;
FOR_EACH_VEC_ELT (new_phis, i, new_phi)
{
int bit_offset;
if (gimple_code (new_phi) == GIMPLE_PHI)
vec_temp = PHI_RESULT (new_phi);
else
vec_temp = gimple_assign_lhs (new_phi);
new_temp = gimple_build (&stmts, BIT_FIELD_REF, compute_type,
vec_temp, bitsize, bitsize_zero_node);
/* In SLP we don't need to apply reduction operation, so we just
collect s' values in SCALAR_RESULTS. */
if (slp_reduc)
scalar_results.safe_push (new_temp);
for (bit_offset = element_bitsize;
bit_offset < vec_size_in_bits;
bit_offset += element_bitsize)
{
tree bitpos = bitsize_int (bit_offset);
new_name = gimple_build (&stmts, BIT_FIELD_REF,
compute_type, vec_temp,
bitsize, bitpos);
if (slp_reduc)
{
/* In SLP we don't need to apply reduction operation, so
we just collect s' values in SCALAR_RESULTS. */
new_temp = new_name;
scalar_results.safe_push (new_name);
}
else
new_temp = gimple_build (&stmts, code, compute_type,
new_name, new_temp);
}
}
/* The only case where we need to reduce scalar results in SLP, is
unrolling. If the size of SCALAR_RESULTS is greater than
REDUC_GROUP_SIZE, we reduce them combining elements modulo
REDUC_GROUP_SIZE. */
if (slp_reduc)
{
tree res, first_res, new_res;
/* Reduce multiple scalar results in case of SLP unrolling. */
for (j = group_size; scalar_results.iterate (j, &res);
j++)
{
first_res = scalar_results[j % group_size];
new_res = gimple_build (&stmts, code, compute_type,
first_res, res);
scalar_results[j % group_size] = new_res;
}
for (k = 0; k < group_size; k++)
scalar_results[k] = gimple_convert (&stmts, scalar_type,
scalar_results[k]);
}
else
{
/* Not SLP - we have one scalar to keep in SCALAR_RESULTS. */
new_temp = gimple_convert (&stmts, scalar_type, new_temp);
scalar_results.safe_push (new_temp);
}
gsi_insert_seq_before (&exit_gsi, stmts, GSI_SAME_STMT);
}
if ((STMT_VINFO_REDUC_TYPE (reduc_info) == INTEGER_INDUC_COND_REDUCTION)
&& induc_val)
{
/* Earlier we set the initial value to be a vector if induc_val
values. Check the result and if it is induc_val then replace
with the original initial value, unless induc_val is
the same as initial_def already. */
tree zcompare = build2 (EQ_EXPR, boolean_type_node, new_temp,
induc_val);
tree tmp = make_ssa_name (new_scalar_dest);
epilog_stmt = gimple_build_assign (tmp, COND_EXPR, zcompare,
initial_def, new_temp);
gsi_insert_before (&exit_gsi, epilog_stmt, GSI_SAME_STMT);
scalar_results[0] = tmp;
}
}
/* 2.5 Adjust the final result by the initial value of the reduction
variable. (When such adjustment is not needed, then
'adjustment_def' is zero). For example, if code is PLUS we create:
new_temp = loop_exit_def + adjustment_def */
if (adjustment_def)
{
gcc_assert (!slp_reduc);
gimple_seq stmts = NULL;
if (nested_in_vect_loop)
{
new_phi = new_phis[0];
gcc_assert (VECTOR_TYPE_P (TREE_TYPE (adjustment_def)));
adjustment_def = gimple_convert (&stmts, vectype, adjustment_def);
new_temp = gimple_build (&stmts, code, vectype,
PHI_RESULT (new_phi), adjustment_def);
}
else
{
new_temp = scalar_results[0];
gcc_assert (TREE_CODE (TREE_TYPE (adjustment_def)) != VECTOR_TYPE);
adjustment_def = gimple_convert (&stmts, scalar_type, adjustment_def);
new_temp = gimple_build (&stmts, code, scalar_type,
new_temp, adjustment_def);
}
epilog_stmt = gimple_seq_last_stmt (stmts);
gsi_insert_seq_before (&exit_gsi, stmts, GSI_SAME_STMT);
if (nested_in_vect_loop)
{
if (!double_reduc)
scalar_results.quick_push (new_temp);
else
scalar_results[0] = new_temp;
}
else
scalar_results[0] = new_temp;
new_phis[0] = epilog_stmt;
}
if (double_reduc)
loop = loop->inner;
/* 2.6 Handle the loop-exit phis. Replace the uses of scalar loop-exit
phis with new adjusted scalar results, i.e., replace use <s_out0>
with use <s_out4>.
Transform:
loop_exit:
s_out0 = phi <s_loop> # (scalar) EXIT_PHI
v_out1 = phi <VECT_DEF> # NEW_EXIT_PHI
v_out2 = reduce <v_out1>
s_out3 = extract_field <v_out2, 0>
s_out4 = adjust_result <s_out3>
use <s_out0>
use <s_out0>
into:
loop_exit:
s_out0 = phi <s_loop> # (scalar) EXIT_PHI
v_out1 = phi <VECT_DEF> # NEW_EXIT_PHI
v_out2 = reduce <v_out1>
s_out3 = extract_field <v_out2, 0>
s_out4 = adjust_result <s_out3>
use <s_out4>
use <s_out4> */
/* In SLP reduction chain we reduce vector results into one vector if
necessary, hence we set here REDUC_GROUP_SIZE to 1. SCALAR_DEST is the
LHS of the last stmt in the reduction chain, since we are looking for
the loop exit phi node. */
if (REDUC_GROUP_FIRST_ELEMENT (stmt_info))
{
stmt_vec_info dest_stmt_info
= vect_orig_stmt (SLP_TREE_SCALAR_STMTS (slp_node)[group_size - 1]);
scalar_dest = gimple_assign_lhs (dest_stmt_info->stmt);
group_size = 1;
}
/* In SLP we may have several statements in NEW_PHIS and REDUCTION_PHIS (in
case that REDUC_GROUP_SIZE is greater than vectorization factor).
Therefore, we need to match SCALAR_RESULTS with corresponding statements.
The first (REDUC_GROUP_SIZE / number of new vector stmts) scalar results
correspond to the first vector stmt, etc.
(RATIO is equal to (REDUC_GROUP_SIZE / number of new vector stmts)). */
if (group_size > new_phis.length ())
gcc_assert (!(group_size % new_phis.length ()));
for (k = 0; k < group_size; k++)
{
if (slp_reduc)
{
stmt_vec_info scalar_stmt_info = SLP_TREE_SCALAR_STMTS (slp_node)[k];
orig_stmt_info = STMT_VINFO_RELATED_STMT (scalar_stmt_info);
/* SLP statements can't participate in patterns. */
gcc_assert (!orig_stmt_info);
scalar_dest = gimple_assign_lhs (scalar_stmt_info->stmt);
}
if (nested_in_vect_loop)
{
if (double_reduc)
loop = outer_loop;
else
gcc_unreachable ();
}
phis.create (3);
/* Find the loop-closed-use at the loop exit of the original scalar
result. (The reduction result is expected to have two immediate uses,
one at the latch block, and one at the loop exit). For double
reductions we are looking for exit phis of the outer loop. */
FOR_EACH_IMM_USE_FAST (use_p, imm_iter, scalar_dest)
{
if (!flow_bb_inside_loop_p (loop, gimple_bb (USE_STMT (use_p))))
{
if (!is_gimple_debug (USE_STMT (use_p)))
phis.safe_push (USE_STMT (use_p));
}
else
{
if (double_reduc && gimple_code (USE_STMT (use_p)) == GIMPLE_PHI)
{
tree phi_res = PHI_RESULT (USE_STMT (use_p));
FOR_EACH_IMM_USE_FAST (phi_use_p, phi_imm_iter, phi_res)
{
if (!flow_bb_inside_loop_p (loop,
gimple_bb (USE_STMT (phi_use_p)))
&& !is_gimple_debug (USE_STMT (phi_use_p)))
phis.safe_push (USE_STMT (phi_use_p));
}
}
}
}
FOR_EACH_VEC_ELT (phis, i, exit_phi)
{
/* Replace the uses: */
orig_name = PHI_RESULT (exit_phi);
scalar_result = scalar_results[k];
FOR_EACH_IMM_USE_STMT (use_stmt, imm_iter, orig_name)
{
FOR_EACH_IMM_USE_ON_STMT (use_p, imm_iter)
SET_USE (use_p, scalar_result);
update_stmt (use_stmt);
}
}
phis.release ();
}
}
/* Return a vector of type VECTYPE that is equal to the vector select
operation "MASK ? VEC : IDENTITY". Insert the select statements
before GSI. */
static tree
merge_with_identity (gimple_stmt_iterator *gsi, tree mask, tree vectype,
tree vec, tree identity)
{
tree cond = make_temp_ssa_name (vectype, NULL, "cond");
gimple *new_stmt = gimple_build_assign (cond, VEC_COND_EXPR,
mask, vec, identity);
gsi_insert_before (gsi, new_stmt, GSI_SAME_STMT);
return cond;
}
/* Successively apply CODE to each element of VECTOR_RHS, in left-to-right
order, starting with LHS. Insert the extraction statements before GSI and
associate the new scalar SSA names with variable SCALAR_DEST.
Return the SSA name for the result. */
static tree
vect_expand_fold_left (gimple_stmt_iterator *gsi, tree scalar_dest,
tree_code code, tree lhs, tree vector_rhs)
{
tree vectype = TREE_TYPE (vector_rhs);
tree scalar_type = TREE_TYPE (vectype);
tree bitsize = TYPE_SIZE (scalar_type);
unsigned HOST_WIDE_INT vec_size_in_bits = tree_to_uhwi (TYPE_SIZE (vectype));
unsigned HOST_WIDE_INT element_bitsize = tree_to_uhwi (bitsize);
for (unsigned HOST_WIDE_INT bit_offset = 0;
bit_offset < vec_size_in_bits;
bit_offset += element_bitsize)
{
tree bitpos = bitsize_int (bit_offset);
tree rhs = build3 (BIT_FIELD_REF, scalar_type, vector_rhs,
bitsize, bitpos);
gassign *stmt = gimple_build_assign (scalar_dest, rhs);
rhs = make_ssa_name (scalar_dest, stmt);
gimple_assign_set_lhs (stmt, rhs);
gsi_insert_before (gsi, stmt, GSI_SAME_STMT);
stmt = gimple_build_assign (scalar_dest, code, lhs, rhs);
tree new_name = make_ssa_name (scalar_dest, stmt);
gimple_assign_set_lhs (stmt, new_name);
gsi_insert_before (gsi, stmt, GSI_SAME_STMT);
lhs = new_name;
}
return lhs;
}
/* Get a masked internal function equivalent to REDUC_FN. VECTYPE_IN is the
type of the vector input. */
static internal_fn
get_masked_reduction_fn (internal_fn reduc_fn, tree vectype_in)
{
internal_fn mask_reduc_fn;
switch (reduc_fn)
{
case IFN_FOLD_LEFT_PLUS:
mask_reduc_fn = IFN_MASK_FOLD_LEFT_PLUS;
break;
default:
return IFN_LAST;
}
if (direct_internal_fn_supported_p (mask_reduc_fn, vectype_in,
OPTIMIZE_FOR_SPEED))
return mask_reduc_fn;
return IFN_LAST;
}
/* Perform an in-order reduction (FOLD_LEFT_REDUCTION). STMT_INFO is the
statement that sets the live-out value. REDUC_DEF_STMT is the phi
statement. CODE is the operation performed by STMT_INFO and OPS are
its scalar operands. REDUC_INDEX is the index of the operand in
OPS that is set by REDUC_DEF_STMT. REDUC_FN is the function that
implements in-order reduction, or IFN_LAST if we should open-code it.
VECTYPE_IN is the type of the vector input. MASKS specifies the masks
that should be used to control the operation in a fully-masked loop. */
static bool
vectorize_fold_left_reduction (loop_vec_info loop_vinfo,
stmt_vec_info stmt_info,
gimple_stmt_iterator *gsi,
gimple **vec_stmt, slp_tree slp_node,
gimple *reduc_def_stmt,
tree_code code, internal_fn reduc_fn,
tree ops[3], tree vectype_in,
int reduc_index, vec_loop_masks *masks)
{
class loop *loop = LOOP_VINFO_LOOP (loop_vinfo);
tree vectype_out = STMT_VINFO_VECTYPE (stmt_info);
internal_fn mask_reduc_fn = get_masked_reduction_fn (reduc_fn, vectype_in);
int ncopies;
if (slp_node)
ncopies = 1;
else
ncopies = vect_get_num_copies (loop_vinfo, vectype_in);
gcc_assert (!nested_in_vect_loop_p (loop, stmt_info));
gcc_assert (ncopies == 1);
gcc_assert (TREE_CODE_LENGTH (code) == binary_op);
if (slp_node)
gcc_assert (known_eq (TYPE_VECTOR_SUBPARTS (vectype_out),
TYPE_VECTOR_SUBPARTS (vectype_in)));
tree op0 = ops[1 - reduc_index];
int group_size = 1;
stmt_vec_info scalar_dest_def_info;
auto_vec<tree> vec_oprnds0;
if (slp_node)
{
auto_vec<vec<tree> > vec_defs (2);
vect_get_slp_defs (loop_vinfo, slp_node, &vec_defs);
vec_oprnds0.safe_splice (vec_defs[1 - reduc_index]);
vec_defs[0].release ();
vec_defs[1].release ();
group_size = SLP_TREE_SCALAR_STMTS (slp_node).length ();
scalar_dest_def_info = SLP_TREE_SCALAR_STMTS (slp_node)[group_size - 1];
}
else
{
vect_get_vec_defs_for_operand (loop_vinfo, stmt_info, 1,
op0, &vec_oprnds0);
scalar_dest_def_info = stmt_info;
}
tree scalar_dest = gimple_assign_lhs (scalar_dest_def_info->stmt);
tree scalar_type = TREE_TYPE (scalar_dest);
tree reduc_var = gimple_phi_result (reduc_def_stmt);
int vec_num = vec_oprnds0.length ();
gcc_assert (vec_num == 1 || slp_node);
tree vec_elem_type = TREE_TYPE (vectype_out);
gcc_checking_assert (useless_type_conversion_p (scalar_type, vec_elem_type));
tree vector_identity = NULL_TREE;
if (LOOP_VINFO_FULLY_MASKED_P (loop_vinfo))
vector_identity = build_zero_cst (vectype_out);
tree scalar_dest_var = vect_create_destination_var (scalar_dest, NULL);
int i;
tree def0;
FOR_EACH_VEC_ELT (vec_oprnds0, i, def0)
{
gimple *new_stmt;
tree mask = NULL_TREE;
if (LOOP_VINFO_FULLY_MASKED_P (loop_vinfo))
mask = vect_get_loop_mask (gsi, masks, vec_num, vectype_in, i);
/* Handle MINUS by adding the negative. */
if (reduc_fn != IFN_LAST && code == MINUS_EXPR)
{
tree negated = make_ssa_name (vectype_out);
new_stmt = gimple_build_assign (negated, NEGATE_EXPR, def0);
gsi_insert_before (gsi, new_stmt, GSI_SAME_STMT);
def0 = negated;
}
if (mask && mask_reduc_fn == IFN_LAST)
def0 = merge_with_identity (gsi, mask, vectype_out, def0,
vector_identity);
/* On the first iteration the input is simply the scalar phi
result, and for subsequent iterations it is the output of
the preceding operation. */
if (reduc_fn != IFN_LAST || (mask && mask_reduc_fn != IFN_LAST))
{
if (mask && mask_reduc_fn != IFN_LAST)
new_stmt = gimple_build_call_internal (mask_reduc_fn, 3, reduc_var,
def0, mask);
else
new_stmt = gimple_build_call_internal (reduc_fn, 2, reduc_var,
def0);
/* For chained SLP reductions the output of the previous reduction
operation serves as the input of the next. For the final statement
the output cannot be a temporary - we reuse the original
scalar destination of the last statement. */
if (i != vec_num - 1)
{
gimple_set_lhs (new_stmt, scalar_dest_var);
reduc_var = make_ssa_name (scalar_dest_var, new_stmt);
gimple_set_lhs (new_stmt, reduc_var);
}
}
else
{
reduc_var = vect_expand_fold_left (gsi, scalar_dest_var, code,
reduc_var, def0);
new_stmt = SSA_NAME_DEF_STMT (reduc_var);
/* Remove the statement, so that we can use the same code paths
as for statements that we've just created. */
gimple_stmt_iterator tmp_gsi = gsi_for_stmt (new_stmt);
gsi_remove (&tmp_gsi, true);
}
if (i == vec_num - 1)
{
gimple_set_lhs (new_stmt, scalar_dest);
vect_finish_replace_stmt (loop_vinfo,
scalar_dest_def_info,
new_stmt);
}
else
vect_finish_stmt_generation (loop_vinfo,
scalar_dest_def_info,
new_stmt, gsi);
if (slp_node)
SLP_TREE_VEC_STMTS (slp_node).quick_push (new_stmt);
else
{
STMT_VINFO_VEC_STMTS (stmt_info).safe_push (new_stmt);
*vec_stmt = new_stmt;
}
}
return true;
}
/* Function is_nonwrapping_integer_induction.
Check if STMT_VINO (which is part of loop LOOP) both increments and
does not cause overflow. */
static bool
is_nonwrapping_integer_induction (stmt_vec_info stmt_vinfo, class loop *loop)
{
gphi *phi = as_a <gphi *> (stmt_vinfo->stmt);
tree base = STMT_VINFO_LOOP_PHI_EVOLUTION_BASE_UNCHANGED (stmt_vinfo);
tree step = STMT_VINFO_LOOP_PHI_EVOLUTION_PART (stmt_vinfo);
tree lhs_type = TREE_TYPE (gimple_phi_result (phi));
widest_int ni, max_loop_value, lhs_max;
wi::overflow_type overflow = wi::OVF_NONE;
/* Make sure the loop is integer based. */
if (TREE_CODE (base) != INTEGER_CST
|| TREE_CODE (step) != INTEGER_CST)
return false;
/* Check that the max size of the loop will not wrap. */
if (TYPE_OVERFLOW_UNDEFINED (lhs_type))
return true;
if (! max_stmt_executions (loop, &ni))
return false;
max_loop_value = wi::mul (wi::to_widest (step), ni, TYPE_SIGN (lhs_type),
&overflow);
if (overflow)
return false;
max_loop_value = wi::add (wi::to_widest (base), max_loop_value,
TYPE_SIGN (lhs_type), &overflow);
if (overflow)
return false;
return (wi::min_precision (max_loop_value, TYPE_SIGN (lhs_type))
<= TYPE_PRECISION (lhs_type));
}
/* Check if masking can be supported by inserting a conditional expression.
CODE is the code for the operation. COND_FN is the conditional internal
function, if it exists. VECTYPE_IN is the type of the vector input. */
static bool
use_mask_by_cond_expr_p (enum tree_code code, internal_fn cond_fn,
tree vectype_in)
{
if (cond_fn != IFN_LAST
&& direct_internal_fn_supported_p (cond_fn, vectype_in,
OPTIMIZE_FOR_SPEED))
return false;
switch (code)
{
case DOT_PROD_EXPR:
case SAD_EXPR:
return true;
default:
return false;
}
}
/* Insert a conditional expression to enable masked vectorization. CODE is the
code for the operation. VOP is the array of operands. MASK is the loop
mask. GSI is a statement iterator used to place the new conditional
expression. */
static void
build_vect_cond_expr (enum tree_code code, tree vop[3], tree mask,
gimple_stmt_iterator *gsi)
{
switch (code)
{
case DOT_PROD_EXPR:
{
tree vectype = TREE_TYPE (vop[1]);
tree zero = build_zero_cst (vectype);
tree masked_op1 = make_temp_ssa_name (vectype, NULL, "masked_op1");
gassign *select = gimple_build_assign (masked_op1, VEC_COND_EXPR,
mask, vop[1], zero);
gsi_insert_before (gsi, select, GSI_SAME_STMT);
vop[1] = masked_op1;
break;
}
case SAD_EXPR:
{
tree vectype = TREE_TYPE (vop[1]);
tree masked_op1 = make_temp_ssa_name (vectype, NULL, "masked_op1");
gassign *select = gimple_build_assign (masked_op1, VEC_COND_EXPR,
mask, vop[1], vop[0]);
gsi_insert_before (gsi, select, GSI_SAME_STMT);
vop[1] = masked_op1;
break;
}
default:
gcc_unreachable ();
}
}
/* Function vectorizable_reduction.
Check if STMT_INFO performs a reduction operation that can be vectorized.
If VEC_STMT is also passed, vectorize STMT_INFO: create a vectorized
stmt to replace it, put it in VEC_STMT, and insert it at GSI.
Return true if STMT_INFO is vectorizable in this way.
This function also handles reduction idioms (patterns) that have been
recognized in advance during vect_pattern_recog. In this case, STMT_INFO
may be of this form:
X = pattern_expr (arg0, arg1, ..., X)
and its STMT_VINFO_RELATED_STMT points to the last stmt in the original
sequence that had been detected and replaced by the pattern-stmt
(STMT_INFO).
This function also handles reduction of condition expressions, for example:
for (int i = 0; i < N; i++)
if (a[i] < value)
last = a[i];
This is handled by vectorising the loop and creating an additional vector
containing the loop indexes for which "a[i] < value" was true. In the
function epilogue this is reduced to a single max value and then used to
index into the vector of results.
In some cases of reduction patterns, the type of the reduction variable X is
different than the type of the other arguments of STMT_INFO.
In such cases, the vectype that is used when transforming STMT_INFO into
a vector stmt is different than the vectype that is used to determine the
vectorization factor, because it consists of a different number of elements
than the actual number of elements that are being operated upon in parallel.
For example, consider an accumulation of shorts into an int accumulator.
On some targets it's possible to vectorize this pattern operating on 8
shorts at a time (hence, the vectype for purposes of determining the
vectorization factor should be V8HI); on the other hand, the vectype that
is used to create the vector form is actually V4SI (the type of the result).
Upon entry to this function, STMT_VINFO_VECTYPE records the vectype that
indicates what is the actual level of parallelism (V8HI in the example), so
that the right vectorization factor would be derived. This vectype
corresponds to the type of arguments to the reduction stmt, and should *NOT*
be used to create the vectorized stmt. The right vectype for the vectorized
stmt is obtained from the type of the result X:
get_vectype_for_scalar_type (vinfo, TREE_TYPE (X))
This means that, contrary to "regular" reductions (or "regular" stmts in
general), the following equation:
STMT_VINFO_VECTYPE == get_vectype_for_scalar_type (vinfo, TREE_TYPE (X))
does *NOT* necessarily hold for reduction patterns. */
bool
vectorizable_reduction (loop_vec_info loop_vinfo,
stmt_vec_info stmt_info, slp_tree slp_node,
slp_instance slp_node_instance,
stmt_vector_for_cost *cost_vec)
{
tree scalar_dest;
tree vectype_in = NULL_TREE;
class loop *loop = LOOP_VINFO_LOOP (loop_vinfo);
enum vect_def_type cond_reduc_dt = vect_unknown_def_type;
stmt_vec_info cond_stmt_vinfo = NULL;
tree scalar_type;
int i;
int ncopies;
bool single_defuse_cycle = false;
bool nested_cycle = false;
bool double_reduc = false;
int vec_num;
tree tem;
tree cr_index_scalar_type = NULL_TREE, cr_index_vector_type = NULL_TREE;
tree cond_reduc_val = NULL_TREE;
/* Make sure it was already recognized as a reduction computation. */
if (STMT_VINFO_DEF_TYPE (stmt_info) != vect_reduction_def
&& STMT_VINFO_DEF_TYPE (stmt_info) != vect_double_reduction_def
&& STMT_VINFO_DEF_TYPE (stmt_info) != vect_nested_cycle)
return false;
/* The stmt we store reduction analysis meta on. */
stmt_vec_info reduc_info = info_for_reduction (loop_vinfo, stmt_info);
reduc_info->is_reduc_info = true;
if (STMT_VINFO_DEF_TYPE (stmt_info) == vect_nested_cycle)
{
if (is_a <gphi *> (stmt_info->stmt))
{
if (slp_node)
{
/* We eventually need to set a vector type on invariant
arguments. */
unsigned j;
slp_tree child;
FOR_EACH_VEC_ELT (SLP_TREE_CHILDREN (slp_node), j, child)
if (!vect_maybe_update_slp_op_vectype
(child, SLP_TREE_VECTYPE (slp_node)))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"incompatible vector types for "
"invariants\n");
return false;
}
}
/* Analysis for double-reduction is done on the outer
loop PHI, nested cycles have no further restrictions. */
STMT_VINFO_TYPE (stmt_info) = cycle_phi_info_type;
}
else
STMT_VINFO_TYPE (stmt_info) = reduc_vec_info_type;
return true;
}
stmt_vec_info orig_stmt_of_analysis = stmt_info;
stmt_vec_info phi_info = stmt_info;
if (STMT_VINFO_DEF_TYPE (stmt_info) == vect_reduction_def
|| STMT_VINFO_DEF_TYPE (stmt_info) == vect_double_reduction_def)
{
if (!is_a <gphi *> (stmt_info->stmt))
{
STMT_VINFO_TYPE (stmt_info) = reduc_vec_info_type;
return true;
}
if (slp_node)
{
slp_node_instance->reduc_phis = slp_node;
/* ??? We're leaving slp_node to point to the PHIs, we only
need it to get at the number of vector stmts which wasn't
yet initialized for the instance root. */
}
if (STMT_VINFO_DEF_TYPE (stmt_info) == vect_reduction_def)
stmt_info = vect_stmt_to_vectorize (STMT_VINFO_REDUC_DEF (stmt_info));
else /* STMT_VINFO_DEF_TYPE (stmt_info) == vect_double_reduction_def */
{
use_operand_p use_p;
gimple *use_stmt;
bool res = single_imm_use (gimple_phi_result (stmt_info->stmt),
&use_p, &use_stmt);
gcc_assert (res);
phi_info = loop_vinfo->lookup_stmt (use_stmt);
stmt_info = vect_stmt_to_vectorize (STMT_VINFO_REDUC_DEF (phi_info));
}
}
/* PHIs should not participate in patterns. */
gcc_assert (!STMT_VINFO_RELATED_STMT (phi_info));
gphi *reduc_def_phi = as_a <gphi *> (phi_info->stmt);
/* Verify following REDUC_IDX from the latch def leads us back to the PHI
and compute the reduction chain length. Discover the real
reduction operation stmt on the way (stmt_info and slp_for_stmt_info). */
tree reduc_def
= PHI_ARG_DEF_FROM_EDGE (reduc_def_phi,
loop_latch_edge
(gimple_bb (reduc_def_phi)->loop_father));
unsigned reduc_chain_length = 0;
bool only_slp_reduc_chain = true;
stmt_info = NULL;
slp_tree slp_for_stmt_info = slp_node ? slp_node_instance->root : NULL;
while (reduc_def != PHI_RESULT (reduc_def_phi))
{
stmt_vec_info def = loop_vinfo->lookup_def (reduc_def);
stmt_vec_info vdef = vect_stmt_to_vectorize (def);
if (STMT_VINFO_REDUC_IDX (vdef) == -1)
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"reduction chain broken by patterns.\n");
return false;
}
if (!REDUC_GROUP_FIRST_ELEMENT (vdef))
only_slp_reduc_chain = false;
/* ??? For epilogue generation live members of the chain need
to point back to the PHI via their original stmt for
info_for_reduction to work. */
if (STMT_VINFO_LIVE_P (vdef))
STMT_VINFO_REDUC_DEF (def) = phi_info;
gassign *assign = dyn_cast <gassign *> (vdef->stmt);
if (!assign)
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"reduction chain includes calls.\n");
return false;
}
if (CONVERT_EXPR_CODE_P (gimple_assign_rhs_code (assign)))
{
if (!tree_nop_conversion_p (TREE_TYPE (gimple_assign_lhs (assign)),
TREE_TYPE (gimple_assign_rhs1 (assign))))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"conversion in the reduction chain.\n");
return false;
}
}
else if (!stmt_info)
/* First non-conversion stmt. */
stmt_info = vdef;
reduc_def = gimple_op (vdef->stmt, 1 + STMT_VINFO_REDUC_IDX (vdef));
reduc_chain_length++;
if (!stmt_info && slp_node)
slp_for_stmt_info = SLP_TREE_CHILDREN (slp_for_stmt_info)[0];
}
/* PHIs should not participate in patterns. */
gcc_assert (!STMT_VINFO_RELATED_STMT (phi_info));
if (nested_in_vect_loop_p (loop, stmt_info))
{
loop = loop->inner;
nested_cycle = true;
}
/* STMT_VINFO_REDUC_DEF doesn't point to the first but the last
element. */
if (slp_node && REDUC_GROUP_FIRST_ELEMENT (stmt_info))
{
gcc_assert (!REDUC_GROUP_NEXT_ELEMENT (stmt_info));
stmt_info = REDUC_GROUP_FIRST_ELEMENT (stmt_info);
}
if (REDUC_GROUP_FIRST_ELEMENT (stmt_info))
gcc_assert (slp_node
&& REDUC_GROUP_FIRST_ELEMENT (stmt_info) == stmt_info);
/* 1. Is vectorizable reduction? */
/* Not supportable if the reduction variable is used in the loop, unless
it's a reduction chain. */
if (STMT_VINFO_RELEVANT (stmt_info) > vect_used_in_outer
&& !REDUC_GROUP_FIRST_ELEMENT (stmt_info))
return false;
/* Reductions that are not used even in an enclosing outer-loop,
are expected to be "live" (used out of the loop). */
if (STMT_VINFO_RELEVANT (stmt_info) == vect_unused_in_scope
&& !STMT_VINFO_LIVE_P (stmt_info))
return false;
/* 2. Has this been recognized as a reduction pattern?
Check if STMT represents a pattern that has been recognized
in earlier analysis stages. For stmts that represent a pattern,
the STMT_VINFO_RELATED_STMT field records the last stmt in
the original sequence that constitutes the pattern. */
stmt_vec_info orig_stmt_info = STMT_VINFO_RELATED_STMT (stmt_info);
if (orig_stmt_info)
{
gcc_assert (STMT_VINFO_IN_PATTERN_P (orig_stmt_info));
gcc_assert (!STMT_VINFO_IN_PATTERN_P (stmt_info));
}
/* 3. Check the operands of the operation. The first operands are defined
inside the loop body. The last operand is the reduction variable,
which is defined by the loop-header-phi. */
tree vectype_out = STMT_VINFO_VECTYPE (stmt_info);
STMT_VINFO_REDUC_VECTYPE (reduc_info) = vectype_out;
gassign *stmt = as_a <gassign *> (stmt_info->stmt);
enum tree_code code = gimple_assign_rhs_code (stmt);
bool lane_reduc_code_p
= (code == DOT_PROD_EXPR || code == WIDEN_SUM_EXPR || code == SAD_EXPR);
int op_type = TREE_CODE_LENGTH (code);
scalar_dest = gimple_assign_lhs (stmt);
scalar_type = TREE_TYPE (scalar_dest);
if (!POINTER_TYPE_P (scalar_type) && !INTEGRAL_TYPE_P (scalar_type)
&& !SCALAR_FLOAT_TYPE_P (scalar_type))
return false;
/* Do not try to vectorize bit-precision reductions. */
if (!type_has_mode_precision_p (scalar_type))
return false;
/* For lane-reducing ops we're reducing the number of reduction PHIs
which means the only use of that may be in the lane-reducing operation. */
if (lane_reduc_code_p
&& reduc_chain_length != 1
&& !only_slp_reduc_chain)
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"lane-reducing reduction with extra stmts.\n");
return false;
}
/* All uses but the last are expected to be defined in the loop.
The last use is the reduction variable. In case of nested cycle this
assumption is not true: we use reduc_index to record the index of the
reduction variable. */
slp_tree *slp_op = XALLOCAVEC (slp_tree, op_type);
/* We need to skip an extra operand for COND_EXPRs with embedded
comparison. */
unsigned opno_adjust = 0;
if (code == COND_EXPR
&& COMPARISON_CLASS_P (gimple_assign_rhs1 (stmt)))
opno_adjust = 1;
for (i = 0; i < op_type; i++)
{
/* The condition of COND_EXPR is checked in vectorizable_condition(). */
if (i == 0 && code == COND_EXPR)
continue;
stmt_vec_info def_stmt_info;
enum vect_def_type dt;
tree op;
if (!vect_is_simple_use (loop_vinfo, stmt_info, slp_for_stmt_info,
i + opno_adjust, &op, &slp_op[i], &dt, &tem,
&def_stmt_info))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"use not simple.\n");
return false;
}
if (i == STMT_VINFO_REDUC_IDX (stmt_info))
continue;
/* There should be only one cycle def in the stmt, the one
leading to reduc_def. */
if (VECTORIZABLE_CYCLE_DEF (dt))
return false;
/* To properly compute ncopies we are interested in the widest
non-reduction input type in case we're looking at a widening
accumulation that we later handle in vect_transform_reduction. */
if (lane_reduc_code_p
&& tem
&& (!vectype_in
|| (GET_MODE_SIZE (SCALAR_TYPE_MODE (TREE_TYPE (vectype_in)))
< GET_MODE_SIZE (SCALAR_TYPE_MODE (TREE_TYPE (tem))))))
vectype_in = tem;
if (code == COND_EXPR)
{
/* Record how the non-reduction-def value of COND_EXPR is defined. */
if (dt == vect_constant_def)
{
cond_reduc_dt = dt;
cond_reduc_val = op;
}
if (dt == vect_induction_def
&& def_stmt_info
&& is_nonwrapping_integer_induction (def_stmt_info, loop))
{
cond_reduc_dt = dt;
cond_stmt_vinfo = def_stmt_info;
}
}
}
if (!vectype_in)
vectype_in = STMT_VINFO_VECTYPE (phi_info);
STMT_VINFO_REDUC_VECTYPE_IN (reduc_info) = vectype_in;
enum vect_reduction_type v_reduc_type = STMT_VINFO_REDUC_TYPE (phi_info);
STMT_VINFO_REDUC_TYPE (reduc_info) = v_reduc_type;
/* If we have a condition reduction, see if we can simplify it further. */
if (v_reduc_type == COND_REDUCTION)
{
if (slp_node)
return false;
/* When the condition uses the reduction value in the condition, fail. */
if (STMT_VINFO_REDUC_IDX (stmt_info) == 0)
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"condition depends on previous iteration\n");
return false;
}
if (reduc_chain_length == 1
&& direct_internal_fn_supported_p (IFN_FOLD_EXTRACT_LAST,
vectype_in, OPTIMIZE_FOR_SPEED))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"optimizing condition reduction with"
" FOLD_EXTRACT_LAST.\n");
STMT_VINFO_REDUC_TYPE (reduc_info) = EXTRACT_LAST_REDUCTION;
}
else if (cond_reduc_dt == vect_induction_def)
{
tree base
= STMT_VINFO_LOOP_PHI_EVOLUTION_BASE_UNCHANGED (cond_stmt_vinfo);
tree step = STMT_VINFO_LOOP_PHI_EVOLUTION_PART (cond_stmt_vinfo);
gcc_assert (TREE_CODE (base) == INTEGER_CST
&& TREE_CODE (step) == INTEGER_CST);
cond_reduc_val = NULL_TREE;
enum tree_code cond_reduc_op_code = ERROR_MARK;
tree res = PHI_RESULT (STMT_VINFO_STMT (cond_stmt_vinfo));
if (!types_compatible_p (TREE_TYPE (res), TREE_TYPE (base)))
;
/* Find a suitable value, for MAX_EXPR below base, for MIN_EXPR
above base; punt if base is the minimum value of the type for
MAX_EXPR or maximum value of the type for MIN_EXPR for now. */
else if (tree_int_cst_sgn (step) == -1)
{
cond_reduc_op_code = MIN_EXPR;
if (tree_int_cst_sgn (base) == -1)
cond_reduc_val = build_int_cst (TREE_TYPE (base), 0);
else if (tree_int_cst_lt (base,
TYPE_MAX_VALUE (TREE_TYPE (base))))
cond_reduc_val
= int_const_binop (PLUS_EXPR, base, integer_one_node);
}
else
{
cond_reduc_op_code = MAX_EXPR;
if (tree_int_cst_sgn (base) == 1)
cond_reduc_val = build_int_cst (TREE_TYPE (base), 0);
else if (tree_int_cst_lt (TYPE_MIN_VALUE (TREE_TYPE (base)),
base))
cond_reduc_val
= int_const_binop (MINUS_EXPR, base, integer_one_node);
}
if (cond_reduc_val)
{
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"condition expression based on "
"integer induction.\n");
STMT_VINFO_REDUC_CODE (reduc_info) = cond_reduc_op_code;
STMT_VINFO_VEC_INDUC_COND_INITIAL_VAL (reduc_info)
= cond_reduc_val;
STMT_VINFO_REDUC_TYPE (reduc_info) = INTEGER_INDUC_COND_REDUCTION;
}
}
else if (cond_reduc_dt == vect_constant_def)
{
enum vect_def_type cond_initial_dt;
tree cond_initial_val
= PHI_ARG_DEF_FROM_EDGE (reduc_def_phi, loop_preheader_edge (loop));
gcc_assert (cond_reduc_val != NULL_TREE);
vect_is_simple_use (cond_initial_val, loop_vinfo, &cond_initial_dt);
if (cond_initial_dt == vect_constant_def
&& types_compatible_p (TREE_TYPE (cond_initial_val),
TREE_TYPE (cond_reduc_val)))
{
tree e = fold_binary (LE_EXPR, boolean_type_node,
cond_initial_val, cond_reduc_val);
if (e && (integer_onep (e) || integer_zerop (e)))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"condition expression based on "
"compile time constant.\n");
/* Record reduction code at analysis stage. */
STMT_VINFO_REDUC_CODE (reduc_info)
= integer_onep (e) ? MAX_EXPR : MIN_EXPR;
STMT_VINFO_REDUC_TYPE (reduc_info) = CONST_COND_REDUCTION;
}
}
}
}
if (STMT_VINFO_LIVE_P (phi_info))
return false;
if (slp_node)
ncopies = 1;
else
ncopies = vect_get_num_copies (loop_vinfo, vectype_in);
gcc_assert (ncopies >= 1);
poly_uint64 nunits_out = TYPE_VECTOR_SUBPARTS (vectype_out);
if (nested_cycle)
{
gcc_assert (STMT_VINFO_DEF_TYPE (reduc_info)
== vect_double_reduction_def);
double_reduc = true;
}
/* 4.2. Check support for the epilog operation.
If STMT represents a reduction pattern, then the type of the
reduction variable may be different than the type of the rest
of the arguments. For example, consider the case of accumulation
of shorts into an int accumulator; The original code:
S1: int_a = (int) short_a;
orig_stmt-> S2: int_acc = plus <int_a ,int_acc>;
was replaced with:
STMT: int_acc = widen_sum <short_a, int_acc>
This means that:
1. The tree-code that is used to create the vector operation in the
epilog code (that reduces the partial results) is not the
tree-code of STMT, but is rather the tree-code of the original
stmt from the pattern that STMT is replacing. I.e, in the example
above we want to use 'widen_sum' in the loop, but 'plus' in the
epilog.
2. The type (mode) we use to check available target support
for the vector operation to be created in the *epilog*, is
determined by the type of the reduction variable (in the example
above we'd check this: optab_handler (plus_optab, vect_int_mode])).
However the type (mode) we use to check available target support
for the vector operation to be created *inside the loop*, is
determined by the type of the other arguments to STMT (in the
example we'd check this: optab_handler (widen_sum_optab,
vect_short_mode)).
This is contrary to "regular" reductions, in which the types of all
the arguments are the same as the type of the reduction variable.
For "regular" reductions we can therefore use the same vector type
(and also the same tree-code) when generating the epilog code and
when generating the code inside the loop. */
enum tree_code orig_code = STMT_VINFO_REDUC_CODE (phi_info);
STMT_VINFO_REDUC_CODE (reduc_info) = orig_code;
vect_reduction_type reduction_type = STMT_VINFO_REDUC_TYPE (reduc_info);
if (reduction_type == TREE_CODE_REDUCTION)
{
/* Check whether it's ok to change the order of the computation.
Generally, when vectorizing a reduction we change the order of the
computation. This may change the behavior of the program in some
cases, so we need to check that this is ok. One exception is when
vectorizing an outer-loop: the inner-loop is executed sequentially,
and therefore vectorizing reductions in the inner-loop during
outer-loop vectorization is safe. Likewise when we are vectorizing
a series of reductions using SLP and the VF is one the reductions
are performed in scalar order. */
if (slp_node
&& !REDUC_GROUP_FIRST_ELEMENT (stmt_info)
&& known_eq (LOOP_VINFO_VECT_FACTOR (loop_vinfo), 1u))
;
else if (needs_fold_left_reduction_p (scalar_type, orig_code))
{
/* When vectorizing a reduction chain w/o SLP the reduction PHI
is not directy used in stmt. */
if (!only_slp_reduc_chain
&& reduc_chain_length != 1)
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"in-order reduction chain without SLP.\n");
return false;
}
STMT_VINFO_REDUC_TYPE (reduc_info)
= reduction_type = FOLD_LEFT_REDUCTION;
}
else if (!commutative_tree_code (orig_code)
|| !associative_tree_code (orig_code))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"reduction: not commutative/associative");
return false;
}
}
if ((double_reduc || reduction_type != TREE_CODE_REDUCTION)
&& ncopies > 1)
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"multiple types in double reduction or condition "
"reduction or fold-left reduction.\n");
return false;
}
internal_fn reduc_fn = IFN_LAST;
if (reduction_type == TREE_CODE_REDUCTION
|| reduction_type == FOLD_LEFT_REDUCTION
|| reduction_type == INTEGER_INDUC_COND_REDUCTION
|| reduction_type == CONST_COND_REDUCTION)
{
if (reduction_type == FOLD_LEFT_REDUCTION
? fold_left_reduction_fn (orig_code, &reduc_fn)
: reduction_fn_for_scalar_code (orig_code, &reduc_fn))
{
if (reduc_fn != IFN_LAST
&& !direct_internal_fn_supported_p (reduc_fn, vectype_out,
OPTIMIZE_FOR_SPEED))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"reduc op not supported by target.\n");
reduc_fn = IFN_LAST;
}
}
else
{
if (!nested_cycle || double_reduc)
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"no reduc code for scalar code.\n");
return false;
}
}
}
else if (reduction_type == COND_REDUCTION)
{
int scalar_precision
= GET_MODE_PRECISION (SCALAR_TYPE_MODE (scalar_type));
cr_index_scalar_type = make_unsigned_type (scalar_precision);
cr_index_vector_type = get_same_sized_vectype (cr_index_scalar_type,
vectype_out);
if (direct_internal_fn_supported_p (IFN_REDUC_MAX, cr_index_vector_type,
OPTIMIZE_FOR_SPEED))
reduc_fn = IFN_REDUC_MAX;
}
STMT_VINFO_REDUC_FN (reduc_info) = reduc_fn;
if (reduction_type != EXTRACT_LAST_REDUCTION
&& (!nested_cycle || double_reduc)
&& reduc_fn == IFN_LAST
&& !nunits_out.is_constant ())
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"missing target support for reduction on"
" variable-length vectors.\n");
return false;
}
/* For SLP reductions, see if there is a neutral value we can use. */
tree neutral_op = NULL_TREE;
if (slp_node)
neutral_op = neutral_op_for_slp_reduction
(slp_node_instance->reduc_phis, vectype_out, orig_code,
REDUC_GROUP_FIRST_ELEMENT (stmt_info) != NULL);
if (double_reduc && reduction_type == FOLD_LEFT_REDUCTION)
{
/* We can't support in-order reductions of code such as this:
for (int i = 0; i < n1; ++i)
for (int j = 0; j < n2; ++j)
l += a[j];
since GCC effectively transforms the loop when vectorizing:
for (int i = 0; i < n1 / VF; ++i)
for (int j = 0; j < n2; ++j)
for (int k = 0; k < VF; ++k)
l += a[j];
which is a reassociation of the original operation. */
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"in-order double reduction not supported.\n");
return false;
}
if (reduction_type == FOLD_LEFT_REDUCTION
&& slp_node
&& !REDUC_GROUP_FIRST_ELEMENT (stmt_info))
{
/* We cannot use in-order reductions in this case because there is
an implicit reassociation of the operations involved. */
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"in-order unchained SLP reductions not supported.\n");
return false;
}
/* For double reductions, and for SLP reductions with a neutral value,
we construct a variable-length initial vector by loading a vector
full of the neutral value and then shift-and-inserting the start
values into the low-numbered elements. */
if ((double_reduc || neutral_op)
&& !nunits_out.is_constant ()
&& !direct_internal_fn_supported_p (IFN_VEC_SHL_INSERT,
vectype_out, OPTIMIZE_FOR_SPEED))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"reduction on variable-length vectors requires"
" target support for a vector-shift-and-insert"
" operation.\n");
return false;
}
/* Check extra constraints for variable-length unchained SLP reductions. */
if (STMT_SLP_TYPE (stmt_info)
&& !REDUC_GROUP_FIRST_ELEMENT (stmt_info)
&& !nunits_out.is_constant ())
{
/* We checked above that we could build the initial vector when
there's a neutral element value. Check here for the case in
which each SLP statement has its own initial value and in which
that value needs to be repeated for every instance of the
statement within the initial vector. */
unsigned int group_size = SLP_TREE_LANES (slp_node);
if (!neutral_op
&& !can_duplicate_and_interleave_p (loop_vinfo, group_size,
TREE_TYPE (vectype_out)))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"unsupported form of SLP reduction for"
" variable-length vectors: cannot build"
" initial vector.\n");
return false;
}
/* The epilogue code relies on the number of elements being a multiple
of the group size. The duplicate-and-interleave approach to setting
up the initial vector does too. */
if (!multiple_p (nunits_out, group_size))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"unsupported form of SLP reduction for"
" variable-length vectors: the vector size"
" is not a multiple of the number of results.\n");
return false;
}
}
if (reduction_type == COND_REDUCTION)
{
widest_int ni;
if (! max_loop_iterations (loop, &ni))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"loop count not known, cannot create cond "
"reduction.\n");
return false;
}
/* Convert backedges to iterations. */
ni += 1;
/* The additional index will be the same type as the condition. Check
that the loop can fit into this less one (because we'll use up the
zero slot for when there are no matches). */
tree max_index = TYPE_MAX_VALUE (cr_index_scalar_type);
if (wi::geu_p (ni, wi::to_widest (max_index)))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"loop size is greater than data size.\n");
return false;
}
}
/* In case the vectorization factor (VF) is bigger than the number
of elements that we can fit in a vectype (nunits), we have to generate
more than one vector stmt - i.e - we need to "unroll" the
vector stmt by a factor VF/nunits. For more details see documentation
in vectorizable_operation. */
/* If the reduction is used in an outer loop we need to generate
VF intermediate results, like so (e.g. for ncopies=2):
r0 = phi (init, r0)
r1 = phi (init, r1)
r0 = x0 + r0;
r1 = x1 + r1;
(i.e. we generate VF results in 2 registers).
In this case we have a separate def-use cycle for each copy, and therefore
for each copy we get the vector def for the reduction variable from the
respective phi node created for this copy.
Otherwise (the reduction is unused in the loop nest), we can combine
together intermediate results, like so (e.g. for ncopies=2):
r = phi (init, r)
r = x0 + r;
r = x1 + r;
(i.e. we generate VF/2 results in a single register).
In this case for each copy we get the vector def for the reduction variable
from the vectorized reduction operation generated in the previous iteration.
This only works when we see both the reduction PHI and its only consumer
in vectorizable_reduction and there are no intermediate stmts
participating. */
if (ncopies > 1
&& (STMT_VINFO_RELEVANT (stmt_info) <= vect_used_only_live)
&& reduc_chain_length == 1)
single_defuse_cycle = true;
if (single_defuse_cycle || lane_reduc_code_p)
{
gcc_assert (code != COND_EXPR);
/* 4. Supportable by target? */
bool ok = true;
/* 4.1. check support for the operation in the loop */
optab optab = optab_for_tree_code (code, vectype_in, optab_vector);
if (!optab)
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"no optab.\n");
ok = false;
}
machine_mode vec_mode = TYPE_MODE (vectype_in);
if (ok && optab_handler (optab, vec_mode) == CODE_FOR_nothing)
{
if (dump_enabled_p ())
dump_printf (MSG_NOTE, "op not supported by target.\n");
if (maybe_ne (GET_MODE_SIZE (vec_mode), UNITS_PER_WORD)
|| !vect_worthwhile_without_simd_p (loop_vinfo, code))
ok = false;
else
if (dump_enabled_p ())
dump_printf (MSG_NOTE, "proceeding using word mode.\n");
}
/* Worthwhile without SIMD support? */
if (ok
&& !VECTOR_MODE_P (TYPE_MODE (vectype_in))
&& !vect_worthwhile_without_simd_p (loop_vinfo, code))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"not worthwhile without SIMD support.\n");
ok = false;
}
/* lane-reducing operations have to go through vect_transform_reduction.
For the other cases try without the single cycle optimization. */
if (!ok)
{
if (lane_reduc_code_p)
return false;
else
single_defuse_cycle = false;
}
}
STMT_VINFO_FORCE_SINGLE_CYCLE (reduc_info) = single_defuse_cycle;
/* If the reduction stmt is one of the patterns that have lane
reduction embedded we cannot handle the case of ! single_defuse_cycle. */
if ((ncopies > 1 && ! single_defuse_cycle)
&& lane_reduc_code_p)
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"multi def-use cycle not possible for lane-reducing "
"reduction operation\n");
return false;
}
if (slp_node
&& !(!single_defuse_cycle
&& code != DOT_PROD_EXPR
&& code != WIDEN_SUM_EXPR
&& code != SAD_EXPR
&& reduction_type != FOLD_LEFT_REDUCTION))
for (i = 0; i < op_type; i++)
if (!vect_maybe_update_slp_op_vectype (slp_op[i], vectype_in))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"incompatible vector types for invariants\n");
return false;
}
if (slp_node)
vec_num = SLP_TREE_NUMBER_OF_VEC_STMTS (slp_node);
else
vec_num = 1;
vect_model_reduction_cost (loop_vinfo, stmt_info, reduc_fn,
reduction_type, ncopies, cost_vec);
/* Cost the reduction op inside the loop if transformed via
vect_transform_reduction. Otherwise this is costed by the
separate vectorizable_* routines. */
if (single_defuse_cycle
|| code == DOT_PROD_EXPR
|| code == WIDEN_SUM_EXPR
|| code == SAD_EXPR)
record_stmt_cost (cost_vec, ncopies, vector_stmt, stmt_info, 0, vect_body);
if (dump_enabled_p ()
&& reduction_type == FOLD_LEFT_REDUCTION)
dump_printf_loc (MSG_NOTE, vect_location,
"using an in-order (fold-left) reduction.\n");
STMT_VINFO_TYPE (orig_stmt_of_analysis) = cycle_phi_info_type;
/* All but single defuse-cycle optimized, lane-reducing and fold-left
reductions go through their own vectorizable_* routines. */
if (!single_defuse_cycle
&& code != DOT_PROD_EXPR
&& code != WIDEN_SUM_EXPR
&& code != SAD_EXPR
&& reduction_type != FOLD_LEFT_REDUCTION)
{
stmt_vec_info tem
= vect_stmt_to_vectorize (STMT_VINFO_REDUC_DEF (phi_info));
if (slp_node && REDUC_GROUP_FIRST_ELEMENT (tem))
{
gcc_assert (!REDUC_GROUP_NEXT_ELEMENT (tem));
tem = REDUC_GROUP_FIRST_ELEMENT (tem);
}
STMT_VINFO_DEF_TYPE (vect_orig_stmt (tem)) = vect_internal_def;
STMT_VINFO_DEF_TYPE (tem) = vect_internal_def;
}
else if (loop_vinfo && LOOP_VINFO_CAN_USE_PARTIAL_VECTORS_P (loop_vinfo))
{
vec_loop_masks *masks = &LOOP_VINFO_MASKS (loop_vinfo);
internal_fn cond_fn = get_conditional_internal_fn (code);
if (reduction_type != FOLD_LEFT_REDUCTION
&& !use_mask_by_cond_expr_p (code, cond_fn, vectype_in)
&& (cond_fn == IFN_LAST
|| !direct_internal_fn_supported_p (cond_fn, vectype_in,
OPTIMIZE_FOR_SPEED)))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"can't operate on partial vectors because"
" no conditional operation is available.\n");
LOOP_VINFO_CAN_USE_PARTIAL_VECTORS_P (loop_vinfo) = false;
}
else if (reduction_type == FOLD_LEFT_REDUCTION
&& reduc_fn == IFN_LAST
&& !expand_vec_cond_expr_p (vectype_in,
truth_type_for (vectype_in),
SSA_NAME))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"can't operate on partial vectors because"
" no conditional operation is available.\n");
LOOP_VINFO_CAN_USE_PARTIAL_VECTORS_P (loop_vinfo) = false;
}
else
vect_record_loop_mask (loop_vinfo, masks, ncopies * vec_num,
vectype_in, NULL);
}
return true;
}
/* Transform the definition stmt STMT_INFO of a reduction PHI backedge
value. */
bool
vect_transform_reduction (loop_vec_info loop_vinfo,
stmt_vec_info stmt_info, gimple_stmt_iterator *gsi,
gimple **vec_stmt, slp_tree slp_node)
{
tree vectype_out = STMT_VINFO_VECTYPE (stmt_info);
class loop *loop = LOOP_VINFO_LOOP (loop_vinfo);
int i;
int ncopies;
int vec_num;
stmt_vec_info reduc_info = info_for_reduction (loop_vinfo, stmt_info);
gcc_assert (reduc_info->is_reduc_info);
if (nested_in_vect_loop_p (loop, stmt_info))
{
loop = loop->inner;
gcc_assert (STMT_VINFO_DEF_TYPE (reduc_info) == vect_double_reduction_def);
}
gassign *stmt = as_a <gassign *> (stmt_info->stmt);
enum tree_code code = gimple_assign_rhs_code (stmt);
int op_type = TREE_CODE_LENGTH (code);
/* Flatten RHS. */
tree ops[3];
switch (get_gimple_rhs_class (code))
{
case GIMPLE_TERNARY_RHS:
ops[2] = gimple_assign_rhs3 (stmt);
/* Fall thru. */
case GIMPLE_BINARY_RHS:
ops[0] = gimple_assign_rhs1 (stmt);
ops[1] = gimple_assign_rhs2 (stmt);
break;
default:
gcc_unreachable ();
}
/* All uses but the last are expected to be defined in the loop.
The last use is the reduction variable. In case of nested cycle this
assumption is not true: we use reduc_index to record the index of the
reduction variable. */
stmt_vec_info phi_info = STMT_VINFO_REDUC_DEF (vect_orig_stmt (stmt_info));
gphi *reduc_def_phi = as_a <gphi *> (phi_info->stmt);
int reduc_index = STMT_VINFO_REDUC_IDX (stmt_info);
tree vectype_in = STMT_VINFO_REDUC_VECTYPE_IN (reduc_info);
if (slp_node)
{
ncopies = 1;
vec_num = SLP_TREE_NUMBER_OF_VEC_STMTS (slp_node);
}
else
{
ncopies = vect_get_num_copies (loop_vinfo, vectype_in);
vec_num = 1;
}
internal_fn cond_fn = get_conditional_internal_fn (code);
vec_loop_masks *masks = &LOOP_VINFO_MASKS (loop_vinfo);
bool mask_by_cond_expr = use_mask_by_cond_expr_p (code, cond_fn, vectype_in);
/* Transform. */
tree new_temp = NULL_TREE;
auto_vec<tree> vec_oprnds0;
auto_vec<tree> vec_oprnds1;
auto_vec<tree> vec_oprnds2;
tree def0;
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location, "transform reduction.\n");
/* FORNOW: Multiple types are not supported for condition. */
if (code == COND_EXPR)
gcc_assert (ncopies == 1);
bool masked_loop_p = LOOP_VINFO_FULLY_MASKED_P (loop_vinfo);
vect_reduction_type reduction_type = STMT_VINFO_REDUC_TYPE (reduc_info);
if (reduction_type == FOLD_LEFT_REDUCTION)
{
internal_fn reduc_fn = STMT_VINFO_REDUC_FN (reduc_info);
return vectorize_fold_left_reduction
(loop_vinfo, stmt_info, gsi, vec_stmt, slp_node, reduc_def_phi, code,
reduc_fn, ops, vectype_in, reduc_index, masks);
}
bool single_defuse_cycle = STMT_VINFO_FORCE_SINGLE_CYCLE (reduc_info);
gcc_assert (single_defuse_cycle
|| code == DOT_PROD_EXPR
|| code == WIDEN_SUM_EXPR
|| code == SAD_EXPR);
/* Create the destination vector */
tree scalar_dest = gimple_assign_lhs (stmt);
tree vec_dest = vect_create_destination_var (scalar_dest, vectype_out);
vect_get_vec_defs (loop_vinfo, stmt_info, slp_node, ncopies,
single_defuse_cycle && reduc_index == 0
? NULL_TREE : ops[0], &vec_oprnds0,
single_defuse_cycle && reduc_index == 1
? NULL_TREE : ops[1], &vec_oprnds1,
op_type == ternary_op
&& !(single_defuse_cycle && reduc_index == 2)
? ops[2] : NULL_TREE, &vec_oprnds2);
if (single_defuse_cycle)
{
gcc_assert (!slp_node);
vect_get_vec_defs_for_operand (loop_vinfo, stmt_info, 1,
ops[reduc_index],
reduc_index == 0 ? &vec_oprnds0
: (reduc_index == 1 ? &vec_oprnds1
: &vec_oprnds2));
}
FOR_EACH_VEC_ELT (vec_oprnds0, i, def0)
{
gimple *new_stmt;
tree vop[3] = { def0, vec_oprnds1[i], NULL_TREE };
if (masked_loop_p && !mask_by_cond_expr)
{
/* Make sure that the reduction accumulator is vop[0]. */
if (reduc_index == 1)
{
gcc_assert (commutative_tree_code (code));
std::swap (vop[0], vop[1]);
}
tree mask = vect_get_loop_mask (gsi, masks, vec_num * ncopies,
vectype_in, i);
gcall *call = gimple_build_call_internal (cond_fn, 4, mask,
vop[0], vop[1], vop[0]);
new_temp = make_ssa_name (vec_dest, call);
gimple_call_set_lhs (call, new_temp);
gimple_call_set_nothrow (call, true);
vect_finish_stmt_generation (loop_vinfo, stmt_info, call, gsi);
new_stmt = call;
}
else
{
if (op_type == ternary_op)
vop[2] = vec_oprnds2[i];
if (masked_loop_p && mask_by_cond_expr)
{
tree mask = vect_get_loop_mask (gsi, masks, vec_num * ncopies,
vectype_in, i);
build_vect_cond_expr (code, vop, mask, gsi);
}
new_stmt = gimple_build_assign (vec_dest, code,
vop[0], vop[1], vop[2]);
new_temp = make_ssa_name (vec_dest, new_stmt);
gimple_assign_set_lhs (new_stmt, new_temp);
vect_finish_stmt_generation (loop_vinfo, stmt_info, new_stmt, gsi);
}
if (slp_node)
SLP_TREE_VEC_STMTS (slp_node).quick_push (new_stmt);
else if (single_defuse_cycle
&& i < ncopies - 1)
{
if (reduc_index == 0)
vec_oprnds0.safe_push (gimple_get_lhs (new_stmt));
else if (reduc_index == 1)
vec_oprnds1.safe_push (gimple_get_lhs (new_stmt));
else if (reduc_index == 2)
vec_oprnds2.safe_push (gimple_get_lhs (new_stmt));
}
else
STMT_VINFO_VEC_STMTS (stmt_info).safe_push (new_stmt);
}
if (!slp_node)
*vec_stmt = STMT_VINFO_VEC_STMTS (stmt_info)[0];
return true;
}
/* Transform phase of a cycle PHI. */
bool
vect_transform_cycle_phi (loop_vec_info loop_vinfo,
stmt_vec_info stmt_info, gimple **vec_stmt,
slp_tree slp_node, slp_instance slp_node_instance)
{
tree vectype_out = STMT_VINFO_VECTYPE (stmt_info);
class loop *loop = LOOP_VINFO_LOOP (loop_vinfo);
int i;
int ncopies;
int j;
bool nested_cycle = false;
int vec_num;
if (nested_in_vect_loop_p (loop, stmt_info))
{
loop = loop->inner;
nested_cycle = true;
}
stmt_vec_info reduc_stmt_info = STMT_VINFO_REDUC_DEF (stmt_info);
reduc_stmt_info = vect_stmt_to_vectorize (reduc_stmt_info);
stmt_vec_info reduc_info = info_for_reduction (loop_vinfo, stmt_info);
gcc_assert (reduc_info->is_reduc_info);
if (STMT_VINFO_REDUC_TYPE (reduc_info) == EXTRACT_LAST_REDUCTION
|| STMT_VINFO_REDUC_TYPE (reduc_info) == FOLD_LEFT_REDUCTION)
/* Leave the scalar phi in place. */
return true;
tree vectype_in = STMT_VINFO_REDUC_VECTYPE_IN (reduc_info);
/* For a nested cycle we do not fill the above. */
if (!vectype_in)
vectype_in = STMT_VINFO_VECTYPE (stmt_info);
gcc_assert (vectype_in);
if (slp_node)
{
/* The size vect_schedule_slp_instance computes is off for us. */
vec_num = vect_get_num_vectors (LOOP_VINFO_VECT_FACTOR (loop_vinfo)
* SLP_TREE_LANES (slp_node), vectype_in);
ncopies = 1;
}
else
{
vec_num = 1;
ncopies = vect_get_num_copies (loop_vinfo, vectype_in);
}
/* Check whether we should use a single PHI node and accumulate
vectors to one before the backedge. */
if (STMT_VINFO_FORCE_SINGLE_CYCLE (reduc_info))
ncopies = 1;
/* Create the destination vector */
gphi *phi = as_a <gphi *> (stmt_info->stmt);
tree vec_dest = vect_create_destination_var (gimple_phi_result (phi),
vectype_out);
/* Get the loop-entry arguments. */
tree vec_initial_def;
auto_vec<tree> vec_initial_defs;
if (slp_node)
{
vec_initial_defs.reserve (vec_num);
if (nested_cycle)
{
unsigned phi_idx = loop_preheader_edge (loop)->dest_idx;
vect_get_slp_defs (SLP_TREE_CHILDREN (slp_node)[phi_idx],
&vec_initial_defs);
}
else
{
gcc_assert (slp_node == slp_node_instance->reduc_phis);
stmt_vec_info first = REDUC_GROUP_FIRST_ELEMENT (reduc_stmt_info);
tree neutral_op
= neutral_op_for_slp_reduction (slp_node, vectype_out,
STMT_VINFO_REDUC_CODE (reduc_info),
first != NULL);
get_initial_defs_for_reduction (loop_vinfo, slp_node_instance->reduc_phis,
&vec_initial_defs, vec_num,
first != NULL, neutral_op);
}
}
else
{
/* Get at the scalar def before the loop, that defines the initial
value of the reduction variable. */
tree initial_def = PHI_ARG_DEF_FROM_EDGE (phi,
loop_preheader_edge (loop));
/* Optimize: if initial_def is for REDUC_MAX smaller than the base
and we can't use zero for induc_val, use initial_def. Similarly
for REDUC_MIN and initial_def larger than the base. */
if (STMT_VINFO_REDUC_TYPE (reduc_info) == INTEGER_INDUC_COND_REDUCTION)
{
tree induc_val = STMT_VINFO_VEC_INDUC_COND_INITIAL_VAL (reduc_info);
if (TREE_CODE (initial_def) == INTEGER_CST
&& !integer_zerop (induc_val)
&& ((STMT_VINFO_REDUC_CODE (reduc_info) == MAX_EXPR
&& tree_int_cst_lt (initial_def, induc_val))
|| (STMT_VINFO_REDUC_CODE (reduc_info) == MIN_EXPR
&& tree_int_cst_lt (induc_val, initial_def))))
{
induc_val = initial_def;
/* Communicate we used the initial_def to epilouge
generation. */
STMT_VINFO_VEC_INDUC_COND_INITIAL_VAL (reduc_info) = NULL_TREE;
}
vec_initial_def = build_vector_from_val (vectype_out, induc_val);
vec_initial_defs.create (ncopies);
for (i = 0; i < ncopies; ++i)
vec_initial_defs.quick_push (vec_initial_def);
}
else if (nested_cycle)
{
/* Do not use an adjustment def as that case is not supported
correctly if ncopies is not one. */
vect_get_vec_defs_for_operand (loop_vinfo, reduc_stmt_info,
ncopies, initial_def,
&vec_initial_defs);
}
else
{
tree adjustment_def = NULL_TREE;
tree *adjustment_defp = &adjustment_def;
enum tree_code code = STMT_VINFO_REDUC_CODE (reduc_info);
if (STMT_VINFO_DEF_TYPE (stmt_info) == vect_double_reduction_def)
adjustment_defp = NULL;
vec_initial_def
= get_initial_def_for_reduction (loop_vinfo, reduc_stmt_info, code,
initial_def, adjustment_defp);
STMT_VINFO_REDUC_EPILOGUE_ADJUSTMENT (reduc_info) = adjustment_def;
vec_initial_defs.create (ncopies);
for (i = 0; i < ncopies; ++i)
vec_initial_defs.quick_push (vec_initial_def);
}
}
/* Generate the reduction PHIs upfront. */
for (i = 0; i < vec_num; i++)
{
tree vec_init_def = vec_initial_defs[i];
for (j = 0; j < ncopies; j++)
{
/* Create the reduction-phi that defines the reduction
operand. */
gphi *new_phi = create_phi_node (vec_dest, loop->header);
/* Set the loop-entry arg of the reduction-phi. */
if (j != 0 && nested_cycle)
vec_init_def = vec_initial_defs[j];
add_phi_arg (new_phi, vec_init_def, loop_preheader_edge (loop),
UNKNOWN_LOCATION);
/* The loop-latch arg is set in epilogue processing. */
if (slp_node)
SLP_TREE_VEC_STMTS (slp_node).quick_push (new_phi);
else
{
if (j == 0)
*vec_stmt = new_phi;
STMT_VINFO_VEC_STMTS (stmt_info).safe_push (new_phi);
}
}
}
return true;
}
/* Vectorizes LC PHIs. */
bool
vectorizable_lc_phi (loop_vec_info loop_vinfo,
stmt_vec_info stmt_info, gimple **vec_stmt,
slp_tree slp_node)
{
if (!loop_vinfo
|| !is_a <gphi *> (stmt_info->stmt)
|| gimple_phi_num_args (stmt_info->stmt) != 1)
return false;
if (STMT_VINFO_DEF_TYPE (stmt_info) != vect_internal_def
&& STMT_VINFO_DEF_TYPE (stmt_info) != vect_double_reduction_def)
return false;
if (!vec_stmt) /* transformation not required. */
{
/* Deal with copies from externs or constants that disguise as
loop-closed PHI nodes (PR97886). */
if (slp_node
&& !vect_maybe_update_slp_op_vectype (SLP_TREE_CHILDREN (slp_node)[0],
SLP_TREE_VECTYPE (slp_node)))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"incompatible vector types for invariants\n");
return false;
}
STMT_VINFO_TYPE (stmt_info) = lc_phi_info_type;
return true;
}
tree vectype = STMT_VINFO_VECTYPE (stmt_info);
tree scalar_dest = gimple_phi_result (stmt_info->stmt);
basic_block bb = gimple_bb (stmt_info->stmt);
edge e = single_pred_edge (bb);
tree vec_dest = vect_create_destination_var (scalar_dest, vectype);
auto_vec<tree> vec_oprnds;
vect_get_vec_defs (loop_vinfo, stmt_info, slp_node,
!slp_node ? vect_get_num_copies (loop_vinfo, vectype) : 1,
gimple_phi_arg_def (stmt_info->stmt, 0), &vec_oprnds);
for (unsigned i = 0; i < vec_oprnds.length (); i++)
{
/* Create the vectorized LC PHI node. */
gphi *new_phi = create_phi_node (vec_dest, bb);
add_phi_arg (new_phi, vec_oprnds[i], e, UNKNOWN_LOCATION);
if (slp_node)
SLP_TREE_VEC_STMTS (slp_node).quick_push (new_phi);
else
STMT_VINFO_VEC_STMTS (stmt_info).safe_push (new_phi);
}
if (!slp_node)
*vec_stmt = STMT_VINFO_VEC_STMTS (stmt_info)[0];
return true;
}
/* Vectorizes PHIs. */
bool
vectorizable_phi (vec_info *,
stmt_vec_info stmt_info, gimple **vec_stmt,
slp_tree slp_node, stmt_vector_for_cost *cost_vec)
{
if (!is_a <gphi *> (stmt_info->stmt) || !slp_node)
return false;
if (STMT_VINFO_DEF_TYPE (stmt_info) != vect_internal_def)
return false;
tree vectype = SLP_TREE_VECTYPE (slp_node);
if (!vec_stmt) /* transformation not required. */
{
slp_tree child;
unsigned i;
FOR_EACH_VEC_ELT (SLP_TREE_CHILDREN (slp_node), i, child)
if (!child)
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"PHI node with unvectorized backedge def\n");
return false;
}
else if (!vect_maybe_update_slp_op_vectype (child, vectype))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"incompatible vector types for invariants\n");
return false;
}
/* For single-argument PHIs assume coalescing which means zero cost
for the scalar and the vector PHIs. This avoids artificially
favoring the vector path (but may pessimize it in some cases). */
if (gimple_phi_num_args (as_a <gphi *> (stmt_info->stmt)) > 1)
record_stmt_cost (cost_vec, SLP_TREE_NUMBER_OF_VEC_STMTS (slp_node),
vector_stmt, stmt_info, vectype, 0, vect_body);
STMT_VINFO_TYPE (stmt_info) = phi_info_type;
return true;
}
tree scalar_dest = gimple_phi_result (stmt_info->stmt);
basic_block bb = gimple_bb (stmt_info->stmt);
tree vec_dest = vect_create_destination_var (scalar_dest, vectype);
auto_vec<gphi *> new_phis;
for (unsigned i = 0; i < gimple_phi_num_args (stmt_info->stmt); ++i)
{
slp_tree child = SLP_TREE_CHILDREN (slp_node)[i];
/* Skip not yet vectorized defs. */
if (SLP_TREE_DEF_TYPE (child) == vect_internal_def
&& SLP_TREE_VEC_STMTS (child).is_empty ())
continue;
auto_vec<tree> vec_oprnds;
vect_get_slp_defs (SLP_TREE_CHILDREN (slp_node)[i], &vec_oprnds);
if (!new_phis.exists ())
{
new_phis.create (vec_oprnds.length ());
for (unsigned j = 0; j < vec_oprnds.length (); j++)
{
/* Create the vectorized LC PHI node. */
new_phis.quick_push (create_phi_node (vec_dest, bb));
SLP_TREE_VEC_STMTS (slp_node).quick_push (new_phis[j]);
}
}
edge e = gimple_phi_arg_edge (as_a <gphi *> (stmt_info->stmt), i);
for (unsigned j = 0; j < vec_oprnds.length (); j++)
add_phi_arg (new_phis[j], vec_oprnds[j], e, UNKNOWN_LOCATION);
}
/* We should have at least one already vectorized child. */
gcc_assert (new_phis.exists ());
return true;
}
/* Function vect_min_worthwhile_factor.
For a loop where we could vectorize the operation indicated by CODE,
return the minimum vectorization factor that makes it worthwhile
to use generic vectors. */
static unsigned int
vect_min_worthwhile_factor (enum tree_code code)
{
switch (code)
{
case PLUS_EXPR:
case MINUS_EXPR:
case NEGATE_EXPR:
return 4;
case BIT_AND_EXPR:
case BIT_IOR_EXPR:
case BIT_XOR_EXPR:
case BIT_NOT_EXPR:
return 2;
default:
return INT_MAX;
}
}
/* Return true if VINFO indicates we are doing loop vectorization and if
it is worth decomposing CODE operations into scalar operations for
that loop's vectorization factor. */
bool
vect_worthwhile_without_simd_p (vec_info *vinfo, tree_code code)
{
loop_vec_info loop_vinfo = dyn_cast <loop_vec_info> (vinfo);
unsigned HOST_WIDE_INT value;
return (loop_vinfo
&& LOOP_VINFO_VECT_FACTOR (loop_vinfo).is_constant (&value)
&& value >= vect_min_worthwhile_factor (code));
}
/* Function vectorizable_induction
Check if STMT_INFO performs an induction computation that can be vectorized.
If VEC_STMT is also passed, vectorize the induction PHI: create a vectorized
phi to replace it, put it in VEC_STMT, and add it to the same basic block.
Return true if STMT_INFO is vectorizable in this way. */
bool
vectorizable_induction (loop_vec_info loop_vinfo,
stmt_vec_info stmt_info,
gimple **vec_stmt, slp_tree slp_node,
stmt_vector_for_cost *cost_vec)
{
class loop *loop = LOOP_VINFO_LOOP (loop_vinfo);
unsigned ncopies;
bool nested_in_vect_loop = false;
class loop *iv_loop;
tree vec_def;
edge pe = loop_preheader_edge (loop);
basic_block new_bb;
tree new_vec, vec_init, vec_step, t;
tree new_name;
gimple *new_stmt;
gphi *induction_phi;
tree induc_def, vec_dest;
tree init_expr, step_expr;
poly_uint64 vf = LOOP_VINFO_VECT_FACTOR (loop_vinfo);
unsigned i;
tree expr;
gimple_stmt_iterator si;
gphi *phi = dyn_cast <gphi *> (stmt_info->stmt);
if (!phi)
return false;
if (!STMT_VINFO_RELEVANT_P (stmt_info))
return false;
/* Make sure it was recognized as induction computation. */
if (STMT_VINFO_DEF_TYPE (stmt_info) != vect_induction_def)
return false;
tree vectype = STMT_VINFO_VECTYPE (stmt_info);
poly_uint64 nunits = TYPE_VECTOR_SUBPARTS (vectype);
if (slp_node)
ncopies = 1;
else
ncopies = vect_get_num_copies (loop_vinfo, vectype);
gcc_assert (ncopies >= 1);
/* FORNOW. These restrictions should be relaxed. */
if (nested_in_vect_loop_p (loop, stmt_info))
{
imm_use_iterator imm_iter;
use_operand_p use_p;
gimple *exit_phi;
edge latch_e;
tree loop_arg;
if (ncopies > 1)
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"multiple types in nested loop.\n");
return false;
}
exit_phi = NULL;
latch_e = loop_latch_edge (loop->inner);
loop_arg = PHI_ARG_DEF_FROM_EDGE (phi, latch_e);
FOR_EACH_IMM_USE_FAST (use_p, imm_iter, loop_arg)
{
gimple *use_stmt = USE_STMT (use_p);
if (is_gimple_debug (use_stmt))
continue;
if (!flow_bb_inside_loop_p (loop->inner, gimple_bb (use_stmt)))
{
exit_phi = use_stmt;
break;
}
}
if (exit_phi)
{
stmt_vec_info exit_phi_vinfo = loop_vinfo->lookup_stmt (exit_phi);
if (!(STMT_VINFO_RELEVANT_P (exit_phi_vinfo)
&& !STMT_VINFO_LIVE_P (exit_phi_vinfo)))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"inner-loop induction only used outside "
"of the outer vectorized loop.\n");
return false;
}
}
nested_in_vect_loop = true;
iv_loop = loop->inner;
}
else
iv_loop = loop;
gcc_assert (iv_loop == (gimple_bb (phi))->loop_father);
if (slp_node && !nunits.is_constant ())
{
/* The current SLP code creates the step value element-by-element. */
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"SLP induction not supported for variable-length"
" vectors.\n");
return false;
}
if (!vec_stmt) /* transformation not required. */
{
unsigned inside_cost = 0, prologue_cost = 0;
if (slp_node)
{
/* We eventually need to set a vector type on invariant
arguments. */
unsigned j;
slp_tree child;
FOR_EACH_VEC_ELT (SLP_TREE_CHILDREN (slp_node), j, child)
if (!vect_maybe_update_slp_op_vectype
(child, SLP_TREE_VECTYPE (slp_node)))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"incompatible vector types for "
"invariants\n");
return false;
}
/* loop cost for vec_loop. */
inside_cost
= record_stmt_cost (cost_vec,
SLP_TREE_NUMBER_OF_VEC_STMTS (slp_node),
vector_stmt, stmt_info, 0, vect_body);
/* prologue cost for vec_init (if not nested) and step. */
prologue_cost = record_stmt_cost (cost_vec, 1 + !nested_in_vect_loop,
scalar_to_vec,
stmt_info, 0, vect_prologue);
}
else /* if (!slp_node) */
{
/* loop cost for vec_loop. */
inside_cost = record_stmt_cost (cost_vec, ncopies, vector_stmt,
stmt_info, 0, vect_body);
/* prologue cost for vec_init and vec_step. */
prologue_cost = record_stmt_cost (cost_vec, 2, scalar_to_vec,
stmt_info, 0, vect_prologue);
}
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"vect_model_induction_cost: inside_cost = %d, "
"prologue_cost = %d .\n", inside_cost,
prologue_cost);
STMT_VINFO_TYPE (stmt_info) = induc_vec_info_type;
DUMP_VECT_SCOPE ("vectorizable_induction");
return true;
}
/* Transform. */
/* Compute a vector variable, initialized with the first VF values of
the induction variable. E.g., for an iv with IV_PHI='X' and
evolution S, for a vector of 4 units, we want to compute:
[X, X + S, X + 2*S, X + 3*S]. */
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location, "transform induction phi.\n");
step_expr = STMT_VINFO_LOOP_PHI_EVOLUTION_PART (stmt_info);
gcc_assert (step_expr != NULL_TREE);
tree step_vectype = get_same_sized_vectype (TREE_TYPE (step_expr), vectype);
pe = loop_preheader_edge (iv_loop);
/* Find the first insertion point in the BB. */
basic_block bb = gimple_bb (phi);
si = gsi_after_labels (bb);
/* For SLP induction we have to generate several IVs as for example
with group size 3 we need
[i0, i1, i2, i0 + S0] [i1 + S1, i2 + S2, i0 + 2*S0, i1 + 2*S1]
[i2 + 2*S2, i0 + 3*S0, i1 + 3*S1, i2 + 3*S2]. */
if (slp_node)
{
/* Enforced above. */
unsigned int const_nunits = nunits.to_constant ();
/* The initial values are vectorized, but any lanes > group_size
need adjustment. */
slp_tree init_node
= SLP_TREE_CHILDREN (slp_node)[pe->dest_idx];
/* Gather steps. Since we do not vectorize inductions as
cycles we have to reconstruct the step from SCEV data. */
unsigned group_size = SLP_TREE_LANES (slp_node);
tree *steps = XALLOCAVEC (tree, group_size);
tree *inits = XALLOCAVEC (tree, group_size);
stmt_vec_info phi_info;
FOR_EACH_VEC_ELT (SLP_TREE_SCALAR_STMTS (slp_node), i, phi_info)
{
steps[i] = STMT_VINFO_LOOP_PHI_EVOLUTION_PART (phi_info);
if (!init_node)
inits[i] = gimple_phi_arg_def (as_a<gphi *> (phi_info->stmt),
pe->dest_idx);
}
/* Now generate the IVs. */
unsigned nvects = SLP_TREE_NUMBER_OF_VEC_STMTS (slp_node);
gcc_assert ((const_nunits * nvects) % group_size == 0);
unsigned nivs;
if (nested_in_vect_loop)
nivs = nvects;
else
{
/* Compute the number of distinct IVs we need. First reduce
group_size if it is a multiple of const_nunits so we get
one IV for a group_size of 4 but const_nunits 2. */
unsigned group_sizep = group_size;
if (group_sizep % const_nunits == 0)
group_sizep = group_sizep / const_nunits;
nivs = least_common_multiple (group_sizep,
const_nunits) / const_nunits;
}
tree stept = TREE_TYPE (step_vectype);
tree lupdate_mul = NULL_TREE;
if (!nested_in_vect_loop)
{
/* The number of iterations covered in one vector iteration. */
unsigned lup_mul = (nvects * const_nunits) / group_size;
lupdate_mul
= build_vector_from_val (step_vectype,
SCALAR_FLOAT_TYPE_P (stept)
? build_real_from_wide (stept, lup_mul,
UNSIGNED)
: build_int_cstu (stept, lup_mul));
}
tree peel_mul = NULL_TREE;
gimple_seq init_stmts = NULL;
if (LOOP_VINFO_MASK_SKIP_NITERS (loop_vinfo))
{
if (SCALAR_FLOAT_TYPE_P (stept))
peel_mul = gimple_build (&init_stmts, FLOAT_EXPR, stept,
LOOP_VINFO_MASK_SKIP_NITERS (loop_vinfo));
else
peel_mul = gimple_convert (&init_stmts, stept,
LOOP_VINFO_MASK_SKIP_NITERS (loop_vinfo));
peel_mul = gimple_build_vector_from_val (&init_stmts,
step_vectype, peel_mul);
}
unsigned ivn;
auto_vec<tree> vec_steps;
for (ivn = 0; ivn < nivs; ++ivn)
{
tree_vector_builder step_elts (step_vectype, const_nunits, 1);
tree_vector_builder init_elts (vectype, const_nunits, 1);
tree_vector_builder mul_elts (step_vectype, const_nunits, 1);
for (unsigned eltn = 0; eltn < const_nunits; ++eltn)
{
/* The scalar steps of the IVs. */
tree elt = steps[(ivn*const_nunits + eltn) % group_size];
elt = gimple_convert (&init_stmts, TREE_TYPE (step_vectype), elt);
step_elts.quick_push (elt);
if (!init_node)
{
/* The scalar inits of the IVs if not vectorized. */
elt = inits[(ivn*const_nunits + eltn) % group_size];
if (!useless_type_conversion_p (TREE_TYPE (vectype),
TREE_TYPE (elt)))
elt = gimple_build (&init_stmts, VIEW_CONVERT_EXPR,
TREE_TYPE (vectype), elt);
init_elts.quick_push (elt);
}
/* The number of steps to add to the initial values. */
unsigned mul_elt = (ivn*const_nunits + eltn) / group_size;
mul_elts.quick_push (SCALAR_FLOAT_TYPE_P (stept)
? build_real_from_wide (stept,
mul_elt, UNSIGNED)
: build_int_cstu (stept, mul_elt));
}
vec_step = gimple_build_vector (&init_stmts, &step_elts);
vec_steps.safe_push (vec_step);
tree step_mul = gimple_build_vector (&init_stmts, &mul_elts);
if (peel_mul)
step_mul = gimple_build (&init_stmts, PLUS_EXPR, step_vectype,
step_mul, peel_mul);
if (!init_node)
vec_init = gimple_build_vector (&init_stmts, &init_elts);
/* Create the induction-phi that defines the induction-operand. */
vec_dest = vect_get_new_vect_var (vectype, vect_simple_var,
"vec_iv_");
induction_phi = create_phi_node (vec_dest, iv_loop->header);
induc_def = PHI_RESULT (induction_phi);
/* Create the iv update inside the loop */
tree up = vec_step;
if (lupdate_mul)
up = gimple_build (&init_stmts, MULT_EXPR, step_vectype,
vec_step, lupdate_mul);
gimple_seq stmts = NULL;
vec_def = gimple_convert (&stmts, step_vectype, induc_def);
vec_def = gimple_build (&stmts,
PLUS_EXPR, step_vectype, vec_def, up);
vec_def = gimple_convert (&stmts, vectype, vec_def);
gsi_insert_seq_before (&si, stmts, GSI_SAME_STMT);
add_phi_arg (induction_phi, vec_def, loop_latch_edge (iv_loop),
UNKNOWN_LOCATION);
if (init_node)
vec_init = vect_get_slp_vect_def (init_node, ivn);
if (!nested_in_vect_loop
&& !integer_zerop (step_mul))
{
vec_def = gimple_convert (&init_stmts, step_vectype, vec_init);
up = gimple_build (&init_stmts, MULT_EXPR, step_vectype,
vec_step, step_mul);
vec_def = gimple_build (&init_stmts, PLUS_EXPR, step_vectype,
vec_def, up);
vec_init = gimple_convert (&init_stmts, vectype, vec_def);
}
/* Set the arguments of the phi node: */
add_phi_arg (induction_phi, vec_init, pe, UNKNOWN_LOCATION);
SLP_TREE_VEC_STMTS (slp_node).quick_push (induction_phi);
}
if (!nested_in_vect_loop)
{
/* Fill up to the number of vectors we need for the whole group. */
nivs = least_common_multiple (group_size,
const_nunits) / const_nunits;
vec_steps.reserve (nivs-ivn);
for (; ivn < nivs; ++ivn)
{
SLP_TREE_VEC_STMTS (slp_node)
.quick_push (SLP_TREE_VEC_STMTS (slp_node)[0]);
vec_steps.quick_push (vec_steps[0]);
}
}
/* Re-use IVs when we can. We are generating further vector
stmts by adding VF' * stride to the IVs generated above. */
if (ivn < nvects)
{
unsigned vfp
= least_common_multiple (group_size, const_nunits) / group_size;
tree lupdate_mul
= build_vector_from_val (step_vectype,
SCALAR_FLOAT_TYPE_P (stept)
? build_real_from_wide (stept,
vfp, UNSIGNED)
: build_int_cstu (stept, vfp));
for (; ivn < nvects; ++ivn)
{
gimple *iv = SLP_TREE_VEC_STMTS (slp_node)[ivn - nivs];
tree def = gimple_get_lhs (iv);
if (ivn < 2*nivs)
vec_steps[ivn - nivs]
= gimple_build (&init_stmts, MULT_EXPR, step_vectype,
vec_steps[ivn - nivs], lupdate_mul);
gimple_seq stmts = NULL;
def = gimple_convert (&stmts, step_vectype, def);
def = gimple_build (&stmts, PLUS_EXPR, step_vectype,
def, vec_steps[ivn % nivs]);
def = gimple_convert (&stmts, vectype, def);
if (gimple_code (iv) == GIMPLE_PHI)
gsi_insert_seq_before (&si, stmts, GSI_SAME_STMT);
else
{
gimple_stmt_iterator tgsi = gsi_for_stmt (iv);
gsi_insert_seq_after (&tgsi, stmts, GSI_CONTINUE_LINKING);
}
SLP_TREE_VEC_STMTS (slp_node)
.quick_push (SSA_NAME_DEF_STMT (def));
}
}
new_bb = gsi_insert_seq_on_edge_immediate (pe, init_stmts);
gcc_assert (!new_bb);
return true;
}
init_expr = PHI_ARG_DEF_FROM_EDGE (phi,
loop_preheader_edge (iv_loop));
gimple_seq stmts = NULL;
if (!nested_in_vect_loop)
{
/* Convert the initial value to the IV update type. */
tree new_type = TREE_TYPE (step_expr);
init_expr = gimple_convert (&stmts, new_type, init_expr);
/* If we are using the loop mask to "peel" for alignment then we need
to adjust the start value here. */
tree skip_niters = LOOP_VINFO_MASK_SKIP_NITERS (loop_vinfo);
if (skip_niters != NULL_TREE)
{
if (FLOAT_TYPE_P (vectype))
skip_niters = gimple_build (&stmts, FLOAT_EXPR, new_type,
skip_niters);
else
skip_niters = gimple_convert (&stmts, new_type, skip_niters);
tree skip_step = gimple_build (&stmts, MULT_EXPR, new_type,
skip_niters, step_expr);
init_expr = gimple_build (&stmts, MINUS_EXPR, new_type,
init_expr, skip_step);
}
}
if (stmts)
{
new_bb = gsi_insert_seq_on_edge_immediate (pe, stmts);
gcc_assert (!new_bb);
}
/* Create the vector that holds the initial_value of the induction. */
if (nested_in_vect_loop)
{
/* iv_loop is nested in the loop to be vectorized. init_expr had already
been created during vectorization of previous stmts. We obtain it
from the STMT_VINFO_VEC_STMT of the defining stmt. */
auto_vec<tree> vec_inits;
vect_get_vec_defs_for_operand (loop_vinfo, stmt_info, 1,
init_expr, &vec_inits);
vec_init = vec_inits[0];
/* If the initial value is not of proper type, convert it. */
if (!useless_type_conversion_p (vectype, TREE_TYPE (vec_init)))
{
new_stmt
= gimple_build_assign (vect_get_new_ssa_name (vectype,
vect_simple_var,
"vec_iv_"),
VIEW_CONVERT_EXPR,
build1 (VIEW_CONVERT_EXPR, vectype,
vec_init));
vec_init = gimple_assign_lhs (new_stmt);
new_bb = gsi_insert_on_edge_immediate (loop_preheader_edge (iv_loop),
new_stmt);
gcc_assert (!new_bb);
}
}
else
{
/* iv_loop is the loop to be vectorized. Create:
vec_init = [X, X+S, X+2*S, X+3*S] (S = step_expr, X = init_expr) */
stmts = NULL;
new_name = gimple_convert (&stmts, TREE_TYPE (step_expr), init_expr);
unsigned HOST_WIDE_INT const_nunits;
if (nunits.is_constant (&const_nunits))
{
tree_vector_builder elts (step_vectype, const_nunits, 1);
elts.quick_push (new_name);
for (i = 1; i < const_nunits; i++)
{
/* Create: new_name_i = new_name + step_expr */
new_name = gimple_build (&stmts, PLUS_EXPR, TREE_TYPE (new_name),
new_name, step_expr);
elts.quick_push (new_name);
}
/* Create a vector from [new_name_0, new_name_1, ...,
new_name_nunits-1] */
vec_init = gimple_build_vector (&stmts, &elts);
}
else if (INTEGRAL_TYPE_P (TREE_TYPE (step_expr)))
/* Build the initial value directly from a VEC_SERIES_EXPR. */
vec_init = gimple_build (&stmts, VEC_SERIES_EXPR, step_vectype,
new_name, step_expr);
else
{
/* Build:
[base, base, base, ...]
+ (vectype) [0, 1, 2, ...] * [step, step, step, ...]. */
gcc_assert (SCALAR_FLOAT_TYPE_P (TREE_TYPE (step_expr)));
gcc_assert (flag_associative_math);
tree index = build_index_vector (step_vectype, 0, 1);
tree base_vec = gimple_build_vector_from_val (&stmts, step_vectype,
new_name);
tree step_vec = gimple_build_vector_from_val (&stmts, step_vectype,
step_expr);
vec_init = gimple_build (&stmts, FLOAT_EXPR, step_vectype, index);
vec_init = gimple_build (&stmts, MULT_EXPR, step_vectype,
vec_init, step_vec);
vec_init = gimple_build (&stmts, PLUS_EXPR, step_vectype,
vec_init, base_vec);
}
vec_init = gimple_convert (&stmts, vectype, vec_init);
if (stmts)
{
new_bb = gsi_insert_seq_on_edge_immediate (pe, stmts);
gcc_assert (!new_bb);
}
}
/* Create the vector that holds the step of the induction. */
if (nested_in_vect_loop)
/* iv_loop is nested in the loop to be vectorized. Generate:
vec_step = [S, S, S, S] */
new_name = step_expr;
else
{
/* iv_loop is the loop to be vectorized. Generate:
vec_step = [VF*S, VF*S, VF*S, VF*S] */
gimple_seq seq = NULL;
if (SCALAR_FLOAT_TYPE_P (TREE_TYPE (step_expr)))
{
expr = build_int_cst (integer_type_node, vf);
expr = gimple_build (&seq, FLOAT_EXPR, TREE_TYPE (step_expr), expr);
}
else
expr = build_int_cst (TREE_TYPE (step_expr), vf);
new_name = gimple_build (&seq, MULT_EXPR, TREE_TYPE (step_expr),
expr, step_expr);
if (seq)
{
new_bb = gsi_insert_seq_on_edge_immediate (pe, seq);
gcc_assert (!new_bb);
}
}
t = unshare_expr (new_name);
gcc_assert (CONSTANT_CLASS_P (new_name)
|| TREE_CODE (new_name) == SSA_NAME);
new_vec = build_vector_from_val (step_vectype, t);
vec_step = vect_init_vector (loop_vinfo, stmt_info,
new_vec, step_vectype, NULL);
/* Create the following def-use cycle:
loop prolog:
vec_init = ...
vec_step = ...
loop:
vec_iv = PHI <vec_init, vec_loop>
...
STMT
...
vec_loop = vec_iv + vec_step; */
/* Create the induction-phi that defines the induction-operand. */
vec_dest = vect_get_new_vect_var (vectype, vect_simple_var, "vec_iv_");
induction_phi = create_phi_node (vec_dest, iv_loop->header);
induc_def = PHI_RESULT (induction_phi);
/* Create the iv update inside the loop */
stmts = NULL;
vec_def = gimple_convert (&stmts, step_vectype, induc_def);
vec_def = gimple_build (&stmts, PLUS_EXPR, step_vectype, vec_def, vec_step);
vec_def = gimple_convert (&stmts, vectype, vec_def);
gsi_insert_seq_before (&si, stmts, GSI_SAME_STMT);
new_stmt = SSA_NAME_DEF_STMT (vec_def);
/* Set the arguments of the phi node: */
add_phi_arg (induction_phi, vec_init, pe, UNKNOWN_LOCATION);
add_phi_arg (induction_phi, vec_def, loop_latch_edge (iv_loop),
UNKNOWN_LOCATION);
STMT_VINFO_VEC_STMTS (stmt_info).safe_push (induction_phi);
*vec_stmt = induction_phi;
/* In case that vectorization factor (VF) is bigger than the number
of elements that we can fit in a vectype (nunits), we have to generate
more than one vector stmt - i.e - we need to "unroll" the
vector stmt by a factor VF/nunits. For more details see documentation
in vectorizable_operation. */
if (ncopies > 1)
{
gimple_seq seq = NULL;
/* FORNOW. This restriction should be relaxed. */
gcc_assert (!nested_in_vect_loop);
/* Create the vector that holds the step of the induction. */
if (SCALAR_FLOAT_TYPE_P (TREE_TYPE (step_expr)))
{
expr = build_int_cst (integer_type_node, nunits);
expr = gimple_build (&seq, FLOAT_EXPR, TREE_TYPE (step_expr), expr);
}
else
expr = build_int_cst (TREE_TYPE (step_expr), nunits);
new_name = gimple_build (&seq, MULT_EXPR, TREE_TYPE (step_expr),
expr, step_expr);
if (seq)
{
new_bb = gsi_insert_seq_on_edge_immediate (pe, seq);
gcc_assert (!new_bb);
}
t = unshare_expr (new_name);
gcc_assert (CONSTANT_CLASS_P (new_name)
|| TREE_CODE (new_name) == SSA_NAME);
new_vec = build_vector_from_val (step_vectype, t);
vec_step = vect_init_vector (loop_vinfo, stmt_info,
new_vec, step_vectype, NULL);
vec_def = induc_def;
for (i = 1; i < ncopies; i++)
{
/* vec_i = vec_prev + vec_step */
gimple_seq stmts = NULL;
vec_def = gimple_convert (&stmts, step_vectype, vec_def);
vec_def = gimple_build (&stmts,
PLUS_EXPR, step_vectype, vec_def, vec_step);
vec_def = gimple_convert (&stmts, vectype, vec_def);
gsi_insert_seq_before (&si, stmts, GSI_SAME_STMT);
new_stmt = SSA_NAME_DEF_STMT (vec_def);
STMT_VINFO_VEC_STMTS (stmt_info).safe_push (new_stmt);
}
}
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"transform induction: created def-use cycle: %G%G",
induction_phi, SSA_NAME_DEF_STMT (vec_def));
return true;
}
/* Function vectorizable_live_operation.
STMT_INFO computes a value that is used outside the loop. Check if
it can be supported. */
bool
vectorizable_live_operation (vec_info *vinfo,
stmt_vec_info stmt_info,
gimple_stmt_iterator *gsi,
slp_tree slp_node, slp_instance slp_node_instance,
int slp_index, bool vec_stmt_p,
stmt_vector_for_cost *cost_vec)
{
loop_vec_info loop_vinfo = dyn_cast <loop_vec_info> (vinfo);
imm_use_iterator imm_iter;
tree lhs, lhs_type, bitsize;
tree vectype = (slp_node
? SLP_TREE_VECTYPE (slp_node)
: STMT_VINFO_VECTYPE (stmt_info));
poly_uint64 nunits = TYPE_VECTOR_SUBPARTS (vectype);
int ncopies;
gimple *use_stmt;
auto_vec<tree> vec_oprnds;
int vec_entry = 0;
poly_uint64 vec_index = 0;
gcc_assert (STMT_VINFO_LIVE_P (stmt_info));
/* If a stmt of a reduction is live, vectorize it via
vect_create_epilog_for_reduction. vectorizable_reduction assessed
validity so just trigger the transform here. */
if (STMT_VINFO_REDUC_DEF (vect_orig_stmt (stmt_info)))
{
if (!vec_stmt_p)
return true;
if (slp_node)
{
/* For reduction chains the meta-info is attached to
the group leader. */
if (REDUC_GROUP_FIRST_ELEMENT (stmt_info))
stmt_info = REDUC_GROUP_FIRST_ELEMENT (stmt_info);
/* For SLP reductions we vectorize the epilogue for
all involved stmts together. */
else if (slp_index != 0)
return true;
else
/* For SLP reductions the meta-info is attached to
the representative. */
stmt_info = SLP_TREE_REPRESENTATIVE (slp_node);
}
stmt_vec_info reduc_info = info_for_reduction (loop_vinfo, stmt_info);
gcc_assert (reduc_info->is_reduc_info);
if (STMT_VINFO_REDUC_TYPE (reduc_info) == FOLD_LEFT_REDUCTION
|| STMT_VINFO_REDUC_TYPE (reduc_info) == EXTRACT_LAST_REDUCTION)
return true;
vect_create_epilog_for_reduction (loop_vinfo, stmt_info, slp_node,
slp_node_instance);
return true;
}
/* If STMT is not relevant and it is a simple assignment and its inputs are
invariant then it can remain in place, unvectorized. The original last
scalar value that it computes will be used. */
if (!STMT_VINFO_RELEVANT_P (stmt_info))
{
gcc_assert (is_simple_and_all_uses_invariant (stmt_info, loop_vinfo));
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"statement is simple and uses invariant. Leaving in "
"place.\n");
return true;
}
if (slp_node)
ncopies = 1;
else
ncopies = vect_get_num_copies (loop_vinfo, vectype);
if (slp_node)
{
gcc_assert (slp_index >= 0);
/* Get the last occurrence of the scalar index from the concatenation of
all the slp vectors. Calculate which slp vector it is and the index
within. */
int num_scalar = SLP_TREE_LANES (slp_node);
int num_vec = SLP_TREE_NUMBER_OF_VEC_STMTS (slp_node);
poly_uint64 pos = (num_vec * nunits) - num_scalar + slp_index;
/* Calculate which vector contains the result, and which lane of
that vector we need. */
if (!can_div_trunc_p (pos, nunits, &vec_entry, &vec_index))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"Cannot determine which vector holds the"
" final result.\n");
return false;
}
}
if (!vec_stmt_p)
{
/* No transformation required. */
if (loop_vinfo && LOOP_VINFO_CAN_USE_PARTIAL_VECTORS_P (loop_vinfo))
{
if (!direct_internal_fn_supported_p (IFN_EXTRACT_LAST, vectype,
OPTIMIZE_FOR_SPEED))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"can't operate on partial vectors "
"because the target doesn't support extract "
"last reduction.\n");
LOOP_VINFO_CAN_USE_PARTIAL_VECTORS_P (loop_vinfo) = false;
}
else if (slp_node)
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"can't operate on partial vectors "
"because an SLP statement is live after "
"the loop.\n");
LOOP_VINFO_CAN_USE_PARTIAL_VECTORS_P (loop_vinfo) = false;
}
else if (ncopies > 1)
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"can't operate on partial vectors "
"because ncopies is greater than 1.\n");
LOOP_VINFO_CAN_USE_PARTIAL_VECTORS_P (loop_vinfo) = false;
}
else
{
gcc_assert (ncopies == 1 && !slp_node);
vect_record_loop_mask (loop_vinfo,
&LOOP_VINFO_MASKS (loop_vinfo),
1, vectype, NULL);
}
}
/* ??? Enable for loop costing as well. */
if (!loop_vinfo)
record_stmt_cost (cost_vec, 1, vec_to_scalar, stmt_info, NULL_TREE,
0, vect_epilogue);
return true;
}
/* Use the lhs of the original scalar statement. */
gimple *stmt = vect_orig_stmt (stmt_info)->stmt;
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location, "extracting lane for live "
"stmt %G", stmt);
lhs = gimple_get_lhs (stmt);
lhs_type = TREE_TYPE (lhs);
bitsize = vector_element_bits_tree (vectype);
/* Get the vectorized lhs of STMT and the lane to use (counted in bits). */
tree vec_lhs, bitstart;
gimple *vec_stmt;
if (slp_node)
{
gcc_assert (!loop_vinfo || !LOOP_VINFO_FULLY_MASKED_P (loop_vinfo));
/* Get the correct slp vectorized stmt. */
vec_stmt = SLP_TREE_VEC_STMTS (slp_node)[vec_entry];
vec_lhs = gimple_get_lhs (vec_stmt);
/* Get entry to use. */
bitstart = bitsize_int (vec_index);
bitstart = int_const_binop (MULT_EXPR, bitsize, bitstart);
}
else
{
/* For multiple copies, get the last copy. */
vec_stmt = STMT_VINFO_VEC_STMTS (stmt_info).last ();
vec_lhs = gimple_get_lhs (vec_stmt);
/* Get the last lane in the vector. */
bitstart = int_const_binop (MULT_EXPR, bitsize, bitsize_int (nunits - 1));
}
if (loop_vinfo)
{
/* Ensure the VEC_LHS for lane extraction stmts satisfy loop-closed PHI
requirement, insert one phi node for it. It looks like:
loop;
BB:
# lhs' = PHI <lhs>
==>
loop;
BB:
# vec_lhs' = PHI <vec_lhs>
new_tree = lane_extract <vec_lhs', ...>;
lhs' = new_tree; */
class loop *loop = LOOP_VINFO_LOOP (loop_vinfo);
basic_block exit_bb = single_exit (loop)->dest;
gcc_assert (single_pred_p (exit_bb));
tree vec_lhs_phi = copy_ssa_name (vec_lhs);
gimple *phi = create_phi_node (vec_lhs_phi, exit_bb);
SET_PHI_ARG_DEF (phi, single_exit (loop)->dest_idx, vec_lhs);
gimple_seq stmts = NULL;
tree new_tree;
if (LOOP_VINFO_FULLY_MASKED_P (loop_vinfo))
{
/* Emit:
SCALAR_RES = EXTRACT_LAST <VEC_LHS, MASK>
where VEC_LHS is the vectorized live-out result and MASK is
the loop mask for the final iteration. */
gcc_assert (ncopies == 1 && !slp_node);
tree scalar_type = TREE_TYPE (STMT_VINFO_VECTYPE (stmt_info));
tree mask = vect_get_loop_mask (gsi, &LOOP_VINFO_MASKS (loop_vinfo),
1, vectype, 0);
tree scalar_res = gimple_build (&stmts, CFN_EXTRACT_LAST, scalar_type,
mask, vec_lhs_phi);
/* Convert the extracted vector element to the scalar type. */
new_tree = gimple_convert (&stmts, lhs_type, scalar_res);
}
else
{
tree bftype = TREE_TYPE (vectype);
if (VECTOR_BOOLEAN_TYPE_P (vectype))
bftype = build_nonstandard_integer_type (tree_to_uhwi (bitsize), 1);
new_tree = build3 (BIT_FIELD_REF, bftype,
vec_lhs_phi, bitsize, bitstart);
new_tree = force_gimple_operand (fold_convert (lhs_type, new_tree),
&stmts, true, NULL_TREE);
}
if (stmts)
{
gimple_stmt_iterator exit_gsi = gsi_after_labels (exit_bb);
gsi_insert_seq_before (&exit_gsi, stmts, GSI_SAME_STMT);
/* Remove existing phi from lhs and create one copy from new_tree. */
tree lhs_phi = NULL_TREE;
gimple_stmt_iterator gsi;
for (gsi = gsi_start_phis (exit_bb);
!gsi_end_p (gsi); gsi_next (&gsi))
{
gimple *phi = gsi_stmt (gsi);
if ((gimple_phi_arg_def (phi, 0) == lhs))
{
remove_phi_node (&gsi, false);
lhs_phi = gimple_phi_result (phi);
gimple *copy = gimple_build_assign (lhs_phi, new_tree);
gsi_insert_before (&exit_gsi, copy, GSI_SAME_STMT);
break;
}
}
}
/* Replace use of lhs with newly computed result. If the use stmt is a
single arg PHI, just replace all uses of PHI result. It's necessary
because lcssa PHI defining lhs may be before newly inserted stmt. */
use_operand_p use_p;
FOR_EACH_IMM_USE_STMT (use_stmt, imm_iter, lhs)
if (!flow_bb_inside_loop_p (loop, gimple_bb (use_stmt))
&& !is_gimple_debug (use_stmt))
{
if (gimple_code (use_stmt) == GIMPLE_PHI
&& gimple_phi_num_args (use_stmt) == 1)
{
replace_uses_by (gimple_phi_result (use_stmt), new_tree);
}
else
{
FOR_EACH_IMM_USE_ON_STMT (use_p, imm_iter)
SET_USE (use_p, new_tree);
}
update_stmt (use_stmt);
}
}
else
{
/* For basic-block vectorization simply insert the lane-extraction. */
tree bftype = TREE_TYPE (vectype);
if (VECTOR_BOOLEAN_TYPE_P (vectype))
bftype = build_nonstandard_integer_type (tree_to_uhwi (bitsize), 1);
tree new_tree = build3 (BIT_FIELD_REF, bftype,
vec_lhs, bitsize, bitstart);
gimple_seq stmts = NULL;
new_tree = force_gimple_operand (fold_convert (lhs_type, new_tree),
&stmts, true, NULL_TREE);
if (TREE_CODE (new_tree) == SSA_NAME
&& SSA_NAME_OCCURS_IN_ABNORMAL_PHI (lhs))
SSA_NAME_OCCURS_IN_ABNORMAL_PHI (new_tree) = 1;
if (is_a <gphi *> (vec_stmt))
{
gimple_stmt_iterator si = gsi_after_labels (gimple_bb (vec_stmt));
gsi_insert_seq_before (&si, stmts, GSI_SAME_STMT);
}
else
{
gimple_stmt_iterator si = gsi_for_stmt (vec_stmt);
gsi_insert_seq_after (&si, stmts, GSI_SAME_STMT);
}
/* Replace use of lhs with newly computed result. If the use stmt is a
single arg PHI, just replace all uses of PHI result. It's necessary
because lcssa PHI defining lhs may be before newly inserted stmt. */
use_operand_p use_p;
stmt_vec_info use_stmt_info;
FOR_EACH_IMM_USE_STMT (use_stmt, imm_iter, lhs)
if (!is_gimple_debug (use_stmt)
&& (!(use_stmt_info = vinfo->lookup_stmt (use_stmt))
|| !PURE_SLP_STMT (vect_stmt_to_vectorize (use_stmt_info))))
{
/* ??? This can happen when the live lane ends up being
used in a vector construction code-generated by an
external SLP node (and code-generation for that already
happened). See gcc.dg/vect/bb-slp-47.c.
Doing this is what would happen if that vector CTOR
were not code-generated yet so it is not too bad.
??? In fact we'd likely want to avoid this situation
in the first place. */
if (TREE_CODE (new_tree) == SSA_NAME
&& !SSA_NAME_IS_DEFAULT_DEF (new_tree)
&& gimple_code (use_stmt) != GIMPLE_PHI
&& !vect_stmt_dominates_stmt_p (SSA_NAME_DEF_STMT (new_tree),
use_stmt))
{
enum tree_code code = gimple_assign_rhs_code (use_stmt);
gcc_assert (code == CONSTRUCTOR
|| code == VIEW_CONVERT_EXPR
|| CONVERT_EXPR_CODE_P (code));
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"Using original scalar computation for "
"live lane because use preceeds vector "
"def\n");
continue;
}
/* ??? It can also happen that we end up pulling a def into
a loop where replacing out-of-loop uses would require
a new LC SSA PHI node. Retain the original scalar in
those cases as well. PR98064. */
if (TREE_CODE (new_tree) == SSA_NAME
&& !SSA_NAME_IS_DEFAULT_DEF (new_tree)
&& (gimple_bb (use_stmt)->loop_father
!= gimple_bb (vec_stmt)->loop_father)
&& !flow_loop_nested_p (gimple_bb (vec_stmt)->loop_father,
gimple_bb (use_stmt)->loop_father))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"Using original scalar computation for "
"live lane because there is an out-of-loop "
"definition for it\n");
continue;
}
FOR_EACH_IMM_USE_ON_STMT (use_p, imm_iter)
SET_USE (use_p, new_tree);
update_stmt (use_stmt);
}
}
return true;
}
/* Kill any debug uses outside LOOP of SSA names defined in STMT_INFO. */
static void
vect_loop_kill_debug_uses (class loop *loop, stmt_vec_info stmt_info)
{
ssa_op_iter op_iter;
imm_use_iterator imm_iter;
def_operand_p def_p;
gimple *ustmt;
FOR_EACH_PHI_OR_STMT_DEF (def_p, stmt_info->stmt, op_iter, SSA_OP_DEF)
{
FOR_EACH_IMM_USE_STMT (ustmt, imm_iter, DEF_FROM_PTR (def_p))
{
basic_block bb;
if (!is_gimple_debug (ustmt))
continue;
bb = gimple_bb (ustmt);
if (!flow_bb_inside_loop_p (loop, bb))
{
if (gimple_debug_bind_p (ustmt))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"killing debug use\n");
gimple_debug_bind_reset_value (ustmt);
update_stmt (ustmt);
}
else
gcc_unreachable ();
}
}
}
}
/* Given loop represented by LOOP_VINFO, return true if computation of
LOOP_VINFO_NITERS (= LOOP_VINFO_NITERSM1 + 1) doesn't overflow, false
otherwise. */
static bool
loop_niters_no_overflow (loop_vec_info loop_vinfo)
{
/* Constant case. */
if (LOOP_VINFO_NITERS_KNOWN_P (loop_vinfo))
{
tree cst_niters = LOOP_VINFO_NITERS (loop_vinfo);
tree cst_nitersm1 = LOOP_VINFO_NITERSM1 (loop_vinfo);
gcc_assert (TREE_CODE (cst_niters) == INTEGER_CST);
gcc_assert (TREE_CODE (cst_nitersm1) == INTEGER_CST);
if (wi::to_widest (cst_nitersm1) < wi::to_widest (cst_niters))
return true;
}
widest_int max;
class loop *loop = LOOP_VINFO_LOOP (loop_vinfo);
/* Check the upper bound of loop niters. */
if (get_max_loop_iterations (loop, &max))
{
tree type = TREE_TYPE (LOOP_VINFO_NITERS (loop_vinfo));
signop sgn = TYPE_SIGN (type);
widest_int type_max = widest_int::from (wi::max_value (type), sgn);
if (max < type_max)
return true;
}
return false;
}
/* Return a mask type with half the number of elements as OLD_TYPE,
given that it should have mode NEW_MODE. */
tree
vect_halve_mask_nunits (tree old_type, machine_mode new_mode)
{
poly_uint64 nunits = exact_div (TYPE_VECTOR_SUBPARTS (old_type), 2);
return build_truth_vector_type_for_mode (nunits, new_mode);
}
/* Return a mask type with twice as many elements as OLD_TYPE,
given that it should have mode NEW_MODE. */
tree
vect_double_mask_nunits (tree old_type, machine_mode new_mode)
{
poly_uint64 nunits = TYPE_VECTOR_SUBPARTS (old_type) * 2;
return build_truth_vector_type_for_mode (nunits, new_mode);
}
/* Record that a fully-masked version of LOOP_VINFO would need MASKS to
contain a sequence of NVECTORS masks that each control a vector of type
VECTYPE. If SCALAR_MASK is nonnull, the fully-masked loop would AND
these vector masks with the vector version of SCALAR_MASK. */
void
vect_record_loop_mask (loop_vec_info loop_vinfo, vec_loop_masks *masks,
unsigned int nvectors, tree vectype, tree scalar_mask)
{
gcc_assert (nvectors != 0);
if (masks->length () < nvectors)
masks->safe_grow_cleared (nvectors, true);
rgroup_controls *rgm = &(*masks)[nvectors - 1];
/* The number of scalars per iteration and the number of vectors are
both compile-time constants. */
unsigned int nscalars_per_iter
= exact_div (nvectors * TYPE_VECTOR_SUBPARTS (vectype),
LOOP_VINFO_VECT_FACTOR (loop_vinfo)).to_constant ();
if (scalar_mask)
{
scalar_cond_masked_key cond (scalar_mask, nvectors);
loop_vinfo->scalar_cond_masked_set.add (cond);
}
if (rgm->max_nscalars_per_iter < nscalars_per_iter)
{
rgm->max_nscalars_per_iter = nscalars_per_iter;
rgm->type = truth_type_for (vectype);
rgm->factor = 1;
}
}
/* Given a complete set of masks MASKS, extract mask number INDEX
for an rgroup that operates on NVECTORS vectors of type VECTYPE,
where 0 <= INDEX < NVECTORS. Insert any set-up statements before GSI.
See the comment above vec_loop_masks for more details about the mask
arrangement. */
tree
vect_get_loop_mask (gimple_stmt_iterator *gsi, vec_loop_masks *masks,
unsigned int nvectors, tree vectype, unsigned int index)
{
rgroup_controls *rgm = &(*masks)[nvectors - 1];
tree mask_type = rgm->type;
/* Populate the rgroup's mask array, if this is the first time we've
used it. */
if (rgm->controls.is_empty ())
{
rgm->controls.safe_grow_cleared (nvectors, true);
for (unsigned int i = 0; i < nvectors; ++i)
{
tree mask = make_temp_ssa_name (mask_type, NULL, "loop_mask");
/* Provide a dummy definition until the real one is available. */
SSA_NAME_DEF_STMT (mask) = gimple_build_nop ();
rgm->controls[i] = mask;
}
}
tree mask = rgm->controls[index];
if (maybe_ne (TYPE_VECTOR_SUBPARTS (mask_type),
TYPE_VECTOR_SUBPARTS (vectype)))
{
/* A loop mask for data type X can be reused for data type Y
if X has N times more elements than Y and if Y's elements
are N times bigger than X's. In this case each sequence
of N elements in the loop mask will be all-zero or all-one.
We can then view-convert the mask so that each sequence of
N elements is replaced by a single element. */
gcc_assert (multiple_p (TYPE_VECTOR_SUBPARTS (mask_type),
TYPE_VECTOR_SUBPARTS (vectype)));
gimple_seq seq = NULL;
mask_type = truth_type_for (vectype);
mask = gimple_build (&seq, VIEW_CONVERT_EXPR, mask_type, mask);
if (seq)
gsi_insert_seq_before (gsi, seq, GSI_SAME_STMT);
}
return mask;
}
/* Record that LOOP_VINFO would need LENS to contain a sequence of NVECTORS
lengths for controlling an operation on VECTYPE. The operation splits
each element of VECTYPE into FACTOR separate subelements, measuring the
length as a number of these subelements. */
void
vect_record_loop_len (loop_vec_info loop_vinfo, vec_loop_lens *lens,
unsigned int nvectors, tree vectype, unsigned int factor)
{
gcc_assert (nvectors != 0);
if (lens->length () < nvectors)
lens->safe_grow_cleared (nvectors, true);
rgroup_controls *rgl = &(*lens)[nvectors - 1];
/* The number of scalars per iteration, scalar occupied bytes and
the number of vectors are both compile-time constants. */
unsigned int nscalars_per_iter
= exact_div (nvectors * TYPE_VECTOR_SUBPARTS (vectype),
LOOP_VINFO_VECT_FACTOR (loop_vinfo)).to_constant ();
if (rgl->max_nscalars_per_iter < nscalars_per_iter)
{
/* For now, we only support cases in which all loads and stores fall back
to VnQI or none do. */
gcc_assert (!rgl->max_nscalars_per_iter
|| (rgl->factor == 1 && factor == 1)
|| (rgl->max_nscalars_per_iter * rgl->factor
== nscalars_per_iter * factor));
rgl->max_nscalars_per_iter = nscalars_per_iter;
rgl->type = vectype;
rgl->factor = factor;
}
}
/* Given a complete set of length LENS, extract length number INDEX for an
rgroup that operates on NVECTORS vectors, where 0 <= INDEX < NVECTORS. */
tree
vect_get_loop_len (loop_vec_info loop_vinfo, vec_loop_lens *lens,
unsigned int nvectors, unsigned int index)
{
rgroup_controls *rgl = &(*lens)[nvectors - 1];
/* Populate the rgroup's len array, if this is the first time we've
used it. */
if (rgl->controls.is_empty ())
{
rgl->controls.safe_grow_cleared (nvectors, true);
for (unsigned int i = 0; i < nvectors; ++i)
{
tree len_type = LOOP_VINFO_RGROUP_COMPARE_TYPE (loop_vinfo);
gcc_assert (len_type != NULL_TREE);
tree len = make_temp_ssa_name (len_type, NULL, "loop_len");
/* Provide a dummy definition until the real one is available. */
SSA_NAME_DEF_STMT (len) = gimple_build_nop ();
rgl->controls[i] = len;
}
}
return rgl->controls[index];
}
/* Scale profiling counters by estimation for LOOP which is vectorized
by factor VF. */
static void
scale_profile_for_vect_loop (class loop *loop, unsigned vf)
{
edge preheader = loop_preheader_edge (loop);
/* Reduce loop iterations by the vectorization factor. */
gcov_type new_est_niter = niter_for_unrolled_loop (loop, vf);
profile_count freq_h = loop->header->count, freq_e = preheader->count ();
if (freq_h.nonzero_p ())
{
profile_probability p;
/* Avoid dropping loop body profile counter to 0 because of zero count
in loop's preheader. */
if (!(freq_e == profile_count::zero ()))
freq_e = freq_e.force_nonzero ();
p = freq_e.apply_scale (new_est_niter + 1, 1).probability_in (freq_h);
scale_loop_frequencies (loop, p);
}
edge exit_e = single_exit (loop);
exit_e->probability = profile_probability::always ()
.apply_scale (1, new_est_niter + 1);
edge exit_l = single_pred_edge (loop->latch);
profile_probability prob = exit_l->probability;
exit_l->probability = exit_e->probability.invert ();
if (prob.initialized_p () && exit_l->probability.initialized_p ())
scale_bbs_frequencies (&loop->latch, 1, exit_l->probability / prob);
}
/* For a vectorized stmt DEF_STMT_INFO adjust all vectorized PHI
latch edge values originally defined by it. */
static void
maybe_set_vectorized_backedge_value (loop_vec_info loop_vinfo,
stmt_vec_info def_stmt_info)
{
tree def = gimple_get_lhs (vect_orig_stmt (def_stmt_info)->stmt);
if (!def || TREE_CODE (def) != SSA_NAME)
return;
stmt_vec_info phi_info;
imm_use_iterator iter;
use_operand_p use_p;
FOR_EACH_IMM_USE_FAST (use_p, iter, def)
if (gphi *phi = dyn_cast <gphi *> (USE_STMT (use_p)))
if (gimple_bb (phi)->loop_father->header == gimple_bb (phi)
&& (phi_info = loop_vinfo->lookup_stmt (phi))
&& STMT_VINFO_RELEVANT_P (phi_info)
&& VECTORIZABLE_CYCLE_DEF (STMT_VINFO_DEF_TYPE (phi_info))
&& STMT_VINFO_REDUC_TYPE (phi_info) != FOLD_LEFT_REDUCTION
&& STMT_VINFO_REDUC_TYPE (phi_info) != EXTRACT_LAST_REDUCTION)
{
loop_p loop = gimple_bb (phi)->loop_father;
edge e = loop_latch_edge (loop);
if (PHI_ARG_DEF_FROM_EDGE (phi, e) == def)
{
vec<gimple *> &phi_defs = STMT_VINFO_VEC_STMTS (phi_info);
vec<gimple *> &latch_defs = STMT_VINFO_VEC_STMTS (def_stmt_info);
gcc_assert (phi_defs.length () == latch_defs.length ());
for (unsigned i = 0; i < phi_defs.length (); ++i)
add_phi_arg (as_a <gphi *> (phi_defs[i]),
gimple_get_lhs (latch_defs[i]), e,
gimple_phi_arg_location (phi, e->dest_idx));
}
}
}
/* Vectorize STMT_INFO if relevant, inserting any new instructions before GSI.
When vectorizing STMT_INFO as a store, set *SEEN_STORE to its
stmt_vec_info. */
static bool
vect_transform_loop_stmt (loop_vec_info loop_vinfo, stmt_vec_info stmt_info,
gimple_stmt_iterator *gsi, stmt_vec_info *seen_store)
{
class loop *loop = LOOP_VINFO_LOOP (loop_vinfo);
poly_uint64 vf = LOOP_VINFO_VECT_FACTOR (loop_vinfo);
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"------>vectorizing statement: %G", stmt_info->stmt);
if (MAY_HAVE_DEBUG_BIND_STMTS && !STMT_VINFO_LIVE_P (stmt_info))
vect_loop_kill_debug_uses (loop, stmt_info);
if (!STMT_VINFO_RELEVANT_P (stmt_info)
&& !STMT_VINFO_LIVE_P (stmt_info))
return false;
if (STMT_VINFO_VECTYPE (stmt_info))
{
poly_uint64 nunits
= TYPE_VECTOR_SUBPARTS (STMT_VINFO_VECTYPE (stmt_info));
if (!STMT_SLP_TYPE (stmt_info)
&& maybe_ne (nunits, vf)
&& dump_enabled_p ())
/* For SLP VF is set according to unrolling factor, and not
to vector size, hence for SLP this print is not valid. */
dump_printf_loc (MSG_NOTE, vect_location, "multiple-types.\n");
}
/* Pure SLP statements have already been vectorized. We still need
to apply loop vectorization to hybrid SLP statements. */
if (PURE_SLP_STMT (stmt_info))
return false;
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location, "transform statement.\n");
if (vect_transform_stmt (loop_vinfo, stmt_info, gsi, NULL, NULL))
*seen_store = stmt_info;
return true;
}
/* Helper function to pass to simplify_replace_tree to enable replacing tree's
in the hash_map with its corresponding values. */
static tree
find_in_mapping (tree t, void *context)
{
hash_map<tree,tree>* mapping = (hash_map<tree, tree>*) context;
tree *value = mapping->get (t);
return value ? *value : t;
}
/* Update EPILOGUE's loop_vec_info. EPILOGUE was constructed as a copy of the
original loop that has now been vectorized.
The inits of the data_references need to be advanced with the number of
iterations of the main loop. This has been computed in vect_do_peeling and
is stored in parameter ADVANCE. We first restore the data_references
initial offset with the values recored in ORIG_DRS_INIT.
Since the loop_vec_info of this EPILOGUE was constructed for the original
loop, its stmt_vec_infos all point to the original statements. These need
to be updated to point to their corresponding copies as well as the SSA_NAMES
in their PATTERN_DEF_SEQs and RELATED_STMTs.
The data_reference's connections also need to be updated. Their
corresponding dr_vec_info need to be reconnected to the EPILOGUE's
stmt_vec_infos, their statements need to point to their corresponding copy,
if they are gather loads or scatter stores then their reference needs to be
updated to point to its corresponding copy and finally we set
'base_misaligned' to false as we have already peeled for alignment in the
prologue of the main loop. */
static void
update_epilogue_loop_vinfo (class loop *epilogue, tree advance)
{
loop_vec_info epilogue_vinfo = loop_vec_info_for_loop (epilogue);
auto_vec<gimple *> stmt_worklist;
hash_map<tree,tree> mapping;
gimple *orig_stmt, *new_stmt;
gimple_stmt_iterator epilogue_gsi;
gphi_iterator epilogue_phi_gsi;
stmt_vec_info stmt_vinfo = NULL, related_vinfo;
basic_block *epilogue_bbs = get_loop_body (epilogue);
unsigned i;
free (LOOP_VINFO_BBS (epilogue_vinfo));
LOOP_VINFO_BBS (epilogue_vinfo) = epilogue_bbs;
/* Advance data_reference's with the number of iterations of the previous
loop and its prologue. */
vect_update_inits_of_drs (epilogue_vinfo, advance, PLUS_EXPR);
/* The EPILOGUE loop is a copy of the original loop so they share the same
gimple UIDs. In this loop we update the loop_vec_info of the EPILOGUE to
point to the copied statements. We also create a mapping of all LHS' in
the original loop and all the LHS' in the EPILOGUE and create worklists to
update teh STMT_VINFO_PATTERN_DEF_SEQs and STMT_VINFO_RELATED_STMTs. */
for (unsigned i = 0; i < epilogue->num_nodes; ++i)
{
for (epilogue_phi_gsi = gsi_start_phis (epilogue_bbs[i]);
!gsi_end_p (epilogue_phi_gsi); gsi_next (&epilogue_phi_gsi))
{
new_stmt = epilogue_phi_gsi.phi ();
gcc_assert (gimple_uid (new_stmt) > 0);
stmt_vinfo
= epilogue_vinfo->stmt_vec_infos[gimple_uid (new_stmt) - 1];
orig_stmt = STMT_VINFO_STMT (stmt_vinfo);
STMT_VINFO_STMT (stmt_vinfo) = new_stmt;
mapping.put (gimple_phi_result (orig_stmt),
gimple_phi_result (new_stmt));
/* PHI nodes can not have patterns or related statements. */
gcc_assert (STMT_VINFO_PATTERN_DEF_SEQ (stmt_vinfo) == NULL
&& STMT_VINFO_RELATED_STMT (stmt_vinfo) == NULL);
}
for (epilogue_gsi = gsi_start_bb (epilogue_bbs[i]);
!gsi_end_p (epilogue_gsi); gsi_next (&epilogue_gsi))
{
new_stmt = gsi_stmt (epilogue_gsi);
if (is_gimple_debug (new_stmt))
continue;
gcc_assert (gimple_uid (new_stmt) > 0);
stmt_vinfo
= epilogue_vinfo->stmt_vec_infos[gimple_uid (new_stmt) - 1];
orig_stmt = STMT_VINFO_STMT (stmt_vinfo);
STMT_VINFO_STMT (stmt_vinfo) = new_stmt;
if (tree old_lhs = gimple_get_lhs (orig_stmt))
mapping.put (old_lhs, gimple_get_lhs (new_stmt));
if (STMT_VINFO_PATTERN_DEF_SEQ (stmt_vinfo))
{
gimple_seq seq = STMT_VINFO_PATTERN_DEF_SEQ (stmt_vinfo);
for (gimple_stmt_iterator gsi = gsi_start (seq);
!gsi_end_p (gsi); gsi_next (&gsi))
stmt_worklist.safe_push (gsi_stmt (gsi));
}
related_vinfo = STMT_VINFO_RELATED_STMT (stmt_vinfo);
if (related_vinfo != NULL && related_vinfo != stmt_vinfo)
{
gimple *stmt = STMT_VINFO_STMT (related_vinfo);
stmt_worklist.safe_push (stmt);
/* Set BB such that the assert in
'get_initial_def_for_reduction' is able to determine that
the BB of the related stmt is inside this loop. */
gimple_set_bb (stmt,
gimple_bb (new_stmt));
related_vinfo = STMT_VINFO_RELATED_STMT (related_vinfo);
gcc_assert (related_vinfo == NULL
|| related_vinfo == stmt_vinfo);
}
}
}
/* The PATTERN_DEF_SEQs and RELATED_STMTs in the epilogue were constructed
using the original main loop and thus need to be updated to refer to the
cloned variables used in the epilogue. */
for (unsigned i = 0; i < stmt_worklist.length (); ++i)
{
gimple *stmt = stmt_worklist[i];
tree *new_op;
for (unsigned j = 1; j < gimple_num_ops (stmt); ++j)
{
tree op = gimple_op (stmt, j);
if ((new_op = mapping.get(op)))
gimple_set_op (stmt, j, *new_op);
else
{
/* PR92429: The last argument of simplify_replace_tree disables
folding when replacing arguments. This is required as
otherwise you might end up with different statements than the
ones analyzed in vect_loop_analyze, leading to different
vectorization. */
op = simplify_replace_tree (op, NULL_TREE, NULL_TREE,
&find_in_mapping, &mapping, false);
gimple_set_op (stmt, j, op);
}
}
}
struct data_reference *dr;
vec<data_reference_p> datarefs = LOOP_VINFO_DATAREFS (epilogue_vinfo);
FOR_EACH_VEC_ELT (datarefs, i, dr)
{
orig_stmt = DR_STMT (dr);
gcc_assert (gimple_uid (orig_stmt) > 0);
stmt_vinfo = epilogue_vinfo->stmt_vec_infos[gimple_uid (orig_stmt) - 1];
/* Data references for gather loads and scatter stores do not use the
updated offset we set using ADVANCE. Instead we have to make sure the
reference in the data references point to the corresponding copy of
the original in the epilogue. */
if (STMT_VINFO_MEMORY_ACCESS_TYPE (vect_stmt_to_vectorize (stmt_vinfo))
== VMAT_GATHER_SCATTER)
{
DR_REF (dr)
= simplify_replace_tree (DR_REF (dr), NULL_TREE, NULL_TREE,
&find_in_mapping, &mapping);
DR_BASE_ADDRESS (dr)
= simplify_replace_tree (DR_BASE_ADDRESS (dr), NULL_TREE, NULL_TREE,
&find_in_mapping, &mapping);
}
DR_STMT (dr) = STMT_VINFO_STMT (stmt_vinfo);
stmt_vinfo->dr_aux.stmt = stmt_vinfo;
/* The vector size of the epilogue is smaller than that of the main loop
so the alignment is either the same or lower. This means the dr will
thus by definition be aligned. */
STMT_VINFO_DR_INFO (stmt_vinfo)->base_misaligned = false;
}
epilogue_vinfo->shared->datarefs_copy.release ();
epilogue_vinfo->shared->save_datarefs ();
}
/* Function vect_transform_loop.
The analysis phase has determined that the loop is vectorizable.
Vectorize the loop - created vectorized stmts to replace the scalar
stmts in the loop, and update the loop exit condition.
Returns scalar epilogue loop if any. */
class loop *
vect_transform_loop (loop_vec_info loop_vinfo, gimple *loop_vectorized_call)
{
class loop *loop = LOOP_VINFO_LOOP (loop_vinfo);
class loop *epilogue = NULL;
basic_block *bbs = LOOP_VINFO_BBS (loop_vinfo);
int nbbs = loop->num_nodes;
int i;
tree niters_vector = NULL_TREE;
tree step_vector = NULL_TREE;
tree niters_vector_mult_vf = NULL_TREE;
poly_uint64 vf = LOOP_VINFO_VECT_FACTOR (loop_vinfo);
unsigned int lowest_vf = constant_lower_bound (vf);
gimple *stmt;
bool check_profitability = false;
unsigned int th;
DUMP_VECT_SCOPE ("vec_transform_loop");
loop_vinfo->shared->check_datarefs ();
/* Use the more conservative vectorization threshold. If the number
of iterations is constant assume the cost check has been performed
by our caller. If the threshold makes all loops profitable that
run at least the (estimated) vectorization factor number of times
checking is pointless, too. */
th = LOOP_VINFO_COST_MODEL_THRESHOLD (loop_vinfo);
if (vect_apply_runtime_profitability_check_p (loop_vinfo))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"Profitability threshold is %d loop iterations.\n",
th);
check_profitability = true;
}
/* Make sure there exists a single-predecessor exit bb. Do this before
versioning. */
edge e = single_exit (loop);
if (! single_pred_p (e->dest))
{
split_loop_exit_edge (e, true);
if (dump_enabled_p ())
dump_printf (MSG_NOTE, "split exit edge\n");
}
/* Version the loop first, if required, so the profitability check
comes first. */
if (LOOP_REQUIRES_VERSIONING (loop_vinfo))
{
class loop *sloop
= vect_loop_versioning (loop_vinfo, loop_vectorized_call);
sloop->force_vectorize = false;
check_profitability = false;
}
/* Make sure there exists a single-predecessor exit bb also on the
scalar loop copy. Do this after versioning but before peeling
so CFG structure is fine for both scalar and if-converted loop
to make slpeel_duplicate_current_defs_from_edges face matched
loop closed PHI nodes on the exit. */
if (LOOP_VINFO_SCALAR_LOOP (loop_vinfo))
{
e = single_exit (LOOP_VINFO_SCALAR_LOOP (loop_vinfo));
if (! single_pred_p (e->dest))
{
split_loop_exit_edge (e, true);
if (dump_enabled_p ())
dump_printf (MSG_NOTE, "split exit edge of scalar loop\n");
}
}
tree niters = vect_build_loop_niters (loop_vinfo);
LOOP_VINFO_NITERS_UNCHANGED (loop_vinfo) = niters;
tree nitersm1 = unshare_expr (LOOP_VINFO_NITERSM1 (loop_vinfo));
bool niters_no_overflow = loop_niters_no_overflow (loop_vinfo);
tree advance;
drs_init_vec orig_drs_init;
epilogue = vect_do_peeling (loop_vinfo, niters, nitersm1, &niters_vector,
&step_vector, &niters_vector_mult_vf, th,
check_profitability, niters_no_overflow,
&advance);
if (LOOP_VINFO_SCALAR_LOOP (loop_vinfo)
&& LOOP_VINFO_SCALAR_LOOP_SCALING (loop_vinfo).initialized_p ())
scale_loop_frequencies (LOOP_VINFO_SCALAR_LOOP (loop_vinfo),
LOOP_VINFO_SCALAR_LOOP_SCALING (loop_vinfo));
if (niters_vector == NULL_TREE)
{
if (LOOP_VINFO_NITERS_KNOWN_P (loop_vinfo)
&& !LOOP_VINFO_USING_PARTIAL_VECTORS_P (loop_vinfo)
&& known_eq (lowest_vf, vf))
{
niters_vector
= build_int_cst (TREE_TYPE (LOOP_VINFO_NITERS (loop_vinfo)),
LOOP_VINFO_INT_NITERS (loop_vinfo) / lowest_vf);
step_vector = build_one_cst (TREE_TYPE (niters));
}
else if (vect_use_loop_mask_for_alignment_p (loop_vinfo))
vect_gen_vector_loop_niters (loop_vinfo, niters, &niters_vector,
&step_vector, niters_no_overflow);
else
/* vect_do_peeling subtracted the number of peeled prologue
iterations from LOOP_VINFO_NITERS. */
vect_gen_vector_loop_niters (loop_vinfo, LOOP_VINFO_NITERS (loop_vinfo),
&niters_vector, &step_vector,
niters_no_overflow);
}
/* 1) Make sure the loop header has exactly two entries
2) Make sure we have a preheader basic block. */
gcc_assert (EDGE_COUNT (loop->header->preds) == 2);
split_edge (loop_preheader_edge (loop));
if (vect_use_loop_mask_for_alignment_p (loop_vinfo))
/* This will deal with any possible peeling. */
vect_prepare_for_masked_peels (loop_vinfo);
/* Schedule the SLP instances first, then handle loop vectorization
below. */
if (!loop_vinfo->slp_instances.is_empty ())
{
DUMP_VECT_SCOPE ("scheduling SLP instances");
vect_schedule_slp (loop_vinfo, LOOP_VINFO_SLP_INSTANCES (loop_vinfo));
}
/* FORNOW: the vectorizer supports only loops which body consist
of one basic block (header + empty latch). When the vectorizer will
support more involved loop forms, the order by which the BBs are
traversed need to be reconsidered. */
for (i = 0; i < nbbs; i++)
{
basic_block bb = bbs[i];
stmt_vec_info stmt_info;
for (gphi_iterator si = gsi_start_phis (bb); !gsi_end_p (si);
gsi_next (&si))
{
gphi *phi = si.phi ();
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"------>vectorizing phi: %G", phi);
stmt_info = loop_vinfo->lookup_stmt (phi);
if (!stmt_info)
continue;
if (MAY_HAVE_DEBUG_BIND_STMTS && !STMT_VINFO_LIVE_P (stmt_info))
vect_loop_kill_debug_uses (loop, stmt_info);
if (!STMT_VINFO_RELEVANT_P (stmt_info)
&& !STMT_VINFO_LIVE_P (stmt_info))
continue;
if (STMT_VINFO_VECTYPE (stmt_info)
&& (maybe_ne
(TYPE_VECTOR_SUBPARTS (STMT_VINFO_VECTYPE (stmt_info)), vf))
&& dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location, "multiple-types.\n");
if ((STMT_VINFO_DEF_TYPE (stmt_info) == vect_induction_def
|| STMT_VINFO_DEF_TYPE (stmt_info) == vect_reduction_def
|| STMT_VINFO_DEF_TYPE (stmt_info) == vect_double_reduction_def
|| STMT_VINFO_DEF_TYPE (stmt_info) == vect_nested_cycle
|| STMT_VINFO_DEF_TYPE (stmt_info) == vect_internal_def)
&& ! PURE_SLP_STMT (stmt_info))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location, "transform phi.\n");
vect_transform_stmt (loop_vinfo, stmt_info, NULL, NULL, NULL);
}
}
for (gphi_iterator si = gsi_start_phis (bb); !gsi_end_p (si);
gsi_next (&si))
{
gphi *phi = si.phi ();
stmt_info = loop_vinfo->lookup_stmt (phi);
if (!stmt_info)
continue;
if (!STMT_VINFO_RELEVANT_P (stmt_info)
&& !STMT_VINFO_LIVE_P (stmt_info))
continue;
if ((STMT_VINFO_DEF_TYPE (stmt_info) == vect_induction_def
|| STMT_VINFO_DEF_TYPE (stmt_info) == vect_reduction_def
|| STMT_VINFO_DEF_TYPE (stmt_info) == vect_double_reduction_def
|| STMT_VINFO_DEF_TYPE (stmt_info) == vect_nested_cycle
|| STMT_VINFO_DEF_TYPE (stmt_info) == vect_internal_def)
&& ! PURE_SLP_STMT (stmt_info))
maybe_set_vectorized_backedge_value (loop_vinfo, stmt_info);
}
for (gimple_stmt_iterator si = gsi_start_bb (bb);
!gsi_end_p (si);)
{
stmt = gsi_stmt (si);
/* During vectorization remove existing clobber stmts. */
if (gimple_clobber_p (stmt))
{
unlink_stmt_vdef (stmt);
gsi_remove (&si, true);
release_defs (stmt);
}
else
{
/* Ignore vector stmts created in the outer loop. */
stmt_info = loop_vinfo->lookup_stmt (stmt);
/* vector stmts created in the outer-loop during vectorization of
stmts in an inner-loop may not have a stmt_info, and do not
need to be vectorized. */
stmt_vec_info seen_store = NULL;
if (stmt_info)
{
if (STMT_VINFO_IN_PATTERN_P (stmt_info))
{
gimple *def_seq = STMT_VINFO_PATTERN_DEF_SEQ (stmt_info);
for (gimple_stmt_iterator subsi = gsi_start (def_seq);
!gsi_end_p (subsi); gsi_next (&subsi))
{
stmt_vec_info pat_stmt_info
= loop_vinfo->lookup_stmt (gsi_stmt (subsi));
vect_transform_loop_stmt (loop_vinfo, pat_stmt_info,
&si, &seen_store);
}
stmt_vec_info pat_stmt_info
= STMT_VINFO_RELATED_STMT (stmt_info);
if (vect_transform_loop_stmt (loop_vinfo, pat_stmt_info,
&si, &seen_store))
maybe_set_vectorized_backedge_value (loop_vinfo,
pat_stmt_info);
}
else
{
if (vect_transform_loop_stmt (loop_vinfo, stmt_info, &si,
&seen_store))
maybe_set_vectorized_backedge_value (loop_vinfo,
stmt_info);
}
}
gsi_next (&si);
if (seen_store)
{
if (STMT_VINFO_GROUPED_ACCESS (seen_store))
/* Interleaving. If IS_STORE is TRUE, the
vectorization of the interleaving chain was
completed - free all the stores in the chain. */
vect_remove_stores (loop_vinfo,
DR_GROUP_FIRST_ELEMENT (seen_store));
else
/* Free the attached stmt_vec_info and remove the stmt. */
loop_vinfo->remove_stmt (stmt_info);
}
}
}
/* Stub out scalar statements that must not survive vectorization.
Doing this here helps with grouped statements, or statements that
are involved in patterns. */
for (gimple_stmt_iterator gsi = gsi_start_bb (bb);
!gsi_end_p (gsi); gsi_next (&gsi))
{
gcall *call = dyn_cast <gcall *> (gsi_stmt (gsi));
if (call && gimple_call_internal_p (call, IFN_MASK_LOAD))
{
tree lhs = gimple_get_lhs (call);
if (!VECTOR_TYPE_P (TREE_TYPE (lhs)))
{
tree zero = build_zero_cst (TREE_TYPE (lhs));
gimple *new_stmt = gimple_build_assign (lhs, zero);
gsi_replace (&gsi, new_stmt, true);
}
}
}
} /* BBs in loop */
/* The vectorization factor is always > 1, so if we use an IV increment of 1.
a zero NITERS becomes a nonzero NITERS_VECTOR. */
if (integer_onep (step_vector))
niters_no_overflow = true;
vect_set_loop_condition (loop, loop_vinfo, niters_vector, step_vector,
niters_vector_mult_vf, !niters_no_overflow);
unsigned int assumed_vf = vect_vf_for_cost (loop_vinfo);
scale_profile_for_vect_loop (loop, assumed_vf);
/* True if the final iteration might not handle a full vector's
worth of scalar iterations. */
bool final_iter_may_be_partial
= LOOP_VINFO_USING_PARTIAL_VECTORS_P (loop_vinfo);
/* The minimum number of iterations performed by the epilogue. This
is 1 when peeling for gaps because we always need a final scalar
iteration. */
int min_epilogue_iters = LOOP_VINFO_PEELING_FOR_GAPS (loop_vinfo) ? 1 : 0;
/* +1 to convert latch counts to loop iteration counts,
-min_epilogue_iters to remove iterations that cannot be performed
by the vector code. */
int bias_for_lowest = 1 - min_epilogue_iters;
int bias_for_assumed = bias_for_lowest;
int alignment_npeels = LOOP_VINFO_PEELING_FOR_ALIGNMENT (loop_vinfo);
if (alignment_npeels && LOOP_VINFO_USING_PARTIAL_VECTORS_P (loop_vinfo))
{
/* When the amount of peeling is known at compile time, the first
iteration will have exactly alignment_npeels active elements.
In the worst case it will have at least one. */
int min_first_active = (alignment_npeels > 0 ? alignment_npeels : 1);
bias_for_lowest += lowest_vf - min_first_active;
bias_for_assumed += assumed_vf - min_first_active;
}
/* In these calculations the "- 1" converts loop iteration counts
back to latch counts. */
if (loop->any_upper_bound)
loop->nb_iterations_upper_bound
= (final_iter_may_be_partial
? wi::udiv_ceil (loop->nb_iterations_upper_bound + bias_for_lowest,
lowest_vf) - 1
: wi::udiv_floor (loop->nb_iterations_upper_bound + bias_for_lowest,
lowest_vf) - 1);
if (loop->any_likely_upper_bound)
loop->nb_iterations_likely_upper_bound
= (final_iter_may_be_partial
? wi::udiv_ceil (loop->nb_iterations_likely_upper_bound
+ bias_for_lowest, lowest_vf) - 1
: wi::udiv_floor (loop->nb_iterations_likely_upper_bound
+ bias_for_lowest, lowest_vf) - 1);
if (loop->any_estimate)
loop->nb_iterations_estimate
= (final_iter_may_be_partial
? wi::udiv_ceil (loop->nb_iterations_estimate + bias_for_assumed,
assumed_vf) - 1
: wi::udiv_floor (loop->nb_iterations_estimate + bias_for_assumed,
assumed_vf) - 1);
if (dump_enabled_p ())
{
if (!LOOP_VINFO_EPILOGUE_P (loop_vinfo))
{
dump_printf_loc (MSG_NOTE, vect_location,
"LOOP VECTORIZED\n");
if (loop->inner)
dump_printf_loc (MSG_NOTE, vect_location,
"OUTER LOOP VECTORIZED\n");
dump_printf (MSG_NOTE, "\n");
}
else
dump_printf_loc (MSG_NOTE, vect_location,
"LOOP EPILOGUE VECTORIZED (MODE=%s)\n",
GET_MODE_NAME (loop_vinfo->vector_mode));
}
/* Loops vectorized with a variable factor won't benefit from
unrolling/peeling. */
if (!vf.is_constant ())
{
loop->unroll = 1;
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location, "Disabling unrolling due to"
" variable-length vectorization factor\n");
}
/* Free SLP instances here because otherwise stmt reference counting
won't work. */
slp_instance instance;
FOR_EACH_VEC_ELT (LOOP_VINFO_SLP_INSTANCES (loop_vinfo), i, instance)
vect_free_slp_instance (instance);
LOOP_VINFO_SLP_INSTANCES (loop_vinfo).release ();
/* Clear-up safelen field since its value is invalid after vectorization
since vectorized loop can have loop-carried dependencies. */
loop->safelen = 0;
if (epilogue)
{
update_epilogue_loop_vinfo (epilogue, advance);
epilogue->simduid = loop->simduid;
epilogue->force_vectorize = loop->force_vectorize;
epilogue->dont_vectorize = false;
}
return epilogue;
}
/* The code below is trying to perform simple optimization - revert
if-conversion for masked stores, i.e. if the mask of a store is zero
do not perform it and all stored value producers also if possible.
For example,
for (i=0; i<n; i++)
if (c[i])
{
p1[i] += 1;
p2[i] = p3[i] +2;
}
this transformation will produce the following semi-hammock:
if (!mask__ifc__42.18_165 == { 0, 0, 0, 0, 0, 0, 0, 0 })
{
vect__11.19_170 = MASK_LOAD (vectp_p1.20_168, 0B, mask__ifc__42.18_165);
vect__12.22_172 = vect__11.19_170 + vect_cst__171;
MASK_STORE (vectp_p1.23_175, 0B, mask__ifc__42.18_165, vect__12.22_172);
vect__18.25_182 = MASK_LOAD (vectp_p3.26_180, 0B, mask__ifc__42.18_165);
vect__19.28_184 = vect__18.25_182 + vect_cst__183;
MASK_STORE (vectp_p2.29_187, 0B, mask__ifc__42.18_165, vect__19.28_184);
}
*/
void
optimize_mask_stores (class loop *loop)
{
basic_block *bbs = get_loop_body (loop);
unsigned nbbs = loop->num_nodes;
unsigned i;
basic_block bb;
class loop *bb_loop;
gimple_stmt_iterator gsi;
gimple *stmt;
auto_vec<gimple *> worklist;
auto_purge_vect_location sentinel;
vect_location = find_loop_location (loop);
/* Pick up all masked stores in loop if any. */
for (i = 0; i < nbbs; i++)
{
bb = bbs[i];
for (gsi = gsi_start_bb (bb); !gsi_end_p (gsi);
gsi_next (&gsi))
{
stmt = gsi_stmt (gsi);
if (gimple_call_internal_p (stmt, IFN_MASK_STORE))
worklist.safe_push (stmt);
}
}
free (bbs);
if (worklist.is_empty ())
return;
/* Loop has masked stores. */
while (!worklist.is_empty ())
{
gimple *last, *last_store;
edge e, efalse;
tree mask;
basic_block store_bb, join_bb;
gimple_stmt_iterator gsi_to;
tree vdef, new_vdef;
gphi *phi;
tree vectype;
tree zero;
last = worklist.pop ();
mask = gimple_call_arg (last, 2);
bb = gimple_bb (last);
/* Create then_bb and if-then structure in CFG, then_bb belongs to
the same loop as if_bb. It could be different to LOOP when two
level loop-nest is vectorized and mask_store belongs to the inner
one. */
e = split_block (bb, last);
bb_loop = bb->loop_father;
gcc_assert (loop == bb_loop || flow_loop_nested_p (loop, bb_loop));
join_bb = e->dest;
store_bb = create_empty_bb (bb);
add_bb_to_loop (store_bb, bb_loop);
e->flags = EDGE_TRUE_VALUE;
efalse = make_edge (bb, store_bb, EDGE_FALSE_VALUE);
/* Put STORE_BB to likely part. */
efalse->probability = profile_probability::unlikely ();
store_bb->count = efalse->count ();
make_single_succ_edge (store_bb, join_bb, EDGE_FALLTHRU);
if (dom_info_available_p (CDI_DOMINATORS))
set_immediate_dominator (CDI_DOMINATORS, store_bb, bb);
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"Create new block %d to sink mask stores.",
store_bb->index);
/* Create vector comparison with boolean result. */
vectype = TREE_TYPE (mask);
zero = build_zero_cst (vectype);
stmt = gimple_build_cond (EQ_EXPR, mask, zero, NULL_TREE, NULL_TREE);
gsi = gsi_last_bb (bb);
gsi_insert_after (&gsi, stmt, GSI_SAME_STMT);
/* Create new PHI node for vdef of the last masked store:
.MEM_2 = VDEF <.MEM_1>
will be converted to
.MEM.3 = VDEF <.MEM_1>
and new PHI node will be created in join bb
.MEM_2 = PHI <.MEM_1, .MEM_3>
*/
vdef = gimple_vdef (last);
new_vdef = make_ssa_name (gimple_vop (cfun), last);
gimple_set_vdef (last, new_vdef);
phi = create_phi_node (vdef, join_bb);
add_phi_arg (phi, new_vdef, EDGE_SUCC (store_bb, 0), UNKNOWN_LOCATION);
/* Put all masked stores with the same mask to STORE_BB if possible. */
while (true)
{
gimple_stmt_iterator gsi_from;
gimple *stmt1 = NULL;
/* Move masked store to STORE_BB. */
last_store = last;
gsi = gsi_for_stmt (last);
gsi_from = gsi;
/* Shift GSI to the previous stmt for further traversal. */
gsi_prev (&gsi);
gsi_to = gsi_start_bb (store_bb);
gsi_move_before (&gsi_from, &gsi_to);
/* Setup GSI_TO to the non-empty block start. */
gsi_to = gsi_start_bb (store_bb);
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"Move stmt to created bb\n%G", last);
/* Move all stored value producers if possible. */
while (!gsi_end_p (gsi))
{
tree lhs;
imm_use_iterator imm_iter;
use_operand_p use_p;
bool res;
/* Skip debug statements. */
if (is_gimple_debug (gsi_stmt (gsi)))
{
gsi_prev (&gsi);
continue;
}
stmt1 = gsi_stmt (gsi);
/* Do not consider statements writing to memory or having
volatile operand. */
if (gimple_vdef (stmt1)
|| gimple_has_volatile_ops (stmt1))
break;
gsi_from = gsi;
gsi_prev (&gsi);
lhs = gimple_get_lhs (stmt1);
if (!lhs)
break;
/* LHS of vectorized stmt must be SSA_NAME. */
if (TREE_CODE (lhs) != SSA_NAME)
break;
if (!VECTOR_TYPE_P (TREE_TYPE (lhs)))
{
/* Remove dead scalar statement. */
if (has_zero_uses (lhs))
{
gsi_remove (&gsi_from, true);
continue;
}
}
/* Check that LHS does not have uses outside of STORE_BB. */
res = true;
FOR_EACH_IMM_USE_FAST (use_p, imm_iter, lhs)
{
gimple *use_stmt;
use_stmt = USE_STMT (use_p);
if (is_gimple_debug (use_stmt))
continue;
if (gimple_bb (use_stmt) != store_bb)
{
res = false;
break;
}
}
if (!res)
break;
if (gimple_vuse (stmt1)
&& gimple_vuse (stmt1) != gimple_vuse (last_store))
break;
/* Can move STMT1 to STORE_BB. */
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"Move stmt to created bb\n%G", stmt1);
gsi_move_before (&gsi_from, &gsi_to);
/* Shift GSI_TO for further insertion. */
gsi_prev (&gsi_to);
}
/* Put other masked stores with the same mask to STORE_BB. */
if (worklist.is_empty ()
|| gimple_call_arg (worklist.last (), 2) != mask
|| worklist.last () != stmt1)
break;
last = worklist.pop ();
}
add_phi_arg (phi, gimple_vuse (last_store), e, UNKNOWN_LOCATION);
}
}
/* Decide whether it is possible to use a zero-based induction variable
when vectorizing LOOP_VINFO with partial vectors. If it is, return
the value that the induction variable must be able to hold in order
to ensure that the rgroups eventually have no active vector elements.
Return -1 otherwise. */
widest_int
vect_iv_limit_for_partial_vectors (loop_vec_info loop_vinfo)
{
tree niters_skip = LOOP_VINFO_MASK_SKIP_NITERS (loop_vinfo);
class loop *loop = LOOP_VINFO_LOOP (loop_vinfo);
unsigned HOST_WIDE_INT max_vf = vect_max_vf (loop_vinfo);
/* Calculate the value that the induction variable must be able
to hit in order to ensure that we end the loop with an all-false mask.
This involves adding the maximum number of inactive trailing scalar
iterations. */
widest_int iv_limit = -1;
if (max_loop_iterations (loop, &iv_limit))
{
if (niters_skip)
{
/* Add the maximum number of skipped iterations to the
maximum iteration count. */
if (TREE_CODE (niters_skip) == INTEGER_CST)
iv_limit += wi::to_widest (niters_skip);
else
iv_limit += max_vf - 1;
}
else if (LOOP_VINFO_PEELING_FOR_ALIGNMENT (loop_vinfo))
/* Make a conservatively-correct assumption. */
iv_limit += max_vf - 1;
/* IV_LIMIT is the maximum number of latch iterations, which is also
the maximum in-range IV value. Round this value down to the previous
vector alignment boundary and then add an extra full iteration. */
poly_uint64 vf = LOOP_VINFO_VECT_FACTOR (loop_vinfo);
iv_limit = (iv_limit & -(int) known_alignment (vf)) + max_vf;
}
return iv_limit;
}
/* For the given rgroup_controls RGC, check whether an induction variable
would ever hit a value that produces a set of all-false masks or zero
lengths before wrapping around. Return true if it's possible to wrap
around before hitting the desirable value, otherwise return false. */
bool
vect_rgroup_iv_might_wrap_p (loop_vec_info loop_vinfo, rgroup_controls *rgc)
{
widest_int iv_limit = vect_iv_limit_for_partial_vectors (loop_vinfo);
if (iv_limit == -1)
return true;
tree compare_type = LOOP_VINFO_RGROUP_COMPARE_TYPE (loop_vinfo);
unsigned int compare_precision = TYPE_PRECISION (compare_type);
unsigned nitems = rgc->max_nscalars_per_iter * rgc->factor;
if (wi::min_precision (iv_limit * nitems, UNSIGNED) > compare_precision)
return true;
return false;
}
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