Add -polly-flatten-schedule pass.
The -polly-flatten-schedule pass reduces the number of scattering
dimensions in its isl_union_map form to make them easier to understand.
It is not meant to be used in production, only for debugging and
regression tests.
To illustrate, how it can make sets simpler, here is a lifetime set
used computed by the porposed DeLICM pass without flattening:
{ Stmt_reduction_for[0, 4] -> [0, 2, o2, o3] : o2 < 0;
Stmt_reduction_for[0, 4] -> [0, 1, o2, o3] : o2 >= 5;
Stmt_reduction_for[0, 4] -> [0, 1, 4, o3] : o3 > 0;
Stmt_reduction_for[0, i1] -> [0, 1, i1, 1] : 0 <= i1 <= 3;
Stmt_reduction_for[0, 4] -> [0, 2, 0, o3] : o3 <= 0 }
And here the same lifetime for a semantically identical one-dimensional
schedule:
{ Stmt_reduction_for[0, i1] -> [2 + 3i1] : 0 <= i1 <= 4 }
Differential Revision: https://reviews.llvm.org/D24310
llvm-svn: 280948
2016-09-08 23:02:36 +08:00
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//===------ FlattenAlgo.cpp ------------------------------------*- C++ -*-===//
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//
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// The LLVM Compiler Infrastructure
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//
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// This file is distributed under the University of Illinois Open Source
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// License. See LICENSE.TXT for details.
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//
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//===----------------------------------------------------------------------===//
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//
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// Main algorithm of the FlattenSchedulePass. This is a separate file to avoid
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// the unittest for this requiring linking against LLVM.
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//
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//===----------------------------------------------------------------------===//
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#include "polly/FlattenAlgo.h"
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#include "llvm/Support/Debug.h"
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#define DEBUG_TYPE "polly-flatten-algo"
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using namespace polly;
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using namespace llvm;
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namespace {
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/// Whether a dimension of a set is bounded (lower and upper) by a constant,
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/// i.e. there are two constants Min and Max, such that every value x of the
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/// chosen dimensions is Min <= x <= Max.
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bool isDimBoundedByConstant(IslPtr<isl_set> Set, unsigned dim) {
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auto ParamDims = isl_set_dim(Set.keep(), isl_dim_param);
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Set = give(isl_set_project_out(Set.take(), isl_dim_param, 0, ParamDims));
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Set = give(isl_set_project_out(Set.take(), isl_dim_set, 0, dim));
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auto SetDims = isl_set_dim(Set.keep(), isl_dim_set);
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Set = give(isl_set_project_out(Set.take(), isl_dim_set, 1, SetDims - 1));
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return isl_set_is_bounded(Set.keep());
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}
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/// Whether a dimension of a set is (lower and upper) bounded by a constant or
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/// parameters, i.e. there are two expressions Min_p and Max_p of the parameters
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/// p, such that every value x of the chosen dimensions is
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/// Min_p <= x <= Max_p.
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bool isDimBoundedByParameter(IslPtr<isl_set> Set, unsigned dim) {
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Set = give(isl_set_project_out(Set.take(), isl_dim_set, 0, dim));
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auto SetDims = isl_set_dim(Set.keep(), isl_dim_set);
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Set = give(isl_set_project_out(Set.take(), isl_dim_set, 1, SetDims - 1));
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return isl_set_is_bounded(Set.keep());
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}
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/// Whether BMap's first out-dimension is not a constant.
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2017-02-24 01:57:27 +08:00
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bool isVariableDim(const IslPtr<isl_basic_map> &BMap) {
|
Add -polly-flatten-schedule pass.
The -polly-flatten-schedule pass reduces the number of scattering
dimensions in its isl_union_map form to make them easier to understand.
It is not meant to be used in production, only for debugging and
regression tests.
To illustrate, how it can make sets simpler, here is a lifetime set
used computed by the porposed DeLICM pass without flattening:
{ Stmt_reduction_for[0, 4] -> [0, 2, o2, o3] : o2 < 0;
Stmt_reduction_for[0, 4] -> [0, 1, o2, o3] : o2 >= 5;
Stmt_reduction_for[0, 4] -> [0, 1, 4, o3] : o3 > 0;
Stmt_reduction_for[0, i1] -> [0, 1, i1, 1] : 0 <= i1 <= 3;
Stmt_reduction_for[0, 4] -> [0, 2, 0, o3] : o3 <= 0 }
And here the same lifetime for a semantically identical one-dimensional
schedule:
{ Stmt_reduction_for[0, i1] -> [2 + 3i1] : 0 <= i1 <= 4 }
Differential Revision: https://reviews.llvm.org/D24310
llvm-svn: 280948
2016-09-08 23:02:36 +08:00
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auto FixedVal =
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give(isl_basic_map_plain_get_val_if_fixed(BMap.keep(), isl_dim_out, 0));
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return !FixedVal || isl_val_is_nan(FixedVal.keep());
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}
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/// Whether Map's first out dimension is no constant nor piecewise constant.
|
2017-02-24 01:57:27 +08:00
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bool isVariableDim(const IslPtr<isl_map> &Map) {
|
Add -polly-flatten-schedule pass.
The -polly-flatten-schedule pass reduces the number of scattering
dimensions in its isl_union_map form to make them easier to understand.
It is not meant to be used in production, only for debugging and
regression tests.
To illustrate, how it can make sets simpler, here is a lifetime set
used computed by the porposed DeLICM pass without flattening:
{ Stmt_reduction_for[0, 4] -> [0, 2, o2, o3] : o2 < 0;
Stmt_reduction_for[0, 4] -> [0, 1, o2, o3] : o2 >= 5;
Stmt_reduction_for[0, 4] -> [0, 1, 4, o3] : o3 > 0;
Stmt_reduction_for[0, i1] -> [0, 1, i1, 1] : 0 <= i1 <= 3;
Stmt_reduction_for[0, 4] -> [0, 2, 0, o3] : o3 <= 0 }
And here the same lifetime for a semantically identical one-dimensional
schedule:
{ Stmt_reduction_for[0, i1] -> [2 + 3i1] : 0 <= i1 <= 4 }
Differential Revision: https://reviews.llvm.org/D24310
llvm-svn: 280948
2016-09-08 23:02:36 +08:00
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return foreachEltWithBreak(Map, [](IslPtr<isl_basic_map> BMap) -> isl_stat {
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if (isVariableDim(BMap))
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return isl_stat_error;
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return isl_stat_ok;
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});
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}
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/// Whether UMap's first out dimension is no (piecewise) constant.
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2017-02-24 01:57:27 +08:00
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bool isVariableDim(const IslPtr<isl_union_map> &UMap) {
|
Add -polly-flatten-schedule pass.
The -polly-flatten-schedule pass reduces the number of scattering
dimensions in its isl_union_map form to make them easier to understand.
It is not meant to be used in production, only for debugging and
regression tests.
To illustrate, how it can make sets simpler, here is a lifetime set
used computed by the porposed DeLICM pass without flattening:
{ Stmt_reduction_for[0, 4] -> [0, 2, o2, o3] : o2 < 0;
Stmt_reduction_for[0, 4] -> [0, 1, o2, o3] : o2 >= 5;
Stmt_reduction_for[0, 4] -> [0, 1, 4, o3] : o3 > 0;
Stmt_reduction_for[0, i1] -> [0, 1, i1, 1] : 0 <= i1 <= 3;
Stmt_reduction_for[0, 4] -> [0, 2, 0, o3] : o3 <= 0 }
And here the same lifetime for a semantically identical one-dimensional
schedule:
{ Stmt_reduction_for[0, i1] -> [2 + 3i1] : 0 <= i1 <= 4 }
Differential Revision: https://reviews.llvm.org/D24310
llvm-svn: 280948
2016-09-08 23:02:36 +08:00
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return foreachEltWithBreak(UMap, [](IslPtr<isl_map> Map) -> isl_stat {
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if (isVariableDim(Map))
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return isl_stat_error;
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return isl_stat_ok;
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});
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}
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/// If @p PwAff maps to a constant, return said constant. If @p Max/@p Min, it
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/// can also be a piecewise constant and it would return the minimum/maximum
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/// value. Otherwise, return NaN.
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IslPtr<isl_val> getConstant(IslPtr<isl_pw_aff> PwAff, bool Max, bool Min) {
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assert(!Max || !Min);
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IslPtr<isl_val> Result;
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foreachPieceWithBreak(
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PwAff, [=, &Result](IslPtr<isl_set> Set, IslPtr<isl_aff> Aff) {
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if (Result && isl_val_is_nan(Result.keep()))
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return isl_stat_ok;
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// TODO: If Min/Max, we can also determine a minimum/maximum value if
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// Set is constant-bounded.
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if (!isl_aff_is_cst(Aff.keep())) {
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Result = give(isl_val_nan(Aff.getCtx()));
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return isl_stat_error;
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}
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auto ThisVal = give(isl_aff_get_constant_val(Aff.keep()));
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if (!Result) {
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Result = ThisVal;
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return isl_stat_ok;
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}
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if (isl_val_eq(Result.keep(), ThisVal.keep()))
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return isl_stat_ok;
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if (Max && isl_val_gt(ThisVal.keep(), Result.keep())) {
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Result = ThisVal;
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return isl_stat_ok;
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}
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if (Min && isl_val_lt(ThisVal.keep(), Result.keep())) {
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Result = ThisVal;
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return isl_stat_ok;
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}
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// Not compatible
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Result = give(isl_val_nan(Aff.getCtx()));
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return isl_stat_error;
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});
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return Result;
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}
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/// Compute @p UPwAff - @p Val.
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IslPtr<isl_union_pw_aff> subtract(IslPtr<isl_union_pw_aff> UPwAff,
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IslPtr<isl_val> Val) {
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if (isl_val_is_zero(Val.keep()))
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return UPwAff;
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auto Result =
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give(isl_union_pw_aff_empty(isl_union_pw_aff_get_space(UPwAff.keep())));
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foreachElt(UPwAff, [=, &Result](IslPtr<isl_pw_aff> PwAff) {
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auto ValAff = give(isl_pw_aff_val_on_domain(
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isl_set_universe(isl_space_domain(isl_pw_aff_get_space(PwAff.keep()))),
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Val.copy()));
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auto Subtracted = give(isl_pw_aff_sub(PwAff.copy(), ValAff.take()));
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Result = give(isl_union_pw_aff_union_add(
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Result.take(), isl_union_pw_aff_from_pw_aff(Subtracted.take())));
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});
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return Result;
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}
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/// Compute @UPwAff * @p Val.
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IslPtr<isl_union_pw_aff> multiply(IslPtr<isl_union_pw_aff> UPwAff,
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IslPtr<isl_val> Val) {
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if (isl_val_is_one(Val.keep()))
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return UPwAff;
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auto Result =
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give(isl_union_pw_aff_empty(isl_union_pw_aff_get_space(UPwAff.keep())));
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foreachElt(UPwAff, [=, &Result](IslPtr<isl_pw_aff> PwAff) {
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auto ValAff = give(isl_pw_aff_val_on_domain(
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isl_set_universe(isl_space_domain(isl_pw_aff_get_space(PwAff.keep()))),
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Val.copy()));
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auto Multiplied = give(isl_pw_aff_mul(PwAff.copy(), ValAff.take()));
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Result = give(isl_union_pw_aff_union_add(
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Result.take(), isl_union_pw_aff_from_pw_aff(Multiplied.take())));
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});
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return Result;
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}
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/// Remove @p n dimensions from @p UMap's range, starting at @p first.
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///
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/// It is assumed that all maps in the maps have at least the necessary number
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/// of out dimensions.
|
2017-02-24 01:57:27 +08:00
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IslPtr<isl_union_map> scheduleProjectOut(const IslPtr<isl_union_map> &UMap,
|
Add -polly-flatten-schedule pass.
The -polly-flatten-schedule pass reduces the number of scattering
dimensions in its isl_union_map form to make them easier to understand.
It is not meant to be used in production, only for debugging and
regression tests.
To illustrate, how it can make sets simpler, here is a lifetime set
used computed by the porposed DeLICM pass without flattening:
{ Stmt_reduction_for[0, 4] -> [0, 2, o2, o3] : o2 < 0;
Stmt_reduction_for[0, 4] -> [0, 1, o2, o3] : o2 >= 5;
Stmt_reduction_for[0, 4] -> [0, 1, 4, o3] : o3 > 0;
Stmt_reduction_for[0, i1] -> [0, 1, i1, 1] : 0 <= i1 <= 3;
Stmt_reduction_for[0, 4] -> [0, 2, 0, o3] : o3 <= 0 }
And here the same lifetime for a semantically identical one-dimensional
schedule:
{ Stmt_reduction_for[0, i1] -> [2 + 3i1] : 0 <= i1 <= 4 }
Differential Revision: https://reviews.llvm.org/D24310
llvm-svn: 280948
2016-09-08 23:02:36 +08:00
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unsigned first, unsigned n) {
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if (n == 0)
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return UMap; /* isl_map_project_out would also reset the tuple, which should
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have no effect on schedule ranges */
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auto Result = give(isl_union_map_empty(isl_union_map_get_space(UMap.keep())));
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foreachElt(UMap, [=, &Result](IslPtr<isl_map> Map) {
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auto Outprojected =
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give(isl_map_project_out(Map.take(), isl_dim_out, first, n));
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Result = give(isl_union_map_add_map(Result.take(), Outprojected.take()));
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});
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return Result;
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}
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/// Return the number of dimensions in the input map's range.
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///
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/// Because this function takes an isl_union_map, the out dimensions could be
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/// different. We return the maximum number in this case. However, a different
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/// number of dimensions is not supported by the other code in this file.
|
2017-02-24 01:57:27 +08:00
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size_t scheduleScatterDims(const IslPtr<isl_union_map> &Schedule) {
|
Add -polly-flatten-schedule pass.
The -polly-flatten-schedule pass reduces the number of scattering
dimensions in its isl_union_map form to make them easier to understand.
It is not meant to be used in production, only for debugging and
regression tests.
To illustrate, how it can make sets simpler, here is a lifetime set
used computed by the porposed DeLICM pass without flattening:
{ Stmt_reduction_for[0, 4] -> [0, 2, o2, o3] : o2 < 0;
Stmt_reduction_for[0, 4] -> [0, 1, o2, o3] : o2 >= 5;
Stmt_reduction_for[0, 4] -> [0, 1, 4, o3] : o3 > 0;
Stmt_reduction_for[0, i1] -> [0, 1, i1, 1] : 0 <= i1 <= 3;
Stmt_reduction_for[0, 4] -> [0, 2, 0, o3] : o3 <= 0 }
And here the same lifetime for a semantically identical one-dimensional
schedule:
{ Stmt_reduction_for[0, i1] -> [2 + 3i1] : 0 <= i1 <= 4 }
Differential Revision: https://reviews.llvm.org/D24310
llvm-svn: 280948
2016-09-08 23:02:36 +08:00
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unsigned Dims = 0;
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foreachElt(Schedule, [&Dims](IslPtr<isl_map> Map) {
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Dims = std::max(Dims, isl_map_dim(Map.keep(), isl_dim_out));
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});
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return Dims;
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}
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/// Return the @p pos' range dimension, converted to an isl_union_pw_aff.
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IslPtr<isl_union_pw_aff> scheduleExtractDimAff(IslPtr<isl_union_map> UMap,
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unsigned pos) {
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auto SingleUMap =
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give(isl_union_map_empty(isl_union_map_get_space(UMap.keep())));
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foreachElt(UMap, [=, &SingleUMap](IslPtr<isl_map> Map) {
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auto MapDims = isl_map_dim(Map.keep(), isl_dim_out);
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auto SingleMap = give(isl_map_project_out(Map.take(), isl_dim_out, 0, pos));
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SingleMap = give(isl_map_project_out(SingleMap.take(), isl_dim_out, 1,
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MapDims - pos - 1));
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SingleUMap =
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give(isl_union_map_add_map(SingleUMap.take(), SingleMap.take()));
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});
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auto UAff = give(isl_union_pw_multi_aff_from_union_map(SingleUMap.take()));
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auto FirstMAff =
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give(isl_multi_union_pw_aff_from_union_pw_multi_aff(UAff.take()));
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return give(isl_multi_union_pw_aff_get_union_pw_aff(FirstMAff.keep(), 0));
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|
}
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/// Flatten a sequence-like first dimension.
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///
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|
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/// A sequence-like scatter dimension is constant, or at least only small
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|
|
/// variation, typically the result of ordering a sequence of different
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|
|
/// statements. An example would be:
|
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|
|
/// { Stmt_A[] -> [0, X, ...]; Stmt_B[] -> [1, Y, ...] }
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|
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/// to schedule all instances of Stmt_A before any instance of Stmt_B.
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///
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|
|
/// To flatten, first begin with an offset of zero. Then determine the lowest
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/// possible value of the dimension, call it "i" [In the example we start at 0].
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|
|
/// Considering only schedules with that value, consider only instances with
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|
/// that value and determine the extent of the next dimension. Let l_X(i) and
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|
|
/// u_X(i) its minimum (lower bound) and maximum (upper bound) value. Add them
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|
|
/// as "Offset + X - l_X(i)" to the new schedule, then add "u_X(i) - l_X(i) + 1"
|
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|
|
/// to Offset and remove all i-instances from the old schedule. Repeat with the
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|
|
/// remaining lowest value i' until there are no instances in the old schedule
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|
|
/// left.
|
|
|
|
/// The example schedule would be transformed to:
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|
|
/// { Stmt_X[] -> [X - l_X, ...]; Stmt_B -> [l_X - u_X + 1 + Y - l_Y, ...] }
|
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|
|
IslPtr<isl_union_map> tryFlattenSequence(IslPtr<isl_union_map> Schedule) {
|
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|
|
auto IslCtx = Schedule.getCtx();
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|
|
auto ScatterSet =
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give(isl_set_from_union_set(isl_union_map_range(Schedule.copy())));
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2016-09-10 00:11:26 +08:00
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auto ParamSpace =
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give(isl_space_params(isl_union_map_get_space(Schedule.keep())));
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Add -polly-flatten-schedule pass.
The -polly-flatten-schedule pass reduces the number of scattering
dimensions in its isl_union_map form to make them easier to understand.
It is not meant to be used in production, only for debugging and
regression tests.
To illustrate, how it can make sets simpler, here is a lifetime set
used computed by the porposed DeLICM pass without flattening:
{ Stmt_reduction_for[0, 4] -> [0, 2, o2, o3] : o2 < 0;
Stmt_reduction_for[0, 4] -> [0, 1, o2, o3] : o2 >= 5;
Stmt_reduction_for[0, 4] -> [0, 1, 4, o3] : o3 > 0;
Stmt_reduction_for[0, i1] -> [0, 1, i1, 1] : 0 <= i1 <= 3;
Stmt_reduction_for[0, 4] -> [0, 2, 0, o3] : o3 <= 0 }
And here the same lifetime for a semantically identical one-dimensional
schedule:
{ Stmt_reduction_for[0, i1] -> [2 + 3i1] : 0 <= i1 <= 4 }
Differential Revision: https://reviews.llvm.org/D24310
llvm-svn: 280948
2016-09-08 23:02:36 +08:00
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auto Dims = isl_set_dim(ScatterSet.keep(), isl_dim_set);
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assert(Dims >= 2);
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// Would cause an infinite loop.
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if (!isDimBoundedByConstant(ScatterSet, 0)) {
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DEBUG(dbgs() << "Abort; dimension is not of fixed size\n");
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return nullptr;
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}
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auto AllDomains = give(isl_union_map_domain(Schedule.copy()));
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auto AllDomainsToNull =
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give(isl_union_pw_multi_aff_from_domain(AllDomains.take()));
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auto NewSchedule = give(isl_union_map_empty(ParamSpace.copy()));
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auto Counter = give(isl_pw_aff_zero_on_domain(isl_local_space_from_space(
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isl_space_set_from_params(ParamSpace.copy()))));
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while (!isl_set_is_empty(ScatterSet.keep())) {
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DEBUG(dbgs() << "Next counter:\n " << Counter << "\n");
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DEBUG(dbgs() << "Remaining scatter set:\n " << ScatterSet << "\n");
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auto ThisSet =
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give(isl_set_project_out(ScatterSet.copy(), isl_dim_set, 1, Dims - 1));
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auto ThisFirst = give(isl_set_lexmin(ThisSet.take()));
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auto ScatterFirst =
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give(isl_set_add_dims(ThisFirst.take(), isl_dim_set, Dims - 1));
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auto SubSchedule = give(isl_union_map_intersect_range(
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Schedule.copy(), isl_union_set_from_set(ScatterFirst.copy())));
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SubSchedule = scheduleProjectOut(std::move(SubSchedule), 0, 1);
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SubSchedule = flattenSchedule(std::move(SubSchedule));
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auto SubDims = scheduleScatterDims(SubSchedule);
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auto FirstSubSchedule = scheduleProjectOut(SubSchedule, 1, SubDims - 1);
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auto FirstScheduleAff = scheduleExtractDimAff(FirstSubSchedule, 0);
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auto RemainingSubSchedule =
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scheduleProjectOut(std::move(SubSchedule), 0, 1);
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auto FirstSubScatter = give(
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isl_set_from_union_set(isl_union_map_range(FirstSubSchedule.take())));
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DEBUG(dbgs() << "Next step in sequence is:\n " << FirstSubScatter << "\n");
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if (!isDimBoundedByParameter(FirstSubScatter, 0)) {
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DEBUG(dbgs() << "Abort; sequence step is not bounded\n");
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return nullptr;
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}
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auto FirstSubScatterMap = give(isl_map_from_range(FirstSubScatter.take()));
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// isl_set_dim_max returns a strange isl_pw_aff with domain tuple_id of
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// 'none'. It doesn't match with any space including a 0-dimensional
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// anonymous tuple.
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// Interesting, one can create such a set using
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// isl_set_universe(ParamSpace). Bug?
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auto PartMin = give(isl_map_dim_min(FirstSubScatterMap.copy(), 0));
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auto PartMax = give(isl_map_dim_max(FirstSubScatterMap.take(), 0));
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auto One = give(isl_pw_aff_val_on_domain(
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isl_set_universe(isl_space_set_from_params(ParamSpace.copy())),
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isl_val_one(IslCtx)));
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auto PartLen = give(isl_pw_aff_add(
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isl_pw_aff_add(PartMax.take(), isl_pw_aff_neg(PartMin.copy())),
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One.take()));
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auto AllPartMin = give(isl_union_pw_aff_pullback_union_pw_multi_aff(
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isl_union_pw_aff_from_pw_aff(PartMin.take()), AllDomainsToNull.copy()));
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auto FirstScheduleAffNormalized =
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give(isl_union_pw_aff_sub(FirstScheduleAff.take(), AllPartMin.take()));
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auto AllCounter = give(isl_union_pw_aff_pullback_union_pw_multi_aff(
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isl_union_pw_aff_from_pw_aff(Counter.copy()), AllDomainsToNull.copy()));
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auto FirstScheduleAffWithOffset = give(isl_union_pw_aff_add(
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FirstScheduleAffNormalized.take(), AllCounter.take()));
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auto ScheduleWithOffset = give(isl_union_map_flat_range_product(
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isl_union_map_from_union_pw_aff(FirstScheduleAffWithOffset.take()),
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RemainingSubSchedule.take()));
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NewSchedule = give(
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isl_union_map_union(NewSchedule.take(), ScheduleWithOffset.take()));
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ScatterSet = give(isl_set_subtract(ScatterSet.take(), ScatterFirst.take()));
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Counter = give(isl_pw_aff_add(Counter.take(), PartLen.take()));
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}
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DEBUG(dbgs() << "Sequence-flatten result is:\n " << NewSchedule << "\n");
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return NewSchedule;
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}
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/// Flatten a loop-like first dimension.
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///
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/// A loop-like dimension is one that depends on a variable (usually a loop's
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/// induction variable). Let the input schedule look like this:
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/// { Stmt[i] -> [i, X, ...] }
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///
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/// To flatten, we determine the largest extent of X which may not depend on the
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/// actual value of i. Let l_X() the smallest possible value of X and u_X() its
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/// largest value. Then, construct a new schedule
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/// { Stmt[i] -> [i * (u_X() - l_X() + 1), ...] }
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IslPtr<isl_union_map> tryFlattenLoop(IslPtr<isl_union_map> Schedule) {
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assert(scheduleScatterDims(Schedule) >= 2);
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auto Remaining = scheduleProjectOut(Schedule, 0, 1);
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auto SubSchedule = flattenSchedule(Remaining);
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auto SubDims = scheduleScatterDims(SubSchedule);
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auto SubExtent =
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give(isl_set_from_union_set(isl_union_map_range(SubSchedule.copy())));
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auto SubExtentDims = isl_set_dim(SubExtent.keep(), isl_dim_param);
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SubExtent = give(
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isl_set_project_out(SubExtent.take(), isl_dim_param, 0, SubExtentDims));
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SubExtent =
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give(isl_set_project_out(SubExtent.take(), isl_dim_set, 1, SubDims - 1));
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if (!isDimBoundedByConstant(SubExtent, 0)) {
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DEBUG(dbgs() << "Abort; dimension not bounded by constant\n");
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return nullptr;
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}
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auto Min = give(isl_set_dim_min(SubExtent.copy(), 0));
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DEBUG(dbgs() << "Min bound:\n " << Min << "\n");
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auto MinVal = getConstant(Min, false, true);
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auto Max = give(isl_set_dim_max(SubExtent.take(), 0));
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DEBUG(dbgs() << "Max bound:\n " << Max << "\n");
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auto MaxVal = getConstant(Max, true, false);
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if (!MinVal || !MaxVal || isl_val_is_nan(MinVal.keep()) ||
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isl_val_is_nan(MaxVal.keep())) {
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DEBUG(dbgs() << "Abort; dimension bounds could not be determined\n");
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return nullptr;
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}
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auto FirstSubScheduleAff = scheduleExtractDimAff(SubSchedule, 0);
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auto RemainingSubSchedule = scheduleProjectOut(std::move(SubSchedule), 0, 1);
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auto LenVal =
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give(isl_val_add_ui(isl_val_sub(MaxVal.take(), MinVal.copy()), 1));
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auto FirstSubScheduleNormalized = subtract(FirstSubScheduleAff, MinVal);
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// TODO: Normalize FirstAff to zero (convert to isl_map, determine minimum,
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// subtract it)
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auto FirstAff = scheduleExtractDimAff(Schedule, 0);
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auto Offset = multiply(FirstAff, LenVal);
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auto Index = give(
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isl_union_pw_aff_add(FirstSubScheduleNormalized.take(), Offset.take()));
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auto IndexMap = give(isl_union_map_from_union_pw_aff(Index.take()));
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auto Result = give(isl_union_map_flat_range_product(
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IndexMap.take(), RemainingSubSchedule.take()));
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DEBUG(dbgs() << "Loop-flatten result is:\n " << Result << "\n");
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return Result;
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}
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} // anonymous namespace
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IslPtr<isl_union_map> polly::flattenSchedule(IslPtr<isl_union_map> Schedule) {
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auto Dims = scheduleScatterDims(Schedule);
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DEBUG(dbgs() << "Recursive schedule to process:\n " << Schedule << "\n");
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// Base case; no dimensions left
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if (Dims == 0) {
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// TODO: Add one dimension?
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return Schedule;
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}
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// Base case; already one-dimensional
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if (Dims == 1)
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return Schedule;
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// Fixed dimension; no need to preserve variabledness.
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if (!isVariableDim(Schedule)) {
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DEBUG(dbgs() << "Fixed dimension; try sequence flattening\n");
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auto NewScheduleSequence = tryFlattenSequence(Schedule);
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if (NewScheduleSequence)
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return NewScheduleSequence;
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}
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// Constant stride
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DEBUG(dbgs() << "Try loop flattening\n");
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auto NewScheduleLoop = tryFlattenLoop(Schedule);
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if (NewScheduleLoop)
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return NewScheduleLoop;
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// Try again without loop condition (may blow up the number of pieces!!)
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DEBUG(dbgs() << "Try sequence flattening again\n");
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auto NewScheduleSequence = tryFlattenSequence(Schedule);
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if (NewScheduleSequence)
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return NewScheduleSequence;
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// Cannot flatten
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return Schedule;
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}
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