Halide 19.0.0
Halide compiler and libraries
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LoopNest.h
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1/** This file defines the LoopNest, which is our
2 * representation of a Halide schedule, and contains methods to
3 * generate candidates for scheduling as well as extract a
4 * featurization that can be used to cost each candidate. */
5
6#ifndef LOOP_NEST_H
7#define LOOP_NEST_H
8
9#include "ASLog.h"
10#include "CostModel.h"
11#include "FunctionDAG.h"
12#include "GPULoopInfo.h"
13#include "GPUMemInfo.h"
14#include "PerfectHashMap.h"
15#include "SearchSpaceOptions.h"
16#include "Statistics.h"
17#include "ThreadInfo.h"
18#include "Tiling.h"
19#include <set>
20#include <vector>
21
22namespace Halide {
23namespace Internal {
24namespace Autoscheduler {
25
26template<typename T>
28
29template<typename T>
31
32enum class GPU_parallelism {
33 Block,
34 Thread,
35 Serial,
36 Simd,
38 None
39};
40
41std::string stringify(GPU_parallelism label);
42
43// inlined => func is inlined so has no memory store location
44enum class GPUMemoryType {
45 Global,
46 Shared,
47 Local,
50};
51
52bool may_subtile(const Anderson2021Params &params);
53
55
57
59
61
63 return 128;
64}
65
66int get_unroll_limit(const Target &target);
67
68bool in_range_zero_one(double x);
69
70bool are_valid_thread_extents(const vector<int64_t> &counts);
71
74
75bool all(const vector<int> &v);
76bool accessed_at_constant_indices(const std::vector<int> &unrolled, const FunctionDAG::Edge *e);
77
78// We're going to do a tree search over possible schedules to find an
79// optimal one. A tree search requires a state, and a function that
80// gives you children of the state (with costs). The following struct
81// represents the state, which is a partial schedule.
82//
83// A partial schedule is a tree. Each node is some portion of the for
84// loop nest of some Func. If there are no children, it's the
85// innermost set of loops. If there are children, it's a loop over
86// tiles of that Func.
87struct LoopNest {
88 mutable RefCount ref_count;
89
90 // The extents of this loop. Put another way, the number of tiles,
91 // not the size of each tile.
92 vector<int64_t> size;
93
94 // The nodes inside the loop body
95 vector<IntrusivePtr<const LoopNest>> children;
96
97 // Funcs inlined into this inner loop, and the number of times
98 // each is called. Only valid if children is empty.
100
101 // Funcs stored inside this loop
102 std::set<const FunctionDAG::Node *> store_at;
103
104 // The total bounds required of any given Func over all iterations
105 // of this loop. In the paper, this is represented using the
106 // little boxes to the left of the loop nest tree figures.
107 mutable NodeMap<Bound> bounds;
108
109 // The Func this loop nest belongs to
110 const FunctionDAG::Node *node = nullptr;
111
112 // The stage of the Func
113 const FunctionDAG::Node::Stage *stage = nullptr;
114
115 // Is this the innermost loop of this func (the SIMD loop)?
116 bool innermost = false;
117
118 // Are we permitted to tile this loop?
119 bool tileable = false;
120
121 // Is this the parallel outer loop?
122 bool parallel = false;
123
124 // What dimension is this Func vectorized over, in terms of the pure args of the Func?
125 int vector_dim = -1;
126
127 // Which loop corresponds to the innermost storage dimension and will be vectorized. -1 means none of them.
128 int vectorized_loop_index = -1;
129
130 // Apply gpu threads to this loop nest
132
144
145 mutable std::map<uint64_t, StageMap<StageMap<FeatureIntermediates>>> feature_intermediates;
146 mutable std::map<uint64_t, StageMap<ScheduleFeatures>> features;
147
148 bool is_gpu_serial(const Target &target) const {
150 }
151
152 bool is_gpu_thread(const Target &target) const {
154 }
155
156 bool is_gpu_block(const Target &target) const {
158 }
159
160 bool is_scalar() const {
161 return size.empty();
162 }
163
164 // given a newly inserted node f into this LoopNest, get union of thread counts in each dimension
165 // across all siblings of f.
166 vector<int64_t> get_union_thread_counts(const FunctionDAG::Node *f) const;
167
168 // given a newly inserted node f into this LoopNest, gets the size of
169 // all of f's stages and their pure_dim indices
171 vector<vector<int64_t>> &stage_sizes,
172 vector<vector<int>> &pure_dims,
173 vector<int> &vectorized_indices) const;
174
175 // given the loop nest of a stage to parallelize at root, figure out if using odd tile sizes
176 // for the vectorized dimension will allow the resulting thread tiles to be multiples of 32
177 // if so, we will include these in the serial loop sizes
178 void generate_vec_dim_serial_tilings(vector<int> &serial_sizes) const;
179
180 // get the loop nests of a newly inserted node, f, that is marked GPU threads. Tiles
181 // the newly inserted loop nests of f into a threads loop outside a serial loop.
182 // V is the vectorized dimension of f. Adds loopnests created from each tiling option in result.
184 const Anderson2021Params &params,
185 const Target &target,
186 int v,
187 vector<IntrusivePtr<const LoopNest>> &result,
188 const vector<int64_t> &max_size);
189
190 void copy_from(const LoopNest &n);
192
193 static void hash_combine(uint64_t &h, uint64_t next) {
194 // From boost
195 h ^= (next + 0x9e3779b9 + (h << 6) + (h >> 2));
196 }
197
198 // Hash the loop structure and sizes up to a fixed depth. This is
199 // used as the hash function for the coarse-to-fine beam search in
200 // the paper.
201 void structural_hash(uint64_t &h, int depth) const;
202
203 // How many funcs are scheduled inside this loop level. Used in
204 // the structural hash.
206 size_t count = inlined.size() + store_at.size();
207 for (const auto &c : children) {
208 count += c->funcs_realized_or_inlined();
209 }
210 return count;
211 }
212
213 // All of a stage's interesting locations in the loop nest. Used to help compute the featurization of a stage.
214 struct Sites {
215 const LoopNest *compute = nullptr; // Its containing compute_at site
216 const LoopNest *store = nullptr; // Its containing store_at site
217 const LoopNest *produce = nullptr; // Its own outermost node
218 const LoopNest *innermost = nullptr; // Its innermost node - usually a SIMD loop
219 const LoopNest *task = nullptr; // The parallel for loop it belongs to
220 const LoopNest *thread = nullptr; // Its containing gpu_thread loop
221 GPUMemoryType gpu_store_memory_type; // global, local, shared?
222 int64_t allocation_size = 0; // Allocation size in bytes
223 bool is_constant_allocation = false; // Does the allocation have constant size?
224 int64_t num_realizations = 0; // Number of times this stage is realized. Only valid for unscheduled producers
225 bool inlined = false; // Is the Func inlined?
226 std::vector<const LoopNest *> inlined_innermosts; // Is the Func inlined?
228
241 };
242
244 bool in_thread,
245 bool is_inlined = false) const;
246
247 std::vector<int> unrolled_loops(const Target &target,
248 const LoopNest *parent,
249 const LoopNest *grandparent) const;
250
252 StageMap<Sites> &sites,
253 NodeMap<bool> &can_be_promoted_to_registers,
254 const LoopNest *grandparent,
255 const LoopNest *parent) const;
256
258 StageMap<Sites> &sites) const;
259
260 // Compute all the sites of interest for each pipeline stage
261 void get_sites(const Target &target,
262 StageMap<Sites> &sites,
263 StageMap<int64_t> &shared_mem_alloc_sizes,
264 const LoopNest *task = nullptr,
265 const LoopNest *parent = nullptr,
266 const LoopNest *current_thread_loop = nullptr) const;
267
268 // A helper for the working_set_at_task feature. Most features are
269 // computed in the recursive pass 'compute_features' below, but
270 // this one must be done in a second separate recursive pass.
273 for (const auto &c : children) {
274 c->set_working_set_at_task_feature(working_set, features);
275 features->get(c->stage).working_set_at_task = working_set;
276 }
277 }
278
280 const LoopNest *parent,
281 bool in_threads_loop) const;
282
284
285 bool has_dynamic_allocation_inside_thread(bool in_thread_loop) const;
286
288
290
292
293 // Get the stride over "node's" storage for a unit increment in the vectorized loop's
294 // index
295 double storage_stride(const LoadJacobian &jac,
296 int innermost_storage_dim,
297 const FunctionDAG::Node *storage_node,
298 const Bound &store_bounds,
299 const LoopNest &root) const;
300
302 int innermost_storage_dim,
303 const FunctionDAG::Node *storage_node,
304 const Bound &store_bounds,
305 const ThreadInfo *thread_info,
306 bool verbose = false) const;
307
309 const FunctionDAG::Node *storage_node,
310 const LoopNest &root) const;
311
312 int get_actual_vector_dim(const Bound &store_bounds) const;
313
315 int consumer_innermost_dim,
316 const FunctionDAG::Node *node,
317 const Bound &consumer_store_bounds,
318 const GPULoopInfo &gpu_loop_info,
319 const std::vector<int64_t> &inner_serial_loop_extents,
320 const Sites &consumer_site,
321 ScheduleFeatures &feat,
322 const LoopNest *parent,
323 const LoopNest &root,
324 GlobalMemInfo &global_mem_loads,
325 SharedMemInfo &shared_mem_loads,
326 LocalMemInfo &local_mem_loads,
327 bool verbose = false) const;
328
330 const FunctionDAG::Node *accessed,
331 int innermost_dim,
332 int loop_index) const;
333
335 const FunctionDAG::Node *accessed,
336 bool accessed_has_been_scheduled,
337 int innermost_dim,
338 int loop_index,
339 const GPUMemoryType &mem_type) const;
340
342 const FunctionDAG::Node *accessed,
343 bool accessed_has_been_scheduled,
344 int innermost_dim,
345 const GPUMemoryType &mem_type,
346 bool verbose = false) const;
347
348 int vectorized_access_size(size_t loop_index,
349 bool verbose = false) const;
350
351 template<typename T>
353 const FunctionDAG::Node *node,
354 const Bound &store_bounds,
355 const ThreadInfo *thread_info,
356 int innermost_dim,
357 double num_requests_per_warp,
358 MemInfoType<T> &mem_info,
359 bool verbose = false) const;
360
361 std::pair<double, double> compute_local_mem_store_features(const LoadJacobian &jac,
362 int consumer_innermost_dim,
363 const FunctionDAG::Node *node,
364 const Bound &consumer_store_bounds,
365 const LoopNest &root,
366 double serial_loop_extents) const;
367
368 template<typename T>
370 int consumer_innermost_dim,
371 const FunctionDAG::Node *node,
372 const Bound &consumer_store_bounds,
373 const ThreadInfo *thread_info,
374 double serial_loop_extents,
375 bool verbose) const;
376
377 template<typename T>
379 int producer_innermost_dim,
380 const FunctionDAG::Node *node,
381 const Bound &producer_store_bounds,
382 bool producer_has_been_scheduled,
383 const ThreadInfo *thread_info,
384 MemInfoType<T> &mem_info,
385 double serial_loop_extents,
386 bool verbose = false) const;
387
388 double compute_local_mem_stride(double stride,
389 double bytes) const;
390
391 // Assumes block, serial, thread or block, thread nesting
393 const LoopNest *grandparent) const;
394
395 std::pair<int64_t, int64_t> get_block_and_serial_extents(const LoopNest *block) const;
396
398
400
402 const GPULoopInfo &gpu_loop_info) const;
403
404 // Assume that when a block is active, all its warps are active
406 ScheduleFeatures &feat,
407 const GPULoopInfo &gpu_loop_info) const;
408
410 const Target &target,
411 int64_t total_shared_mem_alloc_size,
412 ScheduleFeatures &feat) const;
413
414 std::pair<const LoopNest *, const LoopNest *> find_innermost_and_parent() const;
415
417 const Target &target,
418 const GPULoopInfo &gpu_loop_info,
419 const std::vector<const FunctionDAG::Edge *> &edge_chain,
420 const LoadJacobian &jac,
421 const LoopNest *parent,
422 const LoopNest *grandparent,
423 int64_t n,
424 const ScheduleFeatures &feat,
425 const LoadJacobian &serial_jac,
426 bool producer_has_been_scheduled,
427 int producer_innermost_dim,
428 const GPUMemoryType &mem_type,
429 bool verbose) const;
430
432 const LoopNest *parent,
433 const ScheduleFeatures &feat,
434 const LoadJacobian &jac,
435 int producer_dims) const;
436
439
440 vector<pair<int, int>> collect_producers(const StageMap<Sites> &sites) const;
441
443
444 void collect_stages(std::set<const FunctionDAG::Node::Stage *> &stages) const;
445
448
451
454
455 std::pair<int64_t, bool> compute_alloc_size_of_node_here(const FunctionDAG::Node *f) const;
456
457 // Do a recursive walk over the loop nest computing features to feed the cost model.
459 const Anderson2021Params &params,
460 const Target &target,
461 const StageMap<Sites> &sites,
462 int64_t instances,
463 int64_t parallelism,
464 const LoopNest *parent,
465 const LoopNest *grandparent,
466 const LoopNest &root,
467 GPULoopInfo gpu_loop_info,
468 bool use_memoized_features,
469 const StageMap<int64_t> &total_shared_mem_alloc_sizes,
470 int64_t *working_set,
471 int64_t *working_set_local_constant,
472 int64_t *working_set_local_dynamic,
474 Statistics &stats,
475 bool verbose = false) const;
476
477 bool is_root() const {
478 // The root is the sole node without a Func associated with
479 // it.
480 return node == nullptr;
481 }
482
483 // Set the region required of a Func at this site.
484 const Bound &set_bounds(const FunctionDAG::Node *f, BoundContents *b) const {
485 return bounds.emplace(f, b);
486 }
487
488 // Get the region required of a Func at this site, from which we
489 // know what region would be computed if it were scheduled here,
490 // and what its loop nest would be.
491 const Bound &get_bounds(const FunctionDAG::Node *f) const;
492
493 // Get the region required of a Func at this site (but only to satisfy the
494 // consumers along the given edge chain), from which we know what region
495 // would be computed if it were scheduled here and what its loop nest
496 // would be.
498 const vector<const FunctionDAG::Edge *> &edge_chain) const;
499
500 void dump() const;
501
502 std::string to_string() const;
503
504 // Recursively print a loop nest representation to stderr
505 template<typename T>
506 void dump(T &stream, string prefix, const LoopNest *parent) const;
507
508 // Does this loop nest access the given Func
509 bool calls(const FunctionDAG::Node *f) const;
510
511 // What is the maximum number of inlined calls to a Func that
512 // occur within this loop. Used to prune states that would
513 // generate too much code.
515
516 // Does this loop nest access an input buffer? Used to select
517 // trail strategies when splitting loops. We don't want to read
518 // out of bounds on inputs, even if we don't intend to use the
519 // values read. It could create annoying assertion failures for
520 // the user. It's OK to read out of range of the values computed
521 // on internal Funcs though. Allocation bounds inference just pads
522 // out the bounds so that it won't fault.
524
525 // Does this loop nest contain a computation of the given Func.
526 bool computes(const FunctionDAG::Node *f) const;
527
528 // Above here most methods query the loop nest. Below we have
529 // methods that mutate the loop nest.
530
531 // Inline a Func into all consumers within this loop.
533
534 // Compute a Func at this site.
536 bool tileable,
537 int v,
538 bool in_threads_loop,
539 const Anderson2021Params &params,
540 const Target &target);
541
542 // Parallelize this loop according to the given tiling.
544 const LoopNest *parent,
545 const Anderson2021Params &params,
546 const Target &target,
547 bool inner_tiling,
548 bool adjust_tiling,
549 bool move_all_rvars_inward = true,
550 const vector<int> &rvars_to_move_inward = {}) const;
551
552 int64_t get_total_local_mem_alloc_size(bool constant_allocs_only = false,
553 bool in_threads_loop = false) const;
555
556 // All store ats further in than the block level must be fixed
557 // sized allocations. This method checks if f will require a dynamic
558 // allocation
560 const Target &target,
561 bool in_threads_loop) const;
562
563 // Return all possible ways to compute f in tiles somewhere within
564 // this loop nest.
565 // in_threads_loop tracks whether or not function is going to be placed inside a
566 // loop marked gpu_threads, in which case f's loops cannot be gpu_threads
567 vector<IntrusivePtr<const LoopNest>> compute_in_tiles(const FunctionDAG::Node *f,
568 const LoopNest *parent,
569 const Anderson2021Params &params,
570 const Target &target,
571 const SearchSpaceOptions &search_space_options,
572 int v,
573 bool in_realization,
574 bool in_threads_loop,
575 bool is_pre_pass,
576 vector<int64_t> union_counts = vector<int64_t>()) const;
577
578 // Below here we have methods that apply a schedule to a Halide pipeline.
579
580 // A model of the state of the loop nest of a Func while applying
581 // Halide's scheduling directives.
582
583 // Note that StageScheduleState is movable-but-not-copyable thanks to its ostringstream member.
585 // How much parallelism do we need to exploit with this Func?
586 double num_cores = 0;
587
588 // Which storage dimension is vectorized? We need to reorder it innermost
589 int vector_dim = -1;
590 int vectorized_loop_index = -1;
591
592 // The various Vars and RVars used for scheduling a Func.
593 struct FuncVar {
594 // The top-level var or rvar this was split off from
596
597 // This var.
599
600 // Source code to access this Var/RVar. Used for printing
601 // valid Halide source for this schedule.
602 string accessor;
603
604 // Our estimate of the extent of this var. This is exact
605 // when constant_extent flag is true.
606 int64_t extent = 0;
607
608 // Which index in the symbolic loop nest does this var
609 // belong to.
610 size_t index = 0;
611
612 // Some flags.
613 bool innermost_pure_dim = false;
614 bool outermost = false;
615 bool parallel = false;
616 bool exists = false;
617 bool pure = false;
618 bool constant_extent = false;
619
620 bool vectorized = false;
621 bool gpu_threads = false;
622
624 : orig(Var()),
625 var(Var()) {
626 }
627 };
630 bool parallel = false;
631 bool vectorized = false;
634
635 // In order from innermost to outermost. Each group of d is one tiling level.
636 vector<FuncVar> vars;
637
638 // In order from innermost to outermost. Each group of d is one tiling level.
639 vector<FuncVar> ordered_vars;
640 vector<int64_t> gpu_thread_extents;
641
644
645 // From outermost in
646 vector<StageScheduleState *> ancestors;
647
648 std::ostringstream schedule_source;
649 };
650
655 int num_serial_loops() const;
657
659 const NodeMap<bool> &all_inlined) const;
661 const LoopNest *parent) const;
662
663 // Apply the schedule represented by this loop nest to a Halide pipeline.
664 void apply(LoopLevel here,
665 StageMap<std::unique_ptr<StageScheduleState>> &state_map,
666 double num_cores,
667 int depth,
668 const LoopNest *parent,
669 const LoopNest *compute_site,
670 const Target &target,
671 std::vector<StageScheduleState *> &ancestors,
672 const NodeMap<bool> &all_inlined) const;
673
674 double max_idle_lane_wastage(const Target &target,
675 GPULoopInfo gpu_loop_info) const;
676
678
680 NodeMap<bool> &inlined_nodes) const;
681
682 void collect_all_inlined(NodeMap<bool> &all_inlined) const;
683
685 int64_t product_of_descendants(int loop_index) const;
686
688 const LoopNest *compute_root_loop_nest = nullptr) const;
689};
690
691struct Filter {
693 bool logging = false;
694
695 explicit Filter(const LoopNest *loop_nest)
698 if (logging) {
699 std::cerr << "\nState filtered: \n";
700 loop_nest->dump();
701 std::cerr << "Reason: ";
702 }
703 }
704
705 template<typename T>
707 if (logging) {
708 std::cerr << std::forward<T>(x);
709 }
710 return *this;
711 }
712
714};
715
716} // namespace Autoscheduler
717} // namespace Internal
718} // namespace Halide
719
720#endif // LOOP_NEST_H
Data structure containing information about the current GPU loop nest hierarchy of blocks,...
Data structures that help track memory access information.
Data structure containing information about GPU threads for a particular location in the loop nest an...
A class representing a reference count to be used with IntrusivePtr.
A reference to a site in a Halide statement at the top of the body of a particular for loop.
Definition Schedule.h:203
A Halide variable, to be used when defining functions.
Definition Var.h:19
int64_t get_active_block_hardware_limit(const Anderson2021Params &params)
PerfectHashMap< FunctionDAG::Node::Stage, T > StageMap
Definition LoopNest.h:24
bool all(const vector< int > &v)
bool are_valid_thread_extents(const vector< int64_t > &counts)
bool accessed_at_constant_indices(const std::vector< int > &unrolled, const FunctionDAG::Edge *e)
constexpr int64_t get_register_mem_alloc_limit()
Definition LoopNest.h:62
int64_t get_shared_memory_sm_limit(const Anderson2021Params &params)
PerfectHashMap< FunctionDAG::Node, T > NodeMap
Definition LoopNest.h:21
int64_t get_active_warp_hardware_limit(const Anderson2021Params &params)
bool may_subtile(const Anderson2021Params &params)
int get_unroll_limit(const Target &target)
int64_t get_shared_memory_limit(const Anderson2021Params &params)
std::string stringify(GPU_parallelism label)
This file defines the class FunctionDAG, which is our representation of a Halide pipeline,...
@ Internal
Not visible externally, similar to 'static' linkage in C.
unsigned __INT64_TYPE__ uint64_t
signed __INT64_TYPE__ int64_t
Filter(const LoopNest *loop_nest)
Definition LoopNest.h:695
std::vector< const LoopNest * > inlined_innermosts
Definition LoopNest.h:226
NodeMap< std::vector< std::pair< const LoopNest *, std::vector< const FunctionDAG::Edge * > > > > producers_to_be_staged
Definition LoopNest.h:643
vector< pair< int, int > > collect_producers(const StageMap< Sites > &sites) const
bool is_gpu_thread(const Target &target) const
Definition LoopNest.h:152
vector< IntrusivePtr< const LoopNest > > compute_in_tiles(const FunctionDAG::Node *f, const LoopNest *parent, const Anderson2021Params &params, const Target &target, const SearchSpaceOptions &search_space_options, int v, bool in_realization, bool in_threads_loop, bool is_pre_pass, vector< int64_t > union_counts=vector< int64_t >()) const
const LoopNest * get_enclosing_block(const LoopNest *parent, const LoopNest *grandparent) const
int num_serial_loops(const FunctionDAG::Node::Stage *stage) const
int64_t points_accessed_per_thread(const Anderson2021Params &params, const Target &target, const GPULoopInfo &gpu_loop_info, const std::vector< const FunctionDAG::Edge * > &edge_chain, const LoadJacobian &jac, const LoopNest *parent, const LoopNest *grandparent, int64_t n, const ScheduleFeatures &feat, const LoadJacobian &serial_jac, bool producer_has_been_scheduled, int producer_innermost_dim, const GPUMemoryType &mem_type, bool verbose) const
bool has_constant_region_required(const FunctionDAG::Node *node) const
std::map< uint64_t, StageMap< ScheduleFeatures > > features
Definition LoopNest.h:146
int get_pure_stage_vectorized_loop_index(const FunctionDAG::Node *node) const
const FunctionDAG::Node * node
Definition LoopNest.h:57
void dump(T &stream, string prefix, const LoopNest *parent) const
int64_t product_of_self_and_descendants(int loop_index) const
bool all_strides_exist(const LoadJacobian &jac, const FunctionDAG::Node *storage_node, const LoopNest &root) const
void recompute_inlined_features(const StageMap< Sites > &sites, StageMap< ScheduleFeatures > *features) const
void inline_func(const FunctionDAG::Node *f)
void generate_vec_dim_serial_tilings(vector< int > &serial_sizes) const
bool region_computed_shrinks(const FunctionDAG::Node *f, const LoopNest *parent) const
void compute_gpu_store_features(const LoadJacobian &jac, int consumer_innermost_dim, const FunctionDAG::Node *node, const Bound &consumer_store_bounds, const GPULoopInfo &gpu_loop_info, const std::vector< int64_t > &inner_serial_loop_extents, const Sites &consumer_site, ScheduleFeatures &feat, const LoopNest *parent, const LoopNest &root, GlobalMemInfo &global_mem_loads, SharedMemInfo &shared_mem_loads, LocalMemInfo &local_mem_loads, bool verbose=false) const
int64_t compute_licm_amortization(const LoopNest *innermost, const LoopNest *parent, const ScheduleFeatures &feat, const LoadJacobian &jac, int producer_dims) const
int64_t product_of_descendants(int loop_index) const
bool requires_dynamic_allocation(const FunctionDAG::Node *f, const Target &target, bool in_threads_loop) const
bool is_gpu_block(const Target &target) const
Definition LoopNest.h:156
bool node_has_dynamic_region_computed(const FunctionDAG::Node *f) const
bool exceeds_serial_extents_limit(const Target &target, const LoopNest *parent, bool in_threads_loop) const
void collect_stages(std::set< const FunctionDAG::Node::Stage * > &stages) const
Bound get_bounds_along_edge_chain(const FunctionDAG::Node *f, const vector< const FunctionDAG::Edge * > &edge_chain) const
int64_t get_total_constant_local_mem_alloc_size() const
const Bound & get_bounds(const FunctionDAG::Node *f) const
std::pair< double, double > compute_local_mem_store_features(const LoadJacobian &jac, int consumer_innermost_dim, const FunctionDAG::Node *node, const Bound &consumer_store_bounds, const LoopNest &root, double serial_loop_extents) const
int vectorized_load_access_size(const LoadJacobian &jac, const FunctionDAG::Node *accessed, bool accessed_has_been_scheduled, int innermost_dim, const GPUMemoryType &mem_type, bool verbose=false) const
GPUMemoryType get_gpu_memory_type(bool in_block, bool in_thread, bool is_inlined=false) const
void compute_warp_and_block_occupancy(const Anderson2021Params &params, ScheduleFeatures &feat, const GPULoopInfo &gpu_loop_info) const
vector< int64_t > get_union_thread_counts(const FunctionDAG::Node *f) const
void collect_nodes_that_should_be_inlined(const NodeMap< bool > &nodes_to_freeze, NodeMap< bool > &inlined_nodes) const
void apply(LoopLevel here, StageMap< std::unique_ptr< StageScheduleState > > &state_map, double num_cores, int depth, const LoopNest *parent, const LoopNest *compute_site, const Target &target, std::vector< StageScheduleState * > &ancestors, const NodeMap< bool > &all_inlined) const
std::map< uint64_t, StageMap< StageMap< FeatureIntermediates > > > feature_intermediates
Definition LoopNest.h:145
int get_actual_vector_dim(const Bound &store_bounds) const
void compute_shared_mem_occupancy(const Anderson2021Params &params, const Target &target, int64_t total_shared_mem_alloc_size, ScheduleFeatures &feat) const
Strides compute_strides(const LoadJacobian &jac, int innermost_storage_dim, const FunctionDAG::Node *storage_node, const Bound &store_bounds, const ThreadInfo *thread_info, bool verbose=false) const
int get_vectorized_loop_index_from_pure_stage(const LoopNest &root) const
bool computes(const FunctionDAG::Node *f) const
double storage_stride(const LoadJacobian &jac, int innermost_storage_dim, const FunctionDAG::Node *storage_node, const Bound &store_bounds, const LoopNest &root) const
std::vector< IntrusivePtr< const LoopNest > > children
Definition LoopNest.h:42
void set_working_set_at_task_feature(int64_t working_set, StageMap< ScheduleFeatures > *features) const
Definition LoopNest.h:271
bool promote_allocs_to_registers(const Target &target, StageMap< Sites > &sites) const
std::pair< int64_t, int64_t > get_block_and_serial_extents(const LoopNest *block) const
const Bound & set_bounds(const FunctionDAG::Node *f, BoundContents *b) const
Definition LoopNest.h:484
bool has_dynamic_allocation_inside_thread(bool in_thread_loop) const
void memoize_points_computed_minimum(StageMap< ScheduleFeatures > &memoized_features, const StageMap< ScheduleFeatures > *features) const
bool has_constant_region_computed(const FunctionDAG::Node *node) const
double compute_local_mem_stride(double stride, double bytes) const
MemInfoType< T > compute_mem_store_info(const LoadJacobian &jac, int consumer_innermost_dim, const FunctionDAG::Node *node, const Bound &consumer_store_bounds, const ThreadInfo *thread_info, double serial_loop_extents, bool verbose) const
bool add_gpu_thread_tilings(const FunctionDAG::Node *f, const Anderson2021Params &params, const Target &target, int v, vector< IntrusivePtr< const LoopNest > > &result, const vector< int64_t > &max_size)
IntrusivePtr< const LoopNest > parallelize_in_tiles(const vector< int64_t > &tiling, const LoopNest *parent, const Anderson2021Params &params, const Target &target, bool inner_tiling, bool adjust_tiling, bool move_all_rvars_inward=true, const vector< int > &rvars_to_move_inward={}) const
void memoize_features(StageMap< ScheduleFeatures > &memoized_features, const StageMap< ScheduleFeatures > *features) const
void collect_all_inlined(NodeMap< bool > &all_inlined) const
std::vector< int > unrolled_loops(const Target &target, const LoopNest *parent, const LoopNest *grandparent) const
bool is_gpu_serial(const Target &target) const
Definition LoopNest.h:148
double max_idle_lane_wastage(const Target &target, GPULoopInfo gpu_loop_info) const
static void hash_combine(uint64_t &h, uint64_t next)
Definition LoopNest.h:193
const LoopNest * find_pure_stage_loop_nest(const FunctionDAG::Node *node) const
void dump(std::ostream &os, string prefix, const LoopNest *parent) const
void get_stage_sizes(const FunctionDAG::Node *f, vector< vector< int64_t > > &stage_sizes, vector< vector< int > > &pure_dims, vector< int > &vectorized_indices) const
uint64_t compute_hash_of_producers_stored_at_root(const StageMap< Sites > &sites) const
const FunctionDAG::Node::Stage * stage
Definition LoopNest.h:60
bool other_stage_has_same_producer(const FunctionDAG::Node *producer) const
void get_stages_computed_in_each_compute_root_loop(StageMap< StageMap< bool > > &descendants, const LoopNest *compute_root_loop_nest=nullptr) const
void compute_features(const FunctionDAG &dag, const Anderson2021Params &params, const Target &target, const StageMap< Sites > &sites, int64_t instances, int64_t parallelism, const LoopNest *parent, const LoopNest *grandparent, const LoopNest &root, GPULoopInfo gpu_loop_info, bool use_memoized_features, const StageMap< int64_t > &total_shared_mem_alloc_sizes, int64_t *working_set, int64_t *working_set_local_constant, int64_t *working_set_local_dynamic, StageMap< ScheduleFeatures > *features, Statistics &stats, bool verbose=false) const
void get_sites(const Target &target, StageMap< Sites > &sites, StageMap< int64_t > &shared_mem_alloc_sizes, const LoopNest *task=nullptr, const LoopNest *parent=nullptr, const LoopNest *current_thread_loop=nullptr) const
bool calls(const FunctionDAG::Node *f) const
bool producer_computed_here_or_further_in(const FunctionDAG::Node *producer) const
void copy_from_including_features(const LoopNest &n)
bool can_vectorize_access_for_innermost_dim(const LoadJacobian &jac, const FunctionDAG::Node *accessed, int innermost_dim, int loop_index) const
void update_producers_to_be_staged(StageScheduleState &state, const NodeMap< bool > &all_inlined) const
bool can_vectorize_store_access(const LoadJacobian &jac, const FunctionDAG::Node *accessed, bool accessed_has_been_scheduled, int innermost_dim, int loop_index, const GPUMemoryType &mem_type) const
vector< IntrusivePtr< const LoopNest > > children
Definition LoopNest.h:95
void compute_mem_load_features(const LoadJacobian &jac, int producer_innermost_dim, const FunctionDAG::Node *node, const Bound &producer_store_bounds, bool producer_has_been_scheduled, const ThreadInfo *thread_info, MemInfoType< T > &mem_info, double serial_loop_extents, bool verbose=false) const
void get_allocs_that_can_be_promoted_to_registers(const Target &target, StageMap< Sites > &sites, NodeMap< bool > &can_be_promoted_to_registers, const LoopNest *grandparent, const LoopNest *parent) const
void compute_warp_features(ScheduleFeatures &features, const GPULoopInfo &gpu_loop_info) const
void structural_hash(uint64_t &h, int depth) const
void compute_num_mem_accesses_per_block(const LoadJacobian &jac, const FunctionDAG::Node *node, const Bound &store_bounds, const ThreadInfo *thread_info, int innermost_dim, double num_requests_per_warp, MemInfoType< T > &mem_info, bool verbose=false) const
std::pair< const LoopNest *, const LoopNest * > find_innermost_and_parent() const
std::set< const FunctionDAG::Node * > store_at
Definition LoopNest.h:49
std::pair< int64_t, bool > compute_alloc_size_of_node_here(const FunctionDAG::Node *f) const
bool compute_here(const FunctionDAG::Node *f, bool tileable, int v, bool in_threads_loop, const Anderson2021Params &params, const Target &target)
int64_t get_total_local_mem_alloc_size(bool constant_allocs_only=false, bool in_threads_loop=false) const
void compute_working_set_from_features(int64_t *working_set, const StageMap< ScheduleFeatures > *features) const
int vectorized_access_size(size_t loop_index, bool verbose=false) const
Intrusive shared pointers have a reference count (a RefCount object) stored in the class itself.
A struct representing a target machine and os to generate code for.
Definition Target.h:19
bool has_gpu_feature() const
Is a fully feature GPU compute runtime enabled? I.e.
A class that can represent Vars or RVars.
Definition Func.h:29