Bug Summary

File:nnc/ccv_nnc_symbolic_graph_simplify.c
Warning:line 798, column 27
The left expression of the compound assignment is an uninitialized value. The computed value will also be garbage

Annotated Source Code

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clang -cc1 -cc1 -triple x86_64-unknown-linux-gnu -analyze -disable-free -clear-ast-before-backend -disable-llvm-verifier -discard-value-names -main-file-name ccv_nnc_symbolic_graph_simplify.c -analyzer-checker=core -analyzer-checker=apiModeling -analyzer-checker=unix -analyzer-checker=deadcode -analyzer-checker=security.insecureAPI.UncheckedReturn -analyzer-checker=security.insecureAPI.getpw -analyzer-checker=security.insecureAPI.gets -analyzer-checker=security.insecureAPI.mktemp -analyzer-checker=security.insecureAPI.mkstemp -analyzer-checker=security.insecureAPI.vfork -analyzer-checker=nullability.NullPassedToNonnull -analyzer-checker=nullability.NullReturnedFromNonnull -analyzer-output plist -w -setup-static-analyzer -mrelocation-model pic -pic-level 2 -pic-is-pie -mframe-pointer=none -fmath-errno -ffp-contract=on -fno-rounding-math -mconstructor-aliases -funwind-tables=2 -target-cpu x86-64 -target-feature +sse2 -tune-cpu generic -debugger-tuning=gdb -fdebug-compilation-dir=/home/liu/actions-runner/_work/ccv/ccv/lib/nnc -fcoverage-compilation-dir=/home/liu/actions-runner/_work/ccv/ccv/lib/nnc -resource-dir /usr/local/lib/clang/18 -I ../ -I /usr/local/cuda/include -D HAVE_CBLAS -D HAVE_LIBPNG -D HAVE_LIBJPEG -D HAVE_FFTW3 -D HAVE_PTHREAD -D HAVE_LIBLINEAR -D HAVE_TESSERACT -D HAVE_AVCODEC -D HAVE_AVFORMAT -D HAVE_AVUTIL -D HAVE_SWSCALE -D HAVE_SSE2 -D HAVE_GSL -D HAVE_CUDA -D HAVE_CUDNN -D HAVE_NCCL -D USE_SYSTEM_CUB -D HAVE_CUDA_SM80 -I /usr/local/include -internal-isystem /usr/local/lib/clang/18/include -internal-isystem /usr/local/include -internal-isystem /usr/lib/gcc/x86_64-linux-gnu/12/../../../../x86_64-linux-gnu/include -internal-externc-isystem /usr/include/x86_64-linux-gnu -internal-externc-isystem /include -internal-externc-isystem /usr/include -O3 -ferror-limit 19 -fgnuc-version=4.2.1 -fskip-odr-check-in-gmf -vectorize-loops -vectorize-slp -analyzer-output=html -faddrsig -D__GCC_HAVE_DWARF2_CFI_ASM=1 -o /home/liu/actions-runner/_work/ccv/ccv/_analyze/2024-09-14-111742-152955-1 -x c ccv_nnc_symbolic_graph_simplify.c
1#include "ccv_nnc.h"
2#include "ccv_nnc_easy.h"
3#include "ccv_nnc_internal.h"
4#include "ccv_internal.h"
5#include "_ccv_nnc_symbolic_graph.h"
6#include "3rdparty/siphash/siphash24.h"
7
8// MARK - Level-3.5 API
9
10static uint8_t key_siphash[16] = "graphcsekvlibnnc";
11
12typedef struct {
13 int tensor_symbol_info_size;
14 int exec_symbol_info_size;
15 ccv_nnc_symbolic_graph_t* graph;
16 ccv_nnc_graph_visit_t* visit;
17 ccv_nnc_tensor_symbol_info_t* tensor_symbol_info;
18 ccv_nnc_graph_exec_symbol_info_t* exec_symbol_info;
19 uint32_t* exec_dead; // Mark a exec is dead and need to be cleared, each bit represent a exec.
20 uint32_t* tensor_dead; // Mark a tensor is dead and need to be cleared, each bit represent a tensor.
21 int* output_execs; // Mapping from tensor to the exec that generates this tensor.
22} ccv_nnc_symbolic_graph_simplify_t;
23
24static void _ccv_nnc_symbolic_graph_simplify_update_output_execs(ccv_nnc_symbolic_graph_simplify_t* const simplify)
25{
26 int i;
27 for (i = 0; i < simplify->tensor_symbol_info_size; i++)
28 simplify->output_execs[i] = -1;
29 ccv_nnc_graph_visit_for(simplify->visit, simplify->exec_symbol_info, node, idx){ int _i_; for (_i_ = 0; _i_ < (simplify->visit)->size
; _i_++) { const int idx __attribute__((unused)) = (simplify->
visit)->node[_i_].index; const int _node_unused_ __attribute__
((unused)) = (simplify->visit)->node[_i_].term; typeof (
(simplify->exec_symbol_info)) const node __attribute__((unused
)) = (simplify->exec_symbol_info) + idx;
{
30 if (simplify->exec_dead[idx >> 5] & (1u << (idx & 0x1f)))
31 continue;
32 for (i = 0; i < node->output_size; i++)
33 if (node->outputs[i] >= 0)
34 simplify->output_execs[node->outputs[i]] = idx; // A tensor can only be written once.
35 } ccv_nnc_graph_visit_endfor} }
36}
37
38static ccv_nnc_symbolic_graph_simplify_t* _ccv_nnc_symbolic_graph_simplify_new(ccv_nnc_symbolic_graph_t* const graph, const ccv_nnc_graph_exec_symbol_t* const sources, const int source_size, const ccv_nnc_graph_exec_symbol_t* const destinations, const int destination_size)
39{
40 ccv_nnc_symbolic_graph_simplify_t* const simplify = (ccv_nnc_symbolic_graph_simplify_t*)ccmallocmalloc(sizeof(ccv_nnc_symbolic_graph_simplify_t));
41 simplify->graph = graph;
42 simplify->visit = ccv_nnc_graph_visit_new(graph, (ccv_nnc_graph_exec_symbol_info_t*)ccv_array_get(graph->exec_symbol_info, 0), graph->exec_symbol_info->rnum, sources, source_size, destinations, destination_size, 0)({ ccv_nnc_graph_visit_t* _visit_ = (ccv_nnc_graph_visit_t*)malloc
(sizeof(ccv_nnc_graph_visit_t) + sizeof(_visit_->node[0]) *
((graph->exec_symbol_info->rnum) - 1)); _visit_->size
= 0; do { typedef struct { int8_t d; int8_t r; uint16_t c; int32_t
edges; } ccv_nnc_incoming_t; int _i_, _j_; int _incoming_edges_
= 0; for (_i_ = 0; _i_ < (graph->exec_symbol_info->
rnum); _i_++) _incoming_edges_ += (((ccv_nnc_graph_exec_symbol_info_t
*)((void*)(((char*)((graph->exec_symbol_info)->data)) +
(size_t)(graph->exec_symbol_info)->rsize * (size_t)(0)
)))[_i_].outgoings) ? ((ccv_nnc_graph_exec_symbol_info_t*)((void
*)(((char*)((graph->exec_symbol_info)->data)) + (size_t
)(graph->exec_symbol_info)->rsize * (size_t)(0))))[_i_]
.outgoings->rnum : 0; const int _heap_mem_ = ((graph->exec_symbol_info
->rnum) + _incoming_edges_ > 1024); ccv_nnc_incoming_t*
_incomings_; if (_heap_mem_) _incomings_ = (ccv_nnc_incoming_t
*)malloc(sizeof(ccv_nnc_incoming_t) * (graph->exec_symbol_info
->rnum) + sizeof(int32_t) * ((graph->exec_symbol_info->
rnum) * 2 + _incoming_edges_)); else _incomings_ = (ccv_nnc_incoming_t
*)__builtin_alloca (sizeof(ccv_nnc_incoming_t) * (graph->exec_symbol_info
->rnum) + sizeof(int32_t) * ((graph->exec_symbol_info->
rnum) * 2 + _incoming_edges_)); memset(_incomings_, 0, sizeof
(ccv_nnc_incoming_t) * (graph->exec_symbol_info->rnum))
; int32_t* _exists_[2] = { (int32_t*)(_incomings_ + (graph->
exec_symbol_info->rnum)), (int32_t*)(_incomings_ + (graph->
exec_symbol_info->rnum)) + (graph->exec_symbol_info->
rnum), }; int32_t* const _edges_ = _exists_[1] + (graph->exec_symbol_info
->rnum); for (_i_ = 0; _i_ < (source_size); _i_++) { ((
void) sizeof (((sources)[_i_].graph == graph) ? 1 : 0), __extension__
({ if ((sources)[_i_].graph == graph) ; else __assert_fail (
"(sources)[_i_].graph == graph", "ccv_nnc_symbolic_graph_simplify.c"
, 42, __extension__ __PRETTY_FUNCTION__); })); _incomings_[(sources
)[_i_].d].r = 1; _exists_[0][_i_] = (sources)[_i_].d; } int _exist_size_
[2] = { (source_size), 0, }; int _p_ = 0, _q_ = 1; while (_exist_size_
[_p_] > 0) { _exist_size_[_q_] = 0; for (_i_ = 0; _i_ <
_exist_size_[_p_]; _i_++) { const int32_t _idx_ = _exists_[_p_
][_i_]; if (_incomings_[_idx_].r != 1) continue; _incomings_[
_idx_].r = 2; if (((ccv_nnc_graph_exec_symbol_info_t*)((void*
)(((char*)((graph->exec_symbol_info)->data)) + (size_t)
(graph->exec_symbol_info)->rsize * (size_t)(0))))[_idx_
].outgoings) for (_j_ = 0; _j_ < ((ccv_nnc_graph_exec_symbol_info_t
*)((void*)(((char*)((graph->exec_symbol_info)->data)) +
(size_t)(graph->exec_symbol_info)->rsize * (size_t)(0)
)))[_idx_].outgoings->rnum; _j_++) { const int d = *(int*)
((void*)(((char*)((((ccv_nnc_graph_exec_symbol_info_t*)((void
*)(((char*)((graph->exec_symbol_info)->data)) + (size_t
)(graph->exec_symbol_info)->rsize * (size_t)(0))))[_idx_
].outgoings)->data)) + (size_t)(((ccv_nnc_graph_exec_symbol_info_t
*)((void*)(((char*)((graph->exec_symbol_info)->data)) +
(size_t)(graph->exec_symbol_info)->rsize * (size_t)(0)
)))[_idx_].outgoings)->rsize * (size_t)(_j_))); ++_incomings_
[d].c; if (_incomings_[d].r != 0) continue; _incomings_[d].r =
1; ((void) sizeof ((_exist_size_[_q_] < (graph->exec_symbol_info
->rnum)) ? 1 : 0), __extension__ ({ if (_exist_size_[_q_] <
(graph->exec_symbol_info->rnum)) ; else __assert_fail (
"_exist_size_[_q_] < (graph->exec_symbol_info->rnum)"
, "ccv_nnc_symbolic_graph_simplify.c", 42, __extension__ __PRETTY_FUNCTION__
); })); _exists_[_q_][_exist_size_[_q_]] = d; ++_exist_size_[
_q_]; } } ((_i_) = (_p_), (_p_) = (_q_), (_q_) = (_i_)); } for
(_i_ = 0; _i_ < (source_size); _i_++) { ((void) sizeof ((
(sources)[_i_].graph == graph) ? 1 : 0), __extension__ ({ if (
(sources)[_i_].graph == graph) ; else __assert_fail ("(sources)[_i_].graph == graph"
, "ccv_nnc_symbolic_graph_simplify.c", 42, __extension__ __PRETTY_FUNCTION__
); })); _incomings_[(sources)[_i_].d].r = 3; _exists_[0][_i_]
= (sources)[_i_].d; } _exist_size_[0] = (source_size); _exist_size_
[1] = 0; _p_ = 0, _q_ = 1; int _bump_ = 1; while (_exist_size_
[_p_] > 0) { _exist_size_[_q_] = 0; for (_i_ = 0; _i_ <
_exist_size_[_p_]; _i_++) { const int32_t _idx_ = _exists_[_p_
][_i_]; if (_incomings_[_idx_].r != 3) continue; _incomings_[
_idx_].r = 4; if (((ccv_nnc_graph_exec_symbol_info_t*)((void*
)(((char*)((graph->exec_symbol_info)->data)) + (size_t)
(graph->exec_symbol_info)->rsize * (size_t)(0))))[_idx_
].outgoings) for (_j_ = 0; _j_ < ((ccv_nnc_graph_exec_symbol_info_t
*)((void*)(((char*)((graph->exec_symbol_info)->data)) +
(size_t)(graph->exec_symbol_info)->rsize * (size_t)(0)
)))[_idx_].outgoings->rnum; _j_++) { const int d = *(int*)
((void*)(((char*)((((ccv_nnc_graph_exec_symbol_info_t*)((void
*)(((char*)((graph->exec_symbol_info)->data)) + (size_t
)(graph->exec_symbol_info)->rsize * (size_t)(0))))[_idx_
].outgoings)->data)) + (size_t)(((ccv_nnc_graph_exec_symbol_info_t
*)((void*)(((char*)((graph->exec_symbol_info)->data)) +
(size_t)(graph->exec_symbol_info)->rsize * (size_t)(0)
)))[_idx_].outgoings)->rsize * (size_t)(_j_))); if (_incomings_
[d].edges == 0) { _incomings_[d].edges = _bump_; _bump_ += _incomings_
[d].c; _incomings_[d].c = 0; } _edges_[_incomings_[d].edges -
1 + _incomings_[d].c] = _idx_; ++_incomings_[d].c; if (_incomings_
[d].r != 2) continue; _incomings_[d].r = 3; ((void) sizeof ((
_exist_size_[_q_] < (graph->exec_symbol_info->rnum))
? 1 : 0), __extension__ ({ if (_exist_size_[_q_] < (graph
->exec_symbol_info->rnum)) ; else __assert_fail ("_exist_size_[_q_] < (graph->exec_symbol_info->rnum)"
, "ccv_nnc_symbolic_graph_simplify.c", 42, __extension__ __PRETTY_FUNCTION__
); })); _exists_[_q_][_exist_size_[_q_]] = d; ++_exist_size_[
_q_]; } } ((_i_) = (_p_), (_p_) = (_q_), (_q_) = (_i_)); } for
(_i_ = 0; _i_ < (destination_size); _i_++) { ((void) sizeof
(((destinations)[_i_].graph == graph) ? 1 : 0), __extension__
({ if ((destinations)[_i_].graph == graph) ; else __assert_fail
("(destinations)[_i_].graph == graph", "ccv_nnc_symbolic_graph_simplify.c"
, 42, __extension__ __PRETTY_FUNCTION__); })); _incomings_[(destinations
)[_i_].d].r = 5; _exists_[0][_i_] = (destinations)[_i_].d; } _exist_size_
[0] = (destination_size); _exist_size_[1] = 0; _p_ = 0, _q_ =
1; while (_exist_size_[_p_] > 0) { _exist_size_[_q_] = 0;
for (_i_ = 0; _i_ < _exist_size_[_p_]; _i_++) { const int32_t
_idx_ = _exists_[_p_][_i_]; if (_incomings_[_idx_].r != 5) continue
; _incomings_[_idx_].r = 6; if (_incomings_[_idx_].edges >
0) for (_j_ = 0; _j_ < _incomings_[_idx_].c; _j_++) { const
int d = _edges_[_incomings_[_idx_].edges - 1 + _j_]; if (_incomings_
[d].r != 4) continue; _incomings_[d].r = 5; ((void) sizeof ((
_exist_size_[_q_] < (graph->exec_symbol_info->rnum))
? 1 : 0), __extension__ ({ if (_exist_size_[_q_] < (graph
->exec_symbol_info->rnum)) ; else __assert_fail ("_exist_size_[_q_] < (graph->exec_symbol_info->rnum)"
, "ccv_nnc_symbolic_graph_simplify.c", 42, __extension__ __PRETTY_FUNCTION__
); })); _exists_[_q_][_exist_size_[_q_]] = d; ++_exist_size_[
_q_]; } } ((_i_) = (_p_), (_p_) = (_q_), (_q_) = (_i_)); } for
(_i_ = 0; _i_ < (destination_size); _i_++) { ((void) sizeof
(((destinations)[_i_].graph == graph) ? 1 : 0), __extension__
({ if ((destinations)[_i_].graph == graph) ; else __assert_fail
("(destinations)[_i_].graph == graph", "ccv_nnc_symbolic_graph_simplify.c"
, 42, __extension__ __PRETTY_FUNCTION__); })); _incomings_[(destinations
)[_i_].d].d = 1; } for (_i_ = 0; _i_ < (source_size); _i_++
) { ((void) sizeof (((sources)[_i_].graph == graph) ? 1 : 0),
__extension__ ({ if ((sources)[_i_].graph == graph) ; else __assert_fail
("(sources)[_i_].graph == graph", "ccv_nnc_symbolic_graph_simplify.c"
, 42, __extension__ __PRETTY_FUNCTION__); })); _exists_[0][_i_
] = (sources)[_i_].d; } _p_ = 0; _q_ = 1; _exist_size_[0] = (
source_size); _exist_size_[1] = 0; int _d_ = 0; while (_exist_size_
[_p_] > 0) { _exist_size_[_q_] = 0; for (_i_ = 0; _i_ <
_exist_size_[_p_];) { const int32_t _idx_ = _exists_[_p_][_i_
]; _visit_->node[_visit_->size].index = ((_idx_)); _visit_
->node[_visit_->size].term = ((_incomings_[_idx_].d)); ++
_visit_->size;; if (_incomings_[_idx_].d) { ++_d_; _incomings_
[_idx_].r = 7; } if (((ccv_nnc_graph_exec_symbol_info_t*)((void
*)(((char*)((graph->exec_symbol_info)->data)) + (size_t
)(graph->exec_symbol_info)->rsize * (size_t)(0))))[_idx_
].outgoings) { if (((ccv_nnc_graph_exec_symbol_info_t*)((void
*)(((char*)((graph->exec_symbol_info)->data)) + (size_t
)(graph->exec_symbol_info)->rsize * (size_t)(0))))[_idx_
].outgoings->rnum == 1) { const int d = *(int*)((void*)(((
char*)((((ccv_nnc_graph_exec_symbol_info_t*)((void*)(((char*)
((graph->exec_symbol_info)->data)) + (size_t)(graph->
exec_symbol_info)->rsize * (size_t)(0))))[_idx_].outgoings
)->data)) + (size_t)(((ccv_nnc_graph_exec_symbol_info_t*)(
(void*)(((char*)((graph->exec_symbol_info)->data)) + (size_t
)(graph->exec_symbol_info)->rsize * (size_t)(0))))[_idx_
].outgoings)->rsize * (size_t)(0))); --_incomings_[d].c; if
(_incomings_[d].c == 0 && _incomings_[d].r == 6 &&
_d_ < (destination_size)) { _exists_[_p_][_i_] = d; continue
; } } else for (_j_ = 0; _j_ < ((ccv_nnc_graph_exec_symbol_info_t
*)((void*)(((char*)((graph->exec_symbol_info)->data)) +
(size_t)(graph->exec_symbol_info)->rsize * (size_t)(0)
)))[_idx_].outgoings->rnum; _j_++) { const int d = *(int*)
((void*)(((char*)((((ccv_nnc_graph_exec_symbol_info_t*)((void
*)(((char*)((graph->exec_symbol_info)->data)) + (size_t
)(graph->exec_symbol_info)->rsize * (size_t)(0))))[_idx_
].outgoings)->data)) + (size_t)(((ccv_nnc_graph_exec_symbol_info_t
*)((void*)(((char*)((graph->exec_symbol_info)->data)) +
(size_t)(graph->exec_symbol_info)->rsize * (size_t)(0)
)))[_idx_].outgoings)->rsize * (size_t)(_j_))); --_incomings_
[d].c; if (_incomings_[d].c == 0 && _incomings_[d].r ==
6 && _d_ < (destination_size)) { ((void) sizeof (
(_exist_size_[_q_] < (graph->exec_symbol_info->rnum)
) ? 1 : 0), __extension__ ({ if (_exist_size_[_q_] < (graph
->exec_symbol_info->rnum)) ; else __assert_fail ("_exist_size_[_q_] < (graph->exec_symbol_info->rnum)"
, "ccv_nnc_symbolic_graph_simplify.c", 42, __extension__ __PRETTY_FUNCTION__
); })); _exists_[_q_][_exist_size_[_q_]] = d; ++_exist_size_[
_q_]; } } } ++_i_; } ((_i_) = (_p_), (_p_) = (_q_), (_q_) = (
_i_)); } for (_i_ = 0; _i_ < (destination_size); _i_++) { (
(void) sizeof (((destinations)[_i_].graph == graph) ? 1 : 0),
__extension__ ({ if ((destinations)[_i_].graph == graph) ; else
__assert_fail ("(destinations)[_i_].graph == graph", "ccv_nnc_symbolic_graph_simplify.c"
, 42, __extension__ __PRETTY_FUNCTION__); })); if (_incomings_
[(destinations)[_i_].d].r == 7) continue; if (!(0)) { ((void)
sizeof ((_incomings_[(destinations)[_i_].d].c == 0) ? 1 : 0)
, __extension__ ({ if (_incomings_[(destinations)[_i_].d].c ==
0) ; else __assert_fail ("_incomings_[(destinations)[_i_].d].c == 0"
, "ccv_nnc_symbolic_graph_simplify.c", 42, __extension__ __PRETTY_FUNCTION__
); })); } else if (_incomings_[(destinations)[_i_].d].c > 0
) continue; _visit_->node[_visit_->size].index = (((destinations
)[_i_].d)); _visit_->node[_visit_->size].term = ((_incomings_
[(destinations)[_i_].d].d)); ++_visit_->size;; } if (_heap_mem_
) free(_incomings_); } while (0);; ((void) sizeof ((_visit_->
size <= (graph->exec_symbol_info->rnum)) ? 1 : 0), __extension__
({ if (_visit_->size <= (graph->exec_symbol_info->
rnum)) ; else __assert_fail ("_visit_->size <= (graph->exec_symbol_info->rnum)"
, "ccv_nnc_symbolic_graph_simplify.c", 42, __extension__ __PRETTY_FUNCTION__
); })); _visit_; })
;
43 simplify->tensor_symbol_info = (ccv_nnc_tensor_symbol_info_t*)ccmallocmalloc(sizeof(ccv_nnc_tensor_symbol_info_t) * graph->tensor_symbol_info->rnum);
44 simplify->exec_symbol_info = (ccv_nnc_graph_exec_symbol_info_t*)ccmallocmalloc(sizeof(ccv_nnc_graph_exec_symbol_info_t) * graph->exec_symbol_info->rnum);
45 ccv_nnc_symbolic_graph_symbol_infer(graph, simplify->visit, sources, source_size, destinations, destination_size, 0, 0, simplify->tensor_symbol_info, simplify->exec_symbol_info);
46 simplify->tensor_symbol_info_size = graph->tensor_symbol_info->rnum;
47 simplify->exec_symbol_info_size = graph->exec_symbol_info->rnum;
48 simplify->exec_dead = cccalloccalloc(((simplify->exec_symbol_info_size + 31) >> 5) + ((simplify->tensor_symbol_info_size + 31) >> 5), sizeof(uint32_t));
49 simplify->tensor_dead = simplify->exec_dead + ((simplify->exec_symbol_info_size + 31) >> 5);
50 simplify->output_execs = (int*)ccmallocmalloc(sizeof(int) * simplify->tensor_symbol_info_size);
51 return simplify;
52}
53
54static void _ccv_nnc_symbolic_graph_simplify_apply(ccv_nnc_symbolic_graph_simplify_t* const simplify)
55{
56 int i, j;
57 for (i = 0; i < simplify->exec_symbol_info_size; i++)
58 if (simplify->exec_dead[i >> 5] & (1u << (i & 0x1f)))
59 ccv_nnc_graph_exec_symbol_free(simplify->graph, (ccv_nnc_graph_exec_symbol_t){
60 .d = i,
61 .graph = simplify->graph,
62 });
63 else // If it is not marked as dead, go through to unmark tensor
64 for (j = 0; j < simplify->exec_symbol_info[i].output_size; j++)
65 {
66 const int d = simplify->exec_symbol_info[i].outputs[j];
67 if (d >= 0)
68 simplify->tensor_dead[d >> 5] &= ~(1u << (d & 0x1f));
69 }
70 for (i = 0; i < simplify->tensor_symbol_info_size; i++)
71 if (simplify->tensor_dead[i >> 5] & (1u << (i & 0x1f)))
72 ccv_nnc_tensor_symbol_free(simplify->graph, (ccv_nnc_tensor_symbol_t){
73 .d = i,
74 .graph = simplify->graph,
75 });
76}
77
78static void _ccv_nnc_symbolic_graph_simplify_free(ccv_nnc_symbolic_graph_simplify_t* const simplify)
79{
80 ccv_nnc_graph_visit_free(simplify->visit);
81 ccfreefree(simplify->tensor_symbol_info);
82 ccfreefree(simplify->exec_symbol_info);
83 ccfreefree(simplify->exec_dead);
84 ccfreefree(simplify->output_execs);
85 ccfreefree(simplify);
86}
87
88typedef struct {
89 int d;
90 int ifbit;
91 uint64_t hash;
92} ccv_nnc_cse_hash_t;
93
94static int _ccv_nnc_cse_hash_find(ccv_nnc_cse_hash_t* const hash_map, const uint64_t hash, const int map_size)
95{
96 assert(hash > 0)((void) sizeof ((hash > 0) ? 1 : 0), __extension__ ({ if (
hash > 0) ; else __assert_fail ("hash > 0", "ccv_nnc_symbolic_graph_simplify.c"
, 96, __extension__ __PRETTY_FUNCTION__); }))
;
97 int i, j;
98 i = (hash - 1) % map_size;
99 for (j = 0; ; j++, i++)
100 {
101 if (i >= map_size)
102 i = 0;
103 if (j > hash_map[i].ifbit)
104 return -1;
105 if (hash_map[i].hash == hash)
106 return hash_map[i].d;
107 }
108}
109
110static void _ccv_nnc_cse_hash_add(ccv_nnc_cse_hash_t* const hash_map, uint64_t hash, int d, const int map_size)
111{
112 assert(hash > 0)((void) sizeof ((hash > 0) ? 1 : 0), __extension__ ({ if (
hash > 0) ; else __assert_fail ("hash > 0", "ccv_nnc_symbolic_graph_simplify.c"
, 112, __extension__ __PRETTY_FUNCTION__); }))
;
113 int i, j;
114 i = (hash - 1) % map_size;
115 for (j = 0; ; j++, i++)
116 {
117 if (i >= map_size)
118 i = 0;
119 if (hash_map[i].hash == hash) // Already exists, do nothing.
120 return;
121 if (hash_map[i].hash == 0)
122 {
123 // Insert.
124 hash_map[i].d = d;
125 hash_map[i].ifbit = j;
126 hash_map[i].hash = hash;
127 return;
128 }
129 if (j > hash_map[i].ifbit)
130 {
131 const ccv_nnc_cse_hash_t old_hash = hash_map[i];
132 // Swap, and continue, until find an empty slot.
133 hash_map[i].d = d;
134 hash_map[i].ifbit = j;
135 hash_map[i].hash = hash;
136 d = old_hash.d;
137 j = old_hash.ifbit;
138 hash = old_hash.hash;
139 }
140 }
141}
142
143static int _ccv_nnc_symbolic_graph_update_refs(ccv_nnc_symbolic_graph_simplify_t* const simplify, const ccv_nnc_tensor_symbol_t* const outputs, const int output_size, const int* const refs, const int output_exec_ref_dead)
144{
145 int i, j;
146 // Go over refs, if a tensor is an alias, mark it reference to the new one.
147 for (i = 0; i < simplify->tensor_symbol_info_size; i++)
148 if (refs[i] >= 0)
149 // Mark this tensor as dead.
150 simplify->tensor_dead[i >> 5] |= (1u << (i & 0x1f));
151 else if (simplify->tensor_symbol_info[i].alias_ref && refs[simplify->tensor_symbol_info[i].alias_ref - 1] >= 0) {
152 const int alias_ref = simplify->tensor_symbol_info[i].alias_ref - 1;
153 simplify->tensor_symbol_info[i].alias_ref = refs[alias_ref] + 1;
154 ((ccv_nnc_tensor_symbol_info_t*)ccv_array_get(simplify->graph->tensor_symbol_info, i)((void*)(((char*)((simplify->graph->tensor_symbol_info)
->data)) + (size_t)(simplify->graph->tensor_symbol_info
)->rsize * (size_t)(i)))
)->alias_ref = refs[alias_ref] + 1;
155 }
156 for (i = 0; i < output_size; i++)
157 // If the output is an alias, that's fine, because if the alias is re-binded, we are good.
158 simplify->tensor_dead[outputs[i].d >> 5] &= ~(1u << (outputs[i].d & 0x1f)); // Undead for output tensor symbols.
159 // Merge s_refs if the tensor is dead.
160 for (i = 0; i < simplify->tensor_symbol_info_size; i++)
161 if (refs[i] >= 0 && (simplify->tensor_dead[i >> 5] & (1u << (i & 0x1f))))
162 {
163 const int ref = refs[i];
164 if (simplify->tensor_symbol_info[i].s_ref && simplify->tensor_symbol_info[i].s_ref->rnum)
165 {
166 if (!simplify->tensor_symbol_info[ref].s_ref) // If there is no s_ref, simple, just assign the pointer and set the old one to nil.
167 {
168 simplify->tensor_symbol_info[ref].s_ref = simplify->tensor_symbol_info[i].s_ref;
169 simplify->tensor_symbol_info[i].s_ref = 0;
170 ((ccv_nnc_tensor_symbol_info_t*)ccv_array_get(simplify->graph->tensor_symbol_info, i)((void*)(((char*)((simplify->graph->tensor_symbol_info)
->data)) + (size_t)(simplify->graph->tensor_symbol_info
)->rsize * (size_t)(i)))
)->s_ref = 0;
171 } else {
172 ccv_array_t* const ref_s_ref = simplify->tensor_symbol_info[ref].s_ref;
173 ccv_array_t* const i_s_ref = simplify->tensor_symbol_info[i].s_ref;
174 const int ref_s_ref_rnum = ref_s_ref->rnum;
175 int flag = 0;
176 // Detect conflict, if there is, undead.
177 for (j = 0; !flag && j < ccv_min(ref_s_ref_rnum, i_s_ref->rnum)({ typeof (ref_s_ref_rnum) _a = (ref_s_ref_rnum); typeof (i_s_ref
->rnum) _b = (i_s_ref->rnum); (_a < _b) ? _a : _b; }
)
; j++)
178 {
179 const int ref_s_ref_k = *(int*)ccv_array_get(ref_s_ref, j)((void*)(((char*)((ref_s_ref)->data)) + (size_t)(ref_s_ref
)->rsize * (size_t)(j)))
;
180 const int i_s_ref_k = *(int*)ccv_array_get(i_s_ref, j)((void*)(((char*)((i_s_ref)->data)) + (size_t)(i_s_ref)->
rsize * (size_t)(j)))
;
181 // If for the same sub-graph, they have different tensors linked, we cannot merge these two.
182 flag = (ref_s_ref_k > 0 && i_s_ref_k > 0 && ref_s_ref_k != i_s_ref_k);
183 }
184 if (flag)
185 {
186 simplify->tensor_dead[i >> 5] &= ~(1u << (i & 0x1f)); // Undead
187 continue;
188 }
189 if (ref_s_ref_rnum < i_s_ref->rnum)
190 {
191 ccv_array_resize(ref_s_ref, i_s_ref->rnum);
192 memcpy(ccv_array_get(ref_s_ref, ref_s_ref_rnum)((void*)(((char*)((ref_s_ref)->data)) + (size_t)(ref_s_ref
)->rsize * (size_t)(ref_s_ref_rnum)))
, ccv_array_get(i_s_ref, ref_s_ref_rnum)((void*)(((char*)((i_s_ref)->data)) + (size_t)(i_s_ref)->
rsize * (size_t)(ref_s_ref_rnum)))
, sizeof(int) * (i_s_ref->rnum - ref_s_ref_rnum));
193 }
194 for (j = 0; j < ccv_min(ref_s_ref_rnum, i_s_ref->rnum)({ typeof (ref_s_ref_rnum) _a = (ref_s_ref_rnum); typeof (i_s_ref
->rnum) _b = (i_s_ref->rnum); (_a < _b) ? _a : _b; }
)
; j++)
195 {
196 const int ref_s_ref_k = *(int*)ccv_array_get(ref_s_ref, j)((void*)(((char*)((ref_s_ref)->data)) + (size_t)(ref_s_ref
)->rsize * (size_t)(j)))
;
197 const int i_s_ref_k = *(int*)ccv_array_get(i_s_ref, j)((void*)(((char*)((i_s_ref)->data)) + (size_t)(i_s_ref)->
rsize * (size_t)(j)))
;
198 assert(ref_s_ref_k == 0 || i_s_ref_k == 0)((void) sizeof ((ref_s_ref_k == 0 || i_s_ref_k == 0) ? 1 : 0)
, __extension__ ({ if (ref_s_ref_k == 0 || i_s_ref_k == 0) ; else
__assert_fail ("ref_s_ref_k == 0 || i_s_ref_k == 0", "ccv_nnc_symbolic_graph_simplify.c"
, 198, __extension__ __PRETTY_FUNCTION__); }))
;
199 if (i_s_ref_k)
200 *(int*)ccv_array_get(ref_s_ref, j)((void*)(((char*)((ref_s_ref)->data)) + (size_t)(ref_s_ref
)->rsize * (size_t)(j)))
= i_s_ref_k;
201 }
202 ccv_array_free(simplify->tensor_symbol_info[i].s_ref);
203 simplify->tensor_symbol_info[i].s_ref = 0;
204 ((ccv_nnc_tensor_symbol_info_t*)ccv_array_get(simplify->graph->tensor_symbol_info, i)((void*)(((char*)((simplify->graph->tensor_symbol_info)
->data)) + (size_t)(simplify->graph->tensor_symbol_info
)->rsize * (size_t)(i)))
)->s_ref = 0;
205 for (j = 0; j < ref_s_ref->rnum; j++)
206 {
207 const int ref_k = *(int*)ccv_array_get(ref_s_ref, j)((void*)(((char*)((ref_s_ref)->data)) + (size_t)(ref_s_ref
)->rsize * (size_t)(j)))
- 1;
208 if (ref_k >= 0)
209 {
210 ccv_nnc_symbolic_graph_t* const sub_graph = *(ccv_nnc_symbolic_graph_t**)ccv_array_get(simplify->graph->sub_graphs, j)((void*)(((char*)((simplify->graph->sub_graphs)->data
)) + (size_t)(simplify->graph->sub_graphs)->rsize * (
size_t)(j)))
;
211 assert(sub_graph)((void) sizeof ((sub_graph) ? 1 : 0), __extension__ ({ if (sub_graph
) ; else __assert_fail ("sub_graph", "ccv_nnc_symbolic_graph_simplify.c"
, 211, __extension__ __PRETTY_FUNCTION__); }))
;
212 // Update its p_ref.
213 ((ccv_nnc_tensor_symbol_info_t*)ccv_array_get(sub_graph->tensor_symbol_info, ref_k)((void*)(((char*)((sub_graph->tensor_symbol_info)->data
)) + (size_t)(sub_graph->tensor_symbol_info)->rsize * (
size_t)(ref_k)))
)->p_ref = ref + 1;
214 }
215 }
216 }
217 assert(simplify->tensor_symbol_info[i].s_ref == ((ccv_nnc_tensor_symbol_info_t*)ccv_array_get(simplify->graph->tensor_symbol_info, i))->s_ref)((void) sizeof ((simplify->tensor_symbol_info[i].s_ref == (
(ccv_nnc_tensor_symbol_info_t*)((void*)(((char*)((simplify->
graph->tensor_symbol_info)->data)) + (size_t)(simplify->
graph->tensor_symbol_info)->rsize * (size_t)(i))))->
s_ref) ? 1 : 0), __extension__ ({ if (simplify->tensor_symbol_info
[i].s_ref == ((ccv_nnc_tensor_symbol_info_t*)((void*)(((char*
)((simplify->graph->tensor_symbol_info)->data)) + (size_t
)(simplify->graph->tensor_symbol_info)->rsize * (size_t
)(i))))->s_ref) ; else __assert_fail ("simplify->tensor_symbol_info[i].s_ref == ((ccv_nnc_tensor_symbol_info_t*)ccv_array_get(simplify->graph->tensor_symbol_info, i))->s_ref"
, "ccv_nnc_symbolic_graph_simplify.c", 217, __extension__ __PRETTY_FUNCTION__
); }))
;
218 assert(simplify->tensor_symbol_info[ref].s_ref == ((ccv_nnc_tensor_symbol_info_t*)ccv_array_get(simplify->graph->tensor_symbol_info, ref))->s_ref)((void) sizeof ((simplify->tensor_symbol_info[ref].s_ref ==
((ccv_nnc_tensor_symbol_info_t*)((void*)(((char*)((simplify->
graph->tensor_symbol_info)->data)) + (size_t)(simplify->
graph->tensor_symbol_info)->rsize * (size_t)(ref))))->
s_ref) ? 1 : 0), __extension__ ({ if (simplify->tensor_symbol_info
[ref].s_ref == ((ccv_nnc_tensor_symbol_info_t*)((void*)(((char
*)((simplify->graph->tensor_symbol_info)->data)) + (
size_t)(simplify->graph->tensor_symbol_info)->rsize *
(size_t)(ref))))->s_ref) ; else __assert_fail ("simplify->tensor_symbol_info[ref].s_ref == ((ccv_nnc_tensor_symbol_info_t*)ccv_array_get(simplify->graph->tensor_symbol_info, ref))->s_ref"
, "ccv_nnc_symbolic_graph_simplify.c", 218, __extension__ __PRETTY_FUNCTION__
); }))
;
219 }
220 }
221 // Going through refs that we are updating, going through its p_ref to make sure both are updated.
222 for (i = 0; i < simplify->tensor_symbol_info_size; i++)
223 if (refs[i] >= 0 && (simplify->tensor_dead[i >> 5] & (1u << (i & 0x1f))) && simplify->tensor_symbol_info[i].p_ref)
224 {
225 const int ref = refs[i];
226 const int p_ref = simplify->tensor_symbol_info[i].p_ref - 1;
227 assert(p_ref >= 0)((void) sizeof ((p_ref >= 0) ? 1 : 0), __extension__ ({ if
(p_ref >= 0) ; else __assert_fail ("p_ref >= 0", "ccv_nnc_symbolic_graph_simplify.c"
, 227, __extension__ __PRETTY_FUNCTION__); }))
;
228 assert(simplify->graph->p)((void) sizeof ((simplify->graph->p) ? 1 : 0), __extension__
({ if (simplify->graph->p) ; else __assert_fail ("simplify->graph->p"
, "ccv_nnc_symbolic_graph_simplify.c", 228, __extension__ __PRETTY_FUNCTION__
); }))
;
229 ccv_array_t* const s_ref = ((ccv_nnc_tensor_symbol_info_t*)ccv_array_get(simplify->graph->p->tensor_symbol_info, p_ref)((void*)(((char*)((simplify->graph->p->tensor_symbol_info
)->data)) + (size_t)(simplify->graph->p->tensor_symbol_info
)->rsize * (size_t)(p_ref)))
)->s_ref;
230 const int s_idx = simplify->graph->p_idx - 1;
231 assert(s_idx >= 0)((void) sizeof ((s_idx >= 0) ? 1 : 0), __extension__ ({ if
(s_idx >= 0) ; else __assert_fail ("s_idx >= 0", "ccv_nnc_symbolic_graph_simplify.c"
, 231, __extension__ __PRETTY_FUNCTION__); }))
;
232 assert(s_ref && s_ref->rnum > s_idx)((void) sizeof ((s_ref && s_ref->rnum > s_idx) ?
1 : 0), __extension__ ({ if (s_ref && s_ref->rnum
> s_idx) ; else __assert_fail ("s_ref && s_ref->rnum > s_idx"
, "ccv_nnc_symbolic_graph_simplify.c", 232, __extension__ __PRETTY_FUNCTION__
); }))
;
233 *(int*)ccv_array_get(s_ref, s_idx)((void*)(((char*)((s_ref)->data)) + (size_t)(s_ref)->rsize
* (size_t)(s_idx)))
= ref + 1; // Update so it references to the new s_ref.
234 assert(!simplify->tensor_symbol_info[ref].p_ref)((void) sizeof ((!simplify->tensor_symbol_info[ref].p_ref)
? 1 : 0), __extension__ ({ if (!simplify->tensor_symbol_info
[ref].p_ref) ; else __assert_fail ("!simplify->tensor_symbol_info[ref].p_ref"
, "ccv_nnc_symbolic_graph_simplify.c", 234, __extension__ __PRETTY_FUNCTION__
); }))
;
235 simplify->tensor_symbol_info[ref].p_ref = p_ref + 1;
236 ((ccv_nnc_tensor_symbol_info_t*)ccv_array_get(simplify->graph->tensor_symbol_info, ref)((void*)(((char*)((simplify->graph->tensor_symbol_info)
->data)) + (size_t)(simplify->graph->tensor_symbol_info
)->rsize * (size_t)(ref)))
)->p_ref = p_ref + 1;
237 }
238 // Now go over exec to mark them as dead because we don't need these to generate refs.
239 if (output_exec_ref_dead)
240 for (i = 0; i < simplify->tensor_symbol_info_size; i++)
241 if (refs[i] >= 0 && (simplify->tensor_dead[i >> 5] & (1u << (i & 0x1f))))
242 {
243 const int output_exec = simplify->output_execs[i];
244 assert(output_exec >= 0)((void) sizeof ((output_exec >= 0) ? 1 : 0), __extension__
({ if (output_exec >= 0) ; else __assert_fail ("output_exec >= 0"
, "ccv_nnc_symbolic_graph_simplify.c", 244, __extension__ __PRETTY_FUNCTION__
); }))
;
245 const ccv_nnc_graph_exec_symbol_info_t* const symbol_info = simplify->exec_symbol_info + output_exec;
246 int flag = 0;
247 for (j = 0; !flag && j < symbol_info->output_size; j++)
248 {
249 const int d = symbol_info->outputs[j];
250 if (d >= 0)
251 flag = (!(simplify->tensor_dead[d >> 5] & (1u << (d & 0x1f)))); // If some of the output is not dead, we cannot proceed.
252 }
253 if (!flag) // If all outputs are dead, mark the exec as dead.
254 simplify->exec_dead[output_exec >> 5] |= (1u << (output_exec & 0x1f));
255 }
256 int updated_refs = 0;
257 // Go over replace inputs / outputs.
258 ccv_nnc_graph_visit_for(simplify->visit, simplify->exec_symbol_info, node, idx){ int _i_; for (_i_ = 0; _i_ < (simplify->visit)->size
; _i_++) { const int idx __attribute__((unused)) = (simplify->
visit)->node[_i_].index; const int _node_unused_ __attribute__
((unused)) = (simplify->visit)->node[_i_].term; typeof (
(simplify->exec_symbol_info)) const node __attribute__((unused
)) = (simplify->exec_symbol_info) + idx;
{
259 for (i = 0; i < node->input_size; i++)
260 {
261 const int d = node->inputs[i];
262 if (d >= 0 && refs[d] >= 0 && (simplify->tensor_dead[d >> 5] & (1u << (d & 0x1f))))
263 {
264 node->inputs[i] = refs[d]; // It can be replaced.
265 updated_refs = 1;
266 }
267 }
268 for (i = 0; i < node->output_size; i++)
269 {
270 const int d = node->outputs[i];
271 if (d >= 0 && refs[d] >= 0 && (simplify->tensor_dead[d >> 5] & (1u << (d & 0x1f))))
272 {
273 node->outputs[i] = refs[d]; // It can be replaced.
274 updated_refs = 1;
275 }
276 }
277 assert(((ccv_nnc_graph_exec_symbol_info_t*)ccv_array_get(simplify->graph->exec_symbol_info, idx))->inputs == node->inputs)((void) sizeof ((((ccv_nnc_graph_exec_symbol_info_t*)((void*)
(((char*)((simplify->graph->exec_symbol_info)->data)
) + (size_t)(simplify->graph->exec_symbol_info)->rsize
* (size_t)(idx))))->inputs == node->inputs) ? 1 : 0), __extension__
({ if (((ccv_nnc_graph_exec_symbol_info_t*)((void*)(((char*)
((simplify->graph->exec_symbol_info)->data)) + (size_t
)(simplify->graph->exec_symbol_info)->rsize * (size_t
)(idx))))->inputs == node->inputs) ; else __assert_fail
("((ccv_nnc_graph_exec_symbol_info_t*)ccv_array_get(simplify->graph->exec_symbol_info, idx))->inputs == node->inputs"
, "ccv_nnc_symbolic_graph_simplify.c", 277, __extension__ __PRETTY_FUNCTION__
); }))
;
278 assert(((ccv_nnc_graph_exec_symbol_info_t*)ccv_array_get(simplify->graph->exec_symbol_info, idx))->outputs == node->outputs)((void) sizeof ((((ccv_nnc_graph_exec_symbol_info_t*)((void*)
(((char*)((simplify->graph->exec_symbol_info)->data)
) + (size_t)(simplify->graph->exec_symbol_info)->rsize
* (size_t)(idx))))->outputs == node->outputs) ? 1 : 0)
, __extension__ ({ if (((ccv_nnc_graph_exec_symbol_info_t*)((
void*)(((char*)((simplify->graph->exec_symbol_info)->
data)) + (size_t)(simplify->graph->exec_symbol_info)->
rsize * (size_t)(idx))))->outputs == node->outputs) ; else
__assert_fail ("((ccv_nnc_graph_exec_symbol_info_t*)ccv_array_get(simplify->graph->exec_symbol_info, idx))->outputs == node->outputs"
, "ccv_nnc_symbolic_graph_simplify.c", 278, __extension__ __PRETTY_FUNCTION__
); }))
;
279 } ccv_nnc_graph_visit_endfor} }
280 const ccv_nnc_graph_exec_symbol_info_t* const p_node_info = simplify->graph->p ? (ccv_nnc_graph_exec_symbol_info_t*)ccv_array_get(simplify->graph->p->exec_symbol_info, simplify->graph->exec_idx - 1)((void*)(((char*)((simplify->graph->p->exec_symbol_info
)->data)) + (size_t)(simplify->graph->p->exec_symbol_info
)->rsize * (size_t)(simplify->graph->exec_idx - 1)))
: 0;
281 if (p_node_info && (p_node_info->flags & CCV_NNC_GRAPH_EXEC_P_WHILE))
282 // Go over the while inputs as well.
283 for (i = 0; i < p_node_info->p_while.input_size; i++)
284 {
285 const int d = p_node_info->p_while.inputs[i];
286 if (d >= 0 && refs[d] >= 0 && (simplify->tensor_dead[d >> 5] & (1u << (d & 0x1f))))
287 {
288 p_node_info->p_while.inputs[i] = refs[d];
289 updated_refs = 1;
290 }
291 }
292 return updated_refs;
293}
294
295// This is a simple common sub-expression elimination implementation, particularly, we only replace the later computed output
296// with the identical earlier computed output, and let the "elimination" part to the graph pruning.
297static void _ccv_nnc_symbolic_graph_common_subexpression_elimination(ccv_nnc_symbolic_graph_simplify_t* const simplify, const ccv_nnc_tensor_symbol_t* const outputs, const int output_size)
298{
299 _ccv_nnc_symbolic_graph_simplify_update_output_execs(simplify);
300 // tensor_hash starts with 0s, and it is either marked with the tensor index + 1, or the hash of the computations.
301 uint64_t* const tensor_hash = (uint64_t*)cccalloccalloc(simplify->tensor_symbol_info_size, sizeof(uint64_t));
302 int i;
303 ccv_nnc_graph_visit_for(simplify->visit, simplify->exec_symbol_info, node, idx){ int _i_; for (_i_ = 0; _i_ < (simplify->visit)->size
; _i_++) { const int idx __attribute__((unused)) = (simplify->
visit)->node[_i_].index; const int _node_unused_ __attribute__
((unused)) = (simplify->visit)->node[_i_].term; typeof (
(simplify->exec_symbol_info)) const node __attribute__((unused
)) = (simplify->exec_symbol_info) + idx;
{
304 // If already marked as dead, skip.
305 if (simplify->exec_dead[idx >> 5] & (1u << (idx & 0x1f)))
306 continue;
307 for (i = 0; i < node->input_size; i++)
308 {
309 assert(node->inputs[i] < simplify->tensor_symbol_info_size)((void) sizeof ((node->inputs[i] < simplify->tensor_symbol_info_size
) ? 1 : 0), __extension__ ({ if (node->inputs[i] < simplify
->tensor_symbol_info_size) ; else __assert_fail ("node->inputs[i] < simplify->tensor_symbol_info_size"
, "ccv_nnc_symbolic_graph_simplify.c", 309, __extension__ __PRETTY_FUNCTION__
); }))
;
310 // If no hash for the input, use the index + 1 as the hash.
311 if (node->inputs[i] >= 0 && tensor_hash[node->inputs[i]] == 0)
312 tensor_hash[node->inputs[i]] = node->inputs[i] + 1;
313 }
314 // We cannot support graph / custom command (we cannot model them properly).
315 if (node->cmd.cmd == CCV_NNC_GRAPH_FORWARD ||
316 node->cmd.cmd == CCV_NNC_GRAPH_BACKWARD ||
317 node->cmd.cmd == CCV_NNC_CUSTOM_FORWARD ||
318 node->cmd.cmd == CCV_NNC_CUSTOM_BACKWARD ||
319 // No need to process a opt disabled node.
320 (node->flags & CCV_NNC_GRAPH_EXEC_DISABLE_OPT))
321 continue;
322 uint64_t hashout, hashin[3];
323 siphash((uint8_t*)&hashin[0], (const uint8_t*)&node->cmd.info, sizeof(node->cmd.info), key_siphash);
324 siphash((uint8_t*)&hashin[1], (const uint8_t*)&node->hint, sizeof(node->hint), key_siphash);
325 hashin[2] = node->cmd.cmd; // Now actually hash the cmd name.
326 siphash((uint8_t*)&hashout, (const uint8_t*)hashin, sizeof(hashin), key_siphash);
327 // First, hash the cmd and the hints with the cmd.
328 // Note on alias, we cannot really generate proper hash for alias (yet). Thus, just treat alias as normal tensors.
329 for (i = 0; i < node->input_size; i++)
330 {
331 assert(node->inputs[i] < simplify->tensor_symbol_info_size)((void) sizeof ((node->inputs[i] < simplify->tensor_symbol_info_size
) ? 1 : 0), __extension__ ({ if (node->inputs[i] < simplify
->tensor_symbol_info_size) ; else __assert_fail ("node->inputs[i] < simplify->tensor_symbol_info_size"
, "ccv_nnc_symbolic_graph_simplify.c", 331, __extension__ __PRETTY_FUNCTION__
); }))
;
332 if (node->inputs[i] >= 0)
333 {
334 // Hash using the tensor hash.
335 hashin[0] = hashout;
336 hashin[1] = i; // Encode the positional information.
337 hashin[2] = tensor_hash[node->inputs[i]];
338 } else {
339 // Hash using the input integer (could be special integer).
340 hashin[0] = hashout;
341 hashin[1] = i; // Encode the positional information.
342 hashin[2] = node->inputs[i];
343 }
344 siphash((uint8_t*)&hashout, (const uint8_t*)hashin, sizeof(hashin), key_siphash);
345 }
346 for (i = 0; i < node->output_size; i++)
347 if (node->outputs[i] >= 0)
348 {
349 assert(node->outputs[i] < simplify->tensor_symbol_info_size)((void) sizeof ((node->outputs[i] < simplify->tensor_symbol_info_size
) ? 1 : 0), __extension__ ({ if (node->outputs[i] < simplify
->tensor_symbol_info_size) ; else __assert_fail ("node->outputs[i] < simplify->tensor_symbol_info_size"
, "ccv_nnc_symbolic_graph_simplify.c", 349, __extension__ __PRETTY_FUNCTION__
); }))
;
350 // Assigning once, especially now we don't consider aliases.
351 assert(tensor_hash[node->outputs[i]] == 0)((void) sizeof ((tensor_hash[node->outputs[i]] == 0) ? 1 :
0), __extension__ ({ if (tensor_hash[node->outputs[i]] ==
0) ; else __assert_fail ("tensor_hash[node->outputs[i]] == 0"
, "ccv_nnc_symbolic_graph_simplify.c", 351, __extension__ __PRETTY_FUNCTION__
); }))
;
352 hashin[0] = hashout;
353 hashin[1] = i; // Positional information.
354 siphash((uint8_t*)&hashin[2], (const uint8_t*)&simplify->tensor_symbol_info[node->outputs[i]].info,
355 sizeof(simplify->tensor_symbol_info[node->outputs[i]].info), key_siphash);
356 // Generate hash for the output.
357 siphash((uint8_t*)&tensor_hash[node->outputs[i]], (const uint8_t*)hashin, sizeof(hashin), key_siphash);
358 }
359 } ccv_nnc_graph_visit_endfor} }
360 // Allocate 3 / 2 as much space, for the simple robin-hood open address hash map.
361 const int map_size = (simplify->tensor_symbol_info_size * 3 + 1) / 2;
362 int* const refs = (int*)ccmallocmalloc(sizeof(int) * simplify->tensor_symbol_info_size);
363 for (i = 0; i < simplify->tensor_symbol_info_size; i++)
364 refs[i] = -1;
365 ccv_nnc_cse_hash_t* const hash_map = (ccv_nnc_cse_hash_t*)cccalloccalloc(map_size, sizeof(ccv_nnc_cse_hash_t));
366 // Now, all tensors are hashed, identify tensors with the same hash code, replace the ones that accessed later.
367 ccv_nnc_graph_visit_for(simplify->visit, simplify->exec_symbol_info, node, idx){ int _i_; for (_i_ = 0; _i_ < (simplify->visit)->size
; _i_++) { const int idx __attribute__((unused)) = (simplify->
visit)->node[_i_].index; const int _node_unused_ __attribute__
((unused)) = (simplify->visit)->node[_i_].term; typeof (
(simplify->exec_symbol_info)) const node __attribute__((unused
)) = (simplify->exec_symbol_info) + idx;
{
368 // If already marked as dead, skip.
369 if (simplify->exec_dead[idx >> 5] & (1u << (idx & 0x1f)))
370 continue;
371 // No need to visit a opt disabled node.
372 if (node->flags & CCV_NNC_GRAPH_EXEC_DISABLE_OPT)
373 continue;
374 for (i = 0; i < node->input_size; i++)
375 if (node->inputs[i] >= 0)
376 {
377 const int d = node->inputs[i];
378 assert(tensor_hash[d])((void) sizeof ((tensor_hash[d]) ? 1 : 0), __extension__ ({ if
(tensor_hash[d]) ; else __assert_fail ("tensor_hash[d]", "ccv_nnc_symbolic_graph_simplify.c"
, 378, __extension__ __PRETTY_FUNCTION__); }))
;
379 const int new_d = _ccv_nnc_cse_hash_find(hash_map, tensor_hash[d], map_size);
380 if (new_d >= 0 && new_d != d)
381 {
382 // Check whether this can be replaced.
383 assert(refs[d] == -1 || refs[d] == new_d)((void) sizeof ((refs[d] == -1 || refs[d] == new_d) ? 1 : 0),
__extension__ ({ if (refs[d] == -1 || refs[d] == new_d) ; else
__assert_fail ("refs[d] == -1 || refs[d] == new_d", "ccv_nnc_symbolic_graph_simplify.c"
, 383, __extension__ __PRETTY_FUNCTION__); }))
;
384 assert(!simplify->tensor_symbol_info[d].assign_ref)((void) sizeof ((!simplify->tensor_symbol_info[d].assign_ref
) ? 1 : 0), __extension__ ({ if (!simplify->tensor_symbol_info
[d].assign_ref) ; else __assert_fail ("!simplify->tensor_symbol_info[d].assign_ref"
, "ccv_nnc_symbolic_graph_simplify.c", 384, __extension__ __PRETTY_FUNCTION__
); }))
;
385 assert(!simplify->tensor_symbol_info[d].r_assign_ref)((void) sizeof ((!simplify->tensor_symbol_info[d].r_assign_ref
) ? 1 : 0), __extension__ ({ if (!simplify->tensor_symbol_info
[d].r_assign_ref) ; else __assert_fail ("!simplify->tensor_symbol_info[d].r_assign_ref"
, "ccv_nnc_symbolic_graph_simplify.c", 385, __extension__ __PRETTY_FUNCTION__
); }))
;
386 assert(!simplify->tensor_symbol_info[d].bypass_ref)((void) sizeof ((!simplify->tensor_symbol_info[d].bypass_ref
) ? 1 : 0), __extension__ ({ if (!simplify->tensor_symbol_info
[d].bypass_ref) ; else __assert_fail ("!simplify->tensor_symbol_info[d].bypass_ref"
, "ccv_nnc_symbolic_graph_simplify.c", 386, __extension__ __PRETTY_FUNCTION__
); }))
;
387 assert(!simplify->tensor_symbol_info[new_d].assign_ref)((void) sizeof ((!simplify->tensor_symbol_info[new_d].assign_ref
) ? 1 : 0), __extension__ ({ if (!simplify->tensor_symbol_info
[new_d].assign_ref) ; else __assert_fail ("!simplify->tensor_symbol_info[new_d].assign_ref"
, "ccv_nnc_symbolic_graph_simplify.c", 387, __extension__ __PRETTY_FUNCTION__
); }))
;
388 assert(!simplify->tensor_symbol_info[new_d].r_assign_ref)((void) sizeof ((!simplify->tensor_symbol_info[new_d].r_assign_ref
) ? 1 : 0), __extension__ ({ if (!simplify->tensor_symbol_info
[new_d].r_assign_ref) ; else __assert_fail ("!simplify->tensor_symbol_info[new_d].r_assign_ref"
, "ccv_nnc_symbolic_graph_simplify.c", 388, __extension__ __PRETTY_FUNCTION__
); }))
;
389 assert(!simplify->tensor_symbol_info[new_d].bypass_ref)((void) sizeof ((!simplify->tensor_symbol_info[new_d].bypass_ref
) ? 1 : 0), __extension__ ({ if (!simplify->tensor_symbol_info
[new_d].bypass_ref) ; else __assert_fail ("!simplify->tensor_symbol_info[new_d].bypass_ref"
, "ccv_nnc_symbolic_graph_simplify.c", 389, __extension__ __PRETTY_FUNCTION__
); }))
;
390 // Ignore if there is a pair_ref (again, pair_ref has side effect that is deeper (using tape))
391 if (simplify->tensor_symbol_info[d].pair_ref)
392 continue;
393 // If both have p_ref, we cannot merge.
394 if (simplify->tensor_symbol_info[d].p_ref && simplify->tensor_symbol_info[new_d].p_ref)
395 continue;
396 // Merge s_refs from ref[d] later.
397 if (refs[d] != new_d)
398 refs[d] = new_d;
399 assert(simplify->output_execs[new_d] >= 0)((void) sizeof ((simplify->output_execs[new_d] >= 0) ? 1
: 0), __extension__ ({ if (simplify->output_execs[new_d] >=
0) ; else __assert_fail ("simplify->output_execs[new_d] >= 0"
, "ccv_nnc_symbolic_graph_simplify.c", 399, __extension__ __PRETTY_FUNCTION__
); }))
;
400 // Establish new dependency.
401 ccv_nnc_graph_exec_symbol_concat(simplify->graph, (ccv_nnc_graph_exec_symbol_t){
402 .d = simplify->output_execs[new_d],
403 .graph = simplify->graph,
404 }, (ccv_nnc_graph_exec_symbol_t){
405 .d = idx,
406 .graph = simplify->graph,
407 });
408 }
409 }
410 // We can reuse the input, but we cannot do that for output of these commands.
411 if (node->cmd.cmd == CCV_NNC_GRAPH_FORWARD ||
412 node->cmd.cmd == CCV_NNC_GRAPH_BACKWARD ||
413 node->cmd.cmd == CCV_NNC_CUSTOM_FORWARD ||
414 node->cmd.cmd == CCV_NNC_CUSTOM_BACKWARD)
415 continue;
416 for (i = 0; i < node->output_size; i++)
417 if (node->outputs[i] >= 0) // This tensor can be reused by others.
418 _ccv_nnc_cse_hash_add(hash_map, tensor_hash[node->outputs[i]], node->outputs[i], map_size);
419 } ccv_nnc_graph_visit_endfor} }
420 _ccv_nnc_symbolic_graph_update_refs(simplify, outputs, output_size, refs, 1 /* For these exec that generates refs, we don't need them any more. */);
421 ccfreefree(tensor_hash);
422 ccfreefree(hash_map);
423 ccfreefree(refs);
424}
425
426static void _ccv_nnc_symbolic_graph_data_transfer_opt(ccv_nnc_symbolic_graph_simplify_t* const simplify, const ccv_nnc_tensor_symbol_t* const binds, const int bind_size, const ccv_nnc_tensor_symbol_t* const outputs, const int output_size)
427{
428 _ccv_nnc_symbolic_graph_simplify_update_output_execs(simplify);
429 uint32_t* const exec_dead = simplify->exec_dead;
430 const ccv_nnc_tensor_symbol_info_t* const tensor_symbol_info = simplify->tensor_symbol_info;
431 int i;
432 uint32_t* const has_alias = cccalloccalloc(2 * ((simplify->tensor_symbol_info_size + 31) >> 5), sizeof(uint32_t));
433 uint32_t* const has_binds = has_alias + ((simplify->tensor_symbol_info_size + 31) >> 5);
434 for (i = 0; i < bind_size; i++)
435 has_binds[binds[i].d >> 5] |= (1u << (binds[i].d & 0x1f));
436 for (i = 0; i < output_size; i++)
437 has_binds[outputs[i].d >> 5] |= (1u << (outputs[i].d & 0x1f));
438 int* const refs = (int*)ccmallocmalloc(sizeof(int) * simplify->tensor_symbol_info_size);
439 int updated_refs;
440 do {
441 memset(has_alias, 0, sizeof(uint32_t) * ((simplify->tensor_symbol_info_size + 31) >> 5));
442 // Go through until no updates is possible. This won't result an infinite loop because every time,
443 // a tensor is eliminated.
444 for (i = 0; i < simplify->tensor_symbol_info_size; i++)
445 {
446 refs[i] = -1;
447 if (tensor_symbol_info[i].alias_ref)
448 {
449 const int alias_ref = tensor_symbol_info[i].alias_ref - 1;
450 has_alias[alias_ref >> 5] |= (1u << (alias_ref & 0x1f));
451 }
452 }
453 ccv_nnc_graph_visit_for(simplify->visit, simplify->exec_symbol_info, node, idx){ int _i_; for (_i_ = 0; _i_ < (simplify->visit)->size
; _i_++) { const int idx __attribute__((unused)) = (simplify->
visit)->node[_i_].index; const int _node_unused_ __attribute__
((unused)) = (simplify->visit)->node[_i_].term; typeof (
(simplify->exec_symbol_info)) const node __attribute__((unused
)) = (simplify->exec_symbol_info) + idx;
{
454 // If already marked as dead, skip.
455 if (exec_dead[idx >> 5] & (1u << (idx & 0x1f)))
456 continue;
457 if (node->cmd.cmd != CCV_NNC_DATA_TRANSFER_FORWARD &&
458 node->cmd.cmd != CCV_NNC_DATA_TRANSFER_BACKWARD &&
459 // Conversion, if the datatype is the same, it is the data transfer op.
460 node->cmd.cmd != CCV_NNC_DATATYPE_CONVERSION_FORWARD &&
461 node->cmd.cmd != CCV_NNC_DATATYPE_CONVERSION_BACKWARD &&
462 // Format transform, if the format is the same, it is the data transfer op.
463 node->cmd.cmd != CCV_NNC_FORMAT_TRANSFORM_FORWARD &&
464 node->cmd.cmd != CCV_NNC_FORMAT_TRANSFORM_BACKWARD)
465 continue;
466 if (node->flags & CCV_NNC_GRAPH_EXEC_DISABLE_OPT) // If optimization pass disabled, skip.
467 continue;
468 for (i = 0; i < node->output_size; i++) // For data transfer, we only respect output size.
469 if (node->inputs[i] >= 0 && node->outputs[i] >= 0)
470 {
471 assert(node->inputs[i] < simplify->tensor_symbol_info_size)((void) sizeof ((node->inputs[i] < simplify->tensor_symbol_info_size
) ? 1 : 0), __extension__ ({ if (node->inputs[i] < simplify
->tensor_symbol_info_size) ; else __assert_fail ("node->inputs[i] < simplify->tensor_symbol_info_size"
, "ccv_nnc_symbolic_graph_simplify.c", 471, __extension__ __PRETTY_FUNCTION__
); }))
;
472 assert(node->outputs[i] < simplify->tensor_symbol_info_size)((void) sizeof ((node->outputs[i] < simplify->tensor_symbol_info_size
) ? 1 : 0), __extension__ ({ if (node->outputs[i] < simplify
->tensor_symbol_info_size) ; else __assert_fail ("node->outputs[i] < simplify->tensor_symbol_info_size"
, "ccv_nnc_symbolic_graph_simplify.c", 472, __extension__ __PRETTY_FUNCTION__
); }))
;
473 int input_ref = node->inputs[i];
474 while (refs[input_ref] >= 0)
475 input_ref = refs[input_ref];
476 int output_ref = node->outputs[i];
477 while (refs[output_ref] >= 0)
478 output_ref = refs[output_ref];
479 if (input_ref == output_ref)
480 continue;
481 const ccv_nnc_tensor_symbol_info_t* const input = tensor_symbol_info + input_ref;
482 const ccv_nnc_tensor_symbol_info_t* const output = tensor_symbol_info + output_ref;
483 // If they are not the same data type, skip. (Likely data conversion op).
484 if (input->info.datatype != output->info.datatype)
485 continue;
486 // If they are not the same format, skip. (Likely format transform op).
487 if (input->info.format != output->info.format)
488 continue;
489 // If they are not on the same device (even for NUMA), skip.
490 if (input->info.type != output->info.type)
491 continue;
492 // If both are alias, we cannot consolidate this.
493 if (input->alias_ref && output->alias_ref)
494 continue;
495 // If input is alias, and output has alias reference to it, output cannot be the same as input.
496 if (input->alias_ref && (has_alias[output_ref >> 5] & (1u << (output_ref & 0x1f))))
497 continue;
498 // If output is alias, and input has alias reference to it, input cannot be the same as output.
499 if (output->alias_ref && (has_alias[input_ref >> 5] & (1u << (input_ref & 0x1f))))
500 continue;
501 // If either are carry overs (for while), we cannot do anything.
502 if (input->assign_ref || output->assign_ref ||
503 input->r_assign_ref || output->r_assign_ref)
504 continue;
505 // If either are bypasses (for case..of), we cannot do anything.
506 if (input->bypass_ref || output->bypass_ref ||
507 input->r_bypass_ref || output->r_bypass_ref)
508 continue;
509 // If either are inputs / outputs connecting the parent graph, we cannot do anything.
510 if (input->p_ref || output->p_ref)
511 continue;
512 // If the type is the same, check which one is the alias.
513 // We always prefer alias.
514 if (output->alias_ref)
515 {
516 // Input cannot be binds.
517 if (has_binds[input_ref >> 5] & (1u << (input_ref & 0x1f)))
518 continue;
519 refs[input_ref] = output_ref;
520 } else { // if (input->alias_ref), else
521 // Output cannot be binds.
522 if (has_binds[output_ref >> 5] & (1u << (output_ref & 0x1f)))
523 continue;
524 refs[output_ref] = input_ref;
525 }
526 }
527 } ccv_nnc_graph_visit_endfor} }
528 // Make sure refs reference to the end.
529 for (i = 0; i < simplify->tensor_symbol_info_size; i++)
530 if (refs[i] >= 0)
531 {
532 int ref = refs[i];
533 while (refs[ref] >= 0)
534 ref = refs[ref];
535 refs[i] = ref;
536 }
537 updated_refs = _ccv_nnc_symbolic_graph_update_refs(simplify, outputs, output_size, refs, 0 /* We still need these exec that generates the refs. */);
538 } while (updated_refs);
539 ccfreefree(refs);
540 ccfreefree(has_alias);
541 // Now, all references updated, remove data transfers that sources and destinations are the same.
542 ccv_nnc_graph_visit_for(simplify->visit, simplify->exec_symbol_info, node, idx){ int _i_; for (_i_ = 0; _i_ < (simplify->visit)->size
; _i_++) { const int idx __attribute__((unused)) = (simplify->
visit)->node[_i_].index; const int _node_unused_ __attribute__
((unused)) = (simplify->visit)->node[_i_].term; typeof (
(simplify->exec_symbol_info)) const node __attribute__((unused
)) = (simplify->exec_symbol_info) + idx;
{
543 // If already marked as dead, skip.
544 if (exec_dead[idx >> 5] & (1u << (idx & 0x1f)))
545 continue;
546 if (node->cmd.cmd != CCV_NNC_DATA_TRANSFER_FORWARD &&
547 node->cmd.cmd != CCV_NNC_DATA_TRANSFER_BACKWARD &&
548 // Conversion, if the datatype is the same, it is the data transfer op.
549 node->cmd.cmd != CCV_NNC_DATATYPE_CONVERSION_FORWARD &&
550 node->cmd.cmd != CCV_NNC_DATATYPE_CONVERSION_BACKWARD &&
551 // Format transform, if the format is the same, it is the data transfer op.
552 node->cmd.cmd != CCV_NNC_FORMAT_TRANSFORM_FORWARD &&
553 node->cmd.cmd != CCV_NNC_FORMAT_TRANSFORM_BACKWARD)
554 continue;
555 for (i = 0; i < node->output_size; i++) // For data transfer, we only respect output size.
556 if (node->inputs[i] == node->outputs[i])
557 {
558 if (i + 1 < node->output_size)
559 {
560 node->inputs[i] = node->inputs[node->output_size - 1];
561 node->outputs[i] = node->outputs[node->output_size - 1];
562 }
563 --node->output_size;
564 --i;
565 }
566 node->input_size = node->output_size;
567 ((ccv_nnc_graph_exec_symbol_info_t*)ccv_array_get(simplify->graph->exec_symbol_info, idx)((void*)(((char*)((simplify->graph->exec_symbol_info)->
data)) + (size_t)(simplify->graph->exec_symbol_info)->
rsize * (size_t)(idx)))
)->input_size = node->input_size;
568 ((ccv_nnc_graph_exec_symbol_info_t*)ccv_array_get(simplify->graph->exec_symbol_info, idx)((void*)(((char*)((simplify->graph->exec_symbol_info)->
data)) + (size_t)(simplify->graph->exec_symbol_info)->
rsize * (size_t)(idx)))
)->output_size = node->output_size;
569 // Remove this data transfer node if it has no outputs.
570 if (node->output_size == 0)
571 exec_dead[idx >> 5] |= (1u << (idx & 0x1f));
572 } ccv_nnc_graph_visit_endfor} }
573}
574
575typedef struct {
576 uint32_t fused_op; // The final fused op id.
577 uint32_t ops_seq[2]; // The sequence of commands to identify.
578 int ops_seq_size;
579 int ops_info_select; // Which ops' info will be selected.
580 struct {
581 int type; // Whether it is input, or output. It doesn't make sense for example, input in ops_seq, but output in fused_op.
582 int op_idx; // Index into the ops_seq.
583 int from; // The index in ops_seq.
584 int to; // The index in fused_op.
585 } pos[4]; // maps of positions from ops seq to fused_op for inputs (outputs).
586 int pos_size;
587} ccv_nnc_ops_fusion_t;
588
589enum {
590 CCV_NNC_OPS_FUSION_INPUT_INDEX,
591 CCV_NNC_OPS_FUSION_OUTPUT_INDEX,
592};
593
594const static int ccv_nnc_ops_fusion_io_size = 2;
595const static ccv_nnc_ops_fusion_t ccv_nnc_ops_fusions[] = {
596 {
597 .fused_op = CCV_NNC_SOFTMAX_CROSSENTROPY_FORWARD,
598 .ops_seq = {
599 CCV_NNC_SOFTMAX_FORWARD, CCV_NNC_CATEGORICAL_CROSSENTROPY_FORWARD,
600 },
601 .ops_seq_size = 2,
602 .ops_info_select = 1,
603 .pos = {
604 {
605 .type = CCV_NNC_OPS_FUSION_INPUT_INDEX,
606 .op_idx = 0,
607 .from = 0,
608 .to = 0,
609 },
610 {
611 .type = CCV_NNC_OPS_FUSION_INPUT_INDEX,
612 .op_idx = 1,
613 .from = 1,
614 .to = 1,
615 },
616 {
617 .type = CCV_NNC_OPS_FUSION_OUTPUT_INDEX,
618 .op_idx = 0,
619 .from = 0,
620 .to = 1,
621 },
622 {
623 .type = CCV_NNC_OPS_FUSION_OUTPUT_INDEX,
624 .op_idx = 1,
625 .from = 0,
626 .to = 0,
627 },
628 },
629 .pos_size = 4,
630 },
631 {
632 .fused_op = CCV_NNC_SIGMOID_BINARY_CROSSENTROPY_FORWARD,
633 .ops_seq = {
634 CCV_NNC_SIGMOID_FORWARD, CCV_NNC_BINARY_CROSSENTROPY_FORWARD,
635 },
636 .ops_seq_size = 2,
637 .ops_info_select = 1,
638 .pos = {
639 {
640 .type = CCV_NNC_OPS_FUSION_INPUT_INDEX,
641 .op_idx = 0,
642 .from = 0,
643 .to = 0,
644 },
645 {
646 .type = CCV_NNC_OPS_FUSION_INPUT_INDEX,
647 .op_idx = 1,
648 .from = 1,
649 .to = 1,
650 },
651 {
652 .type = CCV_NNC_OPS_FUSION_OUTPUT_INDEX,
653 .op_idx = 0,
654 .from = 0,
655 .to = 1,
656 },
657 {
658 .type = CCV_NNC_OPS_FUSION_OUTPUT_INDEX,
659 .op_idx = 1,
660 .from = 0,
661 .to = 0,
662 },
663 },
664 .pos_size = 4,
665 }
666};
667
668static int _ccv_nnc_find_ops_for_fusion(const ccv_nnc_ops_fusion_t* const fusion, const int ops_idx, const ccv_nnc_graph_exec_symbol_info_t* const exec_symbol_info, const uint32_t* const exec_dead, const int exec_idx, int* const fusing_exec_symbols)
669{
670 if (exec_dead[exec_idx >> 5] & (1u << (exec_idx & 0x1f)))
671 return 0;
672 const ccv_nnc_graph_exec_symbol_info_t* const node = exec_symbol_info + exec_idx;
673 // Doesn't match the ops_seq, return 0.
674 if (fusion->ops_seq[ops_idx] != node->cmd.cmd)
675 return 0;
676 fusing_exec_symbols[ops_idx] = exec_idx;
677 // If already reached the end, we are good.
678 if (ops_idx == fusion->ops_seq_size - 1)
679 return 1;
680 // Otherwise, we need to go on, but don't have any to follow-up.
681 if (!node->outgoings || !node->outgoings->rnum)
682 return 0;
683 int i;
684 for (i = 0; i < node->outgoings->rnum; i++)
685 if (_ccv_nnc_find_ops_for_fusion(fusion, ops_idx + 1, exec_symbol_info, exec_dead, *(int*)ccv_array_get(node->outgoings, i)((void*)(((char*)((node->outgoings)->data)) + (size_t)(
node->outgoings)->rsize * (size_t)(i)))
, fusing_exec_symbols))
686 return 1;
687 return 0;
688}
689
690static void _ccv_nnc_symbolic_graph_ops_fusion(ccv_nnc_symbolic_graph_simplify_t* const simplify, const ccv_nnc_tensor_symbol_t* const outputs, const int output_size)
691{
692 uint32_t* const exec_dead = simplify->exec_dead;
693 ccv_nnc_graph_exec_symbol_info_t* const exec_symbol_info = simplify->exec_symbol_info;
694 int i, j;
695 int fusing_exec_symbols[sizeof(ccv_nnc_ops_fusions->ops_seq)];
696 int fused_inputs[ccv_nnc_ops_fusion_io_size]; // 2 is just a number based on the ops_fusion.
697 int fused_outputs[ccv_nnc_ops_fusion_io_size];
698 ccv_nnc_graph_visit_for(simplify->visit, exec_symbol_info, node, idx){ int _i_; for (_i_ = 0; _i_ < (simplify->visit)->size
; _i_++) { const int idx __attribute__((unused)) = (simplify->
visit)->node[_i_].index; const int _node_unused_ __attribute__
((unused)) = (simplify->visit)->node[_i_].term; typeof (
(exec_symbol_info)) const node __attribute__((unused)) = (exec_symbol_info
) + idx;
{
699 // If already marked as dead, skip.
700 if (exec_dead[idx >> 5] & (1u << (idx & 0x1f)))
701 continue;
702 // Run through rudimentary pattern matching for ops_seq. There are better ways to do this (immediately come to mind, Boyer-Moore). However, this is simpler to code.
703 // If I am running into performance issues with this, I would be very happy.
704 for (i = 0; i < sizeof(ccv_nnc_ops_fusions) / sizeof(ccv_nnc_ops_fusion_t); i++)
705 {
706 const ccv_nnc_ops_fusion_t* const ops_fusion = ccv_nnc_ops_fusions + i;
707 // Check to see if a list of symbols are possible.
708 if (ops_fusion->ops_seq[0] == node->cmd.cmd &&
709 _ccv_nnc_find_ops_for_fusion(ops_fusion, 0, exec_symbol_info, exec_dead, idx, fusing_exec_symbols))
710 {
711 // Go through all the inputs and outputs, check if they exists and are mapped.
712 // TODO: the mapping can be more sophisticated than what we have here.
713 // Also, I need to check if some inputs / outputs cannot be mapped, then we cannot continue.
714 for (j = 0; j < ccv_nnc_ops_fusion_io_size; j++)
715 fused_inputs[j] = fused_outputs[j] = CCV_NNC_NO_TENSOR_SYMBOL;
716 int input_size = 0, output_size = 0;
717 for (j = 0; j < ops_fusion->pos_size; j++)
718 {
719 ccv_nnc_graph_exec_symbol_info_t* const fusing_op = exec_symbol_info + fusing_exec_symbols[ops_fusion->pos[j].op_idx];
720 switch (ops_fusion->pos[j].type)
721 {
722 case CCV_NNC_OPS_FUSION_INPUT_INDEX:
723 fused_inputs[ops_fusion->pos[j].to] = ops_fusion->pos[j].from < fusing_op->input_size ? fusing_op->inputs[ops_fusion->pos[j].from] : CCV_NNC_NO_TENSOR_SYMBOL;
724 input_size = ccv_max(input_size, ops_fusion->pos[j].to + 1)({ typeof (input_size) _a = (input_size); typeof (ops_fusion->
pos[j].to + 1) _b = (ops_fusion->pos[j].to + 1); (_a > _b
) ? _a : _b; })
;
725 break;
726 case CCV_NNC_OPS_FUSION_OUTPUT_INDEX:
727 fused_outputs[ops_fusion->pos[j].to] = ops_fusion->pos[j].from < fusing_op->output_size ? fusing_op->outputs[ops_fusion->pos[j].from] : CCV_NNC_NO_TENSOR_SYMBOL;
728 output_size = ccv_max(output_size, ops_fusion->pos[j].to + 1)({ typeof (output_size) _a = (output_size); typeof (ops_fusion
->pos[j].to + 1) _b = (ops_fusion->pos[j].to + 1); (_a >
_b) ? _a : _b; })
;
729 break;
730 }
731 }
732 const ccv_nnc_cmd_param_t info = exec_symbol_info[fusing_exec_symbols[ops_fusion->ops_info_select]].cmd.info;
733 // Modify the first node so it is the correct type and value. I need to look back to the actual graph to get the info.
734 ccv_nnc_graph_exec_symbol_info_t* const actual_node = (ccv_nnc_graph_exec_symbol_info_t*)ccv_array_get(simplify->graph->exec_symbol_info, idx)((void*)(((char*)((simplify->graph->exec_symbol_info)->
data)) + (size_t)(simplify->graph->exec_symbol_info)->
rsize * (size_t)(idx)))
;
735 actual_node->cmd.cmd = node->cmd.cmd = ops_fusion->fused_op;
736 actual_node->cmd.info = node->cmd.info = info;
737 if (node->input_size + node->output_size < input_size + output_size)
738 actual_node->inputs = node->inputs = node->inputs ? ccreallocrealloc(node->inputs, sizeof(int) * (input_size + output_size)) : ccmallocmalloc(sizeof(int) * (input_size + output_size));
739 actual_node->outputs = node->outputs = node->inputs + input_size;
740 actual_node->input_size = node->input_size = input_size;
741 actual_node->output_size = node->output_size = output_size;
742 memcpy(node->inputs, fused_inputs, sizeof(int) * input_size);
743 memcpy(node->outputs, fused_outputs, sizeof(int) * output_size);
744 // Afterwards, mark the rest as dead.
745 for (j = 1; j < ops_fusion->ops_seq_size; j++)
746 exec_dead[fusing_exec_symbols[j] >> 5] |= (1u << (fusing_exec_symbols[j] & 0x1f));
747 break;
748 }
749 }
750 } ccv_nnc_graph_visit_endfor} }
751}
752
753static void _ccv_nnc_symbolic_graph_pruning_undead_exec(ccv_nnc_symbolic_graph_simplify_t* const simplify, const int exec_idx, uint32_t* const tensor_visited, ccv_array_t* const next)
754{
755 assert(exec_idx >= 0)((void) sizeof ((exec_idx >= 0) ? 1 : 0), __extension__ ({
if (exec_idx >= 0) ; else __assert_fail ("exec_idx >= 0"
, "ccv_nnc_symbolic_graph_simplify.c", 755, __extension__ __PRETTY_FUNCTION__
); }))
;
25
Taking true branch
756 uint32_t* const exec_dead = simplify->exec_dead;
757 uint32_t* const tensor_dead = simplify->tensor_dead;
758 exec_dead[exec_idx >> 5] &= ~(1u << (exec_idx & 0x1f)); // Undead the exec.
759 ccv_nnc_graph_exec_symbol_info_t* const exec_symbol_info = simplify->exec_symbol_info + exec_idx;
760 int i;
761 if (exec_symbol_info->cmd.cmd == CCV_NNC_GRAPH_FORWARD ||
26
Assuming field 'cmd' is not equal to CCV_NNC_GRAPH_FORWARD
30
Taking false branch
762 exec_symbol_info->cmd.cmd == CCV_NNC_GRAPH_BACKWARD ||
27
Assuming field 'cmd' is not equal to CCV_NNC_GRAPH_BACKWARD
763 exec_symbol_info->cmd.cmd == CCV_NNC_CUSTOM_FORWARD ||
28
Assuming field 'cmd' is not equal to CCV_NNC_CUSTOM_FORWARD
764 exec_symbol_info->cmd.cmd == CCV_NNC_CUSTOM_BACKWARD)
29
Assuming field 'cmd' is not equal to CCV_NNC_CUSTOM_BACKWARD
765 {
766 // All of its inputs / outputs need to be undead for these commands.
767 for (i = 0; i < exec_symbol_info->input_size; i++)
768 {
769 const int d = exec_symbol_info->inputs[i];
770 if (d >= 0 && !(tensor_visited[d >> 5] & (1u << (d & 0x1f))))
771 {
772 ccv_array_push(next, &d); // Push to the next round to be undead.
773 tensor_visited[d >> 5] |= (1u << (d & 0x1f));
774 }
775 }
776 for (i = 0; i < exec_symbol_info->output_size; i++)
777 {
778 const int d = exec_symbol_info->outputs[i];
779 if (d >= 0 && !(tensor_visited[d >> 5] & (1u << (d & 0x1f))))
780 {
781 ccv_array_push(next, &d); // Push to the next round to be undead.
782 tensor_visited[d >> 5] |= (1u << (d & 0x1f));
783 }
784 }
785 return;
786 }
787 // Go through the input / output, to make sure that all of them can be available.
788 const int input_bitmask_size = (exec_symbol_info->input_size + 63) >> 6;
789 const int output_bitmask_size = (exec_symbol_info->output_size + 63) >> 6;
790 uint64_t input_bitmasks[ccv_max(1, input_bitmask_size)({ typeof (1) _a = (1); typeof (input_bitmask_size) _b = (input_bitmask_size
); (_a > _b) ? _a : _b; })
];
31
Assuming '_a' is > '_b'
32
'?' condition is true
791 for (i = 0; i
32.1
'i' is >= 'input_bitmask_size'
< input_bitmask_size; i++)
33
Loop condition is false. Execution continues on line 793
792 input_bitmasks[i] = 0;
793 uint64_t output_bitmasks[ccv_max(1, output_bitmask_size)({ typeof (1) _a = (1); typeof (output_bitmask_size) _b = (output_bitmask_size
); (_a > _b) ? _a : _b; })
];
34
Assuming '_a' is > '_b'
35
'?' condition is true
794 for (i = 0; i
35.1
'i' is >= 'output_bitmask_size'
< output_bitmask_size; i++)
36
Loop condition is false. Execution continues on line 796
795 output_bitmasks[i] = 0;
796 for (i = 0; i < exec_symbol_info->input_size; i++)
37
Assuming 'i' is < field 'input_size'
38
Loop condition is true. Entering loop body
797 if (exec_symbol_info->inputs[i] >= 0)
39
Assuming the condition is true
40
Taking true branch
798 input_bitmasks[i >> 6] |= ((uint64_t)1 << (i & 63));
41
The left expression of the compound assignment is an uninitialized value. The computed value will also be garbage
799 for (i = 0; i < exec_symbol_info->output_size; i++)
800 if (exec_symbol_info->outputs[i] >= 0)
801 output_bitmasks[i >> 6] |= ((uint64_t)1 << (i & 63));
802 // First, mark everything with bitmasks, and verify it works.
803 assert(ccv_nnc_cmd_bitmask(exec_symbol_info->cmd, exec_symbol_info->input_size, exec_symbol_info->output_size, input_bitmasks, input_bitmask_size, output_bitmasks, output_bitmask_size))((void) sizeof ((ccv_nnc_cmd_bitmask(exec_symbol_info->cmd
, exec_symbol_info->input_size, exec_symbol_info->output_size
, input_bitmasks, input_bitmask_size, output_bitmasks, output_bitmask_size
)) ? 1 : 0), __extension__ ({ if (ccv_nnc_cmd_bitmask(exec_symbol_info
->cmd, exec_symbol_info->input_size, exec_symbol_info->
output_size, input_bitmasks, input_bitmask_size, output_bitmasks
, output_bitmask_size)) ; else __assert_fail ("ccv_nnc_cmd_bitmask(exec_symbol_info->cmd, exec_symbol_info->input_size, exec_symbol_info->output_size, input_bitmasks, input_bitmask_size, output_bitmasks, output_bitmask_size)"
, "ccv_nnc_symbolic_graph_simplify.c", 803, __extension__ __PRETTY_FUNCTION__
); }))
;
804 int flag;
805 do {
806 flag = 0;
807 // Try to eliminate one at a time. Go over output first.
808 for (i = 0; i < exec_symbol_info->output_size; i++)
809 {
810 const int d = exec_symbol_info->outputs[i];
811 // If this tensor currently is marked as dead, try to see whether it works when we don't have this tensor at all.
812 if (d >= 0 && (tensor_dead[d >> 5] & (1u << (d & 0x1f))) &&
813 (output_bitmasks[i >> 6] & ((uint64_t)1 << (i & 63))))
814 {
815 output_bitmasks[i >> 6] &= ~((uint64_t)1 << (i & 63));
816 if (ccv_nnc_cmd_bitmask(exec_symbol_info->cmd, exec_symbol_info->input_size, exec_symbol_info->output_size, input_bitmasks, input_bitmask_size, output_bitmasks, output_bitmask_size))
817 flag = 1;
818 else // Reset the bitmask.
819 output_bitmasks[i >> 6] |= ((uint64_t)1 << (i & 63));
820 }
821 }
822 // Only check if there are inputs we can remove if it is backward.
823 if (!ccv_nnc_cmd_is_forward(exec_symbol_info->cmd))
824 // For inputs, no matter if it s dead or not, we try to limit our input to the smallest number.
825 for (i = 0; i < exec_symbol_info->input_size; i++)
826 {
827 const int d = exec_symbol_info->inputs[i];
828 // If this tensor currently is marked as dead, try to see whether it works when we don't have this tensor at all.
829 if (d >= 0 && (input_bitmasks[i >> 6] & ((uint64_t)1 << (i & 63))))
830 {
831 input_bitmasks[i >> 6] &= ~((uint64_t)1 << (i & 63));
832 if (ccv_nnc_cmd_bitmask(exec_symbol_info->cmd, exec_symbol_info->input_size, exec_symbol_info->output_size, input_bitmasks, input_bitmask_size, output_bitmasks, output_bitmask_size))
833 flag = 1;
834 else // Reset the bitmask.
835 input_bitmasks[i >> 6] |= ((uint64_t)1 << (i & 63));
836 }
837 }
838 } while (flag);
839 // Now we know which one to keep, which one to undead.
840 for (i = 0; i < exec_symbol_info->input_size; i++)
841 if (input_bitmasks[i >> 6] & ((uint64_t)1 << (i & 63)))
842 {
843 const int d = exec_symbol_info->inputs[i];
844 if (d >= 0 && !(tensor_visited[d >> 5] & (1u << (d & 0x1f))))
845 {
846 ccv_array_push(next, &d); // Push to the next round to be undead.
847 tensor_visited[d >> 5] |= (1u << (d & 0x1f));
848 }
849 } else {
850 // Clean up the inputs.
851 exec_symbol_info->inputs[i] = CCV_NNC_NO_TENSOR_SYMBOL;
852 assert(((ccv_nnc_graph_exec_symbol_info_t*)ccv_array_get(simplify->graph->exec_symbol_info, exec_idx))->inputs == exec_symbol_info->inputs)((void) sizeof ((((ccv_nnc_graph_exec_symbol_info_t*)((void*)
(((char*)((simplify->graph->exec_symbol_info)->data)
) + (size_t)(simplify->graph->exec_symbol_info)->rsize
* (size_t)(exec_idx))))->inputs == exec_symbol_info->inputs
) ? 1 : 0), __extension__ ({ if (((ccv_nnc_graph_exec_symbol_info_t
*)((void*)(((char*)((simplify->graph->exec_symbol_info)
->data)) + (size_t)(simplify->graph->exec_symbol_info
)->rsize * (size_t)(exec_idx))))->inputs == exec_symbol_info
->inputs) ; else __assert_fail ("((ccv_nnc_graph_exec_symbol_info_t*)ccv_array_get(simplify->graph->exec_symbol_info, exec_idx))->inputs == exec_symbol_info->inputs"
, "ccv_nnc_symbolic_graph_simplify.c", 852, __extension__ __PRETTY_FUNCTION__
); }))
;
853 }
854 for (i = 0; i < exec_symbol_info->output_size; i++)
855 if (output_bitmasks[i >> 6] & ((uint64_t)1 << (i & 63)))
856 {
857 const int d = exec_symbol_info->outputs[i];
858 if (d >= 0 && !(tensor_visited[d >> 5] & (1u << (d & 0x1f))))
859 {
860 ccv_array_push(next, &d); // Push to the next round to be undead.
861 tensor_visited[d >> 5] |= (1u << (d & 0x1f));
862 }
863 } else {
864 // Clean up the outputs.
865 exec_symbol_info->outputs[i] = CCV_NNC_NO_TENSOR_SYMBOL;
866 assert(((ccv_nnc_graph_exec_symbol_info_t*)ccv_array_get(simplify->graph->exec_symbol_info, exec_idx))->outputs == exec_symbol_info->outputs)((void) sizeof ((((ccv_nnc_graph_exec_symbol_info_t*)((void*)
(((char*)((simplify->graph->exec_symbol_info)->data)
) + (size_t)(simplify->graph->exec_symbol_info)->rsize
* (size_t)(exec_idx))))->outputs == exec_symbol_info->
outputs) ? 1 : 0), __extension__ ({ if (((ccv_nnc_graph_exec_symbol_info_t
*)((void*)(((char*)((simplify->graph->exec_symbol_info)
->data)) + (size_t)(simplify->graph->exec_symbol_info
)->rsize * (size_t)(exec_idx))))->outputs == exec_symbol_info
->outputs) ; else __assert_fail ("((ccv_nnc_graph_exec_symbol_info_t*)ccv_array_get(simplify->graph->exec_symbol_info, exec_idx))->outputs == exec_symbol_info->outputs"
, "ccv_nnc_symbolic_graph_simplify.c", 866, __extension__ __PRETTY_FUNCTION__
); }))
;
867 }
868}
869
870static void _ccv_nnc_symbolic_graph_pruning(ccv_nnc_symbolic_graph_simplify_t* const simplify, const ccv_nnc_tensor_symbol_t* const outputs, const int output_size)
871{
872 uint32_t* const tensor_visited = (uint32_t*)cccalloccalloc(sizeof(uint32_t), (simplify->tensor_symbol_info_size + 31) >> 5);
873 ccv_array_t* const preserve[2] = {
874 ccv_array_new(sizeof(int), output_size, 0),
875 ccv_array_new(sizeof(int), 0, 0),
876 };
877 int i, j;
878 ccv_array_t** const r_alias_refs = (ccv_array_t**)cccalloccalloc(sizeof(ccv_array_t*), simplify->tensor_symbol_info_size);
879 for (i = 0; i < simplify->tensor_symbol_info_size; i++)
5
Assuming 'i' is >= field 'tensor_symbol_info_size'
6
Loop condition is false. Execution continues on line 888
880 if (simplify->tensor_symbol_info[i].alias_ref)
881 {
882 const int alias_ref = simplify->tensor_symbol_info[i].alias_ref - 1;
883 assert(alias_ref < simplify->tensor_symbol_info_size)((void) sizeof ((alias_ref < simplify->tensor_symbol_info_size
) ? 1 : 0), __extension__ ({ if (alias_ref < simplify->
tensor_symbol_info_size) ; else __assert_fail ("alias_ref < simplify->tensor_symbol_info_size"
, "ccv_nnc_symbolic_graph_simplify.c", 883, __extension__ __PRETTY_FUNCTION__
); }))
;
884 if (!r_alias_refs[alias_ref])
885 r_alias_refs[alias_ref] = ccv_array_new(sizeof(int), 1, 0);
886 ccv_array_push(r_alias_refs[alias_ref], &i);
887 }
888 uint32_t* const exec_dead = simplify->exec_dead;
889 uint32_t* const tensor_dead = simplify->tensor_dead;
890 int* const output_execs = simplify->output_execs;
891 _ccv_nnc_symbolic_graph_simplify_update_output_execs(simplify);
892 // Mark everything visited as dead.
893 ccv_nnc_graph_visit_for(simplify->visit, simplify->exec_symbol_info, node, idx){ int _i_; for (_i_ = 0; _i_ < (simplify->visit)->size
; _i_++) { const int idx __attribute__((unused)) = (simplify->
visit)->node[_i_].index; const int _node_unused_ __attribute__
((unused)) = (simplify->visit)->node[_i_].term; typeof (
(simplify->exec_symbol_info)) const node __attribute__((unused
)) = (simplify->exec_symbol_info) + idx;
{
7
Loop condition is false. Execution continues on line 909
894 exec_dead[idx >> 5] |= (1u << (idx & 0x1f));
895 for (i = 0; i < node->input_size; i++)
896 {
897 const int d = node->inputs[i];
898 if (d >= 0)
899 tensor_dead[d >> 5] |= (1u << (d & 0x1f));
900 }
901 for (i = 0; i < node->output_size; i++)
902 {
903 const int d = node->outputs[i];
904 if (d >= 0)
905 tensor_dead[d >> 5] |= (1u << (d & 0x1f));
906 }
907 } ccv_nnc_graph_visit_endfor} }
908 // If the tensor symbol is used by other exec that is not visited, unmark it.
909 for (i = 0; i < simplify->exec_symbol_info_size; i++)
8
Assuming 'i' is >= field 'exec_symbol_info_size'
9
Loop condition is false. Execution continues on line 929
910 {
911 if (exec_dead[i >> 5] & (1u << (i & 0x1f)))
912 continue;
913 const ccv_nnc_graph_exec_symbol_info_t* const node = simplify->exec_symbol_info + i;
914 for (j = 0; j < node->input_size; j++)
915 {
916 const int d = node->inputs[j];
917 // Undead it.
918 if (d >= 0)
919 tensor_dead[d >> 5] &= ~(1u << (d & 0x1f));
920 }
921 for (j = 0; j < node->output_size; j++)
922 {
923 const int d = node->outputs[j];
924 // Undead it.
925 if (d >= 0)
926 tensor_dead[d >> 5] &= ~(1u << (d & 0x1f));
927 }
928 }
929 for (i = 0; i < output_size; i++)
10
Assuming 'i' is >= 'output_size'
11
Loop condition is false. Execution continues on line 931
930 ccv_array_push(preserve[0], &outputs[i].d);
931 int p = 0, q = 1;
932 // BFS to mark execs / tensors as not dead.
933 while (preserve[p]->rnum > 0)
12
Assuming field 'rnum' is > 0
13
Loop condition is true. Entering loop body
934 {
935 ccv_array_clear(preserve[q]);
936 // First, undead all relevant tensors.
937 for (i = 0; i
13.1
'i' is < field 'rnum'
< preserve[p]->rnum
; i++)
14
Loop condition is true. Entering loop body
19
Assuming 'i' is >= field 'rnum'
20
Loop condition is false. Execution continues on line 957
938 {
939 const int d = *(int*)ccv_array_get(preserve[p], i)((void*)(((char*)((preserve[p])->data)) + (size_t)(preserve
[p])->rsize * (size_t)(i)))
;
940 // Undead the outputs.
941 tensor_dead[d >> 5] &= ~(1u << (d & 0x1f));
942 int alias_ref = d;
943 if (simplify->tensor_symbol_info[d].alias_ref)
15
Assuming field 'alias_ref' is 0
16
Taking false branch
944 {
945 alias_ref = simplify->tensor_symbol_info[d].alias_ref - 1;
946 tensor_dead[alias_ref >> 5] &= ~(1u << (alias_ref & 0x1f));
947 assert(r_alias_refs[alias_ref])((void) sizeof ((r_alias_refs[alias_ref]) ? 1 : 0), __extension__
({ if (r_alias_refs[alias_ref]) ; else __assert_fail ("r_alias_refs[alias_ref]"
, "ccv_nnc_symbolic_graph_simplify.c", 947, __extension__ __PRETTY_FUNCTION__
); }))
;
948 }
949 if (r_alias_refs[alias_ref])
17
Assuming the condition is false
18
Taking false branch
950 for (j = 0; j < r_alias_refs[alias_ref]->rnum; j++)
951 {
952 const int b = *(int*)ccv_array_get(r_alias_refs[alias_ref], j)((void*)(((char*)((r_alias_refs[alias_ref])->data)) + (size_t
)(r_alias_refs[alias_ref])->rsize * (size_t)(j)))
;
953 if (output_execs[b] >= 0) // Only revive if it is written alias.
954 tensor_dead[b >> 5] &= ~(1u << (b & 0x1f));
955 }
956 }
957 for (i = 0; i < preserve[p]->rnum; i++)
21
Loop condition is true. Entering loop body
958 {
959 const int d = *(int*)ccv_array_get(preserve[p], i)((void*)(((char*)((preserve[p])->data)) + (size_t)(preserve
[p])->rsize * (size_t)(i)))
;
960 const int output_exec = output_execs[d];
961 // Undead the exec.
962 if (output_exec >= 0)
22
Assuming 'output_exec' is >= 0
23
Taking true branch
963 _ccv_nnc_symbolic_graph_pruning_undead_exec(simplify, output_exec, tensor_visited, preserve[q]);
24
Calling '_ccv_nnc_symbolic_graph_pruning_undead_exec'
964 int alias_ref = d;
965 if (simplify->tensor_symbol_info[d].alias_ref)
966 {
967 alias_ref = simplify->tensor_symbol_info[d].alias_ref - 1;
968 const int output_exec = output_execs[alias_ref];
969 if (output_exec >= 0)
970 _ccv_nnc_symbolic_graph_pruning_undead_exec(simplify, output_exec, tensor_visited, preserve[q]);
971 }
972 if (r_alias_refs[alias_ref])
973 for (j = 0; j < r_alias_refs[alias_ref]->rnum; j++)
974 {
975 const int b = *(int*)ccv_array_get(r_alias_refs[alias_ref], j)((void*)(((char*)((r_alias_refs[alias_ref])->data)) + (size_t
)(r_alias_refs[alias_ref])->rsize * (size_t)(j)))
;
976 const int output_exec = output_execs[b];
977 if (output_exec >= 0)
978 _ccv_nnc_symbolic_graph_pruning_undead_exec(simplify, output_exec, tensor_visited, preserve[q]);
979 }
980 }
981 CCV_SWAP(p, q, i)((i) = (p), (p) = (q), (q) = (i));
982 }
983 ccfreefree(tensor_visited);
984 ccv_array_free(preserve[0]);
985 ccv_array_free(preserve[1]);
986 for (i = 0; i < simplify->tensor_symbol_info_size; i++)
987 if (r_alias_refs[i])
988 ccv_array_free(r_alias_refs[i]);
989 ccfreefree(r_alias_refs);
990}
991
992void ccv_nnc_symbolic_graph_simplify(ccv_nnc_symbolic_graph_t* const graph, const int* const passes, const int pass_size, const ccv_nnc_tensor_symbol_t* const binds, const int bind_size, const ccv_nnc_tensor_symbol_t* const outputs, const int output_size, const ccv_nnc_graph_exec_symbol_t* const sources, const int source_size, const ccv_nnc_graph_exec_symbol_t* const destinations, const int destination_size)
993{
994 ccv_nnc_symbolic_graph_simplify_t* const simplify = _ccv_nnc_symbolic_graph_simplify_new(graph, sources, source_size, destinations, destination_size);
995 int i;
996 for (i = 0; i < pass_size; i++)
1
Assuming 'i' is < 'pass_size'
2
Loop condition is true. Entering loop body
997 switch (passes[i])
3
Control jumps to 'case CCV_NNC_SIMPLIFY_GRAPH_PRUNING:' at line 1005
998 {
999 case CCV_NNC_SIMPLIFY_COMMON_SUBEXPRESSION_ELIMINATION:
1000 _ccv_nnc_symbolic_graph_common_subexpression_elimination(simplify, outputs, output_size);
1001 break;
1002 case CCV_NNC_SIMPLIFY_DATA_TRANSFER_OPT:
1003 _ccv_nnc_symbolic_graph_data_transfer_opt(simplify, binds, bind_size, outputs, output_size);
1004 break;
1005 case CCV_NNC_SIMPLIFY_GRAPH_PRUNING:
1006 _ccv_nnc_symbolic_graph_pruning(simplify, outputs, output_size);
4
Calling '_ccv_nnc_symbolic_graph_pruning'
1007 break;
1008 case CCV_NNC_SIMPLIFY_OPS_FUSION:
1009 _ccv_nnc_symbolic_graph_ops_fusion(simplify, outputs, output_size);
1010 break;
1011 }
1012 _ccv_nnc_symbolic_graph_simplify_apply(simplify);
1013 _ccv_nnc_symbolic_graph_simplify_free(simplify);
1014}