Bug Summary

File:nnc/ccv_cnnp_model_gradient_checkpointing.c
Warning:line 413, column 26
Array access (via field 'vals') results in a null pointer dereference

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_cnnp_model_gradient_checkpointing.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/2024-09-15-161418-261911-1 -x c ccv_cnnp_model_gradient_checkpointing.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_cnnp_model.h"
6// This can be removed once we organized ccv_cnnp_apply_gradient_checkpoints better.
7#include "_ccv_nnc_symbolic_graph.h"
8
9typedef struct {
10 ccv_array_t* outgoings;
11} ccv_nnc_graph_exec_symbol_reverse_t;
12
13typedef struct {
14 ccv_array_t* tensor_symbols;
15 void* old_tensor_symbol_new_hook_context;
16 ccv_nnc_tensor_symbol_new_hook_f old_tensor_symbol_new_hook;
17 void* old_tensor_symbol_alias_new_hook_context;
18 ccv_nnc_tensor_symbol_alias_new_hook_f old_tensor_symbol_alias_new_hook;
19 ccv_array_t* graph_exec_symbols;
20 ccv_nnc_graph_exec_symbol_new_hook_f old_graph_exec_symbol_new_hook;
21 void* old_graph_exec_symbol_new_hook_context;
22} ccv_cnnp_gradient_checkpoint_build_t;
23
24static void _ccv_cnnp_gradient_checkpoint_tensor_symbol_new_hook(void* context, const ccv_nnc_tensor_symbol_t symbol, const ccv_nnc_tensor_param_t info, const char* const name)
25{
26 ccv_cnnp_gradient_checkpoint_build_t* const build_context = (ccv_cnnp_gradient_checkpoint_build_t*)context;
27 ccv_array_push(build_context->tensor_symbols, &symbol);
28 if (build_context->old_tensor_symbol_new_hook)
29 build_context->old_tensor_symbol_new_hook(build_context->old_tensor_symbol_new_hook_context, symbol, info, name);
30}
31
32static void _ccv_cnnp_gradient_checkpoint_tensor_symbol_alias_new_hook(void* context, const ccv_nnc_tensor_symbol_t symbol, const ccv_nnc_tensor_symbol_t from_symbol, const int ofs[CCV_NNC_MAX_DIM_ALLOC(12)], const int inc[CCV_NNC_MAX_DIM_ALLOC(12)], const ccv_nnc_tensor_param_t info, const char* const name)
33{
34 ccv_cnnp_gradient_checkpoint_build_t* const build_context = (ccv_cnnp_gradient_checkpoint_build_t*)context;
35 ccv_array_push(build_context->tensor_symbols, &symbol);
36 if (build_context->old_tensor_symbol_alias_new_hook)
37 build_context->old_tensor_symbol_alias_new_hook(build_context->old_tensor_symbol_alias_new_hook_context, symbol, from_symbol, ofs, inc, info, name);
38}
39
40static void _ccv_cnnp_model_gradient_checkpoint_graph_exec_symbol_new_hook(void* context, const ccv_nnc_graph_exec_symbol_t symbol, const ccv_nnc_cmd_t cmd, const ccv_nnc_tensor_symbol_t* const inputs, const int input_size, const ccv_nnc_tensor_symbol_t* const outputs, const int output_size, const char* const name)
41{
42 ccv_cnnp_gradient_checkpoint_build_t* const build = (ccv_cnnp_gradient_checkpoint_build_t*)context;
43 ccv_array_push(build->graph_exec_symbols, &symbol);
44 if (build->old_graph_exec_symbol_new_hook)
45 build->old_graph_exec_symbol_new_hook(build->old_graph_exec_symbol_new_hook_context, symbol, cmd, inputs, input_size, outputs, output_size, name);
46}
47
48KHASH_MAP_INIT_INT(ccv_cnnp_tensor_symbol_map, int)typedef struct kh_ccv_cnnp_tensor_symbol_map_s { khint_t n_buckets
, size, n_occupied, upper_bound; khint32_t *flags; khint32_t *
keys; int *vals; } kh_ccv_cnnp_tensor_symbol_map_t; static inline
__attribute__ ((__unused__)) kh_ccv_cnnp_tensor_symbol_map_t
*kh_init_ccv_cnnp_tensor_symbol_map(void) { return (kh_ccv_cnnp_tensor_symbol_map_t
*)calloc(1,sizeof(kh_ccv_cnnp_tensor_symbol_map_t)); } static
inline __attribute__ ((__unused__)) void kh_destroy_ccv_cnnp_tensor_symbol_map
(kh_ccv_cnnp_tensor_symbol_map_t *h) { if (h) { free((void *)
h->keys); free(h->flags); free((void *)h->vals); free
(h); } } static inline __attribute__ ((__unused__)) void kh_clear_ccv_cnnp_tensor_symbol_map
(kh_ccv_cnnp_tensor_symbol_map_t *h) { if (h && h->
flags) { memset(h->flags, 0xaa, ((h->n_buckets) < 16
? 1 : (h->n_buckets)>>4) * sizeof(khint32_t)); h->
size = h->n_occupied = 0; } } static inline __attribute__ (
(__unused__)) khint_t kh_get_ccv_cnnp_tensor_symbol_map(const
kh_ccv_cnnp_tensor_symbol_map_t *h, khint32_t key) { if (h->
n_buckets) { khint_t k, i, last, mask, step = 0; mask = h->
n_buckets - 1; k = (khint32_t)(key); i = k & mask; last =
i; while (!((h->flags[i>>4]>>((i&0xfU)<<
1))&2) && (((h->flags[i>>4]>>((i&
0xfU)<<1))&1) || !((h->keys[i]) == (key)))) { i =
(i + (++step)) & mask; if (i == last) return h->n_buckets
; } return ((h->flags[i>>4]>>((i&0xfU)<<
1))&3)? h->n_buckets : i; } else return 0; } static inline
__attribute__ ((__unused__)) int kh_resize_ccv_cnnp_tensor_symbol_map
(kh_ccv_cnnp_tensor_symbol_map_t *h, khint_t new_n_buckets) {
khint32_t *new_flags = 0; khint_t j = 1; { (--(new_n_buckets
), (new_n_buckets)|=(new_n_buckets)>>1, (new_n_buckets)
|=(new_n_buckets)>>2, (new_n_buckets)|=(new_n_buckets)>>
4, (new_n_buckets)|=(new_n_buckets)>>8, (new_n_buckets)
|=(new_n_buckets)>>16, ++(new_n_buckets)); if (new_n_buckets
< 4) new_n_buckets = 4; if (h->size >= (khint_t)(new_n_buckets
* __ac_HASH_UPPER + 0.5)) j = 0; else { new_flags = (khint32_t
*)malloc(((new_n_buckets) < 16? 1 : (new_n_buckets)>>
4) * sizeof(khint32_t)); if (!new_flags) return -1; memset(new_flags
, 0xaa, ((new_n_buckets) < 16? 1 : (new_n_buckets)>>
4) * sizeof(khint32_t)); if (h->n_buckets < new_n_buckets
) { khint32_t *new_keys = (khint32_t*)realloc((void *)h->keys
,new_n_buckets * sizeof(khint32_t)); if (!new_keys) { free(new_flags
); return -1; } h->keys = new_keys; if (1) { int *new_vals
= (int*)realloc((void *)h->vals,new_n_buckets * sizeof(int
)); if (!new_vals) { free(new_flags); return -1; } h->vals
= new_vals; } } } } if (j) { for (j = 0; j != h->n_buckets
; ++j) { if (((h->flags[j>>4]>>((j&0xfU)<<
1))&3) == 0) { khint32_t key = h->keys[j]; int val; khint_t
new_mask; new_mask = new_n_buckets - 1; if (1) val = h->vals
[j]; (h->flags[j>>4]|=1ul<<((j&0xfU)<<
1)); while (1) { khint_t k, i, step = 0; k = (khint32_t)(key)
; i = k & new_mask; while (!((new_flags[i>>4]>>
((i&0xfU)<<1))&2)) i = (i + (++step)) & new_mask
; (new_flags[i>>4]&=~(2ul<<((i&0xfU)<<
1))); if (i < h->n_buckets && ((h->flags[i>>
4]>>((i&0xfU)<<1))&3) == 0) { { khint32_t
tmp = h->keys[i]; h->keys[i] = key; key = tmp; } if (1
) { int tmp = h->vals[i]; h->vals[i] = val; val = tmp; }
(h->flags[i>>4]|=1ul<<((i&0xfU)<<1)
); } else { h->keys[i] = key; if (1) h->vals[i] = val; break
; } } } } if (h->n_buckets > new_n_buckets) { h->keys
= (khint32_t*)realloc((void *)h->keys,new_n_buckets * sizeof
(khint32_t)); if (1) h->vals = (int*)realloc((void *)h->
vals,new_n_buckets * sizeof(int)); } free(h->flags); h->
flags = new_flags; h->n_buckets = new_n_buckets; h->n_occupied
= h->size; h->upper_bound = (khint_t)(h->n_buckets *
__ac_HASH_UPPER + 0.5); } return 0; } static inline __attribute__
((__unused__)) khint_t kh_put_ccv_cnnp_tensor_symbol_map(kh_ccv_cnnp_tensor_symbol_map_t
*h, khint32_t key, int *ret) { khint_t x; if (h->n_occupied
>= h->upper_bound) { if (h->n_buckets > (h->size
<<1)) { if (kh_resize_ccv_cnnp_tensor_symbol_map(h, h->
n_buckets - 1) < 0) { *ret = -1; return h->n_buckets; }
} else if (kh_resize_ccv_cnnp_tensor_symbol_map(h, h->n_buckets
+ 1) < 0) { *ret = -1; return h->n_buckets; } } { khint_t
k, i, site, last, mask = h->n_buckets - 1, step = 0; x = site
= h->n_buckets; k = (khint32_t)(key); i = k & mask; if
(((h->flags[i>>4]>>((i&0xfU)<<1))&
2)) x = i; else { last = i; while (!((h->flags[i>>4]
>>((i&0xfU)<<1))&2) && (((h->flags
[i>>4]>>((i&0xfU)<<1))&1) || !((h->
keys[i]) == (key)))) { if (((h->flags[i>>4]>>(
(i&0xfU)<<1))&1)) site = i; i = (i + (++step)) &
mask; if (i == last) { x = site; break; } } if (x == h->n_buckets
) { if (((h->flags[i>>4]>>((i&0xfU)<<
1))&2) && site != h->n_buckets) x = site; else
x = i; } } } if (((h->flags[x>>4]>>((x&0xfU
)<<1))&2)) { h->keys[x] = key; (h->flags[x>>
4]&=~(3ul<<((x&0xfU)<<1))); ++h->size;
++h->n_occupied; *ret = 1; } else if (((h->flags[x>>
4]>>((x&0xfU)<<1))&1)) { h->keys[x] = key
; (h->flags[x>>4]&=~(3ul<<((x&0xfU)<<
1))); ++h->size; *ret = 2; } else *ret = 0; return x; } static
inline __attribute__ ((__unused__)) void kh_del_ccv_cnnp_tensor_symbol_map
(kh_ccv_cnnp_tensor_symbol_map_t *h, khint_t x) { if (x != h->
n_buckets && !((h->flags[x>>4]>>((x&
0xfU)<<1))&3)) { (h->flags[x>>4]|=1ul<<
((x&0xfU)<<1)); --h->size; } }
85
←
Null pointer value stored to field 'vals'
→
104
←
Taking true branch
→
105
←
Taking false branch
→
106
←
Calling 'kh_resize_ccv_cnnp_tensor_symbol_map'
→
107
←
Taking true branch
→
108
←
Assuming the condition is false
→
109
←
Taking false branch
→
110
←
'?' condition is true
→
111
←
Assuming 'new_flags' is null
→
112
←
Taking true branch
→
113
←
Returning without writing to 'h->vals'
→
114
←
Returning from 'kh_resize_ccv_cnnp_tensor_symbol_map'
→
115
←
Taking true branch
→
116
←
Returning without writing to 'h->vals'
→
49
50void ccv_cnnp_model_apply_gradient_checkpoints(ccv_cnnp_compiled_data_t* const compiled_data, ccv_nnc_symbolic_graph_t* const graph)
51{
52 ccv_array_t* const gradient_checkpoints = compiled_data->gradient_checkpoints;
53 if (!gradient_checkpoints || gradient_checkpoints->rnum == 0) // No saved gradient checkpoints, this is an easy way out.
1
Assuming 'gradient_checkpoints' is non-null
→
2
←
Assuming field 'rnum' is not equal to 0
→
3
←
Taking false branch
→
54 return;
55 // Otherwise, for each gradient checkpoint, there are 3 steps:
56 // 1. Find currently, what execs exists from inputs to outputs.
57 // 2. Find execs that generates the outputs, and their corresponding backward execs.
58 // 3. Find all backward execs flow from outputs back to inputs.
59 // 4. Generate new ops by calling build again with old inputs, record all new tensors / execs.
60 // 5. Replace inputs in backward execs with the new tensors.
61 // 6. Hook the execs takes inputs with edge from parents of backward execs in step 2.
62 // 7. Delete newly generated execs that has no use (i.e. its outputs are not used by backward pass).
63 // 8. Mark all new execs with DISABLE_OPT to avoid common sub-expression elimination pass.
64 int i, j, k, l;
65 ccv_array_t* input_execs = ccv_array_new(sizeof(ccv_nnc_graph_exec_symbol_t), 0, 0);
66 ccv_array_t* output_execs = ccv_array_new(sizeof(ccv_nnc_graph_exec_symbol_t), 0, 0);
67 ccv_array_t* input_gradient_execs = ccv_array_new(sizeof(ccv_nnc_graph_exec_symbol_t), 0, 0);
68 ccv_array_t* output_gradient_execs = ccv_array_new(sizeof(ccv_nnc_graph_exec_symbol_t), 0, 0);
69 ccv_array_t* visited_backward_execs = ccv_array_new(sizeof(int), 0, 0);
70 ccv_array_t* replaced_backward_execs = ccv_array_new(sizeof(int), 0, 0);
71 const int exec_rnum = graph->exec_symbol_info->rnum;
72 ccv_nnc_graph_exec_symbol_reverse_t* const reversed_nodes = cccalloccalloc(exec_rnum, sizeof(ccv_nnc_graph_exec_symbol_reverse_t));
73 for (i = 0; i < exec_rnum; i++)
4
←
Assuming 'i' is >= 'exec_rnum'
→
5
←
Loop condition is false. Execution continues on line 89
→
74 {
75 const int* tos = 0;
76 int to_size = 0;
77 ccv_nnc_graph_exec_symbol_to(graph, (ccv_nnc_graph_exec_symbol_t){
78 .graph = graph,
79 .d = i
80 }, &tos, &to_size);
81 if (tos)
82 for (j = 0; j < to_size; j++)
83 {
84 if (!reversed_nodes[tos[j]].outgoings)
85 reversed_nodes[tos[j]].outgoings = ccv_array_new(sizeof(int), 1, 0);
86 ccv_array_add_unique_int(reversed_nodes[tos[j]].outgoings, i);
87 }
88 }
89 uint32_t* const maskbit = cccalloccalloc((exec_rnum + 31) >> 5, sizeof(uint32_t));
90 // Temporary for build_data.
91 ccv_array_t* const parameters = ccv_array_new(sizeof(ccv_nnc_tensor_symbol_t), 0, 0);
92 ccv_array_t* const parameter_ids = ccv_array_new(sizeof(char*), 0, 0);
93 ccv_array_t* const parameter_trainables = ccv_array_new(sizeof(int), 0, 0);
94 ccv_array_t* const internals = ccv_array_new(sizeof(ccv_nnc_tensor_symbol_t), 0, 0);
95 ccv_array_t* const internal_ids = ccv_array_new(sizeof(char*), 0, 0);
96 ccv_array_t* const buf = ccv_array_new(sizeof(int), 0, 0);
97 int max_output_size = 0;
98 for (i = 0; i < gradient_checkpoints->rnum; i++)
6
←
Assuming 'i' is < field 'rnum'
→
7
←
Loop condition is true. Entering loop body
→
10
←
Assuming 'i' is >= field 'rnum'
→
11
←
Loop condition is false. Execution continues on line 103
→
99 {
100 ccv_cnnp_model_gradient_checkpoint_t* const checkpoint = (ccv_cnnp_model_gradient_checkpoint_t*)ccv_array_get(gradient_checkpoints, i)((void*)(((char*)((gradient_checkpoints)->data)) + (size_t
)(gradient_checkpoints)->rsize * (size_t)(i)));
101 max_output_size = ccv_max(checkpoint->output_size, max_output_size)({ typeof (checkpoint->output_size) _a = (checkpoint->output_size
); typeof (max_output_size) _b = (max_output_size); (_a > _b
) ? _a : _b; });
8
←
Assuming '_a' is <= '_b'
→
9
←
'?' condition is false
→
102 }
103 ccv_nnc_tensor_symbol_t* max_outputs = ccmallocmalloc(sizeof(ccv_nnc_tensor_symbol_t) * max_output_size);
104 ccv_array_t* newly_used_outputs = ccv_array_new(sizeof(int), 0, 0);
105 for (i = 0; i < gradient_checkpoints->rnum; i++)
12
←
Loop condition is true. Entering loop body
→
106 {
107 ccv_cnnp_model_gradient_checkpoint_t* const checkpoint = (ccv_cnnp_model_gradient_checkpoint_t*)ccv_array_get(gradient_checkpoints, i)((void*)(((char*)((gradient_checkpoints)->data)) + (size_t
)(gradient_checkpoints)->rsize * (size_t)(i)));
108 ccv_array_clear(input_execs);
109 ccv_array_clear(output_execs);
110 ccv_nnc_graph_exec_symbol_info_t* exec_info = (ccv_nnc_graph_exec_symbol_info_t*)ccv_array_get(graph->exec_symbol_info, 0)((void*)(((char*)((graph->exec_symbol_info)->data)) + (
size_t)(graph->exec_symbol_info)->rsize * (size_t)(0)));
111 for (j = 0; j
12.1
'j' is >= 'exec_rnum'
 < exec_rnum; j++)
112 {
113 if (CCV_NNC_GRAPH_EXEC_IS_DEAD(exec_info[j].flags)((exec_info[j].flags) & CCV_NNC_GRAPH_EXEC_DEAD))
114 continue;
115 const int* inputs = exec_info[j].inputs;
116 int input_size = exec_info[j].input_size;
117 const int* outputs = exec_info[j].outputs;
118 int output_size = exec_info[j].output_size;
119 if (input_size == 0 && output_size == 0)
120 continue;
121 // Only go through forward pass.
122 if (ccv_nnc_cmd_is_backward(exec_info[j].cmd))
123 continue;
124 const ccv_nnc_graph_exec_symbol_t symbol = {
125 .graph = graph,
126 .d = j
127 };
128 int flag = 0;
129 for (k = 0; inputs && k < input_size && !flag; k++)
130 if (inputs[k] >= 0)
131 for (l = 0; l < checkpoint->input_size && !flag; l++)
132 if (checkpoint->inputs[l].d >= 0 && inputs[k] == checkpoint->inputs[l].d)
133 flag = 1;
134 if (flag)
135 ccv_array_push(input_execs, &symbol);
136 flag = 0;
137 for (k = 0; outputs && k < output_size && !flag; k++)
138 if (outputs[k] >= 0)
139 for (l = 0; l < checkpoint->output_size && !flag; l++)
140 if (checkpoint->outputs[l].d >= 0 && outputs[k] == checkpoint->outputs[l].d)
141 flag = 1;
142 if (flag)
143 ccv_array_push(output_execs, &symbol);
144 }
145 if (input_execs->rnum <= 0 || output_execs->rnum <= 0)
13
←
Assuming field 'rnum' is > 0
→
14
←
Assuming field 'rnum' is > 0
→
15
←
Taking false branch
→
146 continue;
147 // Fill in blanks (i.e. the backward ops that are not showing in above, but should be included to avoid excluding necessary ones). This is done by flowing gradients from outputs back all the way to inputs.
148 ccv_array_clear(input_gradient_execs);
149 ccv_array_clear(output_gradient_execs);
150 for (j = 0; j
15.1
'j' is < field 'rnum'
 < input_execs->rnum; j++)
16
←
Loop condition is true. Entering loop body
→
19
←
Assuming 'j' is >= field 'rnum'
→
20
←
Loop condition is false. Execution continues on line 196
→
151 {
152 const int d = ((ccv_nnc_graph_exec_symbol_t*)ccv_array_get(input_execs, j)((void*)(((char*)((input_execs)->data)) + (size_t)(input_execs
)->rsize * (size_t)(j))))->d;
153 for (k = 0; k < exec_info[d].input_size; k++)
17
←
Assuming 'k' is >= field 'input_size'
→
154 if (exec_info[d].inputs[k] >= 0)
155 {
156 const ccv_nnc_tensor_symbol_t gradient_symbol = ccv_nnc_tensor_symbol_for_backward(graph, (ccv_nnc_tensor_symbol_t){
157 .graph = graph,
158 .d = exec_info[d].inputs[k]
159 });
160 if (gradient_symbol.d < 0)
161 continue;
162 const ccv_nnc_graph_exec_symbol_t backward = ccv_nnc_graph_exec_symbol_for_backward(graph, gradient_symbol);
163 if (backward.d < 0)
164 continue;
165 if (CCV_NNC_GRAPH_EXEC_IS_DEAD(exec_info[backward.d].flags)((exec_info[backward.d].flags) & CCV_NNC_GRAPH_EXEC_DEAD))
166 continue;
167 int flag = 0;
168 for (l = 0; !flag && l < output_gradient_execs->rnum; l++)
169 if (((ccv_nnc_graph_exec_symbol_t*)ccv_array_get(output_gradient_execs, l)((void*)(((char*)((output_gradient_execs)->data)) + (size_t
)(output_gradient_execs)->rsize * (size_t)(l))))->d == backward.d)
170 flag = 1;
171 if (!flag)
172 ccv_array_push(output_gradient_execs, &backward);
173 }
174 if (exec_info[d].outgoings && exec_info[d].outgoings->rnum > 0)
18
←
Assuming field 'outgoings' is null
→
175 for (k = 0; k < exec_info[d].outgoings->rnum; k++)
176 {
177 const int to_d = *(int*)ccv_array_get(exec_info[d].outgoings, k)((void*)(((char*)((exec_info[d].outgoings)->data)) + (size_t
)(exec_info[d].outgoings)->rsize * (size_t)(k)));
178 if (!ccv_nnc_cmd_is_backward(exec_info[to_d].cmd))
179 continue;
180 int flag = 0;
181 for (l = 0; !flag && l < output_gradient_execs->rnum; l++)
182 if (((ccv_nnc_graph_exec_symbol_t*)ccv_array_get(output_gradient_execs, l)((void*)(((char*)((output_gradient_execs)->data)) + (size_t
)(output_gradient_execs)->rsize * (size_t)(l))))->d == to_d)
183 flag = 1;
184 if (!flag)
185 {
186 const ccv_nnc_graph_exec_symbol_t backward = {
187 .graph = graph,
188 .d = to_d
189 };
190 ccv_array_push(output_gradient_execs, &backward);
191 }
192 }
193 }
194 // For output_gradient_execs, we can be opportunistic and use the wrt symbols (if exists) to find relevant bits.
195 // For input_gradient_execs, there is no other way but to loop over all outgoings, find the ones are direct link as backward execs.
196 for (j = 0; j
20.1
'j' is < field 'rnum'
 < output_execs->rnum; j++)
21
←
Loop condition is true. Entering loop body
→
33
←
Assuming 'j' is >= field 'rnum'
→
34
←
Loop condition is false. Execution continues on line 220
→
197 {
198 const int d = ((ccv_nnc_graph_exec_symbol_t*)ccv_array_get(output_execs, j)((void*)(((char*)((output_execs)->data)) + (size_t)(output_execs
)->rsize * (size_t)(j))))->d;
199 if (exec_info[d].outgoings && exec_info[d].outgoings->rnum > 0)
22
←
Assuming field 'outgoings' is non-null
→
23
←
Assuming field 'rnum' is > 0
→
24
←
Taking true branch
→
200 for (k = 0; k
24.1
'k' is < field 'rnum'
 < exec_info[d].outgoings->rnum; k++)
25
←
Loop condition is true. Entering loop body
→
31
←
Assuming 'k' is >= field 'rnum'
→
32
←
Loop condition is false. Execution continues on line 196
→
201 {
202 const int to_d = *(int*)ccv_array_get(exec_info[d].outgoings, k)((void*)(((char*)((exec_info[d].outgoings)->data)) + (size_t
)(exec_info[d].outgoings)->rsize * (size_t)(k)));
203 if (!ccv_nnc_cmd_is_backward(exec_info[to_d].cmd))
26
←
Assuming the condition is false
→
27
←
Taking false branch
→
204 continue;
205 int flag = 0;
206 for (l = 0; !flag
27.1
'flag' is 0
 && l < input_gradient_execs->rnum; l++)
28
←
Assuming 'l' is >= field 'rnum'
→
29
←
Loop condition is false. Execution continues on line 209
→
207 if (((ccv_nnc_graph_exec_symbol_t*)ccv_array_get(input_gradient_execs, l)((void*)(((char*)((input_gradient_execs)->data)) + (size_t
)(input_gradient_execs)->rsize * (size_t)(l))))->d == to_d)
208 flag = 1;
209 if (!flag
29.1
'flag' is 0
)
30
←
Taking true branch
→
210 {
211 const ccv_nnc_graph_exec_symbol_t backward = {
212 .graph = graph,
213 .d = to_d
214 };
215 ccv_array_push(input_gradient_execs, &backward);
216 }
217 }
218 }
219 // Note that we have to use up-to-date ones because the exec_info might have outgoings that is up-to-date.
220 ccv_nnc_graph_visit_t* const visit = ccv_nnc_graph_visit_new(graph, exec_info, graph->exec_symbol_info->rnum, (ccv_nnc_graph_exec_symbol_t*)ccv_array_get(input_gradient_execs, 0), input_gradient_execs->rnum, (ccv_nnc_graph_exec_symbol_t*)ccv_array_get(output_gradient_execs, 0), output_gradient_execs->rnum, 1)({ 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_ += ((exec_info)[_i_].outgoings
) ? (exec_info)[_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_ < (input_gradient_execs->rnum); _i_
++) { ((void) sizeof ((((ccv_nnc_graph_exec_symbol_t*)((void*
)(((char*)((input_gradient_execs)->data)) + (size_t)(input_gradient_execs
)->rsize * (size_t)(0))))[_i_].graph == graph) ? 1 : 0), __extension__
({ if (((ccv_nnc_graph_exec_symbol_t*)((void*)(((char*)((input_gradient_execs
)->data)) + (size_t)(input_gradient_execs)->rsize * (size_t
)(0))))[_i_].graph == graph) ; else __assert_fail ("((ccv_nnc_graph_exec_symbol_t*)((void*)(((char*)((input_gradient_execs)->data)) + (size_t)(input_gradient_execs)->rsize * (size_t)(0))))[_i_].graph == graph"
, "ccv_cnnp_model_gradient_checkpointing.c", 220, __extension__
__PRETTY_FUNCTION__); })); _incomings_[((ccv_nnc_graph_exec_symbol_t
*)((void*)(((char*)((input_gradient_execs)->data)) + (size_t
)(input_gradient_execs)->rsize * (size_t)(0))))[_i_].d].r =
1; _exists_[0][_i_] = ((ccv_nnc_graph_exec_symbol_t*)((void*
)(((char*)((input_gradient_execs)->data)) + (size_t)(input_gradient_execs
)->rsize * (size_t)(0))))[_i_].d; } int _exist_size_[2] = {
(input_gradient_execs->rnum), 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 ((exec_info)[_idx_].outgoings)
for (_j_ = 0; _j_ < (exec_info)[_idx_].outgoings->rnum
; _j_++) { const int d = *(int*)((void*)(((char*)(((exec_info
)[_idx_].outgoings)->data)) + (size_t)((exec_info)[_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_cnnp_model_gradient_checkpointing.c", 220, __extension__
__PRETTY_FUNCTION__); })); _exists_[_q_][_exist_size_[_q_]] =
d; ++_exist_size_[_q_]; } } ((_i_) = (_p_), (_p_) = (_q_), (
_q_) = (_i_)); } for (_i_ = 0; _i_ < (input_gradient_execs
->rnum); _i_++) { ((void) sizeof ((((ccv_nnc_graph_exec_symbol_t
*)((void*)(((char*)((input_gradient_execs)->data)) + (size_t
)(input_gradient_execs)->rsize * (size_t)(0))))[_i_].graph
== graph) ? 1 : 0), __extension__ ({ if (((ccv_nnc_graph_exec_symbol_t
*)((void*)(((char*)((input_gradient_execs)->data)) + (size_t
)(input_gradient_execs)->rsize * (size_t)(0))))[_i_].graph
== graph) ; else __assert_fail ("((ccv_nnc_graph_exec_symbol_t*)((void*)(((char*)((input_gradient_execs)->data)) + (size_t)(input_gradient_execs)->rsize * (size_t)(0))))[_i_].graph == graph"
, "ccv_cnnp_model_gradient_checkpointing.c", 220, __extension__
__PRETTY_FUNCTION__); })); _incomings_[((ccv_nnc_graph_exec_symbol_t
*)((void*)(((char*)((input_gradient_execs)->data)) + (size_t
)(input_gradient_execs)->rsize * (size_t)(0))))[_i_].d].r =
3; _exists_[0][_i_] = ((ccv_nnc_graph_exec_symbol_t*)((void*
)(((char*)((input_gradient_execs)->data)) + (size_t)(input_gradient_execs
)->rsize * (size_t)(0))))[_i_].d; } _exist_size_[0] = (input_gradient_execs
->rnum); _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 ((exec_info)[_idx_].outgoings)
for (_j_ = 0; _j_ < (exec_info)[_idx_].outgoings->rnum
; _j_++) { const int d = *(int*)((void*)(((char*)(((exec_info
)[_idx_].outgoings)->data)) + (size_t)((exec_info)[_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_cnnp_model_gradient_checkpointing.c", 220, __extension__
__PRETTY_FUNCTION__); })); _exists_[_q_][_exist_size_[_q_]] =
d; ++_exist_size_[_q_]; } } ((_i_) = (_p_), (_p_) = (_q_), (
_q_) = (_i_)); } for (_i_ = 0; _i_ < (output_gradient_execs
->rnum); _i_++) { ((void) sizeof ((((ccv_nnc_graph_exec_symbol_t
*)((void*)(((char*)((output_gradient_execs)->data)) + (size_t
)(output_gradient_execs)->rsize * (size_t)(0))))[_i_].graph
== graph) ? 1 : 0), __extension__ ({ if (((ccv_nnc_graph_exec_symbol_t
*)((void*)(((char*)((output_gradient_execs)->data)) + (size_t
)(output_gradient_execs)->rsize * (size_t)(0))))[_i_].graph
== graph) ; else __assert_fail ("((ccv_nnc_graph_exec_symbol_t*)((void*)(((char*)((output_gradient_execs)->data)) + (size_t)(output_gradient_execs)->rsize * (size_t)(0))))[_i_].graph == graph"
, "ccv_cnnp_model_gradient_checkpointing.c", 220, __extension__
__PRETTY_FUNCTION__); })); _incomings_[((ccv_nnc_graph_exec_symbol_t
*)((void*)(((char*)((output_gradient_execs)->data)) + (size_t
)(output_gradient_execs)->rsize * (size_t)(0))))[_i_].d].r
= 5; _exists_[0][_i_] = ((ccv_nnc_graph_exec_symbol_t*)((void
*)(((char*)((output_gradient_execs)->data)) + (size_t)(output_gradient_execs
)->rsize * (size_t)(0))))[_i_].d; } _exist_size_[0] = (output_gradient_execs
->rnum); _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_cnnp_model_gradient_checkpointing.c", 220, __extension__
__PRETTY_FUNCTION__); })); _exists_[_q_][_exist_size_[_q_]] =
d; ++_exist_size_[_q_]; } } ((_i_) = (_p_), (_p_) = (_q_), (
_q_) = (_i_)); } for (_i_ = 0; _i_ < (output_gradient_execs
->rnum); _i_++) { ((void) sizeof ((((ccv_nnc_graph_exec_symbol_t
*)((void*)(((char*)((output_gradient_execs)->data)) + (size_t
)(output_gradient_execs)->rsize * (size_t)(0))))[_i_].graph
== graph) ? 1 : 0), __extension__ ({ if (((ccv_nnc_graph_exec_symbol_t
*)((void*)(((char*)((output_gradient_execs)->data)) + (size_t
)(output_gradient_execs)->rsize * (size_t)(0))))[_i_].graph
== graph) ; else __assert_fail ("((ccv_nnc_graph_exec_symbol_t*)((void*)(((char*)((output_gradient_execs)->data)) + (size_t)(output_gradient_execs)->rsize * (size_t)(0))))[_i_].graph == graph"
, "ccv_cnnp_model_gradient_checkpointing.c", 220, __extension__
__PRETTY_FUNCTION__); })); _incomings_[((ccv_nnc_graph_exec_symbol_t
*)((void*)(((char*)((output_gradient_execs)->data)) + (size_t
)(output_gradient_execs)->rsize * (size_t)(0))))[_i_].d].d
= 1; } for (_i_ = 0; _i_ < (input_gradient_execs->rnum
); _i_++) { ((void) sizeof ((((ccv_nnc_graph_exec_symbol_t*)(
(void*)(((char*)((input_gradient_execs)->data)) + (size_t)
(input_gradient_execs)->rsize * (size_t)(0))))[_i_].graph ==
graph) ? 1 : 0), __extension__ ({ if (((ccv_nnc_graph_exec_symbol_t
*)((void*)(((char*)((input_gradient_execs)->data)) + (size_t
)(input_gradient_execs)->rsize * (size_t)(0))))[_i_].graph
== graph) ; else __assert_fail ("((ccv_nnc_graph_exec_symbol_t*)((void*)(((char*)((input_gradient_execs)->data)) + (size_t)(input_gradient_execs)->rsize * (size_t)(0))))[_i_].graph == graph"
, "ccv_cnnp_model_gradient_checkpointing.c", 220, __extension__
__PRETTY_FUNCTION__); })); _exists_[0][_i_] = ((ccv_nnc_graph_exec_symbol_t
*)((void*)(((char*)((input_gradient_execs)->data)) + (size_t
)(input_gradient_execs)->rsize * (size_t)(0))))[_i_].d; } _p_
= 0; _q_ = 1; _exist_size_[0] = (input_gradient_execs->rnum
); _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 ((exec_info)[_idx_].outgoings) { if ((exec_info)[_idx_
].outgoings->rnum == 1) { const int d = *(int*)((void*)(((
char*)(((exec_info)[_idx_].outgoings)->data)) + (size_t)((
exec_info)[_idx_].outgoings)->rsize * (size_t)(0))); --_incomings_
[d].c; if (_incomings_[d].c == 0 && _incomings_[d].r ==
6 && _d_ < (output_gradient_execs->rnum)) { _exists_
[_p_][_i_] = d; continue; } } else for (_j_ = 0; _j_ < (exec_info
)[_idx_].outgoings->rnum; _j_++) { const int d = *(int*)((
void*)(((char*)(((exec_info)[_idx_].outgoings)->data)) + (
size_t)((exec_info)[_idx_].outgoings)->rsize * (size_t)(_j_
))); --_incomings_[d].c; if (_incomings_[d].c == 0 &&
_incomings_[d].r == 6 && _d_ < (output_gradient_execs
->rnum)) { ((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_cnnp_model_gradient_checkpointing.c", 220, __extension__
__PRETTY_FUNCTION__); })); _exists_[_q_][_exist_size_[_q_]] =
d; ++_exist_size_[_q_]; } } } ++_i_; } ((_i_) = (_p_), (_p_)
= (_q_), (_q_) = (_i_)); } for (_i_ = 0; _i_ < (output_gradient_execs
->rnum); _i_++) { ((void) sizeof ((((ccv_nnc_graph_exec_symbol_t
*)((void*)(((char*)((output_gradient_execs)->data)) + (size_t
)(output_gradient_execs)->rsize * (size_t)(0))))[_i_].graph
== graph) ? 1 : 0), __extension__ ({ if (((ccv_nnc_graph_exec_symbol_t
*)((void*)(((char*)((output_gradient_execs)->data)) + (size_t
)(output_gradient_execs)->rsize * (size_t)(0))))[_i_].graph
== graph) ; else __assert_fail ("((ccv_nnc_graph_exec_symbol_t*)((void*)(((char*)((output_gradient_execs)->data)) + (size_t)(output_gradient_execs)->rsize * (size_t)(0))))[_i_].graph == graph"
, "ccv_cnnp_model_gradient_checkpointing.c", 220, __extension__
__PRETTY_FUNCTION__); })); if (_incomings_[((ccv_nnc_graph_exec_symbol_t
*)((void*)(((char*)((output_gradient_execs)->data)) + (size_t
)(output_gradient_execs)->rsize * (size_t)(0))))[_i_].d].r
== 7) continue; if (!(1)) { ((void) sizeof ((_incomings_[((ccv_nnc_graph_exec_symbol_t
*)((void*)(((char*)((output_gradient_execs)->data)) + (size_t
)(output_gradient_execs)->rsize * (size_t)(0))))[_i_].d].c
== 0) ? 1 : 0), __extension__ ({ if (_incomings_[((ccv_nnc_graph_exec_symbol_t
*)((void*)(((char*)((output_gradient_execs)->data)) + (size_t
)(output_gradient_execs)->rsize * (size_t)(0))))[_i_].d].c
== 0) ; else __assert_fail ("_incomings_[((ccv_nnc_graph_exec_symbol_t*)((void*)(((char*)((output_gradient_execs)->data)) + (size_t)(output_gradient_execs)->rsize * (size_t)(0))))[_i_].d].c == 0"
, "ccv_cnnp_model_gradient_checkpointing.c", 220, __extension__
__PRETTY_FUNCTION__); })); } else if (_incomings_[((ccv_nnc_graph_exec_symbol_t
*)((void*)(((char*)((output_gradient_execs)->data)) + (size_t
)(output_gradient_execs)->rsize * (size_t)(0))))[_i_].d].c
> 0) continue; _visit_->node[_visit_->size].index =
((((ccv_nnc_graph_exec_symbol_t*)((void*)(((char*)((output_gradient_execs
)->data)) + (size_t)(output_gradient_execs)->rsize * (size_t
)(0))))[_i_].d)); _visit_->node[_visit_->size].term = (
(_incomings_[((ccv_nnc_graph_exec_symbol_t*)((void*)(((char*)
((output_gradient_execs)->data)) + (size_t)(output_gradient_execs
)->rsize * (size_t)(0))))[_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_cnnp_model_gradient_checkpointing.c", 220, __extension__
__PRETTY_FUNCTION__); })); _visit_; });
35
←
Assuming '_i_' is < field 'rnum'
→
36
←
Loop condition is true. Entering loop body
→
37
←
Assuming field 'outgoings' is null
→
38
←
'?' condition is false
→
39
←
Assuming '_i_' is >= field 'rnum'
→
40
←
Loop condition is false. Execution continues on line 220
→
41
←
Taking false branch
→
42
←
Assuming '_i_' is >= field 'rnum'
→
43
←
Loop condition is false. Execution continues on line 220
→
44
←
Loop condition is false. Execution continues on line 220
→
45
←
Loop condition is false. Execution continues on line 220
→
46
←
Loop condition is false. Execution continues on line 220
→
47
←
Assuming '_i_' is >= field 'rnum'
→
48
←
Loop condition is false. Execution continues on line 220
→
49
←
Loop condition is false. Execution continues on line 220
→
50
←
Loop condition is false. Execution continues on line 220
→
51
←
Loop condition is false. Execution continues on line 220
→
52
←
Loop condition is false. Execution continues on line 220
→
53
←
Loop condition is false. Execution continues on line 220
→
54
←
Taking false branch
→
55
←
Loop condition is false. Exiting loop
→
56
←
Taking true branch
→
221 ccv_nnc_graph_visit_for(visit, exec_info, node, idx){ int _i_; for (_i_ = 0; _i_ < (visit)->size; _i_++) { const
int idx __attribute__((unused)) = (visit)->node[_i_].index
; const int _node_unused_ __attribute__((unused)) = (visit)->
node[_i_].term; typeof ((exec_info)) const node __attribute__
((unused)) = (exec_info) + idx; {
57
←
Loop condition is false. Execution continues on line 225
→
222 if (idx < exec_rnum && !CCV_NNC_GRAPH_EXEC_IS_DEAD(node->flags)((node->flags) & CCV_NNC_GRAPH_EXEC_DEAD))
223 maskbit[idx >> 5] |= (1u << (idx & 0x1f));
224 } ccv_nnc_graph_visit_endfor} }
225 ccv_array_clear(visited_backward_execs);
226 // Add more backward pass to the list. Note that we don't add everything, particularly there are new nodes created through gradient checkpointing are ignored.
227#define visitor(node, idx, _) \
228 if (idx < exec_rnum && !CCV_NNC_GRAPH_EXEC_IS_DEAD(exec_info[idx].flags)((exec_info[idx].flags) & CCV_NNC_GRAPH_EXEC_DEAD) && maskbit[idx >> 5] & (1u << (idx & 0x1f))) \
229 ccv_array_add_unique_int(visited_backward_execs, idx);
230 CCV_NNC_GRAPH_VISIT(graph, reversed_nodes, exec_rnum, (ccv_nnc_graph_exec_symbol_t*)ccv_array_get(output_gradient_execs, 0), output_gradient_execs->rnum, (ccv_nnc_graph_exec_symbol_t*)ccv_array_get(input_gradient_execs, 0), input_gradient_execs->rnum, 0, visitor)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_ < (exec_rnum); _i_++) _incoming_edges_
+= ((reversed_nodes)[_i_].outgoings) ? (reversed_nodes)[_i_]
.outgoings->rnum : 0; const int _heap_mem_ = ((exec_rnum) +
_incoming_edges_ > 1024); ccv_nnc_incoming_t* _incomings_
; if (_heap_mem_) _incomings_ = (ccv_nnc_incoming_t*)malloc(sizeof
(ccv_nnc_incoming_t) * (exec_rnum) + sizeof(int32_t) * ((exec_rnum
) * 2 + _incoming_edges_)); else _incomings_ = (ccv_nnc_incoming_t
*)__builtin_alloca (sizeof(ccv_nnc_incoming_t) * (exec_rnum) +
sizeof(int32_t) * ((exec_rnum) * 2 + _incoming_edges_)); memset
(_incomings_, 0, sizeof(ccv_nnc_incoming_t) * (exec_rnum)); int32_t
* _exists_[2] = { (int32_t*)(_incomings_ + (exec_rnum)), (int32_t
*)(_incomings_ + (exec_rnum)) + (exec_rnum), }; int32_t* const
_edges_ = _exists_[1] + (exec_rnum); for (_i_ = 0; _i_ < (
output_gradient_execs->rnum); _i_++) { ((void) sizeof ((((
ccv_nnc_graph_exec_symbol_t*)((void*)(((char*)((output_gradient_execs
)->data)) + (size_t)(output_gradient_execs)->rsize * (size_t
)(0))))[_i_].graph == graph) ? 1 : 0), __extension__ ({ if ((
(ccv_nnc_graph_exec_symbol_t*)((void*)(((char*)((output_gradient_execs
)->data)) + (size_t)(output_gradient_execs)->rsize * (size_t
)(0))))[_i_].graph == graph) ; else __assert_fail ("((ccv_nnc_graph_exec_symbol_t*)((void*)(((char*)((output_gradient_execs)->data)) + (size_t)(output_gradient_execs)->rsize * (size_t)(0))))[_i_].graph == graph"
, "ccv_cnnp_model_gradient_checkpointing.c", 230, __extension__
__PRETTY_FUNCTION__); })); _incomings_[((ccv_nnc_graph_exec_symbol_t
*)((void*)(((char*)((output_gradient_execs)->data)) + (size_t
)(output_gradient_execs)->rsize * (size_t)(0))))[_i_].d].r
= 1; _exists_[0][_i_] = ((ccv_nnc_graph_exec_symbol_t*)((void
*)(((char*)((output_gradient_execs)->data)) + (size_t)(output_gradient_execs
)->rsize * (size_t)(0))))[_i_].d; } int _exist_size_[2] = {
(output_gradient_execs->rnum), 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 ((reversed_nodes)[_idx_].outgoings
) for (_j_ = 0; _j_ < (reversed_nodes)[_idx_].outgoings->
rnum; _j_++) { const int d = *(int*)((void*)(((char*)(((reversed_nodes
)[_idx_].outgoings)->data)) + (size_t)((reversed_nodes)[_idx_
].outgoings)->rsize * (size_t)(_j_))); ++_incomings_[d].c;
if (_incomings_[d].r != 0) continue; _incomings_[d].r = 1; (
(void) sizeof ((_exist_size_[_q_] < (exec_rnum)) ? 1 : 0),
__extension__ ({ if (_exist_size_[_q_] < (exec_rnum)) ; else
__assert_fail ("_exist_size_[_q_] < (exec_rnum)", "ccv_cnnp_model_gradient_checkpointing.c"
, 230, __extension__ __PRETTY_FUNCTION__); })); _exists_[_q_]
[_exist_size_[_q_]] = d; ++_exist_size_[_q_]; } } ((_i_) = (_p_
), (_p_) = (_q_), (_q_) = (_i_)); } for (_i_ = 0; _i_ < (output_gradient_execs
->rnum); _i_++) { ((void) sizeof ((((ccv_nnc_graph_exec_symbol_t
*)((void*)(((char*)((output_gradient_execs)->data)) + (size_t
)(output_gradient_execs)->rsize * (size_t)(0))))[_i_].graph
== graph) ? 1 : 0), __extension__ ({ if (((ccv_nnc_graph_exec_symbol_t
*)((void*)(((char*)((output_gradient_execs)->data)) + (size_t
)(output_gradient_execs)->rsize * (size_t)(0))))[_i_].graph
== graph) ; else __assert_fail ("((ccv_nnc_graph_exec_symbol_t*)((void*)(((char*)((output_gradient_execs)->data)) + (size_t)(output_gradient_execs)->rsize * (size_t)(0))))[_i_].graph == graph"
, "ccv_cnnp_model_gradient_checkpointing.c", 230, __extension__
__PRETTY_FUNCTION__); })); _incomings_[((ccv_nnc_graph_exec_symbol_t
*)((void*)(((char*)((output_gradient_execs)->data)) + (size_t
)(output_gradient_execs)->rsize * (size_t)(0))))[_i_].d].r
= 3; _exists_[0][_i_] = ((ccv_nnc_graph_exec_symbol_t*)((void
*)(((char*)((output_gradient_execs)->data)) + (size_t)(output_gradient_execs
)->rsize * (size_t)(0))))[_i_].d; } _exist_size_[0] = (output_gradient_execs
->rnum); _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 ((reversed_nodes)[_idx_].outgoings
) for (_j_ = 0; _j_ < (reversed_nodes)[_idx_].outgoings->
rnum; _j_++) { const int d = *(int*)((void*)(((char*)(((reversed_nodes
)[_idx_].outgoings)->data)) + (size_t)((reversed_nodes)[_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_] < (exec_rnum)) ? 1 : 0), __extension__ (
{ if (_exist_size_[_q_] < (exec_rnum)) ; else __assert_fail
("_exist_size_[_q_] < (exec_rnum)", "ccv_cnnp_model_gradient_checkpointing.c"
, 230, __extension__ __PRETTY_FUNCTION__); })); _exists_[_q_]
[_exist_size_[_q_]] = d; ++_exist_size_[_q_]; } } ((_i_) = (_p_
), (_p_) = (_q_), (_q_) = (_i_)); } for (_i_ = 0; _i_ < (input_gradient_execs
->rnum); _i_++) { ((void) sizeof ((((ccv_nnc_graph_exec_symbol_t
*)((void*)(((char*)((input_gradient_execs)->data)) + (size_t
)(input_gradient_execs)->rsize * (size_t)(0))))[_i_].graph
== graph) ? 1 : 0), __extension__ ({ if (((ccv_nnc_graph_exec_symbol_t
*)((void*)(((char*)((input_gradient_execs)->data)) + (size_t
)(input_gradient_execs)->rsize * (size_t)(0))))[_i_].graph
== graph) ; else __assert_fail ("((ccv_nnc_graph_exec_symbol_t*)((void*)(((char*)((input_gradient_execs)->data)) + (size_t)(input_gradient_execs)->rsize * (size_t)(0))))[_i_].graph == graph"
, "ccv_cnnp_model_gradient_checkpointing.c", 230, __extension__
__PRETTY_FUNCTION__); })); _incomings_[((ccv_nnc_graph_exec_symbol_t
*)((void*)(((char*)((input_gradient_execs)->data)) + (size_t
)(input_gradient_execs)->rsize * (size_t)(0))))[_i_].d].r =
5; _exists_[0][_i_] = ((ccv_nnc_graph_exec_symbol_t*)((void*
)(((char*)((input_gradient_execs)->data)) + (size_t)(input_gradient_execs
)->rsize * (size_t)(0))))[_i_].d; } _exist_size_[0] = (input_gradient_execs
->rnum); _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_] < (exec_rnum)) ? 1 : 0), __extension__ ({ if (_exist_size_
[_q_] < (exec_rnum)) ; else __assert_fail ("_exist_size_[_q_] < (exec_rnum)"
, "ccv_cnnp_model_gradient_checkpointing.c", 230, __extension__
__PRETTY_FUNCTION__); })); _exists_[_q_][_exist_size_[_q_]] =
d; ++_exist_size_[_q_]; } } ((_i_) = (_p_), (_p_) = (_q_), (
_q_) = (_i_)); } for (_i_ = 0; _i_ < (input_gradient_execs
->rnum); _i_++) { ((void) sizeof ((((ccv_nnc_graph_exec_symbol_t
*)((void*)(((char*)((input_gradient_execs)->data)) + (size_t
)(input_gradient_execs)->rsize * (size_t)(0))))[_i_].graph
== graph) ? 1 : 0), __extension__ ({ if (((ccv_nnc_graph_exec_symbol_t
*)((void*)(((char*)((input_gradient_execs)->data)) + (size_t
)(input_gradient_execs)->rsize * (size_t)(0))))[_i_].graph
== graph) ; else __assert_fail ("((ccv_nnc_graph_exec_symbol_t*)((void*)(((char*)((input_gradient_execs)->data)) + (size_t)(input_gradient_execs)->rsize * (size_t)(0))))[_i_].graph == graph"
, "ccv_cnnp_model_gradient_checkpointing.c", 230, __extension__
__PRETTY_FUNCTION__); })); _incomings_[((ccv_nnc_graph_exec_symbol_t
*)((void*)(((char*)((input_gradient_execs)->data)) + (size_t
)(input_gradient_execs)->rsize * (size_t)(0))))[_i_].d].d =
1; } for (_i_ = 0; _i_ < (output_gradient_execs->rnum)
; _i_++) { ((void) sizeof ((((ccv_nnc_graph_exec_symbol_t*)((
void*)(((char*)((output_gradient_execs)->data)) + (size_t)
(output_gradient_execs)->rsize * (size_t)(0))))[_i_].graph
== graph) ? 1 : 0), __extension__ ({ if (((ccv_nnc_graph_exec_symbol_t
*)((void*)(((char*)((output_gradient_execs)->data)) + (size_t
)(output_gradient_execs)->rsize * (size_t)(0))))[_i_].graph
== graph) ; else __assert_fail ("((ccv_nnc_graph_exec_symbol_t*)((void*)(((char*)((output_gradient_execs)->data)) + (size_t)(output_gradient_execs)->rsize * (size_t)(0))))[_i_].graph == graph"
, "ccv_cnnp_model_gradient_checkpointing.c", 230, __extension__
__PRETTY_FUNCTION__); })); _exists_[0][_i_] = ((ccv_nnc_graph_exec_symbol_t
*)((void*)(((char*)((output_gradient_execs)->data)) + (size_t
)(output_gradient_execs)->rsize * (size_t)(0))))[_i_].d; }
_p_ = 0; _q_ = 1; _exist_size_[0] = (output_gradient_execs->
rnum); _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_]; visitor((
(reversed_nodes) + _idx_), (_idx_), (_incomings_[_idx_].d)); if
(_incomings_[_idx_].d) { ++_d_; _incomings_[_idx_].r = 7; } if
((reversed_nodes)[_idx_].outgoings) { if ((reversed_nodes)[_idx_
].outgoings->rnum == 1) { const int d = *(int*)((void*)(((
char*)(((reversed_nodes)[_idx_].outgoings)->data)) + (size_t
)((reversed_nodes)[_idx_].outgoings)->rsize * (size_t)(0))
); --_incomings_[d].c; if (_incomings_[d].c == 0 && _incomings_
[d].r == 6 && _d_ < (input_gradient_execs->rnum
)) { _exists_[_p_][_i_] = d; continue; } } else for (_j_ = 0;
_j_ < (reversed_nodes)[_idx_].outgoings->rnum; _j_++) {
const int d = *(int*)((void*)(((char*)(((reversed_nodes)[_idx_
].outgoings)->data)) + (size_t)((reversed_nodes)[_idx_].outgoings
)->rsize * (size_t)(_j_))); --_incomings_[d].c; if (_incomings_
[d].c == 0 && _incomings_[d].r == 6 && _d_ <
(input_gradient_execs->rnum)) { ((void) sizeof ((_exist_size_
[_q_] < (exec_rnum)) ? 1 : 0), __extension__ ({ if (_exist_size_
[_q_] < (exec_rnum)) ; else __assert_fail ("_exist_size_[_q_] < (exec_rnum)"
, "ccv_cnnp_model_gradient_checkpointing.c", 230, __extension__
__PRETTY_FUNCTION__); })); _exists_[_q_][_exist_size_[_q_]] =
d; ++_exist_size_[_q_]; } } } ++_i_; } ((_i_) = (_p_), (_p_)
= (_q_), (_q_) = (_i_)); } for (_i_ = 0; _i_ < (input_gradient_execs
->rnum); _i_++) { ((void) sizeof ((((ccv_nnc_graph_exec_symbol_t
*)((void*)(((char*)((input_gradient_execs)->data)) + (size_t
)(input_gradient_execs)->rsize * (size_t)(0))))[_i_].graph
== graph) ? 1 : 0), __extension__ ({ if (((ccv_nnc_graph_exec_symbol_t
*)((void*)(((char*)((input_gradient_execs)->data)) + (size_t
)(input_gradient_execs)->rsize * (size_t)(0))))[_i_].graph
== graph) ; else __assert_fail ("((ccv_nnc_graph_exec_symbol_t*)((void*)(((char*)((input_gradient_execs)->data)) + (size_t)(input_gradient_execs)->rsize * (size_t)(0))))[_i_].graph == graph"
, "ccv_cnnp_model_gradient_checkpointing.c", 230, __extension__
__PRETTY_FUNCTION__); })); if (_incomings_[((ccv_nnc_graph_exec_symbol_t
*)((void*)(((char*)((input_gradient_execs)->data)) + (size_t
)(input_gradient_execs)->rsize * (size_t)(0))))[_i_].d].r ==
7) continue; if (!(0)) { ((void) sizeof ((_incomings_[((ccv_nnc_graph_exec_symbol_t
*)((void*)(((char*)((input_gradient_execs)->data)) + (size_t
)(input_gradient_execs)->rsize * (size_t)(0))))[_i_].d].c ==
0) ? 1 : 0), __extension__ ({ if (_incomings_[((ccv_nnc_graph_exec_symbol_t
*)((void*)(((char*)((input_gradient_execs)->data)) + (size_t
)(input_gradient_execs)->rsize * (size_t)(0))))[_i_].d].c ==
0) ; else __assert_fail ("_incomings_[((ccv_nnc_graph_exec_symbol_t*)((void*)(((char*)((input_gradient_execs)->data)) + (size_t)(input_gradient_execs)->rsize * (size_t)(0))))[_i_].d].c == 0"
, "ccv_cnnp_model_gradient_checkpointing.c", 230, __extension__
__PRETTY_FUNCTION__); })); } else if (_incomings_[((ccv_nnc_graph_exec_symbol_t
*)((void*)(((char*)((input_gradient_execs)->data)) + (size_t
)(input_gradient_execs)->rsize * (size_t)(0))))[_i_].d].c >
0) continue; visitor(((reversed_nodes) + ((ccv_nnc_graph_exec_symbol_t
*)((void*)(((char*)((input_gradient_execs)->data)) + (size_t
)(input_gradient_execs)->rsize * (size_t)(0))))[_i_].d), (
((ccv_nnc_graph_exec_symbol_t*)((void*)(((char*)((input_gradient_execs
)->data)) + (size_t)(input_gradient_execs)->rsize * (size_t
)(0))))[_i_].d), (_incomings_[((ccv_nnc_graph_exec_symbol_t*)
((void*)(((char*)((input_gradient_execs)->data)) + (size_t
)(input_gradient_execs)->rsize * (size_t)(0))))[_i_].d].d)
); } if (_heap_mem_) free(_incomings_); } while (0);;
58
←
Loop condition is false. Execution continues on line 230
→
59
←
Taking false branch
→
60
←
Loop condition is false. Execution continues on line 230
→
61
←
Loop condition is false. Execution continues on line 230
→
62
←
Loop condition is false. Execution continues on line 230
→
63
←
Loop condition is false. Execution continues on line 230
→
64
←
Loop condition is false. Execution continues on line 230
→
65
←
Loop condition is false. Execution continues on line 230
→
66
←
Loop condition is false. Execution continues on line 230
→
67
←
Loop condition is false. Execution continues on line 230
→
68
←
Loop condition is false. Execution continues on line 230
→
69
←
Loop condition is false. Execution continues on line 230
→
70
←
Taking false branch
→
71
←
Loop condition is false. Exiting loop
→
231 for (j = 0; j
71.1
'j' is >= field 'rnum'
 < input_gradient_execs->rnum; j++)
72
←
Loop condition is false. Execution continues on line 235
→
232 ccv_array_add_unique_int(visited_backward_execs, ((ccv_nnc_graph_exec_symbol_t*)ccv_array_get(input_gradient_execs, j)((void*)(((char*)((input_gradient_execs)->data)) + (size_t
)(input_gradient_execs)->rsize * (size_t)(j))))->d);
233#undef visitor
234 ccv_cnnp_gradient_checkpoint_build_t build = {
235 .tensor_symbols = ccv_array_new(sizeof(ccv_nnc_tensor_symbol_t), 0, 0),
236 .graph_exec_symbols = ccv_array_new(sizeof(ccv_nnc_graph_exec_symbol_t), 0, 0),
237 };
238 build.old_tensor_symbol_new_hook_context = ccv_nnc_tensor_symbol_new_hook(graph, _ccv_cnnp_gradient_checkpoint_tensor_symbol_new_hook, &build, &build.old_tensor_symbol_new_hook);
239 build.old_tensor_symbol_alias_new_hook_context = ccv_nnc_tensor_symbol_alias_new_hook(graph, _ccv_cnnp_gradient_checkpoint_tensor_symbol_alias_new_hook, &build, &build.old_tensor_symbol_alias_new_hook);
240 build.old_graph_exec_symbol_new_hook_context = ccv_nnc_graph_exec_symbol_new_hook(graph, _ccv_cnnp_model_gradient_checkpoint_graph_exec_symbol_new_hook, &build, &build.old_graph_exec_symbol_new_hook);
241 ccv_array_clear(parameters);
242 ccv_array_clear(parameter_ids);
243 ccv_array_clear(parameter_trainables);
244 ccv_array_clear(internals);
245 ccv_array_clear(internal_ids);
246 ccv_cnnp_model_sequence_t model_sequence = {
247 .bank = kh_init(ccv_cnnp_model_name_bank)kh_init_ccv_cnnp_model_name_bank()
248 };
249 ccv_cnnp_model_add_to_array_context_t add_to_parameter_context = {
250 .sequence = &model_sequence,
251 .prefix = 't',
252 .symbols = parameters,
253 .ids = parameter_ids,
254 .trainables = parameter_trainables,
255 };
256 ccv_cnnp_model_add_to_array_context_t add_to_output_context = {
257 .sequence = &model_sequence,
258 .prefix = 'r',
259 .symbols = internals,
260 .ids = internal_ids,
261 .trainables = 0,
262 };
263 ccv_cnnp_model_build_data_t build_data = {
264 .is_trainable = checkpoint->is_trainable,
265 .model_sequence = &model_sequence,
266 .add_to_array = ccv_cnnp_model_add_to_array,
267 .parameters = parameters,
268 .context = {
269 .add_to_parameter = &add_to_parameter_context,
270 .add_to_output = &add_to_output_context,
271 },
272 .is_gradient_checkpointing = 1, // Mark this as true so we don't allocate gradient_checkpoints array or override the hooks.
273 .gradient_checkpoints = 0,
274 };
275 checkpoint->model->data = &build_data;
276 checkpoint->build(checkpoint->model, graph, checkpoint->inputs, checkpoint->input_size, max_outputs, checkpoint->output_size);
277 checkpoint->model->data = 0;
278 kh_destroy(ccv_cnnp_model_name_bank, model_sequence.bank)kh_destroy_ccv_cnnp_model_name_bank(model_sequence.bank);
279 if (model_sequence.sequences)
73
←
Assuming field 'sequences' is null
→
74
←
Taking false branch
→
280 ccv_array_free(model_sequence.sequences);
281 ccv_nnc_tensor_symbol_new_hook(graph, build.old_tensor_symbol_new_hook, build.old_tensor_symbol_new_hook_context, 0);
282 ccv_nnc_tensor_symbol_alias_new_hook(graph, build.old_tensor_symbol_alias_new_hook, build.old_tensor_symbol_alias_new_hook_context, 0);
283 ccv_nnc_graph_exec_symbol_autogen(graph, (ccv_nnc_graph_exec_symbol_t*)ccv_array_get(build.graph_exec_symbols, 0)((void*)(((char*)((build.graph_exec_symbols)->data)) + (size_t
)(build.graph_exec_symbols)->rsize * (size_t)(0))), build.graph_exec_symbols->rnum, 0);
284 for (j = 0; j < parameter_ids->rnum; j++)
75
←
Assuming 'j' is >= field 'rnum'
→
76
←
Loop condition is false. Execution continues on line 286
→
285 ccfreefree(*(char**)ccv_array_get(parameter_ids, j)((void*)(((char*)((parameter_ids)->data)) + (size_t)(parameter_ids
)->rsize * (size_t)(j))));
286 for (j = 0; j < internal_ids->rnum; j++)
77
←
Assuming 'j' is >= field 'rnum'
→
78
←
Loop condition is false. Execution continues on line 289
→
287 ccfreefree(*(char**)ccv_array_get(internal_ids, j)((void*)(((char*)((internal_ids)->data)) + (size_t)(internal_ids
)->rsize * (size_t)(j))));
288 // Note that there is no graph optimization applied here.
289 exec_info = (ccv_nnc_graph_exec_symbol_info_t*)ccv_array_get(graph->exec_symbol_info, 0)((void*)(((char*)((graph->exec_symbol_info)->data)) + (
size_t)(graph->exec_symbol_info)->rsize * (size_t)(0)));
290 // Reuse existing one.
291 ccv_array_t* const newly_input_execs = input_execs;
292 ccv_array_t* const newly_output_execs = output_execs;
293 ccv_array_clear(newly_input_execs);
294 ccv_array_clear(newly_output_execs);
295 for (j = 0; j < build.graph_exec_symbols->rnum; j++)
79
←
Assuming 'j' is >= field 'rnum'
→
80
←
Loop condition is false. Execution continues on line 329
→
296 {
297 const int idx = ((ccv_nnc_graph_exec_symbol_t*)ccv_array_get(build.graph_exec_symbols, j)((void*)(((char*)((build.graph_exec_symbols)->data)) + (size_t
)(build.graph_exec_symbols)->rsize * (size_t)(j))))->d;
298 if (idx < 0)
299 continue;
300 if (CCV_NNC_GRAPH_EXEC_IS_DEAD(exec_info[idx].flags)((exec_info[idx].flags) & CCV_NNC_GRAPH_EXEC_DEAD))
301 continue;
302 const ccv_nnc_graph_exec_symbol_t symbol = {
303 .graph = graph,
304 .d = idx
305 };
306 const int* inputs = exec_info[idx].inputs;
307 int input_size = exec_info[idx].input_size;
308 // Only go through forward pass.
309 assert(!ccv_nnc_cmd_is_backward(exec_info[idx].cmd))((void) sizeof ((!ccv_nnc_cmd_is_backward(exec_info[idx].cmd)
) ? 1 : 0), __extension__ ({ if (!ccv_nnc_cmd_is_backward(exec_info
[idx].cmd)) ; else __assert_fail ("!ccv_nnc_cmd_is_backward(exec_info[idx].cmd)"
, "ccv_cnnp_model_gradient_checkpointing.c", 309, __extension__
__PRETTY_FUNCTION__); }));
310 int flag = 0;
311 for (k = 0; inputs && k < input_size && !flag; k++)
312 if (inputs[k] >= 0)
313 for (l = 0; l < checkpoint->input_size && !flag; l++)
314 if (checkpoint->inputs[l].d >= 0 && inputs[k] == checkpoint->inputs[l].d)
315 flag = 1;
316 if (flag)
317 ccv_array_push(newly_input_execs, &symbol);
318 flag = 0;
319 const int* outputs = exec_info[idx].outputs;
320 int output_size = exec_info[idx].output_size;
321 for (k = 0; inputs && k < output_size && !flag; k++)
322 if (outputs[k] >= 0)
323 for (l = 0; l < checkpoint->output_size && !flag; l++)
324 if (max_outputs[l].d >= 0 && outputs[k] == max_outputs[l].d)
325 flag = 1;
326 if (flag)
327 ccv_array_push(newly_output_execs, &symbol);
328 }
329 for (j = 0; j < checkpoint->input_size; j++)
81
←
Assuming 'j' is >= field 'input_size'
→
82
←
Loop condition is false. Execution continues on line 332
→
330 if (checkpoint->inputs[j].d >= 0)
331 ccv_array_push(parameters, checkpoint->inputs + j);
332 ccv_nnc_symbolic_graph_simplify(graph,
333 SYMBOLIC_GRAPH_PASSES(CCV_NNC_SIMPLIFY_COMMON_SUBEXPRESSION_ELIMINATION,(const int []){CCV_NNC_SIMPLIFY_COMMON_SUBEXPRESSION_ELIMINATION
, CCV_NNC_SIMPLIFY_DATA_TRANSFER_OPT, CCV_NNC_SIMPLIFY_OPS_FUSION
}, (1 +1 +1 +1 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +
0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0
+0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +
0 +0 +0 +0 -1)
334 CCV_NNC_SIMPLIFY_DATA_TRANSFER_OPT,(const int []){CCV_NNC_SIMPLIFY_COMMON_SUBEXPRESSION_ELIMINATION
, CCV_NNC_SIMPLIFY_DATA_TRANSFER_OPT, CCV_NNC_SIMPLIFY_OPS_FUSION
}, (1 +1 +1 +1 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +
0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0
+0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +
0 +0 +0 +0 -1)
335 CCV_NNC_SIMPLIFY_OPS_FUSION)(const int []){CCV_NNC_SIMPLIFY_COMMON_SUBEXPRESSION_ELIMINATION
, CCV_NNC_SIMPLIFY_DATA_TRANSFER_OPT, CCV_NNC_SIMPLIFY_OPS_FUSION
}, (1 +1 +1 +1 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +
0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0
+0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +
0 +0 +0 +0 -1),
336 ccv_array_get(parameters, 0)((void*)(((char*)((parameters)->data)) + (size_t)(parameters
)->rsize * (size_t)(0))), parameters->rnum,
337 max_outputs, checkpoint->output_size,
338 ccv_array_get(newly_input_execs, 0)((void*)(((char*)((newly_input_execs)->data)) + (size_t)(newly_input_execs
)->rsize * (size_t)(0))), newly_input_execs->rnum, ccv_array_get(newly_output_execs, 0)((void*)(((char*)((newly_output_execs)->data)) + (size_t)(
newly_output_execs)->rsize * (size_t)(0))), newly_output_execs->rnum);
339 ccv_nnc_graph_exec_symbol_new_hook(graph, build.old_graph_exec_symbol_new_hook, build.old_graph_exec_symbol_new_hook_context, 0);
340 // Need to autogen and redo source / destination.
341 ccv_nnc_graph_exec_symbol_autogen(graph, (ccv_nnc_graph_exec_symbol_t*)ccv_array_get(build.graph_exec_symbols, 0)((void*)(((char*)((build.graph_exec_symbols)->data)) + (size_t
)(build.graph_exec_symbols)->rsize * (size_t)(0))), build.graph_exec_symbols->rnum, 0);
342 exec_info = (ccv_nnc_graph_exec_symbol_info_t*)ccv_array_get(graph->exec_symbol_info, 0)((void*)(((char*)((graph->exec_symbol_info)->data)) + (
size_t)(graph->exec_symbol_info)->rsize * (size_t)(0)));
343 ccv_array_clear(newly_input_execs);
344 for (j = 0; j
82.1
'j' is >= field 'rnum'
 < build.graph_exec_symbols->rnum; j++)
83
←
Loop condition is false. Execution continues on line 369
→
345 {
346 const int idx = ((ccv_nnc_graph_exec_symbol_t*)ccv_array_get(build.graph_exec_symbols, j)((void*)(((char*)((build.graph_exec_symbols)->data)) + (size_t
)(build.graph_exec_symbols)->rsize * (size_t)(j))))->d;
347 if (idx < 0)
348 continue;
349 if (CCV_NNC_GRAPH_EXEC_IS_DEAD(exec_info[idx].flags)((exec_info[idx].flags) & CCV_NNC_GRAPH_EXEC_DEAD))
350 continue;
351 const ccv_nnc_graph_exec_symbol_t symbol = {
352 .graph = graph,
353 .d = idx
354 };
355 const int* inputs = exec_info[idx].inputs;
356 int input_size = exec_info[idx].input_size;
357 // Only go through forward pass.
358 assert(!ccv_nnc_cmd_is_backward(exec_info[idx].cmd))((void) sizeof ((!ccv_nnc_cmd_is_backward(exec_info[idx].cmd)
) ? 1 : 0), __extension__ ({ if (!ccv_nnc_cmd_is_backward(exec_info
[idx].cmd)) ; else __assert_fail ("!ccv_nnc_cmd_is_backward(exec_info[idx].cmd)"
, "ccv_cnnp_model_gradient_checkpointing.c", 358, __extension__
__PRETTY_FUNCTION__); }));
359 int flag = 0;
360 for (k = 0; inputs && k < input_size && !flag; k++)
361 if (inputs[k] >= 0)
362 for (l = 0; l < checkpoint->input_size && !flag; l++)
363 if (checkpoint->inputs[l].d >= 0 && inputs[k] == checkpoint->inputs[l].d)
364 flag = 1;
365 if (flag)
366 ccv_array_push(newly_input_execs, &symbol);
367 }
368 // Build a map between old tensor symbols and new tensor symbols.
369 khash_t(ccv_cnnp_tensor_symbol_map)kh_ccv_cnnp_tensor_symbol_map_t* symbol_map = kh_init(ccv_cnnp_tensor_symbol_map)kh_init_ccv_cnnp_tensor_symbol_map();
84
←
Calling 'kh_init_ccv_cnnp_tensor_symbol_map'
→
86
←
Returning from 'kh_init_ccv_cnnp_tensor_symbol_map'
→
370 assert(build.tensor_symbols->rnum <= checkpoint->tensor_symbols->rnum)((void) sizeof ((build.tensor_symbols->rnum <= checkpoint
->tensor_symbols->rnum) ? 1 : 0), __extension__ ({ if (
build.tensor_symbols->rnum <= checkpoint->tensor_symbols
->rnum) ; else __assert_fail ("build.tensor_symbols->rnum <= checkpoint->tensor_symbols->rnum"
, "ccv_cnnp_model_gradient_checkpointing.c", 370, __extension__
__PRETTY_FUNCTION__); }));
87
←
Assuming 'build.tensor_symbols->rnum' is <= 'checkpoint->tensor_symbols->rnum'
→
88
←
Taking true branch
→
371 // Build a map to potentially map from old input to new input.
372 for (j = 0, k = 0; j < build.tensor_symbols->rnum && k < checkpoint->tensor_symbols->rnum;)
89
←
Assuming 'j' is < field 'rnum'
→
90
←
Assuming 'k' is < field 'rnum'
→
91
←
Loop condition is true. Entering loop body
→
373 {
374 const int from_d = ((ccv_nnc_tensor_symbol_t*)ccv_array_get(checkpoint->tensor_symbols, k)((void*)(((char*)((checkpoint->tensor_symbols)->data)) +
(size_t)(checkpoint->tensor_symbols)->rsize * (size_t)
(k))))->d;
375 assert(from_d >= 0)((void) sizeof ((from_d >= 0) ? 1 : 0), __extension__ ({ if
(from_d >= 0) ; else __assert_fail ("from_d >= 0", "ccv_cnnp_model_gradient_checkpointing.c"
, 375, __extension__ __PRETTY_FUNCTION__); }));
92
←
Assuming 'from_d' is >= 0
→
93
←
Taking true branch
→
376 const int to_d = ((ccv_nnc_tensor_symbol_t*)ccv_array_get(build.tensor_symbols, j)((void*)(((char*)((build.tensor_symbols)->data)) + (size_t
)(build.tensor_symbols)->rsize * (size_t)(j))))->d;
377 assert(to_d >= 0)((void) sizeof ((to_d >= 0) ? 1 : 0), __extension__ ({ if (
to_d >= 0) ; else __assert_fail ("to_d >= 0", "ccv_cnnp_model_gradient_checkpointing.c"
, 377, __extension__ __PRETTY_FUNCTION__); }));
94
←
Assuming 'to_d' is >= 0
→
95
←
Taking true branch
→
378 int from_flag = 0;
379 int to_flag = 0;
380 for (l = 0; (!from_flag
95.1
'from_flag' is 0
 || !to_flag) && l < parameters->rnum; l++)
96
←
Assuming 'l' is >= field 'rnum'
→
381 {
382 const int d = ((ccv_nnc_tensor_symbol_t*)ccv_array_get(parameters, l)((void*)(((char*)((parameters)->data)) + (size_t)(parameters
)->rsize * (size_t)(l))))->d;
383 if (d == from_d)
384 from_flag = 1;
385 if (d == to_d)
386 to_flag = 1;
387 }
388 if (!from_flag
96.1
'from_flag' is 0
 || !to_flag)
389 for (l = 0; (!from_flag
96.2
'from_flag' is 0
 || !to_flag) && l < internals->rnum; l++)
97
←
Assuming 'l' is >= field 'rnum'
→
98
←
Loop condition is false. Execution continues on line 397
→
390 {
391 const int d = ((ccv_nnc_tensor_symbol_t*)ccv_array_get(internals, l)((void*)(((char*)((internals)->data)) + (size_t)(internals
)->rsize * (size_t)(l))))->d;
392 if (d == from_d)
393 from_flag = 1;
394 if (d == to_d)
395 to_flag = 1;
396 }
397 if (from_flag
98.1
'from_flag' is 0
)
99
←
Taking false branch
→
398 ++k;
399 if (to_flag
99.1
'to_flag' is 0
)
400 ++j;
401 if (from_flag
99.2
'from_flag' is 0
 || to_flag
99.3
'to_flag' is 0
)
100
←
Taking false branch
→
402 continue;
403 ++k;
404 ++j;
405 // Skip if from_d is outputs.
406 for (l = 0; l < !from_flag && checkpoint->output_size; l++)
101
←
Loop condition is false. Execution continues on line 409
→
407 if (checkpoint->outputs[l].d == from_d)
408 from_flag = 1;
409 if (from_flag
101.1
'from_flag' is 0
)
102
←
Taking false branch
→
410 continue;
411 int ret = 0;
412 khiter_t h = kh_put(ccv_cnnp_tensor_symbol_map, symbol_map, from_d, &ret)kh_put_ccv_cnnp_tensor_symbol_map(symbol_map, from_d, &ret
);
103
←
Calling 'kh_put_ccv_cnnp_tensor_symbol_map'
→
117
←
Returning from 'kh_put_ccv_cnnp_tensor_symbol_map'
→
413 kh_val(symbol_map, h)((symbol_map)->vals[h]) = to_d;
118
←
Array access (via field 'vals') results in a null pointer dereference
414 }
415 // Now go over all backward passes to replace inputs with the ones from symbol map. Record these that are used.
416 ccv_array_clear(newly_used_outputs);
417 ccv_array_clear(replaced_backward_execs);
418 for (j = 0; j < visited_backward_execs->rnum; j++)
419 {
420 const int idx = *(int*)ccv_array_get(visited_backward_execs, j)((void*)(((char*)((visited_backward_execs)->data)) + (size_t
)(visited_backward_execs)->rsize * (size_t)(j)));
421 if (CCV_NNC_GRAPH_EXEC_IS_DEAD(exec_info[idx].flags)((exec_info[idx].flags) & CCV_NNC_GRAPH_EXEC_DEAD))
422 continue;
423 assert(idx >= 0)((void) sizeof ((idx >= 0) ? 1 : 0), __extension__ ({ if (
idx >= 0) ; else __assert_fail ("idx >= 0", "ccv_cnnp_model_gradient_checkpointing.c"
, 423, __extension__ __PRETTY_FUNCTION__); }));
424 assert(idx < exec_rnum)((void) sizeof ((idx < exec_rnum) ? 1 : 0), __extension__ (
{ if (idx < exec_rnum) ; else __assert_fail ("idx < exec_rnum"
, "ccv_cnnp_model_gradient_checkpointing.c", 424, __extension__
__PRETTY_FUNCTION__); }));
425 if (!ccv_nnc_cmd_is_backward(exec_info[idx].cmd))
426 continue;
427 for (k = 0; k < exec_info[idx].input_size; k++)
428 if (exec_info[idx].inputs[k] >= 0)
429 {
430 const khiter_t h = kh_get(ccv_cnnp_tensor_symbol_map, symbol_map, exec_info[idx].inputs[k])kh_get_ccv_cnnp_tensor_symbol_map(symbol_map, exec_info[idx].
inputs[k]);
431 if (h != kh_end(symbol_map)((symbol_map)->n_buckets)) // Replacing it.
432 {
433 const int newly_created_output = kh_val(symbol_map, h)((symbol_map)->vals[h]);
434 exec_info[idx].inputs[k] = newly_created_output;
435 ccv_array_add_unique_int(newly_used_outputs, newly_created_output);
436 ccv_array_add_unique_int(replaced_backward_execs, idx);
437 }
438 }
439 }
440 for (j = 0; j < build.graph_exec_symbols->rnum; j++)
441 {
442 ccv_nnc_graph_exec_symbol_t* const symbol = (ccv_nnc_graph_exec_symbol_t*)ccv_array_get(build.graph_exec_symbols, j)((void*)(((char*)((build.graph_exec_symbols)->data)) + (size_t
)(build.graph_exec_symbols)->rsize * (size_t)(j)));
443 if (symbol->d < 0)
444 continue;
445 if (CCV_NNC_GRAPH_EXEC_IS_DEAD(exec_info[symbol->d].flags)((exec_info[symbol->d].flags) & CCV_NNC_GRAPH_EXEC_DEAD
))
446 continue;
447 int x, y;
448 for (k = 0; k < replaced_backward_execs->rnum; k++)
449 {
450 const int idx = *(int*)ccv_array_get(replaced_backward_execs, k)((void*)(((char*)((replaced_backward_execs)->data)) + (size_t
)(replaced_backward_execs)->rsize * (size_t)(k)));
451 assert(idx >= 0)((void) sizeof ((idx >= 0) ? 1 : 0), __extension__ ({ if (
idx >= 0) ; else __assert_fail ("idx >= 0", "ccv_cnnp_model_gradient_checkpointing.c"
, 451, __extension__ __PRETTY_FUNCTION__); }));
452 assert(idx < exec_rnum)((void) sizeof ((idx < exec_rnum) ? 1 : 0), __extension__ (
{ if (idx < exec_rnum) ; else __assert_fail ("idx < exec_rnum"
, "ccv_cnnp_model_gradient_checkpointing.c", 452, __extension__
__PRETTY_FUNCTION__); }));
453 assert(ccv_nnc_cmd_is_backward(exec_info[idx].cmd))((void) sizeof ((ccv_nnc_cmd_is_backward(exec_info[idx].cmd))
? 1 : 0), __extension__ ({ if (ccv_nnc_cmd_is_backward(exec_info
[idx].cmd)) ; else __assert_fail ("ccv_nnc_cmd_is_backward(exec_info[idx].cmd)"
, "ccv_cnnp_model_gradient_checkpointing.c", 453, __extension__
__PRETTY_FUNCTION__); }));
454 int flag = 0;
455 for (x = 0; !flag && x < exec_info[idx].input_size; x++)
456 for (y = 0; !flag && y < exec_info[symbol->d].output_size; y++)
457 if (exec_info[idx].inputs[x] == exec_info[symbol->d].outputs[y])
458 flag = 1;
459 if (flag)
460 ccv_nnc_graph_exec_symbol_concat(graph, *symbol, (ccv_nnc_graph_exec_symbol_t){
461 .graph = graph,
462 .d = idx
463 });
464 }
465 }
466 // Find parents to visited_backward_execs, and use that as the starting point of all newly added graph_exec_symbols. Use the visited backward execs as the source, use all its parents as destination, go through with graph visit.
467 ccv_sparse_matrix_t* const exec_dep = ccv_sparse_matrix_new(graph->exec_symbol_info->rnum, graph->exec_symbol_info->rnum, CCV_8U | CCV_C1, CCV_SPARSE_ROW_MAJOR, 0);
468#define for_block(x, val) \
469 do { \
470 if (((uint8_t*)val)[0] != 0) \
471 ccv_array_push(buf, &x); \
472 } while (0)
473 const uint8_t one = 1;
474 // Now go from outputs to inputs, unmark visited ones.
475 ccv_nnc_graph_visit_for(visit, exec_info, node, idx){ int _i_; for (_i_ = 0; _i_ < (visit)->size; _i_++) { const
int idx __attribute__((unused)) = (visit)->node[_i_].index
; const int _node_unused_ __attribute__((unused)) = (visit)->
node[_i_].term; typeof ((exec_info)) const node __attribute__
((unused)) = (exec_info) + idx; {
476 if (idx < exec_rnum && !CCV_NNC_GRAPH_EXEC_IS_DEAD(node->flags)((node->flags) & CCV_NNC_GRAPH_EXEC_DEAD) && maskbit[idx >> 5] & (1u << (idx & 0x1f)))
477 {
478 ccv_array_clear(buf);
479 ccv_sparse_matrix_vector_t* vector = ccv_get_sparse_matrix_vector(exec_dep, idx);
480 if (vector)
481 CCV_SPARSE_VECTOR_FOREACH(exec_dep, vector, for_block)do { switch ((((exec_dep)->type) & 0xFF000)) { case CCV_32S
: { do { int _i_; __attribute__((unused)) const size_t _c_ = (
((exec_dep)->type) & 0xFFF); if ((exec_dep)->type &
CCV_DENSE_VECTOR) { for (_i_ = 0; _i_ < (vector)->size
; _i_++) { for_block((_i_), ((vector)->data.i32 + (_i_ * _c_
))); } } else { const size_t _idx_size_ = sizeof(ccv_sparse_matrix_index_t
) + ((_ccv_get_data_type_size[(((exec_dep)->type) & 0xFF000
) >> 12] * (((exec_dep)->type) & 0xFFF) + 3) &
-4); uint8_t* const _vidx_ = (uint8_t*)(vector)->index; for
(_i_ = 0; _i_ < (vector)->size; _i_++) { ccv_sparse_matrix_index_t
* const _idx_i_ = (ccv_sparse_matrix_index_t*)(_vidx_ + _idx_size_
* _i_); if (_idx_i_->ifbit <= 1) continue; ccv_numeric_data_t
_d_ = { .u8 = (uint8_t*)(_idx_i_ + 1) }; for_block((_idx_i_->
i), (_d_.i32 + (0))); } } } while (0); break; } case CCV_32F:
{ do { int _i_; __attribute__((unused)) const size_t _c_ = (
((exec_dep)->type) & 0xFFF); if ((exec_dep)->type &
CCV_DENSE_VECTOR) { for (_i_ = 0; _i_ < (vector)->size
; _i_++) { for_block((_i_), ((vector)->data.f32 + (_i_ * _c_
))); } } else { const size_t _idx_size_ = sizeof(ccv_sparse_matrix_index_t
) + ((_ccv_get_data_type_size[(((exec_dep)->type) & 0xFF000
) >> 12] * (((exec_dep)->type) & 0xFFF) + 3) &
-4); uint8_t* const _vidx_ = (uint8_t*)(vector)->index; for
(_i_ = 0; _i_ < (vector)->size; _i_++) { ccv_sparse_matrix_index_t
* const _idx_i_ = (ccv_sparse_matrix_index_t*)(_vidx_ + _idx_size_
* _i_); if (_idx_i_->ifbit <= 1) continue; ccv_numeric_data_t
_d_ = { .u8 = (uint8_t*)(_idx_i_ + 1) }; for_block((_idx_i_->
i), (_d_.f32 + (0))); } } } while (0); break; } case CCV_64S:
{ do { int _i_; __attribute__((unused)) const size_t _c_ = (
((exec_dep)->type) & 0xFFF); if ((exec_dep)->type &
CCV_DENSE_VECTOR) { for (_i_ = 0; _i_ < (vector)->size
; _i_++) { for_block((_i_), ((vector)->data.i64 + (_i_ * _c_
))); } } else { const size_t _idx_size_ = sizeof(ccv_sparse_matrix_index_t
) + ((_ccv_get_data_type_size[(((exec_dep)->type) & 0xFF000
) >> 12] * (((exec_dep)->type) & 0xFFF) + 3) &
-4); uint8_t* const _vidx_ = (uint8_t*)(vector)->index; for
(_i_ = 0; _i_ < (vector)->size; _i_++) { ccv_sparse_matrix_index_t
* const _idx_i_ = (ccv_sparse_matrix_index_t*)(_vidx_ + _idx_size_
* _i_); if (_idx_i_->ifbit <= 1) continue; ccv_numeric_data_t
_d_ = { .u8 = (uint8_t*)(_idx_i_ + 1) }; for_block((_idx_i_->
i), (_d_.i64 + (0))); } } } while (0); break; } case CCV_64F:
{ do { int _i_; __attribute__((unused)) const size_t _c_ = (
((exec_dep)->type) & 0xFFF); if ((exec_dep)->type &
CCV_DENSE_VECTOR) { for (_i_ = 0; _i_ < (vector)->size
; _i_++) { for_block((_i_), ((vector)->data.f64 + (_i_ * _c_
))); } } else { const size_t _idx_size_ = sizeof(ccv_sparse_matrix_index_t
) + ((_ccv_get_data_type_size[(((exec_dep)->type) & 0xFF000
) >> 12] * (((exec_dep)->type) & 0xFFF) + 3) &
-4); uint8_t* const _vidx_ = (uint8_t*)(vector)->index; for
(_i_ = 0; _i_ < (vector)->size; _i_++) { ccv_sparse_matrix_index_t
* const _idx_i_ = (ccv_sparse_matrix_index_t*)(_vidx_ + _idx_size_
* _i_); if (_idx_i_->ifbit <= 1) continue; ccv_numeric_data_t
_d_ = { .u8 = (uint8_t*)(_idx_i_ + 1) }; for_block((_idx_i_->
i), (_d_.f64 + (0))); } } } while (0); break; } default: { do
{ int _i_; __attribute__((unused)) const size_t _c_ = (((exec_dep
)->type) & 0xFFF); if ((exec_dep)->type & CCV_DENSE_VECTOR
) { for (_i_ = 0; _i_ < (vector)->size; _i_++) { for_block
((_i_), ((vector)->data.u8 + (_i_ * _c_))); } } else { const
size_t _idx_size_ = sizeof(ccv_sparse_matrix_index_t) + ((_ccv_get_data_type_size
[(((exec_dep)->type) & 0xFF000) >> 12] * (((exec_dep
)->type) & 0xFFF) + 3) & -4); uint8_t* const _vidx_
= (uint8_t*)(vector)->index; for (_i_ = 0; _i_ < (vector
)->size; _i_++) { ccv_sparse_matrix_index_t* const _idx_i_
= (ccv_sparse_matrix_index_t*)(_vidx_ + _idx_size_ * _i_); if
(_idx_i_->ifbit <= 1) continue; ccv_numeric_data_t _d_
= { .u8 = (uint8_t*)(_idx_i_ + 1) }; for_block((_idx_i_->
i), (_d_.u8 + (0))); } } } while (0); } } } while (0);
482 if (node->outgoings && node->outgoings->rnum > 0)
483 {
484 ccv_array_t* const outgoings = node->outgoings;
485 for (k = 0; k < outgoings->rnum; k++)
486 {
487 const int outgoing_d = *(int*)ccv_array_get(outgoings, k)((void*)(((char*)((outgoings)->data)) + (size_t)(outgoings
)->rsize * (size_t)(k)));
488 if (outgoing_d >= exec_rnum)
489 continue;
490 int l;
491 // We cannot avoid the ones that visited, because these may not contain all the deps.
492 ccv_set_sparse_matrix_cell(exec_dep, outgoing_d, idx, &one);
493 for (l = 0; l < buf->rnum; l++)
494 ccv_set_sparse_matrix_cell(exec_dep, outgoing_d, *(int*)ccv_array_get(buf, l)((void*)(((char*)((buf)->data)) + (size_t)(buf)->rsize *
(size_t)(l))), &one);
495 }
496 }
497 }
498 } ccv_nnc_graph_visit_endfor} }
499 // Now go from outputs to inputs, unmark visited ones.
500 ccv_nnc_graph_visit_for(visit, exec_info, node, idx){ int _i_; for (_i_ = 0; _i_ < (visit)->size; _i_++) { const
int idx __attribute__((unused)) = (visit)->node[_i_].index
; const int _node_unused_ __attribute__((unused)) = (visit)->
node[_i_].term; typeof ((exec_info)) const node __attribute__
((unused)) = (exec_info) + idx; {
501 if (idx < exec_rnum)
502 maskbit[idx >> 5] &= ~(1u << (idx & 0x1f));
503 } ccv_nnc_graph_visit_endfor} }
504 ccv_nnc_graph_visit_free(visit);
505#undef for_block
506 // Go through visited backward execs, remove the ones that has no dependency on any replaced backward execs.
507 for (j = 0; j < visited_backward_execs->rnum;)
508 {
509 const int idx = *(int*)ccv_array_get(visited_backward_execs, j)((void*)(((char*)((visited_backward_execs)->data)) + (size_t
)(visited_backward_execs)->rsize * (size_t)(j)));
510 if (ccv_array_contain_int(replaced_backward_execs, idx))
511 {
512 ++j;
513 continue;
514 }
515 ccv_sparse_matrix_vector_t* vector = ccv_get_sparse_matrix_vector(exec_dep, idx);
516 int flag = 0;
517#define for_block(x, val) \
518 do { \
519 if (((uint8_t*)val)[0] != 0) \
520 if (ccv_array_contain_int(replaced_backward_execs, x)) \
521 flag = 1; \
522 } while (0)
523 if (vector)
524 CCV_SPARSE_VECTOR_FOREACH(exec_dep, vector, for_block)do { switch ((((exec_dep)->type) & 0xFF000)) { case CCV_32S
: { do { int _i_; __attribute__((unused)) const size_t _c_ = (
((exec_dep)->type) & 0xFFF); if ((exec_dep)->type &
CCV_DENSE_VECTOR) { for (_i_ = 0; _i_ < (vector)->size
; _i_++) { for_block((_i_), ((vector)->data.i32 + (_i_ * _c_
))); } } else { const size_t _idx_size_ = sizeof(ccv_sparse_matrix_index_t
) + ((_ccv_get_data_type_size[(((exec_dep)->type) & 0xFF000
) >> 12] * (((exec_dep)->type) & 0xFFF) + 3) &
-4); uint8_t* const _vidx_ = (uint8_t*)(vector)->index; for
(_i_ = 0; _i_ < (vector)->size; _i_++) { ccv_sparse_matrix_index_t
* const _idx_i_ = (ccv_sparse_matrix_index_t*)(_vidx_ + _idx_size_
* _i_); if (_idx_i_->ifbit <= 1) continue; ccv_numeric_data_t
_d_ = { .u8 = (uint8_t*)(_idx_i_ + 1) }; for_block((_idx_i_->
i), (_d_.i32 + (0))); } } } while (0); break; } case CCV_32F:
{ do { int _i_; __attribute__((unused)) const size_t _c_ = (
((exec_dep)->type) & 0xFFF); if ((exec_dep)->type &
CCV_DENSE_VECTOR) { for (_i_ = 0; _i_ < (vector)->size
; _i_++) { for_block((_i_), ((vector)->data.f32 + (_i_ * _c_
))); } } else { const size_t _idx_size_ = sizeof(ccv_sparse_matrix_index_t
) + ((_ccv_get_data_type_size[(((exec_dep)->type) & 0xFF000
) >> 12] * (((exec_dep)->type) & 0xFFF) + 3) &
-4); uint8_t* const _vidx_ = (uint8_t*)(vector)->index; for
(_i_ = 0; _i_ < (vector)->size; _i_++) { ccv_sparse_matrix_index_t
* const _idx_i_ = (ccv_sparse_matrix_index_t*)(_vidx_ + _idx_size_
* _i_); if (_idx_i_->ifbit <= 1) continue; ccv_numeric_data_t
_d_ = { .u8 = (uint8_t*)(_idx_i_ + 1) }; for_block((_idx_i_->
i), (_d_.f32 + (0))); } } } while (0); break; } case CCV_64S:
{ do { int _i_; __attribute__((unused)) const size_t _c_ = (
((exec_dep)->type) & 0xFFF); if ((exec_dep)->type &
CCV_DENSE_VECTOR) { for (_i_ = 0; _i_ < (vector)->size
; _i_++) { for_block((_i_), ((vector)->data.i64 + (_i_ * _c_
))); } } else { const size_t _idx_size_ = sizeof(ccv_sparse_matrix_index_t
) + ((_ccv_get_data_type_size[(((exec_dep)->type) & 0xFF000
) >> 12] * (((exec_dep)->type) & 0xFFF) + 3) &
-4); uint8_t* const _vidx_ = (uint8_t*)(vector)->index; for
(_i_ = 0; _i_ < (vector)->size; _i_++) { ccv_sparse_matrix_index_t
* const _idx_i_ = (ccv_sparse_matrix_index_t*)(_vidx_ + _idx_size_
* _i_); if (_idx_i_->ifbit <= 1) continue; ccv_numeric_data_t
_d_ = { .u8 = (uint8_t*)(_idx_i_ + 1) }; for_block((_idx_i_->
i), (_d_.i64 + (0))); } } } while (0); break; } case CCV_64F:
{ do { int _i_; __attribute__((unused)) const size_t _c_ = (
((exec_dep)->type) & 0xFFF); if ((exec_dep)->type &
CCV_DENSE_VECTOR) { for (_i_ = 0; _i_ < (vector)->size
; _i_++) { for_block((_i_), ((vector)->data.f64 + (_i_ * _c_
))); } } else { const size_t _idx_size_ = sizeof(ccv_sparse_matrix_index_t
) + ((_ccv_get_data_type_size[(((exec_dep)->type) & 0xFF000
) >> 12] * (((exec_dep)->type) & 0xFFF) + 3) &
-4); uint8_t* const _vidx_ = (uint8_t*)(vector)->index; for
(_i_ = 0; _i_ < (vector)->size; _i_++) { ccv_sparse_matrix_index_t
* const _idx_i_ = (ccv_sparse_matrix_index_t*)(_vidx_ + _idx_size_
* _i_); if (_idx_i_->ifbit <= 1) continue; ccv_numeric_data_t
_d_ = { .u8 = (uint8_t*)(_idx_i_ + 1) }; for_block((_idx_i_->
i), (_d_.f64 + (0))); } } } while (0); break; } default: { do
{ int _i_; __attribute__((unused)) const size_t _c_ = (((exec_dep
)->type) & 0xFFF); if ((exec_dep)->type & CCV_DENSE_VECTOR
) { for (_i_ = 0; _i_ < (vector)->size; _i_++) { for_block
((_i_), ((vector)->data.u8 + (_i_ * _c_))); } } else { const
size_t _idx_size_ = sizeof(ccv_sparse_matrix_index_t) + ((_ccv_get_data_type_size
[(((exec_dep)->type) & 0xFF000) >> 12] * (((exec_dep
)->type) & 0xFFF) + 3) & -4); uint8_t* const _vidx_
= (uint8_t*)(vector)->index; for (_i_ = 0; _i_ < (vector
)->size; _i_++) { ccv_sparse_matrix_index_t* const _idx_i_
= (ccv_sparse_matrix_index_t*)(_vidx_ + _idx_size_ * _i_); if
(_idx_i_->ifbit <= 1) continue; ccv_numeric_data_t _d_
= { .u8 = (uint8_t*)(_idx_i_ + 1) }; for_block((_idx_i_->
i), (_d_.u8 + (0))); } } } while (0); } } } while (0);
525#undef for_block
526 if (!flag)
527 {
528 if (j < visited_backward_execs->rnum - 1)
529 *(int*)ccv_array_get(visited_backward_execs, j)((void*)(((char*)((visited_backward_execs)->data)) + (size_t
)(visited_backward_execs)->rsize * (size_t)(j))) = *(int*)ccv_array_get(visited_backward_execs, visited_backward_execs->rnum - 1)((void*)(((char*)((visited_backward_execs)->data)) + (size_t
)(visited_backward_execs)->rsize * (size_t)(visited_backward_execs
->rnum - 1)));
530 --visited_backward_execs->rnum;
531 continue;
532 }
533 ++j;
534 }
535 // Now go through all replaced_backward_execs to find the ones has no dependencies in visited_backward_execs.
536 for (j = 0; j < replaced_backward_execs->rnum; j++)
537 {
538 const int idx = *(int*)ccv_array_get(replaced_backward_execs, j)((void*)(((char*)((replaced_backward_execs)->data)) + (size_t
)(replaced_backward_execs)->rsize * (size_t)(j)));
539 ccv_sparse_matrix_vector_t* vector = ccv_get_sparse_matrix_vector(exec_dep, idx);
540 int flag = 0;
541#define for_block(x, val) \
542 do { \
543 if (((uint8_t*)val)[0] != 0) \
544 if (ccv_array_contain_int(visited_backward_execs, x)) \
545 flag = 1; \
546 } while (0)
547 if (vector)
548 CCV_SPARSE_VECTOR_FOREACH(exec_dep, vector, for_block)do { switch ((((exec_dep)->type) & 0xFF000)) { case CCV_32S
: { do { int _i_; __attribute__((unused)) const size_t _c_ = (
((exec_dep)->type) & 0xFFF); if ((exec_dep)->type &
CCV_DENSE_VECTOR) { for (_i_ = 0; _i_ < (vector)->size
; _i_++) { for_block((_i_), ((vector)->data.i32 + (_i_ * _c_
))); } } else { const size_t _idx_size_ = sizeof(ccv_sparse_matrix_index_t
) + ((_ccv_get_data_type_size[(((exec_dep)->type) & 0xFF000
) >> 12] * (((exec_dep)->type) & 0xFFF) + 3) &
-4); uint8_t* const _vidx_ = (uint8_t*)(vector)->index; for
(_i_ = 0; _i_ < (vector)->size; _i_++) { ccv_sparse_matrix_index_t
* const _idx_i_ = (ccv_sparse_matrix_index_t*)(_vidx_ + _idx_size_
* _i_); if (_idx_i_->ifbit <= 1) continue; ccv_numeric_data_t
_d_ = { .u8 = (uint8_t*)(_idx_i_ + 1) }; for_block((_idx_i_->
i), (_d_.i32 + (0))); } } } while (0); break; } case CCV_32F:
{ do { int _i_; __attribute__((unused)) const size_t _c_ = (
((exec_dep)->type) & 0xFFF); if ((exec_dep)->type &
CCV_DENSE_VECTOR) { for (_i_ = 0; _i_ < (vector)->size
; _i_++) { for_block((_i_), ((vector)->data.f32 + (_i_ * _c_
))); } } else { const size_t _idx_size_ = sizeof(ccv_sparse_matrix_index_t
) + ((_ccv_get_data_type_size[(((exec_dep)->type) & 0xFF000
) >> 12] * (((exec_dep)->type) & 0xFFF) + 3) &
-4); uint8_t* const _vidx_ = (uint8_t*)(vector)->index; for
(_i_ = 0; _i_ < (vector)->size; _i_++) { ccv_sparse_matrix_index_t
* const _idx_i_ = (ccv_sparse_matrix_index_t*)(_vidx_ + _idx_size_
* _i_); if (_idx_i_->ifbit <= 1) continue; ccv_numeric_data_t
_d_ = { .u8 = (uint8_t*)(_idx_i_ + 1) }; for_block((_idx_i_->
i), (_d_.f32 + (0))); } } } while (0); break; } case CCV_64S:
{ do { int _i_; __attribute__((unused)) const size_t _c_ = (
((exec_dep)->type) & 0xFFF); if ((exec_dep)->type &
CCV_DENSE_VECTOR) { for (_i_ = 0; _i_ < (vector)->size
; _i_++) { for_block((_i_), ((vector)->data.i64 + (_i_ * _c_
))); } } else { const size_t _idx_size_ = sizeof(ccv_sparse_matrix_index_t
) + ((_ccv_get_data_type_size[(((exec_dep)->type) & 0xFF000
) >> 12] * (((exec_dep)->type) & 0xFFF) + 3) &
-4); uint8_t* const _vidx_ = (uint8_t*)(vector)->index; for
(_i_ = 0; _i_ < (vector)->size; _i_++) { ccv_sparse_matrix_index_t
* const _idx_i_ = (ccv_sparse_matrix_index_t*)(_vidx_ + _idx_size_
* _i_); if (_idx_i_->ifbit <= 1) continue; ccv_numeric_data_t
_d_ = { .u8 = (uint8_t*)(_idx_i_ + 1) }; for_block((_idx_i_->
i), (_d_.i64 + (0))); } } } while (0); break; } case CCV_64F:
{ do { int _i_; __attribute__((unused)) const size_t _c_ = (
((exec_dep)->type) & 0xFFF); if ((exec_dep)->type &
CCV_DENSE_VECTOR) { for (_i_ = 0; _i_ < (vector)->size
; _i_++) { for_block((_i_), ((vector)->data.f64 + (_i_ * _c_
))); } } else { const size_t _idx_size_ = sizeof(ccv_sparse_matrix_index_t
) + ((_ccv_get_data_type_size[(((exec_dep)->type) & 0xFF000
) >> 12] * (((exec_dep)->type) & 0xFFF) + 3) &
-4); uint8_t* const _vidx_ = (uint8_t*)(vector)->index; for
(_i_ = 0; _i_ < (vector)->size; _i_++) { ccv_sparse_matrix_index_t
* const _idx_i_ = (ccv_sparse_matrix_index_t*)(_vidx_ + _idx_size_
* _i_); if (_idx_i_->ifbit <= 1) continue; ccv_numeric_data_t
_d_ = { .u8 = (uint8_t*)(_idx_i_ + 1) }; for_block((_idx_i_->
i), (_d_.f64 + (0))); } } } while (0); break; } default: { do
{ int _i_; __attribute__((unused)) const size_t _c_ = (((exec_dep
)->type) & 0xFFF); if ((exec_dep)->type & CCV_DENSE_VECTOR
) { for (_i_ = 0; _i_ < (vector)->size; _i_++) { for_block
((_i_), ((vector)->data.u8 + (_i_ * _c_))); } } else { const
size_t _idx_size_ = sizeof(ccv_sparse_matrix_index_t) + ((_ccv_get_data_type_size
[(((exec_dep)->type) & 0xFF000) >> 12] * (((exec_dep
)->type) & 0xFFF) + 3) & -4); uint8_t* const _vidx_
= (uint8_t*)(vector)->index; for (_i_ = 0; _i_ < (vector
)->size; _i_++) { ccv_sparse_matrix_index_t* const _idx_i_
= (ccv_sparse_matrix_index_t*)(_vidx_ + _idx_size_ * _i_); if
(_idx_i_->ifbit <= 1) continue; ccv_numeric_data_t _d_
= { .u8 = (uint8_t*)(_idx_i_ + 1) }; for_block((_idx_i_->
i), (_d_.u8 + (0))); } } } while (0); } } } while (0);
549#undef for_block
550 // If this one has no parents that is within the visited_backward_execs, it is a good place for us to add all its parents as dependency for input_execs.
551 if (!flag)
552 {
553 assert(idx < exec_rnum)((void) sizeof ((idx < exec_rnum) ? 1 : 0), __extension__ (
{ if (idx < exec_rnum) ; else __assert_fail ("idx < exec_rnum"
, "ccv_cnnp_model_gradient_checkpointing.c", 553, __extension__
__PRETTY_FUNCTION__); }));
554 ccv_array_t* const outgoings = reversed_nodes[idx].outgoings;
555 assert(outgoings)((void) sizeof ((outgoings) ? 1 : 0), __extension__ ({ if (outgoings
) ; else __assert_fail ("outgoings", "ccv_cnnp_model_gradient_checkpointing.c"
, 555, __extension__ __PRETTY_FUNCTION__); }));
556 for (k = 0; k < outgoings->rnum; k++)
557 {
558 const int d = *(int*)ccv_array_get(outgoings, k)((void*)(((char*)((outgoings)->data)) + (size_t)(outgoings
)->rsize * (size_t)(k)));
559 for (l = 0; l < newly_input_execs->rnum; l++)
560 {
561 ccv_nnc_graph_exec_symbol_concat(graph, (ccv_nnc_graph_exec_symbol_t){
562 .graph = graph,
563 .d = d
564 }, *(ccv_nnc_graph_exec_symbol_t*)ccv_array_get(newly_input_execs, l)((void*)(((char*)((newly_input_execs)->data)) + (size_t)(newly_input_execs
)->rsize * (size_t)(l))));
565 }
566 }
567 }
568 }
569 ccv_matrix_free(exec_dep);
570 // Go through all exec, free ones that doesn't have output used.
571 // Reuse this array because it is not useful any more.
572 ccv_array_t* forward_pass_inputs = visited_backward_execs;
573 int any_deleted;
574 do {
575 // Build a map of still active inputs.
576 ccv_array_clear(forward_pass_inputs);
577 for (j = 0; j < build.graph_exec_symbols->rnum; j++)
578 {
579 ccv_nnc_graph_exec_symbol_t* const symbol = (ccv_nnc_graph_exec_symbol_t*)ccv_array_get(build.graph_exec_symbols, j)((void*)(((char*)((build.graph_exec_symbols)->data)) + (size_t
)(build.graph_exec_symbols)->rsize * (size_t)(j)));
580 if (symbol->d < 0)
581 continue;
582 if (CCV_NNC_GRAPH_EXEC_IS_DEAD(exec_info[symbol->d].flags)((exec_info[symbol->d].flags) & CCV_NNC_GRAPH_EXEC_DEAD
))
583 continue;
584 int* const inputs = exec_info[symbol->d].inputs;
585 const int input_size = exec_info[symbol->d].input_size;
586 for (k = 0; k < input_size; k++)
587 ccv_array_add_unique_int(forward_pass_inputs, inputs[k]);
588 }
589 any_deleted = 0;
590 for (j = 0; j < build.graph_exec_symbols->rnum; j++)
591 {
592 ccv_nnc_graph_exec_symbol_t* const symbol = (ccv_nnc_graph_exec_symbol_t*)ccv_array_get(build.graph_exec_symbols, j)((void*)(((char*)((build.graph_exec_symbols)->data)) + (size_t
)(build.graph_exec_symbols)->rsize * (size_t)(j)));
593 if (symbol->d < 0)
594 continue;
595 if (CCV_NNC_GRAPH_EXEC_IS_DEAD(exec_info[symbol->d].flags)((exec_info[symbol->d].flags) & CCV_NNC_GRAPH_EXEC_DEAD
))
596 continue;
597 int* const outputs = exec_info[symbol->d].outputs;
598 const int output_size = exec_info[symbol->d].output_size;
599 int flag = 0;
600 for (k = 0; !flag && k < output_size; k++)
601 flag = ccv_array_contain_int(newly_used_outputs, outputs[k]) || ccv_array_contain_int(forward_pass_inputs, outputs[k]);
602 if (flag)
603 continue;
604 ccv_nnc_graph_exec_symbol_free(graph, *symbol);
605 symbol->d = -1;
606 symbol->graph = 0;
607 any_deleted = 1;
608 }
609 } while (any_deleted);
610 ccv_array_clear(forward_pass_inputs);
611 for (j = 0; j < build.graph_exec_symbols->rnum; j++)
612 {
613 ccv_nnc_graph_exec_symbol_t* const symbol = (ccv_nnc_graph_exec_symbol_t*)ccv_array_get(build.graph_exec_symbols, j)((void*)(((char*)((build.graph_exec_symbols)->data)) + (size_t
)(build.graph_exec_symbols)->rsize * (size_t)(j)));
614 if (symbol->d < 0)
615 continue;
616 if (CCV_NNC_GRAPH_EXEC_IS_DEAD(exec_info[symbol->d].flags)((exec_info[symbol->d].flags) & CCV_NNC_GRAPH_EXEC_DEAD
))
617 continue;
618 int* const inputs = exec_info[symbol->d].inputs;
619 const int input_size = exec_info[symbol->d].input_size;
620 for (k = 0; k < input_size; k++)
621 ccv_array_add_unique_int(forward_pass_inputs, inputs[k]);
622 int* const outputs = exec_info[symbol->d].outputs;
623 const int output_size = exec_info[symbol->d].output_size;
624 for (k = 0; k < output_size; k++)
625 ccv_array_add_unique_int(forward_pass_inputs, outputs[k]);
626 }
627 // Free unused tensor symbols.
628 for (j = 0; j < build.tensor_symbols->rnum; j++)
629 {
630 const ccv_nnc_tensor_symbol_t* symbol = ((ccv_nnc_tensor_symbol_t*)ccv_array_get(build.tensor_symbols, j)((void*)(((char*)((build.tensor_symbols)->data)) + (size_t
)(build.tensor_symbols)->rsize * (size_t)(j))));
631 if (ccv_array_contain_int(newly_used_outputs, symbol->d) || ccv_array_contain_int(forward_pass_inputs, symbol->d))
632 continue;
633 ccv_nnc_tensor_symbol_free(graph, *symbol);
634 }
635 for (j = 0; j < build.graph_exec_symbols->rnum; j++)
636 {
637 ccv_nnc_graph_exec_symbol_t* const symbol = (ccv_nnc_graph_exec_symbol_t*)ccv_array_get(build.graph_exec_symbols, j)((void*)(((char*)((build.graph_exec_symbols)->data)) + (size_t
)(build.graph_exec_symbols)->rsize * (size_t)(j)));
638 if (symbol->d < 0)
639 continue;
640 if (CCV_NNC_GRAPH_EXEC_IS_DEAD(exec_info[symbol->d].flags)((exec_info[symbol->d].flags) & CCV_NNC_GRAPH_EXEC_DEAD
))
641 continue;
642 ccv_nnc_graph_exec_symbol_set_flags(graph, *symbol, CCV_NNC_GRAPH_EXEC_DISABLE_OPT);
643 }
644 // Free these newly created execs and tensor symbols.
645 ccv_array_free(build.tensor_symbols);
646 ccv_array_free(build.graph_exec_symbols);
647 kh_destroy(ccv_cnnp_tensor_symbol_map, symbol_map)kh_destroy_ccv_cnnp_tensor_symbol_map(symbol_map);
648 }
649 ccfreefree(max_outputs);
650 ccv_array_free(buf);
651 ccv_array_free(newly_used_outputs);
652 ccv_array_free(parameters);
653 ccv_array_free(parameter_ids);
654 ccv_array_free(parameter_trainables);
655 ccv_array_free(internals);
656 ccv_array_free(internal_ids);
657 ccfreefree(maskbit);
658 ccv_array_free(input_gradient_execs);
659 ccv_array_free(output_gradient_execs);
660 ccv_array_free(input_execs);
661 ccv_array_free(output_execs);
662 ccv_array_free(replaced_backward_execs);
663 ccv_array_free(visited_backward_execs);
664 for (i = 0; i < exec_rnum; i++)
665 if (reversed_nodes[i].outgoings)
666 ccv_array_free(reversed_nodes[i].outgoings);
667 ccfreefree(reversed_nodes);
668}