ccv_cnnp_model_gradient_checkpointing.c

Bug Summary

File:	nnc/ccv_cnnp_model_gradient_checkpointing.c
Warning:	line 48, column 1 Assigned value is garbage or undefined

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/2024-09-15-184933-339151-1 -x c ccv_cnnp_model_gradient_checkpointing.c

#include "ccv_nnc.h"

#include "ccv_nnc_easy.h"

#include "ccv_nnc_internal.h"

#include "ccv_internal.h"

#include "_ccv_cnnp_model.h"

// This can be removed once we organized ccv_cnnp_apply_gradient_checkpoints better.

#include "_ccv_nnc_symbolic_graph.h"

typedef struct {

ccv_array_t* outgoings;

} ccv_nnc_graph_exec_symbol_reverse_t;

typedef struct {

ccv_array_t* tensor_symbols;

void* old_tensor_symbol_new_hook_context;

ccv_nnc_tensor_symbol_new_hook_f old_tensor_symbol_new_hook;

void* old_tensor_symbol_alias_new_hook_context;

ccv_nnc_tensor_symbol_alias_new_hook_f old_tensor_symbol_alias_new_hook;

ccv_array_t* graph_exec_symbols;

ccv_nnc_graph_exec_symbol_new_hook_f old_graph_exec_symbol_new_hook;

void* old_graph_exec_symbol_new_hook_context;

} ccv_cnnp_gradient_checkpoint_build_t;

static 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)

{

ccv_cnnp_gradient_checkpoint_build_t* const build_context = (ccv_cnnp_gradient_checkpoint_build_t*)context;

ccv_array_push(build_context->tensor_symbols, &symbol);

if (build_context->old_tensor_symbol_new_hook)

build_context->old_tensor_symbol_new_hook(build_context->old_tensor_symbol_new_hook_context, symbol, info, name);

}

static 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)

{

ccv_cnnp_gradient_checkpoint_build_t* const build_context = (ccv_cnnp_gradient_checkpoint_build_t*)context;

ccv_array_push(build_context->tensor_symbols, &symbol);

if (build_context->old_tensor_symbol_alias_new_hook)

build_context->old_tensor_symbol_alias_new_hook(build_context->old_tensor_symbol_alias_new_hook_context, symbol, from_symbol, ofs, inc, info, name);

}

static 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)

{

ccv_cnnp_gradient_checkpoint_build_t* const build = (ccv_cnnp_gradient_checkpoint_build_t*)context;

ccv_array_push(build->graph_exec_symbols, &symbol);

if (build->old_graph_exec_symbol_new_hook)

build->old_graph_exec_symbol_new_hook(build->old_graph_exec_symbol_new_hook_context, symbol, cmd, inputs, input_size, outputs, output_size, name);

}

KHASH_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; } }

101

Taking true branch

102

Taking false branch

103

Calling 'kh_resize_ccv_cnnp_tensor_symbol_map'

104

Taking true branch

105

Assuming the condition is false

106

Taking false branch

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'?' condition is true

→

108

←

Assuming 'new_flags' is non-null

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Taking false branch

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'?' condition is true

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Taking true branch

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Storing uninitialized value

→

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←

Assuming 'new_keys' is non-null

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Taking false branch

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Taking true branch

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Assuming 'new_vals' is non-null

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Taking false branch

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Taking true branch

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Loop condition is false. Execution continues on line 48

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Taking false branch

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Returning from 'kh_resize_ccv_cnnp_tensor_symbol_map'

122

Taking false branch

123

Assuming right operand of bit shift is less than 32

→

124

←

Assuming the condition is true

125

Taking true branch

126

Assuming the condition is false

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Taking false branch

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Assuming the condition is false

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Taking false branch

144

Assuming field 'n_occupied' is >= field 'upper_bound'

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Taking true branch

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Taking true branch

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Calling 'kh_resize_ccv_cnnp_tensor_symbol_map'

148

Taking false branch

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Assuming the condition is false

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Taking false branch

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'?' condition is true

→

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←

Assuming 'new_flags' is non-null

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Taking false branch

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'?' condition is true

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Taking false branch

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Taking true branch

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The value 0 is assigned to 'j'

→

158

←

Loop condition is true. Entering loop body

→

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←

Assuming the condition is true

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Taking true branch

161

Assigned value is garbage or undefined

void ccv_cnnp_model_apply_gradient_checkpoints(ccv_cnnp_compiled_data_t* const compiled_data, ccv_nnc_symbolic_graph_t* const graph)

{

ccv_array_t* const gradient_checkpoints = compiled_data->gradient_checkpoints;

if (!gradient_checkpoints || gradient_checkpoints->rnum == 0) // No saved gradient checkpoints, this is an easy way out.

Assuming 'gradient_checkpoints' is non-null

→

←

Assuming field 'rnum' is not equal to 0

→

←

Taking false branch

→

return;

// Otherwise, for each gradient checkpoint, there are 3 steps:

// 1. Find currently, what execs exists from inputs to outputs.

// 2. Find execs that generates the outputs, and their corresponding backward execs.

// 3. Find all backward execs flow from outputs back to inputs.

// 4. Generate new ops by calling build again with old inputs, record all new tensors / execs.

// 5. Replace inputs in backward execs with the new tensors.

// 6. Hook the execs takes inputs with edge from parents of backward execs in step 2.

// 7. Delete newly generated execs that has no use (i.e. its outputs are not used by backward pass).

// 8. Mark all new execs with DISABLE_OPT to avoid common sub-expression elimination pass.

int i, j, k, l;

ccv_array_t* input_execs = ccv_array_new(sizeof(ccv_nnc_graph_exec_symbol_t), 0, 0);

ccv_array_t* output_execs = ccv_array_new(sizeof(ccv_nnc_graph_exec_symbol_t), 0, 0);

ccv_array_t* input_gradient_execs = ccv_array_new(sizeof(ccv_nnc_graph_exec_symbol_t), 0, 0);

ccv_array_t* output_gradient_execs = ccv_array_new(sizeof(ccv_nnc_graph_exec_symbol_t), 0, 0);

ccv_array_t* visited_backward_execs = ccv_array_new(sizeof(int), 0, 0);

ccv_array_t* replaced_backward_execs = ccv_array_new(sizeof(int), 0, 0);

const int exec_rnum = graph->exec_symbol_info->rnum;

ccv_nnc_graph_exec_symbol_reverse_t* const reversed_nodes = cccalloccalloc(exec_rnum, sizeof(ccv_nnc_graph_exec_symbol_reverse_t));

for (i = 0; i < exec_rnum; i++)

←

Assuming 'i' is >= 'exec_rnum'

→

←

Loop condition is false. Execution continues on line 89

→

{

const int* tos = 0;

int to_size = 0;

ccv_nnc_graph_exec_symbol_to(graph, (ccv_nnc_graph_exec_symbol_t){

.graph = graph,

.d = i

}, &tos, &to_size);

if (tos)

for (j = 0; j < to_size; j++)

{

if (!reversed_nodes[tos[j]].outgoings)

reversed_nodes[tos[j]].outgoings = ccv_array_new(sizeof(int), 1, 0);

ccv_array_add_unique_int(reversed_nodes[tos[j]].outgoings, i);

}

uint32_t* const maskbit = cccalloccalloc((exec_rnum + 31) >> 5, sizeof(uint32_t));

// Temporary for build_data.

ccv_array_t* const parameters = ccv_array_new(sizeof(ccv_nnc_tensor_symbol_t), 0, 0);

ccv_array_t* const parameter_ids = ccv_array_new(sizeof(char*), 0, 0);

ccv_array_t* const parameter_trainables = ccv_array_new(sizeof(int), 0, 0);

ccv_array_t* const internals = ccv_array_new(sizeof(ccv_nnc_tensor_symbol_t), 0, 0);

ccv_array_t* const internal_ids = ccv_array_new(sizeof(char*), 0, 0);

ccv_array_t* const buf = ccv_array_new(sizeof(int), 0, 0);

int max_output_size = 0;

for (i = 0; i < gradient_checkpoints->rnum; i++)

←

Assuming 'i' is < field 'rnum'

→

←

Loop condition is true. Entering loop body

→

←

Assuming 'i' is >= field 'rnum'

→

←

Loop condition is false. Execution continues on line 103

→

{

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; });

←

Assuming '_a' is <= '_b'

→

←

'?' 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++)

←

Loop condition is true. Entering loop body

→

106

{

107

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)

←

Assuming field 'rnum' is > 0

→

←

Assuming field 'rnum' is > 0

→

←

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++)

←

Loop condition is true. Entering loop body

→

←

Assuming 'j' is >= field 'rnum'

→

←

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++)

←

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)

←

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++)

←

Loop condition is true. Entering loop body

→

←

Assuming 'j' is >= field 'rnum'

→

←

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)

←

Assuming field 'outgoings' is non-null

→

←

Assuming field 'rnum' is > 0

→

←

Taking true branch

→

200

for (k = 0; k

24.1	'k' is < field 'rnum'

< exec_info[d].outgoings->rnum; k++)

←

Loop condition is true. Entering loop body

→

←

Assuming 'k' is >= field 'rnum'

→

←

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))

←

Assuming the condition is false

→

←

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++)

←

Assuming 'l' is >= field 'rnum'

→

←

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

)

←

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_; });

←

Assuming '_i_' is < field 'rnum'

→

←

Loop condition is true. Entering loop body

→

←

Assuming field 'outgoings' is null

→

←

'?' condition is false

→

←

Assuming '_i_' is >= field 'rnum'

→

←

Loop condition is false. Execution continues on line 220

Taking false branch

Assuming '_i_' is >= field 'rnum'

→

←

Loop condition is false. Execution continues on line 220

→

←

Loop condition is false. Execution continues on line 220

→

←

Loop condition is false. Execution continues on line 220

→

←

Loop condition is false. Execution continues on line 220

→

←

Assuming '_i_' is >= field 'rnum'

→

←

Loop condition is false. Execution continues on line 220

→

←

Loop condition is false. Execution continues on line 220

→

←

Loop condition is false. Execution continues on line 220

→

←

Loop condition is false. Execution continues on line 220

→

←

Loop condition is false. Execution continues on line 220

→

←

Loop condition is false. Execution continues on line 220

Taking false branch

Loop condition is false. Exiting loop

→

←

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; {

←

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);;

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Loop condition is false. Execution continues on line 230

Taking false branch

Loop condition is false. Execution continues on line 230

→

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Loop condition is false. Execution continues on line 230

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Loop condition is false. Execution continues on line 230

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Loop condition is false. Execution continues on line 230

→

←

Loop condition is false. Execution continues on line 230

→

←

Loop condition is false. Execution continues on line 230

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Loop condition is false. Execution continues on line 230

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←

Loop condition is false. Execution continues on line 230

→

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Loop condition is false. Execution continues on line 230

→

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Loop condition is false. Execution continues on line 230

Taking false branch

Loop condition is false. Exiting loop

→

231

for (j = 0; j

71.1	'j' is >= field 'rnum'

< input_gradient_execs->rnum; j++)

←

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)

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Assuming field 'sequences' is null

→

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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++)

←

Assuming 'j' is >= field 'rnum'

→

←

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++)

←

Assuming 'j' is >= field 'rnum'

→

←

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++)

←

Assuming 'j' is >= field 'rnum'

→

←

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++)

←

Assuming 'j' is >= field 'input_size'

→

←

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

342

343

ccv_array_clear(newly_input_execs);

344

for (j = 0; j

82.1	'j' is >= field 'rnum'

< build.graph_exec_symbols->rnum; j++)

←

Loop condition is false. Execution continues on line 369

→

345

{

346

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

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();

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__); }));

←

Assuming 'build.tensor_symbols->rnum' is <= 'checkpoint->tensor_symbols->rnum'

→

←

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;)

←

Assuming 'j' is < field 'rnum'

→

←

Assuming 'k' is < field 'rnum'

→

←

Loop condition is true. Entering loop body

→

131

←

Assuming 'j' is < field 'rnum'

→

132

←

Assuming 'k' is < field 'rnum'

→

133

←

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__); }));

←

Assuming 'from_d' is >= 0

Taking true branch

134

Assuming 'from_d' is >= 0

→

135

←

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__); }));

←

Assuming 'to_d' is >= 0

Taking true branch

136

Assuming 'to_d' is >= 0

→

137

←

Taking true branch

→

378

int from_flag = 0;

379

int to_flag = 0;

380

for (l = 0; (!from_flag

92.1	'from_flag' is 0
137.1	'from_flag' is 0

|| !to_flag) && l

137.2

'l' is >= field 'rnum'

< parameters->rnum; l++)

←

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

93.1	'from_flag' is 0
137.3	'from_flag' is 0

|| !to_flag)

389

for (l = 0; (!from_flag

93.2	'from_flag' is 0
137.4	'from_flag' is 0

|| !to_flag) && l

137.5

'l' is >= field 'rnum'

< internals->rnum; l++)

←

Assuming 'l' is >= field 'rnum'

→

←

Loop condition is false. Execution continues on line 397

→

138

←

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

95.1	'from_flag' is 0
138.1	'from_flag' is 0

)

Taking false branch

139

Taking false branch

398

++k;

399

if (to_flag

96.1	'to_flag' is 0
139.1	'to_flag' is 0

)

400

++j;

401

if (from_flag

96.2	'from_flag' is 0
139.2	'from_flag' is 0

|| to_flag

96.3	'to_flag' is 0
139.3	'to_flag' is 0

)

Taking false branch

140

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++)

←

Loop condition is false. Execution continues on line 409

→

141

←

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

98.1	'from_flag' is 0
141.1	'from_flag' is 0

)

Taking false branch

142

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
);

100

←

Calling 'kh_put_ccv_cnnp_tensor_symbol_map'

→

130

←

Returning from 'kh_put_ccv_cnnp_tensor_symbol_map'

→

143

←

Calling 'kh_put_ccv_cnnp_tensor_symbol_map'

→

413

kh_val(symbol_map, h)((symbol_map)->vals[h]) = to_d;

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

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

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

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

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

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

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

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

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

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

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

}