Bug Summary

File:nnc/cmd/pad/ccv_nnc_pad_cpu_ref.c
Warning:line 78, column 20
The right operand of '-' is a garbage value

Annotated Source Code

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clang -cc1 -cc1 -triple x86_64-unknown-linux-gnu -analyze -disable-free -clear-ast-before-backend -disable-llvm-verifier -discard-value-names -main-file-name ccv_nnc_pad_cpu_ref.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/cmd -fcoverage-compilation-dir=/home/liu/actions-runner/_work/ccv/ccv/lib/nnc/cmd -resource-dir /usr/local/lib/clang/19 -I ../../ -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 -I /usr/local/include -internal-isystem /usr/local/lib/clang/19/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/2024-11-07-150833-92989-1 -x c pad/ccv_nnc_pad_cpu_ref.c
1#include "ccv.h"
2#include "ccv_internal.h"
3#include "nnc/ccv_nnc.h"
4#include "nnc/ccv_nnc_easy.h"
5#include "nnc/ccv_nnc_internal.h"
6#ifdef USE_OPENMP
7#include <omp.h>
8#endif
9#ifdef USE_DISPATCH
10#include <dispatch/dispatch.h>
11#endif
12
13static int _ccv_nnc_pad_forw(const ccv_nnc_cmd_t cmd, const ccv_nnc_hint_t hint, const int flags, ccv_nnc_tensor_t* const* const inputs, const int input_size, ccv_nnc_tensor_t* const* const outputs, const int output_size, ccv_nnc_stream_context_t* const stream_context)
14{
15 assert(input_size == 1)((void) sizeof ((input_size == 1) ? 1 : 0), __extension__ ({ if
(input_size == 1) ; else __assert_fail ("input_size == 1", "pad/ccv_nnc_pad_cpu_ref.c"
, 15, __extension__ __PRETTY_FUNCTION__); }));
1
Assuming 'input_size' is equal to 1
→
2
←
Taking true branch
→
16 ccv_nnc_tensor_view_t* const a = (ccv_nnc_tensor_view_t*)inputs[0];
17 ccv_nnc_tensor_view_t* const b = (ccv_nnc_tensor_view_t*)outputs[0];
18 assert(ccv_nnc_tensor_nd(a->info.dim) <= CCV_NNC_MAX_DIM + 2)((void) sizeof ((ccv_nnc_tensor_nd(a->info.dim) <= (2) +
2) ? 1 : 0), __extension__ ({ if (ccv_nnc_tensor_nd(a->info
.dim) <= (2) + 2) ; else __assert_fail ("ccv_nnc_tensor_nd(a->info.dim) <= CCV_NNC_MAX_DIM + 2"
, "pad/ccv_nnc_pad_cpu_ref.c", 18, __extension__ __PRETTY_FUNCTION__
); }));
3
←
Assuming the condition is true
→
4
←
Taking true branch
→
19 assert(ccv_nnc_tensor_nd(b->info.dim) <= CCV_NNC_MAX_DIM + 2)((void) sizeof ((ccv_nnc_tensor_nd(b->info.dim) <= (2) +
2) ? 1 : 0), __extension__ ({ if (ccv_nnc_tensor_nd(b->info
.dim) <= (2) + 2) ; else __assert_fail ("ccv_nnc_tensor_nd(b->info.dim) <= CCV_NNC_MAX_DIM + 2"
, "pad/ccv_nnc_pad_cpu_ref.c", 19, __extension__ __PRETTY_FUNCTION__
); }));
5
←
Assuming the condition is true
→
6
←
Taking true branch
→
20 // Assuming this is float 32.
21 int adim[CCV_NNC_MAX_DIM_ALLOC(12)];
22 int bdim[CCV_NNC_MAX_DIM_ALLOC(12)];
23 ccv_nnc_tensor_view_get_dim(a, adim);
24 ccv_nnc_tensor_view_get_dim(b, bdim);
25 int astride[CCV_NNC_MAX_DIM_ALLOC(12)];
26 int bstride[CCV_NNC_MAX_DIM_ALLOC(12)];
27 assert(CCV_NNC_MAX_DIM == 2)((void) sizeof (((2) == 2) ? 1 : 0), __extension__ ({ if ((2)
== 2) ; else __assert_fail ("CCV_NNC_MAX_DIM == 2", "pad/ccv_nnc_pad_cpu_ref.c"
, 27, __extension__ __PRETTY_FUNCTION__); })); // Need to change this logic for CCV_NNC_MAX_DIM == other number.
7
←
Taking true branch
→
28 ccv_nnc_tensor_view_get_stride(a, astride);
29 ccv_nnc_tensor_view_get_stride(b, bstride);
30 int i[CCV_NNC_MAX_DIM(2) + 2];
31 int x;
32 float* const ap = a->data.f32;
33 float* const bp = b->data.f32;
34 const int nd = ccv_nnc_tensor_nd(a->info.dim);
35 const int offset = CCV_NNC_MAX_DIM(2) + 2 - nd;
36 assert(offset >= 0)((void) sizeof ((offset >= 0) ? 1 : 0), __extension__ ({ if
(offset >= 0) ; else __assert_fail ("offset >= 0", "pad/ccv_nnc_pad_cpu_ref.c"
, 36, __extension__ __PRETTY_FUNCTION__); }));
8
←
Assuming 'offset' is >= 0
→
9
←
Taking true branch
→
37 for (x = 0; x < nd; x++) // We don't support negative pad.
10
←
Assuming 'x' is >= 'nd'
→
11
←
Loop condition is false. Execution continues on line 39
→
38 { assert(cmd.info.size.dim[x] >= 0 && cmd.info.pad.end[x] >= 0)((void) sizeof ((cmd.info.size.dim[x] >= 0 && cmd.
info.pad.end[x] >= 0) ? 1 : 0), __extension__ ({ if (cmd.info
.size.dim[x] >= 0 && cmd.info.pad.end[x] >= 0) ;
else __assert_fail ("cmd.info.size.dim[x] >= 0 && cmd.info.pad.end[x] >= 0"
, "pad/ccv_nnc_pad_cpu_ref.c", 38, __extension__ __PRETTY_FUNCTION__
); })); }
39 int begin[CCV_NNC_MAX_DIM_ALLOC(12)];
40 for (x = 0; x
11.1
'x' is >= 'nd'
 < nd; x++)
12
←
Loop condition is false. Execution continues on line 42
→
41 begin[x + offset] = cmd.info.size.dim[x];
42 for (x = 0; x < offset; x++)
13
←
Assuming 'x' is < 'offset'
→
14
←
Loop condition is true. Entering loop body
→
15
←
Assuming 'x' is < 'offset'
→
16
←
Loop condition is true. Entering loop body
→
17
←
Assuming 'x' is < 'offset'
→
18
←
Loop condition is true. Entering loop body
→
19
←
Assuming 'x' is >= 'offset'
→
20
←
Loop condition is false. Execution continues on line 45
→
43 begin[x] = 0;
44 // Non-optimal case, need to do skip if needed.
45 if (cmd.info.pad.type == CCV_NNC_PAD_ZERO)
21
←
Assuming field 'type' is not equal to CCV_NNC_PAD_ZERO
→
22
←
Taking false branch
→
46 {
47 for (i[0] = 0; i[0] < bdim[0]; i[0]++)
48 {
49 float* const ap0 = (i[0] >= begin[0] && i[0] < adim[0] + begin[0]) ? ap + (i[0] - begin[0]) * astride[0] : 0;
50 float* const bp0 = bp + i[0] * bstride[0];
51 for (i[1] = 0; i[1] < bdim[1]; i[1]++)
52 {
53 float* const ap1 = (ap0 && i[1] >= begin[1] && i[1] < adim[1] + begin[1]) ? ap0 + (i[1] - begin[1]) * astride[1] : 0;
54 float* bp1 = bp0 + i[1] * bstride[1];
55 for (i[2] = 0; i[2] < bdim[2]; i[2]++)
56 {
57 float* const ap2 = (ap1 && i[2] >= begin[2] && i[2] < adim[2] + begin[2]) ? ap1 + (i[2] - begin[2]) * astride[2] : 0;
58 for (x = 0; x < bdim[3]; x++)
59 bp1[x] = (ap2 && x >= begin[3] && x < adim[3] + begin[3]) ? ap2[x - begin[3]] : 0;
60 bp1 += bstride[2];
61 }
62 }
63 }
64 } else {
65 assert(cmd.info.pad.type == CCV_NNC_PAD_REPLICATE)((void) sizeof ((cmd.info.pad.type == CCV_NNC_PAD_REPLICATE) ?
1 : 0), __extension__ ({ if (cmd.info.pad.type == CCV_NNC_PAD_REPLICATE
) ; else __assert_fail ("cmd.info.pad.type == CCV_NNC_PAD_REPLICATE"
, "pad/ccv_nnc_pad_cpu_ref.c", 65, __extension__ __PRETTY_FUNCTION__
); }));
23
←
Assuming field 'type' is equal to CCV_NNC_PAD_REPLICATE
→
24
←
Taking true branch
→
66 for (i[0] = 0; i[0] < bdim[0]; i[0]++)
25
←
Assuming the condition is true
→
26
←
Loop condition is true. Entering loop body
→
67 {
68 float* const ap0 = ap + ccv_min(adim[0] - 1, ccv_max(0, i[0] - begin[0]))({ typeof (adim[0] - 1) _a = (adim[0] - 1); typeof (({ typeof
(0) _a = (0); typeof (i[0] - begin[0]) _b = (i[0] - begin[0]
); (_a > _b) ? _a : _b; })) _b = (({ typeof (0) _a = (0); typeof
(i[0] - begin[0]) _b = (i[0] - begin[0]); (_a > _b) ? _a :
_b; })); (_a < _b) ? _a : _b; }) * astride[0];
27
←
'?' condition is false
→
28
←
Assuming '_a' is >= '_b'
→
29
←
'?' condition is false
→
69 float* const bp0 = bp + i[0] * bstride[0];
70 for (i[1] = 0; i[1] < bdim[1]; i[1]++)
30
←
Assuming the condition is true
→
31
←
Loop condition is true. Entering loop body
→
71 {
72 float* const ap1 = ap0 + ccv_min(adim[1] - 1, ccv_max(0, i[1] - begin[1]))({ typeof (adim[1] - 1) _a = (adim[1] - 1); typeof (({ typeof
(0) _a = (0); typeof (i[1] - begin[1]) _b = (i[1] - begin[1]
); (_a > _b) ? _a : _b; })) _b = (({ typeof (0) _a = (0); typeof
(i[1] - begin[1]) _b = (i[1] - begin[1]); (_a > _b) ? _a :
_b; })); (_a < _b) ? _a : _b; }) * astride[1];
32
←
'?' condition is false
→
33
←
Assuming '_a' is >= '_b'
→
34
←
'?' condition is false
→
73 float* bp1 = bp0 + i[1] * bstride[1];
74 for (i[2] = 0; i[2] < bdim[2]; i[2]++)
35
←
Assuming the condition is true
→
36
←
Loop condition is true. Entering loop body
→
75 {
76 float* const ap2 = ap1 + ccv_min(adim[2] - 1, ccv_max(0, i[2] - begin[2]))({ typeof (adim[2] - 1) _a = (adim[2] - 1); typeof (({ typeof
(0) _a = (0); typeof (i[2] - begin[2]) _b = (i[2] - begin[2]
); (_a > _b) ? _a : _b; })) _b = (({ typeof (0) _a = (0); typeof
(i[2] - begin[2]) _b = (i[2] - begin[2]); (_a > _b) ? _a :
_b; })); (_a < _b) ? _a : _b; }) * astride[2];
37
←
'?' condition is false
→
38
←
Assuming '_a' is >= '_b'
→
39
←
'?' condition is false
→
77 for (x = 0; x < bdim[3]; x++)
40
←
Assuming the condition is true
→
41
←
Loop condition is true. Entering loop body
→
78 bp1[x] = ap2[ccv_min(adim[3] - 1, ccv_max(0, x - begin[3]))({ typeof (adim[3] - 1) _a = (adim[3] - 1); typeof (({ typeof
(0) _a = (0); typeof (x - begin[3]) _b = (x - begin[3]); (_a
> _b) ? _a : _b; })) _b = (({ typeof (0) _a = (0); typeof
(x - begin[3]) _b = (x - begin[3]); (_a > _b) ? _a : _b; }
)); (_a < _b) ? _a : _b; })];
42
←
The right operand of '-' is a garbage value
79 bp1 += bstride[2];
80 }
81 }
82 }
83 }
84 return CCV_NNC_EXEC_SUCCESS;
85}
86
87static int _ccv_nnc_pad_back(const ccv_nnc_cmd_t cmd, const ccv_nnc_hint_t hint, const int flags, ccv_nnc_tensor_t* const* const inputs, const int input_size, ccv_nnc_tensor_t* const* const outputs, const int output_size, ccv_nnc_stream_context_t* const stream_context)
88{
89 assert(input_size == 1)((void) sizeof ((input_size == 1) ? 1 : 0), __extension__ ({ if
(input_size == 1) ; else __assert_fail ("input_size == 1", "pad/ccv_nnc_pad_cpu_ref.c"
, 89, __extension__ __PRETTY_FUNCTION__); }));
90 ccv_nnc_tensor_view_t* const a = (ccv_nnc_tensor_view_t*)inputs[0];
91 ccv_nnc_tensor_view_t* const b = (ccv_nnc_tensor_view_t*)outputs[0];
92 assert(ccv_nnc_tensor_nd(a->info.dim) <= CCV_NNC_MAX_DIM + 2)((void) sizeof ((ccv_nnc_tensor_nd(a->info.dim) <= (2) +
2) ? 1 : 0), __extension__ ({ if (ccv_nnc_tensor_nd(a->info
.dim) <= (2) + 2) ; else __assert_fail ("ccv_nnc_tensor_nd(a->info.dim) <= CCV_NNC_MAX_DIM + 2"
, "pad/ccv_nnc_pad_cpu_ref.c", 92, __extension__ __PRETTY_FUNCTION__
); }));
93 assert(ccv_nnc_tensor_nd(b->info.dim) <= CCV_NNC_MAX_DIM + 2)((void) sizeof ((ccv_nnc_tensor_nd(b->info.dim) <= (2) +
2) ? 1 : 0), __extension__ ({ if (ccv_nnc_tensor_nd(b->info
.dim) <= (2) + 2) ; else __assert_fail ("ccv_nnc_tensor_nd(b->info.dim) <= CCV_NNC_MAX_DIM + 2"
, "pad/ccv_nnc_pad_cpu_ref.c", 93, __extension__ __PRETTY_FUNCTION__
); }));
94 // Assuming this is float 32.
95 int adim[CCV_NNC_MAX_DIM_ALLOC(12)];
96 int bdim[CCV_NNC_MAX_DIM_ALLOC(12)];
97 ccv_nnc_tensor_view_get_dim(a, adim);
98 ccv_nnc_tensor_view_get_dim(b, bdim);
99 int astride[CCV_NNC_MAX_DIM_ALLOC(12)];
100 int bstride[CCV_NNC_MAX_DIM_ALLOC(12)];
101 assert(CCV_NNC_MAX_DIM == 2)((void) sizeof (((2) == 2) ? 1 : 0), __extension__ ({ if ((2)
== 2) ; else __assert_fail ("CCV_NNC_MAX_DIM == 2", "pad/ccv_nnc_pad_cpu_ref.c"
, 101, __extension__ __PRETTY_FUNCTION__); })); // Need to change this logic for CCV_NNC_MAX_DIM == other number.
102 ccv_nnc_tensor_view_get_stride(a, astride);
103 ccv_nnc_tensor_view_get_stride(b, bstride);
104 int i[CCV_NNC_MAX_DIM(2) + 2];
105 int x;
106 float* const ap = a->data.f32;
107 float* const bp = b->data.f32;
108 const int nd = ccv_nnc_tensor_nd(a->info.dim);
109 const int offset = CCV_NNC_MAX_DIM(2) + 2 - nd;
110 assert(offset >= 0)((void) sizeof ((offset >= 0) ? 1 : 0), __extension__ ({ if
(offset >= 0) ; else __assert_fail ("offset >= 0", "pad/ccv_nnc_pad_cpu_ref.c"
, 110, __extension__ __PRETTY_FUNCTION__); }));
111 for (x = 0; x < nd; x++) // We don't support negative pad.
112 { assert(cmd.info.size.dim[x] >= 0 && cmd.info.pad.end[x] >= 0)((void) sizeof ((cmd.info.size.dim[x] >= 0 && cmd.
info.pad.end[x] >= 0) ? 1 : 0), __extension__ ({ if (cmd.info
.size.dim[x] >= 0 && cmd.info.pad.end[x] >= 0) ;
else __assert_fail ("cmd.info.size.dim[x] >= 0 && cmd.info.pad.end[x] >= 0"
, "pad/ccv_nnc_pad_cpu_ref.c", 112, __extension__ __PRETTY_FUNCTION__
); })); }
113 int begin[CCV_NNC_MAX_DIM_ALLOC(12)];
114 for (x = 0; x < nd; x++)
115 begin[x + offset] = cmd.info.size.dim[x];
116 for (x = 0; x < offset; x++)
117 begin[x] = 0;
118 // Non-optimal case, need to do skip if needed.
119 for (i[0] = 0; i[0] < bdim[0]; i[0]++)
120 {
121 float* const ap0 = ap + (i[0] + begin[0]) * astride[0];
122 float* const bp0 = bp + i[0] * bstride[0];
123 for (i[1] = 0; i[1] < bdim[1]; i[1]++)
124 {
125 float* const ap1 = ap0 + (i[1] + begin[1]) * astride[1];
126 float* bp1 = bp0 + i[1] * bstride[1];
127 for (i[2] = 0; i[2] < bdim[2]; i[2]++)
128 {
129 float* const ap2 = ap1 + (i[2] + begin[2]) * astride[2];
130 for (x = 0; x < bdim[3]; x++)
131 bp1[x] = ap2[x + begin[3]];
132 bp1 += bstride[2];
133 }
134 }
135 }
136 return CCV_NNC_EXEC_SUCCESS;
137}
138
139REGISTER_COMMAND_BACKEND(CCV_NNC_PAD_FORWARD, CCV_NNC_BACKEND_CPU_REF)void _register_command_CCV_NNC_PAD_FORWARD_backend_CCV_NNC_BACKEND_CPU_REF(ccv_nnc_cmd_backend_registry_t* const registry)
140{
141 registry->tensor_formats = CCV_TENSOR_FORMAT_NHWC | CCV_TENSOR_FORMAT_NCHW;
142 registry->tensor_datatypes = CCV_32F;
143 registry->tensor_memory = CCV_TENSOR_CPU_MEMORY;
144 registry->algorithms = 1;
145 registry->exec = _ccv_nnc_pad_forw;
146}
147
148REGISTER_COMMAND_BACKEND(CCV_NNC_PAD_BACKWARD, CCV_NNC_BACKEND_CPU_REF)void _register_command_CCV_NNC_PAD_BACKWARD_backend_CCV_NNC_BACKEND_CPU_REF(ccv_nnc_cmd_backend_registry_t* const registry)
149{
150 registry->tensor_formats = CCV_TENSOR_FORMAT_NHWC | CCV_TENSOR_FORMAT_NCHW;
151 registry->tensor_datatypes = CCV_32F;
152 registry->tensor_memory = CCV_TENSOR_CPU_MEMORY;
153 registry->algorithms = 1;
154 registry->exec = _ccv_nnc_pad_back;
155}