Bug Summary

File:nnc/cmd/causal_convolution/ccv_nnc_causal_convolution_cpu_ref.c
Warning:line 28, column 46
Division by zero

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_causal_convolution_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/2026-11-05-170145-808375-1 -x c causal_convolution/ccv_nnc_causal_convolution_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#include <math.h>
7
8static int _ccv_nnc_causal_convolution_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)
9{
10 assert(input_size == 3)((void) sizeof ((input_size == 3) ? 1 : 0), __extension__ ({ if
(input_size == 3) ; else __assert_fail ("input_size == 3", "causal_convolution/ccv_nnc_causal_convolution_cpu_ref.c"
, 10, __extension__ __PRETTY_FUNCTION__); }));
1
Assuming 'input_size' is equal to 3
→
2
←
Taking true branch
→
11 assert(output_size == 2)((void) sizeof ((output_size == 2) ? 1 : 0), __extension__ ({
if (output_size == 2) ; else __assert_fail ("output_size == 2"
, "causal_convolution/ccv_nnc_causal_convolution_cpu_ref.c", 11
, __extension__ __PRETTY_FUNCTION__); }));
3
←
Assuming 'output_size' is equal to 2
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4
←
Taking true branch
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12 const ccv_nnc_tensor_t* const x = inputs[0];
13 const ccv_nnc_tensor_t* const previous = inputs[1];
14 const ccv_nnc_tensor_t* const weight = inputs[2];
15 ccv_nnc_tensor_t* const y = outputs[0];
16 ccv_nnc_tensor_t* const next = outputs[1];
17 int i;
18 for (i = 0; i < 3; i++)
5
←
Loop condition is true. Entering loop body
→
10
←
Loop condition is true. Entering loop body
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13
←
Loop condition is true. Entering loop body
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16
←
Loop condition is false. Execution continues on line 20
→
19 assert(inputs[i]->info.datatype == CCV_32F && CCV_IS_TENSOR_CONTIGUOUS(inputs[i]))((void) sizeof ((inputs[i]->info.datatype == CCV_32F &&
(!((*(int*)(inputs[i])) & CCV_TENSOR_VIEW) || (((ccv_nnc_tensor_view_t
*)inputs[i])->contiguous == 1))) ? 1 : 0), __extension__ (
{ if (inputs[i]->info.datatype == CCV_32F && (!((*
(int*)(inputs[i])) & CCV_TENSOR_VIEW) || (((ccv_nnc_tensor_view_t
*)inputs[i])->contiguous == 1))) ; else __assert_fail ("inputs[i]->info.datatype == CCV_32F && CCV_IS_TENSOR_CONTIGUOUS(inputs[i])"
, "causal_convolution/ccv_nnc_causal_convolution_cpu_ref.c", 19
, __extension__ __PRETTY_FUNCTION__); }));
6
←
Assuming field 'datatype' is equal to CCV_32F
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7
←
Assuming the condition is false
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8
←
Assuming field 'contiguous' is equal to 1
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9
←
Taking true branch
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11
←
Assuming field 'datatype' is equal to CCV_32F
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12
←
Assuming the condition is true
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14
←
Assuming field 'datatype' is equal to CCV_32F
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15
←
Assuming the condition is true
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20 for (i = 0; i < 2; i++)
17
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Loop condition is true. Entering loop body
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22
←
Loop condition is true. Entering loop body
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25
←
Loop condition is false. Execution continues on line 23
→
21 assert(outputs[i]->info.datatype == CCV_32F && CCV_IS_TENSOR_CONTIGUOUS(outputs[i]))((void) sizeof ((outputs[i]->info.datatype == CCV_32F &&
(!((*(int*)(outputs[i])) & CCV_TENSOR_VIEW) || (((ccv_nnc_tensor_view_t
*)outputs[i])->contiguous == 1))) ? 1 : 0), __extension__ (
{ if (outputs[i]->info.datatype == CCV_32F && (!((
*(int*)(outputs[i])) & CCV_TENSOR_VIEW) || (((ccv_nnc_tensor_view_t
*)outputs[i])->contiguous == 1))) ; else __assert_fail ("outputs[i]->info.datatype == CCV_32F && CCV_IS_TENSOR_CONTIGUOUS(outputs[i])"
, "causal_convolution/ccv_nnc_causal_convolution_cpu_ref.c", 21
, __extension__ __PRETTY_FUNCTION__); }));
18
←
Assuming field 'datatype' is equal to CCV_32F
→
19
←
Assuming the condition is false
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20
←
Assuming field 'contiguous' is equal to 1
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21
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Taking true branch
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23
←
Assuming field 'datatype' is equal to CCV_32F
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24
←
Assuming the condition is true
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22 // Shapes only matter through their counts: C is x's last axis and the previous rows hold B batches.
23 const int channels = x->info.dim[ccv_nnc_tensor_nd(x->info.dim) - 1];
24 const int kernel_size = ccv_nnc_tensor_count(weight->info) / channels;
25 // One tap has no previous rows to carry.
26 assert(kernel_size >= 2 && ccv_nnc_tensor_count(weight->info) == (size_t)kernel_size * channels)((void) sizeof ((kernel_size >= 2 && ccv_nnc_tensor_count
(weight->info) == (size_t)kernel_size * channels) ? 1 : 0)
, __extension__ ({ if (kernel_size >= 2 && ccv_nnc_tensor_count
(weight->info) == (size_t)kernel_size * channels) ; else __assert_fail
("kernel_size >= 2 && ccv_nnc_tensor_count(weight->info) == (size_t)kernel_size * channels"
, "causal_convolution/ccv_nnc_causal_convolution_cpu_ref.c", 26
, __extension__ __PRETTY_FUNCTION__); }));
26
←
Assuming 'kernel_size' is >= 2
→
27
←
Assuming the condition is true
→
28
←
Taking true branch
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27 const int B = ccv_nnc_tensor_count(previous->info) / ((size_t)(kernel_size - 1) * channels);
29
←
'B' initialized to 0
→
28 const int T = ccv_nnc_tensor_count(x->info) / ((size_t)B * channels);
30
←
Division by zero
29 const int rows = ccv_nnc_tensor_count(next->info) / ((size_t)B * channels);
30 assert(ccv_nnc_tensor_count(y->info) == (size_t)B * T * channels)((void) sizeof ((ccv_nnc_tensor_count(y->info) == (size_t)
B * T * channels) ? 1 : 0), __extension__ ({ if (ccv_nnc_tensor_count
(y->info) == (size_t)B * T * channels) ; else __assert_fail
("ccv_nnc_tensor_count(y->info) == (size_t)B * T * channels"
, "causal_convolution/ccv_nnc_causal_convolution_cpu_ref.c", 30
, __extension__ __PRETTY_FUNCTION__); }));
31 assert(rows <= kernel_size - 1 + T)((void) sizeof ((rows <= kernel_size - 1 + T) ? 1 : 0), __extension__
({ if (rows <= kernel_size - 1 + T) ; else __assert_fail (
"rows <= kernel_size - 1 + T", "causal_convolution/ccv_nnc_causal_convolution_cpu_ref.c"
, 31, __extension__ __PRETTY_FUNCTION__); }));
32 const int history = kernel_size - 1;
33 // Context row r of batch b is previous row r, then x row r - history.
34#define CONTEXT(b, r, c) ((r) < history ? previous->data.f32[((size_t)(b) * history + (r)) * channels + (c)] : x->data.f32[((size_t)(b) * T + (r) - history) * channels + (c)])
35 const int silu = cmd.info.causal_convolution.silu;
36 int b, t, c, j;
37 for (b = 0; b < B; b++)
38 for (c = 0; c < channels; c++)
39 {
40 const float* const w = weight->data.f32 + (size_t)c * kernel_size;
41 for (t = 0; t < T; t++)
42 {
43 float sum = 0;
44 for (j = 0; j < kernel_size; j++)
45 sum += w[j] * CONTEXT(b, t + j, c);
46 y->data.f32[((size_t)b * T + t) * channels + c] = silu ? sum / (1 + expf(-sum)) : sum;
47 }
48 // The next rows are the context's last rows.
49 const int first = history + T - rows;
50 for (j = 0; j < rows; j++)
51 next->data.f32[((size_t)b * rows + j) * channels + c] = CONTEXT(b, first + j, c);
52 }
53#undef CONTEXT
54 return CCV_NNC_EXEC_SUCCESS;
55}
56
57static int _ccv_nnc_causal_convolution_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)
58{
59 return CCV_NNC_EXEC_INVALID;
60}
61
62REGISTER_COMMAND_BACKEND(CCV_NNC_CAUSAL_CONVOLUTION_FORWARD, CCV_NNC_BACKEND_CPU_REF)void _register_command_CCV_NNC_CAUSAL_CONVOLUTION_FORWARD_backend_CCV_NNC_BACKEND_CPU_REF(ccv_nnc_cmd_backend_registry_t* const registry)
63{
64 registry->tensor_formats = CCV_TENSOR_FORMAT_NHWC | CCV_TENSOR_FORMAT_NCHW | CCV_TENSOR_FORMAT_CHWN;
65 registry->tensor_datatypes = CCV_32F;
66 registry->tensor_memory = CCV_TENSOR_CPU_MEMORY;
67 registry->algorithms = 1;
68 registry->exec = _ccv_nnc_causal_convolution_forw;
69}
70
71REGISTER_COMMAND_BACKEND(CCV_NNC_CAUSAL_CONVOLUTION_BACKWARD, CCV_NNC_BACKEND_CPU_REF)void _register_command_CCV_NNC_CAUSAL_CONVOLUTION_BACKWARD_backend_CCV_NNC_BACKEND_CPU_REF(ccv_nnc_cmd_backend_registry_t* const registry)
72{
73 registry->tensor_formats = CCV_TENSOR_FORMAT_NHWC | CCV_TENSOR_FORMAT_NCHW | CCV_TENSOR_FORMAT_CHWN;
74 registry->tensor_datatypes = CCV_32F;
75 registry->tensor_memory = CCV_TENSOR_CPU_MEMORY;
76 registry->algorithms = 1;
77 registry->exec = _ccv_nnc_causal_convolution_back;
78}