Bug Summary

File:nnc/ccv_nnc_dynamic_graph_evaluate.c
Warning:line 222, column 7
Branch condition evaluates to 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_dynamic_graph_evaluate.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/19 -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/2024-10-25-103032-252541-1 -x c ccv_nnc_dynamic_graph_evaluate.c
1#include "ccv_nnc.h"
2#include "ccv_nnc_easy.h"
3#include "ccv_nnc_internal.h"
4#include "ccv_nnc_easy.h"
5#include "ccv_internal.h"
6#include "_ccv_nnc_dynamic_graph.h"
7#include "_ccv_cnnp_model.h"
8
9// MARK - Level-5.5 API
10
11static int _ccv_cnnp_model_exec(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)
12{
13 ccv_nnc_stateful_exec_t* const stateful_exec = (ccv_nnc_stateful_exec_t*)cmd.data;
14 ccv_cnnp_model_t* const model = (ccv_cnnp_model_t*)stateful_exec->data;
15 // I cannot just use stream context, it cannot synchronize correctly based on existing coroutine implementation.
16 int i;
17 int wait_for_any_neighbor = 0;
18 const int parallel_count = ccv_max(model->parallel_count, 1)({ typeof (model->parallel_count) _a = (model->parallel_count
); typeof (1) _b = (1); (_a > _b) ? _a : _b; })
;
19 if (stream_context) // Find all neighbor context and wait on them all.
20 for (i = 0; i < parallel_count; i++)
21 {
22 ccv_nnc_stream_context_t* const neighbor_context = ccv_nnc_stream_context_find_neighbor(stream_context, i);
23 if (neighbor_context && neighbor_context != stream_context)
24 {
25 ccv_nnc_stream_signal_t* const signal = ccv_nnc_stream_context_emit_signal_new(neighbor_context);
26 ccv_nnc_stream_context_wait_signal(stream_context, signal);
27 wait_for_any_neighbor = 1;
28 }
29 }
30 co_scheduler_t* old_scheduler;
31 co_routine_t* old_main;
32 if (stream_context)
33 {
34 old_main = stream_context->main;
35 old_scheduler = stream_context->scheduler;
36 // We cannot piggyback on old scheduler.
37 stream_context->scheduler = 0;
38 // We will have a new main coroutine when schedule as the root.
39 // Otherwise it will be scheduled after the existing routines all scheduled
40 // out, and that won't be right.
41 stream_context->main = 0;
42 }
43 if (cmd.cmd == CCV_NNC_CUSTOM_FORWARD)
44 {
45 ccv_cnnp_model_evaluate(model, (ccv_cnnp_evaluate_param_t){
46 .requires_grad = stateful_exec->requires_grad,
47 .disable_outgrad = stateful_exec->disable_outgrad,
48 .is_test = stateful_exec->is_test,
49 }, inputs, input_size, outputs, output_size, 0, stream_context);
50 } else {
51 const int ingrad_size = model->output_size * parallel_count;
52 assert(ingrad_size <= input_size)((void) sizeof ((ingrad_size <= input_size) ? 1 : 0), __extension__
({ if (ingrad_size <= input_size) ; else __assert_fail ("ingrad_size <= input_size"
, "ccv_nnc_dynamic_graph_evaluate.c", 52, __extension__ __PRETTY_FUNCTION__
); }))
;
53 if (stateful_exec->disable_outgrad == CCV_CNNP_DISABLE_OUTGRAD_NONE)
54 ccv_cnnp_model_backward(model, inputs, ingrad_size, outputs, output_size, 0, stream_context);
55 else if (stateful_exec->disable_outgrad == CCV_CNNP_DISABLE_OUTGRAD_ALL)
56 ccv_cnnp_model_backward(model, inputs, ingrad_size, 0, 0, 0, stream_context);
57 else {
58 assert(output_size == model->input_size * parallel_count)((void) sizeof ((output_size == model->input_size * parallel_count
) ? 1 : 0), __extension__ ({ if (output_size == model->input_size
* parallel_count) ; else __assert_fail ("output_size == model->input_size * parallel_count"
, "ccv_nnc_dynamic_graph_evaluate.c", 58, __extension__ __PRETTY_FUNCTION__
); }))
;
59 int per_outgrad_size = 0;
60 int i, j, k;
61 for (i = 0; i < model->input_size; i++)
62 if (!(stateful_exec->disable_outgrad & ((uint64_t)1 << i)))
63 ++per_outgrad_size;
64 assert(per_outgrad_size > 0)((void) sizeof ((per_outgrad_size > 0) ? 1 : 0), __extension__
({ if (per_outgrad_size > 0) ; else __assert_fail ("per_outgrad_size > 0"
, "ccv_nnc_dynamic_graph_evaluate.c", 64, __extension__ __PRETTY_FUNCTION__
); }))
;
65 const int outgrad_size = per_outgrad_size * parallel_count;
66 ccv_nnc_tensor_t* outgrads[outgrad_size];
67 for (i = 0; i < parallel_count; i++)
68 for (k = 0, j = 0; j < model->input_size; j++)
69 if (!(stateful_exec->disable_outgrad & ((uint64_t)1 << j)))
70 outgrads[(k++) + i * per_outgrad_size] = outputs[j + i * model->input_size];
71 ccv_cnnp_model_backward(model, inputs, ingrad_size, outgrads, outgrad_size, 0, stream_context);
72 }
73 stateful_exec->did_backward_but_not_apply_gradients = 1;
74 }
75 if (stream_context)
76 {
77 // Should have new scheduler created.
78 assert(stream_context->scheduler)((void) sizeof ((stream_context->scheduler) ? 1 : 0), __extension__
({ if (stream_context->scheduler) ; else __assert_fail ("stream_context->scheduler"
, "ccv_nnc_dynamic_graph_evaluate.c", 78, __extension__ __PRETTY_FUNCTION__
); }))
;
79 // The new scheduler shouldn't be active (everything is scheduled).
80 assert(!co_scheduler_is_active(stream_context->scheduler))((void) sizeof ((!co_scheduler_is_active(stream_context->scheduler
)) ? 1 : 0), __extension__ ({ if (!co_scheduler_is_active(stream_context
->scheduler)) ; else __assert_fail ("!co_scheduler_is_active(stream_context->scheduler)"
, "ccv_nnc_dynamic_graph_evaluate.c", 80, __extension__ __PRETTY_FUNCTION__
); }))
;
81 co_scheduler_free(stream_context->scheduler);
82 // Switch back to the old scheduler.
83 stream_context->scheduler = old_scheduler;
84 // The main coroutine should be cleared.
85 assert(!stream_context->main)((void) sizeof ((!stream_context->main) ? 1 : 0), __extension__
({ if (!stream_context->main) ; else __assert_fail ("!stream_context->main"
, "ccv_nnc_dynamic_graph_evaluate.c", 85, __extension__ __PRETTY_FUNCTION__
); }))
;
86 stream_context->main = old_main;
87 }
88 if (wait_for_any_neighbor) // Find all neighbor context and wait on them all.
89 {
90 assert(stream_context)((void) sizeof ((stream_context) ? 1 : 0), __extension__ ({ if
(stream_context) ; else __assert_fail ("stream_context", "ccv_nnc_dynamic_graph_evaluate.c"
, 90, __extension__ __PRETTY_FUNCTION__); }))
;
91 ccv_nnc_stream_signal_t* const signal = ccv_nnc_stream_context_emit_signal_new(stream_context);
92 for (i = 0; i < parallel_count; i++)
93 {
94 ccv_nnc_stream_context_t* const neighbor_context = ccv_nnc_stream_context_find_neighbor(stream_context, i);
95 if (neighbor_context && neighbor_context != stream_context)
96 ccv_nnc_stream_context_wait_signal(neighbor_context, signal);
97 }
98 }
99 return CCV_NNC_EXEC_SUCCESS;
100}
101
102static void _ccv_cnnp_model_tensor_auto(const ccv_nnc_cmd_t cmd, const ccv_nnc_tensor_param_t* const inputs, const int input_size, const ccv_nnc_hint_t hint, ccv_nnc_tensor_param_t* const outputs, const int output_size)
103{
104 ccv_nnc_stateful_exec_t* const stateful_exec = (ccv_nnc_stateful_exec_t*)cmd.data;
105 ccv_cnnp_model_t* const model = (ccv_cnnp_model_t*)stateful_exec->data;
106 const int parallel_count = ccv_max(model->parallel_count, 1)({ typeof (model->parallel_count) _a = (model->parallel_count
); typeof (1) _b = (1); (_a > _b) ? _a : _b; })
;
107 const int per_input_size = input_size / parallel_count;
108 assert(per_input_size > 0)((void) sizeof ((per_input_size > 0) ? 1 : 0), __extension__
({ if (per_input_size > 0) ; else __assert_fail ("per_input_size > 0"
, "ccv_nnc_dynamic_graph_evaluate.c", 108, __extension__ __PRETTY_FUNCTION__
); }))
;
109 assert((input_size % parallel_count) == 0)((void) sizeof (((input_size % parallel_count) == 0) ? 1 : 0)
, __extension__ ({ if ((input_size % parallel_count) == 0) ; else
__assert_fail ("(input_size % parallel_count) == 0", "ccv_nnc_dynamic_graph_evaluate.c"
, 109, __extension__ __PRETTY_FUNCTION__); }))
;
110 const int per_output_size = output_size / parallel_count;
111 assert(per_output_size > 0)((void) sizeof ((per_output_size > 0) ? 1 : 0), __extension__
({ if (per_output_size > 0) ; else __assert_fail ("per_output_size > 0"
, "ccv_nnc_dynamic_graph_evaluate.c", 111, __extension__ __PRETTY_FUNCTION__
); }))
;
112 assert((output_size % parallel_count) == 0)((void) sizeof (((output_size % parallel_count) == 0) ? 1 : 0
), __extension__ ({ if ((output_size % parallel_count) == 0) ;
else __assert_fail ("(output_size % parallel_count) == 0", "ccv_nnc_dynamic_graph_evaluate.c"
, 112, __extension__ __PRETTY_FUNCTION__); }))
;
113 int i, j;
114 for (i = 0; i < parallel_count; i++)
115 {
116 ccv_cnnp_model_tensor_auto(model, outputs + i * per_output_size, per_output_size);
117 // Set device id to the corresponding inputs' device id.
118 const int device_id = CCV_TENSOR_GET_DEVICE_ID(inputs[i * per_input_size].type)(((inputs[i * per_input_size].type) & 0xfff00) >> 8
)
;
119 for (j = 0; j < per_output_size; j++)
120 CCV_TENSOR_SET_DEVICE_ID(outputs[i * per_output_size + j].type, device_id)(outputs[i * per_output_size + j].type) = (((outputs[i * per_output_size
+ j].type) & ~0xfff00) | (((device_id) & 0xfff) <<
8))
;
121 }
122}
123
124static void _ccv_cnnp_model_apply_gradients(const ccv_nnc_cmd_t cmd, ccv_nnc_stream_context_t* const stream_context)
125{
126 ccv_nnc_stateful_exec_t* const stateful_exec = (ccv_nnc_stateful_exec_t*)cmd.data;
127 ccv_cnnp_model_t* const model = (ccv_cnnp_model_t*)stateful_exec->data;
128 ccv_cnnp_model_apply_gradients(model, stream_context);
129}
130
131static ccv_nnc_stateful_cmd_vtab_t ccv_cnnp_model_exec_isa = {
132 .super = {
133 .exec = _ccv_cnnp_model_exec,
134 .tensor_auto = _ccv_cnnp_model_tensor_auto,
135 },
136 .apply_gradients = _ccv_cnnp_model_apply_gradients,
137};
138
139void ccv_nnc_dynamic_graph_dry_run(ccv_nnc_dynamic_graph_t* const dynamic_graph, ccv_cnnp_model_t* const model, const int is_test, const ccv_nnc_tensor_variable_t* const inputs, const int input_size, ccv_nnc_stream_context_t* const stream_context)
140{
141 assert(input_size > 0)((void) sizeof ((input_size > 0) ? 1 : 0), __extension__ (
{ if (input_size > 0) ; else __assert_fail ("input_size > 0"
, "ccv_nnc_dynamic_graph_evaluate.c", 141, __extension__ __PRETTY_FUNCTION__
); }))
;
1
Assuming 'input_size' is > 0
2
Taking true branch
142 const int parallel_count = ccv_max(model->parallel_count, 1)({ typeof (model->parallel_count) _a = (model->parallel_count
); typeof (1) _b = (1); (_a > _b) ? _a : _b; })
;
3
Assuming '_a' is > '_b'
4
'?' condition is true
143 const int per_input_size = input_size / parallel_count;
144 assert(per_input_size > 0)((void) sizeof ((per_input_size > 0) ? 1 : 0), __extension__
({ if (per_input_size > 0) ; else __assert_fail ("per_input_size > 0"
, "ccv_nnc_dynamic_graph_evaluate.c", 144, __extension__ __PRETTY_FUNCTION__
); }))
;
5
Assuming 'per_input_size' is > 0
6
Taking true branch
145 assert((input_size % parallel_count) == 0)((void) sizeof (((input_size % parallel_count) == 0) ? 1 : 0)
, __extension__ ({ if ((input_size % parallel_count) == 0) ; else
__assert_fail ("(input_size % parallel_count) == 0", "ccv_nnc_dynamic_graph_evaluate.c"
, 145, __extension__ __PRETTY_FUNCTION__); }))
;
7
Assuming the condition is true
8
Taking true branch
146 int i, j;
147 if (!model->graph)
9
Assuming field 'graph' is null
10
Taking true branch
148 {
149 ccv_nnc_tensor_param_t input_params[per_input_size];
150 for (i = 0; i
10.1
'i' is < 'per_input_size'
< per_input_size
; i++)
11
Loop condition is true. Entering loop body
12
Assuming 'i' is >= 'per_input_size'
13
Loop condition is false. Execution continues on line 152
151 input_params[i] = inputs[i]->info;
152 ccv_cnnp_model_compile(model, input_params, per_input_size, CMD_NOOP()ccv_nnc_cmd(CCV_NNC_NOOP, 0, ccv_nnc_cmd_auto, 0), CMD_NOOP()ccv_nnc_cmd(CCV_NNC_NOOP, 0, ccv_nnc_cmd_auto, 0));
153 } else {
154 assert(per_input_size == model->input_size)((void) sizeof ((per_input_size == model->input_size) ? 1 :
0), __extension__ ({ if (per_input_size == model->input_size
) ; else __assert_fail ("per_input_size == model->input_size"
, "ccv_nnc_dynamic_graph_evaluate.c", 154, __extension__ __PRETTY_FUNCTION__
); }))
;
155 ccv_nnc_tensor_param_t input_params[per_input_size];
156 int flag = 0;
157 for (i = 0; i < per_input_size; i++)
158 {
159 input_params[i] = inputs[i]->info;
160 const ccv_nnc_tensor_param_t params = ccv_nnc_tensor_symbol_params(model->graph, model->inputs[i]);
161 // If these two parameters doesn't match, recompile the graph..
162 if (memcmp(&params, &input_params[i], sizeof(params)) != 0)
163 flag = 1;
164 }
165 if (flag) // Recompile the graph.
166 ccv_cnnp_model_compile(model, input_params, per_input_size, ccv_cnnp_model_minimizer(model), CMD_NOOP()ccv_nnc_cmd(CCV_NNC_NOOP, 0, ccv_nnc_cmd_auto, 0));
167 }
168 ccv_nnc_tensor_t* input_tensors[input_size];
169 for (i = 0; i
13.1
'i' is < 'input_size'
< input_size
; i++)
18
Assuming 'i' is < 'input_size'
23
Assuming 'i' is >= 'input_size'
24
Loop condition is false. Execution continues on line 176
170 {
171 // Cannot have the parameter be a partial tensor view for model evaluation.
172 input_tensors[i] = inputs[i] ? ccv_nnc_tensor_from_variable(dynamic_graph, inputs[i], stream_context)ccv_nnc_tensor_from_variable_impl(dynamic_graph, inputs[i], stream_context
)
: 0;
14
Loop condition is true. Entering loop body
15
'?' condition is true
19
Loop condition is true. Entering loop body
20
Assuming the condition is false
21
'?' condition is false
173 if (input_tensors[i])
16
Assuming the condition is false
17
Taking false branch
22
Taking false branch
174 { assert(CCV_IS_TENSOR_CONTIGUOUS(input_tensors[i]))((void) sizeof (((!((*(int*)(input_tensors[i])) & CCV_TENSOR_VIEW
) || (((ccv_nnc_tensor_view_t*)input_tensors[i])->contiguous
== 1))) ? 1 : 0), __extension__ ({ if ((!((*(int*)(input_tensors
[i])) & CCV_TENSOR_VIEW) || (((ccv_nnc_tensor_view_t*)input_tensors
[i])->contiguous == 1))) ; else __assert_fail ("CCV_IS_TENSOR_CONTIGUOUS(input_tensors[i])"
, "ccv_nnc_dynamic_graph_evaluate.c", 174, __extension__ __PRETTY_FUNCTION__
); }))
; }
175 }
176 const int per_output_size = ccv_cnnp_model_output_size(model);
177 ccv_nnc_tensor_param_t output_params[ccv_max(1, per_output_size)({ typeof (1) _a = (1); typeof (per_output_size) _b = (per_output_size
); (_a > _b) ? _a : _b; })
];
25
Assuming '_a' is > '_b'
26
'?' condition is true
178 const int output_size = per_output_size * parallel_count;
179 ccv_nnc_tensor_variable_t outputs[output_size];
180 ccv_nnc_tensor_t* output_tensors[output_size];
181 for (i = 0; i
26.1
'i' is < 'parallel_count'
29.1
'i' is < 'parallel_count'
< parallel_count
; i++)
27
Loop condition is true. Entering loop body
30
Loop condition is true. Entering loop body
33
Assuming 'i' is >= 'parallel_count'
34
Loop condition is false. Execution continues on line 196
182 {
183 for (j = 0; j
27.1
'j' is >= 'per_output_size'
30.1
'j' is >= 'per_output_size'
< per_output_size; j++)
28
Loop condition is false. Execution continues on line 185
31
Loop condition is false. Execution continues on line 185
184 output_params[j] = ccv_nnc_tensor_auto;
185 ccv_cnnp_model_tensor_auto(model, output_params, per_output_size);
186 for (j = 0; j
28.1
'j' is >= 'per_output_size'
31.1
'j' is >= 'per_output_size'
< per_output_size; j++)
29
Loop condition is false. Execution continues on line 181
32
Loop condition is false. Execution continues on line 181
187 if (!ccv_nnc_is_tensor_auto(output_params[j]))
188 {
189 outputs[i * per_output_size + j] = ccv_nnc_tensor_variable_new(dynamic_graph, output_params[j])ccv_nnc_tensor_variable_new_impl(dynamic_graph, output_params
[j])
;
190 output_tensors[i * per_output_size + j] = ccv_nnc_tensor_from_variable(dynamic_graph, outputs[i * per_output_size + j], stream_context)ccv_nnc_tensor_from_variable_impl(dynamic_graph, outputs[i * per_output_size
+ j], stream_context)
;
191 } else {
192 outputs[i * per_output_size + j] = 0;
193 output_tensors[i * per_output_size + j] = 0;
194 }
195 }
196 if (dynamic_graph->no_grad)
35
Assuming field 'no_grad' is not equal to 0
36
Taking true branch
197 {
198 ccv_cnnp_model_dry_run(model, (ccv_cnnp_evaluate_param_t){
199 .requires_grad = 0,
200 .disable_outgrad = CCV_CNNP_DISABLE_OUTGRAD_ALL,
201 .is_test = is_test,
202 }, input_tensors, input_size, output_tensors, output_size);
203 } else {
204 uint64_t disable_outgrad = 0;
205 int count = 0;
206 for (i = 0; i < per_input_size; i++)
207 if (!inputs[i] || inputs[i]->type == CCV_NNC_TENSOR_CONSTANT)
208 {
209 disable_outgrad |= ((uint64_t)1 << i);
210 ++count;
211 }
212 if (count == per_input_size)
213 disable_outgrad = CCV_CNNP_DISABLE_OUTGRAD_ALL;
214 ccv_cnnp_model_dry_run(model, (ccv_cnnp_evaluate_param_t){
215 .requires_grad = 1,
216 .disable_outgrad = disable_outgrad,
217 .is_test = is_test,
218 }, input_tensors, input_size, output_tensors, output_size);
219 }
220 // Free the allocated variables.
221 for (i = 0; i
37.1
'i' is < 'output_size'
< output_size; i++)
37
The value 0 is assigned to 'i'
38
Loop condition is true. Entering loop body
222 if (outputs[i])
39
Branch condition evaluates to a garbage value
223 ccv_nnc_tensor_variable_free(dynamic_graph, outputs[i]);
224}
225
226void ccv_nnc_dynamic_graph_evaluate(ccv_nnc_dynamic_graph_t* const dynamic_graph, ccv_cnnp_model_t* const model, const int is_test, const ccv_nnc_tensor_variable_t* const inputs, const int input_size, ccv_nnc_tensor_variable_t* const outputs, const int output_size, ccv_nnc_tensor_tape_t* const tensor_tape, ccv_nnc_stream_context_t* const stream_context)
227{
228 ccv_nnc_cmd_t cmd = ccv_nnc_cmd(CCV_NNC_CUSTOM_FORWARD, (ccv_nnc_cmd_vtab_t*)&ccv_cnnp_model_exec_isa, (ccv_nnc_cmd_param_t){}, 0);
229 assert(input_size > 0)((void) sizeof ((input_size > 0) ? 1 : 0), __extension__ (
{ if (input_size > 0) ; else __assert_fail ("input_size > 0"
, "ccv_nnc_dynamic_graph_evaluate.c", 229, __extension__ __PRETTY_FUNCTION__
); }))
;
230 const int parallel_count = ccv_max(model->parallel_count, 1)({ typeof (model->parallel_count) _a = (model->parallel_count
); typeof (1) _b = (1); (_a > _b) ? _a : _b; })
;
231 const int per_input_size = input_size / parallel_count;
232 assert(per_input_size > 0)((void) sizeof ((per_input_size > 0) ? 1 : 0), __extension__
({ if (per_input_size > 0) ; else __assert_fail ("per_input_size > 0"
, "ccv_nnc_dynamic_graph_evaluate.c", 232, __extension__ __PRETTY_FUNCTION__
); }))
;
233 assert((input_size % parallel_count) == 0)((void) sizeof (((input_size % parallel_count) == 0) ? 1 : 0)
, __extension__ ({ if ((input_size % parallel_count) == 0) ; else
__assert_fail ("(input_size % parallel_count) == 0", "ccv_nnc_dynamic_graph_evaluate.c"
, 233, __extension__ __PRETTY_FUNCTION__); }))
;
234 int i;
235 if (!model->graph)
236 {
237 ccv_nnc_tensor_param_t input_params[per_input_size];
238 for (i = 0; i < per_input_size; i++)
239 input_params[i] = inputs[i]->info;
240 ccv_cnnp_model_compile(model, input_params, per_input_size, CMD_NOOP()ccv_nnc_cmd(CCV_NNC_NOOP, 0, ccv_nnc_cmd_auto, 0), CMD_NOOP()ccv_nnc_cmd(CCV_NNC_NOOP, 0, ccv_nnc_cmd_auto, 0));
241 } else {
242 assert(per_input_size == model->input_size)((void) sizeof ((per_input_size == model->input_size) ? 1 :
0), __extension__ ({ if (per_input_size == model->input_size
) ; else __assert_fail ("per_input_size == model->input_size"
, "ccv_nnc_dynamic_graph_evaluate.c", 242, __extension__ __PRETTY_FUNCTION__
); }))
;
243 ccv_nnc_tensor_param_t input_params[per_input_size];
244 int flag = 0;
245 for (i = 0; i < per_input_size; i++)
246 {
247 input_params[i] = inputs[i]->info;
248 const ccv_nnc_tensor_param_t params = ccv_nnc_tensor_symbol_params(model->graph, model->inputs[i]);
249 // If these two parameters doesn't match, recompile the graph..
250 if (memcmp(&params, &input_params[i], sizeof(params)) != 0)
251 flag = 1;
252 }
253 if (flag) // Recompile the graph.
254 ccv_cnnp_model_compile(model, input_params, per_input_size, ccv_cnnp_model_minimizer(model), CMD_NOOP()ccv_nnc_cmd(CCV_NNC_NOOP, 0, ccv_nnc_cmd_auto, 0));
255 }
256 for (i = 0; i < input_size; i++)
257 {
258 // Cannot have the parameter be a partial tensor view for model evaluation.
259 ccv_nnc_tensor_t* const tensor = inputs[i] ? ccv_nnc_tensor_from_variable(dynamic_graph, inputs[i], stream_context)ccv_nnc_tensor_from_variable_impl(dynamic_graph, inputs[i], stream_context
)
: 0;
260 if (tensor)
261 { assert(CCV_IS_TENSOR_CONTIGUOUS(tensor))((void) sizeof (((!((*(int*)(tensor)) & CCV_TENSOR_VIEW) ||
(((ccv_nnc_tensor_view_t*)tensor)->contiguous == 1))) ? 1
: 0), __extension__ ({ if ((!((*(int*)(tensor)) & CCV_TENSOR_VIEW
) || (((ccv_nnc_tensor_view_t*)tensor)->contiguous == 1)))
; else __assert_fail ("CCV_IS_TENSOR_CONTIGUOUS(tensor)", "ccv_nnc_dynamic_graph_evaluate.c"
, 261, __extension__ __PRETTY_FUNCTION__); }))
; }
262 }
263 if (dynamic_graph->no_grad)
264 {
265 ccv_nnc_stateful_exec_t stateful_exec = {
266 .requires_grad = 0,
267 .is_test = is_test,
268 .disable_outgrad = CCV_CNNP_DISABLE_OUTGRAD_ALL,
269 .tensor_tape = tensor_tape,
270 .data = model
271 };
272 cmd.data = &stateful_exec;
273 // Parallel parameter doesn't make sense here, the parallel is defined inside the model.
274 ccv_nnc_dynamic_graph_exec_ret(dynamic_graph, cmd, ccv_nnc_no_hint, 0, inputs, input_size, outputs, output_size, 0, stream_context, 0);
275 } else {
276 uint64_t disable_outgrad = 0;
277 int count = 0;
278 for (i = 0; i < per_input_size; i++)
279 if (!inputs[i] || inputs[i]->type == CCV_NNC_TENSOR_CONSTANT)
280 {
281 disable_outgrad |= ((uint64_t)1 << i);
282 ++count;
283 }
284 if (count == per_input_size)
285 disable_outgrad = CCV_CNNP_DISABLE_OUTGRAD_ALL;
286 ccv_nnc_stateful_exec_t* const stateful_exec = (ccv_nnc_stateful_exec_t*)ccmallocmalloc(sizeof(ccv_nnc_stateful_exec_t));
287 cmd.data = stateful_exec;
288 stateful_exec->requires_grad = 1;
289 stateful_exec->is_test = is_test;
290 stateful_exec->did_backward_but_not_apply_gradients = 0;
291 stateful_exec->should_free = 0;
292 stateful_exec->disable_outgrad = disable_outgrad;
293 stateful_exec->tensor_tape = tensor_tape;
294 stateful_exec->data = model;
295 stateful_exec->cmd = cmd;
296 ccv_nnc_graph_exec_symbol_t symbol = {};
297 ccv_nnc_dynamic_graph_exec_ret(dynamic_graph, cmd, ccv_nnc_no_hint, 0, inputs, input_size, outputs, output_size, 0, stream_context, &symbol);
298 if (!symbol.graph) // This is because inputs are all constants.
299 ccfreefree(stateful_exec); // No one records it, there is no cmd.data refer to it.
300 else {
301 if (!dynamic_graph->stateful_execs)
302 {
303 dynamic_graph->stateful_execs = ccv_array_new(sizeof(ccv_nnc_stateful_exec_t*), 1, 0);
304 ccv_array_push(dynamic_graph->stateful_execs, &stateful_exec);
305 stateful_exec->index = dynamic_graph->stateful_execs->rnum - 1;
306 } else {
307 if (dynamic_graph->reuse_stateful_exec >= 0)
308 {
309 *(ccv_nnc_stateful_exec_t**)ccv_array_get(dynamic_graph->stateful_execs, dynamic_graph->reuse_stateful_exec)((void*)(((char*)((dynamic_graph->stateful_execs)->data
)) + (size_t)(dynamic_graph->stateful_execs)->rsize * (
size_t)(dynamic_graph->reuse_stateful_exec)))
= stateful_exec;
310 stateful_exec->index = dynamic_graph->reuse_stateful_exec;
311 int flag = 0;
312 for (i = dynamic_graph->reuse_stateful_exec + 1; !flag && i < dynamic_graph->stateful_execs->rnum; i++)
313 if (*(ccv_nnc_stateful_exec_t**)ccv_array_get(dynamic_graph->stateful_execs, i)((void*)(((char*)((dynamic_graph->stateful_execs)->data
)) + (size_t)(dynamic_graph->stateful_execs)->rsize * (
size_t)(i)))
== 0)
314 dynamic_graph->reuse_stateful_exec = i, flag = 1;
315 if (!flag) // Reset to 1.
316 dynamic_graph->reuse_stateful_exec = -1;
317 } else {
318 // Push new, no reuse available.
319 ccv_array_push(dynamic_graph->stateful_execs, &stateful_exec);
320 stateful_exec->index = dynamic_graph->stateful_execs->rnum - 1;
321 }
322 }
323 }
324 }
325}
326