Bug Summary

File:nnc/ccv_nnc_dynamic_graph_evaluate.c
Warning:line 179, column 2
Declared variable-length array (VLA) has negative size

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/2024-11-04-165540-38187-1 -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 false
→
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
←
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__); }));
6
←
Taking true branch
→
146 int i, j;
147 if (!model->graph)
7
←
Assuming field 'graph' is null
→
8
←
Taking true branch
→
148 {
149 ccv_nnc_tensor_param_t input_params[per_input_size];
150 for (i = 0; i
8.1
'i' is < 'per_input_size'
 < per_input_size; i++)
9
←
Loop condition is true. Entering loop body
→
10
←
Assuming 'i' is >= 'per_input_size'
→
11
←
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 < input_size; i++)
16
←
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;
12
←
Loop condition is true. Entering loop body
→
13
←
'?' condition is true
→
173 if (input_tensors[i])
14
←
Assuming the condition is false
→
15
←
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; })];
17
←
Assuming '_a' is > '_b'
→
18
←
'?' condition is true
→
178 const int output_size = per_output_size * parallel_count;
19
←
'output_size' initialized here
→
179 ccv_nnc_tensor_variable_t outputs[output_size];
20
←
Declared variable-length array (VLA) has negative size
180 ccv_nnc_tensor_t* output_tensors[output_size];
181 for (i = 0; i < parallel_count; i++)
182 {
183 for (j = 0; j < per_output_size; j++)
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 < per_output_size; j++)
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)
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 < output_size; i++)
222 if (outputs[i])
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