Bug Summary

File:nnc/ccv_cnnp_model.c
Warning:line 2835, column 25
Array access (via field 'vals') results in a null pointer dereference

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.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/2026-07-31-163239-403758-1 -x c ccv_cnnp_model.c
1#include "ccv_nnc.h"
2#include "ccv_nnc_easy.h"
3#include "ccv_nnc_internal.h"
4#include "ccv_internal.h"
5#include "_ccv_cnnp_model.h"
6#include "_ccv_nnc_graph.h"
7#include "_ccv_nnc_symbolic_graph.h"
8#ifdef HAVE_CUDA1
9#include "gpu/ccv_nnc_compat.h"
10#endif
11
12// MARK - Level-5 API
13
14ccv_cnnp_model_io_t ccv_cnnp_model_apply(ccv_cnnp_model_t* const model, const ccv_cnnp_model_io_t* const inputs, const int input_size)
15{
16 if (!model->io)
17 model->io = ccv_array_new(sizeof(ccv_cnnp_model_io_t), 1, 0);
18 ccv_cnnp_model_io_t model_io = ccmallocmalloc(sizeof(struct ccv_cnnp_model_io_s) + sizeof(ccv_nnc_tensor_symbol_t) * model->output_size);
19 model_io->param_ref = 0;
20 model_io->param_sel = 0;
21 model_io->visit = 0;
22 model_io->model = model;
23 model_io->dependencies = 0;
24 model_io->dependents = 0;
25 model_io->outgoings = 0;
26 model_io->outputs = (ccv_nnc_tensor_symbol_t*)(model_io + 1);
27 ccv_array_push(model->io, &model_io);
28 if (input_size > 0)
29 {
30 model_io->incomings = ccv_array_new(sizeof(ccv_cnnp_model_io_t), input_size, 0);
31 ccv_array_resize(model_io->incomings, input_size);
32 int i;
33 memcpy(ccv_array_get(model_io->incomings, 0)((void*)(((char*)((model_io->incomings)->data)) + (size_t
)(model_io->incomings)->rsize * (size_t)(0))), inputs, sizeof(ccv_cnnp_model_io_t) * input_size);
34 for (i = 0; i < input_size; i++)
35 {
36 if (!inputs[i]->outgoings)
37 inputs[i]->outgoings = ccv_array_new(sizeof(ccv_cnnp_model_io_t), 1, 0);
38 ccv_array_push(inputs[i]->outgoings, &model_io);
39 }
40 } else {
41 model_io->incomings = 0;
42 }
43 return model_io;
44}
45
46void ccv_cnnp_model_add_dependencies(ccv_cnnp_model_io_t model_io, const ccv_cnnp_model_io_t* const dependencies, const int dependency_size)
47{
48 assert(dependency_size > 0)((void) sizeof ((dependency_size > 0) ? 1 : 0), __extension__
({ if (dependency_size > 0) ; else __assert_fail ("dependency_size > 0"
, "ccv_cnnp_model.c", 48, __extension__ __PRETTY_FUNCTION__);
}));
49 if (!model_io->dependencies)
50 model_io->dependencies = ccv_array_new(sizeof(ccv_cnnp_model_io_t), dependency_size, 0);
51 int i, j;
52 for (i = 0; i < dependency_size; i++)
53 {
54 int flag = 0;
55 // Check if it is already exist or not.
56 for (j = 0; !flag && j < model_io->dependencies->rnum; j++)
57 if (*(ccv_cnnp_model_io_t*)ccv_array_get(model_io->dependencies, j)((void*)(((char*)((model_io->dependencies)->data)) + (size_t
)(model_io->dependencies)->rsize * (size_t)(j))) == dependencies[i])
58 flag = 1;
59 if (flag)
60 continue;
61 ccv_array_push(model_io->dependencies, dependencies + i);
62 ++dependencies[i]->dependents;
63 }
64}
65
66int ccv_cnnp_model_output_size(const ccv_cnnp_model_t* const model)
67{
68 return model->output_size;
69}
70
71int ccv_cnnp_model_is_trainable(const ccv_cnnp_model_t* const model)
72{
73 // If the model is compiled, it is default to 1 unless it is not.
74 if (model->compiled_data)
75 return model->is_trainable >= 0 ? model->is_trainable : 1;
76 return model->is_trainable;
77}
78
79ccv_cnnp_model_io_t ccv_cnnp_model_parameters(ccv_cnnp_model_t* const model, const int selector, const int index)
80{
81 if (!model->io)
82 model->io = ccv_array_new(sizeof(ccv_cnnp_model_io_t), 1, 0);
83 ccv_cnnp_model_io_t model_io = ccmallocmalloc(sizeof(struct ccv_cnnp_model_io_s));
84 model_io->param_ref = index >= 0 ? index + 1 : ALL_PARAMETERS-1;
85 model_io->param_sel = selector >= 0 ? selector + 1 : ALL_PARAMETERS-1;
86 model_io->visit = 0;
87 model_io->model = model;
88 model_io->outputs = 0;
89 model_io->dependencies = 0;
90 model_io->dependents = 0;
91 model_io->incomings = 0;
92 model_io->outgoings = 0;
93 ccv_array_push(model->io, &model_io);
94 return model_io;
95}
96
97void ccv_cnnp_model_notify_hook(ccv_cnnp_model_t* const model, ccv_cnnp_model_notify_f func, void* const context)
98{
99 model->notify_hook.func = func;
100 model->notify_hook.context = context;
101}
102
103void ccv_cnnp_model_notify(const ccv_cnnp_model_t* const model, const int tag, void* const payload)
104{
105 if (model->notify_hook.func)
106 model->notify_hook.func(model, tag, payload, model->notify_hook.context);
107 if (model->isa->notify)
108 model->isa->notify(model, tag, payload);
109}
110
111static int _ccv_nnc_array_dedup_graph_exec_symbols(ccv_nnc_graph_exec_symbol_t* const graph_exec_symbols, int graph_exec_symbol_size)
112{
113 int i, j;
114 for (i = 0; i < graph_exec_symbol_size; i++)
115 {
116 ccv_nnc_graph_exec_symbol_t* const graph_exec_symbol = graph_exec_symbols + i;
117 // Check whether this tensor symbol has any duplicate.
118 for (j = i + 1; j < graph_exec_symbol_size;)
119 {
120 ccv_nnc_graph_exec_symbol_t* const other_symbol = graph_exec_symbols + j;
121 // If there is a same tensor symbol, remove it.
122 if (other_symbol->d == graph_exec_symbol->d && other_symbol->graph == graph_exec_symbol->graph)
123 {
124 if (j + 1 < graph_exec_symbol_size)
125 *other_symbol = graph_exec_symbols[graph_exec_symbol_size - 1];
126 --graph_exec_symbol_size;
127 continue;
128 }
129 ++j;
130 }
131 }
132 return graph_exec_symbol_size;
133}
134
135void ccv_cnnp_model_add_to_array(void* const context, const ccv_nnc_tensor_symbol_t symbol, const int is_trainable)
136{
137 ccv_cnnp_model_add_to_array_context_t* const add_to_array_context = (ccv_cnnp_model_add_to_array_context_t*)context;
138 ccv_cnnp_model_t* const model = add_to_array_context->sequence->model;
139 int i;
140 if (add_to_array_context->add_parameter_indices && !model->parameter_indices)
141 model->parameter_indices = ccv_array_new(sizeof(int), 0, 0);
142 for (i = 0; i < add_to_array_context->symbols->rnum; i++)
143 {
144 const ccv_nnc_tensor_symbol_t other_symbol = *(ccv_nnc_tensor_symbol_t*)ccv_array_get(add_to_array_context->symbols, i)((void*)(((char*)((add_to_array_context->symbols)->data
)) + (size_t)(add_to_array_context->symbols)->rsize * (
size_t)(i)));
145 if (other_symbol.d == symbol.d && other_symbol.graph == symbol.graph)
146 {
147 // Only add to parameter_indices if it is trainable.
148 if (add_to_array_context->add_parameter_indices)
149 ccv_array_add_unique_int(model->parameter_indices, i);
150 // Found it, return, don't add it.
151 return;
152 }
153 }
154 // Only add to parameter_indices if it is trainable.
155 if (add_to_array_context->add_parameter_indices)
156 ccv_array_push(model->parameter_indices, &add_to_array_context->symbols->rnum);
157 // This is a new one, no need to add_unique_int, it is unique.
158 ccv_array_push(add_to_array_context->symbols, &symbol);
159 if (add_to_array_context->trainables)
160 ccv_array_push(add_to_array_context->trainables, &is_trainable);
161 char id[2048];
162 id[0] = add_to_array_context->prefix;
163 id[1] = '-';
164 int total_len = 2;
165 for (i = 0; i < add_to_array_context->sequence->sequences->rnum; i++)
166 {
167 const ccv_cnnp_model_name_t* const name = (ccv_cnnp_model_name_t*)ccv_array_get(add_to_array_context->sequence->sequences, i)((void*)(((char*)((add_to_array_context->sequence->sequences
)->data)) + (size_t)(add_to_array_context->sequence->
sequences)->rsize * (size_t)(i)));
168 int len;
169 if (name->name && name->name[0] != '\0')
170 len = snprintf(id + total_len, 2048 - total_len, "%s-%d-", name->name, name->sequence);
171 else
172 len = snprintf(id + total_len, 2048 - total_len, "%d-", name->sequence);
173 total_len += len;
174 if (total_len >= 2047)
175 break;
176 }
177 if (total_len < 2047)
178 total_len += snprintf(id + total_len, 2048 - total_len, "%d", add_to_array_context->sequence->it);
179 assert(total_len < 2048)((void) sizeof ((total_len < 2048) ? 1 : 0), __extension__
({ if (total_len < 2048) ; else __assert_fail ("total_len < 2048"
, "ccv_cnnp_model.c", 179, __extension__ __PRETTY_FUNCTION__)
; }));
180 char *heap_id = (char*)ccmallocmalloc(total_len + 1);
181 memcpy(heap_id, id, total_len + 1);
182 ccv_array_push(add_to_array_context->ids, &heap_id);
183 ++add_to_array_context->sequence->it;
184}
185
186static void _ccv_cnnp_compiled_data_init(ccv_cnnp_compiled_data_t* const compiled_data, const int output_size, ccv_array_t* const gradient_checkpoints)
187{
188 compiled_data->f = compiled_data->fits + output_size;
189 compiled_data->xpu_alloc.mp_hdr = -1;
190 compiled_data->xpu_alloc.freed = kh_init(dy_str)kh_init_dy_str();
191 compiled_data->xpu_alloc.allocd = kh_init(dy_alloc)kh_init_dy_alloc();
192 compiled_data->gradient_checkpoints = gradient_checkpoints;
193}
194
195static int _ccv_cnnp_model_root_parallel_count(const ccv_cnnp_model_t* const model)
196{
197 return ccv_max(model->parallel_count, 1)({ typeof (model->parallel_count) _a = (model->parallel_count
); typeof (1) _b = (1); (_a > _b) ? _a : _b; });
198}
199
200static int _ccv_cnnp_model_effective_parallel_count(const ccv_cnnp_model_t* const model)
201{
202 int parallel_count = _ccv_cnnp_model_root_parallel_count(model);
203 if (model->graph && model->graph->data_parallel.count > parallel_count)
204 parallel_count = model->graph->data_parallel.count;
205 return parallel_count;
206}
207
208static int _ccv_cnnp_compiled_data_parallel_count(const ccv_cnnp_model_t* const model, const ccv_cnnp_compiled_data_t* const compiled_data)
209{
210 return compiled_data->parallel_count > 0 ? compiled_data->parallel_count : _ccv_cnnp_model_effective_parallel_count(model);
211}
212
213ccv_nnc_tensor_symbol_t ccv_cnnp_model_get_symbol(ccv_cnnp_model_t* const self, const ccv_nnc_tensor_symbol_t symbol)
214{
215 assert(self->data)((void) sizeof ((self->data) ? 1 : 0), __extension__ ({ if
(self->data) ; else __assert_fail ("self->data", "ccv_cnnp_model.c"
, 215, __extension__ __PRETTY_FUNCTION__); }));
216 ccv_cnnp_model_build_data_t* const build_data = (ccv_cnnp_model_build_data_t*)self->data;
217 if (build_data->parallel_count <= 1 || build_data->parallel_rank == 0)
218 return symbol;
219 const int rank = build_data->parallel_rank;
220 assert(rank > 0)((void) sizeof ((rank > 0) ? 1 : 0), __extension__ ({ if (
rank > 0) ; else __assert_fail ("rank > 0", "ccv_cnnp_model.c"
, 220, __extension__ __PRETTY_FUNCTION__); }));
221 assert(rank < build_data->parallel_count)((void) sizeof ((rank < build_data->parallel_count) ? 1
: 0), __extension__ ({ if (rank < build_data->parallel_count
) ; else __assert_fail ("rank < build_data->parallel_count"
, "ccv_cnnp_model.c", 221, __extension__ __PRETTY_FUNCTION__)
; }));
222 ccv_nnc_symbolic_graph_t* const graph = (ccv_nnc_symbolic_graph_t*)symbol.graph;
223 ccv_nnc_tensor_symbol_t copy = ccv_nnc_tensor_symbol_copy(graph, symbol, rank);
224 if (copy.d != CCV_NNC_NO_TENSOR_SYMBOL)
225 return copy;
226 ccv_nnc_tensor_param_t params = ccv_nnc_tensor_symbol_params(graph, symbol);
227 if (CCV_TENSOR_GET_MEMORY(params.type)((params.type) & 0x3) == CCV_TENSOR_GPU_MEMORY)
228 CCV_TENSOR_SET_DEVICE_ID(params.type, rank)(params.type) = (((params.type) & ~0xfff00) | (((rank) &
0xfff) << 8));
229 copy = ccv_nnc_tensor_symbol_new(graph, params, 0);
230 ccv_nnc_tensor_symbol_set_copy(graph, symbol, rank, copy);
231 return copy;
232}
233
234typedef struct {
235 void* old_graph_exec_symbol_new_hook_context;
236 ccv_nnc_graph_exec_symbol_new_hook_f old_graph_exec_symbol_new_hook;
237 ccv_nnc_symbolic_graph_t* graph;
238 ccv_cnnp_model_build_data_t* build_data;
239} ccv_cnnp_model_set_exec_flags_context_t;
240
241static void _ccv_cnnp_model_set_exec_flags(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)
242{
243 ccv_cnnp_model_set_exec_flags_context_t* flags_context = (ccv_cnnp_model_set_exec_flags_context_t*)context;
244 if (flags_context->build_data->exec_flags)
245 ccv_nnc_graph_exec_symbol_set_flags(flags_context->graph, symbol, flags_context->build_data->exec_flags);
246 if (flags_context->old_graph_exec_symbol_new_hook)
247 flags_context->old_graph_exec_symbol_new_hook(flags_context->old_graph_exec_symbol_new_hook_context, symbol, cmd, inputs, input_size, outputs, output_size, name);
248}
249
250static void _ccv_cnnp_model_compile(ccv_cnnp_model_t* const model, const ccv_nnc_tensor_param_t* const inputs, const int input_size, const ccv_nnc_cmd_t loss)
251{
252 assert(model->graph)((void) sizeof ((model->graph) ? 1 : 0), __extension__ ({ if
(model->graph) ; else __assert_fail ("model->graph", "ccv_cnnp_model.c"
, 252, __extension__ __PRETTY_FUNCTION__); }));
253 model->inputs = ccmallocmalloc(sizeof(ccv_nnc_tensor_symbol_t) * input_size);
254 int i;
255 for (i = 0; i < input_size; i++)
256 model->inputs[i] = ccv_nnc_tensor_symbol_new(model->graph, inputs[i], 0);
257 ccv_array_t* const parameters = ccv_array_new(sizeof(ccv_nnc_tensor_symbol_t), 0, 0);
258 ccv_array_t* const parameter_ids = ccv_array_new(sizeof(char*), 0, 0);
259 ccv_array_t* const parameter_trainables = ccv_array_new(sizeof(int), 0, 0);
260 ccv_cnnp_model_sequence_t model_sequence = {
261 .bank = kh_init(ccv_cnnp_model_name_bank)kh_init_ccv_cnnp_model_name_bank()
262 };
263 ccv_cnnp_model_add_to_array_context_t add_to_parameter_context = {
264 .add_parameter_indices = 1,
265 .prefix = 't',
266 .sequence = &model_sequence,
267 .symbols = parameters,
268 .ids = parameter_ids,
269 .trainables = parameter_trainables,
270 };
271 ccv_array_t* const internals = ccv_array_new(sizeof(ccv_nnc_tensor_symbol_t), 0, 0);
272 ccv_array_t* const internal_ids = ccv_array_new(sizeof(char*), 0, 0);
273 ccv_cnnp_model_add_to_array_context_t add_to_output_context = {
274 .add_parameter_indices = 0,
275 .prefix = 'r',
276 .sequence = &model_sequence,
277 .symbols = internals,
278 .ids = internal_ids,
279 .trainables = 0,
280 };
281 ccv_cnnp_model_build_data_t build_data = {
282 .exec_flags = 0,
283 .is_trainable = model->is_trainable >= 0 ? model->is_trainable : 1,
284 .parallel_count = 1,
285 .parallel_rank = 0,
286 .model_sequence = &model_sequence,
287 .add_to_array = ccv_cnnp_model_add_to_array,
288 .parameters = parameters,
289 .context = {
290 .add_to_parameter = &add_to_parameter_context,
291 .add_to_output = &add_to_output_context,
292 },
293 .gradient_checkpoints = 0,
294 };
295 model->data = &build_data;
296 ccv_cnnp_model_set_exec_flags_context_t flags_context = {
297 .graph = model->graph,
298 .build_data = &build_data,
299 .old_graph_exec_symbol_new_hook = 0,
300 .old_graph_exec_symbol_new_hook_context = 0
301 };
302 flags_context.old_graph_exec_symbol_new_hook_context = ccv_nnc_graph_exec_symbol_new_hook(model->graph, _ccv_cnnp_model_set_exec_flags, &flags_context, &flags_context.old_graph_exec_symbol_new_hook);
303 ccv_cnnp_model_build(model, model->graph, model->inputs, input_size, 0, 0);
304 // Reset back to previous hook.
305 ccv_nnc_graph_exec_symbol_new_hook(model->graph, flags_context.old_graph_exec_symbol_new_hook, flags_context.old_graph_exec_symbol_new_hook_context, 0);
306 for (i = 0; i < model->output_size; i++)
307 {
308 const ccv_nnc_tensor_symbol_t output = model->outputs[i];
309 const ccv_nnc_tensor_symbol_t alias_to = ccv_nnc_tensor_symbol_alias_to(model->graph, output);
310 if (alias_to.d == CCV_NNC_NO_TENSOR_SYMBOL)
311 continue;
312 // If output is an alias, insert data transform regardless for result correctness (we cannot bind an alias). You can check ccv_nnc_tensor_bind_symbol method
313 // to see that we can correctly bind a tensor which from it, has aliases, but we cannot bind an alias tensor correctly (this is expected, sort of, to be
314 // honest, because we cannot handle cases of alias is part of the original tensor but bind differently).
315 const ccv_nnc_tensor_param_t output_params = ccv_nnc_tensor_symbol_params(model->graph, output);
316 model->outputs[i] = ccv_nnc_tensor_symbol_new(model->graph, output_params, 0);
317 ccv_nnc_graph_exec_symbol_t make_contiguous = ccv_nnc_graph_exec_symbol_new(model->graph, CMD_FORMAT_TRANSFORM_FORWARD()ccv_nnc_cmd(CCV_NNC_FORMAT_TRANSFORM_FORWARD, 0, ccv_nnc_cmd_auto
, 0), &output, 1, model->outputs + i, 1, "contiguous");
318 ccv_nnc_graph_exec_symbol_set_flags(model->graph, make_contiguous, CCV_NNC_GRAPH_EXEC_DISABLE_OPT);
319 }
320 model->data = 0;
321 kh_destroy(ccv_cnnp_model_name_bank, model_sequence.bank)kh_destroy_ccv_cnnp_model_name_bank(model_sequence.bank);
322 if (model_sequence.sequences)
323 ccv_array_free(model_sequence.sequences);
324 // Check if there are parameters that are not trainables. If there are, we will allocate uint64 bitmap to record that.
325 int not_trainables = 0;
326 // Assert no parameter is alias.
327 for (i = 0; i < parameters->rnum; i++)
328 {
329 const ccv_nnc_tensor_symbol_t parameter = *(ccv_nnc_tensor_symbol_t*)ccv_array_get(parameters, i)((void*)(((char*)((parameters)->data)) + (size_t)(parameters
)->rsize * (size_t)(i)));
330 const ccv_nnc_tensor_symbol_t alias_to = ccv_nnc_tensor_symbol_alias_to(parameter.graph, parameter);
331 assert(alias_to.graph == 0)((void) sizeof ((alias_to.graph == 0) ? 1 : 0), __extension__
({ if (alias_to.graph == 0) ; else __assert_fail ("alias_to.graph == 0"
, "ccv_cnnp_model.c", 331, __extension__ __PRETTY_FUNCTION__)
; })); // Cannot find the one alias to.
332 if (*(int*)ccv_array_get(parameter_trainables, i)((void*)(((char*)((parameter_trainables)->data)) + (size_t
)(parameter_trainables)->rsize * (size_t)(i))) == 0)
333 not_trainables = 1;
334 }
335 assert(parameters->rnum == parameter_trainables->rnum)((void) sizeof ((parameters->rnum == parameter_trainables->
rnum) ? 1 : 0), __extension__ ({ if (parameters->rnum == parameter_trainables
->rnum) ; else __assert_fail ("parameters->rnum == parameter_trainables->rnum"
, "ccv_cnnp_model.c", 335, __extension__ __PRETTY_FUNCTION__)
; }));
336 uint64_t* parameter_flags = 0;
337 if (not_trainables)
338 {
339 parameter_flags = (uint64_t*)cccalloccalloc(((parameters->rnum + 63) >> 6), sizeof(uint64_t));
340 for (i = 0; i < parameter_trainables->rnum; i++)
341 if (*(int*)ccv_array_get(parameter_trainables, i)((void*)(((char*)((parameter_trainables)->data)) + (size_t
)(parameter_trainables)->rsize * (size_t)(i))))
342 parameter_flags[i >> 6] |= ((uint64_t)1 << (i & 63));
343 }
344 ccv_array_free(parameter_trainables);
345 // Assert no internal is alias.
346 for (i = 0; i < internals->rnum; i++)
347 {
348 const ccv_nnc_tensor_symbol_t internal = *(ccv_nnc_tensor_symbol_t*)ccv_array_get(internals, i)((void*)(((char*)((internals)->data)) + (size_t)(internals
)->rsize * (size_t)(i)));
349 const ccv_nnc_tensor_symbol_t alias_to = ccv_nnc_tensor_symbol_alias_to(internal.graph, internal);
350 assert(alias_to.graph == 0)((void) sizeof ((alias_to.graph == 0) ? 1 : 0), __extension__
({ if (alias_to.graph == 0) ; else __assert_fail ("alias_to.graph == 0"
, "ccv_cnnp_model.c", 350, __extension__ __PRETTY_FUNCTION__)
; })); // Cannot find the one alias to.
351 }
352 const int output_size = model->output_size;
353 ccv_nnc_graph_exec_symbol_autogen(model->graph, 0, 0, CCV_NNC_AUTOGEN_ALL_EXECS | CCV_NNC_AUTOGEN_SOURCES_AND_DESTINATIONS);
354 const int parameters_rnum = parameters->rnum;
355 if (input_size > 0)
356 {
357 ccv_array_resize(parameters, parameters_rnum + input_size);
358 memcpy(ccv_array_get(parameters, parameters_rnum)((void*)(((char*)((parameters)->data)) + (size_t)(parameters
)->rsize * (size_t)(parameters_rnum))), model->inputs, input_size * sizeof(ccv_nnc_tensor_symbol_t));
359 }
360 ccv_nnc_symbolic_graph_simplify(model->graph,
361 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
, CCV_NNC_SIMPLIFY_GRAPH_PRUNING}, (1 +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 -1)
362 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
, CCV_NNC_SIMPLIFY_GRAPH_PRUNING}, (1 +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 -1)
363 CCV_NNC_SIMPLIFY_OPS_FUSION,(const int []){CCV_NNC_SIMPLIFY_COMMON_SUBEXPRESSION_ELIMINATION
, CCV_NNC_SIMPLIFY_DATA_TRANSFER_OPT, CCV_NNC_SIMPLIFY_OPS_FUSION
, CCV_NNC_SIMPLIFY_GRAPH_PRUNING}, (1 +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 -1)
364 CCV_NNC_SIMPLIFY_GRAPH_PRUNING)(const int []){CCV_NNC_SIMPLIFY_COMMON_SUBEXPRESSION_ELIMINATION
, CCV_NNC_SIMPLIFY_DATA_TRANSFER_OPT, CCV_NNC_SIMPLIFY_OPS_FUSION
, CCV_NNC_SIMPLIFY_GRAPH_PRUNING}, (1 +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 -1),
365 ccv_array_get(parameters, 0)((void*)(((char*)((parameters)->data)) + (size_t)(parameters
)->rsize * (size_t)(0))), parameters_rnum + input_size,
366 model->outputs, output_size,
367 SYMBOLIC_GRAPH_SOURCES(model->graph)ccv_nnc_symbolic_graph_sources(model->graph), ccv_nnc_symbolic_graph_source_size
(model->graph), SYMBOLIC_GRAPH_DESTINATIONS(model->graph)ccv_nnc_symbolic_graph_destinations(model->graph), ccv_nnc_symbolic_graph_destination_size
(model->graph));
368 ccv_nnc_graph_exec_symbol_autogen(model->graph, 0, 0, CCV_NNC_AUTOGEN_SOURCES_AND_DESTINATIONS);
369 // Size it down.
370 parameters->rnum = parameters_rnum;
371 ccv_cnnp_compiled_data_t* compiled_data = model->compiled_data = cccalloccalloc(1, sizeof(ccv_cnnp_compiled_data_t) + sizeof(ccv_nnc_tensor_symbol_t) * (output_size * 2 - 1));
372 _ccv_cnnp_compiled_data_init(compiled_data, output_size, build_data.gradient_checkpoints);
373 const int evaluate_to_size = compiled_data->evaluate.to_size = ccv_nnc_symbolic_graph_destination_size(model->graph);
374 assert(evaluate_to_size > 0)((void) sizeof ((evaluate_to_size > 0) ? 1 : 0), __extension__
({ if (evaluate_to_size > 0) ; else __assert_fail ("evaluate_to_size > 0"
, "ccv_cnnp_model.c", 374, __extension__ __PRETTY_FUNCTION__)
; }));
375 compiled_data->evaluate.tos = ccmallocmalloc(sizeof(ccv_nnc_graph_exec_symbol_t) * evaluate_to_size);
376 memcpy(compiled_data->evaluate.tos, ccv_nnc_symbolic_graph_destinations(model->graph), sizeof(ccv_nnc_graph_exec_symbol_t) * evaluate_to_size);
377 compiled_data->loss = loss;
378 if (loss.cmd == CCV_NNC_NOOP)
379 {
380 // If no loss function provided, there is no fits.
381 for (i = 0; i < output_size; i++)
382 {
383 compiled_data->fits[i] = NO_TENSOR_SYMBOL(const ccv_nnc_tensor_symbol_t){.d = CCV_NNC_NO_TENSOR_SYMBOL
};
384 const ccv_nnc_tensor_symbol_t alias_to = ccv_nnc_tensor_symbol_alias_to(model->graph, model->outputs[i]);
385 if (alias_to.d < 0)
386 compiled_data->f[i] = model->outputs[i];
387 else { // We cannot differentiate against an alias, therefore, we have to verify this output is full, and we can diff against the original.
388 int ofs[CCV_NNC_MAX_DIM_ALLOC(12)];
389 int inc[CCV_NNC_MAX_DIM_ALLOC(12)];
390 ccv_nnc_tensor_symbol_alias_params(model->graph, model->outputs[i], ofs, inc);
391 int j;
392 for (j = 0; j < CCV_NNC_MAX_DIM_ALLOC(12); j++)
393 { assert(ofs[j] == 0)((void) sizeof ((ofs[j] == 0) ? 1 : 0), __extension__ ({ if (
ofs[j] == 0) ; else __assert_fail ("ofs[j] == 0", "ccv_cnnp_model.c"
, 393, __extension__ __PRETTY_FUNCTION__); })); } // There is no ofs.
394 compiled_data->f[i] = alias_to; // Unfortunately, I cannot assert the size yet.
395 }
396 }
397 } else {
398 for (i = 0; i < output_size; i++)
399 {
400 const ccv_nnc_tensor_param_t info = ccv_nnc_tensor_symbol_params(model->graph, model->outputs[i]);
401 const ccv_nnc_tensor_symbol_t fit = compiled_data->fits[i] = ccv_nnc_tensor_symbol_new(model->graph, info, 0);
402 compiled_data->f[i] = ccv_nnc_tensor_symbol_new(model->graph, ccv_nnc_tensor_auto, 0);
403 ccv_nnc_graph_exec_symbol_new(model->graph, loss, TENSOR_SYMBOL_LIST(model->outputs[i], fit)(const ccv_nnc_tensor_symbol_t []){model->outputs[i], fit}
, (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 +0 -1), TENSOR_SYMBOL_LIST(compiled_data->f[i])(const ccv_nnc_tensor_symbol_t []){compiled_data->f[i]}, (
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
+0 +0 -1), 0);
404 }
405 }
406 if (loss.cmd != CCV_NNC_NOOP)
407 {
408 ccv_nnc_graph_exec_symbol_autogen(model->graph, 0, 0, CCV_NNC_AUTOGEN_ALL_EXECS | CCV_NNC_AUTOGEN_SOURCES_AND_DESTINATIONS);
409 ccv_nnc_symbolic_graph_simplify(model->graph,
410 SYMBOLIC_GRAPH_PASSES(CCV_NNC_SIMPLIFY_OPS_FUSION)(const int []){CCV_NNC_SIMPLIFY_OPS_FUSION}, (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 +0 +0 -1), // Only do Ops fusion, in this way, we can fuse the loss function.
411 0, 0, // No need to provide binds at this point.
412 compiled_data->f, model->output_size,
413 SYMBOLIC_GRAPH_SOURCES(model->graph)ccv_nnc_symbolic_graph_sources(model->graph), ccv_nnc_symbolic_graph_source_size
(model->graph), SYMBOLIC_GRAPH_DESTINATIONS(model->graph)ccv_nnc_symbolic_graph_destinations(model->graph), ccv_nnc_symbolic_graph_destination_size
(model->graph));
414 ccv_nnc_graph_exec_symbol_autogen(model->graph, 0, 0, CCV_NNC_AUTOGEN_SOURCES_AND_DESTINATIONS);
415 }
416 // If inputs are from GPU, stream type is GPU.
417 compiled_data->parameters = parameters;
418 compiled_data->parameter_flags = parameter_flags;
419 compiled_data->internals = internals;
420 compiled_data->ids.parameters = parameter_ids;
421 compiled_data->ids.internals = internal_ids;
422 ccv_cnnp_model_gradient_checkpoints_cleanup_after_build(compiled_data, model->graph);
423}
424
425static void _ccv_cnnp_graph_push_graph_exec_symbol(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)
426{
427 ccv_array_t* const stack = (ccv_array_t*)context;
428 ccv_array_push(stack, &symbol.d);
429}
430
431static void _ccv_nnc_tensor_symbol_reinit(const ccv_nnc_symbolic_graph_t* const src_graph, ccv_nnc_symbolic_graph_t* const dest_graph, const int src_index, const int dest_index)
432{
433 int src_alias_ref = src_index;
434 const ccv_nnc_tensor_symbol_info_t* src_info = (ccv_nnc_tensor_symbol_info_t*)ccv_array_get(src_graph->tensor_symbol_info, src_alias_ref)((void*)(((char*)((src_graph->tensor_symbol_info)->data
)) + (size_t)(src_graph->tensor_symbol_info)->rsize * (
size_t)(src_alias_ref)));
435 while (src_info->alias_ref)
436 {
437 src_alias_ref = src_info->alias_ref - 1;
438 src_info = (ccv_nnc_tensor_symbol_info_t*)ccv_array_get(src_graph->tensor_symbol_info, src_alias_ref)((void*)(((char*)((src_graph->tensor_symbol_info)->data
)) + (size_t)(src_graph->tensor_symbol_info)->rsize * (
size_t)(src_alias_ref)));
439 }
440 int dest_alias_ref = dest_index;
441 const ccv_nnc_tensor_symbol_info_t* dest_info = (ccv_nnc_tensor_symbol_info_t*)ccv_array_get(dest_graph->tensor_symbol_info, dest_alias_ref)((void*)(((char*)((dest_graph->tensor_symbol_info)->data
)) + (size_t)(dest_graph->tensor_symbol_info)->rsize * (
size_t)(dest_alias_ref)));
442 while (dest_info->alias_ref)
443 {
444 dest_alias_ref = dest_info->alias_ref - 1;
445 dest_info = (ccv_nnc_tensor_symbol_info_t*)ccv_array_get(dest_graph->tensor_symbol_info, dest_alias_ref)((void*)(((char*)((dest_graph->tensor_symbol_info)->data
)) + (size_t)(dest_graph->tensor_symbol_info)->rsize * (
size_t)(dest_alias_ref)));
446 }
447 assert((src_alias_ref != src_index) == (dest_alias_ref != dest_index))((void) sizeof (((src_alias_ref != src_index) == (dest_alias_ref
!= dest_index)) ? 1 : 0), __extension__ ({ if ((src_alias_ref
!= src_index) == (dest_alias_ref != dest_index)) ; else __assert_fail
("(src_alias_ref != src_index) == (dest_alias_ref != dest_index)"
, "ccv_cnnp_model.c", 447, __extension__ __PRETTY_FUNCTION__)
; }));
448 if (src_alias_ref != src_index)
449 {
450 const ccv_nnc_tensor_symbol_t src_alias_symbol = {
451 .d = src_alias_ref,
452 .graph = src_graph
453 };
454 const ccv_nnc_tensor_symbol_t dest_alias_symbol = {
455 .d = dest_alias_ref,
456 .graph = dest_graph
457 };
458 const ccv_nnc_tensor_param_t params = ccv_nnc_tensor_symbol_params(src_graph, src_alias_symbol);
459 ccv_nnc_tensor_symbol_set(dest_graph, dest_alias_symbol, params);
460 }
461 const ccv_nnc_tensor_symbol_t src_symbol = {
462 .d = src_index,
463 .graph = src_graph
464 };
465 const ccv_nnc_tensor_symbol_t dest_symbol = {
466 .d = dest_index,
467 .graph = dest_graph
468 };
469 const ccv_nnc_tensor_param_t params = ccv_nnc_tensor_symbol_params(src_graph, src_symbol);
470 ccv_nnc_tensor_symbol_set(dest_graph, dest_symbol, params);
471 int ofs[CCV_NNC_MAX_DIM_ALLOC(12)];
472 int inc[CCV_NNC_MAX_DIM_ALLOC(12)];
473 if (0 == ccv_nnc_tensor_symbol_alias_params(src_graph, src_symbol, ofs, inc))
474 ccv_nnc_tensor_symbol_alias_set(dest_graph, dest_symbol, ofs, inc);
475}
476
477static int _ccv_nnc_tensor_symbol_check_dim(const ccv_nnc_symbolic_graph_t* const src_graph, ccv_nnc_symbolic_graph_t* const dest_graph, const int src_index, const int dest_index)
478{
479 const ccv_nnc_tensor_symbol_t src_symbol = {
480 .d = src_index,
481 .graph = src_graph
482 };
483 const ccv_nnc_tensor_param_t src_params = ccv_nnc_tensor_symbol_params(src_graph, src_symbol);
484 const ccv_nnc_tensor_symbol_t dest_symbol = {
485 .d = dest_index,
486 .graph = dest_graph
487 };
488 const ccv_nnc_tensor_param_t dest_params = ccv_nnc_tensor_symbol_params(dest_graph, dest_symbol);
489 if (src_params.dim[0] == 0 || dest_params.dim[0] == 0)
490 return 1;
491 return memcmp(src_params.dim, dest_params.dim, sizeof(src_params.dim)) == 0;
492}
493
494static void _ccv_cnnp_model_gradient_init(ccv_cnnp_model_t* const model, const int gradient_mode, const uint64_t disable_outgrad, ccv_nnc_tensor_t* const* const fits, const int fit_size);
495static void _ccv_cnnp_compiled_data_graph_free(ccv_cnnp_compiled_data_t* const compiled_data);
496
497typedef struct {
498 int parallel_count;
499 ccv_nnc_symbolic_graph_t* graph;
500 ccv_nnc_graph_exec_arena_t* graph_exec_arena;
501} ccv_nnc_graph_exec_update_t;
502
503static void _ccv_cnnp_cmd_update_for_execs(void* const context, const ccv_nnc_graph_exec_symbol_t symbol, const ccv_nnc_cmd_t cmd, const ccv_nnc_hint_t hint)
504{
505 ccv_nnc_graph_exec_update_t* const graph_exec_update = (ccv_nnc_graph_exec_update_t*)context;
506 ccv_nnc_graph_exec_arena_t* const graph_exec_arena = graph_exec_update->graph_exec_arena;
507 ccv_nnc_graph_exec_t graph_exec = ccv_nnc_graph_exec_from_symbol(graph_exec_arena, symbol);
508 ccv_nnc_graph_exec_set(graph_exec.graph, graph_exec, cmd);
509 ccv_nnc_graph_exec_set_hint(graph_exec.graph, graph_exec, hint);
510 const ccv_nnc_symbolic_graph_t* const graph = graph_exec_update->graph;
511 const int parallel_count = graph_exec_update->parallel_count;
512 int i;
513 for (i = 1; i < parallel_count; i++)
514 {
515 const ccv_nnc_graph_exec_t copy = ccv_nnc_graph_exec_from_symbol(graph_exec_arena, ccv_nnc_graph_exec_symbol_copy(graph, symbol, i));
516 if (!CCV_NO_GRAPH_EXEC(copy)((copy).graph == 0))
517 {
518 ccv_nnc_graph_exec_set(copy.graph, copy, cmd);
519 ccv_nnc_graph_exec_set_hint(copy.graph, copy, hint);
520 }
521 }
522}
523
524void ccv_cnnp_model_absorb(ccv_cnnp_model_t* const model, ccv_cnnp_model_t* const init, const ccv_nnc_tensor_param_t* const inputs, const int input_size)
525{
526 assert(model->graph)((void) sizeof ((model->graph) ? 1 : 0), __extension__ ({ if
(model->graph) ; else __assert_fail ("model->graph", "ccv_cnnp_model.c"
, 526, __extension__ __PRETTY_FUNCTION__); }));
527 assert(model->compiled_data)((void) sizeof ((model->compiled_data) ? 1 : 0), __extension__
({ if (model->compiled_data) ; else __assert_fail ("model->compiled_data"
, "ccv_cnnp_model.c", 527, __extension__ __PRETTY_FUNCTION__)
; }));
528 assert(!init->graph)((void) sizeof ((!init->graph) ? 1 : 0), __extension__ ({ if
(!init->graph) ; else __assert_fail ("!init->graph", "ccv_cnnp_model.c"
, 528, __extension__ __PRETTY_FUNCTION__); }));
529 ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
530 init->graph = ccv_nnc_symbolic_graph_new();
531 ccv_array_t* const stack = ccv_array_new(sizeof(int), 0, 0);
532 ccv_nnc_graph_exec_symbol_new_hook(init->graph, _ccv_cnnp_graph_push_graph_exec_symbol, stack, 0);
533 _ccv_cnnp_model_compile(init, inputs, input_size, compiled_data->loss);
534 init->parallel_count = model->parallel_count;
535 init->memory_compression = model->memory_compression;
536 init->memory_reduction = model->memory_reduction;
537 init->gradient_checkpointing = model->gradient_checkpointing;
538 init->compiled_data->stream_type = model->compiled_data->stream_type;
539 init->compiled_data->minimize.minimizer = model->compiled_data->minimize.minimizer;
540 init->compiled_data->minimize.max_saved_aux_size = model->compiled_data->minimize.max_saved_aux_size;
541 if (model->compiled_data->gradient_mode != CCV_CNNP_COMPILED_DATA_GRADIENT_NONE)
542 _ccv_cnnp_model_gradient_init(init, model->compiled_data->gradient_mode, model->compiled_data->disable_outgrad, 0, 0);
543 ccv_nnc_graph_exec_symbol_new_hook(init->graph, 0, 0, 0);
544 ccv_nnc_symbolic_graph_tensor_auto(init->graph, TRAVERSE_FULL0,0,0,0);
545 int i, j;
546 // Verify parameters, internals and saved_aux in both graph has the same dimensionality.
547 for (i = 0; i < compiled_data->parameters->rnum; i++)
548 {
549 const int d = ((ccv_nnc_tensor_symbol_t*)ccv_array_get(compiled_data->parameters, i)((void*)(((char*)((compiled_data->parameters)->data)) +
(size_t)(compiled_data->parameters)->rsize * (size_t)(
i))))->d;
550 assert(_ccv_nnc_tensor_symbol_check_dim(model->graph, init->graph, d, d))((void) sizeof ((_ccv_nnc_tensor_symbol_check_dim(model->graph
, init->graph, d, d)) ? 1 : 0), __extension__ ({ if (_ccv_nnc_tensor_symbol_check_dim
(model->graph, init->graph, d, d)) ; else __assert_fail
("_ccv_nnc_tensor_symbol_check_dim(model->graph, init->graph, d, d)"
, "ccv_cnnp_model.c", 550, __extension__ __PRETTY_FUNCTION__)
; }));
551 }
552 for (i = 0; i < compiled_data->internals->rnum; i++)
553 {
554 const int d = ((ccv_nnc_tensor_symbol_t*)ccv_array_get(compiled_data->internals, i)((void*)(((char*)((compiled_data->internals)->data)) + (
size_t)(compiled_data->internals)->rsize * (size_t)(i))
))->d;
555 assert(_ccv_nnc_tensor_symbol_check_dim(model->graph, init->graph, d, d))((void) sizeof ((_ccv_nnc_tensor_symbol_check_dim(model->graph
, init->graph, d, d)) ? 1 : 0), __extension__ ({ if (_ccv_nnc_tensor_symbol_check_dim
(model->graph, init->graph, d, d)) ; else __assert_fail
("_ccv_nnc_tensor_symbol_check_dim(model->graph, init->graph, d, d)"
, "ccv_cnnp_model.c", 555, __extension__ __PRETTY_FUNCTION__)
; }));
556 }
557 // Update inputs.
558 assert(model->input_size == init->input_size)((void) sizeof ((model->input_size == init->input_size)
? 1 : 0), __extension__ ({ if (model->input_size == init->
input_size) ; else __assert_fail ("model->input_size == init->input_size"
, "ccv_cnnp_model.c", 558, __extension__ __PRETTY_FUNCTION__)
; }));
559 for (i = 0; i < model->input_size; i++)
560 if (model->inputs[i].d >= 0)
561 {
562 assert(init->inputs[i].d >= 0)((void) sizeof ((init->inputs[i].d >= 0) ? 1 : 0), __extension__
({ if (init->inputs[i].d >= 0) ; else __assert_fail ("init->inputs[i].d >= 0"
, "ccv_cnnp_model.c", 562, __extension__ __PRETTY_FUNCTION__)
; }));
563 _ccv_nnc_tensor_symbol_reinit(init->graph, model->graph, init->inputs[i].d, model->inputs[i].d);
564 }
565 // Update outputs.
566 assert(model->output_size == init->output_size)((void) sizeof ((model->output_size == init->output_size
) ? 1 : 0), __extension__ ({ if (model->output_size == init
->output_size) ; else __assert_fail ("model->output_size == init->output_size"
, "ccv_cnnp_model.c", 566, __extension__ __PRETTY_FUNCTION__)
; }));
567 for (i = 0; i < model->output_size; i++)
568 {
569 if (model->outputs[i].d >= 0)
570 {
571 assert(init->outputs[i].d >= 0)((void) sizeof ((init->outputs[i].d >= 0) ? 1 : 0), __extension__
({ if (init->outputs[i].d >= 0) ; else __assert_fail (
"init->outputs[i].d >= 0", "ccv_cnnp_model.c", 571, __extension__
__PRETTY_FUNCTION__); }));
572 _ccv_nnc_tensor_symbol_reinit(init->graph, model->graph, init->outputs[i].d, model->outputs[i].d);
573 }
574 if (model->outputs[i].d != model->compiled_data->f[i].d)
575 {
576 assert(init->outputs[i].d != init->compiled_data->f[i].d)((void) sizeof ((init->outputs[i].d != init->compiled_data
->f[i].d) ? 1 : 0), __extension__ ({ if (init->outputs[
i].d != init->compiled_data->f[i].d) ; else __assert_fail
("init->outputs[i].d != init->compiled_data->f[i].d"
, "ccv_cnnp_model.c", 576, __extension__ __PRETTY_FUNCTION__)
; }));
577 if (model->compiled_data->f[i].d >= 0)
578 {
579 assert(init->compiled_data->f[i].d >= 0)((void) sizeof ((init->compiled_data->f[i].d >= 0) ?
1 : 0), __extension__ ({ if (init->compiled_data->f[i]
.d >= 0) ; else __assert_fail ("init->compiled_data->f[i].d >= 0"
, "ccv_cnnp_model.c", 579, __extension__ __PRETTY_FUNCTION__)
; }));
580 _ccv_nnc_tensor_symbol_reinit(init->graph, model->graph, init->compiled_data->f[i].d, model->compiled_data->f[i].d);
581 }
582 }
583 }
584 // Go through the graph to set tensor on matching symbols
585 for (i = 0; i < stack->rnum; i++)
586 {
587 const int d = *(int*)ccv_array_get(stack, i)((void*)(((char*)((stack)->data)) + (size_t)(stack)->rsize
* (size_t)(i)));
588 // If exceed range, skip.
589 if (d >= ccv_nnc_graph_exec_symbol_count(init->graph) ||
590 d >= ccv_nnc_graph_exec_symbol_count(model->graph))
591 continue;
592 const ccv_nnc_graph_exec_symbol_t src_symbol = {
593 .d = d,
594 .graph = init->graph
595 };
596 const ccv_nnc_graph_exec_symbol_t dest_symbol = {
597 .d = d,
598 .graph = model->graph
599 };
600 const ccv_nnc_cmd_t src_cmd = ccv_nnc_graph_exec_symbol_cmd(init->graph, src_symbol);
601 const ccv_nnc_cmd_t dest_cmd = ccv_nnc_graph_exec_symbol_cmd(model->graph, dest_symbol);
602 // If the name doesn't match, skip.
603 if (dest_cmd.cmd != src_cmd.cmd && src_cmd.cmd != CCV_NNC_NOOP)
604 continue;
605 // Now get all the inputs and outputs, if matches, set them.
606 const int* src_inputs;
607 int src_input_size;
608 const int* src_outputs;
609 int src_output_size;
610 ccv_nnc_graph_exec_symbol_io(init->graph, src_symbol, &src_inputs, &src_input_size, &src_outputs, &src_output_size);
611 const int* dest_inputs;
612 int dest_input_size;
613 const int* dest_outputs;
614 int dest_output_size;
615 ccv_nnc_graph_exec_symbol_io(model->graph, dest_symbol, &dest_inputs, &dest_input_size, &dest_outputs, &dest_output_size);
616 // We may have unmatched input / output size because this is the minimizer and it has
617 // different saved_aux (for example, when we shrunk with CMD_NOOP).
618 if (src_input_size != dest_input_size)
619 continue;
620 if (src_output_size != dest_output_size)
621 continue;
622 ccv_nnc_graph_exec_symbol_set(model->graph, dest_symbol, src_cmd);
623 // There may be mismatches of the source tensor symbols and destination tensor symbols. The reason is because
624 // we may later passed-in the minimizer, therefore, we may allocate tensors for minimizer later in the original
625 // graph whereas in the newly created graph, it is streamlined (the minimizer exists from the beginning). That
626 // will make the order of tensor symbols creation different, therefore, exact which tensor is which wrong as
627 // well. However, set a new minimizer won't change the exec symbol ordering, because we never create new exec
628 // symbols after gradient init step. Changing a new minimizer just updated that exec symbols setting, it is not
629 // a new exec symbol.
630 for (j = 0; j < src_input_size; j++)
631 if (src_inputs[j] >= 0)
632 _ccv_nnc_tensor_symbol_reinit(init->graph, model->graph, src_inputs[j], dest_inputs[j]);
633 for (j = 0; j < src_output_size; j++)
634 if (src_outputs[j] >= 0)
635 _ccv_nnc_tensor_symbol_reinit(init->graph, model->graph, src_outputs[j], dest_outputs[j]);
636 }
637 ccv_array_free(stack);
638 // After this, we get all tensors in the model graph resolved through tensor_auto.
639 ccv_nnc_symbolic_graph_tensor_auto(model->graph, TRAVERSE_FULL0,0,0,0);
640 // Verify symbols we get matches.
641 const int parameter_size = compiled_data->parameters->rnum;
642 for (i = 0; i < parameter_size; i++)
643 { assert(((ccv_nnc_tensor_symbol_t*)ccv_array_get(compiled_data->parameters, i))->d == ((ccv_nnc_tensor_symbol_t*)ccv_array_get(init->compiled_data->parameters, i))->d)((void) sizeof ((((ccv_nnc_tensor_symbol_t*)((void*)(((char*)
((compiled_data->parameters)->data)) + (size_t)(compiled_data
->parameters)->rsize * (size_t)(i))))->d == ((ccv_nnc_tensor_symbol_t
*)((void*)(((char*)((init->compiled_data->parameters)->
data)) + (size_t)(init->compiled_data->parameters)->
rsize * (size_t)(i))))->d) ? 1 : 0), __extension__ ({ if (
((ccv_nnc_tensor_symbol_t*)((void*)(((char*)((compiled_data->
parameters)->data)) + (size_t)(compiled_data->parameters
)->rsize * (size_t)(i))))->d == ((ccv_nnc_tensor_symbol_t
*)((void*)(((char*)((init->compiled_data->parameters)->
data)) + (size_t)(init->compiled_data->parameters)->
rsize * (size_t)(i))))->d) ; else __assert_fail ("((ccv_nnc_tensor_symbol_t*)ccv_array_get(compiled_data->parameters, i))->d == ((ccv_nnc_tensor_symbol_t*)ccv_array_get(init->compiled_data->parameters, i))->d"
, "ccv_cnnp_model.c", 643, __extension__ __PRETTY_FUNCTION__)
; })); }
644 const int internal_size = compiled_data->internals->rnum;
645 for (i = 0; i < internal_size; i++)
646 { assert(((ccv_nnc_tensor_symbol_t*)ccv_array_get(compiled_data->internals, i))->d == ((ccv_nnc_tensor_symbol_t*)ccv_array_get(init->compiled_data->internals, i))->d)((void) sizeof ((((ccv_nnc_tensor_symbol_t*)((void*)(((char*)
((compiled_data->internals)->data)) + (size_t)(compiled_data
->internals)->rsize * (size_t)(i))))->d == ((ccv_nnc_tensor_symbol_t
*)((void*)(((char*)((init->compiled_data->internals)->
data)) + (size_t)(init->compiled_data->internals)->rsize
* (size_t)(i))))->d) ? 1 : 0), __extension__ ({ if (((ccv_nnc_tensor_symbol_t
*)((void*)(((char*)((compiled_data->internals)->data)) +
(size_t)(compiled_data->internals)->rsize * (size_t)(i
))))->d == ((ccv_nnc_tensor_symbol_t*)((void*)(((char*)((init
->compiled_data->internals)->data)) + (size_t)(init->
compiled_data->internals)->rsize * (size_t)(i))))->d
) ; else __assert_fail ("((ccv_nnc_tensor_symbol_t*)ccv_array_get(compiled_data->internals, i))->d == ((ccv_nnc_tensor_symbol_t*)ccv_array_get(init->compiled_data->internals, i))->d"
, "ccv_cnnp_model.c", 646, __extension__ __PRETTY_FUNCTION__)
; })); }
647 // Go through compiled data.
648 if (compiled_data->tensor_arena)
649 {
650 const int flag = ccv_nnc_tensor_arena_reinit(compiled_data->tensor_arena, model->graph);
651 if (flag == 0 && compiled_data->graph_exec_arena)
652 {
653 ccv_nnc_graph_exec_reinit(compiled_data->graph_exec_arena, compiled_data->graph, model->graph);
654 // Since we will reinit, if we previously set is_test, we need to set it again.
655 if (compiled_data->is_test)
656 {
657 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; });
658 ccv_nnc_graph_exec_update_t update = {
659 .parallel_count = parallel_count,
660 .graph = model->graph,
661 .graph_exec_arena = compiled_data->graph_exec_arena,
662 };
663 ccv_cnnp_model_set_is_test(model, 1, _ccv_cnnp_cmd_update_for_execs, &update);
664 }
665 } else
666 // Free-up tensor arena & graph exec arena.
667 _ccv_cnnp_compiled_data_graph_free(compiled_data);
668 }
669 // There are other compiled graphs, for accum and apply gradients.
670 // However, the main conclusion is, these absorb operations shouldn't impact parameters.
671 // Thus, it won't impact the shape of gradients (only outgrad). Since for outgrad, we
672 // don't allocate ourselves, it is not a concern. For normal gradients, the shape cannot
673 // be changed otherwise parameters' shape will be meaningless. The same goes to internals.
674 // That is why we don't update these compiled graphs at all this point.
675 // Free the model, we've already "absorbed" it.
676 ccv_cnnp_model_free(init);
677}
678
679void ccv_cnnp_model_compile(ccv_cnnp_model_t* const model, const ccv_nnc_tensor_param_t* const inputs, const int input_size, const ccv_nnc_cmd_t minimizer, const ccv_nnc_cmd_t loss)
680{
681 assert(input_size == model->input_size || model->input_size == 0)((void) sizeof ((input_size == model->input_size || model->
input_size == 0) ? 1 : 0), __extension__ ({ if (input_size ==
model->input_size || model->input_size == 0) ; else __assert_fail
("input_size == model->input_size || model->input_size == 0"
, "ccv_cnnp_model.c", 681, __extension__ __PRETTY_FUNCTION__)
; }));
682 if (model->input_size == 0)
683 model->input_size = input_size;
684 if (!model->graph) // The graph is not compiled yet.
685 {
686 model->graph = ccv_nnc_symbolic_graph_new();
687 _ccv_cnnp_model_compile(model, inputs, input_size, loss);
688 assert(model->compiled_data)((void) sizeof ((model->compiled_data) ? 1 : 0), __extension__
({ if (model->compiled_data) ; else __assert_fail ("model->compiled_data"
, "ccv_cnnp_model.c", 688, __extension__ __PRETTY_FUNCTION__)
; }));
689 int i, flag = 0;
690 for (i = 0; !flag && i < input_size; i++)
691 flag = (CCV_TENSOR_GET_MEMORY(inputs[i].type)((inputs[i].type) & 0x3) == CCV_TENSOR_GPU_MEMORY);
692 // If inputs are from GPU, stream type is GPU.
693 model->compiled_data->stream_type = flag ? CCV_STREAM_CONTEXT_GPU : CCV_STREAM_CONTEXT_CPU;
694 model->compiled_data->minimize.minimizer = minimizer;
695 model->compiled_data->minimize.max_saved_aux_size = ccv_nnc_minimizer_saved_aux_size(minimizer);
696 } else {
697 // Now, finally fill in this part. If the graph is already compiled, we make a copy of the model.
698 // And then absorb the "new model" to the old one.
699 ccv_cnnp_model_t* const init = ccv_cnnp_model_copy(model, model->is_trainable);
700 ccv_cnnp_model_absorb(model, init, inputs, input_size);
701 // Reset minimizer.
702 ccv_cnnp_model_set_minimizer(model, minimizer, 1, 0, 0);
703 }
704}
705
706ccv_cnnp_model_t* ccv_cnnp_model_copy(const ccv_cnnp_model_t* const model, const int is_trainable)
707{
708 ccv_cnnp_model_t* const new_model = _ccv_cnnp_model_copy(model, 0);
709 new_model->is_trainable = is_trainable;
710 return new_model;
711}
712
713void ccv_cnnp_model_tensor_auto(ccv_cnnp_model_t* const model, ccv_nnc_tensor_param_t* const outputs, const int output_size)
714{
715 assert(model->graph)((void) sizeof ((model->graph) ? 1 : 0), __extension__ ({ if
(model->graph) ; else __assert_fail ("model->graph", "ccv_cnnp_model.c"
, 715, __extension__ __PRETTY_FUNCTION__); }));
716 assert(output_size == model->output_size)((void) sizeof ((output_size == model->output_size) ? 1 : 0
), __extension__ ({ if (output_size == model->output_size)
; else __assert_fail ("output_size == model->output_size"
, "ccv_cnnp_model.c", 716, __extension__ __PRETTY_FUNCTION__)
; }));
717 ccv_nnc_symbolic_graph_t* const graph = model->graph;
718 ccv_nnc_symbolic_graph_tensor_auto(graph, TRAVERSE_FULL0,0,0,0);
719 int i;
720 for (i = 0; i < output_size; i++)
721 {
722 assert(model->outputs[i].d != CCV_NNC_NO_TENSOR_SYMBOL)((void) sizeof ((model->outputs[i].d != CCV_NNC_NO_TENSOR_SYMBOL
) ? 1 : 0), __extension__ ({ if (model->outputs[i].d != CCV_NNC_NO_TENSOR_SYMBOL
) ; else __assert_fail ("model->outputs[i].d != CCV_NNC_NO_TENSOR_SYMBOL"
, "ccv_cnnp_model.c", 722, __extension__ __PRETTY_FUNCTION__)
; }));
723 outputs[i] = ccv_nnc_tensor_symbol_params(graph, model->outputs[i]);
724 }
725}
726
727void ccv_cnnp_model_set_workspace_size(ccv_cnnp_model_t* const model, size_t workspace_size)
728{
729 if (workspace_size == model->workspace_size)
730 return;
731 model->workspace_size = workspace_size;
732 ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
733 if (compiled_data && compiled_data->graph)
734 ccv_nnc_graph_autotune(compiled_data->graph, workspace_size, 0, TRAVERSE_FULL0,0,0,0);
735}
736
737size_t ccv_cnnp_model_workspace_size(ccv_cnnp_model_t* const model)
738{
739 return model->workspace_size;
740}
741
742void ccv_cnnp_model_set_data_parallel(ccv_cnnp_model_t* const model, const int parallel)
743{
744 if (parallel == 0)
745 model->parallel_count = ccv_nnc_device_count(CCV_STREAM_CONTEXT_GPU);
746 else
747 model->parallel_count = parallel;
748 ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
749 if (compiled_data)
750 { assert(!compiled_data->graph)((void) sizeof ((!compiled_data->graph) ? 1 : 0), __extension__
({ if (!compiled_data->graph) ; else __assert_fail ("!compiled_data->graph"
, "ccv_cnnp_model.c", 750, __extension__ __PRETTY_FUNCTION__)
; })); }
751}
752
753void ccv_cnnp_model_set_max_concurrency(ccv_cnnp_model_t* const model, const int max_stream_count)
754{
755 model->max_stream_count = max_stream_count;
756 ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
757 if (compiled_data)
758 { assert(!compiled_data->graph)((void) sizeof ((!compiled_data->graph) ? 1 : 0), __extension__
({ if (!compiled_data->graph) ; else __assert_fail ("!compiled_data->graph"
, "ccv_cnnp_model.c", 758, __extension__ __PRETTY_FUNCTION__)
; })); }
759}
760
761void ccv_cnnp_model_set_memory_compression(ccv_cnnp_model_t* const model, const int memory_compression)
762{
763 model->memory_compression = memory_compression;
764 ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
765 if (compiled_data)
766 { assert(!compiled_data->graph)((void) sizeof ((!compiled_data->graph) ? 1 : 0), __extension__
({ if (!compiled_data->graph) ; else __assert_fail ("!compiled_data->graph"
, "ccv_cnnp_model.c", 766, __extension__ __PRETTY_FUNCTION__)
; })); }
767}
768
769void ccv_cnnp_model_set_memory_reduction(ccv_cnnp_model_t* const model, const int memory_reduction)
770{
771 model->memory_reduction = memory_reduction;
772 ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
773 if (compiled_data)
774 { assert(!compiled_data->graph)((void) sizeof ((!compiled_data->graph) ? 1 : 0), __extension__
({ if (!compiled_data->graph) ; else __assert_fail ("!compiled_data->graph"
, "ccv_cnnp_model.c", 774, __extension__ __PRETTY_FUNCTION__)
; })); }
775}
776
777void ccv_cnnp_model_set_gradient_checkpointing(ccv_cnnp_model_t* const model, const int gradient_checkpointing)
778{
779 model->gradient_checkpointing = gradient_checkpointing;
780}
781
782int ccv_cnnp_model_gradient_checkpointing(ccv_cnnp_model_t* const model)
783{
784 return model->gradient_checkpointing;
785}
786
787typedef struct {
788 int parallel_count;
789 ccv_nnc_symbolic_graph_t* graph;
790 ccv_cnnp_compiled_data_t* compiled_data;
791 ccv_nnc_tensor_arena_t* tensor_arena;
792} ccv_nnc_tensor_init_states_t;
793
794static int _ccv_cnnp_any_to_init(const ccv_cnnp_compiled_data_t* const compiled_data)
795{
796 int i;
797 const uint32_t* const init_v = CCV_NNC_INIT_V(compiled_data->tensors_init.v)((uint32_t*)((uintptr_t)(compiled_data->tensors_init.v) &
~(uintptr_t)1));
798 for (i = 0; i < compiled_data->parameters->rnum; i++)
799 {
800 const int d = ((ccv_nnc_tensor_symbol_t*)ccv_array_get(compiled_data->parameters, i)((void*)(((char*)((compiled_data->parameters)->data)) +
(size_t)(compiled_data->parameters)->rsize * (size_t)(
i))))->d;
801 if (!(init_v[d >> 5] & (1u << (d & 0x1f))))
802 return 1;
803 }
804 for (i = 0; i < compiled_data->internals->rnum; i++)
805 {
806 const int d = ((ccv_nnc_tensor_symbol_t*)ccv_array_get(compiled_data->internals, i)((void*)(((char*)((compiled_data->internals)->data)) + (
size_t)(compiled_data->internals)->rsize * (size_t)(i))
))->d;
807 if (!(init_v[d >> 5] & (1u << (d & 0x1f))))
808 return 1;
809 }
810 return 0;
811}
812
813static void _ccv_cnnp_init_states_for_tensors(void* const context, const ccv_nnc_cmd_t cmd, const ccv_nnc_hint_t hint, const int flags, ccv_nnc_tensor_t* const input, const ccv_nnc_tensor_symbol_t output_symbol)
814{
815 ccv_nnc_tensor_init_states_t* const tensor_init_states = (ccv_nnc_tensor_init_states_t*)context;
816 ccv_nnc_tensor_arena_t* const tensor_arena = tensor_init_states->tensor_arena;
817 ccv_nnc_tensor_t* const output_tensor = ccv_nnc_tensor_from_symbol(tensor_arena, output_symbol);
818 if (!output_tensor)
819 return;
820 const int d = output_symbol.d;
821 assert(d < tensor_init_states->compiled_data->tensors_init.size)((void) sizeof ((d < tensor_init_states->compiled_data->
tensors_init.size) ? 1 : 0), __extension__ ({ if (d < tensor_init_states
->compiled_data->tensors_init.size) ; else __assert_fail
("d < tensor_init_states->compiled_data->tensors_init.size"
, "ccv_cnnp_model.c", 821, __extension__ __PRETTY_FUNCTION__)
; }));
822 uint32_t* const init_v = CCV_NNC_INIT_V(tensor_init_states->compiled_data->tensors_init.v)((uint32_t*)((uintptr_t)(tensor_init_states->compiled_data
->tensors_init.v) & ~(uintptr_t)1));
823 if (init_v[d >> 5] & (1u << (d & 0x1f)))
824 return;
825 init_v[d >> 5] |= (1u << (d & 0x1f));
826 ccv_nnc_cmd_exec(cmd, hint, flags, &input, input ? 1 : 0, &output_tensor, 1, 0);
827 const ccv_nnc_symbolic_graph_t* const graph = tensor_init_states->graph;
828 const int parallel_count = tensor_init_states->parallel_count;
829 int i;
830 for (i = 1; i < parallel_count; i++)
831 {
832 ccv_nnc_tensor_t* const copy = ccv_nnc_tensor_from_symbol(tensor_arena, ccv_nnc_tensor_symbol_copy(graph, output_symbol, i));
833 if (copy)
834 ccv_nnc_cmd_exec(CMD_DATA_TRANSFER_FORWARD()ccv_nnc_cmd(CCV_NNC_DATA_TRANSFER_FORWARD, 0, ccv_nnc_cmd_auto
, 0), ccv_nnc_no_hint, 0, &output_tensor, 1, &copy, 1, 0);
835 }
836}
837
838// This method can only handle cases we added new tensors and exec, never delete. This invariant is true because
839// we setup everything (including calling simplify method) in ccv_cnnp_model_compile method, before this rewind setup.
840static void _ccv_cnnp_model_rewind_graph(ccv_cnnp_model_t* const model)
841{
842 assert(model->graph)((void) sizeof ((model->graph) ? 1 : 0), __extension__ ({ if
(model->graph) ; else __assert_fail ("model->graph", "ccv_cnnp_model.c"
, 842, __extension__ __PRETTY_FUNCTION__); }));
843 assert(model->compiled_data)((void) sizeof ((model->compiled_data) ? 1 : 0), __extension__
({ if (model->compiled_data) ; else __assert_fail ("model->compiled_data"
, "ccv_cnnp_model.c", 843, __extension__ __PRETTY_FUNCTION__)
; }));
844 ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
845 assert(compiled_data->rewindables)((void) sizeof ((compiled_data->rewindables) ? 1 : 0), __extension__
({ if (compiled_data->rewindables) ; else __assert_fail (
"compiled_data->rewindables", "ccv_cnnp_model.c", 845, __extension__
__PRETTY_FUNCTION__); }));
846 int i;
847 for (i = 0; i < compiled_data->rewindables->rnum; i++)
848 {
849 const ccv_cnnp_rewind_symbol_t* const rewind_symbol = (ccv_cnnp_rewind_symbol_t*)ccv_array_get(compiled_data->rewindables, i)((void*)(((char*)((compiled_data->rewindables)->data)) +
(size_t)(compiled_data->rewindables)->rsize * (size_t)
(i)));
850 if (rewind_symbol->type == CCV_CNNP_REWIND_GRAPH_EXEC)
851 ccv_nnc_graph_exec_symbol_free(model->graph, rewind_symbol->graph_exec);
852 else if (rewind_symbol->type == CCV_CNNP_REWIND_TENSOR)
853 ccv_nnc_tensor_symbol_free(model->graph, rewind_symbol->tensor);
854 }
855 ccv_array_clear(compiled_data->rewindables);
856 ccv_nnc_graph_exec_symbol_autogen(model->graph, 0, 0, CCV_NNC_AUTOGEN_SOURCES_AND_DESTINATIONS);
857}
858
859static void _ccv_cnnp_model_tensor_symbol_new_hook(void* context, const ccv_nnc_tensor_symbol_t symbol, const ccv_nnc_tensor_param_t info, const char* const name)
860{
861 const ccv_cnnp_rewind_symbol_t rewind_symbol = {
862 .type = CCV_CNNP_REWIND_TENSOR,
863 .tensor = symbol
864 };
865 ccv_array_t* const rewind_symbols = (ccv_array_t*)context;
866 ccv_array_push(rewind_symbols, &rewind_symbol);
867}
868
869static void _ccv_cnnp_model_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)
870{
871 const ccv_cnnp_rewind_symbol_t rewind_symbol = {
872 .type = CCV_CNNP_REWIND_TENSOR,
873 .tensor = symbol
874 };
875 ccv_array_t* const rewind_symbols = (ccv_array_t*)context;
876 ccv_array_push(rewind_symbols, &rewind_symbol);
877}
878
879static void _ccv_cnnp_model_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)
880{
881 const ccv_cnnp_rewind_symbol_t rewind_symbol = {
882 .type = CCV_CNNP_REWIND_GRAPH_EXEC,
883 .graph_exec = symbol
884 };
885 ccv_array_t* const rewind_symbols = (ccv_array_t*)context;
886 ccv_array_push(rewind_symbols, &rewind_symbol);
887}
888
889static void _ccv_cnnp_model_graph_symbol_exec_set_for_graph_exec_arena(const ccv_nnc_graph_exec_arena_t* const graph_exec_arena, const int parallel_count, const ccv_nnc_graph_exec_symbol_t exec_symbol, const ccv_nnc_cmd_t cmd, ccv_nnc_symbolic_graph_t* const symbolic_graph)
890{
891 ccv_nnc_graph_exec_t const update_exec = ccv_nnc_graph_exec_from_symbol(graph_exec_arena, exec_symbol);
892 if (!CCV_NO_GRAPH_EXEC(update_exec)((update_exec).graph == 0))
893 ccv_nnc_graph_exec_set(update_exec.graph, update_exec, cmd);
894 int i;
895 for (i = 1; i < parallel_count; i++)
896 {
897 ccv_nnc_graph_exec_symbol_t copy_symbol = ccv_nnc_graph_exec_symbol_copy(symbolic_graph, exec_symbol, i);
898 const ccv_nnc_graph_exec_t copy = ccv_nnc_graph_exec_from_symbol(graph_exec_arena, copy_symbol);
899 if (!CCV_NO_GRAPH_EXEC(copy)((copy).graph == 0))
900 ccv_nnc_graph_exec_set(copy.graph, copy, cmd);
901 }
902}
903
904static void _ccv_cnnp_model_graph_exec_symbol_set(ccv_nnc_symbolic_graph_t* const symbolic_graph, ccv_cnnp_compiled_data_t* const compiled_data, const int parallel_count, const ccv_nnc_graph_exec_symbol_t exec_symbol, const ccv_nnc_cmd_t cmd)
905{
906 assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
(compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 906, __extension__ __PRETTY_FUNCTION__); }));
907 assert(symbolic_graph)((void) sizeof ((symbolic_graph) ? 1 : 0), __extension__ ({ if
(symbolic_graph) ; else __assert_fail ("symbolic_graph", "ccv_cnnp_model.c"
, 907, __extension__ __PRETTY_FUNCTION__); }));
908 ccv_nnc_graph_exec_symbol_set(symbolic_graph, exec_symbol, cmd);
909 int i;
910 for (i = 1; i < parallel_count; i++)
911 {
912 ccv_nnc_graph_exec_symbol_t copy_symbol = ccv_nnc_graph_exec_symbol_copy(symbolic_graph, exec_symbol, i);
913 if (copy_symbol.graph)
914 ccv_nnc_graph_exec_symbol_set(symbolic_graph, copy_symbol, cmd);
915 }
916 ccv_nnc_graph_exec_arena_t* const graph_exec_arena = compiled_data->graph_exec_arena;
917 if (graph_exec_arena)
918 _ccv_cnnp_model_graph_symbol_exec_set_for_graph_exec_arena(graph_exec_arena, parallel_count, exec_symbol, cmd, symbolic_graph);
919 // Skip backward graph exec arena because it is for a specific accum symbolic graph, not the main graph (model->graph)
920 ccv_nnc_graph_exec_arena_t* const gradient_graph_exec_arena = compiled_data->apply_gradients.graph_exec_arena;
921 if (gradient_graph_exec_arena)
922 _ccv_cnnp_model_graph_symbol_exec_set_for_graph_exec_arena(gradient_graph_exec_arena, parallel_count, exec_symbol, cmd, symbolic_graph);
923}
924
925static int _ccv_cnnp_set_minimizer_for_parameter(ccv_nnc_symbolic_graph_t* const graph, ccv_cnnp_compiled_data_t* const compiled_data, ccv_nnc_graph_exec_symbol_t* const update_nodes, ccv_nnc_tensor_symbol_t* const updated_parameters, ccv_nnc_tensor_symbol_map_t* const saved_aux, const int parallel_count, const ccv_nnc_cmd_t minimizer, const int saved_aux_size, const int max_saved_aux_size, const int parameter_indice)
926{
927 int this_parameter_flag = 0;
928 if (update_nodes[parameter_indice].d == CCV_NNC_NO_TENSOR_SYMBOL)
929 return this_parameter_flag;
930 const ccv_nnc_cmd_t old_minimizer = ccv_nnc_graph_exec_symbol_cmd(graph, update_nodes[parameter_indice]);
931 int j, k;
932 // For no-op, we can preserve previous saved_aux_size.
933 if (old_minimizer.cmd != minimizer.cmd && minimizer.cmd != CCV_NNC_NOOP)
934 {
935 // If the old minimizer is a noop, then the old_saved_aux_size should be whatever its previous
936 // saved_aux_size is, otherwise we will reinit the saved_aux repeatedly if you switch between
937 // noop and a minimizer. We don't want that because we do that in high-level frameworks to
938 // make sure some model parameters don't update if we don't want them to.
939 int old_saved_aux_size;
940 if (old_minimizer.cmd == CCV_NNC_NOOP)
941 {
942 int input_size;
943 ccv_nnc_graph_exec_symbol_io(graph, update_nodes[parameter_indice], 0, &input_size, 0, 0);
944 if (input_size < 2) // This is not legit.
945 old_saved_aux_size = ccv_nnc_minimizer_saved_aux_size(old_minimizer);
946 else // See ccv_nnc_minimizer_saved_aux_size, the saved_aux is inputs excluding gradients and parameters.
947 old_saved_aux_size = input_size - 2;
948 } else
949 old_saved_aux_size = ccv_nnc_minimizer_saved_aux_size(old_minimizer);
950 if (old_saved_aux_size != saved_aux_size)
951 {
952 this_parameter_flag = 1;
953 if (saved_aux_size > old_saved_aux_size)
954 {
955 // Allocate new tensor symbols.
956 const ccv_nnc_tensor_param_t info = ccv_nnc_tensor_symbol_params(graph, updated_parameters[parameter_indice]);
957 for (j = old_saved_aux_size; j < saved_aux_size; j++)
958 {
959 saved_aux[parameter_indice * max_saved_aux_size + j].source = ccv_nnc_tensor_symbol_new(graph, info, 0);
960 saved_aux[parameter_indice * max_saved_aux_size + j].destination = ccv_nnc_tensor_symbol_new(graph, info, 0);
961 const int device_id = CCV_TENSOR_GET_DEVICE_ID(info.type)(((info.type) & 0xfff00) >> 8);
962 for (k = 1; k < parallel_count; k++)
963 {
964 ccv_nnc_tensor_param_t dev_info = info;
965 if (k != device_id)
966 CCV_TENSOR_SET_DEVICE_ID(dev_info.type, k)(dev_info.type) = (((dev_info.type) & ~0xfff00) | (((k) &
0xfff) << 8));
967 else
968 CCV_TENSOR_SET_DEVICE_ID(dev_info.type, 0)(dev_info.type) = (((dev_info.type) & ~0xfff00) | (((0) &
0xfff) << 8));
969 const ccv_nnc_tensor_symbol_t src_copy = ccv_nnc_tensor_symbol_new(graph, dev_info, 0);
970 const ccv_nnc_tensor_symbol_t dest_copy = ccv_nnc_tensor_symbol_new(graph, dev_info, 0);
971 ccv_nnc_tensor_symbol_set_copy(graph, saved_aux[parameter_indice * max_saved_aux_size + j].source, k, src_copy);
972 ccv_nnc_tensor_symbol_set_copy(graph, saved_aux[parameter_indice * max_saved_aux_size + j].destination, k, dest_copy);
973 }
974 }
975 } else {
976 for (j = saved_aux_size; j < old_saved_aux_size; j++)
977 {
978 for (k = 1; k < parallel_count; k++)
979 {
980 const ccv_nnc_tensor_symbol_t src_copy = ccv_nnc_tensor_symbol_copy(graph, saved_aux[parameter_indice * max_saved_aux_size + j].source, k);
981 if (src_copy.d >= 0)
982 {
983 ccv_nnc_tensor_symbol_free(graph, src_copy);
984 ccv_nnc_tensor_symbol_set_copy(graph, saved_aux[parameter_indice * max_saved_aux_size + j].source, k, NO_TENSOR_SYMBOL(const ccv_nnc_tensor_symbol_t){.d = CCV_NNC_NO_TENSOR_SYMBOL
});
985 }
986 const ccv_nnc_tensor_symbol_t dest_copy = ccv_nnc_tensor_symbol_copy(graph, saved_aux[parameter_indice * max_saved_aux_size + j].destination, k);
987 if (dest_copy.d >= 0)
988 {
989 ccv_nnc_tensor_symbol_free(graph, dest_copy);
990 ccv_nnc_tensor_symbol_set_copy(graph, saved_aux[parameter_indice * max_saved_aux_size + j].destination, k, NO_TENSOR_SYMBOL(const ccv_nnc_tensor_symbol_t){.d = CCV_NNC_NO_TENSOR_SYMBOL
});
991 }
992 }
993 ccv_nnc_tensor_symbol_free(graph, saved_aux[parameter_indice * max_saved_aux_size + j].source);
994 ccv_nnc_tensor_symbol_free(graph, saved_aux[parameter_indice * max_saved_aux_size + j].destination);
995 saved_aux[parameter_indice * max_saved_aux_size + j].source = saved_aux[parameter_indice * max_saved_aux_size + j].destination = NO_TENSOR_SYMBOL(const ccv_nnc_tensor_symbol_t){.d = CCV_NNC_NO_TENSOR_SYMBOL
};
996 }
997 }
998 }
999 }
1000 _ccv_cnnp_model_graph_exec_symbol_set(graph, compiled_data, parallel_count, update_nodes[parameter_indice], minimizer);
1001 if (this_parameter_flag)
1002 {
1003 ccv_nnc_tensor_symbol_t update_inputs[saved_aux_size + 2];
1004 ccv_nnc_tensor_symbol_t update_outputs[saved_aux_size + 1];
1005 const int* inputs = 0;
1006 int input_size = 0;
1007 ccv_nnc_graph_exec_symbol_io(graph, update_nodes[parameter_indice], &inputs, &input_size, 0, 0);
1008 assert(input_size >= 1)((void) sizeof ((input_size >= 1) ? 1 : 0), __extension__ (
{ if (input_size >= 1) ; else __assert_fail ("input_size >= 1"
, "ccv_cnnp_model.c", 1008, __extension__ __PRETTY_FUNCTION__
); }));
1009 update_inputs[0].d = inputs[0];
1010 update_inputs[0].graph = graph;
1011 update_inputs[1].d = inputs[1];
1012 update_inputs[1].graph = graph;
1013 update_outputs[0] = updated_parameters[parameter_indice];
1014 for (j = 0; j < saved_aux_size; j++)
1015 {
1016 update_inputs[j + 2] = saved_aux[parameter_indice * max_saved_aux_size + j].source;
1017 update_outputs[j + 1] = saved_aux[parameter_indice * max_saved_aux_size + j].destination;
1018 }
1019 ccv_nnc_graph_exec_symbol_set_io(graph, update_nodes[parameter_indice], update_inputs, saved_aux_size + 2, update_outputs, saved_aux_size + 1);
1020 for (k = 1; k < parallel_count; k++)
1021 {
1022 const ccv_nnc_graph_exec_symbol_t copy = ccv_nnc_graph_exec_symbol_copy(graph, update_nodes[parameter_indice], k);
1023 assert(copy.d >= 0)((void) sizeof ((copy.d >= 0) ? 1 : 0), __extension__ ({ if
(copy.d >= 0) ; else __assert_fail ("copy.d >= 0", "ccv_cnnp_model.c"
, 1023, __extension__ __PRETTY_FUNCTION__); }));
1024 ccv_nnc_graph_exec_symbol_io(graph, copy, &inputs, &input_size, 0, 0);
1025 assert(input_size >= 1)((void) sizeof ((input_size >= 1) ? 1 : 0), __extension__ (
{ if (input_size >= 1) ; else __assert_fail ("input_size >= 1"
, "ccv_cnnp_model.c", 1025, __extension__ __PRETTY_FUNCTION__
); }));
1026 update_inputs[0].d = inputs[0];
1027 update_inputs[0].graph = graph;
1028 update_inputs[1].d = inputs[1];
1029 update_inputs[1].graph = graph;
1030 update_outputs[0] = ccv_nnc_tensor_symbol_copy(graph, updated_parameters[parameter_indice], k);
1031 for (j = 0; j < saved_aux_size; j++)
1032 {
1033 update_inputs[j + 2] = ccv_nnc_tensor_symbol_copy(graph, saved_aux[parameter_indice * max_saved_aux_size + j].source, k);
1034 update_outputs[j + 1] = ccv_nnc_tensor_symbol_copy(graph, saved_aux[parameter_indice * max_saved_aux_size + j].destination, k);
1035 }
1036 ccv_nnc_graph_exec_symbol_set_io(graph, copy, update_inputs, saved_aux_size + 2, update_outputs, saved_aux_size + 1);
1037 }
1038 }
1039 return this_parameter_flag;
1040}
1041
1042typedef struct {
1043 int parameter_size;
1044 ccv_nnc_cmd_t minimizer;
1045 ccv_cnnp_model_io_t parameters[1];
1046} ccv_cnnp_set_minimizer_for_parameter_t;
1047
1048static int _ccv_cnnp_apply_parameters_with_minimizer(ccv_cnnp_model_t* const model)
1049{
1050 ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
1051 assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
(compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 1051, __extension__ __PRETTY_FUNCTION__); }));
1052 const int max_saved_aux_size = compiled_data->minimize.max_saved_aux_size;
1053 // We update all parameters, at this point, we have one minimizer.
1054 const int parameter_size = compiled_data->parameters->rnum;
1055 ccv_nnc_graph_exec_symbol_t* const update_nodes = compiled_data->update_nodes;
1056 ccv_nnc_symbolic_graph_t* const symbolic_graph = model->graph;
1057 assert(symbolic_graph)((void) sizeof ((symbolic_graph) ? 1 : 0), __extension__ ({ if
(symbolic_graph) ; else __assert_fail ("symbolic_graph", "ccv_cnnp_model.c"
, 1057, __extension__ __PRETTY_FUNCTION__); }));
1058 const int parallel_count = _ccv_cnnp_model_root_parallel_count(model);
1059 assert(_ccv_cnnp_model_effective_parallel_count(model) == parallel_count && "local replicated stateful models only support forward / no-grad evaluation for now")((void) sizeof ((_ccv_cnnp_model_effective_parallel_count(model
) == parallel_count && "local replicated stateful models only support forward / no-grad evaluation for now"
) ? 1 : 0), __extension__ ({ if (_ccv_cnnp_model_effective_parallel_count
(model) == parallel_count && "local replicated stateful models only support forward / no-grad evaluation for now"
) ; else __assert_fail ("_ccv_cnnp_model_effective_parallel_count(model) == parallel_count && \"local replicated stateful models only support forward / no-grad evaluation for now\""
, "ccv_cnnp_model.c", 1059, __extension__ __PRETTY_FUNCTION__
); }));
1060 ccv_array_t* const parameters = compiled_data->minimize.parameters;
1061 ccv_array_t* const parameter_indices = ccv_array_new(sizeof(int), 0, 0);
1062 int i, j, flag = 0;
1063 for (i = 0; i < parameters->rnum; i++)
1064 {
1065 ccv_cnnp_set_minimizer_for_parameter_t* const set_minimizer_for_parameter = *(ccv_cnnp_set_minimizer_for_parameter_t**)ccv_array_get(parameters, i)((void*)(((char*)((parameters)->data)) + (size_t)(parameters
)->rsize * (size_t)(i)));
1066 for (j = 0; j < set_minimizer_for_parameter->parameter_size; j++)
1067 {
1068 const int param_sel = set_minimizer_for_parameter->parameters[j]->param_sel > 0 ? set_minimizer_for_parameter->parameters[j]->param_sel - 1 : set_minimizer_for_parameter->parameters[j]->param_sel;
1069 assert(set_minimizer_for_parameter->parameters[j]->param_sel != 0)((void) sizeof ((set_minimizer_for_parameter->parameters[j
]->param_sel != 0) ? 1 : 0), __extension__ ({ if (set_minimizer_for_parameter
->parameters[j]->param_sel != 0) ; else __assert_fail (
"set_minimizer_for_parameter->parameters[j]->param_sel != 0"
, "ccv_cnnp_model.c", 1069, __extension__ __PRETTY_FUNCTION__
); }));
1070 const int old_rnum = parameter_indices->rnum;
1071 ccv_cnnp_model_add_to_parameter_indices(set_minimizer_for_parameter->parameters[j]->model, param_sel, parameter_indices);
1072 const int param_ref = set_minimizer_for_parameter->parameters[j]->param_ref > 0 ? set_minimizer_for_parameter->parameters[j]->param_ref - 1 : set_minimizer_for_parameter->parameters[j]->param_ref;
1073 assert(set_minimizer_for_parameter->parameters[j]->param_ref != 0)((void) sizeof ((set_minimizer_for_parameter->parameters[j
]->param_ref != 0) ? 1 : 0), __extension__ ({ if (set_minimizer_for_parameter
->parameters[j]->param_ref != 0) ; else __assert_fail (
"set_minimizer_for_parameter->parameters[j]->param_ref != 0"
, "ccv_cnnp_model.c", 1073, __extension__ __PRETTY_FUNCTION__
); }));
1074 if (param_ref >= 0)
1075 {
1076 assert(param_ref + old_rnum < parameter_indices->rnum)((void) sizeof ((param_ref + old_rnum < parameter_indices->
rnum) ? 1 : 0), __extension__ ({ if (param_ref + old_rnum <
parameter_indices->rnum) ; else __assert_fail ("param_ref + old_rnum < parameter_indices->rnum"
, "ccv_cnnp_model.c", 1076, __extension__ __PRETTY_FUNCTION__
); }));
1077 *(int*)ccv_array_get(parameter_indices, old_rnum)((void*)(((char*)((parameter_indices)->data)) + (size_t)(parameter_indices
)->rsize * (size_t)(old_rnum))) = *(int*)ccv_array_get(parameter_indices, param_ref + old_rnum)((void*)(((char*)((parameter_indices)->data)) + (size_t)(parameter_indices
)->rsize * (size_t)(param_ref + old_rnum)));
1078 parameter_indices->rnum = old_rnum + 1;
1079 }
1080 }
1081 const int saved_aux_size = ccv_nnc_minimizer_saved_aux_size(set_minimizer_for_parameter->minimizer);
1082 // We may have duplicated indices, but that is OK, we will set it twice.
1083 for (j = 0; j < parameter_indices->rnum; j++)
1084 {
1085 const int d = *(int*)ccv_array_get(parameter_indices, j)((void*)(((char*)((parameter_indices)->data)) + (size_t)(parameter_indices
)->rsize * (size_t)(j)));
1086 assert(d <= parameter_size)((void) sizeof ((d <= parameter_size) ? 1 : 0), __extension__
({ if (d <= parameter_size) ; else __assert_fail ("d <= parameter_size"
, "ccv_cnnp_model.c", 1086, __extension__ __PRETTY_FUNCTION__
); }));
1087 if (_ccv_cnnp_set_minimizer_for_parameter(symbolic_graph, compiled_data, update_nodes, compiled_data->updated_parameters, compiled_data->saved_aux, parallel_count, set_minimizer_for_parameter->minimizer, saved_aux_size, max_saved_aux_size, d))
1088 flag = 1;
1089 }
1090 ccv_array_clear(parameter_indices);
1091 }
1092 ccv_array_free(parameter_indices);
1093 return flag;
1094}
1095
1096static void _ccv_cnnp_scatter_saved_aux(ccv_nnc_tensor_symbol_map_t* const saved_aux, const int parameter_size, const int old_saved_aux_size, const int new_saved_aux_size)
1097{
1098 if (new_saved_aux_size == old_saved_aux_size)
1099 return;
1100 assert(new_saved_aux_size > old_saved_aux_size)((void) sizeof ((new_saved_aux_size > old_saved_aux_size) ?
1 : 0), __extension__ ({ if (new_saved_aux_size > old_saved_aux_size
) ; else __assert_fail ("new_saved_aux_size > old_saved_aux_size"
, "ccv_cnnp_model.c", 1100, __extension__ __PRETTY_FUNCTION__
); }));
1101 int i, j;
1102 for (i = parameter_size - 1; i >= 0; i--)
1103 {
1104 for (j = new_saved_aux_size - 1; j >= old_saved_aux_size; j--)
1105 saved_aux[i * new_saved_aux_size + j].source = saved_aux[i * new_saved_aux_size + j].destination = NO_TENSOR_SYMBOL(const ccv_nnc_tensor_symbol_t){.d = CCV_NNC_NO_TENSOR_SYMBOL
};
1106 for (j = old_saved_aux_size - 1; j >= 0; j--)
1107 saved_aux[i * new_saved_aux_size + j] = saved_aux[i * old_saved_aux_size + j];
1108 }
1109}
1110
1111static void _ccv_cnnp_model_set_rewindables(ccv_cnnp_model_t* const model)
1112{
1113 ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
1114 assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
(compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 1114, __extension__ __PRETTY_FUNCTION__); }));
1115 if (!compiled_data->rewindables)
1116 compiled_data->rewindables = ccv_array_new(sizeof(ccv_cnnp_rewind_symbol_t), 0, 0);
1117 ccv_nnc_tensor_symbol_new_hook(model->graph, _ccv_cnnp_model_tensor_symbol_new_hook, compiled_data->rewindables, 0);
1118 ccv_nnc_tensor_symbol_alias_new_hook(model->graph, _ccv_cnnp_model_tensor_symbol_alias_new_hook, compiled_data->rewindables, 0);
1119 ccv_nnc_graph_exec_symbol_new_hook(model->graph, _ccv_cnnp_model_graph_exec_symbol_new_hook, compiled_data->rewindables, 0);
1120}
1121
1122static void _ccv_cnnp_model_gradient_init(ccv_cnnp_model_t* const model, const int gradient_mode, const uint64_t disable_outgrad, ccv_nnc_tensor_t* const* const fits, const int fit_size)
1123{
1124 ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
1125 assert(compiled_data->gradient_mode == CCV_CNNP_COMPILED_DATA_GRADIENT_NONE)((void) sizeof ((compiled_data->gradient_mode == CCV_CNNP_COMPILED_DATA_GRADIENT_NONE
) ? 1 : 0), __extension__ ({ if (compiled_data->gradient_mode
== CCV_CNNP_COMPILED_DATA_GRADIENT_NONE) ; else __assert_fail
("compiled_data->gradient_mode == CCV_CNNP_COMPILED_DATA_GRADIENT_NONE"
, "ccv_cnnp_model.c", 1125, __extension__ __PRETTY_FUNCTION__
); }));
1126 assert(gradient_mode != CCV_CNNP_COMPILED_DATA_GRADIENT_NONE)((void) sizeof ((gradient_mode != CCV_CNNP_COMPILED_DATA_GRADIENT_NONE
) ? 1 : 0), __extension__ ({ if (gradient_mode != CCV_CNNP_COMPILED_DATA_GRADIENT_NONE
) ; else __assert_fail ("gradient_mode != CCV_CNNP_COMPILED_DATA_GRADIENT_NONE"
, "ccv_cnnp_model.c", 1126, __extension__ __PRETTY_FUNCTION__
); }));
1127 const int evaluate_to_size = compiled_data->evaluate.to_size;
1128 assert(evaluate_to_size > 0)((void) sizeof ((evaluate_to_size > 0) ? 1 : 0), __extension__
({ if (evaluate_to_size > 0) ; else __assert_fail ("evaluate_to_size > 0"
, "ccv_cnnp_model.c", 1128, __extension__ __PRETTY_FUNCTION__
); }));
1129 const int parallel_count = _ccv_cnnp_model_root_parallel_count(model);
1130 assert(_ccv_cnnp_model_effective_parallel_count(model) == parallel_count && "local replicated stateful models only support forward / no-grad evaluation for now")((void) sizeof ((_ccv_cnnp_model_effective_parallel_count(model
) == parallel_count && "local replicated stateful models only support forward / no-grad evaluation for now"
) ? 1 : 0), __extension__ ({ if (_ccv_cnnp_model_effective_parallel_count
(model) == parallel_count && "local replicated stateful models only support forward / no-grad evaluation for now"
) ; else __assert_fail ("_ccv_cnnp_model_effective_parallel_count(model) == parallel_count && \"local replicated stateful models only support forward / no-grad evaluation for now\""
, "ccv_cnnp_model.c", 1130, __extension__ __PRETTY_FUNCTION__
); }));
1131 compiled_data->evaluate.tos = ccreallocrealloc(compiled_data->evaluate.tos, sizeof(ccv_nnc_graph_exec_symbol_t) * evaluate_to_size * parallel_count + sizeof(ccv_nnc_graph_exec_t) * evaluate_to_size * parallel_count);
1132 compiled_data->evaluate.to_ops = (ccv_nnc_graph_exec_t*)(compiled_data->evaluate.tos + evaluate_to_size * parallel_count);
1133 int i, j;
1134 const int output_size = model->output_size;
1135 assert(!fits || fit_size == output_size * parallel_count)((void) sizeof ((!fits || fit_size == output_size * parallel_count
) ? 1 : 0), __extension__ ({ if (!fits || fit_size == output_size
* parallel_count) ; else __assert_fail ("!fits || fit_size == output_size * parallel_count"
, "ccv_cnnp_model.c", 1135, __extension__ __PRETTY_FUNCTION__
); }));
1136 if (fits)
1137 for (i = 0; i < output_size; i++)
1138 ccv_nnc_tensor_symbol_set(model->graph, compiled_data->fits[i], fits[i]->info);
1139 const int max_saved_aux_size = compiled_data->minimize.max_saved_aux_size;
1140 const int parameter_size = compiled_data->parameters->rnum;
1141 compiled_data->updated_parameters = (ccv_nnc_tensor_symbol_t*)ccmallocmalloc(sizeof(ccv_nnc_tensor_symbol_t) * parameter_size + sizeof(ccv_nnc_graph_exec_symbol_t) * parameter_size + sizeof(ccv_nnc_tensor_symbol_map_t) * max_saved_aux_size * parameter_size);
1142 compiled_data->update_nodes = (ccv_nnc_graph_exec_symbol_t*)(compiled_data->updated_parameters + parameter_size);
1143 compiled_data->saved_aux = (ccv_nnc_tensor_symbol_map_t*)(compiled_data->update_nodes + parameter_size);
1144 int parameter_size_maybe_more = parameter_size;
1145 compiled_data->disable_outgrad = disable_outgrad;
1146 int outgrad_size;
1147 if (gradient_mode == CCV_CNNP_COMPILED_DATA_GRADIENT_TRAINABLES || model->input_size == 0)
1148 outgrad_size = 0;
1149 else if (disable_outgrad == CCV_CNNP_DISABLE_OUTGRAD_NONE) // Compute minimize with gradients including inputs.
1150 outgrad_size = model->input_size;
1151 else {
1152 assert(disable_outgrad != CCV_CNNP_DISABLE_OUTGRAD_ALL)((void) sizeof ((disable_outgrad != CCV_CNNP_DISABLE_OUTGRAD_ALL
) ? 1 : 0), __extension__ ({ if (disable_outgrad != CCV_CNNP_DISABLE_OUTGRAD_ALL
) ; else __assert_fail ("disable_outgrad != CCV_CNNP_DISABLE_OUTGRAD_ALL"
, "ccv_cnnp_model.c", 1152, __extension__ __PRETTY_FUNCTION__
); })); // If it is disable all, gradient mode won't be this.
1153 outgrad_size = 0;
1154 for (i = 0; i < model->input_size; i++)
1155 if (!(disable_outgrad & ((uint64_t)1 << i)))
1156 ++outgrad_size;
1157 }
1158 compiled_data->outgrad_size = outgrad_size;
1159 parameter_size_maybe_more += outgrad_size;
1160 compiled_data->gradients = (ccv_nnc_tensor_symbol_t*)ccmallocmalloc(sizeof(ccv_nnc_tensor_symbol_t) * parameter_size_maybe_more + sizeof(ccv_nnc_graph_exec_symbol_t) * parameter_size_maybe_more * parallel_count);
1161 compiled_data->outgrads = parameter_size_maybe_more > parameter_size ? compiled_data->gradients + parameter_size : 0;
1162 compiled_data->backward.tos = (ccv_nnc_graph_exec_symbol_t*)(compiled_data->gradients + parameter_size_maybe_more);
1163 compiled_data->backward.to_size = parameter_size_maybe_more;
1164 ccv_nnc_tensor_symbol_t* parameters = (ccv_nnc_tensor_symbol_t*)ccv_array_get(compiled_data->parameters, 0)((void*)(((char*)((compiled_data->parameters)->data)) +
(size_t)(compiled_data->parameters)->rsize * (size_t)(
0)));
1165 if (compiled_data->parameter_flags)
1166 {
1167 parameters = (ccv_nnc_tensor_symbol_t*)ccmallocmalloc(sizeof(ccv_nnc_tensor_symbol_t) * parameter_size);
1168 for (i = 0; i < parameter_size; i++)
1169 if (compiled_data->parameter_flags[i >> 6] & ((uint64_t)1 << (i & 63)))
1170 parameters[i] = *(ccv_nnc_tensor_symbol_t*)ccv_array_get(compiled_data->parameters, i)((void*)(((char*)((compiled_data->parameters)->data)) +
(size_t)(compiled_data->parameters)->rsize * (size_t)(
i)));
1171 else
1172 parameters[i] = NO_TENSOR_SYMBOL(const ccv_nnc_tensor_symbol_t){.d = CCV_NNC_NO_TENSOR_SYMBOL
};
1173 }
1174 if (gradient_mode == CCV_CNNP_COMPILED_DATA_GRADIENT_TRAINABLES || model->input_size == 0)
1175 ccv_nnc_symbolic_graph_minimize(model->graph, compiled_data->minimize.minimizer, compiled_data->f, output_size, parameters, parameter_size, 0, 0, SYMBOLIC_GRAPH_SOURCES(model->graph)ccv_nnc_symbolic_graph_sources(model->graph), ccv_nnc_symbolic_graph_source_size
(model->graph), SYMBOLIC_GRAPH_DESTINATIONS(model->graph)ccv_nnc_symbolic_graph_destinations(model->graph), ccv_nnc_symbolic_graph_destination_size
(model->graph), compiled_data->gradients, compiled_data->updated_parameters, compiled_data->saved_aux, compiled_data->update_nodes);
1176 else if (disable_outgrad == CCV_CNNP_DISABLE_OUTGRAD_NONE) // Compute minimize with gradients including inputs.
1177 ccv_nnc_symbolic_graph_minimize(model->graph, compiled_data->minimize.minimizer, compiled_data->f, output_size, parameters, parameter_size, model->inputs, model->input_size, SYMBOLIC_GRAPH_SOURCES(model->graph)ccv_nnc_symbolic_graph_sources(model->graph), ccv_nnc_symbolic_graph_source_size
(model->graph), SYMBOLIC_GRAPH_DESTINATIONS(model->graph)ccv_nnc_symbolic_graph_destinations(model->graph), ccv_nnc_symbolic_graph_destination_size
(model->graph), compiled_data->gradients, compiled_data->updated_parameters, compiled_data->saved_aux, compiled_data->update_nodes);
1178 else { // Compute minimize with gradients including selected inputs.
1179 assert(model->input_size > 0)((void) sizeof ((model->input_size > 0) ? 1 : 0), __extension__
({ if (model->input_size > 0) ; else __assert_fail ("model->input_size > 0"
, "ccv_cnnp_model.c", 1179, __extension__ __PRETTY_FUNCTION__
); }));
1180 assert(disable_outgrad != CCV_CNNP_DISABLE_OUTGRAD_ALL)((void) sizeof ((disable_outgrad != CCV_CNNP_DISABLE_OUTGRAD_ALL
) ? 1 : 0), __extension__ ({ if (disable_outgrad != CCV_CNNP_DISABLE_OUTGRAD_ALL
) ; else __assert_fail ("disable_outgrad != CCV_CNNP_DISABLE_OUTGRAD_ALL"
, "ccv_cnnp_model.c", 1180, __extension__ __PRETTY_FUNCTION__
); })); // If it is disable all, gradient mode won't be this.
1181 assert(outgrad_size > 0)((void) sizeof ((outgrad_size > 0) ? 1 : 0), __extension__
({ if (outgrad_size > 0) ; else __assert_fail ("outgrad_size > 0"
, "ccv_cnnp_model.c", 1181, __extension__ __PRETTY_FUNCTION__
); }));
1182 ccv_nnc_tensor_symbol_t outgrads[outgrad_size];
1183 j = 0;
1184 for (i = 0; i < model->input_size; i++)
1185 if (!(disable_outgrad & ((uint64_t)1 << i)))
1186 outgrads[j++] = model->inputs[i];
1187 ccv_nnc_symbolic_graph_minimize(model->graph, compiled_data->minimize.minimizer, compiled_data->f, output_size, parameters, parameter_size, outgrads, outgrad_size, SYMBOLIC_GRAPH_SOURCES(model->graph)ccv_nnc_symbolic_graph_sources(model->graph), ccv_nnc_symbolic_graph_source_size
(model->graph), SYMBOLIC_GRAPH_DESTINATIONS(model->graph)ccv_nnc_symbolic_graph_destinations(model->graph), ccv_nnc_symbolic_graph_destination_size
(model->graph), compiled_data->gradients, compiled_data->updated_parameters, compiled_data->saved_aux, compiled_data->update_nodes);
1188 }
1189 if (compiled_data->parameter_flags)
1190 ccfreefree(parameters);
1191 _ccv_cnnp_scatter_saved_aux(compiled_data->saved_aux, parameter_size, ccv_nnc_minimizer_saved_aux_size(compiled_data->minimize.minimizer), compiled_data->minimize.max_saved_aux_size);
1192 if (compiled_data->minimize.parameters)
1193 _ccv_cnnp_apply_parameters_with_minimizer(model);
1194 // Go through gradient checkpoints to generate tensor inputs for backward pass just before executing the backward pass.
1195 ccv_cnnp_model_apply_gradient_checkpoints(compiled_data, model->graph);
1196 for (i = 0; i < output_size; i++)
1197 {
1198 const ccv_nnc_tensor_symbol_t df = ccv_nnc_tensor_symbol_for_backward(model->graph, compiled_data->f[i]);
1199 // Init this to 1 so we can backprop.
1200 ccv_nnc_tensor_symbol_set_flags(model->graph, df, CCV_NNC_TENSOR_SYMBOL_INIT_ONES);
1201 }
1202 compiled_data->backward.to_size = 0;
1203 for (i = 0; i < parameter_size_maybe_more; i++)
1204 if (compiled_data->gradients[i].d != CCV_NNC_NO_TENSOR_SYMBOL)
1205 compiled_data->backward.tos[compiled_data->backward.to_size++] = ccv_nnc_graph_exec_symbol_for_backward(model->graph, compiled_data->gradients[i]);
1206 ccv_nnc_graph_exec_symbol_autogen(model->graph, 0, 0, CCV_NNC_AUTOGEN_ALL_EXECS);
1207 ccv_nnc_symbolic_graph_set_destinations(model->graph, compiled_data->update_nodes, parameter_size);
1208 for (i = 0; i < parameter_size_maybe_more - parameter_size; i++)
1209 {
1210 if (compiled_data->outgrads[i].d < 0) // When we go through input, we might find zero-length inputs, and for these, we cannot have any outgrads.
1211 continue;
1212 const ccv_nnc_graph_exec_symbol_t outgrad = ccv_nnc_graph_exec_symbol_for_backward(model->graph, compiled_data->outgrads[i]);
1213 const int* tos;
1214 int to_size;
1215 ccv_nnc_graph_exec_symbol_to(model->graph, outgrad, &tos, &to_size);
1216 if (to_size == 0) // If this is the end (no minimizers afterwards). We need to attach this as a destination. Otherwise this is covered in update_nodes.
1217 {
1218 const ccv_nnc_graph_exec_symbol_t* destinations = ccv_nnc_symbolic_graph_destinations(model->graph);
1219 const int destination_count = ccv_nnc_symbolic_graph_destination_size(model->graph);
1220 int flag = 0;
1221 const int outgrad_destination_start = ccv_max(0, destination_count - i)({ typeof (0) _a = (0); typeof (destination_count - i) _b = (
destination_count - i); (_a > _b) ? _a : _b; });
1222 for (j = i - 1; !flag && j >= 0; j--)
1223 if (j + outgrad_destination_start < destination_count)
1224 flag = (destinations[j + outgrad_destination_start].d == outgrad.d);
1225 if (!flag) // Only if we cannot find it, we add it.
1226 ccv_nnc_symbolic_graph_add_destination(model->graph, outgrad);
1227 }
1228 }
1229 if (parallel_count > 1)
1230 {
1231 ccv_nnc_symbolic_graph_data_parallel(model->graph, parallel_count,
1232 0, 0,
1233 compiled_data->gradients, parameter_size /* No need to deal with outgrads, we don't allreduce outgrads */,
1234 compiled_data->gradients /* We only care about gradients before allreduce, thus, update our current pointers */,
1235 0, 0, 0,
1236 CCV_NNC_PARALLEL_REDUCE_OP_SUM,
1237 SYMBOLIC_GRAPH_SOURCES(model->graph)ccv_nnc_symbolic_graph_sources(model->graph), ccv_nnc_symbolic_graph_source_size
(model->graph), SYMBOLIC_GRAPH_DESTINATIONS(model->graph)ccv_nnc_symbolic_graph_destinations(model->graph), ccv_nnc_symbolic_graph_destination_size
(model->graph));
1238 ccv_nnc_graph_exec_symbol_autogen(model->graph, 0, 0, CCV_NNC_AUTOGEN_SOURCES_AND_DESTINATIONS);
1239 for (i = 0; i < evaluate_to_size; i++)
1240 for (j = 1; j < parallel_count; j++)
1241 {
1242 const ccv_nnc_graph_exec_symbol_t copy = ccv_nnc_graph_exec_symbol_copy(model->graph, compiled_data->evaluate.tos[i], j);
1243 if (copy.d != CCV_NNC_NO_GRAPH_EXEC_SYMBOL)
1244 compiled_data->evaluate.tos[compiled_data->evaluate.to_size++] = copy;
1245 }
1246 const int backward_to_size = compiled_data->backward.to_size;
1247 for (i = 0; i < backward_to_size; i++)
1248 for (j = 1; j < parallel_count; j++)
1249 {
1250 const ccv_nnc_graph_exec_symbol_t copy = ccv_nnc_graph_exec_symbol_copy(model->graph, compiled_data->backward.tos[i], j);
1251 if (copy.d != CCV_NNC_NO_GRAPH_EXEC_SYMBOL)
1252 compiled_data->backward.tos[compiled_data->backward.to_size++] = copy;
1253 }
1254 }
1255 // Only use memory compression if we are in gradient parameter mode.
1256 if (gradient_mode == CCV_CNNP_COMPILED_DATA_GRADIENT_TRAINABLES || gradient_mode == CCV_CNNP_COMPILED_DATA_GRADIENT_TRAINABLES_AND_INPUTS)
1257 {
1258 if (model->memory_compression)
1259 ccv_nnc_symbolic_graph_memory_compression(model->graph, SYMBOLIC_GRAPH_SOURCES(model->graph)ccv_nnc_symbolic_graph_sources(model->graph), ccv_nnc_symbolic_graph_source_size
(model->graph), SYMBOLIC_GRAPH_DESTINATIONS(model->graph)ccv_nnc_symbolic_graph_destinations(model->graph), ccv_nnc_symbolic_graph_destination_size
(model->graph));
1260 if (model->memory_reduction)
1261 ccv_nnc_symbolic_graph_memory_reduction(model->graph, SYMBOLIC_GRAPH_SOURCES(model->graph)ccv_nnc_symbolic_graph_sources(model->graph), ccv_nnc_symbolic_graph_source_size
(model->graph), SYMBOLIC_GRAPH_DESTINATIONS(model->graph)ccv_nnc_symbolic_graph_destinations(model->graph), ccv_nnc_symbolic_graph_destination_size
(model->graph));
1262 }
1263 compiled_data->backward.to_size = _ccv_nnc_array_dedup_graph_exec_symbols(compiled_data->backward.tos, compiled_data->backward.to_size);
1264 compiled_data->gradient_mode = gradient_mode;
1265}
1266
1267void ccv_cnnp_model_tensors_init_0(const ccv_cnnp_model_t* const model, ccv_cnnp_compiled_data_t* const compiled_data)
1268{
1269 assert(!compiled_data->tensors.parameters)((void) sizeof ((!compiled_data->tensors.parameters) ? 1 :
0), __extension__ ({ if (!compiled_data->tensors.parameters
) ; else __assert_fail ("!compiled_data->tensors.parameters"
, "ccv_cnnp_model.c", 1269, __extension__ __PRETTY_FUNCTION__
); }));
1270 const int parameter_size = compiled_data->parameters->rnum;
1271 const int parallel_count = _ccv_cnnp_model_effective_parallel_count(model);
1272 compiled_data->parallel_count = parallel_count;
1273 const int internal_size = compiled_data->internals->rnum;
1274 compiled_data->tensors_init.size = ccv_nnc_tensor_symbol_count(model->graph);
1275 compiled_data->tensors_init.v = cccalloccalloc(((compiled_data->tensors_init.size + 31) >> 5), sizeof(uint32_t));
1276 compiled_data->tensors.parameters = (ccv_nnc_tensor_t**)cccalloccalloc((parameter_size + internal_size) * parallel_count, sizeof(ccv_nnc_tensor_t*));
1277 compiled_data->tensors.internals = compiled_data->tensors.parameters + parameter_size * parallel_count;
1278}
1279
1280int ccv_cnnp_model_tensors_any_to_alloc(const ccv_cnnp_model_t* const model, ccv_cnnp_compiled_data_t* const compiled_data)
1281{
1282 int i, j;
1283 const int parameter_size = compiled_data->parameters->rnum;
1284 const int parallel_count = _ccv_cnnp_compiled_data_parallel_count(model, compiled_data);
1285 const int internal_size = compiled_data->internals->rnum;
1286 for (i = 0; i < parameter_size; i++)
1287 {
1288 // parameters has to be allocated all together.
1289 if (compiled_data->tensors.parameters[i])
1290 {
1291 for (j = 1; j < parallel_count; j++)
1292 { assert(compiled_data->tensors.parameters[i + j * parameter_size])((void) sizeof ((compiled_data->tensors.parameters[i + j *
parameter_size]) ? 1 : 0), __extension__ ({ if (compiled_data
->tensors.parameters[i + j * parameter_size]) ; else __assert_fail
("compiled_data->tensors.parameters[i + j * parameter_size]"
, "ccv_cnnp_model.c", 1292, __extension__ __PRETTY_FUNCTION__
); })); }
1293 continue;
1294 }
1295 return 1;
1296 }
1297 for (i = 0; i < internal_size; i++)
1298 {
1299 if (!compiled_data->tensors.internals[i])
1300 return 1;
1301 for (j = 1; j < parallel_count; j++)
1302 if (!compiled_data->tensors.internals[i + j * internal_size])
1303 return 1;
1304 }
1305 return 0;
1306}
1307
1308void ccv_cnnp_model_tensors_init_1(const ccv_cnnp_model_t* const model, ccv_cnnp_compiled_data_t* const compiled_data)
1309{
1310 int i, j;
1311 const int parameter_size = compiled_data->parameters->rnum;
1312 const int parallel_count = _ccv_cnnp_compiled_data_parallel_count(model, compiled_data);
1313 compiled_data->parallel_count = parallel_count;
1314 const int internal_size = compiled_data->internals->rnum;
1315 for (i = 0; i < parameter_size; i++)
1316 {
1317 // parameters has to be allocated all together.
1318 if (compiled_data->tensors.parameters[i])
1319 {
1320 for (j = 1; j < parallel_count; j++)
1321 { assert(compiled_data->tensors.parameters[i + j * parameter_size])((void) sizeof ((compiled_data->tensors.parameters[i + j *
parameter_size]) ? 1 : 0), __extension__ ({ if (compiled_data
->tensors.parameters[i + j * parameter_size]) ; else __assert_fail
("compiled_data->tensors.parameters[i + j * parameter_size]"
, "ccv_cnnp_model.c", 1321, __extension__ __PRETTY_FUNCTION__
); })); }
1322 continue;
1323 }
1324 const ccv_nnc_tensor_symbol_t parameter = *(ccv_nnc_tensor_symbol_t*)ccv_array_get(compiled_data->parameters, i)((void*)(((char*)((compiled_data->parameters)->data)) +
(size_t)(compiled_data->parameters)->rsize * (size_t)(
i)));
1325 ccv_nnc_tensor_param_t info = ccv_nnc_tensor_symbol_params(parameter.graph, parameter);
1326 if (CCV_TENSOR_GET_DEVICE(info.type)((info.type) & 0xfff00) == CCV_COMPUTE_DEVICE_ANY)
1327 CCV_TENSOR_SET_DEVICE_ID(info.type, 0)(info.type) = (((info.type) & ~0xfff00) | (((0) & 0xfff
) << 8));
1328 const int device_id = CCV_TENSOR_GET_DEVICE_ID(info.type)(((info.type) & 0xfff00) >> 8);
1329 compiled_data->tensors.parameters[i] = ccv_nnc_tensor_new(0, info, 0);
1330 for (j = 1; j < parallel_count; j++)
1331 {
1332 if (j != device_id)
1333 CCV_TENSOR_SET_DEVICE_ID(info.type, j)(info.type) = (((info.type) & ~0xfff00) | (((j) & 0xfff
) << 8));
1334 else
1335 CCV_TENSOR_SET_DEVICE_ID(info.type, 0)(info.type) = (((info.type) & ~0xfff00) | (((0) & 0xfff
) << 8));
1336 compiled_data->tensors.parameters[i + j * parameter_size] = ccv_nnc_tensor_new(0, info, 0);
1337 }
1338 }
1339 const uint32_t* const init_v = CCV_NNC_INIT_V(compiled_data->tensors_init.v)((uint32_t*)((uintptr_t)(compiled_data->tensors_init.v) &
~(uintptr_t)1));
1340 for (i = 0; i < internal_size; i++)
1341 {
1342 const ccv_nnc_tensor_symbol_t retained = *(ccv_nnc_tensor_symbol_t*)ccv_array_get(compiled_data->internals, i)((void*)(((char*)((compiled_data->internals)->data)) + (
size_t)(compiled_data->internals)->rsize * (size_t)(i))
);
1343 const int d = retained.d;
1344 if (init_v[d >> 5] & (1u << (d & 0x1f)))
1345 continue;
1346 ccv_nnc_tensor_param_t info = ccv_nnc_tensor_symbol_params(retained.graph, retained);
1347 if (CCV_TENSOR_GET_DEVICE(info.type)((info.type) & 0xfff00) == CCV_COMPUTE_DEVICE_ANY)
1348 CCV_TENSOR_SET_DEVICE_ID(info.type, 0)(info.type) = (((info.type) & ~0xfff00) | (((0) & 0xfff
) << 8));
1349 const int device_id = CCV_TENSOR_GET_DEVICE_ID(info.type)(((info.type) & 0xfff00) >> 8);
1350 if (!compiled_data->tensors.internals[i])
1351 compiled_data->tensors.internals[i] = ccv_nnc_tensor_new(0, info, 0);
1352 for (j = 1; j < parallel_count; j++)
1353 {
1354 if (j != device_id)
1355 CCV_TENSOR_SET_DEVICE_ID(info.type, j)(info.type) = (((info.type) & ~0xfff00) | (((j) & 0xfff
) << 8));
1356 else
1357 CCV_TENSOR_SET_DEVICE_ID(info.type, 0)(info.type) = (((info.type) & ~0xfff00) | (((0) & 0xfff
) << 8));
1358 if (!compiled_data->tensors.internals[i + j * internal_size])
1359 compiled_data->tensors.internals[i + j * internal_size] = ccv_nnc_tensor_new(0, info, 0);
1360 }
1361 }
1362 compiled_data->tensors_init.v = CCV_NNC_INIT_V(compiled_data->tensors_init.v)((uint32_t*)((uintptr_t)(compiled_data->tensors_init.v) &
~(uintptr_t)1)); // Remove 1 if any.
1363}
1364
1365static void _ccv_cnnp_model_tensors_init(const ccv_cnnp_model_t* const model, ccv_cnnp_compiled_data_t* const compiled_data)
1366{
1367 ccv_cnnp_model_tensors_init_0(model, compiled_data);
1368 ccv_cnnp_model_tensors_init_1(model, compiled_data);
1369}
1370
1371static void _ccv_cnnp_model_copy_tensors(const uint32_t* const tensors_init, const ccv_nnc_tensor_symbol_t* const tensor_symbols, ccv_nnc_tensor_t* const* const tensors, const int tensor_size, const int parallel_count)
1372{
1373 assert(parallel_count > 0)((void) sizeof ((parallel_count > 0) ? 1 : 0), __extension__
({ if (parallel_count > 0) ; else __assert_fail ("parallel_count > 0"
, "ccv_cnnp_model.c", 1373, __extension__ __PRETTY_FUNCTION__
); }));
1374 int i, j;
1375 for (i = 0; i < tensor_size; i++)
1376 {
1377 if (!tensors[i])
1378 continue;
1379 const int d = tensor_symbols[i].d;
1380 if (!(tensors_init[d >> 5] & (1u << (d & 0x1f))))
1381 continue;
1382 for (j = 1; j < parallel_count; j++)
1383 if (tensors[i + j * tensor_size])
1384 {
1385 ccv_nnc_tensor_t* const input = CCV_NNC_TENSOR(tensors[i])((ccv_nnc_tensor_t*)((uintptr_t)(tensors[i]) & ~(uintptr_t
)1));
1386 ccv_nnc_tensor_t* const output = CCV_NNC_TENSOR(tensors[i + j * tensor_size])((ccv_nnc_tensor_t*)((uintptr_t)(tensors[i + j * tensor_size]
) & ~(uintptr_t)1));
1387 ccv_nnc_cmd_exec(CMD_DATA_TRANSFER_FORWARD()ccv_nnc_cmd(CCV_NNC_DATA_TRANSFER_FORWARD, 0, ccv_nnc_cmd_auto
, 0), ccv_nnc_no_hint, 0, &input, 1, &output, 1, 0);
1388 }
1389 }
1390}
1391
1392static void _ccv_cnnp_model_remove_nocopies(const ccv_nnc_symbolic_graph_t* const graph, const ccv_nnc_tensor_symbol_t* const tensor_symbols, ccv_nnc_tensor_t** const tensors, const int tensor_size, const int parallel_count)
1393{
1394 assert(parallel_count > 0)((void) sizeof ((parallel_count > 0) ? 1 : 0), __extension__
({ if (parallel_count > 0) ; else __assert_fail ("parallel_count > 0"
, "ccv_cnnp_model.c", 1394, __extension__ __PRETTY_FUNCTION__
); }));
1395 int i, j;
1396 for (i = 0; i < tensor_size; i++)
1397 {
1398 const ccv_nnc_tensor_symbol_t tensor_symbol = tensor_symbols[i];
1399 for (j = 1; j < parallel_count; j++)
1400 {
1401 const ccv_nnc_tensor_symbol_t copy = ccv_nnc_tensor_symbol_copy(graph, tensor_symbol, j);
1402 ccv_nnc_tensor_t* copy_tensor = tensors[i + j * tensor_size];
1403 if (copy_tensor && copy.d == CCV_NNC_NO_TENSOR_SYMBOL)
1404 { // We shouldn't allocate this, free it up.
1405 ccv_nnc_tensor_free(tensors[i + j * tensor_size]);
1406 tensors[i + j * tensor_size] = 0;
1407 }
1408 }
1409 }
1410}
1411
1412static void _ccv_cnnp_model_bind_tensors(const ccv_nnc_symbolic_graph_t* const graph, const ccv_nnc_tensor_symbol_t* const tensor_symbols, ccv_nnc_tensor_t* const* const tensors, const int tensor_size, const int parallel_count, ccv_array_t* const tensor_binds)
1413{
1414 assert(parallel_count > 0)((void) sizeof ((parallel_count > 0) ? 1 : 0), __extension__
({ if (parallel_count > 0) ; else __assert_fail ("parallel_count > 0"
, "ccv_cnnp_model.c", 1414, __extension__ __PRETTY_FUNCTION__
); }));
1415 int i, j;
1416 for (i = 0; i < tensor_size; i++)
1417 {
1418 ccv_nnc_tensor_symbol_t tensor_symbol = tensor_symbols[i];
1419 if (tensor_symbol.d == CCV_NNC_NO_TENSOR_SYMBOL)
1420 continue;
1421 if (graph)
1422 {
1423 const ccv_nnc_tensor_symbol_t alias_to = ccv_nnc_tensor_symbol_alias_to(graph, tensor_symbol);
1424 if (alias_to.d != CCV_NNC_NO_TENSOR_SYMBOL)
1425 tensor_symbol = alias_to;
1426 }
1427 ccv_nnc_tensor_t* const tensor = CCV_NNC_TENSOR(tensors[i])((ccv_nnc_tensor_t*)((uintptr_t)(tensors[i]) & ~(uintptr_t
)1));
1428 if (tensor && tensor_symbol.d != CCV_NNC_NO_TENSOR_SYMBOL)
1429 {
1430 const ccv_nnc_tensor_bind_t retained_bind = {
1431 .symbol = tensor_symbol,
1432 .tensor = tensor
1433 };
1434 ccv_array_push(tensor_binds, &retained_bind);
1435 }
1436 for (j = 1; j < parallel_count; j++)
1437 {
1438 const ccv_nnc_tensor_symbol_t copy = ccv_nnc_tensor_symbol_copy(graph, tensor_symbol, j);
1439 ccv_nnc_tensor_t* copy_tensor = tensors[i + j * tensor_size];
1440 if (copy_tensor && copy.d != CCV_NNC_NO_TENSOR_SYMBOL)
1441 {
1442 const ccv_nnc_tensor_bind_t bind = {
1443 .symbol = copy,
1444 .tensor = tensors[i + j * tensor_size]
1445 };
1446 ccv_array_push(tensor_binds, &bind);
1447 }
1448 }
1449 }
1450}
1451
1452static void _ccv_cnnp_compiled_data_graph_free(ccv_cnnp_compiled_data_t* const compiled_data)
1453{
1454 if (compiled_data->graph)
1455 ccv_nnc_graph_free(compiled_data->graph);
1456 compiled_data->graph = 0;
1457 compiled_data->is_test = 0;
1458 if (compiled_data->tensor_arena)
1459 ccv_nnc_tensor_arena_free(compiled_data->tensor_arena);
1460 compiled_data->tensor_arena = 0;
1461 if (compiled_data->graph_exec_arena)
1462 ccv_nnc_graph_exec_arena_free(compiled_data->graph_exec_arena);
1463 compiled_data->graph_exec_arena = 0;
1464 if (compiled_data->backward.from_ops)
1465 ccfreefree(compiled_data->backward.from_ops);
1466 compiled_data->backward.from_ops = 0;
1467 if (compiled_data->evaluate.schedule)
1468 ccv_nnc_graph_static_schedule_free(compiled_data->evaluate.schedule);
1469 compiled_data->evaluate.schedule = 0;
1470 if (compiled_data->backward.schedule)
1471 ccv_nnc_graph_static_schedule_free(compiled_data->backward.schedule);
1472 compiled_data->backward.schedule = 0;
1473}
1474
1475static void _ccv_cnnp_compiled_data_gradient_free(ccv_cnnp_compiled_data_t* const compiled_data)
1476{
1477 if (compiled_data->gradients)
1478 ccfreefree(compiled_data->gradients);
1479 compiled_data->gradients = 0;
1480 if (compiled_data->updated_parameters)
1481 ccfreefree(compiled_data->updated_parameters);
1482 compiled_data->updated_parameters = 0;
1483 compiled_data->update_nodes = 0;
1484 compiled_data->saved_aux = 0;
1485}
1486
1487static void _ccv_cnnp_compiled_data_backward_free(ccv_cnnp_compiled_data_t* const compiled_data)
1488{
1489 if (compiled_data->backward.gradients)
1490 ccfreefree(compiled_data->backward.gradients);
1491 compiled_data->backward.gradients = 0;
1492 if (compiled_data->backward.accum)
1493 ccv_nnc_graph_free(compiled_data->backward.accum);
1494 compiled_data->backward.accum = 0;
1495 if (compiled_data->backward.tensor_arena)
1496 ccv_nnc_tensor_arena_free(compiled_data->backward.tensor_arena);
1497 compiled_data->backward.tensor_arena = 0;
1498 if (compiled_data->backward.graph_exec_arena)
1499 ccv_nnc_graph_exec_arena_free(compiled_data->backward.graph_exec_arena);
1500 compiled_data->backward.graph_exec_arena = 0;
1501}
1502
1503static void _ccv_cnnp_compiled_data_apply_gradients_free(ccv_cnnp_compiled_data_t* const compiled_data)
1504{
1505 if (compiled_data->apply_gradients.graph)
1506 ccv_nnc_graph_free(compiled_data->apply_gradients.graph);
1507 compiled_data->apply_gradients.graph = 0;
1508 if (compiled_data->apply_gradients.tensor_arena)
1509 ccv_nnc_tensor_arena_free(compiled_data->apply_gradients.tensor_arena);
1510 compiled_data->apply_gradients.tensor_arena = 0;
1511 if (compiled_data->apply_gradients.graph_exec_arena)
1512 ccv_nnc_graph_exec_arena_free(compiled_data->apply_gradients.graph_exec_arena);
1513 compiled_data->apply_gradients.graph_exec_arena = 0;
1514}
1515
1516// Compile the graph to run ccv_cnnp_model_fit
1517static void _ccv_cnnp_model_fit_jit(ccv_cnnp_model_t* const model, ccv_nnc_tensor_t* const* const inputs, const int input_size, ccv_nnc_tensor_t* const* const fits, const int fit_size, ccv_nnc_tensor_t* const* const outputs, const int output_size)
1518{
1519 int i, j;
1520 ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
1521 assert(!compiled_data->graph || compiled_data->graph_mode != CCV_CNNP_MODEL_GRAPH_FIT_MODE)((void) sizeof ((!compiled_data->graph || compiled_data->
graph_mode != CCV_CNNP_MODEL_GRAPH_FIT_MODE) ? 1 : 0), __extension__
({ if (!compiled_data->graph || compiled_data->graph_mode
!= CCV_CNNP_MODEL_GRAPH_FIT_MODE) ; else __assert_fail ("!compiled_data->graph || compiled_data->graph_mode != CCV_CNNP_MODEL_GRAPH_FIT_MODE"
, "ccv_cnnp_model.c", 1521, __extension__ __PRETTY_FUNCTION__
); }));
1522 compiled_data->graph_mode = CCV_CNNP_MODEL_GRAPH_FIT_MODE;
1523 const int parallel_count = _ccv_cnnp_model_root_parallel_count(model);
1524 assert(output_size == model->output_size * parallel_count)((void) sizeof ((output_size == model->output_size * parallel_count
) ? 1 : 0), __extension__ ({ if (output_size == model->output_size
* parallel_count) ; else __assert_fail ("output_size == model->output_size * parallel_count"
, "ccv_cnnp_model.c", 1524, __extension__ __PRETTY_FUNCTION__
); }));
1525 assert(!fits || output_size == fit_size)((void) sizeof ((!fits || output_size == fit_size) ? 1 : 0), __extension__
({ if (!fits || output_size == fit_size) ; else __assert_fail
("!fits || output_size == fit_size", "ccv_cnnp_model.c", 1525
, __extension__ __PRETTY_FUNCTION__); }));
1526 assert(output_size > 0)((void) sizeof ((output_size > 0) ? 1 : 0), __extension__ (
{ if (output_size > 0) ; else __assert_fail ("output_size > 0"
, "ccv_cnnp_model.c", 1526, __extension__ __PRETTY_FUNCTION__
); }));
1527 if (compiled_data->gradient_mode == CCV_CNNP_COMPILED_DATA_GRADIENT_NONE)
1528 {
1529 _ccv_cnnp_model_set_rewindables(model);
1530 _ccv_cnnp_model_gradient_init(model, CCV_CNNP_COMPILED_DATA_GRADIENT_TRAINABLES, CCV_CNNP_DISABLE_OUTGRAD_ALL, fits, fit_size);
1531 } else if (compiled_data->gradient_mode != CCV_CNNP_COMPILED_DATA_GRADIENT_TRAINABLES) {
1532 _ccv_cnnp_model_rewind_graph(model);
1533 _ccv_cnnp_compiled_data_gradient_free(compiled_data);
1534 compiled_data->gradient_mode = CCV_CNNP_COMPILED_DATA_GRADIENT_NONE;
1535 _ccv_cnnp_model_gradient_init(model, CCV_CNNP_COMPILED_DATA_GRADIENT_TRAINABLES, CCV_CNNP_DISABLE_OUTGRAD_ALL, fits, fit_size);
1536 }
1537 const int tensors_init = !!compiled_data->tensors_init.v;
1538 if (!tensors_init)
1539 _ccv_cnnp_model_tensors_init(model, compiled_data);
1540 else if ((uintptr_t)compiled_data->tensors_init.v & (uintptr_t)1)
1541 // Check if it is not fully allocated, if it is not, init_1.
1542 ccv_cnnp_model_tensors_init_1(model, compiled_data);
1543 ccv_array_t* const tensor_binds = ccv_array_new(sizeof(ccv_nnc_tensor_bind_t), 0, 0);
1544 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_cnnp_model.c"
, 1544, __extension__ __PRETTY_FUNCTION__); }));
1545 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_cnnp_model.c"
, 1545, __extension__ __PRETTY_FUNCTION__); }));
1546 assert((fit_size % parallel_count) == 0)((void) sizeof (((fit_size % parallel_count) == 0) ? 1 : 0), __extension__
({ if ((fit_size % parallel_count) == 0) ; else __assert_fail
("(fit_size % parallel_count) == 0", "ccv_cnnp_model.c", 1546
, __extension__ __PRETTY_FUNCTION__); }));
1547 const int input_size_per_p = input_size / parallel_count;
1548 _ccv_cnnp_model_bind_tensors(model->graph, model->inputs, inputs, input_size_per_p, parallel_count, tensor_binds);
1549 const int output_size_per_p = output_size / parallel_count;
1550 _ccv_cnnp_model_bind_tensors(model->graph, model->outputs, outputs, output_size_per_p, parallel_count, tensor_binds);
1551 const int fit_size_per_p = fit_size / parallel_count;
1552 _ccv_cnnp_model_bind_tensors(model->graph, compiled_data->fits, fits, fit_size_per_p, parallel_count, tensor_binds);
1553 const int parameter_size = compiled_data->parameters->rnum;
1554 _ccv_cnnp_model_bind_tensors(model->graph, (ccv_nnc_tensor_symbol_t*)ccv_array_get(compiled_data->parameters, 0)((void*)(((char*)((compiled_data->parameters)->data)) +
(size_t)(compiled_data->parameters)->rsize * (size_t)(
0))), compiled_data->tensors.parameters, parameter_size, parallel_count, tensor_binds);
1555 _ccv_cnnp_model_bind_tensors(model->graph, compiled_data->updated_parameters, compiled_data->tensors.parameters, parameter_size, parallel_count, tensor_binds);
1556 const int internal_size = compiled_data->internals->rnum;
1557 _ccv_cnnp_model_remove_nocopies(model->graph, (ccv_nnc_tensor_symbol_t*)ccv_array_get(compiled_data->internals, 0)((void*)(((char*)((compiled_data->internals)->data)) + (
size_t)(compiled_data->internals)->rsize * (size_t)(0))
), compiled_data->tensors.internals, internal_size, parallel_count);
1558 _ccv_cnnp_model_bind_tensors(model->graph, (ccv_nnc_tensor_symbol_t*)ccv_array_get(compiled_data->internals, 0)((void*)(((char*)((compiled_data->internals)->data)) + (
size_t)(compiled_data->internals)->rsize * (size_t)(0))
), compiled_data->tensors.internals, internal_size, parallel_count, tensor_binds);
1559 ccv_nnc_symbolic_graph_compile(model->graph, compiled_data->compile_params, (ccv_nnc_tensor_bind_t*)ccv_array_get(tensor_binds, 0)((void*)(((char*)((tensor_binds)->data)) + (size_t)(tensor_binds
)->rsize * (size_t)(0))), tensor_binds->rnum, 0, 0, SYMBOLIC_GRAPH_SOURCES(model->graph)ccv_nnc_symbolic_graph_sources(model->graph), ccv_nnc_symbolic_graph_source_size
(model->graph), SYMBOLIC_GRAPH_DESTINATIONS(model->graph)ccv_nnc_symbolic_graph_destinations(model->graph), ccv_nnc_symbolic_graph_destination_size
(model->graph), &compiled_data->graph, &compiled_data->tensor_arena, &compiled_data->graph_exec_arena);
1560 ccv_array_free(tensor_binds);
1561 const uint32_t* const init_v = CCV_NNC_INIT_V(compiled_data->tensors_init.v)((uint32_t*)((uintptr_t)(compiled_data->tensors_init.v) &
~(uintptr_t)1));
1562 if (tensors_init && parallel_count > 1)
1563 _ccv_cnnp_model_copy_tensors(init_v, (ccv_nnc_tensor_symbol_t*)ccv_array_get(compiled_data->parameters, 0)((void*)(((char*)((compiled_data->parameters)->data)) +
(size_t)(compiled_data->parameters)->rsize * (size_t)(
0))), compiled_data->tensors.parameters, compiled_data->parameters->rnum, parallel_count);
1564 // If tensor is not init'ed, we need to init states first.
1565 if (_ccv_cnnp_any_to_init(compiled_data))
1566 {
1567 ccv_nnc_tensor_init_states_t tensor_init_states = {
1568 .parallel_count = parallel_count,
1569 .graph = model->graph,
1570 .compiled_data = compiled_data,
1571 .tensor_arena = compiled_data->tensor_arena
1572 };
1573 ccv_cnnp_model_init_states(model, model->graph, _ccv_cnnp_init_states_for_tensors, &tensor_init_states);
1574 }
1575 compiled_data->is_test = 0;
1576 const int saved_aux_size = ccv_nnc_minimizer_saved_aux_size(compiled_data->minimize.minimizer);
1577 // No need to set because it is default to training mode.
1578 // ccv_cnnp_model_set_is_test(model, 0, _ccv_cnnp_cmd_update_for_execs, &update);
1579 for (i = 0; i < saved_aux_size * parameter_size; i++)
1580 {
1581 if (compiled_data->saved_aux[i].source.d == CCV_NNC_NO_TENSOR_SYMBOL)
1582 continue;
1583 ccv_nnc_tensor_t* const tensor = ccv_nnc_tensor_from_symbol(compiled_data->tensor_arena, compiled_data->saved_aux[i].source);
1584 ccv_nnc_cmd_exec(CMD_SET_FORWARD(0)ccv_nnc_cmd(CCV_NNC_SET_FORWARD, 0, (ccv_nnc_cmd_param_t){.size
={.dim={1,1,1}},.blas={.a={0,}}}, 0), ccv_nnc_no_hint, 0, 0, 0, &tensor, 1, 0);
1585 for (j = 1; j < parallel_count; j++)
1586 {
1587 ccv_nnc_tensor_t* const copy = ccv_nnc_tensor_from_symbol(compiled_data->tensor_arena, ccv_nnc_tensor_symbol_copy(model->graph, compiled_data->saved_aux[i].source, j));
1588 if (copy)
1589 ccv_nnc_cmd_exec(CMD_SET_FORWARD(0)ccv_nnc_cmd(CCV_NNC_SET_FORWARD, 0, (ccv_nnc_cmd_param_t){.size
={.dim={1,1,1}},.blas={.a={0,}}}, 0), ccv_nnc_no_hint, 0, 0, 0, &copy, 1, 0);
1590 }
1591 }
1592 const int evaluate_to_size = compiled_data->evaluate.to_size;
1593 compiled_data->evaluate.to_op_size = 0;
1594 for (i = 0; i < evaluate_to_size; i++)
1595 {
1596 ccv_nnc_graph_exec_t const to = ccv_nnc_graph_exec_from_symbol(compiled_data->graph_exec_arena, compiled_data->evaluate.tos[i]);
1597 if (to.graph)
1598 compiled_data->evaluate.to_ops[compiled_data->evaluate.to_op_size++] = to;
1599 }
1600 ccv_nnc_graph_set_default_static_schedule(compiled_data->graph, compiled_data->stream_type, model->max_stream_count);
1601 ccv_nnc_graph_autotune(compiled_data->graph, model->workspace_size, 0, TRAVERSE_FULL0,0,0,0);
1602}
1603
1604ccv_nnc_stream_context_t* ccv_cnnp_model_default_stream(const ccv_cnnp_model_t* const model)
1605{
1606 const ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
1607 if (!compiled_data || !compiled_data->graph)
1608 return 0;
1609 return ccv_nnc_graph_default_stream(compiled_data->graph);
1610}
1611
1612uint64_t ccv_cnnp_model_memory_size(const ccv_cnnp_model_t* const model)
1613{
1614 const ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
1615 if (!compiled_data || !compiled_data->tensor_arena)
1616 return 0;
1617 return ccv_nnc_tensor_arena_size(compiled_data->tensor_arena);
1618}
1619
1620static void _ccv_cnnp_bind_tensors_to_arena(ccv_nnc_tensor_arena_t* const tensor_arena, const ccv_nnc_symbolic_graph_t* const graph, const ccv_nnc_tensor_symbol_t* const tensor_symbols, ccv_nnc_tensor_t* const* const tensors, const int tensor_size, const int parallel_count)
1621{
1622 int i, j;
1623 for (i = 0; i < tensor_size; i++)
1624 {
1625 ccv_nnc_tensor_symbol_t tensor_symbol = tensor_symbols[i];
1626 if (tensor_symbol.d == CCV_NNC_NO_TENSOR_SYMBOL)
1627 continue;
1628 if (graph)
1629 {
1630 const ccv_nnc_tensor_symbol_t alias_to = ccv_nnc_tensor_symbol_alias_to(graph, tensor_symbol);
1631 if (alias_to.d != CCV_NNC_NO_TENSOR_SYMBOL)
1632 tensor_symbol = alias_to;
1633 }
1634 ccv_nnc_tensor_bind_symbol(tensor_arena, tensor_symbol, tensors[i]);
1635 for (j = 1; j < parallel_count; j++)
1636 {
1637 const ccv_nnc_tensor_symbol_t copy = ccv_nnc_tensor_symbol_copy(graph, tensor_symbol, j);
1638 if (copy.d != CCV_NNC_NO_TENSOR_SYMBOL)
1639 ccv_nnc_tensor_bind_symbol(tensor_arena, copy, tensors[i + tensor_size * j]);
1640 }
1641 }
1642}
1643
1644void ccv_cnnp_model_fit(ccv_cnnp_model_t* const model, ccv_nnc_tensor_t* const* const inputs, const int input_size, ccv_nnc_tensor_t* const* const fits, const int fit_size, ccv_nnc_tensor_t* const* const outputs, const int output_size, ccv_nnc_tensor_tape_t* const tensor_tape, ccv_nnc_stream_context_t* const stream_context)
1645{
1646 ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
1647 assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
(compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 1647, __extension__ __PRETTY_FUNCTION__); }));
1648 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; });
1649 assert(output_size == model->output_size * parallel_count)((void) sizeof ((output_size == model->output_size * parallel_count
) ? 1 : 0), __extension__ ({ if (output_size == model->output_size
* parallel_count) ; else __assert_fail ("output_size == model->output_size * parallel_count"
, "ccv_cnnp_model.c", 1649, __extension__ __PRETTY_FUNCTION__
); }));
1650 assert(input_size == model->input_size * parallel_count)((void) sizeof ((input_size == model->input_size * parallel_count
) ? 1 : 0), __extension__ ({ if (input_size == model->input_size
* parallel_count) ; else __assert_fail ("input_size == model->input_size * parallel_count"
, "ccv_cnnp_model.c", 1650, __extension__ __PRETTY_FUNCTION__
); }));
1651 assert(!fits || fit_size == output_size)((void) sizeof ((!fits || fit_size == output_size) ? 1 : 0), __extension__
({ if (!fits || fit_size == output_size) ; else __assert_fail
("!fits || fit_size == output_size", "ccv_cnnp_model.c", 1651
, __extension__ __PRETTY_FUNCTION__); }));
1652 assert(model->graph)((void) sizeof ((model->graph) ? 1 : 0), __extension__ ({ if
(model->graph) ; else __assert_fail ("model->graph", "ccv_cnnp_model.c"
, 1652, __extension__ __PRETTY_FUNCTION__); }));
1653 if (!compiled_data->graph || compiled_data->graph_mode != CCV_CNNP_MODEL_GRAPH_FIT_MODE)
1654 {
1655 _ccv_cnnp_compiled_data_graph_free(compiled_data);
1656 _ccv_cnnp_compiled_data_backward_free(compiled_data);
1657 _ccv_cnnp_compiled_data_apply_gradients_free(compiled_data);
1658 // Compile the symbolic graph down only when needed.
1659 _ccv_cnnp_model_fit_jit(model, inputs, input_size, fits, fit_size, outputs, output_size);
1660 } else {
1661 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_cnnp_model.c"
, 1661, __extension__ __PRETTY_FUNCTION__); }));
1662 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_cnnp_model.c"
, 1662, __extension__ __PRETTY_FUNCTION__); }));
1663 assert((fit_size % parallel_count) == 0)((void) sizeof (((fit_size % parallel_count) == 0) ? 1 : 0), __extension__
({ if ((fit_size % parallel_count) == 0) ; else __assert_fail
("(fit_size % parallel_count) == 0", "ccv_cnnp_model.c", 1663
, __extension__ __PRETTY_FUNCTION__); }));
1664 const int input_size_per_p = input_size / parallel_count;
1665 _ccv_cnnp_bind_tensors_to_arena(compiled_data->tensor_arena, model->graph, model->inputs, inputs, input_size_per_p, parallel_count);
1666 const int output_size_per_p = output_size / parallel_count;
1667 _ccv_cnnp_bind_tensors_to_arena(compiled_data->tensor_arena, model->graph, model->outputs, outputs, output_size_per_p, parallel_count);
1668 const int fit_size_per_p = fit_size / parallel_count;
1669 _ccv_cnnp_bind_tensors_to_arena(compiled_data->tensor_arena, model->graph, compiled_data->fits, fits, fit_size_per_p, parallel_count);
1670 }
1671 if (compiled_data->is_test)
1672 {
1673 compiled_data->is_test = 0;
1674 ccv_nnc_graph_exec_update_t update = {
1675 .parallel_count = parallel_count,
1676 .graph = model->graph,
1677 .graph_exec_arena = compiled_data->graph_exec_arena,
1678 };
1679 ccv_cnnp_model_set_is_test(model, 0, _ccv_cnnp_cmd_update_for_execs, &update);
1680 }
1681 ccv_nnc_graph_run_with_schedule(compiled_data->graph, 0, 0, tensor_tape, stream_context);
1682}
1683
1684// Compile the graph to run ccv_cnnp_model_evaluate with require_grad = false (MULTISTAGE_MODE_NO_GRAD).
1685static void _ccv_cnnp_model_multistage_no_grad_jit(ccv_cnnp_model_t* const model, ccv_nnc_tensor_t* const* const inputs, const int input_size, ccv_nnc_tensor_t* const* const outputs, const int output_size)
1686{
1687 ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
1688 compiled_data->graph_mode = CCV_CNNP_MODEL_GRAPH_MULTISTAGE_MODE_NO_GRAD;
1689 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; });
1690 assert(output_size == model->output_size * parallel_count)((void) sizeof ((output_size == model->output_size * parallel_count
) ? 1 : 0), __extension__ ({ if (output_size == model->output_size
* parallel_count) ; else __assert_fail ("output_size == model->output_size * parallel_count"
, "ccv_cnnp_model.c", 1690, __extension__ __PRETTY_FUNCTION__
); }));
1691 assert(output_size > 0)((void) sizeof ((output_size > 0) ? 1 : 0), __extension__ (
{ if (output_size > 0) ; else __assert_fail ("output_size > 0"
, "ccv_cnnp_model.c", 1691, __extension__ __PRETTY_FUNCTION__
); }));
1692 // If the gradient is not initialized, continue to setup parallel process. We don't init gradient here, but rather,
1693 // we setup proper rewindables so the graph can be rewinded to previous state before we run data parallel.
1694 if (parallel_count > 1 && compiled_data->gradient_mode == CCV_CNNP_COMPILED_DATA_GRADIENT_NONE)
1695 {
1696 const int evaluate_to_size = compiled_data->evaluate.to_size;
1697 compiled_data->evaluate.tos = ccreallocrealloc(compiled_data->evaluate.tos, sizeof(ccv_nnc_graph_exec_symbol_t) * evaluate_to_size * parallel_count + sizeof(ccv_nnc_graph_exec_t) * evaluate_to_size * parallel_count);
1698 _ccv_cnnp_model_set_rewindables(model);
1699 ccv_nnc_symbolic_graph_data_parallel(model->graph, parallel_count,
1700 0, 0,
1701 0, 0, 0,
1702 0, 0, 0,
1703 CCV_NNC_PARALLEL_REDUCE_OP_SUM,
1704 SYMBOLIC_GRAPH_SOURCES(model->graph)ccv_nnc_symbolic_graph_sources(model->graph), ccv_nnc_symbolic_graph_source_size
(model->graph), SYMBOLIC_GRAPH_DESTINATIONS(model->graph)ccv_nnc_symbolic_graph_destinations(model->graph), ccv_nnc_symbolic_graph_destination_size
(model->graph));
1705 ccv_nnc_graph_exec_symbol_autogen(model->graph, 0, 0, CCV_NNC_AUTOGEN_SOURCES_AND_DESTINATIONS);
1706 int i, j;
1707 for (i = 0; i < evaluate_to_size; i++)
1708 for (j = 1; j < parallel_count; j++)
1709 {
1710 const ccv_nnc_graph_exec_symbol_t copy = ccv_nnc_graph_exec_symbol_copy(model->graph, compiled_data->evaluate.tos[i], j);
1711 if (copy.d != CCV_NNC_NO_GRAPH_EXEC_SYMBOL)
1712 compiled_data->evaluate.tos[compiled_data->evaluate.to_size++] = copy;
1713 }
1714 }
1715 const int tensors_init = !!compiled_data->tensors_init.v;
1716 if (!tensors_init)
1717 _ccv_cnnp_model_tensors_init(model, compiled_data);
1718 else if ((uintptr_t)compiled_data->tensors_init.v & (uintptr_t)1)
1719 // Check if it is not fully allocated, if it is not, init_1.
1720 ccv_cnnp_model_tensors_init_1(model, compiled_data);
1721 const int tensor_parallel_count = _ccv_cnnp_compiled_data_parallel_count(model, compiled_data);
1722 ccv_array_t* const tensor_binds = ccv_array_new(sizeof(ccv_nnc_tensor_bind_t), 0, 0);
1723 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_cnnp_model.c"
, 1723, __extension__ __PRETTY_FUNCTION__); }));
1724 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_cnnp_model.c"
, 1724, __extension__ __PRETTY_FUNCTION__); }));
1725 const int input_size_per_p = input_size / parallel_count;
1726 _ccv_cnnp_model_bind_tensors(model->graph, model->inputs, inputs, input_size_per_p, parallel_count, tensor_binds);
1727 const int output_size_per_p = output_size / parallel_count;
1728 _ccv_cnnp_model_bind_tensors(model->graph, model->outputs, outputs, output_size_per_p, parallel_count, tensor_binds);
1729 const int parameter_size = compiled_data->parameters->rnum;
1730 _ccv_cnnp_model_bind_tensors(model->graph, (ccv_nnc_tensor_symbol_t*)ccv_array_get(compiled_data->parameters, 0)((void*)(((char*)((compiled_data->parameters)->data)) +
(size_t)(compiled_data->parameters)->rsize * (size_t)(
0))), compiled_data->tensors.parameters, parameter_size, tensor_parallel_count, tensor_binds);
1731 const int internal_size = compiled_data->internals->rnum;
1732 _ccv_cnnp_model_remove_nocopies(model->graph, (ccv_nnc_tensor_symbol_t*)ccv_array_get(compiled_data->internals, 0)((void*)(((char*)((compiled_data->internals)->data)) + (
size_t)(compiled_data->internals)->rsize * (size_t)(0))
), compiled_data->tensors.internals, internal_size, tensor_parallel_count);
1733 _ccv_cnnp_model_bind_tensors(model->graph, (ccv_nnc_tensor_symbol_t*)ccv_array_get(compiled_data->internals, 0)((void*)(((char*)((compiled_data->internals)->data)) + (
size_t)(compiled_data->internals)->rsize * (size_t)(0))
), compiled_data->tensors.internals, internal_size, tensor_parallel_count, tensor_binds);
1734 // If we generated gradient for the graph, only compile part of the graph because the rest is irrelevant for evaluation.
1735 ccv_nnc_symbolic_graph_compile(model->graph, compiled_data->compile_params, (ccv_nnc_tensor_bind_t*)ccv_array_get(tensor_binds, 0)((void*)(((char*)((tensor_binds)->data)) + (size_t)(tensor_binds
)->rsize * (size_t)(0))), tensor_binds->rnum, 0, 0, SYMBOLIC_GRAPH_SOURCES(model->graph)ccv_nnc_symbolic_graph_sources(model->graph), ccv_nnc_symbolic_graph_source_size
(model->graph), compiled_data->evaluate.tos, compiled_data->evaluate.to_size, &compiled_data->graph, &compiled_data->tensor_arena, &compiled_data->graph_exec_arena);
1736 ccv_array_free(tensor_binds);
1737 const uint32_t* const init_v = CCV_NNC_INIT_V(compiled_data->tensors_init.v)((uint32_t*)((uintptr_t)(compiled_data->tensors_init.v) &
~(uintptr_t)1));
1738 // If tensor is not init'ed, we need to init states first.
1739 if (tensors_init && tensor_parallel_count > 1)
1740 _ccv_cnnp_model_copy_tensors(init_v, (ccv_nnc_tensor_symbol_t*)ccv_array_get(compiled_data->parameters, 0)((void*)(((char*)((compiled_data->parameters)->data)) +
(size_t)(compiled_data->parameters)->rsize * (size_t)(
0))), compiled_data->tensors.parameters, compiled_data->parameters->rnum, tensor_parallel_count);
1741 if (_ccv_cnnp_any_to_init(compiled_data))
1742 {
1743 ccv_nnc_tensor_init_states_t tensor_init_states = {
1744 .parallel_count = tensor_parallel_count,
1745 .graph = model->graph,
1746 .compiled_data = compiled_data,
1747 .tensor_arena = compiled_data->tensor_arena
1748 };
1749 ccv_cnnp_model_init_states(model, model->graph, _ccv_cnnp_init_states_for_tensors, &tensor_init_states);
1750 }
1751 compiled_data->is_test = 1;
1752 ccv_nnc_graph_exec_update_t update = {
1753 .parallel_count = parallel_count,
1754 .graph = model->graph,
1755 .graph_exec_arena = compiled_data->graph_exec_arena,
1756 };
1757 ccv_cnnp_model_set_is_test(model, 1, _ccv_cnnp_cmd_update_for_execs, &update);
1758 ccv_nnc_graph_set_default_static_schedule(compiled_data->graph, compiled_data->stream_type, model->max_stream_count);
1759 ccv_nnc_graph_autotune(compiled_data->graph, model->workspace_size, 0, TRAVERSE_FULL0,0,0,0);
1760}
1761
1762static void _ccv_cnnp_model_gradient_tensors_init(const ccv_cnnp_model_t* const model, ccv_cnnp_compiled_data_t* const compiled_data)
1763{
1764 assert(!compiled_data->tensors.gradients)((void) sizeof ((!compiled_data->tensors.gradients) ? 1 : 0
), __extension__ ({ if (!compiled_data->tensors.gradients)
; else __assert_fail ("!compiled_data->tensors.gradients"
, "ccv_cnnp_model.c", 1764, __extension__ __PRETTY_FUNCTION__
); }));
1765 const int parameter_size = compiled_data->parameters->rnum;
1766 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; });
1767 compiled_data->tensors.gradients = (ccv_nnc_tensor_t**)ccmallocmalloc(sizeof(ccv_nnc_tensor_t*) * parameter_size * 2 * parallel_count);
1768 compiled_data->tensors.accum_gradients = compiled_data->tensors.gradients + parameter_size * parallel_count;
1769 int i, j;
1770 for (i = 0; i < parameter_size; i++)
1771 {
1772 if (compiled_data->parameter_flags && !(compiled_data->parameter_flags[i >> 6] & ((uint64_t)1 << (i & 63))))
1773 {
1774 compiled_data->tensors.gradients[i] = 0;
1775 compiled_data->tensors.accum_gradients[i] = 0;
1776 for (j = 1; j < parallel_count; j++)
1777 {
1778 compiled_data->tensors.gradients[i + j * parameter_size] = 0;
1779 compiled_data->tensors.accum_gradients[i + j * parameter_size] = 0;
1780 }
1781 continue;
1782 }
1783 const ccv_nnc_tensor_symbol_t parameter = *(ccv_nnc_tensor_symbol_t*)ccv_array_get(compiled_data->parameters, i)((void*)(((char*)((compiled_data->parameters)->data)) +
(size_t)(compiled_data->parameters)->rsize * (size_t)(
i)));
1784 ccv_nnc_tensor_param_t info = ccv_nnc_tensor_symbol_params(parameter.graph, parameter);
1785 if (CCV_TENSOR_GET_DEVICE(info.type)((info.type) & 0xfff00) == CCV_COMPUTE_DEVICE_ANY)
1786 CCV_TENSOR_SET_DEVICE_ID(info.type, 0)(info.type) = (((info.type) & ~0xfff00) | (((0) & 0xfff
) << 8));
1787 const int device_id = CCV_TENSOR_GET_DEVICE_ID(info.type)(((info.type) & 0xfff00) >> 8);
1788 compiled_data->tensors.gradients[i] = ccv_nnc_tensor_new(0, info, 0);
1789 compiled_data->tensors.accum_gradients[i] = 0; // delay the accumulated gradient allocation until when we need it.
1790 for (j = 1; j < parallel_count; j++)
1791 {
1792 if (j != device_id)
1793 CCV_TENSOR_SET_DEVICE_ID(info.type, j)(info.type) = (((info.type) & ~0xfff00) | (((j) & 0xfff
) << 8));
1794 else
1795 CCV_TENSOR_SET_DEVICE_ID(info.type, 0)(info.type) = (((info.type) & ~0xfff00) | (((0) & 0xfff
) << 8));
1796 compiled_data->tensors.gradients[i + j * parameter_size] = ccv_nnc_tensor_new(0, info, 0);
1797 compiled_data->tensors.accum_gradients[i + j * parameter_size] = 0;
1798 }
1799 }
1800}
1801
1802static int _ccv_cnnp_is_disable_outgrad_all(const uint64_t disable_outgrad, const int input_size)
1803{
1804 if (disable_outgrad == CCV_CNNP_DISABLE_OUTGRAD_ALL)
1805 return 1;
1806 if (disable_outgrad == CCV_CNNP_DISABLE_OUTGRAD_NONE)
1807 return 0;
1808 int i;
1809 for (i = 0; i < input_size; i++)
1810 if (!(disable_outgrad & ((uint64_t)1 << i)))
1811 return 0;
1812 return 1;
1813}
1814
1815// Compile the graph to run ccv_cnnp_model_evaluate with requires_grad = true (MULTISTAGE_MODE).
1816// Particularly, this method compiles the evaluation and backprop graph (the main graph).
1817static void _ccv_cnnp_model_multistage_jit_0(ccv_cnnp_model_t* const model, const uint64_t disable_outgrad, const int is_test, ccv_nnc_tensor_t* const* const inputs, const int input_size, ccv_nnc_tensor_t* const* const outputs, const int output_size)
1818{
1819 int i, j;
1820 ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
1821 const int target_gradient_mode = _ccv_cnnp_is_disable_outgrad_all(disable_outgrad, model->input_size) ? CCV_CNNP_COMPILED_DATA_GRADIENT_TRAINABLES : CCV_CNNP_COMPILED_DATA_GRADIENT_TRAINABLES_AND_INPUTS;
1822 assert(!compiled_data->graph || compiled_data->graph_mode != CCV_CNNP_MODEL_GRAPH_MULTISTAGE_MODE || compiled_data->gradient_mode != target_gradient_mode)((void) sizeof ((!compiled_data->graph || compiled_data->
graph_mode != CCV_CNNP_MODEL_GRAPH_MULTISTAGE_MODE || compiled_data
->gradient_mode != target_gradient_mode) ? 1 : 0), __extension__
({ if (!compiled_data->graph || compiled_data->graph_mode
!= CCV_CNNP_MODEL_GRAPH_MULTISTAGE_MODE || compiled_data->
gradient_mode != target_gradient_mode) ; else __assert_fail (
"!compiled_data->graph || compiled_data->graph_mode != CCV_CNNP_MODEL_GRAPH_MULTISTAGE_MODE || compiled_data->gradient_mode != target_gradient_mode"
, "ccv_cnnp_model.c", 1822, __extension__ __PRETTY_FUNCTION__
); }));
1823 compiled_data->graph_mode = CCV_CNNP_MODEL_GRAPH_MULTISTAGE_MODE;
1824 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; });
1825 assert(output_size == model->output_size * parallel_count)((void) sizeof ((output_size == model->output_size * parallel_count
) ? 1 : 0), __extension__ ({ if (output_size == model->output_size
* parallel_count) ; else __assert_fail ("output_size == model->output_size * parallel_count"
, "ccv_cnnp_model.c", 1825, __extension__ __PRETTY_FUNCTION__
); }));
1826 assert(output_size > 0)((void) sizeof ((output_size > 0) ? 1 : 0), __extension__ (
{ if (output_size > 0) ; else __assert_fail ("output_size > 0"
, "ccv_cnnp_model.c", 1826, __extension__ __PRETTY_FUNCTION__
); }));
1827 // There shouldn't be a loss function if we evaluate with multistage jit.
1828 assert(compiled_data->loss.cmd == CCV_NNC_NOOP)((void) sizeof ((compiled_data->loss.cmd == CCV_NNC_NOOP) ?
1 : 0), __extension__ ({ if (compiled_data->loss.cmd == CCV_NNC_NOOP
) ; else __assert_fail ("compiled_data->loss.cmd == CCV_NNC_NOOP"
, "ccv_cnnp_model.c", 1828, __extension__ __PRETTY_FUNCTION__
); }));
1829 if (compiled_data->gradient_mode == CCV_CNNP_COMPILED_DATA_GRADIENT_NONE)
1830 {
1831 _ccv_cnnp_model_set_rewindables(model);
1832 _ccv_cnnp_model_gradient_init(model, target_gradient_mode, disable_outgrad, 0, 0); // The type of outputs and fits should be the same. We only use type here.
1833 } else if (compiled_data->gradient_mode != target_gradient_mode) {
1834 _ccv_cnnp_model_rewind_graph(model);
1835 _ccv_cnnp_compiled_data_gradient_free(compiled_data);
1836 compiled_data->gradient_mode = CCV_CNNP_COMPILED_DATA_GRADIENT_NONE;
1837 _ccv_cnnp_model_gradient_init(model, target_gradient_mode, disable_outgrad, 0, 0); // The type of outputs and fits should be the same. We only use type here.
1838 }
1839 const int tensors_init = !!compiled_data->tensors_init.v;
1840 if (!tensors_init)
1841 _ccv_cnnp_model_tensors_init(model, compiled_data);
1842 else if ((uintptr_t)compiled_data->tensors_init.v & (uintptr_t)1)
1843 // Check if it is not fully allocated, if it is not, init_1.
1844 ccv_cnnp_model_tensors_init_1(model, compiled_data);
1845 ccv_array_t* const tensor_binds = ccv_array_new(sizeof(ccv_nnc_tensor_bind_t), 0, 0);
1846 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_cnnp_model.c"
, 1846, __extension__ __PRETTY_FUNCTION__); }));
1847 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_cnnp_model.c"
, 1847, __extension__ __PRETTY_FUNCTION__); }));
1848 const int input_size_per_p = input_size / parallel_count;
1849 _ccv_cnnp_model_bind_tensors(model->graph, model->inputs, inputs, input_size_per_p, parallel_count, tensor_binds);
1850 const int output_size_per_p = output_size / parallel_count;
1851 _ccv_cnnp_model_bind_tensors(model->graph, model->outputs, outputs, output_size_per_p, parallel_count, tensor_binds);
1852 const int parameter_size = compiled_data->parameters->rnum;
1853 _ccv_cnnp_model_bind_tensors(model->graph, (ccv_nnc_tensor_symbol_t*)ccv_array_get(compiled_data->parameters, 0)((void*)(((char*)((compiled_data->parameters)->data)) +
(size_t)(compiled_data->parameters)->rsize * (size_t)(
0))), compiled_data->tensors.parameters, parameter_size, parallel_count, tensor_binds);
1854 const int internal_size = compiled_data->internals->rnum;
1855 _ccv_cnnp_model_remove_nocopies(model->graph, (ccv_nnc_tensor_symbol_t*)ccv_array_get(compiled_data->internals, 0)((void*)(((char*)((compiled_data->internals)->data)) + (
size_t)(compiled_data->internals)->rsize * (size_t)(0))
), compiled_data->tensors.internals, internal_size, parallel_count);
1856 _ccv_cnnp_model_bind_tensors(model->graph, (ccv_nnc_tensor_symbol_t*)ccv_array_get(compiled_data->internals, 0)((void*)(((char*)((compiled_data->internals)->data)) + (
size_t)(compiled_data->internals)->rsize * (size_t)(0))
), compiled_data->tensors.internals, internal_size, parallel_count, tensor_binds);
1857 if (!compiled_data->tensors.gradients)
1858 _ccv_cnnp_model_gradient_tensors_init(model, compiled_data);
1859 _ccv_cnnp_model_bind_tensors(model->graph, compiled_data->gradients, compiled_data->tensors.gradients, parameter_size, parallel_count, tensor_binds);
1860 if (compiled_data->backward.to_size > 0)
1861 ccv_nnc_symbolic_graph_compile(model->graph, compiled_data->compile_params, (ccv_nnc_tensor_bind_t*)ccv_array_get(tensor_binds, 0)((void*)(((char*)((tensor_binds)->data)) + (size_t)(tensor_binds
)->rsize * (size_t)(0))), tensor_binds->rnum, 0, 0, SYMBOLIC_GRAPH_SOURCES(model->graph)ccv_nnc_symbolic_graph_sources(model->graph), ccv_nnc_symbolic_graph_source_size
(model->graph), compiled_data->backward.tos, compiled_data->backward.to_size, &compiled_data->graph, &compiled_data->tensor_arena, &compiled_data->graph_exec_arena);
1862 else
1863 ccv_nnc_symbolic_graph_compile(model->graph, compiled_data->compile_params, (ccv_nnc_tensor_bind_t*)ccv_array_get(tensor_binds, 0)((void*)(((char*)((tensor_binds)->data)) + (size_t)(tensor_binds
)->rsize * (size_t)(0))), tensor_binds->rnum, 0, 0, SYMBOLIC_GRAPH_SOURCES(model->graph)ccv_nnc_symbolic_graph_sources(model->graph), ccv_nnc_symbolic_graph_source_size
(model->graph), compiled_data->evaluate.tos, compiled_data->evaluate.to_size, &compiled_data->graph, &compiled_data->tensor_arena, &compiled_data->graph_exec_arena);
1864 ccv_array_free(tensor_binds);
1865 const uint32_t* const init_v = CCV_NNC_INIT_V(compiled_data->tensors_init.v)((uint32_t*)((uintptr_t)(compiled_data->tensors_init.v) &
~(uintptr_t)1));
1866 if (tensors_init && parallel_count > 1)
1867 _ccv_cnnp_model_copy_tensors(init_v, (ccv_nnc_tensor_symbol_t*)ccv_array_get(compiled_data->parameters, 0)((void*)(((char*)((compiled_data->parameters)->data)) +
(size_t)(compiled_data->parameters)->rsize * (size_t)(
0))), compiled_data->tensors.parameters, compiled_data->parameters->rnum, parallel_count);
1868 // If tensor is not init'ed, we need to init states first.
1869 if (_ccv_cnnp_any_to_init(compiled_data))
1870 {
1871 ccv_nnc_tensor_init_states_t tensor_init_states = {
1872 .parallel_count = parallel_count,
1873 .graph = model->graph,
1874 .compiled_data = compiled_data,
1875 .tensor_arena = compiled_data->tensor_arena
1876 };
1877 ccv_cnnp_model_init_states(model, model->graph, _ccv_cnnp_init_states_for_tensors, &tensor_init_states);
1878 }
1879 compiled_data->is_test = is_test;
1880 ccv_nnc_graph_exec_update_t update = {
1881 .parallel_count = parallel_count,
1882 .graph = model->graph,
1883 .graph_exec_arena = compiled_data->graph_exec_arena,
1884 };
1885 ccv_cnnp_model_set_is_test(model, is_test, _ccv_cnnp_cmd_update_for_execs, &update);
1886 const int evaluate_to_size = compiled_data->evaluate.to_size;
1887 compiled_data->evaluate.to_op_size = 0;
1888 ccv_array_t* const backward_from = ccv_array_new(sizeof(int), 0, 0);
1889 for (i = 0; i < evaluate_to_size; i++)
1890 {
1891 ccv_nnc_graph_exec_t const to_op = ccv_nnc_graph_exec_from_symbol(compiled_data->graph_exec_arena, compiled_data->evaluate.tos[i]);
1892 if (to_op.graph)
1893 compiled_data->evaluate.to_ops[compiled_data->evaluate.to_op_size++] = to_op;
1894 const int* tos;
1895 int to_size;
1896 ccv_nnc_graph_exec_symbol_to(model->graph, compiled_data->evaluate.tos[i], &tos, &to_size);
1897 for (j = 0; j < to_size; j++)
1898 {
1899 ccv_nnc_graph_exec_t const to_op = ccv_nnc_graph_exec_from_symbol(compiled_data->graph_exec_arena, (ccv_nnc_graph_exec_symbol_t){
1900 .d = tos[j],
1901 .graph = model->graph
1902 });
1903 if (to_op.graph)
1904 ccv_array_add_unique_int(backward_from, to_op.d);
1905 }
1906 }
1907 assert(backward_from->rnum > 0)((void) sizeof ((backward_from->rnum > 0) ? 1 : 0), __extension__
({ if (backward_from->rnum > 0) ; else __assert_fail (
"backward_from->rnum > 0", "ccv_cnnp_model.c", 1907, __extension__
__PRETTY_FUNCTION__); }));
1908 compiled_data->backward.from_op_size = backward_from->rnum;
1909 compiled_data->backward.from_ops = (ccv_nnc_graph_exec_t*)ccmallocmalloc(sizeof(ccv_nnc_graph_exec_t) * backward_from->rnum);
1910 for (i = 0; i < backward_from->rnum; i++)
1911 compiled_data->backward.from_ops[i] = (ccv_nnc_graph_exec_t){
1912 .d = *(int*)ccv_array_get(backward_from, i)((void*)(((char*)((backward_from)->data)) + (size_t)(backward_from
)->rsize * (size_t)(i))),
1913 .graph = compiled_data->graph,
1914 };
1915 // If there are any set node (to set some tensors to 0) inserted through backward pass, these won't be executed if we just do sources -> evaluate.to_ops, backward.from_ops -> destinations. We need this logic to find out these nodes and explicitly adding them to backward.from_ops.
1916 ccv_nnc_graph_exec_info_t* const exec_info = (ccv_nnc_graph_exec_info_t*)ccv_array_get(compiled_data->graph->exec_info, 0)((void*)(((char*)((compiled_data->graph->exec_info)->
data)) + (size_t)(compiled_data->graph->exec_info)->
rsize * (size_t)(0)));
1917 const int exec_info_size = compiled_data->graph->exec_info->rnum;
1918 uint32_t* const visited = cccalloccalloc((exec_info_size + 31) >> 5, sizeof(uint32_t));
1919 const ccv_nnc_graph_exec_t* const sources = (ccv_nnc_graph_exec_t*)ccv_array_get(compiled_data->graph->sources, 0)((void*)(((char*)((compiled_data->graph->sources)->data
)) + (size_t)(compiled_data->graph->sources)->rsize *
(size_t)(0)));
1920 const int source_size = compiled_data->graph->sources->rnum;
1921 ccv_nnc_graph_visit_t* visit = ccv_nnc_graph_visit_new(compiled_data->graph, exec_info, exec_info_size, sources, source_size, compiled_data->evaluate.to_ops, compiled_data->evaluate.to_op_size, 0)({ ccv_nnc_graph_visit_t* _visit_ = (ccv_nnc_graph_visit_t*)malloc
(sizeof(ccv_nnc_graph_visit_t) + sizeof(_visit_->node[0]) *
((exec_info_size) - 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_ <
(exec_info_size); _i_++) _incoming_edges_ += ((exec_info)[_i_
].outgoings) ? (exec_info)[_i_].outgoings->rnum : 0; const
int _heap_mem_ = ((exec_info_size) + _incoming_edges_ > 1024
); ccv_nnc_incoming_t* _incomings_; if (_heap_mem_) _incomings_
= (ccv_nnc_incoming_t*)malloc(sizeof(ccv_nnc_incoming_t) * (
exec_info_size) + sizeof(int32_t) * ((exec_info_size) * 2 + _incoming_edges_
)); else _incomings_ = (ccv_nnc_incoming_t*)__builtin_alloca (
sizeof(ccv_nnc_incoming_t) * (exec_info_size) + sizeof(int32_t
) * ((exec_info_size) * 2 + _incoming_edges_)); memset(_incomings_
, 0, sizeof(ccv_nnc_incoming_t) * (exec_info_size)); int32_t*
_exists_[2] = { (int32_t*)(_incomings_ + (exec_info_size)), (
int32_t*)(_incomings_ + (exec_info_size)) + (exec_info_size),
}; int32_t* const _edges_ = _exists_[1] + (exec_info_size); for
(_i_ = 0; _i_ < (source_size); _i_++) { ((void) sizeof ((
(sources)[_i_].graph == compiled_data->graph) ? 1 : 0), __extension__
({ if ((sources)[_i_].graph == compiled_data->graph) ; else
__assert_fail ("(sources)[_i_].graph == compiled_data->graph"
, "ccv_cnnp_model.c", 1921, __extension__ __PRETTY_FUNCTION__
); })); _incomings_[(sources)[_i_].d].r = 1; _exists_[0][_i_]
= (sources)[_i_].d; } int _exist_size_[2] = { (source_size),
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_] <
(exec_info_size)) ? 1 : 0), __extension__ ({ if (_exist_size_
[_q_] < (exec_info_size)) ; else __assert_fail ("_exist_size_[_q_] < (exec_info_size)"
, "ccv_cnnp_model.c", 1921, __extension__ __PRETTY_FUNCTION__
); })); _exists_[_q_][_exist_size_[_q_]] = d; ++_exist_size_[
_q_]; } } ((_i_) = (_p_), (_p_) = (_q_), (_q_) = (_i_)); } for
(_i_ = 0; _i_ < (source_size); _i_++) { ((void) sizeof ((
(sources)[_i_].graph == compiled_data->graph) ? 1 : 0), __extension__
({ if ((sources)[_i_].graph == compiled_data->graph) ; else
__assert_fail ("(sources)[_i_].graph == compiled_data->graph"
, "ccv_cnnp_model.c", 1921, __extension__ __PRETTY_FUNCTION__
); })); _incomings_[(sources)[_i_].d].r = 3; _exists_[0][_i_]
= (sources)[_i_].d; } _exist_size_[0] = (source_size); _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_] <
(exec_info_size)) ? 1 : 0), __extension__ ({ if (_exist_size_
[_q_] < (exec_info_size)) ; else __assert_fail ("_exist_size_[_q_] < (exec_info_size)"
, "ccv_cnnp_model.c", 1921, __extension__ __PRETTY_FUNCTION__
); })); _exists_[_q_][_exist_size_[_q_]] = d; ++_exist_size_[
_q_]; } } ((_i_) = (_p_), (_p_) = (_q_), (_q_) = (_i_)); } for
(_i_ = 0; _i_ < (compiled_data->evaluate.to_op_size); _i_
++) { ((void) sizeof (((compiled_data->evaluate.to_ops)[_i_
].graph == compiled_data->graph) ? 1 : 0), __extension__ (
{ if ((compiled_data->evaluate.to_ops)[_i_].graph == compiled_data
->graph) ; else __assert_fail ("(compiled_data->evaluate.to_ops)[_i_].graph == compiled_data->graph"
, "ccv_cnnp_model.c", 1921, __extension__ __PRETTY_FUNCTION__
); })); _incomings_[(compiled_data->evaluate.to_ops)[_i_].
d].r = 5; _exists_[0][_i_] = (compiled_data->evaluate.to_ops
)[_i_].d; } _exist_size_[0] = (compiled_data->evaluate.to_op_size
); _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_info_size)) ? 1 : 0), __extension__ ({ if (_exist_size_
[_q_] < (exec_info_size)) ; else __assert_fail ("_exist_size_[_q_] < (exec_info_size)"
, "ccv_cnnp_model.c", 1921, __extension__ __PRETTY_FUNCTION__
); })); _exists_[_q_][_exist_size_[_q_]] = d; ++_exist_size_[
_q_]; } } ((_i_) = (_p_), (_p_) = (_q_), (_q_) = (_i_)); } for
(_i_ = 0; _i_ < (compiled_data->evaluate.to_op_size); _i_
++) { ((void) sizeof (((compiled_data->evaluate.to_ops)[_i_
].graph == compiled_data->graph) ? 1 : 0), __extension__ (
{ if ((compiled_data->evaluate.to_ops)[_i_].graph == compiled_data
->graph) ; else __assert_fail ("(compiled_data->evaluate.to_ops)[_i_].graph == compiled_data->graph"
, "ccv_cnnp_model.c", 1921, __extension__ __PRETTY_FUNCTION__
); })); _incomings_[(compiled_data->evaluate.to_ops)[_i_].
d].d = 1; } for (_i_ = 0; _i_ < (source_size); _i_++) { ((
void) sizeof (((sources)[_i_].graph == compiled_data->graph
) ? 1 : 0), __extension__ ({ if ((sources)[_i_].graph == compiled_data
->graph) ; else __assert_fail ("(sources)[_i_].graph == compiled_data->graph"
, "ccv_cnnp_model.c", 1921, __extension__ __PRETTY_FUNCTION__
); })); _exists_[0][_i_] = (sources)[_i_].d; } _p_ = 0; _q_ =
1; _exist_size_[0] = (source_size); _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_ <
(compiled_data->evaluate.to_op_size)) { _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_ < (compiled_data->evaluate.to_op_size
)) { ((void) sizeof ((_exist_size_[_q_] < (exec_info_size)
) ? 1 : 0), __extension__ ({ if (_exist_size_[_q_] < (exec_info_size
)) ; else __assert_fail ("_exist_size_[_q_] < (exec_info_size)"
, "ccv_cnnp_model.c", 1921, __extension__ __PRETTY_FUNCTION__
); })); _exists_[_q_][_exist_size_[_q_]] = d; ++_exist_size_[
_q_]; } } } ++_i_; } ((_i_) = (_p_), (_p_) = (_q_), (_q_) = (
_i_)); } for (_i_ = 0; _i_ < (compiled_data->evaluate.to_op_size
); _i_++) { ((void) sizeof (((compiled_data->evaluate.to_ops
)[_i_].graph == compiled_data->graph) ? 1 : 0), __extension__
({ if ((compiled_data->evaluate.to_ops)[_i_].graph == compiled_data
->graph) ; else __assert_fail ("(compiled_data->evaluate.to_ops)[_i_].graph == compiled_data->graph"
, "ccv_cnnp_model.c", 1921, __extension__ __PRETTY_FUNCTION__
); })); if (_incomings_[(compiled_data->evaluate.to_ops)[_i_
].d].r == 7) continue; if (!(0)) { ((void) sizeof ((_incomings_
[(compiled_data->evaluate.to_ops)[_i_].d].c == 0) ? 1 : 0)
, __extension__ ({ if (_incomings_[(compiled_data->evaluate
.to_ops)[_i_].d].c == 0) ; else __assert_fail ("_incomings_[(compiled_data->evaluate.to_ops)[_i_].d].c == 0"
, "ccv_cnnp_model.c", 1921, __extension__ __PRETTY_FUNCTION__
); })); } else if (_incomings_[(compiled_data->evaluate.to_ops
)[_i_].d].c > 0) continue; _visit_->node[_visit_->size
].index = (((compiled_data->evaluate.to_ops)[_i_].d)); _visit_
->node[_visit_->size].term = ((_incomings_[(compiled_data
->evaluate.to_ops)[_i_].d].d)); ++_visit_->size;; } if (
_heap_mem_) free(_incomings_); } while (0);; ((void) sizeof (
(_visit_->size <= (exec_info_size)) ? 1 : 0), __extension__
({ if (_visit_->size <= (exec_info_size)) ; else __assert_fail
("_visit_->size <= (exec_info_size)", "ccv_cnnp_model.c"
, 1921, __extension__ __PRETTY_FUNCTION__); })); _visit_; });
1922 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; {
1923 visited[(idx >> 5)] |= (1u << (idx & 31));
1924 } ccv_nnc_graph_visit_endfor} }
1925 ccv_nnc_graph_visit_free(visit);
1926 const ccv_nnc_graph_exec_t* const destinations = (ccv_nnc_graph_exec_t*)ccv_array_get(compiled_data->graph->destinations, 0)((void*)(((char*)((compiled_data->graph->destinations)->
data)) + (size_t)(compiled_data->graph->destinations)->
rsize * (size_t)(0)));
1927 const int destination_size = compiled_data->graph->destinations->rnum;
1928 visit = ccv_nnc_graph_visit_new(compiled_data->graph, exec_info, exec_info_size, compiled_data->backward.from_ops, compiled_data->backward.from_op_size, destinations, destination_size, 0)({ ccv_nnc_graph_visit_t* _visit_ = (ccv_nnc_graph_visit_t*)malloc
(sizeof(ccv_nnc_graph_visit_t) + sizeof(_visit_->node[0]) *
((exec_info_size) - 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_ <
(exec_info_size); _i_++) _incoming_edges_ += ((exec_info)[_i_
].outgoings) ? (exec_info)[_i_].outgoings->rnum : 0; const
int _heap_mem_ = ((exec_info_size) + _incoming_edges_ > 1024
); ccv_nnc_incoming_t* _incomings_; if (_heap_mem_) _incomings_
= (ccv_nnc_incoming_t*)malloc(sizeof(ccv_nnc_incoming_t) * (
exec_info_size) + sizeof(int32_t) * ((exec_info_size) * 2 + _incoming_edges_
)); else _incomings_ = (ccv_nnc_incoming_t*)__builtin_alloca (
sizeof(ccv_nnc_incoming_t) * (exec_info_size) + sizeof(int32_t
) * ((exec_info_size) * 2 + _incoming_edges_)); memset(_incomings_
, 0, sizeof(ccv_nnc_incoming_t) * (exec_info_size)); int32_t*
_exists_[2] = { (int32_t*)(_incomings_ + (exec_info_size)), (
int32_t*)(_incomings_ + (exec_info_size)) + (exec_info_size),
}; int32_t* const _edges_ = _exists_[1] + (exec_info_size); for
(_i_ = 0; _i_ < (compiled_data->backward.from_op_size)
; _i_++) { ((void) sizeof (((compiled_data->backward.from_ops
)[_i_].graph == compiled_data->graph) ? 1 : 0), __extension__
({ if ((compiled_data->backward.from_ops)[_i_].graph == compiled_data
->graph) ; else __assert_fail ("(compiled_data->backward.from_ops)[_i_].graph == compiled_data->graph"
, "ccv_cnnp_model.c", 1928, __extension__ __PRETTY_FUNCTION__
); })); _incomings_[(compiled_data->backward.from_ops)[_i_
].d].r = 1; _exists_[0][_i_] = (compiled_data->backward.from_ops
)[_i_].d; } int _exist_size_[2] = { (compiled_data->backward
.from_op_size), 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_] < (exec_info_size)) ? 1 : 0), __extension__ ({ if (_exist_size_
[_q_] < (exec_info_size)) ; else __assert_fail ("_exist_size_[_q_] < (exec_info_size)"
, "ccv_cnnp_model.c", 1928, __extension__ __PRETTY_FUNCTION__
); })); _exists_[_q_][_exist_size_[_q_]] = d; ++_exist_size_[
_q_]; } } ((_i_) = (_p_), (_p_) = (_q_), (_q_) = (_i_)); } for
(_i_ = 0; _i_ < (compiled_data->backward.from_op_size)
; _i_++) { ((void) sizeof (((compiled_data->backward.from_ops
)[_i_].graph == compiled_data->graph) ? 1 : 0), __extension__
({ if ((compiled_data->backward.from_ops)[_i_].graph == compiled_data
->graph) ; else __assert_fail ("(compiled_data->backward.from_ops)[_i_].graph == compiled_data->graph"
, "ccv_cnnp_model.c", 1928, __extension__ __PRETTY_FUNCTION__
); })); _incomings_[(compiled_data->backward.from_ops)[_i_
].d].r = 3; _exists_[0][_i_] = (compiled_data->backward.from_ops
)[_i_].d; } _exist_size_[0] = (compiled_data->backward.from_op_size
); _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_] < (exec_info_size)) ? 1 : 0), __extension__
({ if (_exist_size_[_q_] < (exec_info_size)) ; else __assert_fail
("_exist_size_[_q_] < (exec_info_size)", "ccv_cnnp_model.c"
, 1928, __extension__ __PRETTY_FUNCTION__); })); _exists_[_q_
][_exist_size_[_q_]] = d; ++_exist_size_[_q_]; } } ((_i_) = (
_p_), (_p_) = (_q_), (_q_) = (_i_)); } for (_i_ = 0; _i_ <
(destination_size); _i_++) { ((void) sizeof (((destinations)
[_i_].graph == compiled_data->graph) ? 1 : 0), __extension__
({ if ((destinations)[_i_].graph == compiled_data->graph)
; else __assert_fail ("(destinations)[_i_].graph == compiled_data->graph"
, "ccv_cnnp_model.c", 1928, __extension__ __PRETTY_FUNCTION__
); })); _incomings_[(destinations)[_i_].d].r = 5; _exists_[0]
[_i_] = (destinations)[_i_].d; } _exist_size_[0] = (destination_size
); _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_info_size)) ? 1 : 0), __extension__ ({ if (_exist_size_
[_q_] < (exec_info_size)) ; else __assert_fail ("_exist_size_[_q_] < (exec_info_size)"
, "ccv_cnnp_model.c", 1928, __extension__ __PRETTY_FUNCTION__
); })); _exists_[_q_][_exist_size_[_q_]] = d; ++_exist_size_[
_q_]; } } ((_i_) = (_p_), (_p_) = (_q_), (_q_) = (_i_)); } for
(_i_ = 0; _i_ < (destination_size); _i_++) { ((void) sizeof
(((destinations)[_i_].graph == compiled_data->graph) ? 1 :
0), __extension__ ({ if ((destinations)[_i_].graph == compiled_data
->graph) ; else __assert_fail ("(destinations)[_i_].graph == compiled_data->graph"
, "ccv_cnnp_model.c", 1928, __extension__ __PRETTY_FUNCTION__
); })); _incomings_[(destinations)[_i_].d].d = 1; } for (_i_ =
0; _i_ < (compiled_data->backward.from_op_size); _i_++
) { ((void) sizeof (((compiled_data->backward.from_ops)[_i_
].graph == compiled_data->graph) ? 1 : 0), __extension__ (
{ if ((compiled_data->backward.from_ops)[_i_].graph == compiled_data
->graph) ; else __assert_fail ("(compiled_data->backward.from_ops)[_i_].graph == compiled_data->graph"
, "ccv_cnnp_model.c", 1928, __extension__ __PRETTY_FUNCTION__
); })); _exists_[0][_i_] = (compiled_data->backward.from_ops
)[_i_].d; } _p_ = 0; _q_ = 1; _exist_size_[0] = (compiled_data
->backward.from_op_size); _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_ < (destination_size)
) { _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_ < (destination_size
)) { ((void) sizeof ((_exist_size_[_q_] < (exec_info_size)
) ? 1 : 0), __extension__ ({ if (_exist_size_[_q_] < (exec_info_size
)) ; else __assert_fail ("_exist_size_[_q_] < (exec_info_size)"
, "ccv_cnnp_model.c", 1928, __extension__ __PRETTY_FUNCTION__
); })); _exists_[_q_][_exist_size_[_q_]] = d; ++_exist_size_[
_q_]; } } } ++_i_; } ((_i_) = (_p_), (_p_) = (_q_), (_q_) = (
_i_)); } for (_i_ = 0; _i_ < (destination_size); _i_++) { (
(void) sizeof (((destinations)[_i_].graph == compiled_data->
graph) ? 1 : 0), __extension__ ({ if ((destinations)[_i_].graph
== compiled_data->graph) ; else __assert_fail ("(destinations)[_i_].graph == compiled_data->graph"
, "ccv_cnnp_model.c", 1928, __extension__ __PRETTY_FUNCTION__
); })); if (_incomings_[(destinations)[_i_].d].r == 7) continue
; if (!(0)) { ((void) sizeof ((_incomings_[(destinations)[_i_
].d].c == 0) ? 1 : 0), __extension__ ({ if (_incomings_[(destinations
)[_i_].d].c == 0) ; else __assert_fail ("_incomings_[(destinations)[_i_].d].c == 0"
, "ccv_cnnp_model.c", 1928, __extension__ __PRETTY_FUNCTION__
); })); } else if (_incomings_[(destinations)[_i_].d].c > 0
) continue; _visit_->node[_visit_->size].index = (((destinations
)[_i_].d)); _visit_->node[_visit_->size].term = ((_incomings_
[(destinations)[_i_].d].d)); ++_visit_->size;; } if (_heap_mem_
) free(_incomings_); } while (0);; ((void) sizeof ((_visit_->
size <= (exec_info_size)) ? 1 : 0), __extension__ ({ if (_visit_
->size <= (exec_info_size)) ; else __assert_fail ("_visit_->size <= (exec_info_size)"
, "ccv_cnnp_model.c", 1928, __extension__ __PRETTY_FUNCTION__
); })); _visit_; });
1929 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; {
1930 visited[(idx >> 5)] |= (1u << (idx & 31));
1931 } ccv_nnc_graph_visit_endfor} }
1932 ccv_nnc_graph_visit_free(visit);
1933 visit = ccv_nnc_graph_visit_new(compiled_data->graph, exec_info, exec_info_size, sources, source_size, destinations, destination_size, 0)({ ccv_nnc_graph_visit_t* _visit_ = (ccv_nnc_graph_visit_t*)malloc
(sizeof(ccv_nnc_graph_visit_t) + sizeof(_visit_->node[0]) *
((exec_info_size) - 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_ <
(exec_info_size); _i_++) _incoming_edges_ += ((exec_info)[_i_
].outgoings) ? (exec_info)[_i_].outgoings->rnum : 0; const
int _heap_mem_ = ((exec_info_size) + _incoming_edges_ > 1024
); ccv_nnc_incoming_t* _incomings_; if (_heap_mem_) _incomings_
= (ccv_nnc_incoming_t*)malloc(sizeof(ccv_nnc_incoming_t) * (
exec_info_size) + sizeof(int32_t) * ((exec_info_size) * 2 + _incoming_edges_
)); else _incomings_ = (ccv_nnc_incoming_t*)__builtin_alloca (
sizeof(ccv_nnc_incoming_t) * (exec_info_size) + sizeof(int32_t
) * ((exec_info_size) * 2 + _incoming_edges_)); memset(_incomings_
, 0, sizeof(ccv_nnc_incoming_t) * (exec_info_size)); int32_t*
_exists_[2] = { (int32_t*)(_incomings_ + (exec_info_size)), (
int32_t*)(_incomings_ + (exec_info_size)) + (exec_info_size),
}; int32_t* const _edges_ = _exists_[1] + (exec_info_size); for
(_i_ = 0; _i_ < (source_size); _i_++) { ((void) sizeof ((
(sources)[_i_].graph == compiled_data->graph) ? 1 : 0), __extension__
({ if ((sources)[_i_].graph == compiled_data->graph) ; else
__assert_fail ("(sources)[_i_].graph == compiled_data->graph"
, "ccv_cnnp_model.c", 1933, __extension__ __PRETTY_FUNCTION__
); })); _incomings_[(sources)[_i_].d].r = 1; _exists_[0][_i_]
= (sources)[_i_].d; } int _exist_size_[2] = { (source_size),
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_] <
(exec_info_size)) ? 1 : 0), __extension__ ({ if (_exist_size_
[_q_] < (exec_info_size)) ; else __assert_fail ("_exist_size_[_q_] < (exec_info_size)"
, "ccv_cnnp_model.c", 1933, __extension__ __PRETTY_FUNCTION__
); })); _exists_[_q_][_exist_size_[_q_]] = d; ++_exist_size_[
_q_]; } } ((_i_) = (_p_), (_p_) = (_q_), (_q_) = (_i_)); } for
(_i_ = 0; _i_ < (source_size); _i_++) { ((void) sizeof ((
(sources)[_i_].graph == compiled_data->graph) ? 1 : 0), __extension__
({ if ((sources)[_i_].graph == compiled_data->graph) ; else
__assert_fail ("(sources)[_i_].graph == compiled_data->graph"
, "ccv_cnnp_model.c", 1933, __extension__ __PRETTY_FUNCTION__
); })); _incomings_[(sources)[_i_].d].r = 3; _exists_[0][_i_]
= (sources)[_i_].d; } _exist_size_[0] = (source_size); _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_] <
(exec_info_size)) ? 1 : 0), __extension__ ({ if (_exist_size_
[_q_] < (exec_info_size)) ; else __assert_fail ("_exist_size_[_q_] < (exec_info_size)"
, "ccv_cnnp_model.c", 1933, __extension__ __PRETTY_FUNCTION__
); })); _exists_[_q_][_exist_size_[_q_]] = d; ++_exist_size_[
_q_]; } } ((_i_) = (_p_), (_p_) = (_q_), (_q_) = (_i_)); } for
(_i_ = 0; _i_ < (destination_size); _i_++) { ((void) sizeof
(((destinations)[_i_].graph == compiled_data->graph) ? 1 :
0), __extension__ ({ if ((destinations)[_i_].graph == compiled_data
->graph) ; else __assert_fail ("(destinations)[_i_].graph == compiled_data->graph"
, "ccv_cnnp_model.c", 1933, __extension__ __PRETTY_FUNCTION__
); })); _incomings_[(destinations)[_i_].d].r = 5; _exists_[0]
[_i_] = (destinations)[_i_].d; } _exist_size_[0] = (destination_size
); _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_info_size)) ? 1 : 0), __extension__ ({ if (_exist_size_
[_q_] < (exec_info_size)) ; else __assert_fail ("_exist_size_[_q_] < (exec_info_size)"
, "ccv_cnnp_model.c", 1933, __extension__ __PRETTY_FUNCTION__
); })); _exists_[_q_][_exist_size_[_q_]] = d; ++_exist_size_[
_q_]; } } ((_i_) = (_p_), (_p_) = (_q_), (_q_) = (_i_)); } for
(_i_ = 0; _i_ < (destination_size); _i_++) { ((void) sizeof
(((destinations)[_i_].graph == compiled_data->graph) ? 1 :
0), __extension__ ({ if ((destinations)[_i_].graph == compiled_data
->graph) ; else __assert_fail ("(destinations)[_i_].graph == compiled_data->graph"
, "ccv_cnnp_model.c", 1933, __extension__ __PRETTY_FUNCTION__
); })); _incomings_[(destinations)[_i_].d].d = 1; } for (_i_ =
0; _i_ < (source_size); _i_++) { ((void) sizeof (((sources
)[_i_].graph == compiled_data->graph) ? 1 : 0), __extension__
({ if ((sources)[_i_].graph == compiled_data->graph) ; else
__assert_fail ("(sources)[_i_].graph == compiled_data->graph"
, "ccv_cnnp_model.c", 1933, __extension__ __PRETTY_FUNCTION__
); })); _exists_[0][_i_] = (sources)[_i_].d; } _p_ = 0; _q_ =
1; _exist_size_[0] = (source_size); _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_ <
(destination_size)) { _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_ < (destination_size)) { ((void) sizeof ((_exist_size_
[_q_] < (exec_info_size)) ? 1 : 0), __extension__ ({ if (_exist_size_
[_q_] < (exec_info_size)) ; else __assert_fail ("_exist_size_[_q_] < (exec_info_size)"
, "ccv_cnnp_model.c", 1933, __extension__ __PRETTY_FUNCTION__
); })); _exists_[_q_][_exist_size_[_q_]] = d; ++_exist_size_[
_q_]; } } } ++_i_; } ((_i_) = (_p_), (_p_) = (_q_), (_q_) = (
_i_)); } for (_i_ = 0; _i_ < (destination_size); _i_++) { (
(void) sizeof (((destinations)[_i_].graph == compiled_data->
graph) ? 1 : 0), __extension__ ({ if ((destinations)[_i_].graph
== compiled_data->graph) ; else __assert_fail ("(destinations)[_i_].graph == compiled_data->graph"
, "ccv_cnnp_model.c", 1933, __extension__ __PRETTY_FUNCTION__
); })); if (_incomings_[(destinations)[_i_].d].r == 7) continue
; if (!(0)) { ((void) sizeof ((_incomings_[(destinations)[_i_
].d].c == 0) ? 1 : 0), __extension__ ({ if (_incomings_[(destinations
)[_i_].d].c == 0) ; else __assert_fail ("_incomings_[(destinations)[_i_].d].c == 0"
, "ccv_cnnp_model.c", 1933, __extension__ __PRETTY_FUNCTION__
); })); } else if (_incomings_[(destinations)[_i_].d].c > 0
) continue; _visit_->node[_visit_->size].index = (((destinations
)[_i_].d)); _visit_->node[_visit_->size].term = ((_incomings_
[(destinations)[_i_].d].d)); ++_visit_->size;; } if (_heap_mem_
) free(_incomings_); } while (0);; ((void) sizeof ((_visit_->
size <= (exec_info_size)) ? 1 : 0), __extension__ ({ if (_visit_
->size <= (exec_info_size)) ; else __assert_fail ("_visit_->size <= (exec_info_size)"
, "ccv_cnnp_model.c", 1933, __extension__ __PRETTY_FUNCTION__
); })); _visit_; });
1934 // Find any missing nodes to be added as source. Right now, these are only set nodes.
1935 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; {
1936 if (!(visited[(idx >> 5)] & (1u << (idx & 31))))
1937 {
1938 assert(exec_info[idx].cmd.cmd == CCV_NNC_SET_FORWARD)((void) sizeof ((exec_info[idx].cmd.cmd == CCV_NNC_SET_FORWARD
) ? 1 : 0), __extension__ ({ if (exec_info[idx].cmd.cmd == CCV_NNC_SET_FORWARD
) ; else __assert_fail ("exec_info[idx].cmd.cmd == CCV_NNC_SET_FORWARD"
, "ccv_cnnp_model.c", 1938, __extension__ __PRETTY_FUNCTION__
); }));
1939 if (exec_info[idx].cmd.info.blas.a[0] == 0) // Special-casing for empty out the tensor set function, not for the set grad to 1 one.
1940 ccv_array_add_unique_int(backward_from, idx);
1941 }
1942 } ccv_nnc_graph_visit_endfor} }
1943 ccv_nnc_graph_visit_free(visit);
1944 ccfreefree(visited);
1945 if (backward_from->rnum != compiled_data->backward.from_op_size) // If it doesn't match, need to redo this.
1946 {
1947 compiled_data->backward.from_op_size = backward_from->rnum;
1948 compiled_data->backward.from_ops = (ccv_nnc_graph_exec_t*)ccreallocrealloc(compiled_data->backward.from_ops, sizeof(ccv_nnc_graph_exec_t) * backward_from->rnum);
1949 for (i = 0; i < backward_from->rnum; i++)
1950 compiled_data->backward.from_ops[i] = (ccv_nnc_graph_exec_t){
1951 .d = *(int*)ccv_array_get(backward_from, i)((void*)(((char*)((backward_from)->data)) + (size_t)(backward_from
)->rsize * (size_t)(i))),
1952 .graph = compiled_data->graph,
1953 };
1954 }
1955 ccv_array_free(backward_from);
1956 ccv_nnc_graph_set_default_static_schedule(compiled_data->graph, compiled_data->stream_type, model->max_stream_count);
1957 ccv_nnc_graph_autotune(compiled_data->graph, model->workspace_size, 0, TRAVERSE_FULL0,0,0,0);
1958}
1959
1960void ccv_cnnp_model_dry_run(ccv_cnnp_model_t* const model, const ccv_cnnp_evaluate_param_t params, ccv_nnc_tensor_t* const* const inputs, const int input_size, ccv_nnc_tensor_t* const* const outputs, const int output_size)
1961{
1962 ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
1963 assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
(compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 1963, __extension__ __PRETTY_FUNCTION__); }));
1964 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; });
1965 assert(output_size == model->output_size * parallel_count)((void) sizeof ((output_size == model->output_size * parallel_count
) ? 1 : 0), __extension__ ({ if (output_size == model->output_size
* parallel_count) ; else __assert_fail ("output_size == model->output_size * parallel_count"
, "ccv_cnnp_model.c", 1965, __extension__ __PRETTY_FUNCTION__
); }));
1966 assert(input_size == model->input_size * parallel_count)((void) sizeof ((input_size == model->input_size * parallel_count
) ? 1 : 0), __extension__ ({ if (input_size == model->input_size
* parallel_count) ; else __assert_fail ("input_size == model->input_size * parallel_count"
, "ccv_cnnp_model.c", 1966, __extension__ __PRETTY_FUNCTION__
); }));
1967 assert(model->graph)((void) sizeof ((model->graph) ? 1 : 0), __extension__ ({ if
(model->graph) ; else __assert_fail ("model->graph", "ccv_cnnp_model.c"
, 1967, __extension__ __PRETTY_FUNCTION__); }));
1968 const int target_gradient_mode = _ccv_cnnp_is_disable_outgrad_all(params.disable_outgrad, model->input_size) ? CCV_CNNP_COMPILED_DATA_GRADIENT_TRAINABLES : CCV_CNNP_COMPILED_DATA_GRADIENT_TRAINABLES_AND_INPUTS;
1969 const int mode_mismatch = (params.requires_grad && (compiled_data->graph_mode != CCV_CNNP_MODEL_GRAPH_MULTISTAGE_MODE || compiled_data->gradient_mode != target_gradient_mode || compiled_data->disable_outgrad != params.disable_outgrad));
1970 if (!compiled_data->graph || mode_mismatch)
1971 {
1972 _ccv_cnnp_compiled_data_graph_free(compiled_data);
1973 if (mode_mismatch) // If mode mismatch, we need to redo the backward as well (no need to redo apply_gradients, it doesn't require target_gradient_mode or disable_outgrad.
1974 _ccv_cnnp_compiled_data_backward_free(compiled_data);
1975 if (params.requires_grad)
1976 _ccv_cnnp_model_multistage_jit_0(model, params.disable_outgrad, params.is_test, inputs, input_size, outputs, output_size);
1977 else
1978 _ccv_cnnp_model_multistage_no_grad_jit(model, inputs, input_size, outputs, output_size);
1979 } else {
1980 ccv_nnc_tensor_arena_clear_bindings(compiled_data->tensor_arena);
1981 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_cnnp_model.c"
, 1981, __extension__ __PRETTY_FUNCTION__); }));
1982 const int input_size_per_p = input_size / parallel_count;
1983 _ccv_cnnp_bind_tensors_to_arena(compiled_data->tensor_arena, model->graph, model->inputs, inputs, input_size_per_p, parallel_count);
1984 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_cnnp_model.c"
, 1984, __extension__ __PRETTY_FUNCTION__); }));
1985 const int output_size_per_p = output_size / parallel_count;
1986 _ccv_cnnp_bind_tensors_to_arena(compiled_data->tensor_arena, model->graph, model->outputs, outputs, output_size_per_p, parallel_count);
1987 }
1988 if (compiled_data->is_test != params.is_test)
1989 {
1990 compiled_data->is_test = params.is_test;
1991 ccv_nnc_graph_exec_update_t update = {
1992 .parallel_count = parallel_count,
1993 .graph = model->graph,
1994 .graph_exec_arena = compiled_data->graph_exec_arena,
1995 };
1996 ccv_cnnp_model_set_is_test(model, params.is_test, _ccv_cnnp_cmd_update_for_execs, &update);
1997 }
1998}
1999
2000void ccv_cnnp_model_evaluate(ccv_cnnp_model_t* const model, const ccv_cnnp_evaluate_param_t params, ccv_nnc_tensor_t* const* const inputs, const int input_size, ccv_nnc_tensor_t* const* const outputs, const int output_size, ccv_nnc_tensor_tape_t* const tensor_tape, ccv_nnc_stream_context_t* const stream_context)
2001{
2002 ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
2003 assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
(compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 2003, __extension__ __PRETTY_FUNCTION__); }));
2004 ccv_cnnp_model_dry_run(model, params, inputs, input_size, outputs, output_size);
2005 if (compiled_data->graph_mode == CCV_CNNP_MODEL_GRAPH_MULTISTAGE_MODE_NO_GRAD)
2006 ccv_nnc_graph_run_with_schedule(compiled_data->graph, 0, 0, tensor_tape, stream_context);
2007 else {
2008 if (!compiled_data->evaluate.schedule)
2009 compiled_data->evaluate.schedule = ccv_nnc_graph_static_schedule_new(compiled_data->graph, compiled_data->stream_type, model->max_stream_count, 0, 0, compiled_data->evaluate.to_ops, compiled_data->evaluate.to_op_size);
2010 ccv_nnc_graph_run_with_schedule(compiled_data->graph, 0, compiled_data->evaluate.schedule, tensor_tape, stream_context);
2011 }
2012}
2013
2014// Compile the graph to run ccv_cnnp_model_backward after ccv_cnnp_model_evaluate with requires_grad = true (MULTISTAGE_MODE).
2015// Particularly, this method compiles the accumulator graph.
2016static void _ccv_cnnp_model_multistage_jit_1(ccv_cnnp_model_t* const model)
2017{
2018 ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
2019 assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
(compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 2019, __extension__ __PRETTY_FUNCTION__); }));
2020 assert(compiled_data->graph_mode == CCV_CNNP_MODEL_GRAPH_MULTISTAGE_MODE)((void) sizeof ((compiled_data->graph_mode == CCV_CNNP_MODEL_GRAPH_MULTISTAGE_MODE
) ? 1 : 0), __extension__ ({ if (compiled_data->graph_mode
== CCV_CNNP_MODEL_GRAPH_MULTISTAGE_MODE) ; else __assert_fail
("compiled_data->graph_mode == CCV_CNNP_MODEL_GRAPH_MULTISTAGE_MODE"
, "ccv_cnnp_model.c", 2020, __extension__ __PRETTY_FUNCTION__
); }));
2021 ccv_nnc_symbolic_graph_t* accum = ccv_nnc_symbolic_graph_new();
2022 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; });
2023 const int parameter_size = compiled_data->parameters->rnum;
2024 int i, j;
2025 compiled_data->backward.gradients = (ccv_nnc_tensor_symbol_t*)ccmallocmalloc(sizeof(ccv_nnc_tensor_symbol_t) * parameter_size * parallel_count * 3);
2026 compiled_data->backward.accum_gradients = compiled_data->backward.gradients + parameter_size * parallel_count;
2027 compiled_data->backward.updated_accum_gradients = compiled_data->backward.accum_gradients + parameter_size * parallel_count;
2028 for (i = 0; i < parameter_size; i++)
2029 for (j = 0; j < parallel_count; j++)
2030 if (compiled_data->tensors.gradients[i + j * parameter_size])
2031 {
2032 const ccv_nnc_tensor_param_t info = compiled_data->tensors.gradients[i + j * parameter_size]->info;
2033 // Now, the old gradient is the accumulated gradient, getting new gradient tensor setup so we can collect them.
2034 compiled_data->tensors.accum_gradients[i + j * parameter_size] = compiled_data->tensors.gradients[i + j * parameter_size];
2035 compiled_data->tensors.gradients[i + j * parameter_size] = ccv_nnc_tensor_new(0, info, 0);
2036 ccv_nnc_tensor_symbol_t inputs[2];
2037 inputs[0] = compiled_data->backward.accum_gradients[i + j * parameter_size] = ccv_nnc_tensor_symbol_new(accum, info, 0);
2038 inputs[1] = compiled_data->backward.gradients[i + j * parameter_size] = ccv_nnc_tensor_symbol_new(accum, info, 0);
2039 ccv_nnc_tensor_symbol_t output = compiled_data->backward.updated_accum_gradients[i + j * parameter_size] = ccv_nnc_tensor_symbol_new(accum, info, 0);
2040 ccv_nnc_graph_exec_symbol_new(accum, CMD_EWSUM_FORWARD()ccv_nnc_cmd(CCV_NNC_EWSUM_FORWARD, 0, ccv_nnc_cmd_auto, 0), inputs, 2, &output, 1, 0);
2041 } else {
2042 compiled_data->backward.accum_gradients[i + j * parameter_size] = NO_TENSOR_SYMBOL(const ccv_nnc_tensor_symbol_t){.d = CCV_NNC_NO_TENSOR_SYMBOL
};
2043 compiled_data->backward.gradients[i + j * parameter_size] = NO_TENSOR_SYMBOL(const ccv_nnc_tensor_symbol_t){.d = CCV_NNC_NO_TENSOR_SYMBOL
};
2044 compiled_data->backward.updated_accum_gradients[i + j * parameter_size] = NO_TENSOR_SYMBOL(const ccv_nnc_tensor_symbol_t){.d = CCV_NNC_NO_TENSOR_SYMBOL
};
2045 }
2046 ccv_nnc_graph_exec_symbol_autogen(accum, 0, 0, CCV_NNC_AUTOGEN_ALL_EXECS | CCV_NNC_AUTOGEN_SOURCES_AND_DESTINATIONS);
2047 if (ccv_nnc_symbolic_graph_source_size(accum) == 0)
2048 {
2049 ccv_nnc_symbolic_graph_free(accum);
2050 // Create empty graph.
2051 compiled_data->backward.accum = ccv_nnc_graph_new();
2052 ccv_nnc_graph_topsort(compiled_data->backward.accum, 0, 0);
2053 return;
2054 }
2055 ccv_array_t* const tensor_binds = ccv_array_new(sizeof(ccv_nnc_tensor_bind_t), 0, 0);
2056 _ccv_cnnp_model_bind_tensors(accum, compiled_data->backward.accum_gradients, compiled_data->tensors.accum_gradients, parameter_size * parallel_count, 1, tensor_binds);
2057 _ccv_cnnp_model_bind_tensors(accum, compiled_data->backward.gradients, compiled_data->tensors.gradients, parameter_size * parallel_count, 1, tensor_binds);
2058 _ccv_cnnp_model_bind_tensors(accum, compiled_data->backward.updated_accum_gradients, compiled_data->tensors.accum_gradients, parameter_size * parallel_count, 1, tensor_binds);
2059 ccv_nnc_symbolic_graph_compile(accum, compiled_data->compile_params, (ccv_nnc_tensor_bind_t*)ccv_array_get(tensor_binds, 0)((void*)(((char*)((tensor_binds)->data)) + (size_t)(tensor_binds
)->rsize * (size_t)(0))), tensor_binds->rnum, 0, 0, SYMBOLIC_GRAPH_SOURCES(accum)ccv_nnc_symbolic_graph_sources(accum), ccv_nnc_symbolic_graph_source_size
(accum), SYMBOLIC_GRAPH_DESTINATIONS(accum)ccv_nnc_symbolic_graph_destinations(accum), ccv_nnc_symbolic_graph_destination_size
(accum), &compiled_data->backward.accum, &compiled_data->backward.tensor_arena, &compiled_data->backward.graph_exec_arena);
2060 ccv_nnc_symbolic_graph_free(accum);
2061 ccv_array_free(tensor_binds);
2062 ccv_nnc_graph_set_default_static_schedule(compiled_data->backward.accum, compiled_data->stream_type, model->max_stream_count);
2063}
2064
2065void ccv_cnnp_model_backward(ccv_cnnp_model_t* const model, ccv_nnc_tensor_t* const* const ingrads, const int ingrad_size, ccv_nnc_tensor_t* const* const outgrads, const int outgrad_size, ccv_nnc_tensor_tape_t* const tensor_tape, ccv_nnc_stream_context_t* const stream_context)
2066{
2067 ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
2068 assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
(compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 2068, __extension__ __PRETTY_FUNCTION__); }));
2069 assert(compiled_data->graph_mode == CCV_CNNP_MODEL_GRAPH_MULTISTAGE_MODE)((void) sizeof ((compiled_data->graph_mode == CCV_CNNP_MODEL_GRAPH_MULTISTAGE_MODE
) ? 1 : 0), __extension__ ({ if (compiled_data->graph_mode
== CCV_CNNP_MODEL_GRAPH_MULTISTAGE_MODE) ; else __assert_fail
("compiled_data->graph_mode == CCV_CNNP_MODEL_GRAPH_MULTISTAGE_MODE"
, "ccv_cnnp_model.c", 2069, __extension__ __PRETTY_FUNCTION__
); }));
2070 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; });
2071 assert(ingrad_size == 0 || ingrad_size == model->output_size * parallel_count)((void) sizeof ((ingrad_size == 0 || ingrad_size == model->
output_size * parallel_count) ? 1 : 0), __extension__ ({ if (
ingrad_size == 0 || ingrad_size == model->output_size * parallel_count
) ; else __assert_fail ("ingrad_size == 0 || ingrad_size == model->output_size * parallel_count"
, "ccv_cnnp_model.c", 2071, __extension__ __PRETTY_FUNCTION__
); }));
2072 if (outgrad_size > 0)
2073 { assert(outgrad_size == compiled_data->outgrad_size * parallel_count)((void) sizeof ((outgrad_size == compiled_data->outgrad_size
* parallel_count) ? 1 : 0), __extension__ ({ if (outgrad_size
== compiled_data->outgrad_size * parallel_count) ; else __assert_fail
("outgrad_size == compiled_data->outgrad_size * parallel_count"
, "ccv_cnnp_model.c", 2073, __extension__ __PRETTY_FUNCTION__
); })); }
2074 assert(model->graph)((void) sizeof ((model->graph) ? 1 : 0), __extension__ ({ if
(model->graph) ; else __assert_fail ("model->graph", "ccv_cnnp_model.c"
, 2074, __extension__ __PRETTY_FUNCTION__); }));
2075 assert(compiled_data->graph)((void) sizeof ((compiled_data->graph) ? 1 : 0), __extension__
({ if (compiled_data->graph) ; else __assert_fail ("compiled_data->graph"
, "ccv_cnnp_model.c", 2075, __extension__ __PRETTY_FUNCTION__
); }));
2076 const int parameter_size = compiled_data->parameters->rnum;
2077 // If we need to accumulate the gradients now, do jit on accumulator.
2078 if (compiled_data->backward.count > 0)
2079 {
2080 if (!compiled_data->backward.accum)
2081 _ccv_cnnp_model_multistage_jit_1(model);
2082 else if (compiled_data->backward.count == 1) {
2083 // On this round, we need to switch accumulated gradients with gradients (so we can do accumulation properly).
2084 int i;
2085 for (i = 0; i < parameter_size * parallel_count; i++)
2086 {
2087 ccv_nnc_tensor_t* tensor;
2088 CCV_SWAP(compiled_data->tensors.accum_gradients[i], compiled_data->tensors.gradients[i], tensor)((tensor) = (compiled_data->tensors.accum_gradients[i]), (
compiled_data->tensors.accum_gradients[i]) = (compiled_data
->tensors.gradients[i]), (compiled_data->tensors.gradients
[i]) = (tensor));
2089 }
2090 if (compiled_data->backward.tensor_arena)
2091 {
2092 ccv_nnc_tensor_arena_clear_bindings(compiled_data->backward.tensor_arena);
2093 // Do rebind in case we messed up the binding (we switch accum_gradients and gradients).
2094 _ccv_cnnp_bind_tensors_to_arena(compiled_data->backward.tensor_arena, 0, compiled_data->backward.gradients, compiled_data->tensors.gradients, parameter_size * parallel_count, 1);
2095 _ccv_cnnp_bind_tensors_to_arena(compiled_data->backward.tensor_arena, 0, compiled_data->backward.accum_gradients, compiled_data->tensors.accum_gradients, parameter_size * parallel_count, 1);
2096 _ccv_cnnp_bind_tensors_to_arena(compiled_data->backward.tensor_arena, 0, compiled_data->backward.updated_accum_gradients, compiled_data->tensors.accum_gradients, parameter_size * parallel_count, 1);
2097 }
2098 }
2099 }
2100 const int ingrad_size_per_p = model->output_size;
2101 const int outgrad_size_per_p = compiled_data->outgrad_size;
2102 int i, j;
2103 for (i = 0; i < ingrad_size_per_p; i++)
2104 {
2105 const ccv_nnc_tensor_symbol_t ingrad = ccv_nnc_tensor_symbol_for_backward(model->graph, compiled_data->f[i]);
2106 if (!ingrad_size || !ingrads || ingrads[i] == 0)
2107 {
2108 // Set it to 1 if it is not specified.
2109 ccv_nnc_tensor_t* const ingrad_tensor = ccv_nnc_tensor_from_symbol(compiled_data->tensor_arena, ingrad);
2110 if (ingrad_tensor)
2111 ccv_nnc_cmd_exec(CMD_SET_FORWARD(1)ccv_nnc_cmd(CCV_NNC_SET_FORWARD, 0, (ccv_nnc_cmd_param_t){.size
={.dim={1,1,1}},.blas={.a={1,}}}, 0), ccv_nnc_no_hint, 0, 0, 0, TENSOR_LIST(ingrad_tensor)(ccv_nnc_tensor_t* []){ingrad_tensor}, (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 +0 +0 -1), stream_context);
2112 for (j = 1; j < parallel_count; j++)
2113 {
2114 ccv_nnc_tensor_t* const ingrad_tensor = ccv_nnc_tensor_from_symbol(compiled_data->tensor_arena, ccv_nnc_tensor_symbol_copy(model->graph, ingrad, j));
2115 if (ingrad_tensor)
2116 ccv_nnc_cmd_exec(CMD_SET_FORWARD(1)ccv_nnc_cmd(CCV_NNC_SET_FORWARD, 0, (ccv_nnc_cmd_param_t){.size
={.dim={1,1,1}},.blas={.a={1,}}}, 0), ccv_nnc_no_hint, 0, 0, 0, TENSOR_LIST(ingrad_tensor)(ccv_nnc_tensor_t* []){ingrad_tensor}, (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 +0 +0 -1), stream_context);
2117 }
2118 } else {
2119 // Make sure the length matches, in case it is an alias.
2120 assert(ccv_nnc_tensor_count(ingrads[i]->info) == ccv_nnc_tensor_count(ccv_nnc_tensor_symbol_params(model->graph, ingrad)))((void) sizeof ((ccv_nnc_tensor_count(ingrads[i]->info) ==
ccv_nnc_tensor_count(ccv_nnc_tensor_symbol_params(model->
graph, ingrad))) ? 1 : 0), __extension__ ({ if (ccv_nnc_tensor_count
(ingrads[i]->info) == ccv_nnc_tensor_count(ccv_nnc_tensor_symbol_params
(model->graph, ingrad))) ; else __assert_fail ("ccv_nnc_tensor_count(ingrads[i]->info) == ccv_nnc_tensor_count(ccv_nnc_tensor_symbol_params(model->graph, ingrad))"
, "ccv_cnnp_model.c", 2120, __extension__ __PRETTY_FUNCTION__
); }));
2121 ccv_nnc_tensor_bind_symbol(compiled_data->tensor_arena, ingrad, ingrads[i]);
2122 for (j = 1; j < parallel_count; j++)
2123 ccv_nnc_tensor_bind_symbol(compiled_data->tensor_arena, ccv_nnc_tensor_symbol_copy(model->graph, ingrad, j), ingrads[i + ingrad_size_per_p * j]);
2124 }
2125 }
2126 if (outgrad_size > 0)
2127 {
2128 assert(compiled_data->gradient_mode == CCV_CNNP_COMPILED_DATA_GRADIENT_TRAINABLES_AND_INPUTS && "shouldn't pass disable_outgrad to ccv_cnnp_model_evaluate before if you plan to compute outgrad")((void) sizeof ((compiled_data->gradient_mode == CCV_CNNP_COMPILED_DATA_GRADIENT_TRAINABLES_AND_INPUTS
&& "shouldn't pass disable_outgrad to ccv_cnnp_model_evaluate before if you plan to compute outgrad"
) ? 1 : 0), __extension__ ({ if (compiled_data->gradient_mode
== CCV_CNNP_COMPILED_DATA_GRADIENT_TRAINABLES_AND_INPUTS &&
"shouldn't pass disable_outgrad to ccv_cnnp_model_evaluate before if you plan to compute outgrad"
) ; else __assert_fail ("compiled_data->gradient_mode == CCV_CNNP_COMPILED_DATA_GRADIENT_TRAINABLES_AND_INPUTS && \"shouldn't pass disable_outgrad to ccv_cnnp_model_evaluate before if you plan to compute outgrad\""
, "ccv_cnnp_model.c", 2128, __extension__ __PRETTY_FUNCTION__
); }));
2129 for (i = 0; i < outgrad_size_per_p; i++)
2130 if (outgrads[i])
2131 {
2132 const ccv_nnc_tensor_symbol_t outgrad = compiled_data->outgrads[i];
2133 ccv_nnc_tensor_bind_symbol(compiled_data->tensor_arena, outgrad, outgrads[i]);
2134 for (j = 1; j < parallel_count; j++)
2135 ccv_nnc_tensor_bind_symbol(compiled_data->tensor_arena, ccv_nnc_tensor_symbol_copy(model->graph, outgrad, j), outgrads[i + outgrad_size_per_p * j]);
2136 }
2137 } else {
2138 assert(compiled_data->gradient_mode == CCV_CNNP_COMPILED_DATA_GRADIENT_TRAINABLES ||((void) sizeof ((compiled_data->gradient_mode == CCV_CNNP_COMPILED_DATA_GRADIENT_TRAINABLES
|| compiled_data->gradient_mode == CCV_CNNP_COMPILED_DATA_GRADIENT_TRAINABLES_AND_INPUTS
) ? 1 : 0), __extension__ ({ if (compiled_data->gradient_mode
== CCV_CNNP_COMPILED_DATA_GRADIENT_TRAINABLES || compiled_data
->gradient_mode == CCV_CNNP_COMPILED_DATA_GRADIENT_TRAINABLES_AND_INPUTS
) ; else __assert_fail ("compiled_data->gradient_mode == CCV_CNNP_COMPILED_DATA_GRADIENT_TRAINABLES || compiled_data->gradient_mode == CCV_CNNP_COMPILED_DATA_GRADIENT_TRAINABLES_AND_INPUTS"
, "ccv_cnnp_model.c", 2139, __extension__ __PRETTY_FUNCTION__
); }))
2139 compiled_data->gradient_mode == CCV_CNNP_COMPILED_DATA_GRADIENT_TRAINABLES_AND_INPUTS)((void) sizeof ((compiled_data->gradient_mode == CCV_CNNP_COMPILED_DATA_GRADIENT_TRAINABLES
|| compiled_data->gradient_mode == CCV_CNNP_COMPILED_DATA_GRADIENT_TRAINABLES_AND_INPUTS
) ? 1 : 0), __extension__ ({ if (compiled_data->gradient_mode
== CCV_CNNP_COMPILED_DATA_GRADIENT_TRAINABLES || compiled_data
->gradient_mode == CCV_CNNP_COMPILED_DATA_GRADIENT_TRAINABLES_AND_INPUTS
) ; else __assert_fail ("compiled_data->gradient_mode == CCV_CNNP_COMPILED_DATA_GRADIENT_TRAINABLES || compiled_data->gradient_mode == CCV_CNNP_COMPILED_DATA_GRADIENT_TRAINABLES_AND_INPUTS"
, "ccv_cnnp_model.c", 2139, __extension__ __PRETTY_FUNCTION__
); }));
2140 }
2141 // We need to rebind here because in ccv_cnnp_evaluate, we clear bindings, that will reset all bindings for the gradients.
2142 // For parameters and internals these are fine because when we clear bindings, it restores to original bindings, which are these
2143 // parameters and internals. The same cannot be said for gradients due to the accum_gradients switching.
2144 _ccv_cnnp_bind_tensors_to_arena(compiled_data->tensor_arena, model->graph, compiled_data->gradients, compiled_data->tensors.gradients, parameter_size, parallel_count);
2145 if (!compiled_data->backward.schedule)
2146 compiled_data->backward.schedule = ccv_nnc_graph_static_schedule_new(compiled_data->graph, compiled_data->stream_type, model->max_stream_count, compiled_data->backward.from_ops, compiled_data->backward.from_op_size, 0, 0);
2147 // Run the backward pass.
2148 ccv_nnc_graph_run_with_schedule(compiled_data->graph, 0, compiled_data->backward.schedule, tensor_tape, stream_context);
2149 // If we need to run accumulation round, do that now.
2150 if (compiled_data->backward.count > 0)
2151 ccv_nnc_graph_run_with_schedule(compiled_data->backward.accum, 0, 0, 0, stream_context);
2152 // Update the count, this determines whether we need to accumulate or not.
2153 ++compiled_data->backward.count;
2154}
2155
2156// Compile the graph to run ccv_cnnp_model_apply_gradients after ccv_cnnp_model_backward (MULTISTAGE_MODE).
2157// Particularly, this method compiles the parameter update graph.
2158static void _ccv_cnnp_model_multistage_jit_2(ccv_cnnp_model_t* const model)
2159{
2160 ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
2161 assert(compiled_data->graph_mode == CCV_CNNP_MODEL_GRAPH_MULTISTAGE_MODE)((void) sizeof ((compiled_data->graph_mode == CCV_CNNP_MODEL_GRAPH_MULTISTAGE_MODE
) ? 1 : 0), __extension__ ({ if (compiled_data->graph_mode
== CCV_CNNP_MODEL_GRAPH_MULTISTAGE_MODE) ; else __assert_fail
("compiled_data->graph_mode == CCV_CNNP_MODEL_GRAPH_MULTISTAGE_MODE"
, "ccv_cnnp_model.c", 2161, __extension__ __PRETTY_FUNCTION__
); }));
2162 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; });
2163 const int parameter_size = compiled_data->parameters->rnum;
2164 ccv_array_t* const tensor_binds = ccv_array_new(sizeof(ccv_nnc_tensor_bind_t), 0, 0);
2165 _ccv_cnnp_model_bind_tensors(model->graph, (ccv_nnc_tensor_symbol_t*)ccv_array_get(compiled_data->parameters, 0)((void*)(((char*)((compiled_data->parameters)->data)) +
(size_t)(compiled_data->parameters)->rsize * (size_t)(
0))), compiled_data->tensors.parameters, parameter_size, parallel_count, tensor_binds);
2166 _ccv_cnnp_model_bind_tensors(model->graph, compiled_data->updated_parameters, compiled_data->tensors.parameters, parameter_size, parallel_count, tensor_binds);
2167 // Bind accumulated gradients.
2168 if (compiled_data->backward.count > 1)
2169 _ccv_cnnp_model_bind_tensors(model->graph, compiled_data->gradients, compiled_data->tensors.accum_gradients, parameter_size, parallel_count, tensor_binds);
2170 else
2171 _ccv_cnnp_model_bind_tensors(model->graph, compiled_data->gradients, compiled_data->tensors.gradients, parameter_size, parallel_count, tensor_binds);
2172 ccv_array_t* const apply_gradients_from = ccv_array_new(sizeof(int), 0, 0);
2173 int i, j;
2174 for (i = 0; i < compiled_data->backward.to_size; i++)
2175 {
2176 const int* tos;
2177 int to_size;
2178 ccv_nnc_graph_exec_symbol_to(model->graph, compiled_data->backward.tos[i], &tos, &to_size);
2179 for (j = 0; j < to_size; j++)
2180 {
2181 // Check if this is already show up in the backward graph, if that is the case, it won't be in the apply
2182 // gradients graph.
2183 const ccv_nnc_graph_exec_t exec = ccv_nnc_graph_exec_from_symbol(compiled_data->graph_exec_arena, (ccv_nnc_graph_exec_symbol_t){
2184 .d = tos[j],
2185 .graph = model->graph,
2186 });
2187 if (!exec.graph)
2188 ccv_array_add_unique_int(apply_gradients_from, tos[j]);
2189 }
2190 }
2191 const int from_size = apply_gradients_from->rnum;
2192 if (from_size == 0)
2193 {
2194 ccv_array_free(apply_gradients_from);
2195 ccv_array_free(tensor_binds);
2196 return;
2197 }
2198 ccv_nnc_graph_exec_symbol_t* const froms = (ccv_nnc_graph_exec_symbol_t*)ccmallocmalloc(sizeof(ccv_nnc_graph_exec_symbol_t) * from_size);
2199 for (i = 0; i < from_size; i++)
2200 froms[i] = (ccv_nnc_graph_exec_symbol_t){
2201 .d = *(int*)ccv_array_get(apply_gradients_from, i)((void*)(((char*)((apply_gradients_from)->data)) + (size_t
)(apply_gradients_from)->rsize * (size_t)(i))),
2202 .graph = model->graph
2203 };
2204 ccv_array_free(apply_gradients_from);
2205 // It can only ends with updates on the parameters.
2206 ccv_array_t* const tos = ccv_array_new(sizeof(ccv_nnc_graph_exec_symbol_t), parameter_size * parallel_count, 0);
2207 for (i = 0; i < parameter_size; i++)
2208 {
2209 if (compiled_data->update_nodes[i].d == CCV_NNC_NO_TENSOR_SYMBOL)
2210 continue;
2211 ccv_array_push(tos, &compiled_data->update_nodes[i]);
2212 for (j = 1; j < parallel_count; j++)
2213 {
2214 const ccv_nnc_graph_exec_symbol_t copy = ccv_nnc_graph_exec_symbol_copy(model->graph, compiled_data->update_nodes[i], j);
2215 ccv_array_push(tos, &copy);
2216 }
2217 }
2218 ccv_nnc_symbolic_graph_compile(model->graph, compiled_data->compile_params, (ccv_nnc_tensor_bind_t*)ccv_array_get(tensor_binds, 0)((void*)(((char*)((tensor_binds)->data)) + (size_t)(tensor_binds
)->rsize * (size_t)(0))), tensor_binds->rnum, 0, 0, froms, from_size, (ccv_nnc_graph_exec_symbol_t*)ccv_array_get(tos, 0)((void*)(((char*)((tos)->data)) + (size_t)(tos)->rsize *
(size_t)(0))), tos->rnum, &compiled_data->apply_gradients.graph, &compiled_data->apply_gradients.tensor_arena, &compiled_data->apply_gradients.graph_exec_arena);
2219 ccv_array_free(tos);
2220 ccv_array_free(tensor_binds);
2221 ccfreefree(froms);
2222 const int max_saved_aux_size = compiled_data->minimize.max_saved_aux_size;
2223 for (i = 0; i < max_saved_aux_size * parameter_size; i++)
2224 {
2225 // Skip on no tensor.
2226 if (compiled_data->saved_aux[i].source.d == CCV_NNC_NO_TENSOR_SYMBOL)
2227 continue;
2228 ccv_nnc_tensor_t* const tensor = ccv_nnc_tensor_from_symbol(compiled_data->apply_gradients.tensor_arena, compiled_data->saved_aux[i].source);
2229 ccv_nnc_cmd_exec(CMD_SET_FORWARD(0)ccv_nnc_cmd(CCV_NNC_SET_FORWARD, 0, (ccv_nnc_cmd_param_t){.size
={.dim={1,1,1}},.blas={.a={0,}}}, 0), ccv_nnc_no_hint, 0, 0, 0, &tensor, 1, 0);
2230 for (j = 1; j < parallel_count; j++)
2231 {
2232 ccv_nnc_tensor_t* const copy = ccv_nnc_tensor_from_symbol(compiled_data->apply_gradients.tensor_arena, ccv_nnc_tensor_symbol_copy(model->graph, compiled_data->saved_aux[i].source, j));
2233 if (copy)
2234 ccv_nnc_cmd_exec(CMD_SET_FORWARD(0)ccv_nnc_cmd(CCV_NNC_SET_FORWARD, 0, (ccv_nnc_cmd_param_t){.size
={.dim={1,1,1}},.blas={.a={0,}}}, 0), ccv_nnc_no_hint, 0, 0, 0, &copy, 1, 0);
2235 }
2236 }
2237 ccv_nnc_graph_set_default_static_schedule(compiled_data->apply_gradients.graph, compiled_data->stream_type, model->max_stream_count);
2238}
2239
2240void ccv_cnnp_model_apply_gradients(ccv_cnnp_model_t* const model, ccv_nnc_stream_context_t* const stream_context)
2241{
2242 ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
2243 assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
(compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 2243, __extension__ __PRETTY_FUNCTION__); }));
2244 assert(compiled_data->graph_mode == CCV_CNNP_MODEL_GRAPH_MULTISTAGE_MODE)((void) sizeof ((compiled_data->graph_mode == CCV_CNNP_MODEL_GRAPH_MULTISTAGE_MODE
) ? 1 : 0), __extension__ ({ if (compiled_data->graph_mode
== CCV_CNNP_MODEL_GRAPH_MULTISTAGE_MODE) ; else __assert_fail
("compiled_data->graph_mode == CCV_CNNP_MODEL_GRAPH_MULTISTAGE_MODE"
, "ccv_cnnp_model.c", 2244, __extension__ __PRETTY_FUNCTION__
); }));
2245 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; });
2246 assert(model->graph)((void) sizeof ((model->graph) ? 1 : 0), __extension__ ({ if
(model->graph) ; else __assert_fail ("model->graph", "ccv_cnnp_model.c"
, 2246, __extension__ __PRETTY_FUNCTION__); }));
2247 assert(compiled_data->graph)((void) sizeof ((compiled_data->graph) ? 1 : 0), __extension__
({ if (compiled_data->graph) ; else __assert_fail ("compiled_data->graph"
, "ccv_cnnp_model.c", 2247, __extension__ __PRETTY_FUNCTION__
); }));
2248 // Skip if there is no backward pass.
2249 if (compiled_data->backward.count <= 0)
2250 return;
2251 // Skip if there is no parameters.
2252 if (compiled_data->parameters->rnum == 0)
2253 {
2254 compiled_data->backward.count = 0;
2255 return;
2256 }
2257 if (!compiled_data->apply_gradients.graph)
2258 _ccv_cnnp_model_multistage_jit_2(model);
2259 else {
2260 const int parameter_size = compiled_data->parameters->rnum;
2261 ccv_nnc_tensor_arena_clear_bindings(compiled_data->apply_gradients.tensor_arena);
2262 // Change to bind accum_gradients if we do gradient accumulation (run backward more than once).
2263 if (compiled_data->backward.count > 1)
2264 _ccv_cnnp_bind_tensors_to_arena(compiled_data->apply_gradients.tensor_arena, model->graph, compiled_data->gradients, compiled_data->tensors.accum_gradients, parameter_size, parallel_count);
2265 else
2266 _ccv_cnnp_bind_tensors_to_arena(compiled_data->apply_gradients.tensor_arena, model->graph, compiled_data->gradients, compiled_data->tensors.gradients, parameter_size, parallel_count);
2267 }
2268 if (compiled_data->apply_gradients.graph)
2269 ccv_nnc_graph_run_with_schedule(compiled_data->apply_gradients.graph, 0, 0, 0, stream_context);
2270 // Reset backward count to 0.
2271 compiled_data->backward.count = 0;
2272}
2273
2274void ccv_cnnp_model_set_parameter(ccv_cnnp_model_t* const model, const ccv_cnnp_model_io_t parameter, const ccv_nnc_tensor_t* const tensor)
2275{
2276 ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
2277 const int param_sel = parameter->param_sel > 0 ? parameter->param_sel - 1 : parameter->param_sel;
2278 assert(parameter->param_sel != 0)((void) sizeof ((parameter->param_sel != 0) ? 1 : 0), __extension__
({ if (parameter->param_sel != 0) ; else __assert_fail ("parameter->param_sel != 0"
, "ccv_cnnp_model.c", 2278, __extension__ __PRETTY_FUNCTION__
); }));
2279 const int tensors_init = !!compiled_data->tensors_init.v;
2280 int this_tensor_init = tensors_init;
2281 if (!tensors_init)
2282 ccv_cnnp_model_tensors_init_0(model, compiled_data);
2283 else if ((uintptr_t)compiled_data->tensors_init.v & (uintptr_t)1)
2284 // Check if it is not fully allocated, if it is not, init_1.
2285 this_tensor_init = 0;
2286 ccv_array_t* const parameter_indices = ccv_array_new(sizeof(int), 0, 0);
2287 ccv_cnnp_model_add_to_parameter_indices(parameter->model, param_sel, parameter_indices);
2288 const int param_ref = parameter->param_ref > 0 ? parameter->param_ref - 1 : parameter->param_ref;
2289 if (param_ref < 0)
2290 { assert(parameter_indices->rnum == 1)((void) sizeof ((parameter_indices->rnum == 1) ? 1 : 0), __extension__
({ if (parameter_indices->rnum == 1) ; else __assert_fail
("parameter_indices->rnum == 1", "ccv_cnnp_model.c", 2290
, __extension__ __PRETTY_FUNCTION__); })); }
2291 else
2292 { assert(param_ref < parameter_indices->rnum)((void) sizeof ((param_ref < parameter_indices->rnum) ?
1 : 0), __extension__ ({ if (param_ref < parameter_indices
->rnum) ; else __assert_fail ("param_ref < parameter_indices->rnum"
, "ccv_cnnp_model.c", 2292, __extension__ __PRETTY_FUNCTION__
); })); }
2293 const int d = *(int*)ccv_array_get(parameter_indices, param_ref >= 0 ? param_ref : 0)((void*)(((char*)((parameter_indices)->data)) + (size_t)(parameter_indices
)->rsize * (size_t)(param_ref >= 0 ? param_ref : 0)));
2294 ccv_array_free(parameter_indices);
2295 const int parameter_size = compiled_data->parameters->rnum;
2296 assert(d >= 0)((void) sizeof ((d >= 0) ? 1 : 0), __extension__ ({ if (d >=
0) ; else __assert_fail ("d >= 0", "ccv_cnnp_model.c", 2296
, __extension__ __PRETTY_FUNCTION__); }));
2297 assert(d < parameter_size)((void) sizeof ((d < parameter_size) ? 1 : 0), __extension__
({ if (d < parameter_size) ; else __assert_fail ("d < parameter_size"
, "ccv_cnnp_model.c", 2297, __extension__ __PRETTY_FUNCTION__
); }));
2298 const int parallel_count = _ccv_cnnp_compiled_data_parallel_count(model, compiled_data);
2299 int i;
2300 if (!this_tensor_init)
2301 {
2302 if (compiled_data->tensors.parameters[d])
2303 {
2304 for (i = 1; i < parallel_count; i++)
2305 { assert(compiled_data->tensors.parameters[d + i * parameter_size])((void) sizeof ((compiled_data->tensors.parameters[d + i *
parameter_size]) ? 1 : 0), __extension__ ({ if (compiled_data
->tensors.parameters[d + i * parameter_size]) ; else __assert_fail
("compiled_data->tensors.parameters[d + i * parameter_size]"
, "ccv_cnnp_model.c", 2305, __extension__ __PRETTY_FUNCTION__
); })); }
2306 this_tensor_init = 1;
2307 } else {
2308 const ccv_nnc_tensor_symbol_t parameter = *(ccv_nnc_tensor_symbol_t*)ccv_array_get(compiled_data->parameters, d)((void*)(((char*)((compiled_data->parameters)->data)) +
(size_t)(compiled_data->parameters)->rsize * (size_t)(
d)));
2309 ccv_nnc_tensor_param_t info = ccv_nnc_tensor_symbol_params(parameter.graph, parameter);
2310 if (CCV_TENSOR_GET_DEVICE(info.type)((info.type) & 0xfff00) == CCV_COMPUTE_DEVICE_ANY)
2311 CCV_TENSOR_SET_DEVICE_ID(info.type, 0)(info.type) = (((info.type) & ~0xfff00) | (((0) & 0xfff
) << 8));
2312 const int device_id = CCV_TENSOR_GET_DEVICE_ID(info.type)(((info.type) & 0xfff00) >> 8);
2313 compiled_data->tensors.parameters[d] = ccv_nnc_tensor_new(0, info, 0);
2314 for (i = 1; i < parallel_count; i++)
2315 {
2316 if (i != device_id)
2317 CCV_TENSOR_SET_DEVICE_ID(info.type, i)(info.type) = (((info.type) & ~0xfff00) | (((i) & 0xfff
) << 8));
2318 else
2319 CCV_TENSOR_SET_DEVICE_ID(info.type, 0)(info.type) = (((info.type) & ~0xfff00) | (((0) & 0xfff
) << 8));
2320 compiled_data->tensors.parameters[d + i * parameter_size] = ccv_nnc_tensor_new(0, info, 0);
2321 }
2322 }
2323 }
2324 ccv_nnc_tensor_t* const dest = CCV_NNC_TENSOR(compiled_data->tensors.parameters[d])((ccv_nnc_tensor_t*)((uintptr_t)(compiled_data->tensors.parameters
[d]) & ~(uintptr_t)1));
2325 assert(dest)((void) sizeof ((dest) ? 1 : 0), __extension__ ({ if (dest) ;
else __assert_fail ("dest", "ccv_cnnp_model.c", 2325, __extension__
__PRETTY_FUNCTION__); }));
2326 ccv_nnc_cmd_exec(CMD_DATA_TRANSFER_FORWARD()ccv_nnc_cmd(CCV_NNC_DATA_TRANSFER_FORWARD, 0, ccv_nnc_cmd_auto
, 0), ccv_nnc_no_hint, 0, TENSOR_LIST((ccv_nnc_tensor_t*)tensor)(ccv_nnc_tensor_t* []){(ccv_nnc_tensor_t*)tensor}, (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 +0 +0 -1
), TENSOR_LIST(dest)(ccv_nnc_tensor_t* []){dest}, (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 +0 +0 -1), 0);
2327 for (i = 1; i < parallel_count; i++)
2328 {
2329 ccv_nnc_tensor_t* const copy_tensor = CCV_NNC_TENSOR(compiled_data->tensors.parameters[d + i * parameter_size])((ccv_nnc_tensor_t*)((uintptr_t)(compiled_data->tensors.parameters
[d + i * parameter_size]) & ~(uintptr_t)1));
2330 if (copy_tensor)
2331 ccv_nnc_cmd_exec(CMD_DATA_TRANSFER_FORWARD()ccv_nnc_cmd(CCV_NNC_DATA_TRANSFER_FORWARD, 0, ccv_nnc_cmd_auto
, 0), ccv_nnc_no_hint, 0, TENSOR_LIST(dest)(ccv_nnc_tensor_t* []){dest}, (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 +0 +0 -1), TENSOR_LIST(copy_tensor)(ccv_nnc_tensor_t* []){copy_tensor}, (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 +0 +0 -1), 0);
2332 }
2333 // Mark this symbol as init'ed.
2334 const int s = ((ccv_nnc_tensor_symbol_t*)ccv_array_get(compiled_data->parameters, d)((void*)(((char*)((compiled_data->parameters)->data)) +
(size_t)(compiled_data->parameters)->rsize * (size_t)(
d))))->d;
2335 uint32_t* const init_v = CCV_NNC_INIT_V(compiled_data->tensors_init.v)((uint32_t*)((uintptr_t)(compiled_data->tensors_init.v) &
~(uintptr_t)1));
2336 init_v[s >> 5] |= (1u << (s & 0x1f));
2337 // If we just allocated this tensor, now it is time to check if we need to mark it as fully allocated.
2338 if (!this_tensor_init)
2339 {
2340 if (ccv_cnnp_model_tensors_any_to_alloc(model, compiled_data))
2341 compiled_data->tensors_init.v = (uint32_t*)((uintptr_t)compiled_data->tensors_init.v | (uintptr_t)1);
2342 else // Remove the flag.
2343 compiled_data->tensors_init.v = CCV_NNC_INIT_V(compiled_data->tensors_init.v)((uint32_t*)((uintptr_t)(compiled_data->tensors_init.v) &
~(uintptr_t)1));
2344 }
2345}
2346
2347void ccv_cnnp_model_parameter_copy(ccv_cnnp_model_t* const model, const ccv_cnnp_model_io_t parameter, ccv_nnc_tensor_t* const tensor)
2348{
2349 ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
2350 const int param_sel = parameter->param_sel > 0 ? parameter->param_sel - 1 : parameter->param_sel;
2351 assert(parameter->param_sel != 0)((void) sizeof ((parameter->param_sel != 0) ? 1 : 0), __extension__
({ if (parameter->param_sel != 0) ; else __assert_fail ("parameter->param_sel != 0"
, "ccv_cnnp_model.c", 2351, __extension__ __PRETTY_FUNCTION__
); }));
2352 assert(compiled_data->tensors.parameters)((void) sizeof ((compiled_data->tensors.parameters) ? 1 : 0
), __extension__ ({ if (compiled_data->tensors.parameters)
; else __assert_fail ("compiled_data->tensors.parameters"
, "ccv_cnnp_model.c", 2352, __extension__ __PRETTY_FUNCTION__
); }));
2353 ccv_array_t* const parameter_indices = ccv_array_new(sizeof(int), 0, 0);
2354 ccv_cnnp_model_add_to_parameter_indices(parameter->model, param_sel, parameter_indices);
2355 const int param_ref = parameter->param_ref > 0 ? parameter->param_ref - 1 : parameter->param_ref;
2356 if (param_ref < 0)
2357 { assert(parameter_indices->rnum == 1)((void) sizeof ((parameter_indices->rnum == 1) ? 1 : 0), __extension__
({ if (parameter_indices->rnum == 1) ; else __assert_fail
("parameter_indices->rnum == 1", "ccv_cnnp_model.c", 2357
, __extension__ __PRETTY_FUNCTION__); })); }
2358 else
2359 { assert(param_ref < parameter_indices->rnum)((void) sizeof ((param_ref < parameter_indices->rnum) ?
1 : 0), __extension__ ({ if (param_ref < parameter_indices
->rnum) ; else __assert_fail ("param_ref < parameter_indices->rnum"
, "ccv_cnnp_model.c", 2359, __extension__ __PRETTY_FUNCTION__
); })); }
2360 const int d = *(int*)ccv_array_get(parameter_indices, param_ref >= 0 ? param_ref : 0)((void*)(((char*)((parameter_indices)->data)) + (size_t)(parameter_indices
)->rsize * (size_t)(param_ref >= 0 ? param_ref : 0)));
2361 ccv_array_free(parameter_indices);
2362 const int parameter_size = compiled_data->parameters->rnum;
2363 assert(d >= 0)((void) sizeof ((d >= 0) ? 1 : 0), __extension__ ({ if (d >=
0) ; else __assert_fail ("d >= 0", "ccv_cnnp_model.c", 2363
, __extension__ __PRETTY_FUNCTION__); }));
2364 assert(d < parameter_size)((void) sizeof ((d < parameter_size) ? 1 : 0), __extension__
({ if (d < parameter_size) ; else __assert_fail ("d < parameter_size"
, "ccv_cnnp_model.c", 2364, __extension__ __PRETTY_FUNCTION__
); }));
2365 // We don't need to consider parallel_count, every parameter on each device is identical.
2366 ccv_nnc_tensor_t* const src = CCV_NNC_TENSOR(compiled_data->tensors.parameters[d])((ccv_nnc_tensor_t*)((uintptr_t)(compiled_data->tensors.parameters
[d]) & ~(uintptr_t)1));
2367 assert(src)((void) sizeof ((src) ? 1 : 0), __extension__ ({ if (src) ; else
__assert_fail ("src", "ccv_cnnp_model.c", 2367, __extension__
__PRETTY_FUNCTION__); }));
2368 ccv_nnc_cmd_exec(CMD_DATA_TRANSFER_FORWARD()ccv_nnc_cmd(CCV_NNC_DATA_TRANSFER_FORWARD, 0, ccv_nnc_cmd_auto
, 0), ccv_nnc_no_hint, 0, TENSOR_LIST(src)(ccv_nnc_tensor_t* []){src}, (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 +0 +0 -1), TENSOR_LIST(tensor)(ccv_nnc_tensor_t* []){tensor}, (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 +0 +0 -1), 0);
2369}
2370
2371ccv_nnc_tensor_param_t ccv_cnnp_model_parameter_tensor_params(ccv_cnnp_model_t* const model, const ccv_cnnp_model_io_t parameter)
2372{
2373 ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
2374 const int param_sel = parameter->param_sel > 0 ? parameter->param_sel - 1 : parameter->param_sel;
2375 assert(parameter->param_sel != 0)((void) sizeof ((parameter->param_sel != 0) ? 1 : 0), __extension__
({ if (parameter->param_sel != 0) ; else __assert_fail ("parameter->param_sel != 0"
, "ccv_cnnp_model.c", 2375, __extension__ __PRETTY_FUNCTION__
); }));
2376 assert(compiled_data->tensors.parameters)((void) sizeof ((compiled_data->tensors.parameters) ? 1 : 0
), __extension__ ({ if (compiled_data->tensors.parameters)
; else __assert_fail ("compiled_data->tensors.parameters"
, "ccv_cnnp_model.c", 2376, __extension__ __PRETTY_FUNCTION__
); }));
2377 ccv_array_t* const parameter_indices = ccv_array_new(sizeof(int), 0, 0);
2378 ccv_cnnp_model_add_to_parameter_indices(parameter->model, param_sel, parameter_indices);
2379 const int param_ref = parameter->param_ref > 0 ? parameter->param_ref - 1 : parameter->param_ref;
2380 if (param_ref < 0)
2381 { assert(parameter_indices->rnum == 1)((void) sizeof ((parameter_indices->rnum == 1) ? 1 : 0), __extension__
({ if (parameter_indices->rnum == 1) ; else __assert_fail
("parameter_indices->rnum == 1", "ccv_cnnp_model.c", 2381
, __extension__ __PRETTY_FUNCTION__); })); }
2382 else
2383 { assert(param_ref < parameter_indices->rnum)((void) sizeof ((param_ref < parameter_indices->rnum) ?
1 : 0), __extension__ ({ if (param_ref < parameter_indices
->rnum) ; else __assert_fail ("param_ref < parameter_indices->rnum"
, "ccv_cnnp_model.c", 2383, __extension__ __PRETTY_FUNCTION__
); })); }
2384 const int d = *(int*)ccv_array_get(parameter_indices, param_ref >= 0 ? param_ref : 0)((void*)(((char*)((parameter_indices)->data)) + (size_t)(parameter_indices
)->rsize * (size_t)(param_ref >= 0 ? param_ref : 0)));
2385 ccv_array_free(parameter_indices);
2386 const int parameter_size = compiled_data->parameters->rnum;
2387 assert(d >= 0)((void) sizeof ((d >= 0) ? 1 : 0), __extension__ ({ if (d >=
0) ; else __assert_fail ("d >= 0", "ccv_cnnp_model.c", 2387
, __extension__ __PRETTY_FUNCTION__); }));
2388 assert(d < parameter_size)((void) sizeof ((d < parameter_size) ? 1 : 0), __extension__
({ if (d < parameter_size) ; else __assert_fail ("d < parameter_size"
, "ccv_cnnp_model.c", 2388, __extension__ __PRETTY_FUNCTION__
); }));
2389 // We don't need to consider parallel_count, every parameter on each device is identical.
2390 ccv_nnc_tensor_t* const tensor = CCV_NNC_TENSOR(compiled_data->tensors.parameters[d])((ccv_nnc_tensor_t*)((uintptr_t)(compiled_data->tensors.parameters
[d]) & ~(uintptr_t)1));
2391 assert(tensor)((void) sizeof ((tensor) ? 1 : 0), __extension__ ({ if (tensor
) ; else __assert_fail ("tensor", "ccv_cnnp_model.c", 2391, __extension__
__PRETTY_FUNCTION__); }));
2392 return tensor->info;
2393}
2394
2395const char* ccv_cnnp_model_parameter_name(ccv_cnnp_model_t* const model, const ccv_cnnp_model_io_t parameter)
2396{
2397 ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
2398 const int param_sel = parameter->param_sel > 0 ? parameter->param_sel - 1 : parameter->param_sel;
2399 assert(parameter->param_sel != 0)((void) sizeof ((parameter->param_sel != 0) ? 1 : 0), __extension__
({ if (parameter->param_sel != 0) ; else __assert_fail ("parameter->param_sel != 0"
, "ccv_cnnp_model.c", 2399, __extension__ __PRETTY_FUNCTION__
); }));
2400 ccv_array_t* const parameter_indices = ccv_array_new(sizeof(int), 0, 0);
2401 ccv_cnnp_model_add_to_parameter_indices(parameter->model, param_sel, parameter_indices);
2402 const int param_ref = parameter->param_ref > 0 ? parameter->param_ref - 1 : parameter->param_ref;
2403 if (param_ref < 0)
2404 { assert(parameter_indices->rnum == 1)((void) sizeof ((parameter_indices->rnum == 1) ? 1 : 0), __extension__
({ if (parameter_indices->rnum == 1) ; else __assert_fail
("parameter_indices->rnum == 1", "ccv_cnnp_model.c", 2404
, __extension__ __PRETTY_FUNCTION__); })); }
2405 else
2406 { assert(param_ref < parameter_indices->rnum)((void) sizeof ((param_ref < parameter_indices->rnum) ?
1 : 0), __extension__ ({ if (param_ref < parameter_indices
->rnum) ; else __assert_fail ("param_ref < parameter_indices->rnum"
, "ccv_cnnp_model.c", 2406, __extension__ __PRETTY_FUNCTION__
); })); }
2407 const int d = *(int*)ccv_array_get(parameter_indices, param_ref >= 0 ? param_ref : 0)((void*)(((char*)((parameter_indices)->data)) + (size_t)(parameter_indices
)->rsize * (size_t)(param_ref >= 0 ? param_ref : 0)));
2408 ccv_array_free(parameter_indices);
2409 const int parameter_size = compiled_data->parameters->rnum;
2410 assert(d >= 0)((void) sizeof ((d >= 0) ? 1 : 0), __extension__ ({ if (d >=
0) ; else __assert_fail ("d >= 0", "ccv_cnnp_model.c", 2410
, __extension__ __PRETTY_FUNCTION__); }));
2411 assert(d < parameter_size)((void) sizeof ((d < parameter_size) ? 1 : 0), __extension__
({ if (d < parameter_size) ; else __assert_fail ("d < parameter_size"
, "ccv_cnnp_model.c", 2411, __extension__ __PRETTY_FUNCTION__
); }));
2412 return *(char**)ccv_array_get(compiled_data->ids.parameters, d)((void*)(((char*)((compiled_data->ids.parameters)->data
)) + (size_t)(compiled_data->ids.parameters)->rsize * (
size_t)(d)));
2413}
2414
2415int ccv_cnnp_model_parameter_count(ccv_cnnp_model_t* const model)
2416{
2417 assert(model->compiled_data)((void) sizeof ((model->compiled_data) ? 1 : 0), __extension__
({ if (model->compiled_data) ; else __assert_fail ("model->compiled_data"
, "ccv_cnnp_model.c", 2417, __extension__ __PRETTY_FUNCTION__
); }));
2418 ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
2419 return compiled_data->parameters->rnum;
2420}
2421
2422uint64_t ccv_cnnp_model_parameters_size(ccv_cnnp_model_t* const model)
2423{
2424 assert(model->compiled_data)((void) sizeof ((model->compiled_data) ? 1 : 0), __extension__
({ if (model->compiled_data) ; else __assert_fail ("model->compiled_data"
, "ccv_cnnp_model.c", 2424, __extension__ __PRETTY_FUNCTION__
); }));
2425 ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
2426 const int parameter_size = compiled_data->parameters->rnum;
2427 int i;
2428 const ccv_nnc_symbolic_graph_t* const graph = model->graph;
2429 uint64_t size = 0;
2430 const int tensors_init = !!compiled_data->tensors_init.v;
2431 uint32_t* const init_v = tensors_init ? CCV_NNC_INIT_V(compiled_data->tensors_init.v)((uint32_t*)((uintptr_t)(compiled_data->tensors_init.v) &
~(uintptr_t)1)) : 0;
2432 for (i = 0; i < parameter_size; i++)
2433 {
2434 const int d = ((ccv_nnc_tensor_symbol_t*)ccv_array_get(compiled_data->parameters, i)((void*)(((char*)((compiled_data->parameters)->data)) +
(size_t)(compiled_data->parameters)->rsize * (size_t)(
i))))->d;
2435 if (tensors_init && compiled_data->tensors.parameters && (init_v[d >> 5] | (1u << (d & 0x1f))) && compiled_data->tensors.parameters[i])
2436 {
2437 ccv_nnc_tensor_param_t params = compiled_data->tensors.parameters[i]->info;
2438 size += ccv_nnc_tensor_data_size(params);
2439 continue;
2440 }
2441 ccv_nnc_tensor_param_t params = ccv_nnc_tensor_symbol_params(graph, (ccv_nnc_tensor_symbol_t){
2442 .graph = graph,
2443 .d = d
2444 });
2445 size += ccv_nnc_tensor_data_size(params);
2446 }
2447 return size;
2448}
2449
2450int ccv_cnnp_model_parameters_move(ccv_cnnp_model_t* const model, char** const names, ccv_nnc_tensor_t** const tensors, const int count, int type)
2451{
2452 assert(model->compiled_data)((void) sizeof ((model->compiled_data) ? 1 : 0), __extension__
({ if (model->compiled_data) ; else __assert_fail ("model->compiled_data"
, "ccv_cnnp_model.c", 2452, __extension__ __PRETTY_FUNCTION__
); }));
2453 ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
2454 if (count != compiled_data->parameters->rnum)
2455 return 0;
2456 if (CCV_TENSOR_GET_DEVICE(type)((type) & 0xfff00) == CCV_COMPUTE_DEVICE_ANY)
2457 CCV_TENSOR_SET_DEVICE_ID(type, 0)(type) = (((type) & ~0xfff00) | (((0) & 0xfff) <<
8));
2458 int i;
2459 // We don't need to consider parallel_count, every parameter on each device is identical.
2460 for (i = 0; i < count; i++)
2461 {
2462 ccv_nnc_tensor_t* tensor = compiled_data->tensors.parameters[i];
2463 if ((uintptr_t)tensor & (uintptr_t)1) // If it is not owned. We don't do anything.
2464 {
2465 tensors[i] = 0;
2466 continue;
2467 }
2468 tensor = CCV_NNC_TENSOR(tensor)((ccv_nnc_tensor_t*)((uintptr_t)(tensor) & ~(uintptr_t)1)
);
2469 if (tensor->info.type == type)
2470 tensors[i] = tensor;
2471 else {
2472 ccv_nnc_tensor_param_t info = tensor->info;
2473 info.type = type;
2474 tensors[i] = ccv_nnc_tensor_new(0, info, 0); // Create this tensor, don't initiate copy yet.
2475 }
2476 }
2477 for (i = 0; i < count; i++)
2478 {
2479 ccv_nnc_tensor_t* tensor = compiled_data->tensors.parameters[i];
2480 if ((uintptr_t)tensor & (uintptr_t)1) // If it is not owned. We don't do anything.
2481 continue;
2482 tensor = CCV_NNC_TENSOR(tensor)((ccv_nnc_tensor_t*)((uintptr_t)(tensor) & ~(uintptr_t)1)
);
2483 // Now initiate transfer. We should do this one on a stream.
2484 if (tensor->info.type != type)
2485 ccv_nnc_cmd_exec(CMD_DATA_TRANSFER_FORWARD()ccv_nnc_cmd(CCV_NNC_DATA_TRANSFER_FORWARD, 0, ccv_nnc_cmd_auto
, 0), ccv_nnc_no_hint, 0, TENSOR_LIST(tensor)(ccv_nnc_tensor_t* []){tensor}, (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 +0 +0 -1), TENSOR_LIST(tensors[i])(ccv_nnc_tensor_t* []){tensors[i]}, (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 +0 +0 -1), 0);
2486 }
2487 // Copy names and remove parameters.
2488 for (i = 0; i < count; i++)
2489 {
2490 ccv_nnc_tensor_t* const tensor = compiled_data->tensors.parameters[i];
2491 if ((uintptr_t)tensor & (uintptr_t)1) // If it is not owned. We don't do anything.
2492 {
2493 names[i] = 0;
2494 continue;
2495 }
2496 const char* const name = *(char**)ccv_array_get(compiled_data->ids.parameters, i)((void*)(((char*)((compiled_data->ids.parameters)->data
)) + (size_t)(compiled_data->ids.parameters)->rsize * (
size_t)(i)));
2497 const size_t name_len = ccv_min(strnlen(name, 1023), 1023)({ typeof (strnlen(name, 1023)) _a = (strnlen(name, 1023)); typeof
(1023) _b = (1023); (_a < _b) ? _a : _b; });
2498 names[i] = ccmallocmalloc(name_len + 1);
2499 names[i][name_len] = 0;
2500 memcpy(names[i], name, name_len);
2501 if (tensor->info.type == type)
2502 compiled_data->tensors.parameters[i] = 0; // Only move when it is moved.
2503 }
2504 return 1;
2505}
2506
2507KHASH_MAP_INIT_STR(ccv_cnnp_parameter_id, int)typedef struct kh_ccv_cnnp_parameter_id_s { khint_t n_buckets
, size, n_occupied, upper_bound; khint32_t *flags; kh_cstr_t *
keys; int *vals; } kh_ccv_cnnp_parameter_id_t; static inline __attribute__
((__unused__)) kh_ccv_cnnp_parameter_id_t *kh_init_ccv_cnnp_parameter_id
(void) { return (kh_ccv_cnnp_parameter_id_t*)calloc(1,sizeof(
kh_ccv_cnnp_parameter_id_t)); } static inline __attribute__ (
(__unused__)) void kh_destroy_ccv_cnnp_parameter_id(kh_ccv_cnnp_parameter_id_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_parameter_id(kh_ccv_cnnp_parameter_id_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_parameter_id
(const kh_ccv_cnnp_parameter_id_t *h, kh_cstr_t key) { if (h->
n_buckets) { khint_t k, i, last, mask, step = 0; mask = h->
n_buckets - 1; k = __ac_X31_hash_string(key); i = k & mask
; last = i; while (!((h->flags[i>>4]>>((i&
0xfU)<<1))&2) && (((h->flags[i>>4]
>>((i&0xfU)<<1))&1) || !(strcmp(h->keys
[i], key) == 0))) { 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_parameter_id
(kh_ccv_cnnp_parameter_id_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) { kh_cstr_t *new_keys
= (kh_cstr_t*)realloc((void *)h->keys,new_n_buckets * sizeof
(kh_cstr_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
) { kh_cstr_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 = __ac_X31_hash_string(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) { { kh_cstr_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
= (kh_cstr_t*)realloc((void *)h->keys,new_n_buckets * sizeof
(kh_cstr_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_parameter_id(kh_ccv_cnnp_parameter_id_t
*h, kh_cstr_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_parameter_id(h, h->n_buckets
- 1) < 0) { *ret = -1; return h->n_buckets; } } else if
(kh_resize_ccv_cnnp_parameter_id(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 = __ac_X31_hash_string(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) || !(strcmp
(h->keys[i], key) == 0))) { 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_parameter_id(kh_ccv_cnnp_parameter_id_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; } }
25
←
Null pointer value stored to field 'vals'
→
30
←
Taking true branch
→
31
←
Taking false branch
→
32
←
Calling 'kh_resize_ccv_cnnp_parameter_id'
→
33
←
Taking true branch
→
34
←
Assuming the condition is false
→
35
←
Taking false branch
→
36
←
'?' condition is true
→
37
←
Assuming 'new_flags' is null
→
38
←
Taking true branch
→
39
←
Returning without writing to 'h->vals'
→
40
←
Returning from 'kh_resize_ccv_cnnp_parameter_id'
→
41
←
Taking true branch
→
42
←
Returning without writing to 'h->vals'
→
2508
2509void ccv_cnnp_model_set_parameters_from_key_values(ccv_cnnp_model_t* const model, char* const* const names, ccv_nnc_tensor_t** const tensors, const int count, const int invalidates)
2510{
2511 assert(model->compiled_data)((void) sizeof ((model->compiled_data) ? 1 : 0), __extension__
({ if (model->compiled_data) ; else __assert_fail ("model->compiled_data"
, "ccv_cnnp_model.c", 2511, __extension__ __PRETTY_FUNCTION__
); }));
2512 ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
2513 int i;
2514 khash_t(ccv_cnnp_parameter_id)kh_ccv_cnnp_parameter_id_t* id_map = 0;
2515 if (count != compiled_data->parameters->rnum)
2516 {
2517 id_map = kh_init(ccv_cnnp_parameter_id)kh_init_ccv_cnnp_parameter_id();
2518 // Build the map between name and the index.
2519 for (i = 0; i < count; i++)
2520 {
2521 int ret;
2522 const khiter_t k = kh_put(ccv_cnnp_parameter_id, id_map, names[i], &ret)kh_put_ccv_cnnp_parameter_id(id_map, names[i], &ret);
2523 assert(ret != 0)((void) sizeof ((ret != 0) ? 1 : 0), __extension__ ({ if (ret
!= 0) ; else __assert_fail ("ret != 0", "ccv_cnnp_model.c", 2523
, __extension__ __PRETTY_FUNCTION__); }));
2524 kh_val(id_map, k)((id_map)->vals[k]) = i;
2525 }
2526 }
2527 const int parameter_size = compiled_data->parameters->rnum;
2528 int* copy_back = 0;
2529 const int tensors_init = !!compiled_data->tensors_init.v;
2530 if (!tensors_init)
2531 ccv_cnnp_model_tensors_init_0(model, compiled_data);
2532 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; });
2533 uint32_t* const init_v = CCV_NNC_INIT_V(compiled_data->tensors_init.v)((uint32_t*)((uintptr_t)(compiled_data->tensors_init.v) &
~(uintptr_t)1));
2534 for (i = 0; i < parameter_size; i++)
2535 {
2536 int j = i;
2537 const char* const name = *(char**)ccv_array_get(compiled_data->ids.parameters, i)((void*)(((char*)((compiled_data->ids.parameters)->data
)) + (size_t)(compiled_data->ids.parameters)->rsize * (
size_t)(i)));
2538 if (i >= 0 || strncmp(name, names[i], 1023) != 0)
2539 {
2540 // Build the map.
2541 if (id_map == 0)
2542 {
2543 id_map = kh_init(ccv_cnnp_parameter_id)kh_init_ccv_cnnp_parameter_id();
2544 for (j = 0; j < count; j++)
2545 {
2546 int ret;
2547 const khiter_t k = kh_put(ccv_cnnp_parameter_id, id_map, names[j], &ret)kh_put_ccv_cnnp_parameter_id(id_map, names[j], &ret);
2548 assert(ret != 0)((void) sizeof ((ret != 0) ? 1 : 0), __extension__ ({ if (ret
!= 0) ; else __assert_fail ("ret != 0", "ccv_cnnp_model.c", 2548
, __extension__ __PRETTY_FUNCTION__); }));
2549 kh_val(id_map, k)((id_map)->vals[k]) = j;
2550 }
2551 }
2552 const khiter_t k = kh_get(ccv_cnnp_parameter_id, id_map, name)kh_get_ccv_cnnp_parameter_id(id_map, name);
2553 if (k == kh_end(id_map)((id_map)->n_buckets)) // Cannot find the name, skip.
2554 continue;
2555 j = kh_val(id_map, k)((id_map)->vals[k]);
2556 }
2557 if (compiled_data->tensors.parameters[i]) // Cannot be a shared parameter to read.
2558 { assert(!((uintptr_t)compiled_data->tensors.parameters[i] & (uintptr_t)1))((void) sizeof ((!((uintptr_t)compiled_data->tensors.parameters
[i] & (uintptr_t)1)) ? 1 : 0), __extension__ ({ if (!((uintptr_t
)compiled_data->tensors.parameters[i] & (uintptr_t)1))
; else __assert_fail ("!((uintptr_t)compiled_data->tensors.parameters[i] & (uintptr_t)1)"
, "ccv_cnnp_model.c", 2558, __extension__ __PRETTY_FUNCTION__
); })); }
2559 const ccv_nnc_tensor_symbol_t parameter = *(ccv_nnc_tensor_symbol_t*)ccv_array_get(compiled_data->parameters, i)((void*)(((char*)((compiled_data->parameters)->data)) +
(size_t)(compiled_data->parameters)->rsize * (size_t)(
i)));
2560 ccv_nnc_tensor_param_t info = ccv_nnc_tensor_symbol_params(parameter.graph, parameter);
2561 if (CCV_TENSOR_GET_DEVICE(info.type)((info.type) & 0xfff00) == CCV_COMPUTE_DEVICE_ANY)
2562 CCV_TENSOR_SET_DEVICE_ID(info.type, 0)(info.type) = (((info.type) & ~0xfff00) | (((0) & 0xfff
) << 8));
2563 const int d = parameter.d;
2564 if (info.type == tensors[j]->info.type && invalidates) // Can move.
2565 {
2566 // Deallocate it if needed.
2567 if (!((uintptr_t)compiled_data->tensors.parameters[i] & (uintptr_t)1))
2568 if (compiled_data->tensors.parameters[i])
2569 ccv_nnc_tensor_free(compiled_data->tensors.parameters[i]);
2570 compiled_data->tensors.parameters[i] = tensors[j];
2571 tensors[j] = 0;
2572 } else {
2573 if (!compiled_data->tensors.parameters[i])
2574 { // Not allocated, to allocate first.
2575 // Create new one, make sure we create this by having the right parameters.
2576 const int type = info.type;
2577 info = tensors[j]->info;
2578 info.type = type; // Revert back the type.
2579 compiled_data->tensors.parameters[i] = ccv_nnc_tensor_new(0, info, 0);
2580 }
2581 if (!copy_back)
2582 copy_back = (int*)cccalloccalloc(parameter_size, sizeof(int));
2583 copy_back[i] = j + 1;
2584 }
2585 init_v[d >> 5] |= (1u << (d & 0x1f));
2586 // Create this tensor for other data parallel allocations.
2587 info = compiled_data->tensors.parameters[i]->info; // In case we loaded a different info.
2588 const int device_id = CCV_TENSOR_GET_DEVICE_ID(info.type)(((info.type) & 0xfff00) >> 8);
2589 for (j = 1; j < parallel_count; j++)
2590 if (!compiled_data->tensors.parameters[i + j * parameter_size])
2591 {
2592 if (j != device_id)
2593 CCV_TENSOR_SET_DEVICE_ID(info.type, j)(info.type) = (((info.type) & ~0xfff00) | (((j) & 0xfff
) << 8));
2594 else
2595 CCV_TENSOR_SET_DEVICE_ID(info.type, 0)(info.type) = (((info.type) & ~0xfff00) | (((0) & 0xfff
) << 8));
2596 compiled_data->tensors.parameters[i + j * parameter_size] = ccv_nnc_tensor_new(0, info, 0);
2597 }
2598 // No need to copy over, this is done in ccv_cnnp_model.c's copy_tensors method.
2599 }
2600 if (id_map)
2601 kh_destroy(ccv_cnnp_parameter_id, id_map)kh_destroy_ccv_cnnp_parameter_id(id_map);
2602 // Now do the transfer.
2603 if (copy_back)
2604 {
2605 for (i = 0; i < parameter_size; i++)
2606 {
2607 ccv_nnc_tensor_t* const tensor = CCV_NNC_TENSOR(compiled_data->tensors.parameters[i])((ccv_nnc_tensor_t*)((uintptr_t)(compiled_data->tensors.parameters
[i]) & ~(uintptr_t)1));
2608 if (copy_back[i] == 0)
2609 continue;
2610 const int j = copy_back[i] - 1;
2611 ccv_nnc_cmd_exec(CMD_DATA_TRANSFER_FORWARD()ccv_nnc_cmd(CCV_NNC_DATA_TRANSFER_FORWARD, 0, ccv_nnc_cmd_auto
, 0), ccv_nnc_no_hint, 0, TENSOR_LIST(tensors[j])(ccv_nnc_tensor_t* []){tensors[j]}, (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 +0 +0 -1), TENSOR_LIST(tensor)(ccv_nnc_tensor_t* []){tensor}, (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 +0 +0 -1), 0);
2612 }
2613 ccfreefree(copy_back);
2614 }
2615}
2616
2617ccv_cnnp_model_io_t ccv_cnnp_model_parameter_first(ccv_cnnp_model_t* const model, ccv_cnnp_model_parameters_filter_f first, void* const context)
2618{
2619 ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
2620 assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
(compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 2620, __extension__ __PRETTY_FUNCTION__); }));
2621 const int parameter_size = compiled_data->parameters->rnum;
2622 int i;
2623 for (i = 0; i < parameter_size; i++)
2624 {
2625 const char* const name = *(char**)ccv_array_get(compiled_data->ids.parameters, i)((void*)(((char*)((compiled_data->ids.parameters)->data
)) + (size_t)(compiled_data->ids.parameters)->rsize * (
size_t)(i)));
2626 if (first(model, name, context))
2627 return ccv_cnnp_model_parameters(model, -1, i);
2628 }
2629 return 0;
2630}
2631
2632ccv_array_t* ccv_cnnp_model_parameters_filter(ccv_cnnp_model_t* const model, ccv_cnnp_model_parameters_filter_f filter, void* const context)
2633{
2634 ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
2635 assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
(compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 2635, __extension__ __PRETTY_FUNCTION__); }));
2636 ccv_array_t* const parameters = ccv_array_new(sizeof(ccv_cnnp_model_io_t), 0, 0);
2637 const int parameter_size = compiled_data->parameters->rnum;
2638 int i;
2639 for (i = 0; i < parameter_size; i++)
2640 {
2641 const char* const name = *(char**)ccv_array_get(compiled_data->ids.parameters, i)((void*)(((char*)((compiled_data->ids.parameters)->data
)) + (size_t)(compiled_data->ids.parameters)->rsize * (
size_t)(i)));
2642 if (filter(model, name, context))
2643 {
2644 ccv_cnnp_model_io_t parameter = ccv_cnnp_model_parameters(model, -1, i);
2645 ccv_array_push(parameters, &parameter);
2646 }
2647 }
2648 return parameters;
2649
2650}
2651
2652CCV_WARN_UNUSED(ccv_cnnp_model_io_t)ccv_cnnp_model_io_t __attribute__((warn_unused_result)) ccv_cnnp_model_parameter_first_uninit(ccv_cnnp_model_t* const model)
2653{
2654 ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
2655 assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
(compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 2655, __extension__ __PRETTY_FUNCTION__); }));
2656 const int tensors_init = !!compiled_data->tensors_init.v;
2657 if (!tensors_init) // If nothing initialized, we return parameter 0.
2658 return ccv_cnnp_model_parameters(model, -1, 0);
2659 const int parameter_size = compiled_data->parameters->rnum;
2660 int i;
2661 const uint32_t* const init_v = CCV_NNC_INIT_V(compiled_data->tensors_init.v)((uint32_t*)((uintptr_t)(compiled_data->tensors_init.v) &
~(uintptr_t)1));
2662 for (i = 0; i < parameter_size; i++)
2663 {
2664 const int d = ((ccv_nnc_tensor_symbol_t*)ccv_array_get(compiled_data->parameters, i)((void*)(((char*)((compiled_data->parameters)->data)) +
(size_t)(compiled_data->parameters)->rsize * (size_t)(
i))))->d;
2665 if (!(init_v[d >> 5] & (1u << (d & 0x1f))))
2666 return ccv_cnnp_model_parameters(model, -1, i);
2667 }
2668 return 0;
2669}
2670
2671static ccv_array_t* _ccv_cnnp_model_parameter_indices(const ccv_cnnp_model_t* const model, const ccv_cnnp_model_io_t parameters, int* const param_ref)
2672{
2673 const int to_param_sel = parameters->param_sel > 0 ? parameters->param_sel - 1 : parameters->param_sel;
2674 assert(parameters->param_sel != 0)((void) sizeof ((parameters->param_sel != 0) ? 1 : 0), __extension__
({ if (parameters->param_sel != 0) ; else __assert_fail (
"parameters->param_sel != 0", "ccv_cnnp_model.c", 2674, __extension__
__PRETTY_FUNCTION__); }));
2675 ccv_array_t* const to_parameter_indices = ccv_array_new(sizeof(int), 0, 0);
2676 ccv_cnnp_model_add_to_parameter_indices(parameters->model, to_param_sel, to_parameter_indices);
2677 *param_ref = parameters->param_ref > 0 ? parameters->param_ref - 1 : parameters->param_ref;
2678 return to_parameter_indices;
2679}
2680
2681static void _ccv_cnnp_model_to_parameter_indices_and_from_parameter_indices(ccv_cnnp_model_t* const model, const ccv_cnnp_model_io_t parameters, const ccv_cnnp_model_t* const from_model, const ccv_cnnp_model_io_t from_parameters, ccv_array_t** const parameter_indices, int* const param_ref, ccv_array_t** const from_parameter_indices, int* const from_param_ref, const int only_init_0)
2682{
2683 // If the model is not compiled yet. Compile them now.
2684 if (!model->graph)
2685 {
2686 model->graph = ccv_nnc_symbolic_graph_new();
2687 assert(from_model->compiled_data)((void) sizeof ((from_model->compiled_data) ? 1 : 0), __extension__
({ if (from_model->compiled_data) ; else __assert_fail ("from_model->compiled_data"
, "ccv_cnnp_model.c", 2687, __extension__ __PRETTY_FUNCTION__
); }));
2688 const int input_size = from_model->input_size;
2689 ccv_nnc_tensor_param_t input_params[input_size];
2690 int i;
2691 for (i = 0; i < input_size; i++)
2692 input_params[i] = ccv_nnc_tensor_symbol_params(from_model->graph, from_model->inputs[i]);
2693 _ccv_cnnp_model_compile(model, input_params, input_size, from_model->compiled_data->loss);
2694 model->parallel_count = from_model->parallel_count;
2695 model->memory_compression = from_model->memory_compression;
2696 model->memory_reduction = from_model->memory_reduction;
2697 model->gradient_checkpointing = from_model->gradient_checkpointing;
2698 model->compiled_data->stream_type = from_model->compiled_data->stream_type;
2699 model->compiled_data->minimize.minimizer = from_model->compiled_data->minimize.minimizer;
2700 model->compiled_data->minimize.max_saved_aux_size = from_model->compiled_data->minimize.max_saved_aux_size;
2701 }
2702 ccv_cnnp_compiled_data_t* const to_compiled_data = model->compiled_data;
2703 assert(to_compiled_data)((void) sizeof ((to_compiled_data) ? 1 : 0), __extension__ ({
if (to_compiled_data) ; else __assert_fail ("to_compiled_data"
, "ccv_cnnp_model.c", 2703, __extension__ __PRETTY_FUNCTION__
); }));
2704 const int to_tensors_init = !!to_compiled_data->tensors_init.v;
2705 if (!to_tensors_init)
2706 {
2707 if (only_init_0)
2708 ccv_cnnp_model_tensors_init_0(model, to_compiled_data);
2709 else
2710 _ccv_cnnp_model_tensors_init(model, to_compiled_data);
2711 } else if (!only_init_0 && (uintptr_t)to_compiled_data->tensors_init.v & (uintptr_t)1)
2712 // Check if it is not fully allocated, if it is not, init_1.
2713 ccv_cnnp_model_tensors_init_1(model, to_compiled_data);
2714 assert(to_compiled_data->tensors.parameters)((void) sizeof ((to_compiled_data->tensors.parameters) ? 1
: 0), __extension__ ({ if (to_compiled_data->tensors.parameters
) ; else __assert_fail ("to_compiled_data->tensors.parameters"
, "ccv_cnnp_model.c", 2714, __extension__ __PRETTY_FUNCTION__
); }));
2715 *parameter_indices = _ccv_cnnp_model_parameter_indices(model, parameters, param_ref);
2716 *from_parameter_indices = _ccv_cnnp_model_parameter_indices(from_model, from_parameters, from_param_ref);
2717 if (*from_param_ref < 0 && *param_ref >= 0)
2718 { assert((*from_parameter_indices)->rnum == 1)((void) sizeof (((*from_parameter_indices)->rnum == 1) ? 1
: 0), __extension__ ({ if ((*from_parameter_indices)->rnum
== 1) ; else __assert_fail ("(*from_parameter_indices)->rnum == 1"
, "ccv_cnnp_model.c", 2718, __extension__ __PRETTY_FUNCTION__
); })); }
2719 else if (*from_param_ref >= 0)
2720 { assert(*from_param_ref < (*from_parameter_indices)->rnum)((void) sizeof ((*from_param_ref < (*from_parameter_indices
)->rnum) ? 1 : 0), __extension__ ({ if (*from_param_ref <
(*from_parameter_indices)->rnum) ; else __assert_fail ("*from_param_ref < (*from_parameter_indices)->rnum"
, "ccv_cnnp_model.c", 2720, __extension__ __PRETTY_FUNCTION__
); })); }
2721 if (*param_ref < 0 && *from_param_ref >= 0)
2722 { assert((*parameter_indices)->rnum == 1)((void) sizeof (((*parameter_indices)->rnum == 1) ? 1 : 0)
, __extension__ ({ if ((*parameter_indices)->rnum == 1) ; else
__assert_fail ("(*parameter_indices)->rnum == 1", "ccv_cnnp_model.c"
, 2722, __extension__ __PRETTY_FUNCTION__); })); }
2723 else if (*param_ref >= 0)
2724 { assert(*param_ref < (*parameter_indices)->rnum)((void) sizeof ((*param_ref < (*parameter_indices)->rnum
) ? 1 : 0), __extension__ ({ if (*param_ref < (*parameter_indices
)->rnum) ; else __assert_fail ("*param_ref < (*parameter_indices)->rnum"
, "ccv_cnnp_model.c", 2724, __extension__ __PRETTY_FUNCTION__
); })); }
2725}
2726
2727void ccv_cnnp_model_set_parameters(ccv_cnnp_model_t* const model, const ccv_cnnp_model_io_t parameters, const ccv_cnnp_model_t* const from_model, const ccv_cnnp_model_io_t from_parameters)
2728{
2729 ccv_array_t* to_parameter_indices;
2730 int to_param_ref;
2731 ccv_array_t* from_parameter_indices;
2732 int from_param_ref;
2733 _ccv_cnnp_model_to_parameter_indices_and_from_parameter_indices(model, parameters, from_model, from_parameters, &to_parameter_indices, &to_param_ref, &from_parameter_indices, &from_param_ref, 0);
2734 // Should be exactly the same tensor.
2735 if (to_param_ref < 0 && from_param_ref < 0)
2736 { assert(from_parameter_indices->rnum == to_parameter_indices->rnum)((void) sizeof ((from_parameter_indices->rnum == to_parameter_indices
->rnum) ? 1 : 0), __extension__ ({ if (from_parameter_indices
->rnum == to_parameter_indices->rnum) ; else __assert_fail
("from_parameter_indices->rnum == to_parameter_indices->rnum"
, "ccv_cnnp_model.c", 2736, __extension__ __PRETTY_FUNCTION__
); })); }
2737 // To models.
2738 ccv_cnnp_compiled_data_t* const to_compiled_data = model->compiled_data;
2739 assert(to_compiled_data)((void) sizeof ((to_compiled_data) ? 1 : 0), __extension__ ({
if (to_compiled_data) ; else __assert_fail ("to_compiled_data"
, "ccv_cnnp_model.c", 2739, __extension__ __PRETTY_FUNCTION__
); }));
2740 // From models.
2741 const ccv_cnnp_compiled_data_t* const from_compiled_data = from_model->compiled_data;
2742 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; });
2743 const int to_parameter_size = to_compiled_data->parameters->rnum;
2744 const int rnum = (to_param_ref < 0 && from_param_ref < 0) ? from_parameter_indices->rnum : 1;
2745 int i, j;
2746 const uint32_t* const from_init_v = CCV_NNC_INIT_V(from_compiled_data->tensors_init.v)((uint32_t*)((uintptr_t)(from_compiled_data->tensors_init.
v) & ~(uintptr_t)1));
2747 uint32_t* const to_init_v = CCV_NNC_INIT_V(to_compiled_data->tensors_init.v)((uint32_t*)((uintptr_t)(to_compiled_data->tensors_init.v)
& ~(uintptr_t)1));
2748 for (i = 0; i < rnum; i++)
2749 {
2750 const int src_d = *(int*)ccv_array_get(from_parameter_indices,from_param_ref >= 0 ? from_param_ref : i)((void*)(((char*)((from_parameter_indices)->data)) + (size_t
)(from_parameter_indices)->rsize * (size_t)(from_param_ref
>= 0 ? from_param_ref : i)));
2751 assert(src_d >= 0)((void) sizeof ((src_d >= 0) ? 1 : 0), __extension__ ({ if
(src_d >= 0) ; else __assert_fail ("src_d >= 0", "ccv_cnnp_model.c"
, 2751, __extension__ __PRETTY_FUNCTION__); }));
2752 assert(src_d < from_compiled_data->parameters->rnum)((void) sizeof ((src_d < from_compiled_data->parameters
->rnum) ? 1 : 0), __extension__ ({ if (src_d < from_compiled_data
->parameters->rnum) ; else __assert_fail ("src_d < from_compiled_data->parameters->rnum"
, "ccv_cnnp_model.c", 2752, __extension__ __PRETTY_FUNCTION__
); }));
2753 const int s = ((ccv_nnc_tensor_symbol_t*)ccv_array_get(from_compiled_data->parameters, src_d)((void*)(((char*)((from_compiled_data->parameters)->data
)) + (size_t)(from_compiled_data->parameters)->rsize * (
size_t)(src_d))))->d;
2754 // If the original is not init'ed. We cannot copy from.
2755 if (!(from_init_v[s >> 5] & (1u << (s & 0x1f))))
2756 continue;
2757 const int dest_d = *(int*)ccv_array_get(to_parameter_indices, to_param_ref >= 0 ? to_param_ref : i)((void*)(((char*)((to_parameter_indices)->data)) + (size_t
)(to_parameter_indices)->rsize * (size_t)(to_param_ref >=
0 ? to_param_ref : i)));
2758 assert(dest_d >= 0)((void) sizeof ((dest_d >= 0) ? 1 : 0), __extension__ ({ if
(dest_d >= 0) ; else __assert_fail ("dest_d >= 0", "ccv_cnnp_model.c"
, 2758, __extension__ __PRETTY_FUNCTION__); }));
2759 assert(dest_d < to_compiled_data->parameters->rnum)((void) sizeof ((dest_d < to_compiled_data->parameters->
rnum) ? 1 : 0), __extension__ ({ if (dest_d < to_compiled_data
->parameters->rnum) ; else __assert_fail ("dest_d < to_compiled_data->parameters->rnum"
, "ccv_cnnp_model.c", 2759, __extension__ __PRETTY_FUNCTION__
); }));
2760 ccv_nnc_tensor_t* const src = CCV_NNC_TENSOR(from_compiled_data->tensors.parameters[src_d])((ccv_nnc_tensor_t*)((uintptr_t)(from_compiled_data->tensors
.parameters[src_d]) & ~(uintptr_t)1));
2761 assert(src)((void) sizeof ((src) ? 1 : 0), __extension__ ({ if (src) ; else
__assert_fail ("src", "ccv_cnnp_model.c", 2761, __extension__
__PRETTY_FUNCTION__); }));
2762 ccv_nnc_tensor_t* const dest = CCV_NNC_TENSOR(to_compiled_data->tensors.parameters[dest_d])((ccv_nnc_tensor_t*)((uintptr_t)(to_compiled_data->tensors
.parameters[dest_d]) & ~(uintptr_t)1));
2763 assert(dest)((void) sizeof ((dest) ? 1 : 0), __extension__ ({ if (dest) ;
else __assert_fail ("dest", "ccv_cnnp_model.c", 2763, __extension__
__PRETTY_FUNCTION__); }));
2764 ccv_nnc_cmd_exec(CMD_DATA_TRANSFER_FORWARD()ccv_nnc_cmd(CCV_NNC_DATA_TRANSFER_FORWARD, 0, ccv_nnc_cmd_auto
, 0), ccv_nnc_no_hint, 0, TENSOR_LIST(src)(ccv_nnc_tensor_t* []){src}, (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 +0 +0 -1), TENSOR_LIST(dest)(ccv_nnc_tensor_t* []){dest}, (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 +0 +0 -1), 0);
2765 for (j = 1; j < parallel_count; j++)
2766 {
2767 ccv_nnc_tensor_t* const copy_tensor = CCV_NNC_TENSOR(to_compiled_data->tensors.parameters[dest_d + j * to_parameter_size])((ccv_nnc_tensor_t*)((uintptr_t)(to_compiled_data->tensors
.parameters[dest_d + j * to_parameter_size]) & ~(uintptr_t
)1));
2768 if (copy_tensor)
2769 ccv_nnc_cmd_exec(CMD_DATA_TRANSFER_FORWARD()ccv_nnc_cmd(CCV_NNC_DATA_TRANSFER_FORWARD, 0, ccv_nnc_cmd_auto
, 0), ccv_nnc_no_hint, 0, TENSOR_LIST(dest)(ccv_nnc_tensor_t* []){dest}, (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 +0 +0 -1), TENSOR_LIST(copy_tensor)(ccv_nnc_tensor_t* []){copy_tensor}, (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 +0 +0 -1), 0);
2770 }
2771 // Mark this symbol as init'ed.
2772 const int d = ((ccv_nnc_tensor_symbol_t*)ccv_array_get(to_compiled_data->parameters, dest_d)((void*)(((char*)((to_compiled_data->parameters)->data)
) + (size_t)(to_compiled_data->parameters)->rsize * (size_t
)(dest_d))))->d;
2773 to_init_v[d >> 5] |= (1u << (d & 0x1f));
2774 }
2775 ccv_array_free(to_parameter_indices);
2776 ccv_array_free(from_parameter_indices);
2777}
2778
2779void ccv_cnnp_model_share_parameters(ccv_cnnp_model_t* const model, const ccv_cnnp_model_io_t parameters, const ccv_cnnp_model_t* const from_model, const ccv_cnnp_model_io_t from_parameters, ccv_cnnp_model_parameters_renamer_f renamer, void* const context)
2780{
2781 ccv_array_t* to_parameter_indices;
2782 int to_param_ref;
2783 ccv_array_t* from_parameter_indices;
2784 int from_param_ref;
2785 _ccv_cnnp_model_to_parameter_indices_and_from_parameter_indices(model, parameters, from_model, from_parameters, &to_parameter_indices, &to_param_ref, &from_parameter_indices, &from_param_ref, 1);
2786 // Should be exactly the same tensor.
2787 if (renamer == 0 && to_param_ref < 0 && from_param_ref < 0)
1
Assuming 'renamer' is not equal to null
→
2788 { assert(from_parameter_indices->rnum == to_parameter_indices->rnum)((void) sizeof ((from_parameter_indices->rnum == to_parameter_indices
->rnum) ? 1 : 0), __extension__ ({ if (from_parameter_indices
->rnum == to_parameter_indices->rnum) ; else __assert_fail
("from_parameter_indices->rnum == to_parameter_indices->rnum"
, "ccv_cnnp_model.c", 2788, __extension__ __PRETTY_FUNCTION__
); })); }
2789 // To models.
2790 ccv_cnnp_compiled_data_t* const to_compiled_data = model->compiled_data;
2791 assert(to_compiled_data)((void) sizeof ((to_compiled_data) ? 1 : 0), __extension__ ({
if (to_compiled_data) ; else __assert_fail ("to_compiled_data"
, "ccv_cnnp_model.c", 2791, __extension__ __PRETTY_FUNCTION__
); }));
2
←
Assuming 'to_compiled_data' is non-null
→
3
←
Taking true branch
→
2792 // From models.
2793 const ccv_cnnp_compiled_data_t* const from_compiled_data = from_model->compiled_data;
2794 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; });
4
←
Assuming '_a' is <= '_b'
→
5
←
'?' condition is false
→
2795 assert(parallel_count == ccv_max(from_model->parallel_count, 1))((void) sizeof ((parallel_count == ({ typeof (from_model->
parallel_count) _a = (from_model->parallel_count); typeof (
1) _b = (1); (_a > _b) ? _a : _b; })) ? 1 : 0), __extension__
({ if (parallel_count == ({ typeof (from_model->parallel_count
) _a = (from_model->parallel_count); typeof (1) _b = (1); (
_a > _b) ? _a : _b; })) ; else __assert_fail ("parallel_count == ccv_max(from_model->parallel_count, 1)"
, "ccv_cnnp_model.c", 2795, __extension__ __PRETTY_FUNCTION__
); })); // Should have the same parallel count can share parameters.
6
←
Assuming '_a' is <= '_b'
→
7
←
'?' condition is false
→
8
←
Taking true branch
→
2796 const int from_parameter_size = from_compiled_data->parameters->rnum;
2797 const int to_parameter_size = to_compiled_data->parameters->rnum;
2798 const int rnum = (to_param_ref < 0 && from_param_ref < 0) ? to_parameter_indices->rnum : 1;
9
←
Assuming 'to_param_ref' is >= 0
→
2799 int i, j;
2800 khash_t(ccv_cnnp_parameter_id)kh_ccv_cnnp_parameter_id_t* id_map = 0;
2801 char* updated_name = 0;
2802 const uint32_t* const from_init_v = CCV_NNC_INIT_V(from_compiled_data->tensors_init.v)((uint32_t*)((uintptr_t)(from_compiled_data->tensors_init.
v) & ~(uintptr_t)1));
2803 uint32_t* const to_init_v = CCV_NNC_INIT_V(to_compiled_data->tensors_init.v)((uint32_t*)((uintptr_t)(to_compiled_data->tensors_init.v)
& ~(uintptr_t)1));
2804 for (i = 0; i < rnum; i++)
2805 {
2806 int src_d = (from_param_ref >= 0 ? from_param_ref : i) < from_parameter_indices->rnum ? *(int*)ccv_array_get(from_parameter_indices,from_param_ref >= 0 ? from_param_ref : i)((void*)(((char*)((from_parameter_indices)->data)) + (size_t
)(from_parameter_indices)->rsize * (size_t)(from_param_ref
>= 0 ? from_param_ref : i))) : from_parameter_size;
10
←
Assuming 'from_param_ref' is < 0
→
11
←
'?' condition is false
→
12
←
Assuming the condition is false
→
13
←
'?' condition is false
→
2807 // Need to figure out how to use the renamer here.
2808 const int dest_d = *(int*)ccv_array_get(to_parameter_indices, to_param_ref >= 0 ? to_param_ref : i)((void*)(((char*)((to_parameter_indices)->data)) + (size_t
)(to_parameter_indices)->rsize * (size_t)(to_param_ref >=
0 ? to_param_ref : i)));
14
←
'?' condition is true
→
2809 assert(dest_d >= 0)((void) sizeof ((dest_d >= 0) ? 1 : 0), __extension__ ({ if
(dest_d >= 0) ; else __assert_fail ("dest_d >= 0", "ccv_cnnp_model.c"
, 2809, __extension__ __PRETTY_FUNCTION__); }));
15
←
Assuming 'dest_d' is >= 0
→
16
←
Taking true branch
→
2810 assert(dest_d < to_parameter_size)((void) sizeof ((dest_d < to_parameter_size) ? 1 : 0), __extension__
({ if (dest_d < to_parameter_size) ; else __assert_fail (
"dest_d < to_parameter_size", "ccv_cnnp_model.c", 2810, __extension__
__PRETTY_FUNCTION__); }));
17
←
Assuming 'dest_d' is < 'to_parameter_size'
→
18
←
Taking true branch
→
2811 if (renamer
18.1
'renamer' is non-null
)
2812 {
2813 const char* const src_name = (src_d
18.2
'src_d' is >= 'from_parameter_size'
 < from_parameter_size && src_d >= 0) ? *(char**)ccv_array_get(from_compiled_data->ids.parameters, src_d)((void*)(((char*)((from_compiled_data->ids.parameters)->
data)) + (size_t)(from_compiled_data->ids.parameters)->
rsize * (size_t)(src_d))) : 0;
2814 const char* const dest_name = *(char**)ccv_array_get(to_compiled_data->ids.parameters, dest_d)((void*)(((char*)((to_compiled_data->ids.parameters)->data
)) + (size_t)(to_compiled_data->ids.parameters)->rsize *
(size_t)(dest_d)));
2815 if (!updated_name
18.3
'updated_name' is null
)
19
←
Taking true branch
→
2816 updated_name = (char*)ccmallocmalloc(1024);
2817 const size_t src_name_len = src_name
19.1
'src_name' is equal to null
 == 0 ? 0 : ccv_min(strnlen(src_name, 1023), 1023)({ typeof (strnlen(src_name, 1023)) _a = (strnlen(src_name, 1023
)); typeof (1023) _b = (1023); (_a < _b) ? _a : _b; });
20
←
'?' condition is true
→
2818 if (src_name_len
20.1
'src_name_len' is <= 0
 > 0)
21
←
Taking false branch
→
2819 memcpy(updated_name, src_name, src_name_len);
2820 updated_name[src_name_len] = 0;
2821 if (renamer(context, dest_name, updated_name, 1024) != 0)
22
←
Assuming the condition is false
→
2822 continue; // Skip this.
2823 if (src_name
22.1
'src_name' is equal to null
 != 0 && memcmp(updated_name, src_name, src_name_len) == 0 && strnlen(updated_name, 1023) == src_name_len)
2824 {
2825 // Nothing changed.
2826 } else {
2827 if (!id_map
22.2
'id_map' is null
)
23
←
Taking true branch
→
2828 {
2829 id_map = kh_init(ccv_cnnp_parameter_id)kh_init_ccv_cnnp_parameter_id();
24
←
Calling 'kh_init_ccv_cnnp_parameter_id'
→
26
←
Returning from 'kh_init_ccv_cnnp_parameter_id'
→
2830 for (j = 0; j < from_parameter_size; j++)
27
←
Assuming 'j' is < 'from_parameter_size'
→
28
←
Loop condition is true. Entering loop body
→
2831 {
2832 int ret;
2833 const khiter_t k = kh_put(ccv_cnnp_parameter_id, id_map, *(char**)ccv_array_get(from_compiled_data->ids.parameters, j), &ret)kh_put_ccv_cnnp_parameter_id(id_map, *(char**)((void*)(((char
*)((from_compiled_data->ids.parameters)->data)) + (size_t
)(from_compiled_data->ids.parameters)->rsize * (size_t)
(j))), &ret);
29
←
Calling 'kh_put_ccv_cnnp_parameter_id'
→
43
←
Returning from 'kh_put_ccv_cnnp_parameter_id'
→
2834 assert(ret != 0)((void) sizeof ((ret != 0) ? 1 : 0), __extension__ ({ if (ret
!= 0) ; else __assert_fail ("ret != 0", "ccv_cnnp_model.c", 2834
, __extension__ __PRETTY_FUNCTION__); }));
44
←
Taking true branch
→
2835 kh_val(id_map, k)((id_map)->vals[k]) = j;
45
←
Array access (via field 'vals') results in a null pointer dereference
2836 }
2837 }
2838 const khiter_t k = kh_get(ccv_cnnp_parameter_id, id_map, updated_name)kh_get_ccv_cnnp_parameter_id(id_map, updated_name);
2839 if (k == kh_end(id_map)((id_map)->n_buckets)) // Cannot find the name, skip.
2840 continue;
2841 src_d = kh_val(id_map, k)((id_map)->vals[k]);
2842 assert(src_d >= 0)((void) sizeof ((src_d >= 0) ? 1 : 0), __extension__ ({ if
(src_d >= 0) ; else __assert_fail ("src_d >= 0", "ccv_cnnp_model.c"
, 2842, __extension__ __PRETTY_FUNCTION__); }));
2843 assert(src_d < from_parameter_size)((void) sizeof ((src_d < from_parameter_size) ? 1 : 0), __extension__
({ if (src_d < from_parameter_size) ; else __assert_fail (
"src_d < from_parameter_size", "ccv_cnnp_model.c", 2843, __extension__
__PRETTY_FUNCTION__); }));
2844 }
2845 }
2846 assert(src_d >= 0)((void) sizeof ((src_d >= 0) ? 1 : 0), __extension__ ({ if
(src_d >= 0) ; else __assert_fail ("src_d >= 0", "ccv_cnnp_model.c"
, 2846, __extension__ __PRETTY_FUNCTION__); }));
2847 assert(src_d < from_parameter_size)((void) sizeof ((src_d < from_parameter_size) ? 1 : 0), __extension__
({ if (src_d < from_parameter_size) ; else __assert_fail (
"src_d < from_parameter_size", "ccv_cnnp_model.c", 2847, __extension__
__PRETTY_FUNCTION__); }));
2848 const int s = ((ccv_nnc_tensor_symbol_t*)ccv_array_get(from_compiled_data->parameters, src_d)((void*)(((char*)((from_compiled_data->parameters)->data
)) + (size_t)(from_compiled_data->parameters)->rsize * (
size_t)(src_d))))->d;
2849 // If the original is not init'ed. We cannot share from.
2850 if (!(from_init_v[s >> 5] & (1u << (s & 0x1f))))
2851 continue;
2852 for (j = 0; j < parallel_count; j++)
2853 {
2854 ccv_nnc_tensor_t* const src = CCV_NNC_TENSOR(from_compiled_data->tensors.parameters[src_d + j * from_parameter_size])((ccv_nnc_tensor_t*)((uintptr_t)(from_compiled_data->tensors
.parameters[src_d + j * from_parameter_size]) & ~(uintptr_t
)1));
2855 assert(src)((void) sizeof ((src) ? 1 : 0), __extension__ ({ if (src) ; else
__assert_fail ("src", "ccv_cnnp_model.c", 2855, __extension__
__PRETTY_FUNCTION__); }));
2856 ccv_nnc_tensor_t* const dest = to_compiled_data->tensors.parameters[dest_d + j * to_parameter_size];
2857 if (dest && !((uintptr_t)dest & (uintptr_t)1))
2858 ccv_nnc_tensor_free(dest);
2859 to_compiled_data->tensors.parameters[dest_d + j * to_parameter_size] = (ccv_nnc_tensor_t*)((uintptr_t)src | (uintptr_t)1);
2860 }
2861 // Mark this symbol as init'ed.
2862 const int d = ((ccv_nnc_tensor_symbol_t*)ccv_array_get(to_compiled_data->parameters, dest_d)((void*)(((char*)((to_compiled_data->parameters)->data)
) + (size_t)(to_compiled_data->parameters)->rsize * (size_t
)(dest_d))))->d;
2863 to_init_v[d >> 5] |= (1u << (d & 0x1f));
2864 }
2865 ccv_array_free(to_parameter_indices);
2866 ccv_array_free(from_parameter_indices);
2867 if (id_map)
2868 kh_destroy(ccv_cnnp_parameter_id, id_map)kh_destroy_ccv_cnnp_parameter_id(id_map);
2869 if (updated_name)
2870 ccfreefree(updated_name);
2871 // Mark it as incomplete so we will call init_1.
2872 if (ccv_cnnp_model_tensors_any_to_alloc(model, to_compiled_data))
2873 to_compiled_data->tensors_init.v = (uint32_t*)((uintptr_t)to_compiled_data->tensors_init.v | (uintptr_t)1);
2874 else // Remove the flag.
2875 to_compiled_data->tensors_init.v = CCV_NNC_INIT_V(to_compiled_data->tensors_init.v)((uint32_t*)((uintptr_t)(to_compiled_data->tensors_init.v)
& ~(uintptr_t)1));
2876}
2877
2878ccv_nnc_stream_context_t* ccv_cnnp_compiled_data_get_stream(ccv_cnnp_compiled_data_t* const compiled_data, const int type)
2879{
2880 if (!compiled_data->stream_map)
2881 compiled_data->stream_map = kh_init(stream_map)kh_init_stream_map();
2882 int ret = 0;
2883 khiter_t k = kh_put(stream_map, compiled_data->stream_map, type, &ret)kh_put_stream_map(compiled_data->stream_map, type, &ret
);
2884 assert(ret >= 0)((void) sizeof ((ret >= 0) ? 1 : 0), __extension__ ({ if (
ret >= 0) ; else __assert_fail ("ret >= 0", "ccv_cnnp_model.c"
, 2884, __extension__ __PRETTY_FUNCTION__); }));
2885 ccv_nnc_stream_context_t* stream = kh_val(compiled_data->stream_map, k)((compiled_data->stream_map)->vals[k]);
2886 // If ret == 0, the key already exist, we can return directly, otherwise, create and return.
2887 if (ret != 0)
2888 {
2889 stream = ccv_nnc_stream_context_new(type);
2890 kh_val(compiled_data->stream_map, k)((compiled_data->stream_map)->vals[k]) = stream;
2891 }
2892 return stream;
2893}
2894
2895void ccv_cnnp_model_parameters_zip_map(ccv_cnnp_model_t* const model, const ccv_cnnp_model_io_t parameters, const ccv_nnc_cmd_t cmd, const ccv_nnc_hint_t hint, const int flags, ccv_nnc_tensor_t* const* const aux_ins, const int aux_in_size, ccv_nnc_tensor_t* const* const aux_outs, const int aux_out_size, ccv_nnc_stream_context_t* const stream_context, const ccv_cnnp_model_t* const from_model, const ccv_cnnp_model_io_t from_parameters)
2896{
2897 ccv_array_t* to_parameter_indices;
2898 int to_param_ref;
2899 ccv_array_t* from_parameter_indices;
2900 int from_param_ref;
2901 _ccv_cnnp_model_to_parameter_indices_and_from_parameter_indices(model, parameters, from_model, from_parameters, &to_parameter_indices, &to_param_ref, &from_parameter_indices, &from_param_ref, 0);
2902 // Should be exactly the same tensor.
2903 if (to_param_ref < 0 && from_param_ref < 0)
2904 { assert(from_parameter_indices->rnum == to_parameter_indices->rnum)((void) sizeof ((from_parameter_indices->rnum == to_parameter_indices
->rnum) ? 1 : 0), __extension__ ({ if (from_parameter_indices
->rnum == to_parameter_indices->rnum) ; else __assert_fail
("from_parameter_indices->rnum == to_parameter_indices->rnum"
, "ccv_cnnp_model.c", 2904, __extension__ __PRETTY_FUNCTION__
); })); }
2905 // To models.
2906 ccv_cnnp_compiled_data_t* const to_compiled_data = model->compiled_data;
2907 assert(to_compiled_data)((void) sizeof ((to_compiled_data) ? 1 : 0), __extension__ ({
if (to_compiled_data) ; else __assert_fail ("to_compiled_data"
, "ccv_cnnp_model.c", 2907, __extension__ __PRETTY_FUNCTION__
); }));
2908 // From models.
2909 const ccv_cnnp_compiled_data_t* const from_compiled_data = from_model->compiled_data;
2910 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; });
2911 const int to_parameter_size = to_compiled_data->parameters->rnum;
2912 const int rnum = (to_param_ref < 0 && from_param_ref < 0) ? from_parameter_indices->rnum : 1;
2913 assert(aux_in_size >= 0)((void) sizeof ((aux_in_size >= 0) ? 1 : 0), __extension__
({ if (aux_in_size >= 0) ; else __assert_fail ("aux_in_size >= 0"
, "ccv_cnnp_model.c", 2913, __extension__ __PRETTY_FUNCTION__
); }));
2914 assert(aux_out_size >= 0)((void) sizeof ((aux_out_size >= 0) ? 1 : 0), __extension__
({ if (aux_out_size >= 0) ; else __assert_fail ("aux_out_size >= 0"
, "ccv_cnnp_model.c", 2914, __extension__ __PRETTY_FUNCTION__
); }));
2915 int i, j;
2916 ccv_nnc_tensor_t* inputs[aux_in_size + 2];
2917 ccv_nnc_tensor_t* outputs[aux_out_size + 1];
2918 for (i = 0; i < aux_in_size; i++)
2919 inputs[i + 2] = aux_ins[i];
2920 for (i = 0; i < aux_out_size; i++)
2921 outputs[i + 1] = aux_outs[i];
2922 const uint32_t* const from_init_v = CCV_NNC_INIT_V(from_compiled_data->tensors_init.v)((uint32_t*)((uintptr_t)(from_compiled_data->tensors_init.
v) & ~(uintptr_t)1));
2923 uint32_t* const to_init_v = CCV_NNC_INIT_V(to_compiled_data->tensors_init.v)((uint32_t*)((uintptr_t)(to_compiled_data->tensors_init.v)
& ~(uintptr_t)1));
2924 for (i = 0; i < rnum; i++)
2925 {
2926 const int src_d = *(int*)ccv_array_get(from_parameter_indices,from_param_ref >= 0 ? from_param_ref : i)((void*)(((char*)((from_parameter_indices)->data)) + (size_t
)(from_parameter_indices)->rsize * (size_t)(from_param_ref
>= 0 ? from_param_ref : i)));
2927 assert(src_d >= 0)((void) sizeof ((src_d >= 0) ? 1 : 0), __extension__ ({ if
(src_d >= 0) ; else __assert_fail ("src_d >= 0", "ccv_cnnp_model.c"
, 2927, __extension__ __PRETTY_FUNCTION__); }));
2928 assert(src_d < from_compiled_data->parameters->rnum)((void) sizeof ((src_d < from_compiled_data->parameters
->rnum) ? 1 : 0), __extension__ ({ if (src_d < from_compiled_data
->parameters->rnum) ; else __assert_fail ("src_d < from_compiled_data->parameters->rnum"
, "ccv_cnnp_model.c", 2928, __extension__ __PRETTY_FUNCTION__
); }));
2929 const int s = ((ccv_nnc_tensor_symbol_t*)ccv_array_get(from_compiled_data->parameters, src_d)((void*)(((char*)((from_compiled_data->parameters)->data
)) + (size_t)(from_compiled_data->parameters)->rsize * (
size_t)(src_d))))->d;
2930 // If the original is not init'ed. We cannot copy from.
2931 if (!(from_init_v[s >> 5] & (1u << (s & 0x1f))))
2932 continue;
2933 const int dest_d = *(int*)ccv_array_get(to_parameter_indices, to_param_ref >= 0 ? to_param_ref : i)((void*)(((char*)((to_parameter_indices)->data)) + (size_t
)(to_parameter_indices)->rsize * (size_t)(to_param_ref >=
0 ? to_param_ref : i)));
2934 assert(dest_d >= 0)((void) sizeof ((dest_d >= 0) ? 1 : 0), __extension__ ({ if
(dest_d >= 0) ; else __assert_fail ("dest_d >= 0", "ccv_cnnp_model.c"
, 2934, __extension__ __PRETTY_FUNCTION__); }));
2935 assert(dest_d < to_compiled_data->parameters->rnum)((void) sizeof ((dest_d < to_compiled_data->parameters->
rnum) ? 1 : 0), __extension__ ({ if (dest_d < to_compiled_data
->parameters->rnum) ; else __assert_fail ("dest_d < to_compiled_data->parameters->rnum"
, "ccv_cnnp_model.c", 2935, __extension__ __PRETTY_FUNCTION__
); }));
2936 if (parallel_count > 1)
2937 {
2938 ccv_nnc_stream_context_t* streams[parallel_count];
2939 ccv_nnc_stream_signal_t* signal;
2940 if (stream_context)
2941 signal = ccv_nnc_stream_context_emit_signal_new(stream_context);
2942 for (j = 0; j < parallel_count; j++)
2943 {
2944 ccv_nnc_tensor_t* const src = CCV_NNC_TENSOR(from_compiled_data->tensors.parameters[src_d + j * to_parameter_size])((ccv_nnc_tensor_t*)((uintptr_t)(from_compiled_data->tensors
.parameters[src_d + j * to_parameter_size]) & ~(uintptr_t
)1));
2945 ccv_nnc_tensor_t* const dest = CCV_NNC_TENSOR(to_compiled_data->tensors.parameters[dest_d + j * to_parameter_size])((ccv_nnc_tensor_t*)((uintptr_t)(to_compiled_data->tensors
.parameters[dest_d + j * to_parameter_size]) & ~(uintptr_t
)1));
2946 if (!dest || !src)
2947 {
2948 streams[j] = 0;
2949 continue;
2950 }
2951 // At the moment, can only handle them on the same device.
2952 assert(CCV_TENSOR_GET_MEMORY(src->info.type) == CCV_TENSOR_GET_MEMORY(dest->info.type))((void) sizeof ((((src->info.type) & 0x3) == ((dest->
info.type) & 0x3)) ? 1 : 0), __extension__ ({ if (((src->
info.type) & 0x3) == ((dest->info.type) & 0x3)) ; else
__assert_fail ("CCV_TENSOR_GET_MEMORY(src->info.type) == CCV_TENSOR_GET_MEMORY(dest->info.type)"
, "ccv_cnnp_model.c", 2952, __extension__ __PRETTY_FUNCTION__
); }));
2953 assert(CCV_TENSOR_GET_DEVICE_ID(src->info.type) == CCV_TENSOR_GET_DEVICE_ID(dest->info.type))((void) sizeof (((((src->info.type) & 0xfff00) >>
8) == (((dest->info.type) & 0xfff00) >> 8)) ? 1
: 0), __extension__ ({ if ((((src->info.type) & 0xfff00
) >> 8) == (((dest->info.type) & 0xfff00) >>
8)) ; else __assert_fail ("CCV_TENSOR_GET_DEVICE_ID(src->info.type) == CCV_TENSOR_GET_DEVICE_ID(dest->info.type)"
, "ccv_cnnp_model.c", 2953, __extension__ __PRETTY_FUNCTION__
); }));
2954 const int stream_type = CCV_TENSOR_GET_MEMORY(src->info.type)((src->info.type) & 0x3) == CCV_TENSOR_GPU_MEMORY ? CCV_STREAM_CONTEXT_GPU : CCV_STREAM_CONTEXT_CPU;
2955 const int device_id = CCV_TENSOR_GET_DEVICE_ID(src->info.type)(((src->info.type) & 0xfff00) >> 8);
2956 int type = stream_type;
2957 CCV_STREAM_SET_DEVICE_ID(type, device_id)(type) = (((type) & ~0xfff00) | (((device_id) & 0xfff
) << 8));
2958 ccv_nnc_stream_context_t* const stream_0 = ccv_cnnp_compiled_data_get_stream(to_compiled_data, type);
2959 // Wait signal to finish.
2960 if (stream_context)
2961 ccv_nnc_stream_context_wait_signal(stream_0, signal);
2962 inputs[0] = outputs[0] = dest;
2963 inputs[1] = src;
2964 ccv_nnc_cmd_exec(cmd, hint, flags, inputs, aux_in_size + 2, outputs, aux_out_size + 1, stream_0);
2965 if (stream_context)
2966 {
2967 ccv_nnc_stream_signal_t* const signal = ccv_nnc_stream_context_emit_signal_new(stream_0);
2968 ccv_nnc_stream_context_wait_signal(stream_context, signal);
2969 }
2970 streams[j] = stream_0;
2971 }
2972 // If this should be blocking, blocking it.
2973 if (!stream_context)
2974 for (j = 0; j < parallel_count; j++)
2975 if (streams[j])
2976 ccv_nnc_stream_context_wait(streams[j]);
2977 } else {
2978 ccv_nnc_tensor_t* const src = CCV_NNC_TENSOR(from_compiled_data->tensors.parameters[src_d])((ccv_nnc_tensor_t*)((uintptr_t)(from_compiled_data->tensors
.parameters[src_d]) & ~(uintptr_t)1));
2979 assert(src)((void) sizeof ((src) ? 1 : 0), __extension__ ({ if (src) ; else
__assert_fail ("src", "ccv_cnnp_model.c", 2979, __extension__
__PRETTY_FUNCTION__); }));
2980 ccv_nnc_tensor_t* const dest = CCV_NNC_TENSOR(to_compiled_data->tensors.parameters[dest_d])((ccv_nnc_tensor_t*)((uintptr_t)(to_compiled_data->tensors
.parameters[dest_d]) & ~(uintptr_t)1));
2981 assert(dest)((void) sizeof ((dest) ? 1 : 0), __extension__ ({ if (dest) ;
else __assert_fail ("dest", "ccv_cnnp_model.c", 2981, __extension__
__PRETTY_FUNCTION__); }));
2982 inputs[0] = outputs[0] = dest;
2983 inputs[1] = src;
2984 ccv_nnc_cmd_exec(cmd, hint, flags, inputs, aux_in_size + 2, outputs, aux_out_size + 1, stream_context);
2985 }
2986 // Mark this symbol as init'ed.
2987 const int d = ((ccv_nnc_tensor_symbol_t*)ccv_array_get(to_compiled_data->parameters, dest_d)((void*)(((char*)((to_compiled_data->parameters)->data)
) + (size_t)(to_compiled_data->parameters)->rsize * (size_t
)(dest_d))))->d;
2988 to_init_v[d >> 5] |= (1u << (d & 0x1f));
2989 }
2990 ccv_array_free(to_parameter_indices);
2991 ccv_array_free(from_parameter_indices);
2992}
2993
2994void ccv_cnnp_model_parameters_map(ccv_cnnp_model_t* const model, const ccv_cnnp_model_io_t parameters, const ccv_nnc_cmd_t cmd, const ccv_nnc_hint_t hint, const int flags, ccv_nnc_tensor_t* const* const aux_ins, const int aux_in_size, ccv_nnc_tensor_t* const* const aux_outs, const int aux_out_size, ccv_nnc_stream_context_t* const stream_context)
2995{
2996 int to_param_ref;
2997 ccv_array_t* const to_parameter_indices = _ccv_cnnp_model_parameter_indices(model, parameters, &to_param_ref);
2998 // To models.
2999 ccv_cnnp_compiled_data_t* const to_compiled_data = model->compiled_data;
3000 assert(to_compiled_data)((void) sizeof ((to_compiled_data) ? 1 : 0), __extension__ ({
if (to_compiled_data) ; else __assert_fail ("to_compiled_data"
, "ccv_cnnp_model.c", 3000, __extension__ __PRETTY_FUNCTION__
); }));
3001 // Tensor has to be inited already.
3002 assert(!!to_compiled_data->tensors_init.v)((void) sizeof ((!!to_compiled_data->tensors_init.v) ? 1 :
0), __extension__ ({ if (!!to_compiled_data->tensors_init
.v) ; else __assert_fail ("!!to_compiled_data->tensors_init.v"
, "ccv_cnnp_model.c", 3002, __extension__ __PRETTY_FUNCTION__
); }));
3003 assert(to_compiled_data->tensors.parameters)((void) sizeof ((to_compiled_data->tensors.parameters) ? 1
: 0), __extension__ ({ if (to_compiled_data->tensors.parameters
) ; else __assert_fail ("to_compiled_data->tensors.parameters"
, "ccv_cnnp_model.c", 3003, __extension__ __PRETTY_FUNCTION__
); }));
3004 // From models.
3005 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; });
3006 const int to_parameter_size = to_compiled_data->parameters->rnum;
3007 const int rnum = (to_param_ref < 0) ? to_parameter_indices->rnum : 1;
3008 assert(aux_in_size >= 0)((void) sizeof ((aux_in_size >= 0) ? 1 : 0), __extension__
({ if (aux_in_size >= 0) ; else __assert_fail ("aux_in_size >= 0"
, "ccv_cnnp_model.c", 3008, __extension__ __PRETTY_FUNCTION__
); }));
3009 assert(aux_out_size >= 0)((void) sizeof ((aux_out_size >= 0) ? 1 : 0), __extension__
({ if (aux_out_size >= 0) ; else __assert_fail ("aux_out_size >= 0"
, "ccv_cnnp_model.c", 3009, __extension__ __PRETTY_FUNCTION__
); }));
3010 int i, j;
3011 ccv_nnc_tensor_t* inputs[aux_in_size + 1];
3012 ccv_nnc_tensor_t* outputs[aux_out_size + 1];
3013 for (i = 0; i < aux_in_size; i++)
3014 inputs[i + 1] = aux_ins[i];
3015 for (i = 0; i < aux_out_size; i++)
3016 outputs[i + 1] = aux_outs[i];
3017 for (i = 0; i < rnum; i++)
3018 {
3019 const int dest_d = *(int*)ccv_array_get(to_parameter_indices, to_param_ref >= 0 ? to_param_ref : i)((void*)(((char*)((to_parameter_indices)->data)) + (size_t
)(to_parameter_indices)->rsize * (size_t)(to_param_ref >=
0 ? to_param_ref : i)));
3020 assert(dest_d >= 0)((void) sizeof ((dest_d >= 0) ? 1 : 0), __extension__ ({ if
(dest_d >= 0) ; else __assert_fail ("dest_d >= 0", "ccv_cnnp_model.c"
, 3020, __extension__ __PRETTY_FUNCTION__); }));
3021 assert(dest_d < to_compiled_data->parameters->rnum)((void) sizeof ((dest_d < to_compiled_data->parameters->
rnum) ? 1 : 0), __extension__ ({ if (dest_d < to_compiled_data
->parameters->rnum) ; else __assert_fail ("dest_d < to_compiled_data->parameters->rnum"
, "ccv_cnnp_model.c", 3021, __extension__ __PRETTY_FUNCTION__
); }));
3022 if (parallel_count > 1)
3023 {
3024 ccv_nnc_stream_context_t* streams[parallel_count];
3025 ccv_nnc_stream_signal_t* signal;
3026 if (stream_context)
3027 signal = ccv_nnc_stream_context_emit_signal_new(stream_context);
3028 for (j = 0; j < parallel_count; j++)
3029 {
3030 ccv_nnc_tensor_t* const dest = CCV_NNC_TENSOR(to_compiled_data->tensors.parameters[dest_d + j * to_parameter_size])((ccv_nnc_tensor_t*)((uintptr_t)(to_compiled_data->tensors
.parameters[dest_d + j * to_parameter_size]) & ~(uintptr_t
)1));
3031 if (!dest)
3032 {
3033 streams[j] = 0;
3034 continue;
3035 }
3036 const int stream_type = CCV_TENSOR_GET_MEMORY(dest->info.type)((dest->info.type) & 0x3) == CCV_TENSOR_GPU_MEMORY ? CCV_STREAM_CONTEXT_GPU : CCV_STREAM_CONTEXT_CPU;
3037 const int device_id = CCV_TENSOR_GET_DEVICE_ID(dest->info.type)(((dest->info.type) & 0xfff00) >> 8);
3038 int type = stream_type;
3039 CCV_STREAM_SET_DEVICE_ID(type, device_id)(type) = (((type) & ~0xfff00) | (((device_id) & 0xfff
) << 8));
3040 ccv_nnc_stream_context_t* const stream_0 = ccv_cnnp_compiled_data_get_stream(to_compiled_data, type);
3041 // Wait signal to finish.
3042 if (stream_context)
3043 ccv_nnc_stream_context_wait_signal(stream_0, signal);
3044 inputs[0] = outputs[0] = dest;
3045 ccv_nnc_cmd_exec(cmd, hint, flags, inputs, aux_in_size + 1, outputs, aux_out_size + 1, stream_0);
3046 if (stream_context)
3047 {
3048 ccv_nnc_stream_signal_t* const signal = ccv_nnc_stream_context_emit_signal_new(stream_0);
3049 ccv_nnc_stream_context_wait_signal(stream_context, signal);
3050 }
3051 streams[j] = stream_0;
3052 }
3053 // If this should be blocking, blocking it.
3054 if (!stream_context)
3055 for (j = 0; j < parallel_count; j++)
3056 if (streams[j])
3057 ccv_nnc_stream_context_wait(streams[j]);
3058 } else {
3059 ccv_nnc_tensor_t* const dest = CCV_NNC_TENSOR(to_compiled_data->tensors.parameters[dest_d])((ccv_nnc_tensor_t*)((uintptr_t)(to_compiled_data->tensors
.parameters[dest_d]) & ~(uintptr_t)1));
3060 assert(dest)((void) sizeof ((dest) ? 1 : 0), __extension__ ({ if (dest) ;
else __assert_fail ("dest", "ccv_cnnp_model.c", 3060, __extension__
__PRETTY_FUNCTION__); }));
3061 inputs[0] = outputs[0] = dest;
3062 ccv_nnc_cmd_exec(cmd, hint, flags, inputs, aux_in_size + 1, outputs, aux_out_size + 1, stream_context);
3063 }
3064 // No need to mark this symbol as init'ed, it is already.
3065 }
3066 ccv_array_free(to_parameter_indices);
3067}
3068
3069void ccv_cnnp_model_parameter_gradients_map(ccv_cnnp_model_t* const model, const ccv_cnnp_model_io_t parameters, const ccv_nnc_cmd_t cmd, const ccv_nnc_hint_t hint, const int flags, ccv_nnc_tensor_t* const* const aux_ins, const int aux_in_size, ccv_nnc_tensor_t* const* const aux_outs, const int aux_out_size, ccv_nnc_stream_context_t* const stream_context)
3070{
3071 int to_param_ref;
3072 ccv_array_t* const to_parameter_indices = _ccv_cnnp_model_parameter_indices(model, parameters, &to_param_ref);
3073 // To models.
3074 ccv_cnnp_compiled_data_t* const to_compiled_data = model->compiled_data;
3075 assert(to_compiled_data)((void) sizeof ((to_compiled_data) ? 1 : 0), __extension__ ({
if (to_compiled_data) ; else __assert_fail ("to_compiled_data"
, "ccv_cnnp_model.c", 3075, __extension__ __PRETTY_FUNCTION__
); }));
3076 // Tensor has to be inited already.
3077 assert(!!to_compiled_data->tensors_init.v)((void) sizeof ((!!to_compiled_data->tensors_init.v) ? 1 :
0), __extension__ ({ if (!!to_compiled_data->tensors_init
.v) ; else __assert_fail ("!!to_compiled_data->tensors_init.v"
, "ccv_cnnp_model.c", 3077, __extension__ __PRETTY_FUNCTION__
); }));
3078 ccv_nnc_tensor_t** tensor_gradients;
3079 if (to_compiled_data->backward.count > 1)
3080 tensor_gradients = to_compiled_data->tensors.accum_gradients;
3081 else
3082 tensor_gradients = to_compiled_data->tensors.gradients;
3083 assert(tensor_gradients)((void) sizeof ((tensor_gradients) ? 1 : 0), __extension__ ({
if (tensor_gradients) ; else __assert_fail ("tensor_gradients"
, "ccv_cnnp_model.c", 3083, __extension__ __PRETTY_FUNCTION__
); }));
3084 // From models.
3085 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; });
3086 const int to_parameter_size = to_compiled_data->parameters->rnum;
3087 const int rnum = (to_param_ref < 0) ? to_parameter_indices->rnum : 1;
3088 assert(aux_in_size >= 0)((void) sizeof ((aux_in_size >= 0) ? 1 : 0), __extension__
({ if (aux_in_size >= 0) ; else __assert_fail ("aux_in_size >= 0"
, "ccv_cnnp_model.c", 3088, __extension__ __PRETTY_FUNCTION__
); }));
3089 assert(aux_out_size >= 0)((void) sizeof ((aux_out_size >= 0) ? 1 : 0), __extension__
({ if (aux_out_size >= 0) ; else __assert_fail ("aux_out_size >= 0"
, "ccv_cnnp_model.c", 3089, __extension__ __PRETTY_FUNCTION__
); }));
3090 int i, j;
3091 ccv_nnc_tensor_t* inputs[aux_in_size + 1];
3092 ccv_nnc_tensor_t* outputs[aux_out_size + 1];
3093 for (i = 0; i < aux_in_size; i++)
3094 inputs[i + 1] = aux_ins[i];
3095 for (i = 0; i < aux_out_size; i++)
3096 outputs[i + 1] = aux_outs[i];
3097 for (i = 0; i < rnum; i++)
3098 {
3099 const int dest_d = *(int*)ccv_array_get(to_parameter_indices, to_param_ref >= 0 ? to_param_ref : i)((void*)(((char*)((to_parameter_indices)->data)) + (size_t
)(to_parameter_indices)->rsize * (size_t)(to_param_ref >=
0 ? to_param_ref : i)));
3100 assert(dest_d >= 0)((void) sizeof ((dest_d >= 0) ? 1 : 0), __extension__ ({ if
(dest_d >= 0) ; else __assert_fail ("dest_d >= 0", "ccv_cnnp_model.c"
, 3100, __extension__ __PRETTY_FUNCTION__); }));
3101 assert(dest_d < to_compiled_data->parameters->rnum)((void) sizeof ((dest_d < to_compiled_data->parameters->
rnum) ? 1 : 0), __extension__ ({ if (dest_d < to_compiled_data
->parameters->rnum) ; else __assert_fail ("dest_d < to_compiled_data->parameters->rnum"
, "ccv_cnnp_model.c", 3101, __extension__ __PRETTY_FUNCTION__
); }));
3102 if (parallel_count > 1)
3103 {
3104 ccv_nnc_stream_context_t* streams[parallel_count];
3105 ccv_nnc_stream_signal_t* signal;
3106 if (stream_context)
3107 signal = ccv_nnc_stream_context_emit_signal_new(stream_context);
3108 for (j = 0; j < parallel_count; j++)
3109 {
3110 ccv_nnc_tensor_t* const dest = tensor_gradients[dest_d + j * to_parameter_size];
3111 if (!dest)
3112 {
3113 streams[j] = 0;
3114 continue;
3115 }
3116 const int stream_type = CCV_TENSOR_GET_MEMORY(dest->info.type)((dest->info.type) & 0x3) == CCV_TENSOR_GPU_MEMORY ? CCV_STREAM_CONTEXT_GPU : CCV_STREAM_CONTEXT_CPU;
3117 const int device_id = CCV_TENSOR_GET_DEVICE_ID(dest->info.type)(((dest->info.type) & 0xfff00) >> 8);
3118 int type = stream_type;
3119 CCV_STREAM_SET_DEVICE_ID(type, device_id)(type) = (((type) & ~0xfff00) | (((device_id) & 0xfff
) << 8));
3120 ccv_nnc_stream_context_t* const stream_0 = ccv_cnnp_compiled_data_get_stream(to_compiled_data, type);
3121 // Wait signal to finish.
3122 if (stream_context)
3123 ccv_nnc_stream_context_wait_signal(stream_0, signal);
3124 inputs[0] = outputs[0] = dest;
3125 ccv_nnc_cmd_exec(cmd, hint, flags, inputs, aux_in_size + 1, outputs, aux_out_size + 1, stream_0);
3126 if (stream_context)
3127 {
3128 ccv_nnc_stream_signal_t* const signal = ccv_nnc_stream_context_emit_signal_new(stream_0);
3129 ccv_nnc_stream_context_wait_signal(stream_context, signal);
3130 }
3131 streams[j] = stream_0;
3132 }
3133 // If this should be blocking, blocking it.
3134 if (!stream_context)
3135 for (j = 0; j < parallel_count; j++)
3136 if (streams[j])
3137 ccv_nnc_stream_context_wait(streams[j]);
3138 } else {
3139 ccv_nnc_tensor_t* const dest = tensor_gradients[dest_d];
3140 if (!dest)
3141 continue;
3142 assert(dest)((void) sizeof ((dest) ? 1 : 0), __extension__ ({ if (dest) ;
else __assert_fail ("dest", "ccv_cnnp_model.c", 3142, __extension__
__PRETTY_FUNCTION__); }));
3143 inputs[0] = outputs[0] = dest;
3144 ccv_nnc_cmd_exec(cmd, hint, flags, inputs, aux_in_size + 1, outputs, aux_out_size + 1, stream_context);
3145 }
3146 // No need to mark this symbol as init'ed, it is already.
3147 }
3148 ccv_array_free(to_parameter_indices);
3149}
3150
3151void ccv_cnnp_model_parameters_to_unified_memory(ccv_cnnp_model_t* const model, const ccv_cnnp_model_io_t parameters, ccv_nnc_stream_context_t* const stream_context)
3152{
3153 // Only CUDA backend has this feature.
3154#ifdef HAVE_CUDA1
3155 int to_param_ref;
3156 ccv_array_t* const to_parameter_indices = _ccv_cnnp_model_parameter_indices(model, parameters, &to_param_ref);
3157 // To models.
3158 ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
3159 assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
(compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 3159, __extension__ __PRETTY_FUNCTION__); }));
3160 // Tensor has to be inited already.
3161 assert(!!compiled_data->tensors_init.v)((void) sizeof ((!!compiled_data->tensors_init.v) ? 1 : 0)
, __extension__ ({ if (!!compiled_data->tensors_init.v) ; else
__assert_fail ("!!compiled_data->tensors_init.v", "ccv_cnnp_model.c"
, 3161, __extension__ __PRETTY_FUNCTION__); }));
3162 assert(compiled_data->tensors.parameters)((void) sizeof ((compiled_data->tensors.parameters) ? 1 : 0
), __extension__ ({ if (compiled_data->tensors.parameters)
; else __assert_fail ("compiled_data->tensors.parameters"
, "ccv_cnnp_model.c", 3162, __extension__ __PRETTY_FUNCTION__
); }));
3163 // From models.
3164 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; });
3165 const int rnum = (to_param_ref < 0) ? to_parameter_indices->rnum : 1;
3166 int i;
3167 for (i = 0; i < rnum; i++)
3168 {
3169 const int dest_d = *(int*)ccv_array_get(to_parameter_indices, to_param_ref >= 0 ? to_param_ref : i)((void*)(((char*)((to_parameter_indices)->data)) + (size_t
)(to_parameter_indices)->rsize * (size_t)(to_param_ref >=
0 ? to_param_ref : i)));
3170 assert(dest_d >= 0)((void) sizeof ((dest_d >= 0) ? 1 : 0), __extension__ ({ if
(dest_d >= 0) ; else __assert_fail ("dest_d >= 0", "ccv_cnnp_model.c"
, 3170, __extension__ __PRETTY_FUNCTION__); }));
3171 assert(dest_d < compiled_data->parameters->rnum)((void) sizeof ((dest_d < compiled_data->parameters->
rnum) ? 1 : 0), __extension__ ({ if (dest_d < compiled_data
->parameters->rnum) ; else __assert_fail ("dest_d < compiled_data->parameters->rnum"
, "ccv_cnnp_model.c", 3171, __extension__ __PRETTY_FUNCTION__
); }));
3172 if (parallel_count > 1)
3173 {
3174 assert(0 && "Cannot support this when data parallel is in effect.")((void) sizeof ((0 && "Cannot support this when data parallel is in effect."
) ? 1 : 0), __extension__ ({ if (0 && "Cannot support this when data parallel is in effect."
) ; else __assert_fail ("0 && \"Cannot support this when data parallel is in effect.\""
, "ccv_cnnp_model.c", 3174, __extension__ __PRETTY_FUNCTION__
); }));
3175 } else {
3176 ccv_nnc_tensor_t* const src = CCV_NNC_TENSOR(compiled_data->tensors.parameters[dest_d])((ccv_nnc_tensor_t*)((uintptr_t)(compiled_data->tensors.parameters
[dest_d]) & ~(uintptr_t)1));
3177 assert(src)((void) sizeof ((src) ? 1 : 0), __extension__ ({ if (src) ; else
__assert_fail ("src", "ccv_cnnp_model.c", 3177, __extension__
__PRETTY_FUNCTION__); }));
3178 ccv_nnc_tensor_param_t params = src->info;
3179 if (CCV_TENSOR_GET_MEMORY(params.type)((params.type) & 0x3) != CCV_TENSOR_GPU_MEMORY)
3180 continue;
3181 const size_t size = ccv_nnc_tensor_data_size(params);
3182 if (size <= 0)
3183 continue;
3184 const int should_free = !((uintptr_t)compiled_data->tensors.parameters[dest_d] & (uintptr_t)1);
3185 const int tfb = (CCV_TENSOR_GET_MEMORY(params.type)((params.type) & 0x3) == CCV_TENSOR_CPU_MEMORY && params.format == CCV_TENSOR_FORMAT_NHWC && params.dim[2] > 0 && params.dim[2] <= CCV_MAX_CHANNEL(0xFFF) && params.dim[0] > 0 && params.dim[1] > 0 && params.dim[3] == 0);
3186 ccv_nnc_tensor_t* const tensor = (ccv_nnc_tensor_t*)ccmallocmalloc(sizeof(ccv_nnc_tensor_t));
3187 tensor->dataof = 0;
3188 tensor->alias_ref = 0;
3189 tensor->sig = 0;
3190 tensor->refcount = 1;
3191 tensor->info = params;
3192 if (tfb)
3193 {
3194 tensor->type = CCV_NO_DATA_ALLOC | CCV_MATRIX_DENSE | CCV_GET_DATA_TYPE(params.datatype)((params.datatype) & 0xFF000) | params.dim[2];
3195 // This corresponding to mat->step
3196 tensor->info.dim[4] = CCV_GET_STEP(params.dim[1], (CCV_GET_DATA_TYPE(params.datatype) | params.dim[2]))(((params.dim[1]) * _ccv_get_data_type_size[(((((params.datatype
) & 0xFF000) | params.dim[2])) & 0xFF000) >> 12
] * (((((params.datatype) & 0xFF000) | params.dim[2])) &
0xFFF) + 3) & -4);
3197 } else // This won't be recognized by ccv_dense_matrix_t
3198 tensor->type = CCV_NO_DATA_ALLOC | CCV_MATRIX_DENSE | CCV_GET_DATA_TYPE(params.datatype)((params.datatype) & 0xFF000);
3199 // Remove this flag so it can be deallocated as usual.
3200 tensor->type &= ~CCV_NO_DATA_ALLOC;
3201 assert(CCV_TENSOR_GET_DEVICE(params.type) != CCV_COMPUTE_DEVICE_ANY)((void) sizeof ((((params.type) & 0xfff00) != CCV_COMPUTE_DEVICE_ANY
) ? 1 : 0), __extension__ ({ if (((params.type) & 0xfff00
) != CCV_COMPUTE_DEVICE_ANY) ; else __assert_fail ("CCV_TENSOR_GET_DEVICE(params.type) != CCV_COMPUTE_DEVICE_ANY"
, "ccv_cnnp_model.c", 3201, __extension__ __PRETTY_FUNCTION__
); }));
3202 void* ptr = cumallocmanaged(CCV_TENSOR_GET_DEVICE_ID(params.type)(((params.type) & 0xfff00) >> 8), size);
3203 if (ptr) // If allocated successfully. Otherwise we go through the fallback path.
3204 {
3205 tensor->data.u8 = (uint8_t*)ptr;
3206 tensor->type |= CCV_MAPPED_MEM; // This denotes the tensor is mapped to CPU, and would prefer a explicit prefetch call.
3207 } else {
3208 // Allocation failed.
3209 ccfreefree(tensor);
3210 continue;
3211 }
3212 // TODO: Cannot run this on the stream context yet, due to allocation and deallocations.
3213 ccv_nnc_cmd_exec(CMD_DATA_TRANSFER_FORWARD()ccv_nnc_cmd(CCV_NNC_DATA_TRANSFER_FORWARD, 0, ccv_nnc_cmd_auto
, 0), ccv_nnc_no_hint, 0, &src, 1, &tensor, 1, 0);
3214 cumemadvisereadmostly(CCV_TENSOR_GET_DEVICE_ID(params.type)(((params.type) & 0xfff00) >> 8), tensor->data.u8, size);
3215 compiled_data->tensors.parameters[dest_d] = tensor;
3216 // Can free out the old one.
3217 if (should_free)
3218 ccv_nnc_tensor_free(src);
3219 }
3220 // No need to mark this symbol as init'ed, it is already.
3221 }
3222 ccv_array_free(to_parameter_indices);
3223#endif
3224}
3225
3226ccv_nnc_cmd_t ccv_cnnp_model_minimizer(ccv_cnnp_model_t* const model)
3227{
3228 ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
3229 assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
(compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 3229, __extension__ __PRETTY_FUNCTION__); }));
3230 return compiled_data->minimize.minimizer;
3231}
3232
3233void ccv_cnnp_model_set_minimizer(ccv_cnnp_model_t* const model, const ccv_nnc_cmd_t minimizer, const int reset, const ccv_cnnp_model_io_t* const set_parameters, const int set_parameter_size)
3234{
3235 ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
3236 assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
(compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 3236, __extension__ __PRETTY_FUNCTION__); }));
3237 const int parameter_size = compiled_data->parameters->rnum;
3238 if (parameter_size == 0)
3239 return;
3240 if (reset)
3241 { assert(set_parameters == 0 && set_parameter_size == 0)((void) sizeof ((set_parameters == 0 && set_parameter_size
== 0) ? 1 : 0), __extension__ ({ if (set_parameters == 0 &&
set_parameter_size == 0) ; else __assert_fail ("set_parameters == 0 && set_parameter_size == 0"
, "ccv_cnnp_model.c", 3241, __extension__ __PRETTY_FUNCTION__
); })); }
3242 const int old_max_saved_aux_size = compiled_data->minimize.max_saved_aux_size;
3243 const int saved_aux_size = ccv_nnc_minimizer_saved_aux_size(minimizer);
3244 if (saved_aux_size > compiled_data->minimize.max_saved_aux_size)
3245 compiled_data->minimize.max_saved_aux_size = saved_aux_size;
3246 const int max_saved_aux_size = compiled_data->minimize.max_saved_aux_size;
3247 // We update all parameters, at this point, we have one minimizer.
3248 if (set_parameters == 0 || set_parameter_size == 0)
3249 compiled_data->minimize.minimizer = minimizer;
3250 int i;
3251 if (set_parameters && set_parameter_size)
3252 {
3253 // I need to save what's the minimizer along with this.
3254 if (!compiled_data->minimize.parameters)
3255 compiled_data->minimize.parameters = ccv_array_new(sizeof(ccv_cnnp_set_minimizer_for_parameter_t*), 1, 0);
3256 ccv_cnnp_set_minimizer_for_parameter_t* const set_minimizer_for_parameter = ccmallocmalloc(sizeof(ccv_cnnp_set_minimizer_for_parameter_t) + (set_parameter_size - 1) * sizeof(ccv_cnnp_model_io_t));
3257 set_minimizer_for_parameter->minimizer = minimizer;
3258 set_minimizer_for_parameter->parameter_size = set_parameter_size;
3259 memcpy(set_minimizer_for_parameter->parameters, set_parameters, sizeof(ccv_cnnp_model_io_t) * set_parameter_size);
3260 ccv_array_push(compiled_data->minimize.parameters, &set_minimizer_for_parameter);
3261 }
3262 // If reset is true, clear the parameters array.
3263 if (reset && compiled_data->minimize.parameters)
3264 {
3265 for (i = 0; i < compiled_data->minimize.parameters->rnum; i++)
3266 ccfreefree(*(ccv_cnnp_set_minimizer_for_parameter_t**)ccv_array_get(compiled_data->minimize.parameters, i)((void*)(((char*)((compiled_data->minimize.parameters)->
data)) + (size_t)(compiled_data->minimize.parameters)->
rsize * (size_t)(i))));
3267 ccv_array_clear(compiled_data->minimize.parameters);
3268 }
3269 if (!compiled_data->update_nodes)
3270 return;
3271 ccv_nnc_symbolic_graph_t* const symbolic_graph = model->graph;
3272 assert(symbolic_graph)((void) sizeof ((symbolic_graph) ? 1 : 0), __extension__ ({ if
(symbolic_graph) ; else __assert_fail ("symbolic_graph", "ccv_cnnp_model.c"
, 3272, __extension__ __PRETTY_FUNCTION__); }));
3273 if (saved_aux_size > old_max_saved_aux_size)
3274 {
3275 assert(compiled_data->updated_parameters)((void) sizeof ((compiled_data->updated_parameters) ? 1 : 0
), __extension__ ({ if (compiled_data->updated_parameters)
; else __assert_fail ("compiled_data->updated_parameters"
, "ccv_cnnp_model.c", 3275, __extension__ __PRETTY_FUNCTION__
); }));
3276 // Reallocate first, move them around later.
3277 compiled_data->updated_parameters = (ccv_nnc_tensor_symbol_t*)ccreallocrealloc(compiled_data->updated_parameters, sizeof(ccv_nnc_tensor_symbol_t) * parameter_size + sizeof(ccv_nnc_graph_exec_symbol_t) * parameter_size + sizeof(ccv_nnc_tensor_symbol_map_t) * saved_aux_size * parameter_size);
3278 compiled_data->update_nodes = (ccv_nnc_graph_exec_symbol_t*)(compiled_data->updated_parameters + parameter_size);
3279 compiled_data->saved_aux = (ccv_nnc_tensor_symbol_map_t*)(compiled_data->update_nodes + parameter_size);
3280 // We need to do this from back to front because saved_aux_size > old_saved_aux_size, it could overlap.
3281 _ccv_cnnp_scatter_saved_aux(compiled_data->saved_aux, parameter_size, old_max_saved_aux_size, saved_aux_size);
3282 }
3283 int flag = 0;
3284 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; });
3285 if (set_parameters && set_parameter_size)
3286 {
3287 ccv_array_t* const parameter_indices = ccv_array_new(sizeof(int), 0, 0);
3288 for (i = 0; i < set_parameter_size; i++)
3289 {
3290 const int param_sel = set_parameters[i]->param_sel > 0 ? set_parameters[i]->param_sel - 1 : set_parameters[i]->param_sel;
3291 assert(set_parameters[i]->param_sel != 0)((void) sizeof ((set_parameters[i]->param_sel != 0) ? 1 : 0
), __extension__ ({ if (set_parameters[i]->param_sel != 0)
; else __assert_fail ("set_parameters[i]->param_sel != 0"
, "ccv_cnnp_model.c", 3291, __extension__ __PRETTY_FUNCTION__
); }));
3292 const int old_rnum = parameter_indices->rnum;
3293 ccv_cnnp_model_add_to_parameter_indices(set_parameters[i]->model, param_sel, parameter_indices);
3294 const int param_ref = set_parameters[i]->param_ref > 0 ? set_parameters[i]->param_ref - 1 : set_parameters[i]->param_ref;
3295 assert(set_parameters[i]->param_ref != 0)((void) sizeof ((set_parameters[i]->param_ref != 0) ? 1 : 0
), __extension__ ({ if (set_parameters[i]->param_ref != 0)
; else __assert_fail ("set_parameters[i]->param_ref != 0"
, "ccv_cnnp_model.c", 3295, __extension__ __PRETTY_FUNCTION__
); }));
3296 if (param_ref >= 0)
3297 {
3298 assert(param_ref + old_rnum < parameter_indices->rnum)((void) sizeof ((param_ref + old_rnum < parameter_indices->
rnum) ? 1 : 0), __extension__ ({ if (param_ref + old_rnum <
parameter_indices->rnum) ; else __assert_fail ("param_ref + old_rnum < parameter_indices->rnum"
, "ccv_cnnp_model.c", 3298, __extension__ __PRETTY_FUNCTION__
); }));
3299 *(int*)ccv_array_get(parameter_indices, old_rnum)((void*)(((char*)((parameter_indices)->data)) + (size_t)(parameter_indices
)->rsize * (size_t)(old_rnum))) = *(int*)ccv_array_get(parameter_indices, param_ref + old_rnum)((void*)(((char*)((parameter_indices)->data)) + (size_t)(parameter_indices
)->rsize * (size_t)(param_ref + old_rnum)));
3300 parameter_indices->rnum = old_rnum + 1;
3301 }
3302 }
3303 // We may have duplicated indices, but that is OK, we will set it twice.
3304 for (i = 0; i < parameter_indices->rnum; i++)
3305 {
3306 const int d = *(int*)ccv_array_get(parameter_indices, i)((void*)(((char*)((parameter_indices)->data)) + (size_t)(parameter_indices
)->rsize * (size_t)(i)));
3307 if (_ccv_cnnp_set_minimizer_for_parameter(symbolic_graph, compiled_data, compiled_data->update_nodes, compiled_data->updated_parameters, compiled_data->saved_aux, parallel_count, minimizer, saved_aux_size, max_saved_aux_size, d))
3308 flag = 1;
3309 }
3310 ccv_array_free(parameter_indices);
3311 } else {
3312 for (i = 0; i < parameter_size; i++)
3313 if (_ccv_cnnp_set_minimizer_for_parameter(symbolic_graph, compiled_data, compiled_data->update_nodes, compiled_data->updated_parameters, compiled_data->saved_aux, parallel_count, minimizer, saved_aux_size, max_saved_aux_size, i))
3314 flag = 1;
3315 if (compiled_data->minimize.parameters)
3316 if (_ccv_cnnp_apply_parameters_with_minimizer(model))
3317 flag = 1;
3318 }
3319 if (flag)
3320 {
3321 // If saved_aux_size doesn't match, we need to remove / add new saved_aux to the graph. But first, free up apply gradients graph.
3322 if (compiled_data->graph_mode == CCV_CNNP_MODEL_GRAPH_FIT_MODE)
3323 _ccv_cnnp_compiled_data_graph_free(compiled_data);
3324 _ccv_cnnp_compiled_data_apply_gradients_free(compiled_data);
3325 }
3326}
3327
3328void ccv_cnnp_model_set_compile_params(ccv_cnnp_model_t* const model, const ccv_nnc_symbolic_graph_compile_param_t compile_params)
3329{
3330 ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
3331 assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
(compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 3331, __extension__ __PRETTY_FUNCTION__); }));
3332 compiled_data->compile_params = compile_params;
3333}
3334
3335void ccv_cnnp_model_dot(const ccv_cnnp_model_t* const model, const int flags, FILE** const outs, const int out_size)
3336{
3337 if (model->graph && out_size > 0)
3338 ccv_nnc_symbolic_graph_dot(model->graph, flags, outs[0]);
3339 if (model->compiled_data && model->compiled_data->graph && out_size > 1)
3340 ccv_nnc_graph_dot(model->compiled_data->graph, flags, outs[1]);
3341 if (model->compiled_data && model->compiled_data->backward.accum && out_size > 2)
3342 ccv_nnc_graph_dot(model->compiled_data->backward.accum, flags, outs[2]);
3343 if (model->compiled_data && model->compiled_data->apply_gradients.graph && out_size > 3)
3344 ccv_nnc_graph_dot(model->compiled_data->apply_gradients.graph, flags, outs[3]);
3345}
3346
3347void ccv_cnnp_model_format(const ccv_cnnp_model_t* const model, const ccv_nnc_symbolic_graph_format_f format_fn, void* const context)
3348{
3349 if (model->graph)
3350 ccv_nnc_symbolic_graph_format(model->graph, 0, 0, 0, 0, format_fn, context);
3351}
3352
3353static void _ccv_cnnp_compiled_data_free(const ccv_cnnp_model_t* const model, ccv_cnnp_compiled_data_t* const compiled_data)
3354{
3355 int i;
3356 const int parameter_size = compiled_data->parameters->rnum;
3357 ccv_array_free(compiled_data->parameters);
3358 if (compiled_data->parameter_flags)
3359 ccfreefree(compiled_data->parameter_flags);
3360 const int internal_size = compiled_data->internals->rnum;
3361 ccv_array_free(compiled_data->internals);
3362 assert(compiled_data->ids.parameters->rnum == parameter_size)((void) sizeof ((compiled_data->ids.parameters->rnum ==
parameter_size) ? 1 : 0), __extension__ ({ if (compiled_data
->ids.parameters->rnum == parameter_size) ; else __assert_fail
("compiled_data->ids.parameters->rnum == parameter_size"
, "ccv_cnnp_model.c", 3362, __extension__ __PRETTY_FUNCTION__
); }));
3363 assert(compiled_data->ids.internals->rnum == internal_size)((void) sizeof ((compiled_data->ids.internals->rnum == internal_size
) ? 1 : 0), __extension__ ({ if (compiled_data->ids.internals
->rnum == internal_size) ; else __assert_fail ("compiled_data->ids.internals->rnum == internal_size"
, "ccv_cnnp_model.c", 3363, __extension__ __PRETTY_FUNCTION__
); }));
3364 for (i = 0; i < parameter_size; i++)
3365 ccfreefree(*(char**)ccv_array_get(compiled_data->ids.parameters, i)((void*)(((char*)((compiled_data->ids.parameters)->data
)) + (size_t)(compiled_data->ids.parameters)->rsize * (
size_t)(i))));
3366 ccv_array_free(compiled_data->ids.parameters);
3367 for (i = 0; i < internal_size; i++)
3368 ccfreefree(*(char**)ccv_array_get(compiled_data->ids.internals, i)((void*)(((char*)((compiled_data->ids.internals)->data)
) + (size_t)(compiled_data->ids.internals)->rsize * (size_t
)(i))));
3369 ccv_array_free(compiled_data->ids.internals);
3370 const int parallel_count = compiled_data->parallel_count > 0 ? compiled_data->parallel_count : _ccv_cnnp_model_root_parallel_count(model);
3371 if (compiled_data->tensors.parameters)
3372 {
3373 for (i = 0; i < parameter_size * parallel_count; i++)
3374 // If it is not marked as not belonging, we can free it.
3375 if (!((uintptr_t)compiled_data->tensors.parameters[i] & (uintptr_t)1))
3376 if (compiled_data->tensors.parameters[i])
3377 ccv_nnc_tensor_free(compiled_data->tensors.parameters[i]);
3378 for (i = 0; i < internal_size * parallel_count; i++)
3379 if (compiled_data->tensors.internals[i])
3380 ccv_nnc_tensor_free(compiled_data->tensors.internals[i]);
3381 ccfreefree(compiled_data->tensors.parameters);
3382 }
3383 if (compiled_data->tensors.gradients)
3384 {
3385 for (i = 0; i < parameter_size * parallel_count; i++)
3386 {
3387 if (compiled_data->tensors.gradients[i])
3388 ccv_nnc_tensor_free(compiled_data->tensors.gradients[i]);
3389 if (compiled_data->tensors.accum_gradients[i])
3390 ccv_nnc_tensor_free(compiled_data->tensors.accum_gradients[i]);
3391 }
3392 ccfreefree(compiled_data->tensors.gradients);
3393 }
3394 if (compiled_data->minimize.parameters)
3395 {
3396 for (i = 0; i < compiled_data->minimize.parameters->rnum; i++)
3397 ccfreefree(*(ccv_cnnp_set_minimizer_for_parameter_t**)ccv_array_get(compiled_data->minimize.parameters, i)((void*)(((char*)((compiled_data->minimize.parameters)->
data)) + (size_t)(compiled_data->minimize.parameters)->
rsize * (size_t)(i))));
3398 ccv_array_free(compiled_data->minimize.parameters);
3399 }
3400 if (compiled_data->rewindables)
3401 ccv_array_free(compiled_data->rewindables);
3402 if (compiled_data->tensors_init.v)
3403 ccfreefree(CCV_NNC_INIT_V(compiled_data->tensors_init.v)((uint32_t*)((uintptr_t)(compiled_data->tensors_init.v) &
~(uintptr_t)1)));
3404 if (compiled_data->evaluate.tos)
3405 ccfreefree(compiled_data->evaluate.tos);
3406 compiled_data->evaluate.tos = 0;
3407 if (compiled_data->stream_map)
3408 {
3409 khiter_t k;
3410 for (k = kh_begin(compiled_data->stream_map)(khint_t)(0); k != kh_end(compiled_data->stream_map)((compiled_data->stream_map)->n_buckets); ++k)
3411 {
3412 if (!kh_exist(compiled_data->stream_map, k)(!(((compiled_data->stream_map)->flags[(k)>>4]>>
(((k)&0xfU)<<1))&3)))
3413 continue;
3414 ccv_nnc_stream_context_t* const stream = kh_val(compiled_data->stream_map, k)((compiled_data->stream_map)->vals[k]);
3415 ccv_nnc_stream_context_free(stream);
3416 }
3417 kh_destroy(stream_map, compiled_data->stream_map)kh_destroy_stream_map(compiled_data->stream_map);
3418 }
3419 _ccv_cnnp_compiled_data_graph_free(compiled_data);
3420 _ccv_cnnp_compiled_data_gradient_free(compiled_data);
3421 _ccv_cnnp_compiled_data_backward_free(compiled_data);
3422 _ccv_cnnp_compiled_data_apply_gradients_free(compiled_data);
3423 if (compiled_data->gradient_checkpoints)
3424 {
3425 for (i = 0; i < compiled_data->gradient_checkpoints->rnum; i++)
3426 {
3427 ccv_cnnp_model_gradient_checkpoint_t* const checkpoint = (ccv_cnnp_model_gradient_checkpoint_t*)ccv_array_get(compiled_data->gradient_checkpoints, i)((void*)(((char*)((compiled_data->gradient_checkpoints)->
data)) + (size_t)(compiled_data->gradient_checkpoints)->
rsize * (size_t)(i)));
3428 assert(checkpoint->inputs)((void) sizeof ((checkpoint->inputs) ? 1 : 0), __extension__
({ if (checkpoint->inputs) ; else __assert_fail ("checkpoint->inputs"
, "ccv_cnnp_model.c", 3428, __extension__ __PRETTY_FUNCTION__
); }));
3429 ccfreefree(checkpoint->inputs);
3430 ccv_array_free(checkpoint->tensor_symbols);
3431 }
3432 ccv_array_free(compiled_data->gradient_checkpoints);
3433 }
3434 ccv_nnc_xpu_alloc_destroy(&compiled_data->xpu_alloc);
3435 ccfreefree(compiled_data);
3436}
3437
3438void ccv_cnnp_model_free(ccv_cnnp_model_t* const model)
3439{
3440 ccv_cnnp_model_deinit(model);
3441 if (model->isa->dealloc)
3442 model->isa->dealloc(model);
3443 if (model->io)
3444 {
3445 int i;
3446 for (i = 0; i < model->io->rnum; i++)
3447 {
3448 ccv_cnnp_model_io_t model_io = *(ccv_cnnp_model_io_t*)ccv_array_get(model->io, i)((void*)(((char*)((model->io)->data)) + (size_t)(model->
io)->rsize * (size_t)(i)));
3449 if (model_io->outgoings)
3450 ccv_array_free(model_io->outgoings);
3451 if (model_io->incomings)
3452 ccv_array_free(model_io->incomings);
3453 if (model_io->dependencies)
3454 ccv_array_free(model_io->dependencies);
3455 ccfreefree(model_io);
3456 }
3457 ccv_array_free(model->io);
3458 }
3459 if (model->parameter_indices)
3460 ccv_array_free(model->parameter_indices);
3461 if (model->inputs)
3462 ccfreefree(model->inputs);
3463 if (model->graph)
3464 ccv_nnc_symbolic_graph_free(model->graph);
3465 if (model->compiled_data)
3466 _ccv_cnnp_compiled_data_free(model, model->compiled_data);
3467 if (model->name)
3468 ccfreefree(model->name);
3469 ccfreefree(model);
3470}
3471
3472void ccv_cnnp_model_cancel(ccv_cnnp_model_t* const model)
3473{
3474 ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
3475 if (!compiled_data)
3476 return;
3477 if (compiled_data->graph)
3478 ccv_nnc_graph_cancel(compiled_data->graph);
3479 if (compiled_data->apply_gradients.graph)
3480 ccv_nnc_graph_cancel(compiled_data->apply_gradients.graph);
3481}
3482
3483void ccv_cnnp_model_async_enter(ccv_cnnp_model_t* const model)
3484{
3485 ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
3486 if (!compiled_data)
3487 return;
3488 if (compiled_data->graph)
3489 ccv_nnc_graph_async_enter(compiled_data->graph);
3490 if (compiled_data->apply_gradients.graph)
3491 ccv_nnc_graph_async_enter(compiled_data->apply_gradients.graph);
3492}
3493
3494void ccv_cnnp_model_set_flags(ccv_cnnp_model_t* const model, const int flags)
3495{
3496 model->exec_flags = flags;
3497}
3498
3499int ccv_cnnp_model_flags(ccv_cnnp_model_t* const model)
3500{
3501 return model->exec_flags;
3502}