Bug Summary

File:nnc/ccv_cnnp_model.c
Warning:line 2457, 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/2024-10-18-174139-54102-1 -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
7// MARK - Level-5 API
8
9ccv_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)
10{
11 if (!model->io)
12 model->io = ccv_array_new(sizeof(ccv_cnnp_model_io_t), 1, 0);
13 ccv_cnnp_model_io_t model_io = ccmallocmalloc(sizeof(struct ccv_cnnp_model_io_s) + sizeof(ccv_nnc_tensor_symbol_t) * model->output_size);
14 model_io->param_ref = 0;
15 model_io->param_sel = 0;
16 model_io->visit = 0;
17 model_io->model = model;
18 model_io->dependencies = 0;
19 model_io->dependents = 0;
20 model_io->outgoings = 0;
21 model_io->outputs = (ccv_nnc_tensor_symbol_t*)(model_io + 1);
22 ccv_array_push(model->io, &model_io);
23 if (input_size > 0)
24 {
25 model_io->incomings = ccv_array_new(sizeof(ccv_cnnp_model_io_t), input_size, 0);
26 ccv_array_resize(model_io->incomings, input_size);
27 int i;
28 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);
29 for (i = 0; i < input_size; i++)
30 {
31 if (!inputs[i]->outgoings)
32 inputs[i]->outgoings = ccv_array_new(sizeof(ccv_cnnp_model_io_t), 1, 0);
33 ccv_array_push(inputs[i]->outgoings, &model_io);
34 }
35 } else {
36 model_io->incomings = 0;
37 }
38 return model_io;
39}
40
41void ccv_cnnp_model_add_dependencies(ccv_cnnp_model_io_t model_io, const ccv_cnnp_model_io_t* const dependencies, const int dependency_size)
42{
43 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", 43, __extension__ __PRETTY_FUNCTION__);
}));
44 if (!model_io->dependencies)
45 model_io->dependencies = ccv_array_new(sizeof(ccv_cnnp_model_io_t), dependency_size, 0);
46 int i, j;
47 for (i = 0; i < dependency_size; i++)
48 {
49 int flag = 0;
50 // Check if it is already exist or not.
51 for (j = 0; !flag && j < model_io->dependencies->rnum; j++)
52 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])
53 flag = 1;
54 if (flag)
55 continue;
56 ccv_array_push(model_io->dependencies, dependencies + i);
57 ++dependencies[i]->dependents;
58 }
59}
60
61int ccv_cnnp_model_output_size(const ccv_cnnp_model_t* const model)
62{
63 return model->output_size;
64}
65
66int ccv_cnnp_model_is_trainable(const ccv_cnnp_model_t* const model)
67{
68 // If the model is compiled, it is default to 1 unless it is not.
69 if (model->compiled_data)
70 return model->is_trainable >= 0 ? model->is_trainable : 1;
71 return model->is_trainable;
72}
73
74ccv_cnnp_model_io_t ccv_cnnp_model_parameters(ccv_cnnp_model_t* const model, const int selector, const int index)
75{
76 if (!model->io)
77 model->io = ccv_array_new(sizeof(ccv_cnnp_model_io_t), 1, 0);
78 ccv_cnnp_model_io_t model_io = ccmallocmalloc(sizeof(struct ccv_cnnp_model_io_s));
79 model_io->param_ref = index >= 0 ? index + 1 : ALL_PARAMETERS-1;
80 model_io->param_sel = selector >= 0 ? selector + 1 : ALL_PARAMETERS-1;
81 model_io->visit = 0;
82 model_io->model = model;
83 model_io->outputs = 0;
84 model_io->dependencies = 0;
85 model_io->dependents = 0;
86 model_io->incomings = 0;
87 model_io->outgoings = 0;
88 ccv_array_push(model->io, &model_io);
89 return model_io;
90}
91
92void ccv_cnnp_model_notify_hook(ccv_cnnp_model_t* const model, ccv_cnnp_model_notify_f func, void* const context)
93{
94 model->notify_hook.func = func;
95 model->notify_hook.context = context;
96}
97
98void ccv_cnnp_model_notify(const ccv_cnnp_model_t* const model, const int tag, void* const payload)
99{
100 if (model->notify_hook.func)
101 model->notify_hook.func(model, tag, payload, model->notify_hook.context);
102 if (model->isa->notify)
103 model->isa->notify(model, tag, payload);
104}
105
106static int _ccv_nnc_array_dedup_graph_exec_symbols(ccv_nnc_graph_exec_symbol_t* const graph_exec_symbols, int graph_exec_symbol_size)
107{
108 int i, j;
109 for (i = 0; i < graph_exec_symbol_size; i++)
110 {
111 ccv_nnc_graph_exec_symbol_t* const graph_exec_symbol = graph_exec_symbols + i;
112 // Check whether this tensor symbol has any duplicate.
113 for (j = i + 1; j < graph_exec_symbol_size;)
114 {
115 ccv_nnc_graph_exec_symbol_t* const other_symbol = graph_exec_symbols + j;
116 // If there is a same tensor symbol, remove it.
117 if (other_symbol->d == graph_exec_symbol->d && other_symbol->graph == graph_exec_symbol->graph)
118 {
119 if (j + 1 < graph_exec_symbol_size)
120 *other_symbol = graph_exec_symbols[graph_exec_symbol_size - 1];
121 --graph_exec_symbol_size;
122 continue;
123 }
124 ++j;
125 }
126 }
127 return graph_exec_symbol_size;
128}
129
130void ccv_cnnp_model_add_to_array(void* const context, const ccv_nnc_tensor_symbol_t symbol, const int is_trainable)
131{
132 ccv_cnnp_model_add_to_array_context_t* const add_to_array_context = (ccv_cnnp_model_add_to_array_context_t*)context;
133 ccv_cnnp_model_t* const model = add_to_array_context->sequence->model;
134 int i;
135 if (!model->parameter_indices)
136 model->parameter_indices = ccv_array_new(sizeof(int), 0, 0);
137 for (i = 0; i < add_to_array_context->symbols->rnum; i++)
138 {
139 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)));
140 if (other_symbol.d == symbol.d && other_symbol.graph == symbol.graph)
141 {
142 // Only add to parameter_indices if it is trainable.
143 if (add_to_array_context->prefix == 't')
144 ccv_array_add_unique_int(model->parameter_indices, i);
145 // Found it, return, don't add it.
146 return;
147 }
148 }
149 // Only add to parameter_indices if it is trainable.
150 if (add_to_array_context->prefix == 't')
151 ccv_array_push(model->parameter_indices, &add_to_array_context->symbols->rnum);
152 // This is a new one, no need to add_unique_int, it is unique.
153 ccv_array_push(add_to_array_context->symbols, &symbol);
154 if (add_to_array_context->trainables)
155 ccv_array_push(add_to_array_context->trainables, &is_trainable);
156 char id[2048];
157 id[0] = add_to_array_context->prefix;
158 id[1] = '-';
159 int total_len = 2;
160 for (i = 0; i < add_to_array_context->sequence->sequences->rnum; i++)
161 {
162 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)));
163 int len;
164 if (name->name && name->name[0] != '\0')
165 len = snprintf(id + total_len, 2048 - total_len, "%s-%d-", name->name, name->sequence);
166 else
167 len = snprintf(id + total_len, 2048 - total_len, "%d-", name->sequence);
168 total_len += len;
169 if (total_len >= 2047)
170 break;
171 }
172 if (total_len < 2047)
173 total_len += snprintf(id + total_len, 2048 - total_len, "%d", add_to_array_context->sequence->it);
174 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", 174, __extension__ __PRETTY_FUNCTION__)
; }));
175 char *heap_id = (char*)ccmallocmalloc(total_len + 1);
176 memcpy(heap_id, id, total_len + 1);
177 ccv_array_push(add_to_array_context->ids, &heap_id);
178 ++add_to_array_context->sequence->it;
179}
180
181static void _ccv_cnnp_compiled_data_init(ccv_cnnp_compiled_data_t* const compiled_data, const int output_size, ccv_array_t* const gradient_checkpoints)
182{
183 compiled_data->f = compiled_data->fits + output_size;
184 compiled_data->xpu_alloc.mp_hdr = -1;
185 compiled_data->xpu_alloc.freed = kh_init(dy_str)kh_init_dy_str();
186 compiled_data->xpu_alloc.allocd = kh_init(dy_alloc)kh_init_dy_alloc();
187 compiled_data->gradient_checkpoints = gradient_checkpoints;
188}
189
190static 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)
191{
192 assert(model->graph)((void) sizeof ((model->graph) ? 1 : 0), __extension__ ({ if
(model->graph) ; else __assert_fail ("model->graph", "ccv_cnnp_model.c"
, 192, __extension__ __PRETTY_FUNCTION__); }));
193 model->inputs = ccmallocmalloc(sizeof(ccv_nnc_tensor_symbol_t) * input_size);
194 int i;
195 for (i = 0; i < input_size; i++)
196 model->inputs[i] = ccv_nnc_tensor_symbol_new(model->graph, inputs[i], 0);
197 ccv_array_t* const parameters = ccv_array_new(sizeof(ccv_nnc_tensor_symbol_t), 0, 0);
198 ccv_array_t* const parameter_ids = ccv_array_new(sizeof(char*), 0, 0);
199 ccv_array_t* const parameter_trainables = ccv_array_new(sizeof(int), 0, 0);
200 ccv_cnnp_model_sequence_t model_sequence = {
201 .bank = kh_init(ccv_cnnp_model_name_bank)kh_init_ccv_cnnp_model_name_bank()
202 };
203 ccv_cnnp_model_add_to_array_context_t add_to_parameter_context = {
204 .sequence = &model_sequence,
205 .prefix = 't',
206 .symbols = parameters,
207 .ids = parameter_ids,
208 .trainables = parameter_trainables,
209 };
210 ccv_array_t* const internals = ccv_array_new(sizeof(ccv_nnc_tensor_symbol_t), 0, 0);
211 ccv_array_t* const internal_ids = ccv_array_new(sizeof(char*), 0, 0);
212 ccv_cnnp_model_add_to_array_context_t add_to_output_context = {
213 .sequence = &model_sequence,
214 .prefix = 'r',
215 .symbols = internals,
216 .ids = internal_ids,
217 .trainables = 0,
218 };
219 ccv_cnnp_model_build_data_t build_data = {
220 .is_trainable = model->is_trainable >= 0 ? model->is_trainable : 1,
221 .model_sequence = &model_sequence,
222 .add_to_array = ccv_cnnp_model_add_to_array,
223 .parameters = parameters,
224 .context = {
225 .add_to_parameter = &add_to_parameter_context,
226 .add_to_output = &add_to_output_context,
227 },
228 .gradient_checkpoints = 0,
229 };
230 model->data = &build_data;
231 ccv_cnnp_model_build(model, model->graph, model->inputs, input_size, 0, 0);
232 for (i = 0; i < model->output_size; i++)
233 {
234 const ccv_nnc_tensor_symbol_t output = model->outputs[i];
235 const ccv_nnc_tensor_symbol_t alias_to = ccv_nnc_tensor_symbol_alias_to(model->graph, output);
236 if (alias_to.d == CCV_NNC_NO_TENSOR_SYMBOL)
237 continue;
238 // 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
239 // 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
240 // honest, because we cannot handle cases of alias is part of the original tensor but bind differently).
241 const ccv_nnc_tensor_param_t output_params = ccv_nnc_tensor_symbol_params(model->graph, output);
242 model->outputs[i] = ccv_nnc_tensor_symbol_new(model->graph, output_params, 0);
243 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");
244 ccv_nnc_graph_exec_symbol_set_flags(model->graph, make_contiguous, CCV_NNC_GRAPH_EXEC_DISABLE_OPT);
245 }
246 model->data = 0;
247 kh_destroy(ccv_cnnp_model_name_bank, model_sequence.bank)kh_destroy_ccv_cnnp_model_name_bank(model_sequence.bank);
248 if (model_sequence.sequences)
249 ccv_array_free(model_sequence.sequences);
250 // Check if there are parameters that are not trainables. If there are, we will allocate uint64 bitmap to record that.
251 int not_trainables = 0;
252 // Assert no parameter is alias.
253 for (i = 0; i < parameters->rnum; i++)
254 {
255 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)));
256 const ccv_nnc_tensor_symbol_t alias_to = ccv_nnc_tensor_symbol_alias_to(parameter.graph, parameter);
257 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", 257, __extension__ __PRETTY_FUNCTION__)
; })); // Cannot find the one alias to.
258 if (*(int*)ccv_array_get(parameter_trainables, i)((void*)(((char*)((parameter_trainables)->data)) + (size_t
)(parameter_trainables)->rsize * (size_t)(i))) == 0)
259 not_trainables = 1;
260 }
261 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", 261, __extension__ __PRETTY_FUNCTION__)
; }));
262 uint64_t* parameter_flags = 0;
263 if (not_trainables)
264 {
265 parameter_flags = (uint64_t*)cccalloccalloc(((parameters->rnum + 63) >> 6), sizeof(uint64_t));
266 for (i = 0; i < parameter_trainables->rnum; i++)
267 if (*(int*)ccv_array_get(parameter_trainables, i)((void*)(((char*)((parameter_trainables)->data)) + (size_t
)(parameter_trainables)->rsize * (size_t)(i))))
268 parameter_flags[i >> 6] |= ((uint64_t)1 << (i & 63));
269 }
270 ccv_array_free(parameter_trainables);
271 // Assert no internal is alias.
272 for (i = 0; i < internals->rnum; i++)
273 {
274 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)));
275 const ccv_nnc_tensor_symbol_t alias_to = ccv_nnc_tensor_symbol_alias_to(internal.graph, internal);
276 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", 276, __extension__ __PRETTY_FUNCTION__)
; })); // Cannot find the one alias to.
277 }
278 const int output_size = model->output_size;
279 ccv_nnc_graph_exec_symbol_autogen(model->graph, 0, 0, CCV_NNC_AUTOGEN_ALL_EXECS | CCV_NNC_AUTOGEN_SOURCES_AND_DESTINATIONS);
280 const int parameters_rnum = parameters->rnum;
281 if (input_size > 0)
282 {
283 ccv_array_resize(parameters, parameters_rnum + input_size);
284 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));
285 }
286 ccv_nnc_symbolic_graph_simplify(model->graph,
287 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)
288 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)
289 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)
290 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),
291 ccv_array_get(parameters, 0)((void*)(((char*)((parameters)->data)) + (size_t)(parameters
)->rsize * (size_t)(0))), parameters_rnum + input_size,
292 model->outputs, output_size,
293 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));
294 ccv_nnc_graph_exec_symbol_autogen(model->graph, 0, 0, CCV_NNC_AUTOGEN_SOURCES_AND_DESTINATIONS);
295 // Size it down.
296 parameters->rnum = parameters_rnum;
297 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));
298 _ccv_cnnp_compiled_data_init(compiled_data, output_size, build_data.gradient_checkpoints);
299 const int evaluate_to_size = compiled_data->evaluate.to_size = ccv_nnc_symbolic_graph_destination_size(model->graph);
300 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", 300, __extension__ __PRETTY_FUNCTION__)
; }));
301 compiled_data->evaluate.tos = ccmallocmalloc(sizeof(ccv_nnc_graph_exec_symbol_t) * evaluate_to_size);
302 memcpy(compiled_data->evaluate.tos, ccv_nnc_symbolic_graph_destinations(model->graph), sizeof(ccv_nnc_graph_exec_symbol_t) * evaluate_to_size);
303 compiled_data->loss = loss;
304 if (loss.cmd == CCV_NNC_NOOP)
305 {
306 // If no loss function provided, there is no fits.
307 for (i = 0; i < output_size; i++)
308 {
309 compiled_data->fits[i] = NO_TENSOR_SYMBOL(const ccv_nnc_tensor_symbol_t){.d = CCV_NNC_NO_TENSOR_SYMBOL
};
310 const ccv_nnc_tensor_symbol_t alias_to = ccv_nnc_tensor_symbol_alias_to(model->graph, model->outputs[i]);
311 if (alias_to.d < 0)
312 compiled_data->f[i] = model->outputs[i];
313 else { // We cannot differentiate against an alias, therefore, we have to verify this output is full, and we can diff against the original.
314 int ofs[CCV_NNC_MAX_DIM_ALLOC(12)];
315 int inc[CCV_NNC_MAX_DIM_ALLOC(12)];
316 ccv_nnc_tensor_symbol_alias_params(model->graph, model->outputs[i], ofs, inc);
317 int j;
318 for (j = 0; j < CCV_NNC_MAX_DIM_ALLOC(12); j++)
319 { 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"
, 319, __extension__ __PRETTY_FUNCTION__); })); } // There is no ofs.
320 compiled_data->f[i] = alias_to; // Unfortunately, I cannot assert the size yet.
321 }
322 }
323 } else {
324 for (i = 0; i < output_size; i++)
325 {
326 const ccv_nnc_tensor_param_t info = ccv_nnc_tensor_symbol_params(model->graph, model->outputs[i]);
327 const ccv_nnc_tensor_symbol_t fit = compiled_data->fits[i] = ccv_nnc_tensor_symbol_new(model->graph, info, 0);
328 compiled_data->f[i] = ccv_nnc_tensor_symbol_new(model->graph, ccv_nnc_tensor_auto, 0);
329 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);
330 }
331 }
332 ccv_nnc_graph_exec_symbol_autogen(model->graph, 0, 0, CCV_NNC_AUTOGEN_ALL_EXECS | CCV_NNC_AUTOGEN_SOURCES_AND_DESTINATIONS);
333 ccv_nnc_symbolic_graph_simplify(model->graph,
334 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.
335 0, 0, // No need to provide binds at this point.
336 compiled_data->f, model->output_size,
337 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));
338 ccv_nnc_graph_exec_symbol_autogen(model->graph, 0, 0, CCV_NNC_AUTOGEN_SOURCES_AND_DESTINATIONS);
339 // If inputs are from GPU, stream type is GPU.
340 compiled_data->parameters = parameters;
341 compiled_data->parameter_flags = parameter_flags;
342 compiled_data->internals = internals;
343 compiled_data->ids.parameters = parameter_ids;
344 compiled_data->ids.internals = internal_ids;
345 ccv_cnnp_model_gradient_checkpoints_cleanup_after_build(compiled_data, model->graph);
346}
347
348static 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)
349{
350 ccv_array_t* const stack = (ccv_array_t*)context;
351 ccv_array_push(stack, &symbol.d);
352}
353
354static 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)
355{
356 const ccv_nnc_tensor_symbol_t src_symbol = {
357 .d = src_index,
358 .graph = src_graph
359 };
360 const ccv_nnc_tensor_symbol_t dest_symbol = {
361 .d = dest_index,
362 .graph = dest_graph
363 };
364 const ccv_nnc_tensor_param_t params = ccv_nnc_tensor_symbol_params(src_graph, src_symbol);
365 ccv_nnc_tensor_symbol_set(dest_graph, dest_symbol, params);
366 int ofs[CCV_NNC_MAX_DIM_ALLOC(12)];
367 int inc[CCV_NNC_MAX_DIM_ALLOC(12)];
368 if (0 == ccv_nnc_tensor_symbol_alias_params(src_graph, src_symbol, ofs, inc))
369 ccv_nnc_tensor_symbol_alias_set(dest_graph, dest_symbol, ofs, inc);
370}
371
372static 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)
373{
374 const ccv_nnc_tensor_symbol_t src_symbol = {
375 .d = src_index,
376 .graph = src_graph
377 };
378 const ccv_nnc_tensor_param_t src_params = ccv_nnc_tensor_symbol_params(src_graph, src_symbol);
379 const ccv_nnc_tensor_symbol_t dest_symbol = {
380 .d = dest_index,
381 .graph = dest_graph
382 };
383 const ccv_nnc_tensor_param_t dest_params = ccv_nnc_tensor_symbol_params(dest_graph, dest_symbol);
384 return memcmp(src_params.dim, dest_params.dim, sizeof(src_params.dim)) == 0;
385}
386
387static 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);
388static void _ccv_cnnp_compiled_data_graph_free(ccv_cnnp_compiled_data_t* const compiled_data);
389
390typedef struct {
391 int parallel_count;
392 ccv_nnc_symbolic_graph_t* graph;
393 ccv_nnc_graph_exec_arena_t* graph_exec_arena;
394} ccv_nnc_graph_exec_update_t;
395
396static 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)
397{
398 ccv_nnc_graph_exec_update_t* const graph_exec_update = (ccv_nnc_graph_exec_update_t*)context;
399 ccv_nnc_graph_exec_arena_t* const graph_exec_arena = graph_exec_update->graph_exec_arena;
400 ccv_nnc_graph_exec_t graph_exec = ccv_nnc_graph_exec_from_symbol(graph_exec_arena, symbol);
401 ccv_nnc_graph_exec_set(graph_exec.graph, graph_exec, cmd);
402 ccv_nnc_graph_exec_set_hint(graph_exec.graph, graph_exec, hint);
403 const ccv_nnc_symbolic_graph_t* const graph = graph_exec_update->graph;
404 const int parallel_count = graph_exec_update->parallel_count;
405 int i;
406 for (i = 1; i < parallel_count; i++)
407 {
408 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));
409 if (!CCV_NO_GRAPH_EXEC(copy)((copy).graph == 0))
410 {
411 ccv_nnc_graph_exec_set(copy.graph, copy, cmd);
412 ccv_nnc_graph_exec_set_hint(copy.graph, copy, hint);
413 }
414 }
415}
416
417void 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)
418{
419 assert(model->graph)((void) sizeof ((model->graph) ? 1 : 0), __extension__ ({ if
(model->graph) ; else __assert_fail ("model->graph", "ccv_cnnp_model.c"
, 419, __extension__ __PRETTY_FUNCTION__); }));
420 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", 420, __extension__ __PRETTY_FUNCTION__)
; }));
421 assert(!init->graph)((void) sizeof ((!init->graph) ? 1 : 0), __extension__ ({ if
(!init->graph) ; else __assert_fail ("!init->graph", "ccv_cnnp_model.c"
, 421, __extension__ __PRETTY_FUNCTION__); }));
422 ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
423 init->graph = ccv_nnc_symbolic_graph_new();
424 ccv_array_t* const stack = ccv_array_new(sizeof(int), 0, 0);
425 ccv_nnc_graph_exec_symbol_new_hook(init->graph, _ccv_cnnp_graph_push_graph_exec_symbol, stack, 0);
426 _ccv_cnnp_model_compile(init, inputs, input_size, compiled_data->loss);
427 init->parallel_count = model->parallel_count;
428 init->memory_compression = model->memory_compression;
429 init->memory_reduction = model->memory_reduction;
430 init->gradient_checkpointing = model->gradient_checkpointing;
431 init->compiled_data->stream_type = model->compiled_data->stream_type;
432 init->compiled_data->minimize.minimizer = model->compiled_data->minimize.minimizer;
433 init->compiled_data->minimize.max_saved_aux_size = model->compiled_data->minimize.max_saved_aux_size;
434 if (model->compiled_data->gradient_mode != CCV_CNNP_COMPILED_DATA_GRADIENT_NONE)
435 _ccv_cnnp_model_gradient_init(init, model->compiled_data->gradient_mode, model->compiled_data->disable_outgrad, 0, 0);
436 ccv_nnc_graph_exec_symbol_new_hook(init->graph, 0, 0, 0);
437 ccv_nnc_symbolic_graph_tensor_auto(init->graph, TRAVERSE_FULL0,0,0,0);
438 int i, j;
439 // Verify parameters, internals and saved_aux in both graph has the same dimensionality.
440 for (i = 0; i < compiled_data->parameters->rnum; i++)
441 {
442 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;
443 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", 443, __extension__ __PRETTY_FUNCTION__)
; }));
444 }
445 for (i = 0; i < compiled_data->internals->rnum; i++)
446 {
447 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;
448 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", 448, __extension__ __PRETTY_FUNCTION__)
; }));
449 }
450 // Update inputs.
451 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", 451, __extension__ __PRETTY_FUNCTION__)
; }));
452 for (i = 0; i < model->input_size; i++)
453 if (model->inputs[i].d >= 0)
454 {
455 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", 455, __extension__ __PRETTY_FUNCTION__)
; }));
456 _ccv_nnc_tensor_symbol_reinit(init->graph, model->graph, init->inputs[i].d, model->inputs[i].d);
457 }
458 // Update outputs.
459 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", 459, __extension__ __PRETTY_FUNCTION__)
; }));
460 for (i = 0; i < model->output_size; i++)
461 {
462 if (model->outputs[i].d >= 0)
463 {
464 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", 464, __extension__
__PRETTY_FUNCTION__); }));
465 _ccv_nnc_tensor_symbol_reinit(init->graph, model->graph, init->outputs[i].d, model->outputs[i].d);
466 }
467 if (model->outputs[i].d != model->compiled_data->f[i].d)
468 {
469 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", 469, __extension__ __PRETTY_FUNCTION__)
; }));
470 if (model->compiled_data->f[i].d >= 0)
471 {
472 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", 472, __extension__ __PRETTY_FUNCTION__)
; }));
473 _ccv_nnc_tensor_symbol_reinit(init->graph, model->graph, init->compiled_data->f[i].d, model->compiled_data->f[i].d);
474 }
475 }
476 }
477 // Go through the graph to set tensor on matching symbols
478 for (i = 0; i < stack->rnum; i++)
479 {
480 const int d = *(int*)ccv_array_get(stack, i)((void*)(((char*)((stack)->data)) + (size_t)(stack)->rsize
* (size_t)(i)));
481 // If exceed range, skip.
482 if (d >= ccv_nnc_graph_exec_symbol_count(init->graph) ||
483 d >= ccv_nnc_graph_exec_symbol_count(model->graph))
484 continue;
485 const ccv_nnc_graph_exec_symbol_t src_symbol = {
486 .d = d,
487 .graph = init->graph
488 };
489 const ccv_nnc_graph_exec_symbol_t dest_symbol = {
490 .d = d,
491 .graph = model->graph
492 };
493 const ccv_nnc_cmd_t src_cmd = ccv_nnc_graph_exec_symbol_cmd(init->graph, src_symbol);
494 const ccv_nnc_cmd_t dest_cmd = ccv_nnc_graph_exec_symbol_cmd(model->graph, dest_symbol);
495 // If the name doesn't match, skip.
496 if (dest_cmd.cmd != src_cmd.cmd && src_cmd.cmd != CCV_NNC_NOOP)
497 continue;
498 // Now get all the inputs and outputs, if matches, set them.
499 const int* src_inputs;
500 int src_input_size;
501 const int* src_outputs;
502 int src_output_size;
503 ccv_nnc_graph_exec_symbol_io(init->graph, src_symbol, &src_inputs, &src_input_size, &src_outputs, &src_output_size);
504 const int* dest_inputs;
505 int dest_input_size;
506 const int* dest_outputs;
507 int dest_output_size;
508 ccv_nnc_graph_exec_symbol_io(model->graph, dest_symbol, &dest_inputs, &dest_input_size, &dest_outputs, &dest_output_size);
509 // We may have unmatched input / output size because this is the minimizer and it has
510 // different saved_aux (for example, when we shrunk with CMD_NOOP).
511 if (src_input_size != dest_input_size)
512 continue;
513 if (src_output_size != dest_output_size)
514 continue;
515 ccv_nnc_graph_exec_symbol_set(model->graph, dest_symbol, src_cmd);
516 // There may be mismatches of the source tensor symbols and destination tensor symbols. The reason is because
517 // we may later passed-in the minimizer, therefore, we may allocate tensors for minimizer later in the original
518 // graph whereas in the newly created graph, it is streamlined (the minimizer exists from the beginning). That
519 // will make the order of tensor symbols creation different, therefore, exact which tensor is which wrong as
520 // well. However, set a new minimizer won't change the exec symbol ordering, because we never create new exec
521 // symbols after gradient init step. Changing a new minimizer just updated that exec symbols setting, it is not
522 // a new exec symbol.
523 for (j = 0; j < src_input_size; j++)
524 if (src_inputs[j] >= 0)
525 _ccv_nnc_tensor_symbol_reinit(init->graph, model->graph, src_inputs[j], dest_inputs[j]);
526 for (j = 0; j < src_output_size; j++)
527 if (src_outputs[j] >= 0)
528 _ccv_nnc_tensor_symbol_reinit(init->graph, model->graph, src_outputs[j], dest_outputs[j]);
529 }
530 ccv_array_free(stack);
531 // After this, we get all tensors in the model graph resolved through tensor_auto.
532 ccv_nnc_symbolic_graph_tensor_auto(model->graph, TRAVERSE_FULL0,0,0,0);
533 // Verify symbols we get matches.
534 const int parameter_size = compiled_data->parameters->rnum;
535 for (i = 0; i < parameter_size; i++)
536 { 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", 536, __extension__ __PRETTY_FUNCTION__)
; })); }
537 const int internal_size = compiled_data->internals->rnum;
538 for (i = 0; i < internal_size; i++)
539 { 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", 539, __extension__ __PRETTY_FUNCTION__)
; })); }
540 // Go through compiled data.
541 if (compiled_data->tensor_arena)
542 {
543 const int flag = ccv_nnc_tensor_arena_reinit(compiled_data->tensor_arena, model->graph);
544 if (flag == 0 && compiled_data->graph_exec_arena)
545 {
546 ccv_nnc_graph_exec_reinit(compiled_data->graph_exec_arena, compiled_data->graph, model->graph);
547 // Since we will reinit, if we previously set is_test, we need to set it again.
548 if (compiled_data->is_test)
549 {
550 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; });
551 ccv_nnc_graph_exec_update_t update = {
552 .parallel_count = parallel_count,
553 .graph = model->graph,
554 .graph_exec_arena = compiled_data->graph_exec_arena,
555 };
556 ccv_cnnp_model_set_is_test(model, 1, _ccv_cnnp_cmd_update_for_execs, &update);
557 }
558 } else
559 // Free-up tensor arena & graph exec arena.
560 _ccv_cnnp_compiled_data_graph_free(compiled_data);
561 }
562 // There are other compiled graphs, for accum and apply gradients.
563 // However, the main conclusion is, these absorb operations shouldn't impact parameters.
564 // Thus, it won't impact the shape of gradients (only outgrad). Since for outgrad, we
565 // don't allocate ourselves, it is not a concern. For normal gradients, the shape cannot
566 // be changed otherwise parameters' shape will be meaningless. The same goes to internals.
567 // That is why we don't update these compiled graphs at all this point.
568 // Free the model, we've already "absorbed" it.
569 ccv_cnnp_model_free(init);
570}
571
572void 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)
573{
574 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", 574, __extension__ __PRETTY_FUNCTION__)
; }));
575 if (model->input_size == 0)
576 model->input_size = input_size;
577 if (!model->graph) // The graph is not compiled yet.
578 {
579 model->graph = ccv_nnc_symbolic_graph_new();
580 _ccv_cnnp_model_compile(model, inputs, input_size, loss);
581 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", 581, __extension__ __PRETTY_FUNCTION__)
; }));
582 int i, flag = 0;
583 for (i = 0; !flag && i < input_size; i++)
584 flag = (CCV_TENSOR_GET_MEMORY(inputs[i].type)((inputs[i].type) & 0x3) == CCV_TENSOR_GPU_MEMORY);
585 // If inputs are from GPU, stream type is GPU.
586 model->compiled_data->stream_type = flag ? CCV_STREAM_CONTEXT_GPU : CCV_STREAM_CONTEXT_CPU;
587 model->compiled_data->minimize.minimizer = minimizer;
588 model->compiled_data->minimize.max_saved_aux_size = ccv_nnc_minimizer_saved_aux_size(minimizer);
589 } else {
590 // Now, finally fill in this part. If the graph is already compiled, we make a copy of the model.
591 // And then absorb the "new model" to the old one.
592 ccv_cnnp_model_t* const init = ccv_cnnp_model_copy(model, model->is_trainable);
593 ccv_cnnp_model_absorb(model, init, inputs, input_size);
594 // Reset minimizer.
595 ccv_cnnp_model_set_minimizer(model, minimizer, 1, 0, 0);
596 }
597}
598
599ccv_cnnp_model_t* ccv_cnnp_model_copy(const ccv_cnnp_model_t* const model, const int is_trainable)
600{
601 ccv_cnnp_model_t* const new_model = _ccv_cnnp_model_copy(model, 0);
602 new_model->is_trainable = is_trainable;
603 return new_model;
604}
605
606void ccv_cnnp_model_tensor_auto(ccv_cnnp_model_t* const model, ccv_nnc_tensor_param_t* const outputs, const int output_size)
607{
608 assert(model->graph)((void) sizeof ((model->graph) ? 1 : 0), __extension__ ({ if
(model->graph) ; else __assert_fail ("model->graph", "ccv_cnnp_model.c"
, 608, __extension__ __PRETTY_FUNCTION__); }));
609 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", 609, __extension__ __PRETTY_FUNCTION__)
; }));
610 ccv_nnc_symbolic_graph_t* const graph = model->graph;
611 ccv_nnc_symbolic_graph_tensor_auto(graph, TRAVERSE_FULL0,0,0,0);
612 int i;
613 for (i = 0; i < output_size; i++)
614 {
615 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", 615, __extension__ __PRETTY_FUNCTION__)
; }));
616 outputs[i] = ccv_nnc_tensor_symbol_params(graph, model->outputs[i]);
617 }
618}
619
620void ccv_cnnp_model_set_workspace_size(ccv_cnnp_model_t* const model, size_t workspace_size)
621{
622 if (workspace_size == model->workspace_size)
623 return;
624 model->workspace_size = workspace_size;
625 ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
626 if (compiled_data && compiled_data->graph)
627 ccv_nnc_graph_autotune(compiled_data->graph, workspace_size, 0, TRAVERSE_FULL0,0,0,0);
628}
629
630size_t ccv_cnnp_model_workspace_size(ccv_cnnp_model_t* const model)
631{
632 return model->workspace_size;
633}
634
635void ccv_cnnp_model_set_data_parallel(ccv_cnnp_model_t* const model, const int parallel)
636{
637 if (parallel == 0)
638 model->parallel_count = ccv_nnc_device_count(CCV_STREAM_CONTEXT_GPU);
639 else
640 model->parallel_count = parallel;
641 ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
642 if (compiled_data)
643 { 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", 643, __extension__ __PRETTY_FUNCTION__)
; })); }
644}
645
646void ccv_cnnp_model_set_max_concurrency(ccv_cnnp_model_t* const model, const int max_stream_count)
647{
648 model->max_stream_count = max_stream_count;
649 ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
650 if (compiled_data)
651 { 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", 651, __extension__ __PRETTY_FUNCTION__)
; })); }
652}
653
654void ccv_cnnp_model_set_memory_compression(ccv_cnnp_model_t* const model, const int memory_compression)
655{
656 model->memory_compression = memory_compression;
657 ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
658 if (compiled_data)
659 { 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", 659, __extension__ __PRETTY_FUNCTION__)
; })); }
660}
661
662void ccv_cnnp_model_set_memory_reduction(ccv_cnnp_model_t* const model, const int memory_reduction)
663{
664 model->memory_reduction = memory_reduction;
665 ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
666 if (compiled_data)
667 { 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", 667, __extension__ __PRETTY_FUNCTION__)
; })); }
668}
669
670void ccv_cnnp_model_set_gradient_checkpointing(ccv_cnnp_model_t* const model, const int gradient_checkpointing)
671{
672 model->gradient_checkpointing = gradient_checkpointing;
673}
674
675int ccv_cnnp_model_gradient_checkpointing(ccv_cnnp_model_t* const model)
676{
677 return model->gradient_checkpointing;
678}
679
680typedef struct {
681 int parallel_count;
682 ccv_nnc_symbolic_graph_t* graph;
683 ccv_cnnp_compiled_data_t* compiled_data;
684 ccv_nnc_tensor_arena_t* tensor_arena;
685} ccv_nnc_tensor_init_states_t;
686
687static int _ccv_cnnp_any_to_init(const ccv_cnnp_compiled_data_t* const compiled_data)
688{
689 int i;
690 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));
691 for (i = 0; i < compiled_data->parameters->rnum; i++)
692 {
693 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;
694 if (!(init_v[d >> 5] & (1u << (d & 0x1f))))
695 return 1;
696 }
697 for (i = 0; i < compiled_data->internals->rnum; i++)
698 {
699 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;
700 if (!(init_v[d >> 5] & (1u << (d & 0x1f))))
701 return 1;
702 }
703 return 0;
704}
705
706static 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)
707{
708 ccv_nnc_tensor_init_states_t* const tensor_init_states = (ccv_nnc_tensor_init_states_t*)context;
709 ccv_nnc_tensor_arena_t* const tensor_arena = tensor_init_states->tensor_arena;
710 ccv_nnc_tensor_t* const output_tensor = ccv_nnc_tensor_from_symbol(tensor_arena, output_symbol);
711 if (!output_tensor)
712 return;
713 const int d = output_symbol.d;
714 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", 714, __extension__ __PRETTY_FUNCTION__)
; }));
715 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));
716 if (init_v[d >> 5] & (1u << (d & 0x1f)))
717 return;
718 init_v[d >> 5] |= (1u << (d & 0x1f));
719 ccv_nnc_cmd_exec(cmd, hint, flags, &input, input ? 1 : 0, &output_tensor, 1, 0);
720 const ccv_nnc_symbolic_graph_t* const graph = tensor_init_states->graph;
721 const int parallel_count = tensor_init_states->parallel_count;
722 int i;
723 for (i = 1; i < parallel_count; i++)
724 {
725 ccv_nnc_tensor_t* const copy = ccv_nnc_tensor_from_symbol(tensor_arena, ccv_nnc_tensor_symbol_copy(graph, output_symbol, i));
726 if (copy)
727 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);
728 }
729}
730
731// This method can only handle cases we added new tensors and exec, never delete. This invariant is true because
732// we setup everything (including calling simplify method) in ccv_cnnp_model_compile method, before this rewind setup.
733static void _ccv_cnnp_model_rewind_graph(ccv_cnnp_model_t* const model)
734{
735 assert(model->graph)((void) sizeof ((model->graph) ? 1 : 0), __extension__ ({ if
(model->graph) ; else __assert_fail ("model->graph", "ccv_cnnp_model.c"
, 735, __extension__ __PRETTY_FUNCTION__); }));
736 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", 736, __extension__ __PRETTY_FUNCTION__)
; }));
737 ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
738 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", 738, __extension__
__PRETTY_FUNCTION__); }));
739 int i;
740 for (i = 0; i < compiled_data->rewindables->rnum; i++)
741 {
742 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)));
743 if (rewind_symbol->type == CCV_CNNP_REWIND_GRAPH_EXEC)
744 ccv_nnc_graph_exec_symbol_free(model->graph, rewind_symbol->graph_exec);
745 else if (rewind_symbol->type == CCV_CNNP_REWIND_TENSOR)
746 ccv_nnc_tensor_symbol_free(model->graph, rewind_symbol->tensor);
747 }
748 ccv_array_clear(compiled_data->rewindables);
749 ccv_nnc_graph_exec_symbol_autogen(model->graph, 0, 0, CCV_NNC_AUTOGEN_SOURCES_AND_DESTINATIONS);
750}
751
752static 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)
753{
754 const ccv_cnnp_rewind_symbol_t rewind_symbol = {
755 .type = CCV_CNNP_REWIND_TENSOR,
756 .tensor = symbol
757 };
758 ccv_array_t* const rewind_symbols = (ccv_array_t*)context;
759 ccv_array_push(rewind_symbols, &rewind_symbol);
760}
761
762static 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)
763{
764 const ccv_cnnp_rewind_symbol_t rewind_symbol = {
765 .type = CCV_CNNP_REWIND_TENSOR,
766 .tensor = symbol
767 };
768 ccv_array_t* const rewind_symbols = (ccv_array_t*)context;
769 ccv_array_push(rewind_symbols, &rewind_symbol);
770}
771
772static 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)
773{
774 const ccv_cnnp_rewind_symbol_t rewind_symbol = {
775 .type = CCV_CNNP_REWIND_GRAPH_EXEC,
776 .graph_exec = symbol
777 };
778 ccv_array_t* const rewind_symbols = (ccv_array_t*)context;
779 ccv_array_push(rewind_symbols, &rewind_symbol);
780}
781
782static 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)
783{
784 ccv_nnc_graph_exec_t const update_exec = ccv_nnc_graph_exec_from_symbol(graph_exec_arena, exec_symbol);
785 if (!CCV_NO_GRAPH_EXEC(update_exec)((update_exec).graph == 0))
786 ccv_nnc_graph_exec_set(update_exec.graph, update_exec, cmd);
787 int i;
788 for (i = 1; i < parallel_count; i++)
789 {
790 ccv_nnc_graph_exec_symbol_t copy_symbol = ccv_nnc_graph_exec_symbol_copy(symbolic_graph, exec_symbol, i);
791 const ccv_nnc_graph_exec_t copy = ccv_nnc_graph_exec_from_symbol(graph_exec_arena, copy_symbol);
792 if (!CCV_NO_GRAPH_EXEC(copy)((copy).graph == 0))
793 ccv_nnc_graph_exec_set(copy.graph, copy, cmd);
794 }
795}
796
797static 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)
798{
799 assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
(compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 799, __extension__ __PRETTY_FUNCTION__); }));
800 assert(symbolic_graph)((void) sizeof ((symbolic_graph) ? 1 : 0), __extension__ ({ if
(symbolic_graph) ; else __assert_fail ("symbolic_graph", "ccv_cnnp_model.c"
, 800, __extension__ __PRETTY_FUNCTION__); }));
801 ccv_nnc_graph_exec_symbol_set(symbolic_graph, exec_symbol, cmd);
802 int i;
803 for (i = 1; i < parallel_count; i++)
804 {
805 ccv_nnc_graph_exec_symbol_t copy_symbol = ccv_nnc_graph_exec_symbol_copy(symbolic_graph, exec_symbol, i);
806 if (copy_symbol.graph)
807 ccv_nnc_graph_exec_symbol_set(symbolic_graph, copy_symbol, cmd);
808 }
809 ccv_nnc_graph_exec_arena_t* const graph_exec_arena = compiled_data->graph_exec_arena;
810 if (graph_exec_arena)
811 _ccv_cnnp_model_graph_symbol_exec_set_for_graph_exec_arena(graph_exec_arena, parallel_count, exec_symbol, cmd, symbolic_graph);
812 // Skip backward graph exec arena because it is for a specific accum symbolic graph, not the main graph (model->graph)
813 ccv_nnc_graph_exec_arena_t* const gradient_graph_exec_arena = compiled_data->apply_gradients.graph_exec_arena;
814 if (gradient_graph_exec_arena)
815 _ccv_cnnp_model_graph_symbol_exec_set_for_graph_exec_arena(gradient_graph_exec_arena, parallel_count, exec_symbol, cmd, symbolic_graph);
816}
817
818static 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)
819{
820 int this_parameter_flag = 0;
821 if (update_nodes[parameter_indice].d == CCV_NNC_NO_TENSOR_SYMBOL)
822 return this_parameter_flag;
823 const ccv_nnc_cmd_t old_minimizer = ccv_nnc_graph_exec_symbol_cmd(graph, update_nodes[parameter_indice]);
824 int j, k;
825 // For no-op, we can preserve previous saved_aux_size.
826 if (old_minimizer.cmd != minimizer.cmd && minimizer.cmd != CCV_NNC_NOOP)
827 {
828 // If the old minimizer is a noop, then the old_saved_aux_size should be whatever its previous
829 // saved_aux_size is, otherwise we will reinit the saved_aux repeatedly if you switch between
830 // noop and a minimizer. We don't want that because we do that in high-level frameworks to
831 // make sure some model parameters don't update if we don't want them to.
832 int old_saved_aux_size;
833 if (old_minimizer.cmd == CCV_NNC_NOOP)
834 {
835 int input_size;
836 ccv_nnc_graph_exec_symbol_io(graph, update_nodes[parameter_indice], 0, &input_size, 0, 0);
837 if (input_size < 2) // This is not legit.
838 old_saved_aux_size = ccv_nnc_minimizer_saved_aux_size(old_minimizer);
839 else // See ccv_nnc_minimizer_saved_aux_size, the saved_aux is inputs excluding gradients and parameters.
840 old_saved_aux_size = input_size - 2;
841 } else
842 old_saved_aux_size = ccv_nnc_minimizer_saved_aux_size(old_minimizer);
843 if (old_saved_aux_size != saved_aux_size)
844 {
845 this_parameter_flag = 1;
846 if (saved_aux_size > old_saved_aux_size)
847 {
848 // Allocate new tensor symbols.
849 const ccv_nnc_tensor_param_t info = ccv_nnc_tensor_symbol_params(graph, updated_parameters[parameter_indice]);
850 for (j = old_saved_aux_size; j < saved_aux_size; j++)
851 {
852 saved_aux[parameter_indice * max_saved_aux_size + j].source = ccv_nnc_tensor_symbol_new(graph, info, 0);
853 saved_aux[parameter_indice * max_saved_aux_size + j].destination = ccv_nnc_tensor_symbol_new(graph, info, 0);
854 const int device_id = CCV_TENSOR_GET_DEVICE_ID(info.type)(((info.type) & 0xfff00) >> 8);
855 for (k = 1; k < parallel_count; k++)
856 {
857 ccv_nnc_tensor_param_t dev_info = info;
858 if (k != device_id)
859 CCV_TENSOR_SET_DEVICE_ID(dev_info.type, k)(dev_info.type) = (((dev_info.type) & ~0xfff00) | (((k) &
0xfff) << 8));
860 else
861 CCV_TENSOR_SET_DEVICE_ID(dev_info.type, 0)(dev_info.type) = (((dev_info.type) & ~0xfff00) | (((0) &
0xfff) << 8));
862 const ccv_nnc_tensor_symbol_t src_copy = ccv_nnc_tensor_symbol_new(graph, dev_info, 0);
863 const ccv_nnc_tensor_symbol_t dest_copy = ccv_nnc_tensor_symbol_new(graph, dev_info, 0);
864 ccv_nnc_tensor_symbol_set_copy(graph, saved_aux[parameter_indice * max_saved_aux_size + j].source, k, src_copy);
865 ccv_nnc_tensor_symbol_set_copy(graph, saved_aux[parameter_indice * max_saved_aux_size + j].destination, k, dest_copy);
866 }
867 }
868 } else {
869 for (j = saved_aux_size; j < old_saved_aux_size; j++)
870 {
871 for (k = 1; k < parallel_count; k++)
872 {
873 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);
874 if (src_copy.d >= 0)
875 {
876 ccv_nnc_tensor_symbol_free(graph, src_copy);
877 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
});
878 }
879 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);
880 if (dest_copy.d >= 0)
881 {
882 ccv_nnc_tensor_symbol_free(graph, dest_copy);
883 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
});
884 }
885 }
886 ccv_nnc_tensor_symbol_free(graph, saved_aux[parameter_indice * max_saved_aux_size + j].source);
887 ccv_nnc_tensor_symbol_free(graph, saved_aux[parameter_indice * max_saved_aux_size + j].destination);
888 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
};
889 }
890 }
891 }
892 }
893 _ccv_cnnp_model_graph_exec_symbol_set(graph, compiled_data, parallel_count, update_nodes[parameter_indice], minimizer);
894 if (this_parameter_flag)
895 {
896 ccv_nnc_tensor_symbol_t update_inputs[saved_aux_size + 2];
897 ccv_nnc_tensor_symbol_t update_outputs[saved_aux_size + 1];
898 const int* inputs = 0;
899 int input_size = 0;
900 ccv_nnc_graph_exec_symbol_io(graph, update_nodes[parameter_indice], &inputs, &input_size, 0, 0);
901 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", 901, __extension__ __PRETTY_FUNCTION__)
; }));
902 update_inputs[0].d = inputs[0];
903 update_inputs[0].graph = graph;
904 update_inputs[1].d = inputs[1];
905 update_inputs[1].graph = graph;
906 update_outputs[0] = updated_parameters[parameter_indice];
907 for (j = 0; j < saved_aux_size; j++)
908 {
909 update_inputs[j + 2] = saved_aux[parameter_indice * max_saved_aux_size + j].source;
910 update_outputs[j + 1] = saved_aux[parameter_indice * max_saved_aux_size + j].destination;
911 }
912 ccv_nnc_graph_exec_symbol_set_io(graph, update_nodes[parameter_indice], update_inputs, saved_aux_size + 2, update_outputs, saved_aux_size + 1);
913 for (k = 1; k < parallel_count; k++)
914 {
915 const ccv_nnc_graph_exec_symbol_t copy = ccv_nnc_graph_exec_symbol_copy(graph, update_nodes[parameter_indice], k);
916 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"
, 916, __extension__ __PRETTY_FUNCTION__); }));
917 ccv_nnc_graph_exec_symbol_io(graph, copy, &inputs, &input_size, 0, 0);
918 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", 918, __extension__ __PRETTY_FUNCTION__)
; }));
919 update_inputs[0].d = inputs[0];
920 update_inputs[0].graph = graph;
921 update_inputs[1].d = inputs[1];
922 update_inputs[1].graph = graph;
923 update_outputs[0] = ccv_nnc_tensor_symbol_copy(graph, updated_parameters[parameter_indice], k);
924 for (j = 0; j < saved_aux_size; j++)
925 {
926 update_inputs[j + 2] = ccv_nnc_tensor_symbol_copy(graph, saved_aux[parameter_indice * max_saved_aux_size + j].source, k);
927 update_outputs[j + 1] = ccv_nnc_tensor_symbol_copy(graph, saved_aux[parameter_indice * max_saved_aux_size + j].destination, k);
928 }
929 ccv_nnc_graph_exec_symbol_set_io(graph, copy, update_inputs, saved_aux_size + 2, update_outputs, saved_aux_size + 1);
930 }
931 }
932 return this_parameter_flag;
933}
934
935typedef struct {
936 int parameter_size;
937 ccv_nnc_cmd_t minimizer;
938 ccv_cnnp_model_io_t parameters[1];
939} ccv_cnnp_set_minimizer_for_parameter_t;
940
941static int _ccv_cnnp_apply_parameters_with_minimizer(ccv_cnnp_model_t* const model)
942{
943 ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
944 assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
(compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 944, __extension__ __PRETTY_FUNCTION__); }));
945 const int max_saved_aux_size = compiled_data->minimize.max_saved_aux_size;
946 // We update all parameters, at this point, we have one minimizer.
947 const int parameter_size = compiled_data->parameters->rnum;
948 ccv_nnc_graph_exec_symbol_t* const update_nodes = compiled_data->update_nodes;
949 ccv_nnc_symbolic_graph_t* const symbolic_graph = model->graph;
950 assert(symbolic_graph)((void) sizeof ((symbolic_graph) ? 1 : 0), __extension__ ({ if
(symbolic_graph) ; else __assert_fail ("symbolic_graph", "ccv_cnnp_model.c"
, 950, __extension__ __PRETTY_FUNCTION__); }));
951 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; });
952 ccv_array_t* const parameters = compiled_data->minimize.parameters;
953 ccv_array_t* const parameter_indices = ccv_array_new(sizeof(int), 0, 0);
954 int i, j, flag = 0;
955 for (i = 0; i < parameters->rnum; i++)
956 {
957 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)));
958 for (j = 0; j < set_minimizer_for_parameter->parameter_size; j++)
959 {
960 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;
961 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", 961, __extension__ __PRETTY_FUNCTION__)
; }));
962 const int old_rnum = parameter_indices->rnum;
963 ccv_cnnp_model_add_to_parameter_indices(set_minimizer_for_parameter->parameters[j]->model, param_sel, parameter_indices);
964 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;
965 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", 965, __extension__ __PRETTY_FUNCTION__)
; }));
966 if (param_ref >= 0)
967 {
968 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", 968, __extension__ __PRETTY_FUNCTION__)
; }));
969 *(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)));
970 parameter_indices->rnum = old_rnum + 1;
971 }
972 }
973 const int saved_aux_size = ccv_nnc_minimizer_saved_aux_size(set_minimizer_for_parameter->minimizer);
974 // We may have duplicated indices, but that is OK, we will set it twice.
975 for (j = 0; j < parameter_indices->rnum; j++)
976 {
977 const int d = *(int*)ccv_array_get(parameter_indices, j)((void*)(((char*)((parameter_indices)->data)) + (size_t)(parameter_indices
)->rsize * (size_t)(j)));
978 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", 978, __extension__ __PRETTY_FUNCTION__)
; }));
979 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))
980 flag = 1;
981 }
982 ccv_array_clear(parameter_indices);
983 }
984 ccv_array_free(parameter_indices);
985 return flag;
986}
987
988static 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)
989{
990 if (new_saved_aux_size == old_saved_aux_size)
991 return;
992 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", 992, __extension__ __PRETTY_FUNCTION__)
; }));
993 int i, j;
994 for (i = parameter_size - 1; i >= 0; i--)
995 {
996 for (j = new_saved_aux_size - 1; j >= old_saved_aux_size; j--)
997 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
};
998 for (j = old_saved_aux_size - 1; j >= 0; j--)
999 saved_aux[i * new_saved_aux_size + j] = saved_aux[i * old_saved_aux_size + j];
1000 }
1001}
1002
1003static void _ccv_cnnp_model_set_rewindables(ccv_cnnp_model_t* const model)
1004{
1005 ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
1006 assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
(compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 1006, __extension__ __PRETTY_FUNCTION__); }));
1007 if (!compiled_data->rewindables)
1008 compiled_data->rewindables = ccv_array_new(sizeof(ccv_cnnp_rewind_symbol_t), 0, 0);
1009 ccv_nnc_tensor_symbol_new_hook(model->graph, _ccv_cnnp_model_tensor_symbol_new_hook, compiled_data->rewindables, 0);
1010 ccv_nnc_tensor_symbol_alias_new_hook(model->graph, _ccv_cnnp_model_tensor_symbol_alias_new_hook, compiled_data->rewindables, 0);
1011 ccv_nnc_graph_exec_symbol_new_hook(model->graph, _ccv_cnnp_model_graph_exec_symbol_new_hook, compiled_data->rewindables, 0);
1012}
1013
1014static 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)
1015{
1016 ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
1017 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", 1017, __extension__ __PRETTY_FUNCTION__
); }));
1018 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", 1018, __extension__ __PRETTY_FUNCTION__
); }));
1019 const int evaluate_to_size = compiled_data->evaluate.to_size;
1020 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", 1020, __extension__ __PRETTY_FUNCTION__
); }));
1021 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; });
1022 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);
1023 compiled_data->evaluate.to_ops = (ccv_nnc_graph_exec_t*)(compiled_data->evaluate.tos + evaluate_to_size * parallel_count);
1024 int i, j;
1025 const int output_size = model->output_size;
1026 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", 1026, __extension__ __PRETTY_FUNCTION__
); }));
1027 if (fits)
1028 for (i = 0; i < output_size; i++)
1029 ccv_nnc_tensor_symbol_set(model->graph, compiled_data->fits[i], fits[i]->info);
1030 const int max_saved_aux_size = compiled_data->minimize.max_saved_aux_size;
1031 const int parameter_size = compiled_data->parameters->rnum;
1032 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);
1033 compiled_data->update_nodes = (ccv_nnc_graph_exec_symbol_t*)(compiled_data->updated_parameters + parameter_size);
1034 compiled_data->saved_aux = (ccv_nnc_tensor_symbol_map_t*)(compiled_data->update_nodes + parameter_size);
1035 int parameter_size_maybe_more = parameter_size;
1036 compiled_data->disable_outgrad = disable_outgrad;
1037 int outgrad_size;
1038 if (gradient_mode == CCV_CNNP_COMPILED_DATA_GRADIENT_TRAINABLES || model->input_size == 0)
1039 outgrad_size = 0;
1040 else if (disable_outgrad == CCV_CNNP_DISABLE_OUTGRAD_NONE) // Compute minimize with gradients including inputs.
1041 outgrad_size = model->input_size;
1042 else {
1043 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", 1043, __extension__ __PRETTY_FUNCTION__
); })); // If it is disable all, gradient mode won't be this.
1044 outgrad_size = 0;
1045 for (i = 0; i < model->input_size; i++)
1046 if (!(disable_outgrad & ((uint64_t)1 << i)))
1047 ++outgrad_size;
1048 }
1049 compiled_data->outgrad_size = outgrad_size;
1050 parameter_size_maybe_more += outgrad_size;
1051 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);
1052 compiled_data->outgrads = parameter_size_maybe_more > parameter_size ? compiled_data->gradients + parameter_size : 0;
1053 compiled_data->backward.tos = (ccv_nnc_graph_exec_symbol_t*)(compiled_data->gradients + parameter_size_maybe_more);
1054 compiled_data->backward.to_size = parameter_size_maybe_more;
1055 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)));
1056 if (compiled_data->parameter_flags)
1057 {
1058 parameters = (ccv_nnc_tensor_symbol_t*)ccmallocmalloc(sizeof(ccv_nnc_tensor_symbol_t) * parameter_size);
1059 for (i = 0; i < parameter_size; i++)
1060 if (compiled_data->parameter_flags[i >> 6] & ((uint64_t)1 << (i & 63)))
1061 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)));
1062 else
1063 parameters[i] = NO_TENSOR_SYMBOL(const ccv_nnc_tensor_symbol_t){.d = CCV_NNC_NO_TENSOR_SYMBOL
};
1064 }
1065 if (gradient_mode == CCV_CNNP_COMPILED_DATA_GRADIENT_TRAINABLES || model->input_size == 0)
1066 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);
1067 else if (disable_outgrad == CCV_CNNP_DISABLE_OUTGRAD_NONE) // Compute minimize with gradients including inputs.
1068 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);
1069 else { // Compute minimize with gradients including selected inputs.
1070 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", 1070, __extension__ __PRETTY_FUNCTION__
); }));
1071 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", 1071, __extension__ __PRETTY_FUNCTION__
); })); // If it is disable all, gradient mode won't be this.
1072 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", 1072, __extension__ __PRETTY_FUNCTION__
); }));
1073 ccv_nnc_tensor_symbol_t outgrads[outgrad_size];
1074 j = 0;
1075 for (i = 0; i < model->input_size; i++)
1076 if (!(disable_outgrad & ((uint64_t)1 << i)))
1077 outgrads[j++] = model->inputs[i];
1078 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);
1079 }
1080 if (compiled_data->parameter_flags)
1081 ccfreefree(parameters);
1082 _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);
1083 if (compiled_data->minimize.parameters)
1084 _ccv_cnnp_apply_parameters_with_minimizer(model);
1085 // Go through gradient checkpoints to generate tensor inputs for backward pass just before executing the backward pass.
1086 ccv_cnnp_model_apply_gradient_checkpoints(compiled_data, model->graph);
1087 for (i = 0; i < output_size; i++)
1088 {
1089 const ccv_nnc_tensor_symbol_t df = ccv_nnc_tensor_symbol_for_backward(model->graph, compiled_data->f[i]);
1090 // Init this to 1 so we can backprop.
1091 ccv_nnc_tensor_symbol_set_flags(model->graph, df, CCV_NNC_TENSOR_SYMBOL_INIT_ONES);
1092 }
1093 compiled_data->backward.to_size = 0;
1094 for (i = 0; i < parameter_size_maybe_more; i++)
1095 if (compiled_data->gradients[i].d != CCV_NNC_NO_TENSOR_SYMBOL)
1096 compiled_data->backward.tos[compiled_data->backward.to_size++] = ccv_nnc_graph_exec_symbol_for_backward(model->graph, compiled_data->gradients[i]);
1097 ccv_nnc_graph_exec_symbol_autogen(model->graph, 0, 0, CCV_NNC_AUTOGEN_ALL_EXECS);
1098 ccv_nnc_symbolic_graph_set_destinations(model->graph, compiled_data->update_nodes, parameter_size);
1099 for (i = 0; i < parameter_size_maybe_more - parameter_size; i++)
1100 {
1101 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.
1102 continue;
1103 const ccv_nnc_graph_exec_symbol_t outgrad = ccv_nnc_graph_exec_symbol_for_backward(model->graph, compiled_data->outgrads[i]);
1104 const int* tos;
1105 int to_size;
1106 ccv_nnc_graph_exec_symbol_to(model->graph, outgrad, &tos, &to_size);
1107 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.
1108 {
1109 const ccv_nnc_graph_exec_symbol_t* destinations = ccv_nnc_symbolic_graph_destinations(model->graph);
1110 const int destination_count = ccv_nnc_symbolic_graph_destination_size(model->graph);
1111 int flag = 0;
1112 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; });
1113 for (j = i - 1; !flag && j >= 0; j--)
1114 if (j + outgrad_destination_start < destination_count)
1115 flag = (destinations[j + outgrad_destination_start].d == outgrad.d);
1116 if (!flag) // Only if we cannot find it, we add it.
1117 ccv_nnc_symbolic_graph_add_destination(model->graph, outgrad);
1118 }
1119 }
1120 if (parallel_count > 1)
1121 {
1122 ccv_nnc_symbolic_graph_data_parallel(model->graph, parallel_count,
1123 0, 0,
1124 compiled_data->gradients, parameter_size /* No need to deal with outgrads, we don't allreduce outgrads */,
1125 compiled_data->gradients /* We only care about gradients before allreduce, thus, update our current pointers */,
1126 0, 0, 0,
1127 CCV_NNC_PARALLEL_REDUCE_OP_SUM,
1128 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));
1129 ccv_nnc_graph_exec_symbol_autogen(model->graph, 0, 0, CCV_NNC_AUTOGEN_SOURCES_AND_DESTINATIONS);
1130 for (i = 0; i < evaluate_to_size; i++)
1131 for (j = 1; j < parallel_count; j++)
1132 {
1133 const ccv_nnc_graph_exec_symbol_t copy = ccv_nnc_graph_exec_symbol_copy(model->graph, compiled_data->evaluate.tos[i], j);
1134 if (copy.d != CCV_NNC_NO_GRAPH_EXEC_SYMBOL)
1135 compiled_data->evaluate.tos[compiled_data->evaluate.to_size++] = copy;
1136 }
1137 const int backward_to_size = compiled_data->backward.to_size;
1138 for (i = 0; i < backward_to_size; i++)
1139 for (j = 1; j < parallel_count; j++)
1140 {
1141 const ccv_nnc_graph_exec_symbol_t copy = ccv_nnc_graph_exec_symbol_copy(model->graph, compiled_data->backward.tos[i], j);
1142 if (copy.d != CCV_NNC_NO_GRAPH_EXEC_SYMBOL)
1143 compiled_data->backward.tos[compiled_data->backward.to_size++] = copy;
1144 }
1145 }
1146 // Only use memory compression if we are in gradient parameter mode.
1147 if (gradient_mode == CCV_CNNP_COMPILED_DATA_GRADIENT_TRAINABLES || gradient_mode == CCV_CNNP_COMPILED_DATA_GRADIENT_TRAINABLES_AND_INPUTS)
1148 {
1149 if (model->memory_compression)
1150 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));
1151 if (model->memory_reduction)
1152 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));
1153 }
1154 compiled_data->backward.to_size = _ccv_nnc_array_dedup_graph_exec_symbols(compiled_data->backward.tos, compiled_data->backward.to_size);
1155 compiled_data->gradient_mode = gradient_mode;
1156}
1157
1158void ccv_cnnp_model_tensors_init_0(const ccv_cnnp_model_t* const model, ccv_cnnp_compiled_data_t* const compiled_data)
1159{
1160 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", 1160, __extension__ __PRETTY_FUNCTION__
); }));
1161 const int parameter_size = compiled_data->parameters->rnum;
1162 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; });
1163 const int internal_size = compiled_data->internals->rnum;
1164 compiled_data->tensors_init.size = ccv_nnc_tensor_symbol_count(model->graph);
1165 compiled_data->tensors_init.v = cccalloccalloc(((compiled_data->tensors_init.size + 31) >> 5), sizeof(uint32_t));
1166 compiled_data->tensors.parameters = (ccv_nnc_tensor_t**)cccalloccalloc((parameter_size + internal_size) * parallel_count, sizeof(ccv_nnc_tensor_t*));
1167 compiled_data->tensors.internals = compiled_data->tensors.parameters + parameter_size * parallel_count;
1168}
1169
1170int ccv_cnnp_model_tensors_any_to_alloc(const ccv_cnnp_model_t* const model, ccv_cnnp_compiled_data_t* const compiled_data)
1171{
1172 int i, j;
1173 const int parameter_size = compiled_data->parameters->rnum;
1174 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; });
1175 const int internal_size = compiled_data->internals->rnum;
1176 for (i = 0; i < parameter_size; i++)
1177 {
1178 // parameters has to be allocated all together.
1179 if (compiled_data->tensors.parameters[i])
1180 {
1181 for (j = 1; j < parallel_count; j++)
1182 { 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", 1182, __extension__ __PRETTY_FUNCTION__
); })); }
1183 continue;
1184 }
1185 return 1;
1186 }
1187 for (i = 0; i < internal_size; i++)
1188 {
1189 if (!compiled_data->tensors.internals[i])
1190 return 1;
1191 for (j = 1; j < parallel_count; j++)
1192 if (!compiled_data->tensors.internals[i + j * internal_size])
1193 return 1;
1194 }
1195 return 0;
1196}
1197
1198void ccv_cnnp_model_tensors_init_1(const ccv_cnnp_model_t* const model, ccv_cnnp_compiled_data_t* const compiled_data)
1199{
1200 int i, j;
1201 const int parameter_size = compiled_data->parameters->rnum;
1202 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; });
1203 const int internal_size = compiled_data->internals->rnum;
1204 for (i = 0; i < parameter_size; i++)
1205 {
1206 // parameters has to be allocated all together.
1207 if (compiled_data->tensors.parameters[i])
1208 {
1209 for (j = 1; j < parallel_count; j++)
1210 { 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", 1210, __extension__ __PRETTY_FUNCTION__
); })); }
1211 continue;
1212 }
1213 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)));
1214 ccv_nnc_tensor_param_t info = ccv_nnc_tensor_symbol_params(parameter.graph, parameter);
1215 if (CCV_TENSOR_GET_DEVICE(info.type)((info.type) & 0xfff00) == CCV_COMPUTE_DEVICE_ANY)
1216 CCV_TENSOR_SET_DEVICE_ID(info.type, 0)(info.type) = (((info.type) & ~0xfff00) | (((0) & 0xfff
) << 8));
1217 const int device_id = CCV_TENSOR_GET_DEVICE_ID(info.type)(((info.type) & 0xfff00) >> 8);
1218 compiled_data->tensors.parameters[i] = ccv_nnc_tensor_new(0, info, 0);
1219 for (j = 1; j < parallel_count; j++)
1220 {
1221 if (j != device_id)
1222 CCV_TENSOR_SET_DEVICE_ID(info.type, j)(info.type) = (((info.type) & ~0xfff00) | (((j) & 0xfff
) << 8));
1223 else
1224 CCV_TENSOR_SET_DEVICE_ID(info.type, 0)(info.type) = (((info.type) & ~0xfff00) | (((0) & 0xfff
) << 8));
1225 compiled_data->tensors.parameters[i + j * parameter_size] = ccv_nnc_tensor_new(0, info, 0);
1226 }
1227 }
1228 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));
1229 for (i = 0; i < internal_size; i++)
1230 {
1231 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))
);
1232 const int d = retained.d;
1233 if (init_v[d >> 5] & (1u << (d & 0x1f)))
1234 continue;
1235 ccv_nnc_tensor_param_t info = ccv_nnc_tensor_symbol_params(retained.graph, retained);
1236 if (CCV_TENSOR_GET_DEVICE(info.type)((info.type) & 0xfff00) == CCV_COMPUTE_DEVICE_ANY)
1237 CCV_TENSOR_SET_DEVICE_ID(info.type, 0)(info.type) = (((info.type) & ~0xfff00) | (((0) & 0xfff
) << 8));
1238 const int device_id = CCV_TENSOR_GET_DEVICE_ID(info.type)(((info.type) & 0xfff00) >> 8);
1239 if (!compiled_data->tensors.internals[i])
1240 compiled_data->tensors.internals[i] = ccv_nnc_tensor_new(0, info, 0);
1241 for (j = 1; j < parallel_count; j++)
1242 {
1243 if (j != device_id)
1244 CCV_TENSOR_SET_DEVICE_ID(info.type, j)(info.type) = (((info.type) & ~0xfff00) | (((j) & 0xfff
) << 8));
1245 else
1246 CCV_TENSOR_SET_DEVICE_ID(info.type, 0)(info.type) = (((info.type) & ~0xfff00) | (((0) & 0xfff
) << 8));
1247 if (!compiled_data->tensors.internals[i + j * internal_size])
1248 compiled_data->tensors.internals[i + j * internal_size] = ccv_nnc_tensor_new(0, info, 0);
1249 }
1250 }
1251 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.
1252}
1253
1254static void _ccv_cnnp_model_tensors_init(const ccv_cnnp_model_t* const model, ccv_cnnp_compiled_data_t* const compiled_data)
1255{
1256 ccv_cnnp_model_tensors_init_0(model, compiled_data);
1257 ccv_cnnp_model_tensors_init_1(model, compiled_data);
1258}
1259
1260static 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)
1261{
1262 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", 1262, __extension__ __PRETTY_FUNCTION__
); }));
1263 int i, j;
1264 for (i = 0; i < tensor_size; i++)
1265 {
1266 if (!tensors[i])
1267 continue;
1268 const int d = tensor_symbols[i].d;
1269 if (!(tensors_init[d >> 5] & (1u << (d & 0x1f))))
1270 continue;
1271 for (j = 1; j < parallel_count; j++)
1272 if (tensors[i + j * tensor_size])
1273 {
1274 ccv_nnc_tensor_t* const input = CCV_NNC_TENSOR(tensors[i])((ccv_nnc_tensor_t*)((uintptr_t)(tensors[i]) & ~(uintptr_t
)1));
1275 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));
1276 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);
1277 }
1278 }
1279}
1280
1281static 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)
1282{
1283 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", 1283, __extension__ __PRETTY_FUNCTION__
); }));
1284 int i, j;
1285 for (i = 0; i < tensor_size; i++)
1286 {
1287 const ccv_nnc_tensor_symbol_t tensor_symbol = tensor_symbols[i];
1288 for (j = 1; j < parallel_count; j++)
1289 {
1290 const ccv_nnc_tensor_symbol_t copy = ccv_nnc_tensor_symbol_copy(graph, tensor_symbol, j);
1291 ccv_nnc_tensor_t* copy_tensor = tensors[i + j * tensor_size];
1292 if (copy_tensor && copy.d == CCV_NNC_NO_TENSOR_SYMBOL)
1293 { // We shouldn't allocate this, free it up.
1294 ccv_nnc_tensor_free(tensors[i + j * tensor_size]);
1295 tensors[i + j * tensor_size] = 0;
1296 }
1297 }
1298 }
1299}
1300
1301static 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)
1302{
1303 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", 1303, __extension__ __PRETTY_FUNCTION__
); }));
1304 int i, j;
1305 for (i = 0; i < tensor_size; i++)
1306 {
1307 ccv_nnc_tensor_symbol_t tensor_symbol = tensor_symbols[i];
1308 if (tensor_symbol.d == CCV_NNC_NO_TENSOR_SYMBOL)
1309 continue;
1310 if (graph)
1311 {
1312 const ccv_nnc_tensor_symbol_t alias_to = ccv_nnc_tensor_symbol_alias_to(graph, tensor_symbol);
1313 if (alias_to.d != CCV_NNC_NO_TENSOR_SYMBOL)
1314 tensor_symbol = alias_to;
1315 }
1316 ccv_nnc_tensor_t* const tensor = CCV_NNC_TENSOR(tensors[i])((ccv_nnc_tensor_t*)((uintptr_t)(tensors[i]) & ~(uintptr_t
)1));
1317 if (tensor && tensor_symbol.d != CCV_NNC_NO_TENSOR_SYMBOL)
1318 {
1319 const ccv_nnc_tensor_bind_t retained_bind = {
1320 .symbol = tensor_symbol,
1321 .tensor = tensor
1322 };
1323 ccv_array_push(tensor_binds, &retained_bind);
1324 }
1325 for (j = 1; j < parallel_count; j++)
1326 {
1327 const ccv_nnc_tensor_symbol_t copy = ccv_nnc_tensor_symbol_copy(graph, tensor_symbol, j);
1328 ccv_nnc_tensor_t* copy_tensor = tensors[i + j * tensor_size];
1329 if (copy_tensor && copy.d != CCV_NNC_NO_TENSOR_SYMBOL)
1330 {
1331 const ccv_nnc_tensor_bind_t bind = {
1332 .symbol = copy,
1333 .tensor = tensors[i + j * tensor_size]
1334 };
1335 ccv_array_push(tensor_binds, &bind);
1336 }
1337 }
1338 }
1339}
1340
1341static void _ccv_cnnp_compiled_data_graph_free(ccv_cnnp_compiled_data_t* const compiled_data)
1342{
1343 if (compiled_data->graph)
1344 ccv_nnc_graph_free(compiled_data->graph);
1345 compiled_data->graph = 0;
1346 compiled_data->is_test = 0;
1347 if (compiled_data->tensor_arena)
1348 ccv_nnc_tensor_arena_free(compiled_data->tensor_arena);
1349 compiled_data->tensor_arena = 0;
1350 if (compiled_data->graph_exec_arena)
1351 ccv_nnc_graph_exec_arena_free(compiled_data->graph_exec_arena);
1352 compiled_data->graph_exec_arena = 0;
1353 if (compiled_data->backward.from_ops)
1354 ccfreefree(compiled_data->backward.from_ops);
1355 compiled_data->backward.from_ops = 0;
1356 if (compiled_data->evaluate.schedule)
1357 ccv_nnc_graph_static_schedule_free(compiled_data->evaluate.schedule);
1358 compiled_data->evaluate.schedule = 0;
1359 if (compiled_data->backward.schedule)
1360 ccv_nnc_graph_static_schedule_free(compiled_data->backward.schedule);
1361 compiled_data->backward.schedule = 0;
1362}
1363
1364static void _ccv_cnnp_compiled_data_gradient_free(ccv_cnnp_compiled_data_t* const compiled_data)
1365{
1366 if (compiled_data->gradients)
1367 ccfreefree(compiled_data->gradients);
1368 compiled_data->gradients = 0;
1369 if (compiled_data->updated_parameters)
1370 ccfreefree(compiled_data->updated_parameters);
1371 compiled_data->updated_parameters = 0;
1372 compiled_data->update_nodes = 0;
1373 compiled_data->saved_aux = 0;
1374}
1375
1376static void _ccv_cnnp_compiled_data_backward_free(ccv_cnnp_compiled_data_t* const compiled_data)
1377{
1378 if (compiled_data->backward.gradients)
1379 ccfreefree(compiled_data->backward.gradients);
1380 compiled_data->backward.gradients = 0;
1381 if (compiled_data->backward.accum)
1382 ccv_nnc_graph_free(compiled_data->backward.accum);
1383 compiled_data->backward.accum = 0;
1384 if (compiled_data->backward.tensor_arena)
1385 ccv_nnc_tensor_arena_free(compiled_data->backward.tensor_arena);
1386 compiled_data->backward.tensor_arena = 0;
1387 if (compiled_data->backward.graph_exec_arena)
1388 ccv_nnc_graph_exec_arena_free(compiled_data->backward.graph_exec_arena);
1389 compiled_data->backward.graph_exec_arena = 0;
1390}
1391
1392static void _ccv_cnnp_compiled_data_apply_gradients_free(ccv_cnnp_compiled_data_t* const compiled_data)
1393{
1394 if (compiled_data->apply_gradients.graph)
1395 ccv_nnc_graph_free(compiled_data->apply_gradients.graph);
1396 compiled_data->apply_gradients.graph = 0;
1397 if (compiled_data->apply_gradients.tensor_arena)
1398 ccv_nnc_tensor_arena_free(compiled_data->apply_gradients.tensor_arena);
1399 compiled_data->apply_gradients.tensor_arena = 0;
1400 if (compiled_data->apply_gradients.graph_exec_arena)
1401 ccv_nnc_graph_exec_arena_free(compiled_data->apply_gradients.graph_exec_arena);
1402 compiled_data->apply_gradients.graph_exec_arena = 0;
1403}
1404
1405// Compile the graph to run ccv_cnnp_model_fit
1406static 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)
1407{
1408 int i, j;
1409 ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
1410 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", 1410, __extension__ __PRETTY_FUNCTION__
); }));
1411 compiled_data->graph_mode = CCV_CNNP_MODEL_GRAPH_FIT_MODE;
1412 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; });
1413 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", 1413, __extension__ __PRETTY_FUNCTION__
); }));
1414 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", 1414
, __extension__ __PRETTY_FUNCTION__); }));
1415 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", 1415, __extension__ __PRETTY_FUNCTION__
); }));
1416 if (compiled_data->gradient_mode == CCV_CNNP_COMPILED_DATA_GRADIENT_NONE)
1417 {
1418 _ccv_cnnp_model_set_rewindables(model);
1419 _ccv_cnnp_model_gradient_init(model, CCV_CNNP_COMPILED_DATA_GRADIENT_TRAINABLES, CCV_CNNP_DISABLE_OUTGRAD_ALL, fits, fit_size);
1420 } else if (compiled_data->gradient_mode != CCV_CNNP_COMPILED_DATA_GRADIENT_TRAINABLES) {
1421 _ccv_cnnp_model_rewind_graph(model);
1422 _ccv_cnnp_compiled_data_gradient_free(compiled_data);
1423 compiled_data->gradient_mode = CCV_CNNP_COMPILED_DATA_GRADIENT_NONE;
1424 _ccv_cnnp_model_gradient_init(model, CCV_CNNP_COMPILED_DATA_GRADIENT_TRAINABLES, CCV_CNNP_DISABLE_OUTGRAD_ALL, fits, fit_size);
1425 }
1426 const int tensors_init = !!compiled_data->tensors_init.v;
1427 if (!tensors_init)
1428 _ccv_cnnp_model_tensors_init(model, compiled_data);
1429 else if ((uintptr_t)compiled_data->tensors_init.v & (uintptr_t)1)
1430 // Check if it is not fully allocated, if it is not, init_1.
1431 ccv_cnnp_model_tensors_init_1(model, compiled_data);
1432 ccv_array_t* const tensor_binds = ccv_array_new(sizeof(ccv_nnc_tensor_bind_t), 0, 0);
1433 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"
, 1433, __extension__ __PRETTY_FUNCTION__); }));
1434 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"
, 1434, __extension__ __PRETTY_FUNCTION__); }));
1435 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", 1435
, __extension__ __PRETTY_FUNCTION__); }));
1436 const int input_size_per_p = input_size / parallel_count;
1437 _ccv_cnnp_model_bind_tensors(model->graph, model->inputs, inputs, input_size_per_p, parallel_count, tensor_binds);
1438 const int output_size_per_p = output_size / parallel_count;
1439 _ccv_cnnp_model_bind_tensors(model->graph, model->outputs, outputs, output_size_per_p, parallel_count, tensor_binds);
1440 const int fit_size_per_p = fit_size / parallel_count;
1441 _ccv_cnnp_model_bind_tensors(model->graph, compiled_data->fits, fits, fit_size_per_p, parallel_count, tensor_binds);
1442 const int parameter_size = compiled_data->parameters->rnum;
1443 _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);
1444 _ccv_cnnp_model_bind_tensors(model->graph, compiled_data->updated_parameters, compiled_data->tensors.parameters, parameter_size, parallel_count, tensor_binds);
1445 const int internal_size = compiled_data->internals->rnum;
1446 _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);
1447 _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);
1448 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);
1449 ccv_array_free(tensor_binds);
1450 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));
1451 if (tensors_init && parallel_count > 1)
1452 _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);
1453 // If tensor is not init'ed, we need to init states first.
1454 if (_ccv_cnnp_any_to_init(compiled_data))
1455 {
1456 ccv_nnc_tensor_init_states_t tensor_init_states = {
1457 .parallel_count = parallel_count,
1458 .graph = model->graph,
1459 .compiled_data = compiled_data,
1460 .tensor_arena = compiled_data->tensor_arena
1461 };
1462 ccv_cnnp_model_init_states(model, model->graph, _ccv_cnnp_init_states_for_tensors, &tensor_init_states);
1463 }
1464 compiled_data->is_test = 0;
1465 const int saved_aux_size = ccv_nnc_minimizer_saved_aux_size(compiled_data->minimize.minimizer);
1466 // No need to set because it is default to training mode.
1467 // ccv_cnnp_model_set_is_test(model, 0, _ccv_cnnp_cmd_update_for_execs, &update);
1468 for (i = 0; i < saved_aux_size * parameter_size; i++)
1469 {
1470 if (compiled_data->saved_aux[i].source.d == CCV_NNC_NO_TENSOR_SYMBOL)
1471 continue;
1472 ccv_nnc_tensor_t* const tensor = ccv_nnc_tensor_from_symbol(compiled_data->tensor_arena, compiled_data->saved_aux[i].source);
1473 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);
1474 for (j = 1; j < parallel_count; j++)
1475 {
1476 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));
1477 if (copy)
1478 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);
1479 }
1480 }
1481 const int evaluate_to_size = compiled_data->evaluate.to_size;
1482 compiled_data->evaluate.to_op_size = 0;
1483 for (i = 0; i < evaluate_to_size; i++)
1484 {
1485 ccv_nnc_graph_exec_t const to = ccv_nnc_graph_exec_from_symbol(compiled_data->graph_exec_arena, compiled_data->evaluate.tos[i]);
1486 if (to.graph)
1487 compiled_data->evaluate.to_ops[compiled_data->evaluate.to_op_size++] = to;
1488 }
1489 ccv_nnc_graph_set_default_static_schedule(compiled_data->graph, compiled_data->stream_type, model->max_stream_count);
1490 ccv_nnc_graph_autotune(compiled_data->graph, model->workspace_size, 0, TRAVERSE_FULL0,0,0,0);
1491}
1492
1493ccv_nnc_stream_context_t* ccv_cnnp_model_default_stream(const ccv_cnnp_model_t* const model)
1494{
1495 const ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
1496 if (!compiled_data || !compiled_data->graph)
1497 return 0;
1498 return ccv_nnc_graph_default_stream(compiled_data->graph);
1499}
1500
1501uint64_t ccv_cnnp_model_memory_size(const ccv_cnnp_model_t* const model)
1502{
1503 const ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
1504 if (!compiled_data || !compiled_data->tensor_arena)
1505 return 0;
1506 return ccv_nnc_tensor_arena_size(compiled_data->tensor_arena);
1507}
1508
1509static 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)
1510{
1511 int i, j;
1512 for (i = 0; i < tensor_size; i++)
1513 {
1514 ccv_nnc_tensor_symbol_t tensor_symbol = tensor_symbols[i];
1515 if (tensor_symbol.d == CCV_NNC_NO_TENSOR_SYMBOL)
1516 continue;
1517 if (graph)
1518 {
1519 const ccv_nnc_tensor_symbol_t alias_to = ccv_nnc_tensor_symbol_alias_to(graph, tensor_symbol);
1520 if (alias_to.d != CCV_NNC_NO_TENSOR_SYMBOL)
1521 tensor_symbol = alias_to;
1522 }
1523 ccv_nnc_tensor_bind_symbol(tensor_arena, tensor_symbol, tensors[i]);
1524 for (j = 1; j < parallel_count; j++)
1525 {
1526 const ccv_nnc_tensor_symbol_t copy = ccv_nnc_tensor_symbol_copy(graph, tensor_symbol, j);
1527 if (copy.d != CCV_NNC_NO_TENSOR_SYMBOL)
1528 ccv_nnc_tensor_bind_symbol(tensor_arena, copy, tensors[i + tensor_size * j]);
1529 }
1530 }
1531}
1532
1533void 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)
1534{
1535 ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
1536 assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
(compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 1536, __extension__ __PRETTY_FUNCTION__); }));
1537 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; });
1538 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", 1538, __extension__ __PRETTY_FUNCTION__
); }));
1539 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", 1539, __extension__ __PRETTY_FUNCTION__
); }));
1540 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", 1540
, __extension__ __PRETTY_FUNCTION__); }));
1541 assert(model->graph)((void) sizeof ((model->graph) ? 1 : 0), __extension__ ({ if
(model->graph) ; else __assert_fail ("model->graph", "ccv_cnnp_model.c"
, 1541, __extension__ __PRETTY_FUNCTION__); }));
1542 if (!compiled_data->graph || compiled_data->graph_mode != CCV_CNNP_MODEL_GRAPH_FIT_MODE)
1543 {
1544 _ccv_cnnp_compiled_data_graph_free(compiled_data);
1545 _ccv_cnnp_compiled_data_backward_free(compiled_data);
1546 _ccv_cnnp_compiled_data_apply_gradients_free(compiled_data);
1547 // Compile the symbolic graph down only when needed.
1548 _ccv_cnnp_model_fit_jit(model, inputs, input_size, fits, fit_size, outputs, output_size);
1549 } else {
1550 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"
, 1550, __extension__ __PRETTY_FUNCTION__); }));
1551 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"
, 1551, __extension__ __PRETTY_FUNCTION__); }));
1552 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", 1552
, __extension__ __PRETTY_FUNCTION__); }));
1553 const int input_size_per_p = input_size / parallel_count;
1554 _ccv_cnnp_bind_tensors_to_arena(compiled_data->tensor_arena, model->graph, model->inputs, inputs, input_size_per_p, parallel_count);
1555 const int output_size_per_p = output_size / parallel_count;
1556 _ccv_cnnp_bind_tensors_to_arena(compiled_data->tensor_arena, model->graph, model->outputs, outputs, output_size_per_p, parallel_count);
1557 const int fit_size_per_p = fit_size / parallel_count;
1558 _ccv_cnnp_bind_tensors_to_arena(compiled_data->tensor_arena, model->graph, compiled_data->fits, fits, fit_size_per_p, parallel_count);
1559 }
1560 if (compiled_data->is_test)
1561 {
1562 compiled_data->is_test = 0;
1563 ccv_nnc_graph_exec_update_t update = {
1564 .parallel_count = parallel_count,
1565 .graph = model->graph,
1566 .graph_exec_arena = compiled_data->graph_exec_arena,
1567 };
1568 ccv_cnnp_model_set_is_test(model, 0, _ccv_cnnp_cmd_update_for_execs, &update);
1569 }
1570 ccv_nnc_graph_run_with_schedule(compiled_data->graph, 0, 0, tensor_tape, stream_context);
1571}
1572
1573// Compile the graph to run ccv_cnnp_model_evaluate with require_grad = false (MULTISTAGE_MODE_NO_GRAD).
1574static 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)
1575{
1576 ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
1577 compiled_data->graph_mode = CCV_CNNP_MODEL_GRAPH_MULTISTAGE_MODE_NO_GRAD;
1578 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; });
1579 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", 1579, __extension__ __PRETTY_FUNCTION__
); }));
1580 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", 1580, __extension__ __PRETTY_FUNCTION__
); }));
1581 // If the gradient is not initialized, continue to setup parallel process. We don't init gradient here, but rather,
1582 // we setup proper rewindables so the graph can be rewinded to previous state before we run data parallel.
1583 if (parallel_count > 1 && compiled_data->gradient_mode == CCV_CNNP_COMPILED_DATA_GRADIENT_NONE)
1584 {
1585 const int evaluate_to_size = compiled_data->evaluate.to_size;
1586 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);
1587 _ccv_cnnp_model_set_rewindables(model);
1588 ccv_nnc_symbolic_graph_data_parallel(model->graph, parallel_count,
1589 0, 0,
1590 0, 0, 0,
1591 0, 0, 0,
1592 CCV_NNC_PARALLEL_REDUCE_OP_SUM,
1593 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));
1594 ccv_nnc_graph_exec_symbol_autogen(model->graph, 0, 0, CCV_NNC_AUTOGEN_SOURCES_AND_DESTINATIONS);
1595 int i, j;
1596 for (i = 0; i < evaluate_to_size; i++)
1597 for (j = 1; j < parallel_count; j++)
1598 {
1599 const ccv_nnc_graph_exec_symbol_t copy = ccv_nnc_graph_exec_symbol_copy(model->graph, compiled_data->evaluate.tos[i], j);
1600 if (copy.d != CCV_NNC_NO_GRAPH_EXEC_SYMBOL)
1601 compiled_data->evaluate.tos[compiled_data->evaluate.to_size++] = copy;
1602 }
1603 }
1604 const int tensors_init = !!compiled_data->tensors_init.v;
1605 if (!tensors_init)
1606 _ccv_cnnp_model_tensors_init(model, compiled_data);
1607 else if ((uintptr_t)compiled_data->tensors_init.v & (uintptr_t)1)
1608 // Check if it is not fully allocated, if it is not, init_1.
1609 ccv_cnnp_model_tensors_init_1(model, compiled_data);
1610 ccv_array_t* const tensor_binds = ccv_array_new(sizeof(ccv_nnc_tensor_bind_t), 0, 0);
1611 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"
, 1611, __extension__ __PRETTY_FUNCTION__); }));
1612 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"
, 1612, __extension__ __PRETTY_FUNCTION__); }));
1613 const int input_size_per_p = input_size / parallel_count;
1614 _ccv_cnnp_model_bind_tensors(model->graph, model->inputs, inputs, input_size_per_p, parallel_count, tensor_binds);
1615 const int output_size_per_p = output_size / parallel_count;
1616 _ccv_cnnp_model_bind_tensors(model->graph, model->outputs, outputs, output_size_per_p, parallel_count, tensor_binds);
1617 const int parameter_size = compiled_data->parameters->rnum;
1618 _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);
1619 const int internal_size = compiled_data->internals->rnum;
1620 _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);
1621 _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);
1622 // If we generated gradient for the graph, only compile part of the graph because the rest is irrelevant for evaluation.
1623 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);
1624 ccv_array_free(tensor_binds);
1625 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));
1626 // If tensor is not init'ed, we need to init states first.
1627 if (tensors_init && parallel_count > 1)
1628 _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);
1629 if (_ccv_cnnp_any_to_init(compiled_data))
1630 {
1631 ccv_nnc_tensor_init_states_t tensor_init_states = {
1632 .parallel_count = parallel_count,
1633 .graph = model->graph,
1634 .compiled_data = compiled_data,
1635 .tensor_arena = compiled_data->tensor_arena
1636 };
1637 ccv_cnnp_model_init_states(model, model->graph, _ccv_cnnp_init_states_for_tensors, &tensor_init_states);
1638 }
1639 compiled_data->is_test = 1;
1640 ccv_nnc_graph_exec_update_t update = {
1641 .parallel_count = parallel_count,
1642 .graph = model->graph,
1643 .graph_exec_arena = compiled_data->graph_exec_arena,
1644 };
1645 ccv_cnnp_model_set_is_test(model, 1, _ccv_cnnp_cmd_update_for_execs, &update);
1646 ccv_nnc_graph_set_default_static_schedule(compiled_data->graph, compiled_data->stream_type, model->max_stream_count);
1647 ccv_nnc_graph_autotune(compiled_data->graph, model->workspace_size, 0, TRAVERSE_FULL0,0,0,0);
1648}
1649
1650static void _ccv_cnnp_model_gradient_tensors_init(const ccv_cnnp_model_t* const model, ccv_cnnp_compiled_data_t* const compiled_data)
1651{
1652 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", 1652, __extension__ __PRETTY_FUNCTION__
); }));
1653 const int parameter_size = compiled_data->parameters->rnum;
1654 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; });
1655 compiled_data->tensors.gradients = (ccv_nnc_tensor_t**)ccmallocmalloc(sizeof(ccv_nnc_tensor_t*) * parameter_size * 2 * parallel_count);
1656 compiled_data->tensors.accum_gradients = compiled_data->tensors.gradients + parameter_size * parallel_count;
1657 int i, j;
1658 for (i = 0; i < parameter_size; i++)
1659 {
1660 if (compiled_data->parameter_flags && !(compiled_data->parameter_flags[i >> 6] & ((uint64_t)1 << (i & 63))))
1661 {
1662 compiled_data->tensors.gradients[i] = 0;
1663 compiled_data->tensors.accum_gradients[i] = 0;
1664 for (j = 1; j < parallel_count; j++)
1665 {
1666 compiled_data->tensors.gradients[i + j * parameter_size] = 0;
1667 compiled_data->tensors.accum_gradients[i + j * parameter_size] = 0;
1668 }
1669 continue;
1670 }
1671 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)));
1672 ccv_nnc_tensor_param_t info = ccv_nnc_tensor_symbol_params(parameter.graph, parameter);
1673 if (CCV_TENSOR_GET_DEVICE(info.type)((info.type) & 0xfff00) == CCV_COMPUTE_DEVICE_ANY)
1674 CCV_TENSOR_SET_DEVICE_ID(info.type, 0)(info.type) = (((info.type) & ~0xfff00) | (((0) & 0xfff
) << 8));
1675 const int device_id = CCV_TENSOR_GET_DEVICE_ID(info.type)(((info.type) & 0xfff00) >> 8);
1676 compiled_data->tensors.gradients[i] = ccv_nnc_tensor_new(0, info, 0);
1677 compiled_data->tensors.accum_gradients[i] = 0; // delay the accumulated gradient allocation until when we need it.
1678 for (j = 1; j < parallel_count; j++)
1679 {
1680 if (j != device_id)
1681 CCV_TENSOR_SET_DEVICE_ID(info.type, j)(info.type) = (((info.type) & ~0xfff00) | (((j) & 0xfff
) << 8));
1682 else
1683 CCV_TENSOR_SET_DEVICE_ID(info.type, 0)(info.type) = (((info.type) & ~0xfff00) | (((0) & 0xfff
) << 8));
1684 compiled_data->tensors.gradients[i + j * parameter_size] = ccv_nnc_tensor_new(0, info, 0);
1685 compiled_data->tensors.accum_gradients[i + j * parameter_size] = 0;
1686 }
1687 }
1688}
1689
1690static int _ccv_cnnp_is_disable_outgrad_all(const uint64_t disable_outgrad, const int input_size)
1691{
1692 if (disable_outgrad == CCV_CNNP_DISABLE_OUTGRAD_ALL)
1693 return 1;
1694 if (disable_outgrad == CCV_CNNP_DISABLE_OUTGRAD_NONE)
1695 return 0;
1696 int i;
1697 for (i = 0; i < input_size; i++)
1698 if (!(disable_outgrad & ((uint64_t)1 << i)))
1699 return 0;
1700 return 1;
1701}
1702
1703// Compile the graph to run ccv_cnnp_model_evaluate with requires_grad = true (MULTISTAGE_MODE).
1704// Particularly, this method compiles the evaluation and backprop graph (the main graph).
1705static 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)
1706{
1707 int i, j;
1708 ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
1709 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;
1710 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", 1710, __extension__ __PRETTY_FUNCTION__
); }));
1711 compiled_data->graph_mode = CCV_CNNP_MODEL_GRAPH_MULTISTAGE_MODE;
1712 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; });
1713 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", 1713, __extension__ __PRETTY_FUNCTION__
); }));
1714 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", 1714, __extension__ __PRETTY_FUNCTION__
); }));
1715 // There shouldn't be a loss function if we evaluate with multistage jit.
1716 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", 1716, __extension__ __PRETTY_FUNCTION__
); }));
1717 if (compiled_data->gradient_mode == CCV_CNNP_COMPILED_DATA_GRADIENT_NONE)
1718 {
1719 _ccv_cnnp_model_set_rewindables(model);
1720 _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.
1721 } else if (compiled_data->gradient_mode != target_gradient_mode) {
1722 _ccv_cnnp_model_rewind_graph(model);
1723 _ccv_cnnp_compiled_data_gradient_free(compiled_data);
1724 compiled_data->gradient_mode = CCV_CNNP_COMPILED_DATA_GRADIENT_NONE;
1725 _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.
1726 }
1727 const int tensors_init = !!compiled_data->tensors_init.v;
1728 if (!tensors_init)
1729 _ccv_cnnp_model_tensors_init(model, compiled_data);
1730 else if ((uintptr_t)compiled_data->tensors_init.v & (uintptr_t)1)
1731 // Check if it is not fully allocated, if it is not, init_1.
1732 ccv_cnnp_model_tensors_init_1(model, compiled_data);
1733 ccv_array_t* const tensor_binds = ccv_array_new(sizeof(ccv_nnc_tensor_bind_t), 0, 0);
1734 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"
, 1734, __extension__ __PRETTY_FUNCTION__); }));
1735 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"
, 1735, __extension__ __PRETTY_FUNCTION__); }));
1736 const int input_size_per_p = input_size / parallel_count;
1737 _ccv_cnnp_model_bind_tensors(model->graph, model->inputs, inputs, input_size_per_p, parallel_count, tensor_binds);
1738 const int output_size_per_p = output_size / parallel_count;
1739 _ccv_cnnp_model_bind_tensors(model->graph, model->outputs, outputs, output_size_per_p, parallel_count, tensor_binds);
1740 const int parameter_size = compiled_data->parameters->rnum;
1741 _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);
1742 const int internal_size = compiled_data->internals->rnum;
1743 _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);
1744 _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);
1745 if (!compiled_data->tensors.gradients)
1746 _ccv_cnnp_model_gradient_tensors_init(model, compiled_data);
1747 _ccv_cnnp_model_bind_tensors(model->graph, compiled_data->gradients, compiled_data->tensors.gradients, parameter_size, parallel_count, tensor_binds);
1748 if (compiled_data->backward.to_size > 0)
1749 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);
1750 else
1751 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);
1752 ccv_array_free(tensor_binds);
1753 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));
1754 if (tensors_init && parallel_count > 1)
1755 _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);
1756 // If tensor is not init'ed, we need to init states first.
1757 if (_ccv_cnnp_any_to_init(compiled_data))
1758 {
1759 ccv_nnc_tensor_init_states_t tensor_init_states = {
1760 .parallel_count = parallel_count,
1761 .graph = model->graph,
1762 .compiled_data = compiled_data,
1763 .tensor_arena = compiled_data->tensor_arena
1764 };
1765 ccv_cnnp_model_init_states(model, model->graph, _ccv_cnnp_init_states_for_tensors, &tensor_init_states);
1766 }
1767 compiled_data->is_test = is_test;
1768 ccv_nnc_graph_exec_update_t update = {
1769 .parallel_count = parallel_count,
1770 .graph = model->graph,
1771 .graph_exec_arena = compiled_data->graph_exec_arena,
1772 };
1773 ccv_cnnp_model_set_is_test(model, is_test, _ccv_cnnp_cmd_update_for_execs, &update);
1774 const int evaluate_to_size = compiled_data->evaluate.to_size;
1775 compiled_data->evaluate.to_op_size = 0;
1776 ccv_array_t* const backward_from = ccv_array_new(sizeof(int), 0, 0);
1777 for (i = 0; i < evaluate_to_size; i++)
1778 {
1779 ccv_nnc_graph_exec_t const to_op = ccv_nnc_graph_exec_from_symbol(compiled_data->graph_exec_arena, compiled_data->evaluate.tos[i]);
1780 if (to_op.graph)
1781 compiled_data->evaluate.to_ops[compiled_data->evaluate.to_op_size++] = to_op;
1782 const int* tos;
1783 int to_size;
1784 ccv_nnc_graph_exec_symbol_to(model->graph, compiled_data->evaluate.tos[i], &tos, &to_size);
1785 for (j = 0; j < to_size; j++)
1786 {
1787 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){
1788 .d = tos[j],
1789 .graph = model->graph
1790 });
1791 if (to_op.graph)
1792 ccv_array_add_unique_int(backward_from, to_op.d);
1793 }
1794 }
1795 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", 1795, __extension__
__PRETTY_FUNCTION__); }));
1796 compiled_data->backward.from_op_size = backward_from->rnum;
1797 compiled_data->backward.from_ops = (ccv_nnc_graph_exec_t*)ccmallocmalloc(sizeof(ccv_nnc_graph_exec_t) * backward_from->rnum);
1798 for (i = 0; i < backward_from->rnum; i++)
1799 compiled_data->backward.from_ops[i] = (ccv_nnc_graph_exec_t){
1800 .d = *(int*)ccv_array_get(backward_from, i)((void*)(((char*)((backward_from)->data)) + (size_t)(backward_from
)->rsize * (size_t)(i))),
1801 .graph = compiled_data->graph,
1802 };
1803 ccv_array_free(backward_from);
1804 ccv_nnc_graph_set_default_static_schedule(compiled_data->graph, compiled_data->stream_type, model->max_stream_count);
1805 ccv_nnc_graph_autotune(compiled_data->graph, model->workspace_size, 0, TRAVERSE_FULL0,0,0,0);
1806}
1807
1808void 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)
1809{
1810 ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
1811 assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
(compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 1811, __extension__ __PRETTY_FUNCTION__); }));
1812 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; });
1813 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", 1813, __extension__ __PRETTY_FUNCTION__
); }));
1814 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", 1814, __extension__ __PRETTY_FUNCTION__
); }));
1815 assert(model->graph)((void) sizeof ((model->graph) ? 1 : 0), __extension__ ({ if
(model->graph) ; else __assert_fail ("model->graph", "ccv_cnnp_model.c"
, 1815, __extension__ __PRETTY_FUNCTION__); }));
1816 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;
1817 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));
1818 if (!compiled_data->graph || mode_mismatch)
1819 {
1820 _ccv_cnnp_compiled_data_graph_free(compiled_data);
1821 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.
1822 _ccv_cnnp_compiled_data_backward_free(compiled_data);
1823 if (params.requires_grad)
1824 _ccv_cnnp_model_multistage_jit_0(model, params.disable_outgrad, params.is_test, inputs, input_size, outputs, output_size);
1825 else
1826 _ccv_cnnp_model_multistage_no_grad_jit(model, inputs, input_size, outputs, output_size);
1827 } else {
1828 ccv_nnc_tensor_arena_clear_bindings(compiled_data->tensor_arena);
1829 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"
, 1829, __extension__ __PRETTY_FUNCTION__); }));
1830 const int input_size_per_p = input_size / parallel_count;
1831 _ccv_cnnp_bind_tensors_to_arena(compiled_data->tensor_arena, model->graph, model->inputs, inputs, input_size_per_p, parallel_count);
1832 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"
, 1832, __extension__ __PRETTY_FUNCTION__); }));
1833 const int output_size_per_p = output_size / parallel_count;
1834 _ccv_cnnp_bind_tensors_to_arena(compiled_data->tensor_arena, model->graph, model->outputs, outputs, output_size_per_p, parallel_count);
1835 }
1836 if (compiled_data->is_test != params.is_test)
1837 {
1838 compiled_data->is_test = params.is_test;
1839 ccv_nnc_graph_exec_update_t update = {
1840 .parallel_count = parallel_count,
1841 .graph = model->graph,
1842 .graph_exec_arena = compiled_data->graph_exec_arena,
1843 };
1844 ccv_cnnp_model_set_is_test(model, params.is_test, _ccv_cnnp_cmd_update_for_execs, &update);
1845 }
1846}
1847
1848void 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)
1849{
1850 ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
1851 assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
(compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 1851, __extension__ __PRETTY_FUNCTION__); }));
1852 ccv_cnnp_model_dry_run(model, params, inputs, input_size, outputs, output_size);
1853 if (compiled_data->graph_mode == CCV_CNNP_MODEL_GRAPH_MULTISTAGE_MODE_NO_GRAD)
1854 ccv_nnc_graph_run_with_schedule(compiled_data->graph, 0, 0, tensor_tape, stream_context);
1855 else {
1856 if (!compiled_data->evaluate.schedule)
1857 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);
1858 ccv_nnc_graph_run_with_schedule(compiled_data->graph, 0, compiled_data->evaluate.schedule, tensor_tape, stream_context);
1859 }
1860}
1861
1862// Compile the graph to run ccv_cnnp_model_backward after ccv_cnnp_model_evaluate with requires_grad = true (MULTISTAGE_MODE).
1863// Particularly, this method compiles the accumulator graph.
1864static void _ccv_cnnp_model_multistage_jit_1(ccv_cnnp_model_t* const model)
1865{
1866 ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
1867 assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
(compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 1867, __extension__ __PRETTY_FUNCTION__); }));
1868 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", 1868, __extension__ __PRETTY_FUNCTION__
); }));
1869 ccv_nnc_symbolic_graph_t* accum = ccv_nnc_symbolic_graph_new();
1870 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; });
1871 const int parameter_size = compiled_data->parameters->rnum;
1872 int i, j;
1873 compiled_data->backward.gradients = (ccv_nnc_tensor_symbol_t*)ccmallocmalloc(sizeof(ccv_nnc_tensor_symbol_t) * parameter_size * parallel_count * 3);
1874 compiled_data->backward.accum_gradients = compiled_data->backward.gradients + parameter_size * parallel_count;
1875 compiled_data->backward.updated_accum_gradients = compiled_data->backward.accum_gradients + parameter_size * parallel_count;
1876 for (i = 0; i < parameter_size; i++)
1877 for (j = 0; j < parallel_count; j++)
1878 if (compiled_data->tensors.gradients[i + j * parameter_size])
1879 {
1880 const ccv_nnc_tensor_param_t info = compiled_data->tensors.gradients[i + j * parameter_size]->info;
1881 // Now, the old gradient is the accumulated gradient, getting new gradient tensor setup so we can collect them.
1882 compiled_data->tensors.accum_gradients[i + j * parameter_size] = compiled_data->tensors.gradients[i + j * parameter_size];
1883 compiled_data->tensors.gradients[i + j * parameter_size] = ccv_nnc_tensor_new(0, info, 0);
1884 ccv_nnc_tensor_symbol_t inputs[2];
1885 inputs[0] = compiled_data->backward.accum_gradients[i + j * parameter_size] = ccv_nnc_tensor_symbol_new(accum, info, 0);
1886 inputs[1] = compiled_data->backward.gradients[i + j * parameter_size] = ccv_nnc_tensor_symbol_new(accum, info, 0);
1887 ccv_nnc_tensor_symbol_t output = compiled_data->backward.updated_accum_gradients[i + j * parameter_size] = ccv_nnc_tensor_symbol_new(accum, info, 0);
1888 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);
1889 } else {
1890 compiled_data->backward.accum_gradients[i + j * parameter_size] = NO_TENSOR_SYMBOL(const ccv_nnc_tensor_symbol_t){.d = CCV_NNC_NO_TENSOR_SYMBOL
};
1891 compiled_data->backward.gradients[i + j * parameter_size] = NO_TENSOR_SYMBOL(const ccv_nnc_tensor_symbol_t){.d = CCV_NNC_NO_TENSOR_SYMBOL
};
1892 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
};
1893 }
1894 ccv_nnc_graph_exec_symbol_autogen(accum, 0, 0, CCV_NNC_AUTOGEN_ALL_EXECS | CCV_NNC_AUTOGEN_SOURCES_AND_DESTINATIONS);
1895 if (ccv_nnc_symbolic_graph_source_size(accum) == 0)
1896 {
1897 ccv_nnc_symbolic_graph_free(accum);
1898 // Create empty graph.
1899 compiled_data->backward.accum = ccv_nnc_graph_new();
1900 ccv_nnc_graph_topsort(compiled_data->backward.accum, 0, 0);
1901 return;
1902 }
1903 ccv_array_t* const tensor_binds = ccv_array_new(sizeof(ccv_nnc_tensor_bind_t), 0, 0);
1904 _ccv_cnnp_model_bind_tensors(accum, compiled_data->backward.accum_gradients, compiled_data->tensors.accum_gradients, parameter_size * parallel_count, 1, tensor_binds);
1905 _ccv_cnnp_model_bind_tensors(accum, compiled_data->backward.gradients, compiled_data->tensors.gradients, parameter_size * parallel_count, 1, tensor_binds);
1906 _ccv_cnnp_model_bind_tensors(accum, compiled_data->backward.updated_accum_gradients, compiled_data->tensors.accum_gradients, parameter_size * parallel_count, 1, tensor_binds);
1907 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);
1908 ccv_nnc_symbolic_graph_free(accum);
1909 ccv_array_free(tensor_binds);
1910 ccv_nnc_graph_set_default_static_schedule(compiled_data->backward.accum, compiled_data->stream_type, model->max_stream_count);
1911}
1912
1913void 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)
1914{
1915 ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
1916 assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
(compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 1916, __extension__ __PRETTY_FUNCTION__); }));
1917 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", 1917, __extension__ __PRETTY_FUNCTION__
); }));
1918 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; });
1919 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", 1919, __extension__ __PRETTY_FUNCTION__
); }));
1920 if (outgrad_size > 0)
1921 { 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", 1921, __extension__ __PRETTY_FUNCTION__
); })); }
1922 assert(model->graph)((void) sizeof ((model->graph) ? 1 : 0), __extension__ ({ if
(model->graph) ; else __assert_fail ("model->graph", "ccv_cnnp_model.c"
, 1922, __extension__ __PRETTY_FUNCTION__); }));
1923 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", 1923, __extension__ __PRETTY_FUNCTION__
); }));
1924 const int parameter_size = compiled_data->parameters->rnum;
1925 // If we need to accumulate the gradients now, do jit on accumulator.
1926 if (compiled_data->backward.count > 0)
1927 {
1928 if (!compiled_data->backward.accum)
1929 _ccv_cnnp_model_multistage_jit_1(model);
1930 else if (compiled_data->backward.count == 1) {
1931 // On this round, we need to switch accumulated gradients with gradients (so we can do accumulation properly).
1932 int i;
1933 for (i = 0; i < parameter_size * parallel_count; i++)
1934 {
1935 ccv_nnc_tensor_t* tensor;
1936 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));
1937 }
1938 if (compiled_data->backward.tensor_arena)
1939 {
1940 ccv_nnc_tensor_arena_clear_bindings(compiled_data->backward.tensor_arena);
1941 // Do rebind in case we messed up the binding (we switch accum_gradients and gradients).
1942 _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);
1943 _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);
1944 _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);
1945 }
1946 }
1947 }
1948 const int ingrad_size_per_p = model->output_size;
1949 const int outgrad_size_per_p = compiled_data->outgrad_size;
1950 int i, j;
1951 for (i = 0; i < ingrad_size_per_p; i++)
1952 {
1953 const ccv_nnc_tensor_symbol_t ingrad = ccv_nnc_tensor_symbol_for_backward(model->graph, compiled_data->f[i]);
1954 if (!ingrad_size || !ingrads || ingrads[i] == 0)
1955 {
1956 // Set it to 1 if it is not specified.
1957 ccv_nnc_tensor_t* const ingrad_tensor = ccv_nnc_tensor_from_symbol(compiled_data->tensor_arena, ingrad);
1958 if (ingrad_tensor)
1959 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);
1960 for (j = 1; j < parallel_count; j++)
1961 {
1962 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));
1963 if (ingrad_tensor)
1964 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);
1965 }
1966 } else {
1967 // Make sure the length matches, in case it is an alias.
1968 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", 1968, __extension__ __PRETTY_FUNCTION__
); }));
1969 ccv_nnc_tensor_bind_symbol(compiled_data->tensor_arena, ingrad, ingrads[i]);
1970 for (j = 1; j < parallel_count; j++)
1971 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]);
1972 }
1973 }
1974 if (outgrad_size > 0)
1975 {
1976 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", 1976, __extension__ __PRETTY_FUNCTION__
); }));
1977 for (i = 0; i < outgrad_size_per_p; i++)
1978 if (outgrads[i])
1979 {
1980 const ccv_nnc_tensor_symbol_t outgrad = compiled_data->outgrads[i];
1981 ccv_nnc_tensor_bind_symbol(compiled_data->tensor_arena, outgrad, outgrads[i]);
1982 for (j = 1; j < parallel_count; j++)
1983 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]);
1984 }
1985 } else {
1986 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", 1987, __extension__ __PRETTY_FUNCTION__
); }))
1987 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", 1987, __extension__ __PRETTY_FUNCTION__
); }));
1988 }
1989 // We need to rebind here because in ccv_cnnp_evaluate, we clear bindings, that will reset all bindings for the gradients.
1990 // For parameters and internals these are fine because when we clear bindings, it restores to original bindings, which are these
1991 // parameters and internals. The same cannot be said for gradients due to the accum_gradients switching.
1992 _ccv_cnnp_bind_tensors_to_arena(compiled_data->tensor_arena, model->graph, compiled_data->gradients, compiled_data->tensors.gradients, parameter_size, parallel_count);
1993 if (!compiled_data->backward.schedule)
1994 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);
1995 // Run the backward pass.
1996 ccv_nnc_graph_run_with_schedule(compiled_data->graph, 0, compiled_data->backward.schedule, tensor_tape, stream_context);
1997 // If we need to run accumulation round, do that now.
1998 if (compiled_data->backward.count > 0)
1999 ccv_nnc_graph_run_with_schedule(compiled_data->backward.accum, 0, 0, 0, stream_context);
2000 // Update the count, this determines whether we need to accumulate or not.
2001 ++compiled_data->backward.count;
2002}
2003
2004// Compile the graph to run ccv_cnnp_model_apply_gradients after ccv_cnnp_model_backward (MULTISTAGE_MODE).
2005// Particularly, this method compiles the parameter update graph.
2006static void _ccv_cnnp_model_multistage_jit_2(ccv_cnnp_model_t* const model)
2007{
2008 ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
2009 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", 2009, __extension__ __PRETTY_FUNCTION__
); }));
2010 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; });
2011 const int parameter_size = compiled_data->parameters->rnum;
2012 ccv_array_t* const tensor_binds = ccv_array_new(sizeof(ccv_nnc_tensor_bind_t), 0, 0);
2013 _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);
2014 _ccv_cnnp_model_bind_tensors(model->graph, compiled_data->updated_parameters, compiled_data->tensors.parameters, parameter_size, parallel_count, tensor_binds);
2015 // Bind accumulated gradients.
2016 if (compiled_data->backward.count > 1)
2017 _ccv_cnnp_model_bind_tensors(model->graph, compiled_data->gradients, compiled_data->tensors.accum_gradients, parameter_size, parallel_count, tensor_binds);
2018 else
2019 _ccv_cnnp_model_bind_tensors(model->graph, compiled_data->gradients, compiled_data->tensors.gradients, parameter_size, parallel_count, tensor_binds);
2020 ccv_array_t* const apply_gradients_from = ccv_array_new(sizeof(int), 0, 0);
2021 int i, j;
2022 for (i = 0; i < compiled_data->backward.to_size; i++)
2023 {
2024 const int* tos;
2025 int to_size;
2026 ccv_nnc_graph_exec_symbol_to(model->graph, compiled_data->backward.tos[i], &tos, &to_size);
2027 for (j = 0; j < to_size; j++)
2028 {
2029 // Check if this is already show up in the backward graph, if that is the case, it won't be in the apply
2030 // gradients graph.
2031 const ccv_nnc_graph_exec_t exec = ccv_nnc_graph_exec_from_symbol(compiled_data->graph_exec_arena, (ccv_nnc_graph_exec_symbol_t){
2032 .d = tos[j],
2033 .graph = model->graph,
2034 });
2035 if (!exec.graph)
2036 ccv_array_add_unique_int(apply_gradients_from, tos[j]);
2037 }
2038 }
2039 const int from_size = apply_gradients_from->rnum;
2040 if (from_size == 0)
2041 {
2042 ccv_array_free(apply_gradients_from);
2043 ccv_array_free(tensor_binds);
2044 return;
2045 }
2046 ccv_nnc_graph_exec_symbol_t* const froms = (ccv_nnc_graph_exec_symbol_t*)ccmallocmalloc(sizeof(ccv_nnc_graph_exec_symbol_t) * from_size);
2047 for (i = 0; i < from_size; i++)
2048 froms[i] = (ccv_nnc_graph_exec_symbol_t){
2049 .d = *(int*)ccv_array_get(apply_gradients_from, i)((void*)(((char*)((apply_gradients_from)->data)) + (size_t
)(apply_gradients_from)->rsize * (size_t)(i))),
2050 .graph = model->graph
2051 };
2052 ccv_array_free(apply_gradients_from);
2053 // It can only ends with updates on the parameters.
2054 ccv_array_t* const tos = ccv_array_new(sizeof(ccv_nnc_graph_exec_symbol_t), parameter_size * parallel_count, 0);
2055 for (i = 0; i < parameter_size; i++)
2056 {
2057 if (compiled_data->update_nodes[i].d == CCV_NNC_NO_TENSOR_SYMBOL)
2058 continue;
2059 ccv_array_push(tos, &compiled_data->update_nodes[i]);
2060 for (j = 1; j < parallel_count; j++)
2061 {
2062 const ccv_nnc_graph_exec_symbol_t copy = ccv_nnc_graph_exec_symbol_copy(model->graph, compiled_data->update_nodes[i], j);
2063 ccv_array_push(tos, &copy);
2064 }
2065 }
2066 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);
2067 ccv_array_free(tos);
2068 ccv_array_free(tensor_binds);
2069 ccfreefree(froms);
2070 const int max_saved_aux_size = compiled_data->minimize.max_saved_aux_size;
2071 for (i = 0; i < max_saved_aux_size * parameter_size; i++)
2072 {
2073 // Skip on no tensor.
2074 if (compiled_data->saved_aux[i].source.d == CCV_NNC_NO_TENSOR_SYMBOL)
2075 continue;
2076 ccv_nnc_tensor_t* const tensor = ccv_nnc_tensor_from_symbol(compiled_data->apply_gradients.tensor_arena, compiled_data->saved_aux[i].source);
2077 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);
2078 for (j = 1; j < parallel_count; j++)
2079 {
2080 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));
2081 if (copy)
2082 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);
2083 }
2084 }
2085 ccv_nnc_graph_set_default_static_schedule(compiled_data->apply_gradients.graph, compiled_data->stream_type, model->max_stream_count);
2086}
2087
2088void ccv_cnnp_model_apply_gradients(ccv_cnnp_model_t* const model, ccv_nnc_stream_context_t* const stream_context)
2089{
2090 ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
2091 assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
(compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 2091, __extension__ __PRETTY_FUNCTION__); }));
2092 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", 2092, __extension__ __PRETTY_FUNCTION__
); }));
2093 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; });
2094 assert(model->graph)((void) sizeof ((model->graph) ? 1 : 0), __extension__ ({ if
(model->graph) ; else __assert_fail ("model->graph", "ccv_cnnp_model.c"
, 2094, __extension__ __PRETTY_FUNCTION__); }));
2095 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", 2095, __extension__ __PRETTY_FUNCTION__
); }));
2096 // Skip if there is no backward pass.
2097 if (compiled_data->backward.count <= 0)
2098 return;
2099 // Skip if there is no parameters.
2100 if (compiled_data->parameters->rnum == 0)
2101 {
2102 compiled_data->backward.count = 0;
2103 return;
2104 }
2105 if (!compiled_data->apply_gradients.graph)
2106 _ccv_cnnp_model_multistage_jit_2(model);
2107 else {
2108 const int parameter_size = compiled_data->parameters->rnum;
2109 ccv_nnc_tensor_arena_clear_bindings(compiled_data->apply_gradients.tensor_arena);
2110 // Change to bind accum_gradients if we do gradient accumulation (run backward more than once).
2111 if (compiled_data->backward.count > 1)
2112 _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);
2113 else
2114 _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);
2115 }
2116 if (compiled_data->apply_gradients.graph)
2117 ccv_nnc_graph_run_with_schedule(compiled_data->apply_gradients.graph, 0, 0, 0, stream_context);
2118 // Reset backward count to 0.
2119 compiled_data->backward.count = 0;
2120}
2121
2122void 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)
2123{
2124 ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
2125 const int param_sel = parameter->param_sel > 0 ? parameter->param_sel - 1 : parameter->param_sel;
2126 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", 2126, __extension__ __PRETTY_FUNCTION__
); }));
2127 const int tensors_init = !!compiled_data->tensors_init.v;
2128 if (!tensors_init)
2129 _ccv_cnnp_model_tensors_init(model, compiled_data);
2130 else if ((uintptr_t)compiled_data->tensors_init.v & (uintptr_t)1)
2131 // Check if it is not fully allocated, if it is not, init_1.
2132 ccv_cnnp_model_tensors_init_1(model, compiled_data);
2133 ccv_array_t* const parameter_indices = ccv_array_new(sizeof(int), 0, 0);
2134 ccv_cnnp_model_add_to_parameter_indices(parameter->model, param_sel, parameter_indices);
2135 const int param_ref = parameter->param_ref > 0 ? parameter->param_ref - 1 : parameter->param_ref;
2136 if (param_ref < 0)
2137 { 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", 2137
, __extension__ __PRETTY_FUNCTION__); })); }
2138 else
2139 { 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", 2139, __extension__ __PRETTY_FUNCTION__
); })); }
2140 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)));
2141 ccv_array_free(parameter_indices);
2142 const int parameter_size = compiled_data->parameters->rnum;
2143 assert(d >= 0)((void) sizeof ((d >= 0) ? 1 : 0), __extension__ ({ if (d >=
0) ; else __assert_fail ("d >= 0", "ccv_cnnp_model.c", 2143
, __extension__ __PRETTY_FUNCTION__); }));
2144 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", 2144, __extension__ __PRETTY_FUNCTION__
); }));
2145 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; });
2146 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));
2147 assert(dest)((void) sizeof ((dest) ? 1 : 0), __extension__ ({ if (dest) ;
else __assert_fail ("dest", "ccv_cnnp_model.c", 2147, __extension__
__PRETTY_FUNCTION__); }));
2148 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);
2149 int i;
2150 for (i = 1; i < parallel_count; i++)
2151 {
2152 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));
2153 if (copy_tensor)
2154 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);
2155 }
2156 // Mark this symbol as init'ed.
2157 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;
2158 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));
2159 init_v[s >> 5] |= (1u << (s & 0x1f));
2160}
2161
2162void ccv_cnnp_model_parameter_copy(ccv_cnnp_model_t* const model, const ccv_cnnp_model_io_t parameter, ccv_nnc_tensor_t* const tensor)
2163{
2164 ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
2165 const int param_sel = parameter->param_sel > 0 ? parameter->param_sel - 1 : parameter->param_sel;
2166 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", 2166, __extension__ __PRETTY_FUNCTION__
); }));
2167 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", 2167, __extension__ __PRETTY_FUNCTION__
); }));
2168 ccv_array_t* const parameter_indices = ccv_array_new(sizeof(int), 0, 0);
2169 ccv_cnnp_model_add_to_parameter_indices(parameter->model, param_sel, parameter_indices);
2170 const int param_ref = parameter->param_ref > 0 ? parameter->param_ref - 1 : parameter->param_ref;
2171 if (param_ref < 0)
2172 { 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", 2172
, __extension__ __PRETTY_FUNCTION__); })); }
2173 else
2174 { 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", 2174, __extension__ __PRETTY_FUNCTION__
); })); }
2175 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)));
2176 ccv_array_free(parameter_indices);
2177 const int parameter_size = compiled_data->parameters->rnum;
2178 assert(d >= 0)((void) sizeof ((d >= 0) ? 1 : 0), __extension__ ({ if (d >=
0) ; else __assert_fail ("d >= 0", "ccv_cnnp_model.c", 2178
, __extension__ __PRETTY_FUNCTION__); }));
2179 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", 2179, __extension__ __PRETTY_FUNCTION__
); }));
2180 // We don't need to consider parallel_count, every parameter on each device is identical.
2181 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));
2182 assert(src)((void) sizeof ((src) ? 1 : 0), __extension__ ({ if (src) ; else
__assert_fail ("src", "ccv_cnnp_model.c", 2182, __extension__
__PRETTY_FUNCTION__); }));
2183 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);
2184}
2185
2186ccv_nnc_tensor_param_t ccv_cnnp_model_parameter_tensor_params(ccv_cnnp_model_t* const model, const ccv_cnnp_model_io_t parameter)
2187{
2188 ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
2189 const int param_sel = parameter->param_sel > 0 ? parameter->param_sel - 1 : parameter->param_sel;
2190 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", 2190, __extension__ __PRETTY_FUNCTION__
); }));
2191 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", 2191, __extension__ __PRETTY_FUNCTION__
); }));
2192 ccv_array_t* const parameter_indices = ccv_array_new(sizeof(int), 0, 0);
2193 ccv_cnnp_model_add_to_parameter_indices(parameter->model, param_sel, parameter_indices);
2194 const int param_ref = parameter->param_ref > 0 ? parameter->param_ref - 1 : parameter->param_ref;
2195 if (param_ref < 0)
2196 { 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", 2196
, __extension__ __PRETTY_FUNCTION__); })); }
2197 else
2198 { 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", 2198, __extension__ __PRETTY_FUNCTION__
); })); }
2199 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)));
2200 ccv_array_free(parameter_indices);
2201 const int parameter_size = compiled_data->parameters->rnum;
2202 assert(d >= 0)((void) sizeof ((d >= 0) ? 1 : 0), __extension__ ({ if (d >=
0) ; else __assert_fail ("d >= 0", "ccv_cnnp_model.c", 2202
, __extension__ __PRETTY_FUNCTION__); }));
2203 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", 2203, __extension__ __PRETTY_FUNCTION__
); }));
2204 // We don't need to consider parallel_count, every parameter on each device is identical.
2205 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));
2206 assert(tensor)((void) sizeof ((tensor) ? 1 : 0), __extension__ ({ if (tensor
) ; else __assert_fail ("tensor", "ccv_cnnp_model.c", 2206, __extension__
__PRETTY_FUNCTION__); }));
2207 return tensor->info;
2208}
2209
2210const char* ccv_cnnp_model_parameter_name(ccv_cnnp_model_t* const model, const ccv_cnnp_model_io_t parameter)
2211{
2212 ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
2213 const int param_sel = parameter->param_sel > 0 ? parameter->param_sel - 1 : parameter->param_sel;
2214 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", 2214, __extension__ __PRETTY_FUNCTION__
); }));
2215 ccv_array_t* const parameter_indices = ccv_array_new(sizeof(int), 0, 0);
2216 ccv_cnnp_model_add_to_parameter_indices(parameter->model, param_sel, parameter_indices);
2217 const int param_ref = parameter->param_ref > 0 ? parameter->param_ref - 1 : parameter->param_ref;
2218 if (param_ref < 0)
2219 { 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", 2219
, __extension__ __PRETTY_FUNCTION__); })); }
2220 else
2221 { 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", 2221, __extension__ __PRETTY_FUNCTION__
); })); }
2222 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)));
2223 ccv_array_free(parameter_indices);
2224 const int parameter_size = compiled_data->parameters->rnum;
2225 assert(d >= 0)((void) sizeof ((d >= 0) ? 1 : 0), __extension__ ({ if (d >=
0) ; else __assert_fail ("d >= 0", "ccv_cnnp_model.c", 2225
, __extension__ __PRETTY_FUNCTION__); }));
2226 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", 2226, __extension__ __PRETTY_FUNCTION__
); }));
2227 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)));
2228}
2229
2230int ccv_cnnp_model_parameter_count(ccv_cnnp_model_t* const model)
2231{
2232 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", 2232, __extension__ __PRETTY_FUNCTION__
); }));
2233 ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
2234 return compiled_data->parameters->rnum;
2235}
2236
2237ccv_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)
2238{
2239 ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
2240 assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
(compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 2240, __extension__ __PRETTY_FUNCTION__); }));
2241 const int parameter_size = compiled_data->parameters->rnum;
2242 int i;
2243 for (i = 0; i < parameter_size; i++)
2244 {
2245 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)));
2246 if (first(model, name, context))
2247 return ccv_cnnp_model_parameters(model, -1, i);
2248 }
2249 return 0;
2250}
2251
2252ccv_array_t* ccv_cnnp_model_parameters_filter(ccv_cnnp_model_t* const model, ccv_cnnp_model_parameters_filter_f filter, void* const context)
2253{
2254 ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
2255 assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
(compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 2255, __extension__ __PRETTY_FUNCTION__); }));
2256 ccv_array_t* const parameters = ccv_array_new(sizeof(ccv_cnnp_model_io_t), 0, 0);
2257 const int parameter_size = compiled_data->parameters->rnum;
2258 int i;
2259 for (i = 0; i < parameter_size; i++)
2260 {
2261 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)));
2262 if (filter(model, name, context))
2263 {
2264 ccv_cnnp_model_io_t parameter = ccv_cnnp_model_parameters(model, -1, i);
2265 ccv_array_push(parameters, &parameter);
2266 }
2267 }
2268 return parameters;
2269
2270}
2271
2272CCV_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)
2273{
2274 ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
2275 assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
(compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 2275, __extension__ __PRETTY_FUNCTION__); }));
2276 const int tensors_init = !!compiled_data->tensors_init.v;
2277 if (!tensors_init) // If nothing initialized, we return parameter 0.
2278 return ccv_cnnp_model_parameters(model, -1, 0);
2279 const int parameter_size = compiled_data->parameters->rnum;
2280 int i;
2281 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));
2282 for (i = 0; i < parameter_size; i++)
2283 {
2284 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;
2285 if (!(init_v[d >> 5] & (1u << (d & 0x1f))))
2286 return ccv_cnnp_model_parameters(model, -1, i);
2287 }
2288 return 0;
2289}
2290
2291static 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)
2292{
2293 const int to_param_sel = parameters->param_sel > 0 ? parameters->param_sel - 1 : parameters->param_sel;
2294 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", 2294, __extension__
__PRETTY_FUNCTION__); }));
2295 ccv_array_t* const to_parameter_indices = ccv_array_new(sizeof(int), 0, 0);
2296 ccv_cnnp_model_add_to_parameter_indices(parameters->model, to_param_sel, to_parameter_indices);
2297 *param_ref = parameters->param_ref > 0 ? parameters->param_ref - 1 : parameters->param_ref;
2298 return to_parameter_indices;
2299}
2300
2301static 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)
2302{
2303 // If the model is not compiled yet. Compile them now.
2304 if (!model->graph)
2305 {
2306 model->graph = ccv_nnc_symbolic_graph_new();
2307 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", 2307, __extension__ __PRETTY_FUNCTION__
); }));
2308 const int input_size = from_model->input_size;
2309 ccv_nnc_tensor_param_t input_params[input_size];
2310 int i;
2311 for (i = 0; i < input_size; i++)
2312 input_params[i] = ccv_nnc_tensor_symbol_params(from_model->graph, from_model->inputs[i]);
2313 _ccv_cnnp_model_compile(model, input_params, input_size, from_model->compiled_data->loss);
2314 model->parallel_count = from_model->parallel_count;
2315 model->memory_compression = from_model->memory_compression;
2316 model->memory_reduction = from_model->memory_reduction;
2317 model->gradient_checkpointing = from_model->gradient_checkpointing;
2318 model->compiled_data->stream_type = from_model->compiled_data->stream_type;
2319 model->compiled_data->minimize.minimizer = from_model->compiled_data->minimize.minimizer;
2320 model->compiled_data->minimize.max_saved_aux_size = from_model->compiled_data->minimize.max_saved_aux_size;
2321 }
2322 ccv_cnnp_compiled_data_t* const to_compiled_data = model->compiled_data;
2323 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", 2323, __extension__ __PRETTY_FUNCTION__
); }));
2324 const int to_tensors_init = !!to_compiled_data->tensors_init.v;
2325 if (!to_tensors_init)
2326 {
2327 if (only_init_0)
2328 ccv_cnnp_model_tensors_init_0(model, to_compiled_data);
2329 else
2330 _ccv_cnnp_model_tensors_init(model, to_compiled_data);
2331 } else if (!only_init_0 && (uintptr_t)to_compiled_data->tensors_init.v & (uintptr_t)1)
2332 // Check if it is not fully allocated, if it is not, init_1.
2333 ccv_cnnp_model_tensors_init_1(model, to_compiled_data);
2334 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", 2334, __extension__ __PRETTY_FUNCTION__
); }));
2335 *parameter_indices = _ccv_cnnp_model_parameter_indices(model, parameters, param_ref);
2336 *from_parameter_indices = _ccv_cnnp_model_parameter_indices(from_model, from_parameters, from_param_ref);
2337 if (*from_param_ref < 0 && *param_ref >= 0)
2338 { 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", 2338, __extension__ __PRETTY_FUNCTION__
); })); }
2339 else if (*from_param_ref >= 0)
2340 { 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", 2340, __extension__ __PRETTY_FUNCTION__
); })); }
2341 if (*param_ref < 0 && *from_param_ref >= 0)
2342 { 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"
, 2342, __extension__ __PRETTY_FUNCTION__); })); }
2343 else if (*param_ref >= 0)
2344 { 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", 2344, __extension__ __PRETTY_FUNCTION__
); })); }
2345}
2346
2347void 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)
2348{
2349 ccv_array_t* to_parameter_indices;
2350 int to_param_ref;
2351 ccv_array_t* from_parameter_indices;
2352 int from_param_ref;
2353 _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);
2354 // Should be exactly the same tensor.
2355 if (to_param_ref < 0 && from_param_ref < 0)
2356 { 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", 2356, __extension__ __PRETTY_FUNCTION__
); })); }
2357 // To models.
2358 ccv_cnnp_compiled_data_t* const to_compiled_data = model->compiled_data;
2359 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", 2359, __extension__ __PRETTY_FUNCTION__
); }));
2360 // From models.
2361 const ccv_cnnp_compiled_data_t* const from_compiled_data = from_model->compiled_data;
2362 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; });
2363 const int to_parameter_size = to_compiled_data->parameters->rnum;
2364 const int rnum = (to_param_ref < 0 && from_param_ref < 0) ? from_parameter_indices->rnum : 1;
2365 int i, j;
2366 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));
2367 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));
2368 for (i = 0; i < rnum; i++)
2369 {
2370 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)));
2371 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"
, 2371, __extension__ __PRETTY_FUNCTION__); }));
2372 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", 2372, __extension__ __PRETTY_FUNCTION__
); }));
2373 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;
2374 // If the original is not init'ed. We cannot copy from.
2375 if (!(from_init_v[s >> 5] & (1u << (s & 0x1f))))
2376 continue;
2377 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)));
2378 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"
, 2378, __extension__ __PRETTY_FUNCTION__); }));
2379 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", 2379, __extension__ __PRETTY_FUNCTION__
); }));
2380 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));
2381 assert(src)((void) sizeof ((src) ? 1 : 0), __extension__ ({ if (src) ; else
__assert_fail ("src", "ccv_cnnp_model.c", 2381, __extension__
__PRETTY_FUNCTION__); }));
2382 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));
2383 assert(dest)((void) sizeof ((dest) ? 1 : 0), __extension__ ({ if (dest) ;
else __assert_fail ("dest", "ccv_cnnp_model.c", 2383, __extension__
__PRETTY_FUNCTION__); }));
2384 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);
2385 for (j = 1; j < parallel_count; j++)
2386 {
2387 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));
2388 if (copy_tensor)
2389 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);
2390 }
2391 // Mark this symbol as init'ed.
2392 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;
2393 to_init_v[d >> 5] |= (1u << (d & 0x1f));
2394 }
2395 ccv_array_free(to_parameter_indices);
2396 ccv_array_free(from_parameter_indices);
2397}
2398
2399KHASH_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'
→
2400
2401void 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)
2402{
2403 ccv_array_t* to_parameter_indices;
2404 int to_param_ref;
2405 ccv_array_t* from_parameter_indices;
2406 int from_param_ref;
2407 _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);
2408 // Should be exactly the same tensor.
2409 if (renamer == 0 && to_param_ref < 0 && from_param_ref < 0)
1
Assuming 'renamer' is not equal to null
→
2410 { 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", 2410, __extension__ __PRETTY_FUNCTION__
); })); }
2411 // To models.
2412 ccv_cnnp_compiled_data_t* const to_compiled_data = model->compiled_data;
2413 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", 2413, __extension__ __PRETTY_FUNCTION__
); }));
2
←
Assuming 'to_compiled_data' is non-null
→
3
←
Taking true branch
→
2414 // From models.
2415 const ccv_cnnp_compiled_data_t* const from_compiled_data = from_model->compiled_data;
2416 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
→
2417 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", 2417, __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
→
2418 const int from_parameter_size = from_compiled_data->parameters->rnum;
2419 const int to_parameter_size = to_compiled_data->parameters->rnum;
2420 const int rnum = (to_param_ref < 0 && from_param_ref < 0) ? to_parameter_indices->rnum : 1;
9
←
Assuming 'to_param_ref' is >= 0
→
2421 int i, j;
2422 khash_t(ccv_cnnp_parameter_id)kh_ccv_cnnp_parameter_id_t* id_map = 0;
2423 char* updated_name = 0;
2424 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));
2425 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));
2426 for (i = 0; i < rnum; i++)
2427 {
2428 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
→
2429 // Need to figure out how to use the renamer here.
2430 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
→
2431 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"
, 2431, __extension__ __PRETTY_FUNCTION__); }));
15
←
Assuming 'dest_d' is >= 0
→
16
←
Taking true branch
→
2432 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", 2432, __extension__
__PRETTY_FUNCTION__); }));
17
←
Assuming 'dest_d' is < 'to_parameter_size'
→
18
←
Taking true branch
→
2433 if (renamer
18.1
'renamer' is non-null
)
2434 {
2435 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;
2436 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)));
2437 if (!updated_name
18.3
'updated_name' is null
)
19
←
Taking true branch
→
2438 updated_name = (char*)ccmallocmalloc(1024);
2439 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
→
2440 if (src_name_len
20.1
'src_name_len' is <= 0
 > 0)
21
←
Taking false branch
→
2441 memcpy(updated_name, src_name, src_name_len);
2442 updated_name[src_name_len] = 0;
2443 if (renamer(context, dest_name, updated_name, 1024) != 0)
22
←
Assuming the condition is false
→
2444 continue; // Skip this.
2445 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)
2446 {
2447 // Nothing changed.
2448 } else {
2449 if (!id_map
22.2
'id_map' is null
)
23
←
Taking true branch
→
2450 {
2451 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'
→
2452 for (j = 0; j < from_parameter_size; j++)
27
←
Assuming 'j' is < 'from_parameter_size'
→
28
←
Loop condition is true. Entering loop body
→
2453 {
2454 int ret;
2455 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'
→
2456 assert(ret != 0)((void) sizeof ((ret != 0) ? 1 : 0), __extension__ ({ if (ret
!= 0) ; else __assert_fail ("ret != 0", "ccv_cnnp_model.c", 2456
, __extension__ __PRETTY_FUNCTION__); }));
44
←
Taking true branch
→
2457 kh_val(id_map, k)((id_map)->vals[k]) = j;
45
←
Array access (via field 'vals') results in a null pointer dereference
2458 }
2459 }
2460 const khiter_t k = kh_get(ccv_cnnp_parameter_id, id_map, updated_name)kh_get_ccv_cnnp_parameter_id(id_map, updated_name);
2461 if (k == kh_end(id_map)((id_map)->n_buckets)) // Cannot find the name, skip.
2462 continue;
2463 src_d = kh_val(id_map, k)((id_map)->vals[k]);
2464 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"
, 2464, __extension__ __PRETTY_FUNCTION__); }));
2465 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", 2465, __extension__
__PRETTY_FUNCTION__); }));
2466 }
2467 }
2468 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"
, 2468, __extension__ __PRETTY_FUNCTION__); }));
2469 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", 2469, __extension__
__PRETTY_FUNCTION__); }));
2470 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;
2471 // If the original is not init'ed. We cannot share from.
2472 if (!(from_init_v[s >> 5] & (1u << (s & 0x1f))))
2473 continue;
2474 for (j = 0; j < parallel_count; j++)
2475 {
2476 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));
2477 assert(src)((void) sizeof ((src) ? 1 : 0), __extension__ ({ if (src) ; else
__assert_fail ("src", "ccv_cnnp_model.c", 2477, __extension__
__PRETTY_FUNCTION__); }));
2478 ccv_nnc_tensor_t* const dest = to_compiled_data->tensors.parameters[dest_d + j * to_parameter_size];
2479 if (dest && !((uintptr_t)dest & (uintptr_t)1))
2480 ccv_nnc_tensor_free(dest);
2481 to_compiled_data->tensors.parameters[dest_d + j * to_parameter_size] = (ccv_nnc_tensor_t*)((uintptr_t)src | (uintptr_t)1);
2482 }
2483 // Mark this symbol as init'ed.
2484 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;
2485 to_init_v[d >> 5] |= (1u << (d & 0x1f));
2486 }
2487 ccv_array_free(to_parameter_indices);
2488 ccv_array_free(from_parameter_indices);
2489 if (id_map)
2490 kh_destroy(ccv_cnnp_parameter_id, id_map)kh_destroy_ccv_cnnp_parameter_id(id_map);
2491 if (updated_name)
2492 ccfreefree(updated_name);
2493 // Mark it as incomplete so we will call init_1.
2494 if (ccv_cnnp_model_tensors_any_to_alloc(model, to_compiled_data))
2495 to_compiled_data->tensors_init.v = (uint32_t*)((uintptr_t)to_compiled_data->tensors_init.v | (uintptr_t)1);
2496 else // Remove the flag.
2497 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));
2498}
2499
2500ccv_nnc_stream_context_t* ccv_cnnp_compiled_data_get_stream(ccv_cnnp_compiled_data_t* const compiled_data, const int type)
2501{
2502 if (!compiled_data->stream_map)
2503 compiled_data->stream_map = kh_init(stream_map)kh_init_stream_map();
2504 int ret = 0;
2505 khiter_t k = kh_put(stream_map, compiled_data->stream_map, type, &ret)kh_put_stream_map(compiled_data->stream_map, type, &ret
);
2506 assert(ret >= 0)((void) sizeof ((ret >= 0) ? 1 : 0), __extension__ ({ if (
ret >= 0) ; else __assert_fail ("ret >= 0", "ccv_cnnp_model.c"
, 2506, __extension__ __PRETTY_FUNCTION__); }));
2507 ccv_nnc_stream_context_t* stream = kh_val(compiled_data->stream_map, k)((compiled_data->stream_map)->vals[k]);
2508 // If ret == 0, the key already exist, we can return directly, otherwise, create and return.
2509 if (ret != 0)
2510 {
2511 stream = ccv_nnc_stream_context_new(type);
2512 kh_val(compiled_data->stream_map, k)((compiled_data->stream_map)->vals[k]) = stream;
2513 }
2514 return stream;
2515}
2516
2517void 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)
2518{
2519 ccv_array_t* to_parameter_indices;
2520 int to_param_ref;
2521 ccv_array_t* from_parameter_indices;
2522 int from_param_ref;
2523 _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);
2524 // Should be exactly the same tensor.
2525 if (to_param_ref < 0 && from_param_ref < 0)
2526 { 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", 2526, __extension__ __PRETTY_FUNCTION__
); })); }
2527 // To models.
2528 ccv_cnnp_compiled_data_t* const to_compiled_data = model->compiled_data;
2529 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", 2529, __extension__ __PRETTY_FUNCTION__
); }));
2530 // From models.
2531 const ccv_cnnp_compiled_data_t* const from_compiled_data = from_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 const int to_parameter_size = to_compiled_data->parameters->rnum;
2534 const int rnum = (to_param_ref < 0 && from_param_ref < 0) ? from_parameter_indices->rnum : 1;
2535 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", 2535, __extension__ __PRETTY_FUNCTION__
); }));
2536 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", 2536, __extension__ __PRETTY_FUNCTION__
); }));
2537 int i, j;
2538 ccv_nnc_tensor_t* inputs[aux_in_size + 2];
2539 ccv_nnc_tensor_t* outputs[aux_out_size + 1];
2540 for (i = 0; i < aux_in_size; i++)
2541 inputs[i + 2] = aux_ins[i];
2542 for (i = 0; i < aux_out_size; i++)
2543 outputs[i + 1] = aux_outs[i];
2544 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));
2545 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));
2546 for (i = 0; i < rnum; i++)
2547 {
2548 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)));
2549 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"
, 2549, __extension__ __PRETTY_FUNCTION__); }));
2550 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", 2550, __extension__ __PRETTY_FUNCTION__
); }));
2551 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;
2552 // If the original is not init'ed. We cannot copy from.
2553 if (!(from_init_v[s >> 5] & (1u << (s & 0x1f))))
2554 continue;
2555 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)));
2556 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"
, 2556, __extension__ __PRETTY_FUNCTION__); }));
2557 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", 2557, __extension__ __PRETTY_FUNCTION__
); }));
2558 if (parallel_count > 1)
2559 {
2560 ccv_nnc_stream_context_t* streams[parallel_count];
2561 ccv_nnc_stream_signal_t* signal;
2562 if (stream_context)
2563 signal = ccv_nnc_stream_context_emit_signal_new(stream_context);
2564 for (j = 0; j < parallel_count; j++)
2565 {
2566 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));
2567 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));
2568 if (!dest || !src)
2569 {
2570 streams[j] = 0;
2571 continue;
2572 }
2573 // At the moment, can only handle them on the same device.
2574 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", 2574, __extension__ __PRETTY_FUNCTION__
); }));
2575 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", 2575, __extension__ __PRETTY_FUNCTION__
); }));
2576 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;
2577 const int device_id = CCV_TENSOR_GET_DEVICE_ID(src->info.type)(((src->info.type) & 0xfff00) >> 8);
2578 int type = stream_type;
2579 CCV_STREAM_SET_DEVICE_ID(type, device_id)(type) = (((type) & ~0xfff00) | (((device_id) & 0xfff
) << 8));
2580 ccv_nnc_stream_context_t* const stream_0 = ccv_cnnp_compiled_data_get_stream(to_compiled_data, type);
2581 // Wait signal to finish.
2582 if (stream_context)
2583 ccv_nnc_stream_context_wait_signal(stream_0, signal);
2584 inputs[0] = outputs[0] = dest;
2585 inputs[1] = src;
2586 ccv_nnc_cmd_exec(cmd, hint, flags, inputs, aux_in_size + 2, outputs, aux_out_size + 1, stream_0);
2587 if (stream_context)
2588 {
2589 ccv_nnc_stream_signal_t* const signal = ccv_nnc_stream_context_emit_signal_new(stream_0);
2590 ccv_nnc_stream_context_wait_signal(stream_context, signal);
2591 }
2592 streams[j] = stream_0;
2593 }
2594 // If this should be blocking, blocking it.
2595 if (!stream_context)
2596 for (j = 0; j < parallel_count; j++)
2597 if (streams[j])
2598 ccv_nnc_stream_context_wait(streams[j]);
2599 } else {
2600 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));
2601 assert(src)((void) sizeof ((src) ? 1 : 0), __extension__ ({ if (src) ; else
__assert_fail ("src", "ccv_cnnp_model.c", 2601, __extension__
__PRETTY_FUNCTION__); }));
2602 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));
2603 assert(dest)((void) sizeof ((dest) ? 1 : 0), __extension__ ({ if (dest) ;
else __assert_fail ("dest", "ccv_cnnp_model.c", 2603, __extension__
__PRETTY_FUNCTION__); }));
2604 inputs[0] = outputs[0] = dest;
2605 inputs[1] = src;
2606 ccv_nnc_cmd_exec(cmd, hint, flags, inputs, aux_in_size + 2, outputs, aux_out_size + 1, stream_context);
2607 }
2608 // Mark this symbol as init'ed.
2609 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;
2610 to_init_v[d >> 5] |= (1u << (d & 0x1f));
2611 }
2612 ccv_array_free(to_parameter_indices);
2613 ccv_array_free(from_parameter_indices);
2614}
2615
2616void 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)
2617{
2618 int to_param_ref;
2619 ccv_array_t* const to_parameter_indices = _ccv_cnnp_model_parameter_indices(model, parameters, &to_param_ref);
2620 // To models.
2621 ccv_cnnp_compiled_data_t* const to_compiled_data = model->compiled_data;
2622 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", 2622, __extension__ __PRETTY_FUNCTION__
); }));
2623 // Tensor has to be inited already.
2624 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", 2624, __extension__ __PRETTY_FUNCTION__
); }));
2625 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", 2625, __extension__ __PRETTY_FUNCTION__
); }));
2626 // From models.
2627 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; });
2628 const int to_parameter_size = to_compiled_data->parameters->rnum;
2629 const int rnum = (to_param_ref < 0) ? to_parameter_indices->rnum : 1;
2630 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", 2630, __extension__ __PRETTY_FUNCTION__
); }));
2631 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", 2631, __extension__ __PRETTY_FUNCTION__
); }));
2632 int i, j;
2633 ccv_nnc_tensor_t* inputs[aux_in_size + 1];
2634 ccv_nnc_tensor_t* outputs[aux_out_size + 1];
2635 for (i = 0; i < aux_in_size; i++)
2636 inputs[i + 1] = aux_ins[i];
2637 for (i = 0; i < aux_out_size; i++)
2638 outputs[i + 1] = aux_outs[i];
2639 for (i = 0; i < rnum; i++)
2640 {
2641 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)));
2642 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"
, 2642, __extension__ __PRETTY_FUNCTION__); }));
2643 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", 2643, __extension__ __PRETTY_FUNCTION__
); }));
2644 if (parallel_count > 1)
2645 {
2646 ccv_nnc_stream_context_t* streams[parallel_count];
2647 ccv_nnc_stream_signal_t* signal;
2648 if (stream_context)
2649 signal = ccv_nnc_stream_context_emit_signal_new(stream_context);
2650 for (j = 0; j < parallel_count; j++)
2651 {
2652 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));
2653 if (!dest)
2654 {
2655 streams[j] = 0;
2656 continue;
2657 }
2658 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;
2659 const int device_id = CCV_TENSOR_GET_DEVICE_ID(dest->info.type)(((dest->info.type) & 0xfff00) >> 8);
2660 int type = stream_type;
2661 CCV_STREAM_SET_DEVICE_ID(type, device_id)(type) = (((type) & ~0xfff00) | (((device_id) & 0xfff
) << 8));
2662 ccv_nnc_stream_context_t* const stream_0 = ccv_cnnp_compiled_data_get_stream(to_compiled_data, type);
2663 // Wait signal to finish.
2664 if (stream_context)
2665 ccv_nnc_stream_context_wait_signal(stream_0, signal);
2666 inputs[0] = outputs[0] = dest;
2667 ccv_nnc_cmd_exec(cmd, hint, flags, inputs, aux_in_size + 1, outputs, aux_out_size + 1, stream_0);
2668 if (stream_context)
2669 {
2670 ccv_nnc_stream_signal_t* const signal = ccv_nnc_stream_context_emit_signal_new(stream_0);
2671 ccv_nnc_stream_context_wait_signal(stream_context, signal);
2672 }
2673 streams[j] = stream_0;
2674 }
2675 // If this should be blocking, blocking it.
2676 if (!stream_context)
2677 for (j = 0; j < parallel_count; j++)
2678 if (streams[j])
2679 ccv_nnc_stream_context_wait(streams[j]);
2680 } else {
2681 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));
2682 assert(dest)((void) sizeof ((dest) ? 1 : 0), __extension__ ({ if (dest) ;
else __assert_fail ("dest", "ccv_cnnp_model.c", 2682, __extension__
__PRETTY_FUNCTION__); }));
2683 inputs[0] = outputs[0] = dest;
2684 ccv_nnc_cmd_exec(cmd, hint, flags, inputs, aux_in_size + 1, outputs, aux_out_size + 1, stream_context);
2685 }
2686 // No need to mark this symbol as init'ed, it is already.
2687 }
2688 ccv_array_free(to_parameter_indices);
2689}
2690
2691void 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)
2692{
2693 int to_param_ref;
2694 ccv_array_t* const to_parameter_indices = _ccv_cnnp_model_parameter_indices(model, parameters, &to_param_ref);
2695 // To models.
2696 ccv_cnnp_compiled_data_t* const to_compiled_data = model->compiled_data;
2697 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", 2697, __extension__ __PRETTY_FUNCTION__
); }));
2698 // Tensor has to be inited already.
2699 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", 2699, __extension__ __PRETTY_FUNCTION__
); }));
2700 ccv_nnc_tensor_t** tensor_gradients;
2701 if (to_compiled_data->backward.count > 1)
2702 tensor_gradients = to_compiled_data->tensors.accum_gradients;
2703 else
2704 tensor_gradients = to_compiled_data->tensors.gradients;
2705 assert(tensor_gradients)((void) sizeof ((tensor_gradients) ? 1 : 0), __extension__ ({
if (tensor_gradients) ; else __assert_fail ("tensor_gradients"
, "ccv_cnnp_model.c", 2705, __extension__ __PRETTY_FUNCTION__
); }));
2706 // From models.
2707 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; });
2708 const int to_parameter_size = to_compiled_data->parameters->rnum;
2709 const int rnum = (to_param_ref < 0) ? to_parameter_indices->rnum : 1;
2710 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", 2710, __extension__ __PRETTY_FUNCTION__
); }));
2711 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", 2711, __extension__ __PRETTY_FUNCTION__
); }));
2712 int i, j;
2713 ccv_nnc_tensor_t* inputs[aux_in_size + 1];
2714 ccv_nnc_tensor_t* outputs[aux_out_size + 1];
2715 for (i = 0; i < aux_in_size; i++)
2716 inputs[i + 1] = aux_ins[i];
2717 for (i = 0; i < aux_out_size; i++)
2718 outputs[i + 1] = aux_outs[i];
2719 for (i = 0; i < rnum; i++)
2720 {
2721 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)));
2722 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"
, 2722, __extension__ __PRETTY_FUNCTION__); }));
2723 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", 2723, __extension__ __PRETTY_FUNCTION__
); }));
2724 if (parallel_count > 1)
2725 {
2726 ccv_nnc_stream_context_t* streams[parallel_count];
2727 ccv_nnc_stream_signal_t* signal;
2728 if (stream_context)
2729 signal = ccv_nnc_stream_context_emit_signal_new(stream_context);
2730 for (j = 0; j < parallel_count; j++)
2731 {
2732 ccv_nnc_tensor_t* const dest = tensor_gradients[dest_d + j * to_parameter_size];
2733 if (!dest)
2734 {
2735 streams[j] = 0;
2736 continue;
2737 }
2738 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;
2739 const int device_id = CCV_TENSOR_GET_DEVICE_ID(dest->info.type)(((dest->info.type) & 0xfff00) >> 8);
2740 int type = stream_type;
2741 CCV_STREAM_SET_DEVICE_ID(type, device_id)(type) = (((type) & ~0xfff00) | (((device_id) & 0xfff
) << 8));
2742 ccv_nnc_stream_context_t* const stream_0 = ccv_cnnp_compiled_data_get_stream(to_compiled_data, type);
2743 // Wait signal to finish.
2744 if (stream_context)
2745 ccv_nnc_stream_context_wait_signal(stream_0, signal);
2746 inputs[0] = outputs[0] = dest;
2747 ccv_nnc_cmd_exec(cmd, hint, flags, inputs, aux_in_size + 1, outputs, aux_out_size + 1, stream_0);
2748 if (stream_context)
2749 {
2750 ccv_nnc_stream_signal_t* const signal = ccv_nnc_stream_context_emit_signal_new(stream_0);
2751 ccv_nnc_stream_context_wait_signal(stream_context, signal);
2752 }
2753 streams[j] = stream_0;
2754 }
2755 // If this should be blocking, blocking it.
2756 if (!stream_context)
2757 for (j = 0; j < parallel_count; j++)
2758 if (streams[j])
2759 ccv_nnc_stream_context_wait(streams[j]);
2760 } else {
2761 ccv_nnc_tensor_t* const dest = tensor_gradients[dest_d];
2762 if (!dest)
2763 continue;
2764 assert(dest)((void) sizeof ((dest) ? 1 : 0), __extension__ ({ if (dest) ;
else __assert_fail ("dest", "ccv_cnnp_model.c", 2764, __extension__
__PRETTY_FUNCTION__); }));
2765 inputs[0] = outputs[0] = dest;
2766 ccv_nnc_cmd_exec(cmd, hint, flags, inputs, aux_in_size + 1, outputs, aux_out_size + 1, stream_context);
2767 }
2768 // No need to mark this symbol as init'ed, it is already.
2769 }
2770 ccv_array_free(to_parameter_indices);
2771}
2772
2773ccv_nnc_cmd_t ccv_cnnp_model_minimizer(ccv_cnnp_model_t* const model)
2774{
2775 ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
2776 assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
(compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 2776, __extension__ __PRETTY_FUNCTION__); }));
2777 return compiled_data->minimize.minimizer;
2778}
2779
2780void 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)
2781{
2782 ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
2783 assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
(compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 2783, __extension__ __PRETTY_FUNCTION__); }));
2784 const int parameter_size = compiled_data->parameters->rnum;
2785 if (parameter_size == 0)
2786 return;
2787 if (reset)
2788 { 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", 2788, __extension__ __PRETTY_FUNCTION__
); })); }
2789 const int old_max_saved_aux_size = compiled_data->minimize.max_saved_aux_size;
2790 const int saved_aux_size = ccv_nnc_minimizer_saved_aux_size(minimizer);
2791 if (saved_aux_size > compiled_data->minimize.max_saved_aux_size)
2792 compiled_data->minimize.max_saved_aux_size = saved_aux_size;
2793 const int max_saved_aux_size = compiled_data->minimize.max_saved_aux_size;
2794 // We update all parameters, at this point, we have one minimizer.
2795 if (set_parameters == 0 || set_parameter_size == 0)
2796 compiled_data->minimize.minimizer = minimizer;
2797 int i;
2798 if (set_parameters && set_parameter_size)
2799 {
2800 // I need to save what's the minimizer along with this.
2801 if (!compiled_data->minimize.parameters)
2802 compiled_data->minimize.parameters = ccv_array_new(sizeof(ccv_cnnp_set_minimizer_for_parameter_t*), 1, 0);
2803 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));
2804 set_minimizer_for_parameter->minimizer = minimizer;
2805 set_minimizer_for_parameter->parameter_size = set_parameter_size;
2806 memcpy(set_minimizer_for_parameter->parameters, set_parameters, sizeof(ccv_cnnp_model_io_t) * set_parameter_size);
2807 ccv_array_push(compiled_data->minimize.parameters, &set_minimizer_for_parameter);
2808 }
2809 // If reset is true, clear the parameters array.
2810 if (reset && compiled_data->minimize.parameters)
2811 {
2812 for (i = 0; i < compiled_data->minimize.parameters->rnum; i++)
2813 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))));
2814 ccv_array_clear(compiled_data->minimize.parameters);
2815 }
2816 if (!compiled_data->update_nodes)
2817 return;
2818 ccv_nnc_symbolic_graph_t* const symbolic_graph = model->graph;
2819 assert(symbolic_graph)((void) sizeof ((symbolic_graph) ? 1 : 0), __extension__ ({ if
(symbolic_graph) ; else __assert_fail ("symbolic_graph", "ccv_cnnp_model.c"
, 2819, __extension__ __PRETTY_FUNCTION__); }));
2820 if (saved_aux_size > old_max_saved_aux_size)
2821 {
2822 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", 2822, __extension__ __PRETTY_FUNCTION__
); }));
2823 // Reallocate first, move them around later.
2824 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);
2825 compiled_data->update_nodes = (ccv_nnc_graph_exec_symbol_t*)(compiled_data->updated_parameters + parameter_size);
2826 compiled_data->saved_aux = (ccv_nnc_tensor_symbol_map_t*)(compiled_data->update_nodes + parameter_size);
2827 // We need to do this from back to front because saved_aux_size > old_saved_aux_size, it could overlap.
2828 _ccv_cnnp_scatter_saved_aux(compiled_data->saved_aux, parameter_size, old_max_saved_aux_size, saved_aux_size);
2829 }
2830 int flag = 0;
2831 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; });
2832 if (set_parameters && set_parameter_size)
2833 {
2834 ccv_array_t* const parameter_indices = ccv_array_new(sizeof(int), 0, 0);
2835 for (i = 0; i < set_parameter_size; i++)
2836 {
2837 const int param_sel = set_parameters[i]->param_sel > 0 ? set_parameters[i]->param_sel - 1 : set_parameters[i]->param_sel;
2838 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", 2838, __extension__ __PRETTY_FUNCTION__
); }));
2839 const int old_rnum = parameter_indices->rnum;
2840 ccv_cnnp_model_add_to_parameter_indices(set_parameters[i]->model, param_sel, parameter_indices);
2841 const int param_ref = set_parameters[i]->param_ref > 0 ? set_parameters[i]->param_ref - 1 : set_parameters[i]->param_ref;
2842 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", 2842, __extension__ __PRETTY_FUNCTION__
); }));
2843 if (param_ref >= 0)
2844 {
2845 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", 2845, __extension__ __PRETTY_FUNCTION__
); }));
2846 *(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)));
2847 parameter_indices->rnum = old_rnum + 1;
2848 }
2849 }
2850 // We may have duplicated indices, but that is OK, we will set it twice.
2851 for (i = 0; i < parameter_indices->rnum; i++)
2852 {
2853 const int d = *(int*)ccv_array_get(parameter_indices, i)((void*)(((char*)((parameter_indices)->data)) + (size_t)(parameter_indices
)->rsize * (size_t)(i)));
2854 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))
2855 flag = 1;
2856 }
2857 ccv_array_free(parameter_indices);
2858 } else {
2859 for (i = 0; i < parameter_size; i++)
2860 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))
2861 flag = 1;
2862 if (compiled_data->minimize.parameters)
2863 if (_ccv_cnnp_apply_parameters_with_minimizer(model))
2864 flag = 1;
2865 }
2866 if (flag)
2867 {
2868 // 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.
2869 if (compiled_data->graph_mode == CCV_CNNP_MODEL_GRAPH_FIT_MODE)
2870 _ccv_cnnp_compiled_data_graph_free(compiled_data);
2871 _ccv_cnnp_compiled_data_apply_gradients_free(compiled_data);
2872 }
2873}
2874
2875void ccv_cnnp_model_set_compile_params(ccv_cnnp_model_t* const model, const ccv_nnc_symbolic_graph_compile_param_t compile_params)
2876{
2877 ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
2878 assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
(compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 2878, __extension__ __PRETTY_FUNCTION__); }));
2879 compiled_data->compile_params = compile_params;
2880}
2881
2882void ccv_cnnp_model_dot(const ccv_cnnp_model_t* const model, const int flags, FILE** const outs, const int out_size)
2883{
2884 if (model->graph && out_size > 0)
2885 ccv_nnc_symbolic_graph_dot(model->graph, flags, outs[0]);
2886 if (model->compiled_data && model->compiled_data->graph && out_size > 1)
2887 ccv_nnc_graph_dot(model->compiled_data->graph, flags, outs[1]);
2888 if (model->compiled_data && model->compiled_data->backward.accum && out_size > 2)
2889 ccv_nnc_graph_dot(model->compiled_data->backward.accum, flags, outs[2]);
2890 if (model->compiled_data && model->compiled_data->apply_gradients.graph && out_size > 3)
2891 ccv_nnc_graph_dot(model->compiled_data->apply_gradients.graph, flags, outs[3]);
2892}
2893
2894void ccv_cnnp_model_format(const ccv_cnnp_model_t* const model, const ccv_nnc_symbolic_graph_format_f format_fn, void* const context)
2895{
2896 if (model->graph)
2897 ccv_nnc_symbolic_graph_format(model->graph, 0, 0, 0, 0, format_fn, context);
2898}
2899
2900static void _ccv_cnnp_compiled_data_free(const ccv_cnnp_model_t* const model, ccv_cnnp_compiled_data_t* const compiled_data)
2901{
2902 int i;
2903 const int parameter_size = compiled_data->parameters->rnum;
2904 ccv_array_free(compiled_data->parameters);
2905 if (compiled_data->parameter_flags)
2906 ccfreefree(compiled_data->parameter_flags);
2907 const int internal_size = compiled_data->internals->rnum;
2908 ccv_array_free(compiled_data->internals);
2909 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", 2909, __extension__ __PRETTY_FUNCTION__
); }));
2910 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", 2910, __extension__ __PRETTY_FUNCTION__
); }));
2911 for (i = 0; i < parameter_size; i++)
2912 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))));
2913 ccv_array_free(compiled_data->ids.parameters);
2914 for (i = 0; i < internal_size; i++)
2915 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))));
2916 ccv_array_free(compiled_data->ids.internals);
2917 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; });
2918 if (compiled_data->tensors.parameters)
2919 {
2920 for (i = 0; i < parameter_size * parallel_count; i++)
2921 // If it is not marked as not belonging, we can free it.
2922 if (!((uintptr_t)compiled_data->tensors.parameters[i] & (uintptr_t)1))
2923 if (compiled_data->tensors.parameters[i])
2924 ccv_nnc_tensor_free(compiled_data->tensors.parameters[i]);
2925 for (i = 0; i < internal_size * parallel_count; i++)
2926 if (compiled_data->tensors.internals[i])
2927 ccv_nnc_tensor_free(compiled_data->tensors.internals[i]);
2928 ccfreefree(compiled_data->tensors.parameters);
2929 }
2930 if (compiled_data->tensors.gradients)
2931 {
2932 for (i = 0; i < parameter_size * parallel_count; i++)
2933 {
2934 if (compiled_data->tensors.gradients[i])
2935 ccv_nnc_tensor_free(compiled_data->tensors.gradients[i]);
2936 if (compiled_data->tensors.accum_gradients[i])
2937 ccv_nnc_tensor_free(compiled_data->tensors.accum_gradients[i]);
2938 }
2939 ccfreefree(compiled_data->tensors.gradients);
2940 }
2941 if (compiled_data->minimize.parameters)
2942 {
2943 for (i = 0; i < compiled_data->minimize.parameters->rnum; i++)
2944 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))));
2945 ccv_array_free(compiled_data->minimize.parameters);
2946 }
2947 if (compiled_data->rewindables)
2948 ccv_array_free(compiled_data->rewindables);
2949 if (compiled_data->tensors_init.v)
2950 ccfreefree(CCV_NNC_INIT_V(compiled_data->tensors_init.v)((uint32_t*)((uintptr_t)(compiled_data->tensors_init.v) &
~(uintptr_t)1)));
2951 if (compiled_data->evaluate.tos)
2952 ccfreefree(compiled_data->evaluate.tos);
2953 compiled_data->evaluate.tos = 0;
2954 if (compiled_data->stream_map)
2955 {
2956 khiter_t k;
2957 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)
2958 {
2959 if (!kh_exist(compiled_data->stream_map, k)(!(((compiled_data->stream_map)->flags[(k)>>4]>>
(((k)&0xfU)<<1))&3)))
2960 continue;
2961 ccv_nnc_stream_context_t* const stream = kh_val(compiled_data->stream_map, k)((compiled_data->stream_map)->vals[k]);
2962 ccv_nnc_stream_context_free(stream);
2963 }
2964 kh_destroy(stream_map, compiled_data->stream_map)kh_destroy_stream_map(compiled_data->stream_map);
2965 }
2966 _ccv_cnnp_compiled_data_graph_free(compiled_data);
2967 _ccv_cnnp_compiled_data_gradient_free(compiled_data);
2968 _ccv_cnnp_compiled_data_backward_free(compiled_data);
2969 _ccv_cnnp_compiled_data_apply_gradients_free(compiled_data);
2970 if (compiled_data->gradient_checkpoints)
2971 {
2972 for (i = 0; i < compiled_data->gradient_checkpoints->rnum; i++)
2973 {
2974 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)));
2975 assert(checkpoint->inputs)((void) sizeof ((checkpoint->inputs) ? 1 : 0), __extension__
({ if (checkpoint->inputs) ; else __assert_fail ("checkpoint->inputs"
, "ccv_cnnp_model.c", 2975, __extension__ __PRETTY_FUNCTION__
); }));
2976 ccfreefree(checkpoint->inputs);
2977 ccv_array_free(checkpoint->tensor_symbols);
2978 }
2979 ccv_array_free(compiled_data->gradient_checkpoints);
2980 }
2981 ccv_nnc_xpu_alloc_destroy(&compiled_data->xpu_alloc);
2982 ccfreefree(compiled_data);
2983}
2984
2985void ccv_cnnp_model_free(ccv_cnnp_model_t* const model)
2986{
2987 if (model->isa->deinit)
2988 model->isa->deinit(model);
2989 if (model->io)
2990 {
2991 int i;
2992 for (i = 0; i < model->io->rnum; i++)
2993 {
2994 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)));
2995 if (model_io->outgoings)
2996 ccv_array_free(model_io->outgoings);
2997 if (model_io->incomings)
2998 ccv_array_free(model_io->incomings);
2999 if (model_io->dependencies)
3000 ccv_array_free(model_io->dependencies);
3001 ccfreefree(model_io);
3002 }
3003 ccv_array_free(model->io);
3004 }
3005 if (model->parameter_indices)
3006 ccv_array_free(model->parameter_indices);
3007 if (model->inputs)
3008 ccfreefree(model->inputs);
3009 if (model->graph)
3010 ccv_nnc_symbolic_graph_free(model->graph);
3011 if (model->compiled_data)
3012 _ccv_cnnp_compiled_data_free(model, model->compiled_data);
3013 if (model->name)
3014 ccfreefree(model->name);
3015 ccfreefree(model);
3016}
3017
3018void ccv_cnnp_model_cancel(ccv_cnnp_model_t* const model)
3019{
3020 ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
3021 if (!compiled_data)
3022 return;
3023 if (compiled_data->graph)
3024 ccv_nnc_graph_cancel(compiled_data->graph);
3025 if (compiled_data->apply_gradients.graph)
3026 ccv_nnc_graph_cancel(compiled_data->apply_gradients.graph);
3027}