ccv_cnnp_model.c

Bug Summary

File:	nnc/ccv_cnnp_model.c
Warning:	line 2810, column 13 Array access (via field 'vals') results in a null pointer dereference
Annotated Source Code

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clang -cc1 -cc1 -triple x86_64-unknown-linux-gnu -analyze -disable-free -clear-ast-before-backend -disable-llvm-verifier -discard-value-names -main-file-name ccv_cnnp_model.c -analyzer-checker=core -analyzer-checker=apiModeling -analyzer-checker=unix -analyzer-checker=deadcode -analyzer-checker=security.insecureAPI.UncheckedReturn -analyzer-checker=security.insecureAPI.getpw -analyzer-checker=security.insecureAPI.gets -analyzer-checker=security.insecureAPI.mktemp -analyzer-checker=security.insecureAPI.mkstemp -analyzer-checker=security.insecureAPI.vfork -analyzer-checker=nullability.NullPassedToNonnull -analyzer-checker=nullability.NullReturnedFromNonnull -analyzer-output plist -w -setup-static-analyzer -mrelocation-model pic -pic-level 2 -pic-is-pie -mframe-pointer=none -fmath-errno -ffp-contract=on -fno-rounding-math -mconstructor-aliases -funwind-tables=2 -target-cpu x86-64 -target-feature +sse2 -tune-cpu generic -debugger-tuning=gdb -fdebug-compilation-dir=/home/liu/actions-runner/_work/ccv/ccv/lib/nnc -fcoverage-compilation-dir=/home/liu/actions-runner/_work/ccv/ccv/lib/nnc -resource-dir /usr/local/lib/clang/19 -I ../ -I /usr/local/cuda/include -D HAVE_CBLAS -D HAVE_LIBPNG -D HAVE_LIBJPEG -D HAVE_FFTW3 -D HAVE_PTHREAD -D HAVE_LIBLINEAR -D HAVE_TESSERACT -D HAVE_AVCODEC -D HAVE_AVFORMAT -D HAVE_AVUTIL -D HAVE_SWSCALE -D HAVE_SSE2 -D HAVE_GSL -D HAVE_CUDA -D HAVE_CUDNN -D HAVE_NCCL -D USE_SYSTEM_CUB -I /usr/local/include -internal-isystem /usr/local/lib/clang/19/include -internal-isystem /usr/local/include -internal-isystem /usr/lib/gcc/x86_64-linux-gnu/12/../../../../x86_64-linux-gnu/include -internal-externc-isystem /usr/include/x86_64-linux-gnu -internal-externc-isystem /include -internal-externc-isystem /usr/include -O3 -ferror-limit 19 -fgnuc-version=4.2.1 -fskip-odr-check-in-gmf -vectorize-loops -vectorize-slp -analyzer-output=html -faddrsig -D__GCC_HAVE_DWARF2_CFI_ASM=1 -o /home/liu/actions-runner/_work/ccv/ccv/_analyze/2026-05-17-122821-2676611-1 -x c ccv_cnnp_model.c
1#include "ccv_nnc.h"
2#include "ccv_nnc_easy.h"
3#include "ccv_nnc_internal.h"
4#include "ccv_internal.h"
5#include "_ccv_cnnp_model.h"
6#include "_ccv_nnc_graph.h"
7#include "_ccv_nnc_symbolic_graph.h"
8#ifdef HAVE_CUDA1
9#include "gpu/ccv_nnc_compat.h"
10#endif
11 
12// MARK - Level-5 API
13 
14ccv_cnnp_model_io_t ccv_cnnp_model_apply(ccv_cnnp_model_t* const model, const ccv_cnnp_model_io_t* const inputs, const int input_size)
15{
16	if (!model->io)
17		model->io = ccv_array_new(sizeof(ccv_cnnp_model_io_t), 1, 0);
18	ccv_cnnp_model_io_t model_io = ccmallocmalloc(sizeof(struct ccv_cnnp_model_io_s) + sizeof(ccv_nnc_tensor_symbol_t) * model->output_size);
19	model_io->param_ref = 0;
20	model_io->param_sel = 0;
21	model_io->visit = 0;
22	model_io->model = model;
23	model_io->dependencies = 0;
24	model_io->dependents = 0;
25	model_io->outgoings = 0;
26	model_io->outputs = (ccv_nnc_tensor_symbol_t*)(model_io + 1);
27	ccv_array_push(model->io, &model_io);
28	if (input_size > 0)
29	{
30		model_io->incomings = ccv_array_new(sizeof(ccv_cnnp_model_io_t), input_size, 0);
31		ccv_array_resize(model_io->incomings, input_size);
32		int i;
33		memcpy(ccv_array_get(model_io->incomings, 0)((void*)(((char*)((model_io->incomings)->data)) + (size_t
)(model_io->incomings)->rsize * (size_t)(0))), inputs, sizeof(ccv_cnnp_model_io_t) * input_size);
34		for (i = 0; i < input_size; i++)
35		{
36			if (!inputs[i]->outgoings)
37				inputs[i]->outgoings = ccv_array_new(sizeof(ccv_cnnp_model_io_t), 1, 0);
38			ccv_array_push(inputs[i]->outgoings, &model_io);
39		}
40	} else {
41		model_io->incomings = 0;
42	}
43	return model_io;
44}
45 
46void ccv_cnnp_model_add_dependencies(ccv_cnnp_model_io_t model_io, const ccv_cnnp_model_io_t* const dependencies, const int dependency_size)
47{
48	assert(dependency_size > 0)((void) sizeof ((dependency_size > 0) ? 1 : 0), __extension__
 ({ if (dependency_size > 0) ; else __assert_fail ("dependency_size > 0"
, "ccv_cnnp_model.c", 48, __extension__ __PRETTY_FUNCTION__);
 }));
49	if (!model_io->dependencies)
50		model_io->dependencies = ccv_array_new(sizeof(ccv_cnnp_model_io_t), dependency_size, 0);
51	int i, j;
52	for (i = 0; i < dependency_size; i++)
53	{
54		int flag = 0;
55		// Check if it is already exist or not.
56		for (j = 0; !flag && j < model_io->dependencies->rnum; j++)
57			if (*(ccv_cnnp_model_io_t*)ccv_array_get(model_io->dependencies, j)((void*)(((char*)((model_io->dependencies)->data)) + (size_t
)(model_io->dependencies)->rsize * (size_t)(j))) == dependencies[i])
58				flag = 1;
59		if (flag)
60			continue;
61		ccv_array_push(model_io->dependencies, dependencies + i);
62		++dependencies[i]->dependents;
63	}
64}
65 
66int ccv_cnnp_model_output_size(const ccv_cnnp_model_t* const model)
67{
68	return model->output_size;
69}
70 
71int ccv_cnnp_model_is_trainable(const ccv_cnnp_model_t* const model)
72{
73	// If the model is compiled, it is default to 1 unless it is not.
74	if (model->compiled_data)
75		return model->is_trainable >= 0 ? model->is_trainable : 1;
76	return model->is_trainable;
77}
78 
79ccv_cnnp_model_io_t ccv_cnnp_model_parameters(ccv_cnnp_model_t* const model, const int selector, const int index)
80{
81	if (!model->io)
82		model->io = ccv_array_new(sizeof(ccv_cnnp_model_io_t), 1, 0);
83	ccv_cnnp_model_io_t model_io = ccmallocmalloc(sizeof(struct ccv_cnnp_model_io_s));
84	model_io->param_ref = index >= 0 ? index + 1 : ALL_PARAMETERS-1;
85	model_io->param_sel = selector >= 0 ? selector + 1 : ALL_PARAMETERS-1;
86	model_io->visit = 0;
87	model_io->model = model;
88	model_io->outputs = 0;
89	model_io->dependencies = 0;
90	model_io->dependents = 0;
91	model_io->incomings = 0;
92	model_io->outgoings = 0;
93	ccv_array_push(model->io, &model_io);
94	return model_io;
95}
96 
97void ccv_cnnp_model_notify_hook(ccv_cnnp_model_t* const model, ccv_cnnp_model_notify_f func, void* const context)
98{
99	model->notify_hook.func = func;
100	model->notify_hook.context = context;
101}
102 
103void ccv_cnnp_model_notify(const ccv_cnnp_model_t* const model, const int tag, void* const payload)
104{
105	if (model->notify_hook.func)
106		model->notify_hook.func(model, tag, payload, model->notify_hook.context);
107	if (model->isa->notify)
108		model->isa->notify(model, tag, payload);
109}
110 
111static int _ccv_nnc_array_dedup_graph_exec_symbols(ccv_nnc_graph_exec_symbol_t* const graph_exec_symbols, int graph_exec_symbol_size)
112{
113	int i, j;
114	for (i = 0; i < graph_exec_symbol_size; i++)
115	{
116		ccv_nnc_graph_exec_symbol_t* const graph_exec_symbol = graph_exec_symbols + i;
117		// Check whether this tensor symbol has any duplicate.
118		for (j = i + 1; j < graph_exec_symbol_size;)
119		{
120			ccv_nnc_graph_exec_symbol_t* const other_symbol = graph_exec_symbols + j;
121			// If there is a same tensor symbol, remove it.
122			if (other_symbol->d == graph_exec_symbol->d && other_symbol->graph == graph_exec_symbol->graph)
123			{
124				if (j + 1 < graph_exec_symbol_size)
125					*other_symbol = graph_exec_symbols[graph_exec_symbol_size - 1];
126				--graph_exec_symbol_size;
127				continue;
128			}
129			++j;
130		}
131	}
132	return graph_exec_symbol_size;
133}
134 
135void ccv_cnnp_model_add_to_array(void* const context, const ccv_nnc_tensor_symbol_t symbol, const int is_trainable)
136{
137	ccv_cnnp_model_add_to_array_context_t* const add_to_array_context = (ccv_cnnp_model_add_to_array_context_t*)context;
138	ccv_cnnp_model_t* const model = add_to_array_context->sequence->model;
139	int i;
140	if (add_to_array_context->add_parameter_indices && !model->parameter_indices)
141		model->parameter_indices = ccv_array_new(sizeof(int), 0, 0);
142	for (i = 0; i < add_to_array_context->symbols->rnum; i++)
143	{
144		const ccv_nnc_tensor_symbol_t other_symbol = *(ccv_nnc_tensor_symbol_t*)ccv_array_get(add_to_array_context->symbols, i)((void*)(((char*)((add_to_array_context->symbols)->data
)) + (size_t)(add_to_array_context->symbols)->rsize * (
size_t)(i)));
145		if (other_symbol.d == symbol.d && other_symbol.graph == symbol.graph)
146		{
147			// Only add to parameter_indices if it is trainable.
148			if (add_to_array_context->add_parameter_indices)
149				ccv_array_add_unique_int(model->parameter_indices, i);
150			// Found it, return, don't add it.
151			return;
152		}
153	}
154	// Only add to parameter_indices if it is trainable.
155	if (add_to_array_context->add_parameter_indices)
156		ccv_array_push(model->parameter_indices, &add_to_array_context->symbols->rnum);
157	// This is a new one, no need to add_unique_int, it is unique.
158	ccv_array_push(add_to_array_context->symbols, &symbol);
159	if (add_to_array_context->trainables)
160		ccv_array_push(add_to_array_context->trainables, &is_trainable);
161	char id[2048];
162	id[0] = add_to_array_context->prefix;
163	id[1] = '-';
164	int total_len = 2;
165	for (i = 0; i < add_to_array_context->sequence->sequences->rnum; i++)
166	{
167		const ccv_cnnp_model_name_t* const name = (ccv_cnnp_model_name_t*)ccv_array_get(add_to_array_context->sequence->sequences, i)((void*)(((char*)((add_to_array_context->sequence->sequences
)->data)) + (size_t)(add_to_array_context->sequence->
sequences)->rsize * (size_t)(i)));
168		int len;
169		if (name->name && name->name[0] != '\0')
170			len = snprintf(id + total_len, 2048 - total_len, "%s-%d-", name->name, name->sequence);
171		else
172			len = snprintf(id + total_len, 2048 - total_len, "%d-", name->sequence);
173		total_len += len;
174		if (total_len >= 2047)
175			break;
176	}
177	if (total_len < 2047)
178		total_len += snprintf(id + total_len, 2048 - total_len, "%d", add_to_array_context->sequence->it);
179	assert(total_len < 2048)((void) sizeof ((total_len < 2048) ? 1 : 0), __extension__
 ({ if (total_len < 2048) ; else __assert_fail ("total_len < 2048"
, "ccv_cnnp_model.c", 179, __extension__ __PRETTY_FUNCTION__)
; }));
180	char *heap_id = (char*)ccmallocmalloc(total_len + 1);
181	memcpy(heap_id, id, total_len + 1);
182	ccv_array_push(add_to_array_context->ids, &heap_id);
183	++add_to_array_context->sequence->it;
184}
185 
186static void _ccv_cnnp_compiled_data_init(ccv_cnnp_compiled_data_t* const compiled_data, const int output_size, ccv_array_t* const gradient_checkpoints)
187{
188	compiled_data->f = compiled_data->fits + output_size;
189	compiled_data->xpu_alloc.mp_hdr = -1;
190	compiled_data->xpu_alloc.freed = kh_init(dy_str)kh_init_dy_str();
191	compiled_data->xpu_alloc.allocd = kh_init(dy_alloc)kh_init_dy_alloc();
192	compiled_data->gradient_checkpoints = gradient_checkpoints;
193}
194 
195static int _ccv_cnnp_model_root_parallel_count(const ccv_cnnp_model_t* const model)
196{
197	return ccv_max(model->parallel_count, 1)({ typeof (model->parallel_count) _a = (model->parallel_count
); typeof (1) _b = (1); (_a > _b) ? _a : _b; });
198}
199 
200static int _ccv_cnnp_model_effective_parallel_count(const ccv_cnnp_model_t* const model)
201{
202	int parallel_count = _ccv_cnnp_model_root_parallel_count(model);
203	if (model->graph && model->graph->data_parallel.count > parallel_count)
204		parallel_count = model->graph->data_parallel.count;
205	return parallel_count;
206}
207 
208static int _ccv_cnnp_compiled_data_parallel_count(const ccv_cnnp_model_t* const model, const ccv_cnnp_compiled_data_t* const compiled_data)
209{
210	return compiled_data->parallel_count > 0 ? compiled_data->parallel_count : _ccv_cnnp_model_effective_parallel_count(model);
211}
212 
213ccv_nnc_tensor_symbol_t ccv_cnnp_model_get_symbol(ccv_cnnp_model_t* const self, const ccv_nnc_tensor_symbol_t symbol)
214{
215	assert(self->data)((void) sizeof ((self->data) ? 1 : 0), __extension__ ({ if
 (self->data) ; else __assert_fail ("self->data", "ccv_cnnp_model.c"
, 215, __extension__ __PRETTY_FUNCTION__); }));
216	ccv_cnnp_model_build_data_t* const build_data = (ccv_cnnp_model_build_data_t*)self->data;
217	if (build_data->parallel_count <= 1 || build_data->parallel_rank == 0)
218		return symbol;
219	const int rank = build_data->parallel_rank;
220	assert(rank > 0)((void) sizeof ((rank > 0) ? 1 : 0), __extension__ ({ if (
rank > 0) ; else __assert_fail ("rank > 0", "ccv_cnnp_model.c"
, 220, __extension__ __PRETTY_FUNCTION__); }));
221	assert(rank < build_data->parallel_count)((void) sizeof ((rank < build_data->parallel_count) ? 1
 : 0), __extension__ ({ if (rank < build_data->parallel_count
) ; else __assert_fail ("rank < build_data->parallel_count"
, "ccv_cnnp_model.c", 221, __extension__ __PRETTY_FUNCTION__)
; }));
222	ccv_nnc_symbolic_graph_t* const graph = (ccv_nnc_symbolic_graph_t*)symbol.graph;
223	ccv_nnc_tensor_symbol_t copy = ccv_nnc_tensor_symbol_copy(graph, symbol, rank);
224	if (copy.d != CCV_NNC_NO_TENSOR_SYMBOL)
225		return copy;
226	ccv_nnc_tensor_param_t params = ccv_nnc_tensor_symbol_params(graph, symbol);
227	if (CCV_TENSOR_GET_MEMORY(params.type)((params.type) & 0x3) == CCV_TENSOR_GPU_MEMORY)
228		CCV_TENSOR_SET_DEVICE_ID(params.type, rank)(params.type) = (((params.type) & ~0xfff00) | (((rank) &
 0xfff) << 8));
229	copy = ccv_nnc_tensor_symbol_new(graph, params, 0);
230	ccv_nnc_tensor_symbol_set_copy(graph, symbol, rank, copy);
231	return copy;
232}
233 
234typedef struct {
235	void* old_graph_exec_symbol_new_hook_context;
236	ccv_nnc_graph_exec_symbol_new_hook_f old_graph_exec_symbol_new_hook;
237	ccv_nnc_symbolic_graph_t* graph;
238	ccv_cnnp_model_build_data_t* build_data;
239} ccv_cnnp_model_set_exec_flags_context_t;
240 
241static void _ccv_cnnp_model_set_exec_flags(void* context, const ccv_nnc_graph_exec_symbol_t symbol, const ccv_nnc_cmd_t cmd, const ccv_nnc_tensor_symbol_t* const inputs, const int input_size, const ccv_nnc_tensor_symbol_t* const outputs, const int output_size, const char* const name)
242{
243	ccv_cnnp_model_set_exec_flags_context_t* flags_context = (ccv_cnnp_model_set_exec_flags_context_t*)context;
244	if (flags_context->build_data->exec_flags)
245		ccv_nnc_graph_exec_symbol_set_flags(flags_context->graph, symbol, flags_context->build_data->exec_flags);
246	if (flags_context->old_graph_exec_symbol_new_hook)
247		flags_context->old_graph_exec_symbol_new_hook(flags_context->old_graph_exec_symbol_new_hook_context, symbol, cmd, inputs, input_size, outputs, output_size, name);
248}
249 
250static void _ccv_cnnp_model_compile(ccv_cnnp_model_t* const model, const ccv_nnc_tensor_param_t* const inputs, const int input_size, const ccv_nnc_cmd_t loss)
251{
252	assert(model->graph)((void) sizeof ((model->graph) ? 1 : 0), __extension__ ({ if
 (model->graph) ; else __assert_fail ("model->graph", "ccv_cnnp_model.c"
, 252, __extension__ __PRETTY_FUNCTION__); }));
253	model->inputs = ccmallocmalloc(sizeof(ccv_nnc_tensor_symbol_t) * input_size);
254	int i;
255	for (i = 0; i < input_size; i++)
256		model->inputs[i] = ccv_nnc_tensor_symbol_new(model->graph, inputs[i], 0);
257	ccv_array_t* const parameters = ccv_array_new(sizeof(ccv_nnc_tensor_symbol_t), 0, 0);
258	ccv_array_t* const parameter_ids = ccv_array_new(sizeof(char*), 0, 0);
259	ccv_array_t* const parameter_trainables = ccv_array_new(sizeof(int), 0, 0);
260	ccv_cnnp_model_sequence_t model_sequence = {
261		.bank = kh_init(ccv_cnnp_model_name_bank)kh_init_ccv_cnnp_model_name_bank()
262	};
263	ccv_cnnp_model_add_to_array_context_t add_to_parameter_context = {
264		.add_parameter_indices = 1,
265		.prefix = 't',
266		.sequence = &model_sequence,
267		.symbols = parameters,
268		.ids = parameter_ids,
269		.trainables = parameter_trainables,
270	};
271	ccv_array_t* const internals = ccv_array_new(sizeof(ccv_nnc_tensor_symbol_t), 0, 0);
272	ccv_array_t* const internal_ids = ccv_array_new(sizeof(char*), 0, 0);
273	ccv_cnnp_model_add_to_array_context_t add_to_output_context = {
274		.add_parameter_indices = 0,
275		.prefix = 'r',
276		.sequence = &model_sequence,
277		.symbols = internals,
278		.ids = internal_ids,
279		.trainables = 0,
280	};
281	ccv_cnnp_model_build_data_t build_data = {
282		.exec_flags = 0,
283		.is_trainable = model->is_trainable >= 0 ? model->is_trainable : 1,
284		.parallel_count = 1,
285		.parallel_rank = 0,
286		.model_sequence = &model_sequence,
287		.add_to_array = ccv_cnnp_model_add_to_array,
288		.parameters = parameters,
289		.context = {
290			.add_to_parameter = &add_to_parameter_context,
291			.add_to_output = &add_to_output_context,
292		},
293		.gradient_checkpoints = 0,
294	};
295	model->data = &build_data;
296	ccv_cnnp_model_set_exec_flags_context_t flags_context = {
297		.graph = model->graph,
298		.build_data = &build_data,
299		.old_graph_exec_symbol_new_hook = 0,
300		.old_graph_exec_symbol_new_hook_context = 0
301	};
302	flags_context.old_graph_exec_symbol_new_hook_context = ccv_nnc_graph_exec_symbol_new_hook(model->graph, _ccv_cnnp_model_set_exec_flags, &flags_context, &flags_context.old_graph_exec_symbol_new_hook);
303	ccv_cnnp_model_build(model, model->graph, model->inputs, input_size, 0, 0);
304	// Reset back to previous hook.
305	ccv_nnc_graph_exec_symbol_new_hook(model->graph, flags_context.old_graph_exec_symbol_new_hook, flags_context.old_graph_exec_symbol_new_hook_context, 0);
306	for (i = 0; i < model->output_size; i++)
307	{
308		const ccv_nnc_tensor_symbol_t output = model->outputs[i];
309		const ccv_nnc_tensor_symbol_t alias_to = ccv_nnc_tensor_symbol_alias_to(model->graph, output);
310		if (alias_to.d == CCV_NNC_NO_TENSOR_SYMBOL)
311			continue;
312		// If output is an alias, insert data transform regardless for result correctness (we cannot bind an alias). You can check ccv_nnc_tensor_bind_symbol method
313		// to see that we can correctly bind a tensor which from it, has aliases, but we cannot bind an alias tensor correctly (this is expected, sort of, to be
314		// honest, because we cannot handle cases of alias is part of the original tensor but bind differently).
315		const ccv_nnc_tensor_param_t output_params = ccv_nnc_tensor_symbol_params(model->graph, output);
316		model->outputs[i] = ccv_nnc_tensor_symbol_new(model->graph, output_params, 0);
317		ccv_nnc_graph_exec_symbol_t make_contiguous = ccv_nnc_graph_exec_symbol_new(model->graph, CMD_FORMAT_TRANSFORM_FORWARD()ccv_nnc_cmd(CCV_NNC_FORMAT_TRANSFORM_FORWARD, 0, ccv_nnc_cmd_auto
, 0), &output, 1, model->outputs + i, 1, "contiguous");
318		ccv_nnc_graph_exec_symbol_set_flags(model->graph, make_contiguous, CCV_NNC_GRAPH_EXEC_DISABLE_OPT);
319	}
320	model->data = 0;
321	kh_destroy(ccv_cnnp_model_name_bank, model_sequence.bank)kh_destroy_ccv_cnnp_model_name_bank(model_sequence.bank);
322	if (model_sequence.sequences)
323		ccv_array_free(model_sequence.sequences);
324	// Check if there are parameters that are not trainables. If there are, we will allocate uint64 bitmap to record that.
325	int not_trainables = 0;
326	// Assert no parameter is alias.
327	for (i = 0; i < parameters->rnum; i++)
328	{
329		const ccv_nnc_tensor_symbol_t parameter = *(ccv_nnc_tensor_symbol_t*)ccv_array_get(parameters, i)((void*)(((char*)((parameters)->data)) + (size_t)(parameters
)->rsize * (size_t)(i)));
330		const ccv_nnc_tensor_symbol_t alias_to = ccv_nnc_tensor_symbol_alias_to(parameter.graph, parameter);
331		assert(alias_to.graph == 0)((void) sizeof ((alias_to.graph == 0) ? 1 : 0), __extension__
 ({ if (alias_to.graph == 0) ; else __assert_fail ("alias_to.graph == 0"
, "ccv_cnnp_model.c", 331, __extension__ __PRETTY_FUNCTION__)
; })); // Cannot find the one alias to.
332		if (*(int*)ccv_array_get(parameter_trainables, i)((void*)(((char*)((parameter_trainables)->data)) + (size_t
)(parameter_trainables)->rsize * (size_t)(i))) == 0)
333			not_trainables = 1;
334	}
335	assert(parameters->rnum == parameter_trainables->rnum)((void) sizeof ((parameters->rnum == parameter_trainables->
rnum) ? 1 : 0), __extension__ ({ if (parameters->rnum == parameter_trainables
->rnum) ; else __assert_fail ("parameters->rnum == parameter_trainables->rnum"
, "ccv_cnnp_model.c", 335, __extension__ __PRETTY_FUNCTION__)
; }));
336	uint64_t* parameter_flags = 0;
337	if (not_trainables)
338	{
339		parameter_flags = (uint64_t*)cccalloccalloc(((parameters->rnum + 63) >> 6), sizeof(uint64_t));
340		for (i = 0; i < parameter_trainables->rnum; i++)
341			if (*(int*)ccv_array_get(parameter_trainables, i)((void*)(((char*)((parameter_trainables)->data)) + (size_t
)(parameter_trainables)->rsize * (size_t)(i))))
342				parameter_flags[i >> 6] |= ((uint64_t)1 << (i & 63));
343	}
344	ccv_array_free(parameter_trainables);
345	// Assert no internal is alias.
346	for (i = 0; i < internals->rnum; i++)
347	{
348		const ccv_nnc_tensor_symbol_t internal = *(ccv_nnc_tensor_symbol_t*)ccv_array_get(internals, i)((void*)(((char*)((internals)->data)) + (size_t)(internals
)->rsize * (size_t)(i)));
349		const ccv_nnc_tensor_symbol_t alias_to = ccv_nnc_tensor_symbol_alias_to(internal.graph, internal);
350		assert(alias_to.graph == 0)((void) sizeof ((alias_to.graph == 0) ? 1 : 0), __extension__
 ({ if (alias_to.graph == 0) ; else __assert_fail ("alias_to.graph == 0"
, "ccv_cnnp_model.c", 350, __extension__ __PRETTY_FUNCTION__)
; })); // Cannot find the one alias to.
351	}
352	const int output_size = model->output_size;
353	ccv_nnc_graph_exec_symbol_autogen(model->graph, 0, 0, CCV_NNC_AUTOGEN_ALL_EXECS | CCV_NNC_AUTOGEN_SOURCES_AND_DESTINATIONS);
354	const int parameters_rnum = parameters->rnum;
355	if (input_size > 0)
356	{
357		ccv_array_resize(parameters, parameters_rnum + input_size);
358		memcpy(ccv_array_get(parameters, parameters_rnum)((void*)(((char*)((parameters)->data)) + (size_t)(parameters
)->rsize * (size_t)(parameters_rnum))), model->inputs, input_size * sizeof(ccv_nnc_tensor_symbol_t));
359	}
360	ccv_nnc_symbolic_graph_simplify(model->graph,
361		SYMBOLIC_GRAPH_PASSES(CCV_NNC_SIMPLIFY_COMMON_SUBEXPRESSION_ELIMINATION,(const int []){CCV_NNC_SIMPLIFY_COMMON_SUBEXPRESSION_ELIMINATION
, CCV_NNC_SIMPLIFY_DATA_TRANSFER_OPT, CCV_NNC_SIMPLIFY_OPS_FUSION
, CCV_NNC_SIMPLIFY_GRAPH_PRUNING}, (1 +1 +1 +1 +1 +0 +0 +0 +0
 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +
0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0
 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 -1)
362			CCV_NNC_SIMPLIFY_DATA_TRANSFER_OPT,(const int []){CCV_NNC_SIMPLIFY_COMMON_SUBEXPRESSION_ELIMINATION
, CCV_NNC_SIMPLIFY_DATA_TRANSFER_OPT, CCV_NNC_SIMPLIFY_OPS_FUSION
, CCV_NNC_SIMPLIFY_GRAPH_PRUNING}, (1 +1 +1 +1 +1 +0 +0 +0 +0
 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +
0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0
 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 -1)
363			CCV_NNC_SIMPLIFY_OPS_FUSION,(const int []){CCV_NNC_SIMPLIFY_COMMON_SUBEXPRESSION_ELIMINATION
, CCV_NNC_SIMPLIFY_DATA_TRANSFER_OPT, CCV_NNC_SIMPLIFY_OPS_FUSION
, CCV_NNC_SIMPLIFY_GRAPH_PRUNING}, (1 +1 +1 +1 +1 +0 +0 +0 +0
 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +
0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0
 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 -1)
364			CCV_NNC_SIMPLIFY_GRAPH_PRUNING)(const int []){CCV_NNC_SIMPLIFY_COMMON_SUBEXPRESSION_ELIMINATION
, CCV_NNC_SIMPLIFY_DATA_TRANSFER_OPT, CCV_NNC_SIMPLIFY_OPS_FUSION
, CCV_NNC_SIMPLIFY_GRAPH_PRUNING}, (1 +1 +1 +1 +1 +0 +0 +0 +0
 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +
0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0
 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 -1),
365		ccv_array_get(parameters, 0)((void*)(((char*)((parameters)->data)) + (size_t)(parameters
)->rsize * (size_t)(0))), parameters_rnum + input_size,
366		model->outputs, output_size,
367		SYMBOLIC_GRAPH_SOURCES(model->graph)ccv_nnc_symbolic_graph_sources(model->graph), ccv_nnc_symbolic_graph_source_size
(model->graph), SYMBOLIC_GRAPH_DESTINATIONS(model->graph)ccv_nnc_symbolic_graph_destinations(model->graph), ccv_nnc_symbolic_graph_destination_size
(model->graph));
368	ccv_nnc_graph_exec_symbol_autogen(model->graph, 0, 0, CCV_NNC_AUTOGEN_SOURCES_AND_DESTINATIONS);
369	// Size it down.
370	parameters->rnum = parameters_rnum;
371	ccv_cnnp_compiled_data_t* compiled_data = model->compiled_data = cccalloccalloc(1, sizeof(ccv_cnnp_compiled_data_t) + sizeof(ccv_nnc_tensor_symbol_t) * (output_size * 2 - 1));
372	_ccv_cnnp_compiled_data_init(compiled_data, output_size, build_data.gradient_checkpoints);
373	const int evaluate_to_size = compiled_data->evaluate.to_size = ccv_nnc_symbolic_graph_destination_size(model->graph);
374	assert(evaluate_to_size > 0)((void) sizeof ((evaluate_to_size > 0) ? 1 : 0), __extension__
 ({ if (evaluate_to_size > 0) ; else __assert_fail ("evaluate_to_size > 0"
, "ccv_cnnp_model.c", 374, __extension__ __PRETTY_FUNCTION__)
; }));
375	compiled_data->evaluate.tos = ccmallocmalloc(sizeof(ccv_nnc_graph_exec_symbol_t) * evaluate_to_size);
376	memcpy(compiled_data->evaluate.tos, ccv_nnc_symbolic_graph_destinations(model->graph), sizeof(ccv_nnc_graph_exec_symbol_t) * evaluate_to_size);
377	compiled_data->loss = loss;
378	if (loss.cmd == CCV_NNC_NOOP)
379	{
380		// If no loss function provided, there is no fits.
381		for (i = 0; i < output_size; i++)
382		{
383			compiled_data->fits[i] = NO_TENSOR_SYMBOL(const ccv_nnc_tensor_symbol_t){.d = CCV_NNC_NO_TENSOR_SYMBOL
};
384			const ccv_nnc_tensor_symbol_t alias_to = ccv_nnc_tensor_symbol_alias_to(model->graph, model->outputs[i]);
385			if (alias_to.d < 0)
386				compiled_data->f[i] = model->outputs[i];
387			else { // We cannot differentiate against an alias, therefore, we have to verify this output is full, and we can diff against the original.
388				int ofs[CCV_NNC_MAX_DIM_ALLOC(12)];
389				int inc[CCV_NNC_MAX_DIM_ALLOC(12)];
390				ccv_nnc_tensor_symbol_alias_params(model->graph, model->outputs[i], ofs, inc);
391				int j;
392				for (j = 0; j < CCV_NNC_MAX_DIM_ALLOC(12); j++)
393					{ assert(ofs[j] == 0)((void) sizeof ((ofs[j] == 0) ? 1 : 0), __extension__ ({ if (
ofs[j] == 0) ; else __assert_fail ("ofs[j] == 0", "ccv_cnnp_model.c"
, 393, __extension__ __PRETTY_FUNCTION__); })); } // There is no ofs.
394				compiled_data->f[i] = alias_to; // Unfortunately, I cannot assert the size yet.
395			}
396		}
397	} else {
398		for (i = 0; i < output_size; i++)
399		{
400			const ccv_nnc_tensor_param_t info = ccv_nnc_tensor_symbol_params(model->graph, model->outputs[i]);
401			const ccv_nnc_tensor_symbol_t fit = compiled_data->fits[i] = ccv_nnc_tensor_symbol_new(model->graph, info, 0);
402			compiled_data->f[i] = ccv_nnc_tensor_symbol_new(model->graph, ccv_nnc_tensor_auto, 0);
403			ccv_nnc_graph_exec_symbol_new(model->graph, loss, TENSOR_SYMBOL_LIST(model->outputs[i], fit)(const ccv_nnc_tensor_symbol_t []){model->outputs[i], fit}
, (1 +1 +1 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0
 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +
0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0
 +0 +0 +0 -1), TENSOR_SYMBOL_LIST(compiled_data->f[i])(const ccv_nnc_tensor_symbol_t []){compiled_data->f[i]}, (
1 +1 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0
 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +
0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0
 +0 +0 -1), 0);
404		}
405	}
406	ccv_nnc_graph_exec_symbol_autogen(model->graph, 0, 0, CCV_NNC_AUTOGEN_ALL_EXECS | CCV_NNC_AUTOGEN_SOURCES_AND_DESTINATIONS);
407	ccv_nnc_symbolic_graph_simplify(model->graph,
408		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.
409		0, 0, // No need to provide binds at this point.
410		compiled_data->f, model->output_size,
411		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));
412	ccv_nnc_graph_exec_symbol_autogen(model->graph, 0, 0, CCV_NNC_AUTOGEN_SOURCES_AND_DESTINATIONS);
413	// If inputs are from GPU, stream type is GPU.
414	compiled_data->parameters = parameters;
415	compiled_data->parameter_flags = parameter_flags;
416	compiled_data->internals = internals;
417	compiled_data->ids.parameters = parameter_ids;
418	compiled_data->ids.internals = internal_ids;
419	ccv_cnnp_model_gradient_checkpoints_cleanup_after_build(compiled_data, model->graph);
420}
421 
422static 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)
423{
424	ccv_array_t* const stack = (ccv_array_t*)context;
425	ccv_array_push(stack, &symbol.d);
426}
427 
428static 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)
429{
430	const ccv_nnc_tensor_symbol_t src_symbol = {
431		.d = src_index,
432		.graph = src_graph
433	};
434	const ccv_nnc_tensor_symbol_t dest_symbol = {
435		.d = dest_index,
436		.graph = dest_graph
437	};
438	const ccv_nnc_tensor_param_t params = ccv_nnc_tensor_symbol_params(src_graph, src_symbol);
439	ccv_nnc_tensor_symbol_set(dest_graph, dest_symbol, params);
440	int ofs[CCV_NNC_MAX_DIM_ALLOC(12)];
441	int inc[CCV_NNC_MAX_DIM_ALLOC(12)];
442	if (0 == ccv_nnc_tensor_symbol_alias_params(src_graph, src_symbol, ofs, inc))
443		ccv_nnc_tensor_symbol_alias_set(dest_graph, dest_symbol, ofs, inc);
444}
445 
446static 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)
447{
448	const ccv_nnc_tensor_symbol_t src_symbol = {
449		.d = src_index,
450		.graph = src_graph
451	};
452	const ccv_nnc_tensor_param_t src_params = ccv_nnc_tensor_symbol_params(src_graph, src_symbol);
453	const ccv_nnc_tensor_symbol_t dest_symbol = {
454		.d = dest_index,
455		.graph = dest_graph
456	};
457	const ccv_nnc_tensor_param_t dest_params = ccv_nnc_tensor_symbol_params(dest_graph, dest_symbol);
458	if (src_params.dim[0] == 0 || dest_params.dim[0] == 0)
459		return 1;
460	return memcmp(src_params.dim, dest_params.dim, sizeof(src_params.dim)) == 0;
461}
462 
463static 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);
464static void _ccv_cnnp_compiled_data_graph_free(ccv_cnnp_compiled_data_t* const compiled_data);
465 
466typedef struct {
467	int parallel_count;
468	ccv_nnc_symbolic_graph_t* graph;
469	ccv_nnc_graph_exec_arena_t* graph_exec_arena;
470} ccv_nnc_graph_exec_update_t;
471 
472static 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)
473{
474	ccv_nnc_graph_exec_update_t* const graph_exec_update = (ccv_nnc_graph_exec_update_t*)context;
475	ccv_nnc_graph_exec_arena_t* const graph_exec_arena = graph_exec_update->graph_exec_arena;
476	ccv_nnc_graph_exec_t graph_exec = ccv_nnc_graph_exec_from_symbol(graph_exec_arena, symbol);
477	ccv_nnc_graph_exec_set(graph_exec.graph, graph_exec, cmd);
478	ccv_nnc_graph_exec_set_hint(graph_exec.graph, graph_exec, hint);
479	const ccv_nnc_symbolic_graph_t* const graph = graph_exec_update->graph;
480	const int parallel_count = graph_exec_update->parallel_count;
481	int i;
482	for (i = 1; i < parallel_count; i++)
483	{
484		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));
485		if (!CCV_NO_GRAPH_EXEC(copy)((copy).graph == 0))
486		{
487			ccv_nnc_graph_exec_set(copy.graph, copy, cmd);
488			ccv_nnc_graph_exec_set_hint(copy.graph, copy, hint);
489		}
490	}
491}
492 
493void 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)
494{
495	assert(model->graph)((void) sizeof ((model->graph) ? 1 : 0), __extension__ ({ if
 (model->graph) ; else __assert_fail ("model->graph", "ccv_cnnp_model.c"
, 495, __extension__ __PRETTY_FUNCTION__); }));
496	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", 496, __extension__ __PRETTY_FUNCTION__)
; }));
497	assert(!init->graph)((void) sizeof ((!init->graph) ? 1 : 0), __extension__ ({ if
 (!init->graph) ; else __assert_fail ("!init->graph", "ccv_cnnp_model.c"
, 497, __extension__ __PRETTY_FUNCTION__); }));
498	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
499	init->graph = ccv_nnc_symbolic_graph_new();
500	ccv_array_t* const stack = ccv_array_new(sizeof(int), 0, 0);
501	ccv_nnc_graph_exec_symbol_new_hook(init->graph, _ccv_cnnp_graph_push_graph_exec_symbol, stack, 0);
502	_ccv_cnnp_model_compile(init, inputs, input_size, compiled_data->loss);
503	init->parallel_count = model->parallel_count;
504	init->memory_compression = model->memory_compression;
505	init->memory_reduction = model->memory_reduction;
506	init->gradient_checkpointing = model->gradient_checkpointing;
507	init->compiled_data->stream_type = model->compiled_data->stream_type;
508	init->compiled_data->minimize.minimizer = model->compiled_data->minimize.minimizer;
509	init->compiled_data->minimize.max_saved_aux_size = model->compiled_data->minimize.max_saved_aux_size;
510	if (model->compiled_data->gradient_mode != CCV_CNNP_COMPILED_DATA_GRADIENT_NONE)
511		_ccv_cnnp_model_gradient_init(init, model->compiled_data->gradient_mode, model->compiled_data->disable_outgrad, 0, 0);
512	ccv_nnc_graph_exec_symbol_new_hook(init->graph, 0, 0, 0);
513	ccv_nnc_symbolic_graph_tensor_auto(init->graph, TRAVERSE_FULL0,0,0,0);
514	int i, j;
515	// Verify parameters, internals and saved_aux in both graph has the same dimensionality.
516	for (i = 0; i < compiled_data->parameters->rnum; i++)
517	{
518		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;
519		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", 519, __extension__ __PRETTY_FUNCTION__)
; }));
520	}
521	for (i = 0; i < compiled_data->internals->rnum; i++)
522	{
523		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;
524		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", 524, __extension__ __PRETTY_FUNCTION__)
; }));
525	}
526	// Update inputs.
527	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", 527, __extension__ __PRETTY_FUNCTION__)
; }));
528	for (i = 0; i < model->input_size; i++)
529		if (model->inputs[i].d >= 0)
530		{
531			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", 531, __extension__ __PRETTY_FUNCTION__)
; }));
532			_ccv_nnc_tensor_symbol_reinit(init->graph, model->graph, init->inputs[i].d, model->inputs[i].d);
533		}
534	// Update outputs.
535	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", 535, __extension__ __PRETTY_FUNCTION__)
; }));
536	for (i = 0; i < model->output_size; i++)
537	{
538		if (model->outputs[i].d >= 0)
539		{
540			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", 540, __extension__
 __PRETTY_FUNCTION__); }));
541			_ccv_nnc_tensor_symbol_reinit(init->graph, model->graph, init->outputs[i].d, model->outputs[i].d);
542		}
543		if (model->outputs[i].d != model->compiled_data->f[i].d)
544		{
545			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", 545, __extension__ __PRETTY_FUNCTION__)
; }));
546			if (model->compiled_data->f[i].d >= 0)
547			{
548				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", 548, __extension__ __PRETTY_FUNCTION__)
; }));
549				_ccv_nnc_tensor_symbol_reinit(init->graph, model->graph, init->compiled_data->f[i].d, model->compiled_data->f[i].d);
550			}
551		}
552	}
553	// Go through the graph to set tensor on matching symbols
554	for (i = 0; i < stack->rnum; i++)
555	{
556		const int d = *(int*)ccv_array_get(stack, i)((void*)(((char*)((stack)->data)) + (size_t)(stack)->rsize
 * (size_t)(i)));
557		// If exceed range, skip.
558		if (d >= ccv_nnc_graph_exec_symbol_count(init->graph) ||
559			d >= ccv_nnc_graph_exec_symbol_count(model->graph))
560			continue;
561		const ccv_nnc_graph_exec_symbol_t src_symbol = {
562			.d = d,
563			.graph = init->graph
564		};
565		const ccv_nnc_graph_exec_symbol_t dest_symbol = {
566			.d = d,
567			.graph = model->graph
568		};
569		const ccv_nnc_cmd_t src_cmd = ccv_nnc_graph_exec_symbol_cmd(init->graph, src_symbol);
570		const ccv_nnc_cmd_t dest_cmd = ccv_nnc_graph_exec_symbol_cmd(model->graph, dest_symbol);
571		// If the name doesn't match, skip.
572		if (dest_cmd.cmd != src_cmd.cmd && src_cmd.cmd != CCV_NNC_NOOP)
573			continue;
574		// Now get all the inputs and outputs, if matches, set them.
575		const int* src_inputs;
576		int src_input_size;
577		const int* src_outputs;
578		int src_output_size;
579		ccv_nnc_graph_exec_symbol_io(init->graph, src_symbol, &src_inputs, &src_input_size, &src_outputs, &src_output_size);
580		const int* dest_inputs;
581		int dest_input_size;
582		const int* dest_outputs;
583		int dest_output_size;
584		ccv_nnc_graph_exec_symbol_io(model->graph, dest_symbol, &dest_inputs, &dest_input_size, &dest_outputs, &dest_output_size);
585		// We may have unmatched input / output size because this is the minimizer and it has
586		// different saved_aux (for example, when we shrunk with CMD_NOOP).
587		if (src_input_size != dest_input_size)
588			continue;
589		if (src_output_size != dest_output_size)
590			continue;
591		ccv_nnc_graph_exec_symbol_set(model->graph, dest_symbol, src_cmd);
592		// There may be mismatches of the source tensor symbols and destination tensor symbols. The reason is because
593		// we may later passed-in the minimizer, therefore, we may allocate tensors for minimizer later in the original
594		// graph whereas in the newly created graph, it is streamlined (the minimizer exists from the beginning). That
595		// will make the order of tensor symbols creation different, therefore, exact which tensor is which wrong as
596		// well. However, set a new minimizer won't change the exec symbol ordering, because we never create new exec
597		// symbols after gradient init step. Changing a new minimizer just updated that exec symbols setting, it is not
598		// a new exec symbol.
599		for (j = 0; j < src_input_size; j++)
600			if (src_inputs[j] >= 0)
601				_ccv_nnc_tensor_symbol_reinit(init->graph, model->graph, src_inputs[j], dest_inputs[j]);
602		for (j = 0; j < src_output_size; j++)
603			if (src_outputs[j] >= 0)
604				_ccv_nnc_tensor_symbol_reinit(init->graph, model->graph, src_outputs[j], dest_outputs[j]);
605	}
606	ccv_array_free(stack);
607	// After this, we get all tensors in the model graph resolved through tensor_auto.
608	ccv_nnc_symbolic_graph_tensor_auto(model->graph, TRAVERSE_FULL0,0,0,0);
609	// Verify symbols we get matches.
610	const int parameter_size = compiled_data->parameters->rnum;
611	for (i = 0; i < parameter_size; i++)
612		{ 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", 612, __extension__ __PRETTY_FUNCTION__)
; })); }
613	const int internal_size = compiled_data->internals->rnum;
614	for (i = 0; i < internal_size; i++)
615		{ 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", 615, __extension__ __PRETTY_FUNCTION__)
; })); }
616	// Go through compiled data.
617	if (compiled_data->tensor_arena)
618	{
619		const int flag = ccv_nnc_tensor_arena_reinit(compiled_data->tensor_arena, model->graph);
620		if (flag == 0 && compiled_data->graph_exec_arena)
621		{
622			ccv_nnc_graph_exec_reinit(compiled_data->graph_exec_arena, compiled_data->graph, model->graph);
623			// Since we will reinit, if we previously set is_test, we need to set it again.
624			if (compiled_data->is_test)
625			{
626				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; });
627				ccv_nnc_graph_exec_update_t update = {
628					.parallel_count = parallel_count,
629					.graph = model->graph,
630					.graph_exec_arena = compiled_data->graph_exec_arena,
631				};
632				ccv_cnnp_model_set_is_test(model, 1, _ccv_cnnp_cmd_update_for_execs, &update);
633			}
634		} else
635			// Free-up tensor arena & graph exec arena.
636			_ccv_cnnp_compiled_data_graph_free(compiled_data);
637	}
638	// There are other compiled graphs, for accum and apply gradients.
639	// However, the main conclusion is, these absorb operations shouldn't impact parameters.
640	// Thus, it won't impact the shape of gradients (only outgrad). Since for outgrad, we
641	// don't allocate ourselves, it is not a concern. For normal gradients, the shape cannot
642	// be changed otherwise parameters' shape will be meaningless. The same goes to internals.
643	// That is why we don't update these compiled graphs at all this point.
644	// Free the model, we've already "absorbed" it.
645	ccv_cnnp_model_free(init);
646}
647 
648void 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)
649{
650	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", 650, __extension__ __PRETTY_FUNCTION__)
; }));
651	if (model->input_size == 0)
652		model->input_size = input_size;
653	if (!model->graph) // The graph is not compiled yet.
654	{
655		model->graph = ccv_nnc_symbolic_graph_new();
656		_ccv_cnnp_model_compile(model, inputs, input_size, loss);
657		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", 657, __extension__ __PRETTY_FUNCTION__)
; }));
658		int i, flag = 0;
659		for (i = 0; !flag && i < input_size; i++)
660			flag = (CCV_TENSOR_GET_MEMORY(inputs[i].type)((inputs[i].type) & 0x3) == CCV_TENSOR_GPU_MEMORY);
661		// If inputs are from GPU, stream type is GPU.
662		model->compiled_data->stream_type = flag ? CCV_STREAM_CONTEXT_GPU : CCV_STREAM_CONTEXT_CPU;
663		model->compiled_data->minimize.minimizer = minimizer;
664		model->compiled_data->minimize.max_saved_aux_size = ccv_nnc_minimizer_saved_aux_size(minimizer);
665	} else {
666		// Now, finally fill in this part. If the graph is already compiled, we make a copy of the model.
667		// And then absorb the "new model" to the old one.
668		ccv_cnnp_model_t* const init = ccv_cnnp_model_copy(model, model->is_trainable);
669		ccv_cnnp_model_absorb(model, init, inputs, input_size);
670		// Reset minimizer.
671		ccv_cnnp_model_set_minimizer(model, minimizer, 1, 0, 0);
672	}
673}
674 
675ccv_cnnp_model_t* ccv_cnnp_model_copy(const ccv_cnnp_model_t* const model, const int is_trainable)
676{
677	ccv_cnnp_model_t* const new_model = _ccv_cnnp_model_copy(model, 0);
678	new_model->is_trainable = is_trainable;
679	return new_model;
680}
681 
682void ccv_cnnp_model_tensor_auto(ccv_cnnp_model_t* const model, ccv_nnc_tensor_param_t* const outputs, const int output_size)
683{
684	assert(model->graph)((void) sizeof ((model->graph) ? 1 : 0), __extension__ ({ if
 (model->graph) ; else __assert_fail ("model->graph", "ccv_cnnp_model.c"
, 684, __extension__ __PRETTY_FUNCTION__); }));
685	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", 685, __extension__ __PRETTY_FUNCTION__)
; }));
686	ccv_nnc_symbolic_graph_t* const graph = model->graph;
687	ccv_nnc_symbolic_graph_tensor_auto(graph, TRAVERSE_FULL0,0,0,0);
688	int i;
689	for (i = 0; i < output_size; i++)
690	{
691		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", 691, __extension__ __PRETTY_FUNCTION__)
; }));
692		outputs[i] = ccv_nnc_tensor_symbol_params(graph, model->outputs[i]);
693	}
694}
695 
696void ccv_cnnp_model_set_workspace_size(ccv_cnnp_model_t* const model, size_t workspace_size)
697{
698	if (workspace_size == model->workspace_size)
699		return;
700	model->workspace_size = workspace_size;
701	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
702	if (compiled_data && compiled_data->graph)
703		ccv_nnc_graph_autotune(compiled_data->graph, workspace_size, 0, TRAVERSE_FULL0,0,0,0);
704}
705 
706size_t ccv_cnnp_model_workspace_size(ccv_cnnp_model_t* const model)
707{
708	return model->workspace_size;
709}
710 
711void ccv_cnnp_model_set_data_parallel(ccv_cnnp_model_t* const model, const int parallel)
712{
713	if (parallel == 0)
714		model->parallel_count = ccv_nnc_device_count(CCV_STREAM_CONTEXT_GPU);
715	else
716		model->parallel_count = parallel;
717	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
718	if (compiled_data)
719		{ 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", 719, __extension__ __PRETTY_FUNCTION__)
; })); }
720}
721 
722void ccv_cnnp_model_set_max_concurrency(ccv_cnnp_model_t* const model, const int max_stream_count)
723{
724	model->max_stream_count = max_stream_count;
725	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
726	if (compiled_data)
727		{ 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", 727, __extension__ __PRETTY_FUNCTION__)
; })); }
728}
729 
730void ccv_cnnp_model_set_memory_compression(ccv_cnnp_model_t* const model, const int memory_compression)
731{
732	model->memory_compression = memory_compression;
733	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
734	if (compiled_data)
735		{ 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", 735, __extension__ __PRETTY_FUNCTION__)
; })); }
736}
737 
738void ccv_cnnp_model_set_memory_reduction(ccv_cnnp_model_t* const model, const int memory_reduction)
739{
740	model->memory_reduction = memory_reduction;
741	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
742	if (compiled_data)
743		{ 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", 743, __extension__ __PRETTY_FUNCTION__)
; })); }
744}
745 
746void ccv_cnnp_model_set_gradient_checkpointing(ccv_cnnp_model_t* const model, const int gradient_checkpointing)
747{
748	model->gradient_checkpointing = gradient_checkpointing;
749}
750 
751int ccv_cnnp_model_gradient_checkpointing(ccv_cnnp_model_t* const model)
752{
753	return model->gradient_checkpointing;
754}
755 
756typedef struct {
757	int parallel_count;
758	ccv_nnc_symbolic_graph_t* graph;
759	ccv_cnnp_compiled_data_t* compiled_data;
760	ccv_nnc_tensor_arena_t* tensor_arena;
761} ccv_nnc_tensor_init_states_t;
762 
763static int _ccv_cnnp_any_to_init(const ccv_cnnp_compiled_data_t* const compiled_data)
764{
765	int i;
766	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));
767	for (i = 0; i < compiled_data->parameters->rnum; i++)
768	{
769		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;
770		if (!(init_v[d >> 5] & (1u << (d & 0x1f))))
771			return 1;
772	}
773	for (i = 0; i < compiled_data->internals->rnum; i++)
774	{
775		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;
776		if (!(init_v[d >> 5] & (1u << (d & 0x1f))))
777			return 1;
778	}
779	return 0;
780}
781 
782static 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)
783{
784	ccv_nnc_tensor_init_states_t* const tensor_init_states = (ccv_nnc_tensor_init_states_t*)context;
785	ccv_nnc_tensor_arena_t* const tensor_arena = tensor_init_states->tensor_arena;
786	ccv_nnc_tensor_t* const output_tensor = ccv_nnc_tensor_from_symbol(tensor_arena, output_symbol);
787	if (!output_tensor)
788		return;
789	const int d = output_symbol.d;
790	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", 790, __extension__ __PRETTY_FUNCTION__)
; }));
791  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));
792	if (init_v[d >> 5] & (1u << (d & 0x1f)))
793		return;
794	init_v[d >> 5] |= (1u << (d & 0x1f));
795	ccv_nnc_cmd_exec(cmd, hint, flags, &input, input ? 1 : 0, &output_tensor, 1, 0);
796	const ccv_nnc_symbolic_graph_t* const graph = tensor_init_states->graph;
797	const int parallel_count = tensor_init_states->parallel_count;
798	int i;
799	for (i = 1; i < parallel_count; i++)
800	{
801		ccv_nnc_tensor_t* const copy = ccv_nnc_tensor_from_symbol(tensor_arena, ccv_nnc_tensor_symbol_copy(graph, output_symbol, i));
802		if (copy)
803			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);
804	}
805}
806 
807// This method can only handle cases we added new tensors and exec, never delete. This invariant is true because
808// we setup everything (including calling simplify method) in ccv_cnnp_model_compile method, before this rewind setup.
809static void _ccv_cnnp_model_rewind_graph(ccv_cnnp_model_t* const model)
810{
811	assert(model->graph)((void) sizeof ((model->graph) ? 1 : 0), __extension__ ({ if
 (model->graph) ; else __assert_fail ("model->graph", "ccv_cnnp_model.c"
, 811, __extension__ __PRETTY_FUNCTION__); }));
812	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", 812, __extension__ __PRETTY_FUNCTION__)
; }));
813	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
814	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", 814, __extension__
 __PRETTY_FUNCTION__); }));
815	int i;
816	for (i = 0; i < compiled_data->rewindables->rnum; i++)
817	{
818		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)));
819		if (rewind_symbol->type == CCV_CNNP_REWIND_GRAPH_EXEC)
820			ccv_nnc_graph_exec_symbol_free(model->graph, rewind_symbol->graph_exec);
821		else if (rewind_symbol->type == CCV_CNNP_REWIND_TENSOR)
822			ccv_nnc_tensor_symbol_free(model->graph, rewind_symbol->tensor);
823	}
824	ccv_array_clear(compiled_data->rewindables);
825	ccv_nnc_graph_exec_symbol_autogen(model->graph, 0, 0, CCV_NNC_AUTOGEN_SOURCES_AND_DESTINATIONS);
826}
827 
828static 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)
829{
830	const ccv_cnnp_rewind_symbol_t rewind_symbol = {
831		.type = CCV_CNNP_REWIND_TENSOR,
832		.tensor = symbol
833	};
834	ccv_array_t* const rewind_symbols = (ccv_array_t*)context;
835	ccv_array_push(rewind_symbols, &rewind_symbol);
836}
837 
838static 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)
839{
840	const ccv_cnnp_rewind_symbol_t rewind_symbol = {
841		.type = CCV_CNNP_REWIND_TENSOR,
842		.tensor = symbol
843	};
844	ccv_array_t* const rewind_symbols = (ccv_array_t*)context;
845	ccv_array_push(rewind_symbols, &rewind_symbol);
846}
847 
848static 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)
849{
850	const ccv_cnnp_rewind_symbol_t rewind_symbol = {
851		.type = CCV_CNNP_REWIND_GRAPH_EXEC,
852		.graph_exec = symbol
853	};
854	ccv_array_t* const rewind_symbols = (ccv_array_t*)context;
855	ccv_array_push(rewind_symbols, &rewind_symbol);
856}
857 
858static 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)
859{
860	ccv_nnc_graph_exec_t const update_exec = ccv_nnc_graph_exec_from_symbol(graph_exec_arena, exec_symbol);
861	if (!CCV_NO_GRAPH_EXEC(update_exec)((update_exec).graph == 0))
862		ccv_nnc_graph_exec_set(update_exec.graph, update_exec, cmd);
863	int i;
864	for (i = 1; i < parallel_count; i++)
865	{
866		ccv_nnc_graph_exec_symbol_t copy_symbol = ccv_nnc_graph_exec_symbol_copy(symbolic_graph, exec_symbol, i);
867		const ccv_nnc_graph_exec_t copy = ccv_nnc_graph_exec_from_symbol(graph_exec_arena, copy_symbol);
868		if (!CCV_NO_GRAPH_EXEC(copy)((copy).graph == 0))
869			ccv_nnc_graph_exec_set(copy.graph, copy, cmd);
870	}
871}
872 
873static 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)
874{
875	assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
 (compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 875, __extension__ __PRETTY_FUNCTION__); }));
876	assert(symbolic_graph)((void) sizeof ((symbolic_graph) ? 1 : 0), __extension__ ({ if
 (symbolic_graph) ; else __assert_fail ("symbolic_graph", "ccv_cnnp_model.c"
, 876, __extension__ __PRETTY_FUNCTION__); }));
877	ccv_nnc_graph_exec_symbol_set(symbolic_graph, exec_symbol, cmd);
878	int i;
879	for (i = 1; i < parallel_count; i++)
880	{
881		ccv_nnc_graph_exec_symbol_t copy_symbol = ccv_nnc_graph_exec_symbol_copy(symbolic_graph, exec_symbol, i);
882		if (copy_symbol.graph)
883			ccv_nnc_graph_exec_symbol_set(symbolic_graph, copy_symbol, cmd);
884	}
885	ccv_nnc_graph_exec_arena_t* const graph_exec_arena = compiled_data->graph_exec_arena;
886	if (graph_exec_arena)
887		_ccv_cnnp_model_graph_symbol_exec_set_for_graph_exec_arena(graph_exec_arena, parallel_count, exec_symbol, cmd, symbolic_graph);
888	// Skip backward graph exec arena because it is for a specific accum symbolic graph, not the main graph (model->graph)
889	ccv_nnc_graph_exec_arena_t* const gradient_graph_exec_arena = compiled_data->apply_gradients.graph_exec_arena;
890	if (gradient_graph_exec_arena)
891		_ccv_cnnp_model_graph_symbol_exec_set_for_graph_exec_arena(gradient_graph_exec_arena, parallel_count, exec_symbol, cmd, symbolic_graph);
892}
893 
894static 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)
895{
896	int this_parameter_flag = 0;
897	if (update_nodes[parameter_indice].d == CCV_NNC_NO_TENSOR_SYMBOL)
898		return this_parameter_flag;
899	const ccv_nnc_cmd_t old_minimizer = ccv_nnc_graph_exec_symbol_cmd(graph, update_nodes[parameter_indice]);
900	int j, k;
901	// For no-op, we can preserve previous saved_aux_size.
902	if (old_minimizer.cmd != minimizer.cmd && minimizer.cmd != CCV_NNC_NOOP)
903	{
904		// If the old minimizer is a noop, then the old_saved_aux_size should be whatever its previous
905		// saved_aux_size is, otherwise we will reinit the saved_aux repeatedly if you switch between
906		// noop and a minimizer. We don't want that because we do that in high-level frameworks to
907		// make sure some model parameters don't update if we don't want them to.
908		int old_saved_aux_size;
909		if (old_minimizer.cmd == CCV_NNC_NOOP)
910		{
911			int input_size;
912			ccv_nnc_graph_exec_symbol_io(graph, update_nodes[parameter_indice], 0, &input_size, 0, 0);
913			if (input_size < 2) // This is not legit.
914				old_saved_aux_size = ccv_nnc_minimizer_saved_aux_size(old_minimizer);
915			else // See ccv_nnc_minimizer_saved_aux_size, the saved_aux is inputs excluding gradients and parameters.
916				old_saved_aux_size = input_size - 2;
917		} else
918			old_saved_aux_size = ccv_nnc_minimizer_saved_aux_size(old_minimizer);
919		if (old_saved_aux_size != saved_aux_size)
920		{
921			this_parameter_flag = 1;
922			if (saved_aux_size > old_saved_aux_size)
923			{
924				// Allocate new tensor symbols.
925				const ccv_nnc_tensor_param_t info = ccv_nnc_tensor_symbol_params(graph, updated_parameters[parameter_indice]);
926				for (j = old_saved_aux_size; j < saved_aux_size; j++)
927				{
928					saved_aux[parameter_indice * max_saved_aux_size + j].source = ccv_nnc_tensor_symbol_new(graph, info, 0);
929					saved_aux[parameter_indice * max_saved_aux_size + j].destination = ccv_nnc_tensor_symbol_new(graph, info, 0);
930					const int device_id = CCV_TENSOR_GET_DEVICE_ID(info.type)(((info.type) & 0xfff00) >> 8);
931					for (k = 1; k < parallel_count; k++)
932					{
933						ccv_nnc_tensor_param_t dev_info = info;
934						if (k != device_id)
935							CCV_TENSOR_SET_DEVICE_ID(dev_info.type, k)(dev_info.type) = (((dev_info.type) & ~0xfff00) | (((k) &
 0xfff) << 8));
936						else
937							CCV_TENSOR_SET_DEVICE_ID(dev_info.type, 0)(dev_info.type) = (((dev_info.type) & ~0xfff00) | (((0) &
 0xfff) << 8));
938						const ccv_nnc_tensor_symbol_t src_copy = ccv_nnc_tensor_symbol_new(graph, dev_info, 0);
939						const ccv_nnc_tensor_symbol_t dest_copy = ccv_nnc_tensor_symbol_new(graph, dev_info, 0);
940						ccv_nnc_tensor_symbol_set_copy(graph, saved_aux[parameter_indice * max_saved_aux_size + j].source, k, src_copy);
941						ccv_nnc_tensor_symbol_set_copy(graph, saved_aux[parameter_indice * max_saved_aux_size + j].destination, k, dest_copy);
942					}
943				}
944			} else {
945				for (j = saved_aux_size; j < old_saved_aux_size; j++)
946				{
947					for (k = 1; k < parallel_count; k++)
948					{
949						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);
950						if (src_copy.d >= 0)
951						{
952							ccv_nnc_tensor_symbol_free(graph, src_copy);
953							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
});
954						}
955						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);
956						if (dest_copy.d >= 0)
957						{
958							ccv_nnc_tensor_symbol_free(graph, dest_copy);
959							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
});
960						}
961					}
962					ccv_nnc_tensor_symbol_free(graph, saved_aux[parameter_indice * max_saved_aux_size + j].source);
963					ccv_nnc_tensor_symbol_free(graph, saved_aux[parameter_indice * max_saved_aux_size + j].destination);
964					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
};
965				}
966			}
967		}
968	}
969	_ccv_cnnp_model_graph_exec_symbol_set(graph, compiled_data, parallel_count, update_nodes[parameter_indice], minimizer);
970	if (this_parameter_flag)
971	{
972		ccv_nnc_tensor_symbol_t update_inputs[saved_aux_size + 2];
973		ccv_nnc_tensor_symbol_t update_outputs[saved_aux_size + 1];
974		const int* inputs = 0;
975		int input_size = 0;
976		ccv_nnc_graph_exec_symbol_io(graph, update_nodes[parameter_indice], &inputs, &input_size, 0, 0);
977		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", 977, __extension__ __PRETTY_FUNCTION__)
; }));
978		update_inputs[0].d = inputs[0];
979		update_inputs[0].graph = graph;
980		update_inputs[1].d = inputs[1];
981		update_inputs[1].graph = graph;
982		update_outputs[0] = updated_parameters[parameter_indice];
983		for (j = 0; j < saved_aux_size; j++)
984		{
985			update_inputs[j + 2] = saved_aux[parameter_indice * max_saved_aux_size + j].source;
986			update_outputs[j + 1] = saved_aux[parameter_indice * max_saved_aux_size + j].destination;
987		}
988		ccv_nnc_graph_exec_symbol_set_io(graph, update_nodes[parameter_indice], update_inputs, saved_aux_size + 2, update_outputs, saved_aux_size + 1);
989		for (k = 1; k < parallel_count; k++)
990		{
991			const ccv_nnc_graph_exec_symbol_t copy = ccv_nnc_graph_exec_symbol_copy(graph, update_nodes[parameter_indice], k);
992			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"
, 992, __extension__ __PRETTY_FUNCTION__); }));
993			ccv_nnc_graph_exec_symbol_io(graph, copy, &inputs, &input_size, 0, 0);
994			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", 994, __extension__ __PRETTY_FUNCTION__)
; }));
995			update_inputs[0].d = inputs[0];
996			update_inputs[0].graph = graph;
997			update_inputs[1].d = inputs[1];
998			update_inputs[1].graph = graph;
999			update_outputs[0] = ccv_nnc_tensor_symbol_copy(graph, updated_parameters[parameter_indice], k);
1000			for (j = 0; j < saved_aux_size; j++)
1001			{
1002				update_inputs[j + 2] = ccv_nnc_tensor_symbol_copy(graph, saved_aux[parameter_indice * max_saved_aux_size + j].source, k);
1003				update_outputs[j + 1] = ccv_nnc_tensor_symbol_copy(graph, saved_aux[parameter_indice * max_saved_aux_size + j].destination, k);
1004			}
1005			ccv_nnc_graph_exec_symbol_set_io(graph, copy, update_inputs, saved_aux_size + 2, update_outputs, saved_aux_size + 1);
1006		}
1007	}
1008	return this_parameter_flag;
1009}
1010 
1011typedef struct {
1012	int parameter_size;
1013	ccv_nnc_cmd_t minimizer;
1014	ccv_cnnp_model_io_t parameters[1];
1015} ccv_cnnp_set_minimizer_for_parameter_t;
1016 
1017static int _ccv_cnnp_apply_parameters_with_minimizer(ccv_cnnp_model_t* const model)
1018{
1019	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
1020	assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
 (compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 1020, __extension__ __PRETTY_FUNCTION__); }));
1021	const int max_saved_aux_size = compiled_data->minimize.max_saved_aux_size;
1022	// We update all parameters, at this point, we have one minimizer.
1023	const int parameter_size = compiled_data->parameters->rnum;
1024	ccv_nnc_graph_exec_symbol_t* const update_nodes = compiled_data->update_nodes;
1025	ccv_nnc_symbolic_graph_t* const symbolic_graph = model->graph;
1026	assert(symbolic_graph)((void) sizeof ((symbolic_graph) ? 1 : 0), __extension__ ({ if
 (symbolic_graph) ; else __assert_fail ("symbolic_graph", "ccv_cnnp_model.c"
, 1026, __extension__ __PRETTY_FUNCTION__); }));
1027	const int parallel_count = _ccv_cnnp_model_root_parallel_count(model);
1028	assert(_ccv_cnnp_model_effective_parallel_count(model) == parallel_count && "local replicated stateful models only support forward / no-grad evaluation for now")((void) sizeof ((_ccv_cnnp_model_effective_parallel_count(model
) == parallel_count && "local replicated stateful models only support forward / no-grad evaluation for now"
) ? 1 : 0), __extension__ ({ if (_ccv_cnnp_model_effective_parallel_count
(model) == parallel_count && "local replicated stateful models only support forward / no-grad evaluation for now"
) ; else __assert_fail ("_ccv_cnnp_model_effective_parallel_count(model) == parallel_count && \"local replicated stateful models only support forward / no-grad evaluation for now\""
, "ccv_cnnp_model.c", 1028, __extension__ __PRETTY_FUNCTION__
); }));
1029	ccv_array_t* const parameters = compiled_data->minimize.parameters;
1030	ccv_array_t* const parameter_indices = ccv_array_new(sizeof(int), 0, 0);
1031	int i, j, flag = 0;
1032	for (i = 0; i < parameters->rnum; i++)
1033	{
1034		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)));
1035		for (j = 0; j < set_minimizer_for_parameter->parameter_size; j++)
1036		{
1037			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;
1038			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", 1038, __extension__ __PRETTY_FUNCTION__
); }));
1039			const int old_rnum = parameter_indices->rnum;
1040			ccv_cnnp_model_add_to_parameter_indices(set_minimizer_for_parameter->parameters[j]->model, param_sel, parameter_indices);
1041			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;
1042			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", 1042, __extension__ __PRETTY_FUNCTION__
); }));
1043			if (param_ref >= 0)
1044			{
1045				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", 1045, __extension__ __PRETTY_FUNCTION__
); }));
1046				*(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)));
1047				parameter_indices->rnum = old_rnum + 1;
1048			}
1049		}
1050		const int saved_aux_size = ccv_nnc_minimizer_saved_aux_size(set_minimizer_for_parameter->minimizer);
1051		// We may have duplicated indices, but that is OK, we will set it twice.
1052		for (j = 0; j < parameter_indices->rnum; j++)
1053		{
1054			const int d = *(int*)ccv_array_get(parameter_indices, j)((void*)(((char*)((parameter_indices)->data)) + (size_t)(parameter_indices
)->rsize * (size_t)(j)));
1055			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", 1055, __extension__ __PRETTY_FUNCTION__
); }));
1056			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))
1057				flag = 1;
1058		}
1059		ccv_array_clear(parameter_indices);
1060	}
1061	ccv_array_free(parameter_indices);
1062	return flag;
1063}
1064 
1065static 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)
1066{
1067	if (new_saved_aux_size == old_saved_aux_size)
1068		return;
1069	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", 1069, __extension__ __PRETTY_FUNCTION__
); }));
1070	int i, j;
1071	for (i = parameter_size - 1; i >= 0; i--)
1072	{
1073		for (j = new_saved_aux_size - 1; j >= old_saved_aux_size; j--)
1074			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
};
1075		for (j = old_saved_aux_size - 1; j >= 0; j--)
1076			saved_aux[i * new_saved_aux_size + j] = saved_aux[i * old_saved_aux_size + j];
1077	}
1078}
1079 
1080static void _ccv_cnnp_model_set_rewindables(ccv_cnnp_model_t* const model)
1081{
1082	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
1083	assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
 (compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 1083, __extension__ __PRETTY_FUNCTION__); }));
1084	if (!compiled_data->rewindables)
1085		compiled_data->rewindables = ccv_array_new(sizeof(ccv_cnnp_rewind_symbol_t), 0, 0);
1086	ccv_nnc_tensor_symbol_new_hook(model->graph, _ccv_cnnp_model_tensor_symbol_new_hook, compiled_data->rewindables, 0);
1087	ccv_nnc_tensor_symbol_alias_new_hook(model->graph, _ccv_cnnp_model_tensor_symbol_alias_new_hook, compiled_data->rewindables, 0);
1088	ccv_nnc_graph_exec_symbol_new_hook(model->graph, _ccv_cnnp_model_graph_exec_symbol_new_hook, compiled_data->rewindables, 0);
1089}
1090 
1091static 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)
1092{
1093	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
1094	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", 1094, __extension__ __PRETTY_FUNCTION__
); }));
1095	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", 1095, __extension__ __PRETTY_FUNCTION__
); }));
1096	const int evaluate_to_size = compiled_data->evaluate.to_size;
1097	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", 1097, __extension__ __PRETTY_FUNCTION__
); }));
1098	const int parallel_count = _ccv_cnnp_model_root_parallel_count(model);
1099	assert(_ccv_cnnp_model_effective_parallel_count(model) == parallel_count && "local replicated stateful models only support forward / no-grad evaluation for now")((void) sizeof ((_ccv_cnnp_model_effective_parallel_count(model
) == parallel_count && "local replicated stateful models only support forward / no-grad evaluation for now"
) ? 1 : 0), __extension__ ({ if (_ccv_cnnp_model_effective_parallel_count
(model) == parallel_count && "local replicated stateful models only support forward / no-grad evaluation for now"
) ; else __assert_fail ("_ccv_cnnp_model_effective_parallel_count(model) == parallel_count && \"local replicated stateful models only support forward / no-grad evaluation for now\""
, "ccv_cnnp_model.c", 1099, __extension__ __PRETTY_FUNCTION__
); }));
1100	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);
1101	compiled_data->evaluate.to_ops = (ccv_nnc_graph_exec_t*)(compiled_data->evaluate.tos + evaluate_to_size * parallel_count);
1102	int i, j;
1103	const int output_size = model->output_size;
1104	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", 1104, __extension__ __PRETTY_FUNCTION__
); }));
1105	if (fits)
1106		for (i = 0; i < output_size; i++)
1107			ccv_nnc_tensor_symbol_set(model->graph, compiled_data->fits[i], fits[i]->info);
1108	const int max_saved_aux_size = compiled_data->minimize.max_saved_aux_size;
1109	const int parameter_size = compiled_data->parameters->rnum;
1110	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);
1111	compiled_data->update_nodes = (ccv_nnc_graph_exec_symbol_t*)(compiled_data->updated_parameters + parameter_size);
1112	compiled_data->saved_aux = (ccv_nnc_tensor_symbol_map_t*)(compiled_data->update_nodes + parameter_size);
1113	int parameter_size_maybe_more = parameter_size;
1114	compiled_data->disable_outgrad = disable_outgrad;
1115	int outgrad_size;
1116	if (gradient_mode == CCV_CNNP_COMPILED_DATA_GRADIENT_TRAINABLES || model->input_size == 0)
1117		outgrad_size = 0;
1118	else if (disable_outgrad == CCV_CNNP_DISABLE_OUTGRAD_NONE) // Compute minimize with gradients including inputs.
1119		outgrad_size = model->input_size;
1120	else {
1121		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", 1121, __extension__ __PRETTY_FUNCTION__
); })); // If it is disable all, gradient mode won't be this.
1122		outgrad_size = 0;
1123		for (i = 0; i < model->input_size; i++)
1124			if (!(disable_outgrad & ((uint64_t)1 << i)))
1125				++outgrad_size;
1126	}
1127	compiled_data->outgrad_size = outgrad_size;
1128	parameter_size_maybe_more += outgrad_size;
1129	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);
1130	compiled_data->outgrads = parameter_size_maybe_more > parameter_size ? compiled_data->gradients + parameter_size : 0;
1131	compiled_data->backward.tos = (ccv_nnc_graph_exec_symbol_t*)(compiled_data->gradients + parameter_size_maybe_more);
1132	compiled_data->backward.to_size = parameter_size_maybe_more;
1133	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)));
1134	if (compiled_data->parameter_flags)
1135	{
1136		parameters = (ccv_nnc_tensor_symbol_t*)ccmallocmalloc(sizeof(ccv_nnc_tensor_symbol_t) * parameter_size);
1137		for (i = 0; i < parameter_size; i++)
1138			if (compiled_data->parameter_flags[i >> 6] & ((uint64_t)1 << (i & 63)))
1139				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)));
1140			else
1141				parameters[i] = NO_TENSOR_SYMBOL(const ccv_nnc_tensor_symbol_t){.d = CCV_NNC_NO_TENSOR_SYMBOL
};
1142	}
1143	if (gradient_mode == CCV_CNNP_COMPILED_DATA_GRADIENT_TRAINABLES || model->input_size == 0)
1144		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);
1145	else if (disable_outgrad == CCV_CNNP_DISABLE_OUTGRAD_NONE) // Compute minimize with gradients including inputs.
1146		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);
1147	else { // Compute minimize with gradients including selected inputs.
1148		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", 1148, __extension__ __PRETTY_FUNCTION__
); }));
1149		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", 1149, __extension__ __PRETTY_FUNCTION__
); })); // If it is disable all, gradient mode won't be this.
1150		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", 1150, __extension__ __PRETTY_FUNCTION__
); }));
1151		ccv_nnc_tensor_symbol_t outgrads[outgrad_size];
1152		j = 0;
1153		for (i = 0; i < model->input_size; i++)
1154			if (!(disable_outgrad & ((uint64_t)1 << i)))
1155				outgrads[j++] = model->inputs[i];
1156		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);
1157	}
1158	if (compiled_data->parameter_flags)
1159		ccfreefree(parameters);
1160	_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);
1161	if (compiled_data->minimize.parameters)
1162		_ccv_cnnp_apply_parameters_with_minimizer(model);
1163	// Go through gradient checkpoints to generate tensor inputs for backward pass just before executing the backward pass.
1164	ccv_cnnp_model_apply_gradient_checkpoints(compiled_data, model->graph);
1165	for (i = 0; i < output_size; i++)
1166	{
1167		const ccv_nnc_tensor_symbol_t df = ccv_nnc_tensor_symbol_for_backward(model->graph, compiled_data->f[i]);
1168		// Init this to 1 so we can backprop.
1169		ccv_nnc_tensor_symbol_set_flags(model->graph, df, CCV_NNC_TENSOR_SYMBOL_INIT_ONES);
1170	}
1171	compiled_data->backward.to_size = 0;
1172	for (i = 0; i < parameter_size_maybe_more; i++)
1173		if (compiled_data->gradients[i].d != CCV_NNC_NO_TENSOR_SYMBOL)
1174			compiled_data->backward.tos[compiled_data->backward.to_size++] = ccv_nnc_graph_exec_symbol_for_backward(model->graph, compiled_data->gradients[i]);
1175	ccv_nnc_graph_exec_symbol_autogen(model->graph, 0, 0, CCV_NNC_AUTOGEN_ALL_EXECS);
1176	ccv_nnc_symbolic_graph_set_destinations(model->graph, compiled_data->update_nodes, parameter_size);
1177	for (i = 0; i < parameter_size_maybe_more - parameter_size; i++)
1178	{
1179		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.
1180			continue;
1181		const ccv_nnc_graph_exec_symbol_t outgrad = ccv_nnc_graph_exec_symbol_for_backward(model->graph, compiled_data->outgrads[i]);
1182		const int* tos;
1183		int to_size;
1184		ccv_nnc_graph_exec_symbol_to(model->graph, outgrad, &tos, &to_size);
1185		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.
1186		{
1187			const ccv_nnc_graph_exec_symbol_t* destinations = ccv_nnc_symbolic_graph_destinations(model->graph);
1188			const int destination_count = ccv_nnc_symbolic_graph_destination_size(model->graph);
1189			int flag = 0;
1190			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; });
1191			for (j = i - 1; !flag && j >= 0; j--)
1192				if (j + outgrad_destination_start < destination_count)
1193					flag = (destinations[j + outgrad_destination_start].d == outgrad.d);
1194			if (!flag) // Only if we cannot find it, we add it.
1195				ccv_nnc_symbolic_graph_add_destination(model->graph, outgrad);
1196		}
1197	}
1198	if (parallel_count > 1)
1199	{
1200		ccv_nnc_symbolic_graph_data_parallel(model->graph, parallel_count,
1201			0, 0,
1202			compiled_data->gradients, parameter_size /* No need to deal with outgrads, we don't allreduce outgrads */,
1203			compiled_data->gradients /* We only care about gradients before allreduce, thus, update our current pointers */,
1204			0, 0, 0,
1205			CCV_NNC_PARALLEL_REDUCE_OP_SUM,
1206			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));
1207		ccv_nnc_graph_exec_symbol_autogen(model->graph, 0, 0, CCV_NNC_AUTOGEN_SOURCES_AND_DESTINATIONS);
1208		for (i = 0; i < evaluate_to_size; i++)
1209			for (j = 1; j < parallel_count; j++)
1210			{
1211				const ccv_nnc_graph_exec_symbol_t copy = ccv_nnc_graph_exec_symbol_copy(model->graph, compiled_data->evaluate.tos[i], j);
1212				if (copy.d != CCV_NNC_NO_GRAPH_EXEC_SYMBOL)
1213					compiled_data->evaluate.tos[compiled_data->evaluate.to_size++] = copy;
1214			}
1215		const int backward_to_size = compiled_data->backward.to_size;
1216		for (i = 0; i < backward_to_size; i++)
1217			for (j = 1; j < parallel_count; j++)
1218			{
1219				const ccv_nnc_graph_exec_symbol_t copy = ccv_nnc_graph_exec_symbol_copy(model->graph, compiled_data->backward.tos[i], j);
1220				if (copy.d != CCV_NNC_NO_GRAPH_EXEC_SYMBOL)
1221					compiled_data->backward.tos[compiled_data->backward.to_size++] = copy;
1222			}
1223	}
1224	// Only use memory compression if we are in gradient parameter mode.
1225	if (gradient_mode == CCV_CNNP_COMPILED_DATA_GRADIENT_TRAINABLES || gradient_mode == CCV_CNNP_COMPILED_DATA_GRADIENT_TRAINABLES_AND_INPUTS)
1226	{
1227		if (model->memory_compression)
1228			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));
1229		if (model->memory_reduction)
1230			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));
1231	}
1232	compiled_data->backward.to_size = _ccv_nnc_array_dedup_graph_exec_symbols(compiled_data->backward.tos, compiled_data->backward.to_size);
1233	compiled_data->gradient_mode = gradient_mode;
1234}
1235 
1236void ccv_cnnp_model_tensors_init_0(const ccv_cnnp_model_t* const model, ccv_cnnp_compiled_data_t* const compiled_data)
1237{
1238	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", 1238, __extension__ __PRETTY_FUNCTION__
); }));
1239	const int parameter_size = compiled_data->parameters->rnum;
1240	const int parallel_count = _ccv_cnnp_model_effective_parallel_count(model);
1241	compiled_data->parallel_count = parallel_count;
1242	const int internal_size = compiled_data->internals->rnum;
1243	compiled_data->tensors_init.size = ccv_nnc_tensor_symbol_count(model->graph);
1244	compiled_data->tensors_init.v = cccalloccalloc(((compiled_data->tensors_init.size + 31) >> 5), sizeof(uint32_t));
1245	compiled_data->tensors.parameters = (ccv_nnc_tensor_t**)cccalloccalloc((parameter_size + internal_size) * parallel_count, sizeof(ccv_nnc_tensor_t*));
1246	compiled_data->tensors.internals = compiled_data->tensors.parameters + parameter_size * parallel_count;
1247}
1248 
1249int ccv_cnnp_model_tensors_any_to_alloc(const ccv_cnnp_model_t* const model, ccv_cnnp_compiled_data_t* const compiled_data)
1250{
1251	int i, j;
1252	const int parameter_size = compiled_data->parameters->rnum;
1253	const int parallel_count = _ccv_cnnp_compiled_data_parallel_count(model, compiled_data);
1254	const int internal_size = compiled_data->internals->rnum;
1255	for (i = 0; i < parameter_size; i++)
1256	{
1257		// parameters has to be allocated all together.
1258		if (compiled_data->tensors.parameters[i])
1259		{
1260			for (j = 1; j < parallel_count; j++)
1261				{ 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", 1261, __extension__ __PRETTY_FUNCTION__
); })); }
1262			continue;
1263		}
1264		return 1;
1265	}
1266	for (i = 0; i < internal_size; i++)
1267	{
1268		if (!compiled_data->tensors.internals[i])
1269			return 1;
1270		for (j = 1; j < parallel_count; j++)
1271			if (!compiled_data->tensors.internals[i + j * internal_size])
1272				return 1;
1273	}
1274	return 0;
1275}
1276 
1277void ccv_cnnp_model_tensors_init_1(const ccv_cnnp_model_t* const model, ccv_cnnp_compiled_data_t* const compiled_data)
1278{
1279	int i, j;
1280	const int parameter_size = compiled_data->parameters->rnum;
1281	const int parallel_count = _ccv_cnnp_compiled_data_parallel_count(model, compiled_data);
1282	compiled_data->parallel_count = parallel_count;
1283	const int internal_size = compiled_data->internals->rnum;
1284	for (i = 0; i < parameter_size; i++)
1285	{
1286		// parameters has to be allocated all together.
1287		if (compiled_data->tensors.parameters[i])
1288		{
1289			for (j = 1; j < parallel_count; j++)
1290				{ 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", 1290, __extension__ __PRETTY_FUNCTION__
); })); }
1291			continue;
1292		}
1293		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)));
1294		ccv_nnc_tensor_param_t info = ccv_nnc_tensor_symbol_params(parameter.graph, parameter);
1295		if (CCV_TENSOR_GET_DEVICE(info.type)((info.type) & 0xfff00) == CCV_COMPUTE_DEVICE_ANY)
1296			CCV_TENSOR_SET_DEVICE_ID(info.type, 0)(info.type) = (((info.type) & ~0xfff00) | (((0) & 0xfff
) << 8));
1297		const int device_id = CCV_TENSOR_GET_DEVICE_ID(info.type)(((info.type) & 0xfff00) >> 8);
1298		compiled_data->tensors.parameters[i] = ccv_nnc_tensor_new(0, info, 0);
1299		for (j = 1; j < parallel_count; j++)
1300		{
1301			if (j != device_id)
1302				CCV_TENSOR_SET_DEVICE_ID(info.type, j)(info.type) = (((info.type) & ~0xfff00) | (((j) & 0xfff
) << 8));
1303			else
1304				CCV_TENSOR_SET_DEVICE_ID(info.type, 0)(info.type) = (((info.type) & ~0xfff00) | (((0) & 0xfff
) << 8));
1305			compiled_data->tensors.parameters[i + j * parameter_size] = ccv_nnc_tensor_new(0, info, 0);
1306		}
1307	}
1308	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));
1309	for (i = 0; i < internal_size; i++)
1310	{
1311		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))
);
1312		const int d = retained.d;
1313		if (init_v[d >> 5] & (1u << (d & 0x1f)))
1314			continue;
1315		ccv_nnc_tensor_param_t info = ccv_nnc_tensor_symbol_params(retained.graph, retained);
1316		if (CCV_TENSOR_GET_DEVICE(info.type)((info.type) & 0xfff00) == CCV_COMPUTE_DEVICE_ANY)
1317			CCV_TENSOR_SET_DEVICE_ID(info.type, 0)(info.type) = (((info.type) & ~0xfff00) | (((0) & 0xfff
) << 8));
1318		const int device_id = CCV_TENSOR_GET_DEVICE_ID(info.type)(((info.type) & 0xfff00) >> 8);
1319		if (!compiled_data->tensors.internals[i])
1320			compiled_data->tensors.internals[i] = ccv_nnc_tensor_new(0, info, 0);
1321		for (j = 1; j < parallel_count; j++)
1322		{
1323			if (j != device_id)
1324				CCV_TENSOR_SET_DEVICE_ID(info.type, j)(info.type) = (((info.type) & ~0xfff00) | (((j) & 0xfff
) << 8));
1325			else
1326				CCV_TENSOR_SET_DEVICE_ID(info.type, 0)(info.type) = (((info.type) & ~0xfff00) | (((0) & 0xfff
) << 8));
1327			if (!compiled_data->tensors.internals[i + j * internal_size])
1328				compiled_data->tensors.internals[i + j * internal_size] = ccv_nnc_tensor_new(0, info, 0);
1329		}
1330	}
1331	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.
1332}
1333 
1334static void _ccv_cnnp_model_tensors_init(const ccv_cnnp_model_t* const model, ccv_cnnp_compiled_data_t* const compiled_data)
1335{
1336	ccv_cnnp_model_tensors_init_0(model, compiled_data);
1337	ccv_cnnp_model_tensors_init_1(model, compiled_data);
1338}
1339 
1340static 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)
1341{
1342	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", 1342, __extension__ __PRETTY_FUNCTION__
); }));
1343	int i, j;
1344	for (i = 0; i < tensor_size; i++)
1345	{
1346		if (!tensors[i])
1347			continue;
1348		const int d = tensor_symbols[i].d;
1349		if (!(tensors_init[d >> 5] & (1u << (d & 0x1f))))
1350			continue;
1351		for (j = 1; j < parallel_count; j++)
1352			if (tensors[i + j * tensor_size])
1353			{
1354				ccv_nnc_tensor_t* const input = CCV_NNC_TENSOR(tensors[i])((ccv_nnc_tensor_t*)((uintptr_t)(tensors[i]) & ~(uintptr_t
)1));
1355				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));
1356				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);
1357			}
1358	}
1359}
1360 
1361static 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)
1362{
1363	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", 1363, __extension__ __PRETTY_FUNCTION__
); }));
1364	int i, j;
1365	for (i = 0; i < tensor_size; i++)
1366	{
1367		const ccv_nnc_tensor_symbol_t tensor_symbol = tensor_symbols[i];
1368		for (j = 1; j < parallel_count; j++)
1369		{
1370			const ccv_nnc_tensor_symbol_t copy = ccv_nnc_tensor_symbol_copy(graph, tensor_symbol, j);
1371			ccv_nnc_tensor_t* copy_tensor = tensors[i + j * tensor_size];
1372			if (copy_tensor && copy.d == CCV_NNC_NO_TENSOR_SYMBOL)
1373			{ // We shouldn't allocate this, free it up.
1374				ccv_nnc_tensor_free(tensors[i + j * tensor_size]);
1375				tensors[i + j * tensor_size] = 0;
1376			}
1377		}
1378	}
1379}
1380 
1381static 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)
1382{
1383	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", 1383, __extension__ __PRETTY_FUNCTION__
); }));
1384	int i, j;
1385	for (i = 0; i < tensor_size; i++)
1386	{
1387		ccv_nnc_tensor_symbol_t tensor_symbol = tensor_symbols[i];
1388		if (tensor_symbol.d == CCV_NNC_NO_TENSOR_SYMBOL)
1389			continue;
1390		if (graph)
1391		{
1392			const ccv_nnc_tensor_symbol_t alias_to = ccv_nnc_tensor_symbol_alias_to(graph, tensor_symbol);
1393			if (alias_to.d != CCV_NNC_NO_TENSOR_SYMBOL)
1394				tensor_symbol = alias_to;
1395		}
1396		ccv_nnc_tensor_t* const tensor = CCV_NNC_TENSOR(tensors[i])((ccv_nnc_tensor_t*)((uintptr_t)(tensors[i]) & ~(uintptr_t
)1));
1397		if (tensor && tensor_symbol.d != CCV_NNC_NO_TENSOR_SYMBOL)
1398		{
1399			const ccv_nnc_tensor_bind_t retained_bind = {
1400				.symbol = tensor_symbol,
1401				.tensor = tensor
1402			};
1403			ccv_array_push(tensor_binds, &retained_bind);
1404		}
1405		for (j = 1; j < parallel_count; j++)
1406		{
1407			const ccv_nnc_tensor_symbol_t copy = ccv_nnc_tensor_symbol_copy(graph, tensor_symbol, j);
1408			ccv_nnc_tensor_t* copy_tensor = tensors[i + j * tensor_size];
1409			if (copy_tensor && copy.d != CCV_NNC_NO_TENSOR_SYMBOL)
1410			{
1411				const ccv_nnc_tensor_bind_t bind = {
1412					.symbol = copy,
1413					.tensor = tensors[i + j * tensor_size]
1414				};
1415				ccv_array_push(tensor_binds, &bind);
1416			}
1417		}
1418	}
1419}
1420 
1421static void _ccv_cnnp_compiled_data_graph_free(ccv_cnnp_compiled_data_t* const compiled_data)
1422{
1423	if (compiled_data->graph)
1424		ccv_nnc_graph_free(compiled_data->graph);
1425	compiled_data->graph = 0;
1426	compiled_data->is_test = 0;
1427	if (compiled_data->tensor_arena)
1428		ccv_nnc_tensor_arena_free(compiled_data->tensor_arena);
1429	compiled_data->tensor_arena = 0;
1430	if (compiled_data->graph_exec_arena)
1431		ccv_nnc_graph_exec_arena_free(compiled_data->graph_exec_arena);
1432	compiled_data->graph_exec_arena = 0;
1433	if (compiled_data->backward.from_ops)
1434		ccfreefree(compiled_data->backward.from_ops);
1435	compiled_data->backward.from_ops = 0;
1436	if (compiled_data->evaluate.schedule)
1437		ccv_nnc_graph_static_schedule_free(compiled_data->evaluate.schedule);
1438	compiled_data->evaluate.schedule = 0;
1439	if (compiled_data->backward.schedule)
1440		ccv_nnc_graph_static_schedule_free(compiled_data->backward.schedule);
1441	compiled_data->backward.schedule = 0;
1442}
1443 
1444static void _ccv_cnnp_compiled_data_gradient_free(ccv_cnnp_compiled_data_t* const compiled_data)
1445{
1446	if (compiled_data->gradients)
1447		ccfreefree(compiled_data->gradients);
1448	compiled_data->gradients = 0;
1449	if (compiled_data->updated_parameters)
1450		ccfreefree(compiled_data->updated_parameters);
1451	compiled_data->updated_parameters = 0;
1452	compiled_data->update_nodes = 0;
1453	compiled_data->saved_aux = 0;
1454}
1455 
1456static void _ccv_cnnp_compiled_data_backward_free(ccv_cnnp_compiled_data_t* const compiled_data)
1457{
1458	if (compiled_data->backward.gradients)
1459		ccfreefree(compiled_data->backward.gradients);
1460	compiled_data->backward.gradients = 0;
1461	if (compiled_data->backward.accum)
1462		ccv_nnc_graph_free(compiled_data->backward.accum);
1463	compiled_data->backward.accum = 0;
1464	if (compiled_data->backward.tensor_arena)
1465		ccv_nnc_tensor_arena_free(compiled_data->backward.tensor_arena);
1466	compiled_data->backward.tensor_arena = 0;
1467	if (compiled_data->backward.graph_exec_arena)
1468		ccv_nnc_graph_exec_arena_free(compiled_data->backward.graph_exec_arena);
1469	compiled_data->backward.graph_exec_arena = 0;
1470}
1471 
1472static void _ccv_cnnp_compiled_data_apply_gradients_free(ccv_cnnp_compiled_data_t* const compiled_data)
1473{
1474	if (compiled_data->apply_gradients.graph)
1475		ccv_nnc_graph_free(compiled_data->apply_gradients.graph);
1476	compiled_data->apply_gradients.graph = 0;
1477	if (compiled_data->apply_gradients.tensor_arena)
1478		ccv_nnc_tensor_arena_free(compiled_data->apply_gradients.tensor_arena);
1479	compiled_data->apply_gradients.tensor_arena = 0;
1480	if (compiled_data->apply_gradients.graph_exec_arena)
1481		ccv_nnc_graph_exec_arena_free(compiled_data->apply_gradients.graph_exec_arena);
1482	compiled_data->apply_gradients.graph_exec_arena = 0;
1483}
1484 
1485// Compile the graph to run ccv_cnnp_model_fit
1486static 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)
1487{
1488	int i, j;
1489	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
1490	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", 1490, __extension__ __PRETTY_FUNCTION__
); }));
1491	compiled_data->graph_mode = CCV_CNNP_MODEL_GRAPH_FIT_MODE;
1492	const int parallel_count = _ccv_cnnp_model_root_parallel_count(model);
1493	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", 1493, __extension__ __PRETTY_FUNCTION__
); }));
1494	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", 1494
, __extension__ __PRETTY_FUNCTION__); }));
1495	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", 1495, __extension__ __PRETTY_FUNCTION__
); }));
1496	if (compiled_data->gradient_mode == CCV_CNNP_COMPILED_DATA_GRADIENT_NONE)
1497	{
1498		_ccv_cnnp_model_set_rewindables(model);
1499		_ccv_cnnp_model_gradient_init(model, CCV_CNNP_COMPILED_DATA_GRADIENT_TRAINABLES, CCV_CNNP_DISABLE_OUTGRAD_ALL, fits, fit_size);
1500	} else if (compiled_data->gradient_mode != CCV_CNNP_COMPILED_DATA_GRADIENT_TRAINABLES) {
1501		_ccv_cnnp_model_rewind_graph(model);
1502		_ccv_cnnp_compiled_data_gradient_free(compiled_data);
1503		compiled_data->gradient_mode = CCV_CNNP_COMPILED_DATA_GRADIENT_NONE;
1504		_ccv_cnnp_model_gradient_init(model, CCV_CNNP_COMPILED_DATA_GRADIENT_TRAINABLES, CCV_CNNP_DISABLE_OUTGRAD_ALL, fits, fit_size);
1505	}
1506	const int tensors_init = !!compiled_data->tensors_init.v;
1507	if (!tensors_init)
1508		_ccv_cnnp_model_tensors_init(model, compiled_data);
1509	else if ((uintptr_t)compiled_data->tensors_init.v & (uintptr_t)1)
1510	// Check if it is not fully allocated, if it is not, init_1.
1511		ccv_cnnp_model_tensors_init_1(model, compiled_data);
1512	ccv_array_t* const tensor_binds = ccv_array_new(sizeof(ccv_nnc_tensor_bind_t), 0, 0);
1513	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"
, 1513, __extension__ __PRETTY_FUNCTION__); }));
1514	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"
, 1514, __extension__ __PRETTY_FUNCTION__); }));
1515	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", 1515
, __extension__ __PRETTY_FUNCTION__); }));
1516	const int input_size_per_p = input_size / parallel_count;
1517	_ccv_cnnp_model_bind_tensors(model->graph, model->inputs, inputs, input_size_per_p, parallel_count, tensor_binds);
1518	const int output_size_per_p = output_size / parallel_count;
1519	_ccv_cnnp_model_bind_tensors(model->graph, model->outputs, outputs, output_size_per_p, parallel_count, tensor_binds);
1520	const int fit_size_per_p = fit_size / parallel_count;
1521	_ccv_cnnp_model_bind_tensors(model->graph, compiled_data->fits, fits, fit_size_per_p, parallel_count, tensor_binds);
1522	const int parameter_size = compiled_data->parameters->rnum;
1523	_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);
1524	_ccv_cnnp_model_bind_tensors(model->graph, compiled_data->updated_parameters, compiled_data->tensors.parameters, parameter_size, parallel_count, tensor_binds);
1525	const int internal_size = compiled_data->internals->rnum;
1526	_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);
1527	_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);
1528	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);
1529	ccv_array_free(tensor_binds);
1530	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));
1531	if (tensors_init && parallel_count > 1)
1532		_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);
1533	// If tensor is not init'ed, we need to init states first.
1534	if (_ccv_cnnp_any_to_init(compiled_data))
1535	{
1536		ccv_nnc_tensor_init_states_t tensor_init_states = {
1537			.parallel_count = parallel_count,
1538			.graph = model->graph,
1539			.compiled_data = compiled_data,
1540			.tensor_arena = compiled_data->tensor_arena
1541		};
1542		ccv_cnnp_model_init_states(model, model->graph, _ccv_cnnp_init_states_for_tensors, &tensor_init_states);
1543	}
1544	compiled_data->is_test = 0;
1545	const int saved_aux_size = ccv_nnc_minimizer_saved_aux_size(compiled_data->minimize.minimizer);
1546	// No need to set because it is default to training mode.
1547	// ccv_cnnp_model_set_is_test(model, 0, _ccv_cnnp_cmd_update_for_execs, &update);
1548	for (i = 0; i < saved_aux_size * parameter_size; i++)
1549	{
1550		if (compiled_data->saved_aux[i].source.d == CCV_NNC_NO_TENSOR_SYMBOL)
1551			continue;
1552		ccv_nnc_tensor_t* const tensor = ccv_nnc_tensor_from_symbol(compiled_data->tensor_arena, compiled_data->saved_aux[i].source);
1553		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);
1554		for (j = 1; j < parallel_count; j++)
1555		{
1556			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));
1557			if (copy)
1558				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);
1559		}
1560	}
1561	const int evaluate_to_size = compiled_data->evaluate.to_size;
1562	compiled_data->evaluate.to_op_size = 0;
1563	for (i = 0; i < evaluate_to_size; i++)
1564	{
1565		ccv_nnc_graph_exec_t const to = ccv_nnc_graph_exec_from_symbol(compiled_data->graph_exec_arena, compiled_data->evaluate.tos[i]);
1566		if (to.graph)
1567			compiled_data->evaluate.to_ops[compiled_data->evaluate.to_op_size++] = to;
1568	}
1569	ccv_nnc_graph_set_default_static_schedule(compiled_data->graph, compiled_data->stream_type, model->max_stream_count);
1570	ccv_nnc_graph_autotune(compiled_data->graph, model->workspace_size, 0, TRAVERSE_FULL0,0,0,0);
1571}
1572 
1573ccv_nnc_stream_context_t* ccv_cnnp_model_default_stream(const ccv_cnnp_model_t* const model)
1574{
1575	const ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
1576	if (!compiled_data || !compiled_data->graph)
1577		return 0;
1578	return ccv_nnc_graph_default_stream(compiled_data->graph);
1579}
1580 
1581uint64_t ccv_cnnp_model_memory_size(const ccv_cnnp_model_t* const model)
1582{
1583	const ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
1584	if (!compiled_data || !compiled_data->tensor_arena)
1585		return 0;
1586	return ccv_nnc_tensor_arena_size(compiled_data->tensor_arena);
1587}
1588 
1589static 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)
1590{
1591	int i, j;
1592	for (i = 0; i < tensor_size; i++)
1593	{
1594		ccv_nnc_tensor_symbol_t tensor_symbol = tensor_symbols[i];
1595		if (tensor_symbol.d == CCV_NNC_NO_TENSOR_SYMBOL)
1596			continue;
1597		if (graph)
1598		{
1599			const ccv_nnc_tensor_symbol_t alias_to = ccv_nnc_tensor_symbol_alias_to(graph, tensor_symbol);
1600			if (alias_to.d != CCV_NNC_NO_TENSOR_SYMBOL)
1601				tensor_symbol = alias_to;
1602		}
1603		ccv_nnc_tensor_bind_symbol(tensor_arena, tensor_symbol, tensors[i]);
1604		for (j = 1; j < parallel_count; j++)
1605		{
1606			const ccv_nnc_tensor_symbol_t copy = ccv_nnc_tensor_symbol_copy(graph, tensor_symbol, j);
1607			if (copy.d != CCV_NNC_NO_TENSOR_SYMBOL)
1608				ccv_nnc_tensor_bind_symbol(tensor_arena, copy, tensors[i + tensor_size * j]);
1609		}
1610	}
1611}
1612 
1613void 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)
1614{
1615	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
1616	assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
 (compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 1616, __extension__ __PRETTY_FUNCTION__); }));
1617	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; });
1618	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", 1618, __extension__ __PRETTY_FUNCTION__
); }));
1619	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", 1619, __extension__ __PRETTY_FUNCTION__
); }));
1620	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", 1620
, __extension__ __PRETTY_FUNCTION__); }));
1621	assert(model->graph)((void) sizeof ((model->graph) ? 1 : 0), __extension__ ({ if
 (model->graph) ; else __assert_fail ("model->graph", "ccv_cnnp_model.c"
, 1621, __extension__ __PRETTY_FUNCTION__); }));
1622	if (!compiled_data->graph || compiled_data->graph_mode != CCV_CNNP_MODEL_GRAPH_FIT_MODE)
1623	{
1624		_ccv_cnnp_compiled_data_graph_free(compiled_data);
1625		_ccv_cnnp_compiled_data_backward_free(compiled_data);
1626		_ccv_cnnp_compiled_data_apply_gradients_free(compiled_data);
1627		// Compile the symbolic graph down only when needed.
1628		_ccv_cnnp_model_fit_jit(model, inputs, input_size, fits, fit_size, outputs, output_size);
1629	} else {
1630		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"
, 1630, __extension__ __PRETTY_FUNCTION__); }));
1631		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"
, 1631, __extension__ __PRETTY_FUNCTION__); }));
1632		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", 1632
, __extension__ __PRETTY_FUNCTION__); }));
1633		const int input_size_per_p = input_size / parallel_count;
1634		_ccv_cnnp_bind_tensors_to_arena(compiled_data->tensor_arena, model->graph, model->inputs, inputs, input_size_per_p, parallel_count);
1635		const int output_size_per_p = output_size / parallel_count;
1636		_ccv_cnnp_bind_tensors_to_arena(compiled_data->tensor_arena, model->graph, model->outputs, outputs, output_size_per_p, parallel_count);
1637		const int fit_size_per_p = fit_size / parallel_count;
1638		_ccv_cnnp_bind_tensors_to_arena(compiled_data->tensor_arena, model->graph, compiled_data->fits, fits, fit_size_per_p, parallel_count);
1639	}
1640	if (compiled_data->is_test)
1641	{
1642		compiled_data->is_test = 0;
1643		ccv_nnc_graph_exec_update_t update = {
1644			.parallel_count = parallel_count,
1645			.graph = model->graph,
1646			.graph_exec_arena = compiled_data->graph_exec_arena,
1647		};
1648		ccv_cnnp_model_set_is_test(model, 0, _ccv_cnnp_cmd_update_for_execs, &update);
1649	}
1650	ccv_nnc_graph_run_with_schedule(compiled_data->graph, 0, 0, tensor_tape, stream_context);
1651}
1652 
1653// Compile the graph to run ccv_cnnp_model_evaluate with require_grad = false (MULTISTAGE_MODE_NO_GRAD).
1654static 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)
1655{
1656	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
1657	compiled_data->graph_mode = CCV_CNNP_MODEL_GRAPH_MULTISTAGE_MODE_NO_GRAD;
1658	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; });
1659	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", 1659, __extension__ __PRETTY_FUNCTION__
); }));
1660	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", 1660, __extension__ __PRETTY_FUNCTION__
); }));
1661	// If the gradient is not initialized, continue to setup parallel process. We don't init gradient here, but rather,
1662	// we setup proper rewindables so the graph can be rewinded to previous state before we run data parallel.
1663	if (parallel_count > 1 && compiled_data->gradient_mode == CCV_CNNP_COMPILED_DATA_GRADIENT_NONE)
1664	{
1665		const int evaluate_to_size = compiled_data->evaluate.to_size;
1666		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);
1667		_ccv_cnnp_model_set_rewindables(model);
1668		ccv_nnc_symbolic_graph_data_parallel(model->graph, parallel_count,
1669			0, 0,
1670			0, 0, 0,
1671			0, 0, 0,
1672			CCV_NNC_PARALLEL_REDUCE_OP_SUM,
1673			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));
1674		ccv_nnc_graph_exec_symbol_autogen(model->graph, 0, 0, CCV_NNC_AUTOGEN_SOURCES_AND_DESTINATIONS);
1675		int i, j;
1676		for (i = 0; i < evaluate_to_size; i++)
1677			for (j = 1; j < parallel_count; j++)
1678			{
1679				const ccv_nnc_graph_exec_symbol_t copy = ccv_nnc_graph_exec_symbol_copy(model->graph, compiled_data->evaluate.tos[i], j);
1680				if (copy.d != CCV_NNC_NO_GRAPH_EXEC_SYMBOL)
1681					compiled_data->evaluate.tos[compiled_data->evaluate.to_size++] = copy;
1682			}
1683	}
1684	const int tensors_init = !!compiled_data->tensors_init.v;
1685	if (!tensors_init)
1686		_ccv_cnnp_model_tensors_init(model, compiled_data);
1687	else if ((uintptr_t)compiled_data->tensors_init.v & (uintptr_t)1)
1688	// Check if it is not fully allocated, if it is not, init_1.
1689		ccv_cnnp_model_tensors_init_1(model, compiled_data);
1690	const int tensor_parallel_count = _ccv_cnnp_compiled_data_parallel_count(model, compiled_data);
1691	ccv_array_t* const tensor_binds = ccv_array_new(sizeof(ccv_nnc_tensor_bind_t), 0, 0);
1692	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"
, 1692, __extension__ __PRETTY_FUNCTION__); }));
1693	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"
, 1693, __extension__ __PRETTY_FUNCTION__); }));
1694	const int input_size_per_p = input_size / parallel_count;
1695	_ccv_cnnp_model_bind_tensors(model->graph, model->inputs, inputs, input_size_per_p, parallel_count, tensor_binds);
1696	const int output_size_per_p = output_size / parallel_count;
1697	_ccv_cnnp_model_bind_tensors(model->graph, model->outputs, outputs, output_size_per_p, parallel_count, tensor_binds);
1698	const int parameter_size = compiled_data->parameters->rnum;
1699	_ccv_cnnp_model_bind_tensors(model->graph, (ccv_nnc_tensor_symbol_t*)ccv_array_get(compiled_data->parameters, 0)((void*)(((char*)((compiled_data->parameters)->data)) +
 (size_t)(compiled_data->parameters)->rsize * (size_t)(
0))), compiled_data->tensors.parameters, parameter_size, tensor_parallel_count, tensor_binds);
1700	const int internal_size = compiled_data->internals->rnum;
1701	_ccv_cnnp_model_remove_nocopies(model->graph, (ccv_nnc_tensor_symbol_t*)ccv_array_get(compiled_data->internals, 0)((void*)(((char*)((compiled_data->internals)->data)) + (
size_t)(compiled_data->internals)->rsize * (size_t)(0))
), compiled_data->tensors.internals, internal_size, tensor_parallel_count);
1702	_ccv_cnnp_model_bind_tensors(model->graph, (ccv_nnc_tensor_symbol_t*)ccv_array_get(compiled_data->internals, 0)((void*)(((char*)((compiled_data->internals)->data)) + (
size_t)(compiled_data->internals)->rsize * (size_t)(0))
), compiled_data->tensors.internals, internal_size, tensor_parallel_count, tensor_binds);
1703	// If we generated gradient for the graph, only compile part of the graph because the rest is irrelevant for evaluation.
1704	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);
1705	ccv_array_free(tensor_binds);
1706	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));
1707	// If tensor is not init'ed, we need to init states first.
1708	if (tensors_init && tensor_parallel_count > 1)
1709		_ccv_cnnp_model_copy_tensors(init_v, (ccv_nnc_tensor_symbol_t*)ccv_array_get(compiled_data->parameters, 0)((void*)(((char*)((compiled_data->parameters)->data)) +
 (size_t)(compiled_data->parameters)->rsize * (size_t)(
0))), compiled_data->tensors.parameters, compiled_data->parameters->rnum, tensor_parallel_count);
1710	if (_ccv_cnnp_any_to_init(compiled_data))
1711	{
1712		ccv_nnc_tensor_init_states_t tensor_init_states = {
1713			.parallel_count = tensor_parallel_count,
1714			.graph = model->graph,
1715			.compiled_data = compiled_data,
1716			.tensor_arena = compiled_data->tensor_arena
1717		};
1718		ccv_cnnp_model_init_states(model, model->graph, _ccv_cnnp_init_states_for_tensors, &tensor_init_states);
1719	}
1720	compiled_data->is_test = 1;
1721	ccv_nnc_graph_exec_update_t update = {
1722		.parallel_count = parallel_count,
1723		.graph = model->graph,
1724		.graph_exec_arena = compiled_data->graph_exec_arena,
1725	};
1726	ccv_cnnp_model_set_is_test(model, 1, _ccv_cnnp_cmd_update_for_execs, &update);
1727	ccv_nnc_graph_set_default_static_schedule(compiled_data->graph, compiled_data->stream_type, model->max_stream_count);
1728	ccv_nnc_graph_autotune(compiled_data->graph, model->workspace_size, 0, TRAVERSE_FULL0,0,0,0);
1729}
1730 
1731static void _ccv_cnnp_model_gradient_tensors_init(const ccv_cnnp_model_t* const model, ccv_cnnp_compiled_data_t* const compiled_data)
1732{
1733	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", 1733, __extension__ __PRETTY_FUNCTION__
); }));
1734	const int parameter_size = compiled_data->parameters->rnum;
1735	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; });
1736	compiled_data->tensors.gradients = (ccv_nnc_tensor_t**)ccmallocmalloc(sizeof(ccv_nnc_tensor_t*) * parameter_size * 2 * parallel_count);
1737	compiled_data->tensors.accum_gradients = compiled_data->tensors.gradients + parameter_size * parallel_count;
1738	int i, j;
1739	for (i = 0; i < parameter_size; i++)
1740	{
1741		if (compiled_data->parameter_flags && !(compiled_data->parameter_flags[i >> 6] & ((uint64_t)1 << (i & 63))))
1742		{
1743			compiled_data->tensors.gradients[i] = 0;
1744			compiled_data->tensors.accum_gradients[i] = 0;
1745			for (j = 1; j < parallel_count; j++)
1746			{
1747				compiled_data->tensors.gradients[i + j * parameter_size] = 0;
1748				compiled_data->tensors.accum_gradients[i + j * parameter_size] = 0;
1749			}
1750			continue;
1751		}
1752		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)));
1753		ccv_nnc_tensor_param_t info = ccv_nnc_tensor_symbol_params(parameter.graph, parameter);
1754		if (CCV_TENSOR_GET_DEVICE(info.type)((info.type) & 0xfff00) == CCV_COMPUTE_DEVICE_ANY)
1755			CCV_TENSOR_SET_DEVICE_ID(info.type, 0)(info.type) = (((info.type) & ~0xfff00) | (((0) & 0xfff
) << 8));
1756		const int device_id = CCV_TENSOR_GET_DEVICE_ID(info.type)(((info.type) & 0xfff00) >> 8);
1757		compiled_data->tensors.gradients[i] = ccv_nnc_tensor_new(0, info, 0);
1758		compiled_data->tensors.accum_gradients[i] = 0; // delay the accumulated gradient allocation until when we need it.
1759		for (j = 1; j < parallel_count; j++)
1760		{
1761			if (j != device_id)
1762				CCV_TENSOR_SET_DEVICE_ID(info.type, j)(info.type) = (((info.type) & ~0xfff00) | (((j) & 0xfff
) << 8));
1763			else
1764				CCV_TENSOR_SET_DEVICE_ID(info.type, 0)(info.type) = (((info.type) & ~0xfff00) | (((0) & 0xfff
) << 8));
1765			compiled_data->tensors.gradients[i + j * parameter_size] = ccv_nnc_tensor_new(0, info, 0);
1766			compiled_data->tensors.accum_gradients[i + j * parameter_size] = 0;
1767		}
1768	}
1769}
1770 
1771static int _ccv_cnnp_is_disable_outgrad_all(const uint64_t disable_outgrad, const int input_size)
1772{
1773	if (disable_outgrad == CCV_CNNP_DISABLE_OUTGRAD_ALL)
1774		return 1;
1775	if (disable_outgrad == CCV_CNNP_DISABLE_OUTGRAD_NONE)
1776		return 0;
1777	int i;
1778	for (i = 0; i < input_size; i++)
1779		if (!(disable_outgrad & ((uint64_t)1 << i)))
1780			return 0;
1781	return 1;
1782}
1783 
1784// Compile the graph to run ccv_cnnp_model_evaluate with requires_grad = true (MULTISTAGE_MODE).
1785// Particularly, this method compiles the evaluation and backprop graph (the main graph).
1786static 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)
1787{
1788	int i, j;
1789	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
1790	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;
1791	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", 1791, __extension__ __PRETTY_FUNCTION__
); }));
1792	compiled_data->graph_mode = CCV_CNNP_MODEL_GRAPH_MULTISTAGE_MODE;
1793	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; });
1794	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", 1794, __extension__ __PRETTY_FUNCTION__
); }));
1795	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", 1795, __extension__ __PRETTY_FUNCTION__
); }));
1796	// There shouldn't be a loss function if we evaluate with multistage jit.
1797	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", 1797, __extension__ __PRETTY_FUNCTION__
); }));
1798	if (compiled_data->gradient_mode == CCV_CNNP_COMPILED_DATA_GRADIENT_NONE)
1799	{
1800		_ccv_cnnp_model_set_rewindables(model);
1801		_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.
1802	} else if (compiled_data->gradient_mode != target_gradient_mode) {
1803		_ccv_cnnp_model_rewind_graph(model);
1804		_ccv_cnnp_compiled_data_gradient_free(compiled_data);
1805		compiled_data->gradient_mode = CCV_CNNP_COMPILED_DATA_GRADIENT_NONE;
1806		_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.
1807	}
1808	const int tensors_init = !!compiled_data->tensors_init.v;
1809	if (!tensors_init)
1810		_ccv_cnnp_model_tensors_init(model, compiled_data);
1811	else if ((uintptr_t)compiled_data->tensors_init.v & (uintptr_t)1)
1812	// Check if it is not fully allocated, if it is not, init_1.
1813		ccv_cnnp_model_tensors_init_1(model, compiled_data);
1814	ccv_array_t* const tensor_binds = ccv_array_new(sizeof(ccv_nnc_tensor_bind_t), 0, 0);
1815	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"
, 1815, __extension__ __PRETTY_FUNCTION__); }));
1816	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"
, 1816, __extension__ __PRETTY_FUNCTION__); }));
1817	const int input_size_per_p = input_size / parallel_count;
1818	_ccv_cnnp_model_bind_tensors(model->graph, model->inputs, inputs, input_size_per_p, parallel_count, tensor_binds);
1819	const int output_size_per_p = output_size / parallel_count;
1820	_ccv_cnnp_model_bind_tensors(model->graph, model->outputs, outputs, output_size_per_p, parallel_count, tensor_binds);
1821	const int parameter_size = compiled_data->parameters->rnum;
1822	_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);
1823	const int internal_size = compiled_data->internals->rnum;
1824	_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);
1825	_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);
1826	if (!compiled_data->tensors.gradients)
1827		_ccv_cnnp_model_gradient_tensors_init(model, compiled_data);
1828	_ccv_cnnp_model_bind_tensors(model->graph, compiled_data->gradients, compiled_data->tensors.gradients, parameter_size, parallel_count, tensor_binds);
1829	if (compiled_data->backward.to_size > 0)
1830		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);
1831	else
1832		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);
1833	ccv_array_free(tensor_binds);
1834	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));
1835	if (tensors_init && parallel_count > 1)
1836		_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);
1837	// If tensor is not init'ed, we need to init states first.
1838	if (_ccv_cnnp_any_to_init(compiled_data))
1839	{
1840		ccv_nnc_tensor_init_states_t tensor_init_states = {
1841			.parallel_count = parallel_count,
1842			.graph = model->graph,
1843			.compiled_data = compiled_data,
1844			.tensor_arena = compiled_data->tensor_arena
1845		};
1846		ccv_cnnp_model_init_states(model, model->graph, _ccv_cnnp_init_states_for_tensors, &tensor_init_states);
1847	}
1848	compiled_data->is_test = is_test;
1849	ccv_nnc_graph_exec_update_t update = {
1850		.parallel_count = parallel_count,
1851		.graph = model->graph,
1852		.graph_exec_arena = compiled_data->graph_exec_arena,
1853	};
1854	ccv_cnnp_model_set_is_test(model, is_test, _ccv_cnnp_cmd_update_for_execs, &update);
1855	const int evaluate_to_size = compiled_data->evaluate.to_size;
1856	compiled_data->evaluate.to_op_size = 0;
1857	ccv_array_t* const backward_from = ccv_array_new(sizeof(int), 0, 0);
1858	for (i = 0; i < evaluate_to_size; i++)
1859	{
1860		ccv_nnc_graph_exec_t const to_op = ccv_nnc_graph_exec_from_symbol(compiled_data->graph_exec_arena, compiled_data->evaluate.tos[i]);
1861		if (to_op.graph)
1862			compiled_data->evaluate.to_ops[compiled_data->evaluate.to_op_size++] = to_op;
1863		const int* tos;
1864		int to_size;
1865		ccv_nnc_graph_exec_symbol_to(model->graph, compiled_data->evaluate.tos[i], &tos, &to_size);
1866		for (j = 0; j < to_size; j++)
1867		{
1868			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){
1869				.d = tos[j],
1870				.graph = model->graph
1871			});
1872			if (to_op.graph)
1873				ccv_array_add_unique_int(backward_from, to_op.d);
1874		}
1875	}
1876	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", 1876, __extension__
 __PRETTY_FUNCTION__); }));
1877	compiled_data->backward.from_op_size = backward_from->rnum;
1878	compiled_data->backward.from_ops = (ccv_nnc_graph_exec_t*)ccmallocmalloc(sizeof(ccv_nnc_graph_exec_t) * backward_from->rnum);
1879	for (i = 0; i < backward_from->rnum; i++)
1880		compiled_data->backward.from_ops[i] = (ccv_nnc_graph_exec_t){
1881			.d = *(int*)ccv_array_get(backward_from, i)((void*)(((char*)((backward_from)->data)) + (size_t)(backward_from
)->rsize * (size_t)(i))),
1882			.graph = compiled_data->graph,
1883		};
1884	// If there are any set node (to set some tensors to 0) inserted through backward pass, these won't be executed if we just do sources -> evaluate.to_ops, backward.from_ops -> destinations. We need this logic to find out these nodes and explicitly adding them to backward.from_ops.
1885	ccv_nnc_graph_exec_info_t* const exec_info = (ccv_nnc_graph_exec_info_t*)ccv_array_get(compiled_data->graph->exec_info, 0)((void*)(((char*)((compiled_data->graph->exec_info)->
data)) + (size_t)(compiled_data->graph->exec_info)->
rsize * (size_t)(0)));
1886	const int exec_info_size = compiled_data->graph->exec_info->rnum;
1887	uint32_t* const visited = cccalloccalloc((exec_info_size + 31) >> 5, sizeof(uint32_t));
1888	const ccv_nnc_graph_exec_t* const sources = (ccv_nnc_graph_exec_t*)ccv_array_get(compiled_data->graph->sources, 0)((void*)(((char*)((compiled_data->graph->sources)->data
)) + (size_t)(compiled_data->graph->sources)->rsize *
 (size_t)(0)));
1889	const int source_size = compiled_data->graph->sources->rnum;
1890	ccv_nnc_graph_visit_t* visit = ccv_nnc_graph_visit_new(compiled_data->graph, exec_info, exec_info_size, sources, source_size, compiled_data->evaluate.to_ops, compiled_data->evaluate.to_op_size, 0)({ ccv_nnc_graph_visit_t* _visit_ = (ccv_nnc_graph_visit_t*)malloc
(sizeof(ccv_nnc_graph_visit_t) + sizeof(_visit_->node[0]) *
 ((exec_info_size) - 1)); _visit_->size = 0; do { typedef struct
 { int8_t d; int8_t r; uint16_t c; int32_t edges; } ccv_nnc_incoming_t
; int _i_, _j_; int _incoming_edges_ = 0; for (_i_ = 0; _i_ <
 (exec_info_size); _i_++) _incoming_edges_ += ((exec_info)[_i_
].outgoings) ? (exec_info)[_i_].outgoings->rnum : 0; const
 int _heap_mem_ = ((exec_info_size) + _incoming_edges_ > 1024
); ccv_nnc_incoming_t* _incomings_; if (_heap_mem_) _incomings_
 = (ccv_nnc_incoming_t*)malloc(sizeof(ccv_nnc_incoming_t) * (
exec_info_size) + sizeof(int32_t) * ((exec_info_size) * 2 + _incoming_edges_
)); else _incomings_ = (ccv_nnc_incoming_t*)__builtin_alloca (
sizeof(ccv_nnc_incoming_t) * (exec_info_size) + sizeof(int32_t
) * ((exec_info_size) * 2 + _incoming_edges_)); memset(_incomings_
, 0, sizeof(ccv_nnc_incoming_t) * (exec_info_size)); int32_t*
 _exists_[2] = { (int32_t*)(_incomings_ + (exec_info_size)), (
int32_t*)(_incomings_ + (exec_info_size)) + (exec_info_size),
 }; int32_t* const _edges_ = _exists_[1] + (exec_info_size); for
 (_i_ = 0; _i_ < (source_size); _i_++) { ((void) sizeof ((
(sources)[_i_].graph == compiled_data->graph) ? 1 : 0), __extension__
 ({ if ((sources)[_i_].graph == compiled_data->graph) ; else
 __assert_fail ("(sources)[_i_].graph == compiled_data->graph"
, "ccv_cnnp_model.c", 1890, __extension__ __PRETTY_FUNCTION__
); })); _incomings_[(sources)[_i_].d].r = 1; _exists_[0][_i_]
 = (sources)[_i_].d; } int _exist_size_[2] = { (source_size),
 0, }; int _p_ = 0, _q_ = 1; while (_exist_size_[_p_] > 0)
 { _exist_size_[_q_] = 0; for (_i_ = 0; _i_ < _exist_size_
[_p_]; _i_++) { const int32_t _idx_ = _exists_[_p_][_i_]; if (
_incomings_[_idx_].r != 1) continue; _incomings_[_idx_].r = 2
; if ((exec_info)[_idx_].outgoings) for (_j_ = 0; _j_ < (exec_info
)[_idx_].outgoings->rnum; _j_++) { const int d = *(int*)((
void*)(((char*)(((exec_info)[_idx_].outgoings)->data)) + (
size_t)((exec_info)[_idx_].outgoings)->rsize * (size_t)(_j_
))); ++_incomings_[d].c; if (_incomings_[d].r != 0) continue;
 _incomings_[d].r = 1; ((void) sizeof ((_exist_size_[_q_] <
 (exec_info_size)) ? 1 : 0), __extension__ ({ if (_exist_size_
[_q_] < (exec_info_size)) ; else __assert_fail ("_exist_size_[_q_] < (exec_info_size)"
, "ccv_cnnp_model.c", 1890, __extension__ __PRETTY_FUNCTION__
); })); _exists_[_q_][_exist_size_[_q_]] = d; ++_exist_size_[
_q_]; } } ((_i_) = (_p_), (_p_) = (_q_), (_q_) = (_i_)); } for
 (_i_ = 0; _i_ < (source_size); _i_++) { ((void) sizeof ((
(sources)[_i_].graph == compiled_data->graph) ? 1 : 0), __extension__
 ({ if ((sources)[_i_].graph == compiled_data->graph) ; else
 __assert_fail ("(sources)[_i_].graph == compiled_data->graph"
, "ccv_cnnp_model.c", 1890, __extension__ __PRETTY_FUNCTION__
); })); _incomings_[(sources)[_i_].d].r = 3; _exists_[0][_i_]
 = (sources)[_i_].d; } _exist_size_[0] = (source_size); _exist_size_
[1] = 0; _p_ = 0, _q_ = 1; int _bump_ = 1; while (_exist_size_
[_p_] > 0) { _exist_size_[_q_] = 0; for (_i_ = 0; _i_ <
 _exist_size_[_p_]; _i_++) { const int32_t _idx_ = _exists_[_p_
][_i_]; if (_incomings_[_idx_].r != 3) continue; _incomings_[
_idx_].r = 4; if ((exec_info)[_idx_].outgoings) for (_j_ = 0;
 _j_ < (exec_info)[_idx_].outgoings->rnum; _j_++) { const
 int d = *(int*)((void*)(((char*)(((exec_info)[_idx_].outgoings
)->data)) + (size_t)((exec_info)[_idx_].outgoings)->rsize
 * (size_t)(_j_))); if (_incomings_[d].edges == 0) { _incomings_
[d].edges = _bump_; _bump_ += _incomings_[d].c; _incomings_[d
].c = 0; } _edges_[_incomings_[d].edges - 1 + _incomings_[d].
c] = _idx_; ++_incomings_[d].c; if (_incomings_[d].r != 2) continue
; _incomings_[d].r = 3; ((void) sizeof ((_exist_size_[_q_] <
 (exec_info_size)) ? 1 : 0), __extension__ ({ if (_exist_size_
[_q_] < (exec_info_size)) ; else __assert_fail ("_exist_size_[_q_] < (exec_info_size)"
, "ccv_cnnp_model.c", 1890, __extension__ __PRETTY_FUNCTION__
); })); _exists_[_q_][_exist_size_[_q_]] = d; ++_exist_size_[
_q_]; } } ((_i_) = (_p_), (_p_) = (_q_), (_q_) = (_i_)); } for
 (_i_ = 0; _i_ < (compiled_data->evaluate.to_op_size); _i_
++) { ((void) sizeof (((compiled_data->evaluate.to_ops)[_i_
].graph == compiled_data->graph) ? 1 : 0), __extension__ (
{ if ((compiled_data->evaluate.to_ops)[_i_].graph == compiled_data
->graph) ; else __assert_fail ("(compiled_data->evaluate.to_ops)[_i_].graph == compiled_data->graph"
, "ccv_cnnp_model.c", 1890, __extension__ __PRETTY_FUNCTION__
); })); _incomings_[(compiled_data->evaluate.to_ops)[_i_].
d].r = 5; _exists_[0][_i_] = (compiled_data->evaluate.to_ops
)[_i_].d; } _exist_size_[0] = (compiled_data->evaluate.to_op_size
); _exist_size_[1] = 0; _p_ = 0, _q_ = 1; while (_exist_size_
[_p_] > 0) { _exist_size_[_q_] = 0; for (_i_ = 0; _i_ <
 _exist_size_[_p_]; _i_++) { const int32_t _idx_ = _exists_[_p_
][_i_]; if (_incomings_[_idx_].r != 5) continue; _incomings_[
_idx_].r = 6; if (_incomings_[_idx_].edges > 0) for (_j_ =
 0; _j_ < _incomings_[_idx_].c; _j_++) { const int d = _edges_
[_incomings_[_idx_].edges - 1 + _j_]; if (_incomings_[d].r !=
 4) continue; _incomings_[d].r = 5; ((void) sizeof ((_exist_size_
[_q_] < (exec_info_size)) ? 1 : 0), __extension__ ({ if (_exist_size_
[_q_] < (exec_info_size)) ; else __assert_fail ("_exist_size_[_q_] < (exec_info_size)"
, "ccv_cnnp_model.c", 1890, __extension__ __PRETTY_FUNCTION__
); })); _exists_[_q_][_exist_size_[_q_]] = d; ++_exist_size_[
_q_]; } } ((_i_) = (_p_), (_p_) = (_q_), (_q_) = (_i_)); } for
 (_i_ = 0; _i_ < (compiled_data->evaluate.to_op_size); _i_
++) { ((void) sizeof (((compiled_data->evaluate.to_ops)[_i_
].graph == compiled_data->graph) ? 1 : 0), __extension__ (
{ if ((compiled_data->evaluate.to_ops)[_i_].graph == compiled_data
->graph) ; else __assert_fail ("(compiled_data->evaluate.to_ops)[_i_].graph == compiled_data->graph"
, "ccv_cnnp_model.c", 1890, __extension__ __PRETTY_FUNCTION__
); })); _incomings_[(compiled_data->evaluate.to_ops)[_i_].
d].d = 1; } for (_i_ = 0; _i_ < (source_size); _i_++) { ((
void) sizeof (((sources)[_i_].graph == compiled_data->graph
) ? 1 : 0), __extension__ ({ if ((sources)[_i_].graph == compiled_data
->graph) ; else __assert_fail ("(sources)[_i_].graph == compiled_data->graph"
, "ccv_cnnp_model.c", 1890, __extension__ __PRETTY_FUNCTION__
); })); _exists_[0][_i_] = (sources)[_i_].d; } _p_ = 0; _q_ =
 1; _exist_size_[0] = (source_size); _exist_size_[1] = 0; int
 _d_ = 0; while (_exist_size_[_p_] > 0) { _exist_size_[_q_
] = 0; for (_i_ = 0; _i_ < _exist_size_[_p_];) { const int32_t
 _idx_ = _exists_[_p_][_i_]; _visit_->node[_visit_->size
].index = ((_idx_)); _visit_->node[_visit_->size].term =
 ((_incomings_[_idx_].d)); ++_visit_->size;; if (_incomings_
[_idx_].d) { ++_d_; _incomings_[_idx_].r = 7; } if ((exec_info
)[_idx_].outgoings) { if ((exec_info)[_idx_].outgoings->rnum
 == 1) { const int d = *(int*)((void*)(((char*)(((exec_info)[
_idx_].outgoings)->data)) + (size_t)((exec_info)[_idx_].outgoings
)->rsize * (size_t)(0))); --_incomings_[d].c; if (_incomings_
[d].c == 0 && _incomings_[d].r == 6 && _d_ <
 (compiled_data->evaluate.to_op_size)) { _exists_[_p_][_i_
] = d; continue; } } else for (_j_ = 0; _j_ < (exec_info)[
_idx_].outgoings->rnum; _j_++) { const int d = *(int*)((void
*)(((char*)(((exec_info)[_idx_].outgoings)->data)) + (size_t
)((exec_info)[_idx_].outgoings)->rsize * (size_t)(_j_))); --
_incomings_[d].c; if (_incomings_[d].c == 0 && _incomings_
[d].r == 6 && _d_ < (compiled_data->evaluate.to_op_size
)) { ((void) sizeof ((_exist_size_[_q_] < (exec_info_size)
) ? 1 : 0), __extension__ ({ if (_exist_size_[_q_] < (exec_info_size
)) ; else __assert_fail ("_exist_size_[_q_] < (exec_info_size)"
, "ccv_cnnp_model.c", 1890, __extension__ __PRETTY_FUNCTION__
); })); _exists_[_q_][_exist_size_[_q_]] = d; ++_exist_size_[
_q_]; } } } ++_i_; } ((_i_) = (_p_), (_p_) = (_q_), (_q_) = (
_i_)); } for (_i_ = 0; _i_ < (compiled_data->evaluate.to_op_size
); _i_++) { ((void) sizeof (((compiled_data->evaluate.to_ops
)[_i_].graph == compiled_data->graph) ? 1 : 0), __extension__
 ({ if ((compiled_data->evaluate.to_ops)[_i_].graph == compiled_data
->graph) ; else __assert_fail ("(compiled_data->evaluate.to_ops)[_i_].graph == compiled_data->graph"
, "ccv_cnnp_model.c", 1890, __extension__ __PRETTY_FUNCTION__
); })); if (_incomings_[(compiled_data->evaluate.to_ops)[_i_
].d].r == 7) continue; if (!(0)) { ((void) sizeof ((_incomings_
[(compiled_data->evaluate.to_ops)[_i_].d].c == 0) ? 1 : 0)
, __extension__ ({ if (_incomings_[(compiled_data->evaluate
.to_ops)[_i_].d].c == 0) ; else __assert_fail ("_incomings_[(compiled_data->evaluate.to_ops)[_i_].d].c == 0"
, "ccv_cnnp_model.c", 1890, __extension__ __PRETTY_FUNCTION__
); })); } else if (_incomings_[(compiled_data->evaluate.to_ops
)[_i_].d].c > 0) continue; _visit_->node[_visit_->size
].index = (((compiled_data->evaluate.to_ops)[_i_].d)); _visit_
->node[_visit_->size].term = ((_incomings_[(compiled_data
->evaluate.to_ops)[_i_].d].d)); ++_visit_->size;; } if (
_heap_mem_) free(_incomings_); } while (0);; ((void) sizeof (
(_visit_->size <= (exec_info_size)) ? 1 : 0), __extension__
 ({ if (_visit_->size <= (exec_info_size)) ; else __assert_fail
 ("_visit_->size <= (exec_info_size)", "ccv_cnnp_model.c"
, 1890, __extension__ __PRETTY_FUNCTION__); })); _visit_; });
1891	ccv_nnc_graph_visit_for(visit, exec_info, node, idx){ int _i_; for (_i_ = 0; _i_ < (visit)->size; _i_++) { const
 int idx __attribute__((unused)) = (visit)->node[_i_].index
; const int _node_unused_ __attribute__((unused)) = (visit)->
node[_i_].term; typeof ((exec_info)) const node __attribute__
((unused)) = (exec_info) + idx; {
1892		visited[(idx >> 5)] |= (1u << (idx & 31));
1893	} ccv_nnc_graph_visit_endfor} }
1894	ccv_nnc_graph_visit_free(visit);
1895	const ccv_nnc_graph_exec_t* const destinations = (ccv_nnc_graph_exec_t*)ccv_array_get(compiled_data->graph->destinations, 0)((void*)(((char*)((compiled_data->graph->destinations)->
data)) + (size_t)(compiled_data->graph->destinations)->
rsize * (size_t)(0)));
1896	const int destination_size = compiled_data->graph->destinations->rnum;
1897	visit = ccv_nnc_graph_visit_new(compiled_data->graph, exec_info, exec_info_size, compiled_data->backward.from_ops, compiled_data->backward.from_op_size, destinations, destination_size, 0)({ ccv_nnc_graph_visit_t* _visit_ = (ccv_nnc_graph_visit_t*)malloc
(sizeof(ccv_nnc_graph_visit_t) + sizeof(_visit_->node[0]) *
 ((exec_info_size) - 1)); _visit_->size = 0; do { typedef struct
 { int8_t d; int8_t r; uint16_t c; int32_t edges; } ccv_nnc_incoming_t
; int _i_, _j_; int _incoming_edges_ = 0; for (_i_ = 0; _i_ <
 (exec_info_size); _i_++) _incoming_edges_ += ((exec_info)[_i_
].outgoings) ? (exec_info)[_i_].outgoings->rnum : 0; const
 int _heap_mem_ = ((exec_info_size) + _incoming_edges_ > 1024
); ccv_nnc_incoming_t* _incomings_; if (_heap_mem_) _incomings_
 = (ccv_nnc_incoming_t*)malloc(sizeof(ccv_nnc_incoming_t) * (
exec_info_size) + sizeof(int32_t) * ((exec_info_size) * 2 + _incoming_edges_
)); else _incomings_ = (ccv_nnc_incoming_t*)__builtin_alloca (
sizeof(ccv_nnc_incoming_t) * (exec_info_size) + sizeof(int32_t
) * ((exec_info_size) * 2 + _incoming_edges_)); memset(_incomings_
, 0, sizeof(ccv_nnc_incoming_t) * (exec_info_size)); int32_t*
 _exists_[2] = { (int32_t*)(_incomings_ + (exec_info_size)), (
int32_t*)(_incomings_ + (exec_info_size)) + (exec_info_size),
 }; int32_t* const _edges_ = _exists_[1] + (exec_info_size); for
 (_i_ = 0; _i_ < (compiled_data->backward.from_op_size)
; _i_++) { ((void) sizeof (((compiled_data->backward.from_ops
)[_i_].graph == compiled_data->graph) ? 1 : 0), __extension__
 ({ if ((compiled_data->backward.from_ops)[_i_].graph == compiled_data
->graph) ; else __assert_fail ("(compiled_data->backward.from_ops)[_i_].graph == compiled_data->graph"
, "ccv_cnnp_model.c", 1897, __extension__ __PRETTY_FUNCTION__
); })); _incomings_[(compiled_data->backward.from_ops)[_i_
].d].r = 1; _exists_[0][_i_] = (compiled_data->backward.from_ops
)[_i_].d; } int _exist_size_[2] = { (compiled_data->backward
.from_op_size), 0, }; int _p_ = 0, _q_ = 1; while (_exist_size_
[_p_] > 0) { _exist_size_[_q_] = 0; for (_i_ = 0; _i_ <
 _exist_size_[_p_]; _i_++) { const int32_t _idx_ = _exists_[_p_
][_i_]; if (_incomings_[_idx_].r != 1) continue; _incomings_[
_idx_].r = 2; if ((exec_info)[_idx_].outgoings) for (_j_ = 0;
 _j_ < (exec_info)[_idx_].outgoings->rnum; _j_++) { const
 int d = *(int*)((void*)(((char*)(((exec_info)[_idx_].outgoings
)->data)) + (size_t)((exec_info)[_idx_].outgoings)->rsize
 * (size_t)(_j_))); ++_incomings_[d].c; if (_incomings_[d].r !=
 0) continue; _incomings_[d].r = 1; ((void) sizeof ((_exist_size_
[_q_] < (exec_info_size)) ? 1 : 0), __extension__ ({ if (_exist_size_
[_q_] < (exec_info_size)) ; else __assert_fail ("_exist_size_[_q_] < (exec_info_size)"
, "ccv_cnnp_model.c", 1897, __extension__ __PRETTY_FUNCTION__
); })); _exists_[_q_][_exist_size_[_q_]] = d; ++_exist_size_[
_q_]; } } ((_i_) = (_p_), (_p_) = (_q_), (_q_) = (_i_)); } for
 (_i_ = 0; _i_ < (compiled_data->backward.from_op_size)
; _i_++) { ((void) sizeof (((compiled_data->backward.from_ops
)[_i_].graph == compiled_data->graph) ? 1 : 0), __extension__
 ({ if ((compiled_data->backward.from_ops)[_i_].graph == compiled_data
->graph) ; else __assert_fail ("(compiled_data->backward.from_ops)[_i_].graph == compiled_data->graph"
, "ccv_cnnp_model.c", 1897, __extension__ __PRETTY_FUNCTION__
); })); _incomings_[(compiled_data->backward.from_ops)[_i_
].d].r = 3; _exists_[0][_i_] = (compiled_data->backward.from_ops
)[_i_].d; } _exist_size_[0] = (compiled_data->backward.from_op_size
); _exist_size_[1] = 0; _p_ = 0, _q_ = 1; int _bump_ = 1; while
 (_exist_size_[_p_] > 0) { _exist_size_[_q_] = 0; for (_i_
 = 0; _i_ < _exist_size_[_p_]; _i_++) { const int32_t _idx_
 = _exists_[_p_][_i_]; if (_incomings_[_idx_].r != 3) continue
; _incomings_[_idx_].r = 4; if ((exec_info)[_idx_].outgoings)
 for (_j_ = 0; _j_ < (exec_info)[_idx_].outgoings->rnum
; _j_++) { const int d = *(int*)((void*)(((char*)(((exec_info
)[_idx_].outgoings)->data)) + (size_t)((exec_info)[_idx_].
outgoings)->rsize * (size_t)(_j_))); if (_incomings_[d].edges
 == 0) { _incomings_[d].edges = _bump_; _bump_ += _incomings_
[d].c; _incomings_[d].c = 0; } _edges_[_incomings_[d].edges -
 1 + _incomings_[d].c] = _idx_; ++_incomings_[d].c; if (_incomings_
[d].r != 2) continue; _incomings_[d].r = 3; ((void) sizeof ((
_exist_size_[_q_] < (exec_info_size)) ? 1 : 0), __extension__
 ({ if (_exist_size_[_q_] < (exec_info_size)) ; else __assert_fail
 ("_exist_size_[_q_] < (exec_info_size)", "ccv_cnnp_model.c"
, 1897, __extension__ __PRETTY_FUNCTION__); })); _exists_[_q_
][_exist_size_[_q_]] = d; ++_exist_size_[_q_]; } } ((_i_) = (
_p_), (_p_) = (_q_), (_q_) = (_i_)); } for (_i_ = 0; _i_ <
 (destination_size); _i_++) { ((void) sizeof (((destinations)
[_i_].graph == compiled_data->graph) ? 1 : 0), __extension__
 ({ if ((destinations)[_i_].graph == compiled_data->graph)
 ; else __assert_fail ("(destinations)[_i_].graph == compiled_data->graph"
, "ccv_cnnp_model.c", 1897, __extension__ __PRETTY_FUNCTION__
); })); _incomings_[(destinations)[_i_].d].r = 5; _exists_[0]
[_i_] = (destinations)[_i_].d; } _exist_size_[0] = (destination_size
); _exist_size_[1] = 0; _p_ = 0, _q_ = 1; while (_exist_size_
[_p_] > 0) { _exist_size_[_q_] = 0; for (_i_ = 0; _i_ <
 _exist_size_[_p_]; _i_++) { const int32_t _idx_ = _exists_[_p_
][_i_]; if (_incomings_[_idx_].r != 5) continue; _incomings_[
_idx_].r = 6; if (_incomings_[_idx_].edges > 0) for (_j_ =
 0; _j_ < _incomings_[_idx_].c; _j_++) { const int d = _edges_
[_incomings_[_idx_].edges - 1 + _j_]; if (_incomings_[d].r !=
 4) continue; _incomings_[d].r = 5; ((void) sizeof ((_exist_size_
[_q_] < (exec_info_size)) ? 1 : 0), __extension__ ({ if (_exist_size_
[_q_] < (exec_info_size)) ; else __assert_fail ("_exist_size_[_q_] < (exec_info_size)"
, "ccv_cnnp_model.c", 1897, __extension__ __PRETTY_FUNCTION__
); })); _exists_[_q_][_exist_size_[_q_]] = d; ++_exist_size_[
_q_]; } } ((_i_) = (_p_), (_p_) = (_q_), (_q_) = (_i_)); } for
 (_i_ = 0; _i_ < (destination_size); _i_++) { ((void) sizeof
 (((destinations)[_i_].graph == compiled_data->graph) ? 1 :
 0), __extension__ ({ if ((destinations)[_i_].graph == compiled_data
->graph) ; else __assert_fail ("(destinations)[_i_].graph == compiled_data->graph"
, "ccv_cnnp_model.c", 1897, __extension__ __PRETTY_FUNCTION__
); })); _incomings_[(destinations)[_i_].d].d = 1; } for (_i_ =
 0; _i_ < (compiled_data->backward.from_op_size); _i_++
) { ((void) sizeof (((compiled_data->backward.from_ops)[_i_
].graph == compiled_data->graph) ? 1 : 0), __extension__ (
{ if ((compiled_data->backward.from_ops)[_i_].graph == compiled_data
->graph) ; else __assert_fail ("(compiled_data->backward.from_ops)[_i_].graph == compiled_data->graph"
, "ccv_cnnp_model.c", 1897, __extension__ __PRETTY_FUNCTION__
); })); _exists_[0][_i_] = (compiled_data->backward.from_ops
)[_i_].d; } _p_ = 0; _q_ = 1; _exist_size_[0] = (compiled_data
->backward.from_op_size); _exist_size_[1] = 0; int _d_ = 0
; while (_exist_size_[_p_] > 0) { _exist_size_[_q_] = 0; for
 (_i_ = 0; _i_ < _exist_size_[_p_];) { const int32_t _idx_
 = _exists_[_p_][_i_]; _visit_->node[_visit_->size].index
 = ((_idx_)); _visit_->node[_visit_->size].term = ((_incomings_
[_idx_].d)); ++_visit_->size;; if (_incomings_[_idx_].d) {
 ++_d_; _incomings_[_idx_].r = 7; } if ((exec_info)[_idx_].outgoings
) { if ((exec_info)[_idx_].outgoings->rnum == 1) { const int
 d = *(int*)((void*)(((char*)(((exec_info)[_idx_].outgoings)->
data)) + (size_t)((exec_info)[_idx_].outgoings)->rsize * (
size_t)(0))); --_incomings_[d].c; if (_incomings_[d].c == 0 &&
 _incomings_[d].r == 6 && _d_ < (destination_size)
) { _exists_[_p_][_i_] = d; continue; } } else for (_j_ = 0; _j_
 < (exec_info)[_idx_].outgoings->rnum; _j_++) { const int
 d = *(int*)((void*)(((char*)(((exec_info)[_idx_].outgoings)->
data)) + (size_t)((exec_info)[_idx_].outgoings)->rsize * (
size_t)(_j_))); --_incomings_[d].c; if (_incomings_[d].c == 0
 && _incomings_[d].r == 6 && _d_ < (destination_size
)) { ((void) sizeof ((_exist_size_[_q_] < (exec_info_size)
) ? 1 : 0), __extension__ ({ if (_exist_size_[_q_] < (exec_info_size
)) ; else __assert_fail ("_exist_size_[_q_] < (exec_info_size)"
, "ccv_cnnp_model.c", 1897, __extension__ __PRETTY_FUNCTION__
); })); _exists_[_q_][_exist_size_[_q_]] = d; ++_exist_size_[
_q_]; } } } ++_i_; } ((_i_) = (_p_), (_p_) = (_q_), (_q_) = (
_i_)); } for (_i_ = 0; _i_ < (destination_size); _i_++) { (
(void) sizeof (((destinations)[_i_].graph == compiled_data->
graph) ? 1 : 0), __extension__ ({ if ((destinations)[_i_].graph
 == compiled_data->graph) ; else __assert_fail ("(destinations)[_i_].graph == compiled_data->graph"
, "ccv_cnnp_model.c", 1897, __extension__ __PRETTY_FUNCTION__
); })); if (_incomings_[(destinations)[_i_].d].r == 7) continue
; if (!(0)) { ((void) sizeof ((_incomings_[(destinations)[_i_
].d].c == 0) ? 1 : 0), __extension__ ({ if (_incomings_[(destinations
)[_i_].d].c == 0) ; else __assert_fail ("_incomings_[(destinations)[_i_].d].c == 0"
, "ccv_cnnp_model.c", 1897, __extension__ __PRETTY_FUNCTION__
); })); } else if (_incomings_[(destinations)[_i_].d].c > 0
) continue; _visit_->node[_visit_->size].index = (((destinations
)[_i_].d)); _visit_->node[_visit_->size].term = ((_incomings_
[(destinations)[_i_].d].d)); ++_visit_->size;; } if (_heap_mem_
) free(_incomings_); } while (0);; ((void) sizeof ((_visit_->
size <= (exec_info_size)) ? 1 : 0), __extension__ ({ if (_visit_
->size <= (exec_info_size)) ; else __assert_fail ("_visit_->size <= (exec_info_size)"
, "ccv_cnnp_model.c", 1897, __extension__ __PRETTY_FUNCTION__
); })); _visit_; });
1898	ccv_nnc_graph_visit_for(visit, exec_info, node, idx){ int _i_; for (_i_ = 0; _i_ < (visit)->size; _i_++) { const
 int idx __attribute__((unused)) = (visit)->node[_i_].index
; const int _node_unused_ __attribute__((unused)) = (visit)->
node[_i_].term; typeof ((exec_info)) const node __attribute__
((unused)) = (exec_info) + idx; {
1899		visited[(idx >> 5)] |= (1u << (idx & 31));
1900	} ccv_nnc_graph_visit_endfor} }
1901	ccv_nnc_graph_visit_free(visit);
1902	visit = ccv_nnc_graph_visit_new(compiled_data->graph, exec_info, exec_info_size, sources, source_size, destinations, destination_size, 0)({ ccv_nnc_graph_visit_t* _visit_ = (ccv_nnc_graph_visit_t*)malloc
(sizeof(ccv_nnc_graph_visit_t) + sizeof(_visit_->node[0]) *
 ((exec_info_size) - 1)); _visit_->size = 0; do { typedef struct
 { int8_t d; int8_t r; uint16_t c; int32_t edges; } ccv_nnc_incoming_t
; int _i_, _j_; int _incoming_edges_ = 0; for (_i_ = 0; _i_ <
 (exec_info_size); _i_++) _incoming_edges_ += ((exec_info)[_i_
].outgoings) ? (exec_info)[_i_].outgoings->rnum : 0; const
 int _heap_mem_ = ((exec_info_size) + _incoming_edges_ > 1024
); ccv_nnc_incoming_t* _incomings_; if (_heap_mem_) _incomings_
 = (ccv_nnc_incoming_t*)malloc(sizeof(ccv_nnc_incoming_t) * (
exec_info_size) + sizeof(int32_t) * ((exec_info_size) * 2 + _incoming_edges_
)); else _incomings_ = (ccv_nnc_incoming_t*)__builtin_alloca (
sizeof(ccv_nnc_incoming_t) * (exec_info_size) + sizeof(int32_t
) * ((exec_info_size) * 2 + _incoming_edges_)); memset(_incomings_
, 0, sizeof(ccv_nnc_incoming_t) * (exec_info_size)); int32_t*
 _exists_[2] = { (int32_t*)(_incomings_ + (exec_info_size)), (
int32_t*)(_incomings_ + (exec_info_size)) + (exec_info_size),
 }; int32_t* const _edges_ = _exists_[1] + (exec_info_size); for
 (_i_ = 0; _i_ < (source_size); _i_++) { ((void) sizeof ((
(sources)[_i_].graph == compiled_data->graph) ? 1 : 0), __extension__
 ({ if ((sources)[_i_].graph == compiled_data->graph) ; else
 __assert_fail ("(sources)[_i_].graph == compiled_data->graph"
, "ccv_cnnp_model.c", 1902, __extension__ __PRETTY_FUNCTION__
); })); _incomings_[(sources)[_i_].d].r = 1; _exists_[0][_i_]
 = (sources)[_i_].d; } int _exist_size_[2] = { (source_size),
 0, }; int _p_ = 0, _q_ = 1; while (_exist_size_[_p_] > 0)
 { _exist_size_[_q_] = 0; for (_i_ = 0; _i_ < _exist_size_
[_p_]; _i_++) { const int32_t _idx_ = _exists_[_p_][_i_]; if (
_incomings_[_idx_].r != 1) continue; _incomings_[_idx_].r = 2
; if ((exec_info)[_idx_].outgoings) for (_j_ = 0; _j_ < (exec_info
)[_idx_].outgoings->rnum; _j_++) { const int d = *(int*)((
void*)(((char*)(((exec_info)[_idx_].outgoings)->data)) + (
size_t)((exec_info)[_idx_].outgoings)->rsize * (size_t)(_j_
))); ++_incomings_[d].c; if (_incomings_[d].r != 0) continue;
 _incomings_[d].r = 1; ((void) sizeof ((_exist_size_[_q_] <
 (exec_info_size)) ? 1 : 0), __extension__ ({ if (_exist_size_
[_q_] < (exec_info_size)) ; else __assert_fail ("_exist_size_[_q_] < (exec_info_size)"
, "ccv_cnnp_model.c", 1902, __extension__ __PRETTY_FUNCTION__
); })); _exists_[_q_][_exist_size_[_q_]] = d; ++_exist_size_[
_q_]; } } ((_i_) = (_p_), (_p_) = (_q_), (_q_) = (_i_)); } for
 (_i_ = 0; _i_ < (source_size); _i_++) { ((void) sizeof ((
(sources)[_i_].graph == compiled_data->graph) ? 1 : 0), __extension__
 ({ if ((sources)[_i_].graph == compiled_data->graph) ; else
 __assert_fail ("(sources)[_i_].graph == compiled_data->graph"
, "ccv_cnnp_model.c", 1902, __extension__ __PRETTY_FUNCTION__
); })); _incomings_[(sources)[_i_].d].r = 3; _exists_[0][_i_]
 = (sources)[_i_].d; } _exist_size_[0] = (source_size); _exist_size_
[1] = 0; _p_ = 0, _q_ = 1; int _bump_ = 1; while (_exist_size_
[_p_] > 0) { _exist_size_[_q_] = 0; for (_i_ = 0; _i_ <
 _exist_size_[_p_]; _i_++) { const int32_t _idx_ = _exists_[_p_
][_i_]; if (_incomings_[_idx_].r != 3) continue; _incomings_[
_idx_].r = 4; if ((exec_info)[_idx_].outgoings) for (_j_ = 0;
 _j_ < (exec_info)[_idx_].outgoings->rnum; _j_++) { const
 int d = *(int*)((void*)(((char*)(((exec_info)[_idx_].outgoings
)->data)) + (size_t)((exec_info)[_idx_].outgoings)->rsize
 * (size_t)(_j_))); if (_incomings_[d].edges == 0) { _incomings_
[d].edges = _bump_; _bump_ += _incomings_[d].c; _incomings_[d
].c = 0; } _edges_[_incomings_[d].edges - 1 + _incomings_[d].
c] = _idx_; ++_incomings_[d].c; if (_incomings_[d].r != 2) continue
; _incomings_[d].r = 3; ((void) sizeof ((_exist_size_[_q_] <
 (exec_info_size)) ? 1 : 0), __extension__ ({ if (_exist_size_
[_q_] < (exec_info_size)) ; else __assert_fail ("_exist_size_[_q_] < (exec_info_size)"
, "ccv_cnnp_model.c", 1902, __extension__ __PRETTY_FUNCTION__
); })); _exists_[_q_][_exist_size_[_q_]] = d; ++_exist_size_[
_q_]; } } ((_i_) = (_p_), (_p_) = (_q_), (_q_) = (_i_)); } for
 (_i_ = 0; _i_ < (destination_size); _i_++) { ((void) sizeof
 (((destinations)[_i_].graph == compiled_data->graph) ? 1 :
 0), __extension__ ({ if ((destinations)[_i_].graph == compiled_data
->graph) ; else __assert_fail ("(destinations)[_i_].graph == compiled_data->graph"
, "ccv_cnnp_model.c", 1902, __extension__ __PRETTY_FUNCTION__
); })); _incomings_[(destinations)[_i_].d].r = 5; _exists_[0]
[_i_] = (destinations)[_i_].d; } _exist_size_[0] = (destination_size
); _exist_size_[1] = 0; _p_ = 0, _q_ = 1; while (_exist_size_
[_p_] > 0) { _exist_size_[_q_] = 0; for (_i_ = 0; _i_ <
 _exist_size_[_p_]; _i_++) { const int32_t _idx_ = _exists_[_p_
][_i_]; if (_incomings_[_idx_].r != 5) continue; _incomings_[
_idx_].r = 6; if (_incomings_[_idx_].edges > 0) for (_j_ =
 0; _j_ < _incomings_[_idx_].c; _j_++) { const int d = _edges_
[_incomings_[_idx_].edges - 1 + _j_]; if (_incomings_[d].r !=
 4) continue; _incomings_[d].r = 5; ((void) sizeof ((_exist_size_
[_q_] < (exec_info_size)) ? 1 : 0), __extension__ ({ if (_exist_size_
[_q_] < (exec_info_size)) ; else __assert_fail ("_exist_size_[_q_] < (exec_info_size)"
, "ccv_cnnp_model.c", 1902, __extension__ __PRETTY_FUNCTION__
); })); _exists_[_q_][_exist_size_[_q_]] = d; ++_exist_size_[
_q_]; } } ((_i_) = (_p_), (_p_) = (_q_), (_q_) = (_i_)); } for
 (_i_ = 0; _i_ < (destination_size); _i_++) { ((void) sizeof
 (((destinations)[_i_].graph == compiled_data->graph) ? 1 :
 0), __extension__ ({ if ((destinations)[_i_].graph == compiled_data
->graph) ; else __assert_fail ("(destinations)[_i_].graph == compiled_data->graph"
, "ccv_cnnp_model.c", 1902, __extension__ __PRETTY_FUNCTION__
); })); _incomings_[(destinations)[_i_].d].d = 1; } for (_i_ =
 0; _i_ < (source_size); _i_++) { ((void) sizeof (((sources
)[_i_].graph == compiled_data->graph) ? 1 : 0), __extension__
 ({ if ((sources)[_i_].graph == compiled_data->graph) ; else
 __assert_fail ("(sources)[_i_].graph == compiled_data->graph"
, "ccv_cnnp_model.c", 1902, __extension__ __PRETTY_FUNCTION__
); })); _exists_[0][_i_] = (sources)[_i_].d; } _p_ = 0; _q_ =
 1; _exist_size_[0] = (source_size); _exist_size_[1] = 0; int
 _d_ = 0; while (_exist_size_[_p_] > 0) { _exist_size_[_q_
] = 0; for (_i_ = 0; _i_ < _exist_size_[_p_];) { const int32_t
 _idx_ = _exists_[_p_][_i_]; _visit_->node[_visit_->size
].index = ((_idx_)); _visit_->node[_visit_->size].term =
 ((_incomings_[_idx_].d)); ++_visit_->size;; if (_incomings_
[_idx_].d) { ++_d_; _incomings_[_idx_].r = 7; } if ((exec_info
)[_idx_].outgoings) { if ((exec_info)[_idx_].outgoings->rnum
 == 1) { const int d = *(int*)((void*)(((char*)(((exec_info)[
_idx_].outgoings)->data)) + (size_t)((exec_info)[_idx_].outgoings
)->rsize * (size_t)(0))); --_incomings_[d].c; if (_incomings_
[d].c == 0 && _incomings_[d].r == 6 && _d_ <
 (destination_size)) { _exists_[_p_][_i_] = d; continue; } } else
 for (_j_ = 0; _j_ < (exec_info)[_idx_].outgoings->rnum
; _j_++) { const int d = *(int*)((void*)(((char*)(((exec_info
)[_idx_].outgoings)->data)) + (size_t)((exec_info)[_idx_].
outgoings)->rsize * (size_t)(_j_))); --_incomings_[d].c; if
 (_incomings_[d].c == 0 && _incomings_[d].r == 6 &&
 _d_ < (destination_size)) { ((void) sizeof ((_exist_size_
[_q_] < (exec_info_size)) ? 1 : 0), __extension__ ({ if (_exist_size_
[_q_] < (exec_info_size)) ; else __assert_fail ("_exist_size_[_q_] < (exec_info_size)"
, "ccv_cnnp_model.c", 1902, __extension__ __PRETTY_FUNCTION__
); })); _exists_[_q_][_exist_size_[_q_]] = d; ++_exist_size_[
_q_]; } } } ++_i_; } ((_i_) = (_p_), (_p_) = (_q_), (_q_) = (
_i_)); } for (_i_ = 0; _i_ < (destination_size); _i_++) { (
(void) sizeof (((destinations)[_i_].graph == compiled_data->
graph) ? 1 : 0), __extension__ ({ if ((destinations)[_i_].graph
 == compiled_data->graph) ; else __assert_fail ("(destinations)[_i_].graph == compiled_data->graph"
, "ccv_cnnp_model.c", 1902, __extension__ __PRETTY_FUNCTION__
); })); if (_incomings_[(destinations)[_i_].d].r == 7) continue
; if (!(0)) { ((void) sizeof ((_incomings_[(destinations)[_i_
].d].c == 0) ? 1 : 0), __extension__ ({ if (_incomings_[(destinations
)[_i_].d].c == 0) ; else __assert_fail ("_incomings_[(destinations)[_i_].d].c == 0"
, "ccv_cnnp_model.c", 1902, __extension__ __PRETTY_FUNCTION__
); })); } else if (_incomings_[(destinations)[_i_].d].c > 0
) continue; _visit_->node[_visit_->size].index = (((destinations
)[_i_].d)); _visit_->node[_visit_->size].term = ((_incomings_
[(destinations)[_i_].d].d)); ++_visit_->size;; } if (_heap_mem_
) free(_incomings_); } while (0);; ((void) sizeof ((_visit_->
size <= (exec_info_size)) ? 1 : 0), __extension__ ({ if (_visit_
->size <= (exec_info_size)) ; else __assert_fail ("_visit_->size <= (exec_info_size)"
, "ccv_cnnp_model.c", 1902, __extension__ __PRETTY_FUNCTION__
); })); _visit_; });
1903	// Find any missing nodes to be added as source. Right now, these are only set nodes.
1904	ccv_nnc_graph_visit_for(visit, exec_info, node, idx){ int _i_; for (_i_ = 0; _i_ < (visit)->size; _i_++) { const
 int idx __attribute__((unused)) = (visit)->node[_i_].index
; const int _node_unused_ __attribute__((unused)) = (visit)->
node[_i_].term; typeof ((exec_info)) const node __attribute__
((unused)) = (exec_info) + idx; {
1905		if (!(visited[(idx >> 5)] & (1u << (idx & 31))))
1906		{
1907			assert(exec_info[idx].cmd.cmd == CCV_NNC_SET_FORWARD)((void) sizeof ((exec_info[idx].cmd.cmd == CCV_NNC_SET_FORWARD
) ? 1 : 0), __extension__ ({ if (exec_info[idx].cmd.cmd == CCV_NNC_SET_FORWARD
) ; else __assert_fail ("exec_info[idx].cmd.cmd == CCV_NNC_SET_FORWARD"
, "ccv_cnnp_model.c", 1907, __extension__ __PRETTY_FUNCTION__
); }));
1908			if (exec_info[idx].cmd.info.blas.a[0] == 0) // Special-casing for empty out the tensor set function, not for the set grad to 1 one.
1909				ccv_array_add_unique_int(backward_from, idx);
1910		}
1911	} ccv_nnc_graph_visit_endfor} }
1912	ccv_nnc_graph_visit_free(visit);
1913	ccfreefree(visited);
1914	if (backward_from->rnum != compiled_data->backward.from_op_size) // If it doesn't match, need to redo this.
1915	{
1916		compiled_data->backward.from_op_size = backward_from->rnum;
1917		compiled_data->backward.from_ops = (ccv_nnc_graph_exec_t*)ccreallocrealloc(compiled_data->backward.from_ops, sizeof(ccv_nnc_graph_exec_t) * backward_from->rnum);
1918		for (i = 0; i < backward_from->rnum; i++)
1919			compiled_data->backward.from_ops[i] = (ccv_nnc_graph_exec_t){
1920				.d = *(int*)ccv_array_get(backward_from, i)((void*)(((char*)((backward_from)->data)) + (size_t)(backward_from
)->rsize * (size_t)(i))),
1921				.graph = compiled_data->graph,
1922			};
1923	}
1924	ccv_array_free(backward_from);
1925	ccv_nnc_graph_set_default_static_schedule(compiled_data->graph, compiled_data->stream_type, model->max_stream_count);
1926	ccv_nnc_graph_autotune(compiled_data->graph, model->workspace_size, 0, TRAVERSE_FULL0,0,0,0);
1927}
1928 
1929void 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)
1930{
1931	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
1932	assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
 (compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 1932, __extension__ __PRETTY_FUNCTION__); }));
1933	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; });
1934	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", 1934, __extension__ __PRETTY_FUNCTION__
); }));
1935	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", 1935, __extension__ __PRETTY_FUNCTION__
); }));
1936	assert(model->graph)((void) sizeof ((model->graph) ? 1 : 0), __extension__ ({ if
 (model->graph) ; else __assert_fail ("model->graph", "ccv_cnnp_model.c"
, 1936, __extension__ __PRETTY_FUNCTION__); }));
1937	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;
1938	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));
1939	if (!compiled_data->graph || mode_mismatch)
1940	{
1941		_ccv_cnnp_compiled_data_graph_free(compiled_data);
1942		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.
1943			_ccv_cnnp_compiled_data_backward_free(compiled_data);
1944		if (params.requires_grad)
1945			_ccv_cnnp_model_multistage_jit_0(model, params.disable_outgrad, params.is_test, inputs, input_size, outputs, output_size);
1946		else
1947			_ccv_cnnp_model_multistage_no_grad_jit(model, inputs, input_size, outputs, output_size);
1948	} else {
1949		ccv_nnc_tensor_arena_clear_bindings(compiled_data->tensor_arena);
1950		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"
, 1950, __extension__ __PRETTY_FUNCTION__); }));
1951		const int input_size_per_p = input_size / parallel_count;
1952		_ccv_cnnp_bind_tensors_to_arena(compiled_data->tensor_arena, model->graph, model->inputs, inputs, input_size_per_p, parallel_count);
1953		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"
, 1953, __extension__ __PRETTY_FUNCTION__); }));
1954		const int output_size_per_p = output_size / parallel_count;
1955		_ccv_cnnp_bind_tensors_to_arena(compiled_data->tensor_arena, model->graph, model->outputs, outputs, output_size_per_p, parallel_count);
1956	}
1957	if (compiled_data->is_test != params.is_test)
1958	{
1959		compiled_data->is_test = params.is_test;
1960		ccv_nnc_graph_exec_update_t update = {
1961			.parallel_count = parallel_count,
1962			.graph = model->graph,
1963			.graph_exec_arena = compiled_data->graph_exec_arena,
1964		};
1965		ccv_cnnp_model_set_is_test(model, params.is_test, _ccv_cnnp_cmd_update_for_execs, &update);
1966	}
1967}
1968 
1969void 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)
1970{
1971	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
1972	assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
 (compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 1972, __extension__ __PRETTY_FUNCTION__); }));
1973	ccv_cnnp_model_dry_run(model, params, inputs, input_size, outputs, output_size);
1974	if (compiled_data->graph_mode == CCV_CNNP_MODEL_GRAPH_MULTISTAGE_MODE_NO_GRAD)
1975		ccv_nnc_graph_run_with_schedule(compiled_data->graph, 0, 0, tensor_tape, stream_context);
1976	else {
1977		if (!compiled_data->evaluate.schedule)
1978			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);
1979		ccv_nnc_graph_run_with_schedule(compiled_data->graph, 0, compiled_data->evaluate.schedule, tensor_tape, stream_context);
1980	}
1981}
1982 
1983// Compile the graph to run ccv_cnnp_model_backward after ccv_cnnp_model_evaluate with requires_grad = true (MULTISTAGE_MODE).
1984// Particularly, this method compiles the accumulator graph.
1985static void _ccv_cnnp_model_multistage_jit_1(ccv_cnnp_model_t* const model)
1986{
1987	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
1988	assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
 (compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 1988, __extension__ __PRETTY_FUNCTION__); }));
1989	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", 1989, __extension__ __PRETTY_FUNCTION__
); }));
1990	ccv_nnc_symbolic_graph_t* accum = ccv_nnc_symbolic_graph_new();
1991	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; });
1992	const int parameter_size = compiled_data->parameters->rnum;
1993	int i, j;
1994	compiled_data->backward.gradients = (ccv_nnc_tensor_symbol_t*)ccmallocmalloc(sizeof(ccv_nnc_tensor_symbol_t) * parameter_size * parallel_count * 3);
1995	compiled_data->backward.accum_gradients = compiled_data->backward.gradients + parameter_size * parallel_count;
1996	compiled_data->backward.updated_accum_gradients = compiled_data->backward.accum_gradients + parameter_size * parallel_count;
1997	for (i = 0; i < parameter_size; i++)
1998		for (j = 0; j < parallel_count; j++)
1999			if (compiled_data->tensors.gradients[i + j * parameter_size])
2000			{
2001				const ccv_nnc_tensor_param_t info = compiled_data->tensors.gradients[i + j * parameter_size]->info;
2002				// Now, the old gradient is the accumulated gradient, getting new gradient tensor setup so we can collect them.
2003				compiled_data->tensors.accum_gradients[i + j * parameter_size] = compiled_data->tensors.gradients[i + j * parameter_size];
2004				compiled_data->tensors.gradients[i + j * parameter_size] = ccv_nnc_tensor_new(0, info, 0);
2005				ccv_nnc_tensor_symbol_t inputs[2];
2006				inputs[0] = compiled_data->backward.accum_gradients[i + j * parameter_size] = ccv_nnc_tensor_symbol_new(accum, info, 0);
2007				inputs[1] = compiled_data->backward.gradients[i + j * parameter_size] = ccv_nnc_tensor_symbol_new(accum, info, 0);
2008				ccv_nnc_tensor_symbol_t output = compiled_data->backward.updated_accum_gradients[i + j * parameter_size] = ccv_nnc_tensor_symbol_new(accum, info, 0);
2009				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);
2010			} else {
2011				compiled_data->backward.accum_gradients[i + j * parameter_size] = NO_TENSOR_SYMBOL(const ccv_nnc_tensor_symbol_t){.d = CCV_NNC_NO_TENSOR_SYMBOL
};
2012				compiled_data->backward.gradients[i + j * parameter_size] = NO_TENSOR_SYMBOL(const ccv_nnc_tensor_symbol_t){.d = CCV_NNC_NO_TENSOR_SYMBOL
};
2013				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
};
2014			}
2015	ccv_nnc_graph_exec_symbol_autogen(accum, 0, 0, CCV_NNC_AUTOGEN_ALL_EXECS | CCV_NNC_AUTOGEN_SOURCES_AND_DESTINATIONS);
2016	if (ccv_nnc_symbolic_graph_source_size(accum) == 0)
2017	{
2018		ccv_nnc_symbolic_graph_free(accum);
2019		// Create empty graph.
2020		compiled_data->backward.accum = ccv_nnc_graph_new();
2021		ccv_nnc_graph_topsort(compiled_data->backward.accum, 0, 0);
2022		return;
2023	}
2024	ccv_array_t* const tensor_binds = ccv_array_new(sizeof(ccv_nnc_tensor_bind_t), 0, 0);
2025	_ccv_cnnp_model_bind_tensors(accum, compiled_data->backward.accum_gradients, compiled_data->tensors.accum_gradients, parameter_size * parallel_count, 1, tensor_binds);
2026	_ccv_cnnp_model_bind_tensors(accum, compiled_data->backward.gradients, compiled_data->tensors.gradients, parameter_size * parallel_count, 1, tensor_binds);
2027	_ccv_cnnp_model_bind_tensors(accum, compiled_data->backward.updated_accum_gradients, compiled_data->tensors.accum_gradients, parameter_size * parallel_count, 1, tensor_binds);
2028	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);
2029	ccv_nnc_symbolic_graph_free(accum);
2030	ccv_array_free(tensor_binds);
2031	ccv_nnc_graph_set_default_static_schedule(compiled_data->backward.accum, compiled_data->stream_type, model->max_stream_count);
2032}
2033 
2034void 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)
2035{
2036	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
2037	assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
 (compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 2037, __extension__ __PRETTY_FUNCTION__); }));
2038	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", 2038, __extension__ __PRETTY_FUNCTION__
); }));
2039	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; });
2040	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", 2040, __extension__ __PRETTY_FUNCTION__
); }));
2041	if (outgrad_size > 0)
2042		{ 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", 2042, __extension__ __PRETTY_FUNCTION__
); })); }
2043	assert(model->graph)((void) sizeof ((model->graph) ? 1 : 0), __extension__ ({ if
 (model->graph) ; else __assert_fail ("model->graph", "ccv_cnnp_model.c"
, 2043, __extension__ __PRETTY_FUNCTION__); }));
2044	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", 2044, __extension__ __PRETTY_FUNCTION__
); }));
2045	const int parameter_size = compiled_data->parameters->rnum;
2046	// If we need to accumulate the gradients now, do jit on accumulator.
2047	if (compiled_data->backward.count > 0)
2048	{
2049		if (!compiled_data->backward.accum)
2050			_ccv_cnnp_model_multistage_jit_1(model);
2051		else if (compiled_data->backward.count == 1) {
2052			//  On this round, we need to switch accumulated gradients with gradients (so we can do accumulation properly).
2053			int i;
2054			for (i = 0; i < parameter_size * parallel_count; i++)
2055			{
2056				ccv_nnc_tensor_t* tensor;
2057				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));
2058			}
2059			if (compiled_data->backward.tensor_arena)
2060			{
2061				ccv_nnc_tensor_arena_clear_bindings(compiled_data->backward.tensor_arena);
2062				// Do rebind in case we messed up the binding (we switch accum_gradients and gradients).
2063				_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);
2064				_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);
2065				_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);
2066			}
2067		}
2068	}
2069	const int ingrad_size_per_p = model->output_size;
2070	const int outgrad_size_per_p = compiled_data->outgrad_size;
2071	int i, j;
2072	for (i = 0; i < ingrad_size_per_p; i++)
2073	{
2074		const ccv_nnc_tensor_symbol_t ingrad = ccv_nnc_tensor_symbol_for_backward(model->graph, compiled_data->f[i]);
2075		if (!ingrad_size || !ingrads || ingrads[i] == 0)
2076		{
2077			// Set it to 1 if it is not specified.
2078			ccv_nnc_tensor_t* const ingrad_tensor = ccv_nnc_tensor_from_symbol(compiled_data->tensor_arena, ingrad);
2079			if (ingrad_tensor)
2080				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);
2081			for (j = 1; j < parallel_count; j++)
2082			{
2083				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));
2084				if (ingrad_tensor)
2085					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);
2086			}
2087		} else {
2088			// Make sure the length matches, in case it is an alias.
2089			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", 2089, __extension__ __PRETTY_FUNCTION__
); }));
2090			ccv_nnc_tensor_bind_symbol(compiled_data->tensor_arena, ingrad, ingrads[i]);
2091			for (j = 1; j < parallel_count; j++)
2092				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]);
2093		}
2094	}
2095	if (outgrad_size > 0)
2096	{
2097		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", 2097, __extension__ __PRETTY_FUNCTION__
); }));
2098		for (i = 0; i < outgrad_size_per_p; i++)
2099			if (outgrads[i])
2100			{
2101				const ccv_nnc_tensor_symbol_t outgrad = compiled_data->outgrads[i];
2102				ccv_nnc_tensor_bind_symbol(compiled_data->tensor_arena, outgrad, outgrads[i]);
2103				for (j = 1; j < parallel_count; j++)
2104					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]);
2105			}
2106	} else {
2107		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", 2108, __extension__ __PRETTY_FUNCTION__
); }))
2108			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", 2108, __extension__ __PRETTY_FUNCTION__
); }));
2109	}
2110	// We need to rebind here because in ccv_cnnp_evaluate, we clear bindings, that will reset all bindings for the gradients.
2111	// For parameters and internals these are fine because when we clear bindings, it restores to original bindings, which are these
2112	// parameters and internals. The same cannot be said for gradients due to the accum_gradients switching.
2113	_ccv_cnnp_bind_tensors_to_arena(compiled_data->tensor_arena, model->graph, compiled_data->gradients, compiled_data->tensors.gradients, parameter_size, parallel_count);
2114	if (!compiled_data->backward.schedule)
2115		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);
2116	// Run the backward pass.
2117	ccv_nnc_graph_run_with_schedule(compiled_data->graph, 0, compiled_data->backward.schedule, tensor_tape, stream_context);
2118	// If we need to run accumulation round, do that now.
2119	if (compiled_data->backward.count > 0)
2120		ccv_nnc_graph_run_with_schedule(compiled_data->backward.accum, 0, 0, 0, stream_context);
2121	// Update the count, this determines whether we need to accumulate or not.
2122	++compiled_data->backward.count;
2123}
2124 
2125// Compile the graph to run ccv_cnnp_model_apply_gradients after ccv_cnnp_model_backward (MULTISTAGE_MODE).
2126// Particularly, this method compiles the parameter update graph.
2127static void _ccv_cnnp_model_multistage_jit_2(ccv_cnnp_model_t* const model)
2128{
2129	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
2130	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", 2130, __extension__ __PRETTY_FUNCTION__
); }));
2131	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; });
2132	const int parameter_size = compiled_data->parameters->rnum;
2133	ccv_array_t* const tensor_binds = ccv_array_new(sizeof(ccv_nnc_tensor_bind_t), 0, 0);
2134	_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);
2135	_ccv_cnnp_model_bind_tensors(model->graph, compiled_data->updated_parameters, compiled_data->tensors.parameters, parameter_size, parallel_count, tensor_binds);
2136	// Bind accumulated gradients.
2137	if (compiled_data->backward.count > 1)
2138		_ccv_cnnp_model_bind_tensors(model->graph, compiled_data->gradients, compiled_data->tensors.accum_gradients, parameter_size, parallel_count, tensor_binds);
2139	else
2140		_ccv_cnnp_model_bind_tensors(model->graph, compiled_data->gradients, compiled_data->tensors.gradients, parameter_size, parallel_count, tensor_binds);
2141	ccv_array_t* const apply_gradients_from = ccv_array_new(sizeof(int), 0, 0);
2142	int i, j;
2143	for (i = 0; i < compiled_data->backward.to_size; i++)
2144	{
2145		const int* tos;
2146		int to_size;
2147		ccv_nnc_graph_exec_symbol_to(model->graph, compiled_data->backward.tos[i], &tos, &to_size);
2148		for (j = 0; j < to_size; j++)
2149		{
2150			// Check if this is already show up in the backward graph, if that is the case, it won't be in the apply
2151			// gradients graph.
2152			const ccv_nnc_graph_exec_t exec = ccv_nnc_graph_exec_from_symbol(compiled_data->graph_exec_arena, (ccv_nnc_graph_exec_symbol_t){
2153				.d = tos[j],
2154				.graph = model->graph,
2155			});
2156			if (!exec.graph)
2157				ccv_array_add_unique_int(apply_gradients_from, tos[j]);
2158		}
2159	}
2160	const int from_size = apply_gradients_from->rnum;
2161	if (from_size == 0)
2162	{
2163		ccv_array_free(apply_gradients_from);
2164		ccv_array_free(tensor_binds);
2165		return;
2166	}
2167	ccv_nnc_graph_exec_symbol_t* const froms = (ccv_nnc_graph_exec_symbol_t*)ccmallocmalloc(sizeof(ccv_nnc_graph_exec_symbol_t) * from_size);
2168	for (i = 0; i < from_size; i++)
2169		froms[i] = (ccv_nnc_graph_exec_symbol_t){
2170			.d = *(int*)ccv_array_get(apply_gradients_from, i)((void*)(((char*)((apply_gradients_from)->data)) + (size_t
)(apply_gradients_from)->rsize * (size_t)(i))),
2171			.graph = model->graph
2172		};
2173	ccv_array_free(apply_gradients_from);
2174	// It can only ends with updates on the parameters.
2175	ccv_array_t* const tos = ccv_array_new(sizeof(ccv_nnc_graph_exec_symbol_t), parameter_size * parallel_count, 0);
2176	for (i = 0;  i < parameter_size; i++)
2177	{
2178		if (compiled_data->update_nodes[i].d == CCV_NNC_NO_TENSOR_SYMBOL)
2179			continue;
2180		ccv_array_push(tos, &compiled_data->update_nodes[i]);
2181		for (j = 1; j < parallel_count; j++)
2182		{
2183			const ccv_nnc_graph_exec_symbol_t copy = ccv_nnc_graph_exec_symbol_copy(model->graph, compiled_data->update_nodes[i], j);
2184			ccv_array_push(tos, &copy);
2185		}
2186	}
2187	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);
2188	ccv_array_free(tos);
2189	ccv_array_free(tensor_binds);
2190	ccfreefree(froms);
2191	const int max_saved_aux_size = compiled_data->minimize.max_saved_aux_size;
2192	for (i = 0; i < max_saved_aux_size * parameter_size; i++)
2193	{
2194		// Skip on no tensor.
2195		if (compiled_data->saved_aux[i].source.d == CCV_NNC_NO_TENSOR_SYMBOL)
2196			continue;
2197		ccv_nnc_tensor_t* const tensor = ccv_nnc_tensor_from_symbol(compiled_data->apply_gradients.tensor_arena, compiled_data->saved_aux[i].source);
2198		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);
2199		for (j = 1; j < parallel_count; j++)
2200		{
2201			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));
2202			if (copy)
2203				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);
2204		}
2205	}
2206	ccv_nnc_graph_set_default_static_schedule(compiled_data->apply_gradients.graph, compiled_data->stream_type, model->max_stream_count);
2207}
2208 
2209void ccv_cnnp_model_apply_gradients(ccv_cnnp_model_t* const model, ccv_nnc_stream_context_t* const stream_context)
2210{
2211	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
2212	assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
 (compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 2212, __extension__ __PRETTY_FUNCTION__); }));
2213	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", 2213, __extension__ __PRETTY_FUNCTION__
); }));
2214	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; });
2215	assert(model->graph)((void) sizeof ((model->graph) ? 1 : 0), __extension__ ({ if
 (model->graph) ; else __assert_fail ("model->graph", "ccv_cnnp_model.c"
, 2215, __extension__ __PRETTY_FUNCTION__); }));
2216	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", 2216, __extension__ __PRETTY_FUNCTION__
); }));
2217	// Skip if there is no backward pass.
2218	if (compiled_data->backward.count <= 0)
2219		return;
2220	// Skip if there is no parameters.
2221	if (compiled_data->parameters->rnum == 0)
2222	{
2223		compiled_data->backward.count = 0;
2224		return;
2225	}
2226	if (!compiled_data->apply_gradients.graph)
2227		_ccv_cnnp_model_multistage_jit_2(model);
2228	else {
2229		const int parameter_size = compiled_data->parameters->rnum;
2230		ccv_nnc_tensor_arena_clear_bindings(compiled_data->apply_gradients.tensor_arena);
2231		// Change to bind accum_gradients if we do gradient accumulation (run backward more than once).
2232		if (compiled_data->backward.count > 1)
2233			_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);
2234		else
2235			_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);
2236	}
2237	if (compiled_data->apply_gradients.graph)
2238		ccv_nnc_graph_run_with_schedule(compiled_data->apply_gradients.graph, 0, 0, 0, stream_context);
2239	// Reset backward count to 0.
2240	compiled_data->backward.count = 0;
2241}
2242 
2243void 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)
2244{
2245	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
2246	const int param_sel = parameter->param_sel > 0 ? parameter->param_sel - 1 : parameter->param_sel;
2247	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", 2247, __extension__ __PRETTY_FUNCTION__
); }));
2248	const int tensors_init = !!compiled_data->tensors_init.v;
2249	int this_tensor_init = tensors_init;
2250	if (!tensors_init)
2251		ccv_cnnp_model_tensors_init_0(model, compiled_data);
2252	else if ((uintptr_t)compiled_data->tensors_init.v & (uintptr_t)1)
2253	// Check if it is not fully allocated, if it is not, init_1.
2254		this_tensor_init = 0;
2255	ccv_array_t* const parameter_indices = ccv_array_new(sizeof(int), 0, 0);
2256	ccv_cnnp_model_add_to_parameter_indices(parameter->model, param_sel, parameter_indices);
2257	const int param_ref = parameter->param_ref > 0 ? parameter->param_ref - 1 : parameter->param_ref;
2258	if (param_ref < 0)
2259		{ 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", 2259
, __extension__ __PRETTY_FUNCTION__); })); }
2260	else
2261		{ 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", 2261, __extension__ __PRETTY_FUNCTION__
); })); }
2262	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)));
2263	ccv_array_free(parameter_indices);
2264	const int parameter_size = compiled_data->parameters->rnum;
2265	assert(d >= 0)((void) sizeof ((d >= 0) ? 1 : 0), __extension__ ({ if (d >=
 0) ; else __assert_fail ("d >= 0", "ccv_cnnp_model.c", 2265
, __extension__ __PRETTY_FUNCTION__); }));
2266	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", 2266, __extension__ __PRETTY_FUNCTION__
); }));
2267	const int parallel_count = _ccv_cnnp_compiled_data_parallel_count(model, compiled_data);
2268	int i;
2269	if (!this_tensor_init)
2270	{
2271		if (compiled_data->tensors.parameters[d])
2272		{
2273			for (i = 1; i < parallel_count; i++)
2274				{ assert(compiled_data->tensors.parameters[d + i * parameter_size])((void) sizeof ((compiled_data->tensors.parameters[d + i *
 parameter_size]) ? 1 : 0), __extension__ ({ if (compiled_data
->tensors.parameters[d + i * parameter_size]) ; else __assert_fail
 ("compiled_data->tensors.parameters[d + i * parameter_size]"
, "ccv_cnnp_model.c", 2274, __extension__ __PRETTY_FUNCTION__
); })); }
2275			this_tensor_init = 1;
2276		} else {
2277			const ccv_nnc_tensor_symbol_t parameter = *(ccv_nnc_tensor_symbol_t*)ccv_array_get(compiled_data->parameters, d)((void*)(((char*)((compiled_data->parameters)->data)) +
 (size_t)(compiled_data->parameters)->rsize * (size_t)(
d)));
2278			ccv_nnc_tensor_param_t info = ccv_nnc_tensor_symbol_params(parameter.graph, parameter);
2279			if (CCV_TENSOR_GET_DEVICE(info.type)((info.type) & 0xfff00) == CCV_COMPUTE_DEVICE_ANY)
2280				CCV_TENSOR_SET_DEVICE_ID(info.type, 0)(info.type) = (((info.type) & ~0xfff00) | (((0) & 0xfff
) << 8));
2281			const int device_id = CCV_TENSOR_GET_DEVICE_ID(info.type)(((info.type) & 0xfff00) >> 8);
2282			compiled_data->tensors.parameters[d] = ccv_nnc_tensor_new(0, info, 0);
2283			for (i = 1; i < parallel_count; i++)
2284			{
2285				if (i != device_id)
2286					CCV_TENSOR_SET_DEVICE_ID(info.type, i)(info.type) = (((info.type) & ~0xfff00) | (((i) & 0xfff
) << 8));
2287				else
2288					CCV_TENSOR_SET_DEVICE_ID(info.type, 0)(info.type) = (((info.type) & ~0xfff00) | (((0) & 0xfff
) << 8));
2289				compiled_data->tensors.parameters[d + i * parameter_size] = ccv_nnc_tensor_new(0, info, 0);
2290			}
2291		}
2292	}
2293	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));
2294	assert(dest)((void) sizeof ((dest) ? 1 : 0), __extension__ ({ if (dest) ;
 else __assert_fail ("dest", "ccv_cnnp_model.c", 2294, __extension__
 __PRETTY_FUNCTION__); }));
2295	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);
2296	for (i = 1; i < parallel_count; i++)
2297	{
2298		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));
2299		if (copy_tensor)
2300			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);
2301	}
2302	// Mark this symbol as init'ed.
2303	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;
2304	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));
2305	init_v[s >> 5] |= (1u << (s & 0x1f));
2306	// If we just allocated this tensor, now it is time to check if we need to mark it as fully allocated.
2307	if (!this_tensor_init)
2308	{
2309		if (ccv_cnnp_model_tensors_any_to_alloc(model, compiled_data))
2310			compiled_data->tensors_init.v = (uint32_t*)((uintptr_t)compiled_data->tensors_init.v | (uintptr_t)1);
2311		else // Remove the flag.
2312			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));
2313	}
2314}
2315 
2316void ccv_cnnp_model_parameter_copy(ccv_cnnp_model_t* const model, const ccv_cnnp_model_io_t parameter, ccv_nnc_tensor_t* const tensor)
2317{
2318	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
2319	const int param_sel = parameter->param_sel > 0 ? parameter->param_sel - 1 : parameter->param_sel;
2320	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", 2320, __extension__ __PRETTY_FUNCTION__
); }));
2321	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", 2321, __extension__ __PRETTY_FUNCTION__
); }));
2322	ccv_array_t* const parameter_indices = ccv_array_new(sizeof(int), 0, 0);
2323	ccv_cnnp_model_add_to_parameter_indices(parameter->model, param_sel, parameter_indices);
2324	const int param_ref = parameter->param_ref > 0 ? parameter->param_ref - 1 : parameter->param_ref;
2325	if (param_ref < 0)
2326		{ 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", 2326
, __extension__ __PRETTY_FUNCTION__); })); }
2327	else
2328		{ 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", 2328, __extension__ __PRETTY_FUNCTION__
); })); }
2329	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)));
2330	ccv_array_free(parameter_indices);
2331	const int parameter_size = compiled_data->parameters->rnum;
2332	assert(d >= 0)((void) sizeof ((d >= 0) ? 1 : 0), __extension__ ({ if (d >=
 0) ; else __assert_fail ("d >= 0", "ccv_cnnp_model.c", 2332
, __extension__ __PRETTY_FUNCTION__); }));
2333	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", 2333, __extension__ __PRETTY_FUNCTION__
); }));
2334	// We don't need to consider parallel_count, every parameter on each device is identical.
2335	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));
2336	assert(src)((void) sizeof ((src) ? 1 : 0), __extension__ ({ if (src) ; else
 __assert_fail ("src", "ccv_cnnp_model.c", 2336, __extension__
 __PRETTY_FUNCTION__); }));
2337	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);
2338}
2339 
2340ccv_nnc_tensor_param_t ccv_cnnp_model_parameter_tensor_params(ccv_cnnp_model_t* const model, const ccv_cnnp_model_io_t parameter)
2341{
2342	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
2343	const int param_sel = parameter->param_sel > 0 ? parameter->param_sel - 1 : parameter->param_sel;
2344	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", 2344, __extension__ __PRETTY_FUNCTION__
); }));
2345	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", 2345, __extension__ __PRETTY_FUNCTION__
); }));
2346	ccv_array_t* const parameter_indices = ccv_array_new(sizeof(int), 0, 0);
2347	ccv_cnnp_model_add_to_parameter_indices(parameter->model, param_sel, parameter_indices);
2348	const int param_ref = parameter->param_ref > 0 ? parameter->param_ref - 1 : parameter->param_ref;
2349	if (param_ref < 0)
2350		{ 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", 2350
, __extension__ __PRETTY_FUNCTION__); })); }
2351	else
2352		{ 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", 2352, __extension__ __PRETTY_FUNCTION__
); })); }
2353	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)));
2354	ccv_array_free(parameter_indices);
2355	const int parameter_size = compiled_data->parameters->rnum;
2356	assert(d >= 0)((void) sizeof ((d >= 0) ? 1 : 0), __extension__ ({ if (d >=
 0) ; else __assert_fail ("d >= 0", "ccv_cnnp_model.c", 2356
, __extension__ __PRETTY_FUNCTION__); }));
2357	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", 2357, __extension__ __PRETTY_FUNCTION__
); }));
2358	// We don't need to consider parallel_count, every parameter on each device is identical.
2359	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));
2360	assert(tensor)((void) sizeof ((tensor) ? 1 : 0), __extension__ ({ if (tensor
) ; else __assert_fail ("tensor", "ccv_cnnp_model.c", 2360, __extension__
 __PRETTY_FUNCTION__); }));
2361	return tensor->info;
2362}
2363 
2364const char* ccv_cnnp_model_parameter_name(ccv_cnnp_model_t* const model, const ccv_cnnp_model_io_t parameter)
2365{
2366	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
2367	const int param_sel = parameter->param_sel > 0 ? parameter->param_sel - 1 : parameter->param_sel;
2368	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", 2368, __extension__ __PRETTY_FUNCTION__
); }));
2369	ccv_array_t* const parameter_indices = ccv_array_new(sizeof(int), 0, 0);
2370	ccv_cnnp_model_add_to_parameter_indices(parameter->model, param_sel, parameter_indices);
2371	const int param_ref = parameter->param_ref > 0 ? parameter->param_ref - 1 : parameter->param_ref;
2372	if (param_ref < 0)
2373		{ 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", 2373
, __extension__ __PRETTY_FUNCTION__); })); }
2374	else
2375		{ 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", 2375, __extension__ __PRETTY_FUNCTION__
); })); }
2376	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)));
2377	ccv_array_free(parameter_indices);
2378	const int parameter_size = compiled_data->parameters->rnum;
2379	assert(d >= 0)((void) sizeof ((d >= 0) ? 1 : 0), __extension__ ({ if (d >=
 0) ; else __assert_fail ("d >= 0", "ccv_cnnp_model.c", 2379
, __extension__ __PRETTY_FUNCTION__); }));
2380	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", 2380, __extension__ __PRETTY_FUNCTION__
); }));
2381	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)));
2382}
2383 
2384int ccv_cnnp_model_parameter_count(ccv_cnnp_model_t* const model)
2385{
2386	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", 2386, __extension__ __PRETTY_FUNCTION__
); }));
2387	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
2388	return compiled_data->parameters->rnum;
2389}
2390 
2391uint64_t ccv_cnnp_model_parameters_size(ccv_cnnp_model_t* const model)
2392{
2393	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", 2393, __extension__ __PRETTY_FUNCTION__
); }));
2394	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
2395	const int parameter_size = compiled_data->parameters->rnum;
2396	int i;
2397	const ccv_nnc_symbolic_graph_t* const graph = model->graph;
2398	uint64_t size = 0;
2399	const int tensors_init = !!compiled_data->tensors_init.v;
2400	uint32_t* const init_v = tensors_init ? CCV_NNC_INIT_V(compiled_data->tensors_init.v)((uint32_t*)((uintptr_t)(compiled_data->tensors_init.v) &
 ~(uintptr_t)1)) : 0;
2401	for (i = 0; i < parameter_size; i++)
2402	{
2403		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;
2404		if (tensors_init && compiled_data->tensors.parameters && (init_v[d >> 5] | (1u << (d & 0x1f))) && compiled_data->tensors.parameters[i])
2405		{
2406			ccv_nnc_tensor_param_t params = compiled_data->tensors.parameters[i]->info;
2407			size += ccv_nnc_tensor_data_size(params);
2408			continue;
2409		}
2410		ccv_nnc_tensor_param_t params = ccv_nnc_tensor_symbol_params(graph, (ccv_nnc_tensor_symbol_t){
2411			.graph = graph,
2412			.d = d
2413		});
2414		size += ccv_nnc_tensor_data_size(params);
2415	}
2416	return size;
2417}
2418 
2419int ccv_cnnp_model_parameters_move(ccv_cnnp_model_t* const model, char** const names, ccv_nnc_tensor_t** const tensors, const int count, int type)
2420{
2421	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", 2421, __extension__ __PRETTY_FUNCTION__
); }));
2422	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
2423	if (count != compiled_data->parameters->rnum)
2424		return 0;
2425	if (CCV_TENSOR_GET_DEVICE(type)((type) & 0xfff00) == CCV_COMPUTE_DEVICE_ANY)
2426		CCV_TENSOR_SET_DEVICE_ID(type, 0)(type) = (((type) & ~0xfff00) | (((0) & 0xfff) <<
 8));
2427	int i;
2428	// We don't need to consider parallel_count, every parameter on each device is identical.
2429	for (i = 0; i < count; i++)
2430	{
2431		ccv_nnc_tensor_t* tensor = compiled_data->tensors.parameters[i];
2432		if ((uintptr_t)tensor & (uintptr_t)1) // If it is not owned. We don't do anything.
2433		{
2434			tensors[i] = 0;
2435			continue;
2436		}
2437		tensor = CCV_NNC_TENSOR(tensor)((ccv_nnc_tensor_t*)((uintptr_t)(tensor) & ~(uintptr_t)1)
);
2438		if (tensor->info.type == type)
2439			tensors[i] = tensor;
2440		else {
2441			ccv_nnc_tensor_param_t info = tensor->info;
2442			info.type = type;
2443			tensors[i] = ccv_nnc_tensor_new(0, info, 0); // Create this tensor, don't initiate copy yet.
2444		}
2445	}
2446	for (i = 0; i < count; i++)
2447	{
2448		ccv_nnc_tensor_t* tensor = compiled_data->tensors.parameters[i];
2449		if ((uintptr_t)tensor & (uintptr_t)1) // If it is not owned. We don't do anything.
2450			continue;
2451		tensor = CCV_NNC_TENSOR(tensor)((ccv_nnc_tensor_t*)((uintptr_t)(tensor) & ~(uintptr_t)1)
);
2452		// Now initiate transfer. We should do this one on a stream.
2453		if (tensor->info.type != type)
2454			ccv_nnc_cmd_exec(CMD_DATA_TRANSFER_FORWARD()ccv_nnc_cmd(CCV_NNC_DATA_TRANSFER_FORWARD, 0, ccv_nnc_cmd_auto
, 0), ccv_nnc_no_hint, 0, TENSOR_LIST(tensor)(ccv_nnc_tensor_t* []){tensor}, (1 +1 +0 +0 +0 +0 +0 +0 +0 +0
 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +
0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0
 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 -1), TENSOR_LIST(tensors[i])(ccv_nnc_tensor_t* []){tensors[i]}, (1 +1 +0 +0 +0 +0 +0 +0 +
0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0
 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +
0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 -1), 0);
2455	}
2456	// Copy names and remove parameters.
2457	for (i = 0; i < count; i++)
2458	{
2459		ccv_nnc_tensor_t* const tensor = compiled_data->tensors.parameters[i];
2460		if ((uintptr_t)tensor & (uintptr_t)1) // If it is not owned. We don't do anything.
2461		{
2462			names[i] = 0;
2463			continue;
2464		}
2465		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)));
2466		const size_t name_len = ccv_min(strnlen(name, 1023), 1023)({ typeof (strnlen(name, 1023)) _a = (strnlen(name, 1023)); typeof
 (1023) _b = (1023); (_a < _b) ? _a : _b; });
2467		names[i] = ccmallocmalloc(name_len + 1);
2468		names[i][name_len] = 0;
2469		memcpy(names[i], name, name_len);
2470		if (tensor->info.type == type)
2471			compiled_data->tensors.parameters[i] = 0; // Only move when it is moved.
2472	}
2473	return 1;
2474}
2475 
2476KHASH_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'→
2477 
2478void ccv_cnnp_model_set_parameters_from_key_values(ccv_cnnp_model_t* const model, char* const* const names, ccv_nnc_tensor_t** const tensors, const int count, const int invalidates)
2479{
2480	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", 2480, __extension__ __PRETTY_FUNCTION__
); }));
2481	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
2482	int i;
2483	khash_t(ccv_cnnp_parameter_id)kh_ccv_cnnp_parameter_id_t* id_map = 0;
2484	if (count != compiled_data->parameters->rnum)
2485	{
2486		id_map = kh_init(ccv_cnnp_parameter_id)kh_init_ccv_cnnp_parameter_id();
2487		// Build the map between name and the index.
2488		for (i = 0; i < count; i++)
2489		{
2490			int ret;
2491			const khiter_t k = kh_put(ccv_cnnp_parameter_id, id_map, names[i], &ret)kh_put_ccv_cnnp_parameter_id(id_map, names[i], &ret);
2492			assert(ret != 0)((void) sizeof ((ret != 0) ? 1 : 0), __extension__ ({ if (ret
 != 0) ; else __assert_fail ("ret != 0", "ccv_cnnp_model.c", 2492
, __extension__ __PRETTY_FUNCTION__); }));
2493			kh_val(id_map, k)((id_map)->vals[k]) = i;
2494		}
2495	}
2496	const int parameter_size = compiled_data->parameters->rnum;
2497	int* copy_back = 0;
2498	const int tensors_init = !!compiled_data->tensors_init.v;
2499	if (!tensors_init)
2500		ccv_cnnp_model_tensors_init_0(model, compiled_data);
2501	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; });
2502	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));
2503	for (i = 0; i < parameter_size; i++)
2504	{
2505		int j = i;
2506		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)));
2507		if (i >= 0 || strncmp(name, names[i], 1023) != 0)
2508		{
2509			// Build the map.
2510			if (id_map == 0)
2511			{
2512				id_map = kh_init(ccv_cnnp_parameter_id)kh_init_ccv_cnnp_parameter_id();
2513				for (j = 0; j < count; j++)
2514				{
2515					int ret;
2516					const khiter_t k = kh_put(ccv_cnnp_parameter_id, id_map, names[j], &ret)kh_put_ccv_cnnp_parameter_id(id_map, names[j], &ret);
2517					assert(ret != 0)((void) sizeof ((ret != 0) ? 1 : 0), __extension__ ({ if (ret
 != 0) ; else __assert_fail ("ret != 0", "ccv_cnnp_model.c", 2517
, __extension__ __PRETTY_FUNCTION__); }));
2518					kh_val(id_map, k)((id_map)->vals[k]) = j;
2519				}
2520			}
2521			const khiter_t k = kh_get(ccv_cnnp_parameter_id, id_map, name)kh_get_ccv_cnnp_parameter_id(id_map, name);
2522			if (k == kh_end(id_map)((id_map)->n_buckets)) // Cannot find the name, skip.
2523				continue;
2524			j = kh_val(id_map, k)((id_map)->vals[k]);
2525		}
2526		if (compiled_data->tensors.parameters[i]) // Cannot be a shared parameter to read.
2527			{ assert(!((uintptr_t)compiled_data->tensors.parameters[i] & (uintptr_t)1))((void) sizeof ((!((uintptr_t)compiled_data->tensors.parameters
[i] & (uintptr_t)1)) ? 1 : 0), __extension__ ({ if (!((uintptr_t
)compiled_data->tensors.parameters[i] & (uintptr_t)1))
 ; else __assert_fail ("!((uintptr_t)compiled_data->tensors.parameters[i] & (uintptr_t)1)"
, "ccv_cnnp_model.c", 2527, __extension__ __PRETTY_FUNCTION__
); })); }
2528		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)));
2529		ccv_nnc_tensor_param_t info = ccv_nnc_tensor_symbol_params(parameter.graph, parameter);
2530		if (CCV_TENSOR_GET_DEVICE(info.type)((info.type) & 0xfff00) == CCV_COMPUTE_DEVICE_ANY)
2531			CCV_TENSOR_SET_DEVICE_ID(info.type, 0)(info.type) = (((info.type) & ~0xfff00) | (((0) & 0xfff
) << 8));
2532		const int d = parameter.d;
2533		if (info.type == tensors[j]->info.type && invalidates) // Can move.
2534		{
2535			// Deallocate it if needed.
2536			if (!((uintptr_t)compiled_data->tensors.parameters[i] & (uintptr_t)1))
2537				if (compiled_data->tensors.parameters[i])
2538					ccv_nnc_tensor_free(compiled_data->tensors.parameters[i]);
2539			compiled_data->tensors.parameters[i] = tensors[j];
2540			tensors[j] = 0;
2541		} else {
2542			if (!compiled_data->tensors.parameters[i])
2543			{ // Not allocated, to allocate first.
2544				// Create new one, make sure we create this by having the right parameters.
2545				const int type = info.type;
2546				info = tensors[j]->info;
2547				info.type = type; // Revert back the type.
2548				compiled_data->tensors.parameters[i] = ccv_nnc_tensor_new(0, info, 0);
2549			}
2550			if (!copy_back)
2551				copy_back = (int*)cccalloccalloc(parameter_size, sizeof(int));
2552			copy_back[i] = j + 1;
2553		}
2554		init_v[d >> 5] |= (1u << (d & 0x1f));
2555		// Create this tensor for other data parallel allocations.
2556		info = compiled_data->tensors.parameters[i]->info; // In case we loaded a different info.
2557		const int device_id = CCV_TENSOR_GET_DEVICE_ID(info.type)(((info.type) & 0xfff00) >> 8);
2558		for (j = 1; j < parallel_count; j++)
2559			if (!compiled_data->tensors.parameters[i + j * parameter_size])
2560			{
2561				if (j != device_id)
2562					CCV_TENSOR_SET_DEVICE_ID(info.type, j)(info.type) = (((info.type) & ~0xfff00) | (((j) & 0xfff
) << 8));
2563				else
2564					CCV_TENSOR_SET_DEVICE_ID(info.type, 0)(info.type) = (((info.type) & ~0xfff00) | (((0) & 0xfff
) << 8));
2565				compiled_data->tensors.parameters[i + j * parameter_size] = ccv_nnc_tensor_new(0, info, 0);
2566			}
2567			// No need to copy over, this is done in ccv_cnnp_model.c's copy_tensors method.
2568	}
2569	if (id_map)
2570		kh_destroy(ccv_cnnp_parameter_id, id_map)kh_destroy_ccv_cnnp_parameter_id(id_map);
2571	// Now do the transfer.
2572	if (copy_back)
2573	{
2574		for (i = 0; i < parameter_size; i++)
2575		{
2576			ccv_nnc_tensor_t* const tensor = CCV_NNC_TENSOR(compiled_data->tensors.parameters[i])((ccv_nnc_tensor_t*)((uintptr_t)(compiled_data->tensors.parameters
[i]) & ~(uintptr_t)1));
2577			if (copy_back[i] == 0)
2578				continue;
2579			const int j = copy_back[i] - 1;
2580			ccv_nnc_cmd_exec(CMD_DATA_TRANSFER_FORWARD()ccv_nnc_cmd(CCV_NNC_DATA_TRANSFER_FORWARD, 0, ccv_nnc_cmd_auto
, 0), ccv_nnc_no_hint, 0, TENSOR_LIST(tensors[j])(ccv_nnc_tensor_t* []){tensors[j]}, (1 +1 +0 +0 +0 +0 +0 +0 +
0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0
 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +
0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 -1), TENSOR_LIST(tensor)(ccv_nnc_tensor_t* []){tensor}, (1 +1 +0 +0 +0 +0 +0 +0 +0 +0
 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +
0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0
 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 -1), 0);
2581		}
2582		ccfreefree(copy_back);
2583	}
2584}
2585 
2586ccv_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)
2587{
2588	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
2589	assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
 (compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 2589, __extension__ __PRETTY_FUNCTION__); }));
2590	const int parameter_size = compiled_data->parameters->rnum;
2591	int i;
2592	for (i = 0; i < parameter_size; i++)
2593	{
2594		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)));
2595		if (first(model, name, context))
2596			return ccv_cnnp_model_parameters(model, -1, i);
2597	}
2598	return 0;
2599}
2600 
2601ccv_array_t* ccv_cnnp_model_parameters_filter(ccv_cnnp_model_t* const model, ccv_cnnp_model_parameters_filter_f filter, void* const context)
2602{
2603	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
2604	assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
 (compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 2604, __extension__ __PRETTY_FUNCTION__); }));
2605	ccv_array_t* const parameters = ccv_array_new(sizeof(ccv_cnnp_model_io_t), 0, 0);
2606	const int parameter_size = compiled_data->parameters->rnum;
2607	int i;
2608	for (i = 0; i < parameter_size; i++)
2609	{
2610		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)));
2611		if (filter(model, name, context))
2612		{
2613			ccv_cnnp_model_io_t parameter = ccv_cnnp_model_parameters(model, -1, i);
2614			ccv_array_push(parameters, &parameter);
2615		}
2616	}
2617	return parameters;
2618 
2619}
2620 
2621CCV_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)
2622{
2623	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
2624	assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
 (compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 2624, __extension__ __PRETTY_FUNCTION__); }));
2625	const int tensors_init = !!compiled_data->tensors_init.v;
2626	if (!tensors_init) // If nothing initialized, we return parameter 0.
2627		return ccv_cnnp_model_parameters(model, -1, 0);
2628	const int parameter_size = compiled_data->parameters->rnum;
2629	int i;
2630	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));
2631	for (i = 0; i < parameter_size; i++)
2632	{
2633		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;
2634		if (!(init_v[d >> 5] & (1u << (d & 0x1f))))
2635			return ccv_cnnp_model_parameters(model, -1, i);
2636	}
2637	return 0;
2638}
2639 
2640static 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)
2641{
2642	const int to_param_sel = parameters->param_sel > 0 ? parameters->param_sel - 1 : parameters->param_sel;
2643	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", 2643, __extension__
 __PRETTY_FUNCTION__); }));
2644	ccv_array_t* const to_parameter_indices = ccv_array_new(sizeof(int), 0, 0);
2645	ccv_cnnp_model_add_to_parameter_indices(parameters->model, to_param_sel, to_parameter_indices);
2646	*param_ref = parameters->param_ref > 0 ? parameters->param_ref - 1 : parameters->param_ref;
2647	return to_parameter_indices;
2648}
2649 
2650static 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)
2651{
2652	// If the model is not compiled yet. Compile them now.
2653	if (!model->graph)
2654	{
2655		model->graph = ccv_nnc_symbolic_graph_new();
2656		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", 2656, __extension__ __PRETTY_FUNCTION__
); }));
2657		const int input_size = from_model->input_size;
2658		ccv_nnc_tensor_param_t input_params[input_size];
2659		int i;
2660		for (i = 0; i < input_size; i++)
2661			input_params[i] = ccv_nnc_tensor_symbol_params(from_model->graph, from_model->inputs[i]);
2662		_ccv_cnnp_model_compile(model, input_params, input_size, from_model->compiled_data->loss);
2663		model->parallel_count = from_model->parallel_count;
2664		model->memory_compression = from_model->memory_compression;
2665		model->memory_reduction = from_model->memory_reduction;
2666		model->gradient_checkpointing = from_model->gradient_checkpointing;
2667		model->compiled_data->stream_type = from_model->compiled_data->stream_type;
2668		model->compiled_data->minimize.minimizer = from_model->compiled_data->minimize.minimizer;
2669		model->compiled_data->minimize.max_saved_aux_size = from_model->compiled_data->minimize.max_saved_aux_size;
2670	}
2671	ccv_cnnp_compiled_data_t* const to_compiled_data = model->compiled_data;
2672	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", 2672, __extension__ __PRETTY_FUNCTION__
); }));
2673	const int to_tensors_init = !!to_compiled_data->tensors_init.v;
2674	if (!to_tensors_init)
2675	{
2676		if (only_init_0)
2677			ccv_cnnp_model_tensors_init_0(model, to_compiled_data);
2678		else
2679			_ccv_cnnp_model_tensors_init(model, to_compiled_data);
2680	} else if (!only_init_0 && (uintptr_t)to_compiled_data->tensors_init.v & (uintptr_t)1)
2681		// Check if it is not fully allocated, if it is not, init_1.
2682			ccv_cnnp_model_tensors_init_1(model, to_compiled_data);
2683	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", 2683, __extension__ __PRETTY_FUNCTION__
); }));
2684	*parameter_indices = _ccv_cnnp_model_parameter_indices(model, parameters, param_ref);
2685	*from_parameter_indices = _ccv_cnnp_model_parameter_indices(from_model, from_parameters, from_param_ref);
2686	if (*from_param_ref < 0 && *param_ref >= 0)
2687		{ 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", 2687, __extension__ __PRETTY_FUNCTION__
); })); }
2688	else if (*from_param_ref >= 0)
2689		{ 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", 2689, __extension__ __PRETTY_FUNCTION__
); })); }
2690	if (*param_ref < 0 && *from_param_ref >= 0)
2691		{ 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"
, 2691, __extension__ __PRETTY_FUNCTION__); })); }
2692	else if (*param_ref >= 0)
2693		{ 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", 2693, __extension__ __PRETTY_FUNCTION__
); })); }
2694}
2695 
2696void 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)
2697{
2698	ccv_array_t* to_parameter_indices;
2699	int to_param_ref;
2700	ccv_array_t* from_parameter_indices;
2701	int from_param_ref;
2702	_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);
2703	// Should be exactly the same tensor.
2704	if (to_param_ref < 0 && from_param_ref < 0)
2705		{ 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", 2705, __extension__ __PRETTY_FUNCTION__
); })); }
2706	// To models.
2707	ccv_cnnp_compiled_data_t* const to_compiled_data = model->compiled_data;
2708	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", 2708, __extension__ __PRETTY_FUNCTION__
); }));
2709	// From models.
2710	const ccv_cnnp_compiled_data_t* const from_compiled_data = from_model->compiled_data;
2711	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; });
2712	const int to_parameter_size = to_compiled_data->parameters->rnum;
2713	const int rnum = (to_param_ref < 0 && from_param_ref < 0) ? from_parameter_indices->rnum : 1;
2714	int i, j;
2715	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));
2716  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));
2717	for (i = 0; i < rnum; i++)
2718	{
2719		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)));
2720		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"
, 2720, __extension__ __PRETTY_FUNCTION__); }));
2721		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", 2721, __extension__ __PRETTY_FUNCTION__
); }));
2722		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;
2723		// If the original is not init'ed. We cannot copy from.
2724		if (!(from_init_v[s >> 5] & (1u << (s & 0x1f))))
2725			continue;
2726		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)));
2727		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"
, 2727, __extension__ __PRETTY_FUNCTION__); }));
2728		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", 2728, __extension__ __PRETTY_FUNCTION__
); }));
2729		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));
2730		assert(src)((void) sizeof ((src) ? 1 : 0), __extension__ ({ if (src) ; else
 __assert_fail ("src", "ccv_cnnp_model.c", 2730, __extension__
 __PRETTY_FUNCTION__); }));
2731		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));
2732		assert(dest)((void) sizeof ((dest) ? 1 : 0), __extension__ ({ if (dest) ;
 else __assert_fail ("dest", "ccv_cnnp_model.c", 2732, __extension__
 __PRETTY_FUNCTION__); }));
2733		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);
2734		for (j = 1; j < parallel_count; j++)
2735		{
2736			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));
2737			if (copy_tensor)
2738				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);
2739		}
2740		// Mark this symbol as init'ed.
2741		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;
2742		to_init_v[d >> 5] |= (1u << (d & 0x1f));
2743	}
2744	ccv_array_free(to_parameter_indices);
2745	ccv_array_free(from_parameter_indices);
2746}
2747 
2748void 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)
2749{
2750	ccv_array_t* to_parameter_indices;
2751	int to_param_ref;
2752	ccv_array_t* from_parameter_indices;
2753	int from_param_ref;
2754	_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);
2755	// Should be exactly the same tensor.
2756	if (renamer == 0 && to_param_ref < 0 && from_param_ref < 0)
1
Assuming 'renamer' is not equal to null→
2757		{ 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", 2757, __extension__ __PRETTY_FUNCTION__
); })); }
2758	// To models.
2759	ccv_cnnp_compiled_data_t* const to_compiled_data = model->compiled_data;
2760	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", 2760, __extension__ __PRETTY_FUNCTION__
); }));
2
←
Assuming 'to_compiled_data' is non-null→
3
←
Taking true branch→
2761	// From models.
2762	const ccv_cnnp_compiled_data_t* const from_compiled_data = from_model->compiled_data;
2763	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→
2764	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", 2764, __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→
2765	const int from_parameter_size = from_compiled_data->parameters->rnum;
2766	const int to_parameter_size = to_compiled_data->parameters->rnum;
2767	const int rnum = (to_param_ref < 0 && from_param_ref < 0) ? to_parameter_indices->rnum : 1;
9
←
Assuming 'to_param_ref' is >= 0→
2768	int i, j;
2769	khash_t(ccv_cnnp_parameter_id)kh_ccv_cnnp_parameter_id_t* id_map = 0;
2770	char* updated_name = 0;
2771	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));
2772  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));
2773	for (i = 0; i < rnum; i++)
2774	{
2775		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 true→
12
←
Assuming the condition is false→
13
←
'?' condition is false→
2776		// Need to figure out how to use the renamer here.
2777		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→
2778		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"
, 2778, __extension__ __PRETTY_FUNCTION__); }));
15
←
Assuming 'dest_d' is >= 0→
16
←
Taking true branch→
2779		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", 2779, __extension__
 __PRETTY_FUNCTION__); }));
17
←
Assuming 'dest_d' is < 'to_parameter_size'→
18
←
Taking true branch→
2780		if (renamer18.1
'renamer' is non-null
)
2781		{
2782			const char* const src_name = (src_d18.2
'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;
2783			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)));
2784			if (!updated_name18.3
'updated_name' is null
)
19
←
Taking true branch→
2785				updated_name = (char*)ccmallocmalloc(1024);
2786			const size_t src_name_len = src_name19.1
'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→
2787			if (src_name_len20.1
'src_name_len' is <= 0
 > 0)
21
←
Taking false branch→
2788				memcpy(updated_name, src_name, src_name_len);
2789			updated_name[src_name_len] = 0;
2790			if (renamer(context, dest_name, updated_name, 1024) != 0)
22
←
Assuming the condition is false→
2791				continue; // Skip this.
2792			if (src_name22.1
'src_name' is equal to null
 != 0 && memcmp(updated_name, src_name, src_name_len) == 0 && strnlen(updated_name, 1023) == src_name_len)
2793			{
2794				// Nothing changed.
2795			} else {
2796				if (!id_map22.2
'id_map' is null
)
23
←
Taking true branch→
2797				{
2798					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'→
2799					for (j = 0; j < from_parameter_size; j++)
27
←
Assuming 'j' is >= 'from_parameter_size'→
28
←
Loop condition is false. Execution continues on line 2807→
2800					{
2801						int ret;
2802						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);
2803						assert(ret != 0)((void) sizeof ((ret != 0) ? 1 : 0), __extension__ ({ if (ret
 != 0) ; else __assert_fail ("ret != 0", "ccv_cnnp_model.c", 2803
, __extension__ __PRETTY_FUNCTION__); }));
2804						kh_val(id_map, k)((id_map)->vals[k]) = j;
2805					}
2806				}
2807				const khiter_t k = kh_get(ccv_cnnp_parameter_id, id_map, updated_name)kh_get_ccv_cnnp_parameter_id(id_map, updated_name);
2808				if (k == kh_end(id_map)((id_map)->n_buckets)) // Cannot find the name, skip.
29
←
Assuming 'k' is not equal to field 'n_buckets'→
30
←
Taking false branch→
2809					continue;
2810				src_d = kh_val(id_map, k)((id_map)->vals[k]);
31
←
Array access (via field 'vals') results in a null pointer dereference
2811				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"
, 2811, __extension__ __PRETTY_FUNCTION__); }));
2812				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", 2812, __extension__
 __PRETTY_FUNCTION__); }));
2813			}
2814		}
2815		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"
, 2815, __extension__ __PRETTY_FUNCTION__); }));
2816		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", 2816, __extension__
 __PRETTY_FUNCTION__); }));
2817		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;
2818		// If the original is not init'ed. We cannot share from.
2819		if (!(from_init_v[s >> 5] & (1u << (s & 0x1f))))
2820			continue;
2821		for (j = 0; j < parallel_count; j++)
2822		{
2823			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));
2824			assert(src)((void) sizeof ((src) ? 1 : 0), __extension__ ({ if (src) ; else
 __assert_fail ("src", "ccv_cnnp_model.c", 2824, __extension__
 __PRETTY_FUNCTION__); }));
2825			ccv_nnc_tensor_t* const dest = to_compiled_data->tensors.parameters[dest_d + j * to_parameter_size];
2826			if (dest && !((uintptr_t)dest & (uintptr_t)1))
2827				ccv_nnc_tensor_free(dest);
2828			to_compiled_data->tensors.parameters[dest_d + j * to_parameter_size] = (ccv_nnc_tensor_t*)((uintptr_t)src | (uintptr_t)1);
2829		}
2830		// Mark this symbol as init'ed.
2831		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;
2832		to_init_v[d >> 5] |= (1u << (d & 0x1f));
2833	}
2834	ccv_array_free(to_parameter_indices);
2835	ccv_array_free(from_parameter_indices);
2836	if (id_map)
2837		kh_destroy(ccv_cnnp_parameter_id, id_map)kh_destroy_ccv_cnnp_parameter_id(id_map);
2838	if (updated_name)
2839		ccfreefree(updated_name);
2840	// Mark it as incomplete so we will call init_1.
2841	if (ccv_cnnp_model_tensors_any_to_alloc(model, to_compiled_data))
2842		to_compiled_data->tensors_init.v = (uint32_t*)((uintptr_t)to_compiled_data->tensors_init.v | (uintptr_t)1);
2843	else // Remove the flag.
2844		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));
2845}
2846 
2847ccv_nnc_stream_context_t* ccv_cnnp_compiled_data_get_stream(ccv_cnnp_compiled_data_t* const compiled_data, const int type)
2848{
2849	if (!compiled_data->stream_map)
2850		compiled_data->stream_map = kh_init(stream_map)kh_init_stream_map();
2851	int ret = 0;
2852	khiter_t k = kh_put(stream_map, compiled_data->stream_map, type, &ret)kh_put_stream_map(compiled_data->stream_map, type, &ret
);
2853	assert(ret >= 0)((void) sizeof ((ret >= 0) ? 1 : 0), __extension__ ({ if (
ret >= 0) ; else __assert_fail ("ret >= 0", "ccv_cnnp_model.c"
, 2853, __extension__ __PRETTY_FUNCTION__); }));
2854	ccv_nnc_stream_context_t* stream = kh_val(compiled_data->stream_map, k)((compiled_data->stream_map)->vals[k]);
2855	// If ret == 0, the key already exist, we can return directly, otherwise, create and return.
2856	if (ret != 0)
2857	{
2858		stream = ccv_nnc_stream_context_new(type);
2859		kh_val(compiled_data->stream_map, k)((compiled_data->stream_map)->vals[k]) = stream;
2860	}
2861	return stream;
2862}
2863 
2864void 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)
2865{
2866	ccv_array_t* to_parameter_indices;
2867	int to_param_ref;
2868	ccv_array_t* from_parameter_indices;
2869	int from_param_ref;
2870	_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);
2871	// Should be exactly the same tensor.
2872	if (to_param_ref < 0 && from_param_ref < 0)
2873		{ 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", 2873, __extension__ __PRETTY_FUNCTION__
); })); }
2874	// To models.
2875	ccv_cnnp_compiled_data_t* const to_compiled_data = model->compiled_data;
2876	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", 2876, __extension__ __PRETTY_FUNCTION__
); }));
2877	// From models.
2878	const ccv_cnnp_compiled_data_t* const from_compiled_data = from_model->compiled_data;
2879	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; });
2880	const int to_parameter_size = to_compiled_data->parameters->rnum;
2881	const int rnum = (to_param_ref < 0 && from_param_ref < 0) ? from_parameter_indices->rnum : 1;
2882	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", 2882, __extension__ __PRETTY_FUNCTION__
); }));
2883	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", 2883, __extension__ __PRETTY_FUNCTION__
); }));
2884	int i, j;
2885	ccv_nnc_tensor_t* inputs[aux_in_size + 2];
2886	ccv_nnc_tensor_t* outputs[aux_out_size + 1];
2887	for (i = 0; i < aux_in_size; i++)
2888		inputs[i + 2] = aux_ins[i];
2889	for (i = 0; i < aux_out_size; i++)
2890		outputs[i + 1] = aux_outs[i];
2891	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));
2892	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));
2893	for (i = 0; i < rnum; i++)
2894	{
2895		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)));
2896		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"
, 2896, __extension__ __PRETTY_FUNCTION__); }));
2897		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", 2897, __extension__ __PRETTY_FUNCTION__
); }));
2898		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;
2899		// If the original is not init'ed. We cannot copy from.
2900		if (!(from_init_v[s >> 5] & (1u << (s & 0x1f))))
2901			continue;
2902		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)));
2903		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"
, 2903, __extension__ __PRETTY_FUNCTION__); }));
2904		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", 2904, __extension__ __PRETTY_FUNCTION__
); }));
2905		if (parallel_count > 1)
2906		{
2907			ccv_nnc_stream_context_t* streams[parallel_count];
2908			ccv_nnc_stream_signal_t* signal;
2909			if (stream_context)
2910				signal = ccv_nnc_stream_context_emit_signal_new(stream_context);
2911			for (j = 0; j < parallel_count; j++)
2912			{
2913				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));
2914				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));
2915				if (!dest || !src)
2916				{
2917					streams[j] = 0;
2918					continue;
2919				}
2920				// At the moment, can only handle them on the same device.
2921				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", 2921, __extension__ __PRETTY_FUNCTION__
); }));
2922				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", 2922, __extension__ __PRETTY_FUNCTION__
); }));
2923				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;
2924				const int device_id = CCV_TENSOR_GET_DEVICE_ID(src->info.type)(((src->info.type) & 0xfff00) >> 8);
2925				int type = stream_type;
2926				CCV_STREAM_SET_DEVICE_ID(type, device_id)(type) = (((type) & ~0xfff00) | (((device_id) & 0xfff
) << 8));
2927				ccv_nnc_stream_context_t* const stream_0 = ccv_cnnp_compiled_data_get_stream(to_compiled_data, type);
2928				// Wait signal to finish.
2929				if (stream_context)
2930					ccv_nnc_stream_context_wait_signal(stream_0, signal);
2931				inputs[0] = outputs[0] = dest;
2932				inputs[1] = src;
2933				ccv_nnc_cmd_exec(cmd, hint, flags, inputs, aux_in_size + 2, outputs, aux_out_size + 1, stream_0);
2934				if (stream_context)
2935				{
2936					ccv_nnc_stream_signal_t* const signal = ccv_nnc_stream_context_emit_signal_new(stream_0);
2937					ccv_nnc_stream_context_wait_signal(stream_context, signal);
2938				}
2939				streams[j] = stream_0;
2940			}
2941			// If this should be blocking, blocking it.
2942			if (!stream_context)
2943				for (j = 0; j < parallel_count; j++)
2944					if (streams[j])
2945						ccv_nnc_stream_context_wait(streams[j]);
2946		} else {
2947			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));
2948			assert(src)((void) sizeof ((src) ? 1 : 0), __extension__ ({ if (src) ; else
 __assert_fail ("src", "ccv_cnnp_model.c", 2948, __extension__
 __PRETTY_FUNCTION__); }));
2949			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));
2950			assert(dest)((void) sizeof ((dest) ? 1 : 0), __extension__ ({ if (dest) ;
 else __assert_fail ("dest", "ccv_cnnp_model.c", 2950, __extension__
 __PRETTY_FUNCTION__); }));
2951			inputs[0] = outputs[0] = dest;
2952			inputs[1] = src;
2953			ccv_nnc_cmd_exec(cmd, hint, flags, inputs, aux_in_size + 2, outputs, aux_out_size + 1, stream_context);
2954		}
2955		// Mark this symbol as init'ed.
2956		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;
2957		to_init_v[d >> 5] |= (1u << (d & 0x1f));
2958	}
2959	ccv_array_free(to_parameter_indices);
2960	ccv_array_free(from_parameter_indices);
2961}
2962 
2963void 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)
2964{
2965	int to_param_ref;
2966	ccv_array_t* const to_parameter_indices = _ccv_cnnp_model_parameter_indices(model, parameters, &to_param_ref);
2967	// To models.
2968	ccv_cnnp_compiled_data_t* const to_compiled_data = model->compiled_data;
2969	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", 2969, __extension__ __PRETTY_FUNCTION__
); }));
2970	// Tensor has to be inited already.
2971	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", 2971, __extension__ __PRETTY_FUNCTION__
); }));
2972	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", 2972, __extension__ __PRETTY_FUNCTION__
); }));
2973	// From models.
2974	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; });
2975	const int to_parameter_size = to_compiled_data->parameters->rnum;
2976	const int rnum = (to_param_ref < 0) ? to_parameter_indices->rnum : 1;
2977	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", 2977, __extension__ __PRETTY_FUNCTION__
); }));
2978	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", 2978, __extension__ __PRETTY_FUNCTION__
); }));
2979	int i, j;
2980	ccv_nnc_tensor_t* inputs[aux_in_size + 1];
2981	ccv_nnc_tensor_t* outputs[aux_out_size + 1];
2982	for (i = 0; i < aux_in_size; i++)
2983		inputs[i + 1] = aux_ins[i];
2984	for (i = 0; i < aux_out_size; i++)
2985		outputs[i + 1] = aux_outs[i];
2986	for (i = 0; i < rnum; i++)
2987	{
2988		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)));
2989		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"
, 2989, __extension__ __PRETTY_FUNCTION__); }));
2990		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", 2990, __extension__ __PRETTY_FUNCTION__
); }));
2991		if (parallel_count > 1)
2992		{
2993			ccv_nnc_stream_context_t* streams[parallel_count];
2994			ccv_nnc_stream_signal_t* signal;
2995			if (stream_context)
2996				signal = ccv_nnc_stream_context_emit_signal_new(stream_context);
2997			for (j = 0; j < parallel_count; j++)
2998			{
2999				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));
3000				if (!dest)
3001				{
3002					streams[j] = 0;
3003					continue;
3004				}
3005				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;
3006				const int device_id = CCV_TENSOR_GET_DEVICE_ID(dest->info.type)(((dest->info.type) & 0xfff00) >> 8);
3007				int type = stream_type;
3008				CCV_STREAM_SET_DEVICE_ID(type, device_id)(type) = (((type) & ~0xfff00) | (((device_id) & 0xfff
) << 8));
3009				ccv_nnc_stream_context_t* const stream_0 = ccv_cnnp_compiled_data_get_stream(to_compiled_data, type);
3010				// Wait signal to finish.
3011				if (stream_context)
3012					ccv_nnc_stream_context_wait_signal(stream_0, signal);
3013				inputs[0] = outputs[0] = dest;
3014				ccv_nnc_cmd_exec(cmd, hint, flags, inputs, aux_in_size + 1, outputs, aux_out_size + 1, stream_0);
3015				if (stream_context)
3016				{
3017					ccv_nnc_stream_signal_t* const signal = ccv_nnc_stream_context_emit_signal_new(stream_0);
3018					ccv_nnc_stream_context_wait_signal(stream_context, signal);
3019				}
3020				streams[j] = stream_0;
3021			}
3022			// If this should be blocking, blocking it.
3023			if (!stream_context)
3024				for (j = 0; j < parallel_count; j++)
3025					if (streams[j])
3026						ccv_nnc_stream_context_wait(streams[j]);
3027		} else {
3028			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));
3029			assert(dest)((void) sizeof ((dest) ? 1 : 0), __extension__ ({ if (dest) ;
 else __assert_fail ("dest", "ccv_cnnp_model.c", 3029, __extension__
 __PRETTY_FUNCTION__); }));
3030			inputs[0] = outputs[0] = dest;
3031			ccv_nnc_cmd_exec(cmd, hint, flags, inputs, aux_in_size + 1, outputs, aux_out_size + 1, stream_context);
3032		}
3033		// No need to mark this symbol as init'ed, it is already.
3034	}
3035	ccv_array_free(to_parameter_indices);
3036}
3037 
3038void 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)
3039{
3040	int to_param_ref;
3041	ccv_array_t* const to_parameter_indices = _ccv_cnnp_model_parameter_indices(model, parameters, &to_param_ref);
3042	// To models.
3043	ccv_cnnp_compiled_data_t* const to_compiled_data = model->compiled_data;
3044	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", 3044, __extension__ __PRETTY_FUNCTION__
); }));
3045	// Tensor has to be inited already.
3046	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", 3046, __extension__ __PRETTY_FUNCTION__
); }));
3047	ccv_nnc_tensor_t** tensor_gradients;
3048	if (to_compiled_data->backward.count > 1)
3049		tensor_gradients = to_compiled_data->tensors.accum_gradients;
3050	else
3051		tensor_gradients = to_compiled_data->tensors.gradients;
3052	assert(tensor_gradients)((void) sizeof ((tensor_gradients) ? 1 : 0), __extension__ ({
 if (tensor_gradients) ; else __assert_fail ("tensor_gradients"
, "ccv_cnnp_model.c", 3052, __extension__ __PRETTY_FUNCTION__
); }));
3053	// From models.
3054	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; });
3055	const int to_parameter_size = to_compiled_data->parameters->rnum;
3056	const int rnum = (to_param_ref < 0) ? to_parameter_indices->rnum : 1;
3057	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", 3057, __extension__ __PRETTY_FUNCTION__
); }));
3058	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", 3058, __extension__ __PRETTY_FUNCTION__
); }));
3059	int i, j;
3060	ccv_nnc_tensor_t* inputs[aux_in_size + 1];
3061	ccv_nnc_tensor_t* outputs[aux_out_size + 1];
3062	for (i = 0; i < aux_in_size; i++)
3063		inputs[i + 1] = aux_ins[i];
3064	for (i = 0; i < aux_out_size; i++)
3065		outputs[i + 1] = aux_outs[i];
3066	for (i = 0; i < rnum; i++)
3067	{
3068		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)));
3069		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"
, 3069, __extension__ __PRETTY_FUNCTION__); }));
3070		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", 3070, __extension__ __PRETTY_FUNCTION__
); }));
3071		if (parallel_count > 1)
3072		{
3073			ccv_nnc_stream_context_t* streams[parallel_count];
3074			ccv_nnc_stream_signal_t* signal;
3075			if (stream_context)
3076				signal = ccv_nnc_stream_context_emit_signal_new(stream_context);
3077			for (j = 0; j < parallel_count; j++)
3078			{
3079				ccv_nnc_tensor_t* const dest = tensor_gradients[dest_d + j * to_parameter_size];
3080				if (!dest)
3081				{
3082					streams[j] = 0;
3083					continue;
3084				}
3085				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;
3086				const int device_id = CCV_TENSOR_GET_DEVICE_ID(dest->info.type)(((dest->info.type) & 0xfff00) >> 8);
3087				int type = stream_type;
3088				CCV_STREAM_SET_DEVICE_ID(type, device_id)(type) = (((type) & ~0xfff00) | (((device_id) & 0xfff
) << 8));
3089				ccv_nnc_stream_context_t* const stream_0 = ccv_cnnp_compiled_data_get_stream(to_compiled_data, type);
3090				// Wait signal to finish.
3091				if (stream_context)
3092					ccv_nnc_stream_context_wait_signal(stream_0, signal);
3093				inputs[0] = outputs[0] = dest;
3094				ccv_nnc_cmd_exec(cmd, hint, flags, inputs, aux_in_size + 1, outputs, aux_out_size + 1, stream_0);
3095				if (stream_context)
3096				{
3097					ccv_nnc_stream_signal_t* const signal = ccv_nnc_stream_context_emit_signal_new(stream_0);
3098					ccv_nnc_stream_context_wait_signal(stream_context, signal);
3099				}
3100				streams[j] = stream_0;
3101			}
3102			// If this should be blocking, blocking it.
3103			if (!stream_context)
3104				for (j = 0; j < parallel_count; j++)
3105					if (streams[j])
3106						ccv_nnc_stream_context_wait(streams[j]);
3107		} else {
3108			ccv_nnc_tensor_t* const dest = tensor_gradients[dest_d];
3109			if (!dest)
3110				continue;
3111			assert(dest)((void) sizeof ((dest) ? 1 : 0), __extension__ ({ if (dest) ;
 else __assert_fail ("dest", "ccv_cnnp_model.c", 3111, __extension__
 __PRETTY_FUNCTION__); }));
3112			inputs[0] = outputs[0] = dest;
3113			ccv_nnc_cmd_exec(cmd, hint, flags, inputs, aux_in_size + 1, outputs, aux_out_size + 1, stream_context);
3114		}
3115		// No need to mark this symbol as init'ed, it is already.
3116	}
3117	ccv_array_free(to_parameter_indices);
3118}
3119 
3120void ccv_cnnp_model_parameters_to_unified_memory(ccv_cnnp_model_t* const model, const ccv_cnnp_model_io_t parameters, ccv_nnc_stream_context_t* const stream_context)
3121{
3122	// Only CUDA backend has this feature.
3123#ifdef HAVE_CUDA1
3124	int to_param_ref;
3125	ccv_array_t* const to_parameter_indices = _ccv_cnnp_model_parameter_indices(model, parameters, &to_param_ref);
3126	// To models.
3127	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
3128	assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
 (compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 3128, __extension__ __PRETTY_FUNCTION__); }));
3129	// Tensor has to be inited already.
3130	assert(!!compiled_data->tensors_init.v)((void) sizeof ((!!compiled_data->tensors_init.v) ? 1 : 0)
, __extension__ ({ if (!!compiled_data->tensors_init.v) ; else
 __assert_fail ("!!compiled_data->tensors_init.v", "ccv_cnnp_model.c"
, 3130, __extension__ __PRETTY_FUNCTION__); }));
3131	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", 3131, __extension__ __PRETTY_FUNCTION__
); }));
3132	// From models.
3133	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; });
3134	const int rnum = (to_param_ref < 0) ? to_parameter_indices->rnum : 1;
3135	int i;
3136	for (i = 0; i < rnum; i++)
3137	{
3138		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)));
3139		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"
, 3139, __extension__ __PRETTY_FUNCTION__); }));
3140		assert(dest_d < compiled_data->parameters->rnum)((void) sizeof ((dest_d < compiled_data->parameters->
rnum) ? 1 : 0), __extension__ ({ if (dest_d < compiled_data
->parameters->rnum) ; else __assert_fail ("dest_d < compiled_data->parameters->rnum"
, "ccv_cnnp_model.c", 3140, __extension__ __PRETTY_FUNCTION__
); }));
3141		if (parallel_count > 1)
3142		{
3143			assert(0 && "Cannot support this when data parallel is in effect.")((void) sizeof ((0 && "Cannot support this when data parallel is in effect."
) ? 1 : 0), __extension__ ({ if (0 && "Cannot support this when data parallel is in effect."
) ; else __assert_fail ("0 && \"Cannot support this when data parallel is in effect.\""
, "ccv_cnnp_model.c", 3143, __extension__ __PRETTY_FUNCTION__
); }));
3144		} else {
3145			ccv_nnc_tensor_t* const src = CCV_NNC_TENSOR(compiled_data->tensors.parameters[dest_d])((ccv_nnc_tensor_t*)((uintptr_t)(compiled_data->tensors.parameters
[dest_d]) & ~(uintptr_t)1));
3146			assert(src)((void) sizeof ((src) ? 1 : 0), __extension__ ({ if (src) ; else
 __assert_fail ("src", "ccv_cnnp_model.c", 3146, __extension__
 __PRETTY_FUNCTION__); }));
3147			ccv_nnc_tensor_param_t params = src->info;
3148			if (CCV_TENSOR_GET_MEMORY(params.type)((params.type) & 0x3) != CCV_TENSOR_GPU_MEMORY)
3149				continue;
3150			const size_t size = ccv_nnc_tensor_data_size(params);
3151			if (size <= 0)
3152				continue;
3153			const int should_free = !((uintptr_t)compiled_data->tensors.parameters[dest_d] & (uintptr_t)1);
3154			const int tfb = (CCV_TENSOR_GET_MEMORY(params.type)((params.type) & 0x3) == CCV_TENSOR_CPU_MEMORY && params.format == CCV_TENSOR_FORMAT_NHWC && params.dim[2] > 0 && params.dim[2] <= CCV_MAX_CHANNEL(0xFFF) && params.dim[0] > 0 && params.dim[1] > 0 && params.dim[3] == 0);
3155			ccv_nnc_tensor_t* const tensor = (ccv_nnc_tensor_t*)ccmallocmalloc(sizeof(ccv_nnc_tensor_t));
3156			tensor->dataof = 0;
3157			tensor->alias_ref = 0;
3158			tensor->sig = 0;
3159			tensor->refcount = 1;
3160			tensor->info = params;
3161			if (tfb)
3162			{
3163				tensor->type = CCV_NO_DATA_ALLOC | CCV_MATRIX_DENSE | CCV_GET_DATA_TYPE(params.datatype)((params.datatype) & 0xFF000) | params.dim[2];
3164				// This corresponding to mat->step
3165				tensor->info.dim[4] = CCV_GET_STEP(params.dim[1], (CCV_GET_DATA_TYPE(params.datatype) | params.dim[2]))(((params.dim[1]) * _ccv_get_data_type_size[(((((params.datatype
) & 0xFF000) | params.dim[2])) & 0xFF000) >> 12
] * (((((params.datatype) & 0xFF000) | params.dim[2])) &
 0xFFF) + 3) & -4);
3166			} else // This won't be recognized by ccv_dense_matrix_t
3167				tensor->type = CCV_NO_DATA_ALLOC | CCV_MATRIX_DENSE | CCV_GET_DATA_TYPE(params.datatype)((params.datatype) & 0xFF000);
3168			// Remove this flag so it can be deallocated as usual.
3169			tensor->type &= ~CCV_NO_DATA_ALLOC;
3170			assert(CCV_TENSOR_GET_DEVICE(params.type) != CCV_COMPUTE_DEVICE_ANY)((void) sizeof ((((params.type) & 0xfff00) != CCV_COMPUTE_DEVICE_ANY
) ? 1 : 0), __extension__ ({ if (((params.type) & 0xfff00
) != CCV_COMPUTE_DEVICE_ANY) ; else __assert_fail ("CCV_TENSOR_GET_DEVICE(params.type) != CCV_COMPUTE_DEVICE_ANY"
, "ccv_cnnp_model.c", 3170, __extension__ __PRETTY_FUNCTION__
); }));
3171			void* ptr = cumallocmanaged(CCV_TENSOR_GET_DEVICE_ID(params.type)(((params.type) & 0xfff00) >> 8), size);
3172			if (ptr) // If allocated successfully. Otherwise we go through the fallback path.
3173			{
3174				tensor->data.u8 = (uint8_t*)ptr;
3175				tensor->type |= CCV_MAPPED_MEM; // This denotes the tensor is mapped to CPU, and would prefer a explicit prefetch call.
3176			} else {
3177				// Allocation failed.
3178				ccfreefree(tensor);
3179				continue;
3180			}
3181			// TODO: Cannot run this on the stream context yet, due to allocation and deallocations.
3182			ccv_nnc_cmd_exec(CMD_DATA_TRANSFER_FORWARD()ccv_nnc_cmd(CCV_NNC_DATA_TRANSFER_FORWARD, 0, ccv_nnc_cmd_auto
, 0), ccv_nnc_no_hint, 0, &src, 1, &tensor, 1, 0);
3183			cumemadvisereadmostly(CCV_TENSOR_GET_DEVICE_ID(params.type)(((params.type) & 0xfff00) >> 8), tensor->data.u8, size);
3184			compiled_data->tensors.parameters[dest_d] = tensor;
3185			// Can free out the old one.
3186			if (should_free)
3187				ccv_nnc_tensor_free(src);
3188		}
3189		// No need to mark this symbol as init'ed, it is already.
3190	}
3191	ccv_array_free(to_parameter_indices);
3192#endif
3193}
3194 
3195ccv_nnc_cmd_t ccv_cnnp_model_minimizer(ccv_cnnp_model_t* const model)
3196{
3197	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
3198	assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
 (compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 3198, __extension__ __PRETTY_FUNCTION__); }));
3199	return compiled_data->minimize.minimizer;
3200}
3201 
3202void 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)
3203{
3204	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
3205	assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
 (compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 3205, __extension__ __PRETTY_FUNCTION__); }));
3206	const int parameter_size = compiled_data->parameters->rnum;
3207	if (parameter_size == 0)
3208		return;
3209	if (reset)
3210		{ 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", 3210, __extension__ __PRETTY_FUNCTION__
); })); }
3211	const int old_max_saved_aux_size = compiled_data->minimize.max_saved_aux_size;
3212	const int saved_aux_size = ccv_nnc_minimizer_saved_aux_size(minimizer);
3213	if (saved_aux_size > compiled_data->minimize.max_saved_aux_size)
3214		compiled_data->minimize.max_saved_aux_size = saved_aux_size;
3215	const int max_saved_aux_size = compiled_data->minimize.max_saved_aux_size;
3216	// We update all parameters, at this point, we have one minimizer.
3217	if (set_parameters == 0 || set_parameter_size == 0)
3218		compiled_data->minimize.minimizer = minimizer;
3219	int i;
3220	if (set_parameters && set_parameter_size)
3221	{
3222		// I need to save what's the minimizer along with this.
3223		if (!compiled_data->minimize.parameters)
3224			compiled_data->minimize.parameters = ccv_array_new(sizeof(ccv_cnnp_set_minimizer_for_parameter_t*), 1, 0);
3225		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));
3226		set_minimizer_for_parameter->minimizer = minimizer;
3227		set_minimizer_for_parameter->parameter_size = set_parameter_size;
3228		memcpy(set_minimizer_for_parameter->parameters, set_parameters, sizeof(ccv_cnnp_model_io_t) * set_parameter_size);
3229		ccv_array_push(compiled_data->minimize.parameters, &set_minimizer_for_parameter);
3230	}
3231	// If reset is true, clear the parameters array.
3232	if (reset && compiled_data->minimize.parameters)
3233	{
3234		for (i = 0; i < compiled_data->minimize.parameters->rnum; i++)
3235			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))));
3236		ccv_array_clear(compiled_data->minimize.parameters);
3237	}
3238	if (!compiled_data->update_nodes)
3239		return;
3240	ccv_nnc_symbolic_graph_t* const symbolic_graph = model->graph;
3241	assert(symbolic_graph)((void) sizeof ((symbolic_graph) ? 1 : 0), __extension__ ({ if
 (symbolic_graph) ; else __assert_fail ("symbolic_graph", "ccv_cnnp_model.c"
, 3241, __extension__ __PRETTY_FUNCTION__); }));
3242	if (saved_aux_size > old_max_saved_aux_size)
3243	{
3244		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", 3244, __extension__ __PRETTY_FUNCTION__
); }));
3245		// Reallocate first, move them around later.
3246		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);
3247		compiled_data->update_nodes = (ccv_nnc_graph_exec_symbol_t*)(compiled_data->updated_parameters + parameter_size);
3248		compiled_data->saved_aux = (ccv_nnc_tensor_symbol_map_t*)(compiled_data->update_nodes + parameter_size);
3249		// We need to do this from back to front because saved_aux_size > old_saved_aux_size, it could overlap.
3250		_ccv_cnnp_scatter_saved_aux(compiled_data->saved_aux, parameter_size, old_max_saved_aux_size, saved_aux_size);
3251	}
3252	int flag = 0;
3253	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; });
3254	if (set_parameters && set_parameter_size)
3255	{
3256		ccv_array_t* const parameter_indices = ccv_array_new(sizeof(int), 0, 0);
3257		for (i = 0; i < set_parameter_size; i++)
3258		{
3259			const int param_sel = set_parameters[i]->param_sel > 0 ? set_parameters[i]->param_sel - 1 : set_parameters[i]->param_sel;
3260			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", 3260, __extension__ __PRETTY_FUNCTION__
); }));
3261			const int old_rnum = parameter_indices->rnum;
3262			ccv_cnnp_model_add_to_parameter_indices(set_parameters[i]->model, param_sel, parameter_indices);
3263			const int param_ref = set_parameters[i]->param_ref > 0 ? set_parameters[i]->param_ref - 1 : set_parameters[i]->param_ref;
3264			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", 3264, __extension__ __PRETTY_FUNCTION__
); }));
3265			if (param_ref >= 0)
3266			{
3267				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", 3267, __extension__ __PRETTY_FUNCTION__
); }));
3268				*(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)));
3269				parameter_indices->rnum = old_rnum + 1;
3270			}
3271		}
3272		// We may have duplicated indices, but that is OK, we will set it twice.
3273		for (i = 0; i < parameter_indices->rnum; i++)
3274		{
3275			const int d = *(int*)ccv_array_get(parameter_indices, i)((void*)(((char*)((parameter_indices)->data)) + (size_t)(parameter_indices
)->rsize * (size_t)(i)));
3276			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))
3277				flag = 1;
3278		}
3279		ccv_array_free(parameter_indices);
3280	} else {
3281		for (i = 0; i < parameter_size; i++)
3282			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))
3283				flag = 1;
3284		if (compiled_data->minimize.parameters)
3285			if (_ccv_cnnp_apply_parameters_with_minimizer(model))
3286				flag = 1;
3287	}
3288	if (flag)
3289	{
3290		// 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.
3291		if (compiled_data->graph_mode == CCV_CNNP_MODEL_GRAPH_FIT_MODE)
3292			_ccv_cnnp_compiled_data_graph_free(compiled_data);
3293		_ccv_cnnp_compiled_data_apply_gradients_free(compiled_data);
3294	}
3295}
3296 
3297void ccv_cnnp_model_set_compile_params(ccv_cnnp_model_t* const model, const ccv_nnc_symbolic_graph_compile_param_t compile_params)
3298{
3299	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
3300	assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
 (compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 3300, __extension__ __PRETTY_FUNCTION__); }));
3301	compiled_data->compile_params = compile_params;
3302}
3303 
3304void ccv_cnnp_model_dot(const ccv_cnnp_model_t* const model, const int flags, FILE** const outs, const int out_size)
3305{
3306	if (model->graph && out_size > 0)
3307		ccv_nnc_symbolic_graph_dot(model->graph, flags, outs[0]);
3308	if (model->compiled_data && model->compiled_data->graph && out_size > 1)
3309		ccv_nnc_graph_dot(model->compiled_data->graph, flags, outs[1]);
3310	if (model->compiled_data && model->compiled_data->backward.accum && out_size > 2)
3311		ccv_nnc_graph_dot(model->compiled_data->backward.accum, flags, outs[2]);
3312	if (model->compiled_data && model->compiled_data->apply_gradients.graph && out_size > 3)
3313		ccv_nnc_graph_dot(model->compiled_data->apply_gradients.graph, flags, outs[3]);
3314}
3315 
3316void ccv_cnnp_model_format(const ccv_cnnp_model_t* const model, const ccv_nnc_symbolic_graph_format_f format_fn, void* const context)
3317{
3318	if (model->graph)
3319		ccv_nnc_symbolic_graph_format(model->graph, 0, 0, 0, 0, format_fn, context);
3320}
3321 
3322static void _ccv_cnnp_compiled_data_free(const ccv_cnnp_model_t* const model, ccv_cnnp_compiled_data_t* const compiled_data)
3323{
3324	int i;
3325	const int parameter_size = compiled_data->parameters->rnum;
3326	ccv_array_free(compiled_data->parameters);
3327	if (compiled_data->parameter_flags)
3328		ccfreefree(compiled_data->parameter_flags);
3329	const int internal_size = compiled_data->internals->rnum;
3330	ccv_array_free(compiled_data->internals);
3331	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", 3331, __extension__ __PRETTY_FUNCTION__
); }));
3332	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", 3332, __extension__ __PRETTY_FUNCTION__
); }));
3333	for (i = 0; i < parameter_size; i++)
3334		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))));
3335	ccv_array_free(compiled_data->ids.parameters);
3336	for (i = 0; i < internal_size; i++)
3337		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))));
3338	ccv_array_free(compiled_data->ids.internals);
3339	const int parallel_count = compiled_data->parallel_count > 0 ? compiled_data->parallel_count : _ccv_cnnp_model_root_parallel_count(model);
3340	if (compiled_data->tensors.parameters)
3341	{
3342		for (i = 0; i < parameter_size * parallel_count; i++)
3343			// If it is not marked as not belonging, we can free it.
3344			if (!((uintptr_t)compiled_data->tensors.parameters[i] & (uintptr_t)1))
3345				if (compiled_data->tensors.parameters[i])
3346					ccv_nnc_tensor_free(compiled_data->tensors.parameters[i]);
3347		for (i = 0; i < internal_size * parallel_count; i++)
3348			if (compiled_data->tensors.internals[i])
3349				ccv_nnc_tensor_free(compiled_data->tensors.internals[i]);
3350		ccfreefree(compiled_data->tensors.parameters);
3351	}
3352	if (compiled_data->tensors.gradients)
3353	{
3354		for (i = 0; i < parameter_size * parallel_count; i++)
3355		{
3356			if (compiled_data->tensors.gradients[i])
3357				ccv_nnc_tensor_free(compiled_data->tensors.gradients[i]);
3358			if (compiled_data->tensors.accum_gradients[i])
3359				ccv_nnc_tensor_free(compiled_data->tensors.accum_gradients[i]);
3360		}
3361		ccfreefree(compiled_data->tensors.gradients);
3362	}
3363	if (compiled_data->minimize.parameters)
3364	{
3365		for (i = 0; i < compiled_data->minimize.parameters->rnum; i++)
3366			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))));
3367		ccv_array_free(compiled_data->minimize.parameters);
3368	}
3369	if (compiled_data->rewindables)
3370		ccv_array_free(compiled_data->rewindables);
3371	if (compiled_data->tensors_init.v)
3372		ccfreefree(CCV_NNC_INIT_V(compiled_data->tensors_init.v)((uint32_t*)((uintptr_t)(compiled_data->tensors_init.v) &
 ~(uintptr_t)1)));
3373	if (compiled_data->evaluate.tos)
3374		ccfreefree(compiled_data->evaluate.tos);
3375	compiled_data->evaluate.tos = 0;
3376	if (compiled_data->stream_map)
3377	{
3378		khiter_t k;
3379		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)
3380		{
3381			if (!kh_exist(compiled_data->stream_map, k)(!(((compiled_data->stream_map)->flags[(k)>>4]>>
(((k)&0xfU)<<1))&3)))
3382				continue;
3383			ccv_nnc_stream_context_t* const stream = kh_val(compiled_data->stream_map, k)((compiled_data->stream_map)->vals[k]);
3384			ccv_nnc_stream_context_free(stream);
3385		}
3386		kh_destroy(stream_map, compiled_data->stream_map)kh_destroy_stream_map(compiled_data->stream_map);
3387	}
3388	_ccv_cnnp_compiled_data_graph_free(compiled_data);
3389	_ccv_cnnp_compiled_data_gradient_free(compiled_data);
3390	_ccv_cnnp_compiled_data_backward_free(compiled_data);
3391	_ccv_cnnp_compiled_data_apply_gradients_free(compiled_data);
3392	if (compiled_data->gradient_checkpoints)
3393	{
3394		for (i = 0; i < compiled_data->gradient_checkpoints->rnum; i++)
3395		{
3396			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)));
3397			assert(checkpoint->inputs)((void) sizeof ((checkpoint->inputs) ? 1 : 0), __extension__
 ({ if (checkpoint->inputs) ; else __assert_fail ("checkpoint->inputs"
, "ccv_cnnp_model.c", 3397, __extension__ __PRETTY_FUNCTION__
); }));
3398			ccfreefree(checkpoint->inputs);
3399			ccv_array_free(checkpoint->tensor_symbols);
3400		}
3401		ccv_array_free(compiled_data->gradient_checkpoints);
3402	}
3403	ccv_nnc_xpu_alloc_destroy(&compiled_data->xpu_alloc);
3404	ccfreefree(compiled_data);
3405}
3406 
3407void ccv_cnnp_model_free(ccv_cnnp_model_t* const model)
3408{
3409	ccv_cnnp_model_deinit(model);
3410	if (model->isa->dealloc)
3411		model->isa->dealloc(model);
3412	if (model->io)
3413	{
3414		int i;
3415		for (i = 0; i < model->io->rnum; i++)
3416		{
3417			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)));
3418			if (model_io->outgoings)
3419				ccv_array_free(model_io->outgoings);
3420			if (model_io->incomings)
3421				ccv_array_free(model_io->incomings);
3422			if (model_io->dependencies)
3423				ccv_array_free(model_io->dependencies);
3424			ccfreefree(model_io);
3425		}
3426		ccv_array_free(model->io);
3427	}
3428	if (model->parameter_indices)
3429		ccv_array_free(model->parameter_indices);
3430	if (model->inputs)
3431		ccfreefree(model->inputs);
3432	if (model->graph)
3433		ccv_nnc_symbolic_graph_free(model->graph);
3434	if (model->compiled_data)
3435		_ccv_cnnp_compiled_data_free(model, model->compiled_data);
3436	if (model->name)
3437		ccfreefree(model->name);
3438	ccfreefree(model);
3439}
3440 
3441void ccv_cnnp_model_cancel(ccv_cnnp_model_t* const model)
3442{
3443	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
3444	if (!compiled_data)
3445		return;
3446	if (compiled_data->graph)
3447		ccv_nnc_graph_cancel(compiled_data->graph);
3448	if (compiled_data->apply_gradients.graph)
3449		ccv_nnc_graph_cancel(compiled_data->apply_gradients.graph);
3450}
3451 
3452void ccv_cnnp_model_set_flags(ccv_cnnp_model_t* const model, const int flags)
3453{
3454	model->exec_flags = flags;
3455}
3456 
3457int ccv_cnnp_model_flags(ccv_cnnp_model_t* const model)
3458{
3459	return model->exec_flags;
3460}