ccv_cnnp_model.c

Bug Summary

File:	nnc/ccv_cnnp_model.c
Warning:	line 2728, 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/2025-12-12-091359-782764-1 -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#ifdef HAVE_CUDA1
8#include "gpu/ccv_nnc_compat.h"
9#endif
10 
11// MARK - Level-5 API
12 
13ccv_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)
14{
15	if (!model->io)
16		model->io = ccv_array_new(sizeof(ccv_cnnp_model_io_t), 1, 0);
17	ccv_cnnp_model_io_t model_io = ccmallocmalloc(sizeof(struct ccv_cnnp_model_io_s) + sizeof(ccv_nnc_tensor_symbol_t) * model->output_size);
18	model_io->param_ref = 0;
19	model_io->param_sel = 0;
20	model_io->visit = 0;
21	model_io->model = model;
22	model_io->dependencies = 0;
23	model_io->dependents = 0;
24	model_io->outgoings = 0;
25	model_io->outputs = (ccv_nnc_tensor_symbol_t*)(model_io + 1);
26	ccv_array_push(model->io, &model_io);
27	if (input_size > 0)
28	{
29		model_io->incomings = ccv_array_new(sizeof(ccv_cnnp_model_io_t), input_size, 0);
30		ccv_array_resize(model_io->incomings, input_size);
31		int i;
32		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);
33		for (i = 0; i < input_size; i++)
34		{
35			if (!inputs[i]->outgoings)
36				inputs[i]->outgoings = ccv_array_new(sizeof(ccv_cnnp_model_io_t), 1, 0);
37			ccv_array_push(inputs[i]->outgoings, &model_io);
38		}
39	} else {
40		model_io->incomings = 0;
41	}
42	return model_io;
43}
44 
45void ccv_cnnp_model_add_dependencies(ccv_cnnp_model_io_t model_io, const ccv_cnnp_model_io_t* const dependencies, const int dependency_size)
46{
47	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", 47, __extension__ __PRETTY_FUNCTION__);
 }));
48	if (!model_io->dependencies)
49		model_io->dependencies = ccv_array_new(sizeof(ccv_cnnp_model_io_t), dependency_size, 0);
50	int i, j;
51	for (i = 0; i < dependency_size; i++)
52	{
53		int flag = 0;
54		// Check if it is already exist or not.
55		for (j = 0; !flag && j < model_io->dependencies->rnum; j++)
56			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])
57				flag = 1;
58		if (flag)
59			continue;
60		ccv_array_push(model_io->dependencies, dependencies + i);
61		++dependencies[i]->dependents;
62	}
63}
64 
65int ccv_cnnp_model_output_size(const ccv_cnnp_model_t* const model)
66{
67	return model->output_size;
68}
69 
70int ccv_cnnp_model_is_trainable(const ccv_cnnp_model_t* const model)
71{
72	// If the model is compiled, it is default to 1 unless it is not.
73	if (model->compiled_data)
74		return model->is_trainable >= 0 ? model->is_trainable : 1;
75	return model->is_trainable;
76}
77 
78ccv_cnnp_model_io_t ccv_cnnp_model_parameters(ccv_cnnp_model_t* const model, const int selector, const int index)
79{
80	if (!model->io)
81		model->io = ccv_array_new(sizeof(ccv_cnnp_model_io_t), 1, 0);
82	ccv_cnnp_model_io_t model_io = ccmallocmalloc(sizeof(struct ccv_cnnp_model_io_s));
83	model_io->param_ref = index >= 0 ? index + 1 : ALL_PARAMETERS-1;
84	model_io->param_sel = selector >= 0 ? selector + 1 : ALL_PARAMETERS-1;
85	model_io->visit = 0;
86	model_io->model = model;
87	model_io->outputs = 0;
88	model_io->dependencies = 0;
89	model_io->dependents = 0;
90	model_io->incomings = 0;
91	model_io->outgoings = 0;
92	ccv_array_push(model->io, &model_io);
93	return model_io;
94}
95 
96void ccv_cnnp_model_notify_hook(ccv_cnnp_model_t* const model, ccv_cnnp_model_notify_f func, void* const context)
97{
98	model->notify_hook.func = func;
99	model->notify_hook.context = context;
100}
101 
102void ccv_cnnp_model_notify(const ccv_cnnp_model_t* const model, const int tag, void* const payload)
103{
104	if (model->notify_hook.func)
105		model->notify_hook.func(model, tag, payload, model->notify_hook.context);
106	if (model->isa->notify)
107		model->isa->notify(model, tag, payload);
108}
109 
110static int _ccv_nnc_array_dedup_graph_exec_symbols(ccv_nnc_graph_exec_symbol_t* const graph_exec_symbols, int graph_exec_symbol_size)
111{
112	int i, j;
113	for (i = 0; i < graph_exec_symbol_size; i++)
114	{
115		ccv_nnc_graph_exec_symbol_t* const graph_exec_symbol = graph_exec_symbols + i;
116		// Check whether this tensor symbol has any duplicate.
117		for (j = i + 1; j < graph_exec_symbol_size;)
118		{
119			ccv_nnc_graph_exec_symbol_t* const other_symbol = graph_exec_symbols + j;
120			// If there is a same tensor symbol, remove it.
121			if (other_symbol->d == graph_exec_symbol->d && other_symbol->graph == graph_exec_symbol->graph)
122			{
123				if (j + 1 < graph_exec_symbol_size)
124					*other_symbol = graph_exec_symbols[graph_exec_symbol_size - 1];
125				--graph_exec_symbol_size;
126				continue;
127			}
128			++j;
129		}
130	}
131	return graph_exec_symbol_size;
132}
133 
134void ccv_cnnp_model_add_to_array(void* const context, const ccv_nnc_tensor_symbol_t symbol, const int is_trainable)
135{
136	ccv_cnnp_model_add_to_array_context_t* const add_to_array_context = (ccv_cnnp_model_add_to_array_context_t*)context;
137	ccv_cnnp_model_t* const model = add_to_array_context->sequence->model;
138	int i;
139	if (add_to_array_context->add_parameter_indices && !model->parameter_indices)
140		model->parameter_indices = ccv_array_new(sizeof(int), 0, 0);
141	for (i = 0; i < add_to_array_context->symbols->rnum; i++)
142	{
143		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)));
144		if (other_symbol.d == symbol.d && other_symbol.graph == symbol.graph)
145		{
146			// Only add to parameter_indices if it is trainable.
147			if (add_to_array_context->add_parameter_indices)
148				ccv_array_add_unique_int(model->parameter_indices, i);
149			// Found it, return, don't add it.
150			return;
151		}
152	}
153	// Only add to parameter_indices if it is trainable.
154	if (add_to_array_context->add_parameter_indices)
155		ccv_array_push(model->parameter_indices, &add_to_array_context->symbols->rnum);
156	// This is a new one, no need to add_unique_int, it is unique.
157	ccv_array_push(add_to_array_context->symbols, &symbol);
158	if (add_to_array_context->trainables)
159		ccv_array_push(add_to_array_context->trainables, &is_trainable);
160	char id[2048];
161	id[0] = add_to_array_context->prefix;
162	id[1] = '-';
163	int total_len = 2;
164	for (i = 0; i < add_to_array_context->sequence->sequences->rnum; i++)
165	{
166		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)));
167		int len;
168		if (name->name && name->name[0] != '\0')
169			len = snprintf(id + total_len, 2048 - total_len, "%s-%d-", name->name, name->sequence);
170		else
171			len = snprintf(id + total_len, 2048 - total_len, "%d-", name->sequence);
172		total_len += len;
173		if (total_len >= 2047)
174			break;
175	}
176	if (total_len < 2047)
177		total_len += snprintf(id + total_len, 2048 - total_len, "%d", add_to_array_context->sequence->it);
178	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", 178, __extension__ __PRETTY_FUNCTION__)
; }));
179	char *heap_id = (char*)ccmallocmalloc(total_len + 1);
180	memcpy(heap_id, id, total_len + 1);
181	ccv_array_push(add_to_array_context->ids, &heap_id);
182	++add_to_array_context->sequence->it;
183}
184 
185static void _ccv_cnnp_compiled_data_init(ccv_cnnp_compiled_data_t* const compiled_data, const int output_size, ccv_array_t* const gradient_checkpoints)
186{
187	compiled_data->f = compiled_data->fits + output_size;
188	compiled_data->xpu_alloc.mp_hdr = -1;
189	compiled_data->xpu_alloc.freed = kh_init(dy_str)kh_init_dy_str();
190	compiled_data->xpu_alloc.allocd = kh_init(dy_alloc)kh_init_dy_alloc();
191	compiled_data->gradient_checkpoints = gradient_checkpoints;
192}
193 
194typedef struct {
195	void* old_graph_exec_symbol_new_hook_context;
196	ccv_nnc_graph_exec_symbol_new_hook_f old_graph_exec_symbol_new_hook;
197	ccv_nnc_symbolic_graph_t* graph;
198	ccv_cnnp_model_build_data_t* build_data;
199} ccv_cnnp_model_set_exec_flags_context_t;
200 
201static 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)
202{
203	ccv_cnnp_model_set_exec_flags_context_t* flags_context = (ccv_cnnp_model_set_exec_flags_context_t*)context;
204	if (flags_context->build_data->exec_flags)
205		ccv_nnc_graph_exec_symbol_set_flags(flags_context->graph, symbol, flags_context->build_data->exec_flags);
206	if (flags_context->old_graph_exec_symbol_new_hook)
207		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);
208}
209 
210static 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)
211{
212	assert(model->graph)((void) sizeof ((model->graph) ? 1 : 0), __extension__ ({ if
 (model->graph) ; else __assert_fail ("model->graph", "ccv_cnnp_model.c"
, 212, __extension__ __PRETTY_FUNCTION__); }));
213	model->inputs = ccmallocmalloc(sizeof(ccv_nnc_tensor_symbol_t) * input_size);
214	int i;
215	for (i = 0; i < input_size; i++)
216		model->inputs[i] = ccv_nnc_tensor_symbol_new(model->graph, inputs[i], 0);
217	ccv_array_t* const parameters = ccv_array_new(sizeof(ccv_nnc_tensor_symbol_t), 0, 0);
218	ccv_array_t* const parameter_ids = ccv_array_new(sizeof(char*), 0, 0);
219	ccv_array_t* const parameter_trainables = ccv_array_new(sizeof(int), 0, 0);
220	ccv_cnnp_model_sequence_t model_sequence = {
221		.bank = kh_init(ccv_cnnp_model_name_bank)kh_init_ccv_cnnp_model_name_bank()
222	};
223	ccv_cnnp_model_add_to_array_context_t add_to_parameter_context = {
224		.add_parameter_indices = 1,
225		.prefix = 't',
226		.sequence = &model_sequence,
227		.symbols = parameters,
228		.ids = parameter_ids,
229		.trainables = parameter_trainables,
230	};
231	ccv_array_t* const internals = ccv_array_new(sizeof(ccv_nnc_tensor_symbol_t), 0, 0);
232	ccv_array_t* const internal_ids = ccv_array_new(sizeof(char*), 0, 0);
233	ccv_cnnp_model_add_to_array_context_t add_to_output_context = {
234		.add_parameter_indices = 0,
235		.prefix = 'r',
236		.sequence = &model_sequence,
237		.symbols = internals,
238		.ids = internal_ids,
239		.trainables = 0,
240	};
241	ccv_cnnp_model_build_data_t build_data = {
242		.exec_flags = 0,
243		.is_trainable = model->is_trainable >= 0 ? model->is_trainable : 1,
244		.model_sequence = &model_sequence,
245		.add_to_array = ccv_cnnp_model_add_to_array,
246		.parameters = parameters,
247		.context = {
248			.add_to_parameter = &add_to_parameter_context,
249			.add_to_output = &add_to_output_context,
250		},
251		.gradient_checkpoints = 0,
252	};
253	model->data = &build_data;
254	ccv_cnnp_model_set_exec_flags_context_t flags_context = {
255		.graph = model->graph,
256		.build_data = &build_data,
257		.old_graph_exec_symbol_new_hook = 0,
258		.old_graph_exec_symbol_new_hook_context = 0
259	};
260	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);
261	ccv_cnnp_model_build(model, model->graph, model->inputs, input_size, 0, 0);
262	// Reset back to previous hook.
263	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);
264	for (i = 0; i < model->output_size; i++)
265	{
266		const ccv_nnc_tensor_symbol_t output = model->outputs[i];
267		const ccv_nnc_tensor_symbol_t alias_to = ccv_nnc_tensor_symbol_alias_to(model->graph, output);
268		if (alias_to.d == CCV_NNC_NO_TENSOR_SYMBOL)
269			continue;
270		// 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
271		// 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
272		// honest, because we cannot handle cases of alias is part of the original tensor but bind differently).
273		const ccv_nnc_tensor_param_t output_params = ccv_nnc_tensor_symbol_params(model->graph, output);
274		model->outputs[i] = ccv_nnc_tensor_symbol_new(model->graph, output_params, 0);
275		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");
276		ccv_nnc_graph_exec_symbol_set_flags(model->graph, make_contiguous, CCV_NNC_GRAPH_EXEC_DISABLE_OPT);
277	}
278	model->data = 0;
279	kh_destroy(ccv_cnnp_model_name_bank, model_sequence.bank)kh_destroy_ccv_cnnp_model_name_bank(model_sequence.bank);
280	if (model_sequence.sequences)
281		ccv_array_free(model_sequence.sequences);
282	// Check if there are parameters that are not trainables. If there are, we will allocate uint64 bitmap to record that.
283	int not_trainables = 0;
284	// Assert no parameter is alias.
285	for (i = 0; i < parameters->rnum; i++)
286	{
287		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)));
288		const ccv_nnc_tensor_symbol_t alias_to = ccv_nnc_tensor_symbol_alias_to(parameter.graph, parameter);
289		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", 289, __extension__ __PRETTY_FUNCTION__)
; })); // Cannot find the one alias to.
290		if (*(int*)ccv_array_get(parameter_trainables, i)((void*)(((char*)((parameter_trainables)->data)) + (size_t
)(parameter_trainables)->rsize * (size_t)(i))) == 0)
291			not_trainables = 1;
292	}
293	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", 293, __extension__ __PRETTY_FUNCTION__)
; }));
294	uint64_t* parameter_flags = 0;
295	if (not_trainables)
296	{
297		parameter_flags = (uint64_t*)cccalloccalloc(((parameters->rnum + 63) >> 6), sizeof(uint64_t));
298		for (i = 0; i < parameter_trainables->rnum; i++)
299			if (*(int*)ccv_array_get(parameter_trainables, i)((void*)(((char*)((parameter_trainables)->data)) + (size_t
)(parameter_trainables)->rsize * (size_t)(i))))
300				parameter_flags[i >> 6] |= ((uint64_t)1 << (i & 63));
301	}
302	ccv_array_free(parameter_trainables);
303	// Assert no internal is alias.
304	for (i = 0; i < internals->rnum; i++)
305	{
306		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)));
307		const ccv_nnc_tensor_symbol_t alias_to = ccv_nnc_tensor_symbol_alias_to(internal.graph, internal);
308		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", 308, __extension__ __PRETTY_FUNCTION__)
; })); // Cannot find the one alias to.
309	}
310	const int output_size = model->output_size;
311	ccv_nnc_graph_exec_symbol_autogen(model->graph, 0, 0, CCV_NNC_AUTOGEN_ALL_EXECS | CCV_NNC_AUTOGEN_SOURCES_AND_DESTINATIONS);
312	const int parameters_rnum = parameters->rnum;
313	if (input_size > 0)
314	{
315		ccv_array_resize(parameters, parameters_rnum + input_size);
316		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));
317	}
318	ccv_nnc_symbolic_graph_simplify(model->graph,
319		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)
320			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)
321			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)
322			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),
323		ccv_array_get(parameters, 0)((void*)(((char*)((parameters)->data)) + (size_t)(parameters
)->rsize * (size_t)(0))), parameters_rnum + input_size,
324		model->outputs, output_size,
325		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));
326	ccv_nnc_graph_exec_symbol_autogen(model->graph, 0, 0, CCV_NNC_AUTOGEN_SOURCES_AND_DESTINATIONS);
327	// Size it down.
328	parameters->rnum = parameters_rnum;
329	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));
330	_ccv_cnnp_compiled_data_init(compiled_data, output_size, build_data.gradient_checkpoints);
331	const int evaluate_to_size = compiled_data->evaluate.to_size = ccv_nnc_symbolic_graph_destination_size(model->graph);
332	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", 332, __extension__ __PRETTY_FUNCTION__)
; }));
333	compiled_data->evaluate.tos = ccmallocmalloc(sizeof(ccv_nnc_graph_exec_symbol_t) * evaluate_to_size);
334	memcpy(compiled_data->evaluate.tos, ccv_nnc_symbolic_graph_destinations(model->graph), sizeof(ccv_nnc_graph_exec_symbol_t) * evaluate_to_size);
335	compiled_data->loss = loss;
336	if (loss.cmd == CCV_NNC_NOOP)
337	{
338		// If no loss function provided, there is no fits.
339		for (i = 0; i < output_size; i++)
340		{
341			compiled_data->fits[i] = NO_TENSOR_SYMBOL(const ccv_nnc_tensor_symbol_t){.d = CCV_NNC_NO_TENSOR_SYMBOL
};
342			const ccv_nnc_tensor_symbol_t alias_to = ccv_nnc_tensor_symbol_alias_to(model->graph, model->outputs[i]);
343			if (alias_to.d < 0)
344				compiled_data->f[i] = model->outputs[i];
345			else { // We cannot differentiate against an alias, therefore, we have to verify this output is full, and we can diff against the original.
346				int ofs[CCV_NNC_MAX_DIM_ALLOC(12)];
347				int inc[CCV_NNC_MAX_DIM_ALLOC(12)];
348				ccv_nnc_tensor_symbol_alias_params(model->graph, model->outputs[i], ofs, inc);
349				int j;
350				for (j = 0; j < CCV_NNC_MAX_DIM_ALLOC(12); j++)
351					{ 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"
, 351, __extension__ __PRETTY_FUNCTION__); })); } // There is no ofs.
352				compiled_data->f[i] = alias_to; // Unfortunately, I cannot assert the size yet.
353			}
354		}
355	} else {
356		for (i = 0; i < output_size; i++)
357		{
358			const ccv_nnc_tensor_param_t info = ccv_nnc_tensor_symbol_params(model->graph, model->outputs[i]);
359			const ccv_nnc_tensor_symbol_t fit = compiled_data->fits[i] = ccv_nnc_tensor_symbol_new(model->graph, info, 0);
360			compiled_data->f[i] = ccv_nnc_tensor_symbol_new(model->graph, ccv_nnc_tensor_auto, 0);
361			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);
362		}
363	}
364	ccv_nnc_graph_exec_symbol_autogen(model->graph, 0, 0, CCV_NNC_AUTOGEN_ALL_EXECS | CCV_NNC_AUTOGEN_SOURCES_AND_DESTINATIONS);
365	ccv_nnc_symbolic_graph_simplify(model->graph,
366		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.
367		0, 0, // No need to provide binds at this point.
368		compiled_data->f, model->output_size,
369		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));
370	ccv_nnc_graph_exec_symbol_autogen(model->graph, 0, 0, CCV_NNC_AUTOGEN_SOURCES_AND_DESTINATIONS);
371	// If inputs are from GPU, stream type is GPU.
372	compiled_data->parameters = parameters;
373	compiled_data->parameter_flags = parameter_flags;
374	compiled_data->internals = internals;
375	compiled_data->ids.parameters = parameter_ids;
376	compiled_data->ids.internals = internal_ids;
377	ccv_cnnp_model_gradient_checkpoints_cleanup_after_build(compiled_data, model->graph);
378}
379 
380static 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)
381{
382	ccv_array_t* const stack = (ccv_array_t*)context;
383	ccv_array_push(stack, &symbol.d);
384}
385 
386static 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)
387{
388	const ccv_nnc_tensor_symbol_t src_symbol = {
389		.d = src_index,
390		.graph = src_graph
391	};
392	const ccv_nnc_tensor_symbol_t dest_symbol = {
393		.d = dest_index,
394		.graph = dest_graph
395	};
396	const ccv_nnc_tensor_param_t params = ccv_nnc_tensor_symbol_params(src_graph, src_symbol);
397	ccv_nnc_tensor_symbol_set(dest_graph, dest_symbol, params);
398	int ofs[CCV_NNC_MAX_DIM_ALLOC(12)];
399	int inc[CCV_NNC_MAX_DIM_ALLOC(12)];
400	if (0 == ccv_nnc_tensor_symbol_alias_params(src_graph, src_symbol, ofs, inc))
401		ccv_nnc_tensor_symbol_alias_set(dest_graph, dest_symbol, ofs, inc);
402}
403 
404static 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)
405{
406	const ccv_nnc_tensor_symbol_t src_symbol = {
407		.d = src_index,
408		.graph = src_graph
409	};
410	const ccv_nnc_tensor_param_t src_params = ccv_nnc_tensor_symbol_params(src_graph, src_symbol);
411	const ccv_nnc_tensor_symbol_t dest_symbol = {
412		.d = dest_index,
413		.graph = dest_graph
414	};
415	const ccv_nnc_tensor_param_t dest_params = ccv_nnc_tensor_symbol_params(dest_graph, dest_symbol);
416	return memcmp(src_params.dim, dest_params.dim, sizeof(src_params.dim)) == 0;
417}
418 
419static 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);
420static void _ccv_cnnp_compiled_data_graph_free(ccv_cnnp_compiled_data_t* const compiled_data);
421 
422typedef struct {
423	int parallel_count;
424	ccv_nnc_symbolic_graph_t* graph;
425	ccv_nnc_graph_exec_arena_t* graph_exec_arena;
426} ccv_nnc_graph_exec_update_t;
427 
428static 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)
429{
430	ccv_nnc_graph_exec_update_t* const graph_exec_update = (ccv_nnc_graph_exec_update_t*)context;
431	ccv_nnc_graph_exec_arena_t* const graph_exec_arena = graph_exec_update->graph_exec_arena;
432	ccv_nnc_graph_exec_t graph_exec = ccv_nnc_graph_exec_from_symbol(graph_exec_arena, symbol);
433	ccv_nnc_graph_exec_set(graph_exec.graph, graph_exec, cmd);
434	ccv_nnc_graph_exec_set_hint(graph_exec.graph, graph_exec, hint);
435	const ccv_nnc_symbolic_graph_t* const graph = graph_exec_update->graph;
436	const int parallel_count = graph_exec_update->parallel_count;
437	int i;
438	for (i = 1; i < parallel_count; i++)
439	{
440		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));
441		if (!CCV_NO_GRAPH_EXEC(copy)((copy).graph == 0))
442		{
443			ccv_nnc_graph_exec_set(copy.graph, copy, cmd);
444			ccv_nnc_graph_exec_set_hint(copy.graph, copy, hint);
445		}
446	}
447}
448 
449void 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)
450{
451	assert(model->graph)((void) sizeof ((model->graph) ? 1 : 0), __extension__ ({ if
 (model->graph) ; else __assert_fail ("model->graph", "ccv_cnnp_model.c"
, 451, __extension__ __PRETTY_FUNCTION__); }));
452	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", 452, __extension__ __PRETTY_FUNCTION__)
; }));
453	assert(!init->graph)((void) sizeof ((!init->graph) ? 1 : 0), __extension__ ({ if
 (!init->graph) ; else __assert_fail ("!init->graph", "ccv_cnnp_model.c"
, 453, __extension__ __PRETTY_FUNCTION__); }));
454	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
455	init->graph = ccv_nnc_symbolic_graph_new();
456	ccv_array_t* const stack = ccv_array_new(sizeof(int), 0, 0);
457	ccv_nnc_graph_exec_symbol_new_hook(init->graph, _ccv_cnnp_graph_push_graph_exec_symbol, stack, 0);
458	_ccv_cnnp_model_compile(init, inputs, input_size, compiled_data->loss);
459	init->parallel_count = model->parallel_count;
460	init->memory_compression = model->memory_compression;
461	init->memory_reduction = model->memory_reduction;
462	init->gradient_checkpointing = model->gradient_checkpointing;
463	init->compiled_data->stream_type = model->compiled_data->stream_type;
464	init->compiled_data->minimize.minimizer = model->compiled_data->minimize.minimizer;
465	init->compiled_data->minimize.max_saved_aux_size = model->compiled_data->minimize.max_saved_aux_size;
466	if (model->compiled_data->gradient_mode != CCV_CNNP_COMPILED_DATA_GRADIENT_NONE)
467		_ccv_cnnp_model_gradient_init(init, model->compiled_data->gradient_mode, model->compiled_data->disable_outgrad, 0, 0);
468	ccv_nnc_graph_exec_symbol_new_hook(init->graph, 0, 0, 0);
469	ccv_nnc_symbolic_graph_tensor_auto(init->graph, TRAVERSE_FULL0,0,0,0);
470	int i, j;
471	// Verify parameters, internals and saved_aux in both graph has the same dimensionality.
472	for (i = 0; i < compiled_data->parameters->rnum; i++)
473	{
474		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;
475		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", 475, __extension__ __PRETTY_FUNCTION__)
; }));
476	}
477	for (i = 0; i < compiled_data->internals->rnum; i++)
478	{
479		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;
480		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", 480, __extension__ __PRETTY_FUNCTION__)
; }));
481	}
482	// Update inputs.
483	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", 483, __extension__ __PRETTY_FUNCTION__)
; }));
484	for (i = 0; i < model->input_size; i++)
485		if (model->inputs[i].d >= 0)
486		{
487			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", 487, __extension__ __PRETTY_FUNCTION__)
; }));
488			_ccv_nnc_tensor_symbol_reinit(init->graph, model->graph, init->inputs[i].d, model->inputs[i].d);
489		}
490	// Update outputs.
491	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", 491, __extension__ __PRETTY_FUNCTION__)
; }));
492	for (i = 0; i < model->output_size; i++)
493	{
494		if (model->outputs[i].d >= 0)
495		{
496			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", 496, __extension__
 __PRETTY_FUNCTION__); }));
497			_ccv_nnc_tensor_symbol_reinit(init->graph, model->graph, init->outputs[i].d, model->outputs[i].d);
498		}
499		if (model->outputs[i].d != model->compiled_data->f[i].d)
500		{
501			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", 501, __extension__ __PRETTY_FUNCTION__)
; }));
502			if (model->compiled_data->f[i].d >= 0)
503			{
504				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", 504, __extension__ __PRETTY_FUNCTION__)
; }));
505				_ccv_nnc_tensor_symbol_reinit(init->graph, model->graph, init->compiled_data->f[i].d, model->compiled_data->f[i].d);
506			}
507		}
508	}
509	// Go through the graph to set tensor on matching symbols
510	for (i = 0; i < stack->rnum; i++)
511	{
512		const int d = *(int*)ccv_array_get(stack, i)((void*)(((char*)((stack)->data)) + (size_t)(stack)->rsize
 * (size_t)(i)));
513		// If exceed range, skip.
514		if (d >= ccv_nnc_graph_exec_symbol_count(init->graph) ||
515			d >= ccv_nnc_graph_exec_symbol_count(model->graph))
516			continue;
517		const ccv_nnc_graph_exec_symbol_t src_symbol = {
518			.d = d,
519			.graph = init->graph
520		};
521		const ccv_nnc_graph_exec_symbol_t dest_symbol = {
522			.d = d,
523			.graph = model->graph
524		};
525		const ccv_nnc_cmd_t src_cmd = ccv_nnc_graph_exec_symbol_cmd(init->graph, src_symbol);
526		const ccv_nnc_cmd_t dest_cmd = ccv_nnc_graph_exec_symbol_cmd(model->graph, dest_symbol);
527		// If the name doesn't match, skip.
528		if (dest_cmd.cmd != src_cmd.cmd && src_cmd.cmd != CCV_NNC_NOOP)
529			continue;
530		// Now get all the inputs and outputs, if matches, set them.
531		const int* src_inputs;
532		int src_input_size;
533		const int* src_outputs;
534		int src_output_size;
535		ccv_nnc_graph_exec_symbol_io(init->graph, src_symbol, &src_inputs, &src_input_size, &src_outputs, &src_output_size);
536		const int* dest_inputs;
537		int dest_input_size;
538		const int* dest_outputs;
539		int dest_output_size;
540		ccv_nnc_graph_exec_symbol_io(model->graph, dest_symbol, &dest_inputs, &dest_input_size, &dest_outputs, &dest_output_size);
541		// We may have unmatched input / output size because this is the minimizer and it has
542		// different saved_aux (for example, when we shrunk with CMD_NOOP).
543		if (src_input_size != dest_input_size)
544			continue;
545		if (src_output_size != dest_output_size)
546			continue;
547		ccv_nnc_graph_exec_symbol_set(model->graph, dest_symbol, src_cmd);
548		// There may be mismatches of the source tensor symbols and destination tensor symbols. The reason is because
549		// we may later passed-in the minimizer, therefore, we may allocate tensors for minimizer later in the original
550		// graph whereas in the newly created graph, it is streamlined (the minimizer exists from the beginning). That
551		// will make the order of tensor symbols creation different, therefore, exact which tensor is which wrong as
552		// well. However, set a new minimizer won't change the exec symbol ordering, because we never create new exec
553		// symbols after gradient init step. Changing a new minimizer just updated that exec symbols setting, it is not
554		// a new exec symbol.
555		for (j = 0; j < src_input_size; j++)
556			if (src_inputs[j] >= 0)
557				_ccv_nnc_tensor_symbol_reinit(init->graph, model->graph, src_inputs[j], dest_inputs[j]);
558		for (j = 0; j < src_output_size; j++)
559			if (src_outputs[j] >= 0)
560				_ccv_nnc_tensor_symbol_reinit(init->graph, model->graph, src_outputs[j], dest_outputs[j]);
561	}
562	ccv_array_free(stack);
563	// After this, we get all tensors in the model graph resolved through tensor_auto.
564	ccv_nnc_symbolic_graph_tensor_auto(model->graph, TRAVERSE_FULL0,0,0,0);
565	// Verify symbols we get matches.
566	const int parameter_size = compiled_data->parameters->rnum;
567	for (i = 0; i < parameter_size; i++)
568		{ 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", 568, __extension__ __PRETTY_FUNCTION__)
; })); }
569	const int internal_size = compiled_data->internals->rnum;
570	for (i = 0; i < internal_size; i++)
571		{ 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", 571, __extension__ __PRETTY_FUNCTION__)
; })); }
572	// Go through compiled data.
573	if (compiled_data->tensor_arena)
574	{
575		const int flag = ccv_nnc_tensor_arena_reinit(compiled_data->tensor_arena, model->graph);
576		if (flag == 0 && compiled_data->graph_exec_arena)
577		{
578			ccv_nnc_graph_exec_reinit(compiled_data->graph_exec_arena, compiled_data->graph, model->graph);
579			// Since we will reinit, if we previously set is_test, we need to set it again.
580			if (compiled_data->is_test)
581			{
582				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; });
583				ccv_nnc_graph_exec_update_t update = {
584					.parallel_count = parallel_count,
585					.graph = model->graph,
586					.graph_exec_arena = compiled_data->graph_exec_arena,
587				};
588				ccv_cnnp_model_set_is_test(model, 1, _ccv_cnnp_cmd_update_for_execs, &update);
589			}
590		} else
591			// Free-up tensor arena & graph exec arena.
592			_ccv_cnnp_compiled_data_graph_free(compiled_data);
593	}
594	// There are other compiled graphs, for accum and apply gradients.
595	// However, the main conclusion is, these absorb operations shouldn't impact parameters.
596	// Thus, it won't impact the shape of gradients (only outgrad). Since for outgrad, we
597	// don't allocate ourselves, it is not a concern. For normal gradients, the shape cannot
598	// be changed otherwise parameters' shape will be meaningless. The same goes to internals.
599	// That is why we don't update these compiled graphs at all this point.
600	// Free the model, we've already "absorbed" it.
601	ccv_cnnp_model_free(init);
602}
603 
604void 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)
605{
606	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", 606, __extension__ __PRETTY_FUNCTION__)
; }));
607	if (model->input_size == 0)
608		model->input_size = input_size;
609	if (!model->graph) // The graph is not compiled yet.
610	{
611		model->graph = ccv_nnc_symbolic_graph_new();
612		_ccv_cnnp_model_compile(model, inputs, input_size, loss);
613		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", 613, __extension__ __PRETTY_FUNCTION__)
; }));
614		int i, flag = 0;
615		for (i = 0; !flag && i < input_size; i++)
616			flag = (CCV_TENSOR_GET_MEMORY(inputs[i].type)((inputs[i].type) & 0x3) == CCV_TENSOR_GPU_MEMORY);
617		// If inputs are from GPU, stream type is GPU.
618		model->compiled_data->stream_type = flag ? CCV_STREAM_CONTEXT_GPU : CCV_STREAM_CONTEXT_CPU;
619		model->compiled_data->minimize.minimizer = minimizer;
620		model->compiled_data->minimize.max_saved_aux_size = ccv_nnc_minimizer_saved_aux_size(minimizer);
621	} else {
622		// Now, finally fill in this part. If the graph is already compiled, we make a copy of the model.
623		// And then absorb the "new model" to the old one.
624		ccv_cnnp_model_t* const init = ccv_cnnp_model_copy(model, model->is_trainable);
625		ccv_cnnp_model_absorb(model, init, inputs, input_size);
626		// Reset minimizer.
627		ccv_cnnp_model_set_minimizer(model, minimizer, 1, 0, 0);
628	}
629}
630 
631ccv_cnnp_model_t* ccv_cnnp_model_copy(const ccv_cnnp_model_t* const model, const int is_trainable)
632{
633	ccv_cnnp_model_t* const new_model = _ccv_cnnp_model_copy(model, 0);
634	new_model->is_trainable = is_trainable;
635	return new_model;
636}
637 
638void ccv_cnnp_model_tensor_auto(ccv_cnnp_model_t* const model, ccv_nnc_tensor_param_t* const outputs, const int output_size)
639{
640	assert(model->graph)((void) sizeof ((model->graph) ? 1 : 0), __extension__ ({ if
 (model->graph) ; else __assert_fail ("model->graph", "ccv_cnnp_model.c"
, 640, __extension__ __PRETTY_FUNCTION__); }));
641	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", 641, __extension__ __PRETTY_FUNCTION__)
; }));
642	ccv_nnc_symbolic_graph_t* const graph = model->graph;
643	ccv_nnc_symbolic_graph_tensor_auto(graph, TRAVERSE_FULL0,0,0,0);
644	int i;
645	for (i = 0; i < output_size; i++)
646	{
647		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", 647, __extension__ __PRETTY_FUNCTION__)
; }));
648		outputs[i] = ccv_nnc_tensor_symbol_params(graph, model->outputs[i]);
649	}
650}
651 
652void ccv_cnnp_model_set_workspace_size(ccv_cnnp_model_t* const model, size_t workspace_size)
653{
654	if (workspace_size == model->workspace_size)
655		return;
656	model->workspace_size = workspace_size;
657	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
658	if (compiled_data && compiled_data->graph)
659		ccv_nnc_graph_autotune(compiled_data->graph, workspace_size, 0, TRAVERSE_FULL0,0,0,0);
660}
661 
662size_t ccv_cnnp_model_workspace_size(ccv_cnnp_model_t* const model)
663{
664	return model->workspace_size;
665}
666 
667void ccv_cnnp_model_set_data_parallel(ccv_cnnp_model_t* const model, const int parallel)
668{
669	if (parallel == 0)
670		model->parallel_count = ccv_nnc_device_count(CCV_STREAM_CONTEXT_GPU);
671	else
672		model->parallel_count = parallel;
673	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
674	if (compiled_data)
675		{ 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", 675, __extension__ __PRETTY_FUNCTION__)
; })); }
676}
677 
678void ccv_cnnp_model_set_max_concurrency(ccv_cnnp_model_t* const model, const int max_stream_count)
679{
680	model->max_stream_count = max_stream_count;
681	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
682	if (compiled_data)
683		{ 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", 683, __extension__ __PRETTY_FUNCTION__)
; })); }
684}
685 
686void ccv_cnnp_model_set_memory_compression(ccv_cnnp_model_t* const model, const int memory_compression)
687{
688	model->memory_compression = memory_compression;
689	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
690	if (compiled_data)
691		{ 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", 691, __extension__ __PRETTY_FUNCTION__)
; })); }
692}
693 
694void ccv_cnnp_model_set_memory_reduction(ccv_cnnp_model_t* const model, const int memory_reduction)
695{
696	model->memory_reduction = memory_reduction;
697	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
698	if (compiled_data)
699		{ 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", 699, __extension__ __PRETTY_FUNCTION__)
; })); }
700}
701 
702void ccv_cnnp_model_set_gradient_checkpointing(ccv_cnnp_model_t* const model, const int gradient_checkpointing)
703{
704	model->gradient_checkpointing = gradient_checkpointing;
705}
706 
707int ccv_cnnp_model_gradient_checkpointing(ccv_cnnp_model_t* const model)
708{
709	return model->gradient_checkpointing;
710}
711 
712typedef struct {
713	int parallel_count;
714	ccv_nnc_symbolic_graph_t* graph;
715	ccv_cnnp_compiled_data_t* compiled_data;
716	ccv_nnc_tensor_arena_t* tensor_arena;
717} ccv_nnc_tensor_init_states_t;
718 
719static int _ccv_cnnp_any_to_init(const ccv_cnnp_compiled_data_t* const compiled_data)
720{
721	int i;
722	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));
723	for (i = 0; i < compiled_data->parameters->rnum; i++)
724	{
725		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;
726		if (!(init_v[d >> 5] & (1u << (d & 0x1f))))
727			return 1;
728	}
729	for (i = 0; i < compiled_data->internals->rnum; i++)
730	{
731		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;
732		if (!(init_v[d >> 5] & (1u << (d & 0x1f))))
733			return 1;
734	}
735	return 0;
736}
737 
738static 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)
739{
740	ccv_nnc_tensor_init_states_t* const tensor_init_states = (ccv_nnc_tensor_init_states_t*)context;
741	ccv_nnc_tensor_arena_t* const tensor_arena = tensor_init_states->tensor_arena;
742	ccv_nnc_tensor_t* const output_tensor = ccv_nnc_tensor_from_symbol(tensor_arena, output_symbol);
743	if (!output_tensor)
744		return;
745	const int d = output_symbol.d;
746	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", 746, __extension__ __PRETTY_FUNCTION__)
; }));
747  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));
748	if (init_v[d >> 5] & (1u << (d & 0x1f)))
749		return;
750	init_v[d >> 5] |= (1u << (d & 0x1f));
751	ccv_nnc_cmd_exec(cmd, hint, flags, &input, input ? 1 : 0, &output_tensor, 1, 0);
752	const ccv_nnc_symbolic_graph_t* const graph = tensor_init_states->graph;
753	const int parallel_count = tensor_init_states->parallel_count;
754	int i;
755	for (i = 1; i < parallel_count; i++)
756	{
757		ccv_nnc_tensor_t* const copy = ccv_nnc_tensor_from_symbol(tensor_arena, ccv_nnc_tensor_symbol_copy(graph, output_symbol, i));
758		if (copy)
759			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);
760	}
761}
762 
763// This method can only handle cases we added new tensors and exec, never delete. This invariant is true because
764// we setup everything (including calling simplify method) in ccv_cnnp_model_compile method, before this rewind setup.
765static void _ccv_cnnp_model_rewind_graph(ccv_cnnp_model_t* const model)
766{
767	assert(model->graph)((void) sizeof ((model->graph) ? 1 : 0), __extension__ ({ if
 (model->graph) ; else __assert_fail ("model->graph", "ccv_cnnp_model.c"
, 767, __extension__ __PRETTY_FUNCTION__); }));
768	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", 768, __extension__ __PRETTY_FUNCTION__)
; }));
769	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
770	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", 770, __extension__
 __PRETTY_FUNCTION__); }));
771	int i;
772	for (i = 0; i < compiled_data->rewindables->rnum; i++)
773	{
774		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)));
775		if (rewind_symbol->type == CCV_CNNP_REWIND_GRAPH_EXEC)
776			ccv_nnc_graph_exec_symbol_free(model->graph, rewind_symbol->graph_exec);
777		else if (rewind_symbol->type == CCV_CNNP_REWIND_TENSOR)
778			ccv_nnc_tensor_symbol_free(model->graph, rewind_symbol->tensor);
779	}
780	ccv_array_clear(compiled_data->rewindables);
781	ccv_nnc_graph_exec_symbol_autogen(model->graph, 0, 0, CCV_NNC_AUTOGEN_SOURCES_AND_DESTINATIONS);
782}
783 
784static 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)
785{
786	const ccv_cnnp_rewind_symbol_t rewind_symbol = {
787		.type = CCV_CNNP_REWIND_TENSOR,
788		.tensor = symbol
789	};
790	ccv_array_t* const rewind_symbols = (ccv_array_t*)context;
791	ccv_array_push(rewind_symbols, &rewind_symbol);
792}
793 
794static 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)
795{
796	const ccv_cnnp_rewind_symbol_t rewind_symbol = {
797		.type = CCV_CNNP_REWIND_TENSOR,
798		.tensor = symbol
799	};
800	ccv_array_t* const rewind_symbols = (ccv_array_t*)context;
801	ccv_array_push(rewind_symbols, &rewind_symbol);
802}
803 
804static 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)
805{
806	const ccv_cnnp_rewind_symbol_t rewind_symbol = {
807		.type = CCV_CNNP_REWIND_GRAPH_EXEC,
808		.graph_exec = symbol
809	};
810	ccv_array_t* const rewind_symbols = (ccv_array_t*)context;
811	ccv_array_push(rewind_symbols, &rewind_symbol);
812}
813 
814static 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)
815{
816	ccv_nnc_graph_exec_t const update_exec = ccv_nnc_graph_exec_from_symbol(graph_exec_arena, exec_symbol);
817	if (!CCV_NO_GRAPH_EXEC(update_exec)((update_exec).graph == 0))
818		ccv_nnc_graph_exec_set(update_exec.graph, update_exec, cmd);
819	int i;
820	for (i = 1; i < parallel_count; i++)
821	{
822		ccv_nnc_graph_exec_symbol_t copy_symbol = ccv_nnc_graph_exec_symbol_copy(symbolic_graph, exec_symbol, i);
823		const ccv_nnc_graph_exec_t copy = ccv_nnc_graph_exec_from_symbol(graph_exec_arena, copy_symbol);
824		if (!CCV_NO_GRAPH_EXEC(copy)((copy).graph == 0))
825			ccv_nnc_graph_exec_set(copy.graph, copy, cmd);
826	}
827}
828 
829static 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)
830{
831	assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
 (compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 831, __extension__ __PRETTY_FUNCTION__); }));
832	assert(symbolic_graph)((void) sizeof ((symbolic_graph) ? 1 : 0), __extension__ ({ if
 (symbolic_graph) ; else __assert_fail ("symbolic_graph", "ccv_cnnp_model.c"
, 832, __extension__ __PRETTY_FUNCTION__); }));
833	ccv_nnc_graph_exec_symbol_set(symbolic_graph, exec_symbol, cmd);
834	int i;
835	for (i = 1; i < parallel_count; i++)
836	{
837		ccv_nnc_graph_exec_symbol_t copy_symbol = ccv_nnc_graph_exec_symbol_copy(symbolic_graph, exec_symbol, i);
838		if (copy_symbol.graph)
839			ccv_nnc_graph_exec_symbol_set(symbolic_graph, copy_symbol, cmd);
840	}
841	ccv_nnc_graph_exec_arena_t* const graph_exec_arena = compiled_data->graph_exec_arena;
842	if (graph_exec_arena)
843		_ccv_cnnp_model_graph_symbol_exec_set_for_graph_exec_arena(graph_exec_arena, parallel_count, exec_symbol, cmd, symbolic_graph);
844	// Skip backward graph exec arena because it is for a specific accum symbolic graph, not the main graph (model->graph)
845	ccv_nnc_graph_exec_arena_t* const gradient_graph_exec_arena = compiled_data->apply_gradients.graph_exec_arena;
846	if (gradient_graph_exec_arena)
847		_ccv_cnnp_model_graph_symbol_exec_set_for_graph_exec_arena(gradient_graph_exec_arena, parallel_count, exec_symbol, cmd, symbolic_graph);
848}
849 
850static 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)
851{
852	int this_parameter_flag = 0;
853	if (update_nodes[parameter_indice].d == CCV_NNC_NO_TENSOR_SYMBOL)
854		return this_parameter_flag;
855	const ccv_nnc_cmd_t old_minimizer = ccv_nnc_graph_exec_symbol_cmd(graph, update_nodes[parameter_indice]);
856	int j, k;
857	// For no-op, we can preserve previous saved_aux_size.
858	if (old_minimizer.cmd != minimizer.cmd && minimizer.cmd != CCV_NNC_NOOP)
859	{
860		// If the old minimizer is a noop, then the old_saved_aux_size should be whatever its previous
861		// saved_aux_size is, otherwise we will reinit the saved_aux repeatedly if you switch between
862		// noop and a minimizer. We don't want that because we do that in high-level frameworks to
863		// make sure some model parameters don't update if we don't want them to.
864		int old_saved_aux_size;
865		if (old_minimizer.cmd == CCV_NNC_NOOP)
866		{
867			int input_size;
868			ccv_nnc_graph_exec_symbol_io(graph, update_nodes[parameter_indice], 0, &input_size, 0, 0);
869			if (input_size < 2) // This is not legit.
870				old_saved_aux_size = ccv_nnc_minimizer_saved_aux_size(old_minimizer);
871			else // See ccv_nnc_minimizer_saved_aux_size, the saved_aux is inputs excluding gradients and parameters.
872				old_saved_aux_size = input_size - 2;
873		} else
874			old_saved_aux_size = ccv_nnc_minimizer_saved_aux_size(old_minimizer);
875		if (old_saved_aux_size != saved_aux_size)
876		{
877			this_parameter_flag = 1;
878			if (saved_aux_size > old_saved_aux_size)
879			{
880				// Allocate new tensor symbols.
881				const ccv_nnc_tensor_param_t info = ccv_nnc_tensor_symbol_params(graph, updated_parameters[parameter_indice]);
882				for (j = old_saved_aux_size; j < saved_aux_size; j++)
883				{
884					saved_aux[parameter_indice * max_saved_aux_size + j].source = ccv_nnc_tensor_symbol_new(graph, info, 0);
885					saved_aux[parameter_indice * max_saved_aux_size + j].destination = ccv_nnc_tensor_symbol_new(graph, info, 0);
886					const int device_id = CCV_TENSOR_GET_DEVICE_ID(info.type)(((info.type) & 0xfff00) >> 8);
887					for (k = 1; k < parallel_count; k++)
888					{
889						ccv_nnc_tensor_param_t dev_info = info;
890						if (k != device_id)
891							CCV_TENSOR_SET_DEVICE_ID(dev_info.type, k)(dev_info.type) = (((dev_info.type) & ~0xfff00) | (((k) &
 0xfff) << 8));
892						else
893							CCV_TENSOR_SET_DEVICE_ID(dev_info.type, 0)(dev_info.type) = (((dev_info.type) & ~0xfff00) | (((0) &
 0xfff) << 8));
894						const ccv_nnc_tensor_symbol_t src_copy = ccv_nnc_tensor_symbol_new(graph, dev_info, 0);
895						const ccv_nnc_tensor_symbol_t dest_copy = ccv_nnc_tensor_symbol_new(graph, dev_info, 0);
896						ccv_nnc_tensor_symbol_set_copy(graph, saved_aux[parameter_indice * max_saved_aux_size + j].source, k, src_copy);
897						ccv_nnc_tensor_symbol_set_copy(graph, saved_aux[parameter_indice * max_saved_aux_size + j].destination, k, dest_copy);
898					}
899				}
900			} else {
901				for (j = saved_aux_size; j < old_saved_aux_size; j++)
902				{
903					for (k = 1; k < parallel_count; k++)
904					{
905						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);
906						if (src_copy.d >= 0)
907						{
908							ccv_nnc_tensor_symbol_free(graph, src_copy);
909							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
});
910						}
911						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);
912						if (dest_copy.d >= 0)
913						{
914							ccv_nnc_tensor_symbol_free(graph, dest_copy);
915							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
});
916						}
917					}
918					ccv_nnc_tensor_symbol_free(graph, saved_aux[parameter_indice * max_saved_aux_size + j].source);
919					ccv_nnc_tensor_symbol_free(graph, saved_aux[parameter_indice * max_saved_aux_size + j].destination);
920					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
};
921				}
922			}
923		}
924	}
925	_ccv_cnnp_model_graph_exec_symbol_set(graph, compiled_data, parallel_count, update_nodes[parameter_indice], minimizer);
926	if (this_parameter_flag)
927	{
928		ccv_nnc_tensor_symbol_t update_inputs[saved_aux_size + 2];
929		ccv_nnc_tensor_symbol_t update_outputs[saved_aux_size + 1];
930		const int* inputs = 0;
931		int input_size = 0;
932		ccv_nnc_graph_exec_symbol_io(graph, update_nodes[parameter_indice], &inputs, &input_size, 0, 0);
933		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", 933, __extension__ __PRETTY_FUNCTION__)
; }));
934		update_inputs[0].d = inputs[0];
935		update_inputs[0].graph = graph;
936		update_inputs[1].d = inputs[1];
937		update_inputs[1].graph = graph;
938		update_outputs[0] = updated_parameters[parameter_indice];
939		for (j = 0; j < saved_aux_size; j++)
940		{
941			update_inputs[j + 2] = saved_aux[parameter_indice * max_saved_aux_size + j].source;
942			update_outputs[j + 1] = saved_aux[parameter_indice * max_saved_aux_size + j].destination;
943		}
944		ccv_nnc_graph_exec_symbol_set_io(graph, update_nodes[parameter_indice], update_inputs, saved_aux_size + 2, update_outputs, saved_aux_size + 1);
945		for (k = 1; k < parallel_count; k++)
946		{
947			const ccv_nnc_graph_exec_symbol_t copy = ccv_nnc_graph_exec_symbol_copy(graph, update_nodes[parameter_indice], k);
948			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"
, 948, __extension__ __PRETTY_FUNCTION__); }));
949			ccv_nnc_graph_exec_symbol_io(graph, copy, &inputs, &input_size, 0, 0);
950			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", 950, __extension__ __PRETTY_FUNCTION__)
; }));
951			update_inputs[0].d = inputs[0];
952			update_inputs[0].graph = graph;
953			update_inputs[1].d = inputs[1];
954			update_inputs[1].graph = graph;
955			update_outputs[0] = ccv_nnc_tensor_symbol_copy(graph, updated_parameters[parameter_indice], k);
956			for (j = 0; j < saved_aux_size; j++)
957			{
958				update_inputs[j + 2] = ccv_nnc_tensor_symbol_copy(graph, saved_aux[parameter_indice * max_saved_aux_size + j].source, k);
959				update_outputs[j + 1] = ccv_nnc_tensor_symbol_copy(graph, saved_aux[parameter_indice * max_saved_aux_size + j].destination, k);
960			}
961			ccv_nnc_graph_exec_symbol_set_io(graph, copy, update_inputs, saved_aux_size + 2, update_outputs, saved_aux_size + 1);
962		}
963	}
964	return this_parameter_flag;
965}
966 
967typedef struct {
968	int parameter_size;
969	ccv_nnc_cmd_t minimizer;
970	ccv_cnnp_model_io_t parameters[1];
971} ccv_cnnp_set_minimizer_for_parameter_t;
972 
973static int _ccv_cnnp_apply_parameters_with_minimizer(ccv_cnnp_model_t* const model)
974{
975	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
976	assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
 (compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 976, __extension__ __PRETTY_FUNCTION__); }));
977	const int max_saved_aux_size = compiled_data->minimize.max_saved_aux_size;
978	// We update all parameters, at this point, we have one minimizer.
979	const int parameter_size = compiled_data->parameters->rnum;
980	ccv_nnc_graph_exec_symbol_t* const update_nodes = compiled_data->update_nodes;
981	ccv_nnc_symbolic_graph_t* const symbolic_graph = model->graph;
982	assert(symbolic_graph)((void) sizeof ((symbolic_graph) ? 1 : 0), __extension__ ({ if
 (symbolic_graph) ; else __assert_fail ("symbolic_graph", "ccv_cnnp_model.c"
, 982, __extension__ __PRETTY_FUNCTION__); }));
983	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; });
984	ccv_array_t* const parameters = compiled_data->minimize.parameters;
985	ccv_array_t* const parameter_indices = ccv_array_new(sizeof(int), 0, 0);
986	int i, j, flag = 0;
987	for (i = 0; i < parameters->rnum; i++)
988	{
989		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)));
990		for (j = 0; j < set_minimizer_for_parameter->parameter_size; j++)
991		{
992			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;
993			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", 993, __extension__ __PRETTY_FUNCTION__)
; }));
994			const int old_rnum = parameter_indices->rnum;
995			ccv_cnnp_model_add_to_parameter_indices(set_minimizer_for_parameter->parameters[j]->model, param_sel, parameter_indices);
996			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;
997			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", 997, __extension__ __PRETTY_FUNCTION__)
; }));
998			if (param_ref >= 0)
999			{
1000				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", 1000, __extension__ __PRETTY_FUNCTION__
); }));
1001				*(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)));
1002				parameter_indices->rnum = old_rnum + 1;
1003			}
1004		}
1005		const int saved_aux_size = ccv_nnc_minimizer_saved_aux_size(set_minimizer_for_parameter->minimizer);
1006		// We may have duplicated indices, but that is OK, we will set it twice.
1007		for (j = 0; j < parameter_indices->rnum; j++)
1008		{
1009			const int d = *(int*)ccv_array_get(parameter_indices, j)((void*)(((char*)((parameter_indices)->data)) + (size_t)(parameter_indices
)->rsize * (size_t)(j)));
1010			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", 1010, __extension__ __PRETTY_FUNCTION__
); }));
1011			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))
1012				flag = 1;
1013		}
1014		ccv_array_clear(parameter_indices);
1015	}
1016	ccv_array_free(parameter_indices);
1017	return flag;
1018}
1019 
1020static 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)
1021{
1022	if (new_saved_aux_size == old_saved_aux_size)
1023		return;
1024	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", 1024, __extension__ __PRETTY_FUNCTION__
); }));
1025	int i, j;
1026	for (i = parameter_size - 1; i >= 0; i--)
1027	{
1028		for (j = new_saved_aux_size - 1; j >= old_saved_aux_size; j--)
1029			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
};
1030		for (j = old_saved_aux_size - 1; j >= 0; j--)
1031			saved_aux[i * new_saved_aux_size + j] = saved_aux[i * old_saved_aux_size + j];
1032	}
1033}
1034 
1035static void _ccv_cnnp_model_set_rewindables(ccv_cnnp_model_t* const model)
1036{
1037	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
1038	assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
 (compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 1038, __extension__ __PRETTY_FUNCTION__); }));
1039	if (!compiled_data->rewindables)
1040		compiled_data->rewindables = ccv_array_new(sizeof(ccv_cnnp_rewind_symbol_t), 0, 0);
1041	ccv_nnc_tensor_symbol_new_hook(model->graph, _ccv_cnnp_model_tensor_symbol_new_hook, compiled_data->rewindables, 0);
1042	ccv_nnc_tensor_symbol_alias_new_hook(model->graph, _ccv_cnnp_model_tensor_symbol_alias_new_hook, compiled_data->rewindables, 0);
1043	ccv_nnc_graph_exec_symbol_new_hook(model->graph, _ccv_cnnp_model_graph_exec_symbol_new_hook, compiled_data->rewindables, 0);
1044}
1045 
1046static 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)
1047{
1048	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
1049	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", 1049, __extension__ __PRETTY_FUNCTION__
); }));
1050	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", 1050, __extension__ __PRETTY_FUNCTION__
); }));
1051	const int evaluate_to_size = compiled_data->evaluate.to_size;
1052	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", 1052, __extension__ __PRETTY_FUNCTION__
); }));
1053	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; });
1054	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);
1055	compiled_data->evaluate.to_ops = (ccv_nnc_graph_exec_t*)(compiled_data->evaluate.tos + evaluate_to_size * parallel_count);
1056	int i, j;
1057	const int output_size = model->output_size;
1058	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", 1058, __extension__ __PRETTY_FUNCTION__
); }));
1059	if (fits)
1060		for (i = 0; i < output_size; i++)
1061			ccv_nnc_tensor_symbol_set(model->graph, compiled_data->fits[i], fits[i]->info);
1062	const int max_saved_aux_size = compiled_data->minimize.max_saved_aux_size;
1063	const int parameter_size = compiled_data->parameters->rnum;
1064	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);
1065	compiled_data->update_nodes = (ccv_nnc_graph_exec_symbol_t*)(compiled_data->updated_parameters + parameter_size);
1066	compiled_data->saved_aux = (ccv_nnc_tensor_symbol_map_t*)(compiled_data->update_nodes + parameter_size);
1067	int parameter_size_maybe_more = parameter_size;
1068	compiled_data->disable_outgrad = disable_outgrad;
1069	int outgrad_size;
1070	if (gradient_mode == CCV_CNNP_COMPILED_DATA_GRADIENT_TRAINABLES || model->input_size == 0)
1071		outgrad_size = 0;
1072	else if (disable_outgrad == CCV_CNNP_DISABLE_OUTGRAD_NONE) // Compute minimize with gradients including inputs.
1073		outgrad_size = model->input_size;
1074	else {
1075		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", 1075, __extension__ __PRETTY_FUNCTION__
); })); // If it is disable all, gradient mode won't be this.
1076		outgrad_size = 0;
1077		for (i = 0; i < model->input_size; i++)
1078			if (!(disable_outgrad & ((uint64_t)1 << i)))
1079				++outgrad_size;
1080	}
1081	compiled_data->outgrad_size = outgrad_size;
1082	parameter_size_maybe_more += outgrad_size;
1083	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);
1084	compiled_data->outgrads = parameter_size_maybe_more > parameter_size ? compiled_data->gradients + parameter_size : 0;
1085	compiled_data->backward.tos = (ccv_nnc_graph_exec_symbol_t*)(compiled_data->gradients + parameter_size_maybe_more);
1086	compiled_data->backward.to_size = parameter_size_maybe_more;
1087	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)));
1088	if (compiled_data->parameter_flags)
1089	{
1090		parameters = (ccv_nnc_tensor_symbol_t*)ccmallocmalloc(sizeof(ccv_nnc_tensor_symbol_t) * parameter_size);
1091		for (i = 0; i < parameter_size; i++)
1092			if (compiled_data->parameter_flags[i >> 6] & ((uint64_t)1 << (i & 63)))
1093				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)));
1094			else
1095				parameters[i] = NO_TENSOR_SYMBOL(const ccv_nnc_tensor_symbol_t){.d = CCV_NNC_NO_TENSOR_SYMBOL
};
1096	}
1097	if (gradient_mode == CCV_CNNP_COMPILED_DATA_GRADIENT_TRAINABLES || model->input_size == 0)
1098		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);
1099	else if (disable_outgrad == CCV_CNNP_DISABLE_OUTGRAD_NONE) // Compute minimize with gradients including inputs.
1100		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);
1101	else { // Compute minimize with gradients including selected inputs.
1102		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", 1102, __extension__ __PRETTY_FUNCTION__
); }));
1103		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", 1103, __extension__ __PRETTY_FUNCTION__
); })); // If it is disable all, gradient mode won't be this.
1104		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", 1104, __extension__ __PRETTY_FUNCTION__
); }));
1105		ccv_nnc_tensor_symbol_t outgrads[outgrad_size];
1106		j = 0;
1107		for (i = 0; i < model->input_size; i++)
1108			if (!(disable_outgrad & ((uint64_t)1 << i)))
1109				outgrads[j++] = model->inputs[i];
1110		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);
1111	}
1112	if (compiled_data->parameter_flags)
1113		ccfreefree(parameters);
1114	_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);
1115	if (compiled_data->minimize.parameters)
1116		_ccv_cnnp_apply_parameters_with_minimizer(model);
1117	// Go through gradient checkpoints to generate tensor inputs for backward pass just before executing the backward pass.
1118	ccv_cnnp_model_apply_gradient_checkpoints(compiled_data, model->graph);
1119	for (i = 0; i < output_size; i++)
1120	{
1121		const ccv_nnc_tensor_symbol_t df = ccv_nnc_tensor_symbol_for_backward(model->graph, compiled_data->f[i]);
1122		// Init this to 1 so we can backprop.
1123		ccv_nnc_tensor_symbol_set_flags(model->graph, df, CCV_NNC_TENSOR_SYMBOL_INIT_ONES);
1124	}
1125	compiled_data->backward.to_size = 0;
1126	for (i = 0; i < parameter_size_maybe_more; i++)
1127		if (compiled_data->gradients[i].d != CCV_NNC_NO_TENSOR_SYMBOL)
1128			compiled_data->backward.tos[compiled_data->backward.to_size++] = ccv_nnc_graph_exec_symbol_for_backward(model->graph, compiled_data->gradients[i]);
1129	ccv_nnc_graph_exec_symbol_autogen(model->graph, 0, 0, CCV_NNC_AUTOGEN_ALL_EXECS);
1130	ccv_nnc_symbolic_graph_set_destinations(model->graph, compiled_data->update_nodes, parameter_size);
1131	for (i = 0; i < parameter_size_maybe_more - parameter_size; i++)
1132	{
1133		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.
1134			continue;
1135		const ccv_nnc_graph_exec_symbol_t outgrad = ccv_nnc_graph_exec_symbol_for_backward(model->graph, compiled_data->outgrads[i]);
1136		const int* tos;
1137		int to_size;
1138		ccv_nnc_graph_exec_symbol_to(model->graph, outgrad, &tos, &to_size);
1139		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.
1140		{
1141			const ccv_nnc_graph_exec_symbol_t* destinations = ccv_nnc_symbolic_graph_destinations(model->graph);
1142			const int destination_count = ccv_nnc_symbolic_graph_destination_size(model->graph);
1143			int flag = 0;
1144			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; });
1145			for (j = i - 1; !flag && j >= 0; j--)
1146				if (j + outgrad_destination_start < destination_count)
1147					flag = (destinations[j + outgrad_destination_start].d == outgrad.d);
1148			if (!flag) // Only if we cannot find it, we add it.
1149				ccv_nnc_symbolic_graph_add_destination(model->graph, outgrad);
1150		}
1151	}
1152	if (parallel_count > 1)
1153	{
1154		ccv_nnc_symbolic_graph_data_parallel(model->graph, parallel_count,
1155			0, 0,
1156			compiled_data->gradients, parameter_size /* No need to deal with outgrads, we don't allreduce outgrads */,
1157			compiled_data->gradients /* We only care about gradients before allreduce, thus, update our current pointers */,
1158			0, 0, 0,
1159			CCV_NNC_PARALLEL_REDUCE_OP_SUM,
1160			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));
1161		ccv_nnc_graph_exec_symbol_autogen(model->graph, 0, 0, CCV_NNC_AUTOGEN_SOURCES_AND_DESTINATIONS);
1162		for (i = 0; i < evaluate_to_size; i++)
1163			for (j = 1; j < parallel_count; j++)
1164			{
1165				const ccv_nnc_graph_exec_symbol_t copy = ccv_nnc_graph_exec_symbol_copy(model->graph, compiled_data->evaluate.tos[i], j);
1166				if (copy.d != CCV_NNC_NO_GRAPH_EXEC_SYMBOL)
1167					compiled_data->evaluate.tos[compiled_data->evaluate.to_size++] = copy;
1168			}
1169		const int backward_to_size = compiled_data->backward.to_size;
1170		for (i = 0; i < backward_to_size; i++)
1171			for (j = 1; j < parallel_count; j++)
1172			{
1173				const ccv_nnc_graph_exec_symbol_t copy = ccv_nnc_graph_exec_symbol_copy(model->graph, compiled_data->backward.tos[i], j);
1174				if (copy.d != CCV_NNC_NO_GRAPH_EXEC_SYMBOL)
1175					compiled_data->backward.tos[compiled_data->backward.to_size++] = copy;
1176			}
1177	}
1178	// Only use memory compression if we are in gradient parameter mode.
1179	if (gradient_mode == CCV_CNNP_COMPILED_DATA_GRADIENT_TRAINABLES || gradient_mode == CCV_CNNP_COMPILED_DATA_GRADIENT_TRAINABLES_AND_INPUTS)
1180	{
1181		if (model->memory_compression)
1182			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));
1183		if (model->memory_reduction)
1184			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));
1185	}
1186	compiled_data->backward.to_size = _ccv_nnc_array_dedup_graph_exec_symbols(compiled_data->backward.tos, compiled_data->backward.to_size);
1187	compiled_data->gradient_mode = gradient_mode;
1188}
1189 
1190void ccv_cnnp_model_tensors_init_0(const ccv_cnnp_model_t* const model, ccv_cnnp_compiled_data_t* const compiled_data)
1191{
1192	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", 1192, __extension__ __PRETTY_FUNCTION__
); }));
1193	const int parameter_size = compiled_data->parameters->rnum;
1194	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; });
1195	const int internal_size = compiled_data->internals->rnum;
1196	compiled_data->tensors_init.size = ccv_nnc_tensor_symbol_count(model->graph);
1197	compiled_data->tensors_init.v = cccalloccalloc(((compiled_data->tensors_init.size + 31) >> 5), sizeof(uint32_t));
1198	compiled_data->tensors.parameters = (ccv_nnc_tensor_t**)cccalloccalloc((parameter_size + internal_size) * parallel_count, sizeof(ccv_nnc_tensor_t*));
1199	compiled_data->tensors.internals = compiled_data->tensors.parameters + parameter_size * parallel_count;
1200}
1201 
1202int ccv_cnnp_model_tensors_any_to_alloc(const ccv_cnnp_model_t* const model, ccv_cnnp_compiled_data_t* const compiled_data)
1203{
1204	int i, j;
1205	const int parameter_size = compiled_data->parameters->rnum;
1206	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; });
1207	const int internal_size = compiled_data->internals->rnum;
1208	for (i = 0; i < parameter_size; i++)
1209	{
1210		// parameters has to be allocated all together.
1211		if (compiled_data->tensors.parameters[i])
1212		{
1213			for (j = 1; j < parallel_count; j++)
1214				{ 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", 1214, __extension__ __PRETTY_FUNCTION__
); })); }
1215			continue;
1216		}
1217		return 1;
1218	}
1219	for (i = 0; i < internal_size; i++)
1220	{
1221		if (!compiled_data->tensors.internals[i])
1222			return 1;
1223		for (j = 1; j < parallel_count; j++)
1224			if (!compiled_data->tensors.internals[i + j * internal_size])
1225				return 1;
1226	}
1227	return 0;
1228}
1229 
1230void ccv_cnnp_model_tensors_init_1(const ccv_cnnp_model_t* const model, ccv_cnnp_compiled_data_t* const compiled_data)
1231{
1232	int i, j;
1233	const int parameter_size = compiled_data->parameters->rnum;
1234	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; });
1235	const int internal_size = compiled_data->internals->rnum;
1236	for (i = 0; i < parameter_size; i++)
1237	{
1238		// parameters has to be allocated all together.
1239		if (compiled_data->tensors.parameters[i])
1240		{
1241			for (j = 1; j < parallel_count; j++)
1242				{ 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", 1242, __extension__ __PRETTY_FUNCTION__
); })); }
1243			continue;
1244		}
1245		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)));
1246		ccv_nnc_tensor_param_t info = ccv_nnc_tensor_symbol_params(parameter.graph, parameter);
1247		if (CCV_TENSOR_GET_DEVICE(info.type)((info.type) & 0xfff00) == CCV_COMPUTE_DEVICE_ANY)
1248			CCV_TENSOR_SET_DEVICE_ID(info.type, 0)(info.type) = (((info.type) & ~0xfff00) | (((0) & 0xfff
) << 8));
1249		const int device_id = CCV_TENSOR_GET_DEVICE_ID(info.type)(((info.type) & 0xfff00) >> 8);
1250		compiled_data->tensors.parameters[i] = ccv_nnc_tensor_new(0, info, 0);
1251		for (j = 1; j < parallel_count; j++)
1252		{
1253			if (j != device_id)
1254				CCV_TENSOR_SET_DEVICE_ID(info.type, j)(info.type) = (((info.type) & ~0xfff00) | (((j) & 0xfff
) << 8));
1255			else
1256				CCV_TENSOR_SET_DEVICE_ID(info.type, 0)(info.type) = (((info.type) & ~0xfff00) | (((0) & 0xfff
) << 8));
1257			compiled_data->tensors.parameters[i + j * parameter_size] = ccv_nnc_tensor_new(0, info, 0);
1258		}
1259	}
1260	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));
1261	for (i = 0; i < internal_size; i++)
1262	{
1263		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))
);
1264		const int d = retained.d;
1265		if (init_v[d >> 5] & (1u << (d & 0x1f)))
1266			continue;
1267		ccv_nnc_tensor_param_t info = ccv_nnc_tensor_symbol_params(retained.graph, retained);
1268		if (CCV_TENSOR_GET_DEVICE(info.type)((info.type) & 0xfff00) == CCV_COMPUTE_DEVICE_ANY)
1269			CCV_TENSOR_SET_DEVICE_ID(info.type, 0)(info.type) = (((info.type) & ~0xfff00) | (((0) & 0xfff
) << 8));
1270		const int device_id = CCV_TENSOR_GET_DEVICE_ID(info.type)(((info.type) & 0xfff00) >> 8);
1271		if (!compiled_data->tensors.internals[i])
1272			compiled_data->tensors.internals[i] = ccv_nnc_tensor_new(0, info, 0);
1273		for (j = 1; j < parallel_count; j++)
1274		{
1275			if (j != device_id)
1276				CCV_TENSOR_SET_DEVICE_ID(info.type, j)(info.type) = (((info.type) & ~0xfff00) | (((j) & 0xfff
) << 8));
1277			else
1278				CCV_TENSOR_SET_DEVICE_ID(info.type, 0)(info.type) = (((info.type) & ~0xfff00) | (((0) & 0xfff
) << 8));
1279			if (!compiled_data->tensors.internals[i + j * internal_size])
1280				compiled_data->tensors.internals[i + j * internal_size] = ccv_nnc_tensor_new(0, info, 0);
1281		}
1282	}
1283	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.
1284}
1285 
1286static void _ccv_cnnp_model_tensors_init(const ccv_cnnp_model_t* const model, ccv_cnnp_compiled_data_t* const compiled_data)
1287{
1288	ccv_cnnp_model_tensors_init_0(model, compiled_data);
1289	ccv_cnnp_model_tensors_init_1(model, compiled_data);
1290}
1291 
1292static 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)
1293{
1294	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", 1294, __extension__ __PRETTY_FUNCTION__
); }));
1295	int i, j;
1296	for (i = 0; i < tensor_size; i++)
1297	{
1298		if (!tensors[i])
1299			continue;
1300		const int d = tensor_symbols[i].d;
1301		if (!(tensors_init[d >> 5] & (1u << (d & 0x1f))))
1302			continue;
1303		for (j = 1; j < parallel_count; j++)
1304			if (tensors[i + j * tensor_size])
1305			{
1306				ccv_nnc_tensor_t* const input = CCV_NNC_TENSOR(tensors[i])((ccv_nnc_tensor_t*)((uintptr_t)(tensors[i]) & ~(uintptr_t
)1));
1307				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));
1308				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);
1309			}
1310	}
1311}
1312 
1313static 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)
1314{
1315	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", 1315, __extension__ __PRETTY_FUNCTION__
); }));
1316	int i, j;
1317	for (i = 0; i < tensor_size; i++)
1318	{
1319		const ccv_nnc_tensor_symbol_t tensor_symbol = tensor_symbols[i];
1320		for (j = 1; j < parallel_count; j++)
1321		{
1322			const ccv_nnc_tensor_symbol_t copy = ccv_nnc_tensor_symbol_copy(graph, tensor_symbol, j);
1323			ccv_nnc_tensor_t* copy_tensor = tensors[i + j * tensor_size];
1324			if (copy_tensor && copy.d == CCV_NNC_NO_TENSOR_SYMBOL)
1325			{ // We shouldn't allocate this, free it up.
1326				ccv_nnc_tensor_free(tensors[i + j * tensor_size]);
1327				tensors[i + j * tensor_size] = 0;
1328			}
1329		}
1330	}
1331}
1332 
1333static 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)
1334{
1335	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", 1335, __extension__ __PRETTY_FUNCTION__
); }));
1336	int i, j;
1337	for (i = 0; i < tensor_size; i++)
1338	{
1339		ccv_nnc_tensor_symbol_t tensor_symbol = tensor_symbols[i];
1340		if (tensor_symbol.d == CCV_NNC_NO_TENSOR_SYMBOL)
1341			continue;
1342		if (graph)
1343		{
1344			const ccv_nnc_tensor_symbol_t alias_to = ccv_nnc_tensor_symbol_alias_to(graph, tensor_symbol);
1345			if (alias_to.d != CCV_NNC_NO_TENSOR_SYMBOL)
1346				tensor_symbol = alias_to;
1347		}
1348		ccv_nnc_tensor_t* const tensor = CCV_NNC_TENSOR(tensors[i])((ccv_nnc_tensor_t*)((uintptr_t)(tensors[i]) & ~(uintptr_t
)1));
1349		if (tensor && tensor_symbol.d != CCV_NNC_NO_TENSOR_SYMBOL)
1350		{
1351			const ccv_nnc_tensor_bind_t retained_bind = {
1352				.symbol = tensor_symbol,
1353				.tensor = tensor
1354			};
1355			ccv_array_push(tensor_binds, &retained_bind);
1356		}
1357		for (j = 1; j < parallel_count; j++)
1358		{
1359			const ccv_nnc_tensor_symbol_t copy = ccv_nnc_tensor_symbol_copy(graph, tensor_symbol, j);
1360			ccv_nnc_tensor_t* copy_tensor = tensors[i + j * tensor_size];
1361			if (copy_tensor && copy.d != CCV_NNC_NO_TENSOR_SYMBOL)
1362			{
1363				const ccv_nnc_tensor_bind_t bind = {
1364					.symbol = copy,
1365					.tensor = tensors[i + j * tensor_size]
1366				};
1367				ccv_array_push(tensor_binds, &bind);
1368			}
1369		}
1370	}
1371}
1372 
1373static void _ccv_cnnp_compiled_data_graph_free(ccv_cnnp_compiled_data_t* const compiled_data)
1374{
1375	if (compiled_data->graph)
1376		ccv_nnc_graph_free(compiled_data->graph);
1377	compiled_data->graph = 0;
1378	compiled_data->is_test = 0;
1379	if (compiled_data->tensor_arena)
1380		ccv_nnc_tensor_arena_free(compiled_data->tensor_arena);
1381	compiled_data->tensor_arena = 0;
1382	if (compiled_data->graph_exec_arena)
1383		ccv_nnc_graph_exec_arena_free(compiled_data->graph_exec_arena);
1384	compiled_data->graph_exec_arena = 0;
1385	if (compiled_data->backward.from_ops)
1386		ccfreefree(compiled_data->backward.from_ops);
1387	compiled_data->backward.from_ops = 0;
1388	if (compiled_data->evaluate.schedule)
1389		ccv_nnc_graph_static_schedule_free(compiled_data->evaluate.schedule);
1390	compiled_data->evaluate.schedule = 0;
1391	if (compiled_data->backward.schedule)
1392		ccv_nnc_graph_static_schedule_free(compiled_data->backward.schedule);
1393	compiled_data->backward.schedule = 0;
1394}
1395 
1396static void _ccv_cnnp_compiled_data_gradient_free(ccv_cnnp_compiled_data_t* const compiled_data)
1397{
1398	if (compiled_data->gradients)
1399		ccfreefree(compiled_data->gradients);
1400	compiled_data->gradients = 0;
1401	if (compiled_data->updated_parameters)
1402		ccfreefree(compiled_data->updated_parameters);
1403	compiled_data->updated_parameters = 0;
1404	compiled_data->update_nodes = 0;
1405	compiled_data->saved_aux = 0;
1406}
1407 
1408static void _ccv_cnnp_compiled_data_backward_free(ccv_cnnp_compiled_data_t* const compiled_data)
1409{
1410	if (compiled_data->backward.gradients)
1411		ccfreefree(compiled_data->backward.gradients);
1412	compiled_data->backward.gradients = 0;
1413	if (compiled_data->backward.accum)
1414		ccv_nnc_graph_free(compiled_data->backward.accum);
1415	compiled_data->backward.accum = 0;
1416	if (compiled_data->backward.tensor_arena)
1417		ccv_nnc_tensor_arena_free(compiled_data->backward.tensor_arena);
1418	compiled_data->backward.tensor_arena = 0;
1419	if (compiled_data->backward.graph_exec_arena)
1420		ccv_nnc_graph_exec_arena_free(compiled_data->backward.graph_exec_arena);
1421	compiled_data->backward.graph_exec_arena = 0;
1422}
1423 
1424static void _ccv_cnnp_compiled_data_apply_gradients_free(ccv_cnnp_compiled_data_t* const compiled_data)
1425{
1426	if (compiled_data->apply_gradients.graph)
1427		ccv_nnc_graph_free(compiled_data->apply_gradients.graph);
1428	compiled_data->apply_gradients.graph = 0;
1429	if (compiled_data->apply_gradients.tensor_arena)
1430		ccv_nnc_tensor_arena_free(compiled_data->apply_gradients.tensor_arena);
1431	compiled_data->apply_gradients.tensor_arena = 0;
1432	if (compiled_data->apply_gradients.graph_exec_arena)
1433		ccv_nnc_graph_exec_arena_free(compiled_data->apply_gradients.graph_exec_arena);
1434	compiled_data->apply_gradients.graph_exec_arena = 0;
1435}
1436 
1437// Compile the graph to run ccv_cnnp_model_fit
1438static 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)
1439{
1440	int i, j;
1441	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
1442	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", 1442, __extension__ __PRETTY_FUNCTION__
); }));
1443	compiled_data->graph_mode = CCV_CNNP_MODEL_GRAPH_FIT_MODE;
1444	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; });
1445	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", 1445, __extension__ __PRETTY_FUNCTION__
); }));
1446	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", 1446
, __extension__ __PRETTY_FUNCTION__); }));
1447	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", 1447, __extension__ __PRETTY_FUNCTION__
); }));
1448	if (compiled_data->gradient_mode == CCV_CNNP_COMPILED_DATA_GRADIENT_NONE)
1449	{
1450		_ccv_cnnp_model_set_rewindables(model);
1451		_ccv_cnnp_model_gradient_init(model, CCV_CNNP_COMPILED_DATA_GRADIENT_TRAINABLES, CCV_CNNP_DISABLE_OUTGRAD_ALL, fits, fit_size);
1452	} else if (compiled_data->gradient_mode != CCV_CNNP_COMPILED_DATA_GRADIENT_TRAINABLES) {
1453		_ccv_cnnp_model_rewind_graph(model);
1454		_ccv_cnnp_compiled_data_gradient_free(compiled_data);
1455		compiled_data->gradient_mode = CCV_CNNP_COMPILED_DATA_GRADIENT_NONE;
1456		_ccv_cnnp_model_gradient_init(model, CCV_CNNP_COMPILED_DATA_GRADIENT_TRAINABLES, CCV_CNNP_DISABLE_OUTGRAD_ALL, fits, fit_size);
1457	}
1458	const int tensors_init = !!compiled_data->tensors_init.v;
1459	if (!tensors_init)
1460		_ccv_cnnp_model_tensors_init(model, compiled_data);
1461	else if ((uintptr_t)compiled_data->tensors_init.v & (uintptr_t)1)
1462	// Check if it is not fully allocated, if it is not, init_1.
1463		ccv_cnnp_model_tensors_init_1(model, compiled_data);
1464	ccv_array_t* const tensor_binds = ccv_array_new(sizeof(ccv_nnc_tensor_bind_t), 0, 0);
1465	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"
, 1465, __extension__ __PRETTY_FUNCTION__); }));
1466	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"
, 1466, __extension__ __PRETTY_FUNCTION__); }));
1467	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", 1467
, __extension__ __PRETTY_FUNCTION__); }));
1468	const int input_size_per_p = input_size / parallel_count;
1469	_ccv_cnnp_model_bind_tensors(model->graph, model->inputs, inputs, input_size_per_p, parallel_count, tensor_binds);
1470	const int output_size_per_p = output_size / parallel_count;
1471	_ccv_cnnp_model_bind_tensors(model->graph, model->outputs, outputs, output_size_per_p, parallel_count, tensor_binds);
1472	const int fit_size_per_p = fit_size / parallel_count;
1473	_ccv_cnnp_model_bind_tensors(model->graph, compiled_data->fits, fits, fit_size_per_p, parallel_count, tensor_binds);
1474	const int parameter_size = compiled_data->parameters->rnum;
1475	_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);
1476	_ccv_cnnp_model_bind_tensors(model->graph, compiled_data->updated_parameters, compiled_data->tensors.parameters, parameter_size, parallel_count, tensor_binds);
1477	const int internal_size = compiled_data->internals->rnum;
1478	_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);
1479	_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);
1480	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);
1481	ccv_array_free(tensor_binds);
1482	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));
1483	if (tensors_init && parallel_count > 1)
1484		_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);
1485	// If tensor is not init'ed, we need to init states first.
1486	if (_ccv_cnnp_any_to_init(compiled_data))
1487	{
1488		ccv_nnc_tensor_init_states_t tensor_init_states = {
1489			.parallel_count = parallel_count,
1490			.graph = model->graph,
1491			.compiled_data = compiled_data,
1492			.tensor_arena = compiled_data->tensor_arena
1493		};
1494		ccv_cnnp_model_init_states(model, model->graph, _ccv_cnnp_init_states_for_tensors, &tensor_init_states);
1495	}
1496	compiled_data->is_test = 0;
1497	const int saved_aux_size = ccv_nnc_minimizer_saved_aux_size(compiled_data->minimize.minimizer);
1498	// No need to set because it is default to training mode.
1499	// ccv_cnnp_model_set_is_test(model, 0, _ccv_cnnp_cmd_update_for_execs, &update);
1500	for (i = 0; i < saved_aux_size * parameter_size; i++)
1501	{
1502		if (compiled_data->saved_aux[i].source.d == CCV_NNC_NO_TENSOR_SYMBOL)
1503			continue;
1504		ccv_nnc_tensor_t* const tensor = ccv_nnc_tensor_from_symbol(compiled_data->tensor_arena, compiled_data->saved_aux[i].source);
1505		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);
1506		for (j = 1; j < parallel_count; j++)
1507		{
1508			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));
1509			if (copy)
1510				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);
1511		}
1512	}
1513	const int evaluate_to_size = compiled_data->evaluate.to_size;
1514	compiled_data->evaluate.to_op_size = 0;
1515	for (i = 0; i < evaluate_to_size; i++)
1516	{
1517		ccv_nnc_graph_exec_t const to = ccv_nnc_graph_exec_from_symbol(compiled_data->graph_exec_arena, compiled_data->evaluate.tos[i]);
1518		if (to.graph)
1519			compiled_data->evaluate.to_ops[compiled_data->evaluate.to_op_size++] = to;
1520	}
1521	ccv_nnc_graph_set_default_static_schedule(compiled_data->graph, compiled_data->stream_type, model->max_stream_count);
1522	ccv_nnc_graph_autotune(compiled_data->graph, model->workspace_size, 0, TRAVERSE_FULL0,0,0,0);
1523}
1524 
1525ccv_nnc_stream_context_t* ccv_cnnp_model_default_stream(const ccv_cnnp_model_t* const model)
1526{
1527	const ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
1528	if (!compiled_data || !compiled_data->graph)
1529		return 0;
1530	return ccv_nnc_graph_default_stream(compiled_data->graph);
1531}
1532 
1533uint64_t ccv_cnnp_model_memory_size(const ccv_cnnp_model_t* const model)
1534{
1535	const ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
1536	if (!compiled_data || !compiled_data->tensor_arena)
1537		return 0;
1538	return ccv_nnc_tensor_arena_size(compiled_data->tensor_arena);
1539}
1540 
1541static 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)
1542{
1543	int i, j;
1544	for (i = 0; i < tensor_size; i++)
1545	{
1546		ccv_nnc_tensor_symbol_t tensor_symbol = tensor_symbols[i];
1547		if (tensor_symbol.d == CCV_NNC_NO_TENSOR_SYMBOL)
1548			continue;
1549		if (graph)
1550		{
1551			const ccv_nnc_tensor_symbol_t alias_to = ccv_nnc_tensor_symbol_alias_to(graph, tensor_symbol);
1552			if (alias_to.d != CCV_NNC_NO_TENSOR_SYMBOL)
1553				tensor_symbol = alias_to;
1554		}
1555		ccv_nnc_tensor_bind_symbol(tensor_arena, tensor_symbol, tensors[i]);
1556		for (j = 1; j < parallel_count; j++)
1557		{
1558			const ccv_nnc_tensor_symbol_t copy = ccv_nnc_tensor_symbol_copy(graph, tensor_symbol, j);
1559			if (copy.d != CCV_NNC_NO_TENSOR_SYMBOL)
1560				ccv_nnc_tensor_bind_symbol(tensor_arena, copy, tensors[i + tensor_size * j]);
1561		}
1562	}
1563}
1564 
1565void 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)
1566{
1567	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
1568	assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
 (compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 1568, __extension__ __PRETTY_FUNCTION__); }));
1569	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; });
1570	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", 1570, __extension__ __PRETTY_FUNCTION__
); }));
1571	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", 1571, __extension__ __PRETTY_FUNCTION__
); }));
1572	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", 1572
, __extension__ __PRETTY_FUNCTION__); }));
1573	assert(model->graph)((void) sizeof ((model->graph) ? 1 : 0), __extension__ ({ if
 (model->graph) ; else __assert_fail ("model->graph", "ccv_cnnp_model.c"
, 1573, __extension__ __PRETTY_FUNCTION__); }));
1574	if (!compiled_data->graph || compiled_data->graph_mode != CCV_CNNP_MODEL_GRAPH_FIT_MODE)
1575	{
1576		_ccv_cnnp_compiled_data_graph_free(compiled_data);
1577		_ccv_cnnp_compiled_data_backward_free(compiled_data);
1578		_ccv_cnnp_compiled_data_apply_gradients_free(compiled_data);
1579		// Compile the symbolic graph down only when needed.
1580		_ccv_cnnp_model_fit_jit(model, inputs, input_size, fits, fit_size, outputs, output_size);
1581	} else {
1582		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"
, 1582, __extension__ __PRETTY_FUNCTION__); }));
1583		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"
, 1583, __extension__ __PRETTY_FUNCTION__); }));
1584		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", 1584
, __extension__ __PRETTY_FUNCTION__); }));
1585		const int input_size_per_p = input_size / parallel_count;
1586		_ccv_cnnp_bind_tensors_to_arena(compiled_data->tensor_arena, model->graph, model->inputs, inputs, input_size_per_p, parallel_count);
1587		const int output_size_per_p = output_size / parallel_count;
1588		_ccv_cnnp_bind_tensors_to_arena(compiled_data->tensor_arena, model->graph, model->outputs, outputs, output_size_per_p, parallel_count);
1589		const int fit_size_per_p = fit_size / parallel_count;
1590		_ccv_cnnp_bind_tensors_to_arena(compiled_data->tensor_arena, model->graph, compiled_data->fits, fits, fit_size_per_p, parallel_count);
1591	}
1592	if (compiled_data->is_test)
1593	{
1594		compiled_data->is_test = 0;
1595		ccv_nnc_graph_exec_update_t update = {
1596			.parallel_count = parallel_count,
1597			.graph = model->graph,
1598			.graph_exec_arena = compiled_data->graph_exec_arena,
1599		};
1600		ccv_cnnp_model_set_is_test(model, 0, _ccv_cnnp_cmd_update_for_execs, &update);
1601	}
1602	ccv_nnc_graph_run_with_schedule(compiled_data->graph, 0, 0, tensor_tape, stream_context);
1603}
1604 
1605// Compile the graph to run ccv_cnnp_model_evaluate with require_grad = false (MULTISTAGE_MODE_NO_GRAD).
1606static 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)
1607{
1608	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
1609	compiled_data->graph_mode = CCV_CNNP_MODEL_GRAPH_MULTISTAGE_MODE_NO_GRAD;
1610	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; });
1611	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", 1611, __extension__ __PRETTY_FUNCTION__
); }));
1612	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", 1612, __extension__ __PRETTY_FUNCTION__
); }));
1613	// If the gradient is not initialized, continue to setup parallel process. We don't init gradient here, but rather,
1614	// we setup proper rewindables so the graph can be rewinded to previous state before we run data parallel.
1615	if (parallel_count > 1 && compiled_data->gradient_mode == CCV_CNNP_COMPILED_DATA_GRADIENT_NONE)
1616	{
1617		const int evaluate_to_size = compiled_data->evaluate.to_size;
1618		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);
1619		_ccv_cnnp_model_set_rewindables(model);
1620		ccv_nnc_symbolic_graph_data_parallel(model->graph, parallel_count,
1621			0, 0,
1622			0, 0, 0,
1623			0, 0, 0,
1624			CCV_NNC_PARALLEL_REDUCE_OP_SUM,
1625			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));
1626		ccv_nnc_graph_exec_symbol_autogen(model->graph, 0, 0, CCV_NNC_AUTOGEN_SOURCES_AND_DESTINATIONS);
1627		int i, j;
1628		for (i = 0; i < evaluate_to_size; i++)
1629			for (j = 1; j < parallel_count; j++)
1630			{
1631				const ccv_nnc_graph_exec_symbol_t copy = ccv_nnc_graph_exec_symbol_copy(model->graph, compiled_data->evaluate.tos[i], j);
1632				if (copy.d != CCV_NNC_NO_GRAPH_EXEC_SYMBOL)
1633					compiled_data->evaluate.tos[compiled_data->evaluate.to_size++] = copy;
1634			}
1635	}
1636	const int tensors_init = !!compiled_data->tensors_init.v;
1637	if (!tensors_init)
1638		_ccv_cnnp_model_tensors_init(model, compiled_data);
1639	else if ((uintptr_t)compiled_data->tensors_init.v & (uintptr_t)1)
1640	// Check if it is not fully allocated, if it is not, init_1.
1641		ccv_cnnp_model_tensors_init_1(model, compiled_data);
1642	ccv_array_t* const tensor_binds = ccv_array_new(sizeof(ccv_nnc_tensor_bind_t), 0, 0);
1643	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"
, 1643, __extension__ __PRETTY_FUNCTION__); }));
1644	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"
, 1644, __extension__ __PRETTY_FUNCTION__); }));
1645	const int input_size_per_p = input_size / parallel_count;
1646	_ccv_cnnp_model_bind_tensors(model->graph, model->inputs, inputs, input_size_per_p, parallel_count, tensor_binds);
1647	const int output_size_per_p = output_size / parallel_count;
1648	_ccv_cnnp_model_bind_tensors(model->graph, model->outputs, outputs, output_size_per_p, parallel_count, tensor_binds);
1649	const int parameter_size = compiled_data->parameters->rnum;
1650	_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);
1651	const int internal_size = compiled_data->internals->rnum;
1652	_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);
1653	_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);
1654	// If we generated gradient for the graph, only compile part of the graph because the rest is irrelevant for evaluation.
1655	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);
1656	ccv_array_free(tensor_binds);
1657	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));
1658	// If tensor is not init'ed, we need to init states first.
1659	if (tensors_init && parallel_count > 1)
1660		_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);
1661	if (_ccv_cnnp_any_to_init(compiled_data))
1662	{
1663		ccv_nnc_tensor_init_states_t tensor_init_states = {
1664			.parallel_count = parallel_count,
1665			.graph = model->graph,
1666			.compiled_data = compiled_data,
1667			.tensor_arena = compiled_data->tensor_arena
1668		};
1669		ccv_cnnp_model_init_states(model, model->graph, _ccv_cnnp_init_states_for_tensors, &tensor_init_states);
1670	}
1671	compiled_data->is_test = 1;
1672	ccv_nnc_graph_exec_update_t update = {
1673		.parallel_count = parallel_count,
1674		.graph = model->graph,
1675		.graph_exec_arena = compiled_data->graph_exec_arena,
1676	};
1677	ccv_cnnp_model_set_is_test(model, 1, _ccv_cnnp_cmd_update_for_execs, &update);
1678	ccv_nnc_graph_set_default_static_schedule(compiled_data->graph, compiled_data->stream_type, model->max_stream_count);
1679	ccv_nnc_graph_autotune(compiled_data->graph, model->workspace_size, 0, TRAVERSE_FULL0,0,0,0);
1680}
1681 
1682static void _ccv_cnnp_model_gradient_tensors_init(const ccv_cnnp_model_t* const model, ccv_cnnp_compiled_data_t* const compiled_data)
1683{
1684	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", 1684, __extension__ __PRETTY_FUNCTION__
); }));
1685	const int parameter_size = compiled_data->parameters->rnum;
1686	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; });
1687	compiled_data->tensors.gradients = (ccv_nnc_tensor_t**)ccmallocmalloc(sizeof(ccv_nnc_tensor_t*) * parameter_size * 2 * parallel_count);
1688	compiled_data->tensors.accum_gradients = compiled_data->tensors.gradients + parameter_size * parallel_count;
1689	int i, j;
1690	for (i = 0; i < parameter_size; i++)
1691	{
1692		if (compiled_data->parameter_flags && !(compiled_data->parameter_flags[i >> 6] & ((uint64_t)1 << (i & 63))))
1693		{
1694			compiled_data->tensors.gradients[i] = 0;
1695			compiled_data->tensors.accum_gradients[i] = 0;
1696			for (j = 1; j < parallel_count; j++)
1697			{
1698				compiled_data->tensors.gradients[i + j * parameter_size] = 0;
1699				compiled_data->tensors.accum_gradients[i + j * parameter_size] = 0;
1700			}
1701			continue;
1702		}
1703		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)));
1704		ccv_nnc_tensor_param_t info = ccv_nnc_tensor_symbol_params(parameter.graph, parameter);
1705		if (CCV_TENSOR_GET_DEVICE(info.type)((info.type) & 0xfff00) == CCV_COMPUTE_DEVICE_ANY)
1706			CCV_TENSOR_SET_DEVICE_ID(info.type, 0)(info.type) = (((info.type) & ~0xfff00) | (((0) & 0xfff
) << 8));
1707		const int device_id = CCV_TENSOR_GET_DEVICE_ID(info.type)(((info.type) & 0xfff00) >> 8);
1708		compiled_data->tensors.gradients[i] = ccv_nnc_tensor_new(0, info, 0);
1709		compiled_data->tensors.accum_gradients[i] = 0; // delay the accumulated gradient allocation until when we need it.
1710		for (j = 1; j < parallel_count; j++)
1711		{
1712			if (j != device_id)
1713				CCV_TENSOR_SET_DEVICE_ID(info.type, j)(info.type) = (((info.type) & ~0xfff00) | (((j) & 0xfff
) << 8));
1714			else
1715				CCV_TENSOR_SET_DEVICE_ID(info.type, 0)(info.type) = (((info.type) & ~0xfff00) | (((0) & 0xfff
) << 8));
1716			compiled_data->tensors.gradients[i + j * parameter_size] = ccv_nnc_tensor_new(0, info, 0);
1717			compiled_data->tensors.accum_gradients[i + j * parameter_size] = 0;
1718		}
1719	}
1720}
1721 
1722static int _ccv_cnnp_is_disable_outgrad_all(const uint64_t disable_outgrad, const int input_size)
1723{
1724	if (disable_outgrad == CCV_CNNP_DISABLE_OUTGRAD_ALL)
1725		return 1;
1726	if (disable_outgrad == CCV_CNNP_DISABLE_OUTGRAD_NONE)
1727		return 0;
1728	int i;
1729	for (i = 0; i < input_size; i++)
1730		if (!(disable_outgrad & ((uint64_t)1 << i)))
1731			return 0;
1732	return 1;
1733}
1734 
1735// Compile the graph to run ccv_cnnp_model_evaluate with requires_grad = true (MULTISTAGE_MODE).
1736// Particularly, this method compiles the evaluation and backprop graph (the main graph).
1737static 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)
1738{
1739	int i, j;
1740	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
1741	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;
1742	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", 1742, __extension__ __PRETTY_FUNCTION__
); }));
1743	compiled_data->graph_mode = CCV_CNNP_MODEL_GRAPH_MULTISTAGE_MODE;
1744	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; });
1745	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", 1745, __extension__ __PRETTY_FUNCTION__
); }));
1746	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", 1746, __extension__ __PRETTY_FUNCTION__
); }));
1747	// There shouldn't be a loss function if we evaluate with multistage jit.
1748	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", 1748, __extension__ __PRETTY_FUNCTION__
); }));
1749	if (compiled_data->gradient_mode == CCV_CNNP_COMPILED_DATA_GRADIENT_NONE)
1750	{
1751		_ccv_cnnp_model_set_rewindables(model);
1752		_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.
1753	} else if (compiled_data->gradient_mode != target_gradient_mode) {
1754		_ccv_cnnp_model_rewind_graph(model);
1755		_ccv_cnnp_compiled_data_gradient_free(compiled_data);
1756		compiled_data->gradient_mode = CCV_CNNP_COMPILED_DATA_GRADIENT_NONE;
1757		_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.
1758	}
1759	const int tensors_init = !!compiled_data->tensors_init.v;
1760	if (!tensors_init)
1761		_ccv_cnnp_model_tensors_init(model, compiled_data);
1762	else if ((uintptr_t)compiled_data->tensors_init.v & (uintptr_t)1)
1763	// Check if it is not fully allocated, if it is not, init_1.
1764		ccv_cnnp_model_tensors_init_1(model, compiled_data);
1765	ccv_array_t* const tensor_binds = ccv_array_new(sizeof(ccv_nnc_tensor_bind_t), 0, 0);
1766	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"
, 1766, __extension__ __PRETTY_FUNCTION__); }));
1767	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"
, 1767, __extension__ __PRETTY_FUNCTION__); }));
1768	const int input_size_per_p = input_size / parallel_count;
1769	_ccv_cnnp_model_bind_tensors(model->graph, model->inputs, inputs, input_size_per_p, parallel_count, tensor_binds);
1770	const int output_size_per_p = output_size / parallel_count;
1771	_ccv_cnnp_model_bind_tensors(model->graph, model->outputs, outputs, output_size_per_p, parallel_count, tensor_binds);
1772	const int parameter_size = compiled_data->parameters->rnum;
1773	_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);
1774	const int internal_size = compiled_data->internals->rnum;
1775	_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);
1776	_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);
1777	if (!compiled_data->tensors.gradients)
1778		_ccv_cnnp_model_gradient_tensors_init(model, compiled_data);
1779	_ccv_cnnp_model_bind_tensors(model->graph, compiled_data->gradients, compiled_data->tensors.gradients, parameter_size, parallel_count, tensor_binds);
1780	if (compiled_data->backward.to_size > 0)
1781		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);
1782	else
1783		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);
1784	ccv_array_free(tensor_binds);
1785	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));
1786	if (tensors_init && parallel_count > 1)
1787		_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);
1788	// If tensor is not init'ed, we need to init states first.
1789	if (_ccv_cnnp_any_to_init(compiled_data))
1790	{
1791		ccv_nnc_tensor_init_states_t tensor_init_states = {
1792			.parallel_count = parallel_count,
1793			.graph = model->graph,
1794			.compiled_data = compiled_data,
1795			.tensor_arena = compiled_data->tensor_arena
1796		};
1797		ccv_cnnp_model_init_states(model, model->graph, _ccv_cnnp_init_states_for_tensors, &tensor_init_states);
1798	}
1799	compiled_data->is_test = is_test;
1800	ccv_nnc_graph_exec_update_t update = {
1801		.parallel_count = parallel_count,
1802		.graph = model->graph,
1803		.graph_exec_arena = compiled_data->graph_exec_arena,
1804	};
1805	ccv_cnnp_model_set_is_test(model, is_test, _ccv_cnnp_cmd_update_for_execs, &update);
1806	const int evaluate_to_size = compiled_data->evaluate.to_size;
1807	compiled_data->evaluate.to_op_size = 0;
1808	ccv_array_t* const backward_from = ccv_array_new(sizeof(int), 0, 0);
1809	for (i = 0; i < evaluate_to_size; i++)
1810	{
1811		ccv_nnc_graph_exec_t const to_op = ccv_nnc_graph_exec_from_symbol(compiled_data->graph_exec_arena, compiled_data->evaluate.tos[i]);
1812		if (to_op.graph)
1813			compiled_data->evaluate.to_ops[compiled_data->evaluate.to_op_size++] = to_op;
1814		const int* tos;
1815		int to_size;
1816		ccv_nnc_graph_exec_symbol_to(model->graph, compiled_data->evaluate.tos[i], &tos, &to_size);
1817		for (j = 0; j < to_size; j++)
1818		{
1819			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){
1820				.d = tos[j],
1821				.graph = model->graph
1822			});
1823			if (to_op.graph)
1824				ccv_array_add_unique_int(backward_from, to_op.d);
1825		}
1826	}
1827	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", 1827, __extension__
 __PRETTY_FUNCTION__); }));
1828	compiled_data->backward.from_op_size = backward_from->rnum;
1829	compiled_data->backward.from_ops = (ccv_nnc_graph_exec_t*)ccmallocmalloc(sizeof(ccv_nnc_graph_exec_t) * backward_from->rnum);
1830	for (i = 0; i < backward_from->rnum; i++)
1831		compiled_data->backward.from_ops[i] = (ccv_nnc_graph_exec_t){
1832			.d = *(int*)ccv_array_get(backward_from, i)((void*)(((char*)((backward_from)->data)) + (size_t)(backward_from
)->rsize * (size_t)(i))),
1833			.graph = compiled_data->graph,
1834		};
1835	// 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.
1836	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)));
1837	const int exec_info_size = compiled_data->graph->exec_info->rnum;
1838	uint32_t* const visited = cccalloccalloc((exec_info_size + 31) >> 5, sizeof(uint32_t));
1839	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)));
1840	const int source_size = compiled_data->graph->sources->rnum;
1841	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", 1841, __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", 1841, __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", 1841, __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", 1841, __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", 1841, __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", 1841, __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", 1841, __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", 1841, __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", 1841, __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", 1841, __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", 1841, __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"
, 1841, __extension__ __PRETTY_FUNCTION__); })); _visit_; });
1842	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; {
1843		visited[(idx >> 5)] |= (1u << (idx & 31));
1844	} ccv_nnc_graph_visit_endfor} }
1845	ccv_nnc_graph_visit_free(visit);
1846	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)));
1847	const int destination_size = compiled_data->graph->destinations->rnum;
1848	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", 1848, __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", 1848, __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", 1848, __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"
, 1848, __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", 1848, __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", 1848, __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", 1848, __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", 1848, __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", 1848, __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", 1848, __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", 1848, __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", 1848, __extension__ __PRETTY_FUNCTION__
); })); _visit_; });
1849	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; {
1850		visited[(idx >> 5)] |= (1u << (idx & 31));
1851	} ccv_nnc_graph_visit_endfor} }
1852	ccv_nnc_graph_visit_free(visit);
1853	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", 1853, __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", 1853, __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", 1853, __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", 1853, __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", 1853, __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", 1853, __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", 1853, __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", 1853, __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", 1853, __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", 1853, __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", 1853, __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", 1853, __extension__ __PRETTY_FUNCTION__
); })); _visit_; });
1854	// Find any missing nodes to be added as source. Right now, these are only set nodes.
1855	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; {
1856		if (!(visited[(idx >> 5)] & (1u << (idx & 31))))
1857		{
1858			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", 1858, __extension__ __PRETTY_FUNCTION__
); }));
1859			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.
1860				ccv_array_add_unique_int(backward_from, idx);
1861		}
1862	} ccv_nnc_graph_visit_endfor} }
1863	ccv_nnc_graph_visit_free(visit);
1864	ccfreefree(visited);
1865	if (backward_from->rnum != compiled_data->backward.from_op_size) // If it doesn't match, need to redo this.
1866	{
1867		compiled_data->backward.from_op_size = backward_from->rnum;
1868		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);
1869		for (i = 0; i < backward_from->rnum; i++)
1870			compiled_data->backward.from_ops[i] = (ccv_nnc_graph_exec_t){
1871				.d = *(int*)ccv_array_get(backward_from, i)((void*)(((char*)((backward_from)->data)) + (size_t)(backward_from
)->rsize * (size_t)(i))),
1872				.graph = compiled_data->graph,
1873			};
1874	}
1875	ccv_array_free(backward_from);
1876	ccv_nnc_graph_set_default_static_schedule(compiled_data->graph, compiled_data->stream_type, model->max_stream_count);
1877	ccv_nnc_graph_autotune(compiled_data->graph, model->workspace_size, 0, TRAVERSE_FULL0,0,0,0);
1878}
1879 
1880void 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)
1881{
1882	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
1883	assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
 (compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 1883, __extension__ __PRETTY_FUNCTION__); }));
1884	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; });
1885	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", 1885, __extension__ __PRETTY_FUNCTION__
); }));
1886	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", 1886, __extension__ __PRETTY_FUNCTION__
); }));
1887	assert(model->graph)((void) sizeof ((model->graph) ? 1 : 0), __extension__ ({ if
 (model->graph) ; else __assert_fail ("model->graph", "ccv_cnnp_model.c"
, 1887, __extension__ __PRETTY_FUNCTION__); }));
1888	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;
1889	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));
1890	if (!compiled_data->graph || mode_mismatch)
1891	{
1892		_ccv_cnnp_compiled_data_graph_free(compiled_data);
1893		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.
1894			_ccv_cnnp_compiled_data_backward_free(compiled_data);
1895		if (params.requires_grad)
1896			_ccv_cnnp_model_multistage_jit_0(model, params.disable_outgrad, params.is_test, inputs, input_size, outputs, output_size);
1897		else
1898			_ccv_cnnp_model_multistage_no_grad_jit(model, inputs, input_size, outputs, output_size);
1899	} else {
1900		ccv_nnc_tensor_arena_clear_bindings(compiled_data->tensor_arena);
1901		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"
, 1901, __extension__ __PRETTY_FUNCTION__); }));
1902		const int input_size_per_p = input_size / parallel_count;
1903		_ccv_cnnp_bind_tensors_to_arena(compiled_data->tensor_arena, model->graph, model->inputs, inputs, input_size_per_p, parallel_count);
1904		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"
, 1904, __extension__ __PRETTY_FUNCTION__); }));
1905		const int output_size_per_p = output_size / parallel_count;
1906		_ccv_cnnp_bind_tensors_to_arena(compiled_data->tensor_arena, model->graph, model->outputs, outputs, output_size_per_p, parallel_count);
1907	}
1908	if (compiled_data->is_test != params.is_test)
1909	{
1910		compiled_data->is_test = params.is_test;
1911		ccv_nnc_graph_exec_update_t update = {
1912			.parallel_count = parallel_count,
1913			.graph = model->graph,
1914			.graph_exec_arena = compiled_data->graph_exec_arena,
1915		};
1916		ccv_cnnp_model_set_is_test(model, params.is_test, _ccv_cnnp_cmd_update_for_execs, &update);
1917	}
1918}
1919 
1920void 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)
1921{
1922	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
1923	assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
 (compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 1923, __extension__ __PRETTY_FUNCTION__); }));
1924	ccv_cnnp_model_dry_run(model, params, inputs, input_size, outputs, output_size);
1925	if (compiled_data->graph_mode == CCV_CNNP_MODEL_GRAPH_MULTISTAGE_MODE_NO_GRAD)
1926		ccv_nnc_graph_run_with_schedule(compiled_data->graph, 0, 0, tensor_tape, stream_context);
1927	else {
1928		if (!compiled_data->evaluate.schedule)
1929			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);
1930		ccv_nnc_graph_run_with_schedule(compiled_data->graph, 0, compiled_data->evaluate.schedule, tensor_tape, stream_context);
1931	}
1932}
1933 
1934// Compile the graph to run ccv_cnnp_model_backward after ccv_cnnp_model_evaluate with requires_grad = true (MULTISTAGE_MODE).
1935// Particularly, this method compiles the accumulator graph.
1936static void _ccv_cnnp_model_multistage_jit_1(ccv_cnnp_model_t* const model)
1937{
1938	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
1939	assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
 (compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 1939, __extension__ __PRETTY_FUNCTION__); }));
1940	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", 1940, __extension__ __PRETTY_FUNCTION__
); }));
1941	ccv_nnc_symbolic_graph_t* accum = ccv_nnc_symbolic_graph_new();
1942	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; });
1943	const int parameter_size = compiled_data->parameters->rnum;
1944	int i, j;
1945	compiled_data->backward.gradients = (ccv_nnc_tensor_symbol_t*)ccmallocmalloc(sizeof(ccv_nnc_tensor_symbol_t) * parameter_size * parallel_count * 3);
1946	compiled_data->backward.accum_gradients = compiled_data->backward.gradients + parameter_size * parallel_count;
1947	compiled_data->backward.updated_accum_gradients = compiled_data->backward.accum_gradients + parameter_size * parallel_count;
1948	for (i = 0; i < parameter_size; i++)
1949		for (j = 0; j < parallel_count; j++)
1950			if (compiled_data->tensors.gradients[i + j * parameter_size])
1951			{
1952				const ccv_nnc_tensor_param_t info = compiled_data->tensors.gradients[i + j * parameter_size]->info;
1953				// Now, the old gradient is the accumulated gradient, getting new gradient tensor setup so we can collect them.
1954				compiled_data->tensors.accum_gradients[i + j * parameter_size] = compiled_data->tensors.gradients[i + j * parameter_size];
1955				compiled_data->tensors.gradients[i + j * parameter_size] = ccv_nnc_tensor_new(0, info, 0);
1956				ccv_nnc_tensor_symbol_t inputs[2];
1957				inputs[0] = compiled_data->backward.accum_gradients[i + j * parameter_size] = ccv_nnc_tensor_symbol_new(accum, info, 0);
1958				inputs[1] = compiled_data->backward.gradients[i + j * parameter_size] = ccv_nnc_tensor_symbol_new(accum, info, 0);
1959				ccv_nnc_tensor_symbol_t output = compiled_data->backward.updated_accum_gradients[i + j * parameter_size] = ccv_nnc_tensor_symbol_new(accum, info, 0);
1960				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);
1961			} else {
1962				compiled_data->backward.accum_gradients[i + j * parameter_size] = NO_TENSOR_SYMBOL(const ccv_nnc_tensor_symbol_t){.d = CCV_NNC_NO_TENSOR_SYMBOL
};
1963				compiled_data->backward.gradients[i + j * parameter_size] = NO_TENSOR_SYMBOL(const ccv_nnc_tensor_symbol_t){.d = CCV_NNC_NO_TENSOR_SYMBOL
};
1964				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
};
1965			}
1966	ccv_nnc_graph_exec_symbol_autogen(accum, 0, 0, CCV_NNC_AUTOGEN_ALL_EXECS | CCV_NNC_AUTOGEN_SOURCES_AND_DESTINATIONS);
1967	if (ccv_nnc_symbolic_graph_source_size(accum) == 0)
1968	{
1969		ccv_nnc_symbolic_graph_free(accum);
1970		// Create empty graph.
1971		compiled_data->backward.accum = ccv_nnc_graph_new();
1972		ccv_nnc_graph_topsort(compiled_data->backward.accum, 0, 0);
1973		return;
1974	}
1975	ccv_array_t* const tensor_binds = ccv_array_new(sizeof(ccv_nnc_tensor_bind_t), 0, 0);
1976	_ccv_cnnp_model_bind_tensors(accum, compiled_data->backward.accum_gradients, compiled_data->tensors.accum_gradients, parameter_size * parallel_count, 1, tensor_binds);
1977	_ccv_cnnp_model_bind_tensors(accum, compiled_data->backward.gradients, compiled_data->tensors.gradients, parameter_size * parallel_count, 1, tensor_binds);
1978	_ccv_cnnp_model_bind_tensors(accum, compiled_data->backward.updated_accum_gradients, compiled_data->tensors.accum_gradients, parameter_size * parallel_count, 1, tensor_binds);
1979	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);
1980	ccv_nnc_symbolic_graph_free(accum);
1981	ccv_array_free(tensor_binds);
1982	ccv_nnc_graph_set_default_static_schedule(compiled_data->backward.accum, compiled_data->stream_type, model->max_stream_count);
1983}
1984 
1985void 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)
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	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; });
1991	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", 1991, __extension__ __PRETTY_FUNCTION__
); }));
1992	if (outgrad_size > 0)
1993		{ 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", 1993, __extension__ __PRETTY_FUNCTION__
); })); }
1994	assert(model->graph)((void) sizeof ((model->graph) ? 1 : 0), __extension__ ({ if
 (model->graph) ; else __assert_fail ("model->graph", "ccv_cnnp_model.c"
, 1994, __extension__ __PRETTY_FUNCTION__); }));
1995	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", 1995, __extension__ __PRETTY_FUNCTION__
); }));
1996	const int parameter_size = compiled_data->parameters->rnum;
1997	// If we need to accumulate the gradients now, do jit on accumulator.
1998	if (compiled_data->backward.count > 0)
1999	{
2000		if (!compiled_data->backward.accum)
2001			_ccv_cnnp_model_multistage_jit_1(model);
2002		else if (compiled_data->backward.count == 1) {
2003			//  On this round, we need to switch accumulated gradients with gradients (so we can do accumulation properly).
2004			int i;
2005			for (i = 0; i < parameter_size * parallel_count; i++)
2006			{
2007				ccv_nnc_tensor_t* tensor;
2008				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));
2009			}
2010			if (compiled_data->backward.tensor_arena)
2011			{
2012				ccv_nnc_tensor_arena_clear_bindings(compiled_data->backward.tensor_arena);
2013				// Do rebind in case we messed up the binding (we switch accum_gradients and gradients).
2014				_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);
2015				_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);
2016				_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);
2017			}
2018		}
2019	}
2020	const int ingrad_size_per_p = model->output_size;
2021	const int outgrad_size_per_p = compiled_data->outgrad_size;
2022	int i, j;
2023	for (i = 0; i < ingrad_size_per_p; i++)
2024	{
2025		const ccv_nnc_tensor_symbol_t ingrad = ccv_nnc_tensor_symbol_for_backward(model->graph, compiled_data->f[i]);
2026		if (!ingrad_size || !ingrads || ingrads[i] == 0)
2027		{
2028			// Set it to 1 if it is not specified.
2029			ccv_nnc_tensor_t* const ingrad_tensor = ccv_nnc_tensor_from_symbol(compiled_data->tensor_arena, ingrad);
2030			if (ingrad_tensor)
2031				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);
2032			for (j = 1; j < parallel_count; j++)
2033			{
2034				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));
2035				if (ingrad_tensor)
2036					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);
2037			}
2038		} else {
2039			// Make sure the length matches, in case it is an alias.
2040			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", 2040, __extension__ __PRETTY_FUNCTION__
); }));
2041			ccv_nnc_tensor_bind_symbol(compiled_data->tensor_arena, ingrad, ingrads[i]);
2042			for (j = 1; j < parallel_count; j++)
2043				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]);
2044		}
2045	}
2046	if (outgrad_size > 0)
2047	{
2048		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", 2048, __extension__ __PRETTY_FUNCTION__
); }));
2049		for (i = 0; i < outgrad_size_per_p; i++)
2050			if (outgrads[i])
2051			{
2052				const ccv_nnc_tensor_symbol_t outgrad = compiled_data->outgrads[i];
2053				ccv_nnc_tensor_bind_symbol(compiled_data->tensor_arena, outgrad, outgrads[i]);
2054				for (j = 1; j < parallel_count; j++)
2055					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]);
2056			}
2057	} else {
2058		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", 2059, __extension__ __PRETTY_FUNCTION__
); }))
2059			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", 2059, __extension__ __PRETTY_FUNCTION__
); }));
2060	}
2061	// We need to rebind here because in ccv_cnnp_evaluate, we clear bindings, that will reset all bindings for the gradients.
2062	// For parameters and internals these are fine because when we clear bindings, it restores to original bindings, which are these
2063	// parameters and internals. The same cannot be said for gradients due to the accum_gradients switching.
2064	_ccv_cnnp_bind_tensors_to_arena(compiled_data->tensor_arena, model->graph, compiled_data->gradients, compiled_data->tensors.gradients, parameter_size, parallel_count);
2065	if (!compiled_data->backward.schedule)
2066		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);
2067	// Run the backward pass.
2068	ccv_nnc_graph_run_with_schedule(compiled_data->graph, 0, compiled_data->backward.schedule, tensor_tape, stream_context);
2069	// If we need to run accumulation round, do that now.
2070	if (compiled_data->backward.count > 0)
2071		ccv_nnc_graph_run_with_schedule(compiled_data->backward.accum, 0, 0, 0, stream_context);
2072	// Update the count, this determines whether we need to accumulate or not.
2073	++compiled_data->backward.count;
2074}
2075 
2076// Compile the graph to run ccv_cnnp_model_apply_gradients after ccv_cnnp_model_backward (MULTISTAGE_MODE).
2077// Particularly, this method compiles the parameter update graph.
2078static void _ccv_cnnp_model_multistage_jit_2(ccv_cnnp_model_t* const model)
2079{
2080	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
2081	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", 2081, __extension__ __PRETTY_FUNCTION__
); }));
2082	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; });
2083	const int parameter_size = compiled_data->parameters->rnum;
2084	ccv_array_t* const tensor_binds = ccv_array_new(sizeof(ccv_nnc_tensor_bind_t), 0, 0);
2085	_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);
2086	_ccv_cnnp_model_bind_tensors(model->graph, compiled_data->updated_parameters, compiled_data->tensors.parameters, parameter_size, parallel_count, tensor_binds);
2087	// Bind accumulated gradients.
2088	if (compiled_data->backward.count > 1)
2089		_ccv_cnnp_model_bind_tensors(model->graph, compiled_data->gradients, compiled_data->tensors.accum_gradients, parameter_size, parallel_count, tensor_binds);
2090	else
2091		_ccv_cnnp_model_bind_tensors(model->graph, compiled_data->gradients, compiled_data->tensors.gradients, parameter_size, parallel_count, tensor_binds);
2092	ccv_array_t* const apply_gradients_from = ccv_array_new(sizeof(int), 0, 0);
2093	int i, j;
2094	for (i = 0; i < compiled_data->backward.to_size; i++)
2095	{
2096		const int* tos;
2097		int to_size;
2098		ccv_nnc_graph_exec_symbol_to(model->graph, compiled_data->backward.tos[i], &tos, &to_size);
2099		for (j = 0; j < to_size; j++)
2100		{
2101			// Check if this is already show up in the backward graph, if that is the case, it won't be in the apply
2102			// gradients graph.
2103			const ccv_nnc_graph_exec_t exec = ccv_nnc_graph_exec_from_symbol(compiled_data->graph_exec_arena, (ccv_nnc_graph_exec_symbol_t){
2104				.d = tos[j],
2105				.graph = model->graph,
2106			});
2107			if (!exec.graph)
2108				ccv_array_add_unique_int(apply_gradients_from, tos[j]);
2109		}
2110	}
2111	const int from_size = apply_gradients_from->rnum;
2112	if (from_size == 0)
2113	{
2114		ccv_array_free(apply_gradients_from);
2115		ccv_array_free(tensor_binds);
2116		return;
2117	}
2118	ccv_nnc_graph_exec_symbol_t* const froms = (ccv_nnc_graph_exec_symbol_t*)ccmallocmalloc(sizeof(ccv_nnc_graph_exec_symbol_t) * from_size);
2119	for (i = 0; i < from_size; i++)
2120		froms[i] = (ccv_nnc_graph_exec_symbol_t){
2121			.d = *(int*)ccv_array_get(apply_gradients_from, i)((void*)(((char*)((apply_gradients_from)->data)) + (size_t
)(apply_gradients_from)->rsize * (size_t)(i))),
2122			.graph = model->graph
2123		};
2124	ccv_array_free(apply_gradients_from);
2125	// It can only ends with updates on the parameters.
2126	ccv_array_t* const tos = ccv_array_new(sizeof(ccv_nnc_graph_exec_symbol_t), parameter_size * parallel_count, 0);
2127	for (i = 0;  i < parameter_size; i++)
2128	{
2129		if (compiled_data->update_nodes[i].d == CCV_NNC_NO_TENSOR_SYMBOL)
2130			continue;
2131		ccv_array_push(tos, &compiled_data->update_nodes[i]);
2132		for (j = 1; j < parallel_count; j++)
2133		{
2134			const ccv_nnc_graph_exec_symbol_t copy = ccv_nnc_graph_exec_symbol_copy(model->graph, compiled_data->update_nodes[i], j);
2135			ccv_array_push(tos, &copy);
2136		}
2137	}
2138	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);
2139	ccv_array_free(tos);
2140	ccv_array_free(tensor_binds);
2141	ccfreefree(froms);
2142	const int max_saved_aux_size = compiled_data->minimize.max_saved_aux_size;
2143	for (i = 0; i < max_saved_aux_size * parameter_size; i++)
2144	{
2145		// Skip on no tensor.
2146		if (compiled_data->saved_aux[i].source.d == CCV_NNC_NO_TENSOR_SYMBOL)
2147			continue;
2148		ccv_nnc_tensor_t* const tensor = ccv_nnc_tensor_from_symbol(compiled_data->apply_gradients.tensor_arena, compiled_data->saved_aux[i].source);
2149		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);
2150		for (j = 1; j < parallel_count; j++)
2151		{
2152			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));
2153			if (copy)
2154				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);
2155		}
2156	}
2157	ccv_nnc_graph_set_default_static_schedule(compiled_data->apply_gradients.graph, compiled_data->stream_type, model->max_stream_count);
2158}
2159 
2160void ccv_cnnp_model_apply_gradients(ccv_cnnp_model_t* const model, ccv_nnc_stream_context_t* const stream_context)
2161{
2162	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
2163	assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
 (compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 2163, __extension__ __PRETTY_FUNCTION__); }));
2164	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", 2164, __extension__ __PRETTY_FUNCTION__
); }));
2165	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; });
2166	assert(model->graph)((void) sizeof ((model->graph) ? 1 : 0), __extension__ ({ if
 (model->graph) ; else __assert_fail ("model->graph", "ccv_cnnp_model.c"
, 2166, __extension__ __PRETTY_FUNCTION__); }));
2167	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", 2167, __extension__ __PRETTY_FUNCTION__
); }));
2168	// Skip if there is no backward pass.
2169	if (compiled_data->backward.count <= 0)
2170		return;
2171	// Skip if there is no parameters.
2172	if (compiled_data->parameters->rnum == 0)
2173	{
2174		compiled_data->backward.count = 0;
2175		return;
2176	}
2177	if (!compiled_data->apply_gradients.graph)
2178		_ccv_cnnp_model_multistage_jit_2(model);
2179	else {
2180		const int parameter_size = compiled_data->parameters->rnum;
2181		ccv_nnc_tensor_arena_clear_bindings(compiled_data->apply_gradients.tensor_arena);
2182		// Change to bind accum_gradients if we do gradient accumulation (run backward more than once).
2183		if (compiled_data->backward.count > 1)
2184			_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);
2185		else
2186			_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);
2187	}
2188	if (compiled_data->apply_gradients.graph)
2189		ccv_nnc_graph_run_with_schedule(compiled_data->apply_gradients.graph, 0, 0, 0, stream_context);
2190	// Reset backward count to 0.
2191	compiled_data->backward.count = 0;
2192}
2193 
2194void 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)
2195{
2196	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
2197	const int param_sel = parameter->param_sel > 0 ? parameter->param_sel - 1 : parameter->param_sel;
2198	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", 2198, __extension__ __PRETTY_FUNCTION__
); }));
2199	const int tensors_init = !!compiled_data->tensors_init.v;
2200	if (!tensors_init)
2201		_ccv_cnnp_model_tensors_init(model, compiled_data);
2202	else if ((uintptr_t)compiled_data->tensors_init.v & (uintptr_t)1)
2203	// Check if it is not fully allocated, if it is not, init_1.
2204		ccv_cnnp_model_tensors_init_1(model, compiled_data);
2205	ccv_array_t* const parameter_indices = ccv_array_new(sizeof(int), 0, 0);
2206	ccv_cnnp_model_add_to_parameter_indices(parameter->model, param_sel, parameter_indices);
2207	const int param_ref = parameter->param_ref > 0 ? parameter->param_ref - 1 : parameter->param_ref;
2208	if (param_ref < 0)
2209		{ 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", 2209
, __extension__ __PRETTY_FUNCTION__); })); }
2210	else
2211		{ 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", 2211, __extension__ __PRETTY_FUNCTION__
); })); }
2212	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)));
2213	ccv_array_free(parameter_indices);
2214	const int parameter_size = compiled_data->parameters->rnum;
2215	assert(d >= 0)((void) sizeof ((d >= 0) ? 1 : 0), __extension__ ({ if (d >=
 0) ; else __assert_fail ("d >= 0", "ccv_cnnp_model.c", 2215
, __extension__ __PRETTY_FUNCTION__); }));
2216	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", 2216, __extension__ __PRETTY_FUNCTION__
); }));
2217	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; });
2218	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));
2219	assert(dest)((void) sizeof ((dest) ? 1 : 0), __extension__ ({ if (dest) ;
 else __assert_fail ("dest", "ccv_cnnp_model.c", 2219, __extension__
 __PRETTY_FUNCTION__); }));
2220	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);
2221	int i;
2222	for (i = 1; i < parallel_count; i++)
2223	{
2224		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));
2225		if (copy_tensor)
2226			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);
2227	}
2228	// Mark this symbol as init'ed.
2229	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;
2230  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));
2231	init_v[s >> 5] |= (1u << (s & 0x1f));
2232}
2233 
2234void ccv_cnnp_model_parameter_copy(ccv_cnnp_model_t* const model, const ccv_cnnp_model_io_t parameter, ccv_nnc_tensor_t* const tensor)
2235{
2236	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
2237	const int param_sel = parameter->param_sel > 0 ? parameter->param_sel - 1 : parameter->param_sel;
2238	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", 2238, __extension__ __PRETTY_FUNCTION__
); }));
2239	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", 2239, __extension__ __PRETTY_FUNCTION__
); }));
2240	ccv_array_t* const parameter_indices = ccv_array_new(sizeof(int), 0, 0);
2241	ccv_cnnp_model_add_to_parameter_indices(parameter->model, param_sel, parameter_indices);
2242	const int param_ref = parameter->param_ref > 0 ? parameter->param_ref - 1 : parameter->param_ref;
2243	if (param_ref < 0)
2244		{ 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", 2244
, __extension__ __PRETTY_FUNCTION__); })); }
2245	else
2246		{ 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", 2246, __extension__ __PRETTY_FUNCTION__
); })); }
2247	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)));
2248	ccv_array_free(parameter_indices);
2249	const int parameter_size = compiled_data->parameters->rnum;
2250	assert(d >= 0)((void) sizeof ((d >= 0) ? 1 : 0), __extension__ ({ if (d >=
 0) ; else __assert_fail ("d >= 0", "ccv_cnnp_model.c", 2250
, __extension__ __PRETTY_FUNCTION__); }));
2251	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", 2251, __extension__ __PRETTY_FUNCTION__
); }));
2252	// We don't need to consider parallel_count, every parameter on each device is identical.
2253	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));
2254	assert(src)((void) sizeof ((src) ? 1 : 0), __extension__ ({ if (src) ; else
 __assert_fail ("src", "ccv_cnnp_model.c", 2254, __extension__
 __PRETTY_FUNCTION__); }));
2255	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);
2256}
2257 
2258ccv_nnc_tensor_param_t ccv_cnnp_model_parameter_tensor_params(ccv_cnnp_model_t* const model, const ccv_cnnp_model_io_t parameter)
2259{
2260	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
2261	const int param_sel = parameter->param_sel > 0 ? parameter->param_sel - 1 : parameter->param_sel;
2262	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", 2262, __extension__ __PRETTY_FUNCTION__
); }));
2263	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", 2263, __extension__ __PRETTY_FUNCTION__
); }));
2264	ccv_array_t* const parameter_indices = ccv_array_new(sizeof(int), 0, 0);
2265	ccv_cnnp_model_add_to_parameter_indices(parameter->model, param_sel, parameter_indices);
2266	const int param_ref = parameter->param_ref > 0 ? parameter->param_ref - 1 : parameter->param_ref;
2267	if (param_ref < 0)
2268		{ 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", 2268
, __extension__ __PRETTY_FUNCTION__); })); }
2269	else
2270		{ 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", 2270, __extension__ __PRETTY_FUNCTION__
); })); }
2271	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)));
2272	ccv_array_free(parameter_indices);
2273	const int parameter_size = compiled_data->parameters->rnum;
2274	assert(d >= 0)((void) sizeof ((d >= 0) ? 1 : 0), __extension__ ({ if (d >=
 0) ; else __assert_fail ("d >= 0", "ccv_cnnp_model.c", 2274
, __extension__ __PRETTY_FUNCTION__); }));
2275	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", 2275, __extension__ __PRETTY_FUNCTION__
); }));
2276	// We don't need to consider parallel_count, every parameter on each device is identical.
2277	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));
2278	assert(tensor)((void) sizeof ((tensor) ? 1 : 0), __extension__ ({ if (tensor
) ; else __assert_fail ("tensor", "ccv_cnnp_model.c", 2278, __extension__
 __PRETTY_FUNCTION__); }));
2279	return tensor->info;
2280}
2281 
2282const char* ccv_cnnp_model_parameter_name(ccv_cnnp_model_t* const model, const ccv_cnnp_model_io_t parameter)
2283{
2284	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
2285	const int param_sel = parameter->param_sel > 0 ? parameter->param_sel - 1 : parameter->param_sel;
2286	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", 2286, __extension__ __PRETTY_FUNCTION__
); }));
2287	ccv_array_t* const parameter_indices = ccv_array_new(sizeof(int), 0, 0);
2288	ccv_cnnp_model_add_to_parameter_indices(parameter->model, param_sel, parameter_indices);
2289	const int param_ref = parameter->param_ref > 0 ? parameter->param_ref - 1 : parameter->param_ref;
2290	if (param_ref < 0)
2291		{ 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", 2291
, __extension__ __PRETTY_FUNCTION__); })); }
2292	else
2293		{ 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", 2293, __extension__ __PRETTY_FUNCTION__
); })); }
2294	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)));
2295	ccv_array_free(parameter_indices);
2296	const int parameter_size = compiled_data->parameters->rnum;
2297	assert(d >= 0)((void) sizeof ((d >= 0) ? 1 : 0), __extension__ ({ if (d >=
 0) ; else __assert_fail ("d >= 0", "ccv_cnnp_model.c", 2297
, __extension__ __PRETTY_FUNCTION__); }));
2298	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", 2298, __extension__ __PRETTY_FUNCTION__
); }));
2299	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)));
2300}
2301 
2302int ccv_cnnp_model_parameter_count(ccv_cnnp_model_t* const model)
2303{
2304	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", 2304, __extension__ __PRETTY_FUNCTION__
); }));
2305	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
2306	return compiled_data->parameters->rnum;
2307}
2308 
2309uint64_t ccv_cnnp_model_parameters_size(ccv_cnnp_model_t* const model)
2310{
2311	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", 2311, __extension__ __PRETTY_FUNCTION__
); }));
2312	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
2313	const int parameter_size = compiled_data->parameters->rnum;
2314	int i;
2315	const ccv_nnc_symbolic_graph_t* const graph = model->graph;
2316	uint64_t size = 0;
2317	const int tensors_init = !!compiled_data->tensors_init.v;
2318	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;
2319	for (i = 0; i < parameter_size; i++)
2320	{
2321		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;
2322		if (tensors_init && compiled_data->tensors.parameters && (init_v[d >> 5] | (1u << (d & 0x1f))) && compiled_data->tensors.parameters[i])
2323		{
2324			ccv_nnc_tensor_param_t params = compiled_data->tensors.parameters[i]->info;
2325			size += ccv_nnc_tensor_data_size(params);
2326			continue;
2327		}
2328		ccv_nnc_tensor_param_t params = ccv_nnc_tensor_symbol_params(graph, (ccv_nnc_tensor_symbol_t){
2329			.graph = graph,
2330			.d = d
2331		});
2332		size += ccv_nnc_tensor_data_size(params);
2333	}
2334	return size;
2335}
2336 
2337int 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)
2338{
2339	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", 2339, __extension__ __PRETTY_FUNCTION__
); }));
2340	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
2341	if (count != compiled_data->parameters->rnum)
2342		return 0;
2343	if (CCV_TENSOR_GET_DEVICE(type)((type) & 0xfff00) == CCV_COMPUTE_DEVICE_ANY)
2344		CCV_TENSOR_SET_DEVICE_ID(type, 0)(type) = (((type) & ~0xfff00) | (((0) & 0xfff) <<
 8));
2345	int i;
2346	// We don't need to consider parallel_count, every parameter on each device is identical.
2347	for (i = 0; i < count; i++)
2348	{
2349		ccv_nnc_tensor_t* tensor = compiled_data->tensors.parameters[i];
2350		if ((uintptr_t)tensor & (uintptr_t)1) // If it is not owned. We don't do anything.
2351		{
2352			tensors[i] = 0;
2353			continue;
2354		}
2355		tensor = CCV_NNC_TENSOR(tensor)((ccv_nnc_tensor_t*)((uintptr_t)(tensor) & ~(uintptr_t)1)
);
2356		if (tensor->info.type == type)
2357			tensors[i] = tensor;
2358		else {
2359			ccv_nnc_tensor_param_t info = tensor->info;
2360			info.type = type;
2361			tensors[i] = ccv_nnc_tensor_new(0, info, 0); // Create this tensor, don't initiate copy yet.
2362		}
2363	}
2364	for (i = 0; i < count; i++)
2365	{
2366		ccv_nnc_tensor_t* tensor = compiled_data->tensors.parameters[i];
2367		if ((uintptr_t)tensor & (uintptr_t)1) // If it is not owned. We don't do anything.
2368			continue;
2369		tensor = CCV_NNC_TENSOR(tensor)((ccv_nnc_tensor_t*)((uintptr_t)(tensor) & ~(uintptr_t)1)
);
2370		// Now initiate transfer. We should do this one on a stream.
2371		if (tensor->info.type != type)
2372			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);
2373	}
2374	// Copy names and remove parameters.
2375	for (i = 0; i < count; i++)
2376	{
2377		ccv_nnc_tensor_t* const tensor = compiled_data->tensors.parameters[i];
2378		if ((uintptr_t)tensor & (uintptr_t)1) // If it is not owned. We don't do anything.
2379		{
2380			names[i] = 0;
2381			continue;
2382		}
2383		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)));
2384		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; });
2385		names[i] = ccmallocmalloc(name_len + 1);
2386		names[i][name_len] = 0;
2387		memcpy(names[i], name, name_len);
2388		if (tensor->info.type == type)
2389			compiled_data->tensors.parameters[i] = 0; // Only move when it is moved.
2390	}
2391	return 1;
2392}
2393 
2394KHASH_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'→
2395 
2396void 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)
2397{
2398	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", 2398, __extension__ __PRETTY_FUNCTION__
); }));
2399	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
2400	int i;
2401	khash_t(ccv_cnnp_parameter_id)kh_ccv_cnnp_parameter_id_t* id_map = 0;
2402	if (count != compiled_data->parameters->rnum)
2403	{
2404		id_map = kh_init(ccv_cnnp_parameter_id)kh_init_ccv_cnnp_parameter_id();
2405		// Build the map between name and the index.
2406		for (i = 0; i < count; i++)
2407		{
2408			int ret;
2409			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);
2410			assert(ret != 0)((void) sizeof ((ret != 0) ? 1 : 0), __extension__ ({ if (ret
 != 0) ; else __assert_fail ("ret != 0", "ccv_cnnp_model.c", 2410
, __extension__ __PRETTY_FUNCTION__); }));
2411			kh_val(id_map, k)((id_map)->vals[k]) = i;
2412		}
2413	}
2414	const int parameter_size = compiled_data->parameters->rnum;
2415	int* copy_back = 0;
2416	const int tensors_init = !!compiled_data->tensors_init.v;
2417	if (!tensors_init)
2418		ccv_cnnp_model_tensors_init_0(model, compiled_data);
2419	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; });
2420	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));
2421	for (i = 0; i < parameter_size; i++)
2422	{
2423		int j = i;
2424		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)));
2425		if (i >= 0 || strncmp(name, names[i], 1023) != 0)
2426		{
2427			// Build the map.
2428			if (id_map == 0)
2429			{
2430				id_map = kh_init(ccv_cnnp_parameter_id)kh_init_ccv_cnnp_parameter_id();
2431				for (j = 0; j < count; j++)
2432				{
2433					int ret;
2434					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);
2435					assert(ret != 0)((void) sizeof ((ret != 0) ? 1 : 0), __extension__ ({ if (ret
 != 0) ; else __assert_fail ("ret != 0", "ccv_cnnp_model.c", 2435
, __extension__ __PRETTY_FUNCTION__); }));
2436					kh_val(id_map, k)((id_map)->vals[k]) = j;
2437				}
2438			}
2439			const khiter_t k = kh_get(ccv_cnnp_parameter_id, id_map, name)kh_get_ccv_cnnp_parameter_id(id_map, name);
2440			if (k == kh_end(id_map)((id_map)->n_buckets)) // Cannot find the name, skip.
2441				continue;
2442			j = kh_val(id_map, k)((id_map)->vals[k]);
2443		}
2444		if (compiled_data->tensors.parameters[i]) // Cannot be a shared parameter to read.
2445			{ 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", 2445, __extension__ __PRETTY_FUNCTION__
); })); }
2446		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)));
2447		ccv_nnc_tensor_param_t info = ccv_nnc_tensor_symbol_params(parameter.graph, parameter);
2448		if (CCV_TENSOR_GET_DEVICE(info.type)((info.type) & 0xfff00) == CCV_COMPUTE_DEVICE_ANY)
2449			CCV_TENSOR_SET_DEVICE_ID(info.type, 0)(info.type) = (((info.type) & ~0xfff00) | (((0) & 0xfff
) << 8));
2450		const int d = parameter.d;
2451		if (info.type == tensors[j]->info.type && invalidates) // Can move.
2452		{
2453			// Deallocate it if needed.
2454			if (!((uintptr_t)compiled_data->tensors.parameters[i] & (uintptr_t)1))
2455				if (compiled_data->tensors.parameters[i])
2456					ccv_nnc_tensor_free(compiled_data->tensors.parameters[i]);
2457			compiled_data->tensors.parameters[i] = tensors[j];
2458			tensors[j] = 0;
2459		} else {
2460			if (!compiled_data->tensors.parameters[i])
2461			{ // Not allocated, to allocate first.
2462				// Create new one, make sure we create this by having the right parameters.
2463				const int type = info.type;
2464				info = tensors[j]->info;
2465				info.type = type; // Revert back the type.
2466				compiled_data->tensors.parameters[i] = ccv_nnc_tensor_new(0, info, 0);
2467			}
2468			if (!copy_back)
2469				copy_back = (int*)cccalloccalloc(parameter_size, sizeof(int));
2470			copy_back[i] = j + 1;
2471		}
2472		init_v[d >> 5] |= (1u << (d & 0x1f));
2473		// Create this tensor for other data parallel allocations.
2474		info = compiled_data->tensors.parameters[i]->info; // In case we loaded a different info.
2475		const int device_id = CCV_TENSOR_GET_DEVICE_ID(info.type)(((info.type) & 0xfff00) >> 8);
2476		for (j = 1; j < parallel_count; j++)
2477			if (!compiled_data->tensors.parameters[i + j * parameter_size])
2478			{
2479				if (j != device_id)
2480					CCV_TENSOR_SET_DEVICE_ID(info.type, j)(info.type) = (((info.type) & ~0xfff00) | (((j) & 0xfff
) << 8));
2481				else
2482					CCV_TENSOR_SET_DEVICE_ID(info.type, 0)(info.type) = (((info.type) & ~0xfff00) | (((0) & 0xfff
) << 8));
2483				compiled_data->tensors.parameters[i + j * parameter_size] = ccv_nnc_tensor_new(0, info, 0);
2484			}
2485			// No need to copy over, this is done in ccv_cnnp_model.c's copy_tensors method.
2486	}
2487	if (id_map)
2488		kh_destroy(ccv_cnnp_parameter_id, id_map)kh_destroy_ccv_cnnp_parameter_id(id_map);
2489	// Now do the transfer.
2490	if (copy_back)
2491	{
2492		for (i = 0; i < parameter_size; i++)
2493		{
2494			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));
2495			if (copy_back[i] == 0)
2496				continue;
2497			const int j = copy_back[i] - 1;
2498			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);
2499		}
2500		ccfreefree(copy_back);
2501	}
2502}
2503 
2504ccv_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)
2505{
2506	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
2507	assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
 (compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 2507, __extension__ __PRETTY_FUNCTION__); }));
2508	const int parameter_size = compiled_data->parameters->rnum;
2509	int i;
2510	for (i = 0; i < parameter_size; i++)
2511	{
2512		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)));
2513		if (first(model, name, context))
2514			return ccv_cnnp_model_parameters(model, -1, i);
2515	}
2516	return 0;
2517}
2518 
2519ccv_array_t* ccv_cnnp_model_parameters_filter(ccv_cnnp_model_t* const model, ccv_cnnp_model_parameters_filter_f filter, void* const context)
2520{
2521	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
2522	assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
 (compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 2522, __extension__ __PRETTY_FUNCTION__); }));
2523	ccv_array_t* const parameters = ccv_array_new(sizeof(ccv_cnnp_model_io_t), 0, 0);
2524	const int parameter_size = compiled_data->parameters->rnum;
2525	int i;
2526	for (i = 0; i < parameter_size; i++)
2527	{
2528		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)));
2529		if (filter(model, name, context))
2530		{
2531			ccv_cnnp_model_io_t parameter = ccv_cnnp_model_parameters(model, -1, i);
2532			ccv_array_push(parameters, &parameter);
2533		}
2534	}
2535	return parameters;
2536 
2537}
2538 
2539CCV_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)
2540{
2541	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
2542	assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
 (compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 2542, __extension__ __PRETTY_FUNCTION__); }));
2543	const int tensors_init = !!compiled_data->tensors_init.v;
2544	if (!tensors_init) // If nothing initialized, we return parameter 0.
2545		return ccv_cnnp_model_parameters(model, -1, 0);
2546	const int parameter_size = compiled_data->parameters->rnum;
2547	int i;
2548	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));
2549	for (i = 0; i < parameter_size; i++)
2550	{
2551		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;
2552		if (!(init_v[d >> 5] & (1u << (d & 0x1f))))
2553			return ccv_cnnp_model_parameters(model, -1, i);
2554	}
2555	return 0;
2556}
2557 
2558static 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)
2559{
2560	const int to_param_sel = parameters->param_sel > 0 ? parameters->param_sel - 1 : parameters->param_sel;
2561	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", 2561, __extension__
 __PRETTY_FUNCTION__); }));
2562	ccv_array_t* const to_parameter_indices = ccv_array_new(sizeof(int), 0, 0);
2563	ccv_cnnp_model_add_to_parameter_indices(parameters->model, to_param_sel, to_parameter_indices);
2564	*param_ref = parameters->param_ref > 0 ? parameters->param_ref - 1 : parameters->param_ref;
2565	return to_parameter_indices;
2566}
2567 
2568static 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)
2569{
2570	// If the model is not compiled yet. Compile them now.
2571	if (!model->graph)
2572	{
2573		model->graph = ccv_nnc_symbolic_graph_new();
2574		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", 2574, __extension__ __PRETTY_FUNCTION__
); }));
2575		const int input_size = from_model->input_size;
2576		ccv_nnc_tensor_param_t input_params[input_size];
2577		int i;
2578		for (i = 0; i < input_size; i++)
2579			input_params[i] = ccv_nnc_tensor_symbol_params(from_model->graph, from_model->inputs[i]);
2580		_ccv_cnnp_model_compile(model, input_params, input_size, from_model->compiled_data->loss);
2581		model->parallel_count = from_model->parallel_count;
2582		model->memory_compression = from_model->memory_compression;
2583		model->memory_reduction = from_model->memory_reduction;
2584		model->gradient_checkpointing = from_model->gradient_checkpointing;
2585		model->compiled_data->stream_type = from_model->compiled_data->stream_type;
2586		model->compiled_data->minimize.minimizer = from_model->compiled_data->minimize.minimizer;
2587		model->compiled_data->minimize.max_saved_aux_size = from_model->compiled_data->minimize.max_saved_aux_size;
2588	}
2589	ccv_cnnp_compiled_data_t* const to_compiled_data = model->compiled_data;
2590	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", 2590, __extension__ __PRETTY_FUNCTION__
); }));
2591	const int to_tensors_init = !!to_compiled_data->tensors_init.v;
2592	if (!to_tensors_init)
2593	{
2594		if (only_init_0)
2595			ccv_cnnp_model_tensors_init_0(model, to_compiled_data);
2596		else
2597			_ccv_cnnp_model_tensors_init(model, to_compiled_data);
2598	} else if (!only_init_0 && (uintptr_t)to_compiled_data->tensors_init.v & (uintptr_t)1)
2599		// Check if it is not fully allocated, if it is not, init_1.
2600			ccv_cnnp_model_tensors_init_1(model, to_compiled_data);
2601	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", 2601, __extension__ __PRETTY_FUNCTION__
); }));
2602	*parameter_indices = _ccv_cnnp_model_parameter_indices(model, parameters, param_ref);
2603	*from_parameter_indices = _ccv_cnnp_model_parameter_indices(from_model, from_parameters, from_param_ref);
2604	if (*from_param_ref < 0 && *param_ref >= 0)
2605		{ 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", 2605, __extension__ __PRETTY_FUNCTION__
); })); }
2606	else if (*from_param_ref >= 0)
2607		{ 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", 2607, __extension__ __PRETTY_FUNCTION__
); })); }
2608	if (*param_ref < 0 && *from_param_ref >= 0)
2609		{ 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"
, 2609, __extension__ __PRETTY_FUNCTION__); })); }
2610	else if (*param_ref >= 0)
2611		{ 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", 2611, __extension__ __PRETTY_FUNCTION__
); })); }
2612}
2613 
2614void 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)
2615{
2616	ccv_array_t* to_parameter_indices;
2617	int to_param_ref;
2618	ccv_array_t* from_parameter_indices;
2619	int from_param_ref;
2620	_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);
2621	// Should be exactly the same tensor.
2622	if (to_param_ref < 0 && from_param_ref < 0)
2623		{ 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", 2623, __extension__ __PRETTY_FUNCTION__
); })); }
2624	// To models.
2625	ccv_cnnp_compiled_data_t* const to_compiled_data = model->compiled_data;
2626	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", 2626, __extension__ __PRETTY_FUNCTION__
); }));
2627	// From models.
2628	const ccv_cnnp_compiled_data_t* const from_compiled_data = from_model->compiled_data;
2629	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; });
2630	const int to_parameter_size = to_compiled_data->parameters->rnum;
2631	const int rnum = (to_param_ref < 0 && from_param_ref < 0) ? from_parameter_indices->rnum : 1;
2632	int i, j;
2633	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));
2634  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));
2635	for (i = 0; i < rnum; i++)
2636	{
2637		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)));
2638		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"
, 2638, __extension__ __PRETTY_FUNCTION__); }));
2639		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", 2639, __extension__ __PRETTY_FUNCTION__
); }));
2640		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;
2641		// If the original is not init'ed. We cannot copy from.
2642		if (!(from_init_v[s >> 5] & (1u << (s & 0x1f))))
2643			continue;
2644		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)));
2645		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"
, 2645, __extension__ __PRETTY_FUNCTION__); }));
2646		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", 2646, __extension__ __PRETTY_FUNCTION__
); }));
2647		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));
2648		assert(src)((void) sizeof ((src) ? 1 : 0), __extension__ ({ if (src) ; else
 __assert_fail ("src", "ccv_cnnp_model.c", 2648, __extension__
 __PRETTY_FUNCTION__); }));
2649		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));
2650		assert(dest)((void) sizeof ((dest) ? 1 : 0), __extension__ ({ if (dest) ;
 else __assert_fail ("dest", "ccv_cnnp_model.c", 2650, __extension__
 __PRETTY_FUNCTION__); }));
2651		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);
2652		for (j = 1; j < parallel_count; j++)
2653		{
2654			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));
2655			if (copy_tensor)
2656				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);
2657		}
2658		// Mark this symbol as init'ed.
2659		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;
2660		to_init_v[d >> 5] |= (1u << (d & 0x1f));
2661	}
2662	ccv_array_free(to_parameter_indices);
2663	ccv_array_free(from_parameter_indices);
2664}
2665 
2666void 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)
2667{
2668	ccv_array_t* to_parameter_indices;
2669	int to_param_ref;
2670	ccv_array_t* from_parameter_indices;
2671	int from_param_ref;
2672	_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);
2673	// Should be exactly the same tensor.
2674	if (renamer == 0 && to_param_ref < 0 && from_param_ref < 0)
1
Assuming 'renamer' is not equal to null→
2675		{ 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", 2675, __extension__ __PRETTY_FUNCTION__
); })); }
2676	// To models.
2677	ccv_cnnp_compiled_data_t* const to_compiled_data = model->compiled_data;
2678	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", 2678, __extension__ __PRETTY_FUNCTION__
); }));
2
←
Assuming 'to_compiled_data' is non-null→
3
←
Taking true branch→
2679	// From models.
2680	const ccv_cnnp_compiled_data_t* const from_compiled_data = from_model->compiled_data;
2681	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→
2682	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", 2682, __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→
2683	const int from_parameter_size = from_compiled_data->parameters->rnum;
2684	const int to_parameter_size = to_compiled_data->parameters->rnum;
2685	const int rnum = (to_param_ref < 0 && from_param_ref < 0) ? to_parameter_indices->rnum : 1;
9
←
Assuming 'to_param_ref' is >= 0→
2686	int i, j;
2687	khash_t(ccv_cnnp_parameter_id)kh_ccv_cnnp_parameter_id_t* id_map = 0;
2688	char* updated_name = 0;
2689	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));
2690  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));
2691	for (i = 0; i < rnum; i++)
2692	{
2693		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→
2694		// Need to figure out how to use the renamer here.
2695		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→
2696		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"
, 2696, __extension__ __PRETTY_FUNCTION__); }));
15
←
Assuming 'dest_d' is >= 0→
16
←
Taking true branch→
2697		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", 2697, __extension__
 __PRETTY_FUNCTION__); }));
17
←
Assuming 'dest_d' is < 'to_parameter_size'→
18
←
Taking true branch→
2698		if (renamer18.1
'renamer' is non-null
)
2699		{
2700			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;
2701			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)));
2702			if (!updated_name18.3
'updated_name' is null
)
19
←
Taking true branch→
2703				updated_name = (char*)ccmallocmalloc(1024);
2704			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→
2705			if (src_name_len20.1
'src_name_len' is <= 0
 > 0)
21
←
Taking false branch→
2706				memcpy(updated_name, src_name, src_name_len);
2707			updated_name[src_name_len] = 0;
2708			if (renamer(context, dest_name, updated_name, 1024) != 0)
22
←
Assuming the condition is false→
2709				continue; // Skip this.
2710			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)
2711			{
2712				// Nothing changed.
2713			} else {
2714				if (!id_map22.2
'id_map' is null
)
23
←
Taking true branch→
2715				{
2716					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'→
2717					for (j = 0; j < from_parameter_size; j++)
27
←
Assuming 'j' is >= 'from_parameter_size'→
28
←
Loop condition is false. Execution continues on line 2725→
2718					{
2719						int ret;
2720						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);
2721						assert(ret != 0)((void) sizeof ((ret != 0) ? 1 : 0), __extension__ ({ if (ret
 != 0) ; else __assert_fail ("ret != 0", "ccv_cnnp_model.c", 2721
, __extension__ __PRETTY_FUNCTION__); }));
2722						kh_val(id_map, k)((id_map)->vals[k]) = j;
2723					}
2724				}
2725				const khiter_t k = kh_get(ccv_cnnp_parameter_id, id_map, updated_name)kh_get_ccv_cnnp_parameter_id(id_map, updated_name);
2726				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→
2727					continue;
2728				src_d = kh_val(id_map, k)((id_map)->vals[k]);
31
←
Array access (via field 'vals') results in a null pointer dereference
2729				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"
, 2729, __extension__ __PRETTY_FUNCTION__); }));
2730				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", 2730, __extension__
 __PRETTY_FUNCTION__); }));
2731			}
2732		}
2733		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"
, 2733, __extension__ __PRETTY_FUNCTION__); }));
2734		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", 2734, __extension__
 __PRETTY_FUNCTION__); }));
2735		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;
2736		// If the original is not init'ed. We cannot share from.
2737		if (!(from_init_v[s >> 5] & (1u << (s & 0x1f))))
2738			continue;
2739		for (j = 0; j < parallel_count; j++)
2740		{
2741			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));
2742			assert(src)((void) sizeof ((src) ? 1 : 0), __extension__ ({ if (src) ; else
 __assert_fail ("src", "ccv_cnnp_model.c", 2742, __extension__
 __PRETTY_FUNCTION__); }));
2743			ccv_nnc_tensor_t* const dest = to_compiled_data->tensors.parameters[dest_d + j * to_parameter_size];
2744			if (dest && !((uintptr_t)dest & (uintptr_t)1))
2745				ccv_nnc_tensor_free(dest);
2746			to_compiled_data->tensors.parameters[dest_d + j * to_parameter_size] = (ccv_nnc_tensor_t*)((uintptr_t)src | (uintptr_t)1);
2747		}
2748		// Mark this symbol as init'ed.
2749		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;
2750		to_init_v[d >> 5] |= (1u << (d & 0x1f));
2751	}
2752	ccv_array_free(to_parameter_indices);
2753	ccv_array_free(from_parameter_indices);
2754	if (id_map)
2755		kh_destroy(ccv_cnnp_parameter_id, id_map)kh_destroy_ccv_cnnp_parameter_id(id_map);
2756	if (updated_name)
2757		ccfreefree(updated_name);
2758	// Mark it as incomplete so we will call init_1.
2759	if (ccv_cnnp_model_tensors_any_to_alloc(model, to_compiled_data))
2760		to_compiled_data->tensors_init.v = (uint32_t*)((uintptr_t)to_compiled_data->tensors_init.v | (uintptr_t)1);
2761	else // Remove the flag.
2762		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));
2763}
2764 
2765ccv_nnc_stream_context_t* ccv_cnnp_compiled_data_get_stream(ccv_cnnp_compiled_data_t* const compiled_data, const int type)
2766{
2767	if (!compiled_data->stream_map)
2768		compiled_data->stream_map = kh_init(stream_map)kh_init_stream_map();
2769	int ret = 0;
2770	khiter_t k = kh_put(stream_map, compiled_data->stream_map, type, &ret)kh_put_stream_map(compiled_data->stream_map, type, &ret
);
2771	assert(ret >= 0)((void) sizeof ((ret >= 0) ? 1 : 0), __extension__ ({ if (
ret >= 0) ; else __assert_fail ("ret >= 0", "ccv_cnnp_model.c"
, 2771, __extension__ __PRETTY_FUNCTION__); }));
2772	ccv_nnc_stream_context_t* stream = kh_val(compiled_data->stream_map, k)((compiled_data->stream_map)->vals[k]);
2773	// If ret == 0, the key already exist, we can return directly, otherwise, create and return.
2774	if (ret != 0)
2775	{
2776		stream = ccv_nnc_stream_context_new(type);
2777		kh_val(compiled_data->stream_map, k)((compiled_data->stream_map)->vals[k]) = stream;
2778	}
2779	return stream;
2780}
2781 
2782void 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)
2783{
2784	ccv_array_t* to_parameter_indices;
2785	int to_param_ref;
2786	ccv_array_t* from_parameter_indices;
2787	int from_param_ref;
2788	_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);
2789	// Should be exactly the same tensor.
2790	if (to_param_ref < 0 && from_param_ref < 0)
2791		{ 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", 2791, __extension__ __PRETTY_FUNCTION__
); })); }
2792	// To models.
2793	ccv_cnnp_compiled_data_t* const to_compiled_data = model->compiled_data;
2794	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", 2794, __extension__ __PRETTY_FUNCTION__
); }));
2795	// From models.
2796	const ccv_cnnp_compiled_data_t* const from_compiled_data = from_model->compiled_data;
2797	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; });
2798	const int to_parameter_size = to_compiled_data->parameters->rnum;
2799	const int rnum = (to_param_ref < 0 && from_param_ref < 0) ? from_parameter_indices->rnum : 1;
2800	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", 2800, __extension__ __PRETTY_FUNCTION__
); }));
2801	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", 2801, __extension__ __PRETTY_FUNCTION__
); }));
2802	int i, j;
2803	ccv_nnc_tensor_t* inputs[aux_in_size + 2];
2804	ccv_nnc_tensor_t* outputs[aux_out_size + 1];
2805	for (i = 0; i < aux_in_size; i++)
2806		inputs[i + 2] = aux_ins[i];
2807	for (i = 0; i < aux_out_size; i++)
2808		outputs[i + 1] = aux_outs[i];
2809	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));
2810	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));
2811	for (i = 0; i < rnum; i++)
2812	{
2813		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)));
2814		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"
, 2814, __extension__ __PRETTY_FUNCTION__); }));
2815		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", 2815, __extension__ __PRETTY_FUNCTION__
); }));
2816		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;
2817		// If the original is not init'ed. We cannot copy from.
2818		if (!(from_init_v[s >> 5] & (1u << (s & 0x1f))))
2819			continue;
2820		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)));
2821		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"
, 2821, __extension__ __PRETTY_FUNCTION__); }));
2822		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", 2822, __extension__ __PRETTY_FUNCTION__
); }));
2823		if (parallel_count > 1)
2824		{
2825			ccv_nnc_stream_context_t* streams[parallel_count];
2826			ccv_nnc_stream_signal_t* signal;
2827			if (stream_context)
2828				signal = ccv_nnc_stream_context_emit_signal_new(stream_context);
2829			for (j = 0; j < parallel_count; j++)
2830			{
2831				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));
2832				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));
2833				if (!dest || !src)
2834				{
2835					streams[j] = 0;
2836					continue;
2837				}
2838				// At the moment, can only handle them on the same device.
2839				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", 2839, __extension__ __PRETTY_FUNCTION__
); }));
2840				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", 2840, __extension__ __PRETTY_FUNCTION__
); }));
2841				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;
2842				const int device_id = CCV_TENSOR_GET_DEVICE_ID(src->info.type)(((src->info.type) & 0xfff00) >> 8);
2843				int type = stream_type;
2844				CCV_STREAM_SET_DEVICE_ID(type, device_id)(type) = (((type) & ~0xfff00) | (((device_id) & 0xfff
) << 8));
2845				ccv_nnc_stream_context_t* const stream_0 = ccv_cnnp_compiled_data_get_stream(to_compiled_data, type);
2846				// Wait signal to finish.
2847				if (stream_context)
2848					ccv_nnc_stream_context_wait_signal(stream_0, signal);
2849				inputs[0] = outputs[0] = dest;
2850				inputs[1] = src;
2851				ccv_nnc_cmd_exec(cmd, hint, flags, inputs, aux_in_size + 2, outputs, aux_out_size + 1, stream_0);
2852				if (stream_context)
2853				{
2854					ccv_nnc_stream_signal_t* const signal = ccv_nnc_stream_context_emit_signal_new(stream_0);
2855					ccv_nnc_stream_context_wait_signal(stream_context, signal);
2856				}
2857				streams[j] = stream_0;
2858			}
2859			// If this should be blocking, blocking it.
2860			if (!stream_context)
2861				for (j = 0; j < parallel_count; j++)
2862					if (streams[j])
2863						ccv_nnc_stream_context_wait(streams[j]);
2864		} else {
2865			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));
2866			assert(src)((void) sizeof ((src) ? 1 : 0), __extension__ ({ if (src) ; else
 __assert_fail ("src", "ccv_cnnp_model.c", 2866, __extension__
 __PRETTY_FUNCTION__); }));
2867			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));
2868			assert(dest)((void) sizeof ((dest) ? 1 : 0), __extension__ ({ if (dest) ;
 else __assert_fail ("dest", "ccv_cnnp_model.c", 2868, __extension__
 __PRETTY_FUNCTION__); }));
2869			inputs[0] = outputs[0] = dest;
2870			inputs[1] = src;
2871			ccv_nnc_cmd_exec(cmd, hint, flags, inputs, aux_in_size + 2, outputs, aux_out_size + 1, stream_context);
2872		}
2873		// Mark this symbol as init'ed.
2874		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;
2875		to_init_v[d >> 5] |= (1u << (d & 0x1f));
2876	}
2877	ccv_array_free(to_parameter_indices);
2878	ccv_array_free(from_parameter_indices);
2879}
2880 
2881void 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)
2882{
2883	int to_param_ref;
2884	ccv_array_t* const to_parameter_indices = _ccv_cnnp_model_parameter_indices(model, parameters, &to_param_ref);
2885	// To models.
2886	ccv_cnnp_compiled_data_t* const to_compiled_data = model->compiled_data;
2887	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", 2887, __extension__ __PRETTY_FUNCTION__
); }));
2888	// Tensor has to be inited already.
2889	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", 2889, __extension__ __PRETTY_FUNCTION__
); }));
2890	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", 2890, __extension__ __PRETTY_FUNCTION__
); }));
2891	// From models.
2892	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; });
2893	const int to_parameter_size = to_compiled_data->parameters->rnum;
2894	const int rnum = (to_param_ref < 0) ? to_parameter_indices->rnum : 1;
2895	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", 2895, __extension__ __PRETTY_FUNCTION__
); }));
2896	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", 2896, __extension__ __PRETTY_FUNCTION__
); }));
2897	int i, j;
2898	ccv_nnc_tensor_t* inputs[aux_in_size + 1];
2899	ccv_nnc_tensor_t* outputs[aux_out_size + 1];
2900	for (i = 0; i < aux_in_size; i++)
2901		inputs[i + 1] = aux_ins[i];
2902	for (i = 0; i < aux_out_size; i++)
2903		outputs[i + 1] = aux_outs[i];
2904	for (i = 0; i < rnum; i++)
2905	{
2906		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)));
2907		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"
, 2907, __extension__ __PRETTY_FUNCTION__); }));
2908		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", 2908, __extension__ __PRETTY_FUNCTION__
); }));
2909		if (parallel_count > 1)
2910		{
2911			ccv_nnc_stream_context_t* streams[parallel_count];
2912			ccv_nnc_stream_signal_t* signal;
2913			if (stream_context)
2914				signal = ccv_nnc_stream_context_emit_signal_new(stream_context);
2915			for (j = 0; j < parallel_count; j++)
2916			{
2917				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));
2918				if (!dest)
2919				{
2920					streams[j] = 0;
2921					continue;
2922				}
2923				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;
2924				const int device_id = CCV_TENSOR_GET_DEVICE_ID(dest->info.type)(((dest->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				ccv_nnc_cmd_exec(cmd, hint, flags, inputs, aux_in_size + 1, outputs, aux_out_size + 1, stream_0);
2933				if (stream_context)
2934				{
2935					ccv_nnc_stream_signal_t* const signal = ccv_nnc_stream_context_emit_signal_new(stream_0);
2936					ccv_nnc_stream_context_wait_signal(stream_context, signal);
2937				}
2938				streams[j] = stream_0;
2939			}
2940			// If this should be blocking, blocking it.
2941			if (!stream_context)
2942				for (j = 0; j < parallel_count; j++)
2943					if (streams[j])
2944						ccv_nnc_stream_context_wait(streams[j]);
2945		} else {
2946			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));
2947			assert(dest)((void) sizeof ((dest) ? 1 : 0), __extension__ ({ if (dest) ;
 else __assert_fail ("dest", "ccv_cnnp_model.c", 2947, __extension__
 __PRETTY_FUNCTION__); }));
2948			inputs[0] = outputs[0] = dest;
2949			ccv_nnc_cmd_exec(cmd, hint, flags, inputs, aux_in_size + 1, outputs, aux_out_size + 1, stream_context);
2950		}
2951		// No need to mark this symbol as init'ed, it is already.
2952	}
2953	ccv_array_free(to_parameter_indices);
2954}
2955 
2956void 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)
2957{
2958	int to_param_ref;
2959	ccv_array_t* const to_parameter_indices = _ccv_cnnp_model_parameter_indices(model, parameters, &to_param_ref);
2960	// To models.
2961	ccv_cnnp_compiled_data_t* const to_compiled_data = model->compiled_data;
2962	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", 2962, __extension__ __PRETTY_FUNCTION__
); }));
2963	// Tensor has to be inited already.
2964	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", 2964, __extension__ __PRETTY_FUNCTION__
); }));
2965	ccv_nnc_tensor_t** tensor_gradients;
2966	if (to_compiled_data->backward.count > 1)
2967		tensor_gradients = to_compiled_data->tensors.accum_gradients;
2968	else
2969		tensor_gradients = to_compiled_data->tensors.gradients;
2970	assert(tensor_gradients)((void) sizeof ((tensor_gradients) ? 1 : 0), __extension__ ({
 if (tensor_gradients) ; else __assert_fail ("tensor_gradients"
, "ccv_cnnp_model.c", 2970, __extension__ __PRETTY_FUNCTION__
); }));
2971	// From models.
2972	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; });
2973	const int to_parameter_size = to_compiled_data->parameters->rnum;
2974	const int rnum = (to_param_ref < 0) ? to_parameter_indices->rnum : 1;
2975	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", 2975, __extension__ __PRETTY_FUNCTION__
); }));
2976	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", 2976, __extension__ __PRETTY_FUNCTION__
); }));
2977	int i, j;
2978	ccv_nnc_tensor_t* inputs[aux_in_size + 1];
2979	ccv_nnc_tensor_t* outputs[aux_out_size + 1];
2980	for (i = 0; i < aux_in_size; i++)
2981		inputs[i + 1] = aux_ins[i];
2982	for (i = 0; i < aux_out_size; i++)
2983		outputs[i + 1] = aux_outs[i];
2984	for (i = 0; i < rnum; i++)
2985	{
2986		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)));
2987		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"
, 2987, __extension__ __PRETTY_FUNCTION__); }));
2988		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", 2988, __extension__ __PRETTY_FUNCTION__
); }));
2989		if (parallel_count > 1)
2990		{
2991			ccv_nnc_stream_context_t* streams[parallel_count];
2992			ccv_nnc_stream_signal_t* signal;
2993			if (stream_context)
2994				signal = ccv_nnc_stream_context_emit_signal_new(stream_context);
2995			for (j = 0; j < parallel_count; j++)
2996			{
2997				ccv_nnc_tensor_t* const dest = tensor_gradients[dest_d + j * to_parameter_size];
2998				if (!dest)
2999				{
3000					streams[j] = 0;
3001					continue;
3002				}
3003				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;
3004				const int device_id = CCV_TENSOR_GET_DEVICE_ID(dest->info.type)(((dest->info.type) & 0xfff00) >> 8);
3005				int type = stream_type;
3006				CCV_STREAM_SET_DEVICE_ID(type, device_id)(type) = (((type) & ~0xfff00) | (((device_id) & 0xfff
) << 8));
3007				ccv_nnc_stream_context_t* const stream_0 = ccv_cnnp_compiled_data_get_stream(to_compiled_data, type);
3008				// Wait signal to finish.
3009				if (stream_context)
3010					ccv_nnc_stream_context_wait_signal(stream_0, signal);
3011				inputs[0] = outputs[0] = dest;
3012				ccv_nnc_cmd_exec(cmd, hint, flags, inputs, aux_in_size + 1, outputs, aux_out_size + 1, stream_0);
3013				if (stream_context)
3014				{
3015					ccv_nnc_stream_signal_t* const signal = ccv_nnc_stream_context_emit_signal_new(stream_0);
3016					ccv_nnc_stream_context_wait_signal(stream_context, signal);
3017				}
3018				streams[j] = stream_0;
3019			}
3020			// If this should be blocking, blocking it.
3021			if (!stream_context)
3022				for (j = 0; j < parallel_count; j++)
3023					if (streams[j])
3024						ccv_nnc_stream_context_wait(streams[j]);
3025		} else {
3026			ccv_nnc_tensor_t* const dest = tensor_gradients[dest_d];
3027			if (!dest)
3028				continue;
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_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)
3039{
3040	// Only CUDA backend has this feature.
3041#ifdef HAVE_CUDA1
3042	int to_param_ref;
3043	ccv_array_t* const to_parameter_indices = _ccv_cnnp_model_parameter_indices(model, parameters, &to_param_ref);
3044	// To models.
3045	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
3046	assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
 (compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 3046, __extension__ __PRETTY_FUNCTION__); }));
3047	// Tensor has to be inited already.
3048	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"
, 3048, __extension__ __PRETTY_FUNCTION__); }));
3049	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", 3049, __extension__ __PRETTY_FUNCTION__
); }));
3050	// From models.
3051	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; });
3052	const int rnum = (to_param_ref < 0) ? to_parameter_indices->rnum : 1;
3053	int i;
3054	for (i = 0; i < rnum; i++)
3055	{
3056		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)));
3057		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"
, 3057, __extension__ __PRETTY_FUNCTION__); }));
3058		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", 3058, __extension__ __PRETTY_FUNCTION__
); }));
3059		if (parallel_count > 1)
3060		{
3061			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", 3061, __extension__ __PRETTY_FUNCTION__
); }));
3062		} else {
3063			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));
3064			assert(src)((void) sizeof ((src) ? 1 : 0), __extension__ ({ if (src) ; else
 __assert_fail ("src", "ccv_cnnp_model.c", 3064, __extension__
 __PRETTY_FUNCTION__); }));
3065			ccv_nnc_tensor_param_t params = src->info;
3066			if (CCV_TENSOR_GET_MEMORY(params.type)((params.type) & 0x3) != CCV_TENSOR_GPU_MEMORY)
3067				continue;
3068			const size_t size = ccv_nnc_tensor_data_size(params);
3069			if (size <= 0)
3070				continue;
3071			const int should_free = !((uintptr_t)compiled_data->tensors.parameters[dest_d] & (uintptr_t)1);
3072			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);
3073			ccv_nnc_tensor_t* const tensor = (ccv_nnc_tensor_t*)ccmallocmalloc(sizeof(ccv_nnc_tensor_t));
3074			tensor->dataof = 0;
3075			tensor->alias_ref = 0;
3076			tensor->sig = 0;
3077			tensor->refcount = 1;
3078			tensor->info = params;
3079			if (tfb)
3080			{
3081				tensor->type = CCV_NO_DATA_ALLOC | CCV_MATRIX_DENSE | CCV_GET_DATA_TYPE(params.datatype)((params.datatype) & 0xFF000) | params.dim[2];
3082				// This corresponding to mat->step
3083				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);
3084			} else // This won't be recognized by ccv_dense_matrix_t
3085				tensor->type = CCV_NO_DATA_ALLOC | CCV_MATRIX_DENSE | CCV_GET_DATA_TYPE(params.datatype)((params.datatype) & 0xFF000);
3086			// Remove this flag so it can be deallocated as usual.
3087			tensor->type &= ~CCV_NO_DATA_ALLOC;
3088			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", 3088, __extension__ __PRETTY_FUNCTION__
); }));
3089			void* ptr = cumallocmanaged(CCV_TENSOR_GET_DEVICE_ID(params.type)(((params.type) & 0xfff00) >> 8), size);
3090			if (ptr) // If allocated successfully. Otherwise we go through the fallback path.
3091			{
3092				tensor->data.u8 = (uint8_t*)ptr;
3093				cumemadvisereadmostly(CCV_TENSOR_GET_DEVICE_ID(params.type)(((params.type) & 0xfff00) >> 8), tensor->data.u8, size);
3094				tensor->type |= CCV_MAPPED_MEM; // This denotes the tensor is mapped to CPU, and would prefer a explicit prefetch call.
3095			} else {
3096				// Allocation failed.
3097				ccfreefree(tensor);
3098				continue;
3099			}
3100			// TODO: Cannot run this on the stream context yet, due to allocation and deallocations.
3101			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);
3102			compiled_data->tensors.parameters[dest_d] = tensor;
3103			// Can free out the old one.
3104			if (should_free)
3105				ccv_nnc_tensor_free(src);
3106		}
3107		// No need to mark this symbol as init'ed, it is already.
3108	}
3109	ccv_array_free(to_parameter_indices);
3110#endif
3111}
3112 
3113ccv_nnc_cmd_t ccv_cnnp_model_minimizer(ccv_cnnp_model_t* const model)
3114{
3115	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
3116	assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
 (compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 3116, __extension__ __PRETTY_FUNCTION__); }));
3117	return compiled_data->minimize.minimizer;
3118}
3119 
3120void 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)
3121{
3122	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
3123	assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
 (compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 3123, __extension__ __PRETTY_FUNCTION__); }));
3124	const int parameter_size = compiled_data->parameters->rnum;
3125	if (parameter_size == 0)
3126		return;
3127	if (reset)
3128		{ 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", 3128, __extension__ __PRETTY_FUNCTION__
); })); }
3129	const int old_max_saved_aux_size = compiled_data->minimize.max_saved_aux_size;
3130	const int saved_aux_size = ccv_nnc_minimizer_saved_aux_size(minimizer);
3131	if (saved_aux_size > compiled_data->minimize.max_saved_aux_size)
3132		compiled_data->minimize.max_saved_aux_size = saved_aux_size;
3133	const int max_saved_aux_size = compiled_data->minimize.max_saved_aux_size;
3134	// We update all parameters, at this point, we have one minimizer.
3135	if (set_parameters == 0 || set_parameter_size == 0)
3136		compiled_data->minimize.minimizer = minimizer;
3137	int i;
3138	if (set_parameters && set_parameter_size)
3139	{
3140		// I need to save what's the minimizer along with this.
3141		if (!compiled_data->minimize.parameters)
3142			compiled_data->minimize.parameters = ccv_array_new(sizeof(ccv_cnnp_set_minimizer_for_parameter_t*), 1, 0);
3143		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));
3144		set_minimizer_for_parameter->minimizer = minimizer;
3145		set_minimizer_for_parameter->parameter_size = set_parameter_size;
3146		memcpy(set_minimizer_for_parameter->parameters, set_parameters, sizeof(ccv_cnnp_model_io_t) * set_parameter_size);
3147		ccv_array_push(compiled_data->minimize.parameters, &set_minimizer_for_parameter);
3148	}
3149	// If reset is true, clear the parameters array.
3150	if (reset && compiled_data->minimize.parameters)
3151	{
3152		for (i = 0; i < compiled_data->minimize.parameters->rnum; i++)
3153			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))));
3154		ccv_array_clear(compiled_data->minimize.parameters);
3155	}
3156	if (!compiled_data->update_nodes)
3157		return;
3158	ccv_nnc_symbolic_graph_t* const symbolic_graph = model->graph;
3159	assert(symbolic_graph)((void) sizeof ((symbolic_graph) ? 1 : 0), __extension__ ({ if
 (symbolic_graph) ; else __assert_fail ("symbolic_graph", "ccv_cnnp_model.c"
, 3159, __extension__ __PRETTY_FUNCTION__); }));
3160	if (saved_aux_size > old_max_saved_aux_size)
3161	{
3162		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", 3162, __extension__ __PRETTY_FUNCTION__
); }));
3163		// Reallocate first, move them around later.
3164		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);
3165		compiled_data->update_nodes = (ccv_nnc_graph_exec_symbol_t*)(compiled_data->updated_parameters + parameter_size);
3166		compiled_data->saved_aux = (ccv_nnc_tensor_symbol_map_t*)(compiled_data->update_nodes + parameter_size);
3167		// We need to do this from back to front because saved_aux_size > old_saved_aux_size, it could overlap.
3168		_ccv_cnnp_scatter_saved_aux(compiled_data->saved_aux, parameter_size, old_max_saved_aux_size, saved_aux_size);
3169	}
3170	int flag = 0;
3171	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; });
3172	if (set_parameters && set_parameter_size)
3173	{
3174		ccv_array_t* const parameter_indices = ccv_array_new(sizeof(int), 0, 0);
3175		for (i = 0; i < set_parameter_size; i++)
3176		{
3177			const int param_sel = set_parameters[i]->param_sel > 0 ? set_parameters[i]->param_sel - 1 : set_parameters[i]->param_sel;
3178			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", 3178, __extension__ __PRETTY_FUNCTION__
); }));
3179			const int old_rnum = parameter_indices->rnum;
3180			ccv_cnnp_model_add_to_parameter_indices(set_parameters[i]->model, param_sel, parameter_indices);
3181			const int param_ref = set_parameters[i]->param_ref > 0 ? set_parameters[i]->param_ref - 1 : set_parameters[i]->param_ref;
3182			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", 3182, __extension__ __PRETTY_FUNCTION__
); }));
3183			if (param_ref >= 0)
3184			{
3185				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", 3185, __extension__ __PRETTY_FUNCTION__
); }));
3186				*(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)));
3187				parameter_indices->rnum = old_rnum + 1;
3188			}
3189		}
3190		// We may have duplicated indices, but that is OK, we will set it twice.
3191		for (i = 0; i < parameter_indices->rnum; i++)
3192		{
3193			const int d = *(int*)ccv_array_get(parameter_indices, i)((void*)(((char*)((parameter_indices)->data)) + (size_t)(parameter_indices
)->rsize * (size_t)(i)));
3194			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))
3195				flag = 1;
3196		}
3197		ccv_array_free(parameter_indices);
3198	} else {
3199		for (i = 0; i < parameter_size; i++)
3200			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))
3201				flag = 1;
3202		if (compiled_data->minimize.parameters)
3203			if (_ccv_cnnp_apply_parameters_with_minimizer(model))
3204				flag = 1;
3205	}
3206	if (flag)
3207	{
3208		// 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.
3209		if (compiled_data->graph_mode == CCV_CNNP_MODEL_GRAPH_FIT_MODE)
3210			_ccv_cnnp_compiled_data_graph_free(compiled_data);
3211		_ccv_cnnp_compiled_data_apply_gradients_free(compiled_data);
3212	}
3213}
3214 
3215void ccv_cnnp_model_set_compile_params(ccv_cnnp_model_t* const model, const ccv_nnc_symbolic_graph_compile_param_t compile_params)
3216{
3217	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
3218	assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
 (compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 3218, __extension__ __PRETTY_FUNCTION__); }));
3219	compiled_data->compile_params = compile_params;
3220}
3221 
3222void ccv_cnnp_model_dot(const ccv_cnnp_model_t* const model, const int flags, FILE** const outs, const int out_size)
3223{
3224	if (model->graph && out_size > 0)
3225		ccv_nnc_symbolic_graph_dot(model->graph, flags, outs[0]);
3226	if (model->compiled_data && model->compiled_data->graph && out_size > 1)
3227		ccv_nnc_graph_dot(model->compiled_data->graph, flags, outs[1]);
3228	if (model->compiled_data && model->compiled_data->backward.accum && out_size > 2)
3229		ccv_nnc_graph_dot(model->compiled_data->backward.accum, flags, outs[2]);
3230	if (model->compiled_data && model->compiled_data->apply_gradients.graph && out_size > 3)
3231		ccv_nnc_graph_dot(model->compiled_data->apply_gradients.graph, flags, outs[3]);
3232}
3233 
3234void ccv_cnnp_model_format(const ccv_cnnp_model_t* const model, const ccv_nnc_symbolic_graph_format_f format_fn, void* const context)
3235{
3236	if (model->graph)
3237		ccv_nnc_symbolic_graph_format(model->graph, 0, 0, 0, 0, format_fn, context);
3238}
3239 
3240static void _ccv_cnnp_compiled_data_free(const ccv_cnnp_model_t* const model, ccv_cnnp_compiled_data_t* const compiled_data)
3241{
3242	int i;
3243	const int parameter_size = compiled_data->parameters->rnum;
3244	ccv_array_free(compiled_data->parameters);
3245	if (compiled_data->parameter_flags)
3246		ccfreefree(compiled_data->parameter_flags);
3247	const int internal_size = compiled_data->internals->rnum;
3248	ccv_array_free(compiled_data->internals);
3249	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", 3249, __extension__ __PRETTY_FUNCTION__
); }));
3250	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", 3250, __extension__ __PRETTY_FUNCTION__
); }));
3251	for (i = 0; i < parameter_size; i++)
3252		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))));
3253	ccv_array_free(compiled_data->ids.parameters);
3254	for (i = 0; i < internal_size; i++)
3255		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))));
3256	ccv_array_free(compiled_data->ids.internals);
3257	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; });
3258	if (compiled_data->tensors.parameters)
3259	{
3260		for (i = 0; i < parameter_size * parallel_count; i++)
3261			// If it is not marked as not belonging, we can free it.
3262			if (!((uintptr_t)compiled_data->tensors.parameters[i] & (uintptr_t)1))
3263				if (compiled_data->tensors.parameters[i])
3264					ccv_nnc_tensor_free(compiled_data->tensors.parameters[i]);
3265		for (i = 0; i < internal_size * parallel_count; i++)
3266			if (compiled_data->tensors.internals[i])
3267				ccv_nnc_tensor_free(compiled_data->tensors.internals[i]);
3268		ccfreefree(compiled_data->tensors.parameters);
3269	}
3270	if (compiled_data->tensors.gradients)
3271	{
3272		for (i = 0; i < parameter_size * parallel_count; i++)
3273		{
3274			if (compiled_data->tensors.gradients[i])
3275				ccv_nnc_tensor_free(compiled_data->tensors.gradients[i]);
3276			if (compiled_data->tensors.accum_gradients[i])
3277				ccv_nnc_tensor_free(compiled_data->tensors.accum_gradients[i]);
3278		}
3279		ccfreefree(compiled_data->tensors.gradients);
3280	}
3281	if (compiled_data->minimize.parameters)
3282	{
3283		for (i = 0; i < compiled_data->minimize.parameters->rnum; i++)
3284			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))));
3285		ccv_array_free(compiled_data->minimize.parameters);
3286	}
3287	if (compiled_data->rewindables)
3288		ccv_array_free(compiled_data->rewindables);
3289	if (compiled_data->tensors_init.v)
3290		ccfreefree(CCV_NNC_INIT_V(compiled_data->tensors_init.v)((uint32_t*)((uintptr_t)(compiled_data->tensors_init.v) &
 ~(uintptr_t)1)));
3291	if (compiled_data->evaluate.tos)
3292		ccfreefree(compiled_data->evaluate.tos);
3293	compiled_data->evaluate.tos = 0;
3294	if (compiled_data->stream_map)
3295	{
3296		khiter_t k;
3297		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)
3298		{
3299			if (!kh_exist(compiled_data->stream_map, k)(!(((compiled_data->stream_map)->flags[(k)>>4]>>
(((k)&0xfU)<<1))&3)))
3300				continue;
3301			ccv_nnc_stream_context_t* const stream = kh_val(compiled_data->stream_map, k)((compiled_data->stream_map)->vals[k]);
3302			ccv_nnc_stream_context_free(stream);
3303		}
3304		kh_destroy(stream_map, compiled_data->stream_map)kh_destroy_stream_map(compiled_data->stream_map);
3305	}
3306	_ccv_cnnp_compiled_data_graph_free(compiled_data);
3307	_ccv_cnnp_compiled_data_gradient_free(compiled_data);
3308	_ccv_cnnp_compiled_data_backward_free(compiled_data);
3309	_ccv_cnnp_compiled_data_apply_gradients_free(compiled_data);
3310	if (compiled_data->gradient_checkpoints)
3311	{
3312		for (i = 0; i < compiled_data->gradient_checkpoints->rnum; i++)
3313		{
3314			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)));
3315			assert(checkpoint->inputs)((void) sizeof ((checkpoint->inputs) ? 1 : 0), __extension__
 ({ if (checkpoint->inputs) ; else __assert_fail ("checkpoint->inputs"
, "ccv_cnnp_model.c", 3315, __extension__ __PRETTY_FUNCTION__
); }));
3316			ccfreefree(checkpoint->inputs);
3317			ccv_array_free(checkpoint->tensor_symbols);
3318		}
3319		ccv_array_free(compiled_data->gradient_checkpoints);
3320	}
3321	ccv_nnc_xpu_alloc_destroy(&compiled_data->xpu_alloc);
3322	ccfreefree(compiled_data);
3323}
3324 
3325void ccv_cnnp_model_free(ccv_cnnp_model_t* const model)
3326{
3327	ccv_cnnp_model_deinit(model);
3328	if (model->isa->dealloc)
3329		model->isa->dealloc(model);
3330	if (model->io)
3331	{
3332		int i;
3333		for (i = 0; i < model->io->rnum; i++)
3334		{
3335			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)));
3336			if (model_io->outgoings)
3337				ccv_array_free(model_io->outgoings);
3338			if (model_io->incomings)
3339				ccv_array_free(model_io->incomings);
3340			if (model_io->dependencies)
3341				ccv_array_free(model_io->dependencies);
3342			ccfreefree(model_io);
3343		}
3344		ccv_array_free(model->io);
3345	}
3346	if (model->parameter_indices)
3347		ccv_array_free(model->parameter_indices);
3348	if (model->inputs)
3349		ccfreefree(model->inputs);
3350	if (model->graph)
3351		ccv_nnc_symbolic_graph_free(model->graph);
3352	if (model->compiled_data)
3353		_ccv_cnnp_compiled_data_free(model, model->compiled_data);
3354	if (model->name)
3355		ccfreefree(model->name);
3356	ccfreefree(model);
3357}
3358 
3359void ccv_cnnp_model_cancel(ccv_cnnp_model_t* const model)
3360{
3361	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
3362	if (!compiled_data)
3363		return;
3364	if (compiled_data->graph)
3365		ccv_nnc_graph_cancel(compiled_data->graph);
3366	if (compiled_data->apply_gradients.graph)
3367		ccv_nnc_graph_cancel(compiled_data->apply_gradients.graph);
3368}
3369 
3370void ccv_cnnp_model_set_flags(ccv_cnnp_model_t* const model, const int flags)
3371{
3372	model->exec_flags = flags;
3373}
3374 
3375int ccv_cnnp_model_flags(ccv_cnnp_model_t* const model)
3376{
3377	return model->exec_flags;
3378}