ccv_cnnp_model.c

Bug Summary

File:	nnc/ccv_cnnp_model.c
Warning:	line 2382, column 1 1st function call argument is an uninitialized value
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/2025-05-06-150409-118063-1 -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 
8// MARK - Level-5 API
9 
10ccv_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)
11{
12	if (!model->io)
13		model->io = ccv_array_new(sizeof(ccv_cnnp_model_io_t), 1, 0);
14	ccv_cnnp_model_io_t model_io = ccmallocmalloc(sizeof(struct ccv_cnnp_model_io_s) + sizeof(ccv_nnc_tensor_symbol_t) * model->output_size);
15	model_io->param_ref = 0;
16	model_io->param_sel = 0;
17	model_io->visit = 0;
18	model_io->model = model;
19	model_io->dependencies = 0;
20	model_io->dependents = 0;
21	model_io->outgoings = 0;
22	model_io->outputs = (ccv_nnc_tensor_symbol_t*)(model_io + 1);
23	ccv_array_push(model->io, &model_io);
24	if (input_size > 0)
25	{
26		model_io->incomings = ccv_array_new(sizeof(ccv_cnnp_model_io_t), input_size, 0);
27		ccv_array_resize(model_io->incomings, input_size);
28		int i;
29		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);
30		for (i = 0; i < input_size; i++)
31		{
32			if (!inputs[i]->outgoings)
33				inputs[i]->outgoings = ccv_array_new(sizeof(ccv_cnnp_model_io_t), 1, 0);
34			ccv_array_push(inputs[i]->outgoings, &model_io);
35		}
36	} else {
37		model_io->incomings = 0;
38	}
39	return model_io;
40}
41 
42void ccv_cnnp_model_add_dependencies(ccv_cnnp_model_io_t model_io, const ccv_cnnp_model_io_t* const dependencies, const int dependency_size)
43{
44	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", 44, __extension__ __PRETTY_FUNCTION__);
 }));
45	if (!model_io->dependencies)
46		model_io->dependencies = ccv_array_new(sizeof(ccv_cnnp_model_io_t), dependency_size, 0);
47	int i, j;
48	for (i = 0; i < dependency_size; i++)
49	{
50		int flag = 0;
51		// Check if it is already exist or not.
52		for (j = 0; !flag && j < model_io->dependencies->rnum; j++)
53			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])
54				flag = 1;
55		if (flag)
56			continue;
57		ccv_array_push(model_io->dependencies, dependencies + i);
58		++dependencies[i]->dependents;
59	}
60}
61 
62int ccv_cnnp_model_output_size(const ccv_cnnp_model_t* const model)
63{
64	return model->output_size;
65}
66 
67int ccv_cnnp_model_is_trainable(const ccv_cnnp_model_t* const model)
68{
69	// If the model is compiled, it is default to 1 unless it is not.
70	if (model->compiled_data)
71		return model->is_trainable >= 0 ? model->is_trainable : 1;
72	return model->is_trainable;
73}
74 
75ccv_cnnp_model_io_t ccv_cnnp_model_parameters(ccv_cnnp_model_t* const model, const int selector, const int index)
76{
77	if (!model->io)
78		model->io = ccv_array_new(sizeof(ccv_cnnp_model_io_t), 1, 0);
79	ccv_cnnp_model_io_t model_io = ccmallocmalloc(sizeof(struct ccv_cnnp_model_io_s));
80	model_io->param_ref = index >= 0 ? index + 1 : ALL_PARAMETERS-1;
81	model_io->param_sel = selector >= 0 ? selector + 1 : ALL_PARAMETERS-1;
82	model_io->visit = 0;
83	model_io->model = model;
84	model_io->outputs = 0;
85	model_io->dependencies = 0;
86	model_io->dependents = 0;
87	model_io->incomings = 0;
88	model_io->outgoings = 0;
89	ccv_array_push(model->io, &model_io);
90	return model_io;
91}
92 
93void ccv_cnnp_model_notify_hook(ccv_cnnp_model_t* const model, ccv_cnnp_model_notify_f func, void* const context)
94{
95	model->notify_hook.func = func;
96	model->notify_hook.context = context;
97}
98 
99void ccv_cnnp_model_notify(const ccv_cnnp_model_t* const model, const int tag, void* const payload)
100{
101	if (model->notify_hook.func)
102		model->notify_hook.func(model, tag, payload, model->notify_hook.context);
103	if (model->isa->notify)
104		model->isa->notify(model, tag, payload);
105}
106 
107static int _ccv_nnc_array_dedup_graph_exec_symbols(ccv_nnc_graph_exec_symbol_t* const graph_exec_symbols, int graph_exec_symbol_size)
108{
109	int i, j;
110	for (i = 0; i < graph_exec_symbol_size; i++)
111	{
112		ccv_nnc_graph_exec_symbol_t* const graph_exec_symbol = graph_exec_symbols + i;
113		// Check whether this tensor symbol has any duplicate.
114		for (j = i + 1; j < graph_exec_symbol_size;)
115		{
116			ccv_nnc_graph_exec_symbol_t* const other_symbol = graph_exec_symbols + j;
117			// If there is a same tensor symbol, remove it.
118			if (other_symbol->d == graph_exec_symbol->d && other_symbol->graph == graph_exec_symbol->graph)
119			{
120				if (j + 1 < graph_exec_symbol_size)
121					*other_symbol = graph_exec_symbols[graph_exec_symbol_size - 1];
122				--graph_exec_symbol_size;
123				continue;
124			}
125			++j;
126		}
127	}
128	return graph_exec_symbol_size;
129}
130 
131void ccv_cnnp_model_add_to_array(void* const context, const ccv_nnc_tensor_symbol_t symbol, const int is_trainable)
132{
133	ccv_cnnp_model_add_to_array_context_t* const add_to_array_context = (ccv_cnnp_model_add_to_array_context_t*)context;
134	ccv_cnnp_model_t* const model = add_to_array_context->sequence->model;
135	int i;
136	if (add_to_array_context->add_parameter_indices && !model->parameter_indices)
137		model->parameter_indices = ccv_array_new(sizeof(int), 0, 0);
138	for (i = 0; i < add_to_array_context->symbols->rnum; i++)
139	{
140		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)));
141		if (other_symbol.d == symbol.d && other_symbol.graph == symbol.graph)
142		{
143			// Only add to parameter_indices if it is trainable.
144			if (add_to_array_context->add_parameter_indices)
145				ccv_array_add_unique_int(model->parameter_indices, i);
146			// Found it, return, don't add it.
147			return;
148		}
149	}
150	// Only add to parameter_indices if it is trainable.
151	if (add_to_array_context->add_parameter_indices)
152		ccv_array_push(model->parameter_indices, &add_to_array_context->symbols->rnum);
153	// This is a new one, no need to add_unique_int, it is unique.
154	ccv_array_push(add_to_array_context->symbols, &symbol);
155	if (add_to_array_context->trainables)
156		ccv_array_push(add_to_array_context->trainables, &is_trainable);
157	char id[2048];
158	id[0] = add_to_array_context->prefix;
159	id[1] = '-';
160	int total_len = 2;
161	for (i = 0; i < add_to_array_context->sequence->sequences->rnum; i++)
162	{
163		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)));
164		int len;
165		if (name->name && name->name[0] != '\0')
166			len = snprintf(id + total_len, 2048 - total_len, "%s-%d-", name->name, name->sequence);
167		else
168			len = snprintf(id + total_len, 2048 - total_len, "%d-", name->sequence);
169		total_len += len;
170		if (total_len >= 2047)
171			break;
172	}
173	if (total_len < 2047)
174		total_len += snprintf(id + total_len, 2048 - total_len, "%d", add_to_array_context->sequence->it);
175	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", 175, __extension__ __PRETTY_FUNCTION__)
; }));
176	char *heap_id = (char*)ccmallocmalloc(total_len + 1);
177	memcpy(heap_id, id, total_len + 1);
178	ccv_array_push(add_to_array_context->ids, &heap_id);
179	++add_to_array_context->sequence->it;
180}
181 
182static void _ccv_cnnp_compiled_data_init(ccv_cnnp_compiled_data_t* const compiled_data, const int output_size, ccv_array_t* const gradient_checkpoints)
183{
184	compiled_data->f = compiled_data->fits + output_size;
185	compiled_data->xpu_alloc.mp_hdr = -1;
186	compiled_data->xpu_alloc.freed = kh_init(dy_str)kh_init_dy_str();
187	compiled_data->xpu_alloc.allocd = kh_init(dy_alloc)kh_init_dy_alloc();
188	compiled_data->gradient_checkpoints = gradient_checkpoints;
189}
190 
191typedef struct {
192	void* old_graph_exec_symbol_new_hook_context;
193	ccv_nnc_graph_exec_symbol_new_hook_f old_graph_exec_symbol_new_hook;
194	ccv_nnc_symbolic_graph_t* graph;
195	ccv_cnnp_model_build_data_t* build_data;
196} ccv_cnnp_model_set_exec_flags_context_t;
197 
198static 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)
199{
200	ccv_cnnp_model_set_exec_flags_context_t* flags_context = (ccv_cnnp_model_set_exec_flags_context_t*)context;
201	if (flags_context->build_data->exec_flags)
202		ccv_nnc_graph_exec_symbol_set_flags(flags_context->graph, symbol, flags_context->build_data->exec_flags);
203	if (flags_context->old_graph_exec_symbol_new_hook)
204		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);
205}
206 
207static 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)
208{
209	assert(model->graph)((void) sizeof ((model->graph) ? 1 : 0), __extension__ ({ if
 (model->graph) ; else __assert_fail ("model->graph", "ccv_cnnp_model.c"
, 209, __extension__ __PRETTY_FUNCTION__); }));
210	model->inputs = ccmallocmalloc(sizeof(ccv_nnc_tensor_symbol_t) * input_size);
211	int i;
212	for (i = 0; i < input_size; i++)
213		model->inputs[i] = ccv_nnc_tensor_symbol_new(model->graph, inputs[i], 0);
214	ccv_array_t* const parameters = ccv_array_new(sizeof(ccv_nnc_tensor_symbol_t), 0, 0);
215	ccv_array_t* const parameter_ids = ccv_array_new(sizeof(char*), 0, 0);
216	ccv_array_t* const parameter_trainables = ccv_array_new(sizeof(int), 0, 0);
217	ccv_cnnp_model_sequence_t model_sequence = {
218		.bank = kh_init(ccv_cnnp_model_name_bank)kh_init_ccv_cnnp_model_name_bank()
219	};
220	ccv_cnnp_model_add_to_array_context_t add_to_parameter_context = {
221		.add_parameter_indices = 1,
222		.prefix = 't',
223		.sequence = &model_sequence,
224		.symbols = parameters,
225		.ids = parameter_ids,
226		.trainables = parameter_trainables,
227	};
228	ccv_array_t* const internals = ccv_array_new(sizeof(ccv_nnc_tensor_symbol_t), 0, 0);
229	ccv_array_t* const internal_ids = ccv_array_new(sizeof(char*), 0, 0);
230	ccv_cnnp_model_add_to_array_context_t add_to_output_context = {
231		.add_parameter_indices = 0,
232		.prefix = 'r',
233		.sequence = &model_sequence,
234		.symbols = internals,
235		.ids = internal_ids,
236		.trainables = 0,
237	};
238	ccv_cnnp_model_build_data_t build_data = {
239		.exec_flags = 0,
240		.is_trainable = model->is_trainable >= 0 ? model->is_trainable : 1,
241		.model_sequence = &model_sequence,
242		.add_to_array = ccv_cnnp_model_add_to_array,
243		.parameters = parameters,
244		.context = {
245			.add_to_parameter = &add_to_parameter_context,
246			.add_to_output = &add_to_output_context,
247		},
248		.gradient_checkpoints = 0,
249	};
250	model->data = &build_data;
251	ccv_cnnp_model_set_exec_flags_context_t flags_context = {
252		.graph = model->graph,
253		.build_data = &build_data,
254		.old_graph_exec_symbol_new_hook = 0,
255		.old_graph_exec_symbol_new_hook_context = 0
256	};
257	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);
258	ccv_cnnp_model_build(model, model->graph, model->inputs, input_size, 0, 0);
259	// Reset back to previous hook.
260	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);
261	for (i = 0; i < model->output_size; i++)
262	{
263		const ccv_nnc_tensor_symbol_t output = model->outputs[i];
264		const ccv_nnc_tensor_symbol_t alias_to = ccv_nnc_tensor_symbol_alias_to(model->graph, output);
265		if (alias_to.d == CCV_NNC_NO_TENSOR_SYMBOL)
266			continue;
267		// 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
268		// 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
269		// honest, because we cannot handle cases of alias is part of the original tensor but bind differently).
270		const ccv_nnc_tensor_param_t output_params = ccv_nnc_tensor_symbol_params(model->graph, output);
271		model->outputs[i] = ccv_nnc_tensor_symbol_new(model->graph, output_params, 0);
272		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");
273		ccv_nnc_graph_exec_symbol_set_flags(model->graph, make_contiguous, CCV_NNC_GRAPH_EXEC_DISABLE_OPT);
274	}
275	model->data = 0;
276	kh_destroy(ccv_cnnp_model_name_bank, model_sequence.bank)kh_destroy_ccv_cnnp_model_name_bank(model_sequence.bank);
277	if (model_sequence.sequences)
278		ccv_array_free(model_sequence.sequences);
279	// Check if there are parameters that are not trainables. If there are, we will allocate uint64 bitmap to record that.
280	int not_trainables = 0;
281	// Assert no parameter is alias.
282	for (i = 0; i < parameters->rnum; i++)
283	{
284		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)));
285		const ccv_nnc_tensor_symbol_t alias_to = ccv_nnc_tensor_symbol_alias_to(parameter.graph, parameter);
286		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", 286, __extension__ __PRETTY_FUNCTION__)
; })); // Cannot find the one alias to.
287		if (*(int*)ccv_array_get(parameter_trainables, i)((void*)(((char*)((parameter_trainables)->data)) + (size_t
)(parameter_trainables)->rsize * (size_t)(i))) == 0)
288			not_trainables = 1;
289	}
290	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", 290, __extension__ __PRETTY_FUNCTION__)
; }));
291	uint64_t* parameter_flags = 0;
292	if (not_trainables)
293	{
294		parameter_flags = (uint64_t*)cccalloccalloc(((parameters->rnum + 63) >> 6), sizeof(uint64_t));
295		for (i = 0; i < parameter_trainables->rnum; i++)
296			if (*(int*)ccv_array_get(parameter_trainables, i)((void*)(((char*)((parameter_trainables)->data)) + (size_t
)(parameter_trainables)->rsize * (size_t)(i))))
297				parameter_flags[i >> 6] |= ((uint64_t)1 << (i & 63));
298	}
299	ccv_array_free(parameter_trainables);
300	// Assert no internal is alias.
301	for (i = 0; i < internals->rnum; i++)
302	{
303		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)));
304		const ccv_nnc_tensor_symbol_t alias_to = ccv_nnc_tensor_symbol_alias_to(internal.graph, internal);
305		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", 305, __extension__ __PRETTY_FUNCTION__)
; })); // Cannot find the one alias to.
306	}
307	const int output_size = model->output_size;
308	ccv_nnc_graph_exec_symbol_autogen(model->graph, 0, 0, CCV_NNC_AUTOGEN_ALL_EXECS | CCV_NNC_AUTOGEN_SOURCES_AND_DESTINATIONS);
309	const int parameters_rnum = parameters->rnum;
310	if (input_size > 0)
311	{
312		ccv_array_resize(parameters, parameters_rnum + input_size);
313		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));
314	}
315	ccv_nnc_symbolic_graph_simplify(model->graph,
316		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)
317			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)
318			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)
319			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),
320		ccv_array_get(parameters, 0)((void*)(((char*)((parameters)->data)) + (size_t)(parameters
)->rsize * (size_t)(0))), parameters_rnum + input_size,
321		model->outputs, output_size,
322		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));
323	ccv_nnc_graph_exec_symbol_autogen(model->graph, 0, 0, CCV_NNC_AUTOGEN_SOURCES_AND_DESTINATIONS);
324	// Size it down.
325	parameters->rnum = parameters_rnum;
326	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));
327	_ccv_cnnp_compiled_data_init(compiled_data, output_size, build_data.gradient_checkpoints);
328	const int evaluate_to_size = compiled_data->evaluate.to_size = ccv_nnc_symbolic_graph_destination_size(model->graph);
329	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", 329, __extension__ __PRETTY_FUNCTION__)
; }));
330	compiled_data->evaluate.tos = ccmallocmalloc(sizeof(ccv_nnc_graph_exec_symbol_t) * evaluate_to_size);
331	memcpy(compiled_data->evaluate.tos, ccv_nnc_symbolic_graph_destinations(model->graph), sizeof(ccv_nnc_graph_exec_symbol_t) * evaluate_to_size);
332	compiled_data->loss = loss;
333	if (loss.cmd == CCV_NNC_NOOP)
334	{
335		// If no loss function provided, there is no fits.
336		for (i = 0; i < output_size; i++)
337		{
338			compiled_data->fits[i] = NO_TENSOR_SYMBOL(const ccv_nnc_tensor_symbol_t){.d = CCV_NNC_NO_TENSOR_SYMBOL
};
339			const ccv_nnc_tensor_symbol_t alias_to = ccv_nnc_tensor_symbol_alias_to(model->graph, model->outputs[i]);
340			if (alias_to.d < 0)
341				compiled_data->f[i] = model->outputs[i];
342			else { // We cannot differentiate against an alias, therefore, we have to verify this output is full, and we can diff against the original.
343				int ofs[CCV_NNC_MAX_DIM_ALLOC(12)];
344				int inc[CCV_NNC_MAX_DIM_ALLOC(12)];
345				ccv_nnc_tensor_symbol_alias_params(model->graph, model->outputs[i], ofs, inc);
346				int j;
347				for (j = 0; j < CCV_NNC_MAX_DIM_ALLOC(12); j++)
348					{ 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"
, 348, __extension__ __PRETTY_FUNCTION__); })); } // There is no ofs.
349				compiled_data->f[i] = alias_to; // Unfortunately, I cannot assert the size yet.
350			}
351		}
352	} else {
353		for (i = 0; i < output_size; i++)
354		{
355			const ccv_nnc_tensor_param_t info = ccv_nnc_tensor_symbol_params(model->graph, model->outputs[i]);
356			const ccv_nnc_tensor_symbol_t fit = compiled_data->fits[i] = ccv_nnc_tensor_symbol_new(model->graph, info, 0);
357			compiled_data->f[i] = ccv_nnc_tensor_symbol_new(model->graph, ccv_nnc_tensor_auto, 0);
358			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);
359		}
360	}
361	ccv_nnc_graph_exec_symbol_autogen(model->graph, 0, 0, CCV_NNC_AUTOGEN_ALL_EXECS | CCV_NNC_AUTOGEN_SOURCES_AND_DESTINATIONS);
362	ccv_nnc_symbolic_graph_simplify(model->graph,
363		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.
364		0, 0, // No need to provide binds at this point.
365		compiled_data->f, model->output_size,
366		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));
367	ccv_nnc_graph_exec_symbol_autogen(model->graph, 0, 0, CCV_NNC_AUTOGEN_SOURCES_AND_DESTINATIONS);
368	// If inputs are from GPU, stream type is GPU.
369	compiled_data->parameters = parameters;
370	compiled_data->parameter_flags = parameter_flags;
371	compiled_data->internals = internals;
372	compiled_data->ids.parameters = parameter_ids;
373	compiled_data->ids.internals = internal_ids;
374	ccv_cnnp_model_gradient_checkpoints_cleanup_after_build(compiled_data, model->graph);
375}
376 
377static 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)
378{
379	ccv_array_t* const stack = (ccv_array_t*)context;
380	ccv_array_push(stack, &symbol.d);
381}
382 
383static 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)
384{
385	const ccv_nnc_tensor_symbol_t src_symbol = {
386		.d = src_index,
387		.graph = src_graph
388	};
389	const ccv_nnc_tensor_symbol_t dest_symbol = {
390		.d = dest_index,
391		.graph = dest_graph
392	};
393	const ccv_nnc_tensor_param_t params = ccv_nnc_tensor_symbol_params(src_graph, src_symbol);
394	ccv_nnc_tensor_symbol_set(dest_graph, dest_symbol, params);
395	int ofs[CCV_NNC_MAX_DIM_ALLOC(12)];
396	int inc[CCV_NNC_MAX_DIM_ALLOC(12)];
397	if (0 == ccv_nnc_tensor_symbol_alias_params(src_graph, src_symbol, ofs, inc))
398		ccv_nnc_tensor_symbol_alias_set(dest_graph, dest_symbol, ofs, inc);
399}
400 
401static 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)
402{
403	const ccv_nnc_tensor_symbol_t src_symbol = {
404		.d = src_index,
405		.graph = src_graph
406	};
407	const ccv_nnc_tensor_param_t src_params = ccv_nnc_tensor_symbol_params(src_graph, src_symbol);
408	const ccv_nnc_tensor_symbol_t dest_symbol = {
409		.d = dest_index,
410		.graph = dest_graph
411	};
412	const ccv_nnc_tensor_param_t dest_params = ccv_nnc_tensor_symbol_params(dest_graph, dest_symbol);
413	return memcmp(src_params.dim, dest_params.dim, sizeof(src_params.dim)) == 0;
414}
415 
416static 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);
417static void _ccv_cnnp_compiled_data_graph_free(ccv_cnnp_compiled_data_t* const compiled_data);
418 
419typedef struct {
420	int parallel_count;
421	ccv_nnc_symbolic_graph_t* graph;
422	ccv_nnc_graph_exec_arena_t* graph_exec_arena;
423} ccv_nnc_graph_exec_update_t;
424 
425static 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)
426{
427	ccv_nnc_graph_exec_update_t* const graph_exec_update = (ccv_nnc_graph_exec_update_t*)context;
428	ccv_nnc_graph_exec_arena_t* const graph_exec_arena = graph_exec_update->graph_exec_arena;
429	ccv_nnc_graph_exec_t graph_exec = ccv_nnc_graph_exec_from_symbol(graph_exec_arena, symbol);
430	ccv_nnc_graph_exec_set(graph_exec.graph, graph_exec, cmd);
431	ccv_nnc_graph_exec_set_hint(graph_exec.graph, graph_exec, hint);
432	const ccv_nnc_symbolic_graph_t* const graph = graph_exec_update->graph;
433	const int parallel_count = graph_exec_update->parallel_count;
434	int i;
435	for (i = 1; i < parallel_count; i++)
436	{
437		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));
438		if (!CCV_NO_GRAPH_EXEC(copy)((copy).graph == 0))
439		{
440			ccv_nnc_graph_exec_set(copy.graph, copy, cmd);
441			ccv_nnc_graph_exec_set_hint(copy.graph, copy, hint);
442		}
443	}
444}
445 
446void 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)
447{
448	assert(model->graph)((void) sizeof ((model->graph) ? 1 : 0), __extension__ ({ if
 (model->graph) ; else __assert_fail ("model->graph", "ccv_cnnp_model.c"
, 448, __extension__ __PRETTY_FUNCTION__); }));
449	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", 449, __extension__ __PRETTY_FUNCTION__)
; }));
450	assert(!init->graph)((void) sizeof ((!init->graph) ? 1 : 0), __extension__ ({ if
 (!init->graph) ; else __assert_fail ("!init->graph", "ccv_cnnp_model.c"
, 450, __extension__ __PRETTY_FUNCTION__); }));
451	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
452	init->graph = ccv_nnc_symbolic_graph_new();
453	ccv_array_t* const stack = ccv_array_new(sizeof(int), 0, 0);
454	ccv_nnc_graph_exec_symbol_new_hook(init->graph, _ccv_cnnp_graph_push_graph_exec_symbol, stack, 0);
455	_ccv_cnnp_model_compile(init, inputs, input_size, compiled_data->loss);
456	init->parallel_count = model->parallel_count;
457	init->memory_compression = model->memory_compression;
458	init->memory_reduction = model->memory_reduction;
459	init->gradient_checkpointing = model->gradient_checkpointing;
460	init->compiled_data->stream_type = model->compiled_data->stream_type;
461	init->compiled_data->minimize.minimizer = model->compiled_data->minimize.minimizer;
462	init->compiled_data->minimize.max_saved_aux_size = model->compiled_data->minimize.max_saved_aux_size;
463	if (model->compiled_data->gradient_mode != CCV_CNNP_COMPILED_DATA_GRADIENT_NONE)
464		_ccv_cnnp_model_gradient_init(init, model->compiled_data->gradient_mode, model->compiled_data->disable_outgrad, 0, 0);
465	ccv_nnc_graph_exec_symbol_new_hook(init->graph, 0, 0, 0);
466	ccv_nnc_symbolic_graph_tensor_auto(init->graph, TRAVERSE_FULL0,0,0,0);
467	int i, j;
468	// Verify parameters, internals and saved_aux in both graph has the same dimensionality.
469	for (i = 0; i < compiled_data->parameters->rnum; i++)
470	{
471		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;
472		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", 472, __extension__ __PRETTY_FUNCTION__)
; }));
473	}
474	for (i = 0; i < compiled_data->internals->rnum; i++)
475	{
476		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;
477		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", 477, __extension__ __PRETTY_FUNCTION__)
; }));
478	}
479	// Update inputs.
480	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", 480, __extension__ __PRETTY_FUNCTION__)
; }));
481	for (i = 0; i < model->input_size; i++)
482		if (model->inputs[i].d >= 0)
483		{
484			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", 484, __extension__ __PRETTY_FUNCTION__)
; }));
485			_ccv_nnc_tensor_symbol_reinit(init->graph, model->graph, init->inputs[i].d, model->inputs[i].d);
486		}
487	// Update outputs.
488	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", 488, __extension__ __PRETTY_FUNCTION__)
; }));
489	for (i = 0; i < model->output_size; i++)
490	{
491		if (model->outputs[i].d >= 0)
492		{
493			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", 493, __extension__
 __PRETTY_FUNCTION__); }));
494			_ccv_nnc_tensor_symbol_reinit(init->graph, model->graph, init->outputs[i].d, model->outputs[i].d);
495		}
496		if (model->outputs[i].d != model->compiled_data->f[i].d)
497		{
498			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", 498, __extension__ __PRETTY_FUNCTION__)
; }));
499			if (model->compiled_data->f[i].d >= 0)
500			{
501				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", 501, __extension__ __PRETTY_FUNCTION__)
; }));
502				_ccv_nnc_tensor_symbol_reinit(init->graph, model->graph, init->compiled_data->f[i].d, model->compiled_data->f[i].d);
503			}
504		}
505	}
506	// Go through the graph to set tensor on matching symbols
507	for (i = 0; i < stack->rnum; i++)
508	{
509		const int d = *(int*)ccv_array_get(stack, i)((void*)(((char*)((stack)->data)) + (size_t)(stack)->rsize
 * (size_t)(i)));
510		// If exceed range, skip.
511		if (d >= ccv_nnc_graph_exec_symbol_count(init->graph) ||
512			d >= ccv_nnc_graph_exec_symbol_count(model->graph))
513			continue;
514		const ccv_nnc_graph_exec_symbol_t src_symbol = {
515			.d = d,
516			.graph = init->graph
517		};
518		const ccv_nnc_graph_exec_symbol_t dest_symbol = {
519			.d = d,
520			.graph = model->graph
521		};
522		const ccv_nnc_cmd_t src_cmd = ccv_nnc_graph_exec_symbol_cmd(init->graph, src_symbol);
523		const ccv_nnc_cmd_t dest_cmd = ccv_nnc_graph_exec_symbol_cmd(model->graph, dest_symbol);
524		// If the name doesn't match, skip.
525		if (dest_cmd.cmd != src_cmd.cmd && src_cmd.cmd != CCV_NNC_NOOP)
526			continue;
527		// Now get all the inputs and outputs, if matches, set them.
528		const int* src_inputs;
529		int src_input_size;
530		const int* src_outputs;
531		int src_output_size;
532		ccv_nnc_graph_exec_symbol_io(init->graph, src_symbol, &src_inputs, &src_input_size, &src_outputs, &src_output_size);
533		const int* dest_inputs;
534		int dest_input_size;
535		const int* dest_outputs;
536		int dest_output_size;
537		ccv_nnc_graph_exec_symbol_io(model->graph, dest_symbol, &dest_inputs, &dest_input_size, &dest_outputs, &dest_output_size);
538		// We may have unmatched input / output size because this is the minimizer and it has
539		// different saved_aux (for example, when we shrunk with CMD_NOOP).
540		if (src_input_size != dest_input_size)
541			continue;
542		if (src_output_size != dest_output_size)
543			continue;
544		ccv_nnc_graph_exec_symbol_set(model->graph, dest_symbol, src_cmd);
545		// There may be mismatches of the source tensor symbols and destination tensor symbols. The reason is because
546		// we may later passed-in the minimizer, therefore, we may allocate tensors for minimizer later in the original
547		// graph whereas in the newly created graph, it is streamlined (the minimizer exists from the beginning). That
548		// will make the order of tensor symbols creation different, therefore, exact which tensor is which wrong as
549		// well. However, set a new minimizer won't change the exec symbol ordering, because we never create new exec
550		// symbols after gradient init step. Changing a new minimizer just updated that exec symbols setting, it is not
551		// a new exec symbol.
552		for (j = 0; j < src_input_size; j++)
553			if (src_inputs[j] >= 0)
554				_ccv_nnc_tensor_symbol_reinit(init->graph, model->graph, src_inputs[j], dest_inputs[j]);
555		for (j = 0; j < src_output_size; j++)
556			if (src_outputs[j] >= 0)
557				_ccv_nnc_tensor_symbol_reinit(init->graph, model->graph, src_outputs[j], dest_outputs[j]);
558	}
559	ccv_array_free(stack);
560	// After this, we get all tensors in the model graph resolved through tensor_auto.
561	ccv_nnc_symbolic_graph_tensor_auto(model->graph, TRAVERSE_FULL0,0,0,0);
562	// Verify symbols we get matches.
563	const int parameter_size = compiled_data->parameters->rnum;
564	for (i = 0; i < parameter_size; i++)
565		{ 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", 565, __extension__ __PRETTY_FUNCTION__)
; })); }
566	const int internal_size = compiled_data->internals->rnum;
567	for (i = 0; i < internal_size; i++)
568		{ 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", 568, __extension__ __PRETTY_FUNCTION__)
; })); }
569	// Go through compiled data.
570	if (compiled_data->tensor_arena)
571	{
572		const int flag = ccv_nnc_tensor_arena_reinit(compiled_data->tensor_arena, model->graph);
573		if (flag == 0 && compiled_data->graph_exec_arena)
574		{
575			ccv_nnc_graph_exec_reinit(compiled_data->graph_exec_arena, compiled_data->graph, model->graph);
576			// Since we will reinit, if we previously set is_test, we need to set it again.
577			if (compiled_data->is_test)
578			{
579				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; });
580				ccv_nnc_graph_exec_update_t update = {
581					.parallel_count = parallel_count,
582					.graph = model->graph,
583					.graph_exec_arena = compiled_data->graph_exec_arena,
584				};
585				ccv_cnnp_model_set_is_test(model, 1, _ccv_cnnp_cmd_update_for_execs, &update);
586			}
587		} else
588			// Free-up tensor arena & graph exec arena.
589			_ccv_cnnp_compiled_data_graph_free(compiled_data);
590	}
591	// There are other compiled graphs, for accum and apply gradients.
592	// However, the main conclusion is, these absorb operations shouldn't impact parameters.
593	// Thus, it won't impact the shape of gradients (only outgrad). Since for outgrad, we
594	// don't allocate ourselves, it is not a concern. For normal gradients, the shape cannot
595	// be changed otherwise parameters' shape will be meaningless. The same goes to internals.
596	// That is why we don't update these compiled graphs at all this point.
597	// Free the model, we've already "absorbed" it.
598	ccv_cnnp_model_free(init);
599}
600 
601void 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)
602{
603	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", 603, __extension__ __PRETTY_FUNCTION__)
; }));
604	if (model->input_size == 0)
605		model->input_size = input_size;
606	if (!model->graph) // The graph is not compiled yet.
607	{
608		model->graph = ccv_nnc_symbolic_graph_new();
609		_ccv_cnnp_model_compile(model, inputs, input_size, loss);
610		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", 610, __extension__ __PRETTY_FUNCTION__)
; }));
611		int i, flag = 0;
612		for (i = 0; !flag && i < input_size; i++)
613			flag = (CCV_TENSOR_GET_MEMORY(inputs[i].type)((inputs[i].type) & 0x3) == CCV_TENSOR_GPU_MEMORY);
614		// If inputs are from GPU, stream type is GPU.
615		model->compiled_data->stream_type = flag ? CCV_STREAM_CONTEXT_GPU : CCV_STREAM_CONTEXT_CPU;
616		model->compiled_data->minimize.minimizer = minimizer;
617		model->compiled_data->minimize.max_saved_aux_size = ccv_nnc_minimizer_saved_aux_size(minimizer);
618	} else {
619		// Now, finally fill in this part. If the graph is already compiled, we make a copy of the model.
620		// And then absorb the "new model" to the old one.
621		ccv_cnnp_model_t* const init = ccv_cnnp_model_copy(model, model->is_trainable);
622		ccv_cnnp_model_absorb(model, init, inputs, input_size);
623		// Reset minimizer.
624		ccv_cnnp_model_set_minimizer(model, minimizer, 1, 0, 0);
625	}
626}
627 
628ccv_cnnp_model_t* ccv_cnnp_model_copy(const ccv_cnnp_model_t* const model, const int is_trainable)
629{
630	ccv_cnnp_model_t* const new_model = _ccv_cnnp_model_copy(model, 0);
631	new_model->is_trainable = is_trainable;
632	return new_model;
633}
634 
635void ccv_cnnp_model_tensor_auto(ccv_cnnp_model_t* const model, ccv_nnc_tensor_param_t* const outputs, const int output_size)
636{
637	assert(model->graph)((void) sizeof ((model->graph) ? 1 : 0), __extension__ ({ if
 (model->graph) ; else __assert_fail ("model->graph", "ccv_cnnp_model.c"
, 637, __extension__ __PRETTY_FUNCTION__); }));
638	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", 638, __extension__ __PRETTY_FUNCTION__)
; }));
639	ccv_nnc_symbolic_graph_t* const graph = model->graph;
640	ccv_nnc_symbolic_graph_tensor_auto(graph, TRAVERSE_FULL0,0,0,0);
641	int i;
642	for (i = 0; i < output_size; i++)
643	{
644		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", 644, __extension__ __PRETTY_FUNCTION__)
; }));
645		outputs[i] = ccv_nnc_tensor_symbol_params(graph, model->outputs[i]);
646	}
647}
648 
649void ccv_cnnp_model_set_workspace_size(ccv_cnnp_model_t* const model, size_t workspace_size)
650{
651	if (workspace_size == model->workspace_size)
652		return;
653	model->workspace_size = workspace_size;
654	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
655	if (compiled_data && compiled_data->graph)
656		ccv_nnc_graph_autotune(compiled_data->graph, workspace_size, 0, TRAVERSE_FULL0,0,0,0);
657}
658 
659size_t ccv_cnnp_model_workspace_size(ccv_cnnp_model_t* const model)
660{
661	return model->workspace_size;
662}
663 
664void ccv_cnnp_model_set_data_parallel(ccv_cnnp_model_t* const model, const int parallel)
665{
666	if (parallel == 0)
667		model->parallel_count = ccv_nnc_device_count(CCV_STREAM_CONTEXT_GPU);
668	else
669		model->parallel_count = parallel;
670	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
671	if (compiled_data)
672		{ 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", 672, __extension__ __PRETTY_FUNCTION__)
; })); }
673}
674 
675void ccv_cnnp_model_set_max_concurrency(ccv_cnnp_model_t* const model, const int max_stream_count)
676{
677	model->max_stream_count = max_stream_count;
678	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
679	if (compiled_data)
680		{ 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", 680, __extension__ __PRETTY_FUNCTION__)
; })); }
681}
682 
683void ccv_cnnp_model_set_memory_compression(ccv_cnnp_model_t* const model, const int memory_compression)
684{
685	model->memory_compression = memory_compression;
686	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
687	if (compiled_data)
688		{ 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", 688, __extension__ __PRETTY_FUNCTION__)
; })); }
689}
690 
691void ccv_cnnp_model_set_memory_reduction(ccv_cnnp_model_t* const model, const int memory_reduction)
692{
693	model->memory_reduction = memory_reduction;
694	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
695	if (compiled_data)
696		{ 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", 696, __extension__ __PRETTY_FUNCTION__)
; })); }
697}
698 
699void ccv_cnnp_model_set_gradient_checkpointing(ccv_cnnp_model_t* const model, const int gradient_checkpointing)
700{
701	model->gradient_checkpointing = gradient_checkpointing;
702}
703 
704int ccv_cnnp_model_gradient_checkpointing(ccv_cnnp_model_t* const model)
705{
706	return model->gradient_checkpointing;
707}
708 
709typedef struct {
710	int parallel_count;
711	ccv_nnc_symbolic_graph_t* graph;
712	ccv_cnnp_compiled_data_t* compiled_data;
713	ccv_nnc_tensor_arena_t* tensor_arena;
714} ccv_nnc_tensor_init_states_t;
715 
716static int _ccv_cnnp_any_to_init(const ccv_cnnp_compiled_data_t* const compiled_data)
717{
718	int i;
719	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));
720	for (i = 0; i < compiled_data->parameters->rnum; i++)
721	{
722		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;
723		if (!(init_v[d >> 5] & (1u << (d & 0x1f))))
724			return 1;
725	}
726	for (i = 0; i < compiled_data->internals->rnum; i++)
727	{
728		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;
729		if (!(init_v[d >> 5] & (1u << (d & 0x1f))))
730			return 1;
731	}
732	return 0;
733}
734 
735static 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)
736{
737	ccv_nnc_tensor_init_states_t* const tensor_init_states = (ccv_nnc_tensor_init_states_t*)context;
738	ccv_nnc_tensor_arena_t* const tensor_arena = tensor_init_states->tensor_arena;
739	ccv_nnc_tensor_t* const output_tensor = ccv_nnc_tensor_from_symbol(tensor_arena, output_symbol);
740	if (!output_tensor)
741		return;
742	const int d = output_symbol.d;
743	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", 743, __extension__ __PRETTY_FUNCTION__)
; }));
744  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));
745	if (init_v[d >> 5] & (1u << (d & 0x1f)))
746		return;
747	init_v[d >> 5] |= (1u << (d & 0x1f));
748	ccv_nnc_cmd_exec(cmd, hint, flags, &input, input ? 1 : 0, &output_tensor, 1, 0);
749	const ccv_nnc_symbolic_graph_t* const graph = tensor_init_states->graph;
750	const int parallel_count = tensor_init_states->parallel_count;
751	int i;
752	for (i = 1; i < parallel_count; i++)
753	{
754		ccv_nnc_tensor_t* const copy = ccv_nnc_tensor_from_symbol(tensor_arena, ccv_nnc_tensor_symbol_copy(graph, output_symbol, i));
755		if (copy)
756			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);
757	}
758}
759 
760// This method can only handle cases we added new tensors and exec, never delete. This invariant is true because
761// we setup everything (including calling simplify method) in ccv_cnnp_model_compile method, before this rewind setup.
762static void _ccv_cnnp_model_rewind_graph(ccv_cnnp_model_t* const model)
763{
764	assert(model->graph)((void) sizeof ((model->graph) ? 1 : 0), __extension__ ({ if
 (model->graph) ; else __assert_fail ("model->graph", "ccv_cnnp_model.c"
, 764, __extension__ __PRETTY_FUNCTION__); }));
765	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", 765, __extension__ __PRETTY_FUNCTION__)
; }));
766	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
767	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", 767, __extension__
 __PRETTY_FUNCTION__); }));
768	int i;
769	for (i = 0; i < compiled_data->rewindables->rnum; i++)
770	{
771		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)));
772		if (rewind_symbol->type == CCV_CNNP_REWIND_GRAPH_EXEC)
773			ccv_nnc_graph_exec_symbol_free(model->graph, rewind_symbol->graph_exec);
774		else if (rewind_symbol->type == CCV_CNNP_REWIND_TENSOR)
775			ccv_nnc_tensor_symbol_free(model->graph, rewind_symbol->tensor);
776	}
777	ccv_array_clear(compiled_data->rewindables);
778	ccv_nnc_graph_exec_symbol_autogen(model->graph, 0, 0, CCV_NNC_AUTOGEN_SOURCES_AND_DESTINATIONS);
779}
780 
781static 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)
782{
783	const ccv_cnnp_rewind_symbol_t rewind_symbol = {
784		.type = CCV_CNNP_REWIND_TENSOR,
785		.tensor = symbol
786	};
787	ccv_array_t* const rewind_symbols = (ccv_array_t*)context;
788	ccv_array_push(rewind_symbols, &rewind_symbol);
789}
790 
791static 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)
792{
793	const ccv_cnnp_rewind_symbol_t rewind_symbol = {
794		.type = CCV_CNNP_REWIND_TENSOR,
795		.tensor = symbol
796	};
797	ccv_array_t* const rewind_symbols = (ccv_array_t*)context;
798	ccv_array_push(rewind_symbols, &rewind_symbol);
799}
800 
801static 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)
802{
803	const ccv_cnnp_rewind_symbol_t rewind_symbol = {
804		.type = CCV_CNNP_REWIND_GRAPH_EXEC,
805		.graph_exec = symbol
806	};
807	ccv_array_t* const rewind_symbols = (ccv_array_t*)context;
808	ccv_array_push(rewind_symbols, &rewind_symbol);
809}
810 
811static 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)
812{
813	ccv_nnc_graph_exec_t const update_exec = ccv_nnc_graph_exec_from_symbol(graph_exec_arena, exec_symbol);
814	if (!CCV_NO_GRAPH_EXEC(update_exec)((update_exec).graph == 0))
815		ccv_nnc_graph_exec_set(update_exec.graph, update_exec, cmd);
816	int i;
817	for (i = 1; i < parallel_count; i++)
818	{
819		ccv_nnc_graph_exec_symbol_t copy_symbol = ccv_nnc_graph_exec_symbol_copy(symbolic_graph, exec_symbol, i);
820		const ccv_nnc_graph_exec_t copy = ccv_nnc_graph_exec_from_symbol(graph_exec_arena, copy_symbol);
821		if (!CCV_NO_GRAPH_EXEC(copy)((copy).graph == 0))
822			ccv_nnc_graph_exec_set(copy.graph, copy, cmd);
823	}
824}
825 
826static 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)
827{
828	assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
 (compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 828, __extension__ __PRETTY_FUNCTION__); }));
829	assert(symbolic_graph)((void) sizeof ((symbolic_graph) ? 1 : 0), __extension__ ({ if
 (symbolic_graph) ; else __assert_fail ("symbolic_graph", "ccv_cnnp_model.c"
, 829, __extension__ __PRETTY_FUNCTION__); }));
830	ccv_nnc_graph_exec_symbol_set(symbolic_graph, exec_symbol, cmd);
831	int i;
832	for (i = 1; i < parallel_count; i++)
833	{
834		ccv_nnc_graph_exec_symbol_t copy_symbol = ccv_nnc_graph_exec_symbol_copy(symbolic_graph, exec_symbol, i);
835		if (copy_symbol.graph)
836			ccv_nnc_graph_exec_symbol_set(symbolic_graph, copy_symbol, cmd);
837	}
838	ccv_nnc_graph_exec_arena_t* const graph_exec_arena = compiled_data->graph_exec_arena;
839	if (graph_exec_arena)
840		_ccv_cnnp_model_graph_symbol_exec_set_for_graph_exec_arena(graph_exec_arena, parallel_count, exec_symbol, cmd, symbolic_graph);
841	// Skip backward graph exec arena because it is for a specific accum symbolic graph, not the main graph (model->graph)
842	ccv_nnc_graph_exec_arena_t* const gradient_graph_exec_arena = compiled_data->apply_gradients.graph_exec_arena;
843	if (gradient_graph_exec_arena)
844		_ccv_cnnp_model_graph_symbol_exec_set_for_graph_exec_arena(gradient_graph_exec_arena, parallel_count, exec_symbol, cmd, symbolic_graph);
845}
846 
847static 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)
848{
849	int this_parameter_flag = 0;
850	if (update_nodes[parameter_indice].d == CCV_NNC_NO_TENSOR_SYMBOL)
851		return this_parameter_flag;
852	const ccv_nnc_cmd_t old_minimizer = ccv_nnc_graph_exec_symbol_cmd(graph, update_nodes[parameter_indice]);
853	int j, k;
854	// For no-op, we can preserve previous saved_aux_size.
855	if (old_minimizer.cmd != minimizer.cmd && minimizer.cmd != CCV_NNC_NOOP)
856	{
857		// If the old minimizer is a noop, then the old_saved_aux_size should be whatever its previous
858		// saved_aux_size is, otherwise we will reinit the saved_aux repeatedly if you switch between
859		// noop and a minimizer. We don't want that because we do that in high-level frameworks to
860		// make sure some model parameters don't update if we don't want them to.
861		int old_saved_aux_size;
862		if (old_minimizer.cmd == CCV_NNC_NOOP)
863		{
864			int input_size;
865			ccv_nnc_graph_exec_symbol_io(graph, update_nodes[parameter_indice], 0, &input_size, 0, 0);
866			if (input_size < 2) // This is not legit.
867				old_saved_aux_size = ccv_nnc_minimizer_saved_aux_size(old_minimizer);
868			else // See ccv_nnc_minimizer_saved_aux_size, the saved_aux is inputs excluding gradients and parameters.
869				old_saved_aux_size = input_size - 2;
870		} else
871			old_saved_aux_size = ccv_nnc_minimizer_saved_aux_size(old_minimizer);
872		if (old_saved_aux_size != saved_aux_size)
873		{
874			this_parameter_flag = 1;
875			if (saved_aux_size > old_saved_aux_size)
876			{
877				// Allocate new tensor symbols.
878				const ccv_nnc_tensor_param_t info = ccv_nnc_tensor_symbol_params(graph, updated_parameters[parameter_indice]);
879				for (j = old_saved_aux_size; j < saved_aux_size; j++)
880				{
881					saved_aux[parameter_indice * max_saved_aux_size + j].source = ccv_nnc_tensor_symbol_new(graph, info, 0);
882					saved_aux[parameter_indice * max_saved_aux_size + j].destination = ccv_nnc_tensor_symbol_new(graph, info, 0);
883					const int device_id = CCV_TENSOR_GET_DEVICE_ID(info.type)(((info.type) & 0xfff00) >> 8);
884					for (k = 1; k < parallel_count; k++)
885					{
886						ccv_nnc_tensor_param_t dev_info = info;
887						if (k != device_id)
888							CCV_TENSOR_SET_DEVICE_ID(dev_info.type, k)(dev_info.type) = (((dev_info.type) & ~0xfff00) | (((k) &
 0xfff) << 8));
889						else
890							CCV_TENSOR_SET_DEVICE_ID(dev_info.type, 0)(dev_info.type) = (((dev_info.type) & ~0xfff00) | (((0) &
 0xfff) << 8));
891						const ccv_nnc_tensor_symbol_t src_copy = ccv_nnc_tensor_symbol_new(graph, dev_info, 0);
892						const ccv_nnc_tensor_symbol_t dest_copy = ccv_nnc_tensor_symbol_new(graph, dev_info, 0);
893						ccv_nnc_tensor_symbol_set_copy(graph, saved_aux[parameter_indice * max_saved_aux_size + j].source, k, src_copy);
894						ccv_nnc_tensor_symbol_set_copy(graph, saved_aux[parameter_indice * max_saved_aux_size + j].destination, k, dest_copy);
895					}
896				}
897			} else {
898				for (j = saved_aux_size; j < old_saved_aux_size; j++)
899				{
900					for (k = 1; k < parallel_count; k++)
901					{
902						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);
903						if (src_copy.d >= 0)
904						{
905							ccv_nnc_tensor_symbol_free(graph, src_copy);
906							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
});
907						}
908						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);
909						if (dest_copy.d >= 0)
910						{
911							ccv_nnc_tensor_symbol_free(graph, dest_copy);
912							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
});
913						}
914					}
915					ccv_nnc_tensor_symbol_free(graph, saved_aux[parameter_indice * max_saved_aux_size + j].source);
916					ccv_nnc_tensor_symbol_free(graph, saved_aux[parameter_indice * max_saved_aux_size + j].destination);
917					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
};
918				}
919			}
920		}
921	}
922	_ccv_cnnp_model_graph_exec_symbol_set(graph, compiled_data, parallel_count, update_nodes[parameter_indice], minimizer);
923	if (this_parameter_flag)
924	{
925		ccv_nnc_tensor_symbol_t update_inputs[saved_aux_size + 2];
926		ccv_nnc_tensor_symbol_t update_outputs[saved_aux_size + 1];
927		const int* inputs = 0;
928		int input_size = 0;
929		ccv_nnc_graph_exec_symbol_io(graph, update_nodes[parameter_indice], &inputs, &input_size, 0, 0);
930		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", 930, __extension__ __PRETTY_FUNCTION__)
; }));
931		update_inputs[0].d = inputs[0];
932		update_inputs[0].graph = graph;
933		update_inputs[1].d = inputs[1];
934		update_inputs[1].graph = graph;
935		update_outputs[0] = updated_parameters[parameter_indice];
936		for (j = 0; j < saved_aux_size; j++)
937		{
938			update_inputs[j + 2] = saved_aux[parameter_indice * max_saved_aux_size + j].source;
939			update_outputs[j + 1] = saved_aux[parameter_indice * max_saved_aux_size + j].destination;
940		}
941		ccv_nnc_graph_exec_symbol_set_io(graph, update_nodes[parameter_indice], update_inputs, saved_aux_size + 2, update_outputs, saved_aux_size + 1);
942		for (k = 1; k < parallel_count; k++)
943		{
944			const ccv_nnc_graph_exec_symbol_t copy = ccv_nnc_graph_exec_symbol_copy(graph, update_nodes[parameter_indice], k);
945			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"
, 945, __extension__ __PRETTY_FUNCTION__); }));
946			ccv_nnc_graph_exec_symbol_io(graph, copy, &inputs, &input_size, 0, 0);
947			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", 947, __extension__ __PRETTY_FUNCTION__)
; }));
948			update_inputs[0].d = inputs[0];
949			update_inputs[0].graph = graph;
950			update_inputs[1].d = inputs[1];
951			update_inputs[1].graph = graph;
952			update_outputs[0] = ccv_nnc_tensor_symbol_copy(graph, updated_parameters[parameter_indice], k);
953			for (j = 0; j < saved_aux_size; j++)
954			{
955				update_inputs[j + 2] = ccv_nnc_tensor_symbol_copy(graph, saved_aux[parameter_indice * max_saved_aux_size + j].source, k);
956				update_outputs[j + 1] = ccv_nnc_tensor_symbol_copy(graph, saved_aux[parameter_indice * max_saved_aux_size + j].destination, k);
957			}
958			ccv_nnc_graph_exec_symbol_set_io(graph, copy, update_inputs, saved_aux_size + 2, update_outputs, saved_aux_size + 1);
959		}
960	}
961	return this_parameter_flag;
962}
963 
964typedef struct {
965	int parameter_size;
966	ccv_nnc_cmd_t minimizer;
967	ccv_cnnp_model_io_t parameters[1];
968} ccv_cnnp_set_minimizer_for_parameter_t;
969 
970static int _ccv_cnnp_apply_parameters_with_minimizer(ccv_cnnp_model_t* const model)
971{
972	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
973	assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
 (compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 973, __extension__ __PRETTY_FUNCTION__); }));
974	const int max_saved_aux_size = compiled_data->minimize.max_saved_aux_size;
975	// We update all parameters, at this point, we have one minimizer.
976	const int parameter_size = compiled_data->parameters->rnum;
977	ccv_nnc_graph_exec_symbol_t* const update_nodes = compiled_data->update_nodes;
978	ccv_nnc_symbolic_graph_t* const symbolic_graph = model->graph;
979	assert(symbolic_graph)((void) sizeof ((symbolic_graph) ? 1 : 0), __extension__ ({ if
 (symbolic_graph) ; else __assert_fail ("symbolic_graph", "ccv_cnnp_model.c"
, 979, __extension__ __PRETTY_FUNCTION__); }));
980	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; });
981	ccv_array_t* const parameters = compiled_data->minimize.parameters;
982	ccv_array_t* const parameter_indices = ccv_array_new(sizeof(int), 0, 0);
983	int i, j, flag = 0;
984	for (i = 0; i < parameters->rnum; i++)
985	{
986		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)));
987		for (j = 0; j < set_minimizer_for_parameter->parameter_size; j++)
988		{
989			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;
990			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", 990, __extension__ __PRETTY_FUNCTION__)
; }));
991			const int old_rnum = parameter_indices->rnum;
992			ccv_cnnp_model_add_to_parameter_indices(set_minimizer_for_parameter->parameters[j]->model, param_sel, parameter_indices);
993			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;
994			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", 994, __extension__ __PRETTY_FUNCTION__)
; }));
995			if (param_ref >= 0)
996			{
997				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", 997, __extension__ __PRETTY_FUNCTION__)
; }));
998				*(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)));
999				parameter_indices->rnum = old_rnum + 1;
1000			}
1001		}
1002		const int saved_aux_size = ccv_nnc_minimizer_saved_aux_size(set_minimizer_for_parameter->minimizer);
1003		// We may have duplicated indices, but that is OK, we will set it twice.
1004		for (j = 0; j < parameter_indices->rnum; j++)
1005		{
1006			const int d = *(int*)ccv_array_get(parameter_indices, j)((void*)(((char*)((parameter_indices)->data)) + (size_t)(parameter_indices
)->rsize * (size_t)(j)));
1007			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", 1007, __extension__ __PRETTY_FUNCTION__
); }));
1008			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))
1009				flag = 1;
1010		}
1011		ccv_array_clear(parameter_indices);
1012	}
1013	ccv_array_free(parameter_indices);
1014	return flag;
1015}
1016 
1017static 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)
1018{
1019	if (new_saved_aux_size == old_saved_aux_size)
1020		return;
1021	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", 1021, __extension__ __PRETTY_FUNCTION__
); }));
1022	int i, j;
1023	for (i = parameter_size - 1; i >= 0; i--)
1024	{
1025		for (j = new_saved_aux_size - 1; j >= old_saved_aux_size; j--)
1026			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
};
1027		for (j = old_saved_aux_size - 1; j >= 0; j--)
1028			saved_aux[i * new_saved_aux_size + j] = saved_aux[i * old_saved_aux_size + j];
1029	}
1030}
1031 
1032static void _ccv_cnnp_model_set_rewindables(ccv_cnnp_model_t* const model)
1033{
1034	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
1035	assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
 (compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 1035, __extension__ __PRETTY_FUNCTION__); }));
1036	if (!compiled_data->rewindables)
1037		compiled_data->rewindables = ccv_array_new(sizeof(ccv_cnnp_rewind_symbol_t), 0, 0);
1038	ccv_nnc_tensor_symbol_new_hook(model->graph, _ccv_cnnp_model_tensor_symbol_new_hook, compiled_data->rewindables, 0);
1039	ccv_nnc_tensor_symbol_alias_new_hook(model->graph, _ccv_cnnp_model_tensor_symbol_alias_new_hook, compiled_data->rewindables, 0);
1040	ccv_nnc_graph_exec_symbol_new_hook(model->graph, _ccv_cnnp_model_graph_exec_symbol_new_hook, compiled_data->rewindables, 0);
1041}
1042 
1043static 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)
1044{
1045	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
1046	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", 1046, __extension__ __PRETTY_FUNCTION__
); }));
1047	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", 1047, __extension__ __PRETTY_FUNCTION__
); }));
1048	const int evaluate_to_size = compiled_data->evaluate.to_size;
1049	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", 1049, __extension__ __PRETTY_FUNCTION__
); }));
1050	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; });
1051	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);
1052	compiled_data->evaluate.to_ops = (ccv_nnc_graph_exec_t*)(compiled_data->evaluate.tos + evaluate_to_size * parallel_count);
1053	int i, j;
1054	const int output_size = model->output_size;
1055	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", 1055, __extension__ __PRETTY_FUNCTION__
); }));
1056	if (fits)
1057		for (i = 0; i < output_size; i++)
1058			ccv_nnc_tensor_symbol_set(model->graph, compiled_data->fits[i], fits[i]->info);
1059	const int max_saved_aux_size = compiled_data->minimize.max_saved_aux_size;
1060	const int parameter_size = compiled_data->parameters->rnum;
1061	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);
1062	compiled_data->update_nodes = (ccv_nnc_graph_exec_symbol_t*)(compiled_data->updated_parameters + parameter_size);
1063	compiled_data->saved_aux = (ccv_nnc_tensor_symbol_map_t*)(compiled_data->update_nodes + parameter_size);
1064	int parameter_size_maybe_more = parameter_size;
1065	compiled_data->disable_outgrad = disable_outgrad;
1066	int outgrad_size;
1067	if (gradient_mode == CCV_CNNP_COMPILED_DATA_GRADIENT_TRAINABLES || model->input_size == 0)
1068		outgrad_size = 0;
1069	else if (disable_outgrad == CCV_CNNP_DISABLE_OUTGRAD_NONE) // Compute minimize with gradients including inputs.
1070		outgrad_size = model->input_size;
1071	else {
1072		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", 1072, __extension__ __PRETTY_FUNCTION__
); })); // If it is disable all, gradient mode won't be this.
1073		outgrad_size = 0;
1074		for (i = 0; i < model->input_size; i++)
1075			if (!(disable_outgrad & ((uint64_t)1 << i)))
1076				++outgrad_size;
1077	}
1078	compiled_data->outgrad_size = outgrad_size;
1079	parameter_size_maybe_more += outgrad_size;
1080	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);
1081	compiled_data->outgrads = parameter_size_maybe_more > parameter_size ? compiled_data->gradients + parameter_size : 0;
1082	compiled_data->backward.tos = (ccv_nnc_graph_exec_symbol_t*)(compiled_data->gradients + parameter_size_maybe_more);
1083	compiled_data->backward.to_size = parameter_size_maybe_more;
1084	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)));
1085	if (compiled_data->parameter_flags)
1086	{
1087		parameters = (ccv_nnc_tensor_symbol_t*)ccmallocmalloc(sizeof(ccv_nnc_tensor_symbol_t) * parameter_size);
1088		for (i = 0; i < parameter_size; i++)
1089			if (compiled_data->parameter_flags[i >> 6] & ((uint64_t)1 << (i & 63)))
1090				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)));
1091			else
1092				parameters[i] = NO_TENSOR_SYMBOL(const ccv_nnc_tensor_symbol_t){.d = CCV_NNC_NO_TENSOR_SYMBOL
};
1093	}
1094	if (gradient_mode == CCV_CNNP_COMPILED_DATA_GRADIENT_TRAINABLES || model->input_size == 0)
1095		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);
1096	else if (disable_outgrad == CCV_CNNP_DISABLE_OUTGRAD_NONE) // Compute minimize with gradients including inputs.
1097		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);
1098	else { // Compute minimize with gradients including selected inputs.
1099		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", 1099, __extension__ __PRETTY_FUNCTION__
); }));
1100		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", 1100, __extension__ __PRETTY_FUNCTION__
); })); // If it is disable all, gradient mode won't be this.
1101		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", 1101, __extension__ __PRETTY_FUNCTION__
); }));
1102		ccv_nnc_tensor_symbol_t outgrads[outgrad_size];
1103		j = 0;
1104		for (i = 0; i < model->input_size; i++)
1105			if (!(disable_outgrad & ((uint64_t)1 << i)))
1106				outgrads[j++] = model->inputs[i];
1107		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);
1108	}
1109	if (compiled_data->parameter_flags)
1110		ccfreefree(parameters);
1111	_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);
1112	if (compiled_data->minimize.parameters)
1113		_ccv_cnnp_apply_parameters_with_minimizer(model);
1114	// Go through gradient checkpoints to generate tensor inputs for backward pass just before executing the backward pass.
1115	ccv_cnnp_model_apply_gradient_checkpoints(compiled_data, model->graph);
1116	for (i = 0; i < output_size; i++)
1117	{
1118		const ccv_nnc_tensor_symbol_t df = ccv_nnc_tensor_symbol_for_backward(model->graph, compiled_data->f[i]);
1119		// Init this to 1 so we can backprop.
1120		ccv_nnc_tensor_symbol_set_flags(model->graph, df, CCV_NNC_TENSOR_SYMBOL_INIT_ONES);
1121	}
1122	compiled_data->backward.to_size = 0;
1123	for (i = 0; i < parameter_size_maybe_more; i++)
1124		if (compiled_data->gradients[i].d != CCV_NNC_NO_TENSOR_SYMBOL)
1125			compiled_data->backward.tos[compiled_data->backward.to_size++] = ccv_nnc_graph_exec_symbol_for_backward(model->graph, compiled_data->gradients[i]);
1126	ccv_nnc_graph_exec_symbol_autogen(model->graph, 0, 0, CCV_NNC_AUTOGEN_ALL_EXECS);
1127	ccv_nnc_symbolic_graph_set_destinations(model->graph, compiled_data->update_nodes, parameter_size);
1128	for (i = 0; i < parameter_size_maybe_more - parameter_size; i++)
1129	{
1130		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.
1131			continue;
1132		const ccv_nnc_graph_exec_symbol_t outgrad = ccv_nnc_graph_exec_symbol_for_backward(model->graph, compiled_data->outgrads[i]);
1133		const int* tos;
1134		int to_size;
1135		ccv_nnc_graph_exec_symbol_to(model->graph, outgrad, &tos, &to_size);
1136		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.
1137		{
1138			const ccv_nnc_graph_exec_symbol_t* destinations = ccv_nnc_symbolic_graph_destinations(model->graph);
1139			const int destination_count = ccv_nnc_symbolic_graph_destination_size(model->graph);
1140			int flag = 0;
1141			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; });
1142			for (j = i - 1; !flag && j >= 0; j--)
1143				if (j + outgrad_destination_start < destination_count)
1144					flag = (destinations[j + outgrad_destination_start].d == outgrad.d);
1145			if (!flag) // Only if we cannot find it, we add it.
1146				ccv_nnc_symbolic_graph_add_destination(model->graph, outgrad);
1147		}
1148	}
1149	if (parallel_count > 1)
1150	{
1151		ccv_nnc_symbolic_graph_data_parallel(model->graph, parallel_count,
1152			0, 0,
1153			compiled_data->gradients, parameter_size /* No need to deal with outgrads, we don't allreduce outgrads */,
1154			compiled_data->gradients /* We only care about gradients before allreduce, thus, update our current pointers */,
1155			0, 0, 0,
1156			CCV_NNC_PARALLEL_REDUCE_OP_SUM,
1157			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));
1158		ccv_nnc_graph_exec_symbol_autogen(model->graph, 0, 0, CCV_NNC_AUTOGEN_SOURCES_AND_DESTINATIONS);
1159		for (i = 0; i < evaluate_to_size; i++)
1160			for (j = 1; j < parallel_count; j++)
1161			{
1162				const ccv_nnc_graph_exec_symbol_t copy = ccv_nnc_graph_exec_symbol_copy(model->graph, compiled_data->evaluate.tos[i], j);
1163				if (copy.d != CCV_NNC_NO_GRAPH_EXEC_SYMBOL)
1164					compiled_data->evaluate.tos[compiled_data->evaluate.to_size++] = copy;
1165			}
1166		const int backward_to_size = compiled_data->backward.to_size;
1167		for (i = 0; i < backward_to_size; i++)
1168			for (j = 1; j < parallel_count; j++)
1169			{
1170				const ccv_nnc_graph_exec_symbol_t copy = ccv_nnc_graph_exec_symbol_copy(model->graph, compiled_data->backward.tos[i], j);
1171				if (copy.d != CCV_NNC_NO_GRAPH_EXEC_SYMBOL)
1172					compiled_data->backward.tos[compiled_data->backward.to_size++] = copy;
1173			}
1174	}
1175	// Only use memory compression if we are in gradient parameter mode.
1176	if (gradient_mode == CCV_CNNP_COMPILED_DATA_GRADIENT_TRAINABLES || gradient_mode == CCV_CNNP_COMPILED_DATA_GRADIENT_TRAINABLES_AND_INPUTS)
1177	{
1178		if (model->memory_compression)
1179			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));
1180		if (model->memory_reduction)
1181			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));
1182	}
1183	compiled_data->backward.to_size = _ccv_nnc_array_dedup_graph_exec_symbols(compiled_data->backward.tos, compiled_data->backward.to_size);
1184	compiled_data->gradient_mode = gradient_mode;
1185}
1186 
1187void ccv_cnnp_model_tensors_init_0(const ccv_cnnp_model_t* const model, ccv_cnnp_compiled_data_t* const compiled_data)
1188{
1189	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", 1189, __extension__ __PRETTY_FUNCTION__
); }));
1190	const int parameter_size = compiled_data->parameters->rnum;
1191	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; });
1192	const int internal_size = compiled_data->internals->rnum;
1193	compiled_data->tensors_init.size = ccv_nnc_tensor_symbol_count(model->graph);
1194	compiled_data->tensors_init.v = cccalloccalloc(((compiled_data->tensors_init.size + 31) >> 5), sizeof(uint32_t));
1195	compiled_data->tensors.parameters = (ccv_nnc_tensor_t**)cccalloccalloc((parameter_size + internal_size) * parallel_count, sizeof(ccv_nnc_tensor_t*));
1196	compiled_data->tensors.internals = compiled_data->tensors.parameters + parameter_size * parallel_count;
1197}
1198 
1199int ccv_cnnp_model_tensors_any_to_alloc(const ccv_cnnp_model_t* const model, ccv_cnnp_compiled_data_t* const compiled_data)
1200{
1201	int i, j;
1202	const int parameter_size = compiled_data->parameters->rnum;
1203	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; });
1204	const int internal_size = compiled_data->internals->rnum;
1205	for (i = 0; i < parameter_size; i++)
1206	{
1207		// parameters has to be allocated all together.
1208		if (compiled_data->tensors.parameters[i])
1209		{
1210			for (j = 1; j < parallel_count; j++)
1211				{ 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", 1211, __extension__ __PRETTY_FUNCTION__
); })); }
1212			continue;
1213		}
1214		return 1;
1215	}
1216	for (i = 0; i < internal_size; i++)
1217	{
1218		if (!compiled_data->tensors.internals[i])
1219			return 1;
1220		for (j = 1; j < parallel_count; j++)
1221			if (!compiled_data->tensors.internals[i + j * internal_size])
1222				return 1;
1223	}
1224	return 0;
1225}
1226 
1227void ccv_cnnp_model_tensors_init_1(const ccv_cnnp_model_t* const model, ccv_cnnp_compiled_data_t* const compiled_data)
1228{
1229	int i, j;
1230	const int parameter_size = compiled_data->parameters->rnum;
1231	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; });
1232	const int internal_size = compiled_data->internals->rnum;
1233	for (i = 0; i < parameter_size; i++)
1234	{
1235		// parameters has to be allocated all together.
1236		if (compiled_data->tensors.parameters[i])
1237		{
1238			for (j = 1; j < parallel_count; j++)
1239				{ 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", 1239, __extension__ __PRETTY_FUNCTION__
); })); }
1240			continue;
1241		}
1242		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)));
1243		ccv_nnc_tensor_param_t info = ccv_nnc_tensor_symbol_params(parameter.graph, parameter);
1244		if (CCV_TENSOR_GET_DEVICE(info.type)((info.type) & 0xfff00) == CCV_COMPUTE_DEVICE_ANY)
1245			CCV_TENSOR_SET_DEVICE_ID(info.type, 0)(info.type) = (((info.type) & ~0xfff00) | (((0) & 0xfff
) << 8));
1246		const int device_id = CCV_TENSOR_GET_DEVICE_ID(info.type)(((info.type) & 0xfff00) >> 8);
1247		compiled_data->tensors.parameters[i] = ccv_nnc_tensor_new(0, info, 0);
1248		for (j = 1; j < parallel_count; j++)
1249		{
1250			if (j != device_id)
1251				CCV_TENSOR_SET_DEVICE_ID(info.type, j)(info.type) = (((info.type) & ~0xfff00) | (((j) & 0xfff
) << 8));
1252			else
1253				CCV_TENSOR_SET_DEVICE_ID(info.type, 0)(info.type) = (((info.type) & ~0xfff00) | (((0) & 0xfff
) << 8));
1254			compiled_data->tensors.parameters[i + j * parameter_size] = ccv_nnc_tensor_new(0, info, 0);
1255		}
1256	}
1257	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));
1258	for (i = 0; i < internal_size; i++)
1259	{
1260		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))
);
1261		const int d = retained.d;
1262		if (init_v[d >> 5] & (1u << (d & 0x1f)))
1263			continue;
1264		ccv_nnc_tensor_param_t info = ccv_nnc_tensor_symbol_params(retained.graph, retained);
1265		if (CCV_TENSOR_GET_DEVICE(info.type)((info.type) & 0xfff00) == CCV_COMPUTE_DEVICE_ANY)
1266			CCV_TENSOR_SET_DEVICE_ID(info.type, 0)(info.type) = (((info.type) & ~0xfff00) | (((0) & 0xfff
) << 8));
1267		const int device_id = CCV_TENSOR_GET_DEVICE_ID(info.type)(((info.type) & 0xfff00) >> 8);
1268		if (!compiled_data->tensors.internals[i])
1269			compiled_data->tensors.internals[i] = ccv_nnc_tensor_new(0, info, 0);
1270		for (j = 1; j < parallel_count; j++)
1271		{
1272			if (j != device_id)
1273				CCV_TENSOR_SET_DEVICE_ID(info.type, j)(info.type) = (((info.type) & ~0xfff00) | (((j) & 0xfff
) << 8));
1274			else
1275				CCV_TENSOR_SET_DEVICE_ID(info.type, 0)(info.type) = (((info.type) & ~0xfff00) | (((0) & 0xfff
) << 8));
1276			if (!compiled_data->tensors.internals[i + j * internal_size])
1277				compiled_data->tensors.internals[i + j * internal_size] = ccv_nnc_tensor_new(0, info, 0);
1278		}
1279	}
1280	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.
1281}
1282 
1283static void _ccv_cnnp_model_tensors_init(const ccv_cnnp_model_t* const model, ccv_cnnp_compiled_data_t* const compiled_data)
1284{
1285	ccv_cnnp_model_tensors_init_0(model, compiled_data);
1286	ccv_cnnp_model_tensors_init_1(model, compiled_data);
1287}
1288 
1289static 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)
1290{
1291	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", 1291, __extension__ __PRETTY_FUNCTION__
); }));
1292	int i, j;
1293	for (i = 0; i < tensor_size; i++)
1294	{
1295		if (!tensors[i])
1296			continue;
1297		const int d = tensor_symbols[i].d;
1298		if (!(tensors_init[d >> 5] & (1u << (d & 0x1f))))
1299			continue;
1300		for (j = 1; j < parallel_count; j++)
1301			if (tensors[i + j * tensor_size])
1302			{
1303				ccv_nnc_tensor_t* const input = CCV_NNC_TENSOR(tensors[i])((ccv_nnc_tensor_t*)((uintptr_t)(tensors[i]) & ~(uintptr_t
)1));
1304				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));
1305				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);
1306			}
1307	}
1308}
1309 
1310static 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)
1311{
1312	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", 1312, __extension__ __PRETTY_FUNCTION__
); }));
1313	int i, j;
1314	for (i = 0; i < tensor_size; i++)
1315	{
1316		const ccv_nnc_tensor_symbol_t tensor_symbol = tensor_symbols[i];
1317		for (j = 1; j < parallel_count; j++)
1318		{
1319			const ccv_nnc_tensor_symbol_t copy = ccv_nnc_tensor_symbol_copy(graph, tensor_symbol, j);
1320			ccv_nnc_tensor_t* copy_tensor = tensors[i + j * tensor_size];
1321			if (copy_tensor && copy.d == CCV_NNC_NO_TENSOR_SYMBOL)
1322			{ // We shouldn't allocate this, free it up.
1323				ccv_nnc_tensor_free(tensors[i + j * tensor_size]);
1324				tensors[i + j * tensor_size] = 0;
1325			}
1326		}
1327	}
1328}
1329 
1330static 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)
1331{
1332	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", 1332, __extension__ __PRETTY_FUNCTION__
); }));
1333	int i, j;
1334	for (i = 0; i < tensor_size; i++)
1335	{
1336		ccv_nnc_tensor_symbol_t tensor_symbol = tensor_symbols[i];
1337		if (tensor_symbol.d == CCV_NNC_NO_TENSOR_SYMBOL)
1338			continue;
1339		if (graph)
1340		{
1341			const ccv_nnc_tensor_symbol_t alias_to = ccv_nnc_tensor_symbol_alias_to(graph, tensor_symbol);
1342			if (alias_to.d != CCV_NNC_NO_TENSOR_SYMBOL)
1343				tensor_symbol = alias_to;
1344		}
1345		ccv_nnc_tensor_t* const tensor = CCV_NNC_TENSOR(tensors[i])((ccv_nnc_tensor_t*)((uintptr_t)(tensors[i]) & ~(uintptr_t
)1));
1346		if (tensor && tensor_symbol.d != CCV_NNC_NO_TENSOR_SYMBOL)
1347		{
1348			const ccv_nnc_tensor_bind_t retained_bind = {
1349				.symbol = tensor_symbol,
1350				.tensor = tensor
1351			};
1352			ccv_array_push(tensor_binds, &retained_bind);
1353		}
1354		for (j = 1; j < parallel_count; j++)
1355		{
1356			const ccv_nnc_tensor_symbol_t copy = ccv_nnc_tensor_symbol_copy(graph, tensor_symbol, j);
1357			ccv_nnc_tensor_t* copy_tensor = tensors[i + j * tensor_size];
1358			if (copy_tensor && copy.d != CCV_NNC_NO_TENSOR_SYMBOL)
1359			{
1360				const ccv_nnc_tensor_bind_t bind = {
1361					.symbol = copy,
1362					.tensor = tensors[i + j * tensor_size]
1363				};
1364				ccv_array_push(tensor_binds, &bind);
1365			}
1366		}
1367	}
1368}
1369 
1370static void _ccv_cnnp_compiled_data_graph_free(ccv_cnnp_compiled_data_t* const compiled_data)
1371{
1372	if (compiled_data->graph)
1373		ccv_nnc_graph_free(compiled_data->graph);
1374	compiled_data->graph = 0;
1375	compiled_data->is_test = 0;
1376	if (compiled_data->tensor_arena)
1377		ccv_nnc_tensor_arena_free(compiled_data->tensor_arena);
1378	compiled_data->tensor_arena = 0;
1379	if (compiled_data->graph_exec_arena)
1380		ccv_nnc_graph_exec_arena_free(compiled_data->graph_exec_arena);
1381	compiled_data->graph_exec_arena = 0;
1382	if (compiled_data->backward.from_ops)
1383		ccfreefree(compiled_data->backward.from_ops);
1384	compiled_data->backward.from_ops = 0;
1385	if (compiled_data->evaluate.schedule)
1386		ccv_nnc_graph_static_schedule_free(compiled_data->evaluate.schedule);
1387	compiled_data->evaluate.schedule = 0;
1388	if (compiled_data->backward.schedule)
1389		ccv_nnc_graph_static_schedule_free(compiled_data->backward.schedule);
1390	compiled_data->backward.schedule = 0;
1391}
1392 
1393static void _ccv_cnnp_compiled_data_gradient_free(ccv_cnnp_compiled_data_t* const compiled_data)
1394{
1395	if (compiled_data->gradients)
1396		ccfreefree(compiled_data->gradients);
1397	compiled_data->gradients = 0;
1398	if (compiled_data->updated_parameters)
1399		ccfreefree(compiled_data->updated_parameters);
1400	compiled_data->updated_parameters = 0;
1401	compiled_data->update_nodes = 0;
1402	compiled_data->saved_aux = 0;
1403}
1404 
1405static void _ccv_cnnp_compiled_data_backward_free(ccv_cnnp_compiled_data_t* const compiled_data)
1406{
1407	if (compiled_data->backward.gradients)
1408		ccfreefree(compiled_data->backward.gradients);
1409	compiled_data->backward.gradients = 0;
1410	if (compiled_data->backward.accum)
1411		ccv_nnc_graph_free(compiled_data->backward.accum);
1412	compiled_data->backward.accum = 0;
1413	if (compiled_data->backward.tensor_arena)
1414		ccv_nnc_tensor_arena_free(compiled_data->backward.tensor_arena);
1415	compiled_data->backward.tensor_arena = 0;
1416	if (compiled_data->backward.graph_exec_arena)
1417		ccv_nnc_graph_exec_arena_free(compiled_data->backward.graph_exec_arena);
1418	compiled_data->backward.graph_exec_arena = 0;
1419}
1420 
1421static void _ccv_cnnp_compiled_data_apply_gradients_free(ccv_cnnp_compiled_data_t* const compiled_data)
1422{
1423	if (compiled_data->apply_gradients.graph)
1424		ccv_nnc_graph_free(compiled_data->apply_gradients.graph);
1425	compiled_data->apply_gradients.graph = 0;
1426	if (compiled_data->apply_gradients.tensor_arena)
1427		ccv_nnc_tensor_arena_free(compiled_data->apply_gradients.tensor_arena);
1428	compiled_data->apply_gradients.tensor_arena = 0;
1429	if (compiled_data->apply_gradients.graph_exec_arena)
1430		ccv_nnc_graph_exec_arena_free(compiled_data->apply_gradients.graph_exec_arena);
1431	compiled_data->apply_gradients.graph_exec_arena = 0;
1432}
1433 
1434// Compile the graph to run ccv_cnnp_model_fit
1435static 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)
1436{
1437	int i, j;
1438	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
1439	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", 1439, __extension__ __PRETTY_FUNCTION__
); }));
1440	compiled_data->graph_mode = CCV_CNNP_MODEL_GRAPH_FIT_MODE;
1441	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; });
1442	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", 1442, __extension__ __PRETTY_FUNCTION__
); }));
1443	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", 1443
, __extension__ __PRETTY_FUNCTION__); }));
1444	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", 1444, __extension__ __PRETTY_FUNCTION__
); }));
1445	if (compiled_data->gradient_mode == CCV_CNNP_COMPILED_DATA_GRADIENT_NONE)
1446	{
1447		_ccv_cnnp_model_set_rewindables(model);
1448		_ccv_cnnp_model_gradient_init(model, CCV_CNNP_COMPILED_DATA_GRADIENT_TRAINABLES, CCV_CNNP_DISABLE_OUTGRAD_ALL, fits, fit_size);
1449	} else if (compiled_data->gradient_mode != CCV_CNNP_COMPILED_DATA_GRADIENT_TRAINABLES) {
1450		_ccv_cnnp_model_rewind_graph(model);
1451		_ccv_cnnp_compiled_data_gradient_free(compiled_data);
1452		compiled_data->gradient_mode = CCV_CNNP_COMPILED_DATA_GRADIENT_NONE;
1453		_ccv_cnnp_model_gradient_init(model, CCV_CNNP_COMPILED_DATA_GRADIENT_TRAINABLES, CCV_CNNP_DISABLE_OUTGRAD_ALL, fits, fit_size);
1454	}
1455	const int tensors_init = !!compiled_data->tensors_init.v;
1456	if (!tensors_init)
1457		_ccv_cnnp_model_tensors_init(model, compiled_data);
1458	else if ((uintptr_t)compiled_data->tensors_init.v & (uintptr_t)1)
1459	// Check if it is not fully allocated, if it is not, init_1.
1460		ccv_cnnp_model_tensors_init_1(model, compiled_data);
1461	ccv_array_t* const tensor_binds = ccv_array_new(sizeof(ccv_nnc_tensor_bind_t), 0, 0);
1462	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"
, 1462, __extension__ __PRETTY_FUNCTION__); }));
1463	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"
, 1463, __extension__ __PRETTY_FUNCTION__); }));
1464	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", 1464
, __extension__ __PRETTY_FUNCTION__); }));
1465	const int input_size_per_p = input_size / parallel_count;
1466	_ccv_cnnp_model_bind_tensors(model->graph, model->inputs, inputs, input_size_per_p, parallel_count, tensor_binds);
1467	const int output_size_per_p = output_size / parallel_count;
1468	_ccv_cnnp_model_bind_tensors(model->graph, model->outputs, outputs, output_size_per_p, parallel_count, tensor_binds);
1469	const int fit_size_per_p = fit_size / parallel_count;
1470	_ccv_cnnp_model_bind_tensors(model->graph, compiled_data->fits, fits, fit_size_per_p, parallel_count, tensor_binds);
1471	const int parameter_size = compiled_data->parameters->rnum;
1472	_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);
1473	_ccv_cnnp_model_bind_tensors(model->graph, compiled_data->updated_parameters, compiled_data->tensors.parameters, parameter_size, parallel_count, tensor_binds);
1474	const int internal_size = compiled_data->internals->rnum;
1475	_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);
1476	_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);
1477	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);
1478	ccv_array_free(tensor_binds);
1479	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));
1480	if (tensors_init && parallel_count > 1)
1481		_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);
1482	// If tensor is not init'ed, we need to init states first.
1483	if (_ccv_cnnp_any_to_init(compiled_data))
1484	{
1485		ccv_nnc_tensor_init_states_t tensor_init_states = {
1486			.parallel_count = parallel_count,
1487			.graph = model->graph,
1488			.compiled_data = compiled_data,
1489			.tensor_arena = compiled_data->tensor_arena
1490		};
1491		ccv_cnnp_model_init_states(model, model->graph, _ccv_cnnp_init_states_for_tensors, &tensor_init_states);
1492	}
1493	compiled_data->is_test = 0;
1494	const int saved_aux_size = ccv_nnc_minimizer_saved_aux_size(compiled_data->minimize.minimizer);
1495	// No need to set because it is default to training mode.
1496	// ccv_cnnp_model_set_is_test(model, 0, _ccv_cnnp_cmd_update_for_execs, &update);
1497	for (i = 0; i < saved_aux_size * parameter_size; i++)
1498	{
1499		if (compiled_data->saved_aux[i].source.d == CCV_NNC_NO_TENSOR_SYMBOL)
1500			continue;
1501		ccv_nnc_tensor_t* const tensor = ccv_nnc_tensor_from_symbol(compiled_data->tensor_arena, compiled_data->saved_aux[i].source);
1502		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);
1503		for (j = 1; j < parallel_count; j++)
1504		{
1505			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));
1506			if (copy)
1507				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);
1508		}
1509	}
1510	const int evaluate_to_size = compiled_data->evaluate.to_size;
1511	compiled_data->evaluate.to_op_size = 0;
1512	for (i = 0; i < evaluate_to_size; i++)
1513	{
1514		ccv_nnc_graph_exec_t const to = ccv_nnc_graph_exec_from_symbol(compiled_data->graph_exec_arena, compiled_data->evaluate.tos[i]);
1515		if (to.graph)
1516			compiled_data->evaluate.to_ops[compiled_data->evaluate.to_op_size++] = to;
1517	}
1518	ccv_nnc_graph_set_default_static_schedule(compiled_data->graph, compiled_data->stream_type, model->max_stream_count);
1519	ccv_nnc_graph_autotune(compiled_data->graph, model->workspace_size, 0, TRAVERSE_FULL0,0,0,0);
1520}
1521 
1522ccv_nnc_stream_context_t* ccv_cnnp_model_default_stream(const ccv_cnnp_model_t* const model)
1523{
1524	const ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
1525	if (!compiled_data || !compiled_data->graph)
1526		return 0;
1527	return ccv_nnc_graph_default_stream(compiled_data->graph);
1528}
1529 
1530uint64_t ccv_cnnp_model_memory_size(const ccv_cnnp_model_t* const model)
1531{
1532	const ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
1533	if (!compiled_data || !compiled_data->tensor_arena)
1534		return 0;
1535	return ccv_nnc_tensor_arena_size(compiled_data->tensor_arena);
1536}
1537 
1538static 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)
1539{
1540	int i, j;
1541	for (i = 0; i < tensor_size; i++)
1542	{
1543		ccv_nnc_tensor_symbol_t tensor_symbol = tensor_symbols[i];
1544		if (tensor_symbol.d == CCV_NNC_NO_TENSOR_SYMBOL)
1545			continue;
1546		if (graph)
1547		{
1548			const ccv_nnc_tensor_symbol_t alias_to = ccv_nnc_tensor_symbol_alias_to(graph, tensor_symbol);
1549			if (alias_to.d != CCV_NNC_NO_TENSOR_SYMBOL)
1550				tensor_symbol = alias_to;
1551		}
1552		ccv_nnc_tensor_bind_symbol(tensor_arena, tensor_symbol, tensors[i]);
1553		for (j = 1; j < parallel_count; j++)
1554		{
1555			const ccv_nnc_tensor_symbol_t copy = ccv_nnc_tensor_symbol_copy(graph, tensor_symbol, j);
1556			if (copy.d != CCV_NNC_NO_TENSOR_SYMBOL)
1557				ccv_nnc_tensor_bind_symbol(tensor_arena, copy, tensors[i + tensor_size * j]);
1558		}
1559	}
1560}
1561 
1562void 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)
1563{
1564	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
1565	assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
 (compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 1565, __extension__ __PRETTY_FUNCTION__); }));
1566	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; });
1567	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", 1567, __extension__ __PRETTY_FUNCTION__
); }));
1568	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", 1568, __extension__ __PRETTY_FUNCTION__
); }));
1569	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", 1569
, __extension__ __PRETTY_FUNCTION__); }));
1570	assert(model->graph)((void) sizeof ((model->graph) ? 1 : 0), __extension__ ({ if
 (model->graph) ; else __assert_fail ("model->graph", "ccv_cnnp_model.c"
, 1570, __extension__ __PRETTY_FUNCTION__); }));
1571	if (!compiled_data->graph || compiled_data->graph_mode != CCV_CNNP_MODEL_GRAPH_FIT_MODE)
1572	{
1573		_ccv_cnnp_compiled_data_graph_free(compiled_data);
1574		_ccv_cnnp_compiled_data_backward_free(compiled_data);
1575		_ccv_cnnp_compiled_data_apply_gradients_free(compiled_data);
1576		// Compile the symbolic graph down only when needed.
1577		_ccv_cnnp_model_fit_jit(model, inputs, input_size, fits, fit_size, outputs, output_size);
1578	} else {
1579		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"
, 1579, __extension__ __PRETTY_FUNCTION__); }));
1580		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"
, 1580, __extension__ __PRETTY_FUNCTION__); }));
1581		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", 1581
, __extension__ __PRETTY_FUNCTION__); }));
1582		const int input_size_per_p = input_size / parallel_count;
1583		_ccv_cnnp_bind_tensors_to_arena(compiled_data->tensor_arena, model->graph, model->inputs, inputs, input_size_per_p, parallel_count);
1584		const int output_size_per_p = output_size / parallel_count;
1585		_ccv_cnnp_bind_tensors_to_arena(compiled_data->tensor_arena, model->graph, model->outputs, outputs, output_size_per_p, parallel_count);
1586		const int fit_size_per_p = fit_size / parallel_count;
1587		_ccv_cnnp_bind_tensors_to_arena(compiled_data->tensor_arena, model->graph, compiled_data->fits, fits, fit_size_per_p, parallel_count);
1588	}
1589	if (compiled_data->is_test)
1590	{
1591		compiled_data->is_test = 0;
1592		ccv_nnc_graph_exec_update_t update = {
1593			.parallel_count = parallel_count,
1594			.graph = model->graph,
1595			.graph_exec_arena = compiled_data->graph_exec_arena,
1596		};
1597		ccv_cnnp_model_set_is_test(model, 0, _ccv_cnnp_cmd_update_for_execs, &update);
1598	}
1599	ccv_nnc_graph_run_with_schedule(compiled_data->graph, 0, 0, tensor_tape, stream_context);
1600}
1601 
1602// Compile the graph to run ccv_cnnp_model_evaluate with require_grad = false (MULTISTAGE_MODE_NO_GRAD).
1603static 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)
1604{
1605	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
1606	compiled_data->graph_mode = CCV_CNNP_MODEL_GRAPH_MULTISTAGE_MODE_NO_GRAD;
1607	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; });
1608	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", 1608, __extension__ __PRETTY_FUNCTION__
); }));
1609	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", 1609, __extension__ __PRETTY_FUNCTION__
); }));
1610	// If the gradient is not initialized, continue to setup parallel process. We don't init gradient here, but rather,
1611	// we setup proper rewindables so the graph can be rewinded to previous state before we run data parallel.
1612	if (parallel_count > 1 && compiled_data->gradient_mode == CCV_CNNP_COMPILED_DATA_GRADIENT_NONE)
1613	{
1614		const int evaluate_to_size = compiled_data->evaluate.to_size;
1615		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);
1616		_ccv_cnnp_model_set_rewindables(model);
1617		ccv_nnc_symbolic_graph_data_parallel(model->graph, parallel_count,
1618			0, 0,
1619			0, 0, 0,
1620			0, 0, 0,
1621			CCV_NNC_PARALLEL_REDUCE_OP_SUM,
1622			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));
1623		ccv_nnc_graph_exec_symbol_autogen(model->graph, 0, 0, CCV_NNC_AUTOGEN_SOURCES_AND_DESTINATIONS);
1624		int i, j;
1625		for (i = 0; i < evaluate_to_size; i++)
1626			for (j = 1; j < parallel_count; j++)
1627			{
1628				const ccv_nnc_graph_exec_symbol_t copy = ccv_nnc_graph_exec_symbol_copy(model->graph, compiled_data->evaluate.tos[i], j);
1629				if (copy.d != CCV_NNC_NO_GRAPH_EXEC_SYMBOL)
1630					compiled_data->evaluate.tos[compiled_data->evaluate.to_size++] = copy;
1631			}
1632	}
1633	const int tensors_init = !!compiled_data->tensors_init.v;
1634	if (!tensors_init)
1635		_ccv_cnnp_model_tensors_init(model, compiled_data);
1636	else if ((uintptr_t)compiled_data->tensors_init.v & (uintptr_t)1)
1637	// Check if it is not fully allocated, if it is not, init_1.
1638		ccv_cnnp_model_tensors_init_1(model, compiled_data);
1639	ccv_array_t* const tensor_binds = ccv_array_new(sizeof(ccv_nnc_tensor_bind_t), 0, 0);
1640	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"
, 1640, __extension__ __PRETTY_FUNCTION__); }));
1641	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"
, 1641, __extension__ __PRETTY_FUNCTION__); }));
1642	const int input_size_per_p = input_size / parallel_count;
1643	_ccv_cnnp_model_bind_tensors(model->graph, model->inputs, inputs, input_size_per_p, parallel_count, tensor_binds);
1644	const int output_size_per_p = output_size / parallel_count;
1645	_ccv_cnnp_model_bind_tensors(model->graph, model->outputs, outputs, output_size_per_p, parallel_count, tensor_binds);
1646	const int parameter_size = compiled_data->parameters->rnum;
1647	_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);
1648	const int internal_size = compiled_data->internals->rnum;
1649	_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);
1650	_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);
1651	// If we generated gradient for the graph, only compile part of the graph because the rest is irrelevant for evaluation.
1652	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);
1653	ccv_array_free(tensor_binds);
1654	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));
1655	// If tensor is not init'ed, we need to init states first.
1656	if (tensors_init && parallel_count > 1)
1657		_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);
1658	if (_ccv_cnnp_any_to_init(compiled_data))
1659	{
1660		ccv_nnc_tensor_init_states_t tensor_init_states = {
1661			.parallel_count = parallel_count,
1662			.graph = model->graph,
1663			.compiled_data = compiled_data,
1664			.tensor_arena = compiled_data->tensor_arena
1665		};
1666		ccv_cnnp_model_init_states(model, model->graph, _ccv_cnnp_init_states_for_tensors, &tensor_init_states);
1667	}
1668	compiled_data->is_test = 1;
1669	ccv_nnc_graph_exec_update_t update = {
1670		.parallel_count = parallel_count,
1671		.graph = model->graph,
1672		.graph_exec_arena = compiled_data->graph_exec_arena,
1673	};
1674	ccv_cnnp_model_set_is_test(model, 1, _ccv_cnnp_cmd_update_for_execs, &update);
1675	ccv_nnc_graph_set_default_static_schedule(compiled_data->graph, compiled_data->stream_type, model->max_stream_count);
1676	ccv_nnc_graph_autotune(compiled_data->graph, model->workspace_size, 0, TRAVERSE_FULL0,0,0,0);
1677}
1678 
1679static void _ccv_cnnp_model_gradient_tensors_init(const ccv_cnnp_model_t* const model, ccv_cnnp_compiled_data_t* const compiled_data)
1680{
1681	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", 1681, __extension__ __PRETTY_FUNCTION__
); }));
1682	const int parameter_size = compiled_data->parameters->rnum;
1683	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; });
1684	compiled_data->tensors.gradients = (ccv_nnc_tensor_t**)ccmallocmalloc(sizeof(ccv_nnc_tensor_t*) * parameter_size * 2 * parallel_count);
1685	compiled_data->tensors.accum_gradients = compiled_data->tensors.gradients + parameter_size * parallel_count;
1686	int i, j;
1687	for (i = 0; i < parameter_size; i++)
1688	{
1689		if (compiled_data->parameter_flags && !(compiled_data->parameter_flags[i >> 6] & ((uint64_t)1 << (i & 63))))
1690		{
1691			compiled_data->tensors.gradients[i] = 0;
1692			compiled_data->tensors.accum_gradients[i] = 0;
1693			for (j = 1; j < parallel_count; j++)
1694			{
1695				compiled_data->tensors.gradients[i + j * parameter_size] = 0;
1696				compiled_data->tensors.accum_gradients[i + j * parameter_size] = 0;
1697			}
1698			continue;
1699		}
1700		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)));
1701		ccv_nnc_tensor_param_t info = ccv_nnc_tensor_symbol_params(parameter.graph, parameter);
1702		if (CCV_TENSOR_GET_DEVICE(info.type)((info.type) & 0xfff00) == CCV_COMPUTE_DEVICE_ANY)
1703			CCV_TENSOR_SET_DEVICE_ID(info.type, 0)(info.type) = (((info.type) & ~0xfff00) | (((0) & 0xfff
) << 8));
1704		const int device_id = CCV_TENSOR_GET_DEVICE_ID(info.type)(((info.type) & 0xfff00) >> 8);
1705		compiled_data->tensors.gradients[i] = ccv_nnc_tensor_new(0, info, 0);
1706		compiled_data->tensors.accum_gradients[i] = 0; // delay the accumulated gradient allocation until when we need it.
1707		for (j = 1; j < parallel_count; j++)
1708		{
1709			if (j != device_id)
1710				CCV_TENSOR_SET_DEVICE_ID(info.type, j)(info.type) = (((info.type) & ~0xfff00) | (((j) & 0xfff
) << 8));
1711			else
1712				CCV_TENSOR_SET_DEVICE_ID(info.type, 0)(info.type) = (((info.type) & ~0xfff00) | (((0) & 0xfff
) << 8));
1713			compiled_data->tensors.gradients[i + j * parameter_size] = ccv_nnc_tensor_new(0, info, 0);
1714			compiled_data->tensors.accum_gradients[i + j * parameter_size] = 0;
1715		}
1716	}
1717}
1718 
1719static int _ccv_cnnp_is_disable_outgrad_all(const uint64_t disable_outgrad, const int input_size)
1720{
1721	if (disable_outgrad == CCV_CNNP_DISABLE_OUTGRAD_ALL)
1722		return 1;
1723	if (disable_outgrad == CCV_CNNP_DISABLE_OUTGRAD_NONE)
1724		return 0;
1725	int i;
1726	for (i = 0; i < input_size; i++)
1727		if (!(disable_outgrad & ((uint64_t)1 << i)))
1728			return 0;
1729	return 1;
1730}
1731 
1732// Compile the graph to run ccv_cnnp_model_evaluate with requires_grad = true (MULTISTAGE_MODE).
1733// Particularly, this method compiles the evaluation and backprop graph (the main graph).
1734static 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)
1735{
1736	int i, j;
1737	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
1738	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;
1739	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", 1739, __extension__ __PRETTY_FUNCTION__
); }));
1740	compiled_data->graph_mode = CCV_CNNP_MODEL_GRAPH_MULTISTAGE_MODE;
1741	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; });
1742	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", 1742, __extension__ __PRETTY_FUNCTION__
); }));
1743	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", 1743, __extension__ __PRETTY_FUNCTION__
); }));
1744	// There shouldn't be a loss function if we evaluate with multistage jit.
1745	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", 1745, __extension__ __PRETTY_FUNCTION__
); }));
1746	if (compiled_data->gradient_mode == CCV_CNNP_COMPILED_DATA_GRADIENT_NONE)
1747	{
1748		_ccv_cnnp_model_set_rewindables(model);
1749		_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.
1750	} else if (compiled_data->gradient_mode != target_gradient_mode) {
1751		_ccv_cnnp_model_rewind_graph(model);
1752		_ccv_cnnp_compiled_data_gradient_free(compiled_data);
1753		compiled_data->gradient_mode = CCV_CNNP_COMPILED_DATA_GRADIENT_NONE;
1754		_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.
1755	}
1756	const int tensors_init = !!compiled_data->tensors_init.v;
1757	if (!tensors_init)
1758		_ccv_cnnp_model_tensors_init(model, compiled_data);
1759	else if ((uintptr_t)compiled_data->tensors_init.v & (uintptr_t)1)
1760	// Check if it is not fully allocated, if it is not, init_1.
1761		ccv_cnnp_model_tensors_init_1(model, compiled_data);
1762	ccv_array_t* const tensor_binds = ccv_array_new(sizeof(ccv_nnc_tensor_bind_t), 0, 0);
1763	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"
, 1763, __extension__ __PRETTY_FUNCTION__); }));
1764	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"
, 1764, __extension__ __PRETTY_FUNCTION__); }));
1765	const int input_size_per_p = input_size / parallel_count;
1766	_ccv_cnnp_model_bind_tensors(model->graph, model->inputs, inputs, input_size_per_p, parallel_count, tensor_binds);
1767	const int output_size_per_p = output_size / parallel_count;
1768	_ccv_cnnp_model_bind_tensors(model->graph, model->outputs, outputs, output_size_per_p, parallel_count, tensor_binds);
1769	const int parameter_size = compiled_data->parameters->rnum;
1770	_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);
1771	const int internal_size = compiled_data->internals->rnum;
1772	_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);
1773	_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);
1774	if (!compiled_data->tensors.gradients)
1775		_ccv_cnnp_model_gradient_tensors_init(model, compiled_data);
1776	_ccv_cnnp_model_bind_tensors(model->graph, compiled_data->gradients, compiled_data->tensors.gradients, parameter_size, parallel_count, tensor_binds);
1777	if (compiled_data->backward.to_size > 0)
1778		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);
1779	else
1780		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);
1781	ccv_array_free(tensor_binds);
1782	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));
1783	if (tensors_init && parallel_count > 1)
1784		_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);
1785	// If tensor is not init'ed, we need to init states first.
1786	if (_ccv_cnnp_any_to_init(compiled_data))
1787	{
1788		ccv_nnc_tensor_init_states_t tensor_init_states = {
1789			.parallel_count = parallel_count,
1790			.graph = model->graph,
1791			.compiled_data = compiled_data,
1792			.tensor_arena = compiled_data->tensor_arena
1793		};
1794		ccv_cnnp_model_init_states(model, model->graph, _ccv_cnnp_init_states_for_tensors, &tensor_init_states);
1795	}
1796	compiled_data->is_test = is_test;
1797	ccv_nnc_graph_exec_update_t update = {
1798		.parallel_count = parallel_count,
1799		.graph = model->graph,
1800		.graph_exec_arena = compiled_data->graph_exec_arena,
1801	};
1802	ccv_cnnp_model_set_is_test(model, is_test, _ccv_cnnp_cmd_update_for_execs, &update);
1803	const int evaluate_to_size = compiled_data->evaluate.to_size;
1804	compiled_data->evaluate.to_op_size = 0;
1805	ccv_array_t* const backward_from = ccv_array_new(sizeof(int), 0, 0);
1806	for (i = 0; i < evaluate_to_size; i++)
1807	{
1808		ccv_nnc_graph_exec_t const to_op = ccv_nnc_graph_exec_from_symbol(compiled_data->graph_exec_arena, compiled_data->evaluate.tos[i]);
1809		if (to_op.graph)
1810			compiled_data->evaluate.to_ops[compiled_data->evaluate.to_op_size++] = to_op;
1811		const int* tos;
1812		int to_size;
1813		ccv_nnc_graph_exec_symbol_to(model->graph, compiled_data->evaluate.tos[i], &tos, &to_size);
1814		for (j = 0; j < to_size; j++)
1815		{
1816			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){
1817				.d = tos[j],
1818				.graph = model->graph
1819			});
1820			if (to_op.graph)
1821				ccv_array_add_unique_int(backward_from, to_op.d);
1822		}
1823	}
1824	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", 1824, __extension__
 __PRETTY_FUNCTION__); }));
1825	compiled_data->backward.from_op_size = backward_from->rnum;
1826	compiled_data->backward.from_ops = (ccv_nnc_graph_exec_t*)ccmallocmalloc(sizeof(ccv_nnc_graph_exec_t) * backward_from->rnum);
1827	for (i = 0; i < backward_from->rnum; i++)
1828		compiled_data->backward.from_ops[i] = (ccv_nnc_graph_exec_t){
1829			.d = *(int*)ccv_array_get(backward_from, i)((void*)(((char*)((backward_from)->data)) + (size_t)(backward_from
)->rsize * (size_t)(i))),
1830			.graph = compiled_data->graph,
1831		};
1832	// 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.
1833	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)));
1834	const int exec_info_size = compiled_data->graph->exec_info->rnum;
1835	uint32_t* const visited = cccalloccalloc((exec_info_size + 31) >> 5, sizeof(uint32_t));
1836	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)));
1837	const int source_size = compiled_data->graph->sources->rnum;
1838	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", 1838, __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", 1838, __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", 1838, __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", 1838, __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", 1838, __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", 1838, __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", 1838, __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", 1838, __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", 1838, __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", 1838, __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", 1838, __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"
, 1838, __extension__ __PRETTY_FUNCTION__); })); _visit_; });
1839	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; {
1840		visited[(idx >> 5)] |= (1u << (idx & 31));
1841	} ccv_nnc_graph_visit_endfor} }
1842	ccv_nnc_graph_visit_free(visit);
1843	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)));
1844	const int destination_size = compiled_data->graph->destinations->rnum;
1845	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", 1845, __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", 1845, __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", 1845, __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"
, 1845, __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", 1845, __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", 1845, __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", 1845, __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", 1845, __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", 1845, __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", 1845, __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", 1845, __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", 1845, __extension__ __PRETTY_FUNCTION__
); })); _visit_; });
1846	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; {
1847		visited[(idx >> 5)] |= (1u << (idx & 31));
1848	} ccv_nnc_graph_visit_endfor} }
1849	ccv_nnc_graph_visit_free(visit);
1850	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", 1850, __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", 1850, __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", 1850, __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", 1850, __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", 1850, __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", 1850, __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", 1850, __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", 1850, __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", 1850, __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", 1850, __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", 1850, __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", 1850, __extension__ __PRETTY_FUNCTION__
); })); _visit_; });
1851	// Find any missing nodes to be added as source. Right now, these are only set nodes.
1852	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; {
1853		if (!(visited[(idx >> 5)] & (1u << (idx & 31))))
1854		{
1855			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", 1855, __extension__ __PRETTY_FUNCTION__
); }));
1856			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.
1857				ccv_array_add_unique_int(backward_from, idx);
1858		}
1859	} ccv_nnc_graph_visit_endfor} }
1860	ccv_nnc_graph_visit_free(visit);
1861	ccfreefree(visited);
1862	if (backward_from->rnum != compiled_data->backward.from_op_size) // If it doesn't match, need to redo this.
1863	{
1864		compiled_data->backward.from_op_size = backward_from->rnum;
1865		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);
1866		for (i = 0; i < backward_from->rnum; i++)
1867			compiled_data->backward.from_ops[i] = (ccv_nnc_graph_exec_t){
1868				.d = *(int*)ccv_array_get(backward_from, i)((void*)(((char*)((backward_from)->data)) + (size_t)(backward_from
)->rsize * (size_t)(i))),
1869				.graph = compiled_data->graph,
1870			};
1871	}
1872	ccv_array_free(backward_from);
1873	ccv_nnc_graph_set_default_static_schedule(compiled_data->graph, compiled_data->stream_type, model->max_stream_count);
1874	ccv_nnc_graph_autotune(compiled_data->graph, model->workspace_size, 0, TRAVERSE_FULL0,0,0,0);
1875}
1876 
1877void 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)
1878{
1879	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
1880	assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
 (compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 1880, __extension__ __PRETTY_FUNCTION__); }));
1881	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; });
1882	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", 1882, __extension__ __PRETTY_FUNCTION__
); }));
1883	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", 1883, __extension__ __PRETTY_FUNCTION__
); }));
1884	assert(model->graph)((void) sizeof ((model->graph) ? 1 : 0), __extension__ ({ if
 (model->graph) ; else __assert_fail ("model->graph", "ccv_cnnp_model.c"
, 1884, __extension__ __PRETTY_FUNCTION__); }));
1885	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;
1886	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));
1887	if (!compiled_data->graph || mode_mismatch)
1888	{
1889		_ccv_cnnp_compiled_data_graph_free(compiled_data);
1890		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.
1891			_ccv_cnnp_compiled_data_backward_free(compiled_data);
1892		if (params.requires_grad)
1893			_ccv_cnnp_model_multistage_jit_0(model, params.disable_outgrad, params.is_test, inputs, input_size, outputs, output_size);
1894		else
1895			_ccv_cnnp_model_multistage_no_grad_jit(model, inputs, input_size, outputs, output_size);
1896	} else {
1897		ccv_nnc_tensor_arena_clear_bindings(compiled_data->tensor_arena);
1898		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"
, 1898, __extension__ __PRETTY_FUNCTION__); }));
1899		const int input_size_per_p = input_size / parallel_count;
1900		_ccv_cnnp_bind_tensors_to_arena(compiled_data->tensor_arena, model->graph, model->inputs, inputs, input_size_per_p, parallel_count);
1901		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"
, 1901, __extension__ __PRETTY_FUNCTION__); }));
1902		const int output_size_per_p = output_size / parallel_count;
1903		_ccv_cnnp_bind_tensors_to_arena(compiled_data->tensor_arena, model->graph, model->outputs, outputs, output_size_per_p, parallel_count);
1904	}
1905	if (compiled_data->is_test != params.is_test)
1906	{
1907		compiled_data->is_test = params.is_test;
1908		ccv_nnc_graph_exec_update_t update = {
1909			.parallel_count = parallel_count,
1910			.graph = model->graph,
1911			.graph_exec_arena = compiled_data->graph_exec_arena,
1912		};
1913		ccv_cnnp_model_set_is_test(model, params.is_test, _ccv_cnnp_cmd_update_for_execs, &update);
1914	}
1915}
1916 
1917void 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)
1918{
1919	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
1920	assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
 (compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 1920, __extension__ __PRETTY_FUNCTION__); }));
1921	ccv_cnnp_model_dry_run(model, params, inputs, input_size, outputs, output_size);
1922	if (compiled_data->graph_mode == CCV_CNNP_MODEL_GRAPH_MULTISTAGE_MODE_NO_GRAD)
1923		ccv_nnc_graph_run_with_schedule(compiled_data->graph, 0, 0, tensor_tape, stream_context);
1924	else {
1925		if (!compiled_data->evaluate.schedule)
1926			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);
1927		ccv_nnc_graph_run_with_schedule(compiled_data->graph, 0, compiled_data->evaluate.schedule, tensor_tape, stream_context);
1928	}
1929}
1930 
1931// Compile the graph to run ccv_cnnp_model_backward after ccv_cnnp_model_evaluate with requires_grad = true (MULTISTAGE_MODE).
1932// Particularly, this method compiles the accumulator graph.
1933static void _ccv_cnnp_model_multistage_jit_1(ccv_cnnp_model_t* const model)
1934{
1935	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
1936	assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
 (compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 1936, __extension__ __PRETTY_FUNCTION__); }));
1937	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", 1937, __extension__ __PRETTY_FUNCTION__
); }));
1938	ccv_nnc_symbolic_graph_t* accum = ccv_nnc_symbolic_graph_new();
1939	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; });
1940	const int parameter_size = compiled_data->parameters->rnum;
1941	int i, j;
1942	compiled_data->backward.gradients = (ccv_nnc_tensor_symbol_t*)ccmallocmalloc(sizeof(ccv_nnc_tensor_symbol_t) * parameter_size * parallel_count * 3);
1943	compiled_data->backward.accum_gradients = compiled_data->backward.gradients + parameter_size * parallel_count;
1944	compiled_data->backward.updated_accum_gradients = compiled_data->backward.accum_gradients + parameter_size * parallel_count;
1945	for (i = 0; i < parameter_size; i++)
1946		for (j = 0; j < parallel_count; j++)
1947			if (compiled_data->tensors.gradients[i + j * parameter_size])
1948			{
1949				const ccv_nnc_tensor_param_t info = compiled_data->tensors.gradients[i + j * parameter_size]->info;
1950				// Now, the old gradient is the accumulated gradient, getting new gradient tensor setup so we can collect them.
1951				compiled_data->tensors.accum_gradients[i + j * parameter_size] = compiled_data->tensors.gradients[i + j * parameter_size];
1952				compiled_data->tensors.gradients[i + j * parameter_size] = ccv_nnc_tensor_new(0, info, 0);
1953				ccv_nnc_tensor_symbol_t inputs[2];
1954				inputs[0] = compiled_data->backward.accum_gradients[i + j * parameter_size] = ccv_nnc_tensor_symbol_new(accum, info, 0);
1955				inputs[1] = compiled_data->backward.gradients[i + j * parameter_size] = ccv_nnc_tensor_symbol_new(accum, info, 0);
1956				ccv_nnc_tensor_symbol_t output = compiled_data->backward.updated_accum_gradients[i + j * parameter_size] = ccv_nnc_tensor_symbol_new(accum, info, 0);
1957				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);
1958			} else {
1959				compiled_data->backward.accum_gradients[i + j * parameter_size] = NO_TENSOR_SYMBOL(const ccv_nnc_tensor_symbol_t){.d = CCV_NNC_NO_TENSOR_SYMBOL
};
1960				compiled_data->backward.gradients[i + j * parameter_size] = NO_TENSOR_SYMBOL(const ccv_nnc_tensor_symbol_t){.d = CCV_NNC_NO_TENSOR_SYMBOL
};
1961				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
};
1962			}
1963	ccv_nnc_graph_exec_symbol_autogen(accum, 0, 0, CCV_NNC_AUTOGEN_ALL_EXECS | CCV_NNC_AUTOGEN_SOURCES_AND_DESTINATIONS);
1964	if (ccv_nnc_symbolic_graph_source_size(accum) == 0)
1965	{
1966		ccv_nnc_symbolic_graph_free(accum);
1967		// Create empty graph.
1968		compiled_data->backward.accum = ccv_nnc_graph_new();
1969		ccv_nnc_graph_topsort(compiled_data->backward.accum, 0, 0);
1970		return;
1971	}
1972	ccv_array_t* const tensor_binds = ccv_array_new(sizeof(ccv_nnc_tensor_bind_t), 0, 0);
1973	_ccv_cnnp_model_bind_tensors(accum, compiled_data->backward.accum_gradients, compiled_data->tensors.accum_gradients, parameter_size * parallel_count, 1, tensor_binds);
1974	_ccv_cnnp_model_bind_tensors(accum, compiled_data->backward.gradients, compiled_data->tensors.gradients, parameter_size * parallel_count, 1, tensor_binds);
1975	_ccv_cnnp_model_bind_tensors(accum, compiled_data->backward.updated_accum_gradients, compiled_data->tensors.accum_gradients, parameter_size * parallel_count, 1, tensor_binds);
1976	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);
1977	ccv_nnc_symbolic_graph_free(accum);
1978	ccv_array_free(tensor_binds);
1979	ccv_nnc_graph_set_default_static_schedule(compiled_data->backward.accum, compiled_data->stream_type, model->max_stream_count);
1980}
1981 
1982void 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)
1983{
1984	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
1985	assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
 (compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 1985, __extension__ __PRETTY_FUNCTION__); }));
1986	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", 1986, __extension__ __PRETTY_FUNCTION__
); }));
1987	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; });
1988	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", 1988, __extension__ __PRETTY_FUNCTION__
); }));
1989	if (outgrad_size > 0)
1990		{ 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", 1990, __extension__ __PRETTY_FUNCTION__
); })); }
1991	assert(model->graph)((void) sizeof ((model->graph) ? 1 : 0), __extension__ ({ if
 (model->graph) ; else __assert_fail ("model->graph", "ccv_cnnp_model.c"
, 1991, __extension__ __PRETTY_FUNCTION__); }));
1992	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", 1992, __extension__ __PRETTY_FUNCTION__
); }));
1993	const int parameter_size = compiled_data->parameters->rnum;
1994	// If we need to accumulate the gradients now, do jit on accumulator.
1995	if (compiled_data->backward.count > 0)
1996	{
1997		if (!compiled_data->backward.accum)
1998			_ccv_cnnp_model_multistage_jit_1(model);
1999		else if (compiled_data->backward.count == 1) {
2000			//  On this round, we need to switch accumulated gradients with gradients (so we can do accumulation properly).
2001			int i;
2002			for (i = 0; i < parameter_size * parallel_count; i++)
2003			{
2004				ccv_nnc_tensor_t* tensor;
2005				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));
2006			}
2007			if (compiled_data->backward.tensor_arena)
2008			{
2009				ccv_nnc_tensor_arena_clear_bindings(compiled_data->backward.tensor_arena);
2010				// Do rebind in case we messed up the binding (we switch accum_gradients and gradients).
2011				_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);
2012				_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);
2013				_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);
2014			}
2015		}
2016	}
2017	const int ingrad_size_per_p = model->output_size;
2018	const int outgrad_size_per_p = compiled_data->outgrad_size;
2019	int i, j;
2020	for (i = 0; i < ingrad_size_per_p; i++)
2021	{
2022		const ccv_nnc_tensor_symbol_t ingrad = ccv_nnc_tensor_symbol_for_backward(model->graph, compiled_data->f[i]);
2023		if (!ingrad_size || !ingrads || ingrads[i] == 0)
2024		{
2025			// Set it to 1 if it is not specified.
2026			ccv_nnc_tensor_t* const ingrad_tensor = ccv_nnc_tensor_from_symbol(compiled_data->tensor_arena, ingrad);
2027			if (ingrad_tensor)
2028				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);
2029			for (j = 1; j < parallel_count; j++)
2030			{
2031				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));
2032				if (ingrad_tensor)
2033					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);
2034			}
2035		} else {
2036			// Make sure the length matches, in case it is an alias.
2037			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", 2037, __extension__ __PRETTY_FUNCTION__
); }));
2038			ccv_nnc_tensor_bind_symbol(compiled_data->tensor_arena, ingrad, ingrads[i]);
2039			for (j = 1; j < parallel_count; j++)
2040				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]);
2041		}
2042	}
2043	if (outgrad_size > 0)
2044	{
2045		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", 2045, __extension__ __PRETTY_FUNCTION__
); }));
2046		for (i = 0; i < outgrad_size_per_p; i++)
2047			if (outgrads[i])
2048			{
2049				const ccv_nnc_tensor_symbol_t outgrad = compiled_data->outgrads[i];
2050				ccv_nnc_tensor_bind_symbol(compiled_data->tensor_arena, outgrad, outgrads[i]);
2051				for (j = 1; j < parallel_count; j++)
2052					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]);
2053			}
2054	} else {
2055		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", 2056, __extension__ __PRETTY_FUNCTION__
); }))
2056			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", 2056, __extension__ __PRETTY_FUNCTION__
); }));
2057	}
2058	// We need to rebind here because in ccv_cnnp_evaluate, we clear bindings, that will reset all bindings for the gradients.
2059	// For parameters and internals these are fine because when we clear bindings, it restores to original bindings, which are these
2060	// parameters and internals. The same cannot be said for gradients due to the accum_gradients switching.
2061	_ccv_cnnp_bind_tensors_to_arena(compiled_data->tensor_arena, model->graph, compiled_data->gradients, compiled_data->tensors.gradients, parameter_size, parallel_count);
2062	if (!compiled_data->backward.schedule)
2063		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);
2064	// Run the backward pass.
2065	ccv_nnc_graph_run_with_schedule(compiled_data->graph, 0, compiled_data->backward.schedule, tensor_tape, stream_context);
2066	// If we need to run accumulation round, do that now.
2067	if (compiled_data->backward.count > 0)
2068		ccv_nnc_graph_run_with_schedule(compiled_data->backward.accum, 0, 0, 0, stream_context);
2069	// Update the count, this determines whether we need to accumulate or not.
2070	++compiled_data->backward.count;
2071}
2072 
2073// Compile the graph to run ccv_cnnp_model_apply_gradients after ccv_cnnp_model_backward (MULTISTAGE_MODE).
2074// Particularly, this method compiles the parameter update graph.
2075static void _ccv_cnnp_model_multistage_jit_2(ccv_cnnp_model_t* const model)
2076{
2077	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
2078	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", 2078, __extension__ __PRETTY_FUNCTION__
); }));
2079	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; });
2080	const int parameter_size = compiled_data->parameters->rnum;
2081	ccv_array_t* const tensor_binds = ccv_array_new(sizeof(ccv_nnc_tensor_bind_t), 0, 0);
2082	_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);
2083	_ccv_cnnp_model_bind_tensors(model->graph, compiled_data->updated_parameters, compiled_data->tensors.parameters, parameter_size, parallel_count, tensor_binds);
2084	// Bind accumulated gradients.
2085	if (compiled_data->backward.count > 1)
2086		_ccv_cnnp_model_bind_tensors(model->graph, compiled_data->gradients, compiled_data->tensors.accum_gradients, parameter_size, parallel_count, tensor_binds);
2087	else
2088		_ccv_cnnp_model_bind_tensors(model->graph, compiled_data->gradients, compiled_data->tensors.gradients, parameter_size, parallel_count, tensor_binds);
2089	ccv_array_t* const apply_gradients_from = ccv_array_new(sizeof(int), 0, 0);
2090	int i, j;
2091	for (i = 0; i < compiled_data->backward.to_size; i++)
2092	{
2093		const int* tos;
2094		int to_size;
2095		ccv_nnc_graph_exec_symbol_to(model->graph, compiled_data->backward.tos[i], &tos, &to_size);
2096		for (j = 0; j < to_size; j++)
2097		{
2098			// Check if this is already show up in the backward graph, if that is the case, it won't be in the apply
2099			// gradients graph.
2100			const ccv_nnc_graph_exec_t exec = ccv_nnc_graph_exec_from_symbol(compiled_data->graph_exec_arena, (ccv_nnc_graph_exec_symbol_t){
2101				.d = tos[j],
2102				.graph = model->graph,
2103			});
2104			if (!exec.graph)
2105				ccv_array_add_unique_int(apply_gradients_from, tos[j]);
2106		}
2107	}
2108	const int from_size = apply_gradients_from->rnum;
2109	if (from_size == 0)
2110	{
2111		ccv_array_free(apply_gradients_from);
2112		ccv_array_free(tensor_binds);
2113		return;
2114	}
2115	ccv_nnc_graph_exec_symbol_t* const froms = (ccv_nnc_graph_exec_symbol_t*)ccmallocmalloc(sizeof(ccv_nnc_graph_exec_symbol_t) * from_size);
2116	for (i = 0; i < from_size; i++)
2117		froms[i] = (ccv_nnc_graph_exec_symbol_t){
2118			.d = *(int*)ccv_array_get(apply_gradients_from, i)((void*)(((char*)((apply_gradients_from)->data)) + (size_t
)(apply_gradients_from)->rsize * (size_t)(i))),
2119			.graph = model->graph
2120		};
2121	ccv_array_free(apply_gradients_from);
2122	// It can only ends with updates on the parameters.
2123	ccv_array_t* const tos = ccv_array_new(sizeof(ccv_nnc_graph_exec_symbol_t), parameter_size * parallel_count, 0);
2124	for (i = 0;  i < parameter_size; i++)
2125	{
2126		if (compiled_data->update_nodes[i].d == CCV_NNC_NO_TENSOR_SYMBOL)
2127			continue;
2128		ccv_array_push(tos, &compiled_data->update_nodes[i]);
2129		for (j = 1; j < parallel_count; j++)
2130		{
2131			const ccv_nnc_graph_exec_symbol_t copy = ccv_nnc_graph_exec_symbol_copy(model->graph, compiled_data->update_nodes[i], j);
2132			ccv_array_push(tos, &copy);
2133		}
2134	}
2135	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);
2136	ccv_array_free(tos);
2137	ccv_array_free(tensor_binds);
2138	ccfreefree(froms);
2139	const int max_saved_aux_size = compiled_data->minimize.max_saved_aux_size;
2140	for (i = 0; i < max_saved_aux_size * parameter_size; i++)
2141	{
2142		// Skip on no tensor.
2143		if (compiled_data->saved_aux[i].source.d == CCV_NNC_NO_TENSOR_SYMBOL)
2144			continue;
2145		ccv_nnc_tensor_t* const tensor = ccv_nnc_tensor_from_symbol(compiled_data->apply_gradients.tensor_arena, compiled_data->saved_aux[i].source);
2146		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);
2147		for (j = 1; j < parallel_count; j++)
2148		{
2149			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));
2150			if (copy)
2151				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);
2152		}
2153	}
2154	ccv_nnc_graph_set_default_static_schedule(compiled_data->apply_gradients.graph, compiled_data->stream_type, model->max_stream_count);
2155}
2156 
2157void ccv_cnnp_model_apply_gradients(ccv_cnnp_model_t* const model, ccv_nnc_stream_context_t* const stream_context)
2158{
2159	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
2160	assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
 (compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 2160, __extension__ __PRETTY_FUNCTION__); }));
2161	assert(compiled_data->graph_mode == CCV_CNNP_MODEL_GRAPH_MULTISTAGE_MODE)((void) sizeof ((compiled_data->graph_mode == CCV_CNNP_MODEL_GRAPH_MULTISTAGE_MODE
) ? 1 : 0), __extension__ ({ if (compiled_data->graph_mode
 == CCV_CNNP_MODEL_GRAPH_MULTISTAGE_MODE) ; else __assert_fail
 ("compiled_data->graph_mode == CCV_CNNP_MODEL_GRAPH_MULTISTAGE_MODE"
, "ccv_cnnp_model.c", 2161, __extension__ __PRETTY_FUNCTION__
); }));
2162	const int parallel_count = ccv_max(model->parallel_count, 1)({ typeof (model->parallel_count) _a = (model->parallel_count
); typeof (1) _b = (1); (_a > _b) ? _a : _b; });
2163	assert(model->graph)((void) sizeof ((model->graph) ? 1 : 0), __extension__ ({ if
 (model->graph) ; else __assert_fail ("model->graph", "ccv_cnnp_model.c"
, 2163, __extension__ __PRETTY_FUNCTION__); }));
2164	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", 2164, __extension__ __PRETTY_FUNCTION__
); }));
2165	// Skip if there is no backward pass.
2166	if (compiled_data->backward.count <= 0)
2167		return;
2168	// Skip if there is no parameters.
2169	if (compiled_data->parameters->rnum == 0)
2170	{
2171		compiled_data->backward.count = 0;
2172		return;
2173	}
2174	if (!compiled_data->apply_gradients.graph)
2175		_ccv_cnnp_model_multistage_jit_2(model);
2176	else {
2177		const int parameter_size = compiled_data->parameters->rnum;
2178		ccv_nnc_tensor_arena_clear_bindings(compiled_data->apply_gradients.tensor_arena);
2179		// Change to bind accum_gradients if we do gradient accumulation (run backward more than once).
2180		if (compiled_data->backward.count > 1)
2181			_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);
2182		else
2183			_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);
2184	}
2185	if (compiled_data->apply_gradients.graph)
2186		ccv_nnc_graph_run_with_schedule(compiled_data->apply_gradients.graph, 0, 0, 0, stream_context);
2187	// Reset backward count to 0.
2188	compiled_data->backward.count = 0;
2189}
2190 
2191void 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)
2192{
2193	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
2194	const int param_sel = parameter->param_sel > 0 ? parameter->param_sel - 1 : parameter->param_sel;
2195	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", 2195, __extension__ __PRETTY_FUNCTION__
); }));
2196	const int tensors_init = !!compiled_data->tensors_init.v;
2197	if (!tensors_init)
2198		_ccv_cnnp_model_tensors_init(model, compiled_data);
2199	else if ((uintptr_t)compiled_data->tensors_init.v & (uintptr_t)1)
2200	// Check if it is not fully allocated, if it is not, init_1.
2201		ccv_cnnp_model_tensors_init_1(model, compiled_data);
2202	ccv_array_t* const parameter_indices = ccv_array_new(sizeof(int), 0, 0);
2203	ccv_cnnp_model_add_to_parameter_indices(parameter->model, param_sel, parameter_indices);
2204	const int param_ref = parameter->param_ref > 0 ? parameter->param_ref - 1 : parameter->param_ref;
2205	if (param_ref < 0)
2206		{ 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", 2206
, __extension__ __PRETTY_FUNCTION__); })); }
2207	else
2208		{ 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", 2208, __extension__ __PRETTY_FUNCTION__
); })); }
2209	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)));
2210	ccv_array_free(parameter_indices);
2211	const int parameter_size = compiled_data->parameters->rnum;
2212	assert(d >= 0)((void) sizeof ((d >= 0) ? 1 : 0), __extension__ ({ if (d >=
 0) ; else __assert_fail ("d >= 0", "ccv_cnnp_model.c", 2212
, __extension__ __PRETTY_FUNCTION__); }));
2213	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", 2213, __extension__ __PRETTY_FUNCTION__
); }));
2214	const int parallel_count = ccv_max(model->parallel_count, 1)({ typeof (model->parallel_count) _a = (model->parallel_count
); typeof (1) _b = (1); (_a > _b) ? _a : _b; });
2215	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));
2216	assert(dest)((void) sizeof ((dest) ? 1 : 0), __extension__ ({ if (dest) ;
 else __assert_fail ("dest", "ccv_cnnp_model.c", 2216, __extension__
 __PRETTY_FUNCTION__); }));
2217	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);
2218	int i;
2219	for (i = 1; i < parallel_count; i++)
2220	{
2221		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));
2222		if (copy_tensor)
2223			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);
2224	}
2225	// Mark this symbol as init'ed.
2226	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;
2227  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));
2228	init_v[s >> 5] |= (1u << (s & 0x1f));
2229}
2230 
2231void ccv_cnnp_model_parameter_copy(ccv_cnnp_model_t* const model, const ccv_cnnp_model_io_t parameter, ccv_nnc_tensor_t* const tensor)
2232{
2233	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
2234	const int param_sel = parameter->param_sel > 0 ? parameter->param_sel - 1 : parameter->param_sel;
2235	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", 2235, __extension__ __PRETTY_FUNCTION__
); }));
2236	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", 2236, __extension__ __PRETTY_FUNCTION__
); }));
2237	ccv_array_t* const parameter_indices = ccv_array_new(sizeof(int), 0, 0);
2238	ccv_cnnp_model_add_to_parameter_indices(parameter->model, param_sel, parameter_indices);
2239	const int param_ref = parameter->param_ref > 0 ? parameter->param_ref - 1 : parameter->param_ref;
2240	if (param_ref < 0)
2241		{ 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", 2241
, __extension__ __PRETTY_FUNCTION__); })); }
2242	else
2243		{ 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", 2243, __extension__ __PRETTY_FUNCTION__
); })); }
2244	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)));
2245	ccv_array_free(parameter_indices);
2246	const int parameter_size = compiled_data->parameters->rnum;
2247	assert(d >= 0)((void) sizeof ((d >= 0) ? 1 : 0), __extension__ ({ if (d >=
 0) ; else __assert_fail ("d >= 0", "ccv_cnnp_model.c", 2247
, __extension__ __PRETTY_FUNCTION__); }));
2248	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", 2248, __extension__ __PRETTY_FUNCTION__
); }));
2249	// We don't need to consider parallel_count, every parameter on each device is identical.
2250	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));
2251	assert(src)((void) sizeof ((src) ? 1 : 0), __extension__ ({ if (src) ; else
 __assert_fail ("src", "ccv_cnnp_model.c", 2251, __extension__
 __PRETTY_FUNCTION__); }));
2252	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);
2253}
2254 
2255ccv_nnc_tensor_param_t ccv_cnnp_model_parameter_tensor_params(ccv_cnnp_model_t* const model, const ccv_cnnp_model_io_t parameter)
2256{
2257	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
2258	const int param_sel = parameter->param_sel > 0 ? parameter->param_sel - 1 : parameter->param_sel;
2259	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", 2259, __extension__ __PRETTY_FUNCTION__
); }));
2260	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", 2260, __extension__ __PRETTY_FUNCTION__
); }));
2261	ccv_array_t* const parameter_indices = ccv_array_new(sizeof(int), 0, 0);
2262	ccv_cnnp_model_add_to_parameter_indices(parameter->model, param_sel, parameter_indices);
2263	const int param_ref = parameter->param_ref > 0 ? parameter->param_ref - 1 : parameter->param_ref;
2264	if (param_ref < 0)
2265		{ 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", 2265
, __extension__ __PRETTY_FUNCTION__); })); }
2266	else
2267		{ 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", 2267, __extension__ __PRETTY_FUNCTION__
); })); }
2268	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)));
2269	ccv_array_free(parameter_indices);
2270	const int parameter_size = compiled_data->parameters->rnum;
2271	assert(d >= 0)((void) sizeof ((d >= 0) ? 1 : 0), __extension__ ({ if (d >=
 0) ; else __assert_fail ("d >= 0", "ccv_cnnp_model.c", 2271
, __extension__ __PRETTY_FUNCTION__); }));
2272	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", 2272, __extension__ __PRETTY_FUNCTION__
); }));
2273	// We don't need to consider parallel_count, every parameter on each device is identical.
2274	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));
2275	assert(tensor)((void) sizeof ((tensor) ? 1 : 0), __extension__ ({ if (tensor
) ; else __assert_fail ("tensor", "ccv_cnnp_model.c", 2275, __extension__
 __PRETTY_FUNCTION__); }));
2276	return tensor->info;
2277}
2278 
2279const char* ccv_cnnp_model_parameter_name(ccv_cnnp_model_t* const model, const ccv_cnnp_model_io_t parameter)
2280{
2281	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
2282	const int param_sel = parameter->param_sel > 0 ? parameter->param_sel - 1 : parameter->param_sel;
2283	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", 2283, __extension__ __PRETTY_FUNCTION__
); }));
2284	ccv_array_t* const parameter_indices = ccv_array_new(sizeof(int), 0, 0);
2285	ccv_cnnp_model_add_to_parameter_indices(parameter->model, param_sel, parameter_indices);
2286	const int param_ref = parameter->param_ref > 0 ? parameter->param_ref - 1 : parameter->param_ref;
2287	if (param_ref < 0)
2288		{ 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", 2288
, __extension__ __PRETTY_FUNCTION__); })); }
2289	else
2290		{ 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", 2290, __extension__ __PRETTY_FUNCTION__
); })); }
2291	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)));
2292	ccv_array_free(parameter_indices);
2293	const int parameter_size = compiled_data->parameters->rnum;
2294	assert(d >= 0)((void) sizeof ((d >= 0) ? 1 : 0), __extension__ ({ if (d >=
 0) ; else __assert_fail ("d >= 0", "ccv_cnnp_model.c", 2294
, __extension__ __PRETTY_FUNCTION__); }));
2295	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", 2295, __extension__ __PRETTY_FUNCTION__
); }));
2296	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)));
2297}
2298 
2299int ccv_cnnp_model_parameter_count(ccv_cnnp_model_t* const model)
2300{
2301	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", 2301, __extension__ __PRETTY_FUNCTION__
); }));
2302	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
2303	return compiled_data->parameters->rnum;
2304}
2305 
2306uint64_t ccv_cnnp_model_parameters_size(ccv_cnnp_model_t* const model)
2307{
2308	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", 2308, __extension__ __PRETTY_FUNCTION__
); }));
2309	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
2310	const int parameter_size = compiled_data->parameters->rnum;
2311	int i;
2312	const ccv_nnc_symbolic_graph_t* const graph = model->graph;
2313	uint64_t size = 0;
2314	for (i = 0; i < parameter_size; i++)
2315	{
2316		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;
2317		ccv_nnc_tensor_param_t params = ccv_nnc_tensor_symbol_params(graph, (ccv_nnc_tensor_symbol_t){
2318			.graph = graph,
2319			.d = d
2320		});
2321		size += ccv_nnc_tensor_data_size(params);
2322	}
2323	return size;
2324}
2325 
2326int 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)
2327{
2328	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", 2328, __extension__ __PRETTY_FUNCTION__
); }));
2329	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
2330	if (count != compiled_data->parameters->rnum)
2331		return 0;
2332	if (CCV_TENSOR_GET_DEVICE(type)((type) & 0xfff00) == CCV_COMPUTE_DEVICE_ANY)
2333		CCV_TENSOR_SET_DEVICE_ID(type, 0)(type) = (((type) & ~0xfff00) | (((0) & 0xfff) <<
 8));
2334	int i;
2335	// We don't need to consider parallel_count, every parameter on each device is identical.
2336	for (i = 0; i < count; i++)
2337	{
2338		ccv_nnc_tensor_t* tensor = compiled_data->tensors.parameters[i];
2339		if ((uintptr_t)tensor & (uintptr_t)1) // If it is not owned. We don't do anything.
2340		{
2341			tensors[i] = 0;
2342			continue;
2343		}
2344		tensor = CCV_NNC_TENSOR(tensor)((ccv_nnc_tensor_t*)((uintptr_t)(tensor) & ~(uintptr_t)1)
);
2345		if (tensor->info.type == type)
2346			tensors[i] = tensor;
2347		else {
2348			ccv_nnc_tensor_param_t info = tensor->info;
2349			info.type = type;
2350			tensors[i] = ccv_nnc_tensor_new(0, info, 0); // Create this tensor, don't initiate copy yet.
2351		}
2352	}
2353	for (i = 0; i < count; i++)
2354	{
2355		ccv_nnc_tensor_t* tensor = compiled_data->tensors.parameters[i];
2356		if ((uintptr_t)tensor & (uintptr_t)1) // If it is not owned. We don't do anything.
2357			continue;
2358		tensor = CCV_NNC_TENSOR(tensor)((ccv_nnc_tensor_t*)((uintptr_t)(tensor) & ~(uintptr_t)1)
);
2359		// Now initiate transfer. We should do this one on a stream.
2360		if (tensor->info.type != type)
2361			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);
2362	}
2363	// Copy names and remove parameters.
2364	for (i = 0; i < count; i++)
2365	{
2366		ccv_nnc_tensor_t* const 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		{
2369			names[i] = 0;
2370			continue;
2371		}
2372		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)));
2373		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; });
2374		names[i] = ccmallocmalloc(name_len + 1);
2375		names[i][name_len] = 0;
2376		memcpy(names[i], name, name_len);
2377		compiled_data->tensors.parameters[i] = 0;
2378	}
2379	return 1;
2380}
2381 
2382KHASH_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; } }
27
←
Taking true branch→
28
←
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29
←
Calling 'kh_resize_ccv_cnnp_parameter_id'→
30
←
Taking true branch→
31
←
Assuming the condition is false→
32
←
Taking false branch→
33
←
'?' condition is true→
34
←
Assuming 'new_flags' is non-null→
35
←
Taking false branch→
36
←
'?' condition is true→
37
←
Taking true branch→
38
←
Storing uninitialized value→
39
←
Assuming 'new_keys' is non-null→
40
←
Taking false branch→
41
←
Taking true branch→
42
←
Assuming 'new_vals' is non-null→
43
←
Taking false branch→
44
←
Taking true branch→
45
←
Loop condition is false. Execution continues on line 2382→
46
←
Taking false branch→
47
←
Returning from 'kh_resize_ccv_cnnp_parameter_id'→
48
←
Taking false branch→
49
←
The value 0 is assigned to 'i'→
50
←
Assuming the condition is false→
51
←
Taking false branch→
52
←
Assuming the condition is false→
53
←
1st function call argument is an uninitialized value
2383 
2384void 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)
2385{
2386	assert(model->compiled_data)((void) sizeof ((model->compiled_data) ? 1 : 0), __extension__
 ({ if (model->compiled_data) ; else __assert_fail ("model->compiled_data"
, "ccv_cnnp_model.c", 2386, __extension__ __PRETTY_FUNCTION__
); }));
2387	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
2388	int i;
2389	khash_t(ccv_cnnp_parameter_id)kh_ccv_cnnp_parameter_id_t* id_map = 0;
2390	if (count != compiled_data->parameters->rnum)
2391	{
2392		id_map = kh_init(ccv_cnnp_parameter_id)kh_init_ccv_cnnp_parameter_id();
2393		// Build the map between name and the index.
2394		for (i = 0; i < count; i++)
2395		{
2396			int ret;
2397			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);
2398			assert(ret != 0)((void) sizeof ((ret != 0) ? 1 : 0), __extension__ ({ if (ret
 != 0) ; else __assert_fail ("ret != 0", "ccv_cnnp_model.c", 2398
, __extension__ __PRETTY_FUNCTION__); }));
2399			kh_val(id_map, k)((id_map)->vals[k]) = i;
2400		}
2401	}
2402	const int parameter_size = compiled_data->parameters->rnum;
2403	int* copy_back = 0;
2404	const int tensors_init = !!compiled_data->tensors_init.v;
2405	if (!tensors_init)
2406		ccv_cnnp_model_tensors_init_0(model, compiled_data);
2407	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; });
2408	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));
2409	for (i = 0; i < parameter_size; i++)
2410	{
2411		int j = i;
2412		const char* const name = *(char**)ccv_array_get(compiled_data->ids.parameters, ccv_min(count - 1, i))((void*)(((char*)((compiled_data->ids.parameters)->data
)) + (size_t)(compiled_data->ids.parameters)->rsize * (
size_t)(({ typeof (count - 1) _a = (count - 1); typeof (i) _b
 = (i); (_a < _b) ? _a : _b; }))));
2413		if (strncmp(name, names[i], 1023) != 0)
2414		{
2415			// Build the map.
2416			if (id_map == 0)
2417			{
2418				id_map = kh_init(ccv_cnnp_parameter_id)kh_init_ccv_cnnp_parameter_id();
2419				for (j = 0; j < count; j++)
2420				{
2421					int ret;
2422					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);
2423					assert(ret != 0)((void) sizeof ((ret != 0) ? 1 : 0), __extension__ ({ if (ret
 != 0) ; else __assert_fail ("ret != 0", "ccv_cnnp_model.c", 2423
, __extension__ __PRETTY_FUNCTION__); }));
2424					kh_val(id_map, k)((id_map)->vals[k]) = j;
2425				}
2426			}
2427			const khiter_t k = kh_get(ccv_cnnp_parameter_id, id_map, name)kh_get_ccv_cnnp_parameter_id(id_map, name);
2428			if (k == kh_end(id_map)((id_map)->n_buckets)) // Cannot find the name, skip.
2429				continue;
2430			j = kh_val(id_map, k)((id_map)->vals[k]);
2431		}
2432		if (compiled_data->tensors.parameters[i]) // Cannot be a shared parameter to read.
2433			{ 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", 2433, __extension__ __PRETTY_FUNCTION__
); })); }
2434		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)));
2435		ccv_nnc_tensor_param_t info = ccv_nnc_tensor_symbol_params(parameter.graph, parameter);
2436		if (CCV_TENSOR_GET_DEVICE(info.type)((info.type) & 0xfff00) == CCV_COMPUTE_DEVICE_ANY)
2437			CCV_TENSOR_SET_DEVICE_ID(info.type, 0)(info.type) = (((info.type) & ~0xfff00) | (((0) & 0xfff
) << 8));
2438		const int d = parameter.d;
2439		if (info.type == tensors[j]->info.type && invalidates) // Can move.
2440		{
2441			// Deallocate it if needed.
2442			if (!((uintptr_t)compiled_data->tensors.parameters[i] & (uintptr_t)1))
2443				if (compiled_data->tensors.parameters[i])
2444					ccv_nnc_tensor_free(compiled_data->tensors.parameters[i]);
2445			compiled_data->tensors.parameters[i] = tensors[j];
2446			tensors[j] = 0;
2447		} else {
2448			if (!compiled_data->tensors.parameters[i])
2449			{ // Not allocated, to allocate first.
2450				// Create new one, make sure we create this by having the right parameters.
2451				const int type = info.type;
2452				info = tensors[j]->info;
2453				info.type = type; // Revert back the type.
2454				compiled_data->tensors.parameters[i] = ccv_nnc_tensor_new(0, info, 0);
2455			}
2456			if (!copy_back)
2457				copy_back = (int*)cccalloccalloc(parameter_size, sizeof(int));
2458			copy_back[i] = j + 1;
2459		}
2460		init_v[d >> 5] |= (1u << (d & 0x1f));
2461		// Create this tensor for other data parallel allocations.
2462		info = compiled_data->tensors.parameters[i]->info; // In case we loaded a different info.
2463		const int device_id = CCV_TENSOR_GET_DEVICE_ID(info.type)(((info.type) & 0xfff00) >> 8);
2464		for (j = 1; j < parallel_count; j++)
2465			if (!compiled_data->tensors.parameters[i + j * parameter_size])
2466			{
2467				if (j != device_id)
2468					CCV_TENSOR_SET_DEVICE_ID(info.type, j)(info.type) = (((info.type) & ~0xfff00) | (((j) & 0xfff
) << 8));
2469				else
2470					CCV_TENSOR_SET_DEVICE_ID(info.type, 0)(info.type) = (((info.type) & ~0xfff00) | (((0) & 0xfff
) << 8));
2471				compiled_data->tensors.parameters[i + j * parameter_size] = ccv_nnc_tensor_new(0, info, 0);
2472			}
2473			// No need to copy over, this is done in ccv_cnnp_model.c's copy_tensors method.
2474	}
2475	if (id_map)
2476		kh_destroy(ccv_cnnp_parameter_id, id_map)kh_destroy_ccv_cnnp_parameter_id(id_map);
2477	// Now do the transfer.
2478	if (copy_back)
2479	{
2480		for (i = 0; i < parameter_size; i++)
2481		{
2482			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));
2483			if (copy_back[i] == 0)
2484				continue;
2485			const int j = copy_back[i] - 1;
2486			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);
2487		}
2488		if (invalidates)
2489			for (i = 0; i < parameter_size; i++)
2490			{
2491				if (copy_back[i] == 0)
2492					continue;
2493				const int j = copy_back[i] - 1;
2494				ccv_nnc_tensor_free(tensors[j]);
2495				tensors[j] = 0;
2496			}
2497		ccfreefree(copy_back);
2498	}
2499}
2500 
2501ccv_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)
2502{
2503	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
2504	assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
 (compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 2504, __extension__ __PRETTY_FUNCTION__); }));
2505	const int parameter_size = compiled_data->parameters->rnum;
2506	int i;
2507	for (i = 0; i < parameter_size; i++)
2508	{
2509		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)));
2510		if (first(model, name, context))
2511			return ccv_cnnp_model_parameters(model, -1, i);
2512	}
2513	return 0;
2514}
2515 
2516ccv_array_t* ccv_cnnp_model_parameters_filter(ccv_cnnp_model_t* const model, ccv_cnnp_model_parameters_filter_f filter, void* const context)
2517{
2518	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
2519	assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
 (compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 2519, __extension__ __PRETTY_FUNCTION__); }));
2520	ccv_array_t* const parameters = ccv_array_new(sizeof(ccv_cnnp_model_io_t), 0, 0);
2521	const int parameter_size = compiled_data->parameters->rnum;
2522	int i;
2523	for (i = 0; i < parameter_size; i++)
2524	{
2525		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)));
2526		if (filter(model, name, context))
2527		{
2528			ccv_cnnp_model_io_t parameter = ccv_cnnp_model_parameters(model, -1, i);
2529			ccv_array_push(parameters, &parameter);
2530		}
2531	}
2532	return parameters;
2533 
2534}
2535 
2536CCV_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)
2537{
2538	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
2539	assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
 (compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 2539, __extension__ __PRETTY_FUNCTION__); }));
2540	const int tensors_init = !!compiled_data->tensors_init.v;
2541	if (!tensors_init) // If nothing initialized, we return parameter 0.
2542		return ccv_cnnp_model_parameters(model, -1, 0);
2543	const int parameter_size = compiled_data->parameters->rnum;
2544	int i;
2545	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));
2546	for (i = 0; i < parameter_size; i++)
2547	{
2548		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;
2549		if (!(init_v[d >> 5] & (1u << (d & 0x1f))))
2550			return ccv_cnnp_model_parameters(model, -1, i);
2551	}
2552	return 0;
2553}
2554 
2555static 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)
2556{
2557	const int to_param_sel = parameters->param_sel > 0 ? parameters->param_sel - 1 : parameters->param_sel;
2558	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", 2558, __extension__
 __PRETTY_FUNCTION__); }));
2559	ccv_array_t* const to_parameter_indices = ccv_array_new(sizeof(int), 0, 0);
2560	ccv_cnnp_model_add_to_parameter_indices(parameters->model, to_param_sel, to_parameter_indices);
2561	*param_ref = parameters->param_ref > 0 ? parameters->param_ref - 1 : parameters->param_ref;
2562	return to_parameter_indices;
2563}
2564 
2565static 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)
2566{
2567	// If the model is not compiled yet. Compile them now.
2568	if (!model->graph)
2569	{
2570		model->graph = ccv_nnc_symbolic_graph_new();
2571		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", 2571, __extension__ __PRETTY_FUNCTION__
); }));
2572		const int input_size = from_model->input_size;
2573		ccv_nnc_tensor_param_t input_params[input_size];
2574		int i;
2575		for (i = 0; i < input_size; i++)
2576			input_params[i] = ccv_nnc_tensor_symbol_params(from_model->graph, from_model->inputs[i]);
2577		_ccv_cnnp_model_compile(model, input_params, input_size, from_model->compiled_data->loss);
2578		model->parallel_count = from_model->parallel_count;
2579		model->memory_compression = from_model->memory_compression;
2580		model->memory_reduction = from_model->memory_reduction;
2581		model->gradient_checkpointing = from_model->gradient_checkpointing;
2582		model->compiled_data->stream_type = from_model->compiled_data->stream_type;
2583		model->compiled_data->minimize.minimizer = from_model->compiled_data->minimize.minimizer;
2584		model->compiled_data->minimize.max_saved_aux_size = from_model->compiled_data->minimize.max_saved_aux_size;
2585	}
2586	ccv_cnnp_compiled_data_t* const to_compiled_data = model->compiled_data;
2587	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", 2587, __extension__ __PRETTY_FUNCTION__
); }));
2588	const int to_tensors_init = !!to_compiled_data->tensors_init.v;
2589	if (!to_tensors_init)
2590	{
2591		if (only_init_0)
2592			ccv_cnnp_model_tensors_init_0(model, to_compiled_data);
2593		else
2594			_ccv_cnnp_model_tensors_init(model, to_compiled_data);
2595	} else if (!only_init_0 && (uintptr_t)to_compiled_data->tensors_init.v & (uintptr_t)1)
2596		// Check if it is not fully allocated, if it is not, init_1.
2597			ccv_cnnp_model_tensors_init_1(model, to_compiled_data);
2598	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", 2598, __extension__ __PRETTY_FUNCTION__
); }));
2599	*parameter_indices = _ccv_cnnp_model_parameter_indices(model, parameters, param_ref);
2600	*from_parameter_indices = _ccv_cnnp_model_parameter_indices(from_model, from_parameters, from_param_ref);
2601	if (*from_param_ref < 0 && *param_ref >= 0)
2602		{ 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", 2602, __extension__ __PRETTY_FUNCTION__
); })); }
2603	else if (*from_param_ref >= 0)
2604		{ 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", 2604, __extension__ __PRETTY_FUNCTION__
); })); }
2605	if (*param_ref < 0 && *from_param_ref >= 0)
2606		{ 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"
, 2606, __extension__ __PRETTY_FUNCTION__); })); }
2607	else if (*param_ref >= 0)
2608		{ 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", 2608, __extension__ __PRETTY_FUNCTION__
); })); }
2609}
2610 
2611void 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)
2612{
2613	ccv_array_t* to_parameter_indices;
2614	int to_param_ref;
2615	ccv_array_t* from_parameter_indices;
2616	int from_param_ref;
2617	_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);
2618	// Should be exactly the same tensor.
2619	if (to_param_ref < 0 && from_param_ref < 0)
2620		{ 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", 2620, __extension__ __PRETTY_FUNCTION__
); })); }
2621	// To models.
2622	ccv_cnnp_compiled_data_t* const to_compiled_data = model->compiled_data;
2623	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", 2623, __extension__ __PRETTY_FUNCTION__
); }));
2624	// From models.
2625	const ccv_cnnp_compiled_data_t* const from_compiled_data = from_model->compiled_data;
2626	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; });
2627	const int to_parameter_size = to_compiled_data->parameters->rnum;
2628	const int rnum = (to_param_ref < 0 && from_param_ref < 0) ? from_parameter_indices->rnum : 1;
2629	int i, j;
2630	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));
2631  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));
2632	for (i = 0; i < rnum; i++)
2633	{
2634		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)));
2635		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"
, 2635, __extension__ __PRETTY_FUNCTION__); }));
2636		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", 2636, __extension__ __PRETTY_FUNCTION__
); }));
2637		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;
2638		// If the original is not init'ed. We cannot copy from.
2639		if (!(from_init_v[s >> 5] & (1u << (s & 0x1f))))
2640			continue;
2641		const int dest_d = *(int*)ccv_array_get(to_parameter_indices, to_param_ref >= 0 ? to_param_ref : i)((void*)(((char*)((to_parameter_indices)->data)) + (size_t
)(to_parameter_indices)->rsize * (size_t)(to_param_ref >=
 0 ? to_param_ref : i)));
2642		assert(dest_d >= 0)((void) sizeof ((dest_d >= 0) ? 1 : 0), __extension__ ({ if
 (dest_d >= 0) ; else __assert_fail ("dest_d >= 0", "ccv_cnnp_model.c"
, 2642, __extension__ __PRETTY_FUNCTION__); }));
2643		assert(dest_d < to_compiled_data->parameters->rnum)((void) sizeof ((dest_d < to_compiled_data->parameters->
rnum) ? 1 : 0), __extension__ ({ if (dest_d < to_compiled_data
->parameters->rnum) ; else __assert_fail ("dest_d < to_compiled_data->parameters->rnum"
, "ccv_cnnp_model.c", 2643, __extension__ __PRETTY_FUNCTION__
); }));
2644		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));
2645		assert(src)((void) sizeof ((src) ? 1 : 0), __extension__ ({ if (src) ; else
 __assert_fail ("src", "ccv_cnnp_model.c", 2645, __extension__
 __PRETTY_FUNCTION__); }));
2646		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));
2647		assert(dest)((void) sizeof ((dest) ? 1 : 0), __extension__ ({ if (dest) ;
 else __assert_fail ("dest", "ccv_cnnp_model.c", 2647, __extension__
 __PRETTY_FUNCTION__); }));
2648		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);
2649		for (j = 1; j < parallel_count; j++)
2650		{
2651			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));
2652			if (copy_tensor)
2653				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);
2654		}
2655		// Mark this symbol as init'ed.
2656		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;
2657		to_init_v[d >> 5] |= (1u << (d & 0x1f));
2658	}
2659	ccv_array_free(to_parameter_indices);
2660	ccv_array_free(from_parameter_indices);
2661}
2662 
2663void 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)
2664{
2665	ccv_array_t* to_parameter_indices;
2666	int to_param_ref;
2667	ccv_array_t* from_parameter_indices;
2668	int from_param_ref;
2669	_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);
2670	// Should be exactly the same tensor.
2671	if (renamer == 0 && to_param_ref < 0 && from_param_ref < 0)
1
Assuming 'renamer' is not equal to null→
2672		{ 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", 2672, __extension__ __PRETTY_FUNCTION__
); })); }
2673	// To models.
2674	ccv_cnnp_compiled_data_t* const to_compiled_data = model->compiled_data;
2675	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", 2675, __extension__ __PRETTY_FUNCTION__
); }));
2
←
Assuming 'to_compiled_data' is non-null→
3
←
Taking true branch→
2676	// From models.
2677	const ccv_cnnp_compiled_data_t* const from_compiled_data = from_model->compiled_data;
2678	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→
2679	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", 2679, __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→
2680	const int from_parameter_size = from_compiled_data->parameters->rnum;
2681	const int to_parameter_size = to_compiled_data->parameters->rnum;
2682	const int rnum = (to_param_ref < 0 && from_param_ref < 0) ? to_parameter_indices->rnum : 1;
9
←
Assuming 'to_param_ref' is >= 0→
2683	int i, j;
2684	khash_t(ccv_cnnp_parameter_id)kh_ccv_cnnp_parameter_id_t* id_map = 0;
2685	char* updated_name = 0;
2686	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));
2687  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));
2688	for (i = 0; i < rnum; i++)
2689	{
2690		int src_d = (from_param_ref >= 0 ? from_param_ref : i) < from_parameter_indices->rnum ? *(int*)ccv_array_get(from_parameter_indices,from_param_ref >= 0 ? from_param_ref : i)((void*)(((char*)((from_parameter_indices)->data)) + (size_t
)(from_parameter_indices)->rsize * (size_t)(from_param_ref
 >= 0 ? from_param_ref : i))) : from_parameter_size;
10
←
Assuming 'from_param_ref' is < 0→
11
←
'?' condition is false→
12
←
Assuming the condition is false→
13
←
'?' condition is false→
2691		// Need to figure out how to use the renamer here.
2692		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→
2693		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"
, 2693, __extension__ __PRETTY_FUNCTION__); }));
15
←
Assuming 'dest_d' is >= 0→
16
←
Taking true branch→
2694		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", 2694, __extension__
 __PRETTY_FUNCTION__); }));
17
←
Assuming 'dest_d' is < 'to_parameter_size'→
18
←
Taking true branch→
2695		if (renamer18.1
'renamer' is non-null
)
2696		{
2697			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;
2698			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)));
2699			if (!updated_name18.3
'updated_name' is null
)
19
←
Taking true branch→
2700				updated_name = (char*)ccmallocmalloc(1024);
2701			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→
2702			if (src_name_len20.1
'src_name_len' is <= 0
 > 0)
21
←
Taking false branch→
2703				memcpy(updated_name, src_name, src_name_len);
2704			updated_name[src_name_len] = 0;
2705			if (renamer(context, dest_name, updated_name, 1024) != 0)
22
←
Assuming the condition is false→
2706				continue; // Skip this.
2707			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)
2708			{
2709				// Nothing changed.
2710			} else {
2711				if (!id_map22.2
'id_map' is null
)
23
←
Taking true branch→
2712				{
2713					id_map = kh_init(ccv_cnnp_parameter_id)kh_init_ccv_cnnp_parameter_id();
2714					for (j = 0; j < from_parameter_size; j++)
24
←
Assuming 'j' is < 'from_parameter_size'→
25
←
Loop condition is true.  Entering loop body→
2715					{
2716						int ret;
2717						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);
26
←
Calling 'kh_put_ccv_cnnp_parameter_id'→
2718						assert(ret != 0)((void) sizeof ((ret != 0) ? 1 : 0), __extension__ ({ if (ret
 != 0) ; else __assert_fail ("ret != 0", "ccv_cnnp_model.c", 2718
, __extension__ __PRETTY_FUNCTION__); }));
2719						kh_val(id_map, k)((id_map)->vals[k]) = j;
2720					}
2721				}
2722				const khiter_t k = kh_get(ccv_cnnp_parameter_id, id_map, updated_name)kh_get_ccv_cnnp_parameter_id(id_map, updated_name);
2723				if (k == kh_end(id_map)((id_map)->n_buckets)) // Cannot find the name, skip.
2724					continue;
2725				src_d = kh_val(id_map, k)((id_map)->vals[k]);
2726				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"
, 2726, __extension__ __PRETTY_FUNCTION__); }));
2727				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", 2727, __extension__
 __PRETTY_FUNCTION__); }));
2728			}
2729		}
2730		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"
, 2730, __extension__ __PRETTY_FUNCTION__); }));
2731		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", 2731, __extension__
 __PRETTY_FUNCTION__); }));
2732		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;
2733		// If the original is not init'ed. We cannot share from.
2734		if (!(from_init_v[s >> 5] & (1u << (s & 0x1f))))
2735			continue;
2736		for (j = 0; j < parallel_count; j++)
2737		{
2738			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));
2739			assert(src)((void) sizeof ((src) ? 1 : 0), __extension__ ({ if (src) ; else
 __assert_fail ("src", "ccv_cnnp_model.c", 2739, __extension__
 __PRETTY_FUNCTION__); }));
2740			ccv_nnc_tensor_t* const dest = to_compiled_data->tensors.parameters[dest_d + j * to_parameter_size];
2741			if (dest && !((uintptr_t)dest & (uintptr_t)1))
2742				ccv_nnc_tensor_free(dest);
2743			to_compiled_data->tensors.parameters[dest_d + j * to_parameter_size] = (ccv_nnc_tensor_t*)((uintptr_t)src | (uintptr_t)1);
2744		}
2745		// Mark this symbol as init'ed.
2746		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;
2747		to_init_v[d >> 5] |= (1u << (d & 0x1f));
2748	}
2749	ccv_array_free(to_parameter_indices);
2750	ccv_array_free(from_parameter_indices);
2751	if (id_map)
2752		kh_destroy(ccv_cnnp_parameter_id, id_map)kh_destroy_ccv_cnnp_parameter_id(id_map);
2753	if (updated_name)
2754		ccfreefree(updated_name);
2755	// Mark it as incomplete so we will call init_1.
2756	if (ccv_cnnp_model_tensors_any_to_alloc(model, to_compiled_data))
2757		to_compiled_data->tensors_init.v = (uint32_t*)((uintptr_t)to_compiled_data->tensors_init.v | (uintptr_t)1);
2758	else // Remove the flag.
2759		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));
2760}
2761 
2762ccv_nnc_stream_context_t* ccv_cnnp_compiled_data_get_stream(ccv_cnnp_compiled_data_t* const compiled_data, const int type)
2763{
2764	if (!compiled_data->stream_map)
2765		compiled_data->stream_map = kh_init(stream_map)kh_init_stream_map();
2766	int ret = 0;
2767	khiter_t k = kh_put(stream_map, compiled_data->stream_map, type, &ret)kh_put_stream_map(compiled_data->stream_map, type, &ret
);
2768	assert(ret >= 0)((void) sizeof ((ret >= 0) ? 1 : 0), __extension__ ({ if (
ret >= 0) ; else __assert_fail ("ret >= 0", "ccv_cnnp_model.c"
, 2768, __extension__ __PRETTY_FUNCTION__); }));
2769	ccv_nnc_stream_context_t* stream = kh_val(compiled_data->stream_map, k)((compiled_data->stream_map)->vals[k]);
2770	// If ret == 0, the key already exist, we can return directly, otherwise, create and return.
2771	if (ret != 0)
2772	{
2773		stream = ccv_nnc_stream_context_new(type);
2774		kh_val(compiled_data->stream_map, k)((compiled_data->stream_map)->vals[k]) = stream;
2775	}
2776	return stream;
2777}
2778 
2779void 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)
2780{
2781	ccv_array_t* to_parameter_indices;
2782	int to_param_ref;
2783	ccv_array_t* from_parameter_indices;
2784	int from_param_ref;
2785	_ccv_cnnp_model_to_parameter_indices_and_from_parameter_indices(model, parameters, from_model, from_parameters, &to_parameter_indices, &to_param_ref, &from_parameter_indices, &from_param_ref, 0);
2786	// Should be exactly the same tensor.
2787	if (to_param_ref < 0 && from_param_ref < 0)
2788		{ assert(from_parameter_indices->rnum == to_parameter_indices->rnum)((void) sizeof ((from_parameter_indices->rnum == to_parameter_indices
->rnum) ? 1 : 0), __extension__ ({ if (from_parameter_indices
->rnum == to_parameter_indices->rnum) ; else __assert_fail
 ("from_parameter_indices->rnum == to_parameter_indices->rnum"
, "ccv_cnnp_model.c", 2788, __extension__ __PRETTY_FUNCTION__
); })); }
2789	// To models.
2790	ccv_cnnp_compiled_data_t* const to_compiled_data = model->compiled_data;
2791	assert(to_compiled_data)((void) sizeof ((to_compiled_data) ? 1 : 0), __extension__ ({
 if (to_compiled_data) ; else __assert_fail ("to_compiled_data"
, "ccv_cnnp_model.c", 2791, __extension__ __PRETTY_FUNCTION__
); }));
2792	// From models.
2793	const ccv_cnnp_compiled_data_t* const from_compiled_data = from_model->compiled_data;
2794	const int parallel_count = ccv_max(model->parallel_count, 1)({ typeof (model->parallel_count) _a = (model->parallel_count
); typeof (1) _b = (1); (_a > _b) ? _a : _b; });
2795	const int to_parameter_size = to_compiled_data->parameters->rnum;
2796	const int rnum = (to_param_ref < 0 && from_param_ref < 0) ? from_parameter_indices->rnum : 1;
2797	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", 2797, __extension__ __PRETTY_FUNCTION__
); }));
2798	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", 2798, __extension__ __PRETTY_FUNCTION__
); }));
2799	int i, j;
2800	ccv_nnc_tensor_t* inputs[aux_in_size + 2];
2801	ccv_nnc_tensor_t* outputs[aux_out_size + 1];
2802	for (i = 0; i < aux_in_size; i++)
2803		inputs[i + 2] = aux_ins[i];
2804	for (i = 0; i < aux_out_size; i++)
2805		outputs[i + 1] = aux_outs[i];
2806	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));
2807  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));
2808	for (i = 0; i < rnum; i++)
2809	{
2810		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)));
2811		assert(src_d >= 0)((void) sizeof ((src_d >= 0) ? 1 : 0), __extension__ ({ if
 (src_d >= 0) ; else __assert_fail ("src_d >= 0", "ccv_cnnp_model.c"
, 2811, __extension__ __PRETTY_FUNCTION__); }));
2812		assert(src_d < from_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", 2812, __extension__ __PRETTY_FUNCTION__
); }));
2813		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;
2814		// If the original is not init'ed. We cannot copy from.
2815		if (!(from_init_v[s >> 5] & (1u << (s & 0x1f))))
2816			continue;
2817		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)));
2818		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"
, 2818, __extension__ __PRETTY_FUNCTION__); }));
2819		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", 2819, __extension__ __PRETTY_FUNCTION__
); }));
2820		if (parallel_count > 1)
2821		{
2822			ccv_nnc_stream_context_t* streams[parallel_count];
2823			ccv_nnc_stream_signal_t* signal;
2824			if (stream_context)
2825				signal = ccv_nnc_stream_context_emit_signal_new(stream_context);
2826			for (j = 0; j < parallel_count; j++)
2827			{
2828				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));
2829				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));
2830				if (!dest || !src)
2831				{
2832					streams[j] = 0;
2833					continue;
2834				}
2835				// At the moment, can only handle them on the same device.
2836				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", 2836, __extension__ __PRETTY_FUNCTION__
); }));
2837				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", 2837, __extension__ __PRETTY_FUNCTION__
); }));
2838				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;
2839				const int device_id = CCV_TENSOR_GET_DEVICE_ID(src->info.type)(((src->info.type) & 0xfff00) >> 8);
2840				int type = stream_type;
2841				CCV_STREAM_SET_DEVICE_ID(type, device_id)(type) = (((type) & ~0xfff00) | (((device_id) & 0xfff
) << 8));
2842				ccv_nnc_stream_context_t* const stream_0 = ccv_cnnp_compiled_data_get_stream(to_compiled_data, type);
2843				// Wait signal to finish.
2844				if (stream_context)
2845					ccv_nnc_stream_context_wait_signal(stream_0, signal);
2846				inputs[0] = outputs[0] = dest;
2847				inputs[1] = src;
2848				ccv_nnc_cmd_exec(cmd, hint, flags, inputs, aux_in_size + 2, outputs, aux_out_size + 1, stream_0);
2849				if (stream_context)
2850				{
2851					ccv_nnc_stream_signal_t* const signal = ccv_nnc_stream_context_emit_signal_new(stream_0);
2852					ccv_nnc_stream_context_wait_signal(stream_context, signal);
2853				}
2854				streams[j] = stream_0;
2855			}
2856			// If this should be blocking, blocking it.
2857			if (!stream_context)
2858				for (j = 0; j < parallel_count; j++)
2859					if (streams[j])
2860						ccv_nnc_stream_context_wait(streams[j]);
2861		} else {
2862			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));
2863			assert(src)((void) sizeof ((src) ? 1 : 0), __extension__ ({ if (src) ; else
 __assert_fail ("src", "ccv_cnnp_model.c", 2863, __extension__
 __PRETTY_FUNCTION__); }));
2864			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));
2865			assert(dest)((void) sizeof ((dest) ? 1 : 0), __extension__ ({ if (dest) ;
 else __assert_fail ("dest", "ccv_cnnp_model.c", 2865, __extension__
 __PRETTY_FUNCTION__); }));
2866			inputs[0] = outputs[0] = dest;
2867			inputs[1] = src;
2868			ccv_nnc_cmd_exec(cmd, hint, flags, inputs, aux_in_size + 2, outputs, aux_out_size + 1, stream_context);
2869		}
2870		// Mark this symbol as init'ed.
2871		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;
2872		to_init_v[d >> 5] |= (1u << (d & 0x1f));
2873	}
2874	ccv_array_free(to_parameter_indices);
2875	ccv_array_free(from_parameter_indices);
2876}
2877 
2878void 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)
2879{
2880	int to_param_ref;
2881	ccv_array_t* const to_parameter_indices = _ccv_cnnp_model_parameter_indices(model, parameters, &to_param_ref);
2882	// To models.
2883	ccv_cnnp_compiled_data_t* const to_compiled_data = model->compiled_data;
2884	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", 2884, __extension__ __PRETTY_FUNCTION__
); }));
2885	// Tensor has to be inited already.
2886	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", 2886, __extension__ __PRETTY_FUNCTION__
); }));
2887	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", 2887, __extension__ __PRETTY_FUNCTION__
); }));
2888	// From models.
2889	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; });
2890	const int to_parameter_size = to_compiled_data->parameters->rnum;
2891	const int rnum = (to_param_ref < 0) ? to_parameter_indices->rnum : 1;
2892	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", 2892, __extension__ __PRETTY_FUNCTION__
); }));
2893	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", 2893, __extension__ __PRETTY_FUNCTION__
); }));
2894	int i, j;
2895	ccv_nnc_tensor_t* inputs[aux_in_size + 1];
2896	ccv_nnc_tensor_t* outputs[aux_out_size + 1];
2897	for (i = 0; i < aux_in_size; i++)
2898		inputs[i + 1] = aux_ins[i];
2899	for (i = 0; i < aux_out_size; i++)
2900		outputs[i + 1] = aux_outs[i];
2901	for (i = 0; i < rnum; i++)
2902	{
2903		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)));
2904		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"
, 2904, __extension__ __PRETTY_FUNCTION__); }));
2905		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", 2905, __extension__ __PRETTY_FUNCTION__
); }));
2906		if (parallel_count > 1)
2907		{
2908			ccv_nnc_stream_context_t* streams[parallel_count];
2909			ccv_nnc_stream_signal_t* signal;
2910			if (stream_context)
2911				signal = ccv_nnc_stream_context_emit_signal_new(stream_context);
2912			for (j = 0; j < parallel_count; j++)
2913			{
2914				ccv_nnc_tensor_t* const dest = CCV_NNC_TENSOR(to_compiled_data->tensors.parameters[dest_d + j * to_parameter_size])((ccv_nnc_tensor_t*)((uintptr_t)(to_compiled_data->tensors
.parameters[dest_d + j * to_parameter_size]) & ~(uintptr_t
)1));
2915				if (!dest)
2916				{
2917					streams[j] = 0;
2918					continue;
2919				}
2920				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;
2921				const int device_id = CCV_TENSOR_GET_DEVICE_ID(dest->info.type)(((dest->info.type) & 0xfff00) >> 8);
2922				int type = stream_type;
2923				CCV_STREAM_SET_DEVICE_ID(type, device_id)(type) = (((type) & ~0xfff00) | (((device_id) & 0xfff
) << 8));
2924				ccv_nnc_stream_context_t* const stream_0 = ccv_cnnp_compiled_data_get_stream(to_compiled_data, type);
2925				// Wait signal to finish.
2926				if (stream_context)
2927					ccv_nnc_stream_context_wait_signal(stream_0, signal);
2928				inputs[0] = outputs[0] = dest;
2929				ccv_nnc_cmd_exec(cmd, hint, flags, inputs, aux_in_size + 1, outputs, aux_out_size + 1, stream_0);
2930				if (stream_context)
2931				{
2932					ccv_nnc_stream_signal_t* const signal = ccv_nnc_stream_context_emit_signal_new(stream_0);
2933					ccv_nnc_stream_context_wait_signal(stream_context, signal);
2934				}
2935				streams[j] = stream_0;
2936			}
2937			// If this should be blocking, blocking it.
2938			if (!stream_context)
2939				for (j = 0; j < parallel_count; j++)
2940					if (streams[j])
2941						ccv_nnc_stream_context_wait(streams[j]);
2942		} else {
2943			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));
2944			assert(dest)((void) sizeof ((dest) ? 1 : 0), __extension__ ({ if (dest) ;
 else __assert_fail ("dest", "ccv_cnnp_model.c", 2944, __extension__
 __PRETTY_FUNCTION__); }));
2945			inputs[0] = outputs[0] = dest;
2946			ccv_nnc_cmd_exec(cmd, hint, flags, inputs, aux_in_size + 1, outputs, aux_out_size + 1, stream_context);
2947		}
2948		// No need to mark this symbol as init'ed, it is already.
2949	}
2950	ccv_array_free(to_parameter_indices);
2951}
2952 
2953void 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)
2954{
2955	int to_param_ref;
2956	ccv_array_t* const to_parameter_indices = _ccv_cnnp_model_parameter_indices(model, parameters, &to_param_ref);
2957	// To models.
2958	ccv_cnnp_compiled_data_t* const to_compiled_data = model->compiled_data;
2959	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", 2959, __extension__ __PRETTY_FUNCTION__
); }));
2960	// Tensor has to be inited already.
2961	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", 2961, __extension__ __PRETTY_FUNCTION__
); }));
2962	ccv_nnc_tensor_t** tensor_gradients;
2963	if (to_compiled_data->backward.count > 1)
2964		tensor_gradients = to_compiled_data->tensors.accum_gradients;
2965	else
2966		tensor_gradients = to_compiled_data->tensors.gradients;
2967	assert(tensor_gradients)((void) sizeof ((tensor_gradients) ? 1 : 0), __extension__ ({
 if (tensor_gradients) ; else __assert_fail ("tensor_gradients"
, "ccv_cnnp_model.c", 2967, __extension__ __PRETTY_FUNCTION__
); }));
2968	// From models.
2969	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; });
2970	const int to_parameter_size = to_compiled_data->parameters->rnum;
2971	const int rnum = (to_param_ref < 0) ? to_parameter_indices->rnum : 1;
2972	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", 2972, __extension__ __PRETTY_FUNCTION__
); }));
2973	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", 2973, __extension__ __PRETTY_FUNCTION__
); }));
2974	int i, j;
2975	ccv_nnc_tensor_t* inputs[aux_in_size + 1];
2976	ccv_nnc_tensor_t* outputs[aux_out_size + 1];
2977	for (i = 0; i < aux_in_size; i++)
2978		inputs[i + 1] = aux_ins[i];
2979	for (i = 0; i < aux_out_size; i++)
2980		outputs[i + 1] = aux_outs[i];
2981	for (i = 0; i < rnum; i++)
2982	{
2983		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)));
2984		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"
, 2984, __extension__ __PRETTY_FUNCTION__); }));
2985		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", 2985, __extension__ __PRETTY_FUNCTION__
); }));
2986		if (parallel_count > 1)
2987		{
2988			ccv_nnc_stream_context_t* streams[parallel_count];
2989			ccv_nnc_stream_signal_t* signal;
2990			if (stream_context)
2991				signal = ccv_nnc_stream_context_emit_signal_new(stream_context);
2992			for (j = 0; j < parallel_count; j++)
2993			{
2994				ccv_nnc_tensor_t* const dest = tensor_gradients[dest_d + j * to_parameter_size];
2995				if (!dest)
2996				{
2997					streams[j] = 0;
2998					continue;
2999				}
3000				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;
3001				const int device_id = CCV_TENSOR_GET_DEVICE_ID(dest->info.type)(((dest->info.type) & 0xfff00) >> 8);
3002				int type = stream_type;
3003				CCV_STREAM_SET_DEVICE_ID(type, device_id)(type) = (((type) & ~0xfff00) | (((device_id) & 0xfff
) << 8));
3004				ccv_nnc_stream_context_t* const stream_0 = ccv_cnnp_compiled_data_get_stream(to_compiled_data, type);
3005				// Wait signal to finish.
3006				if (stream_context)
3007					ccv_nnc_stream_context_wait_signal(stream_0, signal);
3008				inputs[0] = outputs[0] = dest;
3009				ccv_nnc_cmd_exec(cmd, hint, flags, inputs, aux_in_size + 1, outputs, aux_out_size + 1, stream_0);
3010				if (stream_context)
3011				{
3012					ccv_nnc_stream_signal_t* const signal = ccv_nnc_stream_context_emit_signal_new(stream_0);
3013					ccv_nnc_stream_context_wait_signal(stream_context, signal);
3014				}
3015				streams[j] = stream_0;
3016			}
3017			// If this should be blocking, blocking it.
3018			if (!stream_context)
3019				for (j = 0; j < parallel_count; j++)
3020					if (streams[j])
3021						ccv_nnc_stream_context_wait(streams[j]);
3022		} else {
3023			ccv_nnc_tensor_t* const dest = tensor_gradients[dest_d];
3024			if (!dest)
3025				continue;
3026			assert(dest)((void) sizeof ((dest) ? 1 : 0), __extension__ ({ if (dest) ;
 else __assert_fail ("dest", "ccv_cnnp_model.c", 3026, __extension__
 __PRETTY_FUNCTION__); }));
3027			inputs[0] = outputs[0] = dest;
3028			ccv_nnc_cmd_exec(cmd, hint, flags, inputs, aux_in_size + 1, outputs, aux_out_size + 1, stream_context);
3029		}
3030		// No need to mark this symbol as init'ed, it is already.
3031	}
3032	ccv_array_free(to_parameter_indices);
3033}
3034 
3035ccv_nnc_cmd_t ccv_cnnp_model_minimizer(ccv_cnnp_model_t* const model)
3036{
3037	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
3038	assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
 (compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 3038, __extension__ __PRETTY_FUNCTION__); }));
3039	return compiled_data->minimize.minimizer;
3040}
3041 
3042void 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)
3043{
3044	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
3045	assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
 (compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 3045, __extension__ __PRETTY_FUNCTION__); }));
3046	const int parameter_size = compiled_data->parameters->rnum;
3047	if (parameter_size == 0)
3048		return;
3049	if (reset)
3050		{ 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", 3050, __extension__ __PRETTY_FUNCTION__
); })); }
3051	const int old_max_saved_aux_size = compiled_data->minimize.max_saved_aux_size;
3052	const int saved_aux_size = ccv_nnc_minimizer_saved_aux_size(minimizer);
3053	if (saved_aux_size > compiled_data->minimize.max_saved_aux_size)
3054		compiled_data->minimize.max_saved_aux_size = saved_aux_size;
3055	const int max_saved_aux_size = compiled_data->minimize.max_saved_aux_size;
3056	// We update all parameters, at this point, we have one minimizer.
3057	if (set_parameters == 0 || set_parameter_size == 0)
3058		compiled_data->minimize.minimizer = minimizer;
3059	int i;
3060	if (set_parameters && set_parameter_size)
3061	{
3062		// I need to save what's the minimizer along with this.
3063		if (!compiled_data->minimize.parameters)
3064			compiled_data->minimize.parameters = ccv_array_new(sizeof(ccv_cnnp_set_minimizer_for_parameter_t*), 1, 0);
3065		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));
3066		set_minimizer_for_parameter->minimizer = minimizer;
3067		set_minimizer_for_parameter->parameter_size = set_parameter_size;
3068		memcpy(set_minimizer_for_parameter->parameters, set_parameters, sizeof(ccv_cnnp_model_io_t) * set_parameter_size);
3069		ccv_array_push(compiled_data->minimize.parameters, &set_minimizer_for_parameter);
3070	}
3071	// If reset is true, clear the parameters array.
3072	if (reset && compiled_data->minimize.parameters)
3073	{
3074		for (i = 0; i < compiled_data->minimize.parameters->rnum; i++)
3075			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))));
3076		ccv_array_clear(compiled_data->minimize.parameters);
3077	}
3078	if (!compiled_data->update_nodes)
3079		return;
3080	ccv_nnc_symbolic_graph_t* const symbolic_graph = model->graph;
3081	assert(symbolic_graph)((void) sizeof ((symbolic_graph) ? 1 : 0), __extension__ ({ if
 (symbolic_graph) ; else __assert_fail ("symbolic_graph", "ccv_cnnp_model.c"
, 3081, __extension__ __PRETTY_FUNCTION__); }));
3082	if (saved_aux_size > old_max_saved_aux_size)
3083	{
3084		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", 3084, __extension__ __PRETTY_FUNCTION__
); }));
3085		// Reallocate first, move them around later.
3086		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);
3087		compiled_data->update_nodes = (ccv_nnc_graph_exec_symbol_t*)(compiled_data->updated_parameters + parameter_size);
3088		compiled_data->saved_aux = (ccv_nnc_tensor_symbol_map_t*)(compiled_data->update_nodes + parameter_size);
3089		// We need to do this from back to front because saved_aux_size > old_saved_aux_size, it could overlap.
3090		_ccv_cnnp_scatter_saved_aux(compiled_data->saved_aux, parameter_size, old_max_saved_aux_size, saved_aux_size);
3091	}
3092	int flag = 0;
3093	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; });
3094	if (set_parameters && set_parameter_size)
3095	{
3096		ccv_array_t* const parameter_indices = ccv_array_new(sizeof(int), 0, 0);
3097		for (i = 0; i < set_parameter_size; i++)
3098		{
3099			const int param_sel = set_parameters[i]->param_sel > 0 ? set_parameters[i]->param_sel - 1 : set_parameters[i]->param_sel;
3100			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", 3100, __extension__ __PRETTY_FUNCTION__
); }));
3101			const int old_rnum = parameter_indices->rnum;
3102			ccv_cnnp_model_add_to_parameter_indices(set_parameters[i]->model, param_sel, parameter_indices);
3103			const int param_ref = set_parameters[i]->param_ref > 0 ? set_parameters[i]->param_ref - 1 : set_parameters[i]->param_ref;
3104			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", 3104, __extension__ __PRETTY_FUNCTION__
); }));
3105			if (param_ref >= 0)
3106			{
3107				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", 3107, __extension__ __PRETTY_FUNCTION__
); }));
3108				*(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)));
3109				parameter_indices->rnum = old_rnum + 1;
3110			}
3111		}
3112		// We may have duplicated indices, but that is OK, we will set it twice.
3113		for (i = 0; i < parameter_indices->rnum; i++)
3114		{
3115			const int d = *(int*)ccv_array_get(parameter_indices, i)((void*)(((char*)((parameter_indices)->data)) + (size_t)(parameter_indices
)->rsize * (size_t)(i)));
3116			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))
3117				flag = 1;
3118		}
3119		ccv_array_free(parameter_indices);
3120	} else {
3121		for (i = 0; i < parameter_size; i++)
3122			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))
3123				flag = 1;
3124		if (compiled_data->minimize.parameters)
3125			if (_ccv_cnnp_apply_parameters_with_minimizer(model))
3126				flag = 1;
3127	}
3128	if (flag)
3129	{
3130		// 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.
3131		if (compiled_data->graph_mode == CCV_CNNP_MODEL_GRAPH_FIT_MODE)
3132			_ccv_cnnp_compiled_data_graph_free(compiled_data);
3133		_ccv_cnnp_compiled_data_apply_gradients_free(compiled_data);
3134	}
3135}
3136 
3137void ccv_cnnp_model_set_compile_params(ccv_cnnp_model_t* const model, const ccv_nnc_symbolic_graph_compile_param_t compile_params)
3138{
3139	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
3140	assert(compiled_data)((void) sizeof ((compiled_data) ? 1 : 0), __extension__ ({ if
 (compiled_data) ; else __assert_fail ("compiled_data", "ccv_cnnp_model.c"
, 3140, __extension__ __PRETTY_FUNCTION__); }));
3141	compiled_data->compile_params = compile_params;
3142}
3143 
3144void ccv_cnnp_model_dot(const ccv_cnnp_model_t* const model, const int flags, FILE** const outs, const int out_size)
3145{
3146	if (model->graph && out_size > 0)
3147		ccv_nnc_symbolic_graph_dot(model->graph, flags, outs[0]);
3148	if (model->compiled_data && model->compiled_data->graph && out_size > 1)
3149		ccv_nnc_graph_dot(model->compiled_data->graph, flags, outs[1]);
3150	if (model->compiled_data && model->compiled_data->backward.accum && out_size > 2)
3151		ccv_nnc_graph_dot(model->compiled_data->backward.accum, flags, outs[2]);
3152	if (model->compiled_data && model->compiled_data->apply_gradients.graph && out_size > 3)
3153		ccv_nnc_graph_dot(model->compiled_data->apply_gradients.graph, flags, outs[3]);
3154}
3155 
3156void ccv_cnnp_model_format(const ccv_cnnp_model_t* const model, const ccv_nnc_symbolic_graph_format_f format_fn, void* const context)
3157{
3158	if (model->graph)
3159		ccv_nnc_symbolic_graph_format(model->graph, 0, 0, 0, 0, format_fn, context);
3160}
3161 
3162static void _ccv_cnnp_compiled_data_free(const ccv_cnnp_model_t* const model, ccv_cnnp_compiled_data_t* const compiled_data)
3163{
3164	int i;
3165	const int parameter_size = compiled_data->parameters->rnum;
3166	ccv_array_free(compiled_data->parameters);
3167	if (compiled_data->parameter_flags)
3168		ccfreefree(compiled_data->parameter_flags);
3169	const int internal_size = compiled_data->internals->rnum;
3170	ccv_array_free(compiled_data->internals);
3171	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", 3171, __extension__ __PRETTY_FUNCTION__
); }));
3172	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", 3172, __extension__ __PRETTY_FUNCTION__
); }));
3173	for (i = 0; i < parameter_size; i++)
3174		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))));
3175	ccv_array_free(compiled_data->ids.parameters);
3176	for (i = 0; i < internal_size; i++)
3177		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))));
3178	ccv_array_free(compiled_data->ids.internals);
3179	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; });
3180	if (compiled_data->tensors.parameters)
3181	{
3182		for (i = 0; i < parameter_size * parallel_count; i++)
3183			// If it is not marked as not belonging, we can free it.
3184			if (!((uintptr_t)compiled_data->tensors.parameters[i] & (uintptr_t)1))
3185				if (compiled_data->tensors.parameters[i])
3186					ccv_nnc_tensor_free(compiled_data->tensors.parameters[i]);
3187		for (i = 0; i < internal_size * parallel_count; i++)
3188			if (compiled_data->tensors.internals[i])
3189				ccv_nnc_tensor_free(compiled_data->tensors.internals[i]);
3190		ccfreefree(compiled_data->tensors.parameters);
3191	}
3192	if (compiled_data->tensors.gradients)
3193	{
3194		for (i = 0; i < parameter_size * parallel_count; i++)
3195		{
3196			if (compiled_data->tensors.gradients[i])
3197				ccv_nnc_tensor_free(compiled_data->tensors.gradients[i]);
3198			if (compiled_data->tensors.accum_gradients[i])
3199				ccv_nnc_tensor_free(compiled_data->tensors.accum_gradients[i]);
3200		}
3201		ccfreefree(compiled_data->tensors.gradients);
3202	}
3203	if (compiled_data->minimize.parameters)
3204	{
3205		for (i = 0; i < compiled_data->minimize.parameters->rnum; i++)
3206			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))));
3207		ccv_array_free(compiled_data->minimize.parameters);
3208	}
3209	if (compiled_data->rewindables)
3210		ccv_array_free(compiled_data->rewindables);
3211	if (compiled_data->tensors_init.v)
3212		ccfreefree(CCV_NNC_INIT_V(compiled_data->tensors_init.v)((uint32_t*)((uintptr_t)(compiled_data->tensors_init.v) &
 ~(uintptr_t)1)));
3213	if (compiled_data->evaluate.tos)
3214		ccfreefree(compiled_data->evaluate.tos);
3215	compiled_data->evaluate.tos = 0;
3216	if (compiled_data->stream_map)
3217	{
3218		khiter_t k;
3219		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)
3220		{
3221			if (!kh_exist(compiled_data->stream_map, k)(!(((compiled_data->stream_map)->flags[(k)>>4]>>
(((k)&0xfU)<<1))&3)))
3222				continue;
3223			ccv_nnc_stream_context_t* const stream = kh_val(compiled_data->stream_map, k)((compiled_data->stream_map)->vals[k]);
3224			ccv_nnc_stream_context_free(stream);
3225		}
3226		kh_destroy(stream_map, compiled_data->stream_map)kh_destroy_stream_map(compiled_data->stream_map);
3227	}
3228	_ccv_cnnp_compiled_data_graph_free(compiled_data);
3229	_ccv_cnnp_compiled_data_gradient_free(compiled_data);
3230	_ccv_cnnp_compiled_data_backward_free(compiled_data);
3231	_ccv_cnnp_compiled_data_apply_gradients_free(compiled_data);
3232	if (compiled_data->gradient_checkpoints)
3233	{
3234		for (i = 0; i < compiled_data->gradient_checkpoints->rnum; i++)
3235		{
3236			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)));
3237			assert(checkpoint->inputs)((void) sizeof ((checkpoint->inputs) ? 1 : 0), __extension__
 ({ if (checkpoint->inputs) ; else __assert_fail ("checkpoint->inputs"
, "ccv_cnnp_model.c", 3237, __extension__ __PRETTY_FUNCTION__
); }));
3238			ccfreefree(checkpoint->inputs);
3239			ccv_array_free(checkpoint->tensor_symbols);
3240		}
3241		ccv_array_free(compiled_data->gradient_checkpoints);
3242	}
3243	ccv_nnc_xpu_alloc_destroy(&compiled_data->xpu_alloc);
3244	ccfreefree(compiled_data);
3245}
3246 
3247void ccv_cnnp_model_free(ccv_cnnp_model_t* const model)
3248{
3249	ccv_cnnp_model_deinit(model);
3250	if (model->isa->dealloc)
3251		model->isa->dealloc(model);
3252	if (model->io)
3253	{
3254		int i;
3255		for (i = 0; i < model->io->rnum; i++)
3256		{
3257			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)));
3258			if (model_io->outgoings)
3259				ccv_array_free(model_io->outgoings);
3260			if (model_io->incomings)
3261				ccv_array_free(model_io->incomings);
3262			if (model_io->dependencies)
3263				ccv_array_free(model_io->dependencies);
3264			ccfreefree(model_io);
3265		}
3266		ccv_array_free(model->io);
3267	}
3268	if (model->parameter_indices)
3269		ccv_array_free(model->parameter_indices);
3270	if (model->inputs)
3271		ccfreefree(model->inputs);
3272	if (model->graph)
3273		ccv_nnc_symbolic_graph_free(model->graph);
3274	if (model->compiled_data)
3275		_ccv_cnnp_compiled_data_free(model, model->compiled_data);
3276	if (model->name)
3277		ccfreefree(model->name);
3278	ccfreefree(model);
3279}
3280 
3281void ccv_cnnp_model_cancel(ccv_cnnp_model_t* const model)
3282{
3283	ccv_cnnp_compiled_data_t* const compiled_data = model->compiled_data;
3284	if (!compiled_data)
3285		return;
3286	if (compiled_data->graph)
3287		ccv_nnc_graph_cancel(compiled_data->graph);
3288	if (compiled_data->apply_gradients.graph)
3289		ccv_nnc_graph_cancel(compiled_data->apply_gradients.graph);
3290}
3291 
3292void ccv_cnnp_model_set_flags(ccv_cnnp_model_t* const model, const int flags)
3293{
3294	model->exec_flags = flags;
3295}
3296 
3297int ccv_cnnp_model_flags(ccv_cnnp_model_t* const model)
3298{
3299	return model->exec_flags;
3300}