ccv_nnc_micro

Bug Summary

File:	nnc/ccv_nnc_micro_core.c
Warning:	line 587, column 40 Potential memory leak

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_nnc_micro_core.c -analyzer-checker=core -analyzer-checker=apiModeling -analyzer-checker=unix -analyzer-checker=deadcode -analyzer-checker=security.insecureAPI.UncheckedReturn -analyzer-checker=security.insecureAPI.getpw -analyzer-checker=security.insecureAPI.gets -analyzer-checker=security.insecureAPI.mktemp -analyzer-checker=security.insecureAPI.mkstemp -analyzer-checker=security.insecureAPI.vfork -analyzer-checker=nullability.NullPassedToNonnull -analyzer-checker=nullability.NullReturnedFromNonnull -analyzer-output plist -w -setup-static-analyzer -mrelocation-model pic -pic-level 2 -pic-is-pie -mframe-pointer=none -fmath-errno -ffp-contract=on -fno-rounding-math -mconstructor-aliases -funwind-tables=2 -target-cpu x86-64 -target-feature +sse2 -tune-cpu generic -debugger-tuning=gdb -fdebug-compilation-dir=/home/liu/actions-runner/_work/ccv/ccv/lib/nnc -fcoverage-compilation-dir=/home/liu/actions-runner/_work/ccv/ccv/lib/nnc -resource-dir /usr/local/lib/clang/19 -I ../ -I /usr/local/cuda/include -D HAVE_CBLAS -D HAVE_LIBPNG -D HAVE_LIBJPEG -D HAVE_FFTW3 -D HAVE_PTHREAD -D HAVE_LIBLINEAR -D HAVE_TESSERACT -D HAVE_AVCODEC -D HAVE_AVFORMAT -D HAVE_AVUTIL -D HAVE_SWSCALE -D HAVE_SSE2 -D HAVE_GSL -D HAVE_CUDA -D HAVE_CUDNN -D HAVE_NCCL -D USE_SYSTEM_CUB -I /usr/local/include -internal-isystem /usr/local/lib/clang/19/include -internal-isystem /usr/local/include -internal-isystem /usr/lib/gcc/x86_64-linux-gnu/12/../../../../x86_64-linux-gnu/include -internal-externc-isystem /usr/include/x86_64-linux-gnu -internal-externc-isystem /include -internal-externc-isystem /usr/include -O3 -ferror-limit 19 -fgnuc-version=4.2.1 -fskip-odr-check-in-gmf -vectorize-loops -vectorize-slp -analyzer-output=html -faddrsig -D__GCC_HAVE_DWARF2_CFI_ASM=1 -o /home/liu/actions-runner/_work/ccv/ccv/_analyze/2024-11-11-003326-289375-1 -x c ccv_nnc_micro_core.c

ccv_nnc_micro_core.c

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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_nnc_micro.h"
6#include "3rdparty/khash/khash.h"
7 
8// MARK - Level-1 API
9 
10const ccv_nnc_micro_io_vtab_t ccv_nnc_micro_io_input_isa = {};
11 
12#define GRAD(_id)(2 * (var_count) - 1 - (_id)) (2 * (var_count) - 1 - (_id))
13 
14ccv_nnc_micro_io_t ccv_nnc_micro_input(const int dimensions)
15{
16	assert(dimensions <= CCV_NNC_MAX_DIM_ALLOC)((void) sizeof ((dimensions <= (12)) ? 1 : 0), __extension__
 ({ if (dimensions <= (12)) ; else __assert_fail ("dimensions <= CCV_NNC_MAX_DIM_ALLOC"
, "ccv_nnc_micro_core.c", 16, __extension__ __PRETTY_FUNCTION__
); }));
17	ccv_nnc_micro_io_t input = cccalloccalloc(1, sizeof(struct ccv_nnc_micro_io_s));
18	input->isa = &ccv_nnc_micro_io_input_isa;
19	input->dimensions = dimensions;
20	input->id = 0;
21	return input;
22}
23struct ccv_nnc_micro_io_grad_s {
24	struct ccv_nnc_micro_io_s super;
25	ccv_nnc_micro_io_t x;
26};
27 
28static void _ccv_nnc_micro_grad_numbering(const ccv_nnc_micro_io_t super, const int id, const int var_count)
29{
30	struct ccv_nnc_micro_io_grad_s* const self = (struct ccv_nnc_micro_io_grad_s*)super;
31	const int sid = self->x->id;
32	self->super.id = GRAD(sid)(2 * (var_count) - 1 - (sid));
33}
34 
35const ccv_nnc_micro_io_vtab_t ccv_nnc_micro_io_grad_isa = {
36	.numbering = _ccv_nnc_micro_grad_numbering
37};
38 
39ccv_nnc_micro_io_t ccv_nnc_micro_grad(const ccv_nnc_micro_io_t x)
40{
41	struct ccv_nnc_micro_io_grad_s* const grad = cccalloccalloc(1, sizeof(struct ccv_nnc_micro_io_grad_s));
42	grad->super.isa = &ccv_nnc_micro_io_grad_isa;
43	grad->super.dimensions = x->dimensions;
44	grad->super.id = 0;
45	grad->x = x;
46	return (ccv_nnc_micro_io_t)grad;
47}
48 
49// A simple recursive descent parser. Omitted tokenisation step.
50static int _accept(const char** const pos, int* const remain_size, const char* symbol, int size)
51{
52	if (*remain_size < size)
53		return 0;
54	if (memcmp(*pos, symbol, size) == 0)
55	{
56		*remain_size -= size;
57		*pos += size;
58		return 1;
59	}
60	return 0;
61}
62 
63static int _expect(const char** const pos, int* const remain_size, const char* symbol, int size)
64{
65	if (_accept(pos, remain_size, symbol, size))
66		return 1;
67	assert(0 && "unexpected symbol")((void) sizeof ((0 && "unexpected symbol") ? 1 : 0), __extension__
 ({ if (0 && "unexpected symbol") ; else __assert_fail
 ("0 && \"unexpected symbol\"", "ccv_nnc_micro_core.c"
, 67, __extension__ __PRETTY_FUNCTION__); }));
68	return 0;
69}
70 
71static int _constant(const char** const pos, int* const remain_size, int* const id)
72{
73	int size = 0;
74	*id = 0;
75	while (*remain_size - size > 0 && pos[0][size] >= '0' && pos[0][size] <= '9')
76	{
77		*id *= 10;
78		*id += (pos[0][size] - '0');
79		++size;
80	}
81	*remain_size -= size;
82	*pos += size;
83	return size > 0;
84}
85 
86static int _index(const char** const pos, int* const remain_size, int* const id)
87{
88	if (!(*remain_size > 0 && pos[0][0] == 'i'))
89		return 0;
90	int size = 1;
91	*id = 0;
92	while (*remain_size - size > 0 && pos[0][size] >= '0' && pos[0][size] <= '9')
93	{
94		*id *= 10;
95		*id += (pos[0][size] - '0');
96		++size;
97	}
98	if (size > 1)
99	{
100		*remain_size -= size;
101		*pos += size;
102		return 1;
103	}
104	return 0;
105}
106 
107static int _dim(const char** const pos, int* const remain_size, int* const id, int* const d, ccv_array_t* const equal_assertions)
108{
109	if (!(*remain_size > 1 && pos[0][0] == 'd'))
110		return 0;
111	if (!(pos[0][1] >= 'A' && pos[0][1] <= 'Z'))
112		return 0;
113	*id = pos[0][1] - 'A';
114	int size = 2;
115	*d = 0;
116	while (*remain_size - size > 0 && pos[0][size] >= '0' && pos[0][size] <= '9')
117	{
118		*d *= 10;
119		*d += (pos[0][size] - '0');
120		++size;
121	}
122	if (size > 1)
123	{
124		*remain_size -= size;
125		*pos += size;
126		while (_accept(pos, remain_size, " ", 1)) {}
127		if (_accept(pos, remain_size, "[", 1))
128		{
129			while (_accept(pos, remain_size, " ", 1)) {}
130			_expect(pos, remain_size, "=", 1);
131			while (_accept(pos, remain_size, " ", 1)) {}
132			int next_id;
133			int next_d;
134			if (!_dim(pos, remain_size, &next_id, &next_d, equal_assertions))
135				{ assert(0 && "unexpected symbol")((void) sizeof ((0 && "unexpected symbol") ? 1 : 0), __extension__
 ({ if (0 && "unexpected symbol") ; else __assert_fail
 ("0 && \"unexpected symbol\"", "ccv_nnc_micro_core.c"
, 135, __extension__ __PRETTY_FUNCTION__); })); }
136			const ccv_nnc_micro_id_equal_assertion_t equal_assertion = {
137				.left = {
138					.type = CCV_NNC_MICRO_AXIS_SIZE_ID,
139					.id = -(*id + 1),
140					.d = *d
141				},
142				.right = {
143					.type = CCV_NNC_MICRO_AXIS_SIZE_ID,
144					.id = -(next_id + 1),
145					.d = next_d
146				}
147			};
148			ccv_array_push(equal_assertions, &equal_assertion);
149			while (_accept(pos, remain_size, " ", 1)) {}
150			_expect(pos, remain_size, "]", 1);
151		}
152		return 1;
153	}
154	return 0;
155}
156 
157static int _var(const char** const pos, int* const remain_size, char** name)
158{
159	if (!(*remain_size > 0 && pos[0][0] == '$'))
160		return 0;
161	int size = 1;
162	while (*remain_size - size > 0 &&
163			((pos[0][size] >= '0' && pos[0][size] <= '9') ||
164			 (pos[0][size] >= 'a' && pos[0][size] <= 'z') ||
165			 (pos[0][size] >= 'A' && pos[0][size] <= 'Z') ||
166			 pos[0][size] == '_'))
167		++size;
168	if (size > 1)
169	{
170		*name = ccmallocmalloc(size + 1);
171		memcpy(*name, *pos, size);
172		name[0][size] = 0;
173		*remain_size -= size;
174		*pos += size;
175		return 1;
176	}
177	return 0;
178}
179 
180static CCV_WARN_UNUSED(ccv_nnc_micro_loop_index_term_t)ccv_nnc_micro_loop_index_term_t __attribute__((warn_unused_result
)) _expression(const char** const pos, int* const remain_size, ccv_array_t* const equal_assertions);
181 
182static ccv_nnc_micro_loop_index_term_t _factor(const char** const pos, int* const remain_size, ccv_array_t* const equal_assertions)
183{
184	ccv_nnc_micro_loop_index_term_t term;
185	while (_accept(pos, remain_size, " ", 1)) {}
186	int id, d;
187	char* name;
188	if (_constant(pos, remain_size, &id)) {
189		term.type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_VAL;
190		term.immediate_value = id;
191	} else if (_index(pos, remain_size, &id)) {
192		term.type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID;
193		term.id.type = CCV_NNC_MICRO_LOOP_ID;
194		term.id.id = id;
195	} else if (_dim(pos, remain_size, &id, &d, equal_assertions)) {
196		term.type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID;
197		term.id.type = CCV_NNC_MICRO_AXIS_SIZE_ID;
198		term.id.d = d;
199		term.id.id = -(id + 1);
200	} else if (_var(pos, remain_size, &name)) {
201		term.type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_UNBOUND_SCALAR;
202		term.name = name;
203	} else if (_accept(pos, remain_size, "(", 1)) {
204		term = _expression(pos, remain_size, equal_assertions);
205		_expect(pos, remain_size, ")", 1);
206	} else {
207		assert(0 && "factor: syntax error")((void) sizeof ((0 && "factor: syntax error") ? 1 : 0
), __extension__ ({ if (0 && "factor: syntax error") ;
 else __assert_fail ("0 && \"factor: syntax error\"",
 "ccv_nnc_micro_core.c", 207, __extension__ __PRETTY_FUNCTION__
); }));
208	}
209	while (_accept(pos, remain_size, " ", 1)) {}
210	return term;
211}
212 
213static ccv_nnc_micro_loop_index_term_t _term(const char** const pos, int* const remain_size, ccv_array_t* const equal_assertions)
214{
215	while (_accept(pos, remain_size, " ", 1)) {}
216	ccv_nnc_micro_loop_index_term_t term = _factor(pos, remain_size, equal_assertions);
217	while (*remain_size > 0 && (pos[0][0] == '*' || pos[0][0] == '/'))
218	{
219		const int op = pos[0][0] == '*' ? CCV_NNC_MICRO_BINARY_OP_MUL : CCV_NNC_MICRO_BINARY_OP_DIV;
220		*remain_size -= 1;
221		*pos += 1;
222		const ccv_nnc_micro_loop_index_term_t left = term;
223		const ccv_nnc_micro_loop_index_term_t right = _factor(pos, remain_size, equal_assertions);
224		term.type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_BINARY;
225		term.binary = (ccv_nnc_micro_loop_index_binary_t*)ccmallocmalloc(sizeof(ccv_nnc_micro_loop_index_binary_t));
226		term.binary->op = op;
227		term.binary->left = left;
228		term.binary->right = right;
229	}
230	while (_accept(pos, remain_size, " ", 1)) {}
231	return term;
232}
233 
234static ccv_nnc_micro_loop_index_term_t _expression(const char** const pos, int* const remain_size, ccv_array_t* const equal_assertions)
235{
236	while (_accept(pos, remain_size, " ", 1)) {}
237	int prefix_op = -1;
238	if (*remain_size > 0 && (pos[0][0] == '+' || pos[0][0] == '-'))
239	{
240		prefix_op = pos[0][0] == '+' ? CCV_NNC_MICRO_BINARY_OP_PLUS : CCV_NNC_MICRO_BINARY_OP_MINUS;
241		*remain_size -= 1;
242		*pos += 1;
243	}
244	ccv_nnc_micro_loop_index_term_t node = _term(pos, remain_size, equal_assertions);
245	while (*remain_size > 0 && (pos[0][0] == '+' || pos[0][0] == '-'))
246	{
247		const int op = pos[0][0] == '+' ? CCV_NNC_MICRO_BINARY_OP_PLUS : CCV_NNC_MICRO_BINARY_OP_MINUS;
248		*remain_size -= 1;
249		*pos += 1;
250		const ccv_nnc_micro_loop_index_term_t left = node;
251		const ccv_nnc_micro_loop_index_term_t right = _term(pos, remain_size, equal_assertions);
252		node.type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_BINARY;
253		node.binary = (ccv_nnc_micro_loop_index_binary_t*)ccmallocmalloc(sizeof(ccv_nnc_micro_loop_index_binary_t));
254		node.binary->op = op;
255		node.binary->left = left;
256		node.binary->right = right;
257	}
258	while (_accept(pos, remain_size, " ", 1)) {}
259	if (prefix_op >= 0)
260	{
261		ccv_nnc_micro_loop_index_binary_t* const expr = (ccv_nnc_micro_loop_index_binary_t*)ccmallocmalloc(sizeof(ccv_nnc_micro_loop_index_binary_t));
262		expr->op = prefix_op;
263		expr->left = node;
264		expr->right.type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_NONE;
265		node.type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_BINARY;
266		node.binary = expr;
267	}
268	return node;
269}
270 
271static void _no_index(const ccv_nnc_micro_loop_index_term_t term)
272{
273	switch (term.type) {
274		case CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID:
275			// Can only be axis size id. No loop index.
276			assert(term.id.type == CCV_NNC_MICRO_AXIS_SIZE_ID)((void) sizeof ((term.id.type == CCV_NNC_MICRO_AXIS_SIZE_ID) ?
 1 : 0), __extension__ ({ if (term.id.type == CCV_NNC_MICRO_AXIS_SIZE_ID
) ; else __assert_fail ("term.id.type == CCV_NNC_MICRO_AXIS_SIZE_ID"
, "ccv_nnc_micro_core.c", 276, __extension__ __PRETTY_FUNCTION__
); }));
277			break;
278		case CCV_NNC_MICRO_LOOP_INDEX_TYPE_BINARY:
279			_no_index(term.binary->left);
280			_no_index(term.binary->right);
281			break;
282	}
283}
284 
285static void _sid_to_axis_size_term(ccv_nnc_micro_loop_index_term_t* const term, const int* const sids, const int sid_count)
286{
287	switch (term->type) {
288		case CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID:
289			// Can only be axis size id. No loop index.
290			if (term->id.type == CCV_NNC_MICRO_AXIS_SIZE_ID && term->id.id < 0)
291			{
292				const int id = -(term->id.id + 1);
293				assert(id >= 0 && id < sid_count)((void) sizeof ((id >= 0 && id < sid_count) ? 1
 : 0), __extension__ ({ if (id >= 0 && id < sid_count
) ; else __assert_fail ("id >= 0 && id < sid_count"
, "ccv_nnc_micro_core.c", 293, __extension__ __PRETTY_FUNCTION__
); }));
294				term->id.id = sids[id];
295			}
296			break;
297		case CCV_NNC_MICRO_LOOP_INDEX_TYPE_BINARY:
298			_sid_to_axis_size_term(&term->binary->left, sids, sid_count);
299			_sid_to_axis_size_term(&term->binary->right, sids, sid_count);
300			break;
301	}
302}
303 
304struct ccv_nnc_micro_io_reindex_s {
305	struct ccv_nnc_micro_io_s super;
306	int s_count;
307	ccv_nnc_micro_io_t x;
308	ccv_nnc_micro_loop_index_term_t* shape;
309	ccv_nnc_micro_loop_index_term_t* reindex;
310	ccv_nnc_micro_io_t* ss;
311	ccv_array_t* equal_assertions;
312};
313 
314static void _ccv_nnc_micro_reindex_numbering(const ccv_nnc_micro_io_t super, const int id, const int var_count)
315{
316	struct ccv_nnc_micro_io_reindex_s* const self = (struct ccv_nnc_micro_io_reindex_s*)super;
317	self->super.id = id;
318	// No need to update axis size.
319	if (self->s_count == 0)
320		return;
321	int sids[self->s_count];
322	int i;
323	for (i = 0; i < self->s_count; i++)
324		sids[i] = self->ss[i]->id;
325	for (i = 0; i < self->super.dimensions; i++)
326		_sid_to_axis_size_term(&self->shape[i], sids, self->s_count);
327	for (i = 0; i < self->x->dimensions; i++)
328		_sid_to_axis_size_term(&self->reindex[i], sids, self->s_count);
329	for (i = 0; i < self->equal_assertions->rnum; i++)
330	{
331		ccv_nnc_micro_id_equal_assertion_t* const equal_assertion = (ccv_nnc_micro_id_equal_assertion_t*)ccv_array_get(self->equal_assertions, i)((void*)(((char*)((self->equal_assertions)->data)) + (size_t
)(self->equal_assertions)->rsize * (size_t)(i)));
332		if (equal_assertion->left.type == CCV_NNC_MICRO_AXIS_SIZE_ID && equal_assertion->left.id < 0)
333		{
334				const int id = -(equal_assertion->left.id + 1);
335				assert(id >= 0 && id < self->s_count)((void) sizeof ((id >= 0 && id < self->s_count
) ? 1 : 0), __extension__ ({ if (id >= 0 && id <
 self->s_count) ; else __assert_fail ("id >= 0 && id < self->s_count"
, "ccv_nnc_micro_core.c", 335, __extension__ __PRETTY_FUNCTION__
); }));
336				equal_assertion->left.id = sids[id];
337		}
338		if (equal_assertion->right.type == CCV_NNC_MICRO_AXIS_SIZE_ID && equal_assertion->right.id < 0)
339		{
340				const int id = -(equal_assertion->right.id + 1);
341				assert(id >= 0 && id < self->s_count)((void) sizeof ((id >= 0 && id < self->s_count
) ? 1 : 0), __extension__ ({ if (id >= 0 && id <
 self->s_count) ; else __assert_fail ("id >= 0 && id < self->s_count"
, "ccv_nnc_micro_core.c", 341, __extension__ __PRETTY_FUNCTION__
); }));
342				equal_assertion->right.id = sids[id];
343		}
344	}
345}
346 
347static void _ccv_nnc_micro_reindex_equal_assertions(const ccv_nnc_micro_io_t super, ccv_array_t* const equal_assertions)
348{
349	struct ccv_nnc_micro_io_reindex_s* const self = (struct ccv_nnc_micro_io_reindex_s*)super;
350	int i;
351	for (i = 0; i < self->equal_assertions->rnum; i++)
352	{
353		ccv_nnc_micro_id_equal_assertion_t* const equal_assertion = (ccv_nnc_micro_id_equal_assertion_t*)ccv_array_get(self->equal_assertions, i)((void*)(((char*)((self->equal_assertions)->data)) + (size_t
)(self->equal_assertions)->rsize * (size_t)(i)));
354		ccv_array_push(equal_assertions, equal_assertion);
355	}
356}
357 
358static void _ccv_nnc_bind_scalars_in_term(ccv_nnc_micro_loop_index_term_t* const term, ccv_nnc_micro_scalar_lookup_f lookup, const void* const context)
359{
360	switch (term->type)
361	{
362		case CCV_NNC_MICRO_LOOP_INDEX_TYPE_BINARY:
363			_ccv_nnc_bind_scalars_in_term(&term->binary->left, lookup, context);
364			_ccv_nnc_bind_scalars_in_term(&term->binary->right, lookup, context);
365			break;
366		case CCV_NNC_MICRO_LOOP_INDEX_TYPE_UNBOUND_SCALAR: {
367			char* const name = term->name;
368			term->id.id = lookup(context, name);
369			ccfreefree(name);
370			term->id.d = 0;
371			term->id.type = CCV_NNC_MICRO_SCALAR_ID;
372			term->type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID;
373			break;
374		}
375	}
376}
377 
378static void _ccv_nnc_micro_reindex_bind_scalars(const ccv_nnc_micro_io_t super, ccv_nnc_micro_scalar_lookup_f lookup, const void* const context)
379{
380	struct ccv_nnc_micro_io_reindex_s* const self = (struct ccv_nnc_micro_io_reindex_s*)super;
381	int i;
382	for (i = 0; i < self->super.dimensions; i++)
383		_ccv_nnc_bind_scalars_in_term(&self->shape[i], lookup, context);
384	for (i = 0; i < self->x->dimensions; i++)
385		_ccv_nnc_bind_scalars_in_term(&self->reindex[i], lookup, context);
386}
387 
388ccv_nnc_micro_loop_index_term_t ccv_nnc_micro_loop_index_deep_copy(const ccv_nnc_micro_loop_index_term_t* const term)
389{
390	switch (term->type)
391	{
392		case CCV_NNC_MICRO_LOOP_INDEX_TYPE_BINARY: {
393			ccv_nnc_micro_loop_index_term_t copy = *term;
394			copy.binary = (ccv_nnc_micro_loop_index_binary_t*)ccmallocmalloc(sizeof(ccv_nnc_micro_loop_index_binary_t));
395			*copy.binary = *term->binary;
396			copy.binary->left = ccv_nnc_micro_loop_index_deep_copy(&term->binary->left);
397			copy.binary->right = ccv_nnc_micro_loop_index_deep_copy(&term->binary->right);
398			return copy;
399		}
400		case CCV_NNC_MICRO_LOOP_INDEX_TYPE_NONE:
401		case CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID:
402		case CCV_NNC_MICRO_LOOP_INDEX_TYPE_VAL:
403		case CCV_NNC_MICRO_LOOP_INDEX_TYPE_UNBOUND_SCALAR:
404			return *term;
405	}
406	return *term;
407}
408 
409static CCV_WARN_UNUSED(ccv_nnc_micro_function_t)ccv_nnc_micro_function_t __attribute__((warn_unused_result)) _ccv_nnc_micro_reindex_emit(const ccv_nnc_micro_io_t super)
410{
411	struct ccv_nnc_micro_io_reindex_s* const self = (struct ccv_nnc_micro_io_reindex_s*)super;
412	const int loop_count = self->super.dimensions;
413	assert(loop_count <= CCV_NNC_MAX_DIM_ALLOC)((void) sizeof ((loop_count <= (12)) ? 1 : 0), __extension__
 ({ if (loop_count <= (12)) ; else __assert_fail ("loop_count <= CCV_NNC_MAX_DIM_ALLOC"
, "ccv_nnc_micro_core.c", 413, __extension__ __PRETTY_FUNCTION__
); }));
414	ccv_nnc_micro_loop_t* const loops = (ccv_nnc_micro_loop_t*)ccmallocmalloc(sizeof(ccv_nnc_micro_loop_t) * loop_count);
415	int i;
416	for (i = 0; i < loop_count; i++)
417		loops[i] = ccv_nnc_micro_for_in(ccv_nnc_micro_index_of_value(0), ccv_nnc_micro_index_of_axis_size(self->super.id, i), i);
418	const ccv_nnc_micro_loop_statement_t statement = ccv_nnc_micro_loop_assignment(
419		ccv_nnc_micro_loop_variable_of_tensor(self->super.id, loop_count, ccv_nnc_micro_index_of_loops(loops, loop_count)(ccv_nnc_micro_loop_index_term_t [(12)]){ { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 0 ? loops[0].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 1 ? loops[1].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 2 ? loops[2].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 3 ? loops[3].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 4 ? loops[4].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 5 ? loops[5].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 6 ? loops[6].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 7 ? loops[7].id : (ccv_nnc_micro_id_t){} } }),
420		ccv_nnc_micro_loop_expression_of_variable(ccv_nnc_micro_loop_variable_of_tensor(self->x->id, self->x->dimensions, self->reindex))
421	);
422	for (i = 0; i < self->x->dimensions; i++)
423		self->reindex[i] = ccv_nnc_micro_loop_index_deep_copy(&self->reindex[i]);
424	loops[loop_count - 1].statement_count = 1;
425	loops[loop_count - 1].statements = (ccv_nnc_micro_loop_statement_t*)ccmallocmalloc(sizeof(ccv_nnc_micro_loop_statement_t));
426	loops[loop_count - 1].statements[0] = statement;
427	return (ccv_nnc_micro_function_t){
428		.block_count = 1,
429		.one_block = {
430			.loop_count = loop_count,
431			.loops = loops
432		}
433	};
434}
435 
436static CCV_WARN_UNUSED(ccv_nnc_micro_function_t)ccv_nnc_micro_function_t __attribute__((warn_unused_result)) _ccv_nnc_micro_reindex_emit_grad(const ccv_nnc_micro_io_t super, const int var_count)
437{
438	// The grad is var_count + original id.
439	struct ccv_nnc_micro_io_reindex_s* const self = (struct ccv_nnc_micro_io_reindex_s*)super;
440	const int reset_loop_count = self->x->dimensions;
441	ccv_nnc_micro_loop_t* const reset_loops = (ccv_nnc_micro_loop_t*)ccmallocmalloc(sizeof(ccv_nnc_micro_loop_t) * reset_loop_count);
442	// This loop reset grad to 0.
443	int i;
444	for (i = 0; i < reset_loop_count; i++)
445		reset_loops[i] = ccv_nnc_micro_for_in(ccv_nnc_micro_index_of_value(0), ccv_nnc_micro_index_of_axis_size(GRAD(self->x->id)(2 * (var_count) - 1 - (self->x->id)), i), i);
446	const ccv_nnc_micro_loop_statement_t reset_statement = ccv_nnc_micro_loop_assignment(
447		ccv_nnc_micro_loop_variable_of_tensor(GRAD(self->x->id)(2 * (var_count) - 1 - (self->x->id)), reset_loop_count, ccv_nnc_micro_index_of_loops(reset_loops, reset_loop_count)(ccv_nnc_micro_loop_index_term_t [(12)]){ { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = reset_loop_count > 0 ? reset_loops[0].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = reset_loop_count
 > 1 ? reset_loops[1].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = reset_loop_count >
 2 ? reset_loops[2].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = reset_loop_count > 3 ? reset_loops[3].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = reset_loop_count
 > 4 ? reset_loops[4].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = reset_loop_count >
 5 ? reset_loops[5].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = reset_loop_count > 6 ? reset_loops[6].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = reset_loop_count
 > 7 ? reset_loops[7].id : (ccv_nnc_micro_id_t){} } }),
448		ccv_nnc_micro_loop_expression_of_value(0)
449	);
450	reset_loops[reset_loop_count - 1].statement_count = 1;
451	reset_loops[reset_loop_count - 1].statements = (ccv_nnc_micro_loop_statement_t*)ccmallocmalloc(sizeof(ccv_nnc_micro_loop_statement_t));
452	reset_loops[reset_loop_count - 1].statements[0] = reset_statement;
453	const int loop_count = self->super.dimensions;
454	ccv_nnc_micro_loop_t* const loops = (ccv_nnc_micro_loop_t*)ccmallocmalloc(sizeof(ccv_nnc_micro_loop_t) * loop_count);
455	for (i = 0; i < loop_count; i++)
456		loops[i] = ccv_nnc_micro_for_in(ccv_nnc_micro_index_of_value(0), ccv_nnc_micro_index_of_axis_size(GRAD(self->super.id)(2 * (var_count) - 1 - (self->super.id)), i), i);
457	const ccv_nnc_micro_loop_statement_t statement = ccv_nnc_micro_loop_compound_assignment_of_tensor(
458		ccv_nnc_micro_loop_variable_of_tensor(GRAD(self->x->id)(2 * (var_count) - 1 - (self->x->id)), self->x->dimensions, self->reindex),
459		ccv_nnc_micro_loop_expression_of_variable(ccv_nnc_micro_loop_variable_of_tensor(GRAD(self->super.id)(2 * (var_count) - 1 - (self->super.id)), loop_count, ccv_nnc_micro_index_of_loops(loops, loop_count)(ccv_nnc_micro_loop_index_term_t [(12)]){ { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 0 ? loops[0].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 1 ? loops[1].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 2 ? loops[2].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 3 ? loops[3].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 4 ? loops[4].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 5 ? loops[5].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 6 ? loops[6].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 7 ? loops[7].id : (ccv_nnc_micro_id_t){} } }))
460	);
461	for (i = 0; i < self->x->dimensions; i++)
462		self->reindex[i] = ccv_nnc_micro_loop_index_deep_copy(&self->reindex[i]);
463	loops[loop_count - 1].statement_count = 1;
464	loops[loop_count - 1].statements = (ccv_nnc_micro_loop_statement_t*)ccmallocmalloc(sizeof(ccv_nnc_micro_loop_statement_t));
465	loops[loop_count - 1].statements[0] = statement;
466	ccv_nnc_micro_loop_block_t* const blocks = (ccv_nnc_micro_loop_block_t*)ccmallocmalloc(sizeof(ccv_nnc_micro_loop_block_t) * 2);
467	blocks[0] = (ccv_nnc_micro_loop_block_t){
468		.loop_count = reset_loop_count,
469		.loops = reset_loops
470	};
471	blocks[1] = (ccv_nnc_micro_loop_block_t){
472		.loop_count = loop_count,
473		.loops = loops
474	};
475	return (ccv_nnc_micro_function_t){
476		.block_count = 2,
477		.blocks = blocks
478	};
479}
480 
481static ccv_nnc_micro_tensor_t _ccv_nnc_micro_reindex_return_shape(const ccv_nnc_micro_io_t super)
482{
483	struct ccv_nnc_micro_io_reindex_s* const self = (struct ccv_nnc_micro_io_reindex_s*)super;
484	ccv_nnc_micro_tensor_t var = {};
485	var.dimensions = self->super.dimensions;
486	var.sibling = -1;
487	var.input = self->x->id;
488	var.shape = (ccv_nnc_micro_loop_index_term_t*)ccmallocmalloc(sizeof(ccv_nnc_micro_loop_index_term_t) * self->super.dimensions);
489	memcpy(var.shape, self->shape, sizeof(ccv_nnc_micro_loop_index_term_t) * self->super.dimensions);
490	return var;
491}
492 
493static void _ccv_nnc_micro_reindex_deinit(const ccv_nnc_micro_io_t super)
494{
495	struct ccv_nnc_micro_io_reindex_s* const self = (struct ccv_nnc_micro_io_reindex_s*)super;
496	int i;
497	for (i = 0; i < self->x->dimensions; i++)
498		ccv_nnc_micro_loop_index_free(&self->reindex[i]);
499	ccv_array_free(self->equal_assertions);
500}
501 
502static const ccv_nnc_micro_io_vtab_t ccv_nnc_micro_io_reindex_isa = {
503	.numbering = _ccv_nnc_micro_reindex_numbering,
504	.equal_assertions = _ccv_nnc_micro_reindex_equal_assertions,
505	.bind_scalars = _ccv_nnc_micro_reindex_bind_scalars,
506	.emit = _ccv_nnc_micro_reindex_emit,
507	.emit_grad = _ccv_nnc_micro_reindex_emit_grad,
508	.return_shape = _ccv_nnc_micro_reindex_return_shape,
509	.deinit = _ccv_nnc_micro_reindex_deinit
510};
511 
512ccv_nnc_micro_io_t ccv_nnc_micro_reindex(const char* const* const shape, const int shape_count, const ccv_nnc_micro_io_t* const ss, const int s_count, const char* const* const reindex, const int reindex_count, const ccv_nnc_micro_io_t x)
513{
514	assert(shape_count <= CCV_NNC_MAX_DIM_ALLOC)((void) sizeof ((shape_count <= (12)) ? 1 : 0), __extension__
 ({ if (shape_count <= (12)) ; else __assert_fail ("shape_count <= CCV_NNC_MAX_DIM_ALLOC"
, "ccv_nnc_micro_core.c", 514, __extension__ __PRETTY_FUNCTION__
); }));
515	assert(reindex_count <= CCV_NNC_MAX_DIM_ALLOC)((void) sizeof ((reindex_count <= (12)) ? 1 : 0), __extension__
 ({ if (reindex_count <= (12)) ; else __assert_fail ("reindex_count <= CCV_NNC_MAX_DIM_ALLOC"
, "ccv_nnc_micro_core.c", 515, __extension__ __PRETTY_FUNCTION__
); }));
516	assert(reindex_count == x->dimensions)((void) sizeof ((reindex_count == x->dimensions) ? 1 : 0),
 __extension__ ({ if (reindex_count == x->dimensions) ; else
 __assert_fail ("reindex_count == x->dimensions", "ccv_nnc_micro_core.c"
, 516, __extension__ __PRETTY_FUNCTION__); }));
517	int i;
518	struct ccv_nnc_micro_io_reindex_s* const self = (struct ccv_nnc_micro_io_reindex_s*)cccalloccalloc(1, sizeof(struct ccv_nnc_micro_io_reindex_s) + sizeof(ccv_nnc_micro_loop_index_term_t) * (shape_count + reindex_count) + sizeof(ccv_nnc_micro_io_t) * (s_count + 1));
519	self->super.isa = &ccv_nnc_micro_io_reindex_isa;
520	self->super.dimensions = shape_count;
521	self->super.id = 0;
522	self->x = x;
523	self->shape = (ccv_nnc_micro_loop_index_term_t*)(self + 1);
524	self->reindex = self->shape + shape_count;
525	self->ss = (ccv_nnc_micro_io_t*)(self->reindex + reindex_count);
526	self->s_count = s_count;
527	self->ss[s_count] = x;
528	self->super.inputs = self->ss;
529	self->super.input_size = s_count + 1;
530	if (s_count > 0)
531		memcpy(self->ss, ss, sizeof(ccv_nnc_micro_io_t) * s_count);
532	ccv_array_t* const equal_assertions = self->equal_assertions = ccv_array_new(sizeof(ccv_nnc_micro_id_equal_assertion_t), 0, 0);
533	// Parse shape into expressions and validate the grammar. Do this upfront so we don't fail on parsing
534	// later, which can be confusing.
535	// CFG:
536	// VAR -> $[a-zA-Z0-9]+
537	// DIM -> d[A-Z]{1}[0-9]+
538	// INDEX -> i[0-9]+
539	// CONST -> [0-9]+
540	// FACTOR -> VAR | DIM | CONST | INDEX
541	// TERM -> FACTOR { ("*" | "/") FACTOR }
542	// EXPRESSION -> ["+" | "-"] TERM { ("+" | "-") TERM }
543	// Also, we choose to reuse the index expression structure even some information (such as id of tensors
544	// and the binding variables) not available. In this way, there is no need to reallocate index expression
545	// later, we just need to simply "patch" it in ccv_nnc_micro_combine_t.
546	for (i = 0; i < shape_count; i++)
547	{
548		int remain_size = strlen(shape[i]);
549		const char* pos = shape[i];
550		ccv_nnc_micro_loop_index_term_t term = _expression(&pos, &remain_size, equal_assertions);
551		_no_index(term); // Make sure this is not index, no loop index.
552		self->shape[i] = term;
553	}
554	// Parse reindex.
555	for (i = 0; i < reindex_count; i++)
556	{
557		int remain_size = strlen(reindex[i]);
558		const char* pos = reindex[i];
559		self->reindex[i] = _expression(&pos, &remain_size, equal_assertions);
560	}
561	return (ccv_nnc_micro_io_t)self;
562}
563 
564struct ccv_nnc_micro_io_unary_s {
565	struct ccv_nnc_micro_io_s super;
566	uint32_t unary_op;
567	ccv_nnc_micro_io_t x;
568};
569 
570static CCV_WARN_UNUSED(ccv_nnc_micro_function_t)ccv_nnc_micro_function_t __attribute__((warn_unused_result)) _ccv_nnc_micro_unary_emit(const ccv_nnc_micro_io_t super)
571{
572	struct ccv_nnc_micro_io_unary_s* const self = (struct ccv_nnc_micro_io_unary_s*)super;
573	const int loop_count = self->super.dimensions;
574	assert(self->x->dimensions == loop_count)((void) sizeof ((self->x->dimensions == loop_count) ? 1
 : 0), __extension__ ({ if (self->x->dimensions == loop_count
) ; else __assert_fail ("self->x->dimensions == loop_count"
, "ccv_nnc_micro_core.c", 574, __extension__ __PRETTY_FUNCTION__
); }));
1
Assuming 'loop_count' is equal to field 'dimensions'→
2
←
Taking true branch→
575	assert(loop_count <= CCV_NNC_MAX_DIM_ALLOC)((void) sizeof ((loop_count <= (12)) ? 1 : 0), __extension__
 ({ if (loop_count <= (12)) ; else __assert_fail ("loop_count <= CCV_NNC_MAX_DIM_ALLOC"
, "ccv_nnc_micro_core.c", 575, __extension__ __PRETTY_FUNCTION__
); }));
3
←
Assuming 'loop_count' is <= 12→
4
←
Taking true branch→
576	ccv_nnc_micro_loop_t* const loops = (ccv_nnc_micro_loop_t*)ccmallocmalloc(sizeof(ccv_nnc_micro_loop_t) * loop_count);
577	int i;
578	for (i = 0; i < loop_count; i++)
5
←
Assuming 'i' is >= 'loop_count'→
6
←
Loop condition is false. Execution continues on line 580→
579		loops[i] = ccv_nnc_micro_for_in(ccv_nnc_micro_index_of_value(0), ccv_nnc_micro_index_of_axis_size(self->super.id, i), i);
580	const ccv_nnc_micro_loop_statement_t statement = ccv_nnc_micro_loop_assignment(
581		ccv_nnc_micro_loop_variable_of_tensor(self->super.id, loop_count, ccv_nnc_micro_index_of_loops(loops, loop_count)(ccv_nnc_micro_loop_index_term_t [(12)]){ { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 0 ? loops[0].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 1 ? loops[1].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 2 ? loops[2].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 3 ? loops[3].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 4 ? loops[4].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 5 ? loops[5].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 6 ? loops[6].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 7 ? loops[7].id : (ccv_nnc_micro_id_t){} } }),
7
←
'?' condition is false→
8
←
'?' condition is false→
9
←
'?' condition is false→
10
←
'?' condition is false→
11
←
'?' condition is false→
12
←
'?' condition is false→
13
←
'?' condition is false→
14
←
'?' condition is false→
582		ccv_nnc_micro_loop_expression_of_unary(
23
←
Calling 'ccv_nnc_micro_loop_expression_of_unary'→
25
←
Returned allocated memory→
583			self->unary_op,
584			ccv_nnc_micro_loop_expression_of_variable(ccv_nnc_micro_loop_variable_of_tensor(self->x->id, loop_count, ccv_nnc_micro_index_of_loops(loops, loop_count)(ccv_nnc_micro_loop_index_term_t [(12)]){ { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 0 ? loops[0].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 1 ? loops[1].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 2 ? loops[2].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 3 ? loops[3].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 4 ? loops[4].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 5 ? loops[5].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 6 ? loops[6].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 7 ? loops[7].id : (ccv_nnc_micro_id_t){} } }))
15
←
'?' condition is false→
16
←
'?' condition is false→
17
←
'?' condition is false→
18
←
'?' condition is false→
19
←
'?' condition is false→
20
←
'?' condition is false→
21
←
'?' condition is false→
22
←
'?' condition is false→
585		)
586	);
587	loops[loop_count - 1].statement_count = 1;
26
←
Potential memory leak
588	loops[loop_count - 1].statements = (ccv_nnc_micro_loop_statement_t*)ccmallocmalloc(sizeof(ccv_nnc_micro_loop_statement_t));
589	loops[loop_count - 1].statements[0] = statement;
590	return (ccv_nnc_micro_function_t){
591		.block_count = 1,
592		.one_block = {
593			.loop_count = loop_count,
594			.loops = loops
595		}
596	};
597}
598 
599static CCV_WARN_UNUSED(ccv_nnc_micro_function_t)ccv_nnc_micro_function_t __attribute__((warn_unused_result)) _ccv_nnc_micro_unary_emit_grad(const ccv_nnc_micro_io_t super, const int var_count)
600{
601	struct ccv_nnc_micro_io_unary_s* const self = (struct ccv_nnc_micro_io_unary_s*)super;
602	const int loop_count = self->super.dimensions;
603	assert(self->x->dimensions == loop_count)((void) sizeof ((self->x->dimensions == loop_count) ? 1
 : 0), __extension__ ({ if (self->x->dimensions == loop_count
) ; else __assert_fail ("self->x->dimensions == loop_count"
, "ccv_nnc_micro_core.c", 603, __extension__ __PRETTY_FUNCTION__
); }));
604	assert(loop_count <= CCV_NNC_MAX_DIM_ALLOC)((void) sizeof ((loop_count <= (12)) ? 1 : 0), __extension__
 ({ if (loop_count <= (12)) ; else __assert_fail ("loop_count <= CCV_NNC_MAX_DIM_ALLOC"
, "ccv_nnc_micro_core.c", 604, __extension__ __PRETTY_FUNCTION__
); }));
605	ccv_nnc_micro_loop_t* const loops = (ccv_nnc_micro_loop_t*)ccmallocmalloc(sizeof(ccv_nnc_micro_loop_t) * loop_count);
606	int i;
607	for (i = 0; i < loop_count; i++)
608		loops[i] = ccv_nnc_micro_for_in(ccv_nnc_micro_index_of_value(0), ccv_nnc_micro_index_of_axis_size(GRAD(self->super.id)(2 * (var_count) - 1 - (self->super.id)), i), i);
609	ccv_nnc_micro_loop_statement_t statement;
610	switch (self->unary_op)
611	{
612		case CCV_NNC_MICRO_UNARY_OP_NEG:
613			statement = ccv_nnc_micro_loop_assignment(
614				ccv_nnc_micro_loop_variable_of_tensor(GRAD(self->x->id)(2 * (var_count) - 1 - (self->x->id)), loop_count, ccv_nnc_micro_index_of_loops(loops, loop_count)(ccv_nnc_micro_loop_index_term_t [(12)]){ { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 0 ? loops[0].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 1 ? loops[1].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 2 ? loops[2].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 3 ? loops[3].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 4 ? loops[4].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 5 ? loops[5].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 6 ? loops[6].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 7 ? loops[7].id : (ccv_nnc_micro_id_t){} } }),
615				ccv_nnc_micro_loop_expression_of_unary(
616					CCV_NNC_MICRO_UNARY_OP_NEG,
617					ccv_nnc_micro_loop_expression_of_variable(ccv_nnc_micro_loop_variable_of_tensor(GRAD(self->super.id)(2 * (var_count) - 1 - (self->super.id)), loop_count, ccv_nnc_micro_index_of_loops(loops, loop_count)(ccv_nnc_micro_loop_index_term_t [(12)]){ { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 0 ? loops[0].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 1 ? loops[1].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 2 ? loops[2].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 3 ? loops[3].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 4 ? loops[4].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 5 ? loops[5].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 6 ? loops[6].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 7 ? loops[7].id : (ccv_nnc_micro_id_t){} } }))
618				)
619			);
620			break;
621		case CCV_NNC_MICRO_UNARY_OP_EXP:
622			statement = ccv_nnc_micro_loop_assignment(
623				ccv_nnc_micro_loop_variable_of_tensor(GRAD(self->x->id)(2 * (var_count) - 1 - (self->x->id)), loop_count, ccv_nnc_micro_index_of_loops(loops, loop_count)(ccv_nnc_micro_loop_index_term_t [(12)]){ { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 0 ? loops[0].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 1 ? loops[1].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 2 ? loops[2].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 3 ? loops[3].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 4 ? loops[4].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 5 ? loops[5].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 6 ? loops[6].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 7 ? loops[7].id : (ccv_nnc_micro_id_t){} } }),
624				ccv_nnc_micro_loop_expression_of_binary(
625					CCV_NNC_MICRO_BINARY_OP_MUL,
626					ccv_nnc_micro_loop_expression_of_variable(ccv_nnc_micro_loop_variable_of_tensor(self->super.id, loop_count, ccv_nnc_micro_index_of_loops(loops, loop_count)(ccv_nnc_micro_loop_index_term_t [(12)]){ { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 0 ? loops[0].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 1 ? loops[1].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 2 ? loops[2].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 3 ? loops[3].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 4 ? loops[4].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 5 ? loops[5].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 6 ? loops[6].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 7 ? loops[7].id : (ccv_nnc_micro_id_t){} } })),
627					ccv_nnc_micro_loop_expression_of_variable(ccv_nnc_micro_loop_variable_of_tensor(GRAD(self->super.id)(2 * (var_count) - 1 - (self->super.id)), loop_count, ccv_nnc_micro_index_of_loops(loops, loop_count)(ccv_nnc_micro_loop_index_term_t [(12)]){ { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 0 ? loops[0].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 1 ? loops[1].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 2 ? loops[2].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 3 ? loops[3].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 4 ? loops[4].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 5 ? loops[5].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 6 ? loops[6].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 7 ? loops[7].id : (ccv_nnc_micro_id_t){} } }))
628				)
629			);
630			break;
631		case CCV_NNC_MICRO_UNARY_OP_LOG:
632			statement = ccv_nnc_micro_loop_assignment(
633				ccv_nnc_micro_loop_variable_of_tensor(GRAD(self->x->id)(2 * (var_count) - 1 - (self->x->id)), loop_count, ccv_nnc_micro_index_of_loops(loops, loop_count)(ccv_nnc_micro_loop_index_term_t [(12)]){ { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 0 ? loops[0].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 1 ? loops[1].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 2 ? loops[2].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 3 ? loops[3].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 4 ? loops[4].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 5 ? loops[5].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 6 ? loops[6].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 7 ? loops[7].id : (ccv_nnc_micro_id_t){} } }),
634				ccv_nnc_micro_loop_expression_of_binary(
635					CCV_NNC_MICRO_BINARY_OP_DIV,
636					ccv_nnc_micro_loop_expression_of_variable(ccv_nnc_micro_loop_variable_of_tensor(GRAD(self->super.id)(2 * (var_count) - 1 - (self->super.id)), loop_count, ccv_nnc_micro_index_of_loops(loops, loop_count)(ccv_nnc_micro_loop_index_term_t [(12)]){ { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 0 ? loops[0].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 1 ? loops[1].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 2 ? loops[2].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 3 ? loops[3].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 4 ? loops[4].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 5 ? loops[5].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 6 ? loops[6].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 7 ? loops[7].id : (ccv_nnc_micro_id_t){} } })),
637					ccv_nnc_micro_loop_expression_of_variable(ccv_nnc_micro_loop_variable_of_tensor(self->x->id, loop_count, ccv_nnc_micro_index_of_loops(loops, loop_count)(ccv_nnc_micro_loop_index_term_t [(12)]){ { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 0 ? loops[0].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 1 ? loops[1].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 2 ? loops[2].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 3 ? loops[3].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 4 ? loops[4].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 5 ? loops[5].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 6 ? loops[6].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 7 ? loops[7].id : (ccv_nnc_micro_id_t){} } }))
638				)
639			);
640			break;
641	}
642	loops[loop_count - 1].statement_count = 1;
643	loops[loop_count - 1].statements = (ccv_nnc_micro_loop_statement_t*)ccmallocmalloc(sizeof(ccv_nnc_micro_loop_statement_t));
644	loops[loop_count - 1].statements[0] = statement;
645	return (ccv_nnc_micro_function_t){
646		.block_count = 1,
647		.one_block = {
648			.loop_count = loop_count,
649			.loops = loops
650		}
651	};
652}
653 
654static ccv_nnc_micro_tensor_t _ccv_nnc_micro_unary_return_shape(const ccv_nnc_micro_io_t super)
655{
656	struct ccv_nnc_micro_io_unary_s* const self = (struct ccv_nnc_micro_io_unary_s*)super;
657	ccv_nnc_micro_tensor_t var = {};
658	var.dimensions = self->super.dimensions;
659	var.input = self->x->id;
660	var.sibling = -1;
661	return var;
662}
663 
664static const ccv_nnc_micro_io_vtab_t ccv_nnc_micro_io_unary_isa = {
665	.emit = _ccv_nnc_micro_unary_emit,
666	.emit_grad = _ccv_nnc_micro_unary_emit_grad,
667	.return_shape = _ccv_nnc_micro_unary_return_shape
668};
669 
670ccv_nnc_micro_io_t ccv_nnc_micro_unary(const uint32_t op, const ccv_nnc_micro_io_t x)
671{
672	struct ccv_nnc_micro_io_unary_s* const self = (struct ccv_nnc_micro_io_unary_s*)cccalloccalloc(1, sizeof(struct ccv_nnc_micro_io_unary_s));
673	self->super.isa = &ccv_nnc_micro_io_unary_isa;
674	self->super.dimensions = x->dimensions;
675	self->super.id = 0;
676	self->super.inputs = &self->x;
677	self->super.input_size = 1;
678	self->unary_op = op;
679	self->x = x;
680	return (ccv_nnc_micro_io_t)self;
681}
682 
683struct ccv_nnc_micro_io_binary_s {
684	struct ccv_nnc_micro_io_s super;
685	uint32_t binary_op;
686	ccv_nnc_micro_io_t left;
687	ccv_nnc_micro_io_t right;
688};
689 
690static CCV_WARN_UNUSED(ccv_nnc_micro_function_t)ccv_nnc_micro_function_t __attribute__((warn_unused_result)) _ccv_nnc_micro_binary_emit(const ccv_nnc_micro_io_t super)
691{
692	struct ccv_nnc_micro_io_binary_s* const self = (struct ccv_nnc_micro_io_binary_s*)super;
693	const int loop_count = self->super.dimensions;
694	assert(self->left->dimensions == loop_count)((void) sizeof ((self->left->dimensions == loop_count) ?
 1 : 0), __extension__ ({ if (self->left->dimensions ==
 loop_count) ; else __assert_fail ("self->left->dimensions == loop_count"
, "ccv_nnc_micro_core.c", 694, __extension__ __PRETTY_FUNCTION__
); }));
695	assert(self->right->dimensions == loop_count)((void) sizeof ((self->right->dimensions == loop_count)
 ? 1 : 0), __extension__ ({ if (self->right->dimensions
 == loop_count) ; else __assert_fail ("self->right->dimensions == loop_count"
, "ccv_nnc_micro_core.c", 695, __extension__ __PRETTY_FUNCTION__
); }));
696	assert(loop_count <= CCV_NNC_MAX_DIM_ALLOC)((void) sizeof ((loop_count <= (12)) ? 1 : 0), __extension__
 ({ if (loop_count <= (12)) ; else __assert_fail ("loop_count <= CCV_NNC_MAX_DIM_ALLOC"
, "ccv_nnc_micro_core.c", 696, __extension__ __PRETTY_FUNCTION__
); }));
697	ccv_nnc_micro_loop_t* const loops = (ccv_nnc_micro_loop_t*)ccmallocmalloc(sizeof(ccv_nnc_micro_loop_t) * loop_count);
698	int i;
699	for (i = 0; i < loop_count; i++)
700		loops[i] = ccv_nnc_micro_for_in(ccv_nnc_micro_index_of_value(0), ccv_nnc_micro_index_of_axis_size(self->super.id, i), i);
701	const ccv_nnc_micro_loop_statement_t statement = ccv_nnc_micro_loop_assignment(
702		ccv_nnc_micro_loop_variable_of_tensor(self->super.id, loop_count, ccv_nnc_micro_index_of_loops(loops, loop_count)(ccv_nnc_micro_loop_index_term_t [(12)]){ { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 0 ? loops[0].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 1 ? loops[1].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 2 ? loops[2].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 3 ? loops[3].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 4 ? loops[4].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 5 ? loops[5].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 6 ? loops[6].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 7 ? loops[7].id : (ccv_nnc_micro_id_t){} } }),
703		ccv_nnc_micro_loop_expression_of_binary(
704			self->binary_op,
705			ccv_nnc_micro_loop_expression_of_variable(ccv_nnc_micro_loop_variable_of_tensor(self->left->id, loop_count, ccv_nnc_micro_index_of_loops(loops, loop_count)(ccv_nnc_micro_loop_index_term_t [(12)]){ { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 0 ? loops[0].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 1 ? loops[1].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 2 ? loops[2].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 3 ? loops[3].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 4 ? loops[4].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 5 ? loops[5].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 6 ? loops[6].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 7 ? loops[7].id : (ccv_nnc_micro_id_t){} } })),
706			ccv_nnc_micro_loop_expression_of_variable(ccv_nnc_micro_loop_variable_of_tensor(self->right->id, loop_count, ccv_nnc_micro_index_of_loops(loops, loop_count)(ccv_nnc_micro_loop_index_term_t [(12)]){ { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 0 ? loops[0].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 1 ? loops[1].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 2 ? loops[2].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 3 ? loops[3].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 4 ? loops[4].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 5 ? loops[5].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 6 ? loops[6].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 7 ? loops[7].id : (ccv_nnc_micro_id_t){} } }))
707		)
708	);
709	loops[loop_count - 1].statement_count = 1;
710	loops[loop_count - 1].statements = (ccv_nnc_micro_loop_statement_t*)ccmallocmalloc(sizeof(ccv_nnc_micro_loop_statement_t));
711	loops[loop_count - 1].statements[0] = statement;
712	return (ccv_nnc_micro_function_t){
713		.block_count = 1,
714		.one_block = {
715			.loop_count = loop_count,
716			.loops = loops
717		}
718	};
719}
720 
721static CCV_WARN_UNUSED(ccv_nnc_micro_function_t)ccv_nnc_micro_function_t __attribute__((warn_unused_result)) _ccv_nnc_micro_binary_emit_grad(const ccv_nnc_micro_io_t super, const int var_count)
722{
723	struct ccv_nnc_micro_io_binary_s* const self = (struct ccv_nnc_micro_io_binary_s*)super;
724	const int loop_count = self->super.dimensions;
725	assert(self->left->dimensions == loop_count)((void) sizeof ((self->left->dimensions == loop_count) ?
 1 : 0), __extension__ ({ if (self->left->dimensions ==
 loop_count) ; else __assert_fail ("self->left->dimensions == loop_count"
, "ccv_nnc_micro_core.c", 725, __extension__ __PRETTY_FUNCTION__
); }));
726	assert(self->right->dimensions == loop_count)((void) sizeof ((self->right->dimensions == loop_count)
 ? 1 : 0), __extension__ ({ if (self->right->dimensions
 == loop_count) ; else __assert_fail ("self->right->dimensions == loop_count"
, "ccv_nnc_micro_core.c", 726, __extension__ __PRETTY_FUNCTION__
); }));
727	assert(loop_count <= CCV_NNC_MAX_DIM_ALLOC)((void) sizeof ((loop_count <= (12)) ? 1 : 0), __extension__
 ({ if (loop_count <= (12)) ; else __assert_fail ("loop_count <= CCV_NNC_MAX_DIM_ALLOC"
, "ccv_nnc_micro_core.c", 727, __extension__ __PRETTY_FUNCTION__
); }));
728	int i;
729	ccv_nnc_micro_loop_t* const left_loops = (ccv_nnc_micro_loop_t*)ccmallocmalloc(sizeof(ccv_nnc_micro_loop_t) * loop_count);
730	for (i = 0; i < loop_count; i++)
731		left_loops[i] = ccv_nnc_micro_for_in(ccv_nnc_micro_index_of_value(0), ccv_nnc_micro_index_of_axis_size(GRAD(self->super.id)(2 * (var_count) - 1 - (self->super.id)), i), i);
732	ccv_nnc_micro_loop_t* const right_loops = (ccv_nnc_micro_loop_t*)ccmallocmalloc(sizeof(ccv_nnc_micro_loop_t) * loop_count);
733	for (i = 0; i < loop_count; i++)
734		right_loops[i] = ccv_nnc_micro_for_in(ccv_nnc_micro_index_of_value(0), ccv_nnc_micro_index_of_axis_size(GRAD(self->super.id)(2 * (var_count) - 1 - (self->super.id)), i), i);
735	ccv_nnc_micro_loop_statement_t left_statement;
736	ccv_nnc_micro_loop_statement_t right_statement;
737	switch (self->binary_op)
738	{
739		case CCV_NNC_MICRO_BINARY_OP_DIV:
740			left_statement = ccv_nnc_micro_loop_assignment(
741				ccv_nnc_micro_loop_variable_of_tensor(GRAD(self->left->id)(2 * (var_count) - 1 - (self->left->id)), loop_count, ccv_nnc_micro_index_of_loops(left_loops, loop_count)(ccv_nnc_micro_loop_index_term_t [(12)]){ { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 0 ? left_loops[0].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 1 ? left_loops[1].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 2 ?
 left_loops[2].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 3 ? left_loops[3].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 4 ? left_loops[4].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 5 ?
 left_loops[5].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 6 ? left_loops[6].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 7 ? left_loops[7].id : (ccv_nnc_micro_id_t){} } }),
742				ccv_nnc_micro_loop_expression_of_binary(
743					CCV_NNC_MICRO_BINARY_OP_DIV,
744					ccv_nnc_micro_loop_expression_of_variable(ccv_nnc_micro_loop_variable_of_tensor(GRAD(self->super.id)(2 * (var_count) - 1 - (self->super.id)), loop_count, ccv_nnc_micro_index_of_loops(left_loops, loop_count)(ccv_nnc_micro_loop_index_term_t [(12)]){ { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 0 ? left_loops[0].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 1 ? left_loops[1].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 2 ?
 left_loops[2].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 3 ? left_loops[3].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 4 ? left_loops[4].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 5 ?
 left_loops[5].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 6 ? left_loops[6].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 7 ? left_loops[7].id : (ccv_nnc_micro_id_t){} } })),
745					ccv_nnc_micro_loop_expression_of_variable(ccv_nnc_micro_loop_variable_of_tensor(self->right->id, loop_count, ccv_nnc_micro_index_of_loops(left_loops, loop_count)(ccv_nnc_micro_loop_index_term_t [(12)]){ { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 0 ? left_loops[0].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 1 ? left_loops[1].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 2 ?
 left_loops[2].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 3 ? left_loops[3].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 4 ? left_loops[4].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 5 ?
 left_loops[5].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 6 ? left_loops[6].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 7 ? left_loops[7].id : (ccv_nnc_micro_id_t){} } }))
746				)
747			);
748			right_statement = ccv_nnc_micro_loop_assignment(
749				ccv_nnc_micro_loop_variable_of_tensor(GRAD(self->right->id)(2 * (var_count) - 1 - (self->right->id)), loop_count, ccv_nnc_micro_index_of_loops(right_loops, loop_count)(ccv_nnc_micro_loop_index_term_t [(12)]){ { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 0 ? right_loops[0].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 1 ? right_loops[1].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 2 ?
 right_loops[2].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 3 ? right_loops[3].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 4 ? right_loops[4].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 5 ?
 right_loops[5].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 6 ? right_loops[6].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 7 ? right_loops[7].id : (ccv_nnc_micro_id_t){} } }),
750				ccv_nnc_micro_loop_expression_of_binary(
751					CCV_NNC_MICRO_BINARY_OP_MUL,
752					ccv_nnc_micro_loop_expression_of_binary(
753						CCV_NNC_MICRO_BINARY_OP_DIV,
754						ccv_nnc_micro_loop_expression_of_variable(ccv_nnc_micro_loop_variable_of_tensor(self->super.id, loop_count, ccv_nnc_micro_index_of_loops(right_loops, loop_count)(ccv_nnc_micro_loop_index_term_t [(12)]){ { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 0 ? right_loops[0].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 1 ? right_loops[1].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 2 ?
 right_loops[2].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 3 ? right_loops[3].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 4 ? right_loops[4].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 5 ?
 right_loops[5].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 6 ? right_loops[6].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 7 ? right_loops[7].id : (ccv_nnc_micro_id_t){} } })),
755						ccv_nnc_micro_loop_expression_of_variable(ccv_nnc_micro_loop_variable_of_tensor(self->right->id, loop_count, ccv_nnc_micro_index_of_loops(right_loops, loop_count)(ccv_nnc_micro_loop_index_term_t [(12)]){ { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 0 ? right_loops[0].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 1 ? right_loops[1].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 2 ?
 right_loops[2].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 3 ? right_loops[3].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 4 ? right_loops[4].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 5 ?
 right_loops[5].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 6 ? right_loops[6].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 7 ? right_loops[7].id : (ccv_nnc_micro_id_t){} } }))
756					),
757					ccv_nnc_micro_loop_expression_of_variable(ccv_nnc_micro_loop_variable_of_tensor(GRAD(self->super.id)(2 * (var_count) - 1 - (self->super.id)), loop_count, ccv_nnc_micro_index_of_loops(right_loops, loop_count)(ccv_nnc_micro_loop_index_term_t [(12)]){ { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 0 ? right_loops[0].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 1 ? right_loops[1].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 2 ?
 right_loops[2].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 3 ? right_loops[3].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 4 ? right_loops[4].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 5 ?
 right_loops[5].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 6 ? right_loops[6].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 7 ? right_loops[7].id : (ccv_nnc_micro_id_t){} } }))
758				)
759			);
760			break;
761		case CCV_NNC_MICRO_BINARY_OP_MUL:
762			left_statement = ccv_nnc_micro_loop_assignment(
763				ccv_nnc_micro_loop_variable_of_tensor(GRAD(self->left->id)(2 * (var_count) - 1 - (self->left->id)), loop_count, ccv_nnc_micro_index_of_loops(left_loops, loop_count)(ccv_nnc_micro_loop_index_term_t [(12)]){ { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 0 ? left_loops[0].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 1 ? left_loops[1].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 2 ?
 left_loops[2].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 3 ? left_loops[3].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 4 ? left_loops[4].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 5 ?
 left_loops[5].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 6 ? left_loops[6].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 7 ? left_loops[7].id : (ccv_nnc_micro_id_t){} } }),
764				ccv_nnc_micro_loop_expression_of_binary(
765					CCV_NNC_MICRO_BINARY_OP_MUL,
766					ccv_nnc_micro_loop_expression_of_variable(ccv_nnc_micro_loop_variable_of_tensor(GRAD(self->super.id)(2 * (var_count) - 1 - (self->super.id)), loop_count, ccv_nnc_micro_index_of_loops(left_loops, loop_count)(ccv_nnc_micro_loop_index_term_t [(12)]){ { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 0 ? left_loops[0].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 1 ? left_loops[1].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 2 ?
 left_loops[2].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 3 ? left_loops[3].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 4 ? left_loops[4].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 5 ?
 left_loops[5].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 6 ? left_loops[6].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 7 ? left_loops[7].id : (ccv_nnc_micro_id_t){} } })),
767					ccv_nnc_micro_loop_expression_of_variable(ccv_nnc_micro_loop_variable_of_tensor(self->right->id, loop_count, ccv_nnc_micro_index_of_loops(left_loops, loop_count)(ccv_nnc_micro_loop_index_term_t [(12)]){ { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 0 ? left_loops[0].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 1 ? left_loops[1].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 2 ?
 left_loops[2].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 3 ? left_loops[3].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 4 ? left_loops[4].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 5 ?
 left_loops[5].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 6 ? left_loops[6].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 7 ? left_loops[7].id : (ccv_nnc_micro_id_t){} } }))
768				)
769			);
770			right_statement = ccv_nnc_micro_loop_assignment(
771				ccv_nnc_micro_loop_variable_of_tensor(GRAD(self->right->id)(2 * (var_count) - 1 - (self->right->id)), loop_count, ccv_nnc_micro_index_of_loops(right_loops, loop_count)(ccv_nnc_micro_loop_index_term_t [(12)]){ { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 0 ? right_loops[0].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 1 ? right_loops[1].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 2 ?
 right_loops[2].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 3 ? right_loops[3].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 4 ? right_loops[4].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 5 ?
 right_loops[5].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 6 ? right_loops[6].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 7 ? right_loops[7].id : (ccv_nnc_micro_id_t){} } }),
772				ccv_nnc_micro_loop_expression_of_binary(
773					CCV_NNC_MICRO_BINARY_OP_MUL,
774					ccv_nnc_micro_loop_expression_of_variable(ccv_nnc_micro_loop_variable_of_tensor(self->left->id, loop_count, ccv_nnc_micro_index_of_loops(right_loops, loop_count)(ccv_nnc_micro_loop_index_term_t [(12)]){ { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 0 ? right_loops[0].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 1 ? right_loops[1].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 2 ?
 right_loops[2].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 3 ? right_loops[3].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 4 ? right_loops[4].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 5 ?
 right_loops[5].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 6 ? right_loops[6].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 7 ? right_loops[7].id : (ccv_nnc_micro_id_t){} } })),
775					ccv_nnc_micro_loop_expression_of_variable(ccv_nnc_micro_loop_variable_of_tensor(GRAD(self->super.id)(2 * (var_count) - 1 - (self->super.id)), loop_count, ccv_nnc_micro_index_of_loops(right_loops, loop_count)(ccv_nnc_micro_loop_index_term_t [(12)]){ { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 0 ? right_loops[0].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 1 ? right_loops[1].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 2 ?
 right_loops[2].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 3 ? right_loops[3].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 4 ? right_loops[4].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 5 ?
 right_loops[5].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 6 ? right_loops[6].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 7 ? right_loops[7].id : (ccv_nnc_micro_id_t){} } }))
776				)
777			);
778			break;
779		case CCV_NNC_MICRO_BINARY_OP_PLUS:
780			left_statement = ccv_nnc_micro_loop_assignment(
781				ccv_nnc_micro_loop_variable_of_tensor(GRAD(self->left->id)(2 * (var_count) - 1 - (self->left->id)), loop_count, ccv_nnc_micro_index_of_loops(left_loops, loop_count)(ccv_nnc_micro_loop_index_term_t [(12)]){ { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 0 ? left_loops[0].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 1 ? left_loops[1].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 2 ?
 left_loops[2].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 3 ? left_loops[3].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 4 ? left_loops[4].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 5 ?
 left_loops[5].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 6 ? left_loops[6].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 7 ? left_loops[7].id : (ccv_nnc_micro_id_t){} } }),
782				ccv_nnc_micro_loop_expression_of_variable(ccv_nnc_micro_loop_variable_of_tensor(GRAD(self->super.id)(2 * (var_count) - 1 - (self->super.id)), loop_count, ccv_nnc_micro_index_of_loops(left_loops, loop_count)(ccv_nnc_micro_loop_index_term_t [(12)]){ { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 0 ? left_loops[0].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 1 ? left_loops[1].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 2 ?
 left_loops[2].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 3 ? left_loops[3].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 4 ? left_loops[4].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 5 ?
 left_loops[5].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 6 ? left_loops[6].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 7 ? left_loops[7].id : (ccv_nnc_micro_id_t){} } }))
783			);
784			right_statement = ccv_nnc_micro_loop_assignment(
785				ccv_nnc_micro_loop_variable_of_tensor(GRAD(self->right->id)(2 * (var_count) - 1 - (self->right->id)), loop_count, ccv_nnc_micro_index_of_loops(right_loops, loop_count)(ccv_nnc_micro_loop_index_term_t [(12)]){ { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 0 ? right_loops[0].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 1 ? right_loops[1].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 2 ?
 right_loops[2].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 3 ? right_loops[3].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 4 ? right_loops[4].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 5 ?
 right_loops[5].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 6 ? right_loops[6].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 7 ? right_loops[7].id : (ccv_nnc_micro_id_t){} } }),
786				ccv_nnc_micro_loop_expression_of_variable(ccv_nnc_micro_loop_variable_of_tensor(GRAD(self->super.id)(2 * (var_count) - 1 - (self->super.id)), loop_count, ccv_nnc_micro_index_of_loops(right_loops, loop_count)(ccv_nnc_micro_loop_index_term_t [(12)]){ { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 0 ? right_loops[0].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 1 ? right_loops[1].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 2 ?
 right_loops[2].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 3 ? right_loops[3].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 4 ? right_loops[4].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 5 ?
 right_loops[5].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 6 ? right_loops[6].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 7 ? right_loops[7].id : (ccv_nnc_micro_id_t){} } }))
787			);
788			break;
789		case CCV_NNC_MICRO_BINARY_OP_MINUS:
790			left_statement = ccv_nnc_micro_loop_assignment(
791				ccv_nnc_micro_loop_variable_of_tensor(GRAD(self->left->id)(2 * (var_count) - 1 - (self->left->id)), loop_count, ccv_nnc_micro_index_of_loops(left_loops, loop_count)(ccv_nnc_micro_loop_index_term_t [(12)]){ { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 0 ? left_loops[0].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 1 ? left_loops[1].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 2 ?
 left_loops[2].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 3 ? left_loops[3].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 4 ? left_loops[4].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 5 ?
 left_loops[5].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 6 ? left_loops[6].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 7 ? left_loops[7].id : (ccv_nnc_micro_id_t){} } }),
792				ccv_nnc_micro_loop_expression_of_variable(ccv_nnc_micro_loop_variable_of_tensor(GRAD(self->super.id)(2 * (var_count) - 1 - (self->super.id)), loop_count, ccv_nnc_micro_index_of_loops(left_loops, loop_count)(ccv_nnc_micro_loop_index_term_t [(12)]){ { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 0 ? left_loops[0].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 1 ? left_loops[1].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 2 ?
 left_loops[2].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 3 ? left_loops[3].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 4 ? left_loops[4].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 5 ?
 left_loops[5].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 6 ? left_loops[6].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 7 ? left_loops[7].id : (ccv_nnc_micro_id_t){} } }))
793			);
794			right_statement = ccv_nnc_micro_loop_assignment(
795				ccv_nnc_micro_loop_variable_of_tensor(GRAD(self->right->id)(2 * (var_count) - 1 - (self->right->id)), loop_count, ccv_nnc_micro_index_of_loops(right_loops, loop_count)(ccv_nnc_micro_loop_index_term_t [(12)]){ { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 0 ? right_loops[0].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 1 ? right_loops[1].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 2 ?
 right_loops[2].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 3 ? right_loops[3].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 4 ? right_loops[4].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 5 ?
 right_loops[5].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 6 ? right_loops[6].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 7 ? right_loops[7].id : (ccv_nnc_micro_id_t){} } }),
796				ccv_nnc_micro_loop_expression_of_unary(
797					CCV_NNC_MICRO_UNARY_OP_NEG,
798					ccv_nnc_micro_loop_expression_of_variable(ccv_nnc_micro_loop_variable_of_tensor(GRAD(self->super.id)(2 * (var_count) - 1 - (self->super.id)), loop_count, ccv_nnc_micro_index_of_loops(right_loops, loop_count)(ccv_nnc_micro_loop_index_term_t [(12)]){ { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 0 ? right_loops[0].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 1 ? right_loops[1].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 2 ?
 right_loops[2].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 3 ? right_loops[3].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 4 ? right_loops[4].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 5 ?
 right_loops[5].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 6 ? right_loops[6].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 7 ? right_loops[7].id : (ccv_nnc_micro_id_t){} } }))
799				)
800			);
801			break;
802		case CCV_NNC_MICRO_BINARY_OP_MIN:
803			left_statement = ccv_nnc_micro_loop_assignment(
804				ccv_nnc_micro_loop_variable_of_tensor(GRAD(self->left->id)(2 * (var_count) - 1 - (self->left->id)), loop_count, ccv_nnc_micro_index_of_loops(left_loops, loop_count)(ccv_nnc_micro_loop_index_term_t [(12)]){ { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 0 ? left_loops[0].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 1 ? left_loops[1].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 2 ?
 left_loops[2].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 3 ? left_loops[3].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 4 ? left_loops[4].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 5 ?
 left_loops[5].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 6 ? left_loops[6].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 7 ? left_loops[7].id : (ccv_nnc_micro_id_t){} } }),
805				ccv_nnc_micro_loop_expression_of_ternary(
806					ccv_nnc_micro_loop_expression_of_binary(CCV_NNC_MICRO_BINARY_OP_LESS_THAN,
807						ccv_nnc_micro_loop_expression_of_variable(ccv_nnc_micro_loop_variable_of_tensor(self->right->id, loop_count, ccv_nnc_micro_index_of_loops(left_loops, loop_count)(ccv_nnc_micro_loop_index_term_t [(12)]){ { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 0 ? left_loops[0].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 1 ? left_loops[1].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 2 ?
 left_loops[2].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 3 ? left_loops[3].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 4 ? left_loops[4].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 5 ?
 left_loops[5].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 6 ? left_loops[6].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 7 ? left_loops[7].id : (ccv_nnc_micro_id_t){} } })),
808						ccv_nnc_micro_loop_expression_of_variable(ccv_nnc_micro_loop_variable_of_tensor(self->left->id, loop_count, ccv_nnc_micro_index_of_loops(left_loops, loop_count)(ccv_nnc_micro_loop_index_term_t [(12)]){ { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 0 ? left_loops[0].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 1 ? left_loops[1].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 2 ?
 left_loops[2].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 3 ? left_loops[3].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 4 ? left_loops[4].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 5 ?
 left_loops[5].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 6 ? left_loops[6].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 7 ? left_loops[7].id : (ccv_nnc_micro_id_t){} } }))
809					),
810					ccv_nnc_micro_loop_expression_of_value(0),
811					ccv_nnc_micro_loop_expression_of_variable(ccv_nnc_micro_loop_variable_of_tensor(GRAD(self->super.id)(2 * (var_count) - 1 - (self->super.id)), loop_count, ccv_nnc_micro_index_of_loops(left_loops, loop_count)(ccv_nnc_micro_loop_index_term_t [(12)]){ { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 0 ? left_loops[0].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 1 ? left_loops[1].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 2 ?
 left_loops[2].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 3 ? left_loops[3].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 4 ? left_loops[4].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 5 ?
 left_loops[5].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 6 ? left_loops[6].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 7 ? left_loops[7].id : (ccv_nnc_micro_id_t){} } }))
812				)
813			);
814			right_statement = ccv_nnc_micro_loop_assignment(
815				ccv_nnc_micro_loop_variable_of_tensor(GRAD(self->right->id)(2 * (var_count) - 1 - (self->right->id)), loop_count, ccv_nnc_micro_index_of_loops(right_loops, loop_count)(ccv_nnc_micro_loop_index_term_t [(12)]){ { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 0 ? right_loops[0].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 1 ? right_loops[1].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 2 ?
 right_loops[2].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 3 ? right_loops[3].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 4 ? right_loops[4].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 5 ?
 right_loops[5].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 6 ? right_loops[6].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 7 ? right_loops[7].id : (ccv_nnc_micro_id_t){} } }),
816				ccv_nnc_micro_loop_expression_of_ternary(
817					ccv_nnc_micro_loop_expression_of_binary(CCV_NNC_MICRO_BINARY_OP_LESS_THAN,
818						ccv_nnc_micro_loop_expression_of_variable(ccv_nnc_micro_loop_variable_of_tensor(self->left->id, loop_count, ccv_nnc_micro_index_of_loops(right_loops, loop_count)(ccv_nnc_micro_loop_index_term_t [(12)]){ { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 0 ? right_loops[0].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 1 ? right_loops[1].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 2 ?
 right_loops[2].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 3 ? right_loops[3].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 4 ? right_loops[4].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 5 ?
 right_loops[5].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 6 ? right_loops[6].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 7 ? right_loops[7].id : (ccv_nnc_micro_id_t){} } })),
819						ccv_nnc_micro_loop_expression_of_variable(ccv_nnc_micro_loop_variable_of_tensor(self->right->id, loop_count, ccv_nnc_micro_index_of_loops(right_loops, loop_count)(ccv_nnc_micro_loop_index_term_t [(12)]){ { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 0 ? right_loops[0].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 1 ? right_loops[1].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 2 ?
 right_loops[2].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 3 ? right_loops[3].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 4 ? right_loops[4].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 5 ?
 right_loops[5].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 6 ? right_loops[6].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 7 ? right_loops[7].id : (ccv_nnc_micro_id_t){} } }))
820					),
821					ccv_nnc_micro_loop_expression_of_value(0),
822					ccv_nnc_micro_loop_expression_of_variable(ccv_nnc_micro_loop_variable_of_tensor(GRAD(self->super.id)(2 * (var_count) - 1 - (self->super.id)), loop_count, ccv_nnc_micro_index_of_loops(right_loops, loop_count)(ccv_nnc_micro_loop_index_term_t [(12)]){ { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 0 ? right_loops[0].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 1 ? right_loops[1].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 2 ?
 right_loops[2].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 3 ? right_loops[3].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 4 ? right_loops[4].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 5 ?
 right_loops[5].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 6 ? right_loops[6].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 7 ? right_loops[7].id : (ccv_nnc_micro_id_t){} } }))
823				)
824			);
825			break;
826		case CCV_NNC_MICRO_BINARY_OP_MAX:
827			left_statement = ccv_nnc_micro_loop_assignment(
828				ccv_nnc_micro_loop_variable_of_tensor(GRAD(self->left->id)(2 * (var_count) - 1 - (self->left->id)), loop_count, ccv_nnc_micro_index_of_loops(left_loops, loop_count)(ccv_nnc_micro_loop_index_term_t [(12)]){ { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 0 ? left_loops[0].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 1 ? left_loops[1].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 2 ?
 left_loops[2].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 3 ? left_loops[3].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 4 ? left_loops[4].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 5 ?
 left_loops[5].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 6 ? left_loops[6].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 7 ? left_loops[7].id : (ccv_nnc_micro_id_t){} } }),
829				ccv_nnc_micro_loop_expression_of_ternary(
830					ccv_nnc_micro_loop_expression_of_binary(CCV_NNC_MICRO_BINARY_OP_LESS_THAN,
831						ccv_nnc_micro_loop_expression_of_variable(ccv_nnc_micro_loop_variable_of_tensor(self->left->id, loop_count, ccv_nnc_micro_index_of_loops(left_loops, loop_count)(ccv_nnc_micro_loop_index_term_t [(12)]){ { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 0 ? left_loops[0].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 1 ? left_loops[1].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 2 ?
 left_loops[2].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 3 ? left_loops[3].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 4 ? left_loops[4].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 5 ?
 left_loops[5].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 6 ? left_loops[6].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 7 ? left_loops[7].id : (ccv_nnc_micro_id_t){} } })),
832						ccv_nnc_micro_loop_expression_of_variable(ccv_nnc_micro_loop_variable_of_tensor(self->right->id, loop_count, ccv_nnc_micro_index_of_loops(left_loops, loop_count)(ccv_nnc_micro_loop_index_term_t [(12)]){ { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 0 ? left_loops[0].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 1 ? left_loops[1].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 2 ?
 left_loops[2].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 3 ? left_loops[3].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 4 ? left_loops[4].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 5 ?
 left_loops[5].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 6 ? left_loops[6].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 7 ? left_loops[7].id : (ccv_nnc_micro_id_t){} } }))
833					),
834					ccv_nnc_micro_loop_expression_of_value(0),
835					ccv_nnc_micro_loop_expression_of_variable(ccv_nnc_micro_loop_variable_of_tensor(GRAD(self->super.id)(2 * (var_count) - 1 - (self->super.id)), loop_count, ccv_nnc_micro_index_of_loops(left_loops, loop_count)(ccv_nnc_micro_loop_index_term_t [(12)]){ { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 0 ? left_loops[0].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 1 ? left_loops[1].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 2 ?
 left_loops[2].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 3 ? left_loops[3].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 4 ? left_loops[4].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 5 ?
 left_loops[5].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 6 ? left_loops[6].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 7 ? left_loops[7].id : (ccv_nnc_micro_id_t){} } }))
836				)
837			);
838			right_statement = ccv_nnc_micro_loop_assignment(
839				ccv_nnc_micro_loop_variable_of_tensor(GRAD(self->right->id)(2 * (var_count) - 1 - (self->right->id)), loop_count, ccv_nnc_micro_index_of_loops(right_loops, loop_count)(ccv_nnc_micro_loop_index_term_t [(12)]){ { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 0 ? right_loops[0].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 1 ? right_loops[1].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 2 ?
 right_loops[2].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 3 ? right_loops[3].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 4 ? right_loops[4].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 5 ?
 right_loops[5].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 6 ? right_loops[6].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 7 ? right_loops[7].id : (ccv_nnc_micro_id_t){} } }),
840				ccv_nnc_micro_loop_expression_of_ternary(
841					ccv_nnc_micro_loop_expression_of_binary(CCV_NNC_MICRO_BINARY_OP_LESS_THAN,
842						ccv_nnc_micro_loop_expression_of_variable(ccv_nnc_micro_loop_variable_of_tensor(self->right->id, loop_count, ccv_nnc_micro_index_of_loops(right_loops, loop_count)(ccv_nnc_micro_loop_index_term_t [(12)]){ { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 0 ? right_loops[0].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 1 ? right_loops[1].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 2 ?
 right_loops[2].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 3 ? right_loops[3].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 4 ? right_loops[4].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 5 ?
 right_loops[5].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 6 ? right_loops[6].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 7 ? right_loops[7].id : (ccv_nnc_micro_id_t){} } })),
843						ccv_nnc_micro_loop_expression_of_variable(ccv_nnc_micro_loop_variable_of_tensor(self->left->id, loop_count, ccv_nnc_micro_index_of_loops(right_loops, loop_count)(ccv_nnc_micro_loop_index_term_t [(12)]){ { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 0 ? right_loops[0].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 1 ? right_loops[1].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 2 ?
 right_loops[2].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 3 ? right_loops[3].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 4 ? right_loops[4].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 5 ?
 right_loops[5].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 6 ? right_loops[6].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 7 ? right_loops[7].id : (ccv_nnc_micro_id_t){} } }))
844					),
845					ccv_nnc_micro_loop_expression_of_value(0),
846					ccv_nnc_micro_loop_expression_of_variable(ccv_nnc_micro_loop_variable_of_tensor(GRAD(self->super.id)(2 * (var_count) - 1 - (self->super.id)), loop_count, ccv_nnc_micro_index_of_loops(right_loops, loop_count)(ccv_nnc_micro_loop_index_term_t [(12)]){ { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 0 ? right_loops[0].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 1 ? right_loops[1].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 2 ?
 right_loops[2].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 3 ? right_loops[3].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 4 ? right_loops[4].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 5 ?
 right_loops[5].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 6 ? right_loops[6].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 7 ? right_loops[7].id : (ccv_nnc_micro_id_t){} } }))
847				)
848			);
849			break;
850	}
851	left_loops[loop_count - 1].statement_count = 1;
852	left_loops[loop_count - 1].statements = (ccv_nnc_micro_loop_statement_t*)ccmallocmalloc(sizeof(ccv_nnc_micro_loop_statement_t));
853	left_loops[loop_count - 1].statements[0] = left_statement;
854	right_loops[loop_count - 1].statement_count = 1;
855	right_loops[loop_count - 1].statements = (ccv_nnc_micro_loop_statement_t*)ccmallocmalloc(sizeof(ccv_nnc_micro_loop_statement_t));
856	right_loops[loop_count - 1].statements[0] = right_statement;
857	ccv_nnc_micro_loop_block_t* const blocks = (ccv_nnc_micro_loop_block_t*)ccmallocmalloc(sizeof(ccv_nnc_micro_loop_block_t) * 2);
858	blocks[0] = (ccv_nnc_micro_loop_block_t){
859		.loop_count = loop_count,
860		.loops = left_loops
861	};
862	blocks[1] = (ccv_nnc_micro_loop_block_t){
863		.loop_count = loop_count,
864		.loops = right_loops
865	};
866	return (ccv_nnc_micro_function_t){
867		.block_count = 2,
868		.blocks = blocks
869	};
870}
871 
872static ccv_nnc_micro_tensor_t _ccv_nnc_micro_binary_return_shape(const ccv_nnc_micro_io_t super)
873{
874	struct ccv_nnc_micro_io_binary_s* const self = (struct ccv_nnc_micro_io_binary_s*)super;
875	ccv_nnc_micro_tensor_t var = {};
876	var.dimensions = self->super.dimensions;
877	var.input = self->left->id;
878	var.sibling = self->right->id;
879	return var;
880}
881 
882static const ccv_nnc_micro_io_vtab_t ccv_nnc_micro_io_binary_isa = {
883	.emit = _ccv_nnc_micro_binary_emit,
884	.emit_grad = _ccv_nnc_micro_binary_emit_grad,
885	.return_shape = _ccv_nnc_micro_binary_return_shape
886};
887 
888ccv_nnc_micro_io_t ccv_nnc_micro_binary(const uint32_t op, const ccv_nnc_micro_io_t x, const ccv_nnc_micro_io_t y)
889{
890	struct ccv_nnc_micro_io_binary_s* const self = (struct ccv_nnc_micro_io_binary_s*)cccalloccalloc(1, sizeof(struct ccv_nnc_micro_io_binary_s));
891	self->super.isa = &ccv_nnc_micro_io_binary_isa;
892	self->super.dimensions = x->dimensions;
893	self->super.id = 0;
894	self->super.inputs = &self->left;
895	self->super.input_size = 2;
896	self->binary_op = op;
897	self->left = x;
898	self->right = y;
899	assert(x->dimensions == y->dimensions)((void) sizeof ((x->dimensions == y->dimensions) ? 1 : 0
), __extension__ ({ if (x->dimensions == y->dimensions)
 ; else __assert_fail ("x->dimensions == y->dimensions"
, "ccv_nnc_micro_core.c", 899, __extension__ __PRETTY_FUNCTION__
); }));
900	return (ccv_nnc_micro_io_t)self;
901}
902 
903struct ccv_nnc_micro_io_reduce_s {
904	struct ccv_nnc_micro_io_s super;
905	uint32_t reduce_op;
906	int axis_count;
907	ccv_nnc_micro_io_t x;
908	int axis[1];
909};
910 
911static CCV_WARN_UNUSED(ccv_nnc_micro_function_t)ccv_nnc_micro_function_t __attribute__((warn_unused_result)) _ccv_nnc_micro_reduce_emit(const ccv_nnc_micro_io_t super)
912{
913	struct ccv_nnc_micro_io_reduce_s* const self = (struct ccv_nnc_micro_io_reduce_s*)super;
914	const int loop_count = self->super.dimensions;
915	assert(self->x->dimensions == loop_count)((void) sizeof ((self->x->dimensions == loop_count) ? 1
 : 0), __extension__ ({ if (self->x->dimensions == loop_count
) ; else __assert_fail ("self->x->dimensions == loop_count"
, "ccv_nnc_micro_core.c", 915, __extension__ __PRETTY_FUNCTION__
); }));
916	// If axis_count == loop_count, we need extra loop to reduce.
917	int has_extra_loop = (self->axis_count == loop_count);
918	ccv_nnc_micro_loop_t* const loops = (ccv_nnc_micro_loop_t*)ccmallocmalloc(sizeof(ccv_nnc_micro_loop_t) * (loop_count + has_extra_loop));
919	int i, j, k;
920	int8_t reduce_axis[loop_count];
921	memset(reduce_axis, 0, sizeof(int8_t) * loop_count);
922	for (i = 0; i < self->axis_count; i++)
923		reduce_axis[self->axis[i]] = 1;
924	j = 0;
925	// If loop_count == reduce_axis_count, we have extra loop for carried variables and blocks.
926	if (has_extra_loop)
927	{
928		loops[0] = ccv_nnc_micro_for_in(ccv_nnc_micro_index_of_value(0), ccv_nnc_micro_index_of_value(1), 0);
929		k = 1;
930	} else
931		k = loop_count - self->axis_count;
932	for (i = 0; i < loop_count; i++)
933		if (reduce_axis[i])
934		{
935			loops[k] = ccv_nnc_micro_for_in(ccv_nnc_micro_index_of_value(0), ccv_nnc_micro_index_of_axis_size(self->x->id, i), i + has_extra_loop);
936			++k;
937		} else {
938			loops[j] = ccv_nnc_micro_for_in(ccv_nnc_micro_index_of_value(0), ccv_nnc_micro_index_of_axis_size(self->x->id, i), i + has_extra_loop);
939			++j;
940		}
941	const int carried_loop_idx = has_extra_loop ? 0 : loop_count - self->axis_count - 1;
942	loops[carried_loop_idx].carried_count = 1;
943	loops[carried_loop_idx].carrieds = (ccv_nnc_micro_loop_carried_t*)ccmallocmalloc(sizeof(ccv_nnc_micro_loop_carried_t));
944	loops[carried_loop_idx].carrieds[0] = ccv_nnc_micro_loop_carried(self->reduce_op, 0);
945	j = 0;
946	k = has_extra_loop ? 1 : loop_count - self->axis_count;
947	// If loop_count == reduce_axis_count, we have extra loop for carrieds and block.
948	ccv_nnc_micro_loop_index_term_t index[CCV_NNC_MAX_DIM_ALLOC(12)];
949	for (i = 0; i < loop_count; i++)
950		if (reduce_axis[i])
951		{
952			index[i] = ccv_nnc_micro_index_of_id(loops[k].id);
953			++k;
954		} else {
955			index[i] = ccv_nnc_micro_index_of_id(loops[j].id);
956			++j;
957		}
958	ccv_nnc_micro_loop_statement_t statement = ccv_nnc_micro_loop_compound_assignment_of_id(
959		loops[carried_loop_idx].carrieds[0].id,
960		ccv_nnc_micro_loop_expression_of_variable(ccv_nnc_micro_loop_variable_of_tensor(self->x->id, loop_count, index))
961	);
962	loops[carried_loop_idx + self->axis_count].statement_count = 1;
963	loops[carried_loop_idx + self->axis_count].statements = (ccv_nnc_micro_loop_statement_t*)ccmallocmalloc(sizeof(ccv_nnc_micro_loop_statement_t));
964	loops[carried_loop_idx + self->axis_count].statements[0] = statement;
965	j = 0;
966	for (i = 0; i < loop_count; i++)
967		if (reduce_axis[i])
968			index[i] = ccv_nnc_micro_index_of_value(0);
969		else {
970			index[i] = ccv_nnc_micro_index_of_id(loops[j].id);
971			++j;
972		}
973	statement = ccv_nnc_micro_loop_assignment(
974		ccv_nnc_micro_loop_variable_of_tensor(self->super.id, loop_count, index),
975		ccv_nnc_micro_loop_expression_of_id(loops[carried_loop_idx].carrieds[0].id)
976	);
977	loops[carried_loop_idx].statement_count = 1;
978	loops[carried_loop_idx].statements = (ccv_nnc_micro_loop_statement_t*)ccmallocmalloc(sizeof(ccv_nnc_micro_loop_statement_t));
979	loops[carried_loop_idx].statements[0] = statement;
980	return (ccv_nnc_micro_function_t){
981		.block_count = 1,
982		.one_block = {
983			.carried_count = 1,
984			.loop_count = loop_count + has_extra_loop,
985			.loops = loops
986		}
987	};
988}
989 
990static CCV_WARN_UNUSED(ccv_nnc_micro_function_t)ccv_nnc_micro_function_t __attribute__((warn_unused_result)) _ccv_nnc_micro_reduce_emit_grad(const ccv_nnc_micro_io_t super, const int var_count)
991{
992	struct ccv_nnc_micro_io_reduce_s* const self = (struct ccv_nnc_micro_io_reduce_s*)super;
993	assert(self->reduce_op == CCV_NNC_MICRO_REDUCE_OP_SUM)((void) sizeof ((self->reduce_op == CCV_NNC_MICRO_REDUCE_OP_SUM
) ? 1 : 0), __extension__ ({ if (self->reduce_op == CCV_NNC_MICRO_REDUCE_OP_SUM
) ; else __assert_fail ("self->reduce_op == CCV_NNC_MICRO_REDUCE_OP_SUM"
, "ccv_nnc_micro_core.c", 993, __extension__ __PRETTY_FUNCTION__
); })); // I haven't figure out how to do mean without add additional opcode.
994	const int loop_count = self->super.dimensions;
995	assert(self->x->dimensions == loop_count)((void) sizeof ((self->x->dimensions == loop_count) ? 1
 : 0), __extension__ ({ if (self->x->dimensions == loop_count
) ; else __assert_fail ("self->x->dimensions == loop_count"
, "ccv_nnc_micro_core.c", 995, __extension__ __PRETTY_FUNCTION__
); }));
996	ccv_nnc_micro_loop_t* const loops = (ccv_nnc_micro_loop_t*)ccmallocmalloc(sizeof(ccv_nnc_micro_loop_t) * loop_count);
997	int i, j, k;
998	int8_t reduce_axis[loop_count];
999	memset(reduce_axis, 0, sizeof(int8_t) * loop_count);
1000	for (i = 0; i < self->axis_count; i++)
1001		reduce_axis[self->axis[i]] = 1;
1002	j = 0;
1003	k = loop_count - self->axis_count;
1004	for (i = 0; i < loop_count; i++)
1005		if (reduce_axis[i])
1006		{
1007			loops[k] = ccv_nnc_micro_for_in(ccv_nnc_micro_index_of_value(0), ccv_nnc_micro_index_of_axis_size(GRAD(self->x->id)(2 * (var_count) - 1 - (self->x->id)), i), i);
1008			++k;
1009		} else {
1010			loops[j] = ccv_nnc_micro_for_in(ccv_nnc_micro_index_of_value(0), ccv_nnc_micro_index_of_axis_size(GRAD(self->x->id)(2 * (var_count) - 1 - (self->x->id)), i), i);
1011			++j;
1012		}
1013	j = 0;
1014	k = loop_count - self->axis_count;
1015	// If loop_count == reduce_axis_count, we have extra loop for carrieds and block.
1016	ccv_nnc_micro_loop_index_term_t index[CCV_NNC_MAX_DIM_ALLOC(12)];
1017	for (i = 0; i < loop_count; i++)
1018		if (reduce_axis[i])
1019		{
1020			index[i] = ccv_nnc_micro_index_of_id(loops[k].id);
1021			++k;
1022		} else {
1023			index[i] = ccv_nnc_micro_index_of_id(loops[j].id);
1024			++j;
1025		}
1026	j = 0;
1027	ccv_nnc_micro_loop_index_term_t reduced_index[CCV_NNC_MAX_DIM_ALLOC(12)];
1028	for (i = 0; i < loop_count; i++)
1029		if (reduce_axis[i])
1030			reduced_index[i] = ccv_nnc_micro_index_of_value(0);
1031		else {
1032			reduced_index[i] = ccv_nnc_micro_index_of_id(loops[j].id);
1033			++j;
1034		}
1035	ccv_nnc_micro_loop_statement_t statement = ccv_nnc_micro_loop_assignment(
1036		ccv_nnc_micro_loop_variable_of_tensor(GRAD(self->x->id)(2 * (var_count) - 1 - (self->x->id)), loop_count, index),
1037		ccv_nnc_micro_loop_expression_of_variable(ccv_nnc_micro_loop_variable_of_tensor(GRAD(self->super.id)(2 * (var_count) - 1 - (self->super.id)), loop_count, reduced_index))
1038	);
1039	loops[loop_count - 1].statement_count = 1;
1040	loops[loop_count - 1].statements = (ccv_nnc_micro_loop_statement_t*)ccmallocmalloc(sizeof(ccv_nnc_micro_loop_statement_t));
1041	loops[loop_count - 1].statements[0] = statement;
1042	return (ccv_nnc_micro_function_t){
1043		.block_count = 1,
1044		.one_block = {
1045			.carried_count = 1,
1046			.loop_count = loop_count,
1047			.loops = loops
1048		}
1049	};
1050}
1051 
1052static ccv_nnc_micro_tensor_t _ccv_nnc_micro_reduce_return_shape(const ccv_nnc_micro_io_t super)
1053{
1054	struct ccv_nnc_micro_io_reduce_s* const self = (struct ccv_nnc_micro_io_reduce_s*)super;
1055	ccv_nnc_micro_tensor_t var = {};
1056	var.dimensions = self->super.dimensions;
1057	var.input = self->x->id;
1058	var.sibling = -1;
1059	var.shape = (ccv_nnc_micro_loop_index_term_t*)ccmallocmalloc(sizeof(ccv_nnc_micro_loop_index_term_t) * self->super.dimensions);
1060	int i;
1061	for (i = 0; i < self->super.dimensions; i++)
1062		var.shape[i] = ccv_nnc_micro_index_of_axis_size(self->x->id, i);
1063	for (i = 0; i < self->axis_count; i++)
1064		var.shape[self->axis[i]] = ccv_nnc_micro_index_of_value(1);
1065	return var;
1066}
1067 
1068static const ccv_nnc_micro_io_vtab_t ccv_nnc_micro_io_reduce_isa = {
1069	.emit = _ccv_nnc_micro_reduce_emit,
1070	.emit_grad = _ccv_nnc_micro_reduce_emit_grad,
1071	.return_shape = _ccv_nnc_micro_reduce_return_shape
1072};
1073 
1074ccv_nnc_micro_io_t ccv_nnc_micro_reduce(const uint8_t op, const int* const axis, const int axis_count, const ccv_nnc_micro_io_t x)
1075{
1076	struct ccv_nnc_micro_io_reduce_s* const self = (struct ccv_nnc_micro_io_reduce_s*)cccalloccalloc(1, sizeof(struct ccv_nnc_micro_io_reduce_s) + sizeof(int) * (axis_count - 1));
1077	self->super.isa = &ccv_nnc_micro_io_reduce_isa;
1078	self->super.dimensions = x->dimensions;
1079	self->super.id = 0;
1080	self->super.inputs = &self->x;
1081	self->super.input_size = 1;
1082	self->reduce_op = op;
1083	self->x = x;
1084	self->axis_count = axis_count;
1085	assert(axis_count <= x->dimensions)((void) sizeof ((axis_count <= x->dimensions) ? 1 : 0),
 __extension__ ({ if (axis_count <= x->dimensions) ; else
 __assert_fail ("axis_count <= x->dimensions", "ccv_nnc_micro_core.c"
, 1085, __extension__ __PRETTY_FUNCTION__); }));
1086	int i;
1087	for (i = 0; i < axis_count; i++)
1088	{ assert(axis[i] < x->dimensions)((void) sizeof ((axis[i] < x->dimensions) ? 1 : 0), __extension__
 ({ if (axis[i] < x->dimensions) ; else __assert_fail (
"axis[i] < x->dimensions", "ccv_nnc_micro_core.c", 1088
, __extension__ __PRETTY_FUNCTION__); })); }
1089	memcpy(self->axis, axis, sizeof(int) * axis_count);
1090	return (ccv_nnc_micro_io_t)self;
1091}
1092 
1093struct ccv_nnc_micro_io_select_s {
1094	struct ccv_nnc_micro_io_s super;
1095	int axis;
1096	ccv_nnc_micro_io_t x;
1097	ccv_nnc_micro_io_t index;
1098};
1099 
1100static CCV_WARN_UNUSED(ccv_nnc_micro_function_t)ccv_nnc_micro_function_t __attribute__((warn_unused_result)) _ccv_nnc_micro_select_emit(const ccv_nnc_micro_io_t super)
1101{
1102	struct ccv_nnc_micro_io_select_s* const self = (struct ccv_nnc_micro_io_select_s*)super;
1103	const int loop_count = self->super.dimensions;
1104	assert(self->x->dimensions == loop_count)((void) sizeof ((self->x->dimensions == loop_count) ? 1
 : 0), __extension__ ({ if (self->x->dimensions == loop_count
) ; else __assert_fail ("self->x->dimensions == loop_count"
, "ccv_nnc_micro_core.c", 1104, __extension__ __PRETTY_FUNCTION__
); }));
1105	assert(self->index->dimensions == loop_count)((void) sizeof ((self->index->dimensions == loop_count)
 ? 1 : 0), __extension__ ({ if (self->index->dimensions
 == loop_count) ; else __assert_fail ("self->index->dimensions == loop_count"
, "ccv_nnc_micro_core.c", 1105, __extension__ __PRETTY_FUNCTION__
); }));
1106	ccv_nnc_micro_loop_t* const loops = (ccv_nnc_micro_loop_t*)ccmallocmalloc(sizeof(ccv_nnc_micro_loop_t) * loop_count);
1107	int i;
1108	for (i = 0; i < loop_count; i++)
1109	{
1110		if (i == self->axis)
1111			loops[i] = ccv_nnc_micro_for_in(ccv_nnc_micro_index_of_value(0), ccv_nnc_micro_index_of_value(1), i);
1112		else
1113			loops[i] = ccv_nnc_micro_for_in(ccv_nnc_micro_index_of_value(0), ccv_nnc_micro_index_of_axis_size(self->super.id, i), i);
1114	}
1115	ccv_nnc_micro_loop_index_term_t index[CCV_NNC_MAX_DIM_ALLOC(12)];
1116	for (i = 0; i < loop_count; i++)
1117	{
1118		if (i == self->axis)
1119			index[i] = ccv_nnc_micro_index_of_id(ccv_nnc_micro_id_of_tensor(self->index->id));
1120		else
1121			index[i] = ccv_nnc_micro_index_of_id(loops[i].id);
1122	}
1123	const ccv_nnc_micro_loop_statement_t statement = ccv_nnc_micro_loop_assignment(
1124		ccv_nnc_micro_loop_variable_of_tensor(self->super.id, loop_count, ccv_nnc_micro_index_of_loops(loops, loop_count)(ccv_nnc_micro_loop_index_term_t [(12)]){ { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 0 ? loops[0].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 1 ? loops[1].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 2 ? loops[2].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 3 ? loops[3].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 4 ? loops[4].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 5 ? loops[5].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 6 ? loops[6].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 7 ? loops[7].id : (ccv_nnc_micro_id_t){} } }),
1125		ccv_nnc_micro_loop_expression_of_variable(ccv_nnc_micro_loop_variable_of_tensor(self->x->id, loop_count, index))
1126	);
1127	loops[loop_count - 1].statement_count = 1;
1128	loops[loop_count - 1].statements = (ccv_nnc_micro_loop_statement_t*)ccmallocmalloc(sizeof(ccv_nnc_micro_loop_statement_t));
1129	loops[loop_count - 1].statements[0] = statement;
1130	return (ccv_nnc_micro_function_t){
1131		.block_count = 1,
1132		.one_block = {
1133			.loop_count = loop_count,
1134			.loops = loops
1135		}
1136	};
1137}
1138 
1139static CCV_WARN_UNUSED(ccv_nnc_micro_function_t)ccv_nnc_micro_function_t __attribute__((warn_unused_result)) _ccv_nnc_micro_select_emit_grad(const ccv_nnc_micro_io_t super, const int var_count)
1140{
1141	struct ccv_nnc_micro_io_select_s* const self = (struct ccv_nnc_micro_io_select_s*)super;
1142	const int loop_count = self->super.dimensions;
1143	assert(self->x->dimensions == loop_count)((void) sizeof ((self->x->dimensions == loop_count) ? 1
 : 0), __extension__ ({ if (self->x->dimensions == loop_count
) ; else __assert_fail ("self->x->dimensions == loop_count"
, "ccv_nnc_micro_core.c", 1143, __extension__ __PRETTY_FUNCTION__
); }));
1144	assert(self->index->dimensions == loop_count)((void) sizeof ((self->index->dimensions == loop_count)
 ? 1 : 0), __extension__ ({ if (self->index->dimensions
 == loop_count) ; else __assert_fail ("self->index->dimensions == loop_count"
, "ccv_nnc_micro_core.c", 1144, __extension__ __PRETTY_FUNCTION__
); }));
1145	ccv_nnc_micro_loop_t* const reset_loops = (ccv_nnc_micro_loop_t*)ccmallocmalloc(sizeof(ccv_nnc_micro_loop_t) * loop_count);
1146	int i;
1147	for (i = 0; i < loop_count; i++)
1148		reset_loops[i] = ccv_nnc_micro_for_in(ccv_nnc_micro_index_of_value(0), ccv_nnc_micro_index_of_axis_size(GRAD(self->x->id)(2 * (var_count) - 1 - (self->x->id)), i), i);
1149	const ccv_nnc_micro_loop_statement_t reset_statement = ccv_nnc_micro_loop_assignment(
1150		ccv_nnc_micro_loop_variable_of_tensor(GRAD(self->x->id)(2 * (var_count) - 1 - (self->x->id)), loop_count, ccv_nnc_micro_index_of_loops(reset_loops, loop_count)(ccv_nnc_micro_loop_index_term_t [(12)]){ { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 0 ? reset_loops[0].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 1 ? reset_loops[1].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 2 ?
 reset_loops[2].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 3 ? reset_loops[3].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 4 ? reset_loops[4].id : (ccv_nnc_micro_id_t){} }, { .type
 = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count > 5 ?
 reset_loops[5].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 6 ? reset_loops[6].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 7 ? reset_loops[7].id : (ccv_nnc_micro_id_t){} } }),
1151		ccv_nnc_micro_loop_expression_of_value(0)
1152	);
1153	reset_loops[loop_count - 1].statement_count = 1;
1154	reset_loops[loop_count - 1].statements = (ccv_nnc_micro_loop_statement_t*)ccmallocmalloc(sizeof(ccv_nnc_micro_loop_statement_t));
1155	reset_loops[loop_count - 1].statements[0] = reset_statement;
1156	ccv_nnc_micro_loop_t* const loops = (ccv_nnc_micro_loop_t*)ccmallocmalloc(sizeof(ccv_nnc_micro_loop_t) * loop_count);
1157	for (i = 0; i < loop_count; i++)
1158		loops[i] = ccv_nnc_micro_for_in(ccv_nnc_micro_index_of_value(0), ccv_nnc_micro_index_of_axis_size(GRAD(self->x->id)(2 * (var_count) - 1 - (self->x->id)), i), i);
1159	ccv_nnc_micro_loop_index_term_t index[CCV_NNC_MAX_DIM_ALLOC(12)];
1160	for (i = 0; i < loop_count; i++)
1161	{
1162		if (i == self->axis)
1163			index[i] = ccv_nnc_micro_index_of_id(ccv_nnc_micro_id_of_tensor(self->index->id));
1164		else
1165			index[i] = ccv_nnc_micro_index_of_id(loops[i].id);
1166	}
1167	// This is only for x, nothing for index.
1168	const ccv_nnc_micro_loop_statement_t statement = ccv_nnc_micro_loop_compound_assignment_of_tensor(
1169		ccv_nnc_micro_loop_variable_of_tensor(GRAD(self->x->id)(2 * (var_count) - 1 - (self->x->id)), loop_count, index),
1170		ccv_nnc_micro_loop_expression_of_variable(ccv_nnc_micro_loop_variable_of_tensor(GRAD(self->super.id)(2 * (var_count) - 1 - (self->super.id)), loop_count, ccv_nnc_micro_index_of_loops(loops, loop_count)(ccv_nnc_micro_loop_index_term_t [(12)]){ { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 0 ? loops[0].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 1 ? loops[1].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 2 ? loops[2].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 3 ? loops[3].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 4 ? loops[4].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 5 ? loops[5].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = loop_count > 6 ? loops[6].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = loop_count
 > 7 ? loops[7].id : (ccv_nnc_micro_id_t){} } }))
1171	);
1172	loops[loop_count - 1].statement_count = 1;
1173	loops[loop_count - 1].statements = (ccv_nnc_micro_loop_statement_t*)ccmallocmalloc(sizeof(ccv_nnc_micro_loop_statement_t));
1174	loops[loop_count - 1].statements[0] = statement;
1175	ccv_nnc_micro_loop_block_t* const blocks = (ccv_nnc_micro_loop_block_t*)ccmallocmalloc(sizeof(ccv_nnc_micro_loop_block_t) * 2);
1176	blocks[0] = (ccv_nnc_micro_loop_block_t){
1177		.loop_count = loop_count,
1178		.loops = reset_loops
1179	};
1180	blocks[1] = (ccv_nnc_micro_loop_block_t){
1181		.loop_count = loop_count,
1182		.loops = loops
1183	};
1184	return (ccv_nnc_micro_function_t){
1185		.block_count = 2,
1186		.blocks = blocks
1187	};
1188}
1189 
1190static ccv_nnc_micro_tensor_t _ccv_nnc_micro_select_return_shape(const ccv_nnc_micro_io_t super)
1191{
1192	struct ccv_nnc_micro_io_select_s* const self = (struct ccv_nnc_micro_io_select_s*)super;
1193	ccv_nnc_micro_tensor_t var = {};
1194	var.dimensions = self->super.dimensions;
1195	var.input = self->x->id;
1196	var.sibling = -1;
1197	var.shape = (ccv_nnc_micro_loop_index_term_t*)ccmallocmalloc(sizeof(ccv_nnc_micro_loop_index_term_t) * self->super.dimensions);
1198	int i;
1199	for (i = 0; i < self->super.dimensions; i++)
1200	{
1201		if (i != self->axis)
1202			var.shape[i] = ccv_nnc_micro_index_of_axis_size(self->x->id, i);
1203		else
1204			var.shape[i] = ccv_nnc_micro_index_of_value(1);
1205	}
1206	return var;
1207}
1208 
1209static const ccv_nnc_micro_io_vtab_t ccv_nnc_micro_io_select_isa = {
1210	.emit = _ccv_nnc_micro_select_emit,
1211	.emit_grad = _ccv_nnc_micro_select_emit_grad,
1212	.return_shape = _ccv_nnc_micro_select_return_shape
1213};
1214 
1215ccv_nnc_micro_io_t ccv_nnc_micro_select(const int axis, const ccv_nnc_micro_io_t x, const ccv_nnc_micro_io_t index)
1216{
1217	struct ccv_nnc_micro_io_select_s* const self = (struct ccv_nnc_micro_io_select_s*)cccalloccalloc(1, sizeof(struct ccv_nnc_micro_io_select_s));
1218	self->super.isa = &ccv_nnc_micro_io_select_isa;
1219	self->super.dimensions = x->dimensions;
1220	self->super.id = 0;
1221	self->super.inputs = &self->x;
1222	self->super.input_size = 2;
1223	self->x = x;
1224	self->index = index;
1225	self->axis = axis;
1226	assert(axis <= CCV_NNC_MAX_DIM_ALLOC)((void) sizeof ((axis <= (12)) ? 1 : 0), __extension__ ({ if
 (axis <= (12)) ; else __assert_fail ("axis <= CCV_NNC_MAX_DIM_ALLOC"
, "ccv_nnc_micro_core.c", 1226, __extension__ __PRETTY_FUNCTION__
); }));
1227	return (ccv_nnc_micro_io_t)self;
1228}

←

./_ccv_nnc_micro.h

1/**********************************************************
2 * C-based/Cached/Core Computer Vision Library
3 * Liu Liu, 2010-02-01
4 **********************************************************/
5 
6/**********************************************************
7 * CCV - Neural Network Collection
8 **********************************************************/
9 
10#ifndef GUARD_ccv_nnc_micro_internal_h
11#define GUARD_ccv_nnc_micro_internal_h
12 
13#include "ccv_nnc.h"
14 
15enum {
16	CCV_NNC_MICRO_LOOP_ID,
17	CCV_NNC_MICRO_LOOP_CARRIED_ID,
18	CCV_NNC_MICRO_AXIS_SIZE_ID,
19	CCV_NNC_MICRO_TENSOR_ID,
20	CCV_NNC_MICRO_SCALAR_ID,
21};
22 
23typedef struct {
24	uint8_t type;
25	uint8_t d; // Only used for axis_size, identify which axis for a tensor.
26	int16_t id;
27} ccv_nnc_micro_id_t;
28 
29typedef struct {
30	ccv_nnc_micro_id_t left;
31	ccv_nnc_micro_id_t right;
32} ccv_nnc_micro_id_equal_assertion_t;
33 
34enum {
35	CCV_NNC_MICRO_LOOP_INDEX_TYPE_NONE,
36	CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID,
37	CCV_NNC_MICRO_LOOP_INDEX_TYPE_VAL,
38	CCV_NNC_MICRO_LOOP_INDEX_TYPE_BINARY,
39	CCV_NNC_MICRO_LOOP_INDEX_TYPE_UNBOUND_SCALAR, // Unbounded scalar variable, shouldn't be there after fully-evaluated.
40};
41 
42typedef struct ccv_nnc_micro_loop_index_binary_s ccv_nnc_micro_loop_index_binary_t;
43 
44typedef struct {
45	int type;
46	union {
47		char* name; // binding variable name.
48		ccv_nnc_micro_id_t id;
49		int immediate_value;
50		ccv_nnc_micro_loop_index_binary_t* binary;
51	};
52} ccv_nnc_micro_loop_index_term_t;
53 
54struct ccv_nnc_micro_loop_index_binary_s {
55	int op;
56	ccv_nnc_micro_loop_index_term_t left;
57	ccv_nnc_micro_loop_index_term_t right;
58};
59 
60typedef struct {
61	ccv_nnc_micro_id_t id;
62	int index_count;
63	int no_check_bound[CCV_NNC_MAX_DIM_ALLOC(12)];
64	ccv_nnc_micro_loop_index_term_t index[CCV_NNC_MAX_DIM_ALLOC(12)];
65} ccv_nnc_micro_loop_variable_t;
66 
67enum {
68	CCV_NNC_MICRO_LOOP_EXPR_TYPE_ID,
69	CCV_NNC_MICRO_LOOP_EXPR_TYPE_VAL,
70	CCV_NNC_MICRO_LOOP_EXPR_TYPE_VAR,
71	CCV_NNC_MICRO_LOOP_EXPR_TYPE_UNARY,
72	CCV_NNC_MICRO_LOOP_EXPR_TYPE_BINARY,
73	CCV_NNC_MICRO_LOOP_EXPR_TYPE_TERNAY,
74};
75 
76typedef struct ccv_nnc_micro_loop_expression_s ccv_nnc_micro_loop_expression_t;
77 
78typedef struct {
79	int unary_op;
80	ccv_nnc_micro_loop_expression_t* x;
81} ccv_nnc_micro_loop_unary_t;
82 
83typedef struct {
84	int binary_op;
85	ccv_nnc_micro_loop_expression_t* left;
86	ccv_nnc_micro_loop_expression_t* right;
87} ccv_nnc_micro_loop_binary_t;
88 
89typedef struct {
90	ccv_nnc_micro_loop_expression_t* pivot; // If it is 0, choose left, otherwise choose right.
91	ccv_nnc_micro_loop_expression_t* left;
92	ccv_nnc_micro_loop_expression_t* right;
93} ccv_nnc_micro_loop_ternary_t;
94 
95struct ccv_nnc_micro_loop_expression_s  {
96	int type;
97	union {
98		ccv_nnc_micro_id_t id; // If this is a compound assignment, the id to that.
99		ccv_nnc_micro_scalar_t immediate_value;
100		ccv_nnc_micro_loop_variable_t variable;
101		ccv_nnc_micro_loop_unary_t unary;
102		ccv_nnc_micro_loop_binary_t binary;
103		ccv_nnc_micro_loop_ternary_t ternary;
104	};
105};
106 
107typedef struct {
108	ccv_nnc_micro_loop_variable_t lvalue;
109	ccv_nnc_micro_loop_expression_t rvalue;
110} ccv_nnc_micro_loop_assignment_t;
111 
112 
113typedef struct  {
114	int type;
115	union {
116		ccv_nnc_micro_id_t id; // If this is a compound assignment, the id to that.
117		ccv_nnc_micro_loop_variable_t variable; // This only implies PLUS at the moment.
118	};
119} ccv_nnc_micro_loop_compound_assignment_lvalue_t;
120 
121typedef struct {
122	ccv_nnc_micro_loop_compound_assignment_lvalue_t lvalue; // It shouldn't be unary or binary, only id or variable.
123	ccv_nnc_micro_loop_expression_t rvalue;
124} ccv_nnc_micro_loop_compound_assignment_t;
125 
126enum {
127	CCV_NNC_MICRO_LOOP_STATEMENT_TYPE_ASSIGNMENT,
128	CCV_NNC_MICRO_LOOP_STATEMENT_TYPE_COMPOUND_ASSIGNMENT,
129};
130 
131// A generic statement within a loop.
132// For our purpose, there will be two types of generic statement:
133// an assignment statement (for tensors).
134// an compound assignment statement (for loop carry overs).
135typedef struct {
136	int type;
137	union {
138		ccv_nnc_micro_loop_assignment_t assignment;
139		ccv_nnc_micro_loop_compound_assignment_t compound_assignment;
140	};
141} ccv_nnc_micro_loop_statement_t;
142 
143typedef struct {
144	ccv_nnc_micro_id_t id;
145} ccv_nnc_micro_loop_carried_t; // The accumulating register.
146 
147// A loop is identified with a loop counter id, some blocks inside this loop, some carry overs within
148// this loop and can be used outside of this loop.
149// If this loop has another loop nested (represented as the next one in the ccv_nnc_micro_nested_loop_t)
150// all blocks are executed after the nested loop finished.
151typedef struct {
152	ccv_nnc_micro_id_t id; // Loop counter's id, this will be used for indexing.
153	int carried_count;
154	int statement_count;
155	ccv_nnc_micro_loop_index_term_t start_index;
156	ccv_nnc_micro_loop_index_term_t end_index;
157	ccv_nnc_micro_loop_carried_t* carrieds;
158	ccv_nnc_micro_loop_statement_t* statements;
159} ccv_nnc_micro_loop_t;
160 
161// A loop block contains many loops within each other.
162typedef struct {
163	int carried_count;
164	int loop_count;
165	ccv_nnc_micro_loop_t* loops;
166} ccv_nnc_micro_loop_block_t;
167 
168typedef struct {
169	int input; // The one it derives its shape from. If shape is nullptr, it has the same shape as input. -1 means it is an input.
170	int sibling; // The sibling that has the same shape.
171	int dimensions;
172	int no_alloc; // No need to allocate this tensor.
173	ccv_nnc_micro_loop_index_term_t* shape;
174} ccv_nnc_micro_tensor_t;
175 
176// A function contains a list of loop blocks that will be executed serially.
177// It also contains references to its dependencies so a function knows its inputs / outputs.
178typedef struct {
179	int block_count;
180	union {
181		ccv_nnc_micro_loop_block_t* blocks; // Heap-allocated blocks.
182		ccv_nnc_micro_loop_block_t one_block; // In-place block to optimize memory allocation for one block cases.
183	};
184} ccv_nnc_micro_function_t;
185 
186typedef struct {
187	int input_size; // Size of inputs.
188	int output_size; // Size of outputs.
189	// Combined ops only have global vars, there is no local vars. All vars are tensors.
190	int var_count;
191	// loops are our constructs of IR ops really. It is hierarchical.
192	int function_count;
193	int* inputs;
194	int* outputs;
195	ccv_nnc_micro_tensor_t* vars;
196	ccv_nnc_micro_function_t* functions;
197} ccv_nnc_micro_program_t;
198 
199// A combined op is constructed with many nested loops. These loops may have data dependencies
200// between each other, but they are ordered in topological order to make sure one is finished
201// after the another.
202struct ccv_nnc_micro_combine_s {
203	int parameter_size; // Size of parameters.
204	ccv_nnc_micro_program_t forward;
205	ccv_nnc_micro_program_t backward;
206	ccv_array_t* equal_assertions;
207};
208 
209typedef uint32_t(*ccv_nnc_micro_scalar_lookup_f)(const void* const context, const char* const name);
210 
211/**
212 * This is the virtual table for micro op.
213 */
214struct ccv_nnc_micro_io_vtab_s {
215	void (*bind_scalars)(const ccv_nnc_micro_io_t self, ccv_nnc_micro_scalar_lookup_f lookup, const void* const context); /**< Bind scalar name to a scoped id. */
216	void (*numbering)(const ccv_nnc_micro_io_t self, const int id, const int var_count); /**< Assign id to the output of this micro op. */
217	void (*equal_assertions)(const ccv_nnc_micro_io_t self, ccv_array_t* const equal_assertions); /**< Collect assertions about id equal. */
218	ccv_nnc_micro_function_t (*emit)(const ccv_nnc_micro_io_t self); /**< Emit instructions for this micro op. */
219	ccv_nnc_micro_function_t (*emit_grad)(const ccv_nnc_micro_io_t self, const int var_count); /**< Emit backward instructions for this micro op. */
220	ccv_nnc_micro_tensor_t (*return_shape)(const ccv_nnc_micro_io_t self); /**< The shape of the returned tensor. */
221	void (*deinit)(const ccv_nnc_micro_io_t self); /**< Deinit this micro io. */
222};
223 
224extern const ccv_nnc_micro_io_vtab_t ccv_nnc_micro_io_input_isa;
225extern const ccv_nnc_micro_io_vtab_t ccv_nnc_micro_io_grad_isa;
226 
227#define CCV_NNC_IS_MICRO_IO_INPUT(x)((x)->isa == &ccv_nnc_micro_io_input_isa) ((x)->isa == &ccv_nnc_micro_io_input_isa)
228#define CCV_NNC_IS_MICRO_IO_GRAD(x)((x)->isa == &ccv_nnc_micro_io_grad_isa) ((x)->isa == &ccv_nnc_micro_io_grad_isa)
229 
230static inline void ccv_nnc_micro_numbering(const ccv_nnc_micro_io_t self, const int id, const int var_count)
231{
232	const ccv_nnc_micro_io_vtab_t* const isa = self->isa;
233	// If we numbering with negative id, we really just enumerate the grad.
234	if (id < 0 && !CCV_NNC_IS_MICRO_IO_GRAD(self)((self)->isa == &ccv_nnc_micro_io_grad_isa))
235		return;
236	if (isa->numbering)
237		isa->numbering(self, id, var_count);
238	else
239		self->id = id;
240}
241 
242static inline void ccv_nnc_micro_equal_assertions(const ccv_nnc_micro_io_t self, ccv_array_t* const equal_assertions)
243{
244	const ccv_nnc_micro_io_vtab_t* const isa = self->isa;
245	if (isa->equal_assertions)
246		isa->equal_assertions(self, equal_assertions);
247}
248 
249static inline void ccv_nnc_micro_bind_scalars(const ccv_nnc_micro_io_t self, ccv_nnc_micro_scalar_lookup_f lookup, const void* const context)
250{
251	const ccv_nnc_micro_io_vtab_t* const isa = self->isa;
252	if (isa->bind_scalars)
253		isa->bind_scalars(self, lookup, context);
254}
255 
256static inline void ccv_nnc_micro_deinit(const ccv_nnc_micro_io_t self)
257{
258	const ccv_nnc_micro_io_vtab_t* const isa = self->isa;
259	if (isa->deinit)
260		isa->deinit(self);
261}
262 
263static inline CCV_WARN_UNUSED(ccv_nnc_micro_function_t)ccv_nnc_micro_function_t __attribute__((warn_unused_result)) ccv_nnc_micro_emit(const ccv_nnc_micro_io_t self)
264{
265	const ccv_nnc_micro_io_vtab_t* const isa = self->isa;
266	return isa->emit(self);
267}
268 
269static inline CCV_WARN_UNUSED(ccv_nnc_micro_function_t)ccv_nnc_micro_function_t __attribute__((warn_unused_result)) ccv_nnc_micro_emit_grad(const ccv_nnc_micro_io_t self, const int var_count)
270{
271	const ccv_nnc_micro_io_vtab_t* const isa = self->isa;
272	return isa->emit_grad(self, var_count);
273}
274 
275static inline CCV_WARN_UNUSED(ccv_nnc_micro_tensor_t)ccv_nnc_micro_tensor_t __attribute__((warn_unused_result)) ccv_nnc_micro_return_shape(const ccv_nnc_micro_io_t self)
276{
277	const ccv_nnc_micro_io_vtab_t* const isa = self->isa;
278	return isa->return_shape(self);
279}
280 
281/**
282 * Helpers to construct the micro IR.
283 */
284 
285static inline ccv_nnc_micro_id_t ccv_nnc_micro_id_of_tensor(const int id)
286{
287	return (ccv_nnc_micro_id_t){
288		.type = CCV_NNC_MICRO_TENSOR_ID,
289		.id = id,
290		.d = 0
291	};
292}
293 
294static inline ccv_nnc_micro_loop_index_term_t ccv_nnc_micro_index_of_value(const int value)
295{
296	return (ccv_nnc_micro_loop_index_term_t){
297		.type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_VAL,
298		.immediate_value = value
299	};
300}
301 
302static inline ccv_nnc_micro_loop_index_term_t ccv_nnc_micro_index_of_id(const ccv_nnc_micro_id_t id)
303{
304	return (ccv_nnc_micro_loop_index_term_t){
305		.type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID,
306		.id = id
307	};
308}
309 
310static inline ccv_nnc_micro_loop_index_term_t ccv_nnc_micro_index_of_axis_size(const int id, const int level)
311{
312	return (ccv_nnc_micro_loop_index_term_t){
313		.type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID,
314		.id = {
315			.type = CCV_NNC_MICRO_AXIS_SIZE_ID,
316			.id = id,
317			.d = level
318		}
319	};
320}
321 
322static inline ccv_nnc_micro_loop_t ccv_nnc_micro_for_in(const ccv_nnc_micro_loop_index_term_t start_index, const ccv_nnc_micro_loop_index_term_t end_index, const int level)
323{
324	return (ccv_nnc_micro_loop_t){
325		.start_index = start_index,
326		.end_index = end_index,
327		.carried_count = 0,
328		.carrieds = 0,
329		.statement_count = 0,
330		.statements = 0,
331		.id = {
332			.type = CCV_NNC_MICRO_LOOP_ID,
333			.d = 0,
334			.id = level,
335		}
336	};
337}
338 
339// This is a macro because C cannot return array type.
340#define ccv_nnc_micro_index_of_loops(_loops, _loop_count)(ccv_nnc_micro_loop_index_term_t [(12)]){ { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = _loop_count > 0 ? _loops[0].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = _loop_count
 > 1 ? _loops[1].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = _loop_count > 2 ? _loops[2].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = _loop_count
 > 3 ? _loops[3].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = _loop_count > 4 ? _loops[4].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = _loop_count
 > 5 ? _loops[5].id : (ccv_nnc_micro_id_t){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID
, .id = _loop_count > 6 ? _loops[6].id : (ccv_nnc_micro_id_t
){} }, { .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = _loop_count
 > 7 ? _loops[7].id : (ccv_nnc_micro_id_t){} } } \
341	(ccv_nnc_micro_loop_index_term_t [CCV_NNC_MAX_DIM_ALLOC(12)]){ \
342		{ .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = _loop_count > 0 ? _loops[0].id : (ccv_nnc_micro_id_t){} }, \
343		{ .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = _loop_count > 1 ? _loops[1].id : (ccv_nnc_micro_id_t){} }, \
344		{ .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = _loop_count > 2 ? _loops[2].id : (ccv_nnc_micro_id_t){} }, \
345		{ .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = _loop_count > 3 ? _loops[3].id : (ccv_nnc_micro_id_t){} }, \
346		{ .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = _loop_count > 4 ? _loops[4].id : (ccv_nnc_micro_id_t){} }, \
347		{ .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = _loop_count > 5 ? _loops[5].id : (ccv_nnc_micro_id_t){} }, \
348		{ .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = _loop_count > 6 ? _loops[6].id : (ccv_nnc_micro_id_t){} }, \
349		{ .type = CCV_NNC_MICRO_LOOP_INDEX_TYPE_ID, .id = _loop_count > 7 ? _loops[7].id : (ccv_nnc_micro_id_t){} } \
350	}
351 
352static inline ccv_nnc_micro_loop_variable_t ccv_nnc_micro_loop_variable_of_tensor(const int id, const int index_count, const ccv_nnc_micro_loop_index_term_t* const index)
353{
354	ccv_nnc_micro_loop_variable_t variable = {
355		.id = ccv_nnc_micro_id_of_tensor(id),
356		.index_count = index_count
357	};
358	int i;
359	for (i = 0; i < index_count; i++)
360		variable.index[i] = index[i];
361	return variable;
362}
363 
364static inline ccv_nnc_micro_loop_expression_t ccv_nnc_micro_loop_expression_of_variable(const ccv_nnc_micro_loop_variable_t variable)
365{
366	return (ccv_nnc_micro_loop_expression_t){
367		.type = CCV_NNC_MICRO_LOOP_EXPR_TYPE_VAR,
368		.variable = variable
369	};
370}
371 
372static inline ccv_nnc_micro_loop_expression_t ccv_nnc_micro_loop_expression_of_value(const float value)
373{
374	return (ccv_nnc_micro_loop_expression_t){
375		.type = CCV_NNC_MICRO_LOOP_EXPR_TYPE_VAL,
376		.immediate_value = {
377			.type = CCV_32F,
378			.f32 = value
379		}
380	};
381}
382 
383static inline ccv_nnc_micro_loop_expression_t ccv_nnc_micro_loop_expression_of_id(const ccv_nnc_micro_id_t id)
384{
385	return (ccv_nnc_micro_loop_expression_t){
386		.type = CCV_NNC_MICRO_LOOP_EXPR_TYPE_ID,
387		.id = id
388	};
389}
390 
391static inline ccv_nnc_micro_loop_statement_t ccv_nnc_micro_loop_assignment(const ccv_nnc_micro_loop_variable_t lvalue, const ccv_nnc_micro_loop_expression_t rvalue)
392{
393	return (ccv_nnc_micro_loop_statement_t){
394		.type = CCV_NNC_MICRO_LOOP_STATEMENT_TYPE_ASSIGNMENT,
395		.assignment = {
396			.lvalue = lvalue,
397			.rvalue = rvalue
398		}
399	};
400}
401 
402static inline ccv_nnc_micro_loop_expression_t ccv_nnc_micro_loop_expression_of_unary(const int unary_op, const ccv_nnc_micro_loop_expression_t x)
403{
404	ccv_nnc_micro_loop_expression_t expression = {
405		.type = CCV_NNC_MICRO_LOOP_EXPR_TYPE_BINARY
406	};
407	expression.unary.unary_op = unary_op;
408	expression.unary.x = (ccv_nnc_micro_loop_expression_t*)ccmallocmalloc(sizeof(ccv_nnc_micro_loop_expression_t));
24
←
Memory is allocated→
409	*expression.unary.x = x;
410	return expression;
411}
412 
413static inline ccv_nnc_micro_loop_expression_t ccv_nnc_micro_loop_expression_of_binary(const int binary_op, const ccv_nnc_micro_loop_expression_t left, const ccv_nnc_micro_loop_expression_t right)
414{
415	ccv_nnc_micro_loop_expression_t expression = {
416		.type = CCV_NNC_MICRO_LOOP_EXPR_TYPE_BINARY
417	};
418	expression.binary.binary_op = binary_op;
419	expression.binary.left = (ccv_nnc_micro_loop_expression_t*)ccmallocmalloc(sizeof(ccv_nnc_micro_loop_expression_t));
420	*expression.binary.left = left;
421	expression.binary.right = (ccv_nnc_micro_loop_expression_t*)ccmallocmalloc(sizeof(ccv_nnc_micro_loop_expression_t));
422	*expression.binary.right = right;
423	return expression;
424}
425 
426static inline ccv_nnc_micro_loop_expression_t ccv_nnc_micro_loop_expression_of_ternary(const ccv_nnc_micro_loop_expression_t pivot, const ccv_nnc_micro_loop_expression_t left, const ccv_nnc_micro_loop_expression_t right)
427{
428	ccv_nnc_micro_loop_expression_t expression = {
429		.type = CCV_NNC_MICRO_LOOP_EXPR_TYPE_TERNAY
430	};
431	expression.ternary.pivot = (ccv_nnc_micro_loop_expression_t*)ccmallocmalloc(sizeof(ccv_nnc_micro_loop_expression_t));
432	*expression.ternary.pivot = pivot;
433	expression.ternary.left = (ccv_nnc_micro_loop_expression_t*)ccmallocmalloc(sizeof(ccv_nnc_micro_loop_expression_t));
434	*expression.ternary.left = left;
435	expression.ternary.right = (ccv_nnc_micro_loop_expression_t*)ccmallocmalloc(sizeof(ccv_nnc_micro_loop_expression_t));
436	*expression.ternary.right = right;
437	return expression;
438}
439 
440static inline ccv_nnc_micro_loop_statement_t ccv_nnc_micro_loop_compound_assignment_of_id(const ccv_nnc_micro_id_t lvalue, const ccv_nnc_micro_loop_expression_t rvalue)
441{
442	return (ccv_nnc_micro_loop_statement_t){
443		.type = CCV_NNC_MICRO_LOOP_STATEMENT_TYPE_COMPOUND_ASSIGNMENT,
444		.compound_assignment = {
445			.lvalue = {
446				.type = CCV_NNC_MICRO_LOOP_EXPR_TYPE_ID,
447				.id = lvalue
448			},
449			.rvalue = rvalue
450		}
451	};
452}
453 
454static inline ccv_nnc_micro_loop_statement_t ccv_nnc_micro_loop_compound_assignment_of_tensor(const ccv_nnc_micro_loop_variable_t lvalue, const ccv_nnc_micro_loop_expression_t rvalue)
455{
456	return (ccv_nnc_micro_loop_statement_t){
457		.type = CCV_NNC_MICRO_LOOP_STATEMENT_TYPE_COMPOUND_ASSIGNMENT,
458		.compound_assignment = {
459			.lvalue = {
460				.type = CCV_NNC_MICRO_LOOP_EXPR_TYPE_VAR,
461				.variable = lvalue
462			},
463			.rvalue = rvalue
464		}
465	};
466}
467 
468static inline ccv_nnc_micro_loop_carried_t ccv_nnc_micro_loop_carried(const uint8_t reduce_op, const int idx)
469{
470	return (ccv_nnc_micro_loop_carried_t){
471		.id = {
472			.type = CCV_NNC_MICRO_LOOP_CARRIED_ID,
473			.d = reduce_op,
474			.id = idx
475		}
476	};
477}
478 
479// This method has to be mutable for efficiency reasons. Hence I kept it private.
480void ccv_nnc_micro_program_simplify(ccv_nnc_micro_program_t* const program, const ccv_nnc_micro_io_t* const inputs, const int input_size, const ccv_nnc_micro_io_t* const outputs, const int output_size, const ccv_array_t* const equal_assertions);
481ccv_nnc_micro_loop_index_term_t ccv_nnc_micro_loop_index_deep_copy(const ccv_nnc_micro_loop_index_term_t* const term);
482void ccv_nnc_micro_loop_index_free(ccv_nnc_micro_loop_index_term_t* const term);
483void ccv_nnc_micro_loop_variable_free(ccv_nnc_micro_loop_variable_t* const var);
484void ccv_nnc_micro_loop_statement_free(ccv_nnc_micro_loop_statement_t* const statement);
485void ccv_nnc_micro_loop_statement_lvalue_free(ccv_nnc_micro_loop_statement_t* const statement);
486void ccv_nnc_micro_loops_free(ccv_nnc_micro_loop_t* const loops, const int loop_count);
487 
488#endif