1 // Copyright (c) Facebook, Inc. and its affiliates.
2 // All rights reserved.
3 //
4 // Copyright 2019 Google LLC
5 //
6 // This source code is licensed under the BSD-style license found in the
7 // LICENSE file in the root directory of this source tree.
8
9 #include <assert.h>
10 #include <math.h>
11 #include <stdbool.h>
12 #include <stddef.h>
13 #include <stdint.h>
14 #include <stdlib.h>
15 #include <string.h>
16
17 #include <xnnpack.h>
18 #include <xnnpack/allocator.h>
19 #include <xnnpack/operator.h>
20 #include <xnnpack/common.h>
21 #include <xnnpack/log.h>
22 #include <xnnpack/math.h>
23 #include <xnnpack/params-init.h>
24 #include <xnnpack/params.h>
25 #include <xnnpack/indirection.h>
26
27
compute_output_dimension(size_t padded_input_dimension,size_t pooling_dimension,size_t stride_dimension)28 static inline size_t compute_output_dimension(
29 size_t padded_input_dimension,
30 size_t pooling_dimension,
31 size_t stride_dimension)
32 {
33 return (padded_input_dimension - pooling_dimension) / stride_dimension + 1;
34 }
35
compute_output_dimension_with_tf_same_padding(size_t input_dimension,size_t stride_dimension)36 static inline size_t compute_output_dimension_with_tf_same_padding(
37 size_t input_dimension,
38 size_t stride_dimension)
39 {
40 return divide_round_up(input_dimension, stride_dimension);
41 }
42
xnn_create_average_pooling2d_nhwc_qu8(uint32_t input_padding_top,uint32_t input_padding_right,uint32_t input_padding_bottom,uint32_t input_padding_left,uint32_t pooling_height,uint32_t pooling_width,uint32_t stride_height,uint32_t stride_width,size_t channels,size_t input_pixel_stride,size_t output_pixel_stride,uint8_t input_zero_point,float input_scale,uint8_t output_zero_point,float output_scale,uint8_t output_min,uint8_t output_max,uint32_t flags,xnn_operator_t * average_pooling_op_out)43 enum xnn_status xnn_create_average_pooling2d_nhwc_qu8(
44 uint32_t input_padding_top,
45 uint32_t input_padding_right,
46 uint32_t input_padding_bottom,
47 uint32_t input_padding_left,
48 uint32_t pooling_height,
49 uint32_t pooling_width,
50 uint32_t stride_height,
51 uint32_t stride_width,
52 size_t channels,
53 size_t input_pixel_stride,
54 size_t output_pixel_stride,
55 uint8_t input_zero_point,
56 float input_scale,
57 uint8_t output_zero_point,
58 float output_scale,
59 uint8_t output_min,
60 uint8_t output_max,
61 uint32_t flags,
62 xnn_operator_t* average_pooling_op_out)
63 {
64 xnn_operator_t average_pooling_op = NULL;
65 enum xnn_status status = xnn_status_uninitialized;
66
67 if ((xnn_params.init_flags & XNN_INIT_FLAG_XNNPACK) == 0) {
68 xnn_log_error("failed to create %s operator: XNNPACK is not initialized",
69 xnn_operator_type_to_string(xnn_operator_type_average_pooling_nhwc_qu8));
70 goto error;
71 }
72
73 status = xnn_status_invalid_parameter;
74
75 const uint32_t pooling_size = pooling_height * pooling_width;
76 if (pooling_size == 0) {
77 xnn_log_error(
78 "failed to create %s operator with %" PRIu32 "x%" PRIu32 " pooling size: "
79 "pooling size dimensions must be non-zero",
80 xnn_operator_type_to_string(xnn_operator_type_average_pooling_nhwc_qu8), pooling_width, pooling_height);
81 goto error;
82 }
83
84 if (pooling_size == 1) {
85 xnn_log_error(
86 "failed to create %s operator with 1 pooling element: 1x1 pooling is meaningless",
87 xnn_operator_type_to_string(xnn_operator_type_average_pooling_nhwc_qu8));
88 goto error;
89 }
90
91 if (stride_height == 0 || stride_width == 0) {
92 xnn_log_error(
93 "failed to create %s operator with %" PRIu32 "x%" PRIu32 " stride: stride dimensions must be non-zero",
94 xnn_operator_type_to_string(xnn_operator_type_average_pooling_nhwc_qu8), stride_width, stride_height);
95 goto error;
96 }
97
98 if (channels == 0) {
99 xnn_log_error(
100 "failed to create %s operator with %zu channels: number of channels must be non-zero",
101 xnn_operator_type_to_string(xnn_operator_type_average_pooling_nhwc_qu8), channels);
102 goto error;
103 }
104
105 if (input_pixel_stride < channels) {
106 xnn_log_error(
107 "failed to create %s operator with input pixel stride of %zu: "
108 "stride must be at least as large as the number of channels (%zu)",
109 xnn_operator_type_to_string(xnn_operator_type_average_pooling_nhwc_qu8), input_pixel_stride, channels);
110 goto error;
111 }
112
113 if (output_pixel_stride < channels) {
114 xnn_log_error(
115 "failed to create %s operator with output pixel stride of %zu: "
116 "stride must be at least as large as the number of channels (%zu)",
117 xnn_operator_type_to_string(xnn_operator_type_average_pooling_nhwc_qu8), output_pixel_stride, channels);
118 goto error;
119 }
120
121 if (input_scale <= 0.0f || !isnormal(input_scale)) {
122 xnn_log_error(
123 "failed to create %s operator with %.7g input scale: scale must be finite, normalized, and positive",
124 xnn_operator_type_to_string(xnn_operator_type_average_pooling_nhwc_qu8), input_scale);
125 goto error;
126 }
127
128 if (output_scale <= 0.0f || !isnormal(output_scale)) {
129 xnn_log_error(
130 "failed to create %s operator with %.7g output scale: scale must be finite, normalized, and positive",
131 xnn_operator_type_to_string(xnn_operator_type_average_pooling_nhwc_qu8), output_scale);
132 goto error;
133 }
134
135 if (output_min >= output_max) {
136 xnn_log_error(
137 "failed to create %s operator with [%" PRIu8 ", %" PRIu8 "] output range: range min must be below range max",
138 xnn_operator_type_to_string(xnn_operator_type_average_pooling_nhwc_qu8), output_min, output_max);
139 goto error;
140 }
141
142 const bool any_padding = (input_padding_left | input_padding_top | input_padding_right | input_padding_bottom) != 0;
143 if ((flags & XNN_FLAG_TENSORFLOW_SAME_PADDING) != 0) {
144 if (any_padding) {
145 xnn_log_error(
146 "failed to create %s operator with %" PRIu32 "+%" PRIu32 "x%" PRIu32 "+%" PRIu32" padding: "
147 "TensorFlow SAME padding can't be combined with explicit padding specification",
148 xnn_operator_type_to_string(xnn_operator_type_average_pooling_nhwc_qu8),
149 input_padding_top, input_padding_left, input_padding_bottom, input_padding_right);
150 goto error;
151 }
152 }
153
154 status = xnn_status_unsupported_parameter;
155
156 const float input_output_scale = input_scale / output_scale;
157 if (input_output_scale < 0x1.0p-8f || input_output_scale >= 0x1.0p+8f) {
158 xnn_log_error(
159 "failed to create %s operator with %.7g input scale and %.7g output scale: "
160 "input-to-output scale ratio (%.7f) must be in [2**-8, 2**8) range",
161 xnn_operator_type_to_string(xnn_operator_type_average_pooling_nhwc_qu8),
162 input_scale, output_scale, input_output_scale);
163 goto error;
164 }
165
166 if (pooling_size >= 16777216) {
167 xnn_log_error(
168 "failed to create %s operator with %"PRIu32" (%" PRIu32 "x%" PRIu32 ") pooling elements: "
169 "the number of elements in the pooling area must be below 2**24",
170 xnn_operator_type_to_string(xnn_operator_type_average_pooling_nhwc_qu8),
171 pooling_size, pooling_width, pooling_height);
172 goto error;
173 }
174
175 status = xnn_status_out_of_memory;
176
177 average_pooling_op = xnn_allocate_zero_simd_memory(sizeof(struct xnn_operator));
178 if (average_pooling_op == NULL) {
179 xnn_log_error(
180 "failed to allocate %zu bytes for %s operator descriptor",
181 sizeof(struct xnn_operator), xnn_operator_type_to_string(xnn_operator_type_average_pooling_nhwc_qu8));
182 goto error;
183 }
184
185 const size_t zero_bytes = channels * sizeof(uint8_t) + XNN_EXTRA_BYTES;
186 void* zero_buffer = xnn_allocate_simd_memory(zero_bytes);
187 if (zero_buffer == NULL) {
188 xnn_log_error(
189 "failed to allocate %zu bytes for %s operator zero padding",
190 zero_bytes, xnn_operator_type_to_string(xnn_operator_type_average_pooling_nhwc_qu8));
191 goto error;
192 }
193 memset(zero_buffer, input_zero_point, channels * sizeof(uint8_t));
194 average_pooling_op->zero_buffer = zero_buffer;
195
196 average_pooling_op->padding_top = input_padding_top;
197 average_pooling_op->padding_right = input_padding_right;
198 average_pooling_op->padding_bottom = input_padding_bottom;
199 average_pooling_op->padding_left = input_padding_left;
200
201 average_pooling_op->kernel_height = pooling_height;
202 average_pooling_op->kernel_width = pooling_width;
203 average_pooling_op->stride_height = stride_height;
204 average_pooling_op->stride_width = stride_width;
205 average_pooling_op->dilation_height = 1;
206 average_pooling_op->dilation_width = 1;
207 average_pooling_op->channels = channels;
208 average_pooling_op->input_pixel_stride = input_pixel_stride;
209 average_pooling_op->output_pixel_stride = output_pixel_stride;
210
211 average_pooling_op->input_zero_point = (int32_t) (uint32_t) input_zero_point;
212 average_pooling_op->output_zero_point = output_zero_point;
213 average_pooling_op->input_scale = input_scale;
214 average_pooling_op->output_scale = output_scale;
215 average_pooling_op->output_min = output_min;
216 average_pooling_op->output_max = output_max;
217
218 // Number of rows read in the AVGPOOL micro-kernel.
219 const size_t avgpool_nrows =
220 round_up(doz(pooling_size, xnn_params.qu8.avgpool.mr), xnn_params.qu8.avgpool.qr) + xnn_params.qu8.avgpool.mr;
221 average_pooling_op->params.qu8_avgpool =
222 xnn_init_qu8_avgpool_params(
223 (int32_t) -((uint32_t) input_zero_point * (uint32_t) avgpool_nrows),
224 input_scale / (output_scale * (float) pooling_size),
225 output_zero_point, output_min, output_max);
226
227 average_pooling_op->type = xnn_operator_type_average_pooling_nhwc_qu8;
228 average_pooling_op->ukernel.type = xnn_ukernel_type_average_pooling;
229 average_pooling_op->flags = flags;
230
231 *average_pooling_op_out = average_pooling_op;
232 return xnn_status_success;
233
234 error:
235 xnn_delete_operator(average_pooling_op);
236 return status;
237 }
238
xnn_create_average_pooling2d_nhwc_f32(uint32_t input_padding_top,uint32_t input_padding_right,uint32_t input_padding_bottom,uint32_t input_padding_left,uint32_t pooling_height,uint32_t pooling_width,uint32_t stride_height,uint32_t stride_width,size_t channels,size_t input_pixel_stride,size_t output_pixel_stride,float output_min,float output_max,uint32_t flags,xnn_operator_t * average_pooling_op_out)239 enum xnn_status xnn_create_average_pooling2d_nhwc_f32(
240 uint32_t input_padding_top,
241 uint32_t input_padding_right,
242 uint32_t input_padding_bottom,
243 uint32_t input_padding_left,
244 uint32_t pooling_height,
245 uint32_t pooling_width,
246 uint32_t stride_height,
247 uint32_t stride_width,
248 size_t channels,
249 size_t input_pixel_stride,
250 size_t output_pixel_stride,
251 float output_min,
252 float output_max,
253 uint32_t flags,
254 xnn_operator_t* average_pooling_op_out)
255 {
256 xnn_operator_t average_pooling_op = NULL;
257 enum xnn_status status = xnn_status_uninitialized;
258
259 if ((xnn_params.init_flags & XNN_INIT_FLAG_XNNPACK) == 0) {
260 xnn_log_error("failed to create %s operator: XNNPACK is not initialized",
261 xnn_operator_type_to_string(xnn_operator_type_average_pooling_nhwc_f32));
262 goto error;
263 }
264
265 status = xnn_status_invalid_parameter;
266
267 const uint32_t pooling_size = pooling_height * pooling_width;
268 if (pooling_size == 0) {
269 xnn_log_error(
270 "failed to create %s operator with %" PRIu32 "x%" PRIu32 " pooling size: "
271 "pooling size dimensions must be non-zero",
272 xnn_operator_type_to_string(xnn_operator_type_average_pooling_nhwc_f32), pooling_width, pooling_height);
273 goto error;
274 }
275
276 if (pooling_size == 1) {
277 xnn_log_error(
278 "failed to create %s operator with 1 pooling element: 1x1 pooling is meaningless",
279 xnn_operator_type_to_string(xnn_operator_type_average_pooling_nhwc_f32));
280 goto error;
281 }
282
283 if (stride_height == 0 || stride_width == 0) {
284 xnn_log_error(
285 "failed to create %s operator with %" PRIu32 "x%" PRIu32 " stride: stride dimensions must be non-zero",
286 xnn_operator_type_to_string(xnn_operator_type_average_pooling_nhwc_f32), stride_width, stride_height);
287 goto error;
288 }
289
290 if (channels == 0) {
291 xnn_log_error(
292 "failed to create %s operator with %zu channels: number of channels must be non-zero",
293 xnn_operator_type_to_string(xnn_operator_type_average_pooling_nhwc_f32), channels);
294 goto error;
295 }
296
297 if (input_pixel_stride < channels) {
298 xnn_log_error(
299 "failed to create %s operator with input pixel stride of %zu: "
300 "stride must be at least as large as the number of channels (%zu)",
301 xnn_operator_type_to_string(xnn_operator_type_average_pooling_nhwc_f32), input_pixel_stride, channels);
302 goto error;
303 }
304
305 if (output_pixel_stride < channels) {
306 xnn_log_error(
307 "failed to create %s operator with output pixel stride of %zu: "
308 "stride must be at least as large as the number of channels (%zu)",
309 xnn_operator_type_to_string(xnn_operator_type_average_pooling_nhwc_f32), output_pixel_stride, channels);
310 goto error;
311 }
312
313 if (isnan(output_min)) {
314 xnn_log_error(
315 "failed to create %s operator with NaN output lower bound: lower bound must be non-NaN",
316 xnn_operator_type_to_string(xnn_operator_type_average_pooling_nhwc_f32));
317 goto error;
318 }
319
320 if (isnan(output_max)) {
321 xnn_log_error(
322 "failed to create %s operator with NaN output upper bound: upper bound must be non-NaN",
323 xnn_operator_type_to_string(xnn_operator_type_average_pooling_nhwc_f32));
324 goto error;
325 }
326
327 if (output_min >= output_max) {
328 xnn_log_error(
329 "failed to create %s operator with [%.7g, %.7g] output range: lower bound must be below upper bound",
330 xnn_operator_type_to_string(xnn_operator_type_average_pooling_nhwc_f32), output_min, output_max);
331 goto error;
332 }
333
334 const bool any_padding = (input_padding_left | input_padding_top | input_padding_right | input_padding_bottom) != 0;
335 if ((flags & XNN_FLAG_TENSORFLOW_SAME_PADDING) != 0) {
336 if (any_padding) {
337 xnn_log_error(
338 "failed to create %s operator with %" PRIu32 "+%" PRIu32 "x%" PRIu32 "+%" PRIu32" padding: "
339 "TensorFlow SAME padding can't be combined with explicit padding specification",
340 xnn_operator_type_to_string(xnn_operator_type_average_pooling_nhwc_f32),
341 input_padding_top, input_padding_left, input_padding_bottom, input_padding_right);
342 goto error;
343 }
344 }
345
346 status = xnn_status_out_of_memory;
347
348 average_pooling_op = xnn_allocate_zero_simd_memory(sizeof(struct xnn_operator));
349 if (average_pooling_op == NULL) {
350 xnn_log_error(
351 "failed to allocate %zu bytes for %s operator descriptor",
352 sizeof(struct xnn_operator), xnn_operator_type_to_string(xnn_operator_type_average_pooling_nhwc_f32));
353 goto error;
354 }
355
356 const size_t zero_bytes = channels * sizeof(float) + XNN_EXTRA_BYTES;
357 void* zero_buffer = xnn_allocate_zero_simd_memory(zero_bytes);
358 if (zero_buffer == NULL) {
359 xnn_log_error(
360 "failed to allocate %zu bytes for %s operator zero padding",
361 zero_bytes, xnn_operator_type_to_string(xnn_operator_type_average_pooling_nhwc_f32));
362 goto error;
363 }
364 average_pooling_op->zero_buffer = zero_buffer;
365
366 average_pooling_op->padding_top = input_padding_top;
367 average_pooling_op->padding_right = input_padding_right;
368 average_pooling_op->padding_bottom = input_padding_bottom;
369 average_pooling_op->padding_left = input_padding_left;
370
371 average_pooling_op->kernel_height = pooling_height;
372 average_pooling_op->kernel_width = pooling_width;
373 average_pooling_op->stride_height = stride_height;
374 average_pooling_op->stride_width = stride_width;
375 average_pooling_op->dilation_height = 1;
376 average_pooling_op->dilation_width = 1;
377 average_pooling_op->channels = channels;
378 average_pooling_op->input_pixel_stride = input_pixel_stride;
379 average_pooling_op->output_pixel_stride = output_pixel_stride;
380
381 average_pooling_op->type = xnn_operator_type_average_pooling_nhwc_f32;
382 average_pooling_op->params.f32_scaleminmax =
383 xnn_init_f32_scaleminmax_params(1.0f / (float) pooling_size, output_min, output_max);
384 const bool tf_same_padding = (flags & XNN_FLAG_TENSORFLOW_SAME_PADDING) != 0;
385 if (any_padding || tf_same_padding) {
386 average_pooling_op->params.f32_minmax =
387 xnn_init_f32_minmax_params(output_min, output_max);
388 average_pooling_op->ukernel.type = xnn_ukernel_type_pixelwise_average_pooling;
389 } else {
390 average_pooling_op->ukernel.type = xnn_ukernel_type_average_pooling;
391 }
392 average_pooling_op->flags = flags;
393
394 *average_pooling_op_out = average_pooling_op;
395 return xnn_status_success;
396
397 error:
398 xnn_delete_operator(average_pooling_op);
399 return status;
400 }
401
setup_average_pooling2d(xnn_operator_t average_pooling_op,size_t batch_size,size_t input_height,size_t input_width,const void * input,void * output,uint32_t log2_input_element_size,uint32_t log2_output_element_size,struct avgpool_parameters avgpool[restrict XNN_MIN_ELEMENTS (1)],struct pavgpool_parameters pavgpool[restrict1],struct gavgpool_parameters gavgpool[restrict XNN_MIN_ELEMENTS (1)],const void * params,size_t params_size,const void * global_params,size_t global_params_size,size_t num_threads,bool is_pixelwise)402 static enum xnn_status setup_average_pooling2d(
403 xnn_operator_t average_pooling_op,
404 size_t batch_size,
405 size_t input_height,
406 size_t input_width,
407 const void* input,
408 void* output,
409 uint32_t log2_input_element_size,
410 uint32_t log2_output_element_size,
411 struct avgpool_parameters avgpool[restrict XNN_MIN_ELEMENTS(1)],
412 struct pavgpool_parameters pavgpool[restrict 1],
413 struct gavgpool_parameters gavgpool[restrict XNN_MIN_ELEMENTS(1)],
414 const void* params,
415 size_t params_size,
416 const void* global_params,
417 size_t global_params_size,
418 size_t num_threads,
419 bool is_pixelwise)
420 {
421 assert(!is_pixelwise || pavgpool != NULL);
422
423 average_pooling_op->state = xnn_run_state_invalid;
424
425 if ((xnn_params.init_flags & XNN_INIT_FLAG_XNNPACK) == 0) {
426 xnn_log_error("failed to setup %s operator: XNNPACK is not initialized",
427 xnn_operator_type_to_string(average_pooling_op->type));
428 return xnn_status_uninitialized;
429 }
430
431 if (input_width == 0 || input_height == 0) {
432 xnn_log_error(
433 "failed to setup %s operator with %zux%zu input: input dimensions must be non-zero",
434 xnn_operator_type_to_string(average_pooling_op->type), input_width, input_height);
435 return xnn_status_invalid_parameter;
436 }
437
438 if (batch_size == 0) {
439 average_pooling_op->state = xnn_run_state_skip;
440 return xnn_status_success;
441 }
442
443 average_pooling_op->input_height = input_height;
444 average_pooling_op->input_width = input_width;
445 average_pooling_op->input = input;
446
447 const bool tf_same_padding = (average_pooling_op->flags & XNN_FLAG_TENSORFLOW_SAME_PADDING) != 0;
448 if (tf_same_padding) {
449 average_pooling_op->output_height = compute_output_dimension_with_tf_same_padding(
450 input_height, average_pooling_op->stride_height);
451 average_pooling_op->output_width = compute_output_dimension_with_tf_same_padding(
452 input_width, average_pooling_op->stride_width);
453
454 const uint32_t kernel_height = average_pooling_op->kernel_height;
455 const uint32_t kernel_width = average_pooling_op->kernel_width;
456 const uint32_t total_padding_height =
457 (average_pooling_op->output_height - 1) * average_pooling_op->stride_height + kernel_height - input_height;
458 const uint32_t total_padding_width =
459 (average_pooling_op->output_width - 1) * average_pooling_op->stride_width + kernel_width - input_width;
460 average_pooling_op->padding_top = total_padding_height / 2;
461 average_pooling_op->padding_left = total_padding_width / 2;
462 average_pooling_op->padding_bottom = total_padding_height - average_pooling_op->padding_top;
463 average_pooling_op->padding_right = total_padding_width - average_pooling_op->padding_left;
464 } else {
465 average_pooling_op->output_height = compute_output_dimension(
466 average_pooling_op->padding_top + input_height + average_pooling_op->padding_bottom,
467 average_pooling_op->kernel_height,
468 average_pooling_op->stride_height);
469 average_pooling_op->output_width = compute_output_dimension(
470 average_pooling_op->padding_left + input_width + average_pooling_op->padding_right,
471 average_pooling_op->kernel_width,
472 average_pooling_op->stride_width);
473 }
474 average_pooling_op->output = output;
475
476 const size_t output_height = average_pooling_op->output_height;
477 const size_t output_width = average_pooling_op->output_width;
478 const size_t padded_input_width = average_pooling_op->padding_left + input_width + average_pooling_op->padding_right;
479 const size_t padded_input_height = average_pooling_op->padding_top + input_height + average_pooling_op->padding_bottom;
480 if (padded_input_width == average_pooling_op->kernel_width && padded_input_height == average_pooling_op->kernel_height) {
481 // Global average pooling
482 const size_t input_elements = input_height * input_width;
483 const size_t input_stride_in_bytes = average_pooling_op->input_pixel_stride << log2_input_element_size;
484 const size_t channels = average_pooling_op->channels;
485 average_pooling_op->context.global_average_pooling_nwc = (struct global_average_pooling_nwc_context) {
486 .input = input,
487 .zero = average_pooling_op->zero_buffer,
488 .input_pixel_stride = input_stride_in_bytes,
489 .input_batch_stride = input_stride_in_bytes * input_elements,
490 .input_elements = input_elements,
491 .channels = channels,
492 .output = output,
493 .output_batch_stride = average_pooling_op->output_pixel_stride << log2_output_element_size,
494 };
495 memcpy(&average_pooling_op->context.global_average_pooling_nwc.params, global_params, global_params_size);
496 average_pooling_op->compute.type = xnn_parallelization_type_1d;
497 average_pooling_op->compute.range[0] = batch_size;
498
499 if (input_elements <= gavgpool->mr) {
500 average_pooling_op->compute.task_1d = (pthreadpool_task_1d_t) xnn_compute_global_average_pooling_nwc_unipass;
501 average_pooling_op->context.global_average_pooling_nwc.unipass_ukernel = gavgpool->up;
502 } else {
503 average_pooling_op->compute.task_1d = (pthreadpool_task_1d_t) xnn_compute_global_average_pooling_nwc_multipass;
504 average_pooling_op->context.global_average_pooling_nwc.multipass_ukernel = gavgpool->mp;
505 }
506 } else {
507 // Non-global average pooling
508 const size_t pooling_height = average_pooling_op->kernel_height;
509 const size_t pooling_width = average_pooling_op->kernel_width;
510 const size_t pooling_size = pooling_height * pooling_width;
511
512 const uint32_t mr = is_pixelwise ? pavgpool->mr : avgpool->mr;
513
514 const size_t step_width = min(average_pooling_op->stride_width, pooling_width);
515 const size_t step_height = pooling_size + (output_width - 1) * step_width * pooling_height;
516
517 const size_t last_input_height = average_pooling_op->last_input_height;
518 const size_t last_input_width = average_pooling_op->last_input_width;
519 if (input_height != last_input_height || input_width != last_input_width) {
520 // Micro-kernel may read up to (mr - 1) elements after the end of indirection buffer.
521 const size_t indirection_buffer_size = sizeof(void*) * ((mr - 1) + output_height * step_height);
522
523 const void** indirection_buffer =
524 (const void**) xnn_reallocate_memory(average_pooling_op->indirection_buffer, indirection_buffer_size);
525 if (indirection_buffer == NULL) {
526 xnn_log_error("failed to allocate %zu bytes for %s operator indirection buffer",
527 indirection_buffer_size, xnn_operator_type_to_string(average_pooling_op->type));
528 return xnn_status_out_of_memory;
529 }
530 average_pooling_op->indirection_buffer = indirection_buffer;
531
532 xnn_indirection_init_dwconv2d(average_pooling_op, step_height, step_width, log2_input_element_size);
533
534 average_pooling_op->last_input = input;
535 average_pooling_op->last_input_height = input_height;
536 average_pooling_op->last_input_width = input_width;
537 }
538
539 const size_t channels = average_pooling_op->channels;
540
541 const size_t indirect_input_height_stride = step_height * sizeof(void*);
542 const size_t output_width_stride = average_pooling_op->output_pixel_stride << log2_output_element_size;
543 const size_t output_height_stride = output_width * output_width_stride;
544
545 if (is_pixelwise) {
546 /* This part is specific to FP32, needs revision if another data types get a PAVGPOOL micro-kernel */
547 if (input_height != last_input_height || input_width != last_input_width) {
548 const size_t pixelwise_buffer_size = output_height * output_width * sizeof(float);
549 float* pixelwise_buffer =
550 (float*) xnn_reallocate_memory(average_pooling_op->pixelwise_buffer, pixelwise_buffer_size);
551 if (pixelwise_buffer == NULL) {
552 xnn_log_error("failed to allocate %zu bytes for %s operator pixelwise buffer",
553 pixelwise_buffer_size, xnn_operator_type_to_string(average_pooling_op->type));
554 return xnn_status_out_of_memory;
555 }
556 average_pooling_op->pixelwise_buffer = pixelwise_buffer;
557
558 float* pixelwise_pointer = pixelwise_buffer;
559 for (size_t output_y = 0; output_y < output_height; output_y++) {
560 const size_t input_y_start = doz(output_y * average_pooling_op->stride_height, average_pooling_op->padding_top);
561 const size_t input_y_end =
562 min(doz(output_y * average_pooling_op->stride_height + average_pooling_op->kernel_height, average_pooling_op->padding_top), input_height);
563 const uint32_t input_y_range = (uint32_t) (input_y_end - input_y_start);
564 for (size_t output_x = 0; output_x < output_width; output_x++) {
565 const size_t input_x_start = doz(output_x * average_pooling_op->stride_width, average_pooling_op->padding_left);
566 const size_t input_x_end =
567 min(doz(output_x * average_pooling_op->stride_width + average_pooling_op->kernel_width, average_pooling_op->padding_left), input_width);
568 const uint32_t input_x_range = (uint32_t) (input_x_end - input_x_start);
569 *pixelwise_pointer++ = 1.0f / ((float) (int32_t) (input_y_range * input_x_range));
570 }
571 }
572 }
573
574 const uint32_t qr = pavgpool->qr;
575 const size_t multipass_adjustment =
576 pooling_size > mr ? round_up(pooling_size - mr, qr) + mr - qr : 0;
577 average_pooling_op->context.pixelwise_average_pooling = (struct pixelwise_average_pooling_context) {
578 .indirect_input = average_pooling_op->indirection_buffer,
579 .indirect_input_height_stride = indirect_input_height_stride,
580 .input_batch_stride = input_height * input_width * average_pooling_op->input_pixel_stride << log2_input_element_size,
581 .input_offset = (size_t) ((uintptr_t) input - (uintptr_t) average_pooling_op->last_input),
582 .pixelwise_buffer = average_pooling_op->pixelwise_buffer,
583 .pixelwise_buffer_height_stride = output_width * sizeof(float),
584 .output = output,
585 .output_batch_stride = output_height * output_height_stride,
586 .output_height_stride = output_height_stride,
587 .output_width = output_width,
588 .pooling_size = pooling_size,
589 .channels = channels,
590 .zero = average_pooling_op->zero_buffer,
591 .input_increment = (pooling_height * step_width - multipass_adjustment) * sizeof(void*),
592 .output_increment = output_width_stride - (channels << log2_output_element_size),
593 };
594 memcpy(&average_pooling_op->context.pixelwise_average_pooling.params, params, params_size);
595 if (pooling_size <= mr) {
596 average_pooling_op->context.pixelwise_average_pooling.unipass_ukernel = pavgpool->up;
597 average_pooling_op->compute.task_2d = (pthreadpool_task_2d_t) xnn_compute_pixelwise_average_pooling_unipass;
598 } else {
599 average_pooling_op->context.pixelwise_average_pooling.multipass_ukernel = pavgpool->mp;
600 average_pooling_op->compute.task_2d = (pthreadpool_task_2d_t) xnn_compute_pixelwise_average_pooling_multipass;
601 }
602 } else {
603 const uint32_t qr = avgpool->qr;
604 const size_t multipass_adjustment =
605 pooling_size > mr ? round_up(pooling_size - mr, qr) + mr - qr : 0;
606 average_pooling_op->context.average_pooling = (struct average_pooling_context) {
607 .indirect_input = average_pooling_op->indirection_buffer,
608 .indirect_input_height_stride = indirect_input_height_stride,
609 .input_offset = (size_t) ((uintptr_t) input - (uintptr_t) average_pooling_op->last_input),
610 .input_batch_stride = input_height * input_width * average_pooling_op->input_pixel_stride << log2_input_element_size,
611 .output = output,
612 .output_batch_stride = output_height * output_height_stride,
613 .output_height_stride = output_height_stride,
614 .output_width = output_width,
615 .pooling_size = pooling_size,
616 .channels = channels,
617 .zero = average_pooling_op->zero_buffer,
618 .input_increment = (pooling_height * step_width - multipass_adjustment) * sizeof(void*),
619 .output_increment = output_width_stride - (channels << log2_output_element_size),
620 .params.f32 = average_pooling_op->params.f32_scaleminmax,
621 };
622 memcpy(&average_pooling_op->context.average_pooling.params, params, params_size);
623 if (pooling_size <= mr) {
624 average_pooling_op->context.average_pooling.unipass_ukernel = avgpool->up;
625 average_pooling_op->compute.task_2d = (pthreadpool_task_2d_t) xnn_compute_average_pooling_unipass;
626 } else {
627 average_pooling_op->context.average_pooling.multipass_ukernel = avgpool->mp;
628 average_pooling_op->compute.task_2d = (pthreadpool_task_2d_t) xnn_compute_average_pooling_multipass;
629 }
630 }
631 average_pooling_op->compute.type = xnn_parallelization_type_2d;
632 average_pooling_op->compute.range[0] = batch_size;
633 average_pooling_op->compute.range[1] = output_height;
634 }
635 average_pooling_op->state = xnn_run_state_ready;
636
637 return xnn_status_success;
638 }
639
xnn_setup_average_pooling2d_nhwc_qu8(xnn_operator_t average_pooling_op,size_t batch_size,size_t input_height,size_t input_width,const uint8_t * input,uint8_t * output,pthreadpool_t threadpool)640 enum xnn_status xnn_setup_average_pooling2d_nhwc_qu8(
641 xnn_operator_t average_pooling_op,
642 size_t batch_size,
643 size_t input_height,
644 size_t input_width,
645 const uint8_t* input,
646 uint8_t* output,
647 pthreadpool_t threadpool)
648 {
649 if (average_pooling_op->type != xnn_operator_type_average_pooling_nhwc_qu8) {
650 xnn_log_error("failed to setup operator: operator type mismatch (expected %s, got %s)",
651 xnn_operator_type_to_string(xnn_operator_type_average_pooling_nhwc_qu8),
652 xnn_operator_type_to_string(average_pooling_op->type));
653 return xnn_status_invalid_parameter;
654 }
655
656 assert(average_pooling_op->ukernel.type == xnn_ukernel_type_average_pooling);
657
658 // Number of rows read in the GAVGPOOL micro-kernel.
659 const size_t input_size = input_height * input_width;
660 const size_t pooling_size = average_pooling_op->kernel_height * average_pooling_op->kernel_width;
661 const size_t gavgpool_nrows = round_up(input_size, xnn_params.qu8.gavgpool.mr);
662 average_pooling_op->params.qu8_gavgpool =
663 xnn_init_qu8_avgpool_params(
664 -(average_pooling_op->input_zero_point * (int32_t) gavgpool_nrows),
665 average_pooling_op->input_scale / (average_pooling_op->output_scale * (float) pooling_size),
666 average_pooling_op->output_zero_point,
667 average_pooling_op->output_min,
668 average_pooling_op->output_max);
669
670 return setup_average_pooling2d(
671 average_pooling_op,
672 batch_size, input_height, input_width,
673 input, output,
674 0 /* log2(sizeof(input element)) = log2(sizeof(uint8_t)) */,
675 0 /* log2(sizeof(output element)) = log2(sizeof(uint8_t)) */,
676 &xnn_params.qu8.avgpool,
677 NULL /* no PAVGPOOL micro-kernel */,
678 &xnn_params.qu8.gavgpool,
679 &average_pooling_op->params.qu8_avgpool,
680 sizeof(average_pooling_op->params.qu8_avgpool),
681 &average_pooling_op->params.qu8_gavgpool,
682 sizeof(average_pooling_op->params.qu8_gavgpool),
683 pthreadpool_get_threads_count(threadpool),
684 false /* pixelwise not supported */);
685 }
686
xnn_setup_average_pooling2d_nhwc_f32(xnn_operator_t average_pooling_op,size_t batch_size,size_t input_height,size_t input_width,const float * input,float * output,pthreadpool_t threadpool)687 enum xnn_status xnn_setup_average_pooling2d_nhwc_f32(
688 xnn_operator_t average_pooling_op,
689 size_t batch_size,
690 size_t input_height,
691 size_t input_width,
692 const float* input,
693 float* output,
694 pthreadpool_t threadpool)
695 {
696 if (average_pooling_op->type != xnn_operator_type_average_pooling_nhwc_f32) {
697 xnn_log_error("failed to setup operator: operator type mismatch (expected %s, got %s)",
698 xnn_operator_type_to_string(xnn_operator_type_average_pooling_nhwc_f32),
699 xnn_operator_type_to_string(average_pooling_op->type));
700 return xnn_status_invalid_parameter;
701 }
702
703 assert(average_pooling_op->ukernel.type == xnn_ukernel_type_average_pooling ||
704 average_pooling_op->ukernel.type == xnn_ukernel_type_pixelwise_average_pooling);
705
706 const bool is_pixelwise = average_pooling_op->ukernel.type == xnn_ukernel_type_pixelwise_average_pooling;
707 if (is_pixelwise) {
708 const size_t input_size = input_height * input_width;
709 xnn_update_f32_scaleminmax_params(&average_pooling_op->params.f32_scaleminmax, 1.0f / (float) input_size);
710 }
711
712 return setup_average_pooling2d(
713 average_pooling_op,
714 batch_size, input_height, input_width,
715 input, output,
716 2 /* log2(sizeof(input element)) = log2(sizeof(float)) */,
717 2 /* log2(sizeof(output element)) = log2(sizeof(float)) */,
718 &xnn_params.f32.avgpool,
719 &xnn_params.f32.pavgpool,
720 &xnn_params.f32.gavgpool,
721 is_pixelwise ? (const void*) &average_pooling_op->params.f32_minmax : (const void*) &average_pooling_op->params.f32_scaleminmax,
722 is_pixelwise ? sizeof(average_pooling_op->params.f32_minmax) : sizeof(average_pooling_op->params.f32_scaleminmax),
723 &average_pooling_op->params.f32_scaleminmax,
724 sizeof(average_pooling_op->params.f32_scaleminmax),
725 pthreadpool_get_threads_count(threadpool),
726 is_pixelwise);
727 }
728