blob: ac1836d0b009e4d21152c1be38b22e618d78736c [file] [log] [blame]
// Copyright 2019 Google LLC
//
// This source code is licensed under the BSD-style license found in the
// LICENSE file in the root directory of this source tree.
#include <assert.h>
#include <math.h>
#include <stdbool.h>
#include <stddef.h>
#include <stdint.h>
#include <stdlib.h>
#include <string.h>
#include <xnnpack.h>
#include <xnnpack/allocator.h>
#include <xnnpack/operator.h>
#include <xnnpack/log.h>
#include <xnnpack/common.h>
#include <xnnpack/math.h>
#include <xnnpack/params.h>
#include <xnnpack/indirection.h>
static enum xnn_status create_resize_bilinear2d_nhwc(
size_t channels,
size_t input_pixel_stride,
size_t output_pixel_stride,
uint32_t flags,
enum xnn_operator_type operator_type,
xnn_operator_t* resize_op_out)
{
xnn_operator_t resize_op = NULL;
enum xnn_status status = xnn_status_uninitialized;
if ((xnn_params.init_flags & XNN_INIT_FLAG_XNNPACK) == 0) {
xnn_log_error("failed to create %s operator: XNNPACK is not initialized",
xnn_operator_type_to_string(operator_type));
goto error;
}
status = xnn_status_invalid_parameter;
if (channels == 0) {
xnn_log_error(
"failed to create %s operator with %zu channels: number of channels must be non-zero",
xnn_operator_type_to_string(operator_type), channels);
goto error;
}
if (input_pixel_stride < channels) {
xnn_log_error(
"failed to create %s operator with input pixel stride of %zu: "
"stride must be at least as large as the number of channels (%zu)",
xnn_operator_type_to_string(operator_type), input_pixel_stride, channels);
goto error;
}
if (output_pixel_stride < channels) {
xnn_log_error(
"failed to create %s operator with output pixel stride of %zu: "
"stride must be at least as large as the number of channels (%zu)",
xnn_operator_type_to_string(operator_type), output_pixel_stride, channels);
goto error;
}
status = xnn_status_out_of_memory;
resize_op = xnn_allocate_zero_simd_memory(sizeof(struct xnn_operator));
if (resize_op == NULL) {
xnn_log_error(
"failed to allocate %zu bytes for %s operator descriptor",
sizeof(struct xnn_operator), xnn_operator_type_to_string(operator_type));
goto error;
}
resize_op->channels = channels;
resize_op->input_pixel_stride = input_pixel_stride;
resize_op->output_pixel_stride = output_pixel_stride;
resize_op->type = operator_type;
resize_op->flags = flags;
resize_op->state = xnn_run_state_invalid;
*resize_op_out = resize_op;
return xnn_status_success;
error:
xnn_delete_operator(resize_op);
return status;
}
enum xnn_status xnn_create_resize_bilinear2d_nhwc_f32(
size_t channels,
size_t input_pixel_stride,
size_t output_pixel_stride,
uint32_t flags,
xnn_operator_t* resize_op_out)
{
return create_resize_bilinear2d_nhwc(
channels,
input_pixel_stride,
output_pixel_stride,
flags,
xnn_operator_type_resize_bilinear_nhwc_f32,
resize_op_out);
}
enum xnn_status xnn_create_resize_bilinear2d_nhwc_s8(
size_t channels,
size_t input_pixel_stride,
size_t output_pixel_stride,
uint32_t flags,
xnn_operator_t* resize_op_out)
{
return create_resize_bilinear2d_nhwc(
channels,
input_pixel_stride,
output_pixel_stride,
flags,
xnn_operator_type_resize_bilinear_nhwc_s8,
resize_op_out);
}
enum xnn_status xnn_create_resize_bilinear2d_nhwc_u8(
size_t channels,
size_t input_pixel_stride,
size_t output_pixel_stride,
uint32_t flags,
xnn_operator_t* resize_op_out)
{
return create_resize_bilinear2d_nhwc(
channels,
input_pixel_stride,
output_pixel_stride,
flags,
xnn_operator_type_resize_bilinear_nhwc_u8,
resize_op_out);
}
static enum xnn_status setup_resize_bilinear2d_nhwc(
xnn_operator_t resize_op,
enum xnn_operator_type expected_operator_type,
size_t batch_size,
size_t input_height,
size_t input_width,
size_t output_height,
size_t output_width,
const void* input,
void* output,
uint32_t log2_element_size,
uint32_t log2_weight_element_size,
xnn_indirection_init_resize_bilinear2d_hwc_fn indirection_init,
const struct ibilinear_parameters ibilinear[restrict XNN_MIN_ELEMENTS(1)],
size_t num_threads)
{
if (resize_op->type != expected_operator_type) {
xnn_log_error("failed to setup operator: operator type mismatch (expected %s, got %s)",
xnn_operator_type_to_string(expected_operator_type),
xnn_operator_type_to_string(resize_op->type));
return xnn_status_invalid_parameter;
}
resize_op->state = xnn_run_state_invalid;
if ((xnn_params.init_flags & XNN_INIT_FLAG_XNNPACK) == 0) {
xnn_log_error("failed to setup %s operator: XNNPACK is not initialized",
xnn_operator_type_to_string(resize_op->type));
return xnn_status_uninitialized;
}
if (input_width == 0 || input_height == 0) {
xnn_log_error(
"failed to setup %s operator with %zux%zu input: input dimensions must be non-zero",
xnn_operator_type_to_string(resize_op->type), input_width, input_height);
return xnn_status_invalid_parameter;
}
if (max(input_width, input_height) >= 16777216) {
xnn_log_error(
"failed to setup %s operator with %zux%zu input: input dimensions must be below 2**24",
xnn_operator_type_to_string(resize_op->type), input_width, input_height);
return xnn_status_unsupported_parameter;
}
if (output_width == 0 || output_height == 0) {
xnn_log_error(
"failed to setup %s operator with %zux%zu output: output dimensions must be non-zero",
xnn_operator_type_to_string(resize_op->type), output_width, output_height);
return xnn_status_invalid_parameter;
}
if (max(output_width, output_height) >= 16777216) {
xnn_log_error(
"failed to setup %s operator with %zux%zu output: output dimensions must be below 2**24",
xnn_operator_type_to_string(resize_op->type), output_width, output_height);
return xnn_status_unsupported_parameter;
}
if (batch_size == 0) {
resize_op->state = xnn_run_state_skip;
return xnn_status_success;
}
if (output_height * output_width != resize_op->last_output_height * resize_op->last_output_width) {
const size_t indirection_buffer_size = sizeof(void*) * (output_height * output_width * 4);
const size_t packed_weights_size = (output_height * output_width * 2) << log2_weight_element_size;
const void** indirection_buffer = (const void**) xnn_reallocate_memory(resize_op->indirection_buffer, indirection_buffer_size);
if (indirection_buffer == NULL) {
xnn_log_error(
"failed to allocate %zu bytes for %s operator indirection buffer",
indirection_buffer_size, xnn_operator_type_to_string(resize_op->type));
return xnn_status_out_of_memory;
}
resize_op->indirection_buffer = indirection_buffer;
// Note: packed weights must be SIMD-aligned, so we can't use xnn_reallocate_memory
xnn_release_simd_memory(resize_op->packed_weights);
resize_op->packed_weights = xnn_allocate_simd_memory(packed_weights_size);
if (resize_op->packed_weights == NULL) {
xnn_log_error(
"failed to allocate %zu bytes for %s operator packed weights",
packed_weights_size, xnn_operator_type_to_string(resize_op->type));
return xnn_status_out_of_memory;
}
}
const size_t input_pixel_stride_in_bytes = resize_op->input_pixel_stride << log2_element_size;
if (input_height != resize_op->last_input_height ||
input_width != resize_op->last_input_width ||
output_height != resize_op->last_output_height ||
output_width != resize_op->last_output_width)
{
const uint32_t flags = resize_op->flags;
indirection_init(
input_pixel_stride_in_bytes,
input_height, input_width,
output_height, output_width,
input, resize_op->indirection_buffer, resize_op->packed_weights,
!!(flags & XNN_FLAG_ALIGN_CORNERS),
!!(flags & XNN_FLAG_TENSORFLOW_LEGACY_MODE));
resize_op->last_input = input;
resize_op->last_input_height = input_height;
resize_op->last_input_width = input_width;
resize_op->last_output_height = output_height;
resize_op->last_output_width = output_width;
}
const size_t output_pixel_stride_in_bytes = resize_op->output_pixel_stride << log2_element_size;
resize_op->context.resize_bilinear = (struct resize_bilinear_context) {
.scaled_channels = resize_op->channels << log2_element_size,
.indirect_input = resize_op->indirection_buffer,
.input_offset = (size_t) ((uintptr_t) input - (uintptr_t) resize_op->last_input),
.input_batch_stride = input_pixel_stride_in_bytes * input_height * input_width,
.packed_weights = resize_op->packed_weights,
.output = output,
.output_pixel_stride = output_pixel_stride_in_bytes,
.output_batch_stride = output_pixel_stride_in_bytes * output_height * output_width,
.log2_wsize = 1 + log2_weight_element_size /* log2(2 * sizeof(weight)) */,
.ukernel = ibilinear->ukernel,
};
const size_t output_size = output_height * output_width;
size_t output_size_tile = output_size;
if (num_threads > 1) {
const size_t target_tiles_per_thread = 5;
const size_t max_output_size_tile = divide_round_up(output_size, num_threads * target_tiles_per_thread);
if (max_output_size_tile < output_size_tile) {
const uint32_t output_size_subtile = ibilinear->pixel_tile;
output_size_tile =
min(output_size_tile,
divide_round_up(output_size_tile, max_output_size_tile * output_size_subtile) * output_size_subtile);
}
}
resize_op->compute.type = xnn_parallelization_type_2d_tile_1d;
resize_op->compute.task_2d_tile_1d = (pthreadpool_task_2d_tile_1d_t) xnn_compute_resize_bilinear;
resize_op->compute.range[0] = batch_size;
resize_op->compute.range[1] = output_size;
resize_op->compute.tile[0] = output_size_tile;
resize_op->state = xnn_run_state_ready;
return xnn_status_success;
}
enum xnn_status xnn_setup_resize_bilinear2d_nhwc_f32(
xnn_operator_t resize_op,
size_t batch_size,
size_t input_height,
size_t input_width,
size_t output_height,
size_t output_width,
const float* input,
float* output,
pthreadpool_t threadpool)
{
return setup_resize_bilinear2d_nhwc(
resize_op,
xnn_operator_type_resize_bilinear_nhwc_f32,
batch_size,
input_height,
input_width,
output_height,
output_width,
input,
output,
2 /* log2(element size) == log2(sizeof(float)) */,
2 /* log2(weight element size) == log2(sizeof(float)) */,
(xnn_indirection_init_resize_bilinear2d_hwc_fn) xnn_indirection_init_resize_bilinear2d_hwc_f32,
&xnn_params.f32.ibilinear,
pthreadpool_get_threads_count(threadpool));
}
enum xnn_status xnn_setup_resize_bilinear2d_nhwc_s8(
xnn_operator_t resize_op,
size_t batch_size,
size_t input_height,
size_t input_width,
size_t output_height,
size_t output_width,
const int8_t* input,
int8_t* output,
pthreadpool_t threadpool)
{
return setup_resize_bilinear2d_nhwc(
resize_op,
xnn_operator_type_resize_bilinear_nhwc_s8,
batch_size,
input_height,
input_width,
output_height,
output_width,
input,
output,
0 /* log2(element size) == log2(sizeof(int8_t)) */,
1 /* log2(weight element size) == log2(sizeof(int16_t)) */,
(xnn_indirection_init_resize_bilinear2d_hwc_fn) xnn_indirection_init_resize_bilinear2d_hwc_q11,
&xnn_params.s8.ibilinear,
pthreadpool_get_threads_count(threadpool));
}
enum xnn_status xnn_setup_resize_bilinear2d_nhwc_u8(
xnn_operator_t resize_op,
size_t batch_size,
size_t input_height,
size_t input_width,
size_t output_height,
size_t output_width,
const uint8_t* input,
uint8_t* output,
pthreadpool_t threadpool)
{
return setup_resize_bilinear2d_nhwc(
resize_op,
xnn_operator_type_resize_bilinear_nhwc_u8,
batch_size,
input_height,
input_width,
output_height,
output_width,
input,
output,
0 /* log2(element size) == log2(sizeof(uint8_t)) */,
1 /* log2(weight element size) == log2(sizeof(int16_t)) */,
(xnn_indirection_init_resize_bilinear2d_hwc_fn) xnn_indirection_init_resize_bilinear2d_hwc_q11,
&xnn_params.u8.ibilinear,
pthreadpool_get_threads_count(threadpool));
}