blob: 79b35d138a3c270516e4ce01470edbde21266045 [file] [log] [blame]
// Copyright (c) Facebook, Inc. and its affiliates.
// All rights reserved.
//
// 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 <stddef.h>
#include <stdint.h>
#include <stdlib.h>
#include <xnnpack.h>
#include <xnnpack/allocator.h>
#include <xnnpack/operator.h>
#include <xnnpack/requantization.h>
#include <xnnpack/log.h>
#include <xnnpack/params.h>
enum xnn_status xnn_create_global_average_pooling_nwc_q8(
size_t channels,
size_t input_stride,
size_t output_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* global_average_pooling_op_out)
{
xnn_operator_t global_average_pooling_op = NULL;
enum xnn_status status = xnn_status_uninitialized;
if (!xnn_params.initialized) {
xnn_log_error("failed to create Global Average Pooling operator: XNNPACK is not initialized");
goto error;
}
status = xnn_status_invalid_parameter;
if (channels == 0) {
xnn_log_error(
"failed to create Global Average Pooling operator with %zu channels: number of channels must be non-zero",
channels);
goto error;
}
if (input_stride < channels) {
xnn_log_error(
"failed to create Global Average Pooling operator with input element stride of %zu: "
"stride must be at least as large as the number of channels (%zu)",
input_stride, channels);
goto error;
}
if (output_stride < channels) {
xnn_log_error(
"failed to create Global Average Pooling operator with output element stride of %zu: "
"stride must be at least as large as the number of channels (%zu)",
output_stride, channels);
goto error;
}
if (input_scale <= 0.0f || !isnormal(input_scale)) {
xnn_log_error(
"failed to create Global Average Pooling operator with %.7g input scale: "
"scale must be finite, normalized, and positive",
input_scale);
goto error;
}
if (output_scale <= 0.0f || !isnormal(output_scale)) {
xnn_log_error(
"failed to create Global Average Pooling operator with %.7g output scale: "
"scale must be finite, normalized, and positive",
output_scale);
goto error;
}
if (output_min >= output_max) {
xnn_log_error(
"failed to create Global Average Pooling operator with [%" PRIu8 ", %" PRIu8 "] output range: "
"range min must be below range max",
output_min, output_max);
goto error;
}
status = xnn_status_unsupported_parameter;
const float input_output_scale = input_scale / output_scale;
if (input_output_scale < 0x1.0p-8f || input_output_scale >= 0x1.0p+8f) {
xnn_log_error(
"failed to create Global Average Pooling operator with %.7g input-to-output scale ratio: "
"scale ratio must be in [2**-8, 2**8) range",
input_output_scale);
goto error;
}
status = xnn_status_out_of_memory;
global_average_pooling_op = xnn_allocate_zero_memory(sizeof(struct xnn_operator));
if (global_average_pooling_op == NULL) {
xnn_log_error("failed to allocate %zu bytes for Global Average Pooling operator descriptor", sizeof(struct xnn_operator));
goto error;
}
void* zero_buffer = xnn_allocate_zero_memory(channels * sizeof(uint8_t) + XNN_EXTRA_BYTES);
if (zero_buffer == NULL) {
xnn_log_error("failed to allocate %zu bytes for Global Average Pooling zero padding",
channels * sizeof(uint8_t) + XNN_EXTRA_BYTES);
goto error;
}
global_average_pooling_op->zero_buffer = zero_buffer;
global_average_pooling_op->channels = channels;
global_average_pooling_op->input_pixel_stride = input_stride;
global_average_pooling_op->output_pixel_stride = output_stride;
global_average_pooling_op->input_zero_point = input_zero_point;
global_average_pooling_op->output_zero_point = output_zero_point;
global_average_pooling_op->input_scale = input_scale;
global_average_pooling_op->output_scale = output_scale;
global_average_pooling_op->output_min = output_min;
global_average_pooling_op->output_max = output_max;
global_average_pooling_op->type = xnn_operator_type_global_average_pooling_q8;
global_average_pooling_op->ukernel.type = xnn_ukernel_type_global_average_pooling;
global_average_pooling_op->state = xnn_run_state_invalid;
*global_average_pooling_op_out = global_average_pooling_op;
return xnn_status_success;
error:
xnn_delete_operator(global_average_pooling_op);
return status;
}
enum xnn_status xnn_create_global_average_pooling_nwc_f32(
size_t channels,
size_t input_stride,
size_t output_stride,
float output_min,
float output_max,
uint32_t flags,
xnn_operator_t* global_average_pooling_op_out)
{
xnn_operator_t global_average_pooling_op = NULL;
enum xnn_status status = xnn_status_uninitialized;
if (!xnn_params.initialized) {
xnn_log_error("failed to create Global Average Pooling operator: XNNPACK is not initialized");
goto error;
}
status = xnn_status_invalid_parameter;
if (channels == 0) {
xnn_log_error(
"failed to create Global Average Pooling operator with %zu channels: number of channels must be non-zero",
channels);
goto error;
}
if (input_stride < channels) {
xnn_log_error(
"failed to create Global Average Pooling operator with input element stride of %zu: "
"stride must be at least as large as the number of channels (%zu)",
input_stride, channels);
goto error;
}
if (output_stride < channels) {
xnn_log_error(
"failed to create Global Average Pooling operator with output element stride of %zu: "
"stride must be at least as large as the number of channels (%zu)",
output_stride, channels);
goto error;
}
if (isnan(output_min)) {
xnn_log_error(
"failed to create Global Average Pooling operator with NaN output lower bound: lower bound must be non-NaN");
goto error;
}
if (isnan(output_max)) {
xnn_log_error(
"failed to create Global Average Pooling operator with NaN output upper bound: upper bound must be non-NaN");
goto error;
}
if (output_min >= output_max) {
xnn_log_error(
"failed to create Global Average Pooling operator with [%.7g, %.7g] output range: "
"lower bound must be below upper bound",
output_min, output_max);
goto error;
}
status = xnn_status_out_of_memory;
global_average_pooling_op = xnn_allocate_zero_memory(sizeof(struct xnn_operator));
if (global_average_pooling_op == NULL) {
xnn_log_error("failed to allocate %zu bytes for Global Average Pooling operator descriptor", sizeof(struct xnn_operator));
goto error;
}
void* zero_buffer = xnn_allocate_zero_memory(channels * sizeof(float) + XNN_EXTRA_BYTES);
if (zero_buffer == NULL) {
xnn_log_error("failed to allocate %zu bytes for Global Average Pooling zero padding",
channels * sizeof(float) + XNN_EXTRA_BYTES);
goto error;
}
global_average_pooling_op->zero_buffer = zero_buffer;
global_average_pooling_op->channels = channels;
global_average_pooling_op->input_pixel_stride = input_stride;
global_average_pooling_op->output_pixel_stride = output_stride;
global_average_pooling_op->f32_avgpool_params =
xnn_compute_f32_avgpool_params(nanf(""), output_min, output_max);
global_average_pooling_op->type = xnn_operator_type_global_average_pooling_f32;
global_average_pooling_op->ukernel.type = xnn_ukernel_type_global_average_pooling;
global_average_pooling_op->state = xnn_run_state_invalid;
*global_average_pooling_op_out = global_average_pooling_op;
return xnn_status_success;
error:
xnn_delete_operator(global_average_pooling_op);
return status;
}
enum xnn_status xnn_setup_global_average_pooling_nwc_q8(
xnn_operator_t global_average_pooling_op,
size_t batch_size,
size_t width,
const uint8_t* input,
uint8_t* output,
pthreadpool_t threadpool)
{
if (global_average_pooling_op->type != xnn_operator_type_global_average_pooling_q8) {
xnn_log_error("failed to setup Global Average Pooling (Q8) operator: operator type mismatch");
return xnn_status_invalid_parameter;
}
global_average_pooling_op->state = xnn_run_state_invalid;
if (!xnn_params.initialized) {
xnn_log_error("failed to setup Global Average Pooling operator: XNNPACK is not initialized");
return xnn_status_uninitialized;
}
if (width == 0) {
xnn_log_error("failed to setup Global Average Pooling operator with width %zu: width must be non-zero", width);
return xnn_status_invalid_parameter;
}
if (batch_size == 0) {
global_average_pooling_op->state = xnn_run_state_skip;
return xnn_status_success;
}
global_average_pooling_op->batch_size = batch_size;
global_average_pooling_op->input_width = width;
global_average_pooling_op->input = input;
global_average_pooling_op->output = output;
global_average_pooling_op->q8_avgpool_params =
xnn_compute_q8_avgpool_params(
-(int32_t) width * (int32_t) (uint32_t) global_average_pooling_op->input_zero_point,
global_average_pooling_op->input_scale / (global_average_pooling_op->output_scale * (float) width),
global_average_pooling_op->output_zero_point,
global_average_pooling_op->output_min,
global_average_pooling_op->output_max);
const size_t input_stride_in_bytes = global_average_pooling_op->input_pixel_stride * sizeof(uint8_t);
const size_t channels = global_average_pooling_op->channels;
global_average_pooling_op->context.global_average_pooling = (struct global_average_pooling_context) {
.input = input,
.zero = global_average_pooling_op->zero_buffer,
.input_pixel_stride = input_stride_in_bytes,
.input_batch_stride = input_stride_in_bytes * width,
.input_elements = width,
.channels = channels,
.output = output,
.output_batch_stride = global_average_pooling_op->output_pixel_stride * sizeof(uint8_t),
.params.q8 = global_average_pooling_op->q8_avgpool_params,
};
global_average_pooling_op->compute.type = xnn_parallelization_type_1d;
global_average_pooling_op->compute.range[0] = batch_size;
if (width <= xnn_params.q8.gavgpool.mr) {
global_average_pooling_op->compute.task_1d = (pthreadpool_task_1d_t) xnn_compute_global_average_pooling_unipass;
global_average_pooling_op->context.global_average_pooling.unipass_ukernel = xnn_params.q8.gavgpool.up;
} else {
global_average_pooling_op->compute.task_1d = (pthreadpool_task_1d_t) xnn_compute_global_average_pooling_multipass;
global_average_pooling_op->context.global_average_pooling.multipass_ukernel = xnn_params.q8.gavgpool.mp;
}
global_average_pooling_op->state = xnn_run_state_ready;
return xnn_status_success;
}
enum xnn_status xnn_setup_global_average_pooling_nwc_f32(
xnn_operator_t global_average_pooling_op,
size_t batch_size,
size_t width,
const float* input,
float* output,
pthreadpool_t threadpool)
{
if (global_average_pooling_op->type != xnn_operator_type_global_average_pooling_f32) {
xnn_log_error("failed to setup Global Average Pooling (F32) operator: operator type mismatch");
return xnn_status_invalid_parameter;
}
global_average_pooling_op->state = xnn_run_state_invalid;
if (!xnn_params.initialized) {
xnn_log_error("failed to setup Global Average Pooling operator: XNNPACK is not initialized");
return xnn_status_uninitialized;
}
if (width == 0) {
xnn_log_error("failed to setup Global Average Pooling operator with width %zu: width must be non-zero", width);
return xnn_status_invalid_parameter;
}
if (batch_size == 0) {
global_average_pooling_op->state = xnn_run_state_skip;
return xnn_status_success;
}
global_average_pooling_op->batch_size = batch_size;
global_average_pooling_op->input_width = width;
global_average_pooling_op->input = input;
global_average_pooling_op->output = output;
xnn_update_f32_avgpool_params(&global_average_pooling_op->f32_avgpool_params, 1.0f / (float) width);
const size_t input_stride_in_bytes = global_average_pooling_op->input_pixel_stride * sizeof(float);
const size_t channels = global_average_pooling_op->channels;
global_average_pooling_op->context.global_average_pooling = (struct global_average_pooling_context) {
.input = input,
.zero = global_average_pooling_op->zero_buffer,
.input_pixel_stride = input_stride_in_bytes,
.input_batch_stride = input_stride_in_bytes * width,
.input_elements = width,
.channels = channels,
.output = output,
.output_batch_stride = global_average_pooling_op->output_pixel_stride * sizeof(float),
.params.f32 = global_average_pooling_op->f32_avgpool_params,
};
global_average_pooling_op->compute.type = xnn_parallelization_type_1d;
global_average_pooling_op->compute.range[0] = batch_size;
if (width <= xnn_params.f32.gavgpool.mr) {
global_average_pooling_op->compute.task_1d = (pthreadpool_task_1d_t) xnn_compute_global_average_pooling_unipass;
global_average_pooling_op->context.global_average_pooling.unipass_ukernel = xnn_params.f32.gavgpool.up;
} else {
global_average_pooling_op->compute.task_1d = (pthreadpool_task_1d_t) xnn_compute_global_average_pooling_multipass;
global_average_pooling_op->context.global_average_pooling.multipass_ukernel = xnn_params.f32.gavgpool.mp;
}
global_average_pooling_op->state = xnn_run_state_ready;
return xnn_status_success;
}