blob: 74ca098b57ac7bfd3ad79ecc7a1c20e71e60bf5a [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/log.h>
enum xnn_status xnn_create_leaky_relu_nc_q8(
size_t channels,
size_t input_stride,
size_t output_stride,
float negative_slope,
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* leaky_relu_op_out)
{
xnn_operator_t leaky_relu_op = NULL;
enum xnn_status status = xnn_status_uninitialized;
if (!xnn_params.initialized) {
xnn_log_error("failed to create Leaky ReLU operator: XNNPACK is not initialized");
goto error;
}
status = xnn_status_invalid_parameter;
if (channels == 0) {
xnn_log_error(
"failed to create Leaky ReLU operator with %zu channels: number of channels must be non-zero", channels);
goto error;
}
if (input_stride < channels) {
xnn_log_error(
"failed to create Leaky ReLU 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 Leaky ReLU 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 (negative_slope <= 0.0f || !isnormal(negative_slope)) {
xnn_log_error(
"failed to create Leaky ReLU operator with %.7g negative slope: slope must be finite, normalized, and positive",
negative_slope);
goto error;
}
if (negative_slope > 1.0f) {
xnn_log_error(
"failed to create Leaky ReLU operator with %.7g negative slope: slope must not exceed 1.0", negative_slope);
goto error;
}
if (input_scale <= 0.0f || !isnormal(input_scale)) {
xnn_log_error(
"failed to create Leaky ReLU 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 Leaky ReLU 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 Leaky ReLU 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 Leaky ReLU 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;
leaky_relu_op = xnn_allocate_zero_memory(sizeof(struct xnn_operator));
if (leaky_relu_op == NULL) {
xnn_log_error("failed to allocate %zu bytes for Leaky ReLU operator descriptor", sizeof(struct xnn_operator));
goto error;
}
leaky_relu_op->lookup_table = xnn_allocate_memory(256 * sizeof(uint8_t));
if (leaky_relu_op->lookup_table == NULL) {
xnn_log_error("failed to allocate 256 bytes for Leaky ReLU lookup table");
goto error;
}
uint8_t* lookup_table = leaky_relu_op->lookup_table;
const float scaled_min_less_zero_point = (float) ((int32_t) output_min - (int32_t) output_zero_point);
const float scaled_max_less_zero_point = (float) ((int32_t) output_max - (int32_t) output_zero_point);
for (int32_t i = 0; i < 256; i++) {
const float x = input_output_scale * (float) (i - (int32_t) (uint32_t) input_zero_point);
float y = x < 0.0f ? x * negative_slope : x;
if (y < scaled_min_less_zero_point) {
y = scaled_min_less_zero_point;
}
if (y > scaled_max_less_zero_point) {
y = scaled_max_less_zero_point;
}
lookup_table[(uint32_t) i] = (uint8_t) (lrintf(y) + (long) output_zero_point);
}
leaky_relu_op->channels = channels;
leaky_relu_op->input_pixel_stride = input_stride;
leaky_relu_op->output_pixel_stride = output_stride;
leaky_relu_op->type = xnn_operator_type_leaky_relu_q8;
leaky_relu_op->ukernel.type = xnn_ukernel_type_lut;
leaky_relu_op->state = xnn_run_state_invalid;
*leaky_relu_op_out = leaky_relu_op;
return xnn_status_success;
error:
xnn_delete_operator(leaky_relu_op);
return status;
}
enum xnn_status xnn_setup_leaky_relu_nc_q8(
xnn_operator_t leaky_relu_op,
size_t batch_size,
const uint8_t* input,
uint8_t* output,
pthreadpool_t threadpool)
{
if (leaky_relu_op->type != xnn_operator_type_leaky_relu_q8) {
xnn_log_error("failed to setup Leaky ReLU (Q8) operator: operator type mismatch");
return xnn_status_invalid_parameter;
}
leaky_relu_op->state = xnn_run_state_invalid;
if (!xnn_params.initialized) {
xnn_log_error("failed to setup Leaky ReLU operator: XNNPACK is not initialized");
return xnn_status_uninitialized;
}
if (batch_size == 0) {
leaky_relu_op->state = xnn_run_state_skip;
return xnn_status_success;
}
const size_t channels = leaky_relu_op->channels;
const size_t input_stride = leaky_relu_op->input_pixel_stride;
const size_t output_stride = leaky_relu_op->output_pixel_stride;
if ((((input_stride ^ channels) | (output_stride ^ channels)) == 0) || batch_size == 1) {
const size_t block_size = 1024;
leaky_relu_op->context.lut_contiguous = (struct lut_contiguous_context) {
.x = input,
.x_stride = input_stride * sizeof(uint8_t),
.t = leaky_relu_op->lookup_table,
.y = output,
.y_stride = output_stride * sizeof(uint8_t),
.ukernel = xnn_params.x8.lut,
};
leaky_relu_op->compute.type = xnn_parallelization_type_1d_tile_1d;
leaky_relu_op->compute.task_1d_tile_1d = (pthreadpool_task_1d_tile_1d_t) xnn_compute_lut_contiguous;
leaky_relu_op->compute.range[0] = batch_size * channels * sizeof(uint8_t);
leaky_relu_op->compute.tile[0] = block_size;
} else {
leaky_relu_op->context.lut_strided = (struct lut_strided_context) {
.n = channels,
.x = input,
.x_stride = input_stride * sizeof(uint8_t),
.t = leaky_relu_op->lookup_table,
.y = output,
.y_stride = output_stride * sizeof(uint8_t),
.ukernel = xnn_params.x8.lut,
};
leaky_relu_op->compute.type = xnn_parallelization_type_1d;
leaky_relu_op->compute.task_1d = (pthreadpool_task_1d_t) xnn_compute_lut_strided;
leaky_relu_op->compute.range[0] = batch_size;
leaky_relu_op->compute.tile[0] = 0;
}
leaky_relu_op->state = xnn_run_state_ready;
return xnn_status_success;
}