blob: 0ef2a8af850bb49bf21355b2cc4c45fd71a1404f [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 <math.h>
#include <stddef.h>
#include <stdint.h>
#include <stdlib.h>
#include <xnnpack.h>
#include <xnnpack/allocator.h>
#include <xnnpack/log.h>
#include <xnnpack/operator.h>
#include <xnnpack/params-init.h>
#include <xnnpack/params.h>
enum xnn_status xnn_create_clamp_nc_u8(
size_t channels,
size_t input_stride,
size_t output_stride,
uint8_t output_min,
uint8_t output_max,
uint32_t flags,
xnn_operator_t* clamp_op_out)
{
xnn_operator_t clamp_op = NULL;
enum xnn_status status = xnn_status_uninitialized;
if (!xnn_params.initialized) {
xnn_log_error("failed to create Clamp operator: XNNPACK is not initialized");
goto error;
}
status = xnn_status_invalid_parameter;
if (channels == 0) {
xnn_log_error(
"failed to create Clamp operator with %zu channels: number of channels must be non-zero", channels);
goto error;
}
if (input_stride < channels) {
xnn_log_error(
"failed to create Clamp 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 Clamp 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 (output_min >= output_max) {
xnn_log_error(
"failed to create Clamp operator with [%" PRIu8 ", %" PRIu8 "] output range: range min must be below range max",
output_min, output_max);
goto error;
}
status = xnn_status_out_of_memory;
clamp_op = xnn_allocate_zero_memory(sizeof(struct xnn_operator));
if (clamp_op == NULL) {
xnn_log_error("failed to allocate %zu bytes for Clamp operator descriptor", sizeof(struct xnn_operator));
goto error;
}
clamp_op->channels = channels;
clamp_op->input_pixel_stride = input_stride;
clamp_op->output_pixel_stride = output_stride;
clamp_op->u8_output_params = xnn_init_u8_output_params(output_min, output_max);
clamp_op->type = xnn_operator_type_clamp_u8;
clamp_op->ukernel.type = xnn_ukernel_type_clamp;
clamp_op->state = xnn_run_state_invalid;
*clamp_op_out = clamp_op;
return xnn_status_success;
error:
xnn_delete_operator(clamp_op);
return status;
}
enum xnn_status xnn_create_clamp_nc_f32(
size_t channels,
size_t input_stride,
size_t output_stride,
float output_min,
float output_max,
uint32_t flags,
xnn_operator_t* clamp_op_out)
{
xnn_operator_t clamp_op = NULL;
enum xnn_status status = xnn_status_uninitialized;
if (!xnn_params.initialized) {
xnn_log_error("failed to create Clamp operator: XNNPACK is not initialized");
goto error;
}
status = xnn_status_invalid_parameter;
if (channels == 0) {
xnn_log_error(
"failed to create Clamp operator with %zu channels: number of channels must be non-zero", channels);
goto error;
}
if (input_stride < channels) {
xnn_log_error(
"failed to create Clamp 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 Clamp 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 Clamp operator with NaN output lower bound: lower bound must be non-NaN");
goto error;
}
if (isnan(output_max)) {
xnn_log_error(
"failed to create Clamp 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 Clamp 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;
clamp_op = xnn_allocate_zero_memory(sizeof(struct xnn_operator));
if (clamp_op == NULL) {
xnn_log_error("failed to allocate %zu bytes for Clamp operator descriptor", sizeof(struct xnn_operator));
goto error;
}
clamp_op->channels = channels;
clamp_op->input_pixel_stride = input_stride;
clamp_op->output_pixel_stride = output_stride;
clamp_op->f32_output_params = xnn_init_f32_output_params(output_min, output_max);
clamp_op->type = xnn_operator_type_clamp_f32;
clamp_op->ukernel.type = xnn_ukernel_type_clamp;
clamp_op->state = xnn_run_state_invalid;
*clamp_op_out = clamp_op;
return xnn_status_success;
error:
xnn_delete_operator(clamp_op);
return status;
}
enum xnn_status xnn_setup_clamp_nc_u8(
xnn_operator_t clamp_op,
size_t batch_size,
const uint8_t* input,
uint8_t* output,
pthreadpool_t threadpool)
{
if (clamp_op->type != xnn_operator_type_clamp_u8) {
xnn_log_error("failed to setup Clamp (U8) operator: operator type mismatch");
return xnn_status_invalid_parameter;
}
clamp_op->state = xnn_run_state_invalid;
if (!xnn_params.initialized) {
xnn_log_error("failed to setup Clamp operator: XNNPACK is not initialized");
return xnn_status_uninitialized;
}
if (batch_size == 0) {
clamp_op->state = xnn_run_state_skip;
return xnn_status_success;
}
const size_t channels = clamp_op->channels;
const size_t input_stride = clamp_op->input_pixel_stride;
const size_t output_stride = clamp_op->output_pixel_stride;
if ((((input_stride ^ channels) | (output_stride ^ channels)) == 0) || batch_size == 1) {
const size_t block_size = 4096;
clamp_op->context.univector_contiguous = (struct univector_contiguous_context) {
.x = input,
.x_stride = input_stride * sizeof(uint8_t),
.y = output,
.y_stride = output_stride * sizeof(uint8_t),
.ukernel = xnn_params.u8.clamp,
.params.u8_output = clamp_op->u8_output_params,
};
clamp_op->compute.type = xnn_parallelization_type_1d_tile_1d;
clamp_op->compute.task_1d_tile_1d = (pthreadpool_task_1d_tile_1d_t) xnn_compute_univector_contiguous;
clamp_op->compute.range[0] = batch_size * channels * sizeof(uint8_t);
clamp_op->compute.tile[0] = block_size;
} else {
clamp_op->context.univector_strided = (struct univector_strided_context) {
.n = channels * sizeof(uint8_t),
.x = input,
.x_stride = input_stride * sizeof(uint8_t),
.y = output,
.y_stride = output_stride * sizeof(uint8_t),
.ukernel = xnn_params.u8.clamp,
.params.u8_output = clamp_op->u8_output_params,
};
clamp_op->compute.type = xnn_parallelization_type_1d_tile_1d;
clamp_op->compute.task_1d_tile_1d = (pthreadpool_task_1d_tile_1d_t) xnn_compute_univector_strided;
clamp_op->compute.range[0] = batch_size;
clamp_op->compute.tile[0] = 1;
}
clamp_op->state = xnn_run_state_ready;
return xnn_status_success;
}
enum xnn_status xnn_setup_clamp_nc_f32(
xnn_operator_t clamp_op,
size_t batch_size,
const float* input,
float* output,
pthreadpool_t threadpool)
{
if (clamp_op->type != xnn_operator_type_clamp_f32) {
xnn_log_error("failed to setup Clamp (F32) operator: operator type mismatch");
return xnn_status_invalid_parameter;
}
clamp_op->state = xnn_run_state_invalid;
if (!xnn_params.initialized) {
xnn_log_error("failed to setup Clamp operator: XNNPACK is not initialized");
return xnn_status_uninitialized;
}
if (batch_size == 0) {
clamp_op->state = xnn_run_state_skip;
return xnn_status_success;
}
const size_t channels = clamp_op->channels;
const size_t input_stride = clamp_op->input_pixel_stride;
const size_t output_stride = clamp_op->output_pixel_stride;
if ((((input_stride ^ channels) | (output_stride ^ channels)) == 0) || batch_size == 1) {
const size_t block_size = 4096;
clamp_op->context.univector_contiguous = (struct univector_contiguous_context) {
.x = input,
.x_stride = input_stride * sizeof(float),
.y = output,
.y_stride = output_stride * sizeof(float),
.ukernel = xnn_params.f32.clamp,
.params.f32_output = clamp_op->f32_output_params,
};
clamp_op->compute.type = xnn_parallelization_type_1d_tile_1d;
clamp_op->compute.task_1d_tile_1d = (pthreadpool_task_1d_tile_1d_t) xnn_compute_univector_contiguous;
clamp_op->compute.range[0] = batch_size * channels * sizeof(float);
clamp_op->compute.tile[0] = block_size;
} else {
clamp_op->context.univector_strided = (struct univector_strided_context) {
.n = channels * sizeof(float),
.x = input,
.x_stride = input_stride * sizeof(float),
.y = output,
.y_stride = output_stride * sizeof(float),
.ukernel = xnn_params.f32.clamp,
.params.f32_output = clamp_op->f32_output_params,
};
clamp_op->compute.type = xnn_parallelization_type_1d_tile_1d;
clamp_op->compute.task_1d_tile_1d = (pthreadpool_task_1d_tile_1d_t) xnn_compute_univector_strided;
clamp_op->compute.range[0] = batch_size;
clamp_op->compute.tile[0] = 1;
}
clamp_op->state = xnn_run_state_ready;
return xnn_status_success;
}