blob: 6351ae86efd799ee10e10a827a21ad7d3f02ae04 [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>
#include <xnnpack/params-init.h>
enum xnn_status xnn_create_softmax_nc_q8(
size_t channels,
size_t input_stride,
size_t output_stride,
float input_scale,
uint8_t output_zero_point,
float output_scale,
uint32_t flags,
xnn_operator_t* softmax_op_out)
{
xnn_operator_t softmax_op = NULL;
enum xnn_status status = xnn_status_uninitialized;
if (!xnn_params.initialized) {
xnn_log_error("failed to create SoftMax operator: XNNPACK is not initialized");
goto error;
}
status = xnn_status_invalid_parameter;
if (channels == 0) {
xnn_log_error(
"failed to create SoftMax operator with %zu channels: number of channels must be non-zero", channels);
goto error;
}
if (input_stride < channels) {
xnn_log_error(
"failed to create SoftMax 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 SoftMax 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 SoftMax 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 SoftMax operator with %.7g output scale: scale must be finite, normalized, and positive",
output_scale);
goto error;
}
status = xnn_status_unsupported_parameter;
if (output_scale != 0x1.0p-8f) {
xnn_log_error(
"failed to create SoftMax operator with %.7g output scale: only output scale of 1/256 is supported",
output_scale);
goto error;
}
if (output_zero_point != 0) {
xnn_log_error(
"failed to create SoftMax operator with %" PRIu8 " output zero point: "
"only output zero point of 0 is supported",
output_zero_point);
goto error;
}
status = xnn_status_out_of_memory;
softmax_op = xnn_allocate_zero_simd_memory(sizeof(struct xnn_operator));
if (softmax_op == NULL) {
xnn_log_error("failed to allocate %zu bytes for SoftMax operator descriptor", sizeof(struct xnn_operator));
goto error;
}
softmax_op->lookup_table = xnn_allocate_simd_memory(256 * sizeof(uint32_t));
if (softmax_op->lookup_table == NULL) {
xnn_log_error("failed to allocate 256 bytes for SoftMax lookup table");
goto error;
}
uint32_t* lookup_table = softmax_op->lookup_table;
const double qscale = fmin(((double) UINT32_MAX) / (double) channels, 8388607.0);
for (int32_t i = 0; i < 256; i++) {
const double scaled_exp_xi = qscale * exp((double) (i - 255) * (double) input_scale);
lookup_table[(uint32_t) i] = (uint32_t) lrint(scaled_exp_xi);
}
softmax_op->channels = channels;
softmax_op->input_pixel_stride = input_stride;
softmax_op->output_pixel_stride = output_stride;
softmax_op->type = xnn_operator_type_softmax_nc_q8;
softmax_op->ukernel.type = xnn_ukernel_type_softmax;
softmax_op->state = xnn_run_state_invalid;
*softmax_op_out = softmax_op;
return xnn_status_success;
error:
xnn_delete_operator(softmax_op);
return status;
}
enum xnn_status xnn_setup_softmax_nc_q8(
xnn_operator_t softmax_op,
size_t batch_size,
const uint8_t* input,
uint8_t* output,
pthreadpool_t threadpool)
{
if (softmax_op->type != xnn_operator_type_softmax_nc_q8) {
xnn_log_error("failed to setup SoftMax (NC, Q8) operator: operator type mismatch");
return xnn_status_invalid_parameter;
}
softmax_op->state = xnn_run_state_invalid;
if (!xnn_params.initialized) {
xnn_log_error("failed to setup SoftMax operator: XNNPACK is not initialized");
return xnn_status_uninitialized;
}
if (batch_size == 0) {
softmax_op->state = xnn_run_state_skip;
return xnn_status_success;
}
softmax_op->batch_size = batch_size;
softmax_op->input = input;
softmax_op->output = output;
softmax_op->context.u8_softmax = (struct u8_softmax_context) {
.n = softmax_op->channels,
.x = input,
.x_stride = softmax_op->input_pixel_stride * sizeof(uint8_t),
.t = softmax_op->lookup_table,
.y = output,
.y_stride = softmax_op->output_pixel_stride * sizeof(uint8_t),
.rmax_ukernel = xnn_params.u8.rmax,
.lut_norm_ukernel = xnn_params.u8.lut32norm,
};
softmax_op->compute.type = xnn_parallelization_type_1d;
softmax_op->compute.task_1d = (pthreadpool_task_1d_t) xnn_compute_u8_softmax;
softmax_op->compute.range[0] = batch_size;
softmax_op->state = xnn_run_state_ready;
return xnn_status_success;
}
enum xnn_status xnn_create_softmax_nc_f32(
size_t channels,
size_t input_stride,
size_t output_stride,
uint32_t flags,
xnn_operator_t* softmax_op_out)
{
xnn_operator_t softmax_op = NULL;
enum xnn_status status = xnn_status_uninitialized;
if (!xnn_params.initialized) {
xnn_log_error("failed to create SoftMax operator: XNNPACK is not initialized");
goto error;
}
status = xnn_status_invalid_parameter;
if (channels == 0) {
xnn_log_error(
"failed to create SoftMax operator with %zu channels: number of channels must be non-zero", channels);
goto error;
}
if (input_stride < channels) {
xnn_log_error(
"failed to create SoftMax 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 SoftMax 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;
}
status = xnn_status_out_of_memory;
softmax_op = xnn_allocate_zero_simd_memory(sizeof(struct xnn_operator));
if (softmax_op == NULL) {
xnn_log_error("failed to allocate %zu bytes for SoftMax operator descriptor", sizeof(struct xnn_operator));
goto error;
}
softmax_op->channels = channels;
softmax_op->input_pixel_stride = input_stride;
softmax_op->output_pixel_stride = output_stride;
softmax_op->type = xnn_operator_type_softmax_nc_f32;
softmax_op->ukernel.type = xnn_ukernel_type_softmax;
softmax_op->state = xnn_run_state_invalid;
*softmax_op_out = softmax_op;
return xnn_status_success;
error:
xnn_delete_operator(softmax_op);
return status;
}
enum xnn_status xnn_setup_softmax_nc_f32(
xnn_operator_t softmax_op,
size_t batch_size,
const float* input,
float* output,
pthreadpool_t threadpool)
{
if (softmax_op->type != xnn_operator_type_softmax_nc_f32) {
xnn_log_error("failed to setup SoftMax (NC, F32) operator: operator type mismatch");
return xnn_status_invalid_parameter;
}
softmax_op->state = xnn_run_state_invalid;
if (!xnn_params.initialized) {
xnn_log_error("failed to setup SoftMax operator: XNNPACK is not initialized");
return xnn_status_uninitialized;
}
if (batch_size == 0) {
softmax_op->state = xnn_run_state_skip;
return xnn_status_success;
}
softmax_op->batch_size = batch_size;
softmax_op->input = input;
softmax_op->output = output;
softmax_op->context.f32_three_pass_softmax = (struct f32_three_pass_softmax_context) {
.n = softmax_op->channels * sizeof(float),
.x = input,
.x_stride = softmax_op->input_pixel_stride * sizeof(float),
.y = output,
.y_stride = softmax_op->output_pixel_stride * sizeof(float),
.rmax_ukernel = xnn_params.f32.rmax,
.raddstoreexpminusmax_ukernel = xnn_params.f32.raddstoreexpminusmax,
.vmulc_ukernel = xnn_params.f32.vmul.opc_ukernel,
.params = xnn_init_f32_minmax_params(-INFINITY, INFINITY),
};
softmax_op->compute.type = xnn_parallelization_type_1d;
softmax_op->compute.task_1d = (pthreadpool_task_1d_t) xnn_compute_f32_three_pass_softmax;
softmax_op->compute.range[0] = batch_size;
softmax_op->state = xnn_run_state_ready;
return xnn_status_success;
}