blob: 1ec6cb509f71ab7604202a5494dde7b10ff58f1d [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/log.h>
#include <xnnpack/operator.h>
#include <xnnpack/params-init.h>
#include <xnnpack/params.h>
enum xnn_status xnn_create_add_nc_q8(
size_t channels,
size_t a_stride,
size_t b_stride,
size_t sum_stride,
uint8_t a_zero_point,
float a_scale,
uint8_t b_zero_point,
float b_scale,
uint8_t sum_zero_point,
float sum_scale,
uint8_t sum_min,
uint8_t sum_max,
uint32_t flags,
xnn_operator_t* add_op_out)
{
xnn_operator_t add_op = NULL;
enum xnn_status status = xnn_status_uninitialized;
if (!xnn_params.initialized) {
xnn_log_error("failed to create Add operator: XNNPACK is not initialized");
goto error;
}
status = xnn_status_invalid_parameter;
if (channels == 0) {
xnn_log_error(
"failed to create Add operator with %zu channels: number of channels must be non-zero", channels);
goto error;
}
if (a_stride < channels) {
xnn_log_error(
"failed to create Add operator with A element stride of %zu: "
"stride must be at least as large as the number of channels (%zu)",
a_stride, channels);
goto error;
}
if (b_stride < channels) {
xnn_log_error(
"failed to create Add operator with B element stride of %zu: "
"stride must be at least as large as the number of channels (%zu)",
b_stride, channels);
goto error;
}
if (sum_stride < channels) {
xnn_log_error(
"failed to create Add operator with Sum element stride of %zu: "
"stride must be at least as large as the number of channels (%zu)",
sum_stride, channels);
goto error;
}
if (a_scale <= 0.0f || !isnormal(a_scale)) {
xnn_log_error(
"failed to create Add operator with %.7g A scale: scale must be finite, normalized, and positive", a_scale);
goto error;
}
if (b_scale <= 0.0f || !isnormal(b_scale)) {
xnn_log_error(
"failed to create Add operator with %.7g B scale: scale must be finite, normalized, and positive", b_scale);
goto error;
}
if (sum_scale <= 0.0f || !isnormal(sum_scale)) {
xnn_log_error(
"failed to create Add operator with %.7g output scale: scale must be finite, normalized, and positive",
sum_scale);
goto error;
}
if (sum_min >= sum_max) {
xnn_log_error(
"failed to create Add operator with [%" PRIu8 ", %" PRIu8 "] output range: range min must be below range max",
sum_min, sum_max);
goto error;
}
status = xnn_status_unsupported_parameter;
const float a_output_scale = a_scale / sum_scale;
if (a_output_scale < 0x1.0p-14f || a_output_scale >= 0x1.0p+8f) {
xnn_log_error(
"failed to create Add operator with %.7g A-to-output scale ratio: scale ratio must be in [2**-14, 2**8) range",
a_output_scale);
goto error;
}
const float b_output_scale = b_scale / sum_scale;
if (b_output_scale < 0x1.0p-14f || b_output_scale >= 0x1.0p+8f) {
xnn_log_error(
"failed to create Add operator with %.7g A-to-output scale ratio: scale ratio must be in [2**-14, 2**8) range",
b_output_scale);
goto error;
}
status = xnn_status_out_of_memory;
add_op = xnn_allocate_zero_simd_memory(sizeof(struct xnn_operator));
if (add_op == NULL) {
xnn_log_error("failed to allocate %zu bytes for Add operator descriptor", sizeof(struct xnn_operator));
goto error;
}
add_op->channels = channels;
add_op->input_pixel_stride = a_stride;
add_op->input2_pixel_stride = b_stride;
add_op->output_pixel_stride = sum_stride;
add_op->q8_add_params =
xnn_init_q8_add_params(
a_zero_point, b_zero_point, sum_zero_point,
a_scale / sum_scale, b_scale / sum_scale,
sum_min, sum_max);
add_op->type = xnn_operator_type_add_nc_q8;
add_op->ukernel.type = xnn_ukernel_type_add;
add_op->state = xnn_run_state_invalid;
*add_op_out = add_op;
return xnn_status_success;
error:
xnn_delete_operator(add_op);
return status;
}
enum xnn_status xnn_create_add_nc_f32(
size_t channels,
size_t a_stride,
size_t b_stride,
size_t sum_stride,
float sum_min,
float sum_max,
uint32_t flags,
xnn_operator_t* add_op_out)
{
xnn_operator_t add_op = NULL;
enum xnn_status status = xnn_status_uninitialized;
if (!xnn_params.initialized) {
xnn_log_error("failed to create Add operator: XNNPACK is not initialized");
goto error;
}
status = xnn_status_invalid_parameter;
if (channels == 0) {
xnn_log_error(
"failed to create add operator with %zu channels: number of channels must be non-zero", channels);
goto error;
}
if (a_stride < channels) {
xnn_log_error(
"failed to create Add operator with A element stride of %zu: "
"stride must be at least as large as the number of channels (%zu)",
a_stride, channels);
goto error;
}
if (b_stride < channels) {
xnn_log_error(
"failed to create Add operator with B element stride of %zu: "
"stride must be at least as large as the number of channels (%zu)",
b_stride, channels);
goto error;
}
if (sum_stride < channels) {
xnn_log_error(
"failed to create Add operator with Sum element stride of %zu: "
"stride must be at least as large as the number of channels (%zu)",
sum_stride, channels);
goto error;
}
if (isnan(sum_min)) {
xnn_log_error(
"failed to create Add operator with NaN output lower bound: lower bound must be non-NaN");
goto error;
}
if (isnan(sum_max)) {
xnn_log_error(
"failed to create Add operator with NaN output upper bound: upper bound must be non-NaN");
goto error;
}
if (sum_min >= sum_max) {
xnn_log_error(
"failed to create Add operator with [%.7g, %.7g] output range: lower bound must be below upper bound",
sum_min, sum_max);
goto error;
}
status = xnn_status_out_of_memory;
add_op = xnn_allocate_zero_simd_memory(sizeof(struct xnn_operator));
if (add_op == NULL) {
xnn_log_error("failed to allocate %zu bytes for Add operator descriptor", sizeof(struct xnn_operator));
goto error;
}
add_op->channels = channels;
add_op->input_pixel_stride = a_stride;
add_op->input2_pixel_stride = b_stride;
add_op->output_pixel_stride = sum_stride;
add_op->f32_minmax_params = xnn_init_f32_minmax_params(sum_min, sum_max);
add_op->type = xnn_operator_type_add_nc_f32;
add_op->ukernel.type = xnn_ukernel_type_add;
add_op->state = xnn_run_state_invalid;
*add_op_out = add_op;
return xnn_status_success;
error:
xnn_delete_operator(add_op);
return status;
}
enum xnn_status xnn_setup_add_nc_q8(
xnn_operator_t add_op,
size_t batch_size,
const uint8_t* a,
const uint8_t* b,
uint8_t* sum,
pthreadpool_t threadpool)
{
if (add_op->type != xnn_operator_type_add_nc_q8) {
xnn_log_error("failed to setup Add (NC, Q8) operator: operator type mismatch");
return xnn_status_invalid_parameter;
}
add_op->state = xnn_run_state_invalid;
if (!xnn_params.initialized) {
xnn_log_error("failed to setup Add operator: XNNPACK is not initialized");
return xnn_status_uninitialized;
}
if (batch_size == 0) {
add_op->state = xnn_run_state_skip;
return xnn_status_success;
}
const size_t channels = add_op->channels;
const size_t a_stride = add_op->input_pixel_stride;
const size_t b_stride = add_op->input2_pixel_stride;
const size_t sum_stride = add_op->output_pixel_stride;
if ((((a_stride ^ channels) | (b_stride ^ channels) | (sum_stride ^ channels)) == 0) || batch_size == 1) {
const size_t block_size = 4096;
add_op->context.add_contiguous = (struct add_contiguous_context) {
.a = a,
.b = b,
.y = sum,
.params.q8 = add_op->q8_add_params,
.ukernel = xnn_params.q8.vadd,
};
add_op->compute.type = xnn_parallelization_type_1d_tile_1d;
add_op->compute.task_1d_tile_1d = (pthreadpool_task_1d_tile_1d_t) xnn_compute_add_contiguous;
add_op->compute.range[0] = batch_size * channels * sizeof(uint8_t);
add_op->compute.tile[0] = block_size;
} else {
add_op->context.add_strided = (struct add_strided_context) {
.a = a,
.a_stride = a_stride * sizeof(uint8_t),
.b = b,
.b_stride = b_stride * sizeof(uint8_t),
.y = sum,
.y_stride = sum_stride * sizeof(uint8_t),
.n = channels,
.params.q8 = add_op->q8_add_params,
.ukernel = xnn_params.q8.vadd,
};
add_op->compute.type = xnn_parallelization_type_1d_tile_1d;
add_op->compute.task_1d_tile_1d = (pthreadpool_task_1d_tile_1d_t) xnn_compute_add_strided;
add_op->compute.range[0] = batch_size;
add_op->compute.tile[0] = 1;
}
add_op->state = xnn_run_state_ready;
return xnn_status_success;
}
enum xnn_status xnn_setup_add_nc_f32(
xnn_operator_t add_op,
size_t batch_size,
const float* a,
const float* b,
float* sum,
pthreadpool_t threadpool)
{
if (add_op->type != xnn_operator_type_add_nc_f32) {
xnn_log_error("failed to setup Add (NC, F32) operator: operator type mismatch");
return xnn_status_invalid_parameter;
}
add_op->state = xnn_run_state_invalid;
if (!xnn_params.initialized) {
xnn_log_error("failed to setup Add operator: XNNPACK is not initialized");
return xnn_status_uninitialized;
}
if (batch_size == 0) {
add_op->state = xnn_run_state_skip;
return xnn_status_success;
}
const size_t channels = add_op->channels;
const size_t a_stride = add_op->input_pixel_stride;
const size_t b_stride = add_op->input2_pixel_stride;
const size_t sum_stride = add_op->output_pixel_stride;
if ((((a_stride ^ channels) | (b_stride ^ channels) | (sum_stride ^ channels)) == 0) || batch_size == 1) {
const size_t block_size = 4096;
add_op->context.add_contiguous = (struct add_contiguous_context) {
.a = a,
.b = b,
.y = sum,
.params.f32 = add_op->f32_minmax_params,
.ukernel = xnn_params.f32.vadd.op_ukernel,
};
add_op->compute.type = xnn_parallelization_type_1d_tile_1d;
add_op->compute.task_1d_tile_1d = (pthreadpool_task_1d_tile_1d_t) xnn_compute_add_contiguous;
add_op->compute.range[0] = batch_size * channels * sizeof(float);
add_op->compute.tile[0] = block_size;
} else {
add_op->context.add_strided = (struct add_strided_context) {
.a = a,
.a_stride = a_stride * sizeof(float),
.b = b,
.b_stride = b_stride * sizeof(float),
.y = sum,
.y_stride = sum_stride * sizeof(float),
.n = channels * sizeof(float),
.params.f32 = add_op->f32_minmax_params,
.ukernel = xnn_params.f32.vadd.op_ukernel,
};
add_op->compute.type = xnn_parallelization_type_1d_tile_1d;
add_op->compute.task_1d_tile_1d = (pthreadpool_task_1d_tile_1d_t) xnn_compute_add_strided;
add_op->compute.range[0] = batch_size;
add_op->compute.tile[0] = 1;
}
add_op->state = xnn_run_state_ready;
return xnn_status_success;
}