test / include /xnnpack.h
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// 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.
#pragma once
#include <stdbool.h>
#include <stddef.h>
#include <stdint.h>
#include <pthreadpool.h>
#ifdef __cplusplus
extern "C" {
#endif
/// The number of bytes XNNPACK may read beyond array bounds.
/// The caller must allocate at least this many extra bytes after the tensor data passed to XNNPACK.
///
/// Note: XNNPACK reads, but never writes beyond array bounds.
#define XNN_EXTRA_BYTES 16
/// Maximum number of dimensions in tensor shape.
#define XNN_MAX_TENSOR_DIMS 6
/// Allow sparse inference in a Runtime.
///
/// Note: this flag hints XNNPACK to consider sparse inference, but does not guarantee it.
#define XNN_FLAG_SPARSE_INFERENCE 0x00000001
#define XNN_FLAG_HINT_SPARSE_INFERENCE XNN_FLAG_SPARSE_INFERENCE
/// Allow IEEE FP16 inference in a Runtime.
///
/// Note: this flag hints XNNPACK to consider IEEE FP16 inference, but does not guarantee it.
#define XNN_FLAG_FP16_INFERENCE 0x00000002
#define XNN_FLAG_HINT_FP16_INFERENCE XNN_FLAG_FP16_INFERENCE
/// Force IEEE FP16 inference in a Runtime, and fail if FP16 inference is not possible.
///
/// Note: this flag guarantees that XNNPACK will use IEEE FP16 inference, or fail to create the Runtime object.
/// Warning: on x86 systems FP16 computations will be emulated at a substantial performance cost.
#define XNN_FLAG_FORCE_FP16_INFERENCE 0x00000004
/// Enable timing of each operator's runtime.
#define XNN_FLAG_BASIC_PROFILING 0x00000008
/// Enable the just-in-time compiler.
#define XNN_FLAG_JIT 0x00000010
/// The convolution operator represents a depthwise convolution, and use HWGo layout for filters.
#define XNN_FLAG_DEPTHWISE_CONVOLUTION 0x00000001
/// Assume transposed weights in a fully connected operator.
#define XNN_FLAG_TRANSPOSE_WEIGHTS 0x00000001
/// The operator assumes NHWC layout for the input, regardless of the output layout.
#define XNN_FLAG_INPUT_NHWC 0x00000002
/// Match "SAME" padding in TensorFlow. Exact padding values are computed dynamically depending on input size.
#define XNN_FLAG_TENSORFLOW_SAME_PADDING 0x00000004
/// Assume transposed weights in a batch matrix multiply operator.
#define XNN_FLAG_TRANSPOSE_B XNN_FLAG_TRANSPOSE_WEIGHTS
/// Assume transposed input in a batch matrix multiply operator.
#define XNN_FLAG_TRANSPOSE_A 0x00000002
/// Implicitly flatten and reshape input of a Fully Connected operator into a 2D tensor.
#define XNN_FLAG_TENSORFLOW_RESHAPE_2D 0x00000004
/// Match behaviour of TensorFlow 1.x.
#define XNN_FLAG_TENSORFLOW_LEGACY_MODE 0x00000004
/// Static weights of the FP16 operator are in FP32 format.
#define XNN_FLAG_FP32_STATIC_WEIGHTS 0x00000008
/// Align corners of input and output images in resize operations.
#define XNN_FLAG_ALIGN_CORNERS 0x00000008
/// Yield worker threads of the thread pool to the system scheduler after the inference.
#define XNN_FLAG_YIELD_WORKERS 0x00000010
/// The number of entries in an array of xnn_dynamic_quantization_params that XNNPACK may read beyond array bounds.
/// The caller must allocate at least this many extra xnn_dynamic_quantization_params before passing the array to XNNPACK.
///
/// Note: XNNPACK reads, but never writes beyond array bounds.
#define XNN_EXTRA_QUANTIZATION_PARAMS 8
struct xnn_dynamic_quantization_params {
int32_t zero_point;
float scale;
};
/// Status code for any XNNPACK function call.
enum xnn_status {
/// The call succeeded, and all output arguments now contain valid data.
xnn_status_success = 0,
xnn_status_uninitialized = 1,
xnn_status_invalid_parameter = 2,
xnn_status_invalid_state = 3,
xnn_status_unsupported_parameter = 4,
xnn_status_unsupported_hardware = 5,
xnn_status_out_of_memory = 6,
};
struct xnn_allocator {
/// User-specified pointer that will be passed as-is to all functions in this structure.
void* context;
/// Pointer to a function to be called for general memory allocation.
///
/// @param context - The user-specified pointer from xnn_allocator structure.
/// @param size - The size of the memory block to allocate, in bytes.
///
/// @returns Pointer to the allocated memory block of at least @ref size bytes.
/// If allocation fails, the function must return NULL.
void* (*allocate)(void* context, size_t size);
/// Pointer to a function to be called for general memory re-allocation, i.e. to increase or shrink a previously
/// allocated memory block. The content of the old memory block is copied to the new memory block.
///
/// @param context - The user-specified pointer from xnn_allocator structure.
/// @param pointer - Pointer to a memory block allocated by @ref allocate or @ref reallocate functions. Can be NULL.
/// If the pointer is NULL, the @ref reallocate call is equivalent to an @ref allocate call.
/// @param size - The new size of the memory block to allocate, in bytes.
///
/// @returns Pointer to the newly allocated memory block of at least @ref size bytes with the content of the previous
/// memory block.
/// If allocation fails, the function must return NULL, but must not release the previous memory block.
void* (*reallocate)(void* context, void* pointer, size_t size);
/// Pointer to a function to be called for general memory de-allocation.
///
/// @param context - The user-specified pointer from xnn_allocator structure.
/// @param pointer - Pointer to a memory block allocated by @ref allocate or @ref reallocate functions. Can be NULL.
/// If the pointer is NULL, the @ref deallocate call is a no-op.
void (*deallocate)(void* context, void* pointer);
/// Pointer to a function to be called for aligned memory allocation.
///
/// @param context - The user-specified pointer from xnn_allocator structure.
/// @param alignment - The alignment of the memory block to allocate, in bytes. Alignment is always a power-of-2.
/// @param size - The size of the memory block to allocate, in bytes.
///
/// @returns Pointer to the allocated memory block of at least @ref size bytes.
/// If allocation fails, the function must return NULL.
void* (*aligned_allocate)(void* context, size_t alignment, size_t size);
/// Pointer to a function to be called for aligned memory de-allocation.
///
/// @param context - The user-specified pointer from xnn_allocator structure.
/// @param pointer - Pointer to a memory block allocated by @ref aligned_allocate function. Can be NULL.
/// If the pointer is NULL, the @ref aligned_deallocate call is a no-op.
void (*aligned_deallocate)(void* context, void* pointer);
};
/// Initialize XNNPACK library.
///
/// XNNPACK must be successfully initialized before use. During initialization, XNNPACK populates internal structures
/// depending on the host processor. Initialization can be time-consuming.
///
/// @param[in] allocator - structure with function pointers to be use for memory allocation and de-allocation.
/// If this argument is NULL, system-provided memory management functions (e.g. malloc/free)
/// will be used.
///
/// @retval xnn_status_success - XNNPACK is successfully initialized and ready to use.
/// @retval xnn_status_out_of_memory - initialization failed due to out-of-memory condition.
/// @retval xnn_status_unsupported_hardware - initialization failed because the host processor does not satisfy the
/// minimum hardware requirements for XNNPACK. E.g. this may happen on x86
/// processors without SSE2 extension, or on 32-bit ARM processors without
/// the NEON SIMD extension.
enum xnn_status xnn_initialize(const struct xnn_allocator* allocator);
/// Deinitialize XNNPACK library.
///
/// To avoid memory and resource leaks, users must call xnn_deinitialize once for each successful xnn_initialize call.
///
/// @retval xnn_status_success - deinitialization call succeeded.
enum xnn_status xnn_deinitialize(void);
/// Subgraph is an abstract representation of a neural network model.
/// Subgraph objects are used to define Values (tensors) and Nodes (operators) comprising the model.
typedef struct xnn_subgraph* xnn_subgraph_t;
/// Create a empty Subgraph object.
///
/// @param external_value_ids - number of Value IDs to reserve for communication with external graph representation.
/// The Subgraph object would avoid creating internal Value IDs in the
/// [0, reserved_value_ids-1] range.
/// @param flags - binary features of the subgraph. No supported flags are currently defined.
/// @param subgraph_out - pointer to the variable that will be initialized with a handle to the Subgraph object upon
/// successful return.
enum xnn_status xnn_create_subgraph(
uint32_t external_value_ids,
uint32_t flags,
xnn_subgraph_t* subgraph_out);
/// Destroy a Subgraph object, as well as Values, and Nodes associated with the subgraph.
///
/// @param subgraph - the Subgraph object to destroy.
enum xnn_status xnn_delete_subgraph(
xnn_subgraph_t subgraph);
#define XNN_VALUE_FLAG_EXTERNAL_INPUT 0x00000001
#define XNN_VALUE_FLAG_EXTERNAL_OUTPUT 0x00000002
#define XNN_VALUE_FLAG_PERSISTENT 0x00000004
#define XNN_INVALID_VALUE_ID UINT32_MAX
/// Type of elements in a Value object.
enum xnn_datatype {
/// Invalid data type. Valid Values never have this datatype.
xnn_datatype_invalid = 0,
/// IEEE754 single-precision floating-point.
xnn_datatype_fp32 = 1,
/// IEEE754 half-precision floating-point.
xnn_datatype_fp16 = 2,
/// Quantized 8-bit signed integer with shared per-Value quantization parameters.
xnn_datatype_qint8 = 3,
/// Quantized 8-bit unsigned integer with shared per-Value quantization parameters.
xnn_datatype_quint8 = 4,
/// Quantized 32-bit signed integer with shared per-Value quantization parameters.
xnn_datatype_qint32 = 5,
/// Quantized 8-bit signed integer with shared per-channel quantization parameters.
xnn_datatype_qcint8 = 6,
/// Quantized 32-bit signed integer with shared per-channel quantization parameters.
xnn_datatype_qcint32 = 7,
/// Quantized 4-bit signed integer with shared per-channel quantization parameters.
xnn_datatype_qcint4 = 8,
};
/// Define a tensor-type Value and add it to a Subgraph.
///
/// @param subgraph - a Subgraph object that will own the created Value.
/// @param datatype - type of the tensor elements.
/// @param num_dims - number of dimensions in the shape.
/// @param dims - pointer to an array of @a num_dims shape dimensions. If num_dims is 0, this pointer can be NULL.
/// XNNPACK does not keep any pointers to this array after the function returns.
/// @param data - pointer to static data used for tensor initialization. If the tensor is not statically initialized,
/// this pointer must be is NULL. If non-NULL, the life-time of the static data must exceed the life-time
/// of the Subgraph object, and of any Runtime objects created from the Subgraph.
/// @param external_id - external ID for the Value. The ID must be within the range of reversed Value IDs specified on
/// the Subgraph creation. If the external ID is XNN_INVALID_VALUE_ID, an internal ID will be
/// created for the Value.
/// @param flags - binary features of the Value. Supported values are any combination of XNN_VALUE_FLAG_EXTERNAL_INPUT
/// and XNN_VALUE_FLAG_EXTERNAL_OUTPUT.
/// @param id_out - pointer to the variable that will be initialized with the Value ID upon successful return. If a
/// valid @a external_id was provided, the variable will be initialized with the @a external_id value.
enum xnn_status xnn_define_tensor_value(
xnn_subgraph_t subgraph,
enum xnn_datatype datatype,
size_t num_dims,
const size_t* dims,
const void* data,
uint32_t external_id,
uint32_t flags,
uint32_t* id_out);
/// Define a quantized tensor-type Value and add it to a Subgraph.
///
/// @param subgraph - a Subgraph object that will own the created Value.
/// @param datatype - type of the tensor elements.
/// @param zero_point - offset from zero to subtract from the quantized elements in the Value.
/// @param scale - multiplication factor to convert quantized elements to real representation.
/// @param num_dims - number of dimensions in the shape.
/// @param dims - pointer to an array of @a num_dims shape dimensions. If num_dims is 0, this pointer can be NULL.
/// XNNPACK does not keep any pointers to this array after the function returns.
/// @param data - pointer to static data used for tensor initialization. If the tensor is not statically initialized,
/// this pointer must be is NULL. If non-NULL, the life-time of the static data must exceed the life-time
/// of the Subgraph object, and of any Runtime objects created from the Subgraph.
/// @param external_id - external ID for the Value. The ID must be within the range of reversed Value IDs specified on
/// the Subgraph creation. If the external ID is XNN_INVALID_VALUE_ID, an internal ID will be
/// created for the Value.
/// @param flags - binary features of the Value. Supported values are any combination of XNN_VALUE_FLAG_EXTERNAL_INPUT
/// and XNN_VALUE_FLAG_EXTERNAL_OUTPUT.
/// @param id_out - pointer to the variable that will be initialized with the Value ID upon successful return. If a
/// valid @a external_id was provided, the variable will be initialized with the @a external_id value.
enum xnn_status xnn_define_quantized_tensor_value(
xnn_subgraph_t subgraph,
enum xnn_datatype datatype,
int32_t zero_point,
float scale,
size_t num_dims,
const size_t* dims,
const void* data,
uint32_t external_id,
uint32_t flags,
uint32_t* id_out);
enum xnn_status xnn_define_channelwise_quantized_tensor_value(
xnn_subgraph_t subgraph,
enum xnn_datatype datatype,
const float* scale,
size_t num_dims,
size_t channel_dim,
const size_t* dims,
const void* data,
uint32_t external_id,
uint32_t flags,
uint32_t* id_out);
/// Define a channelwise quantized tensor-type Value and add it to a Subgraph.
///
/// @param subgraph - a Subgraph object that will own the created Value.
/// @param datatype - type of the tensor elements.
/// @param zero_point - offset from zero to subtract from the quantized elements in the Value.
/// @param scale - per-channel multiplication factors to convert quantized elements to real representation.
/// @param num_dims - number of dimensions in the shape.
/// @param channel_dim - index of the channel dimension in the tensor with per-channel quantization parameters.
/// Typically this is the first dimension (dimension #0) of the filter tensors in the Convolution,
/// Deconvolution, and Fully Connected operators and the last dimension of the filter tensors in
/// the Depthwise Convolution operators.
/// @param dims - pointer to an array of @a num_dims shape dimensions. If num_dims is 0, this pointer can be NULL.
/// XNNPACK does not keep any pointers to this array after the function returns.
/// @param data - pointer to static data used for tensor initialization. If the tensor is not statically initialized,
/// this pointer must be is NULL. If non-NULL, the life-time of the static data must exceed the life-time
/// of the Subgraph object, and of any Runtime objects created from the Subgraph.
/// @param external_id - external ID for the Value. The ID must be within the range of reversed Value IDs specified on
/// the Subgraph creation. If the external ID is XNN_INVALID_VALUE_ID, an internal ID will be
/// created for the Value.
/// @param flags - binary features of the Value. Supported values are any combination of XNN_VALUE_FLAG_EXTERNAL_INPUT
/// and XNN_VALUE_FLAG_EXTERNAL_OUTPUT.
/// @param id_out - pointer to the variable that will be initialized with the Value ID upon successful return. If a
/// valid @a external_id was provided, the variable will be initialized with the @a external_id value.
enum xnn_status xnn_define_channelwise_quantized_tensor_value_v2(
xnn_subgraph_t subgraph,
enum xnn_datatype datatype,
int32_t zero_point,
const float* scale,
size_t num_dims,
size_t channel_dim,
const size_t* dims,
const void* data,
uint32_t external_id,
uint32_t flags,
uint32_t* id_out);
/// Define a Convert Node and add it to a Subgraph.
///
/// @param subgraph - a Subgraph object that will own the created Node.
/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
/// shape must match the shape of the input tensor.
/// @param flags - binary features of the Convert Node. No supported flags are currently defined.
enum xnn_status xnn_define_convert(
xnn_subgraph_t subgraph,
uint32_t input_id,
uint32_t output_id,
uint32_t flags);
/// Define a 2D Convolution Node and add it to a Subgraph.
///
/// @param subgraph - a Subgraph object that will own the created Node.
/// @param input_padding_top - implicit zero-padding above 2D input data. Must be 0 if XNN_FLAG_TENSORFLOW_SAME_PADDING
/// flag is specified.
/// @param input_padding_right - implicit zero-padding to the right of 2D input data. Must be 0 if
/// XNN_FLAG_TENSORFLOW_SAME_PADDING flag is specified.
/// @param input_padding_bottom - implicit zero-padding below 2D input data. Must be 0 if
/// XNN_FLAG_TENSORFLOW_SAME_PADDING flag is specified.
/// @param input_padding_left - implicit zero-padding to the left of 2D input data. Must be 0 if
/// XNN_FLAG_TENSORFLOW_SAME_PADDING flag is specified.
/// @param kernel_height - kernel (filter) height.
/// @param kernel_width - kernel (filter) width.
/// @param subsampling_height - height of subsampling region for convolution output (convolution height stride).
/// @param subsampling_width - width of subsampling region for convolution output (convolution width stride).
/// @param dilation_height - dilation of kernel elements along the height dimension.
/// @param dilation_width - dilation of kernel elements along the width dimension.
/// @param groups - number of convolution groups.
/// @param group_input_channels - number of input channels per group.
/// @param group_output_channels - number of output channels per group.
/// @param output_min - lower bound for clipping output values.
/// @param output_max - upper bound for clipping output values.
/// @param input_id - Value ID for the input tensor. The input tensor must be a 4D tensor defined in the @a subgraph
/// with [N, IH, IW, groups * group_input_channels] dimensions
/// @param filter_id - Value ID for the filter tensor. The filter tensor must ge a 4D tensor defined in the @a subgraph
/// with [groups * group_output_channels, kernel_height, kernel_width, group_input_channels]
/// dimensions.
/// @param bias_id - Value ID for the bias tensor, or XNN_INVALID_VALUE_ID for a 2D Convolution Node without a bias. If
/// present, the bias tensor must be a 1D tensor defined in the @a subgraph with [groups *
/// group_output_channels] dimensions.
/// @param output_id - Value ID for the output tensor. The output tensor must be a 4D tensor defined in the @a subgraph
/// with [N, OH, OW, groups * group_output_channels] dimensions.
/// @param flags - binary features of the 2D Convolution Node. The only currently supported values is
/// XNN_FLAG_TENSORFLOW_SAME_PADDING.
enum xnn_status xnn_define_convolution_2d(
xnn_subgraph_t subgraph,
uint32_t input_padding_top,
uint32_t input_padding_right,
uint32_t input_padding_bottom,
uint32_t input_padding_left,
uint32_t kernel_height,
uint32_t kernel_width,
uint32_t subsampling_height,
uint32_t subsampling_width,
uint32_t dilation_height,
uint32_t dilation_width,
uint32_t groups,
size_t group_input_channels,
size_t group_output_channels,
float output_min,
float output_max,
uint32_t input_id,
uint32_t filter_id,
uint32_t bias_id,
uint32_t output_id,
uint32_t flags);
/// Define a 2D Deconvolution (Transposed Convolution) Node and add it to a Subgraph.
///
/// @param subgraph - a Subgraph object that will own the created Node.
/// @param padding_top - implicit padding above 2D output data.
/// @param padding_right - implicit padding to the right of 2D output data.
/// @param padding_bottom - implicit padding below 2D output data.
/// @param padding_left - implicit padding to the left of 2D output data.
/// @param adjustment_height - additional elements in the bottom of the 2D output data.
/// @param adjustment_width - additional elements to the right of the 2D output data.
/// @param kernel_height - kernel (filter) height.
/// @param kernel_width - kernel (filter) width.
/// @param upsampling_height - height of upsampling region for deconvolution input (deconvolution height stride).
/// @param upsampling_width - width of upsampling region for deconvolution input (deconvolution width stride).
/// @param dilation_height - dilation of kernel elements along the height dimension.
/// @param dilation_width - dilation of kernel elements along the width dimension.
/// @param groups - number of convolution groups.
/// @param group_input_channels - number of input channels per group.
/// @param group_output_channels - number of output channels per group.
/// @param output_min - lower bound for clipping output values.
/// @param output_max - upper bound for clipping output values.
/// @param input_id - Value ID for the input tensor. The input tensor must be a 4D tensor defined in the @a subgraph
/// with [N, IH, IW, groups * group_input_channels] dimensions
/// @param filter_id - Value ID for the filter tensor. The filter tensor must ge a 4D tensor defined in the @a subgraph
/// with [groups * group_output_channels, kernel_height, kernel_width, group_input_channels]
/// dimensions.
/// @param bias_id - Value ID for the bias tensor, or XNN_INVALID_VALUE_ID for a 2D Convolution Node without a bias. If
/// present, the bias tensor must be a 1D tensor defined in the @a subgraph with
/// [groups * group_output_channels] dimensions.
/// @param output_id - Value ID for the output tensor. The output tensor must be a 4D tensor defined in the @a subgraph
/// with [N, OH, OW, groups * group_output_channels] dimensions.
/// @param flags - binary features of the 2D Deconvolution Node. No supported flags are currently defined.
enum xnn_status xnn_define_deconvolution_2d(
xnn_subgraph_t subgraph,
uint32_t padding_top,
uint32_t padding_right,
uint32_t padding_bottom,
uint32_t padding_left,
uint32_t adjustment_height,
uint32_t adjustment_width,
uint32_t kernel_height,
uint32_t kernel_width,
uint32_t upsampling_height,
uint32_t upsampling_width,
uint32_t dilation_height,
uint32_t dilation_width,
uint32_t groups,
size_t group_input_channels,
size_t group_output_channels,
float output_min,
float output_max,
uint32_t input_id,
uint32_t filter_id,
uint32_t bias_id,
uint32_t output_id,
uint32_t flags);
/// Define a 2D Depthwise Convolution Node and add it to a Subgraph.
///
/// @param subgraph - a Subgraph object that will own the created Node.
/// @param input_padding_top - implicit zero-padding above 2D input data. Must be 0 if XNN_FLAG_TENSORFLOW_SAME_PADDING
/// flag is specified.
/// @param input_padding_right - implicit zero-padding to the right of 2D input data. Must be 0 if
/// XNN_FLAG_TENSORFLOW_SAME_PADDING flag is specified.
/// @param input_padding_bottom - implicit zero-padding below 2D input data. Must be 0 if
/// XNN_FLAG_TENSORFLOW_SAME_PADDING flag is specified.
/// @param input_padding_left - implicit zero-padding to the left of 2D input data. Must be 0 if
/// XNN_FLAG_TENSORFLOW_SAME_PADDING flag is specified.
/// @param kernel_height - kernel (filter) height.
/// @param kernel_width - kernel (filter) width.
/// @param subsampling_height - height of subsampling region for convolution output (convolution height stride).
/// @param subsampling_width - width of subsampling region for convolution output (convolution width stride).
/// @param dilation_height - dilation of kernel elements along the height dimension.
/// @param dilation_width - dilation of kernel elements along the width dimension.
/// @param depth_multiplier - ratio of output channels to input channels.
/// @param input_channels - number of input channels.
/// @param output_min - lower bound for clipping output values.
/// @param output_max - upper bound for clipping output values.
/// @param input_id - Value ID for the input tensor. The input tensor must be a 4D tensor defined in the @a subgraph
/// with [N, IH, IW, input_channels] dimensions
/// @param filter_id - Value ID for the filter tensor. The filter tensor must ge a 4D tensor defined in the @a subgraph
/// with [1, kernel_height, kernel_width, input_channels * depth_multiplier] dimensions.
/// @param bias_id - Value ID for the bias tensor, or XNN_INVALID_VALUE_ID for a 2D Depthwise Convolution Node without
/// a bias. If present, the bias tensor must be a 1D tensor defined in the @a subgraph with
/// [input_channels * depth_multiplier] dimensions.
/// @param output_id - Value ID for the output tensor. The output tensor must be a 4D tensor defined in the @a subgraph
/// with [N, OH, OW, input_channels * depth_multiplier] dimensions.
/// @param flags - binary features of the 2D Depthwise Convolution Node. The only currently supported values is
/// XNN_FLAG_TENSORFLOW_SAME_PADDING.
enum xnn_status xnn_define_depthwise_convolution_2d(
xnn_subgraph_t subgraph,
uint32_t input_padding_top,
uint32_t input_padding_right,
uint32_t input_padding_bottom,
uint32_t input_padding_left,
uint32_t kernel_height,
uint32_t kernel_width,
uint32_t subsampling_height,
uint32_t subsampling_width,
uint32_t dilation_height,
uint32_t dilation_width,
uint32_t depth_multiplier,
size_t input_channels,
float output_min,
float output_max,
uint32_t input_id,
uint32_t filter_id,
uint32_t bias_id,
uint32_t output_id,
uint32_t flags);
/// Define a Depth To Space Node and add it to a Subgraph.
///
/// The Depth To Space Node rearranges data from depth into blocks of spatial data (a reverse transform to
/// Space To Depth). For a given input pixel, an output square of pixels with side @a block_size is formed from values
/// in the corresponding number of its channels. The output depth is therefore @a block_size x @a block_size times
/// smaller than that of the input.
///
/// @param subgraph - a Subgraph object that will own the created Node.
/// @param input_id - Value ID for the input tensor. The input tensor must be a 4D tensor defined in the @a subgraph
/// with [N, IH, IW, OC * block_size * block_size] dimensions.
/// @param output_id - Value ID for the output tensor. The output tensor must be a 4D tensor defined in the @a subgraph
/// with [N, IH * block_size, IW * block_size, OC] dimensions.
/// @param block_size - the size of the spatial block.
/// @param flags - binary features of the input_channels Node. No supported flags are currently defined.
enum xnn_status xnn_define_depth_to_space(
xnn_subgraph_t subgraph,
uint32_t input_id,
uint32_t output_id,
uint32_t block_size,
uint32_t flags);
/// Define a 1D Global Average Pooling Node and add it to a Subgraph.
///
/// @param subgraph - a Subgraph object that will own the created Node.
/// @param output_min - lower bound for clipping output values.
/// @param output_max - upper bound for clipping output values.
/// @param input_id - Value ID for the input tensor. The input tensor must be a dense tensor with 2 or more dimensions
/// defined in the @a subgraph. Averaging is performed across the second-innermost dimension.
/// @param output_id - Value ID for the output tensor. The output tensor must be a dense tensor with 2 or more
/// dimensions defined in the @a subgraph.
/// @param flags - binary features of the 1D Global Average Pooling Node. No supported flags are currently defined.
enum xnn_status xnn_define_global_average_pooling_1d(
xnn_subgraph_t subgraph,
float output_min,
float output_max,
uint32_t input_id,
uint32_t output_id,
uint32_t flags);
/// Define a 2D Global Average Pooling Node and add it to a Subgraph.
///
/// @param subgraph - a Subgraph object that will own the created Node.
/// @param output_min - lower bound for clipping output values.
/// @param output_max - upper bound for clipping output values.
/// @param input_id - Value ID for the input tensor. The input tensor must be a dense tensor with 3 or more dimensions
/// defined in the @a subgraph. Averaging is performed across the second- and third-innermost
/// dimensions.
/// @param output_id - Value ID for the output tensor. The output tensor must be a dense tensor with 3 or more
/// dimensions defined in the @a subgraph.
/// @param flags - binary features of the 2D Global Average Pooling Node. No supported flags are currently defined.
enum xnn_status xnn_define_global_average_pooling_2d(
xnn_subgraph_t subgraph,
float output_min,
float output_max,
uint32_t input_id,
uint32_t output_id,
uint32_t flags);
/// Define a 1D Global Sum Pooling Node and add it to a Subgraph.
///
/// @param subgraph - a Subgraph object that will own the created Node.
/// @param output_min - lower bound for clipping output values.
/// @param output_max - upper bound for clipping output values.
/// @param input_id - Value ID for the input tensor. The input tensor must be a dense tensor with 2 or more dimensions
/// defined in the @a subgraph. Averaging is performed across the second-innermost dimension.
/// @param output_id - Value ID for the output tensor. The output tensor must be a dense tensor with 2 or more
/// dimensions defined in the @a subgraph.
/// @param flags - binary features of the 1D Global Sum Pooling Node. No supported flags are currently defined.
enum xnn_status xnn_define_global_sum_pooling_1d(
xnn_subgraph_t subgraph,
float output_min,
float output_max,
uint32_t input_id,
uint32_t output_id,
uint32_t flags);
/// Define a 2D Global Sum Pooling Node and add it to a Subgraph.
///
/// @param subgraph - a Subgraph object that will own the created Node.
/// @param output_min - lower bound for clipping output values.
/// @param output_max - upper bound for clipping output values.
/// @param input_id - Value ID for the input tensor. The input tensor must be a dense tensor with 3 or more dimensions
/// defined in the @a subgraph. Averaging is performed across the second- and third-innermost
/// dimensions.
/// @param output_id - Value ID for the output tensor. The output tensor must be a dense tensor with 3 or more
/// dimensions defined in the @a subgraph.
/// @param flags - binary features of the 2D Global Sum Pooling Node. No supported flags are currently defined.
enum xnn_status xnn_define_global_sum_pooling_2d(
xnn_subgraph_t subgraph,
float output_min,
float output_max,
uint32_t input_id,
uint32_t output_id,
uint32_t flags);
/// Define a 2D Average Pooling Node and add it to a Subgraph.
///
/// @param subgraph - a Subgraph object that will own the created Node.
/// @param input_padding_top - implicit zero-padding above 2D input data. Must be 0 if XNN_FLAG_TENSORFLOW_SAME_PADDING
/// flag is specified.
/// @param input_padding_right - implicit zero-padding to the right of 2D input data. Must be 0 if
/// XNN_FLAG_TENSORFLOW_SAME_PADDING flag is specified.
/// @param input_padding_bottom - implicit zero-padding below 2D input data. Must be 0 if
/// XNN_FLAG_TENSORFLOW_SAME_PADDING flag is specified.
/// @param input_padding_left - implicit zero-padding to the left of 2D input data. Must be 0 if
/// XNN_FLAG_TENSORFLOW_SAME_PADDING flag is specified.
/// @param pooling_height - pooling (kernel) height.
/// @param pooling_width - pooling (kernel) width.
/// @param stride_height - displacing of the pooling window in the vertical dimension of the input pixels corresponding
/// to vertically adjacent output pixels.
/// @param stride_width - displacing of the pooling window in the horizontal dimension of the input pixels corresponding
/// to horizontally adjacent output pixels.
/// @param output_min - lower bound for clipping output values.
/// @param output_max - upper bound for clipping output values.
/// @param input_id - Value ID for the input tensor. The input tensor must be a 4D tensor defined in the @a subgraph
/// with [N, IH, IW, channels] dimensions
/// @param output_id - Value ID for the output tensor. The output tensor must be a 4D tensor defined in the @a subgraph
/// with [N, OH, OW, channels] dimensions.
/// @param flags - binary features of the 2D Average Pooling Node. The only currently supported values is
/// XNN_FLAG_TENSORFLOW_SAME_PADDING.
enum xnn_status xnn_define_average_pooling_2d(
xnn_subgraph_t subgraph,
uint32_t input_padding_top,
uint32_t input_padding_right,
uint32_t input_padding_bottom,
uint32_t input_padding_left,
uint32_t pooling_height,
uint32_t pooling_width,
uint32_t stride_height,
uint32_t stride_width,
float output_min,
float output_max,
uint32_t input_id,
uint32_t output_id,
uint32_t flags);
/// Define a Fully Connected Node and add it to a Subgraph.
///
/// @param subgraph - a Subgraph object that will own the created Node.
/// @param output_min - lower bound for clipping output values.
/// @param output_max - upper bound for clipping output values.
/// @param input_id - Value ID for the input tensor. The input tensor must be an N-dimensional tensor defined in the
/// @a subgraph. If XNN_FLAG_TENSORFLOW_RESHAPE_2D is not specified, the input tensor must be at least
/// 1D and its last dimension must match the last dimension of the filter tensor. In particular, if
/// input is a 2D tensor, it must have [batch_size, input_channels] dimensions.
/// If XNN_FLAG_TENSORFLOW_RESHAPE_2D is specified, the number of elements in the input tensor must be
/// divisible by the input_channels. The tensor will be first flattened into a 1D tensor of
/// [num_input_elements] dimensions, then reshaped into a 2D tensor of
/// [num_input_elements / input_channels, input_channels] dimensions where num_input_elements is the
/// total number of elements in the input tensor.
/// @param filter_id - Value ID for the filter tensor. The filter tensor must a 2D tensor defined in the @a subgraph.
/// If the XNN_FLAG_TRANSPOSE_WEIGHTS flag is not specified, the filter tensor must have
/// [output_channels, input_channels] dimensions. If the XNN_FLAG_TRANSPOSE_WEIGHTS flag is
/// specified, the filter tensor must have [input_channels, output_channels] dimensions.
/// @param bias_id - Value ID for the bias tensor, or XNN_INVALID_VALUE_ID for a Fully Connected Node without a bias.
/// If present, the bias tensor must be a 1D tensor defined in the @a subgraph with [output_channels]
/// dimensions.
/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph.
/// If XNN_FLAG_TENSORFLOW_RESHAPE_2D is not specified, the output tensor must have the same
/// dimensionality as the input tensor, all its dimensions but the last one must match the
/// corresponding dimensions of the input tensor, and the last dimensions of the output tensor must
/// match the first dimension of the filter tensor. In particular, if input is a 2D tensor, output
/// must be a 2D tensor of [batch_size, output_channels] dimensions.
/// If XNN_FLAG_TENSORFLOW_RESHAPE_2D is specified, output must be a 2D tensor of
/// [num_input_elements / input_channels, output_channels] dimensions where num_input_elements is the
/// total number of elements in the input tensor.
/// @param flags - binary features of the Fully Connected Node. The only currently supported values are
/// XNN_FLAG_TENSORFLOW_RESHAPE_2D and XNN_FLAG_TRANSPOSE_WEIGHTS.
enum xnn_status xnn_define_fully_connected(
xnn_subgraph_t subgraph,
float output_min,
float output_max,
uint32_t input_id,
uint32_t filter_id,
uint32_t bias_id,
uint32_t output_id,
uint32_t flags);
/// Define a Sparse Fully Connected Node and add it to a Subgraph.
///
/// This operator is experimental, and will be removed in the future.
///
/// @param subgraph - a Subgraph object that will own the created Node.
/// @param output_min - lower bound for clipping output values.
/// @param output_max - upper bound for clipping output values.
/// @param input_id - Value ID for the input tensor. The input tensor must be an N-dimensional tensor defined in the
/// @a subgraph. If XNN_FLAG_TENSORFLOW_RESHAPE_2D is not specified, the input tensor must be at least
/// 1D and its last dimension must match the last dimension of the filter tensor. In particular, if
/// input is a 2D tensor, it must have [batch_size, input_channels] dimensions.
/// If XNN_FLAG_TENSORFLOW_RESHAPE_2D is specified, the number of elements in the input tensor must be
/// divisible by the input_channels. The tensor will be first flattened into a 1D tensor of
/// [num_input_elements] dimensions, then reshaped into a 2D tensor of
/// [num_input_elements / input_channels, input_channels] dimensions where num_input_elements is the
/// total number of elements in the input tensor.
/// @param filter_id - Value ID for the filter tensor. The filter tensor must a 2D tensor defined in the @a subgraph.
/// If the XNN_FLAG_TRANSPOSE_WEIGHTS flag is not specified, the filter tensor must have
/// [output_channels, input_channels] dimensions. If the XNN_FLAG_TRANSPOSE_WEIGHTS flag is
/// specified, the filter tensor must have [input_channels, output_channels] dimensions.
/// @param bias_id - Value ID for the bias tensor, or XNN_INVALID_VALUE_ID for a Fully Connected Node without a bias.
/// If present, the bias tensor must be a 1D tensor defined in the @a subgraph with [output_channels]
/// dimensions.
/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph.
/// If XNN_FLAG_TENSORFLOW_RESHAPE_2D is not specified, the output tensor must have the same
/// dimensionality as the input tensor, all its dimensions but the last one must match the
/// corresponding dimensions of the input tensor, and the last dimensions of the output tensor must
/// match the first dimension of the filter tensor. In particular, if input is a 2D tensor, output
/// must be a 2D tensor of [batch_size, output_channels] dimensions.
/// If XNN_FLAG_TENSORFLOW_RESHAPE_2D is specified, output must be a 2D tensor of
/// [num_input_elements / input_channels, output_channels] dimensions where num_input_elements is the
/// total number of elements in the input tensor.
/// @param flags - binary features of the Fully Connected Node. The only currently supported values are
/// XNN_FLAG_TENSORFLOW_RESHAPE_2D and XNN_FLAG_TRANSPOSE_WEIGHTS.
enum xnn_status xnn_define_fully_connected_sparse(
xnn_subgraph_t subgraph,
float output_min,
float output_max,
uint32_t input_id,
uint32_t filter_id,
uint32_t bias_id,
uint32_t output_id,
uint32_t flags);
/// Define a 2D Max Pooling Node and add it to a Subgraph.
///
/// @param subgraph - a Subgraph object that will own the created Node.
/// @param input_padding_top - implicit zero-padding above 2D input data. Must be 0 if XNN_FLAG_TENSORFLOW_SAME_PADDING
/// flag is specified.
/// @param input_padding_right - implicit zero-padding to the right of 2D input data. Must be 0 if
/// XNN_FLAG_TENSORFLOW_SAME_PADDING flag is specified.
/// @param input_padding_bottom - implicit zero-padding below 2D input data. Must be 0 if
/// XNN_FLAG_TENSORFLOW_SAME_PADDING flag is specified.
/// @param input_padding_left - implicit zero-padding to the left of 2D input data. Must be 0 if
/// XNN_FLAG_TENSORFLOW_SAME_PADDING flag is specified.
/// @param pooling_height - pooling (kernel) height.
/// @param pooling_width - pooling (kernel) width.
/// @param stride_height - displacing of the pooling window in the vertical dimension of the input pixels corresponding
/// to vertically adjacent output pixels.
/// @param stride_width - displacing of the pooling window in the horizontal dimension of the input pixels corresponding
/// to horizontally adjacent output pixels.
/// @param dilation_height - dilation of pooling elements along the height dimension.
/// @param dilation_width - dilation of pooling elements along the width dimension.
/// @param output_min - lower bound for clipping output values.
/// @param output_max - upper bound for clipping output values.
/// @param input_id - Value ID for the input tensor. The input tensor must be a 4D tensor defined in the @a subgraph
/// with [N, IH, IW, channels] dimensions
/// @param output_id - Value ID for the output tensor. The output tensor must be a 4D tensor defined in the @a subgraph
/// with [N, OH, OW, channels] dimensions.
/// @param flags - binary features of the 2D Max Pooling Node. The only currently supported values is
/// XNN_FLAG_TENSORFLOW_SAME_PADDING.
enum xnn_status xnn_define_max_pooling_2d(
xnn_subgraph_t subgraph,
uint32_t input_padding_top,
uint32_t input_padding_right,
uint32_t input_padding_bottom,
uint32_t input_padding_left,
uint32_t pooling_height,
uint32_t pooling_width,
uint32_t stride_height,
uint32_t stride_width,
uint32_t dilation_height,
uint32_t dilation_width,
float output_min,
float output_max,
uint32_t input_id,
uint32_t output_id,
uint32_t flags);
/// Define a 2D ArgMax Pooling Node and add it to a Subgraph.
///
/// @param subgraph - a Subgraph object that will own the created Node.
/// @param input_padding_top - implicit zero-padding above 2D input data.
/// @param input_padding_right - implicit zero-padding to the right of 2D input data.
/// @param input_padding_bottom - implicit zero-padding below 2D input data.
/// @param input_padding_left - implicit zero-padding to the left of 2D input data.
/// @param pooling_height - pooling (kernel) height. Vertical stride between pooling regions match this value.
/// @param pooling_width - pooling (kernel) width. Horizontal stride between pooling regions match this value.
/// @param input_id - Value ID for the input tensor. The input tensor must be a 4D tensor defined in the @a subgraph
/// with [N, IH, IW, channels] dimensions
/// @param output_value_id - Value ID for the output tensor with the maximum values in the pools. The output tensor must
/// be a 4D tensor defined in the @a subgraph with [N, OH, OW, channels] dimensions.
/// @param output_index_id - Value ID for the output tensor with the indexes of the maximum values in the pools. The
/// output tensor must be a 4D tensor defined in the @a subgraph with [N, OH, OW, channels]
/// dimensions.
/// @param flags - binary features of the 2D ArgMax Pooling Node. No supported flags are currently defined.
enum xnn_status xnn_define_argmax_pooling_2d(
xnn_subgraph_t subgraph,
uint32_t input_padding_top,
uint32_t input_padding_right,
uint32_t input_padding_bottom,
uint32_t input_padding_left,
uint32_t pooling_height,
uint32_t pooling_width,
uint32_t input_id,
uint32_t output_value_id,
uint32_t output_index_id,
uint32_t flags);
/// Define a 2D UnPooling Node and add it to a Subgraph.
///
/// @param subgraph - a Subgraph object that will own the created Node.
/// @param padding_top - implicit padding above 2D output data.
/// @param padding_right - implicit padding to the right of 2D output data.
/// @param padding_bottom - implicit padding below 2D output data.
/// @param padding_left - implicit padding to the left of 2D output data.
/// @param pooling_height - height of the pooling window.
/// @param pooling_width - width of the pooling window.
/// @param input_value_id - Value ID for the input tensor with the max-pooling values to invert. The input value tensor
/// must be a 4D tensor defined in the @a subgraph with [N, IH, IW, channels] dimensions.
/// @param input_index_id - Value ID for the input tensor with the indices of the per-pool maximum values produced by
/// a 2D UnPooling Node. The input tensor must be a 4D tensor defined in the @a subgraph with
/// [N, IH, IW, channels] dimensions.
/// @param output_id - Value ID for the output tensor. The output tensor must be a 4D tensor defined in the @a subgraph
/// with [N, OH, OW, channels] dimensions.
/// @param flags - binary features of the 2D UnPooling Node. No supported flags are currently defined.
enum xnn_status xnn_define_unpooling_2d(
xnn_subgraph_t subgraph,
uint32_t padding_top,
uint32_t padding_right,
uint32_t padding_bottom,
uint32_t padding_left,
uint32_t pooling_height,
uint32_t pooling_width,
uint32_t input_value_id,
uint32_t input_index_id,
uint32_t output_id,
uint32_t flags);
/// Define a 2-Input Add Node and add it to a Subgraph.
///
/// The 2-Input Add Node computes elementwise addition of two tensor inputs with numpy broadcasting rules.
///
/// @param subgraph - a Subgraph object that will own the created Node.
/// @param output_min - lower bound for clipping output values.
/// @param output_max - upper bound for clipping output values.
/// @param input1_id - Value ID for the first input tensor. The input tensor must be an N-dimensional tensor defined in
/// the @a subgraph with each dimension either equal to the corresponding dimension of the second
/// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
/// that dimension.
/// @param input2_id - Value ID for the second input tensor. The input tensor must be an M-dimensional tensor defined in
/// the @a subgraph with each dimension either equal to the corresponding dimension of the first
/// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
/// that dimension.
/// @param output_id - Value ID for the output tensor. The output tensor must be a max(N,M)-dimensional tensor defined
/// in the @a subgraph with each dimension equal to the maximum between the corresponding dimension
/// of the two inputs.
/// @param flags - binary features of the Add Node. No supported flags are currently defined.
enum xnn_status xnn_define_add2(
xnn_subgraph_t subgraph,
float output_min,
float output_max,
uint32_t input1_id,
uint32_t input2_id,
uint32_t output_id,
uint32_t flags);
/// Define a 2-Input Multiply Node and add it to a Subgraph.
///
/// The 2-Input Multiply Node computes elementwise multiplication of two tensor inputs with numpy broadcasting rules.
///
/// @param subgraph - a Subgraph object that will own the created Node.
/// @param output_min - lower bound for clipping output values.
/// @param output_max - upper bound for clipping output values.
/// @param input1_id - Value ID for the first input tensor. The input tensor must be an N-dimensional tensor defined in
/// the @a subgraph with each dimension either equal to the corresponding dimension of the second
/// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
/// that dimension.
/// @param input2_id - Value ID for the second input tensor. The input tensor must be an M-dimensional tensor defined in
/// the @a subgraph with each dimension either equal to the corresponding dimension of the first
/// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
/// that dimension.
/// @param output_id - Value ID for the output tensor. The output tensor must be a max(N,M)-dimensional tensor defined
/// in the @a subgraph with each dimension equal to the maximum between the corresponding dimension
/// of the two inputs.
/// @param flags - binary features of the Multiply Node. No supported flags are currently defined.
enum xnn_status xnn_define_multiply2(
xnn_subgraph_t subgraph,
float output_min,
float output_max,
uint32_t input1_id,
uint32_t input2_id,
uint32_t output_id,
uint32_t flags);
/// Define a Subtract Node and add it to a Subgraph.
///
/// The Subtract Node computes elementwise subtraction of two tensor inputs with numpy broadcasting rules.
///
/// @param subgraph - a Subgraph object that will own the created Node.
/// @param output_min - lower bound for clipping output values.
/// @param output_max - upper bound for clipping output values.
/// @param input1_id - Value ID for the first input tensor. The input tensor must be an N-dimensional tensor defined in
/// the @a subgraph with each dimension either equal to the corresponding dimension of the second
/// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
/// that dimension.
/// @param input2_id - Value ID for the second input tensor. The input tensor must be an M-dimensional tensor defined in
/// the @a subgraph with each dimension either equal to the corresponding dimension of the first
/// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
/// that dimension.
/// @param output_id - Value ID for the output tensor. The output tensor must be a max(N,M)-dimensional tensor defined
/// in the @a subgraph with each dimension equal to the maximum between the corresponding dimension
/// of the two inputs.
/// @param flags - binary features of the Subtract Node. No supported flags are currently defined.
enum xnn_status xnn_define_subtract(
xnn_subgraph_t subgraph,
float output_min,
float output_max,
uint32_t input1_id,
uint32_t input2_id,
uint32_t output_id,
uint32_t flags);
/// Define a Divide Node and add it to a Subgraph.
///
/// The Divide Node computes elementwise division of two tensor inputs with numpy broadcasting rules.
///
/// @param subgraph - a Subgraph object that will own the created Node.
/// @param output_min - lower bound for clipping output values.
/// @param output_max - upper bound for clipping output values.
/// @param input1_id - Value ID for the first input tensor. The input tensor must be an N-dimensional tensor defined in
/// the @a subgraph with each dimension either equal to the corresponding dimension of the second
/// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
/// that dimension.
/// @param input2_id - Value ID for the second input tensor. The input tensor must be an M-dimensional tensor defined in
/// the @a subgraph with each dimension either equal to the corresponding dimension of the first
/// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
/// that dimension.
/// @param output_id - Value ID for the output tensor. The output tensor must be a max(N,M)-dimensional tensor defined
/// in the @a subgraph with each dimension equal to the maximum between the corresponding dimension
/// of the two inputs.
/// @param flags - binary features of the Divide Node. No supported flags are currently defined.
enum xnn_status xnn_define_divide(
xnn_subgraph_t subgraph,
float output_min,
float output_max,
uint32_t input1_id,
uint32_t input2_id,
uint32_t output_id,
uint32_t flags);
/// Define a 2-Input Maximum Node and add it to a Subgraph.
///
/// The 2-Input Maximum Node computes elementwise maximum of two tensor inputs with numpy broadcasting rules.
///
/// @param subgraph - a Subgraph object that will own the created Node.
/// @param input1_id - Value ID for the first input tensor. The input tensor must be an N-dimensional tensor defined in
/// the @a subgraph with each dimension either equal to the corresponding dimension of the second
/// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
/// that dimension.
/// @param input2_id - Value ID for the second input tensor. The input tensor must be an M-dimensional tensor defined in
/// the @a subgraph with each dimension either equal to the corresponding dimension of the first
/// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
/// that dimension.
/// @param output_id - Value ID for the output tensor. The output tensor must be a max(N,M)-dimensional tensor defined
/// in the @a subgraph with each dimension equal to the maximum between the corresponding dimension
/// of the two inputs.
/// @param flags - binary features of the Maximum Node. No supported flags are currently defined.
enum xnn_status xnn_define_maximum2(
xnn_subgraph_t subgraph,
uint32_t input1_id,
uint32_t input2_id,
uint32_t output_id,
uint32_t flags);
/// Define a 2-Input Minimum Node and add it to a Subgraph.
///
/// The 2-Input Minimum Node computes elementwise minimum of two tensor inputs with numpy broadcasting rules.
///
/// @param subgraph - a Subgraph object that will own the created Node.
/// @param input1_id - Value ID for the first input tensor. The input tensor must be an N-dimensional tensor defined in
/// the @a subgraph with each dimension either equal to the corresponding dimension of the second
/// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
/// that dimension.
/// @param input2_id - Value ID for the second input tensor. The input tensor must be an M-dimensional tensor defined in
/// the @a subgraph with each dimension either equal to the corresponding dimension of the first
/// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
/// that dimension.
/// @param output_id - Value ID for the output tensor. The output tensor must be a max(N,M)-dimensional tensor defined
/// in the @a subgraph with each dimension equal to the maximum between the corresponding dimension
/// of the two inputs.
/// @param flags - binary features of the Minimum Node. No supported flags are currently defined.
enum xnn_status xnn_define_minimum2(
xnn_subgraph_t subgraph,
uint32_t input1_id,
uint32_t input2_id,
uint32_t output_id,
uint32_t flags);
/// Define a Squared Difference Node and add it to a Subgraph.
///
/// The Squared Difference Node computes elementwise squared difference of two tensor inputs with numpy broadcasting
/// rules.
///
/// @param subgraph - a Subgraph object that will own the created Node.
/// @param input1_id - Value ID for the first input tensor. The input tensor must be an N-dimensional tensor defined in
/// the @a subgraph with each dimension either equal to the corresponding dimension of the second
/// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
/// that dimension.
/// @param input2_id - Value ID for the second input tensor. The input tensor must be an M-dimensional tensor defined in
/// the @a subgraph with each dimension either equal to the corresponding dimension of the first
/// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
/// that dimension.
/// @param output_id - Value ID for the output tensor. The output tensor must be a max(N,M)-dimensional tensor defined
/// in the @a subgraph with each dimension equal to the maximum between the corresponding dimension
/// of the two inputs.
/// @param flags - binary features of the Squared Difference Node. No supported flags are currently defined.
enum xnn_status xnn_define_squared_difference(
xnn_subgraph_t subgraph,
uint32_t input1_id,
uint32_t input2_id,
uint32_t output_id,
uint32_t flags);
/// Define a Constant Pad Node with static padding specification and add it to a Subgraph.
///
/// @param subgraph - a Subgraph object that will own the created Node.
/// @param pre_paddings - number of padding elements to insert before input elements for every dimension. This array
/// must have as many elements as the number of dimensions in the input tensor.
/// @param post_paddings - number of padding elements to insert after input elements for every dimension. This array
/// must have as many elements as the number of dimensions in the input tensor.
/// @param padding_value - constant value used to initialize padding elements.
/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
/// shape must match the shape of the input tensor with padding.
/// @param flags - binary features of the Constant Pad Node. No supported flags are currently defined.
enum xnn_status xnn_define_static_constant_pad(
xnn_subgraph_t subgraph,
const size_t* pre_paddings,
const size_t* post_paddings,
float padding_value,
uint32_t input_id,
uint32_t output_id,
uint32_t flags);
/// Define a Mean Node and add it to a Subgraph.
///
/// @param subgraph - a Subgraph object that will own the created Node.
/// @param num_reduction_axes - number of axes along which mean is computed.
/// @param reduction_axes - axes along which mean is computed.
/// @param input_id - Value ID for the input tensor. The input tensor must be a dense tensor with at least
/// @a num_reduction_axes dimensions defined in the @a subgraph.
/// @param output_id - Value ID for the output tensor. The output tensor must be a dense tensor defined in the
/// @a subgraph with @a num_reduction_axes fewer dimensions than the input tensor.
/// @param flags - binary features of the Mean Node. No supported flags are currently defined.
enum xnn_status xnn_define_static_mean(
xnn_subgraph_t subgraph,
size_t num_reduction_axes,
const size_t* reduction_axes,
uint32_t input_id,
uint32_t output_id,
uint32_t flags);
/// Define a 2-Input Concatenate Node and add it to a Subgraph.
///
/// The 2-Input Concatenate Node concatenates two tensors along a specified axis.
///
/// @param subgraph - a Subgraph object that will own the created Node.
/// @param axis - the axis to concatenate the two input tensors along
/// @param input1_id - Value ID for the first input tensor. The input tensor must be an N-dimensional tensor defined in
/// the @a subgraph with each dimension, except the axis, equal to the corresponding dimension of the
/// second input.
/// @param input2_id - Value ID for the second input tensor. The input tensor must be an N-dimensional tensor defined in
/// the @a subgraph with each dimension, except the axis, equal to the corresponding dimension of the
/// first input.
/// @param output_id - Value ID for the output tensor. The output tensor must be a N-dimensional tensor defined
/// in the @a subgraph with each dimension equal to the dimension of both inputs, except the axis
/// dimension, where it is the sum of the corresponding dimensions of both inputs.
/// @param flags - binary features of the Concatenate Node. No supported flags are currently defined.
enum xnn_status xnn_define_concatenate2(
xnn_subgraph_t subgraph,
size_t axis,
uint32_t input1_id,
uint32_t input2_id,
uint32_t output_id,
uint32_t flags);
/// Define a 3-Input Concatenate Node and add it to a Subgraph.
///
/// The 3-Input Concatenate Node concatenates three tensors along a specified axis.
///
/// @param subgraph - a Subgraph object that will own the created Node.
/// @param axis - the axis to concatenate the three input tensors along
/// @param input1_id - Value ID for the first input tensor. The input tensor must be an N-dimensional tensor defined in
/// the @a subgraph with each dimension, except the axis, equal to the corresponding dimension of the
/// other inputs.
/// @param input2_id - Value ID for the second input tensor. The input tensor must be an N-dimensional tensor defined in
/// the @a subgraph with each dimension, except the axis, equal to the corresponding dimension of the
/// other inputs.
/// @param input3_id - Value ID for the third input tensor. The input tensor must be an N-dimensional tensor defined in
/// the @a subgraph with each dimension, except the axis, equal to the corresponding dimension of the
/// other inputs.
/// @param output_id - Value ID for the output tensor. The output tensor must be a N-dimensional tensor defined
/// in the @a subgraph with each dimension equal to the dimension of all inputs, except the axis
/// dimension, where it is the sum of the corresponding dimensions of all inputs.
/// @param flags - binary features of the Concatenate Node. No supported flags are currently defined.
enum xnn_status xnn_define_concatenate3(
xnn_subgraph_t subgraph,
size_t axis,
uint32_t input1_id,
uint32_t input2_id,
uint32_t input3_id,
uint32_t output_id,
uint32_t flags);
/// Define a 4-Input Concatenate Node and add it to a Subgraph.
///
/// The 4-Input Concatenate Node concatenates four tensors along a specified axis.
///
/// @param subgraph - a Subgraph object that will own the created Node.
/// @param axis - the axis to concatenate the four input tensors along
/// @param input1_id - Value ID for the first input tensor. The input tensor must be an N-dimensional tensor defined in
/// the @a subgraph with each dimension, except the axis, equal to the corresponding dimension of the
/// other inputs.
/// @param input2_id - Value ID for the second input tensor. The input tensor must be an N-dimensional tensor defined in
/// the @a subgraph with each dimension, except the axis, equal to the corresponding dimension of the
/// other inputs.
/// @param input3_id - Value ID for the third input tensor. The input tensor must be an N-dimensional tensor defined in
/// the @a subgraph with each dimension, except the axis, equal to the corresponding dimension of the
/// other inputs.
/// @param input4_id - Value ID for the fourth input tensor. The input tensor must be an N-dimensional tensor defined in
/// the @a subgraph with each dimension, except the axis, equal to the corresponding dimension of the
/// other inputs.
/// @param output_id - Value ID for the output tensor. The output tensor must be a N-dimensional tensor defined
/// in the @a subgraph with each dimension equal to the dimension of all inputs, except the axis
/// dimension, where it is the sum of the corresponding dimensions of all inputs.
/// @param flags - binary features of the Concatenate Node. No supported flags are currently defined.
enum xnn_status xnn_define_concatenate4(
xnn_subgraph_t subgraph,
size_t axis,
uint32_t input1_id,
uint32_t input2_id,
uint32_t input3_id,
uint32_t input4_id,
uint32_t output_id,
uint32_t flags);
/// Define a Copy Node and add it to a Subgraph.
///
/// The Copy Node copies an input tensor to an output tensor.
///
/// @param subgraph - a Subgraph object that will own the created Node.
/// @param input_id - Value ID for the first input tensor. The input tensor must be defined in the @a subgraph.
/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
/// shape must match the shape of the input tensor.
/// @param flags - binary features of the Copy Node. No supported flags are currently defined.
enum xnn_status xnn_define_copy(
xnn_subgraph_t subgraph,
uint32_t input_id,
uint32_t output_id,
uint32_t flags);
/// Define a 2-Output Split Node and add it to a Subgraph.
///
/// The 2-Output Split Node splits an input tensor into two output tensors along a specified axis evenly.
///
/// @param subgraph - a Subgraph object that will own the created Node.
/// @param split_dim - the dimension to split the input tensor along
/// @param input_id - Value ID for the input tensor. The input tensor must be an N-dimensional tensor defined in the @a
/// subgraph.
/// @param output1_id - Value ID for the first output tensor. The output tensor must be an N-dimensional tensor defined
/// in the @a subgraph with each dimension, except the axis, equal to the corresponding dimension
/// of the second output. The split_dim dimension is half of the input's split_dim.
/// @param output2_id - Value ID for the second output tensor. The output tensor must be an N-dimensional tensor
/// defined in the @a subgraph with each dimension, except the axis, equal to the corresponding
/// dimension of the first output. The split_dim dimension is half of the input's split_dim.
/// @param flags - binary features of the Split Node. No supported flags are currently defined.
enum xnn_status xnn_define_even_split2(
xnn_subgraph_t subgraph,
size_t split_dim,
uint32_t input_id,
uint32_t output1_id,
uint32_t output2_id,
uint32_t flags);
/// Define a 3-Output Split Node and add it to a Subgraph.
///
/// The 3-Output Split Node splits an input tensor into three output tensors along a specified axis evenly.
///
/// @param subgraph - a Subgraph object that will own the created Node.
/// @param split_dim - the dimension to split the input tensor along
/// @param input_id - Value ID for the input tensor. The input tensor must be an N-dimensional tensor defined in the @a
/// subgraph.
/// @param output1_id - Value ID for the first output tensor. The output tensor must be an N-dimensional tensor defined
/// in the @a subgraph with each dimension, except the axis, equal to the corresponding dimension
/// of the second and third output. The split_dim dimension is one third of the input's split_dim.
/// @param output2_id - Value ID for the second output tensor. The output tensor must be an N-dimensional tensor
/// defined in the @a subgraph with each dimension, except the axis, equal to the corresponding
/// dimension of the first and third output. The split_dim dimension is one third of the input's
/// split_dim.
/// @param output3_id - Value ID for the third output tensor. The output tensor must be an N-dimensional tensor
/// defined in the @a subgraph with each dimension, except the axis, equal to the corresponding
/// dimension of the second and third output. The split_dim dimension is one third of the input's
/// split_dim.
/// @param flags - binary features of the Split Node. No supported flags are currently defined.
enum xnn_status xnn_define_even_split3(
xnn_subgraph_t subgraph,
size_t split_dim,
uint32_t input_id,
uint32_t output1_id,
uint32_t output2_id,
uint32_t output3_id,
uint32_t flags);
/// Define a 4-Output Split Node and add it to a Subgraph.
///
/// The 4-Output Split Node splits an input tensor into four output tensors along a specified axis evenly.
///
/// @param subgraph - a Subgraph object that will own the created Node.
/// @param split_dim - the dimension to split the input tensor along
/// @param input_id - Value ID for the input tensor. The input tensor must be an N-dimensional tensor defined in the @a
/// subgraph.
/// @param output1_id - Value ID for the first output tensor. The output tensor must be an N-dimensional tensor defined
/// in the @a subgraph with each dimension, except the axis, equal to the corresponding dimension
/// of the other output tensors. The split_dim dimension is one fourth of the input's split_dim.
/// @param output2_id - Value ID for the second output tensor. The output tensor must be an N-dimensional tensor
/// defined in the @a subgraph with each dimension, except the axis, equal to the corresponding
/// dimension of the other output tensors. The split_dim dimension is one fourth of the input's
/// split_dim.
/// @param output3_id - Value ID for the third output tensor. The output tensor must be an N-dimensional tensor
/// defined in the @a subgraph with each dimension, except the axis, equal to the corresponding
/// dimension of the other output tensors. The split_dim dimension is one fourth of the input's
/// split_dim.
/// @param output4_id - Value ID for the fourth output tensor. The output tensor must be an N-dimensional tensor
/// defined in the @a subgraph with each dimension, except the axis, equal to the corresponding
/// dimension of the other output tensors. The split_dim dimension is one fourth of the input's
/// split_dim.
/// @param flags - binary features of the Split Node. No supported flags are currently defined.
enum xnn_status xnn_define_even_split4(
xnn_subgraph_t subgraph,
size_t split_dim,
uint32_t input_id,
uint32_t output1_id,
uint32_t output2_id,
uint32_t output3_id,
uint32_t output4_id,
uint32_t flags);
/// Define a Reshape Node with static shape specification and add it to a Subgraph.
///
/// @param subgraph - a Subgraph object that will own the created Node.
/// @param num_dims - number of shape dimensions in the output tensor.
/// @param new_shape - shape dimensions of the output tensor.
/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
/// shape must match the shape of the input tensor with padding.
/// @param flags - binary features of the Reshape Node. No supported flags are currently defined.
enum xnn_status xnn_define_static_reshape(
xnn_subgraph_t subgraph,
size_t num_dims,
const size_t* new_shape,
uint32_t input_id,
uint32_t output_id,
uint32_t flags);
/// Define a 2D Resize Bilinear Node with static output height & width specification and add it to a Subgraph.
///
/// @param subgraph - a Subgraph object that will own the created Node.
/// @param new_height - height dimension of the output tensor.
/// @param new_width - width dimension of the output tensor.
/// @param input_id - Value ID for the input tensor. The input tensor must be a 4D tensor defined in the @a subgraph
/// with [N, H, W, C] dimensions.
/// @param output_id - Value ID for the output tensor. The output tensor must be a 4D tensor defined in the @a subgraph
/// with [N, new_height, new_width, C] dimensions.
/// @param flags - binary features of the 2D Resize Bilinear Node. The only currently supported values are
/// XNN_FLAG_TENSORFLOW_LEGACY_MODE and XNN_FLAG_ALIGN_CORNERS, which are mutually exclusive.
enum xnn_status xnn_define_static_resize_bilinear_2d(
xnn_subgraph_t subgraph,
size_t new_height,
size_t new_width,
uint32_t input_id,
uint32_t output_id,
uint32_t flags);
/// Define a PReLU (Parametric ReLU) Node and add it to a Subgraph.
///
/// @param subgraph - a Subgraph object that will own the created Node.
/// @param input_id - Value ID for the input tensor. The input tensor must be a 4D tensor defined in the @a subgraph
/// with [N, H, W, channels] dimensions.
/// @param slope_id - Value ID for the slope tensor. The slope tensor must be a 1D tensor defined in the @a subgraph with
/// [channels] dimensions.
/// @param output_id - Value ID for the output tensor. The output tensor must be a 4D tensor defined in the @a subgraph
/// with [N, H, W, channels] dimensions.
/// @param flags - binary features of the PReLU Node. No supported flags are currently defined.
enum xnn_status xnn_define_prelu(
xnn_subgraph_t subgraph,
uint32_t input_id,
uint32_t slope_id,
uint32_t output_id,
uint32_t flags);
/// Define a RoPE (Rotary Positional Embeddings) Node and add it to a Subgraph.
///
/// @param subgraph - a Subgraph object that will own the created Node.
/// @param max_sequence_size - maximum possible sequence length of the input/output tensors.
/// @param input_id - Value ID for the input tensor. The input tensor must be a 4D tensor defined in the @a subgraph
/// with [batch, sequence_length, heads, channels] dimensions.
/// @param weights_id - Value ID for the weights tensor. The weights tensor must be a 2D tensor defined in the
/// @a subgraph with [max_sequence_length, channels] dimensions.
/// @param output_id - Value ID for the output tensor. The output tensor must be a 4D tensor defined in the @a subgraph
/// with [batch, sequence_length, heads, channels] dimensions.
/// @param flags - binary features of the RoPE Node. No supported flags are currently defined.
enum xnn_status xnn_define_rope(
xnn_subgraph_t subgraph,
size_t max_sequence_size,
uint32_t input_id,
uint32_t weights_id,
uint32_t output_id,
uint32_t flags);
/// Define a Abs Node and add it to a Subgraph.
///
/// @param subgraph - a Subgraph object that will own the created Node.
/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
/// shape must match the shape of the input tensor.
/// @param flags - binary features of the Abs Node. No supported flags are currently defined.
enum xnn_status xnn_define_abs(
xnn_subgraph_t subgraph,
uint32_t input_id,
uint32_t output_id,
uint32_t flags);
/// Define a Bankers' Rounding Node and add it to a Subgraph.
///
/// @param subgraph - a Subgraph object that will own the created Node.
/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
/// shape must match the shape of the input tensor.
/// @param flags - binary features of the Bankers' Rounding Node. No supported flags are currently defined.
enum xnn_status xnn_define_bankers_rounding(
xnn_subgraph_t subgraph,
uint32_t input_id,
uint32_t output_id,
uint32_t flags);
/// Define a Batch Matrix Multiply Node and add it to a Subgraph.
///
/// @param subgraph - a Subgraph object that will own the created Node.
/// @param input1_id - Value ID for the first input tensor. The input tensor must be an N-dimensional tensor defined in
/// the @a subgraph. It must be at least 3D. The first N-2 dimensions must match the second input
/// tensor. The last 2 dimensions are [M, K]. If XNN_FLAG_TRANSPOSE_B is not specified, the last
/// dimension must match the second last dimension of the second input tensor. If
/// XNN_FLAG_TRANSPOSE_B is specified, the last dimension must match the last dimension of the
/// second input tensor.
/// @param input2_id - Value ID for the second input tensor. The input tensor must be an N-dimensional tensor defined
/// in the @a subgraph. It must be at least 3D. The first N-2 dimensions must match the first input
/// tensor. If XNN_FLAG_TRANSPOSE_B is not specified, the last 2 dimensions are [K, N], and the
/// second last dimension must match the last dimension of the first input tensor. If
/// XNN_FLAG_TRANSPOSE_B is specified, the last 2 dimensions are [N, K], and the last dimension must
/// match the last dimension of the first input tensor.
/// @param output_id - Value ID for the output tensor. The output tensor must be an N-dimensional tensor defined in the
/// @a subgraph. It must be at least 3D. The first N-2 dimensions must match the first and second
/// input tensors . The last 2 dimensions must be [M, N].
/// @param flags - binary features of the Batch Matrix Multiply Node. The only currently supported value is
/// XNN_FLAG_TRANSPOSE_B.
enum xnn_status xnn_define_batch_matrix_multiply(
xnn_subgraph_t subgraph,
uint32_t input1_id,
uint32_t input2_id,
uint32_t output_id,
uint32_t flags);
/// Define a Ceiling Node and add it to a Subgraph.
///
/// @param subgraph - a Subgraph object that will own the created Node.
/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
/// shape must match the shape of the input tensor.
/// @param flags - binary features of the Ceiling Node. No supported flags are currently defined.
enum xnn_status xnn_define_ceiling(
xnn_subgraph_t subgraph,
uint32_t input_id,
uint32_t output_id,
uint32_t flags);
/// Define a Clamp Node and add it to a Subgraph.
///
/// @param subgraph - a Subgraph object that will own the created Node.
/// @param output_min - lower bound for clipping output values.
/// @param output_max - upper bound for clipping output values.
/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
/// shape must match the shape of the input tensor.
/// @param flags - binary features of the Clamp Node. No supported flags are currently defined.
enum xnn_status xnn_define_clamp(
xnn_subgraph_t subgraph,
float output_min,
float output_max,
uint32_t input_id,
uint32_t output_id,
uint32_t flags);
/// Define an ELU (Exponential Linear Unit) Node and add it to a Subgraph.
///
/// @param subgraph - a Subgraph object that will own the created Node.
/// @param alpha - scale factor for negative output elements.
/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
/// shape must match the shape of the input tensor.
/// @param flags - binary features of the ELU Node. No supported flags are currently defined.
enum xnn_status xnn_define_elu(
xnn_subgraph_t subgraph,
float alpha,
uint32_t input_id,
uint32_t output_id,
uint32_t flags);
/// Define a Floor Node and add it to a Subgraph.
///
/// @param subgraph - a Subgraph object that will own the created Node.
/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
/// shape must match the shape of the input tensor.
/// @param flags - binary features of the Floor Node. No supported flags are currently defined.
enum xnn_status xnn_define_floor(
xnn_subgraph_t subgraph,
uint32_t input_id,
uint32_t output_id,
uint32_t flags);
/// Define a HardSwish Node and add it to a Subgraph.
///
/// @param subgraph - a Subgraph object that will own the created Node.
/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
/// shape must match the shape of the input tensor.
/// @param flags - binary features of the HardSwish Node. No supported flags are currently defined.
enum xnn_status xnn_define_hardswish(
xnn_subgraph_t subgraph,
uint32_t input_id,
uint32_t output_id,
uint32_t flags);
/// Define a Leaky ReLU Node and add it to a Subgraph.
///
/// @param subgraph - a Subgraph object that will own the created Node.
/// @param negative_slope - scale factor for negative input elements.
/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
/// shape must match the shape of the input tensor.
/// @param flags - binary features of the Leaky ReLU Node. No supported flags are currently defined.
enum xnn_status xnn_define_leaky_relu(
xnn_subgraph_t subgraph,
float negative_slope,
uint32_t input_id,
uint32_t output_id,
uint32_t flags);
/// Define a Negate Node and add it to a Subgraph.
///
/// @param subgraph - a Subgraph object that will own the created Node.
/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
/// shape must match the shape of the input tensor.
/// @param flags - binary features of the Negate Node. No supported flags are currently defined.
enum xnn_status xnn_define_negate(
xnn_subgraph_t subgraph,
uint32_t input_id,
uint32_t output_id,
uint32_t flags);
/// Define a Sigmoid Node and add it to a Subgraph.
///
/// @param subgraph - a Subgraph object that will own the created Node.
/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
/// shape must match the shape of the input tensor.
/// @param flags - binary features of the Sigmoid Node. No supported flags are currently defined.
enum xnn_status xnn_define_sigmoid(
xnn_subgraph_t subgraph,
uint32_t input_id,
uint32_t output_id,
uint32_t flags);
/// Define a SoftMax Node and add it to a Subgraph.
///
/// @param subgraph - a Subgraph object that will own the created Node.
/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph, and have at
/// least one dimension.
/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
/// shape must match the shape of the input tensor.
/// @param flags - binary features of the SoftMax Node. No supported flags are currently defined.
enum xnn_status xnn_define_softmax(
xnn_subgraph_t subgraph,
uint32_t input_id,
uint32_t output_id,
uint32_t flags);
/// Define a Space To Depth 2D Node and add it to a Subgraph.
///
/// The Space To Depth 2D Node rearranges blocks of spatial data into blocks (a reverse transform to Depth To Space 2D).
/// For a given input pixel, an output square of pixels with side @a block_size is formed from values in the
/// corresponding number of its channels. The output depth is therefore @a block_size x @a block_size times greater
/// than that of the input.
///
/// @param subgraph - a Subgraph object that will own the created Node.
/// @param block_size - the size of the spatial block.
/// @param input_id - Value ID for the input tensor. The input tensor must be a 4D tensor defined in the @a subgraph
/// with [N, IH * block_size, IW * block_size, OC] dimensions.
/// @param output_id - Value ID for the output tensor. The output tensor must be a 4D tensor defined in the @a subgraph
/// with [N, IH, IW, OC * block_size * block_size] dimensions.
/// @param flags - binary features of the input_channels Node. No supported flags are currently defined.
enum xnn_status xnn_define_space_to_depth_2d(
xnn_subgraph_t subgraph,
uint32_t block_size,
uint32_t input_id,
uint32_t output_id,
uint32_t flags);
/// Define a Square Node and add it to a Subgraph.
///
/// @param subgraph - a Subgraph object that will own the created Node.
/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
/// shape must match the shape of the input tensor.
/// @param flags - binary features of the Square Node. No supported flags are currently defined.
enum xnn_status xnn_define_square(
xnn_subgraph_t subgraph,
uint32_t input_id,
uint32_t output_id,
uint32_t flags);
/// Define a Square Root Node and add it to a Subgraph.
///
/// @param subgraph - a Subgraph object that will own the created Node.
/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
/// shape must match the shape of the input tensor.
/// @param flags - binary features of the Square Root Node. No supported flags are currently defined.
enum xnn_status xnn_define_square_root(
xnn_subgraph_t subgraph,
uint32_t input_id,
uint32_t output_id,
uint32_t flags);
/// Define a Static Slice Node add it to a Subgraph.
///
/// @param subgraph - a Subgraph object that will own the created Node.
/// @param num_dims - number of shape dimensions in the input and output tensor.
/// @param offsets - offsets in each dimension of the input tensor. This array must have @a num_dims elements.
/// @param sizes - size of each dimension in output tensor. This array must have @a num_dims elements.
/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
/// dimensions must match @a sizes.
/// @param flags - binary features of the Static Slice Node. No supported flags are currently defined.
enum xnn_status xnn_define_static_slice(
xnn_subgraph_t subgraph,
size_t num_dims,
const size_t* offsets,
const size_t* sizes,
uint32_t input_id,
uint32_t output_id,
uint32_t flags);
/// Define a Static Transpose Node and add it to a Subgraph.
///
/// The Static Transpose Node applies a generalized transpose to the input tensor using the permuation in perm.
///
/// @param subgraph - a Subgraph object that will own the created Node.
/// @param input_id - Value ID for the input tensor. The input tensor must be an N-dimensional tensor defined in
/// the @a subgraph.
/// @param output_id - Value ID for the output tensor. The output tensor must be an N-dimensional tensor defined
/// in the @a subgraph with each dimension equal to its corresponding permuted input dimension.
/// @param num_dims - the number of permutation dimensions. This must be equal to the number of input dimensions.
/// @param perm - The permutation of the axis of the input tensor. The perm array must must contain 0 to N-1 in the
/// permuted order.
/// @param flags - binary features of the Static Transpose Node. No supported flags are currently defined.
enum xnn_status xnn_define_static_transpose(
xnn_subgraph_t subgraph,
size_t num_dims,
const size_t* perm,
uint32_t input_id,
uint32_t output_id,
uint32_t flags);
/// Define a Tanh Node and add it to a Subgraph.
///
/// @param subgraph - a Subgraph object that will own the created Node.
/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
/// shape must match the shape of the input tensor.
/// @param flags - binary features of the Tanh Node. No supported flags are currently defined.
enum xnn_status xnn_define_tanh(
xnn_subgraph_t subgraph,
uint32_t input_id,
uint32_t output_id,
uint32_t flags);
/// Code cache is a cache for JIT generated code.
typedef struct xnn_code_cache* xnn_code_cache_t;
/// Weights cache is a cache for packed weights. It can be reused between runtimes.
typedef struct xnn_weights_cache* xnn_weights_cache_t;
enum xnn_status xnn_create_weights_cache(xnn_weights_cache_t* weights_cache_out);
/// Create a weights cache object specifying the initial size of weights cache (in bytes).
/// @size - initial capacity of the weights cache (in bytes), i.e. it can hold size bytes without growing.
/// @param weights_cache_out - pointer to the variable that will be initialized to a handle to the weights cache object
/// upon successful return. Once created, the weights cache object can be shared between
/// different Runtime objects.
enum xnn_status xnn_create_weights_cache_with_size(size_t size, xnn_weights_cache_t* weights_cache_out);
/// Weights cache can be finalized in these ways:
enum xnn_weights_cache_finalization_kind {
/// Weights cache is finalized, no insert operations into the weights cache is allowed, even if the "inserted"
/// weights already exist in thee cache. Weights cache memory will also be trimmed to page boundary and set to
/// read-only (to prevent writes).
xnn_weights_cache_finalization_kind_hard,
/// Weights cache will be finalized with some extra space at the end, this allows for "inserting" into the cache only
/// if the weights are already in the cache, and errors on inserting uncached weights. There is memory overhead.
xnn_weights_cache_finalization_kind_soft,
};
/// Finalizes the weights cache. The kind of finalization is specified by `finalization_kind`.
/// @param weights_cache - the weights cache object to finalize.
/// @param finalization_kind - the kind of finalization.
enum xnn_status xnn_finalize_weights_cache(
xnn_weights_cache_t weights_cache,
enum xnn_weights_cache_finalization_kind finalization_kind);
/// Destroy a weights cache object, as well as memory used for the cache.
/// @param weights_cache - the weights cache object to destroy.
enum xnn_status xnn_delete_weights_cache(xnn_weights_cache_t weights_cache);
typedef struct xnn_workspace* xnn_workspace_t;
/// Create a workspace object.
/// @param workspace_out - pointer to the variable that will be initialized to a handle to the workspace object upon
/// successful return. Once created, the workspace can be shared between different Runtime
/// objects.
enum xnn_status xnn_create_workspace(xnn_workspace_t* workspace_out);
/// Destroy a workspace object, as well as memory used by the workspace. Object destruction can be deferred until all
/// Runtime objects created with this workspace are destroyed.
/// @param workspace - the workspace object to destroy.
enum xnn_status xnn_release_workspace(xnn_workspace_t workspace);
/// Runtime is a combination of an execution plan for subgraph Nodes and a memory manager for subgraph Values.
typedef struct xnn_runtime* xnn_runtime_t;
enum xnn_profile_info {
/// Returns a size_t containing the number of operators.
xnn_profile_info_num_operators,
/// Returns a char[] containing the null character separated names of all operators.
xnn_profile_info_operator_name,
/// Returns a uint64_t[] with the runtimes of all operators in the same order as xnn_profile_info_operator_name.
xnn_profile_info_operator_timing,
};
/// Return profile information for all operators.
///
/// @param runtime - a Runtime object created with @ref xnn_create_runtime, @ref xnn_create_runtime_v2 or
/// @ref xnn_create_runtime_v3.
/// @param param_name - type of profile information required.
/// @param param_value_size - the size in bytes of memory pointed to by param_value. If this is not sufficient then
/// param_value_size_ret will be set to the required size and xnn_status_out_of_memory will be
/// returned.
/// @param param_value - a pointer to memory location where appropriate values for a given param_value will be written.
/// @param param_value_size_ret - returns number of bytes required to write the result if param_value_size is not
/// sufficient.
enum xnn_status xnn_get_runtime_profiling_info(xnn_runtime_t runtime,
enum xnn_profile_info param_name,
size_t param_value_size,
void* param_value,
size_t* param_value_size_ret);
/// Create a Runtime object from a subgraph.
///
/// @param subgraph - a Subgraph object with all Values and Nodes that would be handled by the runtime. No Values or
/// Nodes can be added to the runtime once it is constructed.
/// @param weights_cache - a cache for packed weights. The runtime will look up and reuse packed weights in this cache,
/// this will reduce memory allocated for packed weights.
/// @param workspace - a workspace to hold internal tensors. The runtime will allocate space used for internal tensors
/// and track them using workspace. Workspace can be shared and reused across different runtimes. If
/// workspace is NULL, there will be no sharing: each runtime has its own workspace.
/// @param threadpool - the thread pool to be used for parallelisation of computations in the runtime. If the thread
/// pool is NULL, the computation would run on the caller thread without parallelization.
/// @param flags - binary features of the runtime. The only currently supported values are
/// XNN_FLAG_HINT_SPARSE_INFERENCE, XNN_FLAG_HINT_FP16_INFERENCE, XNN_FLAG_FORCE_FP16_INFERENCE, and
/// XNN_FLAG_YIELD_WORKERS. If XNN_FLAG_YIELD_WORKERS is specified, worker threads would be yielded to
/// the system scheduler after processing the last operator in the Runtime.
/// @param runtime_out - pointer to the variable that will be initialized with a handle to the Runtime object upon
/// successful return. Once constructed, the Runtime object is independent of the Subgraph object
/// used to create it.
enum xnn_status xnn_create_runtime_v4(
xnn_subgraph_t subgraph,
xnn_weights_cache_t weights_cache,
xnn_workspace_t workspace,
pthreadpool_t threadpool,
uint32_t flags,
xnn_runtime_t* runtime_out);
enum xnn_status xnn_create_runtime_v3(
xnn_subgraph_t subgraph,
xnn_weights_cache_t weights_cache,
pthreadpool_t threadpool,
uint32_t flags,
xnn_runtime_t* runtime_out);
enum xnn_status xnn_create_runtime_v2(
xnn_subgraph_t subgraph,
pthreadpool_t threadpool,
uint32_t flags,
xnn_runtime_t* runtime_out);
enum xnn_status xnn_create_runtime(
xnn_subgraph_t subgraph,
xnn_runtime_t* runtime_out);
struct xnn_external_value {
uint32_t id;
void* data;
};
/// Setup data pointers for external inputs and outputs in a Runtime object.
///
/// @param runtime - a Runtime object created with @ref xnn_create_runtime or @ref xnn_create_runtime_v2.
/// @param num_external_values - the number of external inputs and outputs specified in this call. This number must
/// match the number of external inputs and outputs in the runtime, i.e. all external
/// inputs and outputs in the runtime must be specified in one call.
/// @param external_values - array with location information for all external inputs and outputs in the runtime.
enum xnn_status xnn_setup_runtime(
xnn_runtime_t runtime,
size_t num_external_values,
const struct xnn_external_value* external_values);
/// Execute forward pass for all operators in the runtime.
///
/// @param runtime - the Runtime object with the execution plan to invoke.
enum xnn_status xnn_invoke_runtime(
xnn_runtime_t runtime);
/// Destroy a Runtime object, as well as operators and memory associated with it.
///
/// @param runtime - the Runtime object to destroy.
enum xnn_status xnn_delete_runtime(
xnn_runtime_t runtime);
typedef struct xnn_operator* xnn_operator_t;
enum xnn_status xnn_run_operator(
xnn_operator_t op,
pthreadpool_t threadpool);
enum xnn_status xnn_delete_operator(
xnn_operator_t op);
enum xnn_status xnn_create_abs_nc_f32(
size_t channels,
size_t input_stride,
size_t output_stride,
uint32_t flags,
xnn_operator_t* abs_op_out);
enum xnn_status xnn_reshape_abs_nc_f32(
xnn_operator_t abs_op,
size_t batch_size,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_abs_nc_f32(
xnn_operator_t abs_op,
const float* input,
float* output);
enum xnn_status xnn_run_abs_nc_f32(
size_t channels,
size_t input_stride,
size_t output_stride,
size_t batch_size,
const float* input,
float* output,
uint32_t flags,
pthreadpool_t threadpool);
enum xnn_status xnn_create_add_nd_f32(
float output_min,
float output_max,
uint32_t flags,
xnn_operator_t* add_op_out);
enum xnn_status xnn_reshape_add_nd_f32(
xnn_operator_t add_op,
size_t num_input1_dims,
const size_t* input1_shape,
size_t num_input2_dims,
const size_t* input2_shape,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_add_nd_f32(
xnn_operator_t add_op,
const float* input1,
const float* input2,
float* output);
enum xnn_status xnn_run_add_nd_f32(
size_t num_input1_dims,
const size_t* input1_shape,
size_t num_input2_dims,
const size_t* input2_shape,
const float* input1,
const float* input2,
float* output,
float output_min,
float output_max,
uint32_t flags,
pthreadpool_t threadpool);
enum xnn_status xnn_create_argmax_pooling2d_nhwc_f32(
uint32_t input_padding_top,
uint32_t input_padding_right,
uint32_t input_padding_bottom,
uint32_t input_padding_left,
uint32_t pooling_height,
uint32_t pooling_width,
size_t channels,
size_t input_pixel_stride,
size_t output_pixel_stride,
uint32_t flags,
xnn_operator_t* argmax_pooling_op_out);
enum xnn_status xnn_reshape_argmax_pooling2d_nhwc_f32(
xnn_operator_t argmax_pooling_op,
size_t batch_size,
size_t input_height,
size_t input_width,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_argmax_pooling2d_nhwc_f32(
xnn_operator_t argmax_pooling_op,
const float* input,
float* output,
uint32_t* index);
enum xnn_status xnn_create_average_pooling2d_nhwc_f32(
uint32_t input_padding_top,
uint32_t input_padding_right,
uint32_t input_padding_bottom,
uint32_t input_padding_left,
uint32_t pooling_height,
uint32_t pooling_width,
uint32_t stride_height,
uint32_t stride_width,
size_t channels,
size_t input_pixel_stride,
size_t output_pixel_stride,
float output_min,
float output_max,
uint32_t flags,
xnn_operator_t* average_pooling_op_out);
enum xnn_status xnn_reshape_average_pooling2d_nhwc_f32(
xnn_operator_t average_pooling_op,
size_t batch_size,
size_t input_height,
size_t input_width,
size_t* output_height_out,
size_t* output_width_out,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_average_pooling2d_nhwc_f32(
xnn_operator_t average_pooling_op,
const float* input,
float* output);
enum xnn_status xnn_create_bankers_rounding_nc_f32(
size_t channels,
size_t input_stride,
size_t output_stride,
uint32_t flags,
xnn_operator_t* rounding_op_out);
enum xnn_status xnn_reshape_bankers_rounding_nc_f32(
xnn_operator_t rounding_op,
size_t batch_size,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_bankers_rounding_nc_f32(
xnn_operator_t rounding_op,
const float* input,
float* output);
enum xnn_status xnn_run_bankers_rounding_nc_f32(
size_t channels,
size_t input_stride,
size_t output_stride,
size_t batch_size,
const float* input,
float* output,
uint32_t flags,
pthreadpool_t threadpool);
enum xnn_status xnn_create_batch_matrix_multiply_nc_f32(
uint32_t flags,
xnn_operator_t* batch_matrix_multiply_op);
enum xnn_status xnn_reshape_batch_matrix_multiply_nc_f32(
xnn_operator_t batch_matrix_multiply_op,
size_t batch_size,
size_t m,
size_t k,
size_t n,
size_t* workspace_size,
size_t* workspace_alignment,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_batch_matrix_multiply_nc_f32(
xnn_operator_t batch_matrix_multiply_op,
void* workspace,
const float* input1,
const float* input2,
float* output);
enum xnn_status xnn_create_ceiling_nc_f32(
size_t channels,
size_t input_stride,
size_t output_stride,
uint32_t flags,
xnn_operator_t* ceiling_op_out);
enum xnn_status xnn_run_ceiling_nc_f32(
size_t channels,
size_t input_stride,
size_t output_stride,
size_t batch_size,
const float* input,
float* output,
uint32_t flags,
pthreadpool_t threadpool);
enum xnn_status xnn_reshape_ceiling_nc_f32(
xnn_operator_t ceiling_op,
size_t batch_size,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_ceiling_nc_f32(
xnn_operator_t ceiling_op,
const float* input,
float* output);
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);
enum xnn_status xnn_reshape_clamp_nc_f32(
xnn_operator_t clamp_op,
size_t batch_size,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_clamp_nc_f32(
xnn_operator_t clamp_op,
const float* input,
float* output);
enum xnn_status xnn_run_clamp_nc_f32(
size_t channels,
size_t input_stride,
size_t output_stride,
size_t batch_size,
const float* input,
float* output,
float output_min,
float output_max,
uint32_t flags,
pthreadpool_t threadpool);
enum xnn_status xnn_create_convolution2d_nhwc_f32(
uint32_t input_padding_top,
uint32_t input_padding_right,
uint32_t input_padding_bottom,
uint32_t input_padding_left,
uint32_t kernel_height,
uint32_t kernel_width,
uint32_t subsampling_height,
uint32_t subsampling_width,
uint32_t dilation_height,
uint32_t dilation_width,
uint32_t groups,
size_t group_input_channels,
size_t group_output_channels,
size_t input_channel_stride,
size_t output_channel_stride,
const float* kernel,
const float* bias,
float output_min,
float output_max,
uint32_t flags,
xnn_code_cache_t code_cache,
xnn_weights_cache_t weights_cache,
xnn_operator_t* convolution_op_out);
// Forward declare.
struct xnn_post_operation;
/// Create a convolution operator with a number of post operations. The
/// convolution operator created using this function does not have output_min
/// and output_max. The list of operators in post_operations will be applied in
/// order. Convolution with post operations is only supported on JIT platforms
/// and when JIT is enabled.
enum xnn_status xnn_create_fused_convolution2d_nhwc_f32(
uint32_t input_padding_top,
uint32_t input_padding_right,
uint32_t input_padding_bottom,
uint32_t input_padding_left,
uint32_t kernel_height,
uint32_t kernel_width,
uint32_t subsampling_height,
uint32_t subsampling_width,
uint32_t dilation_height,
uint32_t dilation_width,
uint32_t groups,
size_t group_input_channels,
size_t group_output_channels,
size_t input_channel_stride,
size_t output_channel_stride,
const float* kernel,
const float* bias,
size_t num_post_operations,
struct xnn_post_operation* post_operations,
uint32_t flags,
xnn_code_cache_t code_cache,
xnn_weights_cache_t weights_cache,
xnn_operator_t* convolution_op_out);
enum xnn_status xnn_reshape_convolution2d_nhwc_f32(
xnn_operator_t convolution_op,
size_t batch_size,
size_t input_height,
size_t input_width,
size_t* output_height_out,
size_t* output_width_out,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_convolution2d_nhwc_f32(
xnn_operator_t convolution_op,
const float* input,
float* output);
enum xnn_status xnn_create_deconvolution2d_nhwc_f32(
uint32_t output_padding_top,
uint32_t output_padding_right,
uint32_t output_padding_bottom,
uint32_t output_padding_left,
uint32_t kernel_height,
uint32_t kernel_width,
uint32_t stride_height,
uint32_t stride_width,
uint32_t dilation_height,
uint32_t dilation_width,
uint32_t groups,
size_t group_input_channels,
size_t group_output_channels,
size_t input_pixel_stride,
size_t output_pixel_stride,
const float* kernel,
const float* bias,
float output_min,
float output_max,
uint32_t flags,
xnn_code_cache_t code_cache,
xnn_weights_cache_t weights_cache,
xnn_operator_t* deconvolution_op_out);
enum xnn_status xnn_reshape_deconvolution2d_nhwc_f32(
xnn_operator_t deconvolution_op,
size_t batch_size,
size_t input_height,
size_t input_width,
uint32_t adjustment_height,
uint32_t adjustment_width,
size_t* output_height_out,
size_t* output_width_out,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_deconvolution2d_nhwc_f32(
xnn_operator_t deconvolution_op,
const float* input,
float* output);
enum xnn_status xnn_create_divide_nd_f32(
float output_min,
float output_max,
uint32_t flags,
xnn_operator_t* divide_op_out);
enum xnn_status xnn_reshape_divide_nd_f32(
xnn_operator_t divide_op,
size_t num_input1_dims,
const size_t* input1_shape,
size_t num_input2_dims,
const size_t* input2_shape,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_divide_nd_f32(
xnn_operator_t divide_op,
const float* input1,
const float* input2,
float* output);
enum xnn_status xnn_run_divide_nd_f32(
size_t num_input1_dims,
const size_t* input1_shape,
size_t num_input2_dims,
const size_t* input2_shape,
const float* input1,
const float* input2,
float* output,
float output_min,
float output_max,
uint32_t flags,
pthreadpool_t threadpool);
enum xnn_status xnn_create_dynamic_fully_connected_nc_f32(
float output_min,
float output_max,
uint32_t flags,
xnn_operator_t* dynamic_fully_connected_op_out);
enum xnn_status xnn_reshape_dynamic_fully_connected_nc_f32(
xnn_operator_t dynamic_fully_connected_op,
size_t batch_size,
size_t input_channels,
size_t output_channels,
size_t input_stride,
size_t output_stride,
size_t* workspace_size,
size_t* workspace_alignment,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_dynamic_fully_connected_nc_f32(
xnn_operator_t dynamic_fully_connected_op,
void* workspace,
const float* input,
const float* kernel,
const float* bias,
float* output);
enum xnn_status xnn_create_elu_nc_f32(
size_t channels,
size_t input_stride,
size_t output_stride,
float alpha,
uint32_t flags,
xnn_operator_t* elu_op_out);
enum xnn_status xnn_reshape_elu_nc_f32(
xnn_operator_t elu_op,
size_t batch_size,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_elu_nc_f32(
xnn_operator_t elu_op,
const float* input,
float* output);
enum xnn_status xnn_run_elu_nc_f32(
size_t channels,
size_t input_stride,
size_t output_stride,
size_t batch_size,
const float* input,
float* output,
float alpha,
uint32_t flags,
pthreadpool_t threadpool);
enum xnn_status xnn_create_floor_nc_f32(
size_t channels,
size_t input_stride,
size_t output_stride,
uint32_t flags,
xnn_operator_t* floor_op_out);
enum xnn_status xnn_reshape_floor_nc_f32(
xnn_operator_t floor_op,
size_t batch_size,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_floor_nc_f32(
xnn_operator_t floor_op,
const float* input,
float* output);
enum xnn_status xnn_run_floor_nc_f32(
size_t channels,
size_t input_stride,
size_t output_stride,
size_t batch_size,
const float* input,
float* output,
uint32_t flags,
pthreadpool_t threadpool);
enum xnn_status xnn_create_fully_connected_nc_qd8_f32_qc8w(
size_t input_channels,
size_t output_channels,
size_t input_stride,
size_t output_stride,
const float* kernel_scale,
const int8_t* kernel,
const float* bias,
float output_min,
float output_max,
uint32_t flags,
xnn_code_cache_t code_cache,
xnn_weights_cache_t weights_cache,
xnn_operator_t* fully_connected_op_out);
enum xnn_status xnn_setup_fully_connected_nc_qd8_f32_qc8w(
xnn_operator_t fully_connected_op,
const int8_t* input,
float* output,
const struct xnn_dynamic_quantization_params* quantization_params);
enum xnn_status xnn_reshape_fully_connected_nc_qd8_f32_qc8w(
xnn_operator_t fully_connected_op,
size_t batch_size,
pthreadpool_t threadpool);
enum xnn_status xnn_create_fully_connected_nc_f32(
size_t input_channels,
size_t output_channels,
size_t input_stride,
size_t output_stride,
const float* kernel,
const float* bias,
float output_min,
float output_max,
uint32_t flags,
xnn_code_cache_t code_cache,
xnn_weights_cache_t weights_cache,
xnn_operator_t* fully_connected_op_out);
enum xnn_status xnn_reshape_fully_connected_nc_f32(
xnn_operator_t fully_connected_op,
size_t batch_size,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_fully_connected_nc_f32(
xnn_operator_t fully_connected_op,
const float* input,
float* output);
enum xnn_status xnn_create_fully_connected_nc_f32_qc4w(
size_t input_channels,
size_t output_channels,
size_t input_stride,
size_t output_stride,
const float* kernel_scale,
const uint8_t* kernel,
uint8_t kernel_zero_point,
const float* bias,
float output_min,
float output_max,
uint32_t flags,
xnn_code_cache_t code_cache,
xnn_weights_cache_t weights_cache,
xnn_operator_t* fully_connected_op_out);
enum xnn_status xnn_reshape_fully_connected_nc_f32_qc4w(
xnn_operator_t fully_connected_op,
size_t batch_size,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_fully_connected_nc_f32_qc4w(
xnn_operator_t fully_connected_op,
const float* input,
float* output);
enum xnn_status xnn_create_fully_connected_nc_f32_qc8w(
size_t input_channels,
size_t output_channels,
size_t input_stride,
size_t output_stride,
const float* kernel_scale,
const int8_t* kernel,
const float* bias,
float output_min,
float output_max,
uint32_t flags,
xnn_code_cache_t code_cache,
xnn_weights_cache_t weights_cache,
xnn_operator_t* fully_connected_op_out);
enum xnn_status xnn_reshape_fully_connected_nc_f32_qc8w(
xnn_operator_t fully_connected_op,
size_t batch_size,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_fully_connected_nc_f32_qc8w(
xnn_operator_t fully_connected_op,
const float* input,
float* output);
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);
enum xnn_status xnn_reshape_global_average_pooling_nwc_f32(
xnn_operator_t global_average_pooling_op,
size_t batch_size,
size_t width,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_global_average_pooling_nwc_f32(
xnn_operator_t global_average_pooling_op,
const float* input,
float* output);
enum xnn_status xnn_create_global_sum_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_sum_pooling_op_out);
enum xnn_status xnn_reshape_global_sum_pooling_nwc_f32(
xnn_operator_t global_sum_pooling_op,
size_t batch_size,
size_t width,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_global_sum_pooling_nwc_f32(
xnn_operator_t global_sum_pooling_op,
const float* input,
float* output);
enum xnn_status xnn_create_hardswish_nc_f32(
size_t channels,
size_t input_stride,
size_t output_stride,
uint32_t flags,
xnn_operator_t* hardswish_op_out);
enum xnn_status xnn_reshape_hardswish_nc_f32(
xnn_operator_t hardswish_op,
size_t batch_size,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_hardswish_nc_f32(
xnn_operator_t hardswish_op,
const float* input,
float* output);
enum xnn_status xnn_run_hardswish_nc_f32(
size_t channels,
size_t input_stride,
size_t output_stride,
size_t batch_size,
const float* input,
float* output,
uint32_t flags,
pthreadpool_t threadpool);
enum xnn_status xnn_create_leaky_relu_nc_f32(
size_t channels,
size_t input_stride,
size_t output_stride,
float negative_slope,
uint32_t flags,
xnn_operator_t* leaky_relu_op_out);
enum xnn_status xnn_reshape_leaky_relu_nc_f32(
xnn_operator_t leaky_relu_op,
size_t batch_size,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_leaky_relu_nc_f32(
xnn_operator_t leaky_relu_op,
const float* input,
float* output);
enum xnn_status xnn_run_leaky_relu_nc_f32(
size_t channels,
size_t input_stride,
size_t output_stride,
size_t batch_size,
const float* input,
float* output,
float negative_slope,
uint32_t flags,
pthreadpool_t threadpool);
enum xnn_status xnn_create_max_pooling2d_nhwc_f32(
uint32_t input_padding_top,
uint32_t input_padding_right,
uint32_t input_padding_bottom,
uint32_t input_padding_left,
uint32_t pooling_height,
uint32_t pooling_width,
uint32_t stride_height,
uint32_t stride_width,
uint32_t dilation_height,
uint32_t dilation_width,
size_t channels,
size_t input_pixel_stride,
size_t output_pixel_stride,
float output_min,
float output_max,
uint32_t flags,
xnn_operator_t* max_pooling_op_out);
enum xnn_status xnn_reshape_max_pooling2d_nhwc_f32(
xnn_operator_t max_pooling_op,
size_t batch_size,
size_t input_height,
size_t input_width,
size_t* output_height_out,
size_t* output_width_out,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_max_pooling2d_nhwc_f32(
xnn_operator_t max_pooling_op,
const float* input,
float* output);
enum xnn_status xnn_create_maximum_nd_f32(
uint32_t flags,
xnn_operator_t* maximum_op_out);
enum xnn_status xnn_reshape_maximum_nd_f32(
xnn_operator_t maximum_op,
size_t num_input1_dims,
const size_t* input1_shape,
size_t num_input2_dims,
const size_t* input2_shape,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_maximum_nd_f32(
xnn_operator_t maximum_op,
const float* input1,
const float* input2,
float* output);
enum xnn_status xnn_run_maximum_nd_f32(
size_t num_input1_dims,
const size_t* input1_shape,
size_t num_input2_dims,
const size_t* input2_shape,
const float* input1,
const float* input2,
float* output,
uint32_t flags,
pthreadpool_t threadpool);
enum xnn_status xnn_create_mean_nd_f32(
uint32_t flags,
xnn_operator_t* mean_op_out);
enum xnn_status xnn_reshape_mean_nd_f32(
xnn_operator_t mean_op,
size_t num_reduction_axes,
const size_t* reduction_axes,
size_t num_input_dims,
const size_t* input_shape,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_mean_nd_f32(
xnn_operator_t mean_op,
const float* input,
float* output);
enum xnn_status xnn_create_minimum_nd_f32(
uint32_t flags,
xnn_operator_t* minimum_op_out);
enum xnn_status xnn_reshape_minimum_nd_f32(
xnn_operator_t minimum_op,
size_t num_input1_dims,
const size_t* input1_shape,
size_t num_input2_dims,
const size_t* input2_shape,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_minimum_nd_f32(
xnn_operator_t minimum_op,
const float* input1,
const float* input2,
float* output);
enum xnn_status xnn_run_minimum_nd_f32(
size_t num_input1_dims,
const size_t* input1_shape,
size_t num_input2_dims,
const size_t* input2_shape,
const float* input1,
const float* input2,
float* output,
uint32_t flags,
pthreadpool_t threadpool);
enum xnn_status xnn_create_multiply_nd_f32(
float output_min,
float output_max,
uint32_t flags,
xnn_operator_t* multiply_op_out);
enum xnn_status xnn_reshape_multiply_nd_f32(
xnn_operator_t multiply_op,
size_t num_input1_dims,
const size_t* input1_shape,
size_t num_input2_dims,
const size_t* input2_shape,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_multiply_nd_f32(
xnn_operator_t multiply_op,
const float* input1,
const float* input2,
float* output);
enum xnn_status xnn_run_multiply_nd_f32(
size_t num_input1_dims,
const size_t* input1_shape,
size_t num_input2_dims,
const size_t* input2_shape,
const float* input1,
const float* input2,
float* output,
float output_min,
float output_max,
uint32_t flags,
pthreadpool_t threadpool);
enum xnn_status xnn_create_negate_nc_f32(
size_t channels,
size_t input_stride,
size_t output_stride,
uint32_t flags,
xnn_operator_t* negate_op_out);
enum xnn_status xnn_reshape_negate_nc_f32(
xnn_operator_t negate_op,
size_t batch_size,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_negate_nc_f32(
xnn_operator_t negate_op,
const float* input,
float* output);
enum xnn_status xnn_run_negate_nc_f32(
size_t channels,
size_t input_stride,
size_t output_stride,
size_t batch_size,
const float* input,
float* output,
uint32_t flags,
pthreadpool_t threadpool);
enum xnn_status xnn_create_prelu_nc_f32(
size_t channels,
size_t input_stride,
size_t output_stride,
const float* negative_slope,
uint32_t flags,
xnn_code_cache_t code_cache,
xnn_weights_cache_t weights_cache,
xnn_operator_t* prelu_op_out);
enum xnn_status xnn_reshape_prelu_nc_f32(
xnn_operator_t prelu_op,
size_t batch_size,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_prelu_nc_f32(
xnn_operator_t prelu_op,
const float* input,
float* output);
enum xnn_status xnn_create_resize_bilinear2d_nhwc_f32(
size_t channels,
size_t input_pixel_stride,
size_t output_pixel_stride,
uint32_t flags,
xnn_operator_t* resize_op_out);
enum xnn_status xnn_reshape_resize_bilinear2d_nhwc_f32(
xnn_operator_t resize_op,
size_t batch_size,
size_t input_height,
size_t input_width,
size_t output_height,
size_t output_width,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_resize_bilinear2d_nhwc_f32(
xnn_operator_t resize_op,
const float* input,
float* output);
enum xnn_status xnn_create_rope_nthc_f32(
size_t max_sequence_size,
size_t channels,
const float* weights,
uint32_t flags,
xnn_operator_t* rope_op_out);
enum xnn_status xnn_reshape_rope_nthc_f32(
xnn_operator_t rope_op,
size_t batch_size,
size_t sequence_size,
size_t heads,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_rope_nthc_f32(
xnn_operator_t rope_op,
const float* input,
float* output);
enum xnn_status xnn_create_sigmoid_nc_f32(
size_t channels,
size_t input_stride,
size_t output_stride,
uint32_t flags,
xnn_operator_t* sigmoid_op_out);
enum xnn_status xnn_reshape_sigmoid_nc_f32(
xnn_operator_t sigmoid_op,
size_t batch_size,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_sigmoid_nc_f32(
xnn_operator_t sigmoid_op,
const float* input,
float* output);
enum xnn_status xnn_run_sigmoid_nc_f32(
size_t channels,
size_t input_stride,
size_t output_stride,
size_t batch_size,
const float* input,
float* output,
uint32_t flags,
pthreadpool_t threadpool);
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);
enum xnn_status xnn_reshape_softmax_nc_f32(
xnn_operator_t softmax_op,
size_t batch_size,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_softmax_nc_f32(
xnn_operator_t softmax_op,
const float* input,
float* output);
enum xnn_status xnn_create_square_nc_f32(
size_t channels,
size_t input_stride,
size_t output_stride,
uint32_t flags,
xnn_operator_t* square_op_out);
enum xnn_status xnn_reshape_square_nc_f32(
xnn_operator_t square_op,
size_t batch_size,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_square_nc_f32(
xnn_operator_t square_op,
const float* input,
float* output);
enum xnn_status xnn_run_square_nc_f32(
size_t channels,
size_t input_stride,
size_t output_stride,
size_t batch_size,
const float* input,
float* output,
uint32_t flags,
pthreadpool_t threadpool);
enum xnn_status xnn_create_square_root_nc_f32(
size_t channels,
size_t input_stride,
size_t output_stride,
uint32_t flags,
xnn_operator_t* sqrt_op_out);
enum xnn_status xnn_reshape_square_root_nc_f32(
xnn_operator_t sqrt_op,
size_t batch_size,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_square_root_nc_f32(
xnn_operator_t sqrt_op,
const float* input,
float* output);
enum xnn_status xnn_run_square_root_nc_f32(
size_t channels,
size_t input_stride,
size_t output_stride,
size_t batch_size,
const float* input,
float* output,
uint32_t flags,
pthreadpool_t threadpool);
enum xnn_status xnn_create_squared_difference_nd_f32(
uint32_t flags,
xnn_operator_t* squared_difference_op_out);
enum xnn_status xnn_reshape_squared_difference_nd_f32(
xnn_operator_t squared_difference_op,
size_t num_input1_dims,
const size_t* input1_shape,
size_t num_input2_dims,
const size_t* input2_shape,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_squared_difference_nd_f32(
xnn_operator_t squared_difference_op,
const float* input1,
const float* input2,
float* output);
enum xnn_status xnn_run_squared_difference_nd_f32(
size_t num_input1_dims,
const size_t* input1_shape,
size_t num_input2_dims,
const size_t* input2_shape,
const float* input1,
const float* input2,
float* output,
uint32_t flags,
pthreadpool_t threadpool);
enum xnn_status xnn_create_subtract_nd_f32(
float output_min,
float output_max,
uint32_t flags,
xnn_operator_t* subtract_op_out);
enum xnn_status xnn_reshape_subtract_nd_f32(
xnn_operator_t subtract_op,
size_t num_input1_dims,
const size_t* input1_shape,
size_t num_input2_dims,
const size_t* input2_shape,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_subtract_nd_f32(
xnn_operator_t subtract_op,
const float* input1,
const float* input2,
float* output);
enum xnn_status xnn_run_subtract_nd_f32(
size_t num_input1_dims,
const size_t* input1_shape,
size_t num_input2_dims,
const size_t* input2_shape,
const float* input1,
const float* input2,
float* output,
float output_min,
float output_max,
uint32_t flags,
pthreadpool_t threadpool);
enum xnn_status xnn_create_tanh_nc_f32(
size_t channels,
size_t input_stride,
size_t output_stride,
uint32_t flags,
xnn_operator_t* tanh_op_out);
enum xnn_status xnn_reshape_tanh_nc_f32(
xnn_operator_t tanh_op,
size_t batch_size,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_tanh_nc_f32(
xnn_operator_t tanh_op,
const float* input,
float* output);
enum xnn_status xnn_run_tanh_nc_f32(
size_t channels,
size_t input_stride,
size_t output_stride,
size_t batch_size,
const float* input,
float* output,
uint32_t flags,
pthreadpool_t threadpool);
enum xnn_status xnn_create_truncation_nc_f32(
size_t channels,
size_t input_stride,
size_t output_stride,
uint32_t flags,
xnn_operator_t* truncation_op_out);
enum xnn_status xnn_reshape_truncation_nc_f32(
xnn_operator_t truncation_op,
size_t batch_size,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_truncation_nc_f32(
xnn_operator_t truncation_op,
const float* input,
float* output);
enum xnn_status xnn_run_truncation_nc_f32(
size_t channels,
size_t input_stride,
size_t output_stride,
size_t batch_size,
const float* input,
float* output,
uint32_t flags,
pthreadpool_t threadpool);
enum xnn_status xnn_create_depth_to_space_nchw2nhwc_x32(
size_t output_channels,
size_t input_channel_stride,
size_t output_channel_stride,
uint32_t block_size,
uint32_t flags,
xnn_operator_t* depth_to_space_op_out);
enum xnn_status xnn_reshape_depth_to_space_nchw2nhwc_x32(
xnn_operator_t depth_to_space_op,
size_t batch_size,
size_t input_height,
size_t input_width,
size_t* output_height_out,
size_t* output_width_out,
size_t* output_channels_out,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_depth_to_space_nchw2nhwc_x32(
xnn_operator_t depth_to_space_op,
const void* input,
void* output);
enum xnn_status xnn_create_convolution2d_nchw_f32(
uint32_t input_padding_top,
uint32_t input_padding_right,
uint32_t input_padding_bottom,
uint32_t input_padding_left,
uint32_t kernel_height,
uint32_t kernel_width,
uint32_t subsampling_height,
uint32_t subsampling_width,
uint32_t dilation_height,
uint32_t dilation_width,
uint32_t groups,
size_t group_input_channels,
size_t group_output_channels,
size_t input_channel_stride,
size_t output_channel_stride,
const float* kernel,
const float* bias,
float output_min,
float output_max,
uint32_t flags,
xnn_code_cache_t code_cache,
xnn_weights_cache_t weights_cache,
xnn_operator_t* convolution_op_out);
enum xnn_status xnn_reshape_convolution2d_nchw_f32(
xnn_operator_t convolution_op,
size_t batch_size,
size_t input_height,
size_t input_width,
size_t* output_height_out,
size_t* output_width_out,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_convolution2d_nchw_f32(
xnn_operator_t convolution_op,
const float* input,
float* output);
enum xnn_status xnn_create_global_average_pooling_ncw_f32(
size_t channels,
float output_min,
float output_max,
uint32_t flags,
xnn_operator_t* global_average_pooling_op_out);
enum xnn_status xnn_reshape_global_average_pooling_ncw_f32(
xnn_operator_t global_average_pooling_op,
size_t batch_size,
size_t width,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_global_average_pooling_ncw_f32(
xnn_operator_t global_average_pooling_op,
const float* input,
float* output);
enum xnn_status xnn_create_resize_bilinear2d_nchw_f32(
size_t channels,
size_t input_pixel_stride,
size_t output_pixel_stride,
uint32_t flags,
xnn_operator_t* resize_op_out);
enum xnn_status xnn_reshape_resize_bilinear2d_nchw_f32(
xnn_operator_t resize_op,
size_t batch_size,
size_t input_height,
size_t input_width,
size_t output_height,
size_t output_width,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_resize_bilinear2d_nchw_f32(
xnn_operator_t resize_op,
const float* input,
float* output);
enum xnn_status xnn_create_channel_shuffle_nc_x32(
size_t groups,
size_t group_channels,
size_t input_stride,
size_t output_stride,
uint32_t flags,
xnn_operator_t* channel_shuffle_op_out);
enum xnn_status xnn_reshape_channel_shuffle_nc_x32(
xnn_operator_t channel_shuffle_op,
size_t batch_size,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_channel_shuffle_nc_x32(
xnn_operator_t channel_shuffle_op,
const void* input,
void* output);
enum xnn_status xnn_create_constant_pad_nd_x32(
const void* padding_value,
uint32_t flags,
xnn_operator_t* constant_pad_op_out);
enum xnn_status xnn_reshape_constant_pad_nd_x32(
xnn_operator_t constant_pad_op,
size_t num_dims,
const size_t* input_shape,
const size_t* pre_padding,
const size_t* post_padding,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_constant_pad_nd_x32(
xnn_operator_t constant_pad_op,
const void* input,
void* output);
enum xnn_status xnn_run_constant_pad_nd_x32(
uint32_t flags,
size_t num_dims,
const size_t* input_shape,
const size_t* pre_paddings,
const size_t* post_paddings,
const void* input,
void* output,
const void* padding_value,
pthreadpool_t threadpool);
enum xnn_status xnn_create_copy_nc_x32(
size_t channels,
size_t input_stride,
size_t output_stride,
uint32_t flags,
xnn_operator_t* copy_op_out);
enum xnn_status xnn_reshape_copy_nc_x32(
xnn_operator_t copy_op,
size_t batch_size,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_copy_nc_x32(
xnn_operator_t copy_op,
const void* input,
void* output);
enum xnn_status xnn_run_copy_nc_x32(
size_t channels,
size_t input_stride,
size_t output_stride,
size_t batch_size,
const uint32_t* input,
uint32_t* output,
uint32_t flags,
pthreadpool_t threadpool);
enum xnn_status xnn_create_depth_to_space_nhwc_x32(
size_t output_channels,
size_t input_channel_stride,
size_t output_channel_stride,
uint32_t block_size,
uint32_t flags,
xnn_operator_t* depth_to_space_op_out);
enum xnn_status xnn_reshape_depth_to_space_nhwc_x32(
xnn_operator_t depth_to_space_op,
size_t batch_size,
size_t input_height,
size_t input_width,
size_t* output_height_out,
size_t* output_width_out,
size_t* output_channels_out,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_depth_to_space_nhwc_x32(
xnn_operator_t depth_to_space_op,
const void* input,
void* output);
enum xnn_status xnn_create_slice_nd_x32(
uint32_t flags,
xnn_operator_t* slice_op_out);
enum xnn_status xnn_reshape_slice_nd_x32(
xnn_operator_t slice_op,
size_t num_dims,
const size_t* input_shape,
const size_t* offsets,
const size_t* sizes,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_slice_nd_x32(
xnn_operator_t slice_op,
const void* input,
void* output);
enum xnn_status xnn_run_slice_nd_x32(
size_t num_dims,
const size_t* input_shape,
const size_t* offsets,
const size_t* sizes,
const void* input,
void* output,
uint32_t flags,
pthreadpool_t threadpool);
enum xnn_status xnn_create_space_to_depth_nhwc_x32(
size_t input_channels,
size_t input_channel_stride,
size_t output_channel_stride,
uint32_t block_size,
uint32_t flags,
xnn_operator_t* space_to_depth_op_out);
enum xnn_status xnn_reshape_space_to_depth_nhwc_x32(
xnn_operator_t space_to_depth_op,
size_t batch_size,
size_t input_height,
size_t input_width,
size_t* output_height_out,
size_t* output_width_out,
size_t* output_channels_out,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_space_to_depth_nhwc_x32(
xnn_operator_t space_to_depth_op,
const void* input,
void* output);
enum xnn_status xnn_create_transpose_nd_x32(
uint32_t flags,
xnn_operator_t* transpose_op_out);
enum xnn_status xnn_reshape_transpose_nd_x32(
xnn_operator_t transpose_op,
size_t num_dims,
const size_t* input_shape,
const size_t* output_perm,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_transpose_nd_x32(
xnn_operator_t transpose_op,
const void* input,
void* output);
enum xnn_status xnn_run_transpose_nd_x32(
const void* input,
void* output,
size_t num_dims,
const size_t* input_shape,
const size_t* output_perm,
uint32_t flags,
pthreadpool_t threadpool);
enum xnn_status xnn_create_unpooling2d_nhwc_x32(
uint32_t input_padding_top,
uint32_t input_padding_right,
uint32_t input_padding_bottom,
uint32_t input_padding_left,
uint32_t pooling_height,
uint32_t pooling_width,
size_t channels,
size_t input_pixel_stride,
size_t output_pixel_stride,
uint32_t flags,
xnn_operator_t* unpooling_op_out);
enum xnn_status xnn_reshape_unpooling2d_nhwc_x32(
xnn_operator_t unpooling_op,
size_t batch_size,
size_t input_height,
size_t input_width,
size_t* output_height_out,
size_t* output_width_out,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_unpooling2d_nhwc_x32(
xnn_operator_t unpooling_op,
const void* input,
const uint32_t* index,
void* output);
enum xnn_status xnn_create_abs_nc_f16(
size_t channels,
size_t input_stride,
size_t output_stride,
uint32_t flags,
xnn_operator_t* abs_op_out);
enum xnn_status xnn_reshape_abs_nc_f16(
xnn_operator_t abs_op,
size_t batch_size,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_abs_nc_f16(
xnn_operator_t abs_op,
const void* input,
void* output);
enum xnn_status xnn_create_add_nd_f16(
float output_min,
float output_max,
uint32_t flags,
xnn_operator_t* add_op_out);
enum xnn_status xnn_reshape_add_nd_f16(
xnn_operator_t add_op,
size_t num_input1_dims,
const size_t* input1_shape,
size_t num_input2_dims,
const size_t* input2_shape,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_add_nd_f16(
xnn_operator_t add_op,
const void* input1,
const void* input2,
void* output);
enum xnn_status xnn_create_average_pooling2d_nhwc_f16(
uint32_t input_padding_top,
uint32_t input_padding_right,
uint32_t input_padding_bottom,
uint32_t input_padding_left,
uint32_t pooling_height,
uint32_t pooling_width,
uint32_t stride_height,
uint32_t stride_width,
size_t channels,
size_t input_pixel_stride,
size_t output_pixel_stride,
float output_min,
float output_max,
uint32_t flags,
xnn_operator_t* average_pooling_op_out);
enum xnn_status xnn_reshape_average_pooling2d_nhwc_f16(
xnn_operator_t average_pooling_op,
size_t batch_size,
size_t input_height,
size_t input_width,
size_t* output_height_out,
size_t* output_width_out,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_average_pooling2d_nhwc_f16(
xnn_operator_t average_pooling_op,
const void* input,
void* output);
enum xnn_status xnn_create_bankers_rounding_nc_f16(
size_t channels,
size_t input_stride,
size_t output_stride,
uint32_t flags,
xnn_operator_t* rounding_op_out);
enum xnn_status xnn_reshape_bankers_rounding_nc_f16(
xnn_operator_t rounding_op,
size_t batch_size,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_bankers_rounding_nc_f16(
xnn_operator_t rounding_op,
const void* input,
void* output);
enum xnn_status xnn_create_ceiling_nc_f16(
size_t channels,
size_t input_stride,
size_t output_stride,
uint32_t flags,
xnn_operator_t* ceiling_op_out);
enum xnn_status xnn_reshape_ceiling_nc_f16(
xnn_operator_t ceiling_op,
size_t batch_size,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_ceiling_nc_f16(
xnn_operator_t ceiling_op,
const void* input,
void* output);
enum xnn_status xnn_create_clamp_nc_f16(
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);
enum xnn_status xnn_reshape_clamp_nc_f16(
xnn_operator_t clamp_op,
size_t batch_size,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_clamp_nc_f16(
xnn_operator_t clamp_op,
const void* input,
void* output);
enum xnn_status xnn_create_convolution2d_nhwc_f16(
uint32_t input_padding_top,
uint32_t input_padding_right,
uint32_t input_padding_bottom,
uint32_t input_padding_left,
uint32_t kernel_height,
uint32_t kernel_width,
uint32_t subsampling_height,
uint32_t subsampling_width,
uint32_t dilation_height,
uint32_t dilation_width,
uint32_t groups,
size_t group_input_channels,
size_t group_output_channels,
size_t input_channel_stride,
size_t output_channel_stride,
const void* kernel,
const void* bias,
float output_min,
float output_max,
uint32_t flags,
xnn_code_cache_t code_cache,
xnn_weights_cache_t weights_cache,
xnn_operator_t* convolution_op_out);
enum xnn_status xnn_reshape_convolution2d_nhwc_f16(
xnn_operator_t convolution_op,
size_t batch_size,
size_t input_height,
size_t input_width,
size_t* output_height_out,
size_t* output_width_out,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_convolution2d_nhwc_f16(
xnn_operator_t convolution_op,
const void* input,
void* output);
enum xnn_status xnn_create_deconvolution2d_nhwc_f16(
uint32_t output_padding_top,
uint32_t output_padding_right,
uint32_t output_padding_bottom,
uint32_t output_padding_left,
uint32_t kernel_height,
uint32_t kernel_width,
uint32_t stride_height,
uint32_t stride_width,
uint32_t dilation_height,
uint32_t dilation_width,
uint32_t groups,
size_t group_input_channels,
size_t group_output_channels,
size_t input_pixel_stride,
size_t output_pixel_stride,
const void* kernel,
const void* bias,
float output_min,
float output_max,
uint32_t flags,
xnn_code_cache_t code_cache,
xnn_weights_cache_t weights_cache,
xnn_operator_t* deconvolution_op_out);
enum xnn_status xnn_reshape_deconvolution2d_nhwc_f16(
xnn_operator_t deconvolution_op,
size_t batch_size,
size_t input_height,
size_t input_width,
uint32_t adjustment_height,
uint32_t adjustment_width,
size_t* output_height_out,
size_t* output_width_out,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_deconvolution2d_nhwc_f16(
xnn_operator_t deconvolution_op,
const void* input,
void* output);
enum xnn_status xnn_create_divide_nd_f16(
float output_min,
float output_max,
uint32_t flags,
xnn_operator_t* divide_op_out);
enum xnn_status xnn_reshape_divide_nd_f16(
xnn_operator_t divide_op,
size_t num_input1_dims,
const size_t* input1_shape,
size_t num_input2_dims,
const size_t* input2_shape,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_divide_nd_f16(
xnn_operator_t divide_op,
const void* input1,
const void* input2,
void* output);
enum xnn_status xnn_create_dynamic_fully_connected_nc_f16(
float output_min,
float output_max,
uint32_t flags,
xnn_operator_t* dynamic_fully_connected_op_out);
enum xnn_status xnn_reshape_dynamic_fully_connected_nc_f16(
xnn_operator_t dynamic_fully_connected_op,
size_t batch_size,
size_t input_channels,
size_t output_channels,
size_t input_stride,
size_t output_stride,
size_t* workspace_size,
size_t* workspace_alignment,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_dynamic_fully_connected_nc_f16(
xnn_operator_t dynamic_fully_connected_op,
void* workspace,
const void* input,
const void* kernel,
const void* bias,
void* output);
enum xnn_status xnn_create_elu_nc_f16(
size_t channels,
size_t input_stride,
size_t output_stride,
float alpha,
uint32_t flags,
xnn_operator_t* elu_op_out);
enum xnn_status xnn_reshape_elu_nc_f16(
xnn_operator_t elu_op,
size_t batch_size,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_elu_nc_f16(
xnn_operator_t elu_op,
const void* input,
void* output);
enum xnn_status xnn_create_floor_nc_f16(
size_t channels,
size_t input_stride,
size_t output_stride,
uint32_t flags,
xnn_operator_t* floor_op_out);
enum xnn_status xnn_reshape_floor_nc_f16(
xnn_operator_t floor_op,
size_t batch_size,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_floor_nc_f16(
xnn_operator_t floor_op,
const void* input,
void* output);
enum xnn_status xnn_create_fully_connected_nc_f16(
size_t input_channels,
size_t output_channels,
size_t input_stride,
size_t output_stride,
const void* kernel,
const void* bias,
float output_min,
float output_max,
uint32_t flags,
xnn_code_cache_t code_cache,
xnn_weights_cache_t weights_cache,
xnn_operator_t* fully_connected_op_out);
enum xnn_status xnn_reshape_fully_connected_nc_f16(
xnn_operator_t fully_connected_op,
size_t batch_size,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_fully_connected_nc_f16(
xnn_operator_t fully_connected_op,
const void* input,
void* output);
enum xnn_status xnn_create_global_average_pooling_nwc_f16(
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);
enum xnn_status xnn_reshape_global_average_pooling_nwc_f16(
xnn_operator_t global_average_pooling_op,
size_t batch_size,
size_t width,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_global_average_pooling_nwc_f16(
xnn_operator_t global_average_pooling_op,
const void* input,
void* output);
enum xnn_status xnn_create_global_sum_pooling_nwc_f16(
size_t channels,
size_t input_stride,
size_t output_stride,
float output_min,
float output_max,
uint32_t flags,
xnn_operator_t* global_sum_pooling_op_out);
enum xnn_status xnn_reshape_global_sum_pooling_nwc_f16(
xnn_operator_t global_sum_pooling_op,
size_t batch_size,
size_t width,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_global_sum_pooling_nwc_f16(
xnn_operator_t global_sum_pooling_op,
const void* input,
void* output);
enum xnn_status xnn_create_hardswish_nc_f16(
size_t channels,
size_t input_stride,
size_t output_stride,
uint32_t flags,
xnn_operator_t* hardswish_op_out);
enum xnn_status xnn_reshape_hardswish_nc_f16(
xnn_operator_t hardswish_op,
size_t batch_size,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_hardswish_nc_f16(
xnn_operator_t hardswish_op,
const void* input,
void* output);
enum xnn_status xnn_create_leaky_relu_nc_f16(
size_t channels,
size_t input_stride,
size_t output_stride,
float negative_slope,
uint32_t flags,
xnn_operator_t* leaky_relu_op_out);
enum xnn_status xnn_reshape_leaky_relu_nc_f16(
xnn_operator_t leaky_relu_op,
size_t batch_size,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_leaky_relu_nc_f16(
xnn_operator_t leaky_relu_op,
const void* input,
void* output);
enum xnn_status xnn_create_max_pooling2d_nhwc_f16(
uint32_t input_padding_top,
uint32_t input_padding_right,
uint32_t input_padding_bottom,
uint32_t input_padding_left,
uint32_t pooling_height,
uint32_t pooling_width,
uint32_t stride_height,
uint32_t stride_width,
uint32_t dilation_height,
uint32_t dilation_width,
size_t channels,
size_t input_pixel_stride,
size_t output_pixel_stride,
float output_min,
float output_max,
uint32_t flags,
xnn_operator_t* max_pooling_op_out);
enum xnn_status xnn_reshape_max_pooling2d_nhwc_f16(
xnn_operator_t max_pooling_op,
size_t batch_size,
size_t input_height,
size_t input_width,
size_t* output_height_out,
size_t* output_width_out,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_max_pooling2d_nhwc_f16(
xnn_operator_t max_pooling_op,
const void* input,
void* output);
enum xnn_status xnn_create_maximum_nd_f16(
uint32_t flags,
xnn_operator_t* maximum_op_out);
enum xnn_status xnn_reshape_maximum_nd_f16(
xnn_operator_t maximum_op,
size_t num_input1_dims,
const size_t* input1_shape,
size_t num_input2_dims,
const size_t* input2_shape,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_maximum_nd_f16(
xnn_operator_t maximum_op,
const void* input1,
const void* input2,
void* output);
enum xnn_status xnn_create_mean_nd_f16(
uint32_t flags,
xnn_operator_t* mean_op_out);
enum xnn_status xnn_reshape_mean_nd_f16(
xnn_operator_t mean_op,
size_t num_reduction_axes,
const size_t* reduction_axes,
size_t num_input_dims,
const size_t* input_shape,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_mean_nd_f16(
xnn_operator_t mean_op,
const void* input,
void* output);
enum xnn_status xnn_create_minimum_nd_f16(
uint32_t flags,
xnn_operator_t* minimum_op_out);
enum xnn_status xnn_reshape_minimum_nd_f16(
xnn_operator_t minimum_op,
size_t num_input1_dims,
const size_t* input1_shape,
size_t num_input2_dims,
const size_t* input2_shape,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_minimum_nd_f16(
xnn_operator_t minimum_op,
const void* input1,
const void* input2,
void* output);
enum xnn_status xnn_create_multiply_nd_f16(
float output_min,
float output_max,
uint32_t flags,
xnn_operator_t* multiply_op_out);
enum xnn_status xnn_reshape_multiply_nd_f16(
xnn_operator_t multiply_op,
size_t num_input1_dims,
const size_t* input1_shape,
size_t num_input2_dims,
const size_t* input2_shape,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_multiply_nd_f16(
xnn_operator_t multiply_op,
const void* input1,
const void* input2,
void* output);
enum xnn_status xnn_create_negate_nc_f16(
size_t channels,
size_t input_stride,
size_t output_stride,
uint32_t flags,
xnn_operator_t* negate_op_out);
enum xnn_status xnn_reshape_negate_nc_f16(
xnn_operator_t negate_op,
size_t batch_size,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_negate_nc_f16(
xnn_operator_t negate_op,
const void* input,
void* output);
enum xnn_status xnn_create_prelu_nc_f16(
size_t channels,
size_t input_stride,
size_t output_stride,
const void* negative_slope,
uint32_t flags,
xnn_code_cache_t code_cache,
xnn_weights_cache_t weights_cache,
xnn_operator_t* prelu_op_out);
enum xnn_status xnn_reshape_prelu_nc_f16(
xnn_operator_t prelu_op,
size_t batch_size,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_prelu_nc_f16(
xnn_operator_t prelu_op,
const void* input,
void* output);
enum xnn_status xnn_create_resize_bilinear2d_nhwc_f16(
size_t channels,
size_t input_pixel_stride,
size_t output_pixel_stride,
uint32_t flags,
xnn_operator_t* resize_op_out);
enum xnn_status xnn_reshape_resize_bilinear2d_nhwc_f16(
xnn_operator_t resize_op,
size_t batch_size,
size_t input_height,
size_t input_width,
size_t output_height,
size_t output_width,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_resize_bilinear2d_nhwc_f16(
xnn_operator_t resize_op,
const void* input,
void* output);
enum xnn_status xnn_create_sigmoid_nc_f16(
size_t channels,
size_t input_stride,
size_t output_stride,
uint32_t flags,
xnn_operator_t* sigmoid_op_out);
enum xnn_status xnn_reshape_sigmoid_nc_f16(
xnn_operator_t sigmoid_op,
size_t batch_size,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_sigmoid_nc_f16(
xnn_operator_t sigmoid_op,
const void* input,
void* output);
enum xnn_status xnn_create_softmax_nc_f16(
size_t channels,
size_t input_stride,
size_t output_stride,
uint32_t flags,
xnn_operator_t* softmax_op_out);
enum xnn_status xnn_reshape_softmax_nc_f16(
xnn_operator_t softmax_op,
size_t batch_size,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_softmax_nc_f16(
xnn_operator_t softmax_op,
const void* input,
void* output);
enum xnn_status xnn_create_square_nc_f16(
size_t channels,
size_t input_stride,
size_t output_stride,
uint32_t flags,
xnn_operator_t* square_op_out);
enum xnn_status xnn_reshape_square_nc_f16(
xnn_operator_t square_op,
size_t batch_size,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_square_nc_f16(
xnn_operator_t square_op,
const void* input,
void* output);
enum xnn_status xnn_create_square_root_nc_f16(
size_t channels,
size_t input_stride,
size_t output_stride,
uint32_t flags,
xnn_operator_t* sqrt_op_out);
enum xnn_status xnn_reshape_square_root_nc_f16(
xnn_operator_t sqrt_op,
size_t batch_size,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_square_root_nc_f16(
xnn_operator_t sqrt_op,
const void* input,
void* output);
enum xnn_status xnn_create_squared_difference_nd_f16(
uint32_t flags,
xnn_operator_t* squared_difference_op_out);
enum xnn_status xnn_reshape_squared_difference_nd_f16(
xnn_operator_t squared_difference_op,
size_t num_input1_dims,
const size_t* input1_shape,
size_t num_input2_dims,
const size_t* input2_shape,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_squared_difference_nd_f16(
xnn_operator_t squared_difference_op,
const void* input1,
const void* input2,
void* output);
enum xnn_status xnn_create_subtract_nd_f16(
float output_min,
float output_max,
uint32_t flags,
xnn_operator_t* subtract_op_out);
enum xnn_status xnn_reshape_subtract_nd_f16(
xnn_operator_t subtract_op,
size_t num_input1_dims,
const size_t* input1_shape,
size_t num_input2_dims,
const size_t* input2_shape,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_subtract_nd_f16(
xnn_operator_t subtract_op,
const void* input1,
const void* input2,
void* output);
enum xnn_status xnn_create_tanh_nc_f16(
size_t channels,
size_t input_stride,
size_t output_stride,
uint32_t flags,
xnn_operator_t* tanh_op_out);
enum xnn_status xnn_reshape_tanh_nc_f16(
xnn_operator_t tanh_op,
size_t batch_size,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_tanh_nc_f16(
xnn_operator_t tanh_op,
const void* input,
void* output);
enum xnn_status xnn_create_truncation_nc_f16(
size_t channels,
size_t input_stride,
size_t output_stride,
uint32_t flags,
xnn_operator_t* truncation_op_out);
enum xnn_status xnn_reshape_truncation_nc_f16(
xnn_operator_t truncation_op,
size_t batch_size,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_truncation_nc_f16(
xnn_operator_t truncation_op,
const void* input,
void* output);
enum xnn_status xnn_create_convolution2d_nchw_f16(
uint32_t input_padding_top,
uint32_t input_padding_right,
uint32_t input_padding_bottom,
uint32_t input_padding_left,
uint32_t kernel_height,
uint32_t kernel_width,
uint32_t subsampling_height,
uint32_t subsampling_width,
uint32_t dilation_height,
uint32_t dilation_width,
uint32_t groups,
size_t group_input_channels,
size_t group_output_channels,
size_t input_channel_stride,
size_t output_channel_stride,
const void* kernel,
const void* bias,
float output_min,
float output_max,
uint32_t flags,
xnn_code_cache_t code_cache,
xnn_weights_cache_t weights_cache,
xnn_operator_t* convolution_op_out);
enum xnn_status xnn_reshape_convolution2d_nchw_f16(
xnn_operator_t convolution_op,
size_t batch_size,
size_t input_height,
size_t input_width,
size_t* output_height_out,
size_t* output_width_out,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_convolution2d_nchw_f16(
xnn_operator_t convolution_op,
const void* input,
void* output);
enum xnn_status xnn_create_depth_to_space_nchw2nhwc_x16(
size_t output_channels,
size_t input_channel_stride,
size_t output_channel_stride,
uint32_t block_size,
uint32_t flags,
xnn_operator_t* depth_to_space_op_out);
enum xnn_status xnn_reshape_depth_to_space_nchw2nhwc_x16(
xnn_operator_t depth_to_space_op,
size_t batch_size,
size_t input_height,
size_t input_width,
size_t* output_height_out,
size_t* output_width_out,
size_t* output_channels_out,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_depth_to_space_nchw2nhwc_x16(
xnn_operator_t depth_to_space_op,
const void* input,
void* output);
enum xnn_status xnn_create_global_average_pooling_ncw_f16(
size_t channels,
float output_min,
float output_max,
uint32_t flags,
xnn_operator_t* global_average_pooling_op_out);
enum xnn_status xnn_reshape_global_average_pooling_ncw_f16(
xnn_operator_t global_average_pooling_op,
size_t batch_size,
size_t width,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_global_average_pooling_ncw_f16(
xnn_operator_t global_average_pooling_op,
const void* input,
void* output);
enum xnn_status xnn_create_resize_bilinear2d_nchw_f16(
size_t channels,
size_t input_pixel_stride,
size_t output_pixel_stride,
uint32_t flags,
xnn_operator_t* resize_op_out);
enum xnn_status xnn_reshape_resize_bilinear2d_nchw_f16(
xnn_operator_t resize_op,
size_t batch_size,
size_t input_height,
size_t input_width,
size_t output_height,
size_t output_width,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_resize_bilinear2d_nchw_f16(
xnn_operator_t resize_op,
const void* input,
void* output);
enum xnn_status xnn_create_constant_pad_nd_x16(
const void* padding_value,
uint32_t flags,
xnn_operator_t* constant_pad_op_out);
enum xnn_status xnn_reshape_constant_pad_nd_x16(
xnn_operator_t constant_pad_op,
size_t num_dims,
const size_t* input_shape,
const size_t* pre_padding,
const size_t* post_padding,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_constant_pad_nd_x16(
xnn_operator_t constant_pad_op,
const void* input,
void* output);
enum xnn_status xnn_run_constant_pad_nd_x16(
uint32_t flags,
size_t num_dims,
const size_t* input_shape,
const size_t* pre_paddings,
const size_t* post_paddings,
const void* input,
void* output,
const void* padding_value,
pthreadpool_t threadpool);
enum xnn_status xnn_create_copy_nc_x16(
size_t channels,
size_t input_stride,
size_t output_stride,
uint32_t flags,
xnn_operator_t* copy_op_out);
enum xnn_status xnn_reshape_copy_nc_x16(
xnn_operator_t copy_op,
size_t batch_size,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_copy_nc_x16(
xnn_operator_t copy_op,
const void* input,
void* output);
enum xnn_status xnn_create_depth_to_space_nhwc_x16(
size_t output_channels,
size_t input_channel_stride,
size_t output_channel_stride,
uint32_t block_size,
uint32_t flags,
xnn_operator_t* depth_to_space_op_out);
enum xnn_status xnn_reshape_depth_to_space_nhwc_x16(
xnn_operator_t depth_to_space_op,
size_t batch_size,
size_t input_height,
size_t input_width,
size_t* output_height_out,
size_t* output_width_out,
size_t* output_channels_out,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_depth_to_space_nhwc_x16(
xnn_operator_t depth_to_space_op,
const void* input,
void* output);
enum xnn_status xnn_create_slice_nd_x16(
uint32_t flags,
xnn_operator_t* slice_op_out);
enum xnn_status xnn_reshape_slice_nd_x16(
xnn_operator_t slice_op,
size_t num_dims,
const size_t* input_shape,
const size_t* offsets,
const size_t* sizes,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_slice_nd_x16(
xnn_operator_t slice_op,
const void* input,
void* output);
enum xnn_status xnn_create_space_to_depth_nhwc_x16(
size_t input_channels,
size_t input_channel_stride,
size_t output_channel_stride,
uint32_t block_size,
uint32_t flags,
xnn_operator_t* space_to_depth_op_out);
enum xnn_status xnn_reshape_space_to_depth_nhwc_x16(
xnn_operator_t space_to_depth_op,
size_t batch_size,
size_t input_height,
size_t input_width,
size_t* output_height_out,
size_t* output_width_out,
size_t* output_channels_out,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_space_to_depth_nhwc_x16(
xnn_operator_t space_to_depth_op,
const void* input,
void* output);
enum xnn_status xnn_create_transpose_nd_x16(
uint32_t flags,
xnn_operator_t* transpose_op_out);
enum xnn_status xnn_reshape_transpose_nd_x16(
xnn_operator_t transpose_op,
size_t num_dims,
const size_t* input_shape,
const size_t* output_perm,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_transpose_nd_x16(
xnn_operator_t transpose_op,
const void* input,
void* output);
enum xnn_status xnn_run_transpose_nd_x16(
const void* input,
void* output,
size_t num_dims,
const size_t* input_shape,
const size_t* output_perm,
uint32_t flags,
pthreadpool_t threadpool);
enum xnn_status xnn_create_convolution2d_nhwc_qs8_qc8w(
uint32_t input_padding_top,
uint32_t input_padding_right,
uint32_t input_padding_bottom,
uint32_t input_padding_left,
uint32_t kernel_height,
uint32_t kernel_width,
uint32_t subsampling_height,
uint32_t subsampling_width,
uint32_t dilation_height,
uint32_t dilation_width,
uint32_t groups,
size_t group_input_channels,
size_t group_output_channels,
size_t input_channel_stride,
size_t output_channel_stride,
int8_t input_zero_point,
float input_scale,
const float* kernel_scale,
const int8_t* kernel,
const int32_t* bias,
int8_t output_zero_point,
float output_scale,
int8_t output_min,
int8_t output_max,
uint32_t flags,
xnn_code_cache_t code_cache,
xnn_weights_cache_t weights_cache,
xnn_operator_t* convolution_op_out);
enum xnn_status xnn_reshape_convolution2d_nhwc_qs8_qc8w(
xnn_operator_t convolution_op,
size_t batch_size,
size_t input_height,
size_t input_width,
size_t* output_height_out,
size_t* output_width_out,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_convolution2d_nhwc_qs8_qc8w(
xnn_operator_t convolution_op,
const int8_t* input,
int8_t* output);
enum xnn_status xnn_create_add_nd_qs8(
int8_t input1_zero_point,
float input1_scale,
int8_t input2_zero_point,
float input2_scale,
int8_t output_zero_point,
float output_scale,
int8_t output_min,
int8_t output_max,
uint32_t flags,
xnn_operator_t* add_op_out);
enum xnn_status xnn_reshape_add_nd_qs8(
xnn_operator_t add_op,
size_t num_input1_dims,
const size_t* input1_shape,
size_t num_input2_dims,
const size_t* input2_shape,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_add_nd_qs8(
xnn_operator_t add_op,
const int8_t* input1,
const int8_t* input2,
int8_t* output);
enum xnn_status xnn_run_add_nd_qs8(
size_t num_input1_dims,
const size_t* input1_shape,
int8_t input1_zero_point,
float input1_scale,
size_t num_input2_dims,
const size_t* input2_shape,
int8_t input2_zero_point,
float input2_scale,
const int8_t* input1,
const int8_t* input2,
int8_t* output,
int8_t output_zero_point,
float output_scale,
int8_t output_min,
int8_t output_max,
uint32_t flags,
pthreadpool_t threadpool);
enum xnn_status xnn_create_convolution2d_nhwc_qs8(
uint32_t input_padding_top,
uint32_t input_padding_right,
uint32_t input_padding_bottom,
uint32_t input_padding_left,
uint32_t kernel_height,
uint32_t kernel_width,
uint32_t subsampling_height,
uint32_t subsampling_width,
uint32_t dilation_height,
uint32_t dilation_width,
uint32_t groups,
size_t group_input_channels,
size_t group_output_channels,
size_t input_channel_stride,
size_t output_channel_stride,
int8_t input_zero_point,
float input_scale,
float kernel_scale,
const int8_t* kernel,
const int32_t* bias,
int8_t output_zero_point,
float output_scale,
int8_t output_min,
int8_t output_max,
uint32_t flags,
xnn_code_cache_t code_cache,
xnn_weights_cache_t weights_cache,
xnn_operator_t* convolution_op_out);
enum xnn_status xnn_reshape_convolution2d_nhwc_qs8(
xnn_operator_t convolution_op,
size_t batch_size,
size_t input_height,
size_t input_width,
size_t* output_height_out,
size_t* output_width_out,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_convolution2d_nhwc_qs8(
xnn_operator_t convolution_op,
const int8_t* input,
int8_t* output);
enum xnn_status xnn_create_deconvolution2d_nhwc_qs8(
uint32_t output_padding_top,
uint32_t output_padding_right,
uint32_t output_padding_bottom,
uint32_t output_padding_left,
uint32_t kernel_height,
uint32_t kernel_width,
uint32_t stride_height,
uint32_t stride_width,
uint32_t dilation_height,
uint32_t dilation_width,
uint32_t groups,
size_t group_input_channels,
size_t group_output_channels,
size_t input_pixel_stride,
size_t output_pixel_stride,
int8_t input_zero_point,
float input_scale,
float kernel_scale,
const int8_t* kernel,
const int32_t* bias,
int8_t output_zero_point,
float output_scale,
int8_t output_min,
int8_t output_max,
uint32_t flags,
xnn_code_cache_t code_cache,
xnn_weights_cache_t weights_cache,
xnn_operator_t* deconvolution_op_out);
enum xnn_status xnn_reshape_deconvolution2d_nhwc_qs8(
xnn_operator_t deconvolution_op,
size_t batch_size,
size_t input_height,
size_t input_width,
uint32_t adjustment_height,
uint32_t adjustment_width,
size_t* output_height_out,
size_t* output_width_out,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_deconvolution2d_nhwc_qs8(
xnn_operator_t deconvolution_op,
const int8_t* input,
int8_t* output);
enum xnn_status xnn_create_elu_nc_qs8(
size_t channels,
size_t input_stride,
size_t output_stride,
float alpha,
int8_t input_zero_point,
float input_scale,
int8_t output_zero_point,
float output_scale,
int8_t output_min,
int8_t output_max,
uint32_t flags,
xnn_operator_t* elu_op_out);
enum xnn_status xnn_reshape_elu_nc_qs8(
xnn_operator_t elu_op,
size_t batch_size,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_elu_nc_qs8(
xnn_operator_t elu_op,
const int8_t* input,
int8_t* output);
enum xnn_status xnn_create_fully_connected_nc_qs8(
size_t input_channels,
size_t output_channels,
size_t input_stride,
size_t output_stride,
int8_t input_zero_point,
float input_scale,
float kernel_scale,
const int8_t* kernel,
const int32_t* bias,
int8_t output_zero_point,
float output_scale,
int8_t output_min,
int8_t output_max,
uint32_t flags,
xnn_code_cache_t code_cache,
xnn_weights_cache_t weights_cache,
xnn_operator_t* fully_connected_op_out);
enum xnn_status xnn_reshape_fully_connected_nc_qs8(
xnn_operator_t fully_connected_op,
size_t batch_size,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_fully_connected_nc_qs8(
xnn_operator_t fully_connected_op,
const int8_t* input,
int8_t* output);
enum xnn_status xnn_create_global_average_pooling_nwc_qs8(
size_t channels,
size_t input_stride,
size_t output_stride,
int8_t input_zero_point,
float input_scale,
int8_t output_zero_point,
float output_scale,
int8_t output_min,
int8_t output_max,
uint32_t flags,
xnn_operator_t* global_average_pooling_op_out);
enum xnn_status xnn_reshape_global_average_pooling_nwc_qs8(
xnn_operator_t global_average_pooling_op,
size_t batch_size,
size_t width,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_global_average_pooling_nwc_qs8(
xnn_operator_t global_average_pooling_op,
const int8_t* input,
int8_t* output);
enum xnn_status xnn_create_multiply_nd_qs8(
int8_t input1_zero_point,
float input1_scale,
int8_t input2_zero_point,
float input2_scale,
int8_t output_zero_point,
float output_scale,
int8_t output_min,
int8_t output_max,
uint32_t flags,
xnn_operator_t* multiply_op_out);
enum xnn_status xnn_reshape_multiply_nd_qs8(
xnn_operator_t multiply_op,
size_t num_input1_dims,
const size_t* input1_shape,
size_t num_input2_dims,
const size_t* input2_shape,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_multiply_nd_qs8(
xnn_operator_t multiply_op,
const int8_t* input1,
const int8_t* input2,
int8_t* output);
enum xnn_status xnn_run_multiply_nd_qs8(
size_t num_input1_dims,
const size_t* input1_shape,
int8_t input1_zero_point,
float input1_scale,
size_t num_input2_dims,
const size_t* input2_shape,
int8_t input2_zero_point,
float input2_scale,
const int8_t* input1,
const int8_t* input2,
int8_t* output,
int8_t output_zero_point,
float output_scale,
int8_t output_min,
int8_t output_max,
uint32_t flags,
pthreadpool_t threadpool);
enum xnn_status xnn_create_leaky_relu_nc_qs8(
size_t channels,
size_t input_stride,
size_t output_stride,
float negative_slope,
int8_t input_zero_point,
float input_scale,
int8_t output_zero_point,
float output_scale,
uint32_t flags,
xnn_operator_t* leaky_relu_op_out);
enum xnn_status xnn_reshape_leaky_relu_nc_qs8(
xnn_operator_t leaky_relu_op,
size_t batch_size,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_leaky_relu_nc_qs8(
xnn_operator_t leaky_relu_op,
const int8_t* input,
int8_t* output);
enum xnn_status xnn_create_sigmoid_nc_qs8(
size_t channels,
size_t input_stride,
size_t output_stride,
int8_t input_zero_point,
float input_scale,
int8_t output_zero_point,
float output_scale,
int8_t output_min,
int8_t output_max,
uint32_t flags,
xnn_operator_t* sigmoid_op_out);
enum xnn_status xnn_reshape_sigmoid_nc_qs8(
xnn_operator_t sigmoid_op,
size_t batch_size,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_sigmoid_nc_qs8(
xnn_operator_t sigmoid_op,
const int8_t* input,
int8_t* output);
enum xnn_status xnn_create_subtract_nd_qs8(
int8_t input1_zero_point,
float input1_scale,
int8_t input2_zero_point,
float input2_scale,
int8_t output_zero_point,
float output_scale,
int8_t output_min,
int8_t output_max,
uint32_t flags,
xnn_operator_t* subtract_op_out);
enum xnn_status xnn_reshape_subtract_nd_qs8(
xnn_operator_t subtract_op,
size_t num_input1_dims,
const size_t* input1_shape,
size_t num_input2_dims,
const size_t* input2_shape,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_subtract_nd_qs8(
xnn_operator_t subtract_op,
const int8_t* input1,
const int8_t* input2,
int8_t* output);
enum xnn_status xnn_run_subtract_nd_qs8(
size_t num_input1_dims,
const size_t* input1_shape,
int8_t input1_zero_point,
float input1_scale,
size_t num_input2_dims,
const size_t* input2_shape,
int8_t input2_zero_point,
float input2_scale,
const int8_t* input1,
const int8_t* input2,
int8_t* output,
int8_t output_zero_point,
float output_scale,
int8_t output_min,
int8_t output_max,
uint32_t flags,
pthreadpool_t threadpool);
enum xnn_status xnn_create_tanh_nc_qs8(
size_t channels,
size_t input_stride,
size_t output_stride,
int8_t input_zero_point,
float input_scale,
int8_t output_zero_point,
float output_scale,
int8_t output_min,
int8_t output_max,
uint32_t flags,
xnn_operator_t* tanh_op_out);
enum xnn_status xnn_reshape_tanh_nc_qs8(
xnn_operator_t tanh_op,
size_t batch_size,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_tanh_nc_qs8(
xnn_operator_t tanh_op,
const int8_t* input,
int8_t* output);
enum xnn_status xnn_create_add_nd_qu8(
uint8_t input1_zero_point,
float input1_scale,
uint8_t input2_zero_point,
float input2_scale,
uint8_t output_zero_point,
float output_scale,
uint8_t output_min,
uint8_t output_max,
uint32_t flags,
xnn_operator_t* add_op_out);
enum xnn_status xnn_reshape_add_nd_qu8(
xnn_operator_t add_op,
size_t num_input1_dims,
const size_t* input1_shape,
size_t num_input2_dims,
const size_t* input2_shape,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_add_nd_qu8(
xnn_operator_t add_op,
const uint8_t* input1,
const uint8_t* input2,
uint8_t* output);
enum xnn_status xnn_run_add_nd_qu8(
size_t num_input1_dims,
const size_t* input1_shape,
uint8_t input1_zero_point,
float input1_scale,
size_t num_input2_dims,
const size_t* input2_shape,
uint8_t input2_zero_point,
float input2_scale,
const uint8_t* input1,
const uint8_t* input2,
uint8_t* output,
uint8_t output_zero_point,
float output_scale,
uint8_t output_min,
uint8_t output_max,
uint32_t flags,
pthreadpool_t threadpool);
enum xnn_status xnn_create_average_pooling2d_nhwc_qu8(
uint32_t input_padding_top,
uint32_t input_padding_right,
uint32_t input_padding_bottom,
uint32_t input_padding_left,
uint32_t pooling_height,
uint32_t pooling_width,
uint32_t stride_height,
uint32_t stride_width,
size_t channels,
size_t input_pixel_stride,
size_t output_pixel_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* average_pooling_op_out);
enum xnn_status xnn_reshape_average_pooling2d_nhwc_qu8(
xnn_operator_t average_pooling_op,
size_t batch_size,
size_t input_height,
size_t input_width,
size_t* output_height_out,
size_t* output_width_out,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_average_pooling2d_nhwc_qu8(
xnn_operator_t average_pooling_op,
const uint8_t* input,
uint8_t* output);
enum xnn_status xnn_create_convolution2d_nhwc_qu8(
uint32_t input_padding_top,
uint32_t input_padding_right,
uint32_t input_padding_bottom,
uint32_t input_padding_left,
uint32_t kernel_height,
uint32_t kernel_width,
uint32_t subsampling_height,
uint32_t subsampling_width,
uint32_t dilation_height,
uint32_t dilation_width,
uint32_t groups,
size_t group_input_channels,
size_t group_output_channels,
size_t input_channel_stride,
size_t output_channel_stride,
uint8_t input_zero_point,
float input_scale,
uint8_t kernel_zero_point,
float kernel_scale,
const uint8_t* kernel,
const int32_t* bias,
uint8_t output_zero_point,
float output_scale,
uint8_t output_min,
uint8_t output_max,
uint32_t flags,
xnn_code_cache_t code_cache,
xnn_weights_cache_t weights_cache,
xnn_operator_t* convolution_op_out);
enum xnn_status xnn_reshape_convolution2d_nhwc_qu8(
xnn_operator_t convolution_op,
size_t batch_size,
size_t input_height,
size_t input_width,
size_t* output_height_out,
size_t* output_width_out,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_convolution2d_nhwc_qu8(
xnn_operator_t convolution_op,
const uint8_t* input,
uint8_t* output);
enum xnn_status xnn_create_deconvolution2d_nhwc_qu8(
uint32_t output_padding_top,
uint32_t output_padding_right,
uint32_t output_padding_bottom,
uint32_t output_padding_left,
uint32_t kernel_height,
uint32_t kernel_width,
uint32_t stride_height,
uint32_t stride_width,
uint32_t dilation_height,
uint32_t dilation_width,
uint32_t groups,
size_t group_input_channels,
size_t group_output_channels,
size_t input_pixel_stride,
size_t output_pixel_stride,
uint8_t input_zero_point,
float input_scale,
uint8_t kernel_zero_point,
float kernel_scale,
const uint8_t* kernel,
const int32_t* bias,
uint8_t output_zero_point,
float output_scale,
uint8_t output_min,
uint8_t output_max,
uint32_t flags,
xnn_code_cache_t code_cache,
xnn_weights_cache_t weights_cache,
xnn_operator_t* deconvolution_op_out);
enum xnn_status xnn_reshape_deconvolution2d_nhwc_qu8(
xnn_operator_t deconvolution_op,
size_t batch_size,
size_t input_height,
size_t input_width,
uint32_t adjustment_height,
uint32_t adjustment_width,
size_t* output_height_out,
size_t* output_width_out,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_deconvolution2d_nhwc_qu8(
xnn_operator_t deconvolution_op,
const uint8_t* input,
uint8_t* output);
enum xnn_status xnn_create_fully_connected_nc_qu8(
size_t input_channels,
size_t output_channels,
size_t input_stride,
size_t output_stride,
uint8_t input_zero_point,
float input_scale,
uint8_t kernel_zero_point,
float kernel_scale,
const uint8_t* kernel,
const int32_t* bias,
uint8_t output_zero_point,
float output_scale,
uint8_t output_min,
uint8_t output_max,
uint32_t flags,
xnn_code_cache_t code_cache,
xnn_weights_cache_t weights_cache,
xnn_operator_t* fully_connected_op_out);
enum xnn_status xnn_reshape_fully_connected_nc_qu8(
xnn_operator_t fully_connected_op,
size_t batch_size,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_fully_connected_nc_qu8(
xnn_operator_t fully_connected_op,
const uint8_t* input,
uint8_t* output);
enum xnn_status xnn_create_global_average_pooling_nwc_qu8(
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);
enum xnn_status xnn_reshape_global_average_pooling_nwc_qu8(
xnn_operator_t global_average_pooling_op,
size_t batch_size,
size_t width,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_global_average_pooling_nwc_qu8(
xnn_operator_t global_average_pooling_op,
const uint8_t* input,
uint8_t* output);
enum xnn_status xnn_create_leaky_relu_nc_qu8(
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,
uint32_t flags,
xnn_operator_t* leaky_relu_op_out);
enum xnn_status xnn_reshape_leaky_relu_nc_qu8(
xnn_operator_t leaky_relu_op,
size_t batch_size,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_leaky_relu_nc_qu8(
xnn_operator_t leaky_relu_op,
const uint8_t* input,
uint8_t* output);
enum xnn_status xnn_create_multiply_nd_qu8(
uint8_t input1_zero_point,
float input1_scale,
uint8_t input2_zero_point,
float input2_scale,
uint8_t output_zero_point,
float output_scale,
uint8_t output_min,
uint8_t output_max,
uint32_t flags,
xnn_operator_t* multiply_op_out);
enum xnn_status xnn_reshape_multiply_nd_qu8(
xnn_operator_t multiply_op,
size_t num_input1_dims,
const size_t* input1_shape,
size_t num_input2_dims,
const size_t* input2_shape,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_multiply_nd_qu8(
xnn_operator_t multiply_op,
const uint8_t* input1,
const uint8_t* input2,
uint8_t* output);
enum xnn_status xnn_run_multiply_nd_qu8(
size_t num_input1_dims,
const size_t* input1_shape,
uint8_t input1_zero_point,
float input1_scale,
size_t num_input2_dims,
const size_t* input2_shape,
uint8_t input2_zero_point,
float input2_scale,
const uint8_t* input1,
const uint8_t* input2,
uint8_t* output,
uint8_t output_zero_point,
float output_scale,
uint8_t output_min,
uint8_t output_max,
uint32_t flags,
pthreadpool_t threadpool);
enum xnn_status xnn_create_sigmoid_nc_qu8(
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* sigmoid_op_out);
enum xnn_status xnn_reshape_sigmoid_nc_qu8(
xnn_operator_t sigmoid_op,
size_t batch_size,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_sigmoid_nc_qu8(
xnn_operator_t sigmoid_op,
const uint8_t* input,
uint8_t* output);
enum xnn_status xnn_create_softmax_nc_qu8(
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);
enum xnn_status xnn_reshape_softmax_nc_qu8(
xnn_operator_t softmax_op,
size_t batch_size,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_softmax_nc_qu8(
xnn_operator_t softmax_op,
const uint8_t* input,
uint8_t* output);
enum xnn_status xnn_create_subtract_nd_qu8(
uint8_t input1_zero_point,
float input1_scale,
uint8_t input2_zero_point,
float input2_scale,
uint8_t output_zero_point,
float output_scale,
uint8_t output_min,
uint8_t output_max,
uint32_t flags,
xnn_operator_t* subtract_op_out);
enum xnn_status xnn_reshape_subtract_nd_qu8(
xnn_operator_t subtract_op,
size_t num_input1_dims,
const size_t* input1_shape,
size_t num_input2_dims,
const size_t* input2_shape,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_subtract_nd_qu8(
xnn_operator_t subtract_op,
const uint8_t* input1,
const uint8_t* input2,
uint8_t* output);
enum xnn_status xnn_run_subtract_nd_qu8(
size_t num_input1_dims,
const size_t* input1_shape,
uint8_t input1_zero_point,
float input1_scale,
size_t num_input2_dims,
const size_t* input2_shape,
uint8_t input2_zero_point,
float input2_scale,
const uint8_t* input1,
const uint8_t* input2,
uint8_t* output,
uint8_t output_zero_point,
float output_scale,
uint8_t output_min,
uint8_t output_max,
uint32_t flags,
pthreadpool_t threadpool);
enum xnn_status xnn_create_tanh_nc_qu8(
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* tanh_op_out);
enum xnn_status xnn_reshape_tanh_nc_qu8(
xnn_operator_t tanh_op,
size_t batch_size,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_tanh_nc_qu8(
xnn_operator_t tanh_op,
const uint8_t* input,
uint8_t* output);
enum xnn_status xnn_create_clamp_nc_s8(
size_t channels,
size_t input_stride,
size_t output_stride,
int8_t output_min,
int8_t output_max,
uint32_t flags,
xnn_operator_t* clamp_op_out);
enum xnn_status xnn_reshape_clamp_nc_s8(
xnn_operator_t clamp_op,
size_t batch_size,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_clamp_nc_s8(
xnn_operator_t clamp_op,
const int8_t* input,
int8_t* output);
enum xnn_status xnn_create_max_pooling2d_nhwc_s8(
uint32_t input_padding_top,
uint32_t input_padding_right,
uint32_t input_padding_bottom,
uint32_t input_padding_left,
uint32_t pooling_height,
uint32_t pooling_width,
uint32_t stride_height,
uint32_t stride_width,
uint32_t dilation_height,
uint32_t dilation_width,
size_t channels,
size_t input_pixel_stride,
size_t output_pixel_stride,
int8_t output_min,
int8_t output_max,
uint32_t flags,
xnn_operator_t* max_pooling_op_out);
enum xnn_status xnn_reshape_max_pooling2d_nhwc_s8(
xnn_operator_t max_pooling_op,
size_t batch_size,
size_t input_height,
size_t input_width,
size_t* output_height_out,
size_t* output_width_out,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_max_pooling2d_nhwc_s8(
xnn_operator_t max_pooling_op,
const int8_t* input,
int8_t* output);
enum xnn_status xnn_create_resize_bilinear2d_nhwc_s8(
size_t channels,
size_t input_pixel_stride,
size_t output_pixel_stride,
uint32_t flags,
xnn_operator_t* resize_op_out);
enum xnn_status xnn_reshape_resize_bilinear2d_nhwc_s8(
xnn_operator_t resize_op,
size_t batch_size,
size_t input_height,
size_t input_width,
size_t output_height,
size_t output_width,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_resize_bilinear2d_nhwc_s8(
xnn_operator_t resize_op,
const int8_t* input,
int8_t* output);
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);
enum xnn_status xnn_reshape_clamp_nc_u8(
xnn_operator_t clamp_op,
size_t batch_size,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_clamp_nc_u8(
xnn_operator_t clamp_op,
const uint8_t* input,
uint8_t* output);
enum xnn_status xnn_create_max_pooling2d_nhwc_u8(
uint32_t input_padding_top,
uint32_t input_padding_right,
uint32_t input_padding_bottom,
uint32_t input_padding_left,
uint32_t pooling_height,
uint32_t pooling_width,
uint32_t stride_height,
uint32_t stride_width,
uint32_t dilation_height,
uint32_t dilation_width,
size_t channels,
size_t input_pixel_stride,
size_t output_pixel_stride,
uint8_t output_min,
uint8_t output_max,
uint32_t flags,
xnn_operator_t* max_pooling_op_out);
enum xnn_status xnn_reshape_max_pooling2d_nhwc_u8(
xnn_operator_t max_pooling_op,
size_t batch_size,
size_t input_height,
size_t input_width,
size_t* output_height_out,
size_t* output_width_out,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_max_pooling2d_nhwc_u8(
xnn_operator_t max_pooling_op,
const uint8_t* input,
uint8_t* output);
enum xnn_status xnn_create_resize_bilinear2d_nhwc_u8(
size_t channels,
size_t input_pixel_stride,
size_t output_pixel_stride,
uint32_t flags,
xnn_operator_t* resize_op_out);
enum xnn_status xnn_reshape_resize_bilinear2d_nhwc_u8(
xnn_operator_t resize_op,
size_t batch_size,
size_t input_height,
size_t input_width,
size_t output_height,
size_t output_width,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_resize_bilinear2d_nhwc_u8(
xnn_operator_t resize_op,
const uint8_t* input,
uint8_t* output);
enum xnn_status xnn_create_copy_nc_x8(
size_t channels,
size_t input_stride,
size_t output_stride,
uint32_t flags,
xnn_operator_t* copy_op_out);
enum xnn_status xnn_reshape_copy_nc_x8(
xnn_operator_t copy_op,
size_t batch_size,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_copy_nc_x8(
xnn_operator_t copy_op,
const void* input,
void* output);
enum xnn_status xnn_create_channel_shuffle_nc_x8(
size_t groups,
size_t group_channels,
size_t input_stride,
size_t output_stride,
uint32_t flags,
xnn_operator_t* channel_shuffle_op_out);
enum xnn_status xnn_reshape_channel_shuffle_nc_x8(
xnn_operator_t channel_shuffle_op,
size_t batch_size,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_channel_shuffle_nc_x8(
xnn_operator_t channel_shuffle_op,
const void* input,
void* output);
enum xnn_status xnn_create_constant_pad_nd_x8(
const void* padding_value,
uint32_t flags,
xnn_operator_t* constant_pad_op_out);
enum xnn_status xnn_reshape_constant_pad_nd_x8(
xnn_operator_t constant_pad_op,
size_t num_dims,
const size_t* input_shape,
const size_t* pre_padding,
const size_t* post_padding,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_constant_pad_nd_x8(
xnn_operator_t constant_pad_op,
const void* input,
void* output);
enum xnn_status xnn_run_constant_pad_nd_x8(
uint32_t flags,
size_t num_dims,
const size_t* input_shape,
const size_t* pre_paddings,
const size_t* post_paddings,
const void* input,
void* output,
const void* padding_value,
pthreadpool_t threadpool);
enum xnn_status xnn_create_depth_to_space_nhwc_x8(
size_t output_channels,
size_t input_channel_stride,
size_t output_channel_stride,
uint32_t block_size,
uint32_t flags,
xnn_operator_t* depth_to_space_op_out);
enum xnn_status xnn_reshape_depth_to_space_nhwc_x8(
xnn_operator_t depth_to_space_op,
size_t batch_size,
size_t input_height,
size_t input_width,
size_t* output_height_out,
size_t* output_width_out,
size_t* output_channels_out,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_depth_to_space_nhwc_x8(
xnn_operator_t depth_to_space_op,
const void* input,
void* output);
enum xnn_status xnn_create_slice_nd_x8(
uint32_t flags,
xnn_operator_t* slice_op_out);
enum xnn_status xnn_reshape_slice_nd_x8(
xnn_operator_t slice_op,
size_t num_dims,
const size_t* input_shape,
const size_t* offsets,
const size_t* sizes,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_slice_nd_x8(
xnn_operator_t slice_op,
const void* input,
void* output);
enum xnn_status xnn_create_space_to_depth_nhwc_x8(
size_t input_channels,
size_t input_channel_stride,
size_t output_channel_stride,
uint32_t block_size,
uint32_t flags,
xnn_operator_t* space_to_depth_op_out);
enum xnn_status xnn_reshape_space_to_depth_nhwc_x8(
xnn_operator_t space_to_depth_op,
size_t batch_size,
size_t input_height,
size_t input_width,
size_t* output_height_out,
size_t* output_width_out,
size_t* output_channels_out,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_space_to_depth_nhwc_x8(
xnn_operator_t space_to_depth_op,
const void* input,
void* output);
enum xnn_status xnn_create_transpose_nd_x8(
uint32_t flags,
xnn_operator_t* transpose_op_out);
enum xnn_status xnn_reshape_transpose_nd_x8(
xnn_operator_t transpose_op,
size_t num_dims,
const size_t* input_shape,
const size_t* output_perm,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_transpose_nd_x8(
xnn_operator_t transpose_op,
const void* input,
void* output);
enum xnn_status xnn_run_transpose_nd_x8(
const void* input,
void* output,
size_t num_dims,
const size_t* input_shape,
const size_t* output_perm,
uint32_t flags,
pthreadpool_t threadpool);
enum xnn_status xnn_create_convert_nc_f32_qd8(
size_t channels,
size_t input_stride,
size_t output_stride,
uint32_t flags,
xnn_operator_t* convert_op_out);
enum xnn_status xnn_reshape_convert_nc_f32_qd8(
xnn_operator_t convert_op,
size_t batch_size,
pthreadpool_t threadpool);
// quantization_params must be padded with at least XNN_EXTRA_QUANTIZATION_PARAMS entries.
enum xnn_status xnn_setup_convert_nc_f32_qd8(
xnn_operator_t convert_op,
const float* input,
int8_t* output,
struct xnn_dynamic_quantization_params* quantization_params);
enum xnn_status xnn_create_convert_nc_f16_f32(
size_t channels,
size_t input_stride,
size_t output_stride,
uint32_t flags,
xnn_operator_t* convert_op_out);
enum xnn_status xnn_reshape_convert_nc_f16_f32(
xnn_operator_t convert_op,
size_t batch_size,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_convert_nc_f16_f32(
xnn_operator_t convert_op,
const void* input,
float* output);
enum xnn_status xnn_run_convert_nc_f16_f32(
size_t channels,
size_t input_stride,
size_t output_stride,
size_t batch_size,
const void* input,
float* output,
uint32_t flags,
pthreadpool_t threadpool);
enum xnn_status xnn_create_convert_nc_f32_f16(
size_t channels,
size_t input_stride,
size_t output_stride,
uint32_t flags,
xnn_operator_t* convert_op_out);
enum xnn_status xnn_reshape_convert_nc_f32_f16(
xnn_operator_t convert_op,
size_t batch_size,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_convert_nc_f32_f16(
xnn_operator_t convert_op,
const float* input,
void* output);
enum xnn_status xnn_run_convert_nc_f32_f16(
size_t channels,
size_t input_stride,
size_t output_stride,
size_t batch_size,
const float* input,
void* output,
uint32_t flags,
pthreadpool_t threadpool);
enum xnn_status xnn_create_convert_nc_f32_qs8(
size_t channels,
size_t input_stride,
size_t output_stride,
float output_scale,
int8_t output_zero_point,
int8_t output_min,
int8_t output_max,
uint32_t flags,
xnn_operator_t* convert_op_out);
enum xnn_status xnn_reshape_convert_nc_f32_qs8(
xnn_operator_t convert_op,
size_t batch_size,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_convert_nc_f32_qs8(
xnn_operator_t convert_op,
const float* input,
int8_t* output);
enum xnn_status xnn_run_convert_nc_f32_qs8(
size_t channels,
size_t input_stride,
size_t output_stride,
size_t batch_size,
const float* input,
int8_t* output,
float output_scale,
int8_t output_zero_point,
uint32_t flags,
pthreadpool_t threadpool);
enum xnn_status xnn_create_convert_nc_f32_qu8(
size_t channels,
size_t input_stride,
size_t output_stride,
float output_scale,
uint8_t output_zero_point,
uint8_t output_min,
uint8_t output_max,
uint32_t flags,
xnn_operator_t* convert_op_out);
enum xnn_status xnn_reshape_convert_nc_f32_qu8(
xnn_operator_t convert_op,
size_t batch_size,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_convert_nc_f32_qu8(
xnn_operator_t convert_op,
const float* input,
uint8_t* output);
enum xnn_status xnn_run_convert_nc_f32_qu8(
size_t channels,
size_t input_stride,
size_t output_stride,
size_t batch_size,
const float* input,
uint8_t* output,
float output_scale,
uint8_t output_zero_point,
uint32_t flags,
pthreadpool_t threadpool);
enum xnn_status xnn_create_convert_nc_qs8(
size_t channels,
size_t input_stride,
size_t output_stride,
float input_scale,
int8_t input_zero_point,
float output_scale,
int8_t output_zero_point,
uint32_t flags,
xnn_operator_t* convert_op_out);
enum xnn_status xnn_reshape_convert_nc_qs8(
xnn_operator_t convert_op,
size_t batch_size,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_convert_nc_qs8(
xnn_operator_t convert_op,
const int8_t* input,
int8_t* output);
enum xnn_status xnn_create_convert_nc_qs8_f32(
size_t channels,
size_t input_stride,
size_t output_stride,
float input_scale,
int8_t input_zero_point,
uint32_t flags,
xnn_operator_t* convert_op_out);
enum xnn_status xnn_reshape_convert_nc_qs8_f32(
xnn_operator_t convert_op,
size_t batch_size,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_convert_nc_qs8_f32(
xnn_operator_t convert_op,
const int8_t* input,
float* output);
enum xnn_status xnn_run_convert_nc_qs8_f32(
size_t channels,
size_t input_stride,
size_t output_stride,
size_t batch_size,
const int8_t* input,
float* output,
float input_scale,
int8_t input_zero_point,
uint32_t flags,
pthreadpool_t threadpool);
enum xnn_status xnn_create_convert_nc_qs16_qs8(
size_t channels,
size_t input_stride,
size_t output_stride,
float input_scale,
float output_scale,
int8_t output_zero_point,
uint32_t flags,
xnn_operator_t* convert_op_out);
enum xnn_status xnn_reshape_convert_nc_qs16_qs8(
xnn_operator_t convert_op,
size_t batch_size,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_convert_nc_qs16_qs8(
xnn_operator_t convert_op,
const int16_t* input,
int8_t* output);
enum xnn_status xnn_run_convert_nc_qs16_qs8(
size_t channels,
size_t input_stride,
size_t output_stride,
size_t batch_size,
const int16_t* input,
int8_t* output,
float input_scale,
float output_scale,
int8_t output_zero_point,
uint32_t flags,
pthreadpool_t threadpool);
enum xnn_status xnn_create_convert_nc_qu8(
size_t channels,
size_t input_stride,
size_t output_stride,
float input_scale,
uint8_t input_zero_point,
float output_scale,
uint8_t output_zero_point,
uint32_t flags,
xnn_operator_t* convert_op_out);
enum xnn_status xnn_reshape_convert_nc_qu8(
xnn_operator_t convert_op,
size_t batch_size,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_convert_nc_qu8(
xnn_operator_t convert_op,
const uint8_t* input,
uint8_t* output);
enum xnn_status xnn_create_convert_nc_qu8_f32(
size_t channels,
size_t input_stride,
size_t output_stride,
float input_scale,
uint8_t input_zero_point,
uint32_t flags,
xnn_operator_t* convert_op_out);
enum xnn_status xnn_reshape_convert_nc_qu8_f32(
xnn_operator_t convert_op,
size_t batch_size,
pthreadpool_t threadpool);
enum xnn_status xnn_setup_convert_nc_qu8_f32(
xnn_operator_t convert_op,
const uint8_t* input,
float* output);
enum xnn_status xnn_run_convert_nc_qu8_f32(
size_t channels,
size_t input_stride,
size_t output_stride,
size_t batch_size,
const uint8_t* input,
float* output,
float input_scale,
uint8_t input_zero_point,
uint32_t flags,
pthreadpool_t threadpool);
#ifdef __cplusplus
} // extern "C"
#endif