// 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 #include #include #include #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