// 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 forces XNNPACK to consider sparse inference, but does not guarantee it. #define XNN_FLAG_SPARSE_INFERENCE 0x00000001 /// 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 /// 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 /// Align corners of input and output images in resize operations. #define XNN_FLAG_ALIGN_CORNERS 0x00000008 /// 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 host processor. It 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 succesfully 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_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, }; /// 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 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. 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. 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. 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 DepthToSpace Node and add it to a Subgraph. /// /// The DepthToSpace Node rearranges data from depth into blocks of spatial data (a reverse transform for SpaceToDepth). /// 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 divisible by @a block_size in the channel dimension. /// @param output_id - Value ID for the output tensor. /// @param block_size - the size of the spatial block. /// @param flags - binary features of the DepthToSpace 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 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 /// 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, 1, 1, C] /// dimensions. /// @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 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 ge /// a 2D tensor defined in the @a subgraph /// with [output_channels, input_channels] dimensions. /// @param bias_id - Value ID for the bias tensor. 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 value is XNN_FLAG_TENSORFLOW_RESHAPE_2D. 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 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 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 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 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 bias tensor. The bias 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 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 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 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); /// 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; /// 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 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 value is XNN_FLAG_SPARSE_INFERENCE. /// @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_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); #ifndef XNN_NO_F32_OPERATORS 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_setup_abs_nc_f32( xnn_operator_t abs_op, size_t batch_size, const float* input, float* output, 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_setup_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, const float* input1, const float* input2, float* output, 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_setup_argmax_pooling2d_nhwc_f32( xnn_operator_t argmax_pooling_op, size_t batch_size, size_t input_height, size_t input_width, const float* input, float* output, uint32_t* index, pthreadpool_t threadpool); 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_setup_average_pooling2d_nhwc_f32( xnn_operator_t average_pooling_op, size_t batch_size, size_t input_height, size_t input_width, const float* input, float* output, pthreadpool_t threadpool); 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_setup_bankers_rounding_nc_f32( xnn_operator_t rounding_op, size_t batch_size, const float* input, float* output, pthreadpool_t threadpool); 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_setup_ceiling_nc_f32( xnn_operator_t ceiling_op, size_t batch_size, const float* input, float* output, pthreadpool_t threadpool); 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_setup_clamp_nc_f32( xnn_operator_t clamp_op, size_t batch_size, const float* input, float* output, 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_operator_t* convolution_op_out); enum xnn_status xnn_setup_convolution2d_nhwc_f32( xnn_operator_t convolution_op, size_t batch_size, size_t input_height, size_t input_width, const float* input, float* output, pthreadpool_t threadpool); 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_operator_t* deconvolution_op_out); enum xnn_status xnn_setup_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, const float* input, float* output, pthreadpool_t threadpool); 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_setup_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, const float* input1, const float* input2, float* output, pthreadpool_t threadpool); 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_setup_elu_nc_f32( xnn_operator_t elu_op, size_t batch_size, const float* input, float* output, 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_operator_t* fully_connected_op_out); enum xnn_status xnn_setup_fully_connected_nc_f32( xnn_operator_t fully_connected_op, size_t batch_size, const float* input, float* output, 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_setup_floor_nc_f32( xnn_operator_t floor_op, size_t batch_size, const float* input, float* output, pthreadpool_t threadpool); 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_setup_global_average_pooling_nwc_f32( xnn_operator_t global_average_pooling_op, size_t batch_size, size_t width, const float* input, float* output, pthreadpool_t threadpool); 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_setup_hardswish_nc_f32( xnn_operator_t hardswish_op, size_t batch_size, const float* input, float* output, 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_setup_leaky_relu_nc_f32( xnn_operator_t leaky_relu_op, size_t batch_size, const float* input, float* output, 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_setup_max_pooling2d_nhwc_f32( xnn_operator_t max_pooling_op, size_t batch_size, size_t input_height, size_t input_width, const float* input, float* output, pthreadpool_t threadpool); enum xnn_status xnn_create_maximum_nd_f32( uint32_t flags, xnn_operator_t* maximum_op_out); enum xnn_status xnn_setup_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, const float* input1, const float* input2, float* output, pthreadpool_t threadpool); enum xnn_status xnn_create_minimum_nd_f32( uint32_t flags, xnn_operator_t* minimum_op_out); enum xnn_status xnn_setup_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, const float* input1, const float* input2, float* output, 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_setup_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, const float* input1, const float* input2, float* output, 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_setup_negate_nc_f32( xnn_operator_t negate_op, size_t batch_size, const float* input, float* output, 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_operator_t* prelu_op_out); enum xnn_status xnn_setup_prelu_nc_f32( xnn_operator_t prelu_op, size_t batch_size, const float* input, float* output, pthreadpool_t threadpool); 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_setup_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, const float* input, float* output, pthreadpool_t threadpool); 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_setup_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, const float* input, float* output, pthreadpool_t threadpool); 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_setup_sigmoid_nc_f32( xnn_operator_t sigmoid_op, size_t batch_size, const float* input, float* output, 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_setup_softmax_nc_f32( xnn_operator_t softmax_op, size_t batch_size, const float* input, float* output, pthreadpool_t threadpool); 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_setup_square_nc_f32( xnn_operator_t square_op, size_t batch_size, const float* input, float* output, 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_setup_square_root_nc_f32( xnn_operator_t sqrt_op, size_t batch_size, const float* input, float* output, 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_setup_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, const float* input1, const float* input2, float* output, 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_setup_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, const float* input1, const float* input2, float* output, 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_setup_truncation_nc_f32( xnn_operator_t truncation_op, size_t batch_size, const float* input, float* output, pthreadpool_t threadpool); #ifndef XNN_NO_NCHW_OPERATORS 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_operator_t* convolution_op_out); enum xnn_status xnn_setup_convolution2d_nchw_f32( xnn_operator_t convolution_op, size_t batch_size, size_t input_height, size_t input_width, const float* input, float* output, pthreadpool_t threadpool); 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_setup_global_average_pooling_ncw_f32( xnn_operator_t global_average_pooling_op, size_t batch_size, size_t width, const float* input, float* output, pthreadpool_t threadpool); #endif // XNN_NO_NCHW_OPERATORS #endif // XNN_NO_F32_OPERATORS #ifndef XNN_NO_X32_OPERATORS 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_setup_channel_shuffle_nc_x32( xnn_operator_t channel_shuffle_op, size_t batch_size, const void* input, void* output, pthreadpool_t threadpool); 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_setup_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, const void* input, void* output, 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_setup_copy_nc_x32( xnn_operator_t copy_op, size_t batch_size, const void* input, void* output, 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_setup_depth_to_space_nhwc_x32( xnn_operator_t depth_to_space_op, size_t batch_size, size_t input_height, size_t input_width, const void* input, void* output, 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_setup_depth_to_space_nchw2nhwc_x32( xnn_operator_t depth_to_space_op, size_t batch_size, size_t input_height, size_t input_width, const void* input, void* output, 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_setup_unpooling2d_nhwc_x32( xnn_operator_t unpooling_op, size_t batch_size, size_t input_height, size_t input_width, const void* input, const uint32_t* index, void* output, pthreadpool_t threadpool); #endif // XNN_NO_X32_OPERATORS #ifndef XNN_NO_F16_OPERATORS 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_setup_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, const void* input1, const void* input2, void* output, pthreadpool_t threadpool); 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_operator_t* convolution_op_out); enum xnn_status xnn_setup_convolution2d_nhwc_f16( xnn_operator_t convolution_op, size_t batch_size, size_t input_height, size_t input_width, const void* input, void* output, pthreadpool_t threadpool); 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_setup_global_average_pooling_nwc_f16( xnn_operator_t global_average_pooling_op, size_t batch_size, size_t width, const void* input, void* output, pthreadpool_t threadpool); 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_setup_hardswish_nc_f16( xnn_operator_t hardswish_op, size_t batch_size, const void* input, void* output, pthreadpool_t threadpool); 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_setup_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, const void* input1, const void* input2, void* output, pthreadpool_t threadpool); #endif // XNN_NO_F16_OPERATORS #ifndef XNN_NO_QS8_OPERATORS 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_setup_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, const int8_t* input1, const int8_t* input2, int8_t* output, 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_operator_t* convolution_op_out); enum xnn_status xnn_setup_convolution2d_nhwc_qs8( xnn_operator_t convolution_op, size_t batch_size, size_t input_height, size_t input_width, const int8_t* input, int8_t* output, pthreadpool_t threadpool); 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_setup_global_average_pooling_nwc_qs8( xnn_operator_t global_average_pooling_op, size_t batch_size, size_t width, const int8_t* input, int8_t* output, pthreadpool_t threadpool); #endif // XNN_NO_QS8_OPERATORS #ifndef XNN_NO_QU8_OPERATORS 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_setup_average_pooling2d_nhwc_qu8( xnn_operator_t average_pooling_op, size_t batch_size, size_t input_height, size_t input_width, const uint8_t* input, uint8_t* output, pthreadpool_t threadpool); 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_operator_t* convolution_op_out); enum xnn_status xnn_setup_convolution2d_nhwc_qu8( xnn_operator_t convolution_op, size_t batch_size, size_t input_height, size_t input_width, const uint8_t* input, uint8_t* output, pthreadpool_t threadpool); 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_operator_t* deconvolution_op_out); enum xnn_status xnn_setup_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, const uint8_t* input, uint8_t* output, pthreadpool_t threadpool); 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_operator_t* fully_connected_op_out); enum xnn_status xnn_setup_fully_connected_nc_qu8( xnn_operator_t fully_connected_op, size_t batch_size, const uint8_t* input, uint8_t* output, pthreadpool_t threadpool); 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_setup_global_average_pooling_nwc_qu8( xnn_operator_t global_average_pooling_op, size_t batch_size, size_t width, const uint8_t* input, uint8_t* output, pthreadpool_t threadpool); 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, uint8_t output_min, uint8_t output_max, uint32_t flags, xnn_operator_t* leaky_relu_op_out); enum xnn_status xnn_setup_leaky_relu_nc_qu8( xnn_operator_t leaky_relu_op, size_t batch_size, const uint8_t* input, uint8_t* output, 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_setup_sigmoid_nc_qu8( xnn_operator_t sigmoid_op, size_t batch_size, const uint8_t* input, uint8_t* output, pthreadpool_t threadpool); 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_setup_softmax_nc_qu8( xnn_operator_t softmax_op, size_t batch_size, const uint8_t* input, uint8_t* output, pthreadpool_t threadpool); #endif // XNN_NO_QU8_OPERATORS #ifndef XNN_NO_U8_OPERATORS 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_setup_clamp_nc_u8( xnn_operator_t clamp_op, size_t batch_size, const uint8_t* input, uint8_t* output, pthreadpool_t threadpool); 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_setup_max_pooling2d_nhwc_u8( xnn_operator_t max_pooling_op, size_t batch_size, size_t input_height, size_t input_width, const uint8_t* input, uint8_t* output, pthreadpool_t threadpool); #endif // XNN_NO_U8_OPERATORS #ifndef XNN_NO_X8_OPERATORS 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_setup_channel_shuffle_nc_x8( xnn_operator_t channel_shuffle_op, size_t batch_size, const void* input, void* output, pthreadpool_t threadpool); #endif // XNN_NO_X8_OPERATORS #ifdef __cplusplus } // extern "C" #endif