// 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 this 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 /// 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 /// 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. /// @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 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 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. /// @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 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 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 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 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 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 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); /// 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 empty 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 subgraph. No supported flags are currently defined. /// @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_add_nc_f32( size_t channels, size_t a_stride, size_t b_stride, size_t sum_stride, float sum_min, float sum_max, uint32_t flags, xnn_operator_t* add_op_out); enum xnn_status xnn_setup_add_nc_f32( xnn_operator_t add_op, size_t batch_size, const float* a, const float* b, float* sum, pthreadpool_t threadpool); 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, float output_min, float output_max, 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_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_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* 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_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_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_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_prelu_nc_f32( size_t channels, size_t input_stride, size_t output_stride, const float* negative_slope, float output_min, float output_max, 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_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_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); #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, 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_batch_stride, size_t output_batch_stride, 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_pad_nc_x32( size_t input_channels, size_t pad_before_channels, size_t pad_after_channels, size_t input_stride, size_t output_stride, const void* pad_value, uint32_t flags, xnn_operator_t* channel_pad_op_out); enum xnn_status xnn_setup_channel_pad_nc_x32( xnn_operator_t channel_pad_op, size_t batch_size, const void* input, void* output, pthreadpool_t threadpool); 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_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_Q8_OPERATORS enum xnn_status xnn_create_add_nc_q8( size_t channels, size_t a_stride, size_t b_stride, size_t sum_stride, uint8_t a_zero_point, float a_scale, uint8_t b_zero_point, float b_scale, uint8_t sum_zero_point, float sum_scale, uint8_t sum_min, uint8_t sum_max, uint32_t flags, xnn_operator_t* add_op_out); enum xnn_status xnn_setup_add_nc_q8( xnn_operator_t add_op, size_t batch_size, const uint8_t* a, const uint8_t* b, uint8_t* sum, pthreadpool_t threadpool); enum xnn_status xnn_create_average_pooling2d_nhwc_q8( 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_q8( 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_q8( 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_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* convolution_op_out); enum xnn_status xnn_setup_convolution2d_nhwc_q8( 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_q8( 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_q8( 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_q8( 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_q8( 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_q8( 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_q8( 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_q8( 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_q8( 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_q8( 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_q8( 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_q8( size_t channels, size_t input_stride, size_t output_stride, float input_scale, uint8_t output_zero_point, float output_scale, uint32_t flags, xnn_operator_t* softmax_op_out); enum xnn_status xnn_setup_softmax_nc_q8( xnn_operator_t softmax_op, size_t batch_size, const uint8_t* input, uint8_t* output, pthreadpool_t threadpool); #endif // XNN_NO_Q8_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