6173 lines
218 KiB
C
6173 lines
218 KiB
C
// Copyright (c) Facebook, Inc. and its affiliates.
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// All rights reserved.
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//
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// Copyright 2019 Google LLC
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//
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// This source code is licensed under the BSD-style license found in the
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// LICENSE file in the root directory of this source tree.
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#pragma once
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#include <stdbool.h>
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#include <stddef.h>
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#include <stdint.h>
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#include <pthreadpool.h>
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#ifdef __cplusplus
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extern "C" {
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#endif
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/// The number of bytes XNNPACK may read beyond array bounds.
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/// The caller must allocate at least this many extra bytes after the tensor data passed to XNNPACK.
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///
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/// Note: XNNPACK reads, but never writes beyond array bounds.
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#define XNN_EXTRA_BYTES 16
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/// Maximum number of dimensions in tensor shape.
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#define XNN_MAX_TENSOR_DIMS 6
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/// Allow sparse inference in a Runtime.
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///
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/// Note: this flag hints XNNPACK to consider sparse inference, but does not guarantee it.
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#define XNN_FLAG_HINT_SPARSE_INFERENCE 0x00000001
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/// Allow IEEE FP16 inference in a Runtime.
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///
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/// Note: this flag hints XNNPACK to consider IEEE FP16 inference, but does not guarantee it.
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#define XNN_FLAG_HINT_FP16_INFERENCE 0x00000002
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/// Force IEEE FP16 inference in a Runtime, and fail if FP16 inference is not possible.
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///
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/// Note: this flag guarantees that XNNPACK will use IEEE FP16 inference, or fail to create the Runtime object.
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/// Warning: on x86 systems FP16 computations will be emulated at a substantial performance cost.
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#define XNN_FLAG_FORCE_FP16_INFERENCE 0x00000004
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/// Enable timing of each operator's runtime.
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#define XNN_FLAG_BASIC_PROFILING 0x00000008
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/// Enable the just-in-time compiler.
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#define XNN_FLAG_JIT 0x00000010
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/// The convolution operator represents a depthwise convolution, and use HWGo layout for filters.
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#define XNN_FLAG_DEPTHWISE_CONVOLUTION 0x00000001
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/// Assume transposed weights in a fully connected operator.
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#define XNN_FLAG_TRANSPOSE_WEIGHTS 0x00000001
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/// The operator assumes NHWC layout for the input, regardless of the output layout.
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#define XNN_FLAG_INPUT_NHWC 0x00000002
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/// Match "SAME" padding in TensorFlow. Exact padding values are computed dynamically depending on input size.
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#define XNN_FLAG_TENSORFLOW_SAME_PADDING 0x00000004
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/// Assume transposed weights in a batch matrix multiply operator.
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#define XNN_FLAG_TRANSPOSE_B XNN_FLAG_TRANSPOSE_WEIGHTS
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/// Assume transposed input in a batch matrix multiply operator.
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#define XNN_FLAG_TRANSPOSE_A 0x00000002
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/// Implicitly flatten and reshape input of a Fully Connected operator into a 2D tensor.
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#define XNN_FLAG_TENSORFLOW_RESHAPE_2D 0x00000004
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/// Match behaviour of TensorFlow 1.x.
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#define XNN_FLAG_TENSORFLOW_LEGACY_MODE 0x00000004
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/// Static weights of the FP16 operator are in FP32 format.
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#define XNN_FLAG_FP32_STATIC_WEIGHTS 0x00000008
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/// Align corners of input and output images in resize operations.
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#define XNN_FLAG_ALIGN_CORNERS 0x00000008
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/// Yield worker threads of the thread pool to the system scheduler after the inference.
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#define XNN_FLAG_YIELD_WORKERS 0x00000010
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/// Use transient indirection buffer to reduce memory footprint
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#define XNN_FLAG_TRANSIENT_INDIRECTION_BUFFER 0x00000020
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/// Reduce the dimensions.
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#define XNN_FLAG_REDUCE_DIMS 0x00000040
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/// The number of entries in an array of xnn_dynamic_quantization_params that XNNPACK may read beyond array bounds.
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/// The caller must allocate at least this many extra xnn_dynamic_quantization_params before passing the array to XNNPACK.
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///
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/// Note: XNNPACK reads, but never writes beyond array bounds.
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#define XNN_EXTRA_QUANTIZATION_PARAMS 8
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struct xnn_dynamic_quantization_params {
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int32_t zero_point;
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float scale;
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};
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/// Status code for any XNNPACK function call.
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enum xnn_status {
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/// The call succeeded, and all output arguments now contain valid data.
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xnn_status_success = 0,
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xnn_status_uninitialized = 1,
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xnn_status_invalid_parameter = 2,
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xnn_status_invalid_state = 3,
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xnn_status_unsupported_parameter = 4,
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xnn_status_unsupported_hardware = 5,
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xnn_status_out_of_memory = 6,
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xnn_status_reallocation_required = 7,
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};
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struct xnn_allocator {
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/// User-specified pointer that will be passed as-is to all functions in this structure.
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void* context;
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/// Pointer to a function to be called for general memory allocation.
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///
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/// @param context - The user-specified pointer from xnn_allocator structure.
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/// @param size - The size of the memory block to allocate, in bytes.
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///
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/// @returns Pointer to the allocated memory block of at least @ref size bytes.
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/// If allocation fails, the function must return NULL.
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void* (*allocate)(void* context, size_t size);
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/// Pointer to a function to be called for general memory re-allocation, i.e. to increase or shrink a previously
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/// allocated memory block. The content of the old memory block is copied to the new memory block.
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///
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/// @param context - The user-specified pointer from xnn_allocator structure.
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/// @param pointer - Pointer to a memory block allocated by @ref allocate or @ref reallocate functions. Can be NULL.
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/// If the pointer is NULL, the @ref reallocate call is equivalent to an @ref allocate call.
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/// @param size - The new size of the memory block to allocate, in bytes.
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///
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/// @returns Pointer to the newly allocated memory block of at least @ref size bytes with the content of the previous
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/// memory block.
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/// If allocation fails, the function must return NULL, but must not release the previous memory block.
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void* (*reallocate)(void* context, void* pointer, size_t size);
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/// Pointer to a function to be called for general memory de-allocation.
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///
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/// @param context - The user-specified pointer from xnn_allocator structure.
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/// @param pointer - Pointer to a memory block allocated by @ref allocate or @ref reallocate functions. Can be NULL.
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/// If the pointer is NULL, the @ref deallocate call is a no-op.
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void (*deallocate)(void* context, void* pointer);
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/// Pointer to a function to be called for aligned memory allocation.
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///
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/// @param context - The user-specified pointer from xnn_allocator structure.
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/// @param alignment - The alignment of the memory block to allocate, in bytes. Alignment is always a power-of-2.
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/// @param size - The size of the memory block to allocate, in bytes.
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///
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/// @returns Pointer to the allocated memory block of at least @ref size bytes.
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/// If allocation fails, the function must return NULL.
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void* (*aligned_allocate)(void* context, size_t alignment, size_t size);
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/// Pointer to a function to be called for aligned memory de-allocation.
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///
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/// @param context - The user-specified pointer from xnn_allocator structure.
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/// @param pointer - Pointer to a memory block allocated by @ref aligned_allocate function. Can be NULL.
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/// If the pointer is NULL, the @ref aligned_deallocate call is a no-op.
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void (*aligned_deallocate)(void* context, void* pointer);
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};
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/// Initialize XNNPACK library.
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///
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/// XNNPACK must be successfully initialized before use. During initialization, XNNPACK populates internal structures
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/// depending on the host processor. Initialization can be time-consuming.
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///
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/// @param[in] allocator - structure with function pointers to be use for memory allocation and de-allocation.
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/// If this argument is NULL, system-provided memory management functions (e.g. malloc/free)
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/// will be used.
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///
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/// @retval xnn_status_success - XNNPACK is successfully initialized and ready to use.
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/// @retval xnn_status_out_of_memory - initialization failed due to out-of-memory condition.
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/// @retval xnn_status_unsupported_hardware - initialization failed because the host processor does not satisfy the
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/// minimum hardware requirements for XNNPACK. E.g. this may happen on x86
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/// processors without SSE2 extension, or on 32-bit ARM processors without
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/// the NEON SIMD extension.
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enum xnn_status xnn_initialize(const struct xnn_allocator* allocator);
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/// Deinitialize XNNPACK library.
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///
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/// To avoid memory and resource leaks, users must call xnn_deinitialize once for each successful xnn_initialize call.
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///
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/// @retval xnn_status_success - deinitialization call succeeded.
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enum xnn_status xnn_deinitialize(void);
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/// Subgraph is an abstract representation of a neural network model.
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/// Subgraph objects are used to define Values (tensors) and Nodes (operators) comprising the model.
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typedef struct xnn_subgraph* xnn_subgraph_t;
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/// Create a empty Subgraph object.
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///
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/// @param external_value_ids - number of Value IDs to reserve for communication with external graph representation.
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/// The Subgraph object would avoid creating internal Value IDs in the
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/// [0, reserved_value_ids-1] range.
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/// @param flags - binary features of the subgraph. No supported flags are currently defined.
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/// @param subgraph_out - pointer to the variable that will be initialized with a handle to the Subgraph object upon
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/// successful return.
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enum xnn_status xnn_create_subgraph(
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uint32_t external_value_ids,
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uint32_t flags,
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xnn_subgraph_t* subgraph_out);
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/// Destroy a Subgraph object, as well as Values, and Nodes associated with the subgraph.
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///
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/// @param subgraph - the Subgraph object to destroy.
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enum xnn_status xnn_delete_subgraph(
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xnn_subgraph_t subgraph);
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#define XNN_VALUE_FLAG_EXTERNAL_INPUT 0x00000001
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#define XNN_VALUE_FLAG_EXTERNAL_OUTPUT 0x00000002
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#define XNN_VALUE_FLAG_PERSISTENT 0x00000004
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#define XNN_INVALID_VALUE_ID UINT32_MAX
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/// Type of elements in a Value object.
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enum xnn_datatype {
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/// Invalid data type. Valid Values never have this datatype.
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xnn_datatype_invalid = 0,
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/// IEEE754 single-precision floating-point.
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xnn_datatype_fp32 = 1,
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/// IEEE754 half-precision floating-point.
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xnn_datatype_fp16 = 2,
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/// Quantized 8-bit signed integer with shared per-Value quantization parameters.
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xnn_datatype_qint8 = 3,
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/// Quantized 8-bit unsigned integer with shared per-Value quantization parameters.
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xnn_datatype_quint8 = 4,
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/// Quantized 32-bit signed integer with shared per-Value quantization parameters.
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xnn_datatype_qint32 = 5,
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/// Quantized 8-bit signed integer with shared per-channel quantization parameters.
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xnn_datatype_qcint8 = 6,
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/// Quantized 32-bit signed integer with shared per-channel quantization parameters.
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xnn_datatype_qcint32 = 7,
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/// Quantized 4-bit signed integer with shared per-channel quantization parameters.
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xnn_datatype_qcint4 = 8,
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/// Dynamically quantized 8-bit signed integer with per-batch quantization parameters.
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xnn_datatype_qdint8 = 9,
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};
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/// Define a tensor-type Value and add it to a Subgraph.
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///
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/// @param subgraph - a Subgraph object that will own the created Value.
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/// @param datatype - type of the tensor elements.
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/// @param num_dims - number of dimensions in the shape.
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/// @param dims - pointer to an array of @a num_dims shape dimensions. If num_dims is 0, this pointer can be NULL.
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/// XNNPACK does not keep any pointers to this array after the function returns.
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/// @param data - pointer to static data used for tensor initialization. If the tensor is not statically initialized,
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/// this pointer must be is NULL. If non-NULL, the life-time of the static data must exceed the life-time
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/// of the Subgraph object, and of any Runtime objects created from the Subgraph.
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/// @param external_id - external ID for the Value. The ID must be within the range of reversed Value IDs specified on
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/// the Subgraph creation. If the external ID is XNN_INVALID_VALUE_ID, an internal ID will be
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/// created for the Value.
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/// @param flags - binary features of the Value. Supported values are any combination of XNN_VALUE_FLAG_EXTERNAL_INPUT
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/// and XNN_VALUE_FLAG_EXTERNAL_OUTPUT.
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/// @param id_out - pointer to the variable that will be initialized with the Value ID upon successful return. If a
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/// valid @a external_id was provided, the variable will be initialized with the @a external_id value.
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enum xnn_status xnn_define_tensor_value(
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xnn_subgraph_t subgraph,
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enum xnn_datatype datatype,
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size_t num_dims,
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const size_t* dims,
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const void* data,
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uint32_t external_id,
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uint32_t flags,
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uint32_t* id_out);
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/// Define a quantized tensor-type Value and add it to a Subgraph.
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///
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/// @param subgraph - a Subgraph object that will own the created Value.
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/// @param datatype - type of the tensor elements.
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/// @param zero_point - offset from zero to subtract from the quantized elements in the Value.
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/// @param scale - multiplication factor to convert quantized elements to real representation.
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/// @param num_dims - number of dimensions in the shape.
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/// @param dims - pointer to an array of @a num_dims shape dimensions. If num_dims is 0, this pointer can be NULL.
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/// XNNPACK does not keep any pointers to this array after the function returns.
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/// @param data - pointer to static data used for tensor initialization. If the tensor is not statically initialized,
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/// this pointer must be is NULL. If non-NULL, the life-time of the static data must exceed the life-time
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/// of the Subgraph object, and of any Runtime objects created from the Subgraph.
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/// @param external_id - external ID for the Value. The ID must be within the range of reversed Value IDs specified on
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/// the Subgraph creation. If the external ID is XNN_INVALID_VALUE_ID, an internal ID will be
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/// created for the Value.
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/// @param flags - binary features of the Value. Supported values are any combination of XNN_VALUE_FLAG_EXTERNAL_INPUT
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/// and XNN_VALUE_FLAG_EXTERNAL_OUTPUT.
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/// @param id_out - pointer to the variable that will be initialized with the Value ID upon successful return. If a
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/// valid @a external_id was provided, the variable will be initialized with the @a external_id value.
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enum xnn_status xnn_define_quantized_tensor_value(
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xnn_subgraph_t subgraph,
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enum xnn_datatype datatype,
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int32_t zero_point,
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float scale,
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size_t num_dims,
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const size_t* dims,
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const void* data,
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uint32_t external_id,
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uint32_t flags,
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uint32_t* id_out);
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enum xnn_status xnn_define_channelwise_quantized_tensor_value(
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xnn_subgraph_t subgraph,
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enum xnn_datatype datatype,
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const float* scale,
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size_t num_dims,
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size_t channel_dim,
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const size_t* dims,
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const void* data,
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uint32_t external_id,
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uint32_t flags,
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uint32_t* id_out);
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/// Validate the dimensions, channel_dim, zero point, datatype, and scale of a quantized tensor-type.
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///
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/// @param datatype - type of the tensor elements.
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/// @param zero_point - offset from zero to subtract from the quantized elements in the Value.
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/// @param scale - multiplication factor to convert quantized elements to real representation.
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/// @param num_dims - number of dimensions in the shape.
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/// @param dims - pointer to an array of @a num_dims shape dimensions. If num_dims is 0, this pointer can be NULL.
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/// XNNPACK does not keep any pointers to this array after the function returns.
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enum xnn_status xnn_validate_quantized_tensor(
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enum xnn_datatype datatype,
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int32_t zero_point,
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float scale,
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size_t num_dims,
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const size_t* dims);
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/// Validate the dimensions, channel_dim, zero point, datatype, and scales of a channelwise quantized tensor-type.
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///
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/// @param datatype - type of the tensor elements.
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/// @param zero_point - offset from zero to subtract from the quantized elements in the Value.
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/// @param scale - per-channel multiplication factors to convert quantized elements to real representation.
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/// @param num_dims - number of dimensions in the shape.
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/// @param channel_dim - index of the channel dimension in the tensor with per-channel quantization parameters.
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/// Typically this is the first dimension (dimension #0) of the filter tensors in the Convolution,
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/// Deconvolution, and Fully Connected operators and the last dimension of the filter tensors in
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/// the Depthwise Convolution operators.
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/// @param dims - pointer to an array of @a num_dims shape dimensions. If num_dims is 0, this pointer can be NULL.
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/// XNNPACK does not keep any pointers to this array after the function returns.
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enum xnn_status xnn_validate_channelwise_quantized_tensor(
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enum xnn_datatype datatype,
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int32_t zero_point,
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const float* scale,
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size_t num_dims,
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size_t channel_dim,
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const size_t* dims);
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/// Define a channelwise quantized tensor-type Value and add it to a Subgraph.
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///
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/// @param subgraph - a Subgraph object that will own the created Value.
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/// @param datatype - type of the tensor elements.
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/// @param zero_point - offset from zero to subtract from the quantized elements in the Value.
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/// @param scale - per-channel multiplication factors to convert quantized elements to real representation.
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/// @param num_dims - number of dimensions in the shape.
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/// @param channel_dim - index of the channel dimension in the tensor with per-channel quantization parameters.
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/// Typically this is the first dimension (dimension #0) of the filter tensors in the Convolution,
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/// Deconvolution, and Fully Connected operators and the last dimension of the filter tensors in
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/// the Depthwise Convolution operators.
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/// @param dims - pointer to an array of @a num_dims shape dimensions. If num_dims is 0, this pointer can be NULL.
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/// XNNPACK does not keep any pointers to this array after the function returns.
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/// @param data - pointer to static data used for tensor initialization. If the tensor is not statically initialized,
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/// this pointer must be is NULL. If non-NULL, the life-time of the static data must exceed the life-time
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/// of the Subgraph object, and of any Runtime objects created from the Subgraph.
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/// @param external_id - external ID for the Value. The ID must be within the range of reversed Value IDs specified on
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/// the Subgraph creation. If the external ID is XNN_INVALID_VALUE_ID, an internal ID will be
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/// created for the Value.
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/// @param flags - binary features of the Value. Supported values are any combination of XNN_VALUE_FLAG_EXTERNAL_INPUT
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/// and XNN_VALUE_FLAG_EXTERNAL_OUTPUT.
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/// @param id_out - pointer to the variable that will be initialized with the Value ID upon successful return. If a
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/// valid @a external_id was provided, the variable will be initialized with the @a external_id value.
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enum xnn_status xnn_define_channelwise_quantized_tensor_value_v2(
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xnn_subgraph_t subgraph,
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enum xnn_datatype datatype,
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int32_t zero_point,
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const float* scale,
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size_t num_dims,
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size_t channel_dim,
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const size_t* dims,
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const void* data,
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uint32_t external_id,
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uint32_t flags,
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uint32_t* id_out);
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/// Define a dynamically quantized tensor-type Value and add it to a Subgraph.
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///
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/// @param subgraph - a Subgraph object that will own the created Value.
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/// @param datatype - type of the tensor elements.
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/// @param num_dims - number of dimensions in the shape.
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/// @param num_non_batch_dims - number of non-batch dimensions in the shape. The leading (num_dims - num_non_batch_dims)
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/// dimensions will be flattened and treated as batch size. A set of quantization parameters
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/// will be calculated for each batch element.
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/// @param dims - pointer to an array of @a num_dims shape dimensions. If num_dims is 0, this pointer can be NULL.
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/// XNNPACK does not keep any pointers to this array after the function returns.
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/// @param external_id - external ID for the Value. The ID must be within the range of reversed Value IDs specified on
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/// the Subgraph creation. If the external ID is XNN_INVALID_VALUE_ID, an internal ID will be
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/// created for the Value.
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/// @param flags - binary features of the Value. No supported flags are currently defined.
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/// @param id_out - pointer to the variable that will be initialized with the Value ID upon successful return. If a
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/// valid @a external_id was provided, the variable will be initialized with the @a external_id value.
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enum xnn_status xnn_define_dynamically_quantized_tensor_value(
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xnn_subgraph_t subgraph,
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enum xnn_datatype datatype,
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size_t num_dims,
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size_t num_nonbatch_dims,
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const size_t* dims,
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uint32_t external_id,
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uint32_t flags,
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uint32_t* id_out);
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/// Define a Convert Node and add it to a Subgraph.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
|
|
/// shape must match the shape of the input tensor.
|
|
/// @param flags - binary features of the Convert Node. No supported flags are currently defined.
|
|
enum xnn_status xnn_define_convert(
|
|
xnn_subgraph_t subgraph,
|
|
uint32_t input_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a 2D Convolution Node and add it to a Subgraph.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param input_padding_top - implicit zero-padding above 2D input data. Must be 0 if XNN_FLAG_TENSORFLOW_SAME_PADDING
|
|
/// flag is specified.
|
|
/// @param input_padding_right - implicit zero-padding to the right of 2D input data. Must be 0 if
|
|
/// XNN_FLAG_TENSORFLOW_SAME_PADDING flag is specified.
|
|
/// @param input_padding_bottom - implicit zero-padding below 2D input data. Must be 0 if
|
|
/// XNN_FLAG_TENSORFLOW_SAME_PADDING flag is specified.
|
|
/// @param input_padding_left - implicit zero-padding to the left of 2D input data. Must be 0 if
|
|
/// XNN_FLAG_TENSORFLOW_SAME_PADDING flag is specified.
|
|
/// @param kernel_height - kernel (filter) height.
|
|
/// @param kernel_width - kernel (filter) width.
|
|
/// @param subsampling_height - height of subsampling region for convolution output (convolution height stride).
|
|
/// @param subsampling_width - width of subsampling region for convolution output (convolution width stride).
|
|
/// @param dilation_height - dilation of kernel elements along the height dimension.
|
|
/// @param dilation_width - dilation of kernel elements along the width dimension.
|
|
/// @param groups - number of convolution groups.
|
|
/// @param group_input_channels - number of input channels per group.
|
|
/// @param group_output_channels - number of output channels per group.
|
|
/// @param output_min - lower bound for clipping output values.
|
|
/// @param output_max - upper bound for clipping output values.
|
|
/// @param input_id - Value ID for the input tensor. The input tensor must be a 4D tensor defined in the @a subgraph
|
|
/// with [N, IH, IW, groups * group_input_channels] dimensions
|
|
/// @param filter_id - Value ID for the filter tensor. The filter tensor must ge a 4D tensor defined in the @a subgraph
|
|
/// with [groups * group_output_channels, kernel_height, kernel_width, group_input_channels]
|
|
/// dimensions.
|
|
/// @param bias_id - Value ID for the bias tensor, or XNN_INVALID_VALUE_ID for a 2D Convolution Node without a bias. If
|
|
/// present, the bias tensor must be a 1D tensor defined in the @a subgraph with [groups *
|
|
/// group_output_channels] dimensions.
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be a 4D tensor defined in the @a subgraph
|
|
/// with [N, OH, OW, groups * group_output_channels] dimensions.
|
|
/// @param flags - binary features of the 2D Convolution Node. The only currently supported values is
|
|
/// XNN_FLAG_TENSORFLOW_SAME_PADDING.
|
|
enum xnn_status xnn_define_convolution_2d(
|
|
xnn_subgraph_t subgraph,
|
|
uint32_t input_padding_top,
|
|
uint32_t input_padding_right,
|
|
uint32_t input_padding_bottom,
|
|
uint32_t input_padding_left,
|
|
uint32_t kernel_height,
|
|
uint32_t kernel_width,
|
|
uint32_t subsampling_height,
|
|
uint32_t subsampling_width,
|
|
uint32_t dilation_height,
|
|
uint32_t dilation_width,
|
|
uint32_t groups,
|
|
size_t group_input_channels,
|
|
size_t group_output_channels,
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t input_id,
|
|
uint32_t filter_id,
|
|
uint32_t bias_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a 2D Deconvolution (Transposed Convolution) Node and add it to a Subgraph.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param padding_top - implicit padding above 2D output data.
|
|
/// @param padding_right - implicit padding to the right of 2D output data.
|
|
/// @param padding_bottom - implicit padding below 2D output data.
|
|
/// @param padding_left - implicit padding to the left of 2D output data.
|
|
/// @param adjustment_height - additional elements in the bottom of the 2D output data.
|
|
/// @param adjustment_width - additional elements to the right of the 2D output data.
|
|
/// @param kernel_height - kernel (filter) height.
|
|
/// @param kernel_width - kernel (filter) width.
|
|
/// @param upsampling_height - height of upsampling region for deconvolution input (deconvolution height stride).
|
|
/// @param upsampling_width - width of upsampling region for deconvolution input (deconvolution width stride).
|
|
/// @param dilation_height - dilation of kernel elements along the height dimension.
|
|
/// @param dilation_width - dilation of kernel elements along the width dimension.
|
|
/// @param groups - number of convolution groups.
|
|
/// @param group_input_channels - number of input channels per group.
|
|
/// @param group_output_channels - number of output channels per group.
|
|
/// @param output_min - lower bound for clipping output values.
|
|
/// @param output_max - upper bound for clipping output values.
|
|
/// @param input_id - Value ID for the input tensor. The input tensor must be a 4D tensor defined in the @a subgraph
|
|
/// with [N, IH, IW, groups * group_input_channels] dimensions
|
|
/// @param filter_id - Value ID for the filter tensor. The filter tensor must ge a 4D tensor defined in the @a subgraph
|
|
/// with [groups * group_output_channels, kernel_height, kernel_width, group_input_channels]
|
|
/// dimensions.
|
|
/// @param bias_id - Value ID for the bias tensor, or XNN_INVALID_VALUE_ID for a 2D Convolution Node without a bias. If
|
|
/// present, the bias tensor must be a 1D tensor defined in the @a subgraph with
|
|
/// [groups * group_output_channels] dimensions.
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be a 4D tensor defined in the @a subgraph
|
|
/// with [N, OH, OW, groups * group_output_channels] dimensions.
|
|
/// @param flags - binary features of the 2D Deconvolution Node. No supported flags are currently defined.
|
|
enum xnn_status xnn_define_deconvolution_2d(
|
|
xnn_subgraph_t subgraph,
|
|
uint32_t padding_top,
|
|
uint32_t padding_right,
|
|
uint32_t padding_bottom,
|
|
uint32_t padding_left,
|
|
uint32_t adjustment_height,
|
|
uint32_t adjustment_width,
|
|
uint32_t kernel_height,
|
|
uint32_t kernel_width,
|
|
uint32_t upsampling_height,
|
|
uint32_t upsampling_width,
|
|
uint32_t dilation_height,
|
|
uint32_t dilation_width,
|
|
uint32_t groups,
|
|
size_t group_input_channels,
|
|
size_t group_output_channels,
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t input_id,
|
|
uint32_t filter_id,
|
|
uint32_t bias_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a 2D Depthwise Convolution Node and add it to a Subgraph.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param input_padding_top - implicit zero-padding above 2D input data. Must be 0 if XNN_FLAG_TENSORFLOW_SAME_PADDING
|
|
/// flag is specified.
|
|
/// @param input_padding_right - implicit zero-padding to the right of 2D input data. Must be 0 if
|
|
/// XNN_FLAG_TENSORFLOW_SAME_PADDING flag is specified.
|
|
/// @param input_padding_bottom - implicit zero-padding below 2D input data. Must be 0 if
|
|
/// XNN_FLAG_TENSORFLOW_SAME_PADDING flag is specified.
|
|
/// @param input_padding_left - implicit zero-padding to the left of 2D input data. Must be 0 if
|
|
/// XNN_FLAG_TENSORFLOW_SAME_PADDING flag is specified.
|
|
/// @param kernel_height - kernel (filter) height.
|
|
/// @param kernel_width - kernel (filter) width.
|
|
/// @param subsampling_height - height of subsampling region for convolution output (convolution height stride).
|
|
/// @param subsampling_width - width of subsampling region for convolution output (convolution width stride).
|
|
/// @param dilation_height - dilation of kernel elements along the height dimension.
|
|
/// @param dilation_width - dilation of kernel elements along the width dimension.
|
|
/// @param depth_multiplier - ratio of output channels to input channels.
|
|
/// @param input_channels - number of input channels.
|
|
/// @param output_min - lower bound for clipping output values.
|
|
/// @param output_max - upper bound for clipping output values.
|
|
/// @param input_id - Value ID for the input tensor. The input tensor must be a 4D tensor defined in the @a subgraph
|
|
/// with [N, IH, IW, input_channels] dimensions
|
|
/// @param filter_id - Value ID for the filter tensor. The filter tensor must ge a 4D tensor defined in the @a subgraph
|
|
/// with [1, kernel_height, kernel_width, input_channels * depth_multiplier] dimensions.
|
|
/// @param bias_id - Value ID for the bias tensor, or XNN_INVALID_VALUE_ID for a 2D Depthwise Convolution Node without
|
|
/// a bias. If present, the bias tensor must be a 1D tensor defined in the @a subgraph with
|
|
/// [input_channels * depth_multiplier] dimensions.
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be a 4D tensor defined in the @a subgraph
|
|
/// with [N, OH, OW, input_channels * depth_multiplier] dimensions.
|
|
/// @param flags - binary features of the 2D Depthwise Convolution Node. The only currently supported values is
|
|
/// XNN_FLAG_TENSORFLOW_SAME_PADDING.
|
|
enum xnn_status xnn_define_depthwise_convolution_2d(
|
|
xnn_subgraph_t subgraph,
|
|
uint32_t input_padding_top,
|
|
uint32_t input_padding_right,
|
|
uint32_t input_padding_bottom,
|
|
uint32_t input_padding_left,
|
|
uint32_t kernel_height,
|
|
uint32_t kernel_width,
|
|
uint32_t subsampling_height,
|
|
uint32_t subsampling_width,
|
|
uint32_t dilation_height,
|
|
uint32_t dilation_width,
|
|
uint32_t depth_multiplier,
|
|
size_t input_channels,
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t input_id,
|
|
uint32_t filter_id,
|
|
uint32_t bias_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a Depth To Space Node 2D and add it to a Subgraph.
|
|
///
|
|
/// The Depth To Space 2D Node rearranges data from depth into blocks of spatial data (a reverse transform to
|
|
/// Space To Depth). For a given input pixel, an output square of pixels with side @a block_size is formed from values
|
|
/// in the corresponding number of its channels. The output depth is therefore @a block_size x @a block_size times
|
|
/// smaller than that of the input.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param block_size - the size of the spatial block.
|
|
/// @param input_id - Value ID for the input tensor. The input tensor must be a 4D tensor defined in the @a subgraph
|
|
/// with [N, IH, IW, OC * block_size * block_size] dimensions.
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be a 4D tensor defined in the @a subgraph
|
|
/// with [N, IH * block_size, IW * block_size, OC] dimensions.
|
|
/// @param flags - binary features of the input_channels Node. No supported flags are currently defined.
|
|
enum xnn_status xnn_define_depth_to_space_2d(
|
|
xnn_subgraph_t subgraph,
|
|
uint32_t block_size,
|
|
uint32_t input_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
enum xnn_status xnn_define_depth_to_space(
|
|
xnn_subgraph_t subgraph,
|
|
uint32_t input_id,
|
|
uint32_t output_id,
|
|
uint32_t block_size,
|
|
uint32_t flags);
|
|
|
|
/// Define a 1D Global Average Pooling Node and add it to a Subgraph.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param output_min - lower bound for clipping output values.
|
|
/// @param output_max - upper bound for clipping output values.
|
|
/// @param input_id - Value ID for the input tensor. The input tensor must be a dense tensor with 2 or more dimensions
|
|
/// defined in the @a subgraph. Averaging is performed across the second-innermost dimension.
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be a dense tensor with 2 or more
|
|
/// dimensions defined in the @a subgraph.
|
|
/// @param flags - binary features of the 1D Global Average Pooling Node. The only currently supported value is
|
|
/// XNN_FLAG_REDUCE_DIMS.
|
|
enum xnn_status xnn_define_global_average_pooling_1d(
|
|
xnn_subgraph_t subgraph,
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t input_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a 2D Global Average Pooling Node and add it to a Subgraph.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param output_min - lower bound for clipping output values.
|
|
/// @param output_max - upper bound for clipping output values.
|
|
/// @param input_id - Value ID for the input tensor. The input tensor must be a dense tensor with 3 or more dimensions
|
|
/// defined in the @a subgraph. Averaging is performed across the second- and third-innermost
|
|
/// dimensions.
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be a dense tensor with 3 or more
|
|
/// dimensions defined in the @a subgraph.
|
|
/// @param flags - binary features of the 2D Global Average Pooling Node. The only currently supported value is
|
|
/// XNN_FLAG_REDUCE_DIMS.
|
|
enum xnn_status xnn_define_global_average_pooling_2d(
|
|
xnn_subgraph_t subgraph,
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t input_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a 1D Global Sum Pooling Node and add it to a Subgraph.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param output_min - lower bound for clipping output values.
|
|
/// @param output_max - upper bound for clipping output values.
|
|
/// @param input_id - Value ID for the input tensor. The input tensor must be a dense tensor with 2 or more dimensions
|
|
/// defined in the @a subgraph. Averaging is performed across the second-innermost dimension.
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be a dense tensor with 2 or more
|
|
/// dimensions defined in the @a subgraph.
|
|
/// @param flags - binary features of the 1D Global Sum Pooling Node. The only currently supported value is
|
|
/// XNN_FLAG_REDUCE_DIMS.
|
|
enum xnn_status xnn_define_global_sum_pooling_1d(
|
|
xnn_subgraph_t subgraph,
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t input_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a 2D Global Sum Pooling Node and add it to a Subgraph.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param output_min - lower bound for clipping output values.
|
|
/// @param output_max - upper bound for clipping output values.
|
|
/// @param input_id - Value ID for the input tensor. The input tensor must be a dense tensor with 3 or more dimensions
|
|
/// defined in the @a subgraph. Averaging is performed across the second- and third-innermost
|
|
/// dimensions.
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be a dense tensor with 3 or more
|
|
/// dimensions defined in the @a subgraph.
|
|
/// @param flags - binary features of the 2D Global Sum Pooling Node. The only currently supported value is
|
|
/// XNN_FLAG_REDUCE_DIMS.
|
|
enum xnn_status xnn_define_global_sum_pooling_2d(
|
|
xnn_subgraph_t subgraph,
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t input_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a 2D Average Pooling Node and add it to a Subgraph.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param input_padding_top - implicit zero-padding above 2D input data. Must be 0 if XNN_FLAG_TENSORFLOW_SAME_PADDING
|
|
/// flag is specified.
|
|
/// @param input_padding_right - implicit zero-padding to the right of 2D input data. Must be 0 if
|
|
/// XNN_FLAG_TENSORFLOW_SAME_PADDING flag is specified.
|
|
/// @param input_padding_bottom - implicit zero-padding below 2D input data. Must be 0 if
|
|
/// XNN_FLAG_TENSORFLOW_SAME_PADDING flag is specified.
|
|
/// @param input_padding_left - implicit zero-padding to the left of 2D input data. Must be 0 if
|
|
/// XNN_FLAG_TENSORFLOW_SAME_PADDING flag is specified.
|
|
/// @param pooling_height - pooling (kernel) height.
|
|
/// @param pooling_width - pooling (kernel) width.
|
|
/// @param stride_height - displacing of the pooling window in the vertical dimension of the input pixels corresponding
|
|
/// to vertically adjacent output pixels.
|
|
/// @param stride_width - displacing of the pooling window in the horizontal dimension of the input pixels corresponding
|
|
/// to horizontally adjacent output pixels.
|
|
/// @param output_min - lower bound for clipping output values.
|
|
/// @param output_max - upper bound for clipping output values.
|
|
/// @param input_id - Value ID for the input tensor. The input tensor must be a 4D tensor defined in the @a subgraph
|
|
/// with [N, IH, IW, channels] dimensions
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be a 4D tensor defined in the @a subgraph
|
|
/// with [N, OH, OW, channels] dimensions.
|
|
/// @param flags - binary features of the 2D Average Pooling Node. The only currently supported values is
|
|
/// XNN_FLAG_TENSORFLOW_SAME_PADDING.
|
|
enum xnn_status xnn_define_average_pooling_2d(
|
|
xnn_subgraph_t subgraph,
|
|
uint32_t input_padding_top,
|
|
uint32_t input_padding_right,
|
|
uint32_t input_padding_bottom,
|
|
uint32_t input_padding_left,
|
|
uint32_t pooling_height,
|
|
uint32_t pooling_width,
|
|
uint32_t stride_height,
|
|
uint32_t stride_width,
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t input_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a Fully Connected Node and add it to a Subgraph.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param output_min - lower bound for clipping output values.
|
|
/// @param output_max - upper bound for clipping output values.
|
|
/// @param input_id - Value ID for the input tensor. The input tensor must be an N-dimensional tensor defined in the
|
|
/// @a subgraph. If XNN_FLAG_TENSORFLOW_RESHAPE_2D is not specified, the input tensor must be at least
|
|
/// 1D and its last dimension must match the last dimension of the filter tensor. In particular, if
|
|
/// input is a 2D tensor, it must have [batch_size, input_channels] dimensions.
|
|
/// If XNN_FLAG_TENSORFLOW_RESHAPE_2D is specified, the number of elements in the input tensor must be
|
|
/// divisible by the input_channels. The tensor will be first flattened into a 1D tensor of
|
|
/// [num_input_elements] dimensions, then reshaped into a 2D tensor of
|
|
/// [num_input_elements / input_channels, input_channels] dimensions where num_input_elements is the
|
|
/// total number of elements in the input tensor.
|
|
/// @param filter_id - Value ID for the filter tensor. The filter tensor must a 2D tensor defined in the @a subgraph.
|
|
/// If the XNN_FLAG_TRANSPOSE_WEIGHTS flag is not specified, the filter tensor must have
|
|
/// [output_channels, input_channels] dimensions. If the XNN_FLAG_TRANSPOSE_WEIGHTS flag is
|
|
/// specified, the filter tensor must have [input_channels, output_channels] dimensions.
|
|
/// @param bias_id - Value ID for the bias tensor, or XNN_INVALID_VALUE_ID for a Fully Connected Node without a bias.
|
|
/// If present, the bias tensor must be a 1D tensor defined in the @a subgraph with [output_channels]
|
|
/// dimensions.
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph.
|
|
/// If XNN_FLAG_TENSORFLOW_RESHAPE_2D is not specified, the output tensor must have the same
|
|
/// dimensionality as the input tensor, all its dimensions but the last one must match the
|
|
/// corresponding dimensions of the input tensor, and the last dimensions of the output tensor must
|
|
/// match the first dimension of the filter tensor. In particular, if input is a 2D tensor, output
|
|
/// must be a 2D tensor of [batch_size, output_channels] dimensions.
|
|
/// If XNN_FLAG_TENSORFLOW_RESHAPE_2D is specified, output must be a 2D tensor of
|
|
/// [num_input_elements / input_channels, output_channels] dimensions where num_input_elements is the
|
|
/// total number of elements in the input tensor.
|
|
/// @param flags - binary features of the Fully Connected Node. The only currently supported values are
|
|
/// XNN_FLAG_TENSORFLOW_RESHAPE_2D and XNN_FLAG_TRANSPOSE_WEIGHTS.
|
|
enum xnn_status xnn_define_fully_connected(
|
|
xnn_subgraph_t subgraph,
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t input_id,
|
|
uint32_t filter_id,
|
|
uint32_t bias_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a Sparse Fully Connected Node and add it to a Subgraph.
|
|
///
|
|
/// This operator is experimental, and will be removed in the future.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param output_min - lower bound for clipping output values.
|
|
/// @param output_max - upper bound for clipping output values.
|
|
/// @param input_id - Value ID for the input tensor. The input tensor must be an N-dimensional tensor defined in the
|
|
/// @a subgraph. If XNN_FLAG_TENSORFLOW_RESHAPE_2D is not specified, the input tensor must be at least
|
|
/// 1D and its last dimension must match the last dimension of the filter tensor. In particular, if
|
|
/// input is a 2D tensor, it must have [batch_size, input_channels] dimensions.
|
|
/// If XNN_FLAG_TENSORFLOW_RESHAPE_2D is specified, the number of elements in the input tensor must be
|
|
/// divisible by the input_channels. The tensor will be first flattened into a 1D tensor of
|
|
/// [num_input_elements] dimensions, then reshaped into a 2D tensor of
|
|
/// [num_input_elements / input_channels, input_channels] dimensions where num_input_elements is the
|
|
/// total number of elements in the input tensor.
|
|
/// @param filter_id - Value ID for the filter tensor. The filter tensor must a 2D tensor defined in the @a subgraph.
|
|
/// If the XNN_FLAG_TRANSPOSE_WEIGHTS flag is not specified, the filter tensor must have
|
|
/// [output_channels, input_channels] dimensions. If the XNN_FLAG_TRANSPOSE_WEIGHTS flag is
|
|
/// specified, the filter tensor must have [input_channels, output_channels] dimensions.
|
|
/// @param bias_id - Value ID for the bias tensor, or XNN_INVALID_VALUE_ID for a Fully Connected Node without a bias.
|
|
/// If present, the bias tensor must be a 1D tensor defined in the @a subgraph with [output_channels]
|
|
/// dimensions.
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph.
|
|
/// If XNN_FLAG_TENSORFLOW_RESHAPE_2D is not specified, the output tensor must have the same
|
|
/// dimensionality as the input tensor, all its dimensions but the last one must match the
|
|
/// corresponding dimensions of the input tensor, and the last dimensions of the output tensor must
|
|
/// match the first dimension of the filter tensor. In particular, if input is a 2D tensor, output
|
|
/// must be a 2D tensor of [batch_size, output_channels] dimensions.
|
|
/// If XNN_FLAG_TENSORFLOW_RESHAPE_2D is specified, output must be a 2D tensor of
|
|
/// [num_input_elements / input_channels, output_channels] dimensions where num_input_elements is the
|
|
/// total number of elements in the input tensor.
|
|
/// @param flags - binary features of the Fully Connected Node. The only currently supported values are
|
|
/// XNN_FLAG_TENSORFLOW_RESHAPE_2D and XNN_FLAG_TRANSPOSE_WEIGHTS.
|
|
enum xnn_status xnn_define_fully_connected_sparse(
|
|
xnn_subgraph_t subgraph,
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t input_id,
|
|
uint32_t filter_id,
|
|
uint32_t bias_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a 2D Max Pooling Node and add it to a Subgraph.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param input_padding_top - implicit zero-padding above 2D input data. Must be 0 if XNN_FLAG_TENSORFLOW_SAME_PADDING
|
|
/// flag is specified.
|
|
/// @param input_padding_right - implicit zero-padding to the right of 2D input data. Must be 0 if
|
|
/// XNN_FLAG_TENSORFLOW_SAME_PADDING flag is specified.
|
|
/// @param input_padding_bottom - implicit zero-padding below 2D input data. Must be 0 if
|
|
/// XNN_FLAG_TENSORFLOW_SAME_PADDING flag is specified.
|
|
/// @param input_padding_left - implicit zero-padding to the left of 2D input data. Must be 0 if
|
|
/// XNN_FLAG_TENSORFLOW_SAME_PADDING flag is specified.
|
|
/// @param pooling_height - pooling (kernel) height.
|
|
/// @param pooling_width - pooling (kernel) width.
|
|
/// @param stride_height - displacing of the pooling window in the vertical dimension of the input pixels corresponding
|
|
/// to vertically adjacent output pixels.
|
|
/// @param stride_width - displacing of the pooling window in the horizontal dimension of the input pixels corresponding
|
|
/// to horizontally adjacent output pixels.
|
|
/// @param dilation_height - dilation of pooling elements along the height dimension.
|
|
/// @param dilation_width - dilation of pooling elements along the width dimension.
|
|
/// @param output_min - lower bound for clipping output values.
|
|
/// @param output_max - upper bound for clipping output values.
|
|
/// @param input_id - Value ID for the input tensor. The input tensor must be a 4D tensor defined in the @a subgraph
|
|
/// with [N, IH, IW, channels] dimensions
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be a 4D tensor defined in the @a subgraph
|
|
/// with [N, OH, OW, channels] dimensions.
|
|
/// @param flags - binary features of the 2D Max Pooling Node. The only currently supported values is
|
|
/// XNN_FLAG_TENSORFLOW_SAME_PADDING.
|
|
enum xnn_status xnn_define_max_pooling_2d(
|
|
xnn_subgraph_t subgraph,
|
|
uint32_t input_padding_top,
|
|
uint32_t input_padding_right,
|
|
uint32_t input_padding_bottom,
|
|
uint32_t input_padding_left,
|
|
uint32_t pooling_height,
|
|
uint32_t pooling_width,
|
|
uint32_t stride_height,
|
|
uint32_t stride_width,
|
|
uint32_t dilation_height,
|
|
uint32_t dilation_width,
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t input_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a 2D ArgMax Pooling Node and add it to a Subgraph.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param input_padding_top - implicit zero-padding above 2D input data.
|
|
/// @param input_padding_right - implicit zero-padding to the right of 2D input data.
|
|
/// @param input_padding_bottom - implicit zero-padding below 2D input data.
|
|
/// @param input_padding_left - implicit zero-padding to the left of 2D input data.
|
|
/// @param pooling_height - pooling (kernel) height. Vertical stride between pooling regions match this value.
|
|
/// @param pooling_width - pooling (kernel) width. Horizontal stride between pooling regions match this value.
|
|
/// @param input_id - Value ID for the input tensor. The input tensor must be a 4D tensor defined in the @a subgraph
|
|
/// with [N, IH, IW, channels] dimensions
|
|
/// @param output_value_id - Value ID for the output tensor with the maximum values in the pools. The output tensor must
|
|
/// be a 4D tensor defined in the @a subgraph with [N, OH, OW, channels] dimensions.
|
|
/// @param output_index_id - Value ID for the output tensor with the indexes of the maximum values in the pools. The
|
|
/// output tensor must be a 4D tensor defined in the @a subgraph with [N, OH, OW, channels]
|
|
/// dimensions.
|
|
/// @param flags - binary features of the 2D ArgMax Pooling Node. No supported flags are currently defined.
|
|
enum xnn_status xnn_define_argmax_pooling_2d(
|
|
xnn_subgraph_t subgraph,
|
|
uint32_t input_padding_top,
|
|
uint32_t input_padding_right,
|
|
uint32_t input_padding_bottom,
|
|
uint32_t input_padding_left,
|
|
uint32_t pooling_height,
|
|
uint32_t pooling_width,
|
|
uint32_t input_id,
|
|
uint32_t output_value_id,
|
|
uint32_t output_index_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a 2D UnPooling Node and add it to a Subgraph.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param padding_top - implicit padding above 2D output data.
|
|
/// @param padding_right - implicit padding to the right of 2D output data.
|
|
/// @param padding_bottom - implicit padding below 2D output data.
|
|
/// @param padding_left - implicit padding to the left of 2D output data.
|
|
/// @param pooling_height - height of the pooling window.
|
|
/// @param pooling_width - width of the pooling window.
|
|
/// @param input_value_id - Value ID for the input tensor with the max-pooling values to invert. The input value tensor
|
|
/// must be a 4D tensor defined in the @a subgraph with [N, IH, IW, channels] dimensions.
|
|
/// @param input_index_id - Value ID for the input tensor with the indices of the per-pool maximum values produced by
|
|
/// a 2D UnPooling Node. The input tensor must be a 4D tensor defined in the @a subgraph with
|
|
/// [N, IH, IW, channels] dimensions.
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be a 4D tensor defined in the @a subgraph
|
|
/// with [N, OH, OW, channels] dimensions.
|
|
/// @param flags - binary features of the 2D UnPooling Node. No supported flags are currently defined.
|
|
enum xnn_status xnn_define_unpooling_2d(
|
|
xnn_subgraph_t subgraph,
|
|
uint32_t padding_top,
|
|
uint32_t padding_right,
|
|
uint32_t padding_bottom,
|
|
uint32_t padding_left,
|
|
uint32_t pooling_height,
|
|
uint32_t pooling_width,
|
|
uint32_t input_value_id,
|
|
uint32_t input_index_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a 2-Input Add Node and add it to a Subgraph.
|
|
///
|
|
/// The 2-Input Add Node computes elementwise addition of two tensor inputs with numpy broadcasting rules.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param output_min - lower bound for clipping output values.
|
|
/// @param output_max - upper bound for clipping output values.
|
|
/// @param input1_id - Value ID for the first input tensor. The input tensor must be an N-dimensional tensor defined in
|
|
/// the @a subgraph with each dimension either equal to the corresponding dimension of the second
|
|
/// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
|
|
/// that dimension.
|
|
/// @param input2_id - Value ID for the second input tensor. The input tensor must be an M-dimensional tensor defined in
|
|
/// the @a subgraph with each dimension either equal to the corresponding dimension of the first
|
|
/// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
|
|
/// that dimension.
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be a max(N,M)-dimensional tensor defined
|
|
/// in the @a subgraph with each dimension equal to the maximum between the corresponding dimension
|
|
/// of the two inputs.
|
|
/// @param flags - binary features of the Add Node. No supported flags are currently defined.
|
|
enum xnn_status xnn_define_add2(
|
|
xnn_subgraph_t subgraph,
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t input1_id,
|
|
uint32_t input2_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a 2-Input Multiply Node and add it to a Subgraph.
|
|
///
|
|
/// The 2-Input Multiply Node computes elementwise multiplication of two tensor inputs with numpy broadcasting rules.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param output_min - lower bound for clipping output values.
|
|
/// @param output_max - upper bound for clipping output values.
|
|
/// @param input1_id - Value ID for the first input tensor. The input tensor must be an N-dimensional tensor defined in
|
|
/// the @a subgraph with each dimension either equal to the corresponding dimension of the second
|
|
/// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
|
|
/// that dimension.
|
|
/// @param input2_id - Value ID for the second input tensor. The input tensor must be an M-dimensional tensor defined in
|
|
/// the @a subgraph with each dimension either equal to the corresponding dimension of the first
|
|
/// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
|
|
/// that dimension.
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be a max(N,M)-dimensional tensor defined
|
|
/// in the @a subgraph with each dimension equal to the maximum between the corresponding dimension
|
|
/// of the two inputs.
|
|
/// @param flags - binary features of the Multiply Node. No supported flags are currently defined.
|
|
enum xnn_status xnn_define_multiply2(
|
|
xnn_subgraph_t subgraph,
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t input1_id,
|
|
uint32_t input2_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
// Cap operations applied to logits (Q * K) of attention operator.
|
|
enum xnn_attention_logits_cap_type {
|
|
// No capping.
|
|
xnn_attention_logits_cap_type_none = 0,
|
|
// Cap the absolute values of logits by tanh: tanh(logits / cap) * cap
|
|
xnn_attention_logits_cap_type_tanh
|
|
};
|
|
|
|
// Params when the cap type is xnn_attention_logits_cap_type_tanh.
|
|
struct xnn_attention_logits_cap_tanh_params {
|
|
float cap;
|
|
};
|
|
|
|
/// Define a Scaled Dot-Product Attention Node and add it to a Subgraph.
|
|
///
|
|
/// This operator is experimental.
|
|
///
|
|
/// The Scaled Dot-Product Attention Node computes a multi-head or multi-query scaled dot attention on the query, key,
|
|
/// and value tensors.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param cap_type - type of cap to be applied to the logits.
|
|
/// @param cap_params - parameters for the cap. Must be a pointer to xnn_attention_logits_cap_tanh_params if cap_type
|
|
/// is xnn_attention_logits_cap_type_tanh.
|
|
/// @param query_id - Value ID for the query tensor. The query tensor must be a 3+-dimensional tensor defined in the
|
|
/// @a subgraph with the dimensions as [*, H, T, C], where H/T/C are the heads/tokens/channels, and *
|
|
/// is the 0 or more dimensions treated as batch size.
|
|
/// @param key_id - Value ID for the key tensor. The key tensor must be a 2+--dimensional tensor defined in the
|
|
/// @a subgraph. It can have the same number of dimensions as the query, with the dimensions as
|
|
/// [*, H, U, C] (multi-head), or have 1 less dimension than the query, with the dimensions as
|
|
/// as [*, U, C] (multi-query, number of heads omitted implies single head), where H/U/C are the
|
|
/// heads/key_value_tokens/channels, and * is the 0 or more dimensions treated as batch size. These
|
|
/// batch size dimensions must be the same as query.
|
|
/// @param value_id - Value ID for the value tensor. The value tensor must be a 2+--dimensional tensor defined in the
|
|
/// @a subgraph. It can have the same number of dimensions as the query, with the dimensions as
|
|
/// [*, H, U, D] (multi-head), or have 1 less dimension than the query, with the dimensions as
|
|
/// as [*, U, D] (multi-query, number of heads omitted implies single head), where H/U/D are the
|
|
/// heads/key_value_tokens/value_channels, and * is the 0 or more dimensions treated as batch size.
|
|
/// These batch size dimensions must be the same as query and key.
|
|
/// @param scale_id - Value ID for the scale tensor. The scale tensor must be a 1D tensor defined in the @a subgraph
|
|
/// with [C] dimensions. The query tensor is multiplied with this scale tensor before the dot product
|
|
/// with the key tensor.
|
|
/// @param mask_id - Value ID for the mask tensor. The mask tensor must be a 2D tensor defined in the @a subgraph with
|
|
/// [T, U] dimensions. The mask tensor is added to the logits (query dot value).
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be a 3+-dimensional tensor defined in the
|
|
/// @a subgraph with the dimensions as [*, H, T, D], where H/T/D are the heads/tokens/value_channels,
|
|
/// and * is the 0 or more dimensions treated as batch size. These batch size dimensions must be the
|
|
/// same as query, key, and value.
|
|
/// @param flags - binary features of the Scaled Dot Product Attention Node. No supported flags are currently defined.
|
|
enum xnn_status xnn_define_scaled_dot_product_attention(
|
|
xnn_subgraph_t subgraph,
|
|
enum xnn_attention_logits_cap_type cap_type,
|
|
const void* cap_params,
|
|
uint32_t query_id,
|
|
uint32_t key_id,
|
|
uint32_t value_id,
|
|
uint32_t scale_id,
|
|
uint32_t mask_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a Subtract Node and add it to a Subgraph.
|
|
///
|
|
/// The Subtract Node computes elementwise subtraction of two tensor inputs with numpy broadcasting rules.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param output_min - lower bound for clipping output values.
|
|
/// @param output_max - upper bound for clipping output values.
|
|
/// @param input1_id - Value ID for the first input tensor. The input tensor must be an N-dimensional tensor defined in
|
|
/// the @a subgraph with each dimension either equal to the corresponding dimension of the second
|
|
/// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
|
|
/// that dimension.
|
|
/// @param input2_id - Value ID for the second input tensor. The input tensor must be an M-dimensional tensor defined in
|
|
/// the @a subgraph with each dimension either equal to the corresponding dimension of the first
|
|
/// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
|
|
/// that dimension.
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be a max(N,M)-dimensional tensor defined
|
|
/// in the @a subgraph with each dimension equal to the maximum between the corresponding dimension
|
|
/// of the two inputs.
|
|
/// @param flags - binary features of the Subtract Node. No supported flags are currently defined.
|
|
enum xnn_status xnn_define_subtract(
|
|
xnn_subgraph_t subgraph,
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t input1_id,
|
|
uint32_t input2_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a Divide Node and add it to a Subgraph.
|
|
///
|
|
/// The Divide Node computes elementwise division of two tensor inputs with numpy broadcasting rules.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param output_min - lower bound for clipping output values.
|
|
/// @param output_max - upper bound for clipping output values.
|
|
/// @param input1_id - Value ID for the first input tensor. The input tensor must be an N-dimensional tensor defined in
|
|
/// the @a subgraph with each dimension either equal to the corresponding dimension of the second
|
|
/// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
|
|
/// that dimension.
|
|
/// @param input2_id - Value ID for the second input tensor. The input tensor must be an M-dimensional tensor defined in
|
|
/// the @a subgraph with each dimension either equal to the corresponding dimension of the first
|
|
/// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
|
|
/// that dimension.
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be a max(N,M)-dimensional tensor defined
|
|
/// in the @a subgraph with each dimension equal to the maximum between the corresponding dimension
|
|
/// of the two inputs.
|
|
/// @param flags - binary features of the Divide Node. No supported flags are currently defined.
|
|
enum xnn_status xnn_define_divide(
|
|
xnn_subgraph_t subgraph,
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t input1_id,
|
|
uint32_t input2_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a 2-Input Maximum Node and add it to a Subgraph.
|
|
///
|
|
/// The 2-Input Maximum Node computes elementwise maximum of two tensor inputs with numpy broadcasting rules.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param input1_id - Value ID for the first input tensor. The input tensor must be an N-dimensional tensor defined in
|
|
/// the @a subgraph with each dimension either equal to the corresponding dimension of the second
|
|
/// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
|
|
/// that dimension.
|
|
/// @param input2_id - Value ID for the second input tensor. The input tensor must be an M-dimensional tensor defined in
|
|
/// the @a subgraph with each dimension either equal to the corresponding dimension of the first
|
|
/// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
|
|
/// that dimension.
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be a max(N,M)-dimensional tensor defined
|
|
/// in the @a subgraph with each dimension equal to the maximum between the corresponding dimension
|
|
/// of the two inputs.
|
|
/// @param flags - binary features of the Maximum Node. No supported flags are currently defined.
|
|
enum xnn_status xnn_define_maximum2(
|
|
xnn_subgraph_t subgraph,
|
|
uint32_t input1_id,
|
|
uint32_t input2_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a 2-Input Minimum Node and add it to a Subgraph.
|
|
///
|
|
/// The 2-Input Minimum Node computes elementwise minimum of two tensor inputs with numpy broadcasting rules.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param input1_id - Value ID for the first input tensor. The input tensor must be an N-dimensional tensor defined in
|
|
/// the @a subgraph with each dimension either equal to the corresponding dimension of the second
|
|
/// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
|
|
/// that dimension.
|
|
/// @param input2_id - Value ID for the second input tensor. The input tensor must be an M-dimensional tensor defined in
|
|
/// the @a subgraph with each dimension either equal to the corresponding dimension of the first
|
|
/// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
|
|
/// that dimension.
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be a max(N,M)-dimensional tensor defined
|
|
/// in the @a subgraph with each dimension equal to the maximum between the corresponding dimension
|
|
/// of the two inputs.
|
|
/// @param flags - binary features of the Minimum Node. No supported flags are currently defined.
|
|
enum xnn_status xnn_define_minimum2(
|
|
xnn_subgraph_t subgraph,
|
|
uint32_t input1_id,
|
|
uint32_t input2_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a Squared Difference Node and add it to a Subgraph.
|
|
///
|
|
/// The Squared Difference Node computes elementwise squared difference of two tensor inputs with numpy broadcasting
|
|
/// rules.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param input1_id - Value ID for the first input tensor. The input tensor must be an N-dimensional tensor defined in
|
|
/// the @a subgraph with each dimension either equal to the corresponding dimension of the second
|
|
/// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
|
|
/// that dimension.
|
|
/// @param input2_id - Value ID for the second input tensor. The input tensor must be an M-dimensional tensor defined in
|
|
/// the @a subgraph with each dimension either equal to the corresponding dimension of the first
|
|
/// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
|
|
/// that dimension.
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be a max(N,M)-dimensional tensor defined
|
|
/// in the @a subgraph with each dimension equal to the maximum between the corresponding dimension
|
|
/// of the two inputs.
|
|
/// @param flags - binary features of the Squared Difference Node. No supported flags are currently defined.
|
|
enum xnn_status xnn_define_squared_difference(
|
|
xnn_subgraph_t subgraph,
|
|
uint32_t input1_id,
|
|
uint32_t input2_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a Constant Pad Node with static padding specification and add it to a Subgraph.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param pre_paddings - number of padding elements to insert before input elements for every dimension. This array
|
|
/// must have as many elements as the number of dimensions in the input tensor.
|
|
/// @param post_paddings - number of padding elements to insert after input elements for every dimension. This array
|
|
/// must have as many elements as the number of dimensions in the input tensor.
|
|
/// @param padding_value - constant value used to initialize padding elements.
|
|
/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
|
|
/// shape must match the shape of the input tensor with padding.
|
|
/// @param flags - binary features of the Constant Pad Node. No supported flags are currently defined.
|
|
enum xnn_status xnn_define_static_constant_pad(
|
|
xnn_subgraph_t subgraph,
|
|
const size_t* pre_paddings,
|
|
const size_t* post_paddings,
|
|
float padding_value,
|
|
uint32_t input_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a Mean Node and add it to a Subgraph.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param num_reduction_axes - number of axes along which mean is computed.
|
|
/// @param reduction_axes - axes along which mean is computed.
|
|
/// @param input_id - Value ID for the input tensor. The input tensor must be a dense tensor with at least
|
|
/// @a num_reduction_axes dimensions defined in the @a subgraph.
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be a dense tensor defined in the
|
|
/// @a subgraph with @a num_reduction_axes fewer dimensions than the input tensor (if
|
|
/// XNN_FLAG_REDUCE_DIMS is specified), or has same dimension rank but the dimension at
|
|
/// @a reduction_axes reduced to 1 (if XNN_FLAG_REDUCE_DIMS is not specified).
|
|
/// @param flags - binary features of the Mean Node. The only currently supported value is XNN_FLAG_REDUCE_DIMS
|
|
enum xnn_status xnn_define_static_mean(
|
|
xnn_subgraph_t subgraph,
|
|
size_t num_reduction_axes,
|
|
const size_t* reduction_axes,
|
|
uint32_t input_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a 2-Input Concatenate Node and add it to a Subgraph.
|
|
///
|
|
/// The 2-Input Concatenate Node concatenates two tensors along a specified axis.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param axis - the axis to concatenate the two input tensors along
|
|
/// @param input1_id - Value ID for the first input tensor. The input tensor must be an N-dimensional tensor defined in
|
|
/// the @a subgraph with each dimension, except the axis, equal to the corresponding dimension of the
|
|
/// second input.
|
|
/// @param input2_id - Value ID for the second input tensor. The input tensor must be an N-dimensional tensor defined in
|
|
/// the @a subgraph with each dimension, except the axis, equal to the corresponding dimension of the
|
|
/// first input.
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be a N-dimensional tensor defined
|
|
/// in the @a subgraph with each dimension equal to the dimension of both inputs, except the axis
|
|
/// dimension, where it is the sum of the corresponding dimensions of both inputs.
|
|
/// @param flags - binary features of the Concatenate Node. No supported flags are currently defined.
|
|
enum xnn_status xnn_define_concatenate2(
|
|
xnn_subgraph_t subgraph,
|
|
size_t axis,
|
|
uint32_t input1_id,
|
|
uint32_t input2_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a 3-Input Concatenate Node and add it to a Subgraph.
|
|
///
|
|
/// The 3-Input Concatenate Node concatenates three tensors along a specified axis.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param axis - the axis to concatenate the three input tensors along
|
|
/// @param input1_id - Value ID for the first input tensor. The input tensor must be an N-dimensional tensor defined in
|
|
/// the @a subgraph with each dimension, except the axis, equal to the corresponding dimension of the
|
|
/// other inputs.
|
|
/// @param input2_id - Value ID for the second input tensor. The input tensor must be an N-dimensional tensor defined in
|
|
/// the @a subgraph with each dimension, except the axis, equal to the corresponding dimension of the
|
|
/// other inputs.
|
|
/// @param input3_id - Value ID for the third input tensor. The input tensor must be an N-dimensional tensor defined in
|
|
/// the @a subgraph with each dimension, except the axis, equal to the corresponding dimension of the
|
|
/// other inputs.
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be a N-dimensional tensor defined
|
|
/// in the @a subgraph with each dimension equal to the dimension of all inputs, except the axis
|
|
/// dimension, where it is the sum of the corresponding dimensions of all inputs.
|
|
/// @param flags - binary features of the Concatenate Node. No supported flags are currently defined.
|
|
enum xnn_status xnn_define_concatenate3(
|
|
xnn_subgraph_t subgraph,
|
|
size_t axis,
|
|
uint32_t input1_id,
|
|
uint32_t input2_id,
|
|
uint32_t input3_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a 4-Input Concatenate Node and add it to a Subgraph.
|
|
///
|
|
/// The 4-Input Concatenate Node concatenates four tensors along a specified axis.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param axis - the axis to concatenate the four input tensors along
|
|
/// @param input1_id - Value ID for the first input tensor. The input tensor must be an N-dimensional tensor defined in
|
|
/// the @a subgraph with each dimension, except the axis, equal to the corresponding dimension of the
|
|
/// other inputs.
|
|
/// @param input2_id - Value ID for the second input tensor. The input tensor must be an N-dimensional tensor defined in
|
|
/// the @a subgraph with each dimension, except the axis, equal to the corresponding dimension of the
|
|
/// other inputs.
|
|
/// @param input3_id - Value ID for the third input tensor. The input tensor must be an N-dimensional tensor defined in
|
|
/// the @a subgraph with each dimension, except the axis, equal to the corresponding dimension of the
|
|
/// other inputs.
|
|
/// @param input4_id - Value ID for the fourth input tensor. The input tensor must be an N-dimensional tensor defined in
|
|
/// the @a subgraph with each dimension, except the axis, equal to the corresponding dimension of the
|
|
/// other inputs.
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be a N-dimensional tensor defined
|
|
/// in the @a subgraph with each dimension equal to the dimension of all inputs, except the axis
|
|
/// dimension, where it is the sum of the corresponding dimensions of all inputs.
|
|
/// @param flags - binary features of the Concatenate Node. No supported flags are currently defined.
|
|
enum xnn_status xnn_define_concatenate4(
|
|
xnn_subgraph_t subgraph,
|
|
size_t axis,
|
|
uint32_t input1_id,
|
|
uint32_t input2_id,
|
|
uint32_t input3_id,
|
|
uint32_t input4_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
enum xnn_status xnn_define_concatenate5(
|
|
xnn_subgraph_t subgraph,
|
|
size_t axis,
|
|
uint32_t input1_id,
|
|
uint32_t input2_id,
|
|
uint32_t input3_id,
|
|
uint32_t input4_id,
|
|
uint32_t input5_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a Copy Node and add it to a Subgraph.
|
|
///
|
|
/// The Copy Node copies an input tensor to an output tensor.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param input_id - Value ID for the first input tensor. The input tensor must be defined in the @a subgraph.
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
|
|
/// shape must match the shape of the input tensor.
|
|
/// @param flags - binary features of the Copy Node. No supported flags are currently defined.
|
|
enum xnn_status xnn_define_copy(
|
|
xnn_subgraph_t subgraph,
|
|
uint32_t input_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a 2-Output Split Node and add it to a Subgraph.
|
|
///
|
|
/// The 2-Output Split Node splits an input tensor into two output tensors along a specified axis evenly.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param split_dim - the dimension to split the input tensor along
|
|
/// @param input_id - Value ID for the input tensor. The input tensor must be an N-dimensional tensor defined in the @a
|
|
/// subgraph.
|
|
/// @param output1_id - Value ID for the first output tensor. The output tensor must be an N-dimensional tensor defined
|
|
/// in the @a subgraph with each dimension, except the axis, equal to the corresponding dimension
|
|
/// of the second output. The split_dim dimension is half of the input's split_dim.
|
|
/// @param output2_id - Value ID for the second output tensor. The output tensor must be an N-dimensional tensor
|
|
/// defined in the @a subgraph with each dimension, except the axis, equal to the corresponding
|
|
/// dimension of the first output. The split_dim dimension is half of the input's split_dim.
|
|
/// @param flags - binary features of the Split Node. No supported flags are currently defined.
|
|
enum xnn_status xnn_define_even_split2(
|
|
xnn_subgraph_t subgraph,
|
|
size_t split_dim,
|
|
uint32_t input_id,
|
|
uint32_t output1_id,
|
|
uint32_t output2_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a 3-Output Split Node and add it to a Subgraph.
|
|
///
|
|
/// The 3-Output Split Node splits an input tensor into three output tensors along a specified axis evenly.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param split_dim - the dimension to split the input tensor along
|
|
/// @param input_id - Value ID for the input tensor. The input tensor must be an N-dimensional tensor defined in the @a
|
|
/// subgraph.
|
|
/// @param output1_id - Value ID for the first output tensor. The output tensor must be an N-dimensional tensor defined
|
|
/// in the @a subgraph with each dimension, except the axis, equal to the corresponding dimension
|
|
/// of the second and third output. The split_dim dimension is one third of the input's split_dim.
|
|
/// @param output2_id - Value ID for the second output tensor. The output tensor must be an N-dimensional tensor
|
|
/// defined in the @a subgraph with each dimension, except the axis, equal to the corresponding
|
|
/// dimension of the first and third output. The split_dim dimension is one third of the input's
|
|
/// split_dim.
|
|
/// @param output3_id - Value ID for the third output tensor. The output tensor must be an N-dimensional tensor
|
|
/// defined in the @a subgraph with each dimension, except the axis, equal to the corresponding
|
|
/// dimension of the second and third output. The split_dim dimension is one third of the input's
|
|
/// split_dim.
|
|
/// @param flags - binary features of the Split Node. No supported flags are currently defined.
|
|
enum xnn_status xnn_define_even_split3(
|
|
xnn_subgraph_t subgraph,
|
|
size_t split_dim,
|
|
uint32_t input_id,
|
|
uint32_t output1_id,
|
|
uint32_t output2_id,
|
|
uint32_t output3_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a 4-Output Split Node and add it to a Subgraph.
|
|
///
|
|
/// The 4-Output Split Node splits an input tensor into four output tensors along a specified axis evenly.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param split_dim - the dimension to split the input tensor along
|
|
/// @param input_id - Value ID for the input tensor. The input tensor must be an N-dimensional tensor defined in the @a
|
|
/// subgraph.
|
|
/// @param output1_id - Value ID for the first output tensor. The output tensor must be an N-dimensional tensor defined
|
|
/// in the @a subgraph with each dimension, except the axis, equal to the corresponding dimension
|
|
/// of the other output tensors. The split_dim dimension is one fourth of the input's split_dim.
|
|
/// @param output2_id - Value ID for the second output tensor. The output tensor must be an N-dimensional tensor
|
|
/// defined in the @a subgraph with each dimension, except the axis, equal to the corresponding
|
|
/// dimension of the other output tensors. The split_dim dimension is one fourth of the input's
|
|
/// split_dim.
|
|
/// @param output3_id - Value ID for the third output tensor. The output tensor must be an N-dimensional tensor
|
|
/// defined in the @a subgraph with each dimension, except the axis, equal to the corresponding
|
|
/// dimension of the other output tensors. The split_dim dimension is one fourth of the input's
|
|
/// split_dim.
|
|
/// @param output4_id - Value ID for the fourth output tensor. The output tensor must be an N-dimensional tensor
|
|
/// defined in the @a subgraph with each dimension, except the axis, equal to the corresponding
|
|
/// dimension of the other output tensors. The split_dim dimension is one fourth of the input's
|
|
/// split_dim.
|
|
/// @param flags - binary features of the Split Node. No supported flags are currently defined.
|
|
enum xnn_status xnn_define_even_split4(
|
|
xnn_subgraph_t subgraph,
|
|
size_t split_dim,
|
|
uint32_t input_id,
|
|
uint32_t output1_id,
|
|
uint32_t output2_id,
|
|
uint32_t output3_id,
|
|
uint32_t output4_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a Reshape Node with static shape specification and add it to a Subgraph.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param num_dims - number of shape dimensions in the output tensor.
|
|
/// @param new_shape - shape dimensions of the output tensor.
|
|
/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
|
|
/// shape must match the shape of the input tensor with padding.
|
|
/// @param flags - binary features of the Reshape Node. No supported flags are currently defined.
|
|
enum xnn_status xnn_define_static_reshape(
|
|
xnn_subgraph_t subgraph,
|
|
size_t num_dims,
|
|
const size_t* new_shape,
|
|
uint32_t input_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a Node that reshapes a tensor to two dimensions, retaining the
|
|
/// trailing dimension, and add it to a Subgraph.
|
|
///
|
|
/// This operator is experimental.
|
|
///
|
|
/// @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
|
|
/// size 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_reshape_2d(xnn_subgraph_t subgraph,
|
|
uint32_t input_id, uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a 2D Resize Bilinear Node with static output height & width specification and add it to a Subgraph.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param new_height - height dimension of the output tensor.
|
|
/// @param new_width - width dimension of the output tensor.
|
|
/// @param input_id - Value ID for the input tensor. The input tensor must be a 4D tensor defined in the @a subgraph
|
|
/// with [N, H, W, C] dimensions.
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be a 4D tensor defined in the @a subgraph
|
|
/// with [N, new_height, new_width, C] dimensions.
|
|
/// @param flags - binary features of the 2D Resize Bilinear Node. The only currently supported values are
|
|
/// XNN_FLAG_TENSORFLOW_LEGACY_MODE and XNN_FLAG_ALIGN_CORNERS, which are mutually exclusive.
|
|
enum xnn_status xnn_define_static_resize_bilinear_2d(
|
|
xnn_subgraph_t subgraph,
|
|
size_t new_height,
|
|
size_t new_width,
|
|
uint32_t input_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a PReLU (Parametric ReLU) Node and add it to a Subgraph.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param input_id - Value ID for the input tensor. The input tensor must be a 4D tensor defined in the @a subgraph
|
|
/// with [N, H, W, channels] dimensions.
|
|
/// @param slope_id - Value ID for the slope tensor. The slope tensor must be a 1D tensor defined in the @a subgraph with
|
|
/// [channels] dimensions.
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be a 4D tensor defined in the @a subgraph
|
|
/// with [N, H, W, channels] dimensions.
|
|
/// @param flags - binary features of the PReLU Node. No supported flags are currently defined.
|
|
enum xnn_status xnn_define_prelu(
|
|
xnn_subgraph_t subgraph,
|
|
uint32_t input_id,
|
|
uint32_t slope_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a RoPE (Rotary Positional Embeddings) Node and add it to a Subgraph.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param max_tokens - maximum possible number of tokens (maximum sequence length) of the input/output tensors.
|
|
/// @param input_id - Value ID for the input tensor. The input tensor must be a 4D tensor defined in the @a subgraph
|
|
/// with [batch, tokens, heads, channels] dimensions.
|
|
/// @param weights_id - Value ID for the weights tensor. The weights tensor must be a 2D tensor defined in the
|
|
/// @a subgraph with [max_tokens, channels] dimensions.
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be a 4D tensor defined in the @a subgraph
|
|
/// with [batch, tokens, heads, channels] dimensions.
|
|
/// @param flags - binary features of the RoPE Node. No supported flags are currently defined.
|
|
enum xnn_status xnn_define_rope(
|
|
xnn_subgraph_t subgraph,
|
|
size_t max_sequence_size,
|
|
uint32_t input_id,
|
|
uint32_t weights_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a Abs Node and add it to a Subgraph.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
|
|
/// shape must match the shape of the input tensor.
|
|
/// @param flags - binary features of the Abs Node. No supported flags are currently defined.
|
|
enum xnn_status xnn_define_abs(
|
|
xnn_subgraph_t subgraph,
|
|
uint32_t input_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a Bankers' Rounding Node and add it to a Subgraph.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
|
|
/// shape must match the shape of the input tensor.
|
|
/// @param flags - binary features of the Bankers' Rounding Node. No supported flags are currently defined.
|
|
enum xnn_status xnn_define_bankers_rounding(
|
|
xnn_subgraph_t subgraph,
|
|
uint32_t input_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a Batch Matrix Multiply Node and add it to a Subgraph.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param input1_id - Value ID for the first input tensor. The input tensor must be an N-dimensional tensor defined in
|
|
/// the @a subgraph. It must be at least 3D. The first N-2 dimensions must match the second input
|
|
/// tensor. The last 2 dimensions are [M, K]. If XNN_FLAG_TRANSPOSE_B is not specified, the last
|
|
/// dimension must match the second last dimension of the second input tensor. If
|
|
/// XNN_FLAG_TRANSPOSE_B is specified, the last dimension must match the last dimension of the
|
|
/// second input tensor.
|
|
/// @param input2_id - Value ID for the second input tensor. The input tensor must be an N-dimensional tensor defined
|
|
/// in the @a subgraph. It must be at least 3D. The first N-2 dimensions must match the first input
|
|
/// tensor. If XNN_FLAG_TRANSPOSE_B is not specified, the last 2 dimensions are [K, N], and the
|
|
/// second last dimension must match the last dimension of the first input tensor. If
|
|
/// XNN_FLAG_TRANSPOSE_B is specified, the last 2 dimensions are [N, K], and the last dimension must
|
|
/// match the last dimension of the first input tensor.
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be an N-dimensional tensor defined in the
|
|
/// @a subgraph. It must be at least 3D. The first N-2 dimensions must match the first and second
|
|
/// input tensors . The last 2 dimensions must be [M, N].
|
|
/// @param flags - binary features of the Batch Matrix Multiply Node. The only currently supported value is
|
|
/// XNN_FLAG_TRANSPOSE_B.
|
|
enum xnn_status xnn_define_batch_matrix_multiply(
|
|
xnn_subgraph_t subgraph,
|
|
uint32_t input1_id,
|
|
uint32_t input2_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a Ceiling Node and add it to a Subgraph.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
|
|
/// shape must match the shape of the input tensor.
|
|
/// @param flags - binary features of the Ceiling Node. No supported flags are currently defined.
|
|
enum xnn_status xnn_define_ceiling(
|
|
xnn_subgraph_t subgraph,
|
|
uint32_t input_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a Clamp Node and add it to a Subgraph.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param output_min - lower bound for clipping output values.
|
|
/// @param output_max - upper bound for clipping output values.
|
|
/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
|
|
/// shape must match the shape of the input tensor.
|
|
/// @param flags - binary features of the Clamp Node. No supported flags are currently defined.
|
|
enum xnn_status xnn_define_clamp(
|
|
xnn_subgraph_t subgraph,
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t input_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define an ELU (Exponential Linear Unit) Node and add it to a Subgraph.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param alpha - scale factor for negative output elements.
|
|
/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
|
|
/// shape must match the shape of the input tensor.
|
|
/// @param flags - binary features of the ELU Node. No supported flags are currently defined.
|
|
enum xnn_status xnn_define_elu(
|
|
xnn_subgraph_t subgraph,
|
|
float alpha,
|
|
uint32_t input_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a Floor Node and add it to a Subgraph.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
|
|
/// shape must match the shape of the input tensor.
|
|
/// @param flags - binary features of the Floor Node. No supported flags are currently defined.
|
|
enum xnn_status xnn_define_floor(
|
|
xnn_subgraph_t subgraph,
|
|
uint32_t input_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a HardSwish Node and add it to a Subgraph.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
|
|
/// shape must match the shape of the input tensor.
|
|
/// @param flags - binary features of the HardSwish Node. No supported flags are currently defined.
|
|
enum xnn_status xnn_define_hardswish(
|
|
xnn_subgraph_t subgraph,
|
|
uint32_t input_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a Leaky ReLU Node and add it to a Subgraph.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param negative_slope - scale factor for negative input elements.
|
|
/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
|
|
/// shape must match the shape of the input tensor.
|
|
/// @param flags - binary features of the Leaky ReLU Node. No supported flags are currently defined.
|
|
enum xnn_status xnn_define_leaky_relu(
|
|
xnn_subgraph_t subgraph,
|
|
float negative_slope,
|
|
uint32_t input_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a Negate Node and add it to a Subgraph.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
|
|
/// shape must match the shape of the input tensor.
|
|
/// @param flags - binary features of the Negate Node. No supported flags are currently defined.
|
|
enum xnn_status xnn_define_negate(
|
|
xnn_subgraph_t subgraph,
|
|
uint32_t input_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a Sigmoid Node and add it to a Subgraph.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
|
|
/// shape must match the shape of the input tensor.
|
|
/// @param flags - binary features of the Sigmoid Node. No supported flags are currently defined.
|
|
enum xnn_status xnn_define_sigmoid(
|
|
xnn_subgraph_t subgraph,
|
|
uint32_t input_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a SoftMax Node and add it to a Subgraph.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph, and have at
|
|
/// least one dimension.
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
|
|
/// shape must match the shape of the input tensor.
|
|
/// @param flags - binary features of the SoftMax Node. No supported flags are currently defined.
|
|
enum xnn_status xnn_define_softmax(
|
|
xnn_subgraph_t subgraph,
|
|
uint32_t input_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a Space To Depth 2D Node and add it to a Subgraph.
|
|
///
|
|
/// The Space To Depth 2D Node rearranges blocks of spatial data into blocks (a reverse transform to Depth To Space 2D).
|
|
/// For a given input pixel, an output square of pixels with side @a block_size is formed from values in the
|
|
/// corresponding number of its channels. The output depth is therefore @a block_size x @a block_size times greater
|
|
/// than that of the input.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param block_size - the size of the spatial block.
|
|
/// @param input_id - Value ID for the input tensor. The input tensor must be a 4D tensor defined in the @a subgraph
|
|
/// with [N, IH * block_size, IW * block_size, OC] dimensions.
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be a 4D tensor defined in the @a subgraph
|
|
/// with [N, IH, IW, OC * block_size * block_size] dimensions.
|
|
/// @param flags - binary features of the input_channels Node. No supported flags are currently defined.
|
|
enum xnn_status xnn_define_space_to_depth_2d(
|
|
xnn_subgraph_t subgraph,
|
|
uint32_t block_size,
|
|
uint32_t input_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a Square Node and add it to a Subgraph.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
|
|
/// shape must match the shape of the input tensor.
|
|
/// @param flags - binary features of the Square Node. No supported flags are currently defined.
|
|
enum xnn_status xnn_define_square(
|
|
xnn_subgraph_t subgraph,
|
|
uint32_t input_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a Square Root Node and add it to a Subgraph.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
|
|
/// shape must match the shape of the input tensor.
|
|
/// @param flags - binary features of the Square Root Node. No supported flags are currently defined.
|
|
enum xnn_status xnn_define_square_root(
|
|
xnn_subgraph_t subgraph,
|
|
uint32_t input_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a Reciprocal 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_reciprocal_square_root(xnn_subgraph_t subgraph,
|
|
uint32_t input_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a Static Slice Node add it to a Subgraph.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param num_dims - number of shape dimensions in the input and output tensor.
|
|
/// @param offsets - offsets in each dimension of the input tensor. This array must have @a num_dims elements.
|
|
/// @param sizes - size of each dimension in output tensor. This array must have @a num_dims elements.
|
|
/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
|
|
/// dimensions must match @a sizes.
|
|
/// @param flags - binary features of the Static Slice Node. No supported flags are currently defined.
|
|
enum xnn_status xnn_define_static_slice(
|
|
xnn_subgraph_t subgraph,
|
|
size_t num_dims,
|
|
const size_t* offsets,
|
|
const size_t* sizes,
|
|
uint32_t input_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a Static Transpose Node and add it to a Subgraph.
|
|
///
|
|
/// The Static Transpose Node applies a generalized transpose to the input tensor using the permuation in perm.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param input_id - Value ID for the input tensor. The input tensor must be an N-dimensional tensor defined in
|
|
/// the @a subgraph.
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be an N-dimensional tensor defined
|
|
/// in the @a subgraph with each dimension equal to its corresponding permuted input dimension.
|
|
/// @param num_dims - the number of permutation dimensions. This must be equal to the number of input dimensions.
|
|
/// @param perm - The permutation of the axis of the input tensor. The perm array must must contain 0 to N-1 in the
|
|
/// permuted order.
|
|
/// @param flags - binary features of the Static Transpose Node. No supported flags are currently defined.
|
|
enum xnn_status xnn_define_static_transpose(
|
|
xnn_subgraph_t subgraph,
|
|
size_t num_dims,
|
|
const size_t* perm,
|
|
uint32_t input_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Define a Tanh Node and add it to a Subgraph.
|
|
///
|
|
/// @param subgraph - a Subgraph object that will own the created Node.
|
|
/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
|
|
/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
|
|
/// shape must match the shape of the input tensor.
|
|
/// @param flags - binary features of the Tanh Node. No supported flags are currently defined.
|
|
enum xnn_status xnn_define_tanh(
|
|
xnn_subgraph_t subgraph,
|
|
uint32_t input_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
/// Code cache is a cache for JIT generated code.
|
|
typedef struct xnn_code_cache* xnn_code_cache_t;
|
|
|
|
/// Weights cache can be finalized in these ways:
|
|
enum xnn_weights_cache_finalization_kind {
|
|
/// Weights cache is finalized, no insert operations into the weights cache is allowed, even if the "inserted"
|
|
/// weights already exist in thee cache. Weights cache memory will also be trimmed to page boundary and set to
|
|
/// read-only (to prevent writes).
|
|
xnn_weights_cache_finalization_kind_hard,
|
|
/// Weights cache will be finalized with some extra space at the end, this allows for "inserting" into the cache only
|
|
/// if the weights are already in the cache, and errors on inserting uncached weights. There is memory overhead.
|
|
xnn_weights_cache_finalization_kind_soft,
|
|
};
|
|
|
|
/// A combination of multiple factors to uniquely locate the weights cache.
|
|
struct xnn_weights_cache_look_up_key {
|
|
/// The unique seed for each ukernel. It is guaranteed that each ukernel provides
|
|
/// a consistent and identical seed.
|
|
uint32_t seed;
|
|
/// Pointer to the original kernel.
|
|
const void* kernel;
|
|
/// Pointer to the original bias, could be NULL.
|
|
const void* bias;
|
|
};
|
|
|
|
/// A group of function pointers to manage weights cache. All functions may be
|
|
/// called on multi threads.
|
|
struct xnn_weights_cache_provider {
|
|
/// User-specified pointer that will be passed as-is to all functions in this
|
|
/// structure.
|
|
void* context;
|
|
|
|
/// Looks up the tuple of {cache_key, kernel, bias} in the cache. If it is found,
|
|
/// returns the offset to the found entry for reuse. Otherwise, returns SIZE_MAX.
|
|
/// @param context - The user-specified pointer from xnn_weights_cache_provider structure.
|
|
/// @param cache_key - The key used to locate the weights cache entry.
|
|
size_t (*look_up)(void* context, const struct xnn_weights_cache_look_up_key* cache_key);
|
|
|
|
/// Ensures that cache has enough space for `n` bytes. Returns the address to
|
|
/// store weight cache. Returns NULL if fails to reserve space.
|
|
/// @param context - The user-specified pointer from xnn_weights_cache_provider structure.
|
|
/// @param n - size to be reserved.
|
|
void* (*reserve_space)(void* context, size_t n);
|
|
|
|
/// Looks up packed weights at `ptr` in the cache. If it is found, reuse it.
|
|
/// Otherwise, it is added to the cache. Returns the offset to the cache.
|
|
/// @param context - The user-specified pointer from xnn_weights_cache_provider structure.
|
|
/// @param cache_key - The key used to locate the weights cache entry.
|
|
/// @param ptr - pointer pointing to the packed weight.
|
|
/// @param size - size of the packed weight.
|
|
size_t (*look_up_or_insert)(void* context, const struct xnn_weights_cache_look_up_key* cache_key, void* ptr, size_t size);
|
|
|
|
/// Returns whether the cache is finalized.
|
|
/// @param context - The user-specified pointer from xnn_weights_cache_provider structure.
|
|
bool (*is_finalized)(void* context);
|
|
|
|
/// Returns the absolute pointer corresponding to `offset`, where the offset is returned from
|
|
/// `look_up` or `get_or_insert`. This function must be called after finalize.
|
|
/// @param context - The user-specified pointer from xnn_weights_cache_provider structure.
|
|
/// @param offset - offset to the start of internal buffer
|
|
void* (*offset_to_addr)(void* context, size_t offset);
|
|
|
|
/// Destroy a weights cache object, as well as memory used for the cache.
|
|
/// @param context - The user-specified pointer from xnn_weights_cache_provider structure.
|
|
enum xnn_status (*delete_cache)(void* context);
|
|
};
|
|
|
|
/// Weights cache is a cache for packed weights. It can be reused between runtimes.
|
|
typedef struct xnn_weights_cache_provider* xnn_weights_cache_t;
|
|
|
|
/// Create a weights cache object specifying the initial size of weights cache (in bytes).
|
|
///
|
|
/// @param[in] size - initial capacity of the weights cache (in bytes), i.e. it can hold size bytes without growing.
|
|
/// @param weights_cache_out - pointer to the variable that will be initialized to a handle to the weights cache provider
|
|
/// upon successful return. Once created, the weights cache provider can be shared between
|
|
/// different Runtime objects.
|
|
enum xnn_status xnn_create_weights_cache_with_size(size_t size, xnn_weights_cache_t* weights_cache_out);
|
|
|
|
enum xnn_status xnn_create_weights_cache(xnn_weights_cache_t* weights_cache_out);
|
|
|
|
/// Finalizes the weights cache. The kind of finalization is specified by `finalization_kind`.
|
|
/// @param weights_cache - the weights cache object to finalize.
|
|
/// @param finalization_kind - the kind of finalization.
|
|
enum xnn_status xnn_finalize_weights_cache(
|
|
xnn_weights_cache_t weights_cache,
|
|
enum xnn_weights_cache_finalization_kind finalization_kind);
|
|
|
|
/// Destroy a weights cache object, as well as memory used for the cache.
|
|
/// @param weights_cache - the weights cache object to destroy.
|
|
enum xnn_status xnn_delete_weights_cache(xnn_weights_cache_t weights_cache);
|
|
|
|
typedef struct xnn_workspace* xnn_workspace_t;
|
|
|
|
/// Create a workspace object.
|
|
/// @param workspace_out - pointer to the variable that will be initialized to a handle to the workspace object upon
|
|
/// successful return. Once created, the workspace can be shared between different Runtime
|
|
/// objects.
|
|
enum xnn_status xnn_create_workspace(xnn_workspace_t* workspace_out);
|
|
/// Destroy a workspace object, as well as memory used by the workspace. Object destruction can be deferred until all
|
|
/// Runtime objects created with this workspace are destroyed.
|
|
/// @param workspace - the workspace object to destroy.
|
|
enum xnn_status xnn_release_workspace(xnn_workspace_t workspace);
|
|
|
|
/// Runtime is a combination of an execution plan for subgraph Nodes and a memory manager for subgraph Values.
|
|
typedef struct xnn_runtime* xnn_runtime_t;
|
|
|
|
enum xnn_profile_info {
|
|
/// Returns a size_t containing the number of operators.
|
|
xnn_profile_info_num_operators,
|
|
/// Returns a char[] containing the null character separated names of all operators.
|
|
xnn_profile_info_operator_name,
|
|
/// Returns a uint64_t[] with the runtimes of all operators in the same order as xnn_profile_info_operator_name.
|
|
xnn_profile_info_operator_timing,
|
|
};
|
|
|
|
/// Return profile information for all operators.
|
|
///
|
|
/// @param runtime - a Runtime object created with @ref xnn_create_runtime, @ref xnn_create_runtime_v2 or
|
|
/// @ref xnn_create_runtime_v3.
|
|
/// @param param_name - type of profile information required.
|
|
/// @param param_value_size - the size in bytes of memory pointed to by param_value. If this is not sufficient then
|
|
/// param_value_size_ret will be set to the required size and xnn_status_out_of_memory will be
|
|
/// returned.
|
|
/// @param param_value - a pointer to memory location where appropriate values for a given param_value will be written.
|
|
/// @param param_value_size_ret - returns number of bytes required to write the result if param_value_size is not
|
|
/// sufficient.
|
|
enum xnn_status xnn_get_runtime_profiling_info(xnn_runtime_t runtime,
|
|
enum xnn_profile_info param_name,
|
|
size_t param_value_size,
|
|
void* param_value,
|
|
size_t* param_value_size_ret);
|
|
|
|
/// Create a Runtime object from a subgraph.
|
|
///
|
|
/// @param subgraph - a Subgraph object with all Values and Nodes that would be handled by the runtime. No Values or
|
|
/// Nodes can be added to the runtime once it is constructed.
|
|
/// @param weights_cache - a cache for packed weights. The runtime will look up and reuse packed weights in this cache,
|
|
/// this will reduce memory allocated for packed weights.
|
|
/// @param workspace - a workspace to hold internal tensors. The runtime will allocate space used for internal tensors
|
|
/// and track them using workspace. Workspace can be shared and reused across different runtimes. If
|
|
/// workspace is NULL, there will be no sharing: each runtime has its own workspace.
|
|
/// @param threadpool - the thread pool to be used for parallelisation of computations in the runtime. If the thread
|
|
/// pool is NULL, the computation would run on the caller thread without parallelization.
|
|
/// @param flags - binary features of the runtime. The only currently supported values are
|
|
/// XNN_FLAG_HINT_SPARSE_INFERENCE, XNN_FLAG_HINT_FP16_INFERENCE, XNN_FLAG_FORCE_FP16_INFERENCE,
|
|
/// XNN_FLAG_YIELD_WORKERS, and XNN_FLAG_TRANSIENT_INDIRECTION_BUFFER. If XNN_FLAG_YIELD_WORKERS is
|
|
/// specified, worker threads would be yielded to the system scheduler after processing the last operator
|
|
/// in the Runtime. If XNN_FLAG_TRANSIENT_INDIRECTION_BUFFER is specified, convolution operators will
|
|
/// initialize indirection buffers on each inference run using temporary memory in the workspace, instead
|
|
/// of initializing persistent indirection buffers once.
|
|
/// @param runtime_out - pointer to the variable that will be initialized with a handle to the Runtime object upon
|
|
/// successful return. Once constructed, the Runtime object is independent of the Subgraph object
|
|
/// used to create it.
|
|
enum xnn_status xnn_create_runtime_v4(
|
|
xnn_subgraph_t subgraph,
|
|
xnn_weights_cache_t weights_cache,
|
|
xnn_workspace_t workspace,
|
|
pthreadpool_t threadpool,
|
|
uint32_t flags,
|
|
xnn_runtime_t* runtime_out);
|
|
|
|
enum xnn_status xnn_create_runtime_v3(
|
|
xnn_subgraph_t subgraph,
|
|
xnn_weights_cache_t weights_cache,
|
|
pthreadpool_t threadpool,
|
|
uint32_t flags,
|
|
xnn_runtime_t* runtime_out);
|
|
|
|
enum xnn_status xnn_create_runtime_v2(
|
|
xnn_subgraph_t subgraph,
|
|
pthreadpool_t threadpool,
|
|
uint32_t flags,
|
|
xnn_runtime_t* runtime_out);
|
|
|
|
enum xnn_status xnn_create_runtime(
|
|
xnn_subgraph_t subgraph,
|
|
xnn_runtime_t* runtime_out);
|
|
|
|
struct xnn_external_value {
|
|
uint32_t id;
|
|
void* data;
|
|
};
|
|
|
|
/// Reshape an external value.
|
|
///
|
|
/// @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 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.
|
|
enum xnn_status xnn_reshape_external_value(
|
|
xnn_runtime_t runtime,
|
|
uint32_t external_id,
|
|
size_t num_dims,
|
|
const size_t* dims);
|
|
|
|
/// Get the external value shape.
|
|
///
|
|
/// @param external_id - external ID for the Value. The ID must be within the range of reversed Value IDs specified on
|
|
/// the Subgraph creation. The external ID can not be XNN_INVALID_VALUE_ID.
|
|
/// @param num_dims - A valid pointer into which the number of dimensions in the shape will be written. It can not be larger than XNN_MAX_TENSOR_DIMS.
|
|
/// @param dims - pointer to an array of @a num_dims shape dimensions. This pointer can't be NULL. It must be large enough to hold
|
|
/// at least @a num_dims elements. XNNPACK does not keep any pointers to this array after the function returns.
|
|
enum xnn_status xnn_get_external_value_shape(
|
|
xnn_runtime_t runtime,
|
|
uint32_t external_id,
|
|
size_t* num_dims,
|
|
size_t* dims);
|
|
|
|
/// Reshape the XNNPACK runtime.
|
|
///
|
|
/// Propgates the shapes of input tensors through the graph to determine the shapes of intermediate and output tensors.
|
|
/// Memory is allocated if required. Output tensor shapes are returned by xnn_get_external_value_shape.
|
|
///
|
|
/// @param runtime - a Runtime object created with @ref xnn_create_runtime or @ref xnn_create_runtime_v2.
|
|
enum xnn_status xnn_reshape_runtime(
|
|
xnn_runtime_t runtime);
|
|
|
|
/// Deprecated. Use xnn_reshape_runtime and xnn_setup_runtime_v2.
|
|
///
|
|
/// Setup data pointers for external inputs and outputs in a Runtime object and
|
|
/// allocate memory.
|
|
///
|
|
/// @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);
|
|
|
|
/// Setup data pointers for external inputs and outputs in a Runtime object.
|
|
/// Should be called after xnn_reshape_runtime.
|
|
///
|
|
/// @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_v2(
|
|
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);
|
|
|
|
|
|
/// Operator API:
|
|
/// - create operator will create and populate a xnn_operator_t
|
|
/// - reshape operator will update fields in xnn_operator_t with shape/dimensions and parallelization information
|
|
/// - setup operator will update pointers to input and outputs
|
|
/// Each supported operator must have a create, reshape, and setup function. (Optionally a run function.)
|
|
/// Operators listed below are in alphabetical order by operator name; within each operator, we sort alphabetically by
|
|
/// data layout and type. We also group create, reshape, setup (and optionally run) functions of each operator together.
|
|
|
|
enum xnn_status xnn_create_abs_nc_f16(
|
|
uint32_t flags,
|
|
xnn_operator_t* abs_op_out);
|
|
|
|
enum xnn_status xnn_reshape_abs_nc_f16(
|
|
xnn_operator_t abs_op,
|
|
size_t batch_size,
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_abs_nc_f16(
|
|
xnn_operator_t abs_op,
|
|
const void* input,
|
|
void* output);
|
|
|
|
enum xnn_status xnn_create_abs_nc_f32(
|
|
uint32_t flags,
|
|
xnn_operator_t* abs_op_out);
|
|
|
|
enum xnn_status xnn_reshape_abs_nc_f32(
|
|
xnn_operator_t abs_op,
|
|
size_t batch_size,
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_abs_nc_f32(
|
|
xnn_operator_t abs_op,
|
|
const float* input,
|
|
float* output);
|
|
|
|
enum xnn_status xnn_run_abs_nc_f32(
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
size_t batch_size,
|
|
const float* input,
|
|
float* output,
|
|
uint32_t flags,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_add_nd_f16(
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t flags,
|
|
xnn_operator_t* add_op_out);
|
|
|
|
enum xnn_status xnn_reshape_add_nd_f16(
|
|
xnn_operator_t add_op,
|
|
size_t num_input1_dims,
|
|
const size_t* input1_shape,
|
|
size_t num_input2_dims,
|
|
const size_t* input2_shape,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_add_nd_f16(
|
|
xnn_operator_t add_op,
|
|
const void* input1,
|
|
const void* input2,
|
|
void* output);
|
|
|
|
enum xnn_status xnn_create_add_nd_f32(
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t flags,
|
|
xnn_operator_t* add_op_out);
|
|
|
|
enum xnn_status xnn_reshape_add_nd_f32(
|
|
xnn_operator_t add_op,
|
|
size_t num_input1_dims,
|
|
const size_t* input1_shape,
|
|
size_t num_input2_dims,
|
|
const size_t* input2_shape,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_add_nd_f32(
|
|
xnn_operator_t add_op,
|
|
const float* input1,
|
|
const float* input2,
|
|
float* output);
|
|
|
|
enum xnn_status xnn_run_add_nd_f32(
|
|
size_t num_input1_dims,
|
|
const size_t* input1_shape,
|
|
size_t num_input2_dims,
|
|
const size_t* input2_shape,
|
|
const float* input1,
|
|
const float* input2,
|
|
float* output,
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t flags,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_add_nd_qs8(
|
|
int8_t input1_zero_point,
|
|
float input1_scale,
|
|
int8_t input2_zero_point,
|
|
float input2_scale,
|
|
int8_t output_zero_point,
|
|
float output_scale,
|
|
int8_t output_min,
|
|
int8_t output_max,
|
|
uint32_t flags,
|
|
xnn_operator_t* add_op_out);
|
|
|
|
enum xnn_status xnn_reshape_add_nd_qs8(
|
|
xnn_operator_t add_op,
|
|
size_t num_input1_dims,
|
|
const size_t* input1_shape,
|
|
size_t num_input2_dims,
|
|
const size_t* input2_shape,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_add_nd_qs8(
|
|
xnn_operator_t add_op,
|
|
const int8_t* input1,
|
|
const int8_t* input2,
|
|
int8_t* output);
|
|
|
|
enum xnn_status xnn_run_add_nd_qs8(
|
|
size_t num_input1_dims,
|
|
const size_t* input1_shape,
|
|
int8_t input1_zero_point,
|
|
float input1_scale,
|
|
size_t num_input2_dims,
|
|
const size_t* input2_shape,
|
|
int8_t input2_zero_point,
|
|
float input2_scale,
|
|
const int8_t* input1,
|
|
const int8_t* input2,
|
|
int8_t* output,
|
|
int8_t output_zero_point,
|
|
float output_scale,
|
|
int8_t output_min,
|
|
int8_t output_max,
|
|
uint32_t flags,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_add_nd_qu8(
|
|
uint8_t input1_zero_point,
|
|
float input1_scale,
|
|
uint8_t input2_zero_point,
|
|
float input2_scale,
|
|
uint8_t output_zero_point,
|
|
float output_scale,
|
|
uint8_t output_min,
|
|
uint8_t output_max,
|
|
uint32_t flags,
|
|
xnn_operator_t* add_op_out);
|
|
|
|
enum xnn_status xnn_reshape_add_nd_qu8(
|
|
xnn_operator_t add_op,
|
|
size_t num_input1_dims,
|
|
const size_t* input1_shape,
|
|
size_t num_input2_dims,
|
|
const size_t* input2_shape,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_add_nd_qu8(
|
|
xnn_operator_t add_op,
|
|
const uint8_t* input1,
|
|
const uint8_t* input2,
|
|
uint8_t* output);
|
|
|
|
enum xnn_status xnn_run_add_nd_qu8(
|
|
size_t num_input1_dims,
|
|
const size_t* input1_shape,
|
|
uint8_t input1_zero_point,
|
|
float input1_scale,
|
|
size_t num_input2_dims,
|
|
const size_t* input2_shape,
|
|
uint8_t input2_zero_point,
|
|
float input2_scale,
|
|
const uint8_t* input1,
|
|
const uint8_t* input2,
|
|
uint8_t* output,
|
|
uint8_t output_zero_point,
|
|
float output_scale,
|
|
uint8_t output_min,
|
|
uint8_t output_max,
|
|
uint32_t flags,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_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,
|
|
uint32_t flags,
|
|
xnn_operator_t* argmax_pooling_op_out);
|
|
|
|
enum xnn_status xnn_reshape_argmax_pooling2d_nhwc_f32(
|
|
xnn_operator_t argmax_pooling_op,
|
|
size_t batch_size,
|
|
size_t input_height,
|
|
size_t input_width,
|
|
size_t channels,
|
|
size_t input_pixel_stride,
|
|
size_t output_pixel_stride,
|
|
size_t* workspace_size,
|
|
size_t* workspace_alignment,
|
|
size_t* output_height_out,
|
|
size_t* output_width_out,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_argmax_pooling2d_nhwc_f32(
|
|
xnn_operator_t argmax_pooling_op,
|
|
void* workspace,
|
|
const float* input,
|
|
float* output,
|
|
uint32_t* index);
|
|
|
|
enum xnn_status xnn_create_average_pooling2d_nhwc_f16(
|
|
uint32_t input_padding_top,
|
|
uint32_t input_padding_right,
|
|
uint32_t input_padding_bottom,
|
|
uint32_t input_padding_left,
|
|
uint32_t pooling_height,
|
|
uint32_t pooling_width,
|
|
uint32_t stride_height,
|
|
uint32_t stride_width,
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t flags,
|
|
xnn_operator_t* average_pooling_op_out);
|
|
|
|
enum xnn_status xnn_reshape_average_pooling2d_nhwc_f16(
|
|
xnn_operator_t average_pooling_op,
|
|
size_t batch_size,
|
|
size_t input_height,
|
|
size_t input_width,
|
|
size_t channels,
|
|
size_t input_pixel_stride,
|
|
size_t output_pixel_stride,
|
|
size_t* workspace_size,
|
|
size_t* workspace_alignment,
|
|
size_t* output_height_out,
|
|
size_t* output_width_out,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_average_pooling2d_nhwc_f16(
|
|
xnn_operator_t average_pooling_op,
|
|
void* workspace,
|
|
const void* input,
|
|
void* output);
|
|
|
|
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,
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t flags,
|
|
xnn_operator_t* average_pooling_op_out);
|
|
|
|
enum xnn_status xnn_reshape_average_pooling2d_nhwc_f32(
|
|
xnn_operator_t average_pooling_op,
|
|
size_t batch_size,
|
|
size_t input_height,
|
|
size_t input_width,
|
|
size_t channels,
|
|
size_t input_pixel_stride,
|
|
size_t output_pixel_stride,
|
|
size_t* workspace_size,
|
|
size_t* workspace_alignment,
|
|
size_t* output_height_out,
|
|
size_t* output_width_out,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_average_pooling2d_nhwc_f32(
|
|
xnn_operator_t average_pooling_op,
|
|
void* workspace,
|
|
const float* input,
|
|
float* output);
|
|
|
|
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,
|
|
uint8_t input_zero_point,
|
|
float input_scale,
|
|
uint8_t output_zero_point,
|
|
float output_scale,
|
|
uint8_t output_min,
|
|
uint8_t output_max,
|
|
uint32_t flags,
|
|
xnn_operator_t* average_pooling_op_out);
|
|
|
|
enum xnn_status xnn_reshape_average_pooling2d_nhwc_qu8(
|
|
xnn_operator_t average_pooling_op,
|
|
size_t batch_size,
|
|
size_t input_height,
|
|
size_t input_width,
|
|
size_t channels,
|
|
size_t input_pixel_stride,
|
|
size_t output_pixel_stride,
|
|
size_t* workspace_size,
|
|
size_t* workspace_alignment,
|
|
size_t* output_height_out,
|
|
size_t* output_width_out,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_average_pooling2d_nhwc_qu8(
|
|
xnn_operator_t average_pooling_op,
|
|
void* workspace,
|
|
const uint8_t* input,
|
|
uint8_t* output);
|
|
|
|
enum xnn_status xnn_create_bankers_rounding_nc_f16(
|
|
uint32_t flags,
|
|
xnn_operator_t* rounding_op_out);
|
|
|
|
enum xnn_status xnn_reshape_bankers_rounding_nc_f16(
|
|
xnn_operator_t rounding_op,
|
|
size_t batch_size,
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_bankers_rounding_nc_f16(
|
|
xnn_operator_t rounding_op,
|
|
const void* input,
|
|
void* output);
|
|
|
|
enum xnn_status xnn_create_bankers_rounding_nc_f32(
|
|
uint32_t flags,
|
|
xnn_operator_t* rounding_op_out);
|
|
|
|
enum xnn_status xnn_reshape_bankers_rounding_nc_f32(
|
|
xnn_operator_t rounding_op,
|
|
size_t batch_size,
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_bankers_rounding_nc_f32(
|
|
xnn_operator_t rounding_op,
|
|
const float* input,
|
|
float* output);
|
|
|
|
enum xnn_status xnn_run_bankers_rounding_nc_f32(
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
size_t batch_size,
|
|
const float* input,
|
|
float* output,
|
|
uint32_t flags,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_batch_matrix_multiply_nc_f16(
|
|
uint32_t flags,
|
|
xnn_operator_t* batch_matrix_multiply_op);
|
|
|
|
enum xnn_status xnn_reshape_batch_matrix_multiply_nc_f16(
|
|
xnn_operator_t batch_matrix_multiply_op,
|
|
size_t batch_size,
|
|
size_t m,
|
|
size_t k,
|
|
size_t n,
|
|
size_t* workspace_size,
|
|
size_t* workspace_alignment,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_batch_matrix_multiply_nc_f16(
|
|
xnn_operator_t batch_matrix_multiply_op,
|
|
void* workspace,
|
|
const void* lhs_input,
|
|
const void* rhs_input,
|
|
void* output);
|
|
|
|
enum xnn_status xnn_create_batch_matrix_multiply_nc_f32(
|
|
uint32_t flags,
|
|
xnn_operator_t* batch_matrix_multiply_op);
|
|
|
|
enum xnn_status xnn_reshape_batch_matrix_multiply_nc_f32(
|
|
xnn_operator_t batch_matrix_multiply_op,
|
|
size_t batch_size,
|
|
size_t m,
|
|
size_t k,
|
|
size_t n,
|
|
size_t* workspace_size,
|
|
size_t* workspace_alignment,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_batch_matrix_multiply_nc_f32(
|
|
xnn_operator_t batch_matrix_multiply_op,
|
|
void* workspace,
|
|
const float* lhs_input,
|
|
const float* rhs_input,
|
|
float* output);
|
|
|
|
enum xnn_status xnn_create_ceiling_nc_f16(
|
|
uint32_t flags,
|
|
xnn_operator_t* ceiling_op_out);
|
|
|
|
enum xnn_status xnn_reshape_ceiling_nc_f16(
|
|
xnn_operator_t ceiling_op,
|
|
size_t batch_size,
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_ceiling_nc_f16(
|
|
xnn_operator_t ceiling_op,
|
|
const void* input,
|
|
void* output);
|
|
|
|
enum xnn_status xnn_create_ceiling_nc_f32(
|
|
uint32_t flags,
|
|
xnn_operator_t* ceiling_op_out);
|
|
|
|
enum xnn_status xnn_run_ceiling_nc_f32(
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
size_t batch_size,
|
|
const float* input,
|
|
float* output,
|
|
uint32_t flags,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_reshape_ceiling_nc_f32(
|
|
xnn_operator_t ceiling_op,
|
|
size_t batch_size,
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_ceiling_nc_f32(
|
|
xnn_operator_t ceiling_op,
|
|
const float* input,
|
|
float* output);
|
|
|
|
enum xnn_status xnn_create_channel_shuffle_nc_x8(
|
|
size_t groups,
|
|
size_t group_channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
uint32_t flags,
|
|
xnn_operator_t* channel_shuffle_op_out);
|
|
|
|
enum xnn_status xnn_reshape_channel_shuffle_nc_x8(
|
|
xnn_operator_t channel_shuffle_op,
|
|
size_t batch_size,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_channel_shuffle_nc_x8(
|
|
xnn_operator_t channel_shuffle_op,
|
|
const void* input,
|
|
void* output);
|
|
|
|
enum xnn_status xnn_create_channel_shuffle_nc_x32(
|
|
size_t groups,
|
|
size_t group_channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
uint32_t flags,
|
|
xnn_operator_t* channel_shuffle_op_out);
|
|
|
|
enum xnn_status xnn_reshape_channel_shuffle_nc_x32(
|
|
xnn_operator_t channel_shuffle_op,
|
|
size_t batch_size,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_channel_shuffle_nc_x32(
|
|
xnn_operator_t channel_shuffle_op,
|
|
const void* input,
|
|
void* output);
|
|
|
|
enum xnn_status xnn_create_clamp_nc_f16(
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t flags,
|
|
xnn_operator_t* clamp_op_out);
|
|
|
|
enum xnn_status xnn_reshape_clamp_nc_f16(
|
|
xnn_operator_t clamp_op,
|
|
size_t batch_size,
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_clamp_nc_f16(
|
|
xnn_operator_t clamp_op,
|
|
const void* input,
|
|
void* output);
|
|
|
|
enum xnn_status xnn_create_clamp_nc_f32(
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t flags,
|
|
xnn_operator_t* clamp_op_out);
|
|
|
|
enum xnn_status xnn_reshape_clamp_nc_f32(
|
|
xnn_operator_t clamp_op,
|
|
size_t batch_size,
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_clamp_nc_f32(
|
|
xnn_operator_t clamp_op,
|
|
const float* input,
|
|
float* output);
|
|
|
|
enum xnn_status xnn_run_clamp_nc_f32(
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
size_t batch_size,
|
|
const float* input,
|
|
float* output,
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t flags,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_clamp_nc_s8(
|
|
int8_t output_min,
|
|
int8_t output_max,
|
|
uint32_t flags,
|
|
xnn_operator_t* clamp_op_out);
|
|
|
|
enum xnn_status xnn_reshape_clamp_nc_s8(
|
|
xnn_operator_t clamp_op,
|
|
size_t batch_size,
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_clamp_nc_s8(
|
|
xnn_operator_t clamp_op,
|
|
const int8_t* input,
|
|
int8_t* output);
|
|
|
|
enum xnn_status xnn_create_clamp_nc_u8(
|
|
uint8_t output_min,
|
|
uint8_t output_max,
|
|
uint32_t flags,
|
|
xnn_operator_t* clamp_op_out);
|
|
|
|
enum xnn_status xnn_reshape_clamp_nc_u8(
|
|
xnn_operator_t clamp_op,
|
|
size_t batch_size,
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_clamp_nc_u8(
|
|
xnn_operator_t clamp_op,
|
|
const uint8_t* input,
|
|
uint8_t* output);
|
|
|
|
enum xnn_status xnn_create_constant_pad_nd_x8(
|
|
const void* padding_value,
|
|
uint32_t flags,
|
|
xnn_operator_t* constant_pad_op_out);
|
|
|
|
enum xnn_status xnn_reshape_constant_pad_nd_x8(
|
|
xnn_operator_t constant_pad_op,
|
|
size_t num_dims,
|
|
const size_t* input_shape,
|
|
const size_t* pre_padding,
|
|
const size_t* post_padding,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_constant_pad_nd_x8(
|
|
xnn_operator_t constant_pad_op,
|
|
const void* input,
|
|
void* output);
|
|
|
|
enum xnn_status xnn_run_constant_pad_nd_x8(
|
|
uint32_t flags,
|
|
size_t num_dims,
|
|
const size_t* input_shape,
|
|
const size_t* pre_paddings,
|
|
const size_t* post_paddings,
|
|
const void* input,
|
|
void* output,
|
|
const void* padding_value,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_constant_pad_nd_x16(
|
|
const void* padding_value,
|
|
uint32_t flags,
|
|
xnn_operator_t* constant_pad_op_out);
|
|
|
|
enum xnn_status xnn_reshape_constant_pad_nd_x16(
|
|
xnn_operator_t constant_pad_op,
|
|
size_t num_dims,
|
|
const size_t* input_shape,
|
|
const size_t* pre_padding,
|
|
const size_t* post_padding,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_constant_pad_nd_x16(
|
|
xnn_operator_t constant_pad_op,
|
|
const void* input,
|
|
void* output);
|
|
|
|
enum xnn_status xnn_run_constant_pad_nd_x16(
|
|
uint32_t flags,
|
|
size_t num_dims,
|
|
const size_t* input_shape,
|
|
const size_t* pre_paddings,
|
|
const size_t* post_paddings,
|
|
const void* input,
|
|
void* output,
|
|
const void* padding_value,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_constant_pad_nd_x32(
|
|
const void* padding_value,
|
|
uint32_t flags,
|
|
xnn_operator_t* constant_pad_op_out);
|
|
|
|
enum xnn_status xnn_reshape_constant_pad_nd_x32(
|
|
xnn_operator_t constant_pad_op,
|
|
size_t num_dims,
|
|
const size_t* input_shape,
|
|
const size_t* pre_padding,
|
|
const size_t* post_padding,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_constant_pad_nd_x32(
|
|
xnn_operator_t constant_pad_op,
|
|
const void* input,
|
|
void* output);
|
|
|
|
enum xnn_status xnn_run_constant_pad_nd_x32(
|
|
uint32_t flags,
|
|
size_t num_dims,
|
|
const size_t* input_shape,
|
|
const size_t* pre_paddings,
|
|
const size_t* post_paddings,
|
|
const void* input,
|
|
void* output,
|
|
const void* padding_value,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_convert_nc_f16_f32(
|
|
uint32_t flags,
|
|
xnn_operator_t* convert_op_out);
|
|
|
|
enum xnn_status xnn_reshape_convert_nc_f16_f32(
|
|
xnn_operator_t convert_op,
|
|
size_t batch_size,
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_convert_nc_f16_f32(
|
|
xnn_operator_t convert_op,
|
|
const void* input,
|
|
float* output);
|
|
|
|
enum xnn_status xnn_run_convert_nc_f16_f32(
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
size_t batch_size,
|
|
const void* input,
|
|
float* output,
|
|
uint32_t flags,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_convert_nc_f16_qd8(
|
|
uint32_t flags,
|
|
xnn_operator_t* convert_op_out);
|
|
|
|
enum xnn_status xnn_reshape_convert_nc_f16_qd8(
|
|
xnn_operator_t convert_op,
|
|
size_t batch_size,
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
pthreadpool_t threadpool);
|
|
|
|
// quantization_params must be padded with at least XNN_EXTRA_QUANTIZATION_PARAMS entries.
|
|
enum xnn_status xnn_setup_convert_nc_f16_qd8(
|
|
xnn_operator_t convert_op,
|
|
const void* input,
|
|
int8_t* output,
|
|
struct xnn_dynamic_quantization_params* quantization_params);
|
|
|
|
enum xnn_status xnn_create_convert_nc_f32_qd8(
|
|
uint32_t flags,
|
|
xnn_operator_t* convert_op_out);
|
|
|
|
enum xnn_status xnn_reshape_convert_nc_f32_qd8(
|
|
xnn_operator_t convert_op,
|
|
size_t batch_size,
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
pthreadpool_t threadpool);
|
|
|
|
// quantization_params must be padded with at least XNN_EXTRA_QUANTIZATION_PARAMS entries.
|
|
enum xnn_status xnn_setup_convert_nc_f32_qd8(
|
|
xnn_operator_t convert_op,
|
|
const float* input,
|
|
int8_t* output,
|
|
struct xnn_dynamic_quantization_params* quantization_params);
|
|
|
|
enum xnn_status xnn_create_convert_nc_f32_f16(
|
|
uint32_t flags,
|
|
xnn_operator_t* convert_op_out);
|
|
|
|
enum xnn_status xnn_reshape_convert_nc_f32_f16(
|
|
xnn_operator_t convert_op,
|
|
size_t batch_size,
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_convert_nc_f32_f16(
|
|
xnn_operator_t convert_op,
|
|
const float* input,
|
|
void* output);
|
|
|
|
enum xnn_status xnn_run_convert_nc_f32_f16(
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
size_t batch_size,
|
|
const float* input,
|
|
void* output,
|
|
uint32_t flags,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_convert_nc_f32_qs8(
|
|
float output_scale,
|
|
int8_t output_zero_point,
|
|
int8_t output_min,
|
|
int8_t output_max,
|
|
uint32_t flags,
|
|
xnn_operator_t* convert_op_out);
|
|
|
|
enum xnn_status xnn_reshape_convert_nc_f32_qs8(
|
|
xnn_operator_t convert_op,
|
|
size_t batch_size,
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_convert_nc_f32_qs8(
|
|
xnn_operator_t convert_op,
|
|
const float* input,
|
|
int8_t* output);
|
|
|
|
enum xnn_status xnn_run_convert_nc_f32_qs8(
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
size_t batch_size,
|
|
const float* input,
|
|
int8_t* output,
|
|
float output_scale,
|
|
int8_t output_zero_point,
|
|
uint32_t flags,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_convert_nc_f32_qu8(
|
|
float output_scale,
|
|
uint8_t output_zero_point,
|
|
uint8_t output_min,
|
|
uint8_t output_max,
|
|
uint32_t flags,
|
|
xnn_operator_t* convert_op_out);
|
|
|
|
enum xnn_status xnn_reshape_convert_nc_f32_qu8(
|
|
xnn_operator_t convert_op,
|
|
size_t batch_size,
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_convert_nc_f32_qu8(
|
|
xnn_operator_t convert_op,
|
|
const float* input,
|
|
uint8_t* output);
|
|
|
|
enum xnn_status xnn_run_convert_nc_f32_qu8(
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
size_t batch_size,
|
|
const float* input,
|
|
uint8_t* output,
|
|
float output_scale,
|
|
uint8_t output_zero_point,
|
|
uint32_t flags,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_convert_nc_qs8(
|
|
float input_scale,
|
|
int8_t input_zero_point,
|
|
float output_scale,
|
|
int8_t output_zero_point,
|
|
uint32_t flags,
|
|
xnn_operator_t* convert_op_out);
|
|
|
|
enum xnn_status xnn_reshape_convert_nc_qs8(
|
|
xnn_operator_t convert_op,
|
|
size_t batch_size,
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_convert_nc_qs8(
|
|
xnn_operator_t convert_op,
|
|
const int8_t* input,
|
|
int8_t* output);
|
|
|
|
enum xnn_status xnn_create_convert_nc_qs8_f16(
|
|
float input_scale,
|
|
int8_t input_zero_point,
|
|
uint32_t flags,
|
|
xnn_operator_t* convert_op_out);
|
|
|
|
enum xnn_status xnn_reshape_convert_nc_qs8_f16(
|
|
xnn_operator_t convert_op,
|
|
size_t batch_size,
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_convert_nc_qs8_f16(
|
|
xnn_operator_t convert_op,
|
|
const int8_t* input,
|
|
void* output);
|
|
|
|
enum xnn_status xnn_create_convert_nc_qs8_f32(
|
|
float input_scale,
|
|
int8_t input_zero_point,
|
|
uint32_t flags,
|
|
xnn_operator_t* convert_op_out);
|
|
|
|
enum xnn_status xnn_reshape_convert_nc_qs8_f32(
|
|
xnn_operator_t convert_op,
|
|
size_t batch_size,
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_convert_nc_qs8_f32(
|
|
xnn_operator_t convert_op,
|
|
const int8_t* input,
|
|
float* output);
|
|
|
|
enum xnn_status xnn_run_convert_nc_qs8_f32(
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
size_t batch_size,
|
|
const int8_t* input,
|
|
float* output,
|
|
float input_scale,
|
|
int8_t input_zero_point,
|
|
uint32_t flags,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_convert_nc_qs16_qs8(
|
|
float input_scale,
|
|
float output_scale,
|
|
int8_t output_zero_point,
|
|
uint32_t flags,
|
|
xnn_operator_t* convert_op_out);
|
|
|
|
enum xnn_status xnn_reshape_convert_nc_qs16_qs8(
|
|
xnn_operator_t convert_op,
|
|
size_t batch_size,
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_convert_nc_qs16_qs8(
|
|
xnn_operator_t convert_op,
|
|
const int16_t* input,
|
|
int8_t* output);
|
|
|
|
enum xnn_status xnn_run_convert_nc_qs16_qs8(
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
size_t batch_size,
|
|
const int16_t* input,
|
|
int8_t* output,
|
|
float input_scale,
|
|
float output_scale,
|
|
int8_t output_zero_point,
|
|
uint32_t flags,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_convert_nc_qu8(
|
|
float input_scale,
|
|
uint8_t input_zero_point,
|
|
float output_scale,
|
|
uint8_t output_zero_point,
|
|
uint32_t flags,
|
|
xnn_operator_t* convert_op_out);
|
|
|
|
enum xnn_status xnn_reshape_convert_nc_qu8(
|
|
xnn_operator_t convert_op,
|
|
size_t batch_size,
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_convert_nc_qu8(
|
|
xnn_operator_t convert_op,
|
|
const uint8_t* input,
|
|
uint8_t* output);
|
|
|
|
enum xnn_status xnn_create_convert_nc_qu8_f32(
|
|
float input_scale,
|
|
uint8_t input_zero_point,
|
|
uint32_t flags,
|
|
xnn_operator_t* convert_op_out);
|
|
|
|
enum xnn_status xnn_reshape_convert_nc_qu8_f32(
|
|
xnn_operator_t convert_op,
|
|
size_t batch_size,
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_convert_nc_qu8_f32(
|
|
xnn_operator_t convert_op,
|
|
const uint8_t* input,
|
|
float* output);
|
|
|
|
enum xnn_status xnn_run_convert_nc_qu8_f32(
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
size_t batch_size,
|
|
const uint8_t* input,
|
|
float* output,
|
|
float input_scale,
|
|
uint8_t input_zero_point,
|
|
uint32_t flags,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_convolution2d_nchw_f16(
|
|
uint32_t input_padding_top,
|
|
uint32_t input_padding_right,
|
|
uint32_t input_padding_bottom,
|
|
uint32_t input_padding_left,
|
|
uint32_t kernel_height,
|
|
uint32_t kernel_width,
|
|
uint32_t subsampling_height,
|
|
uint32_t subsampling_width,
|
|
uint32_t dilation_height,
|
|
uint32_t dilation_width,
|
|
uint32_t groups,
|
|
size_t group_input_channels,
|
|
size_t group_output_channels,
|
|
size_t input_channel_stride,
|
|
size_t output_channel_stride,
|
|
const void* kernel,
|
|
const void* bias,
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t flags,
|
|
xnn_code_cache_t code_cache,
|
|
xnn_weights_cache_t weights_cache,
|
|
xnn_operator_t* convolution_op_out);
|
|
|
|
enum xnn_status xnn_reshape_convolution2d_nchw_f16(
|
|
xnn_operator_t convolution_op,
|
|
size_t batch_size,
|
|
size_t input_height,
|
|
size_t input_width,
|
|
size_t* output_height_out,
|
|
size_t* output_width_out,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_convolution2d_nchw_f16(
|
|
xnn_operator_t convolution_op,
|
|
const void* input,
|
|
void* output);
|
|
|
|
enum xnn_status xnn_create_convolution2d_nchw_f32(
|
|
uint32_t input_padding_top,
|
|
uint32_t input_padding_right,
|
|
uint32_t input_padding_bottom,
|
|
uint32_t input_padding_left,
|
|
uint32_t kernel_height,
|
|
uint32_t kernel_width,
|
|
uint32_t subsampling_height,
|
|
uint32_t subsampling_width,
|
|
uint32_t dilation_height,
|
|
uint32_t dilation_width,
|
|
uint32_t groups,
|
|
size_t group_input_channels,
|
|
size_t group_output_channels,
|
|
size_t input_channel_stride,
|
|
size_t output_channel_stride,
|
|
const float* kernel,
|
|
const float* bias,
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t flags,
|
|
xnn_code_cache_t code_cache,
|
|
xnn_weights_cache_t weights_cache,
|
|
xnn_operator_t* convolution_op_out);
|
|
|
|
enum xnn_status xnn_reshape_convolution2d_nchw_f32(
|
|
xnn_operator_t convolution_op,
|
|
size_t batch_size,
|
|
size_t input_height,
|
|
size_t input_width,
|
|
size_t* output_height_out,
|
|
size_t* output_width_out,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_convolution2d_nchw_f32(
|
|
xnn_operator_t convolution_op,
|
|
const float* input,
|
|
float* output);
|
|
|
|
enum xnn_status xnn_create_convolution2d_nhwc_f16(
|
|
uint32_t input_padding_top,
|
|
uint32_t input_padding_right,
|
|
uint32_t input_padding_bottom,
|
|
uint32_t input_padding_left,
|
|
uint32_t kernel_height,
|
|
uint32_t kernel_width,
|
|
uint32_t subsampling_height,
|
|
uint32_t subsampling_width,
|
|
uint32_t dilation_height,
|
|
uint32_t dilation_width,
|
|
uint32_t groups,
|
|
size_t group_input_channels,
|
|
size_t group_output_channels,
|
|
size_t input_channel_stride,
|
|
size_t output_channel_stride,
|
|
const void* kernel,
|
|
const void* bias,
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t flags,
|
|
xnn_code_cache_t code_cache,
|
|
xnn_weights_cache_t weights_cache,
|
|
xnn_operator_t* convolution_op_out);
|
|
|
|
enum xnn_status xnn_reshape_convolution2d_nhwc_f16(
|
|
xnn_operator_t convolution_op,
|
|
size_t batch_size,
|
|
size_t input_height,
|
|
size_t input_width,
|
|
size_t* workspace_size,
|
|
size_t* workspace_alignment,
|
|
size_t* output_height_out,
|
|
size_t* output_width_out,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_convolution2d_nhwc_f16(
|
|
xnn_operator_t convolution_op,
|
|
void* workspace,
|
|
const void* input,
|
|
void* output);
|
|
|
|
enum xnn_status xnn_create_convolution2d_nhwc_f32(
|
|
uint32_t input_padding_top,
|
|
uint32_t input_padding_right,
|
|
uint32_t input_padding_bottom,
|
|
uint32_t input_padding_left,
|
|
uint32_t kernel_height,
|
|
uint32_t kernel_width,
|
|
uint32_t subsampling_height,
|
|
uint32_t subsampling_width,
|
|
uint32_t dilation_height,
|
|
uint32_t dilation_width,
|
|
uint32_t groups,
|
|
size_t group_input_channels,
|
|
size_t group_output_channels,
|
|
size_t input_channel_stride,
|
|
size_t output_channel_stride,
|
|
const float* kernel,
|
|
const float* bias,
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t flags,
|
|
xnn_code_cache_t code_cache,
|
|
xnn_weights_cache_t weights_cache,
|
|
xnn_operator_t* convolution_op_out);
|
|
|
|
// Forward declare.
|
|
struct xnn_post_operation;
|
|
|
|
/// Create a convolution operator with a number of post operations. The
|
|
/// convolution operator created using this function does not have output_min
|
|
/// and output_max. The list of operators in post_operations will be applied in
|
|
/// order. Convolution with post operations is only supported on JIT platforms
|
|
/// and when JIT is enabled.
|
|
enum xnn_status xnn_create_fused_convolution2d_nhwc_f32(
|
|
uint32_t input_padding_top,
|
|
uint32_t input_padding_right,
|
|
uint32_t input_padding_bottom,
|
|
uint32_t input_padding_left,
|
|
uint32_t kernel_height,
|
|
uint32_t kernel_width,
|
|
uint32_t subsampling_height,
|
|
uint32_t subsampling_width,
|
|
uint32_t dilation_height,
|
|
uint32_t dilation_width,
|
|
uint32_t groups,
|
|
size_t group_input_channels,
|
|
size_t group_output_channels,
|
|
size_t input_channel_stride,
|
|
size_t output_channel_stride,
|
|
const float* kernel,
|
|
const float* bias,
|
|
size_t num_post_operations,
|
|
struct xnn_post_operation* post_operations,
|
|
uint32_t flags,
|
|
xnn_code_cache_t code_cache,
|
|
xnn_weights_cache_t weights_cache,
|
|
xnn_operator_t* convolution_op_out);
|
|
|
|
enum xnn_status xnn_reshape_convolution2d_nhwc_f32(
|
|
xnn_operator_t convolution_op,
|
|
size_t batch_size,
|
|
size_t input_height,
|
|
size_t input_width,
|
|
size_t* workspace_size,
|
|
size_t* workspace_alignment,
|
|
size_t* output_height_out,
|
|
size_t* output_width_out,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_convolution2d_nhwc_f32(
|
|
xnn_operator_t convolution_op,
|
|
void* workspace,
|
|
const float* input,
|
|
float* output);
|
|
|
|
enum xnn_status xnn_create_convolution2d_nhwc_qd8_f16_qc8w(
|
|
uint32_t input_padding_top, uint32_t input_padding_right,
|
|
uint32_t input_padding_bottom, uint32_t input_padding_left,
|
|
uint32_t kernel_height, uint32_t kernel_width, uint32_t subsampling_height,
|
|
uint32_t subsampling_width, uint32_t dilation_height,
|
|
uint32_t dilation_width, uint32_t groups, size_t group_input_channels,
|
|
size_t group_output_channels, size_t input_channel_stride,
|
|
size_t output_channel_stride, const float* kernel_scale,
|
|
const int8_t* kernel, const float* bias, float output_min, float output_max,
|
|
uint32_t flags, xnn_code_cache_t code_cache,
|
|
xnn_weights_cache_t weights_cache, xnn_operator_t* convolution_op_out);
|
|
|
|
enum xnn_status xnn_create_convolution2d_nhwc_qd8_f32_qc8w(
|
|
uint32_t input_padding_top, uint32_t input_padding_right,
|
|
uint32_t input_padding_bottom, uint32_t input_padding_left,
|
|
uint32_t kernel_height, uint32_t kernel_width, uint32_t subsampling_height,
|
|
uint32_t subsampling_width, uint32_t dilation_height,
|
|
uint32_t dilation_width, uint32_t groups, size_t group_input_channels,
|
|
size_t group_output_channels, size_t input_channel_stride,
|
|
size_t output_channel_stride, const float* kernel_scale,
|
|
const int8_t* kernel, const float* bias, float output_min, float output_max,
|
|
uint32_t flags, xnn_code_cache_t code_cache,
|
|
xnn_weights_cache_t weights_cache, xnn_operator_t* convolution_op_out);
|
|
|
|
enum xnn_status xnn_create_convolution2d_nhwc_qs8(
|
|
uint32_t input_padding_top,
|
|
uint32_t input_padding_right,
|
|
uint32_t input_padding_bottom,
|
|
uint32_t input_padding_left,
|
|
uint32_t kernel_height,
|
|
uint32_t kernel_width,
|
|
uint32_t subsampling_height,
|
|
uint32_t subsampling_width,
|
|
uint32_t dilation_height,
|
|
uint32_t dilation_width,
|
|
uint32_t groups,
|
|
size_t group_input_channels,
|
|
size_t group_output_channels,
|
|
size_t input_channel_stride,
|
|
size_t output_channel_stride,
|
|
int8_t input_zero_point,
|
|
float input_scale,
|
|
float kernel_scale,
|
|
const int8_t* kernel,
|
|
const int32_t* bias,
|
|
int8_t output_zero_point,
|
|
float output_scale,
|
|
int8_t output_min,
|
|
int8_t output_max,
|
|
uint32_t flags,
|
|
xnn_code_cache_t code_cache,
|
|
xnn_weights_cache_t weights_cache,
|
|
xnn_operator_t* convolution_op_out);
|
|
|
|
enum xnn_status xnn_reshape_convolution2d_nhwc_qd8_f16_qc8w(
|
|
xnn_operator_t convolution_op, size_t batch_size, size_t input_height,
|
|
size_t input_width, size_t* workspace_size, size_t* workspace_alignment,
|
|
size_t* output_height_out, size_t* output_width_out,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_reshape_convolution2d_nhwc_qd8_f32_qc8w(
|
|
xnn_operator_t convolution_op, size_t batch_size, size_t input_height,
|
|
size_t input_width, size_t* workspace_size, size_t* workspace_alignment,
|
|
size_t* output_height_out, size_t* output_width_out,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_reshape_convolution2d_nhwc_qs8(
|
|
xnn_operator_t convolution_op,
|
|
size_t batch_size,
|
|
size_t input_height,
|
|
size_t input_width,
|
|
size_t* workspace_size,
|
|
size_t* workspace_alignment,
|
|
size_t* output_height_out,
|
|
size_t* output_width_out,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_convolution2d_nhwc_qd8_f16_qc8w(
|
|
xnn_operator_t convolution_op, void* workspace, const int8_t* input,
|
|
void* output,
|
|
const struct xnn_dynamic_quantization_params* quantization_params);
|
|
|
|
enum xnn_status xnn_setup_convolution2d_nhwc_qd8_f32_qc8w(
|
|
xnn_operator_t convolution_op, void* workspace, const int8_t* input,
|
|
float* output,
|
|
const struct xnn_dynamic_quantization_params* quantization_params);
|
|
|
|
enum xnn_status xnn_setup_convolution2d_nhwc_qs8(
|
|
xnn_operator_t convolution_op,
|
|
void* workspace,
|
|
const int8_t* input,
|
|
int8_t* output);
|
|
|
|
enum xnn_status xnn_create_convolution2d_nhwc_qs8_qc8w(
|
|
uint32_t input_padding_top,
|
|
uint32_t input_padding_right,
|
|
uint32_t input_padding_bottom,
|
|
uint32_t input_padding_left,
|
|
uint32_t kernel_height,
|
|
uint32_t kernel_width,
|
|
uint32_t subsampling_height,
|
|
uint32_t subsampling_width,
|
|
uint32_t dilation_height,
|
|
uint32_t dilation_width,
|
|
uint32_t groups,
|
|
size_t group_input_channels,
|
|
size_t group_output_channels,
|
|
size_t input_channel_stride,
|
|
size_t output_channel_stride,
|
|
int8_t input_zero_point,
|
|
float input_scale,
|
|
const float* kernel_scale,
|
|
const int8_t* kernel,
|
|
const int32_t* bias,
|
|
int8_t output_zero_point,
|
|
float output_scale,
|
|
int8_t output_min,
|
|
int8_t output_max,
|
|
uint32_t flags,
|
|
xnn_code_cache_t code_cache,
|
|
xnn_weights_cache_t weights_cache,
|
|
xnn_operator_t* convolution_op_out);
|
|
|
|
enum xnn_status xnn_reshape_convolution2d_nhwc_qs8_qc8w(
|
|
xnn_operator_t convolution_op,
|
|
size_t batch_size,
|
|
size_t input_height,
|
|
size_t input_width,
|
|
size_t* workspace_size,
|
|
size_t* workspace_alignment,
|
|
size_t* output_height_out,
|
|
size_t* output_width_out,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_convolution2d_nhwc_qs8_qc8w(
|
|
xnn_operator_t convolution_op,
|
|
void* workspace,
|
|
const int8_t* input,
|
|
int8_t* output);
|
|
|
|
enum xnn_status xnn_create_convolution2d_nhwc_qu8(
|
|
uint32_t input_padding_top,
|
|
uint32_t input_padding_right,
|
|
uint32_t input_padding_bottom,
|
|
uint32_t input_padding_left,
|
|
uint32_t kernel_height,
|
|
uint32_t kernel_width,
|
|
uint32_t subsampling_height,
|
|
uint32_t subsampling_width,
|
|
uint32_t dilation_height,
|
|
uint32_t dilation_width,
|
|
uint32_t groups,
|
|
size_t group_input_channels,
|
|
size_t group_output_channels,
|
|
size_t input_channel_stride,
|
|
size_t output_channel_stride,
|
|
uint8_t input_zero_point,
|
|
float input_scale,
|
|
uint8_t kernel_zero_point,
|
|
float kernel_scale,
|
|
const uint8_t* kernel,
|
|
const int32_t* bias,
|
|
uint8_t output_zero_point,
|
|
float output_scale,
|
|
uint8_t output_min,
|
|
uint8_t output_max,
|
|
uint32_t flags,
|
|
xnn_code_cache_t code_cache,
|
|
xnn_weights_cache_t weights_cache,
|
|
xnn_operator_t* convolution_op_out);
|
|
|
|
enum xnn_status xnn_reshape_convolution2d_nhwc_qu8(
|
|
xnn_operator_t convolution_op,
|
|
size_t batch_size,
|
|
size_t input_height,
|
|
size_t input_width,
|
|
size_t* workspace_size,
|
|
size_t* workspace_alignment,
|
|
size_t* output_height_out,
|
|
size_t* output_width_out,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_convolution2d_nhwc_qu8(
|
|
xnn_operator_t convolution_op,
|
|
void* workspace,
|
|
const uint8_t* input,
|
|
uint8_t* output);
|
|
|
|
enum xnn_status xnn_create_copy_nc_x8(
|
|
uint32_t flags,
|
|
xnn_operator_t* copy_op_out);
|
|
|
|
enum xnn_status xnn_reshape_copy_nc_x8(
|
|
xnn_operator_t copy_op,
|
|
size_t batch_size,
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_copy_nc_x8(
|
|
xnn_operator_t copy_op,
|
|
const void* input,
|
|
void* output);
|
|
|
|
enum xnn_status xnn_create_copy_nc_x16(
|
|
uint32_t flags,
|
|
xnn_operator_t* copy_op_out);
|
|
|
|
enum xnn_status xnn_reshape_copy_nc_x16(
|
|
xnn_operator_t copy_op,
|
|
size_t batch_size,
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_copy_nc_x16(
|
|
xnn_operator_t copy_op,
|
|
const void* input,
|
|
void* output);
|
|
|
|
enum xnn_status xnn_create_copy_nc_x32(
|
|
uint32_t flags,
|
|
xnn_operator_t* copy_op_out);
|
|
|
|
enum xnn_status xnn_reshape_copy_nc_x32(
|
|
xnn_operator_t copy_op,
|
|
size_t batch_size,
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_copy_nc_x32(
|
|
xnn_operator_t copy_op,
|
|
const void* input,
|
|
void* output);
|
|
|
|
enum xnn_status xnn_run_copy_nc_x32(
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
size_t batch_size,
|
|
const uint32_t* input,
|
|
uint32_t* output,
|
|
uint32_t flags,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_deconvolution2d_nhwc_f16(
|
|
uint32_t output_padding_top,
|
|
uint32_t output_padding_right,
|
|
uint32_t output_padding_bottom,
|
|
uint32_t output_padding_left,
|
|
uint32_t kernel_height,
|
|
uint32_t kernel_width,
|
|
uint32_t stride_height,
|
|
uint32_t stride_width,
|
|
uint32_t dilation_height,
|
|
uint32_t dilation_width,
|
|
uint32_t groups,
|
|
size_t group_input_channels,
|
|
size_t group_output_channels,
|
|
size_t input_pixel_stride,
|
|
size_t output_pixel_stride,
|
|
const void* kernel,
|
|
const void* bias,
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t flags,
|
|
xnn_code_cache_t code_cache,
|
|
xnn_weights_cache_t weights_cache,
|
|
xnn_operator_t* deconvolution_op_out);
|
|
|
|
enum xnn_status xnn_reshape_deconvolution2d_nhwc_f16(
|
|
xnn_operator_t deconvolution_op,
|
|
size_t batch_size,
|
|
size_t input_height,
|
|
size_t input_width,
|
|
uint32_t adjustment_height,
|
|
uint32_t adjustment_width,
|
|
size_t* output_height_out,
|
|
size_t* output_width_out,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_deconvolution2d_nhwc_f16(
|
|
xnn_operator_t deconvolution_op,
|
|
const void* input,
|
|
void* output);
|
|
|
|
enum xnn_status xnn_create_deconvolution2d_nhwc_f32(
|
|
uint32_t output_padding_top,
|
|
uint32_t output_padding_right,
|
|
uint32_t output_padding_bottom,
|
|
uint32_t output_padding_left,
|
|
uint32_t kernel_height,
|
|
uint32_t kernel_width,
|
|
uint32_t stride_height,
|
|
uint32_t stride_width,
|
|
uint32_t dilation_height,
|
|
uint32_t dilation_width,
|
|
uint32_t groups,
|
|
size_t group_input_channels,
|
|
size_t group_output_channels,
|
|
size_t input_pixel_stride,
|
|
size_t output_pixel_stride,
|
|
const float* kernel,
|
|
const float* bias,
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t flags,
|
|
xnn_code_cache_t code_cache,
|
|
xnn_weights_cache_t weights_cache,
|
|
xnn_operator_t* deconvolution_op_out);
|
|
|
|
enum xnn_status xnn_reshape_deconvolution2d_nhwc_f32(
|
|
xnn_operator_t deconvolution_op,
|
|
size_t batch_size,
|
|
size_t input_height,
|
|
size_t input_width,
|
|
uint32_t adjustment_height,
|
|
uint32_t adjustment_width,
|
|
size_t* output_height_out,
|
|
size_t* output_width_out,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_deconvolution2d_nhwc_f32(
|
|
xnn_operator_t deconvolution_op,
|
|
const float* input,
|
|
float* output);
|
|
|
|
enum xnn_status xnn_create_deconvolution2d_nhwc_qs8(
|
|
uint32_t output_padding_top,
|
|
uint32_t output_padding_right,
|
|
uint32_t output_padding_bottom,
|
|
uint32_t output_padding_left,
|
|
uint32_t kernel_height,
|
|
uint32_t kernel_width,
|
|
uint32_t stride_height,
|
|
uint32_t stride_width,
|
|
uint32_t dilation_height,
|
|
uint32_t dilation_width,
|
|
uint32_t groups,
|
|
size_t group_input_channels,
|
|
size_t group_output_channels,
|
|
size_t input_pixel_stride,
|
|
size_t output_pixel_stride,
|
|
int8_t input_zero_point,
|
|
float input_scale,
|
|
float kernel_scale,
|
|
const int8_t* kernel,
|
|
const int32_t* bias,
|
|
int8_t output_zero_point,
|
|
float output_scale,
|
|
int8_t output_min,
|
|
int8_t output_max,
|
|
uint32_t flags,
|
|
xnn_code_cache_t code_cache,
|
|
xnn_weights_cache_t weights_cache,
|
|
xnn_operator_t* deconvolution_op_out);
|
|
|
|
enum xnn_status xnn_reshape_deconvolution2d_nhwc_qs8(
|
|
xnn_operator_t deconvolution_op,
|
|
size_t batch_size,
|
|
size_t input_height,
|
|
size_t input_width,
|
|
uint32_t adjustment_height,
|
|
uint32_t adjustment_width,
|
|
size_t* output_height_out,
|
|
size_t* output_width_out,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_deconvolution2d_nhwc_qs8(
|
|
xnn_operator_t deconvolution_op,
|
|
const int8_t* input,
|
|
int8_t* output);
|
|
|
|
enum xnn_status xnn_create_deconvolution2d_nhwc_qu8(
|
|
uint32_t output_padding_top,
|
|
uint32_t output_padding_right,
|
|
uint32_t output_padding_bottom,
|
|
uint32_t output_padding_left,
|
|
uint32_t kernel_height,
|
|
uint32_t kernel_width,
|
|
uint32_t stride_height,
|
|
uint32_t stride_width,
|
|
uint32_t dilation_height,
|
|
uint32_t dilation_width,
|
|
uint32_t groups,
|
|
size_t group_input_channels,
|
|
size_t group_output_channels,
|
|
size_t input_pixel_stride,
|
|
size_t output_pixel_stride,
|
|
uint8_t input_zero_point,
|
|
float input_scale,
|
|
uint8_t kernel_zero_point,
|
|
float kernel_scale,
|
|
const uint8_t* kernel,
|
|
const int32_t* bias,
|
|
uint8_t output_zero_point,
|
|
float output_scale,
|
|
uint8_t output_min,
|
|
uint8_t output_max,
|
|
uint32_t flags,
|
|
xnn_code_cache_t code_cache,
|
|
xnn_weights_cache_t weights_cache,
|
|
xnn_operator_t* deconvolution_op_out);
|
|
|
|
enum xnn_status xnn_reshape_deconvolution2d_nhwc_qu8(
|
|
xnn_operator_t deconvolution_op,
|
|
size_t batch_size,
|
|
size_t input_height,
|
|
size_t input_width,
|
|
uint32_t adjustment_height,
|
|
uint32_t adjustment_width,
|
|
size_t* output_height_out,
|
|
size_t* output_width_out,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_deconvolution2d_nhwc_qu8(
|
|
xnn_operator_t deconvolution_op,
|
|
const uint8_t* input,
|
|
uint8_t* output);
|
|
|
|
enum xnn_status xnn_create_depth_to_space_nchw2nhwc_x16(
|
|
uint32_t block_size,
|
|
uint32_t flags,
|
|
xnn_operator_t* depth_to_space_op_out);
|
|
|
|
enum xnn_status xnn_reshape_depth_to_space_nchw2nhwc_x16(
|
|
xnn_operator_t depth_to_space_op,
|
|
size_t batch_size,
|
|
size_t input_height,
|
|
size_t input_width,
|
|
size_t input_channels,
|
|
size_t* output_height_out,
|
|
size_t* output_width_out,
|
|
size_t* output_channels_out,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_depth_to_space_nchw2nhwc_x16(
|
|
xnn_operator_t depth_to_space_op,
|
|
const void* input,
|
|
void* output);
|
|
|
|
enum xnn_status xnn_create_depth_to_space_nchw2nhwc_x32(
|
|
uint32_t block_size,
|
|
uint32_t flags,
|
|
xnn_operator_t* depth_to_space_op_out);
|
|
|
|
enum xnn_status xnn_reshape_depth_to_space_nchw2nhwc_x32(
|
|
xnn_operator_t depth_to_space_op,
|
|
size_t batch_size,
|
|
size_t input_height,
|
|
size_t input_width,
|
|
size_t input_channels,
|
|
size_t* output_height_out,
|
|
size_t* output_width_out,
|
|
size_t* output_channels_out,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_depth_to_space_nchw2nhwc_x32(
|
|
xnn_operator_t depth_to_space_op,
|
|
const void* input,
|
|
void* output);
|
|
|
|
enum xnn_status xnn_create_depth_to_space_nhwc_x8(
|
|
uint32_t block_size,
|
|
uint32_t flags,
|
|
xnn_operator_t* depth_to_space_op_out);
|
|
|
|
enum xnn_status xnn_reshape_depth_to_space_nhwc_x8(
|
|
xnn_operator_t depth_to_space_op,
|
|
size_t batch_size,
|
|
size_t input_height,
|
|
size_t input_width,
|
|
size_t input_channels,
|
|
size_t* output_height_out,
|
|
size_t* output_width_out,
|
|
size_t* output_channels_out,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_depth_to_space_nhwc_x8(
|
|
xnn_operator_t depth_to_space_op,
|
|
const void* input,
|
|
void* output);
|
|
|
|
enum xnn_status xnn_create_depth_to_space_nhwc_x16(
|
|
uint32_t block_size,
|
|
uint32_t flags,
|
|
xnn_operator_t* depth_to_space_op_out);
|
|
|
|
enum xnn_status xnn_reshape_depth_to_space_nhwc_x16(
|
|
xnn_operator_t depth_to_space_op,
|
|
size_t batch_size,
|
|
size_t input_height,
|
|
size_t input_width,
|
|
size_t input_channels,
|
|
size_t* output_height_out,
|
|
size_t* output_width_out,
|
|
size_t* output_channels_out,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_depth_to_space_nhwc_x16(
|
|
xnn_operator_t depth_to_space_op,
|
|
const void* input,
|
|
void* output);
|
|
|
|
enum xnn_status xnn_create_depth_to_space_nhwc_x32(
|
|
uint32_t block_size,
|
|
uint32_t flags,
|
|
xnn_operator_t* depth_to_space_op_out);
|
|
|
|
enum xnn_status xnn_reshape_depth_to_space_nhwc_x32(
|
|
xnn_operator_t depth_to_space_op,
|
|
size_t batch_size,
|
|
size_t input_height,
|
|
size_t input_width,
|
|
size_t input_channels,
|
|
size_t* output_height_out,
|
|
size_t* output_width_out,
|
|
size_t* output_channels_out,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_depth_to_space_nhwc_x32(
|
|
xnn_operator_t depth_to_space_op,
|
|
const void* input,
|
|
void* output);
|
|
|
|
enum xnn_status xnn_create_divide_nd_f16(
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t flags,
|
|
xnn_operator_t* divide_op_out);
|
|
|
|
enum xnn_status xnn_reshape_divide_nd_f16(
|
|
xnn_operator_t divide_op,
|
|
size_t num_input1_dims,
|
|
const size_t* input1_shape,
|
|
size_t num_input2_dims,
|
|
const size_t* input2_shape,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_divide_nd_f16(
|
|
xnn_operator_t divide_op,
|
|
const void* input1,
|
|
const void* input2,
|
|
void* output);
|
|
|
|
enum xnn_status xnn_create_divide_nd_f32(
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t flags,
|
|
xnn_operator_t* divide_op_out);
|
|
|
|
enum xnn_status xnn_reshape_divide_nd_f32(
|
|
xnn_operator_t divide_op,
|
|
size_t num_input1_dims,
|
|
const size_t* input1_shape,
|
|
size_t num_input2_dims,
|
|
const size_t* input2_shape,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_divide_nd_f32(
|
|
xnn_operator_t divide_op,
|
|
const float* input1,
|
|
const float* input2,
|
|
float* output);
|
|
|
|
enum xnn_status xnn_run_divide_nd_f32(
|
|
size_t num_input1_dims,
|
|
const size_t* input1_shape,
|
|
size_t num_input2_dims,
|
|
const size_t* input2_shape,
|
|
const float* input1,
|
|
const float* input2,
|
|
float* output,
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t flags,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_dynamic_fully_connected_nc_f16(
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t flags,
|
|
xnn_operator_t* dynamic_fully_connected_op_out);
|
|
|
|
enum xnn_status xnn_reshape_dynamic_fully_connected_nc_f16(
|
|
xnn_operator_t dynamic_fully_connected_op,
|
|
size_t batch_size,
|
|
size_t input_channels,
|
|
size_t output_channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
size_t* workspace_size,
|
|
size_t* workspace_alignment,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_dynamic_fully_connected_nc_f16(
|
|
xnn_operator_t dynamic_fully_connected_op,
|
|
void* workspace,
|
|
const void* input,
|
|
const void* kernel,
|
|
const void* bias,
|
|
void* output);
|
|
|
|
enum xnn_status xnn_create_dynamic_fully_connected_nc_f32(
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t flags,
|
|
xnn_operator_t* dynamic_fully_connected_op_out);
|
|
|
|
enum xnn_status xnn_reshape_dynamic_fully_connected_nc_f32(
|
|
xnn_operator_t dynamic_fully_connected_op,
|
|
size_t batch_size,
|
|
size_t input_channels,
|
|
size_t output_channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
size_t* workspace_size,
|
|
size_t* workspace_alignment,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_dynamic_fully_connected_nc_f32(
|
|
xnn_operator_t dynamic_fully_connected_op,
|
|
void* workspace,
|
|
const float* input,
|
|
const float* kernel,
|
|
const float* bias,
|
|
float* output);
|
|
|
|
enum xnn_status xnn_create_elu_nc_f16(
|
|
float alpha,
|
|
uint32_t flags,
|
|
xnn_operator_t* elu_op_out);
|
|
|
|
enum xnn_status xnn_reshape_elu_nc_f16(
|
|
xnn_operator_t elu_op,
|
|
size_t batch_size,
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_elu_nc_f16(
|
|
xnn_operator_t elu_op,
|
|
const void* input,
|
|
void* output);
|
|
|
|
enum xnn_status xnn_create_elu_nc_f32(
|
|
float alpha,
|
|
uint32_t flags,
|
|
xnn_operator_t* elu_op_out);
|
|
|
|
enum xnn_status xnn_reshape_elu_nc_f32(
|
|
xnn_operator_t elu_op,
|
|
size_t batch_size,
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_elu_nc_f32(
|
|
xnn_operator_t elu_op,
|
|
const float* input,
|
|
float* output);
|
|
|
|
enum xnn_status xnn_run_elu_nc_f32(
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
size_t batch_size,
|
|
const float* input,
|
|
float* output,
|
|
float alpha,
|
|
uint32_t flags,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_elu_nc_qs8(
|
|
float alpha,
|
|
int8_t input_zero_point,
|
|
float input_scale,
|
|
int8_t output_zero_point,
|
|
float output_scale,
|
|
int8_t output_min,
|
|
int8_t output_max,
|
|
uint32_t flags,
|
|
xnn_operator_t* elu_op_out);
|
|
|
|
enum xnn_status xnn_reshape_elu_nc_qs8(
|
|
xnn_operator_t elu_op,
|
|
size_t batch_size,
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_elu_nc_qs8(
|
|
xnn_operator_t elu_op,
|
|
const int8_t* input,
|
|
int8_t* output);
|
|
|
|
enum xnn_status xnn_create_floor_nc_f16(
|
|
uint32_t flags,
|
|
xnn_operator_t* floor_op_out);
|
|
|
|
enum xnn_status xnn_reshape_floor_nc_f16(
|
|
xnn_operator_t floor_op,
|
|
size_t batch_size,
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_floor_nc_f16(
|
|
xnn_operator_t floor_op,
|
|
const void* input,
|
|
void* output);
|
|
|
|
enum xnn_status xnn_create_floor_nc_f32(
|
|
uint32_t flags,
|
|
xnn_operator_t* floor_op_out);
|
|
|
|
enum xnn_status xnn_reshape_floor_nc_f32(
|
|
xnn_operator_t floor_op,
|
|
size_t batch_size,
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_floor_nc_f32(
|
|
xnn_operator_t floor_op,
|
|
const float* input,
|
|
float* output);
|
|
|
|
enum xnn_status xnn_run_floor_nc_f32(
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
size_t batch_size,
|
|
const float* input,
|
|
float* output,
|
|
uint32_t flags,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_fully_connected_nc_f16(
|
|
size_t input_channels,
|
|
size_t output_channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
const void* kernel,
|
|
const void* bias,
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t flags,
|
|
xnn_code_cache_t code_cache,
|
|
xnn_weights_cache_t weights_cache,
|
|
xnn_operator_t* fully_connected_op_out);
|
|
|
|
enum xnn_status xnn_reshape_fully_connected_nc_f16(
|
|
xnn_operator_t fully_connected_op,
|
|
size_t batch_size,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_fully_connected_nc_f16(
|
|
xnn_operator_t fully_connected_op,
|
|
const void* input,
|
|
void* output);
|
|
|
|
enum xnn_status xnn_create_fully_connected_nc_f32(
|
|
size_t input_channels,
|
|
size_t output_channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
const float* kernel,
|
|
const float* bias,
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t flags,
|
|
xnn_code_cache_t code_cache,
|
|
xnn_weights_cache_t weights_cache,
|
|
xnn_operator_t* fully_connected_op_out);
|
|
|
|
enum xnn_status xnn_reshape_fully_connected_nc_f32(
|
|
xnn_operator_t fully_connected_op,
|
|
size_t batch_size,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_fully_connected_nc_f32(
|
|
xnn_operator_t fully_connected_op,
|
|
const float* input,
|
|
float* output);
|
|
|
|
enum xnn_status xnn_create_fully_connected_nc_f32_qc4w(
|
|
size_t input_channels,
|
|
size_t output_channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
uint8_t kernel_zero_point,
|
|
const float* kernel_scale,
|
|
const uint8_t* kernel,
|
|
const float* bias,
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t flags,
|
|
xnn_code_cache_t code_cache,
|
|
xnn_weights_cache_t weights_cache,
|
|
xnn_operator_t* fully_connected_op_out);
|
|
|
|
enum xnn_status xnn_reshape_fully_connected_nc_f32_qc4w(
|
|
xnn_operator_t fully_connected_op,
|
|
size_t batch_size,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_fully_connected_nc_f32_qc4w(
|
|
xnn_operator_t fully_connected_op,
|
|
const float* input,
|
|
float* output);
|
|
|
|
enum xnn_status xnn_create_fully_connected_nc_f32_qc8w(
|
|
size_t input_channels,
|
|
size_t output_channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
const float* kernel_scale,
|
|
const int8_t* kernel,
|
|
const float* bias,
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t flags,
|
|
xnn_code_cache_t code_cache,
|
|
xnn_weights_cache_t weights_cache,
|
|
xnn_operator_t* fully_connected_op_out);
|
|
|
|
enum xnn_status xnn_reshape_fully_connected_nc_f32_qc8w(
|
|
xnn_operator_t fully_connected_op,
|
|
size_t batch_size,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_fully_connected_nc_f32_qc8w(
|
|
xnn_operator_t fully_connected_op,
|
|
const float* input,
|
|
float* output);
|
|
|
|
enum xnn_status xnn_create_fully_connected_nc_qd8_f16_qc4w(
|
|
size_t input_channels,
|
|
size_t output_channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
uint8_t kernel_zero_point,
|
|
const float* kernel_scale,
|
|
const void* kernel,
|
|
const float* bias,
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t flags,
|
|
xnn_code_cache_t code_cache,
|
|
xnn_weights_cache_t weights_cache,
|
|
xnn_operator_t* fully_connected_op_out);
|
|
|
|
enum xnn_status xnn_setup_fully_connected_nc_qd8_f16_qc4w(
|
|
xnn_operator_t fully_connected_op,
|
|
const int8_t* input,
|
|
void* output,
|
|
const struct xnn_dynamic_quantization_params* quantization_params);
|
|
|
|
enum xnn_status xnn_reshape_fully_connected_nc_qd8_f16_qc4w(
|
|
xnn_operator_t fully_connected_op,
|
|
size_t batch_size,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_fully_connected_nc_qd8_f32_qc4w(
|
|
size_t input_channels,
|
|
size_t output_channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
uint8_t kernel_zero_point,
|
|
const float* kernel_scale,
|
|
const void* kernel,
|
|
const float* bias,
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t flags,
|
|
xnn_code_cache_t code_cache,
|
|
xnn_weights_cache_t weights_cache,
|
|
xnn_operator_t* fully_connected_op_out);
|
|
|
|
enum xnn_status xnn_setup_fully_connected_nc_qd8_f32_qc4w(
|
|
xnn_operator_t fully_connected_op,
|
|
const int8_t* input,
|
|
float* output,
|
|
const struct xnn_dynamic_quantization_params* quantization_params);
|
|
|
|
enum xnn_status xnn_reshape_fully_connected_nc_qd8_f32_qc4w(
|
|
xnn_operator_t fully_connected_op,
|
|
size_t batch_size,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_fully_connected_nc_qd8_f16_qc8w(
|
|
size_t input_channels,
|
|
size_t output_channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
const float* kernel_scale,
|
|
const int8_t* kernel,
|
|
const float* bias,
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t flags,
|
|
xnn_code_cache_t code_cache,
|
|
xnn_weights_cache_t weights_cache,
|
|
xnn_operator_t* fully_connected_op_out);
|
|
|
|
enum xnn_status xnn_setup_fully_connected_nc_qd8_f16_qc8w(
|
|
xnn_operator_t fully_connected_op,
|
|
const int8_t* input,
|
|
void* output,
|
|
const struct xnn_dynamic_quantization_params* quantization_params);
|
|
|
|
enum xnn_status xnn_reshape_fully_connected_nc_qd8_f16_qc8w(
|
|
xnn_operator_t fully_connected_op,
|
|
size_t batch_size,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_fully_connected_nc_qd8_f32_qc8w(
|
|
size_t input_channels,
|
|
size_t output_channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
const float* kernel_scale,
|
|
const int8_t* kernel,
|
|
const float* bias,
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t flags,
|
|
xnn_code_cache_t code_cache,
|
|
xnn_weights_cache_t weights_cache,
|
|
xnn_operator_t* fully_connected_op_out);
|
|
|
|
enum xnn_status xnn_setup_fully_connected_nc_qd8_f32_qc8w(
|
|
xnn_operator_t fully_connected_op,
|
|
const int8_t* input,
|
|
float* output,
|
|
const struct xnn_dynamic_quantization_params* quantization_params);
|
|
|
|
enum xnn_status xnn_reshape_fully_connected_nc_qd8_f32_qc8w(
|
|
xnn_operator_t fully_connected_op,
|
|
size_t batch_size,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_fully_connected_nc_qs8(
|
|
size_t input_channels,
|
|
size_t output_channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
int8_t input_zero_point,
|
|
float input_scale,
|
|
float kernel_scale,
|
|
const int8_t* kernel,
|
|
const int32_t* bias,
|
|
int8_t output_zero_point,
|
|
float output_scale,
|
|
int8_t output_min,
|
|
int8_t output_max,
|
|
uint32_t flags,
|
|
xnn_code_cache_t code_cache,
|
|
xnn_weights_cache_t weights_cache,
|
|
xnn_operator_t* fully_connected_op_out);
|
|
|
|
enum xnn_status xnn_reshape_fully_connected_nc_qs8(
|
|
xnn_operator_t fully_connected_op,
|
|
size_t batch_size,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_fully_connected_nc_qs8(
|
|
xnn_operator_t fully_connected_op,
|
|
const int8_t* input,
|
|
int8_t* output);
|
|
|
|
enum xnn_status xnn_create_fully_connected_nc_qs8_qc8w(
|
|
size_t input_channels,
|
|
size_t output_channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
int8_t input_zero_point,
|
|
float input_scale,
|
|
const float* kernel_scale,
|
|
const int8_t* kernel,
|
|
const int32_t* bias,
|
|
int8_t output_zero_point,
|
|
float output_scale,
|
|
int8_t output_min,
|
|
int8_t output_max,
|
|
uint32_t flags,
|
|
xnn_code_cache_t code_cache,
|
|
xnn_weights_cache_t weights_cache,
|
|
xnn_operator_t* fully_connected_op_out);
|
|
|
|
enum xnn_status xnn_reshape_fully_connected_nc_qs8_qc8w(
|
|
xnn_operator_t fully_connected_op,
|
|
size_t batch_size,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_fully_connected_nc_qs8_qc8w(
|
|
xnn_operator_t fully_connected_op,
|
|
const int8_t* input,
|
|
int8_t* output);
|
|
|
|
enum xnn_status xnn_create_fully_connected_nc_qu8(
|
|
size_t input_channels,
|
|
size_t output_channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
uint8_t input_zero_point,
|
|
float input_scale,
|
|
uint8_t kernel_zero_point,
|
|
float kernel_scale,
|
|
const uint8_t* kernel,
|
|
const int32_t* bias,
|
|
uint8_t output_zero_point,
|
|
float output_scale,
|
|
uint8_t output_min,
|
|
uint8_t output_max,
|
|
uint32_t flags,
|
|
xnn_code_cache_t code_cache,
|
|
xnn_weights_cache_t weights_cache,
|
|
xnn_operator_t* fully_connected_op_out);
|
|
|
|
enum xnn_status xnn_reshape_fully_connected_nc_qu8(
|
|
xnn_operator_t fully_connected_op,
|
|
size_t batch_size,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_fully_connected_nc_qu8(
|
|
xnn_operator_t fully_connected_op,
|
|
const uint8_t* input,
|
|
uint8_t* output);
|
|
|
|
enum xnn_status xnn_create_global_average_pooling_ncw_f16(
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t flags,
|
|
xnn_operator_t* global_average_pooling_op_out);
|
|
|
|
enum xnn_status xnn_reshape_global_average_pooling_ncw_f16(
|
|
xnn_operator_t global_average_pooling_op,
|
|
size_t batch_size,
|
|
size_t width,
|
|
size_t channels,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_global_average_pooling_ncw_f16(
|
|
xnn_operator_t global_average_pooling_op,
|
|
const void* input,
|
|
void* output);
|
|
|
|
enum xnn_status xnn_create_global_average_pooling_ncw_f32(
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t flags,
|
|
xnn_operator_t* global_average_pooling_op_out);
|
|
|
|
enum xnn_status xnn_reshape_global_average_pooling_ncw_f32(
|
|
xnn_operator_t global_average_pooling_op,
|
|
size_t batch_size,
|
|
size_t width,
|
|
size_t channels,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_global_average_pooling_ncw_f32(
|
|
xnn_operator_t global_average_pooling_op,
|
|
const float* input,
|
|
float* output);
|
|
|
|
enum xnn_status xnn_create_global_average_pooling_nwc_f16(
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t flags,
|
|
xnn_operator_t* global_average_pooling_op_out);
|
|
|
|
enum xnn_status xnn_reshape_global_average_pooling_nwc_f16(
|
|
xnn_operator_t global_average_pooling_op,
|
|
size_t batch_size,
|
|
size_t width,
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
size_t* workspace_size,
|
|
size_t* workspace_alignment,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_global_average_pooling_nwc_f16(
|
|
xnn_operator_t global_average_pooling_op,
|
|
void* workspace,
|
|
const void* input,
|
|
void* output);
|
|
|
|
enum xnn_status xnn_create_global_average_pooling_nwc_f32(
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t flags,
|
|
xnn_operator_t* global_average_pooling_op_out);
|
|
|
|
enum xnn_status xnn_reshape_global_average_pooling_nwc_f32(
|
|
xnn_operator_t global_average_pooling_op,
|
|
size_t batch_size,
|
|
size_t width,
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
size_t* workspace_size,
|
|
size_t* workspace_alignment,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_global_average_pooling_nwc_f32(
|
|
xnn_operator_t global_average_pooling_op,
|
|
void* workspace,
|
|
const float* input,
|
|
float* output);
|
|
|
|
enum xnn_status xnn_create_global_average_pooling_nwc_qs8(
|
|
int8_t input_zero_point,
|
|
float input_scale,
|
|
int8_t output_zero_point,
|
|
float output_scale,
|
|
int8_t output_min,
|
|
int8_t output_max,
|
|
uint32_t flags,
|
|
xnn_operator_t* global_average_pooling_op_out);
|
|
|
|
enum xnn_status xnn_reshape_global_average_pooling_nwc_qs8(
|
|
xnn_operator_t global_average_pooling_op,
|
|
size_t batch_size,
|
|
size_t width,
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
size_t* workspace_size,
|
|
size_t* workspace_alignment,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_global_average_pooling_nwc_qs8(
|
|
xnn_operator_t global_average_pooling_op,
|
|
void* workspace,
|
|
const int8_t* input,
|
|
int8_t* output);
|
|
|
|
enum xnn_status xnn_create_global_average_pooling_nwc_qu8(
|
|
uint8_t input_zero_point,
|
|
float input_scale,
|
|
uint8_t output_zero_point,
|
|
float output_scale,
|
|
uint8_t output_min,
|
|
uint8_t output_max,
|
|
uint32_t flags,
|
|
xnn_operator_t* global_average_pooling_op_out);
|
|
|
|
enum xnn_status xnn_reshape_global_average_pooling_nwc_qu8(
|
|
xnn_operator_t global_average_pooling_op,
|
|
size_t batch_size,
|
|
size_t width,
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
size_t* workspace_size,
|
|
size_t* workspace_alignment,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_global_average_pooling_nwc_qu8(
|
|
xnn_operator_t global_average_pooling_op,
|
|
void* workspace,
|
|
const uint8_t* input,
|
|
uint8_t* output);
|
|
|
|
enum xnn_status xnn_create_global_sum_pooling_nwc_f16(
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t flags,
|
|
xnn_operator_t* global_sum_pooling_op_out);
|
|
|
|
enum xnn_status xnn_reshape_global_sum_pooling_nwc_f16(
|
|
xnn_operator_t global_sum_pooling_op,
|
|
size_t batch_size,
|
|
size_t width,
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
size_t* workspace_size,
|
|
size_t* workspace_alignment,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_global_sum_pooling_nwc_f16(
|
|
xnn_operator_t global_sum_pooling_op,
|
|
void* workspace,
|
|
const void* input,
|
|
void* output);
|
|
|
|
enum xnn_status xnn_create_global_sum_pooling_nwc_f32(
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t flags,
|
|
xnn_operator_t* global_sum_pooling_op_out);
|
|
|
|
enum xnn_status xnn_reshape_global_sum_pooling_nwc_f32(
|
|
xnn_operator_t global_sum_pooling_op,
|
|
size_t batch_size,
|
|
size_t width,
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
size_t* workspace_size,
|
|
size_t* workspace_alignment,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_global_sum_pooling_nwc_f32(
|
|
xnn_operator_t global_sum_pooling_op,
|
|
void* workspace,
|
|
const float* input,
|
|
float* output);
|
|
|
|
enum xnn_status xnn_create_hardswish_nc_f16(
|
|
uint32_t flags,
|
|
xnn_operator_t* hardswish_op_out);
|
|
|
|
enum xnn_status xnn_reshape_hardswish_nc_f16(
|
|
xnn_operator_t hardswish_op,
|
|
size_t batch_size,
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_hardswish_nc_f16(
|
|
xnn_operator_t hardswish_op,
|
|
const void* input,
|
|
void* output);
|
|
|
|
enum xnn_status xnn_create_hardswish_nc_f32(
|
|
uint32_t flags,
|
|
xnn_operator_t* hardswish_op_out);
|
|
|
|
enum xnn_status xnn_reshape_hardswish_nc_f32(
|
|
xnn_operator_t hardswish_op,
|
|
size_t batch_size,
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_hardswish_nc_f32(
|
|
xnn_operator_t hardswish_op,
|
|
const float* input,
|
|
float* output);
|
|
|
|
enum xnn_status xnn_run_hardswish_nc_f32(
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
size_t batch_size,
|
|
const float* input,
|
|
float* output,
|
|
uint32_t flags,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_leaky_relu_nc_f16(
|
|
float negative_slope,
|
|
uint32_t flags,
|
|
xnn_operator_t* leaky_relu_op_out);
|
|
|
|
enum xnn_status xnn_reshape_leaky_relu_nc_f16(
|
|
xnn_operator_t leaky_relu_op,
|
|
size_t batch_size,
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_leaky_relu_nc_f16(
|
|
xnn_operator_t leaky_relu_op,
|
|
const void* input,
|
|
void* output);
|
|
|
|
enum xnn_status xnn_create_leaky_relu_nc_f32(
|
|
float negative_slope,
|
|
uint32_t flags,
|
|
xnn_operator_t* leaky_relu_op_out);
|
|
|
|
enum xnn_status xnn_reshape_leaky_relu_nc_f32(
|
|
xnn_operator_t leaky_relu_op,
|
|
size_t batch_size,
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_leaky_relu_nc_f32(
|
|
xnn_operator_t leaky_relu_op,
|
|
const float* input,
|
|
float* output);
|
|
|
|
enum xnn_status xnn_run_leaky_relu_nc_f32(
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
size_t batch_size,
|
|
const float* input,
|
|
float* output,
|
|
float negative_slope,
|
|
uint32_t flags,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_leaky_relu_nc_qs8(
|
|
float negative_slope,
|
|
int8_t input_zero_point,
|
|
float input_scale,
|
|
int8_t output_zero_point,
|
|
float output_scale,
|
|
uint32_t flags,
|
|
xnn_operator_t* leaky_relu_op_out);
|
|
|
|
enum xnn_status xnn_reshape_leaky_relu_nc_qs8(
|
|
xnn_operator_t leaky_relu_op,
|
|
size_t batch_size,
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_leaky_relu_nc_qs8(
|
|
xnn_operator_t leaky_relu_op,
|
|
const int8_t* input,
|
|
int8_t* output);
|
|
|
|
enum xnn_status xnn_create_leaky_relu_nc_qu8(
|
|
float negative_slope,
|
|
uint8_t input_zero_point,
|
|
float input_scale,
|
|
uint8_t output_zero_point,
|
|
float output_scale,
|
|
uint32_t flags,
|
|
xnn_operator_t* leaky_relu_op_out);
|
|
|
|
enum xnn_status xnn_reshape_leaky_relu_nc_qu8(
|
|
xnn_operator_t leaky_relu_op,
|
|
size_t batch_size,
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_leaky_relu_nc_qu8(
|
|
xnn_operator_t leaky_relu_op,
|
|
const uint8_t* input,
|
|
uint8_t* output);
|
|
|
|
enum xnn_status xnn_create_max_pooling2d_nhwc_f16(
|
|
uint32_t input_padding_top,
|
|
uint32_t input_padding_right,
|
|
uint32_t input_padding_bottom,
|
|
uint32_t input_padding_left,
|
|
uint32_t pooling_height,
|
|
uint32_t pooling_width,
|
|
uint32_t stride_height,
|
|
uint32_t stride_width,
|
|
uint32_t dilation_height,
|
|
uint32_t dilation_width,
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t flags,
|
|
xnn_operator_t* max_pooling_op_out);
|
|
|
|
enum xnn_status xnn_reshape_max_pooling2d_nhwc_f16(
|
|
xnn_operator_t max_pooling_op,
|
|
size_t batch_size,
|
|
size_t input_height,
|
|
size_t input_width,
|
|
size_t channels,
|
|
size_t input_pixel_stride,
|
|
size_t output_pixel_stride,
|
|
size_t* output_height_out,
|
|
size_t* output_width_out,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_max_pooling2d_nhwc_f16(
|
|
xnn_operator_t max_pooling_op,
|
|
const void* input,
|
|
void* output);
|
|
|
|
enum xnn_status xnn_create_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,
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t flags,
|
|
xnn_operator_t* max_pooling_op_out);
|
|
|
|
enum xnn_status xnn_reshape_max_pooling2d_nhwc_f32(
|
|
xnn_operator_t max_pooling_op,
|
|
size_t batch_size,
|
|
size_t input_height,
|
|
size_t input_width,
|
|
size_t channels,
|
|
size_t input_pixel_stride,
|
|
size_t output_pixel_stride,
|
|
size_t* output_height_out,
|
|
size_t* output_width_out,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_max_pooling2d_nhwc_f32(
|
|
xnn_operator_t max_pooling_op,
|
|
const float* input,
|
|
float* output);
|
|
|
|
enum xnn_status xnn_create_max_pooling2d_nhwc_s8(
|
|
uint32_t input_padding_top,
|
|
uint32_t input_padding_right,
|
|
uint32_t input_padding_bottom,
|
|
uint32_t input_padding_left,
|
|
uint32_t pooling_height,
|
|
uint32_t pooling_width,
|
|
uint32_t stride_height,
|
|
uint32_t stride_width,
|
|
uint32_t dilation_height,
|
|
uint32_t dilation_width,
|
|
int8_t output_min,
|
|
int8_t output_max,
|
|
uint32_t flags,
|
|
xnn_operator_t* max_pooling_op_out);
|
|
|
|
enum xnn_status xnn_reshape_max_pooling2d_nhwc_s8(
|
|
xnn_operator_t max_pooling_op,
|
|
size_t batch_size,
|
|
size_t input_height,
|
|
size_t input_width,
|
|
size_t channels,
|
|
size_t input_pixel_stride,
|
|
size_t output_pixel_stride,
|
|
size_t* output_height_out,
|
|
size_t* output_width_out,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_max_pooling2d_nhwc_s8(
|
|
xnn_operator_t max_pooling_op,
|
|
const int8_t* input,
|
|
int8_t* output);
|
|
|
|
enum xnn_status xnn_create_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,
|
|
uint8_t output_min,
|
|
uint8_t output_max,
|
|
uint32_t flags,
|
|
xnn_operator_t* max_pooling_op_out);
|
|
|
|
enum xnn_status xnn_reshape_max_pooling2d_nhwc_u8(
|
|
xnn_operator_t max_pooling_op,
|
|
size_t batch_size,
|
|
size_t input_height,
|
|
size_t input_width,
|
|
size_t channels,
|
|
size_t input_pixel_stride,
|
|
size_t output_pixel_stride,
|
|
size_t* output_height_out,
|
|
size_t* output_width_out,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_max_pooling2d_nhwc_u8(
|
|
xnn_operator_t max_pooling_op,
|
|
const uint8_t* input,
|
|
uint8_t* output);
|
|
|
|
enum xnn_status xnn_create_maximum_nd_f16(
|
|
uint32_t flags,
|
|
xnn_operator_t* maximum_op_out);
|
|
|
|
enum xnn_status xnn_reshape_maximum_nd_f16(
|
|
xnn_operator_t maximum_op,
|
|
size_t num_input1_dims,
|
|
const size_t* input1_shape,
|
|
size_t num_input2_dims,
|
|
const size_t* input2_shape,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_maximum_nd_f16(
|
|
xnn_operator_t maximum_op,
|
|
const void* input1,
|
|
const void* input2,
|
|
void* output);
|
|
|
|
enum xnn_status xnn_create_maximum_nd_f32(
|
|
uint32_t flags,
|
|
xnn_operator_t* maximum_op_out);
|
|
|
|
enum xnn_status xnn_reshape_maximum_nd_f32(
|
|
xnn_operator_t maximum_op,
|
|
size_t num_input1_dims,
|
|
const size_t* input1_shape,
|
|
size_t num_input2_dims,
|
|
const size_t* input2_shape,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_maximum_nd_f32(
|
|
xnn_operator_t maximum_op,
|
|
const float* input1,
|
|
const float* input2,
|
|
float* output);
|
|
|
|
enum xnn_status xnn_run_maximum_nd_f32(
|
|
size_t num_input1_dims,
|
|
const size_t* input1_shape,
|
|
size_t num_input2_dims,
|
|
const size_t* input2_shape,
|
|
const float* input1,
|
|
const float* input2,
|
|
float* output,
|
|
uint32_t flags,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_mean_nd_f16(
|
|
uint32_t flags,
|
|
xnn_operator_t* mean_op_out);
|
|
|
|
enum xnn_status xnn_reshape_mean_nd_f16(
|
|
xnn_operator_t mean_op,
|
|
size_t num_reduction_axes,
|
|
const size_t* reduction_axes,
|
|
size_t num_input_dims,
|
|
const size_t* input_shape,
|
|
size_t* workspace_size,
|
|
size_t* workspace_alignment,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_mean_nd_f16(
|
|
xnn_operator_t mean_op,
|
|
void* workspace,
|
|
const void* input,
|
|
void* output);
|
|
|
|
enum xnn_status xnn_create_mean_nd_f32(
|
|
uint32_t flags,
|
|
xnn_operator_t* mean_op_out);
|
|
|
|
enum xnn_status xnn_reshape_mean_nd_f32(
|
|
xnn_operator_t mean_op,
|
|
size_t num_reduction_axes,
|
|
const size_t* reduction_axes,
|
|
size_t num_input_dims,
|
|
const size_t* input_shape,
|
|
size_t* workspace_size,
|
|
size_t* workspace_alignment,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_mean_nd_f32(
|
|
xnn_operator_t mean_op,
|
|
void* workspace,
|
|
const float* input,
|
|
float* output);
|
|
|
|
enum xnn_status xnn_create_minimum_nd_f16(
|
|
uint32_t flags,
|
|
xnn_operator_t* minimum_op_out);
|
|
|
|
enum xnn_status xnn_reshape_minimum_nd_f16(
|
|
xnn_operator_t minimum_op,
|
|
size_t num_input1_dims,
|
|
const size_t* input1_shape,
|
|
size_t num_input2_dims,
|
|
const size_t* input2_shape,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_minimum_nd_f16(
|
|
xnn_operator_t minimum_op,
|
|
const void* input1,
|
|
const void* input2,
|
|
void* output);
|
|
|
|
enum xnn_status xnn_create_minimum_nd_f32(
|
|
uint32_t flags,
|
|
xnn_operator_t* minimum_op_out);
|
|
|
|
enum xnn_status xnn_reshape_minimum_nd_f32(
|
|
xnn_operator_t minimum_op,
|
|
size_t num_input1_dims,
|
|
const size_t* input1_shape,
|
|
size_t num_input2_dims,
|
|
const size_t* input2_shape,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_minimum_nd_f32(
|
|
xnn_operator_t minimum_op,
|
|
const float* input1,
|
|
const float* input2,
|
|
float* output);
|
|
|
|
enum xnn_status xnn_run_minimum_nd_f32(
|
|
size_t num_input1_dims,
|
|
const size_t* input1_shape,
|
|
size_t num_input2_dims,
|
|
const size_t* input2_shape,
|
|
const float* input1,
|
|
const float* input2,
|
|
float* output,
|
|
uint32_t flags,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_multiply_nd_f16(
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t flags,
|
|
xnn_operator_t* multiply_op_out);
|
|
|
|
enum xnn_status xnn_reshape_multiply_nd_f16(
|
|
xnn_operator_t multiply_op,
|
|
size_t num_input1_dims,
|
|
const size_t* input1_shape,
|
|
size_t num_input2_dims,
|
|
const size_t* input2_shape,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_multiply_nd_f16(
|
|
xnn_operator_t multiply_op,
|
|
const void* input1,
|
|
const void* input2,
|
|
void* output);
|
|
|
|
enum xnn_status xnn_create_multiply_nd_f32(
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t flags,
|
|
xnn_operator_t* multiply_op_out);
|
|
|
|
enum xnn_status xnn_reshape_multiply_nd_f32(
|
|
xnn_operator_t multiply_op,
|
|
size_t num_input1_dims,
|
|
const size_t* input1_shape,
|
|
size_t num_input2_dims,
|
|
const size_t* input2_shape,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_multiply_nd_f32(
|
|
xnn_operator_t multiply_op,
|
|
const float* input1,
|
|
const float* input2,
|
|
float* output);
|
|
|
|
enum xnn_status xnn_run_multiply_nd_f32(
|
|
size_t num_input1_dims,
|
|
const size_t* input1_shape,
|
|
size_t num_input2_dims,
|
|
const size_t* input2_shape,
|
|
const float* input1,
|
|
const float* input2,
|
|
float* output,
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t flags,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_multiply_nd_qs8(
|
|
int8_t input1_zero_point,
|
|
float input1_scale,
|
|
int8_t input2_zero_point,
|
|
float input2_scale,
|
|
int8_t output_zero_point,
|
|
float output_scale,
|
|
int8_t output_min,
|
|
int8_t output_max,
|
|
uint32_t flags,
|
|
xnn_operator_t* multiply_op_out);
|
|
|
|
enum xnn_status xnn_reshape_multiply_nd_qs8(
|
|
xnn_operator_t multiply_op,
|
|
size_t num_input1_dims,
|
|
const size_t* input1_shape,
|
|
size_t num_input2_dims,
|
|
const size_t* input2_shape,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_multiply_nd_qs8(
|
|
xnn_operator_t multiply_op,
|
|
const int8_t* input1,
|
|
const int8_t* input2,
|
|
int8_t* output);
|
|
|
|
enum xnn_status xnn_run_multiply_nd_qs8(
|
|
size_t num_input1_dims,
|
|
const size_t* input1_shape,
|
|
int8_t input1_zero_point,
|
|
float input1_scale,
|
|
size_t num_input2_dims,
|
|
const size_t* input2_shape,
|
|
int8_t input2_zero_point,
|
|
float input2_scale,
|
|
const int8_t* input1,
|
|
const int8_t* input2,
|
|
int8_t* output,
|
|
int8_t output_zero_point,
|
|
float output_scale,
|
|
int8_t output_min,
|
|
int8_t output_max,
|
|
uint32_t flags,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_multiply_nd_qu8(
|
|
uint8_t input1_zero_point,
|
|
float input1_scale,
|
|
uint8_t input2_zero_point,
|
|
float input2_scale,
|
|
uint8_t output_zero_point,
|
|
float output_scale,
|
|
uint8_t output_min,
|
|
uint8_t output_max,
|
|
uint32_t flags,
|
|
xnn_operator_t* multiply_op_out);
|
|
|
|
enum xnn_status xnn_reshape_multiply_nd_qu8(
|
|
xnn_operator_t multiply_op,
|
|
size_t num_input1_dims,
|
|
const size_t* input1_shape,
|
|
size_t num_input2_dims,
|
|
const size_t* input2_shape,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_multiply_nd_qu8(
|
|
xnn_operator_t multiply_op,
|
|
const uint8_t* input1,
|
|
const uint8_t* input2,
|
|
uint8_t* output);
|
|
|
|
enum xnn_status xnn_run_multiply_nd_qu8(
|
|
size_t num_input1_dims,
|
|
const size_t* input1_shape,
|
|
uint8_t input1_zero_point,
|
|
float input1_scale,
|
|
size_t num_input2_dims,
|
|
const size_t* input2_shape,
|
|
uint8_t input2_zero_point,
|
|
float input2_scale,
|
|
const uint8_t* input1,
|
|
const uint8_t* input2,
|
|
uint8_t* output,
|
|
uint8_t output_zero_point,
|
|
float output_scale,
|
|
uint8_t output_min,
|
|
uint8_t output_max,
|
|
uint32_t flags,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_negate_nc_f16(
|
|
uint32_t flags,
|
|
xnn_operator_t* negate_op_out);
|
|
|
|
enum xnn_status xnn_reshape_negate_nc_f16(
|
|
xnn_operator_t negate_op,
|
|
size_t batch_size,
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_negate_nc_f16(
|
|
xnn_operator_t negate_op,
|
|
const void* input,
|
|
void* output);
|
|
|
|
enum xnn_status xnn_create_negate_nc_f32(
|
|
uint32_t flags,
|
|
xnn_operator_t* negate_op_out);
|
|
|
|
enum xnn_status xnn_reshape_negate_nc_f32(
|
|
xnn_operator_t negate_op,
|
|
size_t batch_size,
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_negate_nc_f32(
|
|
xnn_operator_t negate_op,
|
|
const float* input,
|
|
float* output);
|
|
|
|
enum xnn_status xnn_run_negate_nc_f32(
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
size_t batch_size,
|
|
const float* input,
|
|
float* output,
|
|
uint32_t flags,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_prelu_nc_f16(
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
const void* negative_slope,
|
|
uint32_t flags,
|
|
xnn_code_cache_t code_cache,
|
|
xnn_weights_cache_t weights_cache,
|
|
xnn_operator_t* prelu_op_out);
|
|
|
|
enum xnn_status xnn_reshape_prelu_nc_f16(
|
|
xnn_operator_t prelu_op,
|
|
size_t batch_size,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_prelu_nc_f16(
|
|
xnn_operator_t prelu_op,
|
|
const void* input,
|
|
void* output);
|
|
|
|
enum xnn_status xnn_create_prelu_nc_f32(
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
const float* negative_slope,
|
|
uint32_t flags,
|
|
xnn_code_cache_t code_cache,
|
|
xnn_weights_cache_t weights_cache,
|
|
xnn_operator_t* prelu_op_out);
|
|
|
|
enum xnn_status xnn_reshape_prelu_nc_f32(
|
|
xnn_operator_t prelu_op,
|
|
size_t batch_size,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_prelu_nc_f32(
|
|
xnn_operator_t prelu_op,
|
|
const float* input,
|
|
float* output);
|
|
|
|
enum xnn_status xnn_create_resize_bilinear2d_nchw_f32(
|
|
size_t output_height,
|
|
size_t output_width,
|
|
uint32_t flags,
|
|
xnn_operator_t* resize_op_out);
|
|
|
|
enum xnn_status xnn_reshape_resize_bilinear2d_nchw_f32(
|
|
xnn_operator_t resize_op,
|
|
size_t batch_size,
|
|
size_t input_height,
|
|
size_t input_width,
|
|
size_t channels,
|
|
size_t input_pixel_stride,
|
|
size_t output_pixel_stride,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_resize_bilinear2d_nchw_f32(
|
|
xnn_operator_t resize_op,
|
|
const float* input,
|
|
float* output);
|
|
|
|
enum xnn_status xnn_create_resize_bilinear2d_nchw_f16(
|
|
size_t output_height,
|
|
size_t output_width,
|
|
uint32_t flags,
|
|
xnn_operator_t* resize_op_out);
|
|
|
|
enum xnn_status xnn_reshape_resize_bilinear2d_nchw_f16(
|
|
xnn_operator_t resize_op,
|
|
size_t batch_size,
|
|
size_t input_height,
|
|
size_t input_width,
|
|
size_t channels,
|
|
size_t input_pixel_stride,
|
|
size_t output_pixel_stride,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_resize_bilinear2d_nchw_f16(
|
|
xnn_operator_t resize_op,
|
|
const void* input,
|
|
void* output);
|
|
|
|
enum xnn_status xnn_create_resize_bilinear2d_nhwc_f16(
|
|
size_t output_height,
|
|
size_t output_width,
|
|
uint32_t flags,
|
|
xnn_operator_t* resize_op_out);
|
|
|
|
enum xnn_status xnn_reshape_resize_bilinear2d_nhwc_f16(
|
|
xnn_operator_t resize_op,
|
|
size_t batch_size,
|
|
size_t input_height,
|
|
size_t input_width,
|
|
size_t channels,
|
|
size_t input_pixel_stride,
|
|
size_t output_pixel_stride,
|
|
size_t* workspace_size,
|
|
size_t* workspace_alignment,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_resize_bilinear2d_nhwc_f16(
|
|
xnn_operator_t resize_op,
|
|
void* workspace,
|
|
const void* input,
|
|
void* output);
|
|
|
|
enum xnn_status xnn_create_resize_bilinear2d_nhwc_f32(
|
|
size_t output_height,
|
|
size_t output_width,
|
|
uint32_t flags,
|
|
xnn_operator_t* resize_op_out);
|
|
|
|
enum xnn_status xnn_reshape_resize_bilinear2d_nhwc_f32(
|
|
xnn_operator_t resize_op,
|
|
size_t batch_size,
|
|
size_t input_height,
|
|
size_t input_width,
|
|
size_t channels,
|
|
size_t input_pixel_stride,
|
|
size_t output_pixel_stride,
|
|
size_t* workspace_size,
|
|
size_t* workspace_alignment,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_resize_bilinear2d_nhwc_f32(
|
|
xnn_operator_t resize_op,
|
|
void* workspace,
|
|
const float* input,
|
|
float* output);
|
|
|
|
enum xnn_status xnn_create_resize_bilinear2d_nhwc_s8(
|
|
size_t output_height,
|
|
size_t output_width,
|
|
uint32_t flags,
|
|
xnn_operator_t* resize_op_out);
|
|
|
|
enum xnn_status xnn_reshape_resize_bilinear2d_nhwc_s8(
|
|
xnn_operator_t resize_op,
|
|
size_t batch_size,
|
|
size_t input_height,
|
|
size_t input_width,
|
|
size_t channels,
|
|
size_t input_pixel_stride,
|
|
size_t output_pixel_stride,
|
|
size_t* workspace_size,
|
|
size_t* workspace,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_resize_bilinear2d_nhwc_s8(
|
|
xnn_operator_t resize_op,
|
|
void* workspace,
|
|
const int8_t* input,
|
|
int8_t* output);
|
|
|
|
enum xnn_status xnn_create_resize_bilinear2d_nhwc_u8(
|
|
size_t output_height,
|
|
size_t output_width,
|
|
uint32_t flags,
|
|
xnn_operator_t* resize_op_out);
|
|
|
|
enum xnn_status xnn_reshape_resize_bilinear2d_nhwc_u8(
|
|
xnn_operator_t resize_op,
|
|
size_t batch_size,
|
|
size_t input_height,
|
|
size_t input_width,
|
|
size_t channels,
|
|
size_t input_pixel_stride,
|
|
size_t output_pixel_stride,
|
|
size_t* workspace_size,
|
|
size_t* workspace_alignment,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_resize_bilinear2d_nhwc_u8(
|
|
xnn_operator_t resize_op,
|
|
void* workspace,
|
|
const uint8_t* input,
|
|
uint8_t* output);
|
|
|
|
enum xnn_status xnn_create_rope_nthc_f16(
|
|
size_t max_tokens,
|
|
uint32_t flags,
|
|
xnn_operator_t* rope_op_out);
|
|
|
|
enum xnn_status xnn_reshape_rope_nthc_f16(
|
|
xnn_operator_t rope_op,
|
|
size_t batch_size,
|
|
size_t tokens,
|
|
size_t heads,
|
|
size_t channels,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_rope_nthc_f16(
|
|
xnn_operator_t rope_op,
|
|
const void* input,
|
|
const void* weights,
|
|
void* output);
|
|
|
|
enum xnn_status xnn_create_rope_nthc_f32(
|
|
size_t max_tokens,
|
|
uint32_t flags,
|
|
xnn_operator_t* rope_op_out);
|
|
|
|
enum xnn_status xnn_reshape_rope_nthc_f32(
|
|
xnn_operator_t rope_op,
|
|
size_t batch_size,
|
|
size_t tokens,
|
|
size_t heads,
|
|
size_t channels,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_rope_nthc_f32(
|
|
xnn_operator_t rope_op,
|
|
const float* input,
|
|
const float* weights,
|
|
float* output);
|
|
|
|
// N: batch size
|
|
// H: number of heads
|
|
// T: tokens (sequence length)
|
|
// C: channels (head dimension)
|
|
enum xnn_status xnn_create_scaled_dot_product_attention_nhtc_f16(
|
|
enum xnn_attention_logits_cap_type cap_type,
|
|
const void* cap_params,
|
|
uint32_t flags,
|
|
xnn_operator_t* attention_op_out);
|
|
|
|
enum xnn_status xnn_reshape_scaled_dot_product_attention_nhtc_f16(
|
|
xnn_operator_t attention_op,
|
|
size_t batch_size,
|
|
size_t query_heads,
|
|
// Number of tokens in query.
|
|
size_t query_tokens,
|
|
size_t key_value_heads,
|
|
// Number of tokens in key/value. For self-attention, this is same as tokens.
|
|
size_t key_value_tokens,
|
|
size_t query_key_channels,
|
|
size_t value_channels,
|
|
size_t* workspace_size,
|
|
size_t* workspace_alignment,
|
|
pthreadpool_t threadpool);
|
|
|
|
// Query is of dimension [batch_size, query_heads, query_tokens, channels].
|
|
// Key and value are of dimension [batch_size, key_value_heads, key_value_tokens, channels].
|
|
// Scale is of dimension [channels].
|
|
// Mask is of dimension [query_tokens, key_value_tokens].
|
|
enum xnn_status xnn_setup_scaled_dot_product_attention_nhtc_f16(
|
|
xnn_operator_t attention_op,
|
|
void* workspace,
|
|
const void* query,
|
|
const void* key,
|
|
const void* value,
|
|
const void* scale,
|
|
const void* mask,
|
|
void* output);
|
|
|
|
// N: batch size
|
|
// H: number of heads
|
|
// T: tokens (sequence length)
|
|
// C: channels (head dimension)
|
|
enum xnn_status xnn_create_scaled_dot_product_attention_nhtc_f32(
|
|
enum xnn_attention_logits_cap_type cap_type,
|
|
const void* cap_params,
|
|
uint32_t flags,
|
|
xnn_operator_t* attention_op_out);
|
|
|
|
enum xnn_status xnn_reshape_scaled_dot_product_attention_nhtc_f32(
|
|
xnn_operator_t attention_op,
|
|
size_t batch_size,
|
|
size_t query_heads,
|
|
// Number of tokens in query.
|
|
size_t query_tokens,
|
|
size_t key_value_heads,
|
|
// Number of tokens in key/value. For self-attention, this is same as tokens.
|
|
size_t key_value_tokens,
|
|
size_t query_key_channels,
|
|
size_t value_channels,
|
|
size_t* workspace_size,
|
|
size_t* workspace_alignment,
|
|
pthreadpool_t threadpool);
|
|
|
|
// Query is of dimension [batch_size, query_heads, query_tokens, query_key_channels].
|
|
// Key and value are of dimension [batch_size, key_value_heads, key_value_tokens, query_key_channels].
|
|
// Scale is of dimension [query_key_channels].
|
|
// Mask is of dimension [query_tokens, key_value_tokens].
|
|
// Output is of dimension [batch_size, query_heads, query_tokens, value_channels].
|
|
enum xnn_status xnn_setup_scaled_dot_product_attention_nhtc_f32(
|
|
xnn_operator_t attention_op,
|
|
void* workspace,
|
|
const float* query,
|
|
const float* key,
|
|
const float* value,
|
|
const float* scale,
|
|
const float* mask,
|
|
float* output);
|
|
|
|
enum xnn_status xnn_create_sigmoid_nc_f16(
|
|
uint32_t flags,
|
|
xnn_operator_t* sigmoid_op_out);
|
|
|
|
enum xnn_status xnn_reshape_sigmoid_nc_f16(
|
|
xnn_operator_t sigmoid_op,
|
|
size_t batch_size,
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_sigmoid_nc_f16(
|
|
xnn_operator_t sigmoid_op,
|
|
const void* input,
|
|
void* output);
|
|
|
|
enum xnn_status xnn_create_sigmoid_nc_f32(
|
|
uint32_t flags,
|
|
xnn_operator_t* sigmoid_op_out);
|
|
|
|
enum xnn_status xnn_reshape_sigmoid_nc_f32(
|
|
xnn_operator_t sigmoid_op,
|
|
size_t batch_size,
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_sigmoid_nc_f32(
|
|
xnn_operator_t sigmoid_op,
|
|
const float* input,
|
|
float* output);
|
|
|
|
enum xnn_status xnn_run_sigmoid_nc_f32(
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
size_t batch_size,
|
|
const float* input,
|
|
float* output,
|
|
uint32_t flags,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_sigmoid_nc_qs8(
|
|
int8_t input_zero_point,
|
|
float input_scale,
|
|
int8_t output_zero_point,
|
|
float output_scale,
|
|
int8_t output_min,
|
|
int8_t output_max,
|
|
uint32_t flags,
|
|
xnn_operator_t* sigmoid_op_out);
|
|
|
|
enum xnn_status xnn_reshape_sigmoid_nc_qs8(
|
|
xnn_operator_t sigmoid_op,
|
|
size_t batch_size,
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_sigmoid_nc_qs8(
|
|
xnn_operator_t sigmoid_op,
|
|
const int8_t* input,
|
|
int8_t* output);
|
|
|
|
enum xnn_status xnn_create_sigmoid_nc_qu8(
|
|
uint8_t input_zero_point,
|
|
float input_scale,
|
|
uint8_t output_zero_point,
|
|
float output_scale,
|
|
uint8_t output_min,
|
|
uint8_t output_max,
|
|
uint32_t flags,
|
|
xnn_operator_t* sigmoid_op_out);
|
|
|
|
enum xnn_status xnn_reshape_sigmoid_nc_qu8(
|
|
xnn_operator_t sigmoid_op,
|
|
size_t batch_size,
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_sigmoid_nc_qu8(
|
|
xnn_operator_t sigmoid_op,
|
|
const uint8_t* input,
|
|
uint8_t* output);
|
|
|
|
enum xnn_status xnn_create_slice_nd_x16(
|
|
uint32_t flags,
|
|
xnn_operator_t* slice_op_out);
|
|
|
|
enum xnn_status xnn_reshape_slice_nd_x16(
|
|
xnn_operator_t slice_op,
|
|
size_t num_dims,
|
|
const size_t* input_shape,
|
|
const size_t* offsets,
|
|
const size_t* sizes,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_slice_nd_x16(
|
|
xnn_operator_t slice_op,
|
|
const void* input,
|
|
void* output);
|
|
|
|
enum xnn_status xnn_create_slice_nd_x32(
|
|
uint32_t flags,
|
|
xnn_operator_t* slice_op_out);
|
|
|
|
enum xnn_status xnn_reshape_slice_nd_x32(
|
|
xnn_operator_t slice_op,
|
|
size_t num_dims,
|
|
const size_t* input_shape,
|
|
const size_t* offsets,
|
|
const size_t* sizes,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_slice_nd_x32(
|
|
xnn_operator_t slice_op,
|
|
const void* input,
|
|
void* output);
|
|
|
|
enum xnn_status xnn_run_slice_nd_x32(
|
|
size_t num_dims,
|
|
const size_t* input_shape,
|
|
const size_t* offsets,
|
|
const size_t* sizes,
|
|
const void* input,
|
|
void* output,
|
|
uint32_t flags,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_softmax_nc_f16(
|
|
uint32_t flags,
|
|
xnn_operator_t* softmax_op_out);
|
|
|
|
enum xnn_status xnn_reshape_softmax_nc_f16(
|
|
xnn_operator_t softmax_op,
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
size_t batch_size,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_softmax_nc_f16(
|
|
xnn_operator_t softmax_op,
|
|
const void* input,
|
|
void* output);
|
|
|
|
enum xnn_status xnn_create_softmax_nc_f32(
|
|
uint32_t flags,
|
|
xnn_operator_t* softmax_op_out);
|
|
|
|
enum xnn_status xnn_reshape_softmax_nc_f32(
|
|
xnn_operator_t softmax_op,
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
size_t batch_size,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_softmax_nc_f32(
|
|
xnn_operator_t softmax_op,
|
|
const float* input,
|
|
float* output);
|
|
|
|
enum xnn_status xnn_create_softmax_nc_qu8(
|
|
float input_scale,
|
|
uint8_t output_zero_point,
|
|
float output_scale,
|
|
uint32_t flags,
|
|
xnn_operator_t* softmax_op_out);
|
|
|
|
enum xnn_status xnn_reshape_softmax_nc_qu8(
|
|
xnn_operator_t softmax_op,
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
size_t batch_size,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_softmax_nc_qu8(
|
|
xnn_operator_t softmax_op,
|
|
const uint8_t* input,
|
|
uint8_t* output);
|
|
|
|
enum xnn_status xnn_create_space_to_depth_nhwc_x16(
|
|
uint32_t block_size,
|
|
uint32_t flags,
|
|
xnn_operator_t* space_to_depth_op_out);
|
|
|
|
enum xnn_status xnn_reshape_space_to_depth_nhwc_x16(
|
|
xnn_operator_t space_to_depth_op,
|
|
size_t batch_size,
|
|
size_t input_height,
|
|
size_t input_width,
|
|
size_t input_channels,
|
|
size_t* output_height_out,
|
|
size_t* output_width_out,
|
|
size_t* output_channels_out,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_space_to_depth_nhwc_x16(
|
|
xnn_operator_t space_to_depth_op,
|
|
const void* input,
|
|
void* output);
|
|
|
|
enum xnn_status xnn_create_space_to_depth_nhwc_x32(
|
|
uint32_t block_size,
|
|
uint32_t flags,
|
|
xnn_operator_t* space_to_depth_op_out);
|
|
|
|
enum xnn_status xnn_reshape_space_to_depth_nhwc_x32(
|
|
xnn_operator_t space_to_depth_op,
|
|
size_t batch_size,
|
|
size_t input_height,
|
|
size_t input_width,
|
|
size_t input_channels,
|
|
size_t* output_height_out,
|
|
size_t* output_width_out,
|
|
size_t* output_channels_out,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_space_to_depth_nhwc_x32(
|
|
xnn_operator_t space_to_depth_op,
|
|
const void* input,
|
|
void* output);
|
|
|
|
enum xnn_status xnn_create_square_nc_f16(
|
|
uint32_t flags,
|
|
xnn_operator_t* square_op_out);
|
|
|
|
enum xnn_status xnn_reshape_square_nc_f16(
|
|
xnn_operator_t square_op,
|
|
size_t batch_size,
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_square_nc_f16(
|
|
xnn_operator_t square_op,
|
|
const void* input,
|
|
void* output);
|
|
|
|
enum xnn_status xnn_create_square_nc_f32(
|
|
uint32_t flags,
|
|
xnn_operator_t* square_op_out);
|
|
|
|
enum xnn_status xnn_reshape_square_nc_f32(
|
|
xnn_operator_t square_op,
|
|
size_t batch_size,
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_square_nc_f32(
|
|
xnn_operator_t square_op,
|
|
const float* input,
|
|
float* output);
|
|
|
|
enum xnn_status xnn_run_square_nc_f32(
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
size_t batch_size,
|
|
const float* input,
|
|
float* output,
|
|
uint32_t flags,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_square_root_nc_f16(
|
|
uint32_t flags,
|
|
xnn_operator_t* sqrt_op_out);
|
|
|
|
enum xnn_status xnn_reshape_square_root_nc_f16(
|
|
xnn_operator_t sqrt_op,
|
|
size_t batch_size,
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_square_root_nc_f16(
|
|
xnn_operator_t sqrt_op,
|
|
const void* input,
|
|
void* output);
|
|
|
|
enum xnn_status xnn_create_square_root_nc_f32(
|
|
uint32_t flags,
|
|
xnn_operator_t* sqrt_op_out);
|
|
|
|
enum xnn_status xnn_reshape_square_root_nc_f32(
|
|
xnn_operator_t sqrt_op,
|
|
size_t batch_size,
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_square_root_nc_f32(
|
|
xnn_operator_t sqrt_op,
|
|
const float* input,
|
|
float* output);
|
|
|
|
enum xnn_status xnn_run_square_root_nc_f32(
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
size_t batch_size,
|
|
const float* input,
|
|
float* output,
|
|
uint32_t flags,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_reciprocal_square_root_nc_f32(
|
|
uint32_t flags, xnn_operator_t* sqrt_op_out);
|
|
|
|
enum xnn_status xnn_reshape_reciprocal_square_root_nc_f32(
|
|
xnn_operator_t sqrt_op, size_t batch_size, size_t channels,
|
|
size_t input_stride, size_t output_stride, pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_reciprocal_square_root_nc_f32(xnn_operator_t sqrt_op,
|
|
const float* input,
|
|
float* output);
|
|
|
|
enum xnn_status xnn_run_reciprocal_square_root_nc_f32(
|
|
size_t channels, size_t input_stride, size_t output_stride,
|
|
size_t batch_size, const float* input, float* output, uint32_t flags,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_squared_difference_nd_f16(
|
|
uint32_t flags,
|
|
xnn_operator_t* squared_difference_op_out);
|
|
|
|
enum xnn_status xnn_reshape_squared_difference_nd_f16(
|
|
xnn_operator_t squared_difference_op,
|
|
size_t num_input1_dims,
|
|
const size_t* input1_shape,
|
|
size_t num_input2_dims,
|
|
const size_t* input2_shape,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_squared_difference_nd_f16(
|
|
xnn_operator_t squared_difference_op,
|
|
const void* input1,
|
|
const void* input2,
|
|
void* output);
|
|
|
|
enum xnn_status xnn_create_squared_difference_nd_f32(
|
|
uint32_t flags,
|
|
xnn_operator_t* squared_difference_op_out);
|
|
|
|
enum xnn_status xnn_reshape_squared_difference_nd_f32(
|
|
xnn_operator_t squared_difference_op,
|
|
size_t num_input1_dims,
|
|
const size_t* input1_shape,
|
|
size_t num_input2_dims,
|
|
const size_t* input2_shape,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_squared_difference_nd_f32(
|
|
xnn_operator_t squared_difference_op,
|
|
const float* input1,
|
|
const float* input2,
|
|
float* output);
|
|
|
|
enum xnn_status xnn_run_squared_difference_nd_f32(
|
|
size_t num_input1_dims,
|
|
const size_t* input1_shape,
|
|
size_t num_input2_dims,
|
|
const size_t* input2_shape,
|
|
const float* input1,
|
|
const float* input2,
|
|
float* output,
|
|
uint32_t flags,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_subtract_nd_f16(
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t flags,
|
|
xnn_operator_t* subtract_op_out);
|
|
|
|
enum xnn_status xnn_reshape_subtract_nd_f16(
|
|
xnn_operator_t subtract_op,
|
|
size_t num_input1_dims,
|
|
const size_t* input1_shape,
|
|
size_t num_input2_dims,
|
|
const size_t* input2_shape,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_subtract_nd_f16(
|
|
xnn_operator_t subtract_op,
|
|
const void* input1,
|
|
const void* input2,
|
|
void* output);
|
|
|
|
enum xnn_status xnn_create_subtract_nd_f32(
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t flags,
|
|
xnn_operator_t* subtract_op_out);
|
|
|
|
enum xnn_status xnn_reshape_subtract_nd_f32(
|
|
xnn_operator_t subtract_op,
|
|
size_t num_input1_dims,
|
|
const size_t* input1_shape,
|
|
size_t num_input2_dims,
|
|
const size_t* input2_shape,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_subtract_nd_f32(
|
|
xnn_operator_t subtract_op,
|
|
const float* input1,
|
|
const float* input2,
|
|
float* output);
|
|
|
|
enum xnn_status xnn_run_subtract_nd_f32(
|
|
size_t num_input1_dims,
|
|
const size_t* input1_shape,
|
|
size_t num_input2_dims,
|
|
const size_t* input2_shape,
|
|
const float* input1,
|
|
const float* input2,
|
|
float* output,
|
|
float output_min,
|
|
float output_max,
|
|
uint32_t flags,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_subtract_nd_qs8(
|
|
int8_t input1_zero_point,
|
|
float input1_scale,
|
|
int8_t input2_zero_point,
|
|
float input2_scale,
|
|
int8_t output_zero_point,
|
|
float output_scale,
|
|
int8_t output_min,
|
|
int8_t output_max,
|
|
uint32_t flags,
|
|
xnn_operator_t* subtract_op_out);
|
|
|
|
enum xnn_status xnn_reshape_subtract_nd_qs8(
|
|
xnn_operator_t subtract_op,
|
|
size_t num_input1_dims,
|
|
const size_t* input1_shape,
|
|
size_t num_input2_dims,
|
|
const size_t* input2_shape,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_subtract_nd_qs8(
|
|
xnn_operator_t subtract_op,
|
|
const int8_t* input1,
|
|
const int8_t* input2,
|
|
int8_t* output);
|
|
|
|
enum xnn_status xnn_run_subtract_nd_qs8(
|
|
size_t num_input1_dims,
|
|
const size_t* input1_shape,
|
|
int8_t input1_zero_point,
|
|
float input1_scale,
|
|
size_t num_input2_dims,
|
|
const size_t* input2_shape,
|
|
int8_t input2_zero_point,
|
|
float input2_scale,
|
|
const int8_t* input1,
|
|
const int8_t* input2,
|
|
int8_t* output,
|
|
int8_t output_zero_point,
|
|
float output_scale,
|
|
int8_t output_min,
|
|
int8_t output_max,
|
|
uint32_t flags,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_subtract_nd_qu8(
|
|
uint8_t input1_zero_point,
|
|
float input1_scale,
|
|
uint8_t input2_zero_point,
|
|
float input2_scale,
|
|
uint8_t output_zero_point,
|
|
float output_scale,
|
|
uint8_t output_min,
|
|
uint8_t output_max,
|
|
uint32_t flags,
|
|
xnn_operator_t* subtract_op_out);
|
|
|
|
enum xnn_status xnn_reshape_subtract_nd_qu8(
|
|
xnn_operator_t subtract_op,
|
|
size_t num_input1_dims,
|
|
const size_t* input1_shape,
|
|
size_t num_input2_dims,
|
|
const size_t* input2_shape,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_subtract_nd_qu8(
|
|
xnn_operator_t subtract_op,
|
|
const uint8_t* input1,
|
|
const uint8_t* input2,
|
|
uint8_t* output);
|
|
|
|
enum xnn_status xnn_run_subtract_nd_qu8(
|
|
size_t num_input1_dims,
|
|
const size_t* input1_shape,
|
|
uint8_t input1_zero_point,
|
|
float input1_scale,
|
|
size_t num_input2_dims,
|
|
const size_t* input2_shape,
|
|
uint8_t input2_zero_point,
|
|
float input2_scale,
|
|
const uint8_t* input1,
|
|
const uint8_t* input2,
|
|
uint8_t* output,
|
|
uint8_t output_zero_point,
|
|
float output_scale,
|
|
uint8_t output_min,
|
|
uint8_t output_max,
|
|
uint32_t flags,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_tanh_nc_f16(
|
|
uint32_t flags,
|
|
xnn_operator_t* tanh_op_out);
|
|
|
|
enum xnn_status xnn_reshape_tanh_nc_f16(
|
|
xnn_operator_t tanh_op,
|
|
size_t batch_size,
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_tanh_nc_f16(
|
|
xnn_operator_t tanh_op,
|
|
const void* input,
|
|
void* output);
|
|
|
|
enum xnn_status xnn_create_tanh_nc_f32(
|
|
uint32_t flags,
|
|
xnn_operator_t* tanh_op_out);
|
|
|
|
enum xnn_status xnn_reshape_tanh_nc_f32(
|
|
xnn_operator_t tanh_op,
|
|
size_t batch_size,
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_tanh_nc_f32(
|
|
xnn_operator_t tanh_op,
|
|
const float* input,
|
|
float* output);
|
|
|
|
enum xnn_status xnn_run_tanh_nc_f32(
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
size_t batch_size,
|
|
const float* input,
|
|
float* output,
|
|
uint32_t flags,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_tanh_nc_qs8(
|
|
int8_t input_zero_point,
|
|
float input_scale,
|
|
int8_t output_zero_point,
|
|
float output_scale,
|
|
int8_t output_min,
|
|
int8_t output_max,
|
|
uint32_t flags,
|
|
xnn_operator_t* tanh_op_out);
|
|
|
|
enum xnn_status xnn_reshape_tanh_nc_qs8(
|
|
xnn_operator_t tanh_op,
|
|
size_t batch_size,
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_tanh_nc_qs8(
|
|
xnn_operator_t tanh_op,
|
|
const int8_t* input,
|
|
int8_t* output);
|
|
|
|
enum xnn_status xnn_create_tanh_nc_qu8(
|
|
uint8_t input_zero_point,
|
|
float input_scale,
|
|
uint8_t output_zero_point,
|
|
float output_scale,
|
|
uint8_t output_min,
|
|
uint8_t output_max,
|
|
uint32_t flags,
|
|
xnn_operator_t* tanh_op_out);
|
|
|
|
enum xnn_status xnn_reshape_tanh_nc_qu8(
|
|
xnn_operator_t tanh_op,
|
|
size_t batch_size,
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_tanh_nc_qu8(
|
|
xnn_operator_t tanh_op,
|
|
const uint8_t* input,
|
|
uint8_t* output);
|
|
|
|
enum xnn_status xnn_create_transpose_nd_x8(
|
|
uint32_t flags,
|
|
xnn_operator_t* transpose_op_out);
|
|
|
|
enum xnn_status xnn_reshape_transpose_nd_x8(
|
|
xnn_operator_t transpose_op,
|
|
size_t num_dims,
|
|
const size_t* input_shape,
|
|
const size_t* output_perm,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_transpose_nd_x8(
|
|
xnn_operator_t transpose_op,
|
|
const void* input,
|
|
void* output);
|
|
|
|
enum xnn_status xnn_run_transpose_nd_x8(
|
|
const void* input,
|
|
void* output,
|
|
size_t num_dims,
|
|
const size_t* input_shape,
|
|
const size_t* output_perm,
|
|
uint32_t flags,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_transpose_nd_x16(
|
|
uint32_t flags,
|
|
xnn_operator_t* transpose_op_out);
|
|
|
|
enum xnn_status xnn_reshape_transpose_nd_x16(
|
|
xnn_operator_t transpose_op,
|
|
size_t num_dims,
|
|
const size_t* input_shape,
|
|
const size_t* output_perm,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_transpose_nd_x16(
|
|
xnn_operator_t transpose_op,
|
|
const void* input,
|
|
void* output);
|
|
|
|
enum xnn_status xnn_run_transpose_nd_x16(
|
|
const void* input,
|
|
void* output,
|
|
size_t num_dims,
|
|
const size_t* input_shape,
|
|
const size_t* output_perm,
|
|
uint32_t flags,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_transpose_nd_x32(
|
|
uint32_t flags,
|
|
xnn_operator_t* transpose_op_out);
|
|
|
|
enum xnn_status xnn_reshape_transpose_nd_x32(
|
|
xnn_operator_t transpose_op,
|
|
size_t num_dims,
|
|
const size_t* input_shape,
|
|
const size_t* output_perm,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_transpose_nd_x32(
|
|
xnn_operator_t transpose_op,
|
|
const void* input,
|
|
void* output);
|
|
|
|
enum xnn_status xnn_run_transpose_nd_x32(
|
|
const void* input,
|
|
void* output,
|
|
size_t num_dims,
|
|
const size_t* input_shape,
|
|
const size_t* output_perm,
|
|
uint32_t flags,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_transpose_nd_x64(
|
|
uint32_t flags,
|
|
xnn_operator_t* transpose_op_out);
|
|
|
|
enum xnn_status xnn_reshape_transpose_nd_x64(
|
|
xnn_operator_t transpose_op,
|
|
size_t num_dims,
|
|
const size_t* input_shape,
|
|
const size_t* output_perm,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_transpose_nd_x64(
|
|
xnn_operator_t transpose_op,
|
|
const void* input,
|
|
void* output);
|
|
|
|
enum xnn_status xnn_run_transpose_nd_x64(
|
|
const void* input,
|
|
void* output,
|
|
size_t num_dims,
|
|
const size_t* input_shape,
|
|
const size_t* output_perm,
|
|
uint32_t flags,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_truncation_nc_f16(
|
|
uint32_t flags,
|
|
xnn_operator_t* truncation_op_out);
|
|
|
|
enum xnn_status xnn_reshape_truncation_nc_f16(
|
|
xnn_operator_t truncation_op,
|
|
size_t batch_size,
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_truncation_nc_f16(
|
|
xnn_operator_t truncation_op,
|
|
const void* input,
|
|
void* output);
|
|
|
|
enum xnn_status xnn_create_truncation_nc_f32(
|
|
uint32_t flags,
|
|
xnn_operator_t* truncation_op_out);
|
|
|
|
enum xnn_status xnn_reshape_truncation_nc_f32(
|
|
xnn_operator_t truncation_op,
|
|
size_t batch_size,
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_truncation_nc_f32(
|
|
xnn_operator_t truncation_op,
|
|
const float* input,
|
|
float* output);
|
|
|
|
enum xnn_status xnn_run_truncation_nc_f32(
|
|
size_t channels,
|
|
size_t input_stride,
|
|
size_t output_stride,
|
|
size_t batch_size,
|
|
const float* input,
|
|
float* output,
|
|
uint32_t flags,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_create_unpooling2d_nhwc_x32(
|
|
uint32_t input_padding_top,
|
|
uint32_t input_padding_right,
|
|
uint32_t input_padding_bottom,
|
|
uint32_t input_padding_left,
|
|
uint32_t pooling_height,
|
|
uint32_t pooling_width,
|
|
size_t channels,
|
|
size_t input_pixel_stride,
|
|
size_t output_pixel_stride,
|
|
uint32_t flags,
|
|
xnn_operator_t* unpooling_op_out);
|
|
|
|
enum xnn_status xnn_reshape_unpooling2d_nhwc_x32(
|
|
xnn_operator_t unpooling_op,
|
|
size_t batch_size,
|
|
size_t input_height,
|
|
size_t input_width,
|
|
size_t* output_height_out,
|
|
size_t* output_width_out,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_unpooling2d_nhwc_x32(
|
|
xnn_operator_t unpooling_op,
|
|
const void* input,
|
|
const uint32_t* index,
|
|
void* output);
|
|
|
|
enum xnn_status xnn_create_slice_nd_x8(
|
|
uint32_t flags,
|
|
xnn_operator_t* slice_op_out);
|
|
|
|
enum xnn_status xnn_reshape_slice_nd_x8(
|
|
xnn_operator_t slice_op,
|
|
size_t num_dims,
|
|
const size_t* input_shape,
|
|
const size_t* offsets,
|
|
const size_t* sizes,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_slice_nd_x8(
|
|
xnn_operator_t slice_op,
|
|
const void* input,
|
|
void* output);
|
|
|
|
enum xnn_status xnn_create_space_to_depth_nhwc_x8(
|
|
uint32_t block_size,
|
|
uint32_t flags,
|
|
xnn_operator_t* space_to_depth_op_out);
|
|
|
|
enum xnn_status xnn_reshape_space_to_depth_nhwc_x8(
|
|
xnn_operator_t space_to_depth_op,
|
|
size_t batch_size,
|
|
size_t input_height,
|
|
size_t input_width,
|
|
size_t input_channels,
|
|
size_t* output_height_out,
|
|
size_t* output_width_out,
|
|
size_t* output_channels_out,
|
|
pthreadpool_t threadpool);
|
|
|
|
enum xnn_status xnn_setup_space_to_depth_nhwc_x8(
|
|
xnn_operator_t space_to_depth_op,
|
|
const void* input,
|
|
void* output);
|
|
|
|
#ifdef __cplusplus
|
|
} // extern "C"
|
|
#endif
|