1 | // Copyright (c) Facebook, Inc. and its affiliates. |
2 | // All rights reserved. |
3 | // |
4 | // Copyright 2019 Google LLC |
5 | // |
6 | // This source code is licensed under the BSD-style license found in the |
7 | // LICENSE file in the root directory of this source tree. |
8 | |
9 | #pragma once |
10 | |
11 | #include <stdbool.h> |
12 | #include <stddef.h> |
13 | #include <stdint.h> |
14 | |
15 | #include <pthreadpool.h> |
16 | |
17 | #ifdef __cplusplus |
18 | extern "C" { |
19 | #endif |
20 | |
21 | /// The number of bytes XNNPACK may read beyond array bounds. |
22 | /// The caller must allocate at least this many extra bytes after the tensor data passed to XNNPACK. |
23 | /// |
24 | /// Note: XNNPACK reads, but never writes beyond array bounds. |
25 | #define 16 |
26 | |
27 | /// Maximum number of dimensions in tensor shape. |
28 | #define XNN_MAX_TENSOR_DIMS 6 |
29 | |
30 | /// Allow sparse inference in a Runtime. |
31 | /// |
32 | /// Note: this flag hints XNNPACK to consider sparse inference, but does not guarantee it. |
33 | #define XNN_FLAG_SPARSE_INFERENCE 0x00000001 |
34 | #define XNN_FLAG_HINT_SPARSE_INFERENCE XNN_FLAG_SPARSE_INFERENCE |
35 | |
36 | /// Allow IEEE FP16 inference in a Runtime. |
37 | /// |
38 | /// Note: this flag hints XNNPACK to consider IEEE FP16 inference, but does not guarantee it. |
39 | #define XNN_FLAG_FP16_INFERENCE 0x00000002 |
40 | #define XNN_FLAG_HINT_FP16_INFERENCE XNN_FLAG_FP16_INFERENCE |
41 | |
42 | /// Force IEEE FP16 inference in a Runtime, and fail if FP16 inference is not possible. |
43 | /// |
44 | /// Note: this flag guarantees that XNNPACK will use IEEE FP16 inference, or fail to create the Runtime object. |
45 | /// Warning: on x86 systems FP16 computations will be emulated at a substantial performance cost. |
46 | #define XNN_FLAG_FORCE_FP16_INFERENCE 0x00000004 |
47 | |
48 | /// Enable timing of each operator's runtime. |
49 | #define XNN_FLAG_BASIC_PROFILING 0x00000008 |
50 | |
51 | /// The convolution operator represents a depthwise convolution, and use HWGo layout for filters. |
52 | #define XNN_FLAG_DEPTHWISE_CONVOLUTION 0x00000001 |
53 | |
54 | /// Assume transposed weights in a fully connected operator. |
55 | #define XNN_FLAG_TRANSPOSE_WEIGHTS 0x00000001 |
56 | |
57 | /// The operator assumes NHWC layout for the input, regardless of the output layout. |
58 | #define XNN_FLAG_INPUT_NHWC 0x00000002 |
59 | |
60 | /// Match "SAME" padding in TensorFlow. Exact padding values are computed dynamically depending on input size. |
61 | #define XNN_FLAG_TENSORFLOW_SAME_PADDING 0x00000004 |
62 | |
63 | /// Implicitly flatten and reshape input of a Fully Connected operator into a 2D tensor. |
64 | #define XNN_FLAG_TENSORFLOW_RESHAPE_2D 0x00000004 |
65 | |
66 | /// Match behaviour of TensorFlow 1.x. |
67 | #define XNN_FLAG_TENSORFLOW_LEGACY_MODE 0x00000004 |
68 | |
69 | /// Static weights of the FP16 operator are in FP32 format. |
70 | #define XNN_FLAG_FP32_STATIC_WEIGHTS 0x00000008 |
71 | |
72 | /// Align corners of input and output images in resize operations. |
73 | #define XNN_FLAG_ALIGN_CORNERS 0x00000008 |
74 | |
75 | /// Yield worker threads of the thread pool to the system scheduler after the inference. |
76 | #define XNN_FLAG_YIELD_WORKERS 0x00000010 |
77 | |
78 | /// Status code for any XNNPACK function call. |
79 | enum xnn_status { |
80 | /// The call succeeded, and all output arguments now contain valid data. |
81 | xnn_status_success = 0, |
82 | xnn_status_uninitialized = 1, |
83 | xnn_status_invalid_parameter = 2, |
84 | xnn_status_invalid_state = 3, |
85 | xnn_status_unsupported_parameter = 4, |
86 | xnn_status_unsupported_hardware = 5, |
87 | xnn_status_out_of_memory = 6, |
88 | }; |
89 | |
90 | struct xnn_allocator { |
91 | /// User-specified pointer that will be passed as-is to all functions in this structure. |
92 | void* context; |
93 | /// Pointer to a function to be called for general memory allocation. |
94 | /// |
95 | /// @param context - The user-specified pointer from xnn_allocator structure. |
96 | /// @param size - The size of the memory block to allocate, in bytes. |
97 | /// |
98 | /// @returns Pointer to the allocated memory block of at least @ref size bytes. |
99 | /// If allocation fails, the function must return NULL. |
100 | void* (*allocate)(void* context, size_t size); |
101 | /// Pointer to a function to be called for general memory re-allocation, i.e. to increase or shrink a previously |
102 | /// allocated memory block. The content of the old memory block is copied to the new memory block. |
103 | /// |
104 | /// @param context - The user-specified pointer from xnn_allocator structure. |
105 | /// @param pointer - Pointer to a memory block allocated by @ref allocate or @ref reallocate functions. Can be NULL. |
106 | /// If the pointer is NULL, the @ref reallocate call is equivalent to an @ref allocate call. |
107 | /// @param size - The new size of the memory block to allocate, in bytes. |
108 | /// |
109 | /// @returns Pointer to the newly allocated memory block of at least @ref size bytes with the content of the previous |
110 | /// memory block. |
111 | /// If allocation fails, the function must return NULL, but must not release the previous memory block. |
112 | void* (*reallocate)(void* context, void* pointer, size_t size); |
113 | /// Pointer to a function to be called for general memory de-allocation. |
114 | /// |
115 | /// @param context - The user-specified pointer from xnn_allocator structure. |
116 | /// @param pointer - Pointer to a memory block allocated by @ref allocate or @ref reallocate functions. Can be NULL. |
117 | /// If the pointer is NULL, the @ref deallocate call is a no-op. |
118 | void (*deallocate)(void* context, void* pointer); |
119 | /// Pointer to a function to be called for aligned memory allocation. |
120 | /// |
121 | /// @param context - The user-specified pointer from xnn_allocator structure. |
122 | /// @param alignment - The alignment of the memory block to allocate, in bytes. Alignment is always a power-of-2. |
123 | /// @param size - The size of the memory block to allocate, in bytes. |
124 | /// |
125 | /// @returns Pointer to the allocated memory block of at least @ref size bytes. |
126 | /// If allocation fails, the function must return NULL. |
127 | void* (*aligned_allocate)(void* context, size_t alignment, size_t size); |
128 | /// Pointer to a function to be called for aligned memory de-allocation. |
129 | /// |
130 | /// @param context - The user-specified pointer from xnn_allocator structure. |
131 | /// @param pointer - Pointer to a memory block allocated by @ref aligned_allocate function. Can be NULL. |
132 | /// If the pointer is NULL, the @ref aligned_deallocate call is a no-op. |
133 | void (*aligned_deallocate)(void* context, void* pointer); |
134 | }; |
135 | |
136 | /// Initialize XNNPACK library. |
137 | /// |
138 | /// XNNPACK must be successfully initialized before use. During initialization, XNNPACK populates internal structures |
139 | /// depending on the host processor. Initialization can be time-consuming. |
140 | /// |
141 | /// @param[in] allocator - structure with function pointers to be use for memory allocation and de-allocation. |
142 | /// If this argument is NULL, system-provided memory management functions (e.g. malloc/free) |
143 | /// will be used. |
144 | /// |
145 | /// @retval xnn_status_success - XNNPACK is successfully initialized and ready to use. |
146 | /// @retval xnn_status_out_of_memory - initialization failed due to out-of-memory condition. |
147 | /// @retval xnn_status_unsupported_hardware - initialization failed because the host processor does not satisfy the |
148 | /// minimum hardware requirements for XNNPACK. E.g. this may happen on x86 |
149 | /// processors without SSE2 extension, or on 32-bit ARM processors without |
150 | /// the NEON SIMD extension. |
151 | enum xnn_status xnn_initialize(const struct xnn_allocator* allocator); |
152 | |
153 | /// Deinitialize XNNPACK library. |
154 | /// |
155 | /// To avoid memory and resource leaks, users must call xnn_deinitialize once for each successful xnn_initialize call. |
156 | /// |
157 | /// @retval xnn_status_success - deinitialization call succeeded. |
158 | enum xnn_status xnn_deinitialize(void); |
159 | |
160 | /// Subgraph is an abstract representation of a neural network model. |
161 | /// Subgraph objects are used to define Values (tensors) and Nodes (operators) comprising the model. |
162 | typedef struct xnn_subgraph* xnn_subgraph_t; |
163 | |
164 | /// Create a empty Subgraph object. |
165 | /// |
166 | /// @param external_value_ids - number of Value IDs to reserve for communication with external graph representation. |
167 | /// The Subgraph object would avoid creating internal Value IDs in the |
168 | /// [0, reserved_value_ids-1] range. |
169 | /// @param flags - binary features of the subgraph. No supported flags are currently defined. |
170 | /// @param subgraph_out - pointer to the variable that will be initialized with a handle to the Subgraph object upon |
171 | /// successful return. |
172 | enum xnn_status xnn_create_subgraph( |
173 | uint32_t external_value_ids, |
174 | uint32_t flags, |
175 | xnn_subgraph_t* subgraph_out); |
176 | |
177 | /// Destroy a Subgraph object, as well as Values, and Nodes associated with the subgraph. |
178 | /// |
179 | /// @param subgraph - the Subgraph object to destroy. |
180 | enum xnn_status xnn_delete_subgraph( |
181 | xnn_subgraph_t subgraph); |
182 | |
183 | #define XNN_VALUE_FLAG_EXTERNAL_INPUT 0x00000001 |
184 | #define XNN_VALUE_FLAG_EXTERNAL_OUTPUT 0x00000002 |
185 | #define XNN_VALUE_FLAG_PERSISTENT 0x00000004 |
186 | |
187 | #define XNN_INVALID_VALUE_ID UINT32_MAX |
188 | |
189 | /// Type of elements in a Value object. |
190 | enum xnn_datatype { |
191 | /// Invalid data type. Valid Values never have this datatype. |
192 | xnn_datatype_invalid = 0, |
193 | /// IEEE754 single-precision floating-point. |
194 | xnn_datatype_fp32 = 1, |
195 | /// IEEE754 half-precision floating-point. |
196 | xnn_datatype_fp16 = 2, |
197 | /// Quantized 8-bit signed integer with shared per-Value quantization parameters. |
198 | xnn_datatype_qint8 = 3, |
199 | /// Quantized 8-bit unsigned integer with shared per-Value quantization parameters. |
200 | xnn_datatype_quint8 = 4, |
201 | /// Quantized 32-bit signed integer with shared per-Value quantization parameters. |
202 | xnn_datatype_qint32 = 5, |
203 | /// Quantized 8-bit signed integer with shared per-channel quantization parameters. |
204 | xnn_datatype_qcint8 = 6, |
205 | /// Quantized 32-bit signed integer with shared per-channel quantization parameters. |
206 | xnn_datatype_qcint32 = 7, |
207 | }; |
208 | |
209 | /// Define a tensor-type Value and add it to a Subgraph. |
210 | /// |
211 | /// @param subgraph - a Subgraph object that will own the created Value. |
212 | /// @param datatype - type of the tensor elements. |
213 | /// @param num_dims - number of dimensions in the shape. |
214 | /// @param dims - pointer to an array of @a num_dims shape dimensions. If num_dims is 0, this pointer can be NULL. |
215 | /// XNNPACK does not keep any pointers to this array after the function returns. |
216 | /// @param data - pointer to static data used for tensor initialization. If the tensor is not statically initialized, |
217 | /// this pointer must be is NULL. If non-NULL, the life-time of the static data must exceed the life-time |
218 | /// of the Subgraph object, and of any Runtime objects created from the Subgraph. |
219 | /// @param external_id - external ID for the Value. The ID must be within the range of reversed Value IDs specified on |
220 | /// the Subgraph creation. If the external ID is XNN_INVALID_VALUE_ID, an internal ID will be |
221 | /// created for the Value. |
222 | /// @param flags - binary features of the Value. Supported values are any combination of XNN_VALUE_FLAG_EXTERNAL_INPUT |
223 | /// and XNN_VALUE_FLAG_EXTERNAL_OUTPUT. |
224 | /// @param id_out - pointer to the variable that will be initialized with the Value ID upon successful return. If a |
225 | /// valid @a external_id was provided, the variable will be initialized with the @a external_id value. |
226 | enum xnn_status xnn_define_tensor_value( |
227 | xnn_subgraph_t subgraph, |
228 | enum xnn_datatype datatype, |
229 | size_t num_dims, |
230 | const size_t* dims, |
231 | const void* data, |
232 | uint32_t external_id, |
233 | uint32_t flags, |
234 | uint32_t* id_out); |
235 | |
236 | /// Define a quantized tensor-type Value and add it to a Subgraph. |
237 | /// |
238 | /// @param subgraph - a Subgraph object that will own the created Value. |
239 | /// @param datatype - type of the tensor elements. |
240 | /// @param zero_point - offset from zero to subtract from the quantized elements in the Value. |
241 | /// @param scale - multiplication factor to convert quantized elements to real representation. |
242 | /// @param num_dims - number of dimensions in the shape. |
243 | /// @param dims - pointer to an array of @a num_dims shape dimensions. If num_dims is 0, this pointer can be NULL. |
244 | /// XNNPACK does not keep any pointers to this array after the function returns. |
245 | /// @param data - pointer to static data used for tensor initialization. If the tensor is not statically initialized, |
246 | /// this pointer must be is NULL. If non-NULL, the life-time of the static data must exceed the life-time |
247 | /// of the Subgraph object, and of any Runtime objects created from the Subgraph. |
248 | /// @param external_id - external ID for the Value. The ID must be within the range of reversed Value IDs specified on |
249 | /// the Subgraph creation. If the external ID is XNN_INVALID_VALUE_ID, an internal ID will be |
250 | /// created for the Value. |
251 | /// @param flags - binary features of the Value. Supported values are any combination of XNN_VALUE_FLAG_EXTERNAL_INPUT |
252 | /// and XNN_VALUE_FLAG_EXTERNAL_OUTPUT. |
253 | /// @param id_out - pointer to the variable that will be initialized with the Value ID upon successful return. If a |
254 | /// valid @a external_id was provided, the variable will be initialized with the @a external_id value. |
255 | enum xnn_status xnn_define_quantized_tensor_value( |
256 | xnn_subgraph_t subgraph, |
257 | enum xnn_datatype datatype, |
258 | int32_t zero_point, |
259 | float scale, |
260 | size_t num_dims, |
261 | const size_t* dims, |
262 | const void* data, |
263 | uint32_t external_id, |
264 | uint32_t flags, |
265 | uint32_t* id_out); |
266 | |
267 | /// Define a channelwise quantized tensor-type Value and add it to a Subgraph. |
268 | /// |
269 | /// @param subgraph - a Subgraph object that will own the created Value. |
270 | /// @param datatype - type of the tensor elements. |
271 | /// @param scale - per-channel multiplication factors to convert quantized elements to real representation. |
272 | /// @param num_dims - number of dimensions in the shape. |
273 | /// @param channel_dim - index of the channel dimension in the tensor with per-channel quantization parameters. |
274 | /// Typically this is the first dimension (dimension #0) of the filter tensors in the Convolution, |
275 | /// Deconvolution, and Fully Connected operators and the last dimension of the filter tensors in |
276 | /// the Depthwise Convolution operators. |
277 | /// @param dims - pointer to an array of @a num_dims shape dimensions. If num_dims is 0, this pointer can be NULL. |
278 | /// XNNPACK does not keep any pointers to this array after the function returns. |
279 | /// @param data - pointer to static data used for tensor initialization. If the tensor is not statically initialized, |
280 | /// this pointer must be is NULL. If non-NULL, the life-time of the static data must exceed the life-time |
281 | /// of the Subgraph object, and of any Runtime objects created from the Subgraph. |
282 | /// @param external_id - external ID for the Value. The ID must be within the range of reversed Value IDs specified on |
283 | /// the Subgraph creation. If the external ID is XNN_INVALID_VALUE_ID, an internal ID will be |
284 | /// created for the Value. |
285 | /// @param flags - binary features of the Value. Supported values are any combination of XNN_VALUE_FLAG_EXTERNAL_INPUT |
286 | /// and XNN_VALUE_FLAG_EXTERNAL_OUTPUT. |
287 | /// @param id_out - pointer to the variable that will be initialized with the Value ID upon successful return. If a |
288 | /// valid @a external_id was provided, the variable will be initialized with the @a external_id value. |
289 | enum xnn_status xnn_define_channelwise_quantized_tensor_value( |
290 | xnn_subgraph_t subgraph, |
291 | enum xnn_datatype datatype, |
292 | const float* scale, |
293 | size_t num_dims, |
294 | size_t channel_dim, |
295 | const size_t* dims, |
296 | const void* data, |
297 | uint32_t external_id, |
298 | uint32_t flags, |
299 | uint32_t* id_out); |
300 | |
301 | /// Define a Convert Node and add it to a Subgraph. |
302 | /// |
303 | /// @param subgraph - a Subgraph object that will own the created Node. |
304 | /// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph. |
305 | /// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its |
306 | /// shape must match the shape of the input tensor. |
307 | /// @param flags - binary features of the Convert Node. No supported flags are currently defined. |
308 | enum xnn_status xnn_define_convert( |
309 | xnn_subgraph_t subgraph, |
310 | uint32_t input_id, |
311 | uint32_t output_id, |
312 | uint32_t flags); |
313 | |
314 | /// Define a 2D Convolution Node and add it to a Subgraph. |
315 | /// |
316 | /// @param subgraph - a Subgraph object that will own the created Node. |
317 | /// @param input_padding_top - implicit zero-padding above 2D input data. Must be 0 if XNN_FLAG_TENSORFLOW_SAME_PADDING |
318 | /// flag is specified. |
319 | /// @param input_padding_right - implicit zero-padding to the right of 2D input data. Must be 0 if |
320 | /// XNN_FLAG_TENSORFLOW_SAME_PADDING flag is specified. |
321 | /// @param input_padding_bottom - implicit zero-padding below 2D input data. Must be 0 if |
322 | /// XNN_FLAG_TENSORFLOW_SAME_PADDING flag is specified. |
323 | /// @param input_padding_left - implicit zero-padding to the left of 2D input data. Must be 0 if |
324 | /// XNN_FLAG_TENSORFLOW_SAME_PADDING flag is specified. |
325 | /// @param kernel_height - kernel (filter) height. |
326 | /// @param kernel_width - kernel (filter) width. |
327 | /// @param subsampling_height - height of subsampling region for convolution output (convolution height stride). |
328 | /// @param subsampling_width - width of subsampling region for convolution output (convolution width stride). |
329 | /// @param dilation_height - dilation of kernel elements along the height dimension. |
330 | /// @param dilation_width - dilation of kernel elements along the width dimension. |
331 | /// @param groups - number of convolution groups. |
332 | /// @param group_input_channels - number of input channels per group. |
333 | /// @param group_output_channels - number of output channels per group. |
334 | /// @param output_min - lower bound for clipping output values. |
335 | /// @param output_max - upper bound for clipping output values. |
336 | /// @param input_id - Value ID for the input tensor. The input tensor must be a 4D tensor defined in the @a subgraph |
337 | /// with [N, IH, IW, groups * group_input_channels] dimensions |
338 | /// @param filter_id - Value ID for the filter tensor. The filter tensor must ge a 4D tensor defined in the @a subgraph |
339 | /// with [groups * group_output_channels, kernel_height, kernel_width, group_input_channels] |
340 | /// dimensions. |
341 | /// @param bias_id - Value ID for the bias tensor, or XNN_INVALID_VALUE_ID for a 2D Convolution Node without a bias. If |
342 | /// present, the bias tensor must be a 1D tensor defined in the @a subgraph with [groups * |
343 | /// group_output_channels] dimensions. |
344 | /// @param output_id - Value ID for the output tensor. The output tensor must be a 4D tensor defined in the @a subgraph |
345 | /// with [N, OH, OW, groups * group_output_channels] dimensions. |
346 | /// @param flags - binary features of the 2D Convolution Node. The only currently supported values is |
347 | /// XNN_FLAG_TENSORFLOW_SAME_PADDING. |
348 | enum xnn_status xnn_define_convolution_2d( |
349 | xnn_subgraph_t subgraph, |
350 | uint32_t input_padding_top, |
351 | uint32_t input_padding_right, |
352 | uint32_t input_padding_bottom, |
353 | uint32_t input_padding_left, |
354 | uint32_t kernel_height, |
355 | uint32_t kernel_width, |
356 | uint32_t subsampling_height, |
357 | uint32_t subsampling_width, |
358 | uint32_t dilation_height, |
359 | uint32_t dilation_width, |
360 | uint32_t groups, |
361 | size_t group_input_channels, |
362 | size_t group_output_channels, |
363 | float output_min, |
364 | float output_max, |
365 | uint32_t input_id, |
366 | uint32_t filter_id, |
367 | uint32_t bias_id, |
368 | uint32_t output_id, |
369 | uint32_t flags); |
370 | |
371 | /// Define a 2D Deconvolution (Transposed Convolution) Node and add it to a Subgraph. |
372 | /// |
373 | /// @param subgraph - a Subgraph object that will own the created Node. |
374 | /// @param padding_top - implicit padding above 2D output data. |
375 | /// @param padding_right - implicit padding to the right of 2D output data. |
376 | /// @param padding_bottom - implicit padding below 2D output data. |
377 | /// @param padding_left - implicit padding to the left of 2D output data. |
378 | /// @param adjustment_height - additional elements in the bottom of the 2D output data. |
379 | /// @param adjustment_width - additional elements to the right of the 2D output data. |
380 | /// @param kernel_height - kernel (filter) height. |
381 | /// @param kernel_width - kernel (filter) width. |
382 | /// @param upsampling_height - height of upsampling region for deconvolution input (deconvolution height stride). |
383 | /// @param upsampling_width - width of upsampling region for deconvolution input (deconvolution width stride). |
384 | /// @param dilation_height - dilation of kernel elements along the height dimension. |
385 | /// @param dilation_width - dilation of kernel elements along the width dimension. |
386 | /// @param groups - number of convolution groups. |
387 | /// @param group_input_channels - number of input channels per group. |
388 | /// @param group_output_channels - number of output channels per group. |
389 | /// @param output_min - lower bound for clipping output values. |
390 | /// @param output_max - upper bound for clipping output values. |
391 | /// @param input_id - Value ID for the input tensor. The input tensor must be a 4D tensor defined in the @a subgraph |
392 | /// with [N, IH, IW, groups * group_input_channels] dimensions |
393 | /// @param filter_id - Value ID for the filter tensor. The filter tensor must ge a 4D tensor defined in the @a subgraph |
394 | /// with [groups * group_output_channels, kernel_height, kernel_width, group_input_channels] |
395 | /// dimensions. |
396 | /// @param bias_id - Value ID for the bias tensor, or XNN_INVALID_VALUE_ID for a 2D Convolution Node without a bias. If |
397 | /// present, the bias tensor must be a 1D tensor defined in the @a subgraph with |
398 | /// [groups * group_output_channels] dimensions. |
399 | /// @param output_id - Value ID for the output tensor. The output tensor must be a 4D tensor defined in the @a subgraph |
400 | /// with [N, OH, OW, groups * group_output_channels] dimensions. |
401 | /// @param flags - binary features of the 2D Deconvolution Node. No supported flags are currently defined. |
402 | enum xnn_status xnn_define_deconvolution_2d( |
403 | xnn_subgraph_t subgraph, |
404 | uint32_t padding_top, |
405 | uint32_t padding_right, |
406 | uint32_t padding_bottom, |
407 | uint32_t padding_left, |
408 | uint32_t adjustment_height, |
409 | uint32_t adjustment_width, |
410 | uint32_t kernel_height, |
411 | uint32_t kernel_width, |
412 | uint32_t upsampling_height, |
413 | uint32_t upsampling_width, |
414 | uint32_t dilation_height, |
415 | uint32_t dilation_width, |
416 | uint32_t groups, |
417 | size_t group_input_channels, |
418 | size_t group_output_channels, |
419 | float output_min, |
420 | float output_max, |
421 | uint32_t input_id, |
422 | uint32_t filter_id, |
423 | uint32_t bias_id, |
424 | uint32_t output_id, |
425 | uint32_t flags); |
426 | |
427 | /// Define a 2D Depthwise Convolution Node and add it to a Subgraph. |
428 | /// |
429 | /// @param subgraph - a Subgraph object that will own the created Node. |
430 | /// @param input_padding_top - implicit zero-padding above 2D input data. Must be 0 if XNN_FLAG_TENSORFLOW_SAME_PADDING |
431 | /// flag is specified. |
432 | /// @param input_padding_right - implicit zero-padding to the right of 2D input data. Must be 0 if |
433 | /// XNN_FLAG_TENSORFLOW_SAME_PADDING flag is specified. |
434 | /// @param input_padding_bottom - implicit zero-padding below 2D input data. Must be 0 if |
435 | /// XNN_FLAG_TENSORFLOW_SAME_PADDING flag is specified. |
436 | /// @param input_padding_left - implicit zero-padding to the left of 2D input data. Must be 0 if |
437 | /// XNN_FLAG_TENSORFLOW_SAME_PADDING flag is specified. |
438 | /// @param kernel_height - kernel (filter) height. |
439 | /// @param kernel_width - kernel (filter) width. |
440 | /// @param subsampling_height - height of subsampling region for convolution output (convolution height stride). |
441 | /// @param subsampling_width - width of subsampling region for convolution output (convolution width stride). |
442 | /// @param dilation_height - dilation of kernel elements along the height dimension. |
443 | /// @param dilation_width - dilation of kernel elements along the width dimension. |
444 | /// @param depth_multiplier - ratio of output channels to input channels. |
445 | /// @param input_channels - number of input channels. |
446 | /// @param output_min - lower bound for clipping output values. |
447 | /// @param output_max - upper bound for clipping output values. |
448 | /// @param input_id - Value ID for the input tensor. The input tensor must be a 4D tensor defined in the @a subgraph |
449 | /// with [N, IH, IW, input_channels] dimensions |
450 | /// @param filter_id - Value ID for the filter tensor. The filter tensor must ge a 4D tensor defined in the @a subgraph |
451 | /// with [1, kernel_height, kernel_width, input_channels * depth_multiplier] dimensions. |
452 | /// @param bias_id - Value ID for the bias tensor, or XNN_INVALID_VALUE_ID for a 2D Depthwise Convolution Node without |
453 | /// a bias. If present, the bias tensor must be a 1D tensor defined in the @a subgraph with |
454 | /// [input_channels * depth_multiplier] dimensions. |
455 | /// @param output_id - Value ID for the output tensor. The output tensor must be a 4D tensor defined in the @a subgraph |
456 | /// with [N, OH, OW, input_channels * depth_multiplier] dimensions. |
457 | /// @param flags - binary features of the 2D Depthwise Convolution Node. The only currently supported values is |
458 | /// XNN_FLAG_TENSORFLOW_SAME_PADDING. |
459 | enum xnn_status xnn_define_depthwise_convolution_2d( |
460 | xnn_subgraph_t subgraph, |
461 | uint32_t input_padding_top, |
462 | uint32_t input_padding_right, |
463 | uint32_t input_padding_bottom, |
464 | uint32_t input_padding_left, |
465 | uint32_t kernel_height, |
466 | uint32_t kernel_width, |
467 | uint32_t subsampling_height, |
468 | uint32_t subsampling_width, |
469 | uint32_t dilation_height, |
470 | uint32_t dilation_width, |
471 | uint32_t depth_multiplier, |
472 | size_t input_channels, |
473 | float output_min, |
474 | float output_max, |
475 | uint32_t input_id, |
476 | uint32_t filter_id, |
477 | uint32_t bias_id, |
478 | uint32_t output_id, |
479 | uint32_t flags); |
480 | |
481 | /// Define a Depth To Space Node and add it to a Subgraph. |
482 | /// |
483 | /// The Depth To Space Node rearranges data from depth into blocks of spatial data (a reverse transform to |
484 | /// Space To Depth). For a given input pixel, an output square of pixels with side @a block_size is formed from values |
485 | /// in the corresponding number of its channels. The output depth is therefore @a block_size x @a block_size times |
486 | /// smaller than that of the input. |
487 | /// |
488 | /// @param subgraph - a Subgraph object that will own the created Node. |
489 | /// @param input_id - Value ID for the input tensor. The input tensor must be a 4D tensor defined in the @a subgraph |
490 | /// with [N, IH, IW, OC * block_size * block_size] dimensions. |
491 | /// @param output_id - Value ID for the output tensor. The output tensor must be a 4D tensor defined in the @a subgraph |
492 | /// with [N, IH * block_size, IW * block_size, OC] dimensions. |
493 | /// @param block_size - the size of the spatial block. |
494 | /// @param flags - binary features of the input_channels Node. No supported flags are currently defined. |
495 | enum xnn_status xnn_define_depth_to_space( |
496 | xnn_subgraph_t subgraph, |
497 | uint32_t input_id, |
498 | uint32_t output_id, |
499 | uint32_t block_size, |
500 | uint32_t flags); |
501 | |
502 | /// Define a 1D Global Average Pooling Node and add it to a Subgraph. |
503 | /// |
504 | /// @param subgraph - a Subgraph object that will own the created Node. |
505 | /// @param output_min - lower bound for clipping output values. |
506 | /// @param output_max - upper bound for clipping output values. |
507 | /// @param input_id - Value ID for the input tensor. The input tensor must be a dense tensor with 2 or more dimensions |
508 | /// defined in the @a subgraph. Averaging is performed across the second-innermost dimension. |
509 | /// @param output_id - Value ID for the output tensor. The output tensor must be a dense tensor with 2 or more |
510 | /// dimensions defined in the @a subgraph. |
511 | /// @param flags - binary features of the 1D Global Average Pooling Node. No supported flags are currently defined. |
512 | enum xnn_status xnn_define_global_average_pooling_1d( |
513 | xnn_subgraph_t subgraph, |
514 | float output_min, |
515 | float output_max, |
516 | uint32_t input_id, |
517 | uint32_t output_id, |
518 | uint32_t flags); |
519 | |
520 | /// Define a 2D Global Average Pooling Node and add it to a Subgraph. |
521 | /// |
522 | /// @param subgraph - a Subgraph object that will own the created Node. |
523 | /// @param output_min - lower bound for clipping output values. |
524 | /// @param output_max - upper bound for clipping output values. |
525 | /// @param input_id - Value ID for the input tensor. The input tensor must be a dense tensor with 3 or more dimensions |
526 | /// defined in the @a subgraph. Averaging is performed across the second- and third-innermost |
527 | /// dimensions. |
528 | /// @param output_id - Value ID for the output tensor. The output tensor must be a dense tensor with 3 or more |
529 | /// dimensions defined in the @a subgraph. |
530 | /// @param flags - binary features of the 2D Global Average Pooling Node. No supported flags are currently defined. |
531 | enum xnn_status xnn_define_global_average_pooling_2d( |
532 | xnn_subgraph_t subgraph, |
533 | float output_min, |
534 | float output_max, |
535 | uint32_t input_id, |
536 | uint32_t output_id, |
537 | uint32_t flags); |
538 | |
539 | /// Define a 2D Average Pooling Node and add it to a Subgraph. |
540 | /// |
541 | /// @param subgraph - a Subgraph object that will own the created Node. |
542 | /// @param input_padding_top - implicit zero-padding above 2D input data. Must be 0 if XNN_FLAG_TENSORFLOW_SAME_PADDING |
543 | /// flag is specified. |
544 | /// @param input_padding_right - implicit zero-padding to the right of 2D input data. Must be 0 if |
545 | /// XNN_FLAG_TENSORFLOW_SAME_PADDING flag is specified. |
546 | /// @param input_padding_bottom - implicit zero-padding below 2D input data. Must be 0 if |
547 | /// XNN_FLAG_TENSORFLOW_SAME_PADDING flag is specified. |
548 | /// @param input_padding_left - implicit zero-padding to the left of 2D input data. Must be 0 if |
549 | /// XNN_FLAG_TENSORFLOW_SAME_PADDING flag is specified. |
550 | /// @param pooling_height - pooling (kernel) height. |
551 | /// @param pooling_width - pooling (kernel) width. |
552 | /// @param stride_height - displacing of the pooling window in the vertical dimension of the input pixels corresponding |
553 | /// to vertically adjacent output pixels. |
554 | /// @param stride_width - displacing of the pooling window in the horizontal dimension of the input pixels corresponding |
555 | /// to horizontally adjacent output pixels. |
556 | /// @param output_min - lower bound for clipping output values. |
557 | /// @param output_max - upper bound for clipping output values. |
558 | /// @param input_id - Value ID for the input tensor. The input tensor must be a 4D tensor defined in the @a subgraph |
559 | /// with [N, IH, IW, channels] dimensions |
560 | /// @param output_id - Value ID for the output tensor. The output tensor must be a 4D tensor defined in the @a subgraph |
561 | /// with [N, OH, OW, channels] dimensions. |
562 | /// @param flags - binary features of the 2D Average Pooling Node. The only currently supported values is |
563 | /// XNN_FLAG_TENSORFLOW_SAME_PADDING. |
564 | enum xnn_status xnn_define_average_pooling_2d( |
565 | xnn_subgraph_t subgraph, |
566 | uint32_t input_padding_top, |
567 | uint32_t input_padding_right, |
568 | uint32_t input_padding_bottom, |
569 | uint32_t input_padding_left, |
570 | uint32_t pooling_height, |
571 | uint32_t pooling_width, |
572 | uint32_t stride_height, |
573 | uint32_t stride_width, |
574 | float output_min, |
575 | float output_max, |
576 | uint32_t input_id, |
577 | uint32_t output_id, |
578 | uint32_t flags); |
579 | |
580 | /// Define a Fully Connected Node and add it to a Subgraph. |
581 | /// |
582 | /// @param subgraph - a Subgraph object that will own the created Node. |
583 | /// @param output_min - lower bound for clipping output values. |
584 | /// @param output_max - upper bound for clipping output values. |
585 | /// @param input_id - Value ID for the input tensor. The input tensor must be an N-dimensional tensor defined in the |
586 | /// @a subgraph. If XNN_FLAG_TENSORFLOW_RESHAPE_2D is not specified, the input tensor must be at least |
587 | /// 1D and its last dimension must match the last dimension of the filter tensor. In particular, if |
588 | /// input is a 2D tensor, it must have [batch_size, input_channels] dimensions. |
589 | /// If XNN_FLAG_TENSORFLOW_RESHAPE_2D is specified, the number of elements in the input tensor must be |
590 | /// divisible by the input_channels. The tensor will be first flattened into a 1D tensor of |
591 | /// [num_input_elements] dimensions, then reshaped into a 2D tensor of |
592 | /// [num_input_elements / input_channels, input_channels] dimensions where num_input_elements is the |
593 | /// total number of elements in the input tensor. |
594 | /// @param filter_id - Value ID for the filter tensor. The filter tensor must a 2D tensor defined in the @a subgraph. |
595 | /// If the XNN_FLAG_TRANSPOSE_WEIGHTS flag is not specified, the filter tensor must have |
596 | /// [output_channels, input_channels] dimensions. If the XNN_FLAG_TRANSPOSE_WEIGHTS flag is |
597 | /// specified, the filter tensor must have [input_channels, output_channels] dimensions. |
598 | /// @param bias_id - Value ID for the bias tensor, or XNN_INVALID_VALUE_ID for a Fully Connected Node without a bias. |
599 | /// If present, the bias tensor must be a 1D tensor defined in the @a subgraph with [output_channels] |
600 | /// dimensions. |
601 | /// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph. |
602 | /// If XNN_FLAG_TENSORFLOW_RESHAPE_2D is not specified, the output tensor must have the same |
603 | /// dimensionality as the input tensor, all its dimensions but the last one must match the |
604 | /// corresponding dimensions of the input tensor, and the last dimensions of the output tensor must |
605 | /// match the first dimension of the filter tensor. In particular, if input is a 2D tensor, output |
606 | /// must be a 2D tensor of [batch_size, output_channels] dimensions. |
607 | /// If XNN_FLAG_TENSORFLOW_RESHAPE_2D is specified, output must be a 2D tensor of |
608 | /// [num_input_elements / input_channels, output_channels] dimensions where num_input_elements is the |
609 | /// total number of elements in the input tensor. |
610 | /// @param flags - binary features of the Fully Connected Node. The only currently supported values are |
611 | /// XNN_FLAG_TENSORFLOW_RESHAPE_2D and XNN_FLAG_TRANSPOSE_WEIGHTS. |
612 | enum xnn_status xnn_define_fully_connected( |
613 | xnn_subgraph_t subgraph, |
614 | float output_min, |
615 | float output_max, |
616 | uint32_t input_id, |
617 | uint32_t filter_id, |
618 | uint32_t bias_id, |
619 | uint32_t output_id, |
620 | uint32_t flags); |
621 | |
622 | /// Define a 2D Max Pooling Node and add it to a Subgraph. |
623 | /// |
624 | /// @param subgraph - a Subgraph object that will own the created Node. |
625 | /// @param input_padding_top - implicit zero-padding above 2D input data. Must be 0 if XNN_FLAG_TENSORFLOW_SAME_PADDING |
626 | /// flag is specified. |
627 | /// @param input_padding_right - implicit zero-padding to the right of 2D input data. Must be 0 if |
628 | /// XNN_FLAG_TENSORFLOW_SAME_PADDING flag is specified. |
629 | /// @param input_padding_bottom - implicit zero-padding below 2D input data. Must be 0 if |
630 | /// XNN_FLAG_TENSORFLOW_SAME_PADDING flag is specified. |
631 | /// @param input_padding_left - implicit zero-padding to the left of 2D input data. Must be 0 if |
632 | /// XNN_FLAG_TENSORFLOW_SAME_PADDING flag is specified. |
633 | /// @param pooling_height - pooling (kernel) height. |
634 | /// @param pooling_width - pooling (kernel) width. |
635 | /// @param stride_height - displacing of the pooling window in the vertical dimension of the input pixels corresponding |
636 | /// to vertically adjacent output pixels. |
637 | /// @param stride_width - displacing of the pooling window in the horizontal dimension of the input pixels corresponding |
638 | /// to horizontally adjacent output pixels. |
639 | /// @param dilation_height - dilation of pooling elements along the height dimension. |
640 | /// @param dilation_width - dilation of pooling elements along the width dimension. |
641 | /// @param output_min - lower bound for clipping output values. |
642 | /// @param output_max - upper bound for clipping output values. |
643 | /// @param input_id - Value ID for the input tensor. The input tensor must be a 4D tensor defined in the @a subgraph |
644 | /// with [N, IH, IW, channels] dimensions |
645 | /// @param output_id - Value ID for the output tensor. The output tensor must be a 4D tensor defined in the @a subgraph |
646 | /// with [N, OH, OW, channels] dimensions. |
647 | /// @param flags - binary features of the 2D Max Pooling Node. The only currently supported values is |
648 | /// XNN_FLAG_TENSORFLOW_SAME_PADDING. |
649 | enum xnn_status xnn_define_max_pooling_2d( |
650 | xnn_subgraph_t subgraph, |
651 | uint32_t input_padding_top, |
652 | uint32_t input_padding_right, |
653 | uint32_t input_padding_bottom, |
654 | uint32_t input_padding_left, |
655 | uint32_t pooling_height, |
656 | uint32_t pooling_width, |
657 | uint32_t stride_height, |
658 | uint32_t stride_width, |
659 | uint32_t dilation_height, |
660 | uint32_t dilation_width, |
661 | float output_min, |
662 | float output_max, |
663 | uint32_t input_id, |
664 | uint32_t output_id, |
665 | uint32_t flags); |
666 | |
667 | /// Define a 2D ArgMax Pooling Node and add it to a Subgraph. |
668 | /// |
669 | /// @param subgraph - a Subgraph object that will own the created Node. |
670 | /// @param input_padding_top - implicit zero-padding above 2D input data. |
671 | /// @param input_padding_right - implicit zero-padding to the right of 2D input data. |
672 | /// @param input_padding_bottom - implicit zero-padding below 2D input data. |
673 | /// @param input_padding_left - implicit zero-padding to the left of 2D input data. |
674 | /// @param pooling_height - pooling (kernel) height. Vertical stride between pooling regions match this value. |
675 | /// @param pooling_width - pooling (kernel) width. Horizontal stride between pooling regions match this value. |
676 | /// @param input_id - Value ID for the input tensor. The input tensor must be a 4D tensor defined in the @a subgraph |
677 | /// with [N, IH, IW, channels] dimensions |
678 | /// @param output_value_id - Value ID for the output tensor with the maximum values in the pools. The output tensor must |
679 | /// be a 4D tensor defined in the @a subgraph with [N, OH, OW, channels] dimensions. |
680 | /// @param output_index_id - Value ID for the output tensor with the indexes of the maximum values in the pools. The |
681 | /// output tensor must be a 4D tensor defined in the @a subgraph with [N, OH, OW, channels] |
682 | /// dimensions. |
683 | /// @param flags - binary features of the 2D ArgMax Pooling Node. No supported flags are currently defined. |
684 | enum xnn_status xnn_define_argmax_pooling_2d( |
685 | xnn_subgraph_t subgraph, |
686 | uint32_t input_padding_top, |
687 | uint32_t input_padding_right, |
688 | uint32_t input_padding_bottom, |
689 | uint32_t input_padding_left, |
690 | uint32_t pooling_height, |
691 | uint32_t pooling_width, |
692 | uint32_t input_id, |
693 | uint32_t output_value_id, |
694 | uint32_t output_index_id, |
695 | uint32_t flags); |
696 | |
697 | /// Define a 2D UnPooling Node and add it to a Subgraph. |
698 | /// |
699 | /// @param subgraph - a Subgraph object that will own the created Node. |
700 | /// @param padding_top - implicit padding above 2D output data. |
701 | /// @param padding_right - implicit padding to the right of 2D output data. |
702 | /// @param padding_bottom - implicit padding below 2D output data. |
703 | /// @param padding_left - implicit padding to the left of 2D output data. |
704 | /// @param pooling_height - height of the pooling window. |
705 | /// @param pooling_width - width of the pooling window. |
706 | /// @param input_value_id - Value ID for the input tensor with the max-pooling values to invert. The input value tensor |
707 | /// must be a 4D tensor defined in the @a subgraph with [N, IH, IW, channels] dimensions. |
708 | /// @param input_index_id - Value ID for the input tensor with the indices of the per-pool maximum values produced by |
709 | /// a 2D UnPooling Node. The input tensor must be a 4D tensor defined in the @a subgraph with |
710 | /// [N, IH, IW, channels] dimensions. |
711 | /// @param output_id - Value ID for the output tensor. The output tensor must be a 4D tensor defined in the @a subgraph |
712 | /// with [N, OH, OW, channels] dimensions. |
713 | /// @param flags - binary features of the 2D UnPooling Node. No supported flags are currently defined. |
714 | enum xnn_status xnn_define_unpooling_2d( |
715 | xnn_subgraph_t subgraph, |
716 | uint32_t padding_top, |
717 | uint32_t padding_right, |
718 | uint32_t padding_bottom, |
719 | uint32_t padding_left, |
720 | uint32_t pooling_height, |
721 | uint32_t pooling_width, |
722 | uint32_t input_value_id, |
723 | uint32_t input_index_id, |
724 | uint32_t output_id, |
725 | uint32_t flags); |
726 | |
727 | /// Define a 2-Input Add Node and add it to a Subgraph. |
728 | /// |
729 | /// The 2-Input Add Node computes elementwise addition of two tensor inputs with numpy broadcasting rules. |
730 | /// |
731 | /// @param subgraph - a Subgraph object that will own the created Node. |
732 | /// @param output_min - lower bound for clipping output values. |
733 | /// @param output_max - upper bound for clipping output values. |
734 | /// @param input1_id - Value ID for the first input tensor. The input tensor must be an N-dimensional tensor defined in |
735 | /// the @a subgraph with each dimension either equal to the corresponding dimension of the second |
736 | /// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along |
737 | /// that dimension. |
738 | /// @param input2_id - Value ID for the second input tensor. The input tensor must be an M-dimensional tensor defined in |
739 | /// the @a subgraph with each dimension either equal to the corresponding dimension of the first |
740 | /// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along |
741 | /// that dimension. |
742 | /// @param output_id - Value ID for the output tensor. The output tensor must be a max(N,M)-dimensional tensor defined |
743 | /// in the @a subgraph with each dimension equal to the maximum between the corresponding dimension |
744 | /// of the two inputs. |
745 | /// @param flags - binary features of the Add Node. No supported flags are currently defined. |
746 | enum xnn_status xnn_define_add2( |
747 | xnn_subgraph_t subgraph, |
748 | float output_min, |
749 | float output_max, |
750 | uint32_t input1_id, |
751 | uint32_t input2_id, |
752 | uint32_t output_id, |
753 | uint32_t flags); |
754 | |
755 | /// Define a 2-Input Multiply Node and add it to a Subgraph. |
756 | /// |
757 | /// The 2-Input Multiply Node computes elementwise multiplication of two tensor inputs with numpy broadcasting rules. |
758 | /// |
759 | /// @param subgraph - a Subgraph object that will own the created Node. |
760 | /// @param output_min - lower bound for clipping output values. |
761 | /// @param output_max - upper bound for clipping output values. |
762 | /// @param input1_id - Value ID for the first input tensor. The input tensor must be an N-dimensional tensor defined in |
763 | /// the @a subgraph with each dimension either equal to the corresponding dimension of the second |
764 | /// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along |
765 | /// that dimension. |
766 | /// @param input2_id - Value ID for the second input tensor. The input tensor must be an M-dimensional tensor defined in |
767 | /// the @a subgraph with each dimension either equal to the corresponding dimension of the first |
768 | /// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along |
769 | /// that dimension. |
770 | /// @param output_id - Value ID for the output tensor. The output tensor must be a max(N,M)-dimensional tensor defined |
771 | /// in the @a subgraph with each dimension equal to the maximum between the corresponding dimension |
772 | /// of the two inputs. |
773 | /// @param flags - binary features of the Multiply Node. No supported flags are currently defined. |
774 | enum xnn_status xnn_define_multiply2( |
775 | xnn_subgraph_t subgraph, |
776 | float output_min, |
777 | float output_max, |
778 | uint32_t input1_id, |
779 | uint32_t input2_id, |
780 | uint32_t output_id, |
781 | uint32_t flags); |
782 | |
783 | /// Define a Subtract Node and add it to a Subgraph. |
784 | /// |
785 | /// The Subtract Node computes elementwise subtraction of two tensor inputs with numpy broadcasting rules. |
786 | /// |
787 | /// @param subgraph - a Subgraph object that will own the created Node. |
788 | /// @param output_min - lower bound for clipping output values. |
789 | /// @param output_max - upper bound for clipping output values. |
790 | /// @param input1_id - Value ID for the first input tensor. The input tensor must be an N-dimensional tensor defined in |
791 | /// the @a subgraph with each dimension either equal to the corresponding dimension of the second |
792 | /// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along |
793 | /// that dimension. |
794 | /// @param input2_id - Value ID for the second input tensor. The input tensor must be an M-dimensional tensor defined in |
795 | /// the @a subgraph with each dimension either equal to the corresponding dimension of the first |
796 | /// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along |
797 | /// that dimension. |
798 | /// @param output_id - Value ID for the output tensor. The output tensor must be a max(N,M)-dimensional tensor defined |
799 | /// in the @a subgraph with each dimension equal to the maximum between the corresponding dimension |
800 | /// of the two inputs. |
801 | /// @param flags - binary features of the Subtract Node. No supported flags are currently defined. |
802 | enum xnn_status xnn_define_subtract( |
803 | xnn_subgraph_t subgraph, |
804 | float output_min, |
805 | float output_max, |
806 | uint32_t input1_id, |
807 | uint32_t input2_id, |
808 | uint32_t output_id, |
809 | uint32_t flags); |
810 | |
811 | /// Define a Divide Node and add it to a Subgraph. |
812 | /// |
813 | /// The Divide Node computes elementwise division of two tensor inputs with numpy broadcasting rules. |
814 | /// |
815 | /// @param subgraph - a Subgraph object that will own the created Node. |
816 | /// @param output_min - lower bound for clipping output values. |
817 | /// @param output_max - upper bound for clipping output values. |
818 | /// @param input1_id - Value ID for the first input tensor. The input tensor must be an N-dimensional tensor defined in |
819 | /// the @a subgraph with each dimension either equal to the corresponding dimension of the second |
820 | /// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along |
821 | /// that dimension. |
822 | /// @param input2_id - Value ID for the second input tensor. The input tensor must be an M-dimensional tensor defined in |
823 | /// the @a subgraph with each dimension either equal to the corresponding dimension of the first |
824 | /// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along |
825 | /// that dimension. |
826 | /// @param output_id - Value ID for the output tensor. The output tensor must be a max(N,M)-dimensional tensor defined |
827 | /// in the @a subgraph with each dimension equal to the maximum between the corresponding dimension |
828 | /// of the two inputs. |
829 | /// @param flags - binary features of the Divide Node. No supported flags are currently defined. |
830 | enum xnn_status xnn_define_divide( |
831 | xnn_subgraph_t subgraph, |
832 | float output_min, |
833 | float output_max, |
834 | uint32_t input1_id, |
835 | uint32_t input2_id, |
836 | uint32_t output_id, |
837 | uint32_t flags); |
838 | |
839 | /// Define a 2-Input Maximum Node and add it to a Subgraph. |
840 | /// |
841 | /// The 2-Input Maximum Node computes elementwise maximum of two tensor inputs with numpy broadcasting rules. |
842 | /// |
843 | /// @param subgraph - a Subgraph object that will own the created Node. |
844 | /// @param input1_id - Value ID for the first input tensor. The input tensor must be an N-dimensional tensor defined in |
845 | /// the @a subgraph with each dimension either equal to the corresponding dimension of the second |
846 | /// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along |
847 | /// that dimension. |
848 | /// @param input2_id - Value ID for the second input tensor. The input tensor must be an M-dimensional tensor defined in |
849 | /// the @a subgraph with each dimension either equal to the corresponding dimension of the first |
850 | /// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along |
851 | /// that dimension. |
852 | /// @param output_id - Value ID for the output tensor. The output tensor must be a max(N,M)-dimensional tensor defined |
853 | /// in the @a subgraph with each dimension equal to the maximum between the corresponding dimension |
854 | /// of the two inputs. |
855 | /// @param flags - binary features of the Maximum Node. No supported flags are currently defined. |
856 | enum xnn_status xnn_define_maximum2( |
857 | xnn_subgraph_t subgraph, |
858 | uint32_t input1_id, |
859 | uint32_t input2_id, |
860 | uint32_t output_id, |
861 | uint32_t flags); |
862 | |
863 | /// Define a 2-Input Minimum Node and add it to a Subgraph. |
864 | /// |
865 | /// The 2-Input Minimum Node computes elementwise minimum of two tensor inputs with numpy broadcasting rules. |
866 | /// |
867 | /// @param subgraph - a Subgraph object that will own the created Node. |
868 | /// @param input1_id - Value ID for the first input tensor. The input tensor must be an N-dimensional tensor defined in |
869 | /// the @a subgraph with each dimension either equal to the corresponding dimension of the second |
870 | /// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along |
871 | /// that dimension. |
872 | /// @param input2_id - Value ID for the second input tensor. The input tensor must be an M-dimensional tensor defined in |
873 | /// the @a subgraph with each dimension either equal to the corresponding dimension of the first |
874 | /// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along |
875 | /// that dimension. |
876 | /// @param output_id - Value ID for the output tensor. The output tensor must be a max(N,M)-dimensional tensor defined |
877 | /// in the @a subgraph with each dimension equal to the maximum between the corresponding dimension |
878 | /// of the two inputs. |
879 | /// @param flags - binary features of the Minimum Node. No supported flags are currently defined. |
880 | enum xnn_status xnn_define_minimum2( |
881 | xnn_subgraph_t subgraph, |
882 | uint32_t input1_id, |
883 | uint32_t input2_id, |
884 | uint32_t output_id, |
885 | uint32_t flags); |
886 | |
887 | /// Define a Squared Difference Node and add it to a Subgraph. |
888 | /// |
889 | /// The Squared Difference Node computes elementwise squared difference of two tensor inputs with numpy broadcasting |
890 | /// rules. |
891 | /// |
892 | /// @param subgraph - a Subgraph object that will own the created Node. |
893 | /// @param input1_id - Value ID for the first input tensor. The input tensor must be an N-dimensional tensor defined in |
894 | /// the @a subgraph with each dimension either equal to the corresponding dimension of the second |
895 | /// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along |
896 | /// that dimension. |
897 | /// @param input2_id - Value ID for the second input tensor. The input tensor must be an M-dimensional tensor defined in |
898 | /// the @a subgraph with each dimension either equal to the corresponding dimension of the first |
899 | /// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along |
900 | /// that dimension. |
901 | /// @param output_id - Value ID for the output tensor. The output tensor must be a max(N,M)-dimensional tensor defined |
902 | /// in the @a subgraph with each dimension equal to the maximum between the corresponding dimension |
903 | /// of the two inputs. |
904 | /// @param flags - binary features of the Squared Difference Node. No supported flags are currently defined. |
905 | enum xnn_status xnn_define_squared_difference( |
906 | xnn_subgraph_t subgraph, |
907 | uint32_t input1_id, |
908 | uint32_t input2_id, |
909 | uint32_t output_id, |
910 | uint32_t flags); |
911 | |
912 | /// Define a Constant Pad Node with static padding specification and add it to a Subgraph. |
913 | /// |
914 | /// @param subgraph - a Subgraph object that will own the created Node. |
915 | /// @param pre_paddings - number of padding elements to insert before input elements for every dimension. This array |
916 | /// must have as many elements as the number of dimensions in the input tensor. |
917 | /// @param post_paddings - number of padding elements to insert after input elements for every dimension. This array |
918 | /// must have as many elements as the number of dimensions in the input tensor. |
919 | /// @param padding_value - constant value used to initialize padding elements. |
920 | /// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph. |
921 | /// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its |
922 | /// shape must match the shape of the input tensor with padding. |
923 | /// @param flags - binary features of the Constant Pad Node. No supported flags are currently defined. |
924 | enum xnn_status xnn_define_static_constant_pad( |
925 | xnn_subgraph_t subgraph, |
926 | const size_t* pre_paddings, |
927 | const size_t* post_paddings, |
928 | float padding_value, |
929 | uint32_t input_id, |
930 | uint32_t output_id, |
931 | uint32_t flags); |
932 | |
933 | /// Define a 2-Input Concatenate Node and add it to a Subgraph. |
934 | /// |
935 | /// The 2-Input Concatenate Node concatenates two tensors along a specified axis. |
936 | /// |
937 | /// @param subgraph - a Subgraph object that will own the created Node. |
938 | /// @param axis - the axis to concatenate the two input tensors along |
939 | /// @param input1_id - Value ID for the first input tensor. The input tensor must be an N-dimensional tensor defined in |
940 | /// the @a subgraph with each dimension, except the axis, equal to the corresponding dimension of the |
941 | /// second input. |
942 | /// @param input2_id - Value ID for the second input tensor. The input tensor must be an N-dimensional tensor defined in |
943 | /// the @a subgraph with each dimension, except the axis, equal to the corresponding dimension of the |
944 | /// first input. |
945 | /// @param output_id - Value ID for the output tensor. The output tensor must be a N-dimensional tensor defined |
946 | /// in the @a subgraph with each dimension equal to the dimension of both inputs, except the axis |
947 | /// dimension, where it is the sum of the corresponding dimensions of both inputs. |
948 | /// @param flags - binary features of the Concatenate Node. No supported flags are currently defined. |
949 | enum xnn_status xnn_define_concatenate2( |
950 | xnn_subgraph_t subgraph, |
951 | size_t axis, |
952 | uint32_t input1_id, |
953 | uint32_t input2_id, |
954 | uint32_t output_id, |
955 | uint32_t flags); |
956 | |
957 | /// Define a 3-Input Concatenate Node and add it to a Subgraph. |
958 | /// |
959 | /// The 3-Input Concatenate Node concatenates three tensors along a specified axis. |
960 | /// |
961 | /// @param subgraph - a Subgraph object that will own the created Node. |
962 | /// @param axis - the axis to concatenate the three input tensors along |
963 | /// @param input1_id - Value ID for the first input tensor. The input tensor must be an N-dimensional tensor defined in |
964 | /// the @a subgraph with each dimension, except the axis, equal to the corresponding dimension of the |
965 | /// other inputs. |
966 | /// @param input2_id - Value ID for the second input tensor. The input tensor must be an N-dimensional tensor defined in |
967 | /// the @a subgraph with each dimension, except the axis, equal to the corresponding dimension of the |
968 | /// other inputs. |
969 | /// @param input3_id - Value ID for the third input tensor. The input tensor must be an N-dimensional tensor defined in |
970 | /// the @a subgraph with each dimension, except the axis, equal to the corresponding dimension of the |
971 | /// other inputs. |
972 | /// @param output_id - Value ID for the output tensor. The output tensor must be a N-dimensional tensor defined |
973 | /// in the @a subgraph with each dimension equal to the dimension of all inputs, except the axis |
974 | /// dimension, where it is the sum of the corresponding dimensions of all inputs. |
975 | /// @param flags - binary features of the Concatenate Node. No supported flags are currently defined. |
976 | enum xnn_status xnn_define_concatenate3( |
977 | xnn_subgraph_t subgraph, |
978 | size_t axis, |
979 | uint32_t input1_id, |
980 | uint32_t input2_id, |
981 | uint32_t input3_id, |
982 | uint32_t output_id, |
983 | uint32_t flags); |
984 | |
985 | /// Define a 4-Input Concatenate Node and add it to a Subgraph. |
986 | /// |
987 | /// The 4-Input Concatenate Node concatenates four tensors along a specified axis. |
988 | /// |
989 | /// @param subgraph - a Subgraph object that will own the created Node. |
990 | /// @param axis - the axis to concatenate the four input tensors along |
991 | /// @param input1_id - Value ID for the first input tensor. The input tensor must be an N-dimensional tensor defined in |
992 | /// the @a subgraph with each dimension, except the axis, equal to the corresponding dimension of the |
993 | /// other inputs. |
994 | /// @param input2_id - Value ID for the second input tensor. The input tensor must be an N-dimensional tensor defined in |
995 | /// the @a subgraph with each dimension, except the axis, equal to the corresponding dimension of the |
996 | /// other inputs. |
997 | /// @param input3_id - Value ID for the third input tensor. The input tensor must be an N-dimensional tensor defined in |
998 | /// the @a subgraph with each dimension, except the axis, equal to the corresponding dimension of the |
999 | /// other inputs. |
1000 | /// @param input4_id - Value ID for the fourth input tensor. The input tensor must be an N-dimensional tensor defined in |
1001 | /// the @a subgraph with each dimension, except the axis, equal to the corresponding dimension of the |
1002 | /// other inputs. |
1003 | /// @param output_id - Value ID for the output tensor. The output tensor must be a N-dimensional tensor defined |
1004 | /// in the @a subgraph with each dimension equal to the dimension of all inputs, except the axis |
1005 | /// dimension, where it is the sum of the corresponding dimensions of all inputs. |
1006 | /// @param flags - binary features of the Concatenate Node. No supported flags are currently defined. |
1007 | enum xnn_status xnn_define_concatenate4( |
1008 | xnn_subgraph_t subgraph, |
1009 | size_t axis, |
1010 | uint32_t input1_id, |
1011 | uint32_t input2_id, |
1012 | uint32_t input3_id, |
1013 | uint32_t input4_id, |
1014 | uint32_t output_id, |
1015 | uint32_t flags); |
1016 | |
1017 | /// Define a Copy Node and add it to a Subgraph. |
1018 | /// |
1019 | /// The Copy Node copies an input tensor to an output tensor. |
1020 | /// |
1021 | /// @param subgraph - a Subgraph object that will own the created Node. |
1022 | /// @param input_id - Value ID for the first input tensor. The input tensor must be defined in the @a subgraph. |
1023 | /// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its |
1024 | /// shape must match the shape of the input tensor. |
1025 | /// @param flags - binary features of the Copy Node. No supported flags are currently defined. |
1026 | enum xnn_status xnn_define_copy( |
1027 | xnn_subgraph_t subgraph, |
1028 | uint32_t input_id, |
1029 | uint32_t output_id, |
1030 | uint32_t flags); |
1031 | |
1032 | /// Define a 2-Output Split Node and add it to a Subgraph. |
1033 | /// |
1034 | /// The 2-Output Split Node splits an input tensor into two output tensors along a specified axis evenly. |
1035 | /// |
1036 | /// @param subgraph - a Subgraph object that will own the created Node. |
1037 | /// @param split_dim - the dimension to split the input tensor along |
1038 | /// @param input_id - Value ID for the input tensor. The input tensor must be an N-dimensional tensor defined in the @a |
1039 | /// subgraph. |
1040 | /// @param output1_id - Value ID for the first output tensor. The output tensor must be an N-dimensional tensor defined |
1041 | /// in the @a subgraph with each dimension, except the axis, equal to the corresponding dimension |
1042 | /// of the second output. The split_dim dimension is half of the input's split_dim. |
1043 | /// @param output2_id - Value ID for the second output tensor. The output tensor must be an N-dimensional tensor |
1044 | /// defined in the @a subgraph with each dimension, except the axis, equal to the corresponding |
1045 | /// dimension of the first output. The split_dim dimension is half of the input's split_dim. |
1046 | /// @param flags - binary features of the Split Node. No supported flags are currently defined. |
1047 | enum xnn_status xnn_define_even_split2( |
1048 | xnn_subgraph_t subgraph, |
1049 | size_t split_dim, |
1050 | uint32_t input_id, |
1051 | uint32_t output1_id, |
1052 | uint32_t output2_id, |
1053 | uint32_t flags); |
1054 | |
1055 | /// Define a 3-Output Split Node and add it to a Subgraph. |
1056 | /// |
1057 | /// The 3-Output Split Node splits an input tensor into three output tensors along a specified axis evenly. |
1058 | /// |
1059 | /// @param subgraph - a Subgraph object that will own the created Node. |
1060 | /// @param split_dim - the dimension to split the input tensor along |
1061 | /// @param input_id - Value ID for the input tensor. The input tensor must be an N-dimensional tensor defined in the @a |
1062 | /// subgraph. |
1063 | /// @param output1_id - Value ID for the first output tensor. The output tensor must be an N-dimensional tensor defined |
1064 | /// in the @a subgraph with each dimension, except the axis, equal to the corresponding dimension |
1065 | /// of the second and third output. The split_dim dimension is one third of the input's split_dim. |
1066 | /// @param output2_id - Value ID for the second output tensor. The output tensor must be an N-dimensional tensor |
1067 | /// defined in the @a subgraph with each dimension, except the axis, equal to the corresponding |
1068 | /// dimension of the first and third output. The split_dim dimension is one third of the input's |
1069 | /// split_dim. |
1070 | /// @param output3_id - Value ID for the third output tensor. The output tensor must be an N-dimensional tensor |
1071 | /// defined in the @a subgraph with each dimension, except the axis, equal to the corresponding |
1072 | /// dimension of the second and third output. The split_dim dimension is one third of the input's |
1073 | /// split_dim. |
1074 | /// @param flags - binary features of the Split Node. No supported flags are currently defined. |
1075 | enum xnn_status xnn_define_even_split3( |
1076 | xnn_subgraph_t subgraph, |
1077 | size_t split_dim, |
1078 | uint32_t input_id, |
1079 | uint32_t output1_id, |
1080 | uint32_t output2_id, |
1081 | uint32_t output3_id, |
1082 | uint32_t flags); |
1083 | |
1084 | /// Define a 4-Output Split Node and add it to a Subgraph. |
1085 | /// |
1086 | /// The 4-Output Split Node splits an input tensor into four output tensors along a specified axis evenly. |
1087 | /// |
1088 | /// @param subgraph - a Subgraph object that will own the created Node. |
1089 | /// @param split_dim - the dimension to split the input tensor along |
1090 | /// @param input_id - Value ID for the input tensor. The input tensor must be an N-dimensional tensor defined in the @a |
1091 | /// subgraph. |
1092 | /// @param output1_id - Value ID for the first output tensor. The output tensor must be an N-dimensional tensor defined |
1093 | /// in the @a subgraph with each dimension, except the axis, equal to the corresponding dimension |
1094 | /// of the other output tensors. The split_dim dimension is one fourth of the input's split_dim. |
1095 | /// @param output2_id - Value ID for the second output tensor. The output tensor must be an N-dimensional tensor |
1096 | /// defined in the @a subgraph with each dimension, except the axis, equal to the corresponding |
1097 | /// dimension of the other output tensors. The split_dim dimension is one fourth of the input's |
1098 | /// split_dim. |
1099 | /// @param output3_id - Value ID for the third output tensor. The output tensor must be an N-dimensional tensor |
1100 | /// defined in the @a subgraph with each dimension, except the axis, equal to the corresponding |
1101 | /// dimension of the other output tensors. The split_dim dimension is one fourth of the input's |
1102 | /// split_dim. |
1103 | /// @param output4_id - Value ID for the fourth output tensor. The output tensor must be an N-dimensional tensor |
1104 | /// defined in the @a subgraph with each dimension, except the axis, equal to the corresponding |
1105 | /// dimension of the other output tensors. The split_dim dimension is one fourth of the input's |
1106 | /// split_dim. |
1107 | /// @param flags - binary features of the Split Node. No supported flags are currently defined. |
1108 | enum xnn_status xnn_define_even_split4( |
1109 | xnn_subgraph_t subgraph, |
1110 | size_t split_dim, |
1111 | uint32_t input_id, |
1112 | uint32_t output1_id, |
1113 | uint32_t output2_id, |
1114 | uint32_t output3_id, |
1115 | uint32_t output4_id, |
1116 | uint32_t flags); |
1117 | |
1118 | /// Define a Reshape Node with static shape specification and add it to a Subgraph. |
1119 | /// |
1120 | /// @param subgraph - a Subgraph object that will own the created Node. |
1121 | /// @param num_dims - number of shape dimensions in the output tensor. |
1122 | /// @param new_shape - shape dimensions of the output tensor. |
1123 | /// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph. |
1124 | /// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its |
1125 | /// shape must match the shape of the input tensor with padding. |
1126 | /// @param flags - binary features of the Reshape Node. No supported flags are currently defined. |
1127 | enum xnn_status xnn_define_static_reshape( |
1128 | xnn_subgraph_t subgraph, |
1129 | size_t num_dims, |
1130 | const size_t* new_shape, |
1131 | uint32_t input_id, |
1132 | uint32_t output_id, |
1133 | uint32_t flags); |
1134 | |
1135 | /// Define a 2D Resize Bilinear Node with static output height & width specification and add it to a Subgraph. |
1136 | /// |
1137 | /// @param subgraph - a Subgraph object that will own the created Node. |
1138 | /// @param new_height - height dimension of the output tensor. |
1139 | /// @param new_width - width dimension of the output tensor. |
1140 | /// @param input_id - Value ID for the input tensor. The input tensor must be a 4D tensor defined in the @a subgraph |
1141 | /// with [N, H, W, C] dimensions. |
1142 | /// @param output_id - Value ID for the output tensor. The output tensor must be a 4D tensor defined in the @a subgraph |
1143 | /// with [N, new_height, new_width, C] dimensions. |
1144 | /// @param flags - binary features of the 2D Resize Bilinear Node. The only currently supported values are |
1145 | /// XNN_FLAG_TENSORFLOW_LEGACY_MODE and XNN_FLAG_ALIGN_CORNERS, which are mutually exclusive. |
1146 | enum xnn_status xnn_define_static_resize_bilinear_2d( |
1147 | xnn_subgraph_t subgraph, |
1148 | size_t new_height, |
1149 | size_t new_width, |
1150 | uint32_t input_id, |
1151 | uint32_t output_id, |
1152 | uint32_t flags); |
1153 | |
1154 | /// Define a PReLU (Parametric ReLU) Node and add it to a Subgraph. |
1155 | /// |
1156 | /// @param subgraph - a Subgraph object that will own the created Node. |
1157 | /// @param input_id - Value ID for the input tensor. The input tensor must be a 4D tensor defined in the @a subgraph |
1158 | /// with [N, H, W, channels] dimensions. |
1159 | /// @param slope_id - Value ID for the bias tensor. The bias tensor must be a 1D tensor defined in the @a subgraph with |
1160 | /// [channels] dimensions. |
1161 | /// @param output_id - Value ID for the output tensor. The output tensor must be a 4D tensor defined in the @a subgraph |
1162 | /// with [N, H, W, channels] dimensions. |
1163 | /// @param flags - binary features of the PReLU Node. No supported flags are currently defined. |
1164 | enum xnn_status xnn_define_prelu( |
1165 | xnn_subgraph_t subgraph, |
1166 | uint32_t input_id, |
1167 | uint32_t slope_id, |
1168 | uint32_t output_id, |
1169 | uint32_t flags); |
1170 | |
1171 | /// Define a Abs Node and add it to a Subgraph. |
1172 | /// |
1173 | /// @param subgraph - a Subgraph object that will own the created Node. |
1174 | /// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph. |
1175 | /// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its |
1176 | /// shape must match the shape of the input tensor. |
1177 | /// @param flags - binary features of the Abs Node. No supported flags are currently defined. |
1178 | enum xnn_status xnn_define_abs( |
1179 | xnn_subgraph_t subgraph, |
1180 | uint32_t input_id, |
1181 | uint32_t output_id, |
1182 | uint32_t flags); |
1183 | |
1184 | /// Define a Bankers' Rounding Node and add it to a Subgraph. |
1185 | /// |
1186 | /// @param subgraph - a Subgraph object that will own the created Node. |
1187 | /// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph. |
1188 | /// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its |
1189 | /// shape must match the shape of the input tensor. |
1190 | /// @param flags - binary features of the Bankers' Rounding Node. No supported flags are currently defined. |
1191 | enum xnn_status xnn_define_bankers_rounding( |
1192 | xnn_subgraph_t subgraph, |
1193 | uint32_t input_id, |
1194 | uint32_t output_id, |
1195 | uint32_t flags); |
1196 | |
1197 | /// Define a Ceiling Node and add it to a Subgraph. |
1198 | /// |
1199 | /// @param subgraph - a Subgraph object that will own the created Node. |
1200 | /// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph. |
1201 | /// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its |
1202 | /// shape must match the shape of the input tensor. |
1203 | /// @param flags - binary features of the Ceiling Node. No supported flags are currently defined. |
1204 | enum xnn_status xnn_define_ceiling( |
1205 | xnn_subgraph_t subgraph, |
1206 | uint32_t input_id, |
1207 | uint32_t output_id, |
1208 | uint32_t flags); |
1209 | |
1210 | /// Define a Clamp Node and add it to a Subgraph. |
1211 | /// |
1212 | /// @param subgraph - a Subgraph object that will own the created Node. |
1213 | /// @param output_min - lower bound for clipping output values. |
1214 | /// @param output_max - upper bound for clipping output values. |
1215 | /// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph. |
1216 | /// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its |
1217 | /// shape must match the shape of the input tensor. |
1218 | /// @param flags - binary features of the Clamp Node. No supported flags are currently defined. |
1219 | enum xnn_status xnn_define_clamp( |
1220 | xnn_subgraph_t subgraph, |
1221 | float output_min, |
1222 | float output_max, |
1223 | uint32_t input_id, |
1224 | uint32_t output_id, |
1225 | uint32_t flags); |
1226 | |
1227 | /// Define an ELU (Exponential Linear Unit) Node and add it to a Subgraph. |
1228 | /// |
1229 | /// @param subgraph - a Subgraph object that will own the created Node. |
1230 | /// @param alpha - scale factor for negative output elements. |
1231 | /// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph. |
1232 | /// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its |
1233 | /// shape must match the shape of the input tensor. |
1234 | /// @param flags - binary features of the ELU Node. No supported flags are currently defined. |
1235 | enum xnn_status xnn_define_elu( |
1236 | xnn_subgraph_t subgraph, |
1237 | float alpha, |
1238 | uint32_t input_id, |
1239 | uint32_t output_id, |
1240 | uint32_t flags); |
1241 | |
1242 | /// Define a Floor Node and add it to a Subgraph. |
1243 | /// |
1244 | /// @param subgraph - a Subgraph object that will own the created Node. |
1245 | /// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph. |
1246 | /// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its |
1247 | /// shape must match the shape of the input tensor. |
1248 | /// @param flags - binary features of the Floor Node. No supported flags are currently defined. |
1249 | enum xnn_status xnn_define_floor( |
1250 | xnn_subgraph_t subgraph, |
1251 | uint32_t input_id, |
1252 | uint32_t output_id, |
1253 | uint32_t flags); |
1254 | |
1255 | /// Define a HardSwish Node and add it to a Subgraph. |
1256 | /// |
1257 | /// @param subgraph - a Subgraph object that will own the created Node. |
1258 | /// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph. |
1259 | /// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its |
1260 | /// shape must match the shape of the input tensor. |
1261 | /// @param flags - binary features of the HardSwish Node. No supported flags are currently defined. |
1262 | enum xnn_status xnn_define_hardswish( |
1263 | xnn_subgraph_t subgraph, |
1264 | uint32_t input_id, |
1265 | uint32_t output_id, |
1266 | uint32_t flags); |
1267 | |
1268 | /// Define a Leaky ReLU Node and add it to a Subgraph. |
1269 | /// |
1270 | /// @param subgraph - a Subgraph object that will own the created Node. |
1271 | /// @param negative_slope - scale factor for negative input elements. |
1272 | /// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph. |
1273 | /// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its |
1274 | /// shape must match the shape of the input tensor. |
1275 | /// @param flags - binary features of the Leaky ReLU Node. No supported flags are currently defined. |
1276 | enum xnn_status xnn_define_leaky_relu( |
1277 | xnn_subgraph_t subgraph, |
1278 | float negative_slope, |
1279 | uint32_t input_id, |
1280 | uint32_t output_id, |
1281 | uint32_t flags); |
1282 | |
1283 | /// Define a Negate Node and add it to a Subgraph. |
1284 | /// |
1285 | /// @param subgraph - a Subgraph object that will own the created Node. |
1286 | /// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph. |
1287 | /// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its |
1288 | /// shape must match the shape of the input tensor. |
1289 | /// @param flags - binary features of the Negate Node. No supported flags are currently defined. |
1290 | enum xnn_status xnn_define_negate( |
1291 | xnn_subgraph_t subgraph, |
1292 | uint32_t input_id, |
1293 | uint32_t output_id, |
1294 | uint32_t flags); |
1295 | |
1296 | /// Define a Sigmoid Node and add it to a Subgraph. |
1297 | /// |
1298 | /// @param subgraph - a Subgraph object that will own the created Node. |
1299 | /// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph. |
1300 | /// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its |
1301 | /// shape must match the shape of the input tensor. |
1302 | /// @param flags - binary features of the Sigmoid Node. No supported flags are currently defined. |
1303 | enum xnn_status xnn_define_sigmoid( |
1304 | xnn_subgraph_t subgraph, |
1305 | uint32_t input_id, |
1306 | uint32_t output_id, |
1307 | uint32_t flags); |
1308 | |
1309 | /// Define a SoftMax Node and add it to a Subgraph. |
1310 | /// |
1311 | /// @param subgraph - a Subgraph object that will own the created Node. |
1312 | /// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph, and have at |
1313 | /// least one dimension. |
1314 | /// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its |
1315 | /// shape must match the shape of the input tensor. |
1316 | /// @param flags - binary features of the SoftMax Node. No supported flags are currently defined. |
1317 | enum xnn_status xnn_define_softmax( |
1318 | xnn_subgraph_t subgraph, |
1319 | uint32_t input_id, |
1320 | uint32_t output_id, |
1321 | uint32_t flags); |
1322 | |
1323 | /// Define a Space To Depth 2D Node and add it to a Subgraph. |
1324 | /// |
1325 | /// The Space To Depth 2D Node rearranges blocks of spatial data into blocks (a reverse transform to Depth To Space 2D). |
1326 | /// For a given input pixel, an output square of pixels with side @a block_size is formed from values in the |
1327 | /// corresponding number of its channels. The output depth is therefore @a block_size x @a block_size times greater |
1328 | /// than that of the input. |
1329 | /// |
1330 | /// @param subgraph - a Subgraph object that will own the created Node. |
1331 | /// @param block_size - the size of the spatial block. |
1332 | /// @param input_id - Value ID for the input tensor. The input tensor must be a 4D tensor defined in the @a subgraph |
1333 | /// with [N, IH * block_size, IW * block_size, OC] dimensions. |
1334 | /// @param output_id - Value ID for the output tensor. The output tensor must be a 4D tensor defined in the @a subgraph |
1335 | /// with [N, IH, IW, OC * block_size * block_size] dimensions. |
1336 | /// @param flags - binary features of the input_channels Node. No supported flags are currently defined. |
1337 | enum xnn_status xnn_define_space_to_depth_2d( |
1338 | xnn_subgraph_t subgraph, |
1339 | uint32_t block_size, |
1340 | uint32_t input_id, |
1341 | uint32_t output_id, |
1342 | uint32_t flags); |
1343 | |
1344 | /// Define a Square Node and add it to a Subgraph. |
1345 | /// |
1346 | /// @param subgraph - a Subgraph object that will own the created Node. |
1347 | /// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph. |
1348 | /// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its |
1349 | /// shape must match the shape of the input tensor. |
1350 | /// @param flags - binary features of the Square Node. No supported flags are currently defined. |
1351 | enum xnn_status xnn_define_square( |
1352 | xnn_subgraph_t subgraph, |
1353 | uint32_t input_id, |
1354 | uint32_t output_id, |
1355 | uint32_t flags); |
1356 | |
1357 | /// Define a Square Root Node and add it to a Subgraph. |
1358 | /// |
1359 | /// @param subgraph - a Subgraph object that will own the created Node. |
1360 | /// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph. |
1361 | /// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its |
1362 | /// shape must match the shape of the input tensor. |
1363 | /// @param flags - binary features of the Square Root Node. No supported flags are currently defined. |
1364 | enum xnn_status xnn_define_square_root( |
1365 | xnn_subgraph_t subgraph, |
1366 | uint32_t input_id, |
1367 | uint32_t output_id, |
1368 | uint32_t flags); |
1369 | |
1370 | /// Define a Static Slice Node add it to a Subgraph. |
1371 | /// |
1372 | /// @param subgraph - a Subgraph object that will own the created Node. |
1373 | /// @param num_dims - number of shape dimensions in the input and output tensor. |
1374 | /// @param offsets - offsets in each dimension of the input tensor. This array must have @a num_dims elements. |
1375 | /// @param sizes - size of each dimension in output tensor. This array must have @a num_dims elements. |
1376 | /// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph. |
1377 | /// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its |
1378 | /// dimensions must match @a sizes. |
1379 | /// @param flags - binary features of the Static Slice Node. No supported flags are currently defined. |
1380 | enum xnn_status xnn_define_static_slice( |
1381 | xnn_subgraph_t subgraph, |
1382 | size_t num_dims, |
1383 | const size_t* offsets, |
1384 | const size_t* sizes, |
1385 | uint32_t input_id, |
1386 | uint32_t output_id, |
1387 | uint32_t flags); |
1388 | |
1389 | /// Define a Static Transpose Node and add it to a Subgraph. |
1390 | /// |
1391 | /// The Static Transpose Node applies a generalized transpose to the input tensor using the permuation in perm. |
1392 | /// |
1393 | /// @param subgraph - a Subgraph object that will own the created Node. |
1394 | /// @param input_id - Value ID for the input tensor. The input tensor must be an N-dimensional tensor defined in |
1395 | /// the @a subgraph. |
1396 | /// @param output_id - Value ID for the output tensor. The output tensor must be an N-dimensional tensor defined |
1397 | /// in the @a subgraph with each dimension equal to its corresponding permuted input dimension. |
1398 | /// @param num_dims - the number of permutation dimensions. This must be equal to the number of input dimensions. |
1399 | /// @param perm - The permutation of the axis of the input tensor. The perm array must must contain 0 to N-1 in the |
1400 | /// permuted order. |
1401 | /// @param flags - binary features of the Static Transpose Node. No supported flags are currently defined. |
1402 | enum xnn_status xnn_define_static_transpose( |
1403 | xnn_subgraph_t subgraph, |
1404 | size_t num_dims, |
1405 | const size_t* perm, |
1406 | uint32_t input_id, |
1407 | uint32_t output_id, |
1408 | uint32_t flags); |
1409 | |
1410 | /// Weights cache is a cache for packed weights. It can be reused between runtimes. |
1411 | typedef struct xnn_weights_cache* xnn_weights_cache_t; |
1412 | |
1413 | enum xnn_status xnn_create_weights_cache(xnn_weights_cache_t* weights_cache_out); |
1414 | |
1415 | /// Create a weights cache object specifying the initial size of weights cache (in bytes). |
1416 | /// @size - initial capacity of the weights cache (in bytes), i.e. it can hold size bytes without growing. |
1417 | /// @param weights_cache_out - pointer to the variable that will be initialized to a handle to the weights cache object |
1418 | /// upon successful return. Once created, the weights cache object can be shared between |
1419 | /// different Runtime objects. |
1420 | enum xnn_status xnn_create_weights_cache_with_size(size_t size, xnn_weights_cache_t* weights_cache_out); |
1421 | |
1422 | |
1423 | /// Weights cache can be finalized in these ways: |
1424 | enum xnn_weights_cache_finalization_kind { |
1425 | /// Weights cache is finalized, no insert operations into the weights cache is allowed, even if the "inserted" |
1426 | /// weights already exist in thee cache. Weights cache memory will also be trimmed to page boundary and set to |
1427 | /// read-only (to prevent writes). |
1428 | xnn_weights_cache_finalization_kind_hard, |
1429 | /// Weights cache will be finalized with some extra space at the end, this allows for "inserting" into the cache only |
1430 | /// if the weights are already in the cache, and errors on inserting uncached weights. There is memory overhead. |
1431 | xnn_weights_cache_finalization_kind_soft, |
1432 | }; |
1433 | |
1434 | /// Finalizes the weights cache. The kind of finalization is specified by `finalization_kind`. |
1435 | /// @param weights_cache - the weights cache object to finalize. |
1436 | /// @param finalization_kind - the kind of finalization. |
1437 | enum xnn_status xnn_finalize_weights_cache( |
1438 | xnn_weights_cache_t weights_cache, |
1439 | enum xnn_weights_cache_finalization_kind finalization_kind); |
1440 | |
1441 | /// Destroy a weights cache object, as well as memory used for the cache. |
1442 | /// @param weights_cache - the weights cache object to destroy. |
1443 | enum xnn_status xnn_delete_weights_cache(xnn_weights_cache_t weights_cache); |
1444 | |
1445 | typedef struct xnn_workspace* xnn_workspace_t; |
1446 | |
1447 | /// Create a workspace object. |
1448 | /// @param workspace_out - pointer to the variable that will be initialized to a handle to the workspace object upon |
1449 | /// successful return. Once created, the workspace can be shared between different Runtime |
1450 | /// objects. |
1451 | enum xnn_status xnn_create_workspace(xnn_workspace_t* workspace_out); |
1452 | /// Destroy a workspace object, as well as memory used by the workspace. Object destruction can be deferred until all |
1453 | /// Runtime objects created with this workspace are destroyed. |
1454 | /// @param workspace - the workspace object to destroy. |
1455 | enum xnn_status xnn_release_workspace(xnn_workspace_t workspace); |
1456 | |
1457 | /// Runtime is a combination of an execution plan for subgraph Nodes and a memory manager for subgraph Values. |
1458 | typedef struct xnn_runtime* xnn_runtime_t; |
1459 | |
1460 | enum xnn_profile_info { |
1461 | /// Returns a size_t containing the number of operators. |
1462 | xnn_profile_info_num_operators, |
1463 | /// Returns a char[] containing the null character separated names of all operators. |
1464 | xnn_profile_info_operator_name, |
1465 | /// Returns a uint64_t[] with the runtimes of all operators in the same order as xnn_profile_info_operator_name. |
1466 | xnn_profile_info_operator_timing, |
1467 | }; |
1468 | |
1469 | /// Return profile information for all operators. |
1470 | /// |
1471 | /// @param runtime - a Runtime object created with @ref xnn_create_runtime, @ref xnn_create_runtime_v2 or |
1472 | /// @ref xnn_create_runtime_v3. |
1473 | /// @param param_name - type of profile information required. |
1474 | /// @param param_value_size - the size in bytes of memory pointed to by param_value. If this is not sufficient then |
1475 | /// param_value_size_ret will be set to the required size and xnn_status_out_of_memory will be |
1476 | /// returned. |
1477 | /// @param param_value - a pointer to memory location where appropriate values for a given param_value will be written. |
1478 | /// @param param_value_size_ret - returns number of bytes required to write the result if param_value_size is not |
1479 | /// sufficient. |
1480 | enum xnn_status xnn_get_runtime_profiling_info(xnn_runtime_t runtime, |
1481 | enum xnn_profile_info param_name, |
1482 | size_t param_value_size, |
1483 | void* param_value, |
1484 | size_t* param_value_size_ret); |
1485 | |
1486 | /// Create a Runtime object from a subgraph. |
1487 | /// |
1488 | /// @param subgraph - a Subgraph object with all Values and Nodes that would be handled by the runtime. No Values or |
1489 | /// Nodes can be added to the runtime once it is constructed. |
1490 | /// @param weights_cache - a cache for packed weights. The runtime will look up and reuse packed weights in this cache, |
1491 | /// this will reduce memory allocated for packed weights. |
1492 | /// @param workspace - a workspace to hold internal tensors. The runtime will allocate space used for internal tensors |
1493 | /// and track them using workspace. Workspace can be shared and reused across different runtimes. If |
1494 | /// workspace is NULL, there will be no sharing: each runtime has its own workspace. |
1495 | /// @param threadpool - the thread pool to be used for parallelisation of computations in the runtime. If the thread |
1496 | /// pool is NULL, the computation would run on the caller thread without parallelization. |
1497 | /// @param flags - binary features of the runtime. The only currently supported values are |
1498 | /// XNN_FLAG_HINT_SPARSE_INFERENCE, XNN_FLAG_HINT_FP16_INFERENCE, XNN_FLAG_FORCE_FP16_INFERENCE, and |
1499 | /// XNN_FLAG_YIELD_WORKERS. If XNN_FLAG_YIELD_WORKERS is specified, worker threads would be yielded to |
1500 | /// the system scheduler after processing the last operator in the Runtime. |
1501 | /// @param runtime_out - pointer to the variable that will be initialized with a handle to the Runtime object upon |
1502 | /// successful return. Once constructed, the Runtime object is independent of the Subgraph object |
1503 | /// used to create it. |
1504 | enum xnn_status xnn_create_runtime_v4( |
1505 | xnn_subgraph_t subgraph, |
1506 | xnn_weights_cache_t weights_cache, |
1507 | xnn_workspace_t workspace, |
1508 | pthreadpool_t threadpool, |
1509 | uint32_t flags, |
1510 | xnn_runtime_t* runtime_out); |
1511 | |
1512 | enum xnn_status xnn_create_runtime_v3( |
1513 | xnn_subgraph_t subgraph, |
1514 | xnn_weights_cache_t weights_cache, |
1515 | pthreadpool_t threadpool, |
1516 | uint32_t flags, |
1517 | xnn_runtime_t* runtime_out); |
1518 | |
1519 | enum xnn_status xnn_create_runtime_v2( |
1520 | xnn_subgraph_t subgraph, |
1521 | pthreadpool_t threadpool, |
1522 | uint32_t flags, |
1523 | xnn_runtime_t* runtime_out); |
1524 | |
1525 | enum xnn_status xnn_create_runtime( |
1526 | xnn_subgraph_t subgraph, |
1527 | xnn_runtime_t* runtime_out); |
1528 | |
1529 | struct xnn_external_value { |
1530 | uint32_t id; |
1531 | void* data; |
1532 | }; |
1533 | |
1534 | /// Setup data pointers for external inputs and outputs in a Runtime object. |
1535 | /// |
1536 | /// @param runtime - a Runtime object created with @ref xnn_create_runtime or @ref xnn_create_runtime_v2. |
1537 | /// @param num_external_values - the number of external inputs and outputs specified in this call. This number must |
1538 | /// match the number of external inputs and outputs in the runtime, i.e. all external |
1539 | /// inputs and outputs in the runtime must be specified in one call. |
1540 | /// @param external_values - array with location information for all external inputs and outputs in the runtime. |
1541 | enum xnn_status xnn_setup_runtime( |
1542 | xnn_runtime_t runtime, |
1543 | size_t num_external_values, |
1544 | const struct xnn_external_value* external_values); |
1545 | |
1546 | /// Execute forward pass for all operators in the runtime. |
1547 | /// |
1548 | /// @param runtime - the Runtime object with the execution plan to invoke. |
1549 | enum xnn_status xnn_invoke_runtime( |
1550 | xnn_runtime_t runtime); |
1551 | |
1552 | /// Destroy a Runtime object, as well as operators and memory associated with it. |
1553 | /// |
1554 | /// @param runtime - the Runtime object to destroy. |
1555 | enum xnn_status xnn_delete_runtime( |
1556 | xnn_runtime_t runtime); |
1557 | |
1558 | typedef struct xnn_operator* xnn_operator_t; |
1559 | |
1560 | enum xnn_status xnn_run_operator( |
1561 | xnn_operator_t op, |
1562 | pthreadpool_t threadpool); |
1563 | |
1564 | enum xnn_status xnn_delete_operator( |
1565 | xnn_operator_t op); |
1566 | |
1567 | #ifndef XNN_NO_F32_OPERATORS |
1568 | |
1569 | enum xnn_status xnn_create_abs_nc_f32( |
1570 | size_t channels, |
1571 | size_t input_stride, |
1572 | size_t output_stride, |
1573 | uint32_t flags, |
1574 | xnn_operator_t* abs_op_out); |
1575 | |
1576 | enum xnn_status xnn_setup_abs_nc_f32( |
1577 | xnn_operator_t abs_op, |
1578 | size_t batch_size, |
1579 | const float* input, |
1580 | float* output, |
1581 | pthreadpool_t threadpool); |
1582 | |
1583 | enum xnn_status xnn_run_abs_nc_f32( |
1584 | size_t channels, |
1585 | size_t input_stride, |
1586 | size_t output_stride, |
1587 | size_t batch_size, |
1588 | const float* input, |
1589 | float* output, |
1590 | uint32_t flags, |
1591 | pthreadpool_t threadpool); |
1592 | |
1593 | enum xnn_status xnn_create_add_nd_f32( |
1594 | float output_min, |
1595 | float output_max, |
1596 | uint32_t flags, |
1597 | xnn_operator_t* add_op_out); |
1598 | |
1599 | enum xnn_status xnn_setup_add_nd_f32( |
1600 | xnn_operator_t add_op, |
1601 | size_t num_input1_dims, |
1602 | const size_t* input1_shape, |
1603 | size_t num_input2_dims, |
1604 | const size_t* input2_shape, |
1605 | const float* input1, |
1606 | const float* input2, |
1607 | float* output, |
1608 | pthreadpool_t threadpool); |
1609 | |
1610 | enum xnn_status xnn_run_add_nd_f32( |
1611 | size_t num_input1_dims, |
1612 | const size_t* input1_shape, |
1613 | size_t num_input2_dims, |
1614 | const size_t* input2_shape, |
1615 | const float* input1, |
1616 | const float* input2, |
1617 | float* output, |
1618 | float output_min, |
1619 | float output_max, |
1620 | uint32_t flags, |
1621 | pthreadpool_t threadpool); |
1622 | |
1623 | enum xnn_status xnn_create_argmax_pooling2d_nhwc_f32( |
1624 | uint32_t input_padding_top, |
1625 | uint32_t input_padding_right, |
1626 | uint32_t input_padding_bottom, |
1627 | uint32_t input_padding_left, |
1628 | uint32_t pooling_height, |
1629 | uint32_t pooling_width, |
1630 | size_t channels, |
1631 | size_t input_pixel_stride, |
1632 | size_t output_pixel_stride, |
1633 | uint32_t flags, |
1634 | xnn_operator_t* argmax_pooling_op_out); |
1635 | |
1636 | enum xnn_status xnn_setup_argmax_pooling2d_nhwc_f32( |
1637 | xnn_operator_t argmax_pooling_op, |
1638 | size_t batch_size, |
1639 | size_t input_height, |
1640 | size_t input_width, |
1641 | const float* input, |
1642 | float* output, |
1643 | uint32_t* index, |
1644 | pthreadpool_t threadpool); |
1645 | |
1646 | enum xnn_status xnn_create_average_pooling2d_nhwc_f32( |
1647 | uint32_t input_padding_top, |
1648 | uint32_t input_padding_right, |
1649 | uint32_t input_padding_bottom, |
1650 | uint32_t input_padding_left, |
1651 | uint32_t pooling_height, |
1652 | uint32_t pooling_width, |
1653 | uint32_t stride_height, |
1654 | uint32_t stride_width, |
1655 | size_t channels, |
1656 | size_t input_pixel_stride, |
1657 | size_t output_pixel_stride, |
1658 | float output_min, |
1659 | float output_max, |
1660 | uint32_t flags, |
1661 | xnn_operator_t* average_pooling_op_out); |
1662 | |
1663 | enum xnn_status xnn_setup_average_pooling2d_nhwc_f32( |
1664 | xnn_operator_t average_pooling_op, |
1665 | size_t batch_size, |
1666 | size_t input_height, |
1667 | size_t input_width, |
1668 | const float* input, |
1669 | float* output, |
1670 | pthreadpool_t threadpool); |
1671 | |
1672 | enum xnn_status xnn_create_bankers_rounding_nc_f32( |
1673 | size_t channels, |
1674 | size_t input_stride, |
1675 | size_t output_stride, |
1676 | uint32_t flags, |
1677 | xnn_operator_t* rounding_op_out); |
1678 | |
1679 | enum xnn_status xnn_setup_bankers_rounding_nc_f32( |
1680 | xnn_operator_t rounding_op, |
1681 | size_t batch_size, |
1682 | const float* input, |
1683 | float* output, |
1684 | pthreadpool_t threadpool); |
1685 | |
1686 | enum xnn_status xnn_run_bankers_rounding_nc_f32( |
1687 | size_t channels, |
1688 | size_t input_stride, |
1689 | size_t output_stride, |
1690 | size_t batch_size, |
1691 | const float* input, |
1692 | float* output, |
1693 | uint32_t flags, |
1694 | pthreadpool_t threadpool); |
1695 | |
1696 | enum xnn_status xnn_create_ceiling_nc_f32( |
1697 | size_t channels, |
1698 | size_t input_stride, |
1699 | size_t output_stride, |
1700 | uint32_t flags, |
1701 | xnn_operator_t* ceiling_op_out); |
1702 | |
1703 | enum xnn_status xnn_run_ceiling_nc_f32( |
1704 | size_t channels, |
1705 | size_t input_stride, |
1706 | size_t output_stride, |
1707 | size_t batch_size, |
1708 | const float* input, |
1709 | float* output, |
1710 | uint32_t flags, |
1711 | pthreadpool_t threadpool); |
1712 | |
1713 | enum xnn_status xnn_setup_ceiling_nc_f32( |
1714 | xnn_operator_t ceiling_op, |
1715 | size_t batch_size, |
1716 | const float* input, |
1717 | float* output, |
1718 | pthreadpool_t threadpool); |
1719 | |
1720 | enum xnn_status xnn_create_clamp_nc_f32( |
1721 | size_t channels, |
1722 | size_t input_stride, |
1723 | size_t output_stride, |
1724 | float output_min, |
1725 | float output_max, |
1726 | uint32_t flags, |
1727 | xnn_operator_t* clamp_op_out); |
1728 | |
1729 | enum xnn_status xnn_setup_clamp_nc_f32( |
1730 | xnn_operator_t clamp_op, |
1731 | size_t batch_size, |
1732 | const float* input, |
1733 | float* output, |
1734 | pthreadpool_t threadpool); |
1735 | |
1736 | enum xnn_status xnn_run_clamp_nc_f32( |
1737 | size_t channels, |
1738 | size_t input_stride, |
1739 | size_t output_stride, |
1740 | size_t batch_size, |
1741 | const float* input, |
1742 | float* output, |
1743 | float output_min, |
1744 | float output_max, |
1745 | uint32_t flags, |
1746 | pthreadpool_t threadpool); |
1747 | |
1748 | typedef const struct xnn_caches* xnn_caches_t; |
1749 | |
1750 | enum xnn_status xnn_create_convolution2d_nhwc_f32( |
1751 | uint32_t input_padding_top, |
1752 | uint32_t input_padding_right, |
1753 | uint32_t input_padding_bottom, |
1754 | uint32_t input_padding_left, |
1755 | uint32_t kernel_height, |
1756 | uint32_t kernel_width, |
1757 | uint32_t subsampling_height, |
1758 | uint32_t subsampling_width, |
1759 | uint32_t dilation_height, |
1760 | uint32_t dilation_width, |
1761 | uint32_t groups, |
1762 | size_t group_input_channels, |
1763 | size_t group_output_channels, |
1764 | size_t input_channel_stride, |
1765 | size_t output_channel_stride, |
1766 | const float* kernel, |
1767 | const float* bias, |
1768 | float output_min, |
1769 | float output_max, |
1770 | uint32_t flags, |
1771 | xnn_caches_t caches, |
1772 | xnn_operator_t* convolution_op_out); |
1773 | |
1774 | // Forward declare. |
1775 | struct xnn_post_operation; |
1776 | |
1777 | /// Create a convolution operator with a number of post operations. The |
1778 | /// convolution operator created using this function does not have output_min |
1779 | /// and output_max. The list of operators in post_operations will be applied in |
1780 | /// order. Convolution with post operations is only supported on JIT platforms |
1781 | /// and when JIT is enabled. |
1782 | enum xnn_status xnn_create_fused_convolution2d_nhwc_f32( |
1783 | uint32_t input_padding_top, |
1784 | uint32_t input_padding_right, |
1785 | uint32_t input_padding_bottom, |
1786 | uint32_t input_padding_left, |
1787 | uint32_t kernel_height, |
1788 | uint32_t kernel_width, |
1789 | uint32_t subsampling_height, |
1790 | uint32_t subsampling_width, |
1791 | uint32_t dilation_height, |
1792 | uint32_t dilation_width, |
1793 | uint32_t groups, |
1794 | size_t group_input_channels, |
1795 | size_t group_output_channels, |
1796 | size_t input_channel_stride, |
1797 | size_t output_channel_stride, |
1798 | const float* kernel, |
1799 | const float* bias, |
1800 | size_t num_post_operations, |
1801 | struct xnn_post_operation* post_operations, |
1802 | uint32_t flags, |
1803 | xnn_caches_t caches, |
1804 | xnn_operator_t* convolution_op_out); |
1805 | |
1806 | enum xnn_status xnn_setup_convolution2d_nhwc_f32( |
1807 | xnn_operator_t convolution_op, |
1808 | size_t batch_size, |
1809 | size_t input_height, |
1810 | size_t input_width, |
1811 | const float* input, |
1812 | float* output, |
1813 | pthreadpool_t threadpool); |
1814 | |
1815 | enum xnn_status xnn_create_deconvolution2d_nhwc_f32( |
1816 | uint32_t output_padding_top, |
1817 | uint32_t output_padding_right, |
1818 | uint32_t output_padding_bottom, |
1819 | uint32_t output_padding_left, |
1820 | uint32_t kernel_height, |
1821 | uint32_t kernel_width, |
1822 | uint32_t stride_height, |
1823 | uint32_t stride_width, |
1824 | uint32_t dilation_height, |
1825 | uint32_t dilation_width, |
1826 | uint32_t groups, |
1827 | size_t group_input_channels, |
1828 | size_t group_output_channels, |
1829 | size_t input_pixel_stride, |
1830 | size_t output_pixel_stride, |
1831 | const float* kernel, |
1832 | const float* bias, |
1833 | float output_min, |
1834 | float output_max, |
1835 | uint32_t flags, |
1836 | xnn_caches_t caches, |
1837 | xnn_operator_t* deconvolution_op_out); |
1838 | |
1839 | enum xnn_status xnn_setup_deconvolution2d_nhwc_f32( |
1840 | xnn_operator_t deconvolution_op, |
1841 | size_t batch_size, |
1842 | size_t input_height, |
1843 | size_t input_width, |
1844 | uint32_t adjustment_height, |
1845 | uint32_t adjustment_width, |
1846 | const float* input, |
1847 | float* output, |
1848 | pthreadpool_t threadpool); |
1849 | |
1850 | enum xnn_status xnn_create_divide_nd_f32( |
1851 | float output_min, |
1852 | float output_max, |
1853 | uint32_t flags, |
1854 | xnn_operator_t* divide_op_out); |
1855 | |
1856 | enum xnn_status xnn_setup_divide_nd_f32( |
1857 | xnn_operator_t divide_op, |
1858 | size_t num_input1_dims, |
1859 | const size_t* input1_shape, |
1860 | size_t num_input2_dims, |
1861 | const size_t* input2_shape, |
1862 | const float* input1, |
1863 | const float* input2, |
1864 | float* output, |
1865 | pthreadpool_t threadpool); |
1866 | |
1867 | enum xnn_status xnn_run_divide_nd_f32( |
1868 | size_t num_input1_dims, |
1869 | const size_t* input1_shape, |
1870 | size_t num_input2_dims, |
1871 | const size_t* input2_shape, |
1872 | const float* input1, |
1873 | const float* input2, |
1874 | float* output, |
1875 | float output_min, |
1876 | float output_max, |
1877 | uint32_t flags, |
1878 | pthreadpool_t threadpool); |
1879 | |
1880 | enum xnn_status xnn_create_elu_nc_f32( |
1881 | size_t channels, |
1882 | size_t input_stride, |
1883 | size_t output_stride, |
1884 | float alpha, |
1885 | uint32_t flags, |
1886 | xnn_operator_t* elu_op_out); |
1887 | |
1888 | enum xnn_status xnn_setup_elu_nc_f32( |
1889 | xnn_operator_t elu_op, |
1890 | size_t batch_size, |
1891 | const float* input, |
1892 | float* output, |
1893 | pthreadpool_t threadpool); |
1894 | |
1895 | enum xnn_status xnn_run_elu_nc_f32( |
1896 | size_t channels, |
1897 | size_t input_stride, |
1898 | size_t output_stride, |
1899 | size_t batch_size, |
1900 | const float* input, |
1901 | float* output, |
1902 | float alpha, |
1903 | uint32_t flags, |
1904 | pthreadpool_t threadpool); |
1905 | |
1906 | enum xnn_status xnn_create_floor_nc_f32( |
1907 | size_t channels, |
1908 | size_t input_stride, |
1909 | size_t output_stride, |
1910 | uint32_t flags, |
1911 | xnn_operator_t* floor_op_out); |
1912 | |
1913 | enum xnn_status xnn_setup_floor_nc_f32( |
1914 | xnn_operator_t floor_op, |
1915 | size_t batch_size, |
1916 | const float* input, |
1917 | float* output, |
1918 | pthreadpool_t threadpool); |
1919 | |
1920 | enum xnn_status xnn_run_floor_nc_f32( |
1921 | size_t channels, |
1922 | size_t input_stride, |
1923 | size_t output_stride, |
1924 | size_t batch_size, |
1925 | const float* input, |
1926 | float* output, |
1927 | uint32_t flags, |
1928 | pthreadpool_t threadpool); |
1929 | |
1930 | enum xnn_status xnn_create_fully_connected_nc_f32( |
1931 | size_t input_channels, |
1932 | size_t output_channels, |
1933 | size_t input_stride, |
1934 | size_t output_stride, |
1935 | const float* kernel, |
1936 | const float* bias, |
1937 | float output_min, |
1938 | float output_max, |
1939 | uint32_t flags, |
1940 | const xnn_caches_t caches, |
1941 | xnn_operator_t* fully_connected_op_out); |
1942 | |
1943 | enum xnn_status xnn_setup_fully_connected_nc_f32( |
1944 | xnn_operator_t fully_connected_op, |
1945 | size_t batch_size, |
1946 | const float* input, |
1947 | float* output, |
1948 | pthreadpool_t threadpool); |
1949 | |
1950 | enum xnn_status xnn_create_global_average_pooling_nwc_f32( |
1951 | size_t channels, |
1952 | size_t input_stride, |
1953 | size_t output_stride, |
1954 | float output_min, |
1955 | float output_max, |
1956 | uint32_t flags, |
1957 | xnn_operator_t* global_average_pooling_op_out); |
1958 | |
1959 | enum xnn_status xnn_setup_global_average_pooling_nwc_f32( |
1960 | xnn_operator_t global_average_pooling_op, |
1961 | size_t batch_size, |
1962 | size_t width, |
1963 | const float* input, |
1964 | float* output, |
1965 | pthreadpool_t threadpool); |
1966 | |
1967 | enum xnn_status xnn_create_hardswish_nc_f32( |
1968 | size_t channels, |
1969 | size_t input_stride, |
1970 | size_t output_stride, |
1971 | uint32_t flags, |
1972 | xnn_operator_t* hardswish_op_out); |
1973 | |
1974 | enum xnn_status xnn_setup_hardswish_nc_f32( |
1975 | xnn_operator_t hardswish_op, |
1976 | size_t batch_size, |
1977 | const float* input, |
1978 | float* output, |
1979 | pthreadpool_t threadpool); |
1980 | |
1981 | enum xnn_status xnn_run_hardswish_nc_f32( |
1982 | size_t channels, |
1983 | size_t input_stride, |
1984 | size_t output_stride, |
1985 | size_t batch_size, |
1986 | const float* input, |
1987 | float* output, |
1988 | uint32_t flags, |
1989 | pthreadpool_t threadpool); |
1990 | |
1991 | enum xnn_status xnn_create_leaky_relu_nc_f32( |
1992 | size_t channels, |
1993 | size_t input_stride, |
1994 | size_t output_stride, |
1995 | float negative_slope, |
1996 | uint32_t flags, |
1997 | xnn_operator_t* leaky_relu_op_out); |
1998 | |
1999 | enum xnn_status xnn_setup_leaky_relu_nc_f32( |
2000 | xnn_operator_t leaky_relu_op, |
2001 | size_t batch_size, |
2002 | const float* input, |
2003 | float* output, |
2004 | pthreadpool_t threadpool); |
2005 | |
2006 | enum xnn_status xnn_run_leaky_relu_nc_f32( |
2007 | size_t channels, |
2008 | size_t input_stride, |
2009 | size_t output_stride, |
2010 | size_t batch_size, |
2011 | const float* input, |
2012 | float* output, |
2013 | float negative_slope, |
2014 | uint32_t flags, |
2015 | pthreadpool_t threadpool); |
2016 | |
2017 | enum xnn_status xnn_create_max_pooling2d_nhwc_f32( |
2018 | uint32_t input_padding_top, |
2019 | uint32_t input_padding_right, |
2020 | uint32_t input_padding_bottom, |
2021 | uint32_t input_padding_left, |
2022 | uint32_t pooling_height, |
2023 | uint32_t pooling_width, |
2024 | uint32_t stride_height, |
2025 | uint32_t stride_width, |
2026 | uint32_t dilation_height, |
2027 | uint32_t dilation_width, |
2028 | size_t channels, |
2029 | size_t input_pixel_stride, |
2030 | size_t output_pixel_stride, |
2031 | float output_min, |
2032 | float output_max, |
2033 | uint32_t flags, |
2034 | xnn_operator_t* max_pooling_op_out); |
2035 | |
2036 | enum xnn_status xnn_setup_max_pooling2d_nhwc_f32( |
2037 | xnn_operator_t max_pooling_op, |
2038 | size_t batch_size, |
2039 | size_t input_height, |
2040 | size_t input_width, |
2041 | const float* input, |
2042 | float* output, |
2043 | pthreadpool_t threadpool); |
2044 | |
2045 | enum xnn_status xnn_create_maximum_nd_f32( |
2046 | uint32_t flags, |
2047 | xnn_operator_t* maximum_op_out); |
2048 | |
2049 | enum xnn_status xnn_setup_maximum_nd_f32( |
2050 | xnn_operator_t maximum_op, |
2051 | size_t num_input1_dims, |
2052 | const size_t* input1_shape, |
2053 | size_t num_input2_dims, |
2054 | const size_t* input2_shape, |
2055 | const float* input1, |
2056 | const float* input2, |
2057 | float* output, |
2058 | pthreadpool_t threadpool); |
2059 | |
2060 | enum xnn_status xnn_run_maximum_nd_f32( |
2061 | size_t num_input1_dims, |
2062 | const size_t* input1_shape, |
2063 | size_t num_input2_dims, |
2064 | const size_t* input2_shape, |
2065 | const float* input1, |
2066 | const float* input2, |
2067 | float* output, |
2068 | float output_min, |
2069 | float output_max, |
2070 | uint32_t flags, |
2071 | pthreadpool_t threadpool); |
2072 | |
2073 | enum xnn_status xnn_create_minimum_nd_f32( |
2074 | uint32_t flags, |
2075 | xnn_operator_t* minimum_op_out); |
2076 | |
2077 | enum xnn_status xnn_setup_minimum_nd_f32( |
2078 | xnn_operator_t minimum_op, |
2079 | size_t num_input1_dims, |
2080 | const size_t* input1_shape, |
2081 | size_t num_input2_dims, |
2082 | const size_t* input2_shape, |
2083 | const float* input1, |
2084 | const float* input2, |
2085 | float* output, |
2086 | pthreadpool_t threadpool); |
2087 | |
2088 | enum xnn_status xnn_run_minimum_nd_f32( |
2089 | size_t num_input1_dims, |
2090 | const size_t* input1_shape, |
2091 | size_t num_input2_dims, |
2092 | const size_t* input2_shape, |
2093 | const float* input1, |
2094 | const float* input2, |
2095 | float* output, |
2096 | float output_min, |
2097 | float output_max, |
2098 | uint32_t flags, |
2099 | pthreadpool_t threadpool); |
2100 | |
2101 | enum xnn_status xnn_create_multiply_nd_f32( |
2102 | float output_min, |
2103 | float output_max, |
2104 | uint32_t flags, |
2105 | xnn_operator_t* multiply_op_out); |
2106 | |
2107 | enum xnn_status xnn_setup_multiply_nd_f32( |
2108 | xnn_operator_t multiply_op, |
2109 | size_t num_input1_dims, |
2110 | const size_t* input1_shape, |
2111 | size_t num_input2_dims, |
2112 | const size_t* input2_shape, |
2113 | const float* input1, |
2114 | const float* input2, |
2115 | float* output, |
2116 | pthreadpool_t threadpool); |
2117 | |
2118 | enum xnn_status xnn_run_multiply_nd_f32( |
2119 | size_t num_input1_dims, |
2120 | const size_t* input1_shape, |
2121 | size_t num_input2_dims, |
2122 | const size_t* input2_shape, |
2123 | const float* input1, |
2124 | const float* input2, |
2125 | float* output, |
2126 | float output_min, |
2127 | float output_max, |
2128 | uint32_t flags, |
2129 | pthreadpool_t threadpool); |
2130 | |
2131 | enum xnn_status xnn_create_negate_nc_f32( |
2132 | size_t channels, |
2133 | size_t input_stride, |
2134 | size_t output_stride, |
2135 | uint32_t flags, |
2136 | xnn_operator_t* negate_op_out); |
2137 | |
2138 | enum xnn_status xnn_setup_negate_nc_f32( |
2139 | xnn_operator_t negate_op, |
2140 | size_t batch_size, |
2141 | const float* input, |
2142 | float* output, |
2143 | pthreadpool_t threadpool); |
2144 | |
2145 | enum xnn_status xnn_run_negate_nc_f32( |
2146 | size_t channels, |
2147 | size_t input_stride, |
2148 | size_t output_stride, |
2149 | size_t batch_size, |
2150 | const float* input, |
2151 | float* output, |
2152 | uint32_t flags, |
2153 | pthreadpool_t threadpool); |
2154 | |
2155 | enum xnn_status xnn_create_prelu_nc_f32( |
2156 | size_t channels, |
2157 | size_t input_stride, |
2158 | size_t output_stride, |
2159 | const float* negative_slope, |
2160 | uint32_t flags, |
2161 | xnn_caches_t caches, |
2162 | xnn_operator_t* prelu_op_out); |
2163 | |
2164 | enum xnn_status xnn_setup_prelu_nc_f32( |
2165 | xnn_operator_t prelu_op, |
2166 | size_t batch_size, |
2167 | const float* input, |
2168 | float* output, |
2169 | pthreadpool_t threadpool); |
2170 | |
2171 | enum xnn_status xnn_create_resize_bilinear2d_nhwc_f32( |
2172 | size_t channels, |
2173 | size_t input_pixel_stride, |
2174 | size_t output_pixel_stride, |
2175 | uint32_t flags, |
2176 | xnn_operator_t* resize_op_out); |
2177 | |
2178 | enum xnn_status xnn_setup_resize_bilinear2d_nhwc_f32( |
2179 | xnn_operator_t resize_op, |
2180 | size_t batch_size, |
2181 | size_t input_height, |
2182 | size_t input_width, |
2183 | size_t output_height, |
2184 | size_t output_width, |
2185 | const float* input, |
2186 | float* output, |
2187 | pthreadpool_t threadpool); |
2188 | |
2189 | enum xnn_status xnn_create_sigmoid_nc_f32( |
2190 | size_t channels, |
2191 | size_t input_stride, |
2192 | size_t output_stride, |
2193 | uint32_t flags, |
2194 | xnn_operator_t* sigmoid_op_out); |
2195 | |
2196 | enum xnn_status xnn_setup_sigmoid_nc_f32( |
2197 | xnn_operator_t sigmoid_op, |
2198 | size_t batch_size, |
2199 | const float* input, |
2200 | float* output, |
2201 | pthreadpool_t threadpool); |
2202 | |
2203 | enum xnn_status xnn_run_sigmoid_nc_f32( |
2204 | size_t channels, |
2205 | size_t input_stride, |
2206 | size_t output_stride, |
2207 | size_t batch_size, |
2208 | const float* input, |
2209 | float* output, |
2210 | uint32_t flags, |
2211 | pthreadpool_t threadpool); |
2212 | |
2213 | enum xnn_status xnn_create_softmax_nc_f32( |
2214 | size_t channels, |
2215 | size_t input_stride, |
2216 | size_t output_stride, |
2217 | uint32_t flags, |
2218 | xnn_operator_t* softmax_op_out); |
2219 | |
2220 | enum xnn_status xnn_setup_softmax_nc_f32( |
2221 | xnn_operator_t softmax_op, |
2222 | size_t batch_size, |
2223 | const float* input, |
2224 | float* output, |
2225 | pthreadpool_t threadpool); |
2226 | |
2227 | enum xnn_status xnn_create_square_nc_f32( |
2228 | size_t channels, |
2229 | size_t input_stride, |
2230 | size_t output_stride, |
2231 | uint32_t flags, |
2232 | xnn_operator_t* square_op_out); |
2233 | |
2234 | enum xnn_status xnn_setup_square_nc_f32( |
2235 | xnn_operator_t square_op, |
2236 | size_t batch_size, |
2237 | const float* input, |
2238 | float* output, |
2239 | pthreadpool_t threadpool); |
2240 | |
2241 | enum xnn_status xnn_run_square_nc_f32( |
2242 | size_t channels, |
2243 | size_t input_stride, |
2244 | size_t output_stride, |
2245 | size_t batch_size, |
2246 | const float* input, |
2247 | float* output, |
2248 | uint32_t flags, |
2249 | pthreadpool_t threadpool); |
2250 | |
2251 | enum xnn_status xnn_create_square_root_nc_f32( |
2252 | size_t channels, |
2253 | size_t input_stride, |
2254 | size_t output_stride, |
2255 | uint32_t flags, |
2256 | xnn_operator_t* sqrt_op_out); |
2257 | |
2258 | enum xnn_status xnn_setup_square_root_nc_f32( |
2259 | xnn_operator_t sqrt_op, |
2260 | size_t batch_size, |
2261 | const float* input, |
2262 | float* output, |
2263 | pthreadpool_t threadpool); |
2264 | |
2265 | enum xnn_status xnn_run_square_root_nc_f32( |
2266 | size_t channels, |
2267 | size_t input_stride, |
2268 | size_t output_stride, |
2269 | size_t batch_size, |
2270 | const float* input, |
2271 | float* output, |
2272 | uint32_t flags, |
2273 | pthreadpool_t threadpool); |
2274 | |
2275 | enum xnn_status xnn_create_squared_difference_nd_f32( |
2276 | uint32_t flags, |
2277 | xnn_operator_t* squared_difference_op_out); |
2278 | |
2279 | enum xnn_status xnn_setup_squared_difference_nd_f32( |
2280 | xnn_operator_t squared_difference_op, |
2281 | size_t num_input1_dims, |
2282 | const size_t* input1_shape, |
2283 | size_t num_input2_dims, |
2284 | const size_t* input2_shape, |
2285 | const float* input1, |
2286 | const float* input2, |
2287 | float* output, |
2288 | pthreadpool_t threadpool); |
2289 | |
2290 | enum xnn_status xnn_run_squared_difference_nd_f32( |
2291 | size_t num_input1_dims, |
2292 | const size_t* input1_shape, |
2293 | size_t num_input2_dims, |
2294 | const size_t* input2_shape, |
2295 | const float* input1, |
2296 | const float* input2, |
2297 | float* output, |
2298 | float output_min, |
2299 | float output_max, |
2300 | uint32_t flags, |
2301 | pthreadpool_t threadpool); |
2302 | |
2303 | enum xnn_status xnn_create_subtract_nd_f32( |
2304 | float output_min, |
2305 | float output_max, |
2306 | uint32_t flags, |
2307 | xnn_operator_t* subtract_op_out); |
2308 | |
2309 | enum xnn_status xnn_setup_subtract_nd_f32( |
2310 | xnn_operator_t subtract_op, |
2311 | size_t num_input1_dims, |
2312 | const size_t* input1_shape, |
2313 | size_t num_input2_dims, |
2314 | const size_t* input2_shape, |
2315 | const float* input1, |
2316 | const float* input2, |
2317 | float* output, |
2318 | pthreadpool_t threadpool); |
2319 | |
2320 | enum xnn_status xnn_run_subtract_nd_f32( |
2321 | size_t num_input1_dims, |
2322 | const size_t* input1_shape, |
2323 | size_t num_input2_dims, |
2324 | const size_t* input2_shape, |
2325 | const float* input1, |
2326 | const float* input2, |
2327 | float* output, |
2328 | float output_min, |
2329 | float output_max, |
2330 | uint32_t flags, |
2331 | pthreadpool_t threadpool); |
2332 | |
2333 | enum xnn_status xnn_create_truncation_nc_f32( |
2334 | size_t channels, |
2335 | size_t input_stride, |
2336 | size_t output_stride, |
2337 | uint32_t flags, |
2338 | xnn_operator_t* truncation_op_out); |
2339 | |
2340 | enum xnn_status xnn_setup_truncation_nc_f32( |
2341 | xnn_operator_t truncation_op, |
2342 | size_t batch_size, |
2343 | const float* input, |
2344 | float* output, |
2345 | pthreadpool_t threadpool); |
2346 | |
2347 | enum xnn_status xnn_run_truncation_nc_f32( |
2348 | size_t channels, |
2349 | size_t input_stride, |
2350 | size_t output_stride, |
2351 | size_t batch_size, |
2352 | const float* input, |
2353 | float* output, |
2354 | uint32_t flags, |
2355 | pthreadpool_t threadpool); |
2356 | |
2357 | #ifndef XNN_NO_NCHW_OPERATORS |
2358 | |
2359 | enum xnn_status xnn_create_depth_to_space_nchw2nhwc_x32( |
2360 | size_t output_channels, |
2361 | size_t input_channel_stride, |
2362 | size_t output_channel_stride, |
2363 | uint32_t block_size, |
2364 | uint32_t flags, |
2365 | xnn_operator_t* depth_to_space_op_out); |
2366 | |
2367 | enum xnn_status xnn_setup_depth_to_space_nchw2nhwc_x32( |
2368 | xnn_operator_t depth_to_space_op, |
2369 | size_t batch_size, |
2370 | size_t input_height, |
2371 | size_t input_width, |
2372 | const void* input, |
2373 | void* output, |
2374 | pthreadpool_t threadpool); |
2375 | |
2376 | enum xnn_status xnn_create_convolution2d_nchw_f32( |
2377 | uint32_t input_padding_top, |
2378 | uint32_t input_padding_right, |
2379 | uint32_t input_padding_bottom, |
2380 | uint32_t input_padding_left, |
2381 | uint32_t kernel_height, |
2382 | uint32_t kernel_width, |
2383 | uint32_t subsampling_height, |
2384 | uint32_t subsampling_width, |
2385 | uint32_t dilation_height, |
2386 | uint32_t dilation_width, |
2387 | uint32_t groups, |
2388 | size_t group_input_channels, |
2389 | size_t group_output_channels, |
2390 | size_t input_channel_stride, |
2391 | size_t output_channel_stride, |
2392 | const float* kernel, |
2393 | const float* bias, |
2394 | float output_min, |
2395 | float output_max, |
2396 | uint32_t flags, |
2397 | xnn_caches_t caches, |
2398 | xnn_operator_t* convolution_op_out); |
2399 | |
2400 | enum xnn_status xnn_setup_convolution2d_nchw_f32( |
2401 | xnn_operator_t convolution_op, |
2402 | size_t batch_size, |
2403 | size_t input_height, |
2404 | size_t input_width, |
2405 | const float* input, |
2406 | float* output, |
2407 | pthreadpool_t threadpool); |
2408 | |
2409 | enum xnn_status xnn_create_global_average_pooling_ncw_f32( |
2410 | size_t channels, |
2411 | float output_min, |
2412 | float output_max, |
2413 | uint32_t flags, |
2414 | xnn_operator_t* global_average_pooling_op_out); |
2415 | |
2416 | enum xnn_status xnn_setup_global_average_pooling_ncw_f32( |
2417 | xnn_operator_t global_average_pooling_op, |
2418 | size_t batch_size, |
2419 | size_t width, |
2420 | const float* input, |
2421 | float* output, |
2422 | pthreadpool_t threadpool); |
2423 | |
2424 | enum xnn_status xnn_create_resize_bilinear2d_nchw_f32( |
2425 | size_t channels, |
2426 | size_t input_pixel_stride, |
2427 | size_t output_pixel_stride, |
2428 | uint32_t flags, |
2429 | xnn_operator_t* resize_op_out); |
2430 | |
2431 | enum xnn_status xnn_setup_resize_bilinear2d_nchw_f32( |
2432 | xnn_operator_t resize_op, |
2433 | size_t batch_size, |
2434 | size_t input_height, |
2435 | size_t input_width, |
2436 | size_t output_height, |
2437 | size_t output_width, |
2438 | const float* input, |
2439 | float* output, |
2440 | pthreadpool_t threadpool); |
2441 | |
2442 | #endif // XNN_NO_NCHW_OPERATORS |
2443 | |
2444 | #endif // XNN_NO_F32_OPERATORS |
2445 | |
2446 | #ifndef XNN_NO_X32_OPERATORS |
2447 | |
2448 | enum xnn_status xnn_create_channel_shuffle_nc_x32( |
2449 | size_t groups, |
2450 | size_t group_channels, |
2451 | size_t input_stride, |
2452 | size_t output_stride, |
2453 | uint32_t flags, |
2454 | xnn_operator_t* channel_shuffle_op_out); |
2455 | |
2456 | enum xnn_status xnn_setup_channel_shuffle_nc_x32( |
2457 | xnn_operator_t channel_shuffle_op, |
2458 | size_t batch_size, |
2459 | const void* input, |
2460 | void* output, |
2461 | pthreadpool_t threadpool); |
2462 | |
2463 | enum xnn_status xnn_create_constant_pad_nd_x32( |
2464 | const void* padding_value, |
2465 | uint32_t flags, |
2466 | xnn_operator_t* constant_pad_op_out); |
2467 | |
2468 | enum xnn_status xnn_setup_constant_pad_nd_x32( |
2469 | xnn_operator_t constant_pad_op, |
2470 | size_t num_dims, |
2471 | const size_t* input_shape, |
2472 | const size_t* pre_padding, |
2473 | const size_t* post_padding, |
2474 | const void* input, |
2475 | void* output, |
2476 | pthreadpool_t threadpool); |
2477 | |
2478 | enum xnn_status xnn_run_constant_pad_nd_x32( |
2479 | uint32_t flags, |
2480 | size_t num_dims, |
2481 | const size_t* input_shape, |
2482 | const size_t* pre_paddings, |
2483 | const size_t* post_paddings, |
2484 | const void* input, |
2485 | void* output, |
2486 | const void* padding_value, |
2487 | pthreadpool_t threadpool); |
2488 | |
2489 | enum xnn_status xnn_create_copy_nc_x32( |
2490 | size_t channels, |
2491 | size_t input_stride, |
2492 | size_t output_stride, |
2493 | uint32_t flags, |
2494 | xnn_operator_t* copy_op_out); |
2495 | |
2496 | enum xnn_status xnn_setup_copy_nc_x32( |
2497 | xnn_operator_t copy_op, |
2498 | size_t batch_size, |
2499 | const void* input, |
2500 | void* output, |
2501 | pthreadpool_t threadpool); |
2502 | |
2503 | enum xnn_status xnn_run_copy_nc_x32( |
2504 | size_t channels, |
2505 | size_t input_stride, |
2506 | size_t output_stride, |
2507 | size_t batch_size, |
2508 | const uint32_t* input, |
2509 | uint32_t* output, |
2510 | uint32_t flags, |
2511 | pthreadpool_t threadpool); |
2512 | |
2513 | enum xnn_status xnn_create_depth_to_space_nhwc_x32( |
2514 | size_t output_channels, |
2515 | size_t input_channel_stride, |
2516 | size_t output_channel_stride, |
2517 | uint32_t block_size, |
2518 | uint32_t flags, |
2519 | xnn_operator_t* depth_to_space_op_out); |
2520 | |
2521 | enum xnn_status xnn_setup_depth_to_space_nhwc_x32( |
2522 | xnn_operator_t depth_to_space_op, |
2523 | size_t batch_size, |
2524 | size_t input_height, |
2525 | size_t input_width, |
2526 | const void* input, |
2527 | void* output, |
2528 | pthreadpool_t threadpool); |
2529 | |
2530 | enum xnn_status xnn_create_slice_nd_x32( |
2531 | uint32_t flags, |
2532 | xnn_operator_t* slice_op_out); |
2533 | |
2534 | enum xnn_status xnn_setup_slice_nd_x32( |
2535 | xnn_operator_t slice_op, |
2536 | size_t num_dims, |
2537 | const size_t* input_shape, |
2538 | const size_t* offsets, |
2539 | const size_t* sizes, |
2540 | const void* input, |
2541 | void* output, |
2542 | pthreadpool_t threadpool); |
2543 | |
2544 | enum xnn_status xnn_run_slice_nd_x32( |
2545 | size_t num_dims, |
2546 | const size_t* input_shape, |
2547 | const size_t* offsets, |
2548 | const size_t* sizes, |
2549 | const void* input, |
2550 | void* output, |
2551 | uint32_t flags, |
2552 | pthreadpool_t threadpool); |
2553 | |
2554 | enum xnn_status xnn_create_space_to_depth_nhwc_x32( |
2555 | size_t input_channels, |
2556 | size_t input_channel_stride, |
2557 | size_t output_channel_stride, |
2558 | uint32_t block_size, |
2559 | uint32_t flags, |
2560 | xnn_operator_t* space_to_depth_op_out); |
2561 | |
2562 | enum xnn_status xnn_setup_space_to_depth_nhwc_x32( |
2563 | xnn_operator_t space_to_depth_op, |
2564 | size_t batch_size, |
2565 | size_t input_height, |
2566 | size_t input_width, |
2567 | const void* input, |
2568 | void* output, |
2569 | pthreadpool_t threadpool); |
2570 | |
2571 | enum xnn_status xnn_create_transpose_nd_x32( |
2572 | uint32_t flags, |
2573 | xnn_operator_t* transpose_op_out); |
2574 | |
2575 | enum xnn_status xnn_setup_transpose_nd_x32( |
2576 | xnn_operator_t transpose_op, |
2577 | const void* input, |
2578 | void* output, |
2579 | const size_t num_dims, |
2580 | const size_t* input_shape, |
2581 | const size_t* output_perm, |
2582 | pthreadpool_t threadpool); |
2583 | |
2584 | enum xnn_status xnn_run_transpose_nd_x32( |
2585 | const void* input, |
2586 | void* output, |
2587 | const size_t num_dims, |
2588 | const size_t* input_shape, |
2589 | const size_t* output_perm, |
2590 | uint32_t flags, |
2591 | pthreadpool_t threadpool); |
2592 | |
2593 | enum xnn_status xnn_create_unpooling2d_nhwc_x32( |
2594 | uint32_t input_padding_top, |
2595 | uint32_t input_padding_right, |
2596 | uint32_t input_padding_bottom, |
2597 | uint32_t input_padding_left, |
2598 | uint32_t pooling_height, |
2599 | uint32_t pooling_width, |
2600 | size_t channels, |
2601 | size_t input_pixel_stride, |
2602 | size_t output_pixel_stride, |
2603 | uint32_t flags, |
2604 | xnn_operator_t* unpooling_op_out); |
2605 | |
2606 | enum xnn_status xnn_setup_unpooling2d_nhwc_x32( |
2607 | xnn_operator_t unpooling_op, |
2608 | size_t batch_size, |
2609 | size_t input_height, |
2610 | size_t input_width, |
2611 | const void* input, |
2612 | const uint32_t* index, |
2613 | void* output, |
2614 | pthreadpool_t threadpool); |
2615 | |
2616 | #endif // XNN_NO_X32_OPERATORS |
2617 | |
2618 | #ifndef XNN_NO_F16_OPERATORS |
2619 | |
2620 | enum xnn_status xnn_create_abs_nc_f16( |
2621 | size_t channels, |
2622 | size_t input_stride, |
2623 | size_t output_stride, |
2624 | uint32_t flags, |
2625 | xnn_operator_t* abs_op_out); |
2626 | |
2627 | enum xnn_status xnn_setup_abs_nc_f16( |
2628 | xnn_operator_t abs_op, |
2629 | size_t batch_size, |
2630 | const void* input, |
2631 | void* output, |
2632 | pthreadpool_t threadpool); |
2633 | |
2634 | enum xnn_status xnn_create_add_nd_f16( |
2635 | float output_min, |
2636 | float output_max, |
2637 | uint32_t flags, |
2638 | xnn_operator_t* add_op_out); |
2639 | |
2640 | enum xnn_status xnn_setup_add_nd_f16( |
2641 | xnn_operator_t add_op, |
2642 | size_t num_input1_dims, |
2643 | const size_t* input1_shape, |
2644 | size_t num_input2_dims, |
2645 | const size_t* input2_shape, |
2646 | const void* input1, |
2647 | const void* input2, |
2648 | void* output, |
2649 | pthreadpool_t threadpool); |
2650 | |
2651 | enum xnn_status xnn_create_average_pooling2d_nhwc_f16( |
2652 | uint32_t input_padding_top, |
2653 | uint32_t input_padding_right, |
2654 | uint32_t input_padding_bottom, |
2655 | uint32_t input_padding_left, |
2656 | uint32_t pooling_height, |
2657 | uint32_t pooling_width, |
2658 | uint32_t stride_height, |
2659 | uint32_t stride_width, |
2660 | size_t channels, |
2661 | size_t input_pixel_stride, |
2662 | size_t output_pixel_stride, |
2663 | float output_min, |
2664 | float output_max, |
2665 | uint32_t flags, |
2666 | xnn_operator_t* average_pooling_op_out); |
2667 | |
2668 | enum xnn_status xnn_setup_average_pooling2d_nhwc_f16( |
2669 | xnn_operator_t average_pooling_op, |
2670 | size_t batch_size, |
2671 | size_t input_height, |
2672 | size_t input_width, |
2673 | const void* input, |
2674 | void* output, |
2675 | pthreadpool_t threadpool); |
2676 | |
2677 | enum xnn_status xnn_create_bankers_rounding_nc_f16( |
2678 | size_t channels, |
2679 | size_t input_stride, |
2680 | size_t output_stride, |
2681 | uint32_t flags, |
2682 | xnn_operator_t* rounding_op_out); |
2683 | |
2684 | enum xnn_status xnn_setup_bankers_rounding_nc_f16( |
2685 | xnn_operator_t rounding_op, |
2686 | size_t batch_size, |
2687 | const void* input, |
2688 | void* output, |
2689 | pthreadpool_t threadpool); |
2690 | |
2691 | enum xnn_status xnn_create_ceiling_nc_f16( |
2692 | size_t channels, |
2693 | size_t input_stride, |
2694 | size_t output_stride, |
2695 | uint32_t flags, |
2696 | xnn_operator_t* ceiling_op_out); |
2697 | |
2698 | enum xnn_status xnn_setup_ceiling_nc_f16( |
2699 | xnn_operator_t ceiling_op, |
2700 | size_t batch_size, |
2701 | const void* input, |
2702 | void* output, |
2703 | pthreadpool_t threadpool); |
2704 | |
2705 | enum xnn_status xnn_create_clamp_nc_f16( |
2706 | size_t channels, |
2707 | size_t input_stride, |
2708 | size_t output_stride, |
2709 | float output_min, |
2710 | float output_max, |
2711 | uint32_t flags, |
2712 | xnn_operator_t* clamp_op_out); |
2713 | |
2714 | enum xnn_status xnn_setup_clamp_nc_f16( |
2715 | xnn_operator_t clamp_op, |
2716 | size_t batch_size, |
2717 | const void* input, |
2718 | void* output, |
2719 | pthreadpool_t threadpool); |
2720 | |
2721 | enum xnn_status xnn_create_convolution2d_nhwc_f16( |
2722 | uint32_t input_padding_top, |
2723 | uint32_t input_padding_right, |
2724 | uint32_t input_padding_bottom, |
2725 | uint32_t input_padding_left, |
2726 | uint32_t kernel_height, |
2727 | uint32_t kernel_width, |
2728 | uint32_t subsampling_height, |
2729 | uint32_t subsampling_width, |
2730 | uint32_t dilation_height, |
2731 | uint32_t dilation_width, |
2732 | uint32_t groups, |
2733 | size_t group_input_channels, |
2734 | size_t group_output_channels, |
2735 | size_t input_channel_stride, |
2736 | size_t output_channel_stride, |
2737 | const void* kernel, |
2738 | const void* bias, |
2739 | float output_min, |
2740 | float output_max, |
2741 | uint32_t flags, |
2742 | xnn_caches_t caches, |
2743 | xnn_operator_t* convolution_op_out); |
2744 | |
2745 | enum xnn_status xnn_setup_convolution2d_nhwc_f16( |
2746 | xnn_operator_t convolution_op, |
2747 | size_t batch_size, |
2748 | size_t input_height, |
2749 | size_t input_width, |
2750 | const void* input, |
2751 | void* output, |
2752 | pthreadpool_t threadpool); |
2753 | |
2754 | enum xnn_status xnn_create_deconvolution2d_nhwc_f16( |
2755 | uint32_t output_padding_top, |
2756 | uint32_t output_padding_right, |
2757 | uint32_t output_padding_bottom, |
2758 | uint32_t output_padding_left, |
2759 | uint32_t kernel_height, |
2760 | uint32_t kernel_width, |
2761 | uint32_t stride_height, |
2762 | uint32_t stride_width, |
2763 | uint32_t dilation_height, |
2764 | uint32_t dilation_width, |
2765 | uint32_t groups, |
2766 | size_t group_input_channels, |
2767 | size_t group_output_channels, |
2768 | size_t input_pixel_stride, |
2769 | size_t output_pixel_stride, |
2770 | const void* kernel, |
2771 | const void* bias, |
2772 | float output_min, |
2773 | float output_max, |
2774 | uint32_t flags, |
2775 | xnn_caches_t caches, |
2776 | xnn_operator_t* deconvolution_op_out); |
2777 | |
2778 | enum xnn_status xnn_setup_deconvolution2d_nhwc_f16( |
2779 | xnn_operator_t deconvolution_op, |
2780 | size_t batch_size, |
2781 | size_t input_height, |
2782 | size_t input_width, |
2783 | uint32_t adjustment_height, |
2784 | uint32_t adjustment_width, |
2785 | const void* input, |
2786 | void* output, |
2787 | pthreadpool_t threadpool); |
2788 | |
2789 | enum xnn_status xnn_create_divide_nd_f16( |
2790 | float output_min, |
2791 | float output_max, |
2792 | uint32_t flags, |
2793 | xnn_operator_t* divide_op_out); |
2794 | |
2795 | enum xnn_status xnn_setup_divide_nd_f16( |
2796 | xnn_operator_t divide_op, |
2797 | size_t num_input1_dims, |
2798 | const size_t* input1_shape, |
2799 | size_t num_input2_dims, |
2800 | const size_t* input2_shape, |
2801 | const void* input1, |
2802 | const void* input2, |
2803 | void* output, |
2804 | pthreadpool_t threadpool); |
2805 | |
2806 | enum xnn_status xnn_create_elu_nc_f16( |
2807 | size_t channels, |
2808 | size_t input_stride, |
2809 | size_t output_stride, |
2810 | float alpha, |
2811 | uint32_t flags, |
2812 | xnn_operator_t* elu_op_out); |
2813 | |
2814 | enum xnn_status xnn_setup_elu_nc_f16( |
2815 | xnn_operator_t elu_op, |
2816 | size_t batch_size, |
2817 | const void* input, |
2818 | void* output, |
2819 | pthreadpool_t threadpool); |
2820 | |
2821 | enum xnn_status xnn_create_floor_nc_f16( |
2822 | size_t channels, |
2823 | size_t input_stride, |
2824 | size_t output_stride, |
2825 | uint32_t flags, |
2826 | xnn_operator_t* floor_op_out); |
2827 | |
2828 | enum xnn_status xnn_setup_floor_nc_f16( |
2829 | xnn_operator_t floor_op, |
2830 | size_t batch_size, |
2831 | const void* input, |
2832 | void* output, |
2833 | pthreadpool_t threadpool); |
2834 | |
2835 | enum xnn_status xnn_create_fully_connected_nc_f16( |
2836 | size_t input_channels, |
2837 | size_t output_channels, |
2838 | size_t input_stride, |
2839 | size_t output_stride, |
2840 | const void* kernel, |
2841 | const void* bias, |
2842 | float output_min, |
2843 | float output_max, |
2844 | uint32_t flags, |
2845 | xnn_caches_t caches, |
2846 | xnn_operator_t* fully_connected_op_out); |
2847 | |
2848 | enum xnn_status xnn_setup_fully_connected_nc_f16( |
2849 | xnn_operator_t fully_connected_op, |
2850 | size_t batch_size, |
2851 | const void* input, |
2852 | void* output, |
2853 | pthreadpool_t threadpool); |
2854 | |
2855 | enum xnn_status xnn_create_global_average_pooling_nwc_f16( |
2856 | size_t channels, |
2857 | size_t input_stride, |
2858 | size_t output_stride, |
2859 | float output_min, |
2860 | float output_max, |
2861 | uint32_t flags, |
2862 | xnn_operator_t* global_average_pooling_op_out); |
2863 | |
2864 | enum xnn_status xnn_setup_global_average_pooling_nwc_f16( |
2865 | xnn_operator_t global_average_pooling_op, |
2866 | size_t batch_size, |
2867 | size_t width, |
2868 | const void* input, |
2869 | void* output, |
2870 | pthreadpool_t threadpool); |
2871 | |
2872 | enum xnn_status xnn_create_hardswish_nc_f16( |
2873 | size_t channels, |
2874 | size_t input_stride, |
2875 | size_t output_stride, |
2876 | uint32_t flags, |
2877 | xnn_operator_t* hardswish_op_out); |
2878 | |
2879 | enum xnn_status xnn_setup_hardswish_nc_f16( |
2880 | xnn_operator_t hardswish_op, |
2881 | size_t batch_size, |
2882 | const void* input, |
2883 | void* output, |
2884 | pthreadpool_t threadpool); |
2885 | |
2886 | enum xnn_status xnn_create_leaky_relu_nc_f16( |
2887 | size_t channels, |
2888 | size_t input_stride, |
2889 | size_t output_stride, |
2890 | float negative_slope, |
2891 | uint32_t flags, |
2892 | xnn_operator_t* leaky_relu_op_out); |
2893 | |
2894 | enum xnn_status xnn_setup_leaky_relu_nc_f16( |
2895 | xnn_operator_t leaky_relu_op, |
2896 | size_t batch_size, |
2897 | const void* input, |
2898 | void* output, |
2899 | pthreadpool_t threadpool); |
2900 | |
2901 | enum xnn_status xnn_create_max_pooling2d_nhwc_f16( |
2902 | uint32_t input_padding_top, |
2903 | uint32_t input_padding_right, |
2904 | uint32_t input_padding_bottom, |
2905 | uint32_t input_padding_left, |
2906 | uint32_t pooling_height, |
2907 | uint32_t pooling_width, |
2908 | uint32_t stride_height, |
2909 | uint32_t stride_width, |
2910 | uint32_t dilation_height, |
2911 | uint32_t dilation_width, |
2912 | size_t channels, |
2913 | size_t input_pixel_stride, |
2914 | size_t output_pixel_stride, |
2915 | float output_min, |
2916 | float output_max, |
2917 | uint32_t flags, |
2918 | xnn_operator_t* max_pooling_op_out); |
2919 | |
2920 | enum xnn_status xnn_setup_max_pooling2d_nhwc_f16( |
2921 | xnn_operator_t max_pooling_op, |
2922 | size_t batch_size, |
2923 | size_t input_height, |
2924 | size_t input_width, |
2925 | const void* input, |
2926 | void* output, |
2927 | pthreadpool_t threadpool); |
2928 | |
2929 | enum xnn_status xnn_create_maximum_nd_f16( |
2930 | uint32_t flags, |
2931 | xnn_operator_t* maximum_op_out); |
2932 | |
2933 | enum xnn_status xnn_setup_maximum_nd_f16( |
2934 | xnn_operator_t maximum_op, |
2935 | size_t num_input1_dims, |
2936 | const size_t* input1_shape, |
2937 | size_t num_input2_dims, |
2938 | const size_t* input2_shape, |
2939 | const void* input1, |
2940 | const void* input2, |
2941 | void* output, |
2942 | pthreadpool_t threadpool); |
2943 | |
2944 | enum xnn_status xnn_create_minimum_nd_f16( |
2945 | uint32_t flags, |
2946 | xnn_operator_t* minimum_op_out); |
2947 | |
2948 | enum xnn_status xnn_setup_minimum_nd_f16( |
2949 | xnn_operator_t minimum_op, |
2950 | size_t num_input1_dims, |
2951 | const size_t* input1_shape, |
2952 | size_t num_input2_dims, |
2953 | const size_t* input2_shape, |
2954 | const void* input1, |
2955 | const void* input2, |
2956 | void* output, |
2957 | pthreadpool_t threadpool); |
2958 | |
2959 | enum xnn_status xnn_create_multiply_nd_f16( |
2960 | float output_min, |
2961 | float output_max, |
2962 | uint32_t flags, |
2963 | xnn_operator_t* multiply_op_out); |
2964 | |
2965 | enum xnn_status xnn_setup_multiply_nd_f16( |
2966 | xnn_operator_t multiply_op, |
2967 | size_t num_input1_dims, |
2968 | const size_t* input1_shape, |
2969 | size_t num_input2_dims, |
2970 | const size_t* input2_shape, |
2971 | const void* input1, |
2972 | const void* input2, |
2973 | void* output, |
2974 | pthreadpool_t threadpool); |
2975 | |
2976 | enum xnn_status xnn_create_negate_nc_f16( |
2977 | size_t channels, |
2978 | size_t input_stride, |
2979 | size_t output_stride, |
2980 | uint32_t flags, |
2981 | xnn_operator_t* negate_op_out); |
2982 | |
2983 | enum xnn_status xnn_setup_negate_nc_f16( |
2984 | xnn_operator_t negate_op, |
2985 | size_t batch_size, |
2986 | const void* input, |
2987 | void* output, |
2988 | pthreadpool_t threadpool); |
2989 | |
2990 | enum xnn_status xnn_create_prelu_nc_f16( |
2991 | size_t channels, |
2992 | size_t input_stride, |
2993 | size_t output_stride, |
2994 | const void* negative_slope, |
2995 | uint32_t flags, |
2996 | xnn_caches_t caches, |
2997 | xnn_operator_t* prelu_op_out); |
2998 | |
2999 | enum xnn_status xnn_setup_prelu_nc_f16( |
3000 | xnn_operator_t prelu_op, |
3001 | size_t batch_size, |
3002 | const void* input, |
3003 | void* output, |
3004 | pthreadpool_t threadpool); |
3005 | |
3006 | enum xnn_status xnn_create_resize_bilinear2d_nhwc_f16( |
3007 | size_t channels, |
3008 | size_t input_pixel_stride, |
3009 | size_t output_pixel_stride, |
3010 | uint32_t flags, |
3011 | xnn_operator_t* resize_op_out); |
3012 | |
3013 | enum xnn_status xnn_setup_resize_bilinear2d_nhwc_f16( |
3014 | xnn_operator_t resize_op, |
3015 | size_t batch_size, |
3016 | size_t input_height, |
3017 | size_t input_width, |
3018 | size_t output_height, |
3019 | size_t output_width, |
3020 | const void* input, |
3021 | void* output, |
3022 | pthreadpool_t threadpool); |
3023 | |
3024 | enum xnn_status xnn_create_sigmoid_nc_f16( |
3025 | size_t channels, |
3026 | size_t input_stride, |
3027 | size_t output_stride, |
3028 | uint32_t flags, |
3029 | xnn_operator_t* sigmoid_op_out); |
3030 | |
3031 | enum xnn_status xnn_setup_sigmoid_nc_f16( |
3032 | xnn_operator_t sigmoid_op, |
3033 | size_t batch_size, |
3034 | const void* input, |
3035 | void* output, |
3036 | pthreadpool_t threadpool); |
3037 | |
3038 | enum xnn_status xnn_create_softmax_nc_f16( |
3039 | size_t channels, |
3040 | size_t input_stride, |
3041 | size_t output_stride, |
3042 | uint32_t flags, |
3043 | xnn_operator_t* softmax_op_out); |
3044 | |
3045 | enum xnn_status xnn_setup_softmax_nc_f16( |
3046 | xnn_operator_t softmax_op, |
3047 | size_t batch_size, |
3048 | const void* input, |
3049 | void* output, |
3050 | pthreadpool_t threadpool); |
3051 | |
3052 | enum xnn_status xnn_create_square_nc_f16( |
3053 | size_t channels, |
3054 | size_t input_stride, |
3055 | size_t output_stride, |
3056 | uint32_t flags, |
3057 | xnn_operator_t* square_op_out); |
3058 | |
3059 | enum xnn_status xnn_setup_square_nc_f16( |
3060 | xnn_operator_t square_op, |
3061 | size_t batch_size, |
3062 | const void* input, |
3063 | void* output, |
3064 | pthreadpool_t threadpool); |
3065 | |
3066 | enum xnn_status xnn_create_square_root_nc_f16( |
3067 | size_t channels, |
3068 | size_t input_stride, |
3069 | size_t output_stride, |
3070 | uint32_t flags, |
3071 | xnn_operator_t* sqrt_op_out); |
3072 | |
3073 | enum xnn_status xnn_setup_square_root_nc_f16( |
3074 | xnn_operator_t sqrt_op, |
3075 | size_t batch_size, |
3076 | const void* input, |
3077 | void* output, |
3078 | pthreadpool_t threadpool); |
3079 | |
3080 | enum xnn_status xnn_create_squared_difference_nd_f16( |
3081 | uint32_t flags, |
3082 | xnn_operator_t* squared_difference_op_out); |
3083 | |
3084 | enum xnn_status xnn_setup_squared_difference_nd_f16( |
3085 | xnn_operator_t squared_difference_op, |
3086 | size_t num_input1_dims, |
3087 | const size_t* input1_shape, |
3088 | size_t num_input2_dims, |
3089 | const size_t* input2_shape, |
3090 | const void* input1, |
3091 | const void* input2, |
3092 | void* output, |
3093 | pthreadpool_t threadpool); |
3094 | |
3095 | enum xnn_status xnn_create_subtract_nd_f16( |
3096 | float output_min, |
3097 | float output_max, |
3098 | uint32_t flags, |
3099 | xnn_operator_t* subtract_op_out); |
3100 | |
3101 | enum xnn_status xnn_setup_subtract_nd_f16( |
3102 | xnn_operator_t subtract_op, |
3103 | size_t num_input1_dims, |
3104 | const size_t* input1_shape, |
3105 | size_t num_input2_dims, |
3106 | const size_t* input2_shape, |
3107 | const void* input1, |
3108 | const void* input2, |
3109 | void* output, |
3110 | pthreadpool_t threadpool); |
3111 | |
3112 | enum xnn_status xnn_create_truncation_nc_f16( |
3113 | size_t channels, |
3114 | size_t input_stride, |
3115 | size_t output_stride, |
3116 | uint32_t flags, |
3117 | xnn_operator_t* truncation_op_out); |
3118 | |
3119 | enum xnn_status xnn_setup_truncation_nc_f16( |
3120 | xnn_operator_t truncation_op, |
3121 | size_t batch_size, |
3122 | const void* input, |
3123 | void* output, |
3124 | pthreadpool_t threadpool); |
3125 | |
3126 | #ifndef XNN_NO_NCHW_OPERATORS |
3127 | |
3128 | enum xnn_status xnn_create_convolution2d_nchw_f16( |
3129 | uint32_t input_padding_top, |
3130 | uint32_t input_padding_right, |
3131 | uint32_t input_padding_bottom, |
3132 | uint32_t input_padding_left, |
3133 | uint32_t kernel_height, |
3134 | uint32_t kernel_width, |
3135 | uint32_t subsampling_height, |
3136 | uint32_t subsampling_width, |
3137 | uint32_t dilation_height, |
3138 | uint32_t dilation_width, |
3139 | uint32_t groups, |
3140 | size_t group_input_channels, |
3141 | size_t group_output_channels, |
3142 | size_t input_channel_stride, |
3143 | size_t output_channel_stride, |
3144 | const void* kernel, |
3145 | const void* bias, |
3146 | float output_min, |
3147 | float output_max, |
3148 | uint32_t flags, |
3149 | xnn_caches_t caches, |
3150 | xnn_operator_t* convolution_op_out); |
3151 | |
3152 | enum xnn_status xnn_setup_convolution2d_nchw_f16( |
3153 | xnn_operator_t convolution_op, |
3154 | size_t batch_size, |
3155 | size_t input_height, |
3156 | size_t input_width, |
3157 | const void* input, |
3158 | void* output, |
3159 | pthreadpool_t threadpool); |
3160 | |
3161 | enum xnn_status xnn_create_depth_to_space_nchw2nhwc_x16( |
3162 | size_t output_channels, |
3163 | size_t input_channel_stride, |
3164 | size_t output_channel_stride, |
3165 | uint32_t block_size, |
3166 | uint32_t flags, |
3167 | xnn_operator_t* depth_to_space_op_out); |
3168 | |
3169 | enum xnn_status xnn_setup_depth_to_space_nchw2nhwc_x16( |
3170 | xnn_operator_t depth_to_space_op, |
3171 | size_t batch_size, |
3172 | size_t input_height, |
3173 | size_t input_width, |
3174 | const void* input, |
3175 | void* output, |
3176 | pthreadpool_t threadpool); |
3177 | |
3178 | enum xnn_status xnn_create_global_average_pooling_ncw_f16( |
3179 | size_t channels, |
3180 | float output_min, |
3181 | float output_max, |
3182 | uint32_t flags, |
3183 | xnn_operator_t* global_average_pooling_op_out); |
3184 | |
3185 | enum xnn_status xnn_setup_global_average_pooling_ncw_f16( |
3186 | xnn_operator_t global_average_pooling_op, |
3187 | size_t batch_size, |
3188 | size_t width, |
3189 | const void* input, |
3190 | void* output, |
3191 | pthreadpool_t threadpool); |
3192 | |
3193 | enum xnn_status xnn_create_resize_bilinear2d_nchw_f16( |
3194 | size_t channels, |
3195 | size_t input_pixel_stride, |
3196 | size_t output_pixel_stride, |
3197 | uint32_t flags, |
3198 | xnn_operator_t* resize_op_out); |
3199 | |
3200 | enum xnn_status xnn_setup_resize_bilinear2d_nchw_f16( |
3201 | xnn_operator_t resize_op, |
3202 | size_t batch_size, |
3203 | size_t input_height, |
3204 | size_t input_width, |
3205 | size_t output_height, |
3206 | size_t output_width, |
3207 | const void* input, |
3208 | void* output, |
3209 | pthreadpool_t threadpool); |
3210 | |
3211 | #endif // XNN_NO_NCHW_OPERATORS |
3212 | |
3213 | #endif // XNN_NO_F16_OPERATORS |
3214 | |
3215 | #ifndef XNN_NO_X16_OPERATORS |
3216 | |
3217 | enum xnn_status xnn_create_constant_pad_nd_x16( |
3218 | const void* padding_value, |
3219 | uint32_t flags, |
3220 | xnn_operator_t* constant_pad_op_out); |
3221 | |
3222 | enum xnn_status xnn_setup_constant_pad_nd_x16( |
3223 | xnn_operator_t constant_pad_op, |
3224 | size_t num_dims, |
3225 | const size_t* input_shape, |
3226 | const size_t* pre_padding, |
3227 | const size_t* post_padding, |
3228 | const void* input, |
3229 | void* output, |
3230 | pthreadpool_t threadpool); |
3231 | |
3232 | enum xnn_status xnn_run_constant_pad_nd_x16( |
3233 | uint32_t flags, |
3234 | size_t num_dims, |
3235 | const size_t* input_shape, |
3236 | const size_t* pre_paddings, |
3237 | const size_t* post_paddings, |
3238 | const void* input, |
3239 | void* output, |
3240 | const void* padding_value, |
3241 | pthreadpool_t threadpool); |
3242 | |
3243 | enum xnn_status xnn_create_copy_nc_x16( |
3244 | size_t channels, |
3245 | size_t input_stride, |
3246 | size_t output_stride, |
3247 | uint32_t flags, |
3248 | xnn_operator_t* copy_op_out); |
3249 | |
3250 | enum xnn_status xnn_setup_copy_nc_x16( |
3251 | xnn_operator_t copy_op, |
3252 | size_t batch_size, |
3253 | const void* input, |
3254 | void* output, |
3255 | pthreadpool_t threadpool); |
3256 | |
3257 | enum xnn_status xnn_create_depth_to_space_nhwc_x16( |
3258 | size_t output_channels, |
3259 | size_t input_channel_stride, |
3260 | size_t output_channel_stride, |
3261 | uint32_t block_size, |
3262 | uint32_t flags, |
3263 | xnn_operator_t* depth_to_space_op_out); |
3264 | |
3265 | enum xnn_status xnn_setup_depth_to_space_nhwc_x16( |
3266 | xnn_operator_t depth_to_space_op, |
3267 | size_t batch_size, |
3268 | size_t input_height, |
3269 | size_t input_width, |
3270 | const void* input, |
3271 | void* output, |
3272 | pthreadpool_t threadpool); |
3273 | |
3274 | enum xnn_status xnn_create_slice_nd_x16( |
3275 | uint32_t flags, |
3276 | xnn_operator_t* slice_op_out); |
3277 | |
3278 | enum xnn_status xnn_setup_slice_nd_x16( |
3279 | xnn_operator_t slice_op, |
3280 | size_t num_dims, |
3281 | const size_t* input_shape, |
3282 | const size_t* offsets, |
3283 | const size_t* sizes, |
3284 | const void* input, |
3285 | void* output, |
3286 | pthreadpool_t threadpool); |
3287 | |
3288 | enum xnn_status xnn_create_space_to_depth_nhwc_x16( |
3289 | size_t input_channels, |
3290 | size_t input_channel_stride, |
3291 | size_t output_channel_stride, |
3292 | uint32_t block_size, |
3293 | uint32_t flags, |
3294 | xnn_operator_t* space_to_depth_op_out); |
3295 | |
3296 | enum xnn_status xnn_setup_space_to_depth_nhwc_x16( |
3297 | xnn_operator_t space_to_depth_op, |
3298 | size_t batch_size, |
3299 | size_t input_height, |
3300 | size_t input_width, |
3301 | const void* input, |
3302 | void* output, |
3303 | pthreadpool_t threadpool); |
3304 | |
3305 | enum xnn_status xnn_create_transpose_nd_x16( |
3306 | uint32_t flags, |
3307 | xnn_operator_t* transpose_op_out); |
3308 | |
3309 | enum xnn_status xnn_setup_transpose_nd_x16( |
3310 | xnn_operator_t transpose_op, |
3311 | const void* input, |
3312 | void* output, |
3313 | const size_t num_dims, |
3314 | const size_t* input_shape, |
3315 | const size_t* output_perm, |
3316 | pthreadpool_t threadpool); |
3317 | |
3318 | enum xnn_status xnn_run_transpose_nd_x16( |
3319 | const void* input, |
3320 | void* output, |
3321 | const size_t num_dims, |
3322 | const size_t* input_shape, |
3323 | const size_t* output_perm, |
3324 | uint32_t flags, |
3325 | pthreadpool_t threadpool); |
3326 | |
3327 | #endif // XNN_NO_X16_OPERATORS |
3328 | |
3329 | #ifndef XNN_NO_QC8_OPERATORS |
3330 | |
3331 | enum xnn_status xnn_create_convolution2d_nhwc_qc8( |
3332 | uint32_t input_padding_top, |
3333 | uint32_t input_padding_right, |
3334 | uint32_t input_padding_bottom, |
3335 | uint32_t input_padding_left, |
3336 | uint32_t kernel_height, |
3337 | uint32_t kernel_width, |
3338 | uint32_t subsampling_height, |
3339 | uint32_t subsampling_width, |
3340 | uint32_t dilation_height, |
3341 | uint32_t dilation_width, |
3342 | uint32_t groups, |
3343 | size_t group_input_channels, |
3344 | size_t group_output_channels, |
3345 | size_t input_channel_stride, |
3346 | size_t output_channel_stride, |
3347 | int8_t input_zero_point, |
3348 | float input_scale, |
3349 | const float* kernel_scale, |
3350 | const int8_t* kernel, |
3351 | const int32_t* bias, |
3352 | int8_t output_zero_point, |
3353 | float output_scale, |
3354 | int8_t output_min, |
3355 | int8_t output_max, |
3356 | uint32_t flags, |
3357 | xnn_caches_t caches, |
3358 | xnn_operator_t* convolution_op_out); |
3359 | |
3360 | enum xnn_status xnn_setup_convolution2d_nhwc_qc8( |
3361 | xnn_operator_t convolution_op, |
3362 | size_t batch_size, |
3363 | size_t input_height, |
3364 | size_t input_width, |
3365 | const int8_t* input, |
3366 | int8_t* output, |
3367 | pthreadpool_t threadpool); |
3368 | |
3369 | #endif // XNN_NO_QC8_OPERATORS |
3370 | |
3371 | #ifndef XNN_NO_QS8_OPERATORS |
3372 | |
3373 | enum xnn_status xnn_create_add_nd_qs8( |
3374 | int8_t input1_zero_point, |
3375 | float input1_scale, |
3376 | int8_t input2_zero_point, |
3377 | float input2_scale, |
3378 | int8_t output_zero_point, |
3379 | float output_scale, |
3380 | int8_t output_min, |
3381 | int8_t output_max, |
3382 | uint32_t flags, |
3383 | xnn_operator_t* add_op_out); |
3384 | |
3385 | enum xnn_status xnn_setup_add_nd_qs8( |
3386 | xnn_operator_t add_op, |
3387 | size_t num_input1_dims, |
3388 | const size_t* input1_shape, |
3389 | size_t num_input2_dims, |
3390 | const size_t* input2_shape, |
3391 | const int8_t* input1, |
3392 | const int8_t* input2, |
3393 | int8_t* output, |
3394 | pthreadpool_t threadpool); |
3395 | |
3396 | enum xnn_status xnn_run_add_nd_qs8( |
3397 | size_t num_input1_dims, |
3398 | const size_t* input1_shape, |
3399 | int8_t input1_zero_point, |
3400 | float input1_scale, |
3401 | size_t num_input2_dims, |
3402 | const size_t* input2_shape, |
3403 | int8_t input2_zero_point, |
3404 | float input2_scale, |
3405 | const int8_t* input1, |
3406 | const int8_t* input2, |
3407 | int8_t* output, |
3408 | int8_t output_zero_point, |
3409 | float output_scale, |
3410 | int8_t output_min, |
3411 | int8_t output_max, |
3412 | uint32_t flags, |
3413 | pthreadpool_t threadpool); |
3414 | |
3415 | enum xnn_status xnn_create_convolution2d_nhwc_qs8( |
3416 | uint32_t input_padding_top, |
3417 | uint32_t input_padding_right, |
3418 | uint32_t input_padding_bottom, |
3419 | uint32_t input_padding_left, |
3420 | uint32_t kernel_height, |
3421 | uint32_t kernel_width, |
3422 | uint32_t subsampling_height, |
3423 | uint32_t subsampling_width, |
3424 | uint32_t dilation_height, |
3425 | uint32_t dilation_width, |
3426 | uint32_t groups, |
3427 | size_t group_input_channels, |
3428 | size_t group_output_channels, |
3429 | size_t input_channel_stride, |
3430 | size_t output_channel_stride, |
3431 | int8_t input_zero_point, |
3432 | float input_scale, |
3433 | float kernel_scale, |
3434 | const int8_t* kernel, |
3435 | const int32_t* bias, |
3436 | int8_t output_zero_point, |
3437 | float output_scale, |
3438 | int8_t output_min, |
3439 | int8_t output_max, |
3440 | uint32_t flags, |
3441 | xnn_caches_t caches, |
3442 | xnn_operator_t* convolution_op_out); |
3443 | |
3444 | enum xnn_status xnn_setup_convolution2d_nhwc_qs8( |
3445 | xnn_operator_t convolution_op, |
3446 | size_t batch_size, |
3447 | size_t input_height, |
3448 | size_t input_width, |
3449 | const int8_t* input, |
3450 | int8_t* output, |
3451 | pthreadpool_t threadpool); |
3452 | |
3453 | enum xnn_status xnn_create_deconvolution2d_nhwc_qs8( |
3454 | uint32_t output_padding_top, |
3455 | uint32_t output_padding_right, |
3456 | uint32_t output_padding_bottom, |
3457 | uint32_t output_padding_left, |
3458 | uint32_t kernel_height, |
3459 | uint32_t kernel_width, |
3460 | uint32_t stride_height, |
3461 | uint32_t stride_width, |
3462 | uint32_t dilation_height, |
3463 | uint32_t dilation_width, |
3464 | uint32_t groups, |
3465 | size_t group_input_channels, |
3466 | size_t group_output_channels, |
3467 | size_t input_pixel_stride, |
3468 | size_t output_pixel_stride, |
3469 | int8_t input_zero_point, |
3470 | float input_scale, |
3471 | float kernel_scale, |
3472 | const int8_t* kernel, |
3473 | const int32_t* bias, |
3474 | int8_t output_zero_point, |
3475 | float output_scale, |
3476 | int8_t output_min, |
3477 | int8_t output_max, |
3478 | uint32_t flags, |
3479 | xnn_caches_t caches, |
3480 | xnn_operator_t* deconvolution_op_out); |
3481 | |
3482 | enum xnn_status xnn_setup_deconvolution2d_nhwc_qs8( |
3483 | xnn_operator_t deconvolution_op, |
3484 | size_t batch_size, |
3485 | size_t input_height, |
3486 | size_t input_width, |
3487 | uint32_t adjustment_height, |
3488 | uint32_t adjustment_width, |
3489 | const int8_t* input, |
3490 | int8_t* output, |
3491 | pthreadpool_t threadpool); |
3492 | |
3493 | enum xnn_status xnn_create_elu_nc_qs8( |
3494 | size_t channels, |
3495 | size_t input_stride, |
3496 | size_t output_stride, |
3497 | float alpha, |
3498 | int8_t input_zero_point, |
3499 | float input_scale, |
3500 | int8_t output_zero_point, |
3501 | float output_scale, |
3502 | int8_t output_min, |
3503 | int8_t output_max, |
3504 | uint32_t flags, |
3505 | xnn_operator_t* elu_op_out); |
3506 | |
3507 | enum xnn_status xnn_setup_elu_nc_qs8( |
3508 | xnn_operator_t elu_op, |
3509 | size_t batch_size, |
3510 | const int8_t* input, |
3511 | int8_t* output, |
3512 | pthreadpool_t threadpool); |
3513 | |
3514 | enum xnn_status xnn_create_fully_connected_nc_qs8( |
3515 | size_t input_channels, |
3516 | size_t output_channels, |
3517 | size_t input_stride, |
3518 | size_t output_stride, |
3519 | int8_t input_zero_point, |
3520 | float input_scale, |
3521 | float kernel_scale, |
3522 | const int8_t* kernel, |
3523 | const int32_t* bias, |
3524 | int8_t output_zero_point, |
3525 | float output_scale, |
3526 | int8_t output_min, |
3527 | int8_t output_max, |
3528 | uint32_t flags, |
3529 | xnn_caches_t caches, |
3530 | xnn_operator_t* fully_connected_op_out); |
3531 | |
3532 | enum xnn_status xnn_setup_fully_connected_nc_qs8( |
3533 | xnn_operator_t fully_connected_op, |
3534 | size_t batch_size, |
3535 | const int8_t* input, |
3536 | int8_t* output, |
3537 | pthreadpool_t threadpool); |
3538 | |
3539 | enum xnn_status xnn_create_global_average_pooling_nwc_qs8( |
3540 | size_t channels, |
3541 | size_t input_stride, |
3542 | size_t output_stride, |
3543 | int8_t input_zero_point, |
3544 | float input_scale, |
3545 | int8_t output_zero_point, |
3546 | float output_scale, |
3547 | int8_t output_min, |
3548 | int8_t output_max, |
3549 | uint32_t flags, |
3550 | xnn_operator_t* global_average_pooling_op_out); |
3551 | |
3552 | enum xnn_status xnn_setup_global_average_pooling_nwc_qs8( |
3553 | xnn_operator_t global_average_pooling_op, |
3554 | size_t batch_size, |
3555 | size_t width, |
3556 | const int8_t* input, |
3557 | int8_t* output, |
3558 | pthreadpool_t threadpool); |
3559 | |
3560 | enum xnn_status xnn_create_multiply_nd_qs8( |
3561 | int8_t input1_zero_point, |
3562 | float input1_scale, |
3563 | int8_t input2_zero_point, |
3564 | float input2_scale, |
3565 | int8_t output_zero_point, |
3566 | float output_scale, |
3567 | int8_t output_min, |
3568 | int8_t output_max, |
3569 | uint32_t flags, |
3570 | xnn_operator_t* multiply_op_out); |
3571 | |
3572 | enum xnn_status xnn_setup_multiply_nd_qs8( |
3573 | xnn_operator_t multiply_op, |
3574 | size_t num_input1_dims, |
3575 | const size_t* input1_shape, |
3576 | size_t num_input2_dims, |
3577 | const size_t* input2_shape, |
3578 | const int8_t* input1, |
3579 | const int8_t* input2, |
3580 | int8_t* output, |
3581 | pthreadpool_t threadpool); |
3582 | |
3583 | enum xnn_status xnn_run_multiply_nd_qs8( |
3584 | size_t num_input1_dims, |
3585 | const size_t* input1_shape, |
3586 | int8_t input1_zero_point, |
3587 | float input1_scale, |
3588 | size_t num_input2_dims, |
3589 | const size_t* input2_shape, |
3590 | int8_t input2_zero_point, |
3591 | float input2_scale, |
3592 | const int8_t* input1, |
3593 | const int8_t* input2, |
3594 | int8_t* output, |
3595 | int8_t output_zero_point, |
3596 | float output_scale, |
3597 | int8_t output_min, |
3598 | int8_t output_max, |
3599 | uint32_t flags, |
3600 | pthreadpool_t threadpool); |
3601 | |
3602 | enum xnn_status xnn_create_leaky_relu_nc_qs8( |
3603 | size_t channels, |
3604 | size_t input_stride, |
3605 | size_t output_stride, |
3606 | float negative_slope, |
3607 | int8_t input_zero_point, |
3608 | float input_scale, |
3609 | int8_t output_zero_point, |
3610 | float output_scale, |
3611 | uint32_t flags, |
3612 | xnn_operator_t* leaky_relu_op_out); |
3613 | |
3614 | enum xnn_status xnn_setup_leaky_relu_nc_qs8( |
3615 | xnn_operator_t leaky_relu_op, |
3616 | size_t batch_size, |
3617 | const int8_t* input, |
3618 | int8_t* output, |
3619 | pthreadpool_t threadpool); |
3620 | |
3621 | enum xnn_status xnn_create_sigmoid_nc_qs8( |
3622 | size_t channels, |
3623 | size_t input_stride, |
3624 | size_t output_stride, |
3625 | int8_t input_zero_point, |
3626 | float input_scale, |
3627 | int8_t output_zero_point, |
3628 | float output_scale, |
3629 | int8_t output_min, |
3630 | int8_t output_max, |
3631 | uint32_t flags, |
3632 | xnn_operator_t* sigmoid_op_out); |
3633 | |
3634 | enum xnn_status xnn_setup_sigmoid_nc_qs8( |
3635 | xnn_operator_t sigmoid_op, |
3636 | size_t batch_size, |
3637 | const int8_t* input, |
3638 | int8_t* output, |
3639 | pthreadpool_t threadpool); |
3640 | |
3641 | enum xnn_status xnn_create_subtract_nd_qs8( |
3642 | int8_t input1_zero_point, |
3643 | float input1_scale, |
3644 | int8_t input2_zero_point, |
3645 | float input2_scale, |
3646 | int8_t output_zero_point, |
3647 | float output_scale, |
3648 | int8_t output_min, |
3649 | int8_t output_max, |
3650 | uint32_t flags, |
3651 | xnn_operator_t* subtract_op_out); |
3652 | |
3653 | enum xnn_status xnn_setup_subtract_nd_qs8( |
3654 | xnn_operator_t subtract_op, |
3655 | size_t num_input1_dims, |
3656 | const size_t* input1_shape, |
3657 | size_t num_input2_dims, |
3658 | const size_t* input2_shape, |
3659 | const int8_t* input1, |
3660 | const int8_t* input2, |
3661 | int8_t* output, |
3662 | pthreadpool_t threadpool); |
3663 | |
3664 | enum xnn_status xnn_run_subtract_nd_qs8( |
3665 | size_t num_input1_dims, |
3666 | const size_t* input1_shape, |
3667 | int8_t input1_zero_point, |
3668 | float input1_scale, |
3669 | size_t num_input2_dims, |
3670 | const size_t* input2_shape, |
3671 | int8_t input2_zero_point, |
3672 | float input2_scale, |
3673 | const int8_t* input1, |
3674 | const int8_t* input2, |
3675 | int8_t* output, |
3676 | int8_t output_zero_point, |
3677 | float output_scale, |
3678 | int8_t output_min, |
3679 | int8_t output_max, |
3680 | uint32_t flags, |
3681 | pthreadpool_t threadpool); |
3682 | |
3683 | enum xnn_status xnn_create_tanh_nc_qs8( |
3684 | size_t channels, |
3685 | size_t input_stride, |
3686 | size_t output_stride, |
3687 | int8_t input_zero_point, |
3688 | float input_scale, |
3689 | int8_t output_zero_point, |
3690 | float output_scale, |
3691 | int8_t output_min, |
3692 | int8_t output_max, |
3693 | uint32_t flags, |
3694 | xnn_operator_t* tanh_op_out); |
3695 | |
3696 | enum xnn_status xnn_setup_tanh_nc_qs8( |
3697 | xnn_operator_t tanh_op, |
3698 | size_t batch_size, |
3699 | const int8_t* input, |
3700 | int8_t* output, |
3701 | pthreadpool_t threadpool); |
3702 | |
3703 | #endif // XNN_NO_QS8_OPERATORS |
3704 | |
3705 | #ifndef XNN_NO_QU8_OPERATORS |
3706 | |
3707 | enum xnn_status xnn_create_add_nd_qu8( |
3708 | uint8_t input1_zero_point, |
3709 | float input1_scale, |
3710 | uint8_t input2_zero_point, |
3711 | float input2_scale, |
3712 | uint8_t output_zero_point, |
3713 | float output_scale, |
3714 | uint8_t output_min, |
3715 | uint8_t output_max, |
3716 | uint32_t flags, |
3717 | xnn_operator_t* add_op_out); |
3718 | |
3719 | enum xnn_status xnn_setup_add_nd_qu8( |
3720 | xnn_operator_t add_op, |
3721 | size_t num_input1_dims, |
3722 | const size_t* input1_shape, |
3723 | size_t num_input2_dims, |
3724 | const size_t* input2_shape, |
3725 | const uint8_t* input1, |
3726 | const uint8_t* input2, |
3727 | uint8_t* output, |
3728 | pthreadpool_t threadpool); |
3729 | |
3730 | enum xnn_status xnn_run_add_nd_qu8( |
3731 | size_t num_input1_dims, |
3732 | const size_t* input1_shape, |
3733 | uint8_t input1_zero_point, |
3734 | float input1_scale, |
3735 | size_t num_input2_dims, |
3736 | const size_t* input2_shape, |
3737 | uint8_t input2_zero_point, |
3738 | float input2_scale, |
3739 | const uint8_t* input1, |
3740 | const uint8_t* input2, |
3741 | uint8_t* output, |
3742 | uint8_t output_zero_point, |
3743 | float output_scale, |
3744 | uint8_t output_min, |
3745 | uint8_t output_max, |
3746 | uint32_t flags, |
3747 | pthreadpool_t threadpool); |
3748 | |
3749 | enum xnn_status xnn_create_average_pooling2d_nhwc_qu8( |
3750 | uint32_t input_padding_top, |
3751 | uint32_t input_padding_right, |
3752 | uint32_t input_padding_bottom, |
3753 | uint32_t input_padding_left, |
3754 | uint32_t pooling_height, |
3755 | uint32_t pooling_width, |
3756 | uint32_t stride_height, |
3757 | uint32_t stride_width, |
3758 | size_t channels, |
3759 | size_t input_pixel_stride, |
3760 | size_t output_pixel_stride, |
3761 | uint8_t input_zero_point, |
3762 | float input_scale, |
3763 | uint8_t output_zero_point, |
3764 | float output_scale, |
3765 | uint8_t output_min, |
3766 | uint8_t output_max, |
3767 | uint32_t flags, |
3768 | xnn_operator_t* average_pooling_op_out); |
3769 | |
3770 | enum xnn_status xnn_setup_average_pooling2d_nhwc_qu8( |
3771 | xnn_operator_t average_pooling_op, |
3772 | size_t batch_size, |
3773 | size_t input_height, |
3774 | size_t input_width, |
3775 | const uint8_t* input, |
3776 | uint8_t* output, |
3777 | pthreadpool_t threadpool); |
3778 | |
3779 | enum xnn_status xnn_create_convolution2d_nhwc_qu8( |
3780 | uint32_t input_padding_top, |
3781 | uint32_t input_padding_right, |
3782 | uint32_t input_padding_bottom, |
3783 | uint32_t input_padding_left, |
3784 | uint32_t kernel_height, |
3785 | uint32_t kernel_width, |
3786 | uint32_t subsampling_height, |
3787 | uint32_t subsampling_width, |
3788 | uint32_t dilation_height, |
3789 | uint32_t dilation_width, |
3790 | uint32_t groups, |
3791 | size_t group_input_channels, |
3792 | size_t group_output_channels, |
3793 | size_t input_channel_stride, |
3794 | size_t output_channel_stride, |
3795 | uint8_t input_zero_point, |
3796 | float input_scale, |
3797 | uint8_t kernel_zero_point, |
3798 | float kernel_scale, |
3799 | const uint8_t* kernel, |
3800 | const int32_t* bias, |
3801 | uint8_t output_zero_point, |
3802 | float output_scale, |
3803 | uint8_t output_min, |
3804 | uint8_t output_max, |
3805 | uint32_t flags, |
3806 | xnn_caches_t caches, |
3807 | xnn_operator_t* convolution_op_out); |
3808 | |
3809 | enum xnn_status xnn_setup_convolution2d_nhwc_qu8( |
3810 | xnn_operator_t convolution_op, |
3811 | size_t batch_size, |
3812 | size_t input_height, |
3813 | size_t input_width, |
3814 | const uint8_t* input, |
3815 | uint8_t* output, |
3816 | pthreadpool_t threadpool); |
3817 | |
3818 | enum xnn_status xnn_create_deconvolution2d_nhwc_qu8( |
3819 | uint32_t output_padding_top, |
3820 | uint32_t output_padding_right, |
3821 | uint32_t output_padding_bottom, |
3822 | uint32_t output_padding_left, |
3823 | uint32_t kernel_height, |
3824 | uint32_t kernel_width, |
3825 | uint32_t stride_height, |
3826 | uint32_t stride_width, |
3827 | uint32_t dilation_height, |
3828 | uint32_t dilation_width, |
3829 | uint32_t groups, |
3830 | size_t group_input_channels, |
3831 | size_t group_output_channels, |
3832 | size_t input_pixel_stride, |
3833 | size_t output_pixel_stride, |
3834 | uint8_t input_zero_point, |
3835 | float input_scale, |
3836 | uint8_t kernel_zero_point, |
3837 | float kernel_scale, |
3838 | const uint8_t* kernel, |
3839 | const int32_t* bias, |
3840 | uint8_t output_zero_point, |
3841 | float output_scale, |
3842 | uint8_t output_min, |
3843 | uint8_t output_max, |
3844 | uint32_t flags, |
3845 | xnn_caches_t caches, |
3846 | xnn_operator_t* deconvolution_op_out); |
3847 | |
3848 | enum xnn_status xnn_setup_deconvolution2d_nhwc_qu8( |
3849 | xnn_operator_t deconvolution_op, |
3850 | size_t batch_size, |
3851 | size_t input_height, |
3852 | size_t input_width, |
3853 | uint32_t adjustment_height, |
3854 | uint32_t adjustment_width, |
3855 | const uint8_t* input, |
3856 | uint8_t* output, |
3857 | pthreadpool_t threadpool); |
3858 | |
3859 | enum xnn_status xnn_create_fully_connected_nc_qu8( |
3860 | size_t input_channels, |
3861 | size_t output_channels, |
3862 | size_t input_stride, |
3863 | size_t output_stride, |
3864 | uint8_t input_zero_point, |
3865 | float input_scale, |
3866 | uint8_t kernel_zero_point, |
3867 | float kernel_scale, |
3868 | const uint8_t* kernel, |
3869 | const int32_t* bias, |
3870 | uint8_t output_zero_point, |
3871 | float output_scale, |
3872 | uint8_t output_min, |
3873 | uint8_t output_max, |
3874 | uint32_t flags, |
3875 | xnn_caches_t caches, |
3876 | xnn_operator_t* fully_connected_op_out); |
3877 | |
3878 | enum xnn_status xnn_setup_fully_connected_nc_qu8( |
3879 | xnn_operator_t fully_connected_op, |
3880 | size_t batch_size, |
3881 | const uint8_t* input, |
3882 | uint8_t* output, |
3883 | pthreadpool_t threadpool); |
3884 | |
3885 | enum xnn_status xnn_create_global_average_pooling_nwc_qu8( |
3886 | size_t channels, |
3887 | size_t input_stride, |
3888 | size_t output_stride, |
3889 | uint8_t input_zero_point, |
3890 | float input_scale, |
3891 | uint8_t output_zero_point, |
3892 | float output_scale, |
3893 | uint8_t output_min, |
3894 | uint8_t output_max, |
3895 | uint32_t flags, |
3896 | xnn_operator_t* global_average_pooling_op_out); |
3897 | |
3898 | enum xnn_status xnn_setup_global_average_pooling_nwc_qu8( |
3899 | xnn_operator_t global_average_pooling_op, |
3900 | size_t batch_size, |
3901 | size_t width, |
3902 | const uint8_t* input, |
3903 | uint8_t* output, |
3904 | pthreadpool_t threadpool); |
3905 | |
3906 | enum xnn_status xnn_create_leaky_relu_nc_qu8( |
3907 | size_t channels, |
3908 | size_t input_stride, |
3909 | size_t output_stride, |
3910 | float negative_slope, |
3911 | uint8_t input_zero_point, |
3912 | float input_scale, |
3913 | uint8_t output_zero_point, |
3914 | float output_scale, |
3915 | uint32_t flags, |
3916 | xnn_operator_t* leaky_relu_op_out); |
3917 | |
3918 | enum xnn_status xnn_setup_leaky_relu_nc_qu8( |
3919 | xnn_operator_t leaky_relu_op, |
3920 | size_t batch_size, |
3921 | const uint8_t* input, |
3922 | uint8_t* output, |
3923 | pthreadpool_t threadpool); |
3924 | |
3925 | enum xnn_status xnn_create_multiply_nd_qu8( |
3926 | uint8_t input1_zero_point, |
3927 | float input1_scale, |
3928 | uint8_t input2_zero_point, |
3929 | float input2_scale, |
3930 | uint8_t output_zero_point, |
3931 | float output_scale, |
3932 | uint8_t output_min, |
3933 | uint8_t output_max, |
3934 | uint32_t flags, |
3935 | xnn_operator_t* multiply_op_out); |
3936 | |
3937 | enum xnn_status xnn_setup_multiply_nd_qu8( |
3938 | xnn_operator_t multiply_op, |
3939 | size_t num_input1_dims, |
3940 | const size_t* input1_shape, |
3941 | size_t num_input2_dims, |
3942 | const size_t* input2_shape, |
3943 | const uint8_t* input1, |
3944 | const uint8_t* input2, |
3945 | uint8_t* output, |
3946 | pthreadpool_t threadpool); |
3947 | |
3948 | enum xnn_status xnn_run_multiply_nd_qu8( |
3949 | size_t num_input1_dims, |
3950 | const size_t* input1_shape, |
3951 | uint8_t input1_zero_point, |
3952 | float input1_scale, |
3953 | size_t num_input2_dims, |
3954 | const size_t* input2_shape, |
3955 | uint8_t input2_zero_point, |
3956 | float input2_scale, |
3957 | const uint8_t* input1, |
3958 | const uint8_t* input2, |
3959 | uint8_t* output, |
3960 | uint8_t output_zero_point, |
3961 | float output_scale, |
3962 | uint8_t output_min, |
3963 | uint8_t output_max, |
3964 | uint32_t flags, |
3965 | pthreadpool_t threadpool); |
3966 | |
3967 | enum xnn_status xnn_create_sigmoid_nc_qu8( |
3968 | size_t channels, |
3969 | size_t input_stride, |
3970 | size_t output_stride, |
3971 | uint8_t input_zero_point, |
3972 | float input_scale, |
3973 | uint8_t output_zero_point, |
3974 | float output_scale, |
3975 | uint8_t output_min, |
3976 | uint8_t output_max, |
3977 | uint32_t flags, |
3978 | xnn_operator_t* sigmoid_op_out); |
3979 | |
3980 | enum xnn_status xnn_setup_sigmoid_nc_qu8( |
3981 | xnn_operator_t sigmoid_op, |
3982 | size_t batch_size, |
3983 | const uint8_t* input, |
3984 | uint8_t* output, |
3985 | pthreadpool_t threadpool); |
3986 | |
3987 | enum xnn_status xnn_create_softmax_nc_qu8( |
3988 | size_t channels, |
3989 | size_t input_stride, |
3990 | size_t output_stride, |
3991 | float input_scale, |
3992 | uint8_t output_zero_point, |
3993 | float output_scale, |
3994 | uint32_t flags, |
3995 | xnn_operator_t* softmax_op_out); |
3996 | |
3997 | enum xnn_status xnn_setup_softmax_nc_qu8( |
3998 | xnn_operator_t softmax_op, |
3999 | size_t batch_size, |
4000 | const uint8_t* input, |
4001 | uint8_t* output, |
4002 | pthreadpool_t threadpool); |
4003 | |
4004 | enum xnn_status xnn_create_subtract_nd_qu8( |
4005 | uint8_t input1_zero_point, |
4006 | float input1_scale, |
4007 | uint8_t input2_zero_point, |
4008 | float input2_scale, |
4009 | uint8_t output_zero_point, |
4010 | float output_scale, |
4011 | uint8_t output_min, |
4012 | uint8_t output_max, |
4013 | uint32_t flags, |
4014 | xnn_operator_t* subtract_op_out); |
4015 | |
4016 | enum xnn_status xnn_setup_subtract_nd_qu8( |
4017 | xnn_operator_t subtract_op, |
4018 | size_t num_input1_dims, |
4019 | const size_t* input1_shape, |
4020 | size_t num_input2_dims, |
4021 | const size_t* input2_shape, |
4022 | const uint8_t* input1, |
4023 | const uint8_t* input2, |
4024 | uint8_t* output, |
4025 | pthreadpool_t threadpool); |
4026 | |
4027 | enum xnn_status xnn_run_subtract_nd_qu8( |
4028 | size_t num_input1_dims, |
4029 | const size_t* input1_shape, |
4030 | uint8_t input1_zero_point, |
4031 | float input1_scale, |
4032 | size_t num_input2_dims, |
4033 | const size_t* input2_shape, |
4034 | uint8_t input2_zero_point, |
4035 | float input2_scale, |
4036 | const uint8_t* input1, |
4037 | const uint8_t* input2, |
4038 | uint8_t* output, |
4039 | uint8_t output_zero_point, |
4040 | float output_scale, |
4041 | uint8_t output_min, |
4042 | uint8_t output_max, |
4043 | uint32_t flags, |
4044 | pthreadpool_t threadpool); |
4045 | |
4046 | enum xnn_status xnn_create_tanh_nc_qu8( |
4047 | size_t channels, |
4048 | size_t input_stride, |
4049 | size_t output_stride, |
4050 | uint8_t input_zero_point, |
4051 | float input_scale, |
4052 | uint8_t output_zero_point, |
4053 | float output_scale, |
4054 | uint8_t output_min, |
4055 | uint8_t output_max, |
4056 | uint32_t flags, |
4057 | xnn_operator_t* tanh_op_out); |
4058 | |
4059 | enum xnn_status xnn_setup_tanh_nc_qu8( |
4060 | xnn_operator_t tanh_op, |
4061 | size_t batch_size, |
4062 | const uint8_t* input, |
4063 | uint8_t* output, |
4064 | pthreadpool_t threadpool); |
4065 | |
4066 | #endif // XNN_NO_QU8_OPERATORS |
4067 | |
4068 | #ifndef XNN_NO_S8_OPERATORS |
4069 | |
4070 | enum xnn_status xnn_create_clamp_nc_s8( |
4071 | size_t channels, |
4072 | size_t input_stride, |
4073 | size_t output_stride, |
4074 | int8_t output_min, |
4075 | int8_t output_max, |
4076 | uint32_t flags, |
4077 | xnn_operator_t* clamp_op_out); |
4078 | |
4079 | enum xnn_status xnn_setup_clamp_nc_s8( |
4080 | xnn_operator_t clamp_op, |
4081 | size_t batch_size, |
4082 | const int8_t* input, |
4083 | int8_t* output, |
4084 | pthreadpool_t threadpool); |
4085 | |
4086 | enum xnn_status xnn_create_max_pooling2d_nhwc_s8( |
4087 | uint32_t input_padding_top, |
4088 | uint32_t input_padding_right, |
4089 | uint32_t input_padding_bottom, |
4090 | uint32_t input_padding_left, |
4091 | uint32_t pooling_height, |
4092 | uint32_t pooling_width, |
4093 | uint32_t stride_height, |
4094 | uint32_t stride_width, |
4095 | uint32_t dilation_height, |
4096 | uint32_t dilation_width, |
4097 | size_t channels, |
4098 | size_t input_pixel_stride, |
4099 | size_t output_pixel_stride, |
4100 | int8_t output_min, |
4101 | int8_t output_max, |
4102 | uint32_t flags, |
4103 | xnn_operator_t* max_pooling_op_out); |
4104 | |
4105 | enum xnn_status xnn_setup_max_pooling2d_nhwc_s8( |
4106 | xnn_operator_t max_pooling_op, |
4107 | size_t batch_size, |
4108 | size_t input_height, |
4109 | size_t input_width, |
4110 | const int8_t* input, |
4111 | int8_t* output, |
4112 | pthreadpool_t threadpool); |
4113 | |
4114 | enum xnn_status xnn_create_resize_bilinear2d_nhwc_s8( |
4115 | size_t channels, |
4116 | size_t input_pixel_stride, |
4117 | size_t output_pixel_stride, |
4118 | uint32_t flags, |
4119 | xnn_operator_t* resize_op_out); |
4120 | |
4121 | enum xnn_status xnn_setup_resize_bilinear2d_nhwc_s8( |
4122 | xnn_operator_t resize_op, |
4123 | size_t batch_size, |
4124 | size_t input_height, |
4125 | size_t input_width, |
4126 | size_t output_height, |
4127 | size_t output_width, |
4128 | const int8_t* input, |
4129 | int8_t* output, |
4130 | pthreadpool_t threadpool); |
4131 | |
4132 | #endif // XNN_NO_S8_OPERATORS |
4133 | |
4134 | #ifndef XNN_NO_U8_OPERATORS |
4135 | |
4136 | enum xnn_status xnn_create_clamp_nc_u8( |
4137 | size_t channels, |
4138 | size_t input_stride, |
4139 | size_t output_stride, |
4140 | uint8_t output_min, |
4141 | uint8_t output_max, |
4142 | uint32_t flags, |
4143 | xnn_operator_t* clamp_op_out); |
4144 | |
4145 | enum xnn_status xnn_setup_clamp_nc_u8( |
4146 | xnn_operator_t clamp_op, |
4147 | size_t batch_size, |
4148 | const uint8_t* input, |
4149 | uint8_t* output, |
4150 | pthreadpool_t threadpool); |
4151 | |
4152 | enum xnn_status xnn_create_max_pooling2d_nhwc_u8( |
4153 | uint32_t input_padding_top, |
4154 | uint32_t input_padding_right, |
4155 | uint32_t input_padding_bottom, |
4156 | uint32_t input_padding_left, |
4157 | uint32_t pooling_height, |
4158 | uint32_t pooling_width, |
4159 | uint32_t stride_height, |
4160 | uint32_t stride_width, |
4161 | uint32_t dilation_height, |
4162 | uint32_t dilation_width, |
4163 | size_t channels, |
4164 | size_t input_pixel_stride, |
4165 | size_t output_pixel_stride, |
4166 | uint8_t output_min, |
4167 | uint8_t output_max, |
4168 | uint32_t flags, |
4169 | xnn_operator_t* max_pooling_op_out); |
4170 | |
4171 | enum xnn_status xnn_setup_max_pooling2d_nhwc_u8( |
4172 | xnn_operator_t max_pooling_op, |
4173 | size_t batch_size, |
4174 | size_t input_height, |
4175 | size_t input_width, |
4176 | const uint8_t* input, |
4177 | uint8_t* output, |
4178 | pthreadpool_t threadpool); |
4179 | |
4180 | enum xnn_status xnn_create_resize_bilinear2d_nhwc_u8( |
4181 | size_t channels, |
4182 | size_t input_pixel_stride, |
4183 | size_t output_pixel_stride, |
4184 | uint32_t flags, |
4185 | xnn_operator_t* resize_op_out); |
4186 | |
4187 | enum xnn_status xnn_setup_resize_bilinear2d_nhwc_u8( |
4188 | xnn_operator_t resize_op, |
4189 | size_t batch_size, |
4190 | size_t input_height, |
4191 | size_t input_width, |
4192 | size_t output_height, |
4193 | size_t output_width, |
4194 | const uint8_t* input, |
4195 | uint8_t* output, |
4196 | pthreadpool_t threadpool); |
4197 | |
4198 | #endif // XNN_NO_U8_OPERATORS |
4199 | |
4200 | #ifndef XNN_NO_X8_OPERATORS |
4201 | |
4202 | enum xnn_status xnn_create_copy_nc_x8( |
4203 | size_t channels, |
4204 | size_t input_stride, |
4205 | size_t output_stride, |
4206 | uint32_t flags, |
4207 | xnn_operator_t* copy_op_out); |
4208 | |
4209 | enum xnn_status xnn_setup_copy_nc_x8( |
4210 | xnn_operator_t copy_op, |
4211 | size_t batch_size, |
4212 | const void* input, |
4213 | void* output, |
4214 | pthreadpool_t threadpool); |
4215 | |
4216 | enum xnn_status xnn_create_channel_shuffle_nc_x8( |
4217 | size_t groups, |
4218 | size_t group_channels, |
4219 | size_t input_stride, |
4220 | size_t output_stride, |
4221 | uint32_t flags, |
4222 | xnn_operator_t* channel_shuffle_op_out); |
4223 | |
4224 | enum xnn_status xnn_setup_channel_shuffle_nc_x8( |
4225 | xnn_operator_t channel_shuffle_op, |
4226 | size_t batch_size, |
4227 | const void* input, |
4228 | void* output, |
4229 | pthreadpool_t threadpool); |
4230 | |
4231 | enum xnn_status xnn_create_constant_pad_nd_x8( |
4232 | const void* padding_value, |
4233 | uint32_t flags, |
4234 | xnn_operator_t* constant_pad_op_out); |
4235 | |
4236 | enum xnn_status xnn_setup_constant_pad_nd_x8( |
4237 | xnn_operator_t constant_pad_op, |
4238 | size_t num_dims, |
4239 | const size_t* input_shape, |
4240 | const size_t* pre_padding, |
4241 | const size_t* post_padding, |
4242 | const void* input, |
4243 | void* output, |
4244 | pthreadpool_t threadpool); |
4245 | |
4246 | enum xnn_status xnn_run_constant_pad_nd_x8( |
4247 | uint32_t flags, |
4248 | size_t num_dims, |
4249 | const size_t* input_shape, |
4250 | const size_t* pre_paddings, |
4251 | const size_t* post_paddings, |
4252 | const void* input, |
4253 | void* output, |
4254 | const void* padding_value, |
4255 | pthreadpool_t threadpool); |
4256 | |
4257 | enum xnn_status xnn_create_depth_to_space_nhwc_x8( |
4258 | size_t output_channels, |
4259 | size_t input_channel_stride, |
4260 | size_t output_channel_stride, |
4261 | uint32_t block_size, |
4262 | uint32_t flags, |
4263 | xnn_operator_t* depth_to_space_op_out); |
4264 | |
4265 | enum xnn_status xnn_setup_depth_to_space_nhwc_x8( |
4266 | xnn_operator_t depth_to_space_op, |
4267 | size_t batch_size, |
4268 | size_t input_height, |
4269 | size_t input_width, |
4270 | const void* input, |
4271 | void* output, |
4272 | pthreadpool_t threadpool); |
4273 | |
4274 | enum xnn_status xnn_create_slice_nd_x8( |
4275 | uint32_t flags, |
4276 | xnn_operator_t* slice_op_out); |
4277 | |
4278 | enum xnn_status xnn_setup_slice_nd_x8( |
4279 | xnn_operator_t slice_op, |
4280 | size_t num_dims, |
4281 | const size_t* input_shape, |
4282 | const size_t* offsets, |
4283 | const size_t* sizes, |
4284 | const void* input, |
4285 | void* output, |
4286 | pthreadpool_t threadpool); |
4287 | |
4288 | enum xnn_status xnn_create_space_to_depth_nhwc_x8( |
4289 | size_t input_channels, |
4290 | size_t input_channel_stride, |
4291 | size_t output_channel_stride, |
4292 | uint32_t block_size, |
4293 | uint32_t flags, |
4294 | xnn_operator_t* space_to_depth_op_out); |
4295 | |
4296 | enum xnn_status xnn_setup_space_to_depth_nhwc_x8( |
4297 | xnn_operator_t space_to_depth_op, |
4298 | size_t batch_size, |
4299 | size_t input_height, |
4300 | size_t input_width, |
4301 | const void* input, |
4302 | void* output, |
4303 | pthreadpool_t threadpool); |
4304 | |
4305 | enum xnn_status xnn_create_transpose_nd_x8( |
4306 | uint32_t flags, |
4307 | xnn_operator_t* transpose_op_out); |
4308 | |
4309 | enum xnn_status xnn_setup_transpose_nd_x8( |
4310 | xnn_operator_t transpose_op, |
4311 | const void* input, |
4312 | void* output, |
4313 | const size_t num_dims, |
4314 | const size_t* input_shape, |
4315 | const size_t* output_perm, |
4316 | pthreadpool_t threadpool); |
4317 | |
4318 | enum xnn_status xnn_run_transpose_nd_x8( |
4319 | const void* input, |
4320 | void* output, |
4321 | const size_t num_dims, |
4322 | const size_t* input_shape, |
4323 | const size_t* output_perm, |
4324 | uint32_t flags, |
4325 | pthreadpool_t threadpool); |
4326 | |
4327 | #endif // XNN_NO_X8_OPERATORS |
4328 | |
4329 | #ifndef XNN_NO_CVT_OPERATORS |
4330 | |
4331 | enum xnn_status xnn_create_convert_nc_f16_f32( |
4332 | size_t channels, |
4333 | size_t input_stride, |
4334 | size_t output_stride, |
4335 | uint32_t flags, |
4336 | xnn_operator_t* convert_op_out); |
4337 | |
4338 | enum xnn_status xnn_setup_convert_nc_f16_f32( |
4339 | xnn_operator_t convert_op, |
4340 | size_t batch_size, |
4341 | const void* input, |
4342 | float* output, |
4343 | pthreadpool_t threadpool); |
4344 | |
4345 | enum xnn_status xnn_run_convert_nc_f16_f32( |
4346 | size_t channels, |
4347 | size_t input_stride, |
4348 | size_t output_stride, |
4349 | size_t batch_size, |
4350 | const void* input, |
4351 | float* output, |
4352 | uint32_t flags, |
4353 | pthreadpool_t threadpool); |
4354 | |
4355 | enum xnn_status xnn_create_convert_nc_f32_f16( |
4356 | size_t channels, |
4357 | size_t input_stride, |
4358 | size_t output_stride, |
4359 | uint32_t flags, |
4360 | xnn_operator_t* convert_op_out); |
4361 | |
4362 | enum xnn_status xnn_setup_convert_nc_f32_f16( |
4363 | xnn_operator_t convert_op, |
4364 | size_t batch_size, |
4365 | const float* input, |
4366 | void* output, |
4367 | pthreadpool_t threadpool); |
4368 | |
4369 | enum xnn_status xnn_run_convert_nc_f32_f16( |
4370 | size_t channels, |
4371 | size_t input_stride, |
4372 | size_t output_stride, |
4373 | size_t batch_size, |
4374 | const float* input, |
4375 | void* output, |
4376 | uint32_t flags, |
4377 | pthreadpool_t threadpool); |
4378 | |
4379 | enum xnn_status xnn_create_convert_nc_f32_qs8( |
4380 | size_t channels, |
4381 | size_t input_stride, |
4382 | size_t output_stride, |
4383 | float output_scale, |
4384 | int8_t output_zero_point, |
4385 | int8_t output_min, |
4386 | int8_t output_max, |
4387 | uint32_t flags, |
4388 | xnn_operator_t* convert_op_out); |
4389 | |
4390 | enum xnn_status xnn_setup_convert_nc_f32_qs8( |
4391 | xnn_operator_t convert_op, |
4392 | size_t batch_size, |
4393 | const float* input, |
4394 | int8_t* output, |
4395 | pthreadpool_t threadpool); |
4396 | |
4397 | enum xnn_status xnn_run_convert_nc_f32_qs8( |
4398 | size_t channels, |
4399 | size_t input_stride, |
4400 | size_t output_stride, |
4401 | size_t batch_size, |
4402 | const float* input, |
4403 | int8_t* output, |
4404 | float output_scale, |
4405 | int8_t output_zero_point, |
4406 | uint32_t flags, |
4407 | pthreadpool_t threadpool); |
4408 | |
4409 | enum xnn_status xnn_create_convert_nc_f32_qu8( |
4410 | size_t channels, |
4411 | size_t input_stride, |
4412 | size_t output_stride, |
4413 | float output_scale, |
4414 | uint8_t output_zero_point, |
4415 | uint8_t output_min, |
4416 | uint8_t output_max, |
4417 | uint32_t flags, |
4418 | xnn_operator_t* convert_op_out); |
4419 | |
4420 | enum xnn_status xnn_setup_convert_nc_f32_qu8( |
4421 | xnn_operator_t convert_op, |
4422 | size_t batch_size, |
4423 | const float* input, |
4424 | uint8_t* output, |
4425 | pthreadpool_t threadpool); |
4426 | |
4427 | enum xnn_status xnn_run_convert_nc_f32_qu8( |
4428 | size_t channels, |
4429 | size_t input_stride, |
4430 | size_t output_stride, |
4431 | size_t batch_size, |
4432 | const float* input, |
4433 | uint8_t* output, |
4434 | float output_scale, |
4435 | uint8_t output_zero_point, |
4436 | uint32_t flags, |
4437 | pthreadpool_t threadpool); |
4438 | |
4439 | enum xnn_status xnn_create_convert_nc_qs8( |
4440 | size_t channels, |
4441 | size_t input_stride, |
4442 | size_t output_stride, |
4443 | float input_scale, |
4444 | int8_t input_zero_point, |
4445 | float output_scale, |
4446 | int8_t output_zero_point, |
4447 | uint32_t flags, |
4448 | xnn_operator_t* convert_op_out); |
4449 | |
4450 | enum xnn_status xnn_setup_convert_nc_qs8( |
4451 | xnn_operator_t convert_op, |
4452 | size_t batch_size, |
4453 | const int8_t* input, |
4454 | int8_t* output, |
4455 | pthreadpool_t threadpool); |
4456 | |
4457 | enum xnn_status xnn_create_convert_nc_qs8_f32( |
4458 | size_t channels, |
4459 | size_t input_stride, |
4460 | size_t output_stride, |
4461 | float input_scale, |
4462 | int8_t input_zero_point, |
4463 | uint32_t flags, |
4464 | xnn_operator_t* convert_op_out); |
4465 | |
4466 | enum xnn_status xnn_setup_convert_nc_qs8_f32( |
4467 | xnn_operator_t convert_op, |
4468 | size_t batch_size, |
4469 | const int8_t* input, |
4470 | float* output, |
4471 | pthreadpool_t threadpool); |
4472 | |
4473 | enum xnn_status xnn_run_convert_nc_qs8_f32( |
4474 | size_t channels, |
4475 | size_t input_stride, |
4476 | size_t output_stride, |
4477 | size_t batch_size, |
4478 | const int8_t* input, |
4479 | float* output, |
4480 | float input_scale, |
4481 | int8_t input_zero_point, |
4482 | uint32_t flags, |
4483 | pthreadpool_t threadpool); |
4484 | |
4485 | enum xnn_status xnn_create_convert_nc_qu8( |
4486 | size_t channels, |
4487 | size_t input_stride, |
4488 | size_t output_stride, |
4489 | float input_scale, |
4490 | uint8_t input_zero_point, |
4491 | float output_scale, |
4492 | uint8_t output_zero_point, |
4493 | uint32_t flags, |
4494 | xnn_operator_t* convert_op_out); |
4495 | |
4496 | enum xnn_status xnn_setup_convert_nc_qu8( |
4497 | xnn_operator_t convert_op, |
4498 | size_t batch_size, |
4499 | const uint8_t* input, |
4500 | uint8_t* output, |
4501 | pthreadpool_t threadpool); |
4502 | |
4503 | enum xnn_status xnn_create_convert_nc_qu8_f32( |
4504 | size_t channels, |
4505 | size_t input_stride, |
4506 | size_t output_stride, |
4507 | float input_scale, |
4508 | uint8_t input_zero_point, |
4509 | uint32_t flags, |
4510 | xnn_operator_t* convert_op_out); |
4511 | |
4512 | enum xnn_status xnn_setup_convert_nc_qu8_f32( |
4513 | xnn_operator_t convert_op, |
4514 | size_t batch_size, |
4515 | const uint8_t* input, |
4516 | float* output, |
4517 | pthreadpool_t threadpool); |
4518 | |
4519 | enum xnn_status xnn_run_convert_nc_qu8_f32( |
4520 | size_t channels, |
4521 | size_t input_stride, |
4522 | size_t output_stride, |
4523 | size_t batch_size, |
4524 | const uint8_t* input, |
4525 | float* output, |
4526 | float input_scale, |
4527 | uint8_t input_zero_point, |
4528 | uint32_t flags, |
4529 | pthreadpool_t threadpool); |
4530 | |
4531 | #endif // XNN_NO_CVT_OPERATORS |
4532 | |
4533 | #ifdef __cplusplus |
4534 | } // extern "C" |
4535 | #endif |
4536 | |