Compute Library
 22.11
CpuGemmLowpOffsetContributionKernel.cpp
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25 
26 #include "arm_compute/core/Error.h"
30 #include "arm_compute/core/Types.h"
31 #include "arm_compute/core/Utils.h"
36 
37 #include <arm_neon.h>
38 
39 namespace arm_compute
40 {
41 namespace cpu
42 {
43 namespace kernels
44 {
45 namespace
46 {
47 Status validate_arguments(const ITensorInfo *mm_result, const ITensorInfo *vector_sum_col, const ITensorInfo *vector_sum_row,
48  int32_t a_offset, int32_t b_offset)
49 {
51 
52  // If a_offset == 0, vector_sum_col can be a nullptr
53  if(a_offset != 0)
54  {
56  ARM_COMPUTE_RETURN_ERROR_ON(vector_sum_col->dimension(0) != mm_result->dimension(0));
57  }
58 
59  // If b_offset == 0, vector_sum_row can be a nullptr
60  if(b_offset != 0)
61  {
63 
64  // Check if input is a 3D reinterpretation
65  const bool reinterpret_as_3d = mm_result->num_dimensions() > 1 && mm_result->tensor_shape().y() != vector_sum_row->tensor_shape().x();
66 
67  // Validate input
68  ARM_COMPUTE_RETURN_ERROR_ON(reinterpret_as_3d && vector_sum_row->dimension(0) != (mm_result->dimension(1) * mm_result->dimension(2)));
69  ARM_COMPUTE_RETURN_ERROR_ON(!reinterpret_as_3d && vector_sum_row->dimension(0) != mm_result->dimension(1));
70 
71  TensorShape output_shape = mm_result->tensor_shape();
72  if(output_shape.num_dimensions() > 1)
73  {
74  const unsigned int output_batch_idx = reinterpret_as_3d ? 3 : 2;
75 
76  TensorShape vector_sum_row_shape = vector_sum_row->tensor_shape();
77  vector_sum_row_shape.collapse_from(1);
78  output_shape.collapse_from(output_batch_idx);
79 
80  ARM_COMPUTE_RETURN_ERROR_ON_MSG(vector_sum_row_shape[1] != output_shape[output_batch_idx],
81  "mm_result tensor must have the same number of batches of output tensor");
82 
83  if(a_offset != 0)
84  {
85  TensorShape vector_sum_col_shape = vector_sum_col->tensor_shape();
86  vector_sum_col_shape.collapse_from(1);
87 
88  ARM_COMPUTE_RETURN_ERROR_ON_MSG(vector_sum_col_shape[1] != 1 && vector_sum_col_shape[1] != vector_sum_row_shape[1],
89  "vector_sum_col tensor must have the same number of batches of vector_sum_row_shape or the number of batches must be set to 1");
90  }
91  }
92  }
93 
94  return Status{};
95 }
96 
97 void run_offset_contribution(const Window &window,
98  ITensor *mm_result, const ITensor *vector_sum_col, const ITensor *vector_sum_row,
99  int32_t a_offset, int32_t b_offset, int32_t k_offset, bool slide_vector_sum_col, bool is_gemm3d)
100 {
101  Window collapsed_window = window.collapse_if_possible(window, Window::DimZ);
102  collapsed_window.set(Window::DimX, Window::Dimension(0, 1, 1));
103 
104  const int height_input = is_gemm3d ? mm_result->info()->dimension(1) : 0;
105  const int depth_input = is_gemm3d ? mm_result->info()->dimension(2) : 1;
106 
107  const int window_start_x = window.x().start();
108  const int window_end_x = window.x().end();
109  const int window_step_x = 16;
110 
111  // if vector_sum_col is nullptr then stride_y is 0, else get stride_y
112  const size_t sum_col_stride_y = (vector_sum_col != nullptr) ? (vector_sum_col->info()->strides_in_bytes().y()) : 0;
113  Iterator mm_result_it(mm_result, collapsed_window);
114 
115  if((a_offset != 0) && (b_offset != 0) && (vector_sum_col != nullptr) && (vector_sum_row != nullptr)) // true, true
116  {
117  // Set window for vector_sum_col
118  Window win_vector_sum_col(collapsed_window);
119  win_vector_sum_col.set(Window::DimY, Window::Dimension(0, 0, 0));
120  win_vector_sum_col.set(Window::DimZ, Window::Dimension(0, 0, 0));
121 
122  // Set window for vector_sum_row
123  Window win_vector_sum_row(collapsed_window);
124  win_vector_sum_row.set(Window::DimX, Window::Dimension(0, 0, 0));
125  win_vector_sum_row.set(Window::DimY, Window::Dimension(0, 0, 0));
126  win_vector_sum_row.set(Window::DimZ, Window::Dimension(0, 0, 0));
127 
128  Iterator vector_sum_col_it(vector_sum_col, win_vector_sum_col);
129  Iterator vector_sum_row_it(vector_sum_row, win_vector_sum_row);
130 
131  const size_t sum_row_stride_y = vector_sum_row->info()->strides_in_bytes().y();
132 
133  // Offset in case vector_sum_col is batched
134  const int vector_sum_col_batch_offset = slide_vector_sum_col ? vector_sum_col->info()->strides_in_bytes().z() : 0;
135 
136  execute_window_loop(collapsed_window, [&](const Coordinates & id)
137  {
138  const int batch_id = id.z() / depth_input;
139  const size_t batch_offset_col = batch_id * (sum_col_stride_y );
140  auto vector_sum_col_ptr = reinterpret_cast<const int32_t *>(vector_sum_col_it.ptr() + batch_offset_col + batch_id * vector_sum_col_batch_offset);
141  auto mm_result_ptr = reinterpret_cast<int32_t *>(mm_result_it.ptr());
142 
143  // Compute the leftover term due to b_offset.
144  int32_t b_offset_term_s32 = *(reinterpret_cast<const int32_t *>(vector_sum_row_it.ptr() + batch_id * sum_row_stride_y) + id.y() + (id.z() % depth_input) * height_input);
145  b_offset_term_s32 *= b_offset;
146 
147  const int32x4_t b_offset_term_s32_vec = vdupq_n_s32(b_offset_term_s32);
148 
149  int x = window_start_x;
150  for(; x <= (window_end_x - window_step_x); x += window_step_x)
151  {
152  // Compute the leftover term due to a_offset.
153  int32x4x4_t a_offset_term_s32 =
154  {
155  {
156  vld1q_s32(vector_sum_col_ptr + x + 0),
157  vld1q_s32(vector_sum_col_ptr + x + 4),
158  vld1q_s32(vector_sum_col_ptr + x + 8),
159  vld1q_s32(vector_sum_col_ptr + x + 12)
160  }
161  };
162 
163  a_offset_term_s32.val[0] = vmulq_n_s32(a_offset_term_s32.val[0], a_offset);
164  a_offset_term_s32.val[1] = vmulq_n_s32(a_offset_term_s32.val[1], a_offset);
165  a_offset_term_s32.val[2] = vmulq_n_s32(a_offset_term_s32.val[2], a_offset);
166  a_offset_term_s32.val[3] = vmulq_n_s32(a_offset_term_s32.val[3], a_offset);
167 
168  // Add a_offset_term_s32 and b_offset_term_s32
169  int32x4x4_t offset_term_s32 =
170  {
171  {
172  vdupq_n_s32(k_offset),
173  vdupq_n_s32(k_offset),
174  vdupq_n_s32(k_offset),
175  vdupq_n_s32(k_offset)
176  }
177  };
178 
179  offset_term_s32.val[0] = vaddq_s32(offset_term_s32.val[0], vaddq_s32(a_offset_term_s32.val[0], b_offset_term_s32_vec));
180  offset_term_s32.val[1] = vaddq_s32(offset_term_s32.val[1], vaddq_s32(a_offset_term_s32.val[1], b_offset_term_s32_vec));
181  offset_term_s32.val[2] = vaddq_s32(offset_term_s32.val[2], vaddq_s32(a_offset_term_s32.val[2], b_offset_term_s32_vec));
182  offset_term_s32.val[3] = vaddq_s32(offset_term_s32.val[3], vaddq_s32(a_offset_term_s32.val[3], b_offset_term_s32_vec));
183 
184  int32x4x4_t in_s32 =
185  {
186  {
187  vld1q_s32(mm_result_ptr + x + 0),
188  vld1q_s32(mm_result_ptr + x + 4),
189  vld1q_s32(mm_result_ptr + x + 8),
190  vld1q_s32(mm_result_ptr + x + 12)
191  }
192  };
193 
194  // Add the offset terms to GEMM's result
195  in_s32.val[0] = vaddq_s32(in_s32.val[0], offset_term_s32.val[0]);
196  in_s32.val[1] = vaddq_s32(in_s32.val[1], offset_term_s32.val[1]);
197  in_s32.val[2] = vaddq_s32(in_s32.val[2], offset_term_s32.val[2]);
198  in_s32.val[3] = vaddq_s32(in_s32.val[3], offset_term_s32.val[3]);
199 
200  // Store the result with the offset contribution
201  vst1q_s32(mm_result_ptr + x + 0, in_s32.val[0]);
202  vst1q_s32(mm_result_ptr + x + 4, in_s32.val[1]);
203  vst1q_s32(mm_result_ptr + x + 8, in_s32.val[2]);
204  vst1q_s32(mm_result_ptr + x + 12, in_s32.val[3]);
205  }
206 
207  // Left-overs loop
208  for(; x < window_end_x; ++x)
209  {
210  // Compute the leftover term due to a_offset.
211  int32_t a_offset_term_s32 = *(vector_sum_col_ptr + x);
212 
213  a_offset_term_s32 *= a_offset;
214 
215  // Add the offset terms to GEMM's result
216  // Store the result with the offset contribution
217  mm_result_ptr[x] += k_offset + a_offset_term_s32 + b_offset_term_s32;
218  }
219  },
220  vector_sum_col_it, vector_sum_row_it, mm_result_it);
221  }
222  else if((a_offset == 0) && (b_offset != 0) && (vector_sum_row != nullptr)) // false, true
223  {
224  ARM_COMPUTE_ERROR_ON_NULLPTR(vector_sum_row);
225 
226  // Set window for vector_sum_row
227  Window win_vector_sum_row(collapsed_window);
228  win_vector_sum_row.set(Window::DimX, Window::Dimension(0, 0, 0));
229  win_vector_sum_row.set(Window::DimY, Window::Dimension(0, 0, 0));
230  win_vector_sum_row.set(Window::DimZ, Window::Dimension(0, 0, 0));
231 
232  Iterator vector_sum_row_it(vector_sum_row, win_vector_sum_row);
233 
234  const size_t sum_row_stride_y = vector_sum_row->info()->strides_in_bytes().y();
235 
236  execute_window_loop(collapsed_window, [&](const Coordinates & id)
237  {
238  const int batch_id = id.z() / depth_input;
239  auto mm_result_ptr = reinterpret_cast<int32_t *>(mm_result_it.ptr());
240 
241  // Compute the leftover term due to b_offset.
242  int32_t b_offset_term_s32 = *(reinterpret_cast<const int32_t *>(vector_sum_row_it.ptr() + batch_id * sum_row_stride_y) + id.y() + (id.z() % depth_input) * height_input);
243  b_offset_term_s32 *= b_offset;
244 
245  const int32x4_t b_offset_term_s32_vec = vdupq_n_s32(b_offset_term_s32);
246 
247  int x = window_start_x;
248  for(; x <= (window_end_x - window_step_x); x += window_step_x)
249  {
250  int32x4x4_t in_s32 =
251  {
252  {
253  vld1q_s32(mm_result_ptr + x + 0),
254  vld1q_s32(mm_result_ptr + x + 4),
255  vld1q_s32(mm_result_ptr + x + 8),
256  vld1q_s32(mm_result_ptr + x + 12)
257  }
258  };
259 
260  // Add the offset terms to GEMM's result
261  in_s32.val[0] = vaddq_s32(in_s32.val[0], b_offset_term_s32_vec);
262  in_s32.val[1] = vaddq_s32(in_s32.val[1], b_offset_term_s32_vec);
263  in_s32.val[2] = vaddq_s32(in_s32.val[2], b_offset_term_s32_vec);
264  in_s32.val[3] = vaddq_s32(in_s32.val[3], b_offset_term_s32_vec);
265 
266  // Store the result with the offset contribution
267  vst1q_s32(mm_result_ptr + x + 0, in_s32.val[0]);
268  vst1q_s32(mm_result_ptr + x + 4, in_s32.val[1]);
269  vst1q_s32(mm_result_ptr + x + 8, in_s32.val[2]);
270  vst1q_s32(mm_result_ptr + x + 12, in_s32.val[3]);
271  }
272 
273  // Left-overs loop
274  for(; x < window_end_x; ++x)
275  {
276  // Add the offset terms to GEMM's result
277  // Store the result with the offset contribution
278  mm_result_ptr[x] += b_offset_term_s32;
279  }
280  },
281  vector_sum_row_it, mm_result_it);
282  }
283  else if((a_offset != 0) && (b_offset == 0) && (vector_sum_col != nullptr)) // true, false
284  {
285  // Set window for vector_sum_col
286  Window win_vector_sum_col(collapsed_window);
287  win_vector_sum_col.set(Window::DimY, Window::Dimension(0, 0, 0));
288  win_vector_sum_col.set(Window::DimZ, Window::Dimension(0, 0, 0));
289 
290  Iterator vector_sum_col_it(vector_sum_col, win_vector_sum_col);
291 
292  // Offset in case vector_sum_col is batched
293  const int vector_sum_col_batch_offset = slide_vector_sum_col ? vector_sum_col->info()->strides_in_bytes().z() : 0;
294 
295  execute_window_loop(collapsed_window, [&](const Coordinates & id)
296  {
297  const int batch_id = id.z() / depth_input;
298  const size_t batch_offset_col = batch_id * (sum_col_stride_y ); // Value to offset vector_sum_col_ptr to allow for iteration of y values in tensor
299  auto vector_sum_col_ptr = reinterpret_cast<const int32_t *>(vector_sum_col_it.ptr() + batch_offset_col + batch_id * vector_sum_col_batch_offset);
300  auto mm_result_ptr = reinterpret_cast<int32_t *>(mm_result_it.ptr());
301 
302  int x = window_start_x;
303  for(; x <= (window_end_x - window_step_x); x += window_step_x)
304  {
305  // Compute the leftover term due to a_offset.
306  int32x4x4_t a_offset_term_s32 =
307  {
308  {
309  vld1q_s32(vector_sum_col_ptr + x + 0),
310  vld1q_s32(vector_sum_col_ptr + x + 4),
311  vld1q_s32(vector_sum_col_ptr + x + 8),
312  vld1q_s32(vector_sum_col_ptr + x + 12)
313  }
314  };
315 
316  a_offset_term_s32.val[0] = vmulq_n_s32(a_offset_term_s32.val[0], a_offset);
317  a_offset_term_s32.val[1] = vmulq_n_s32(a_offset_term_s32.val[1], a_offset);
318  a_offset_term_s32.val[2] = vmulq_n_s32(a_offset_term_s32.val[2], a_offset);
319  a_offset_term_s32.val[3] = vmulq_n_s32(a_offset_term_s32.val[3], a_offset);
320 
321  int32x4x4_t in_s32 =
322  {
323  {
324  vld1q_s32(mm_result_ptr + x + 0),
325  vld1q_s32(mm_result_ptr + x + 4),
326  vld1q_s32(mm_result_ptr + x + 8),
327  vld1q_s32(mm_result_ptr + x + 12)
328  }
329  };
330 
331  // Add the offset terms to GEMM's result
332  in_s32.val[0] = vaddq_s32(in_s32.val[0], a_offset_term_s32.val[0]);
333  in_s32.val[1] = vaddq_s32(in_s32.val[1], a_offset_term_s32.val[1]);
334  in_s32.val[2] = vaddq_s32(in_s32.val[2], a_offset_term_s32.val[2]);
335  in_s32.val[3] = vaddq_s32(in_s32.val[3], a_offset_term_s32.val[3]);
336 
337  // Store the result with the offset contribution
338  vst1q_s32(mm_result_ptr + x + 0, in_s32.val[0]);
339  vst1q_s32(mm_result_ptr + x + 4, in_s32.val[1]);
340  vst1q_s32(mm_result_ptr + x + 8, in_s32.val[2]);
341  vst1q_s32(mm_result_ptr + x + 12, in_s32.val[3]);
342  }
343 
344  // Left-overs loop
345  for(; x < window_end_x; ++x)
346  {
347  // Compute the leftover term due to a_offset.
348  const int32_t a_offset_term_s32 = *(vector_sum_col_ptr + x);
349 
350  // Add the offset terms to GEMM's result
351  // Store the result with the offset contribution
352  mm_result_ptr[x] += a_offset_term_s32 * a_offset;
353  }
354  },
355  vector_sum_col_it, mm_result_it);
356  }
357  else // false, false
358  {
359  // No offset contribution from matrix A and matrix B
360  return;
361  }
362 }
363 } // namespace
364 
365 void CpuGemmLowpOffsetContributionKernel::configure(ITensorInfo *mm_result, ITensorInfo *vector_sum_col, ITensorInfo *vector_sum_row, int32_t k, int32_t a_offset, int32_t b_offset)
366 {
367  // Perform validate step
368  ARM_COMPUTE_UNUSED(vector_sum_row);
369  ARM_COMPUTE_ERROR_ON_NULLPTR(mm_result);
370  ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(mm_result, vector_sum_col, vector_sum_row, a_offset, b_offset));
371 
372  _a_offset = a_offset;
373  _b_offset = b_offset;
374  _k_offset = a_offset * b_offset * k;
375 
376  // If a_offset == 0, vector_sum_col can be a nullptr
377  if(a_offset != 0)
378  {
379  // Check if vector_sum_col_shape should be slidden or not
380  // Don't slide vector_sum_col_shape along the y dimension if vector_sum_col_shape has just 1 dimension and vector_sum_row_shape more than 1
381  // This scenario can happen when the the matrix multiplication is used to perform a convolution operation
382  _slide_vector_sum_col = vector_sum_col->tensor_shape().num_dimensions() > 1;
383  }
384 
385  // Configure kernel window
386  Window win = calculate_max_window(*mm_result, Steps());
387  ICpuKernel::configure(win);
388 }
389 
390 Status CpuGemmLowpOffsetContributionKernel::validate(const ITensorInfo *mm_result, const ITensorInfo *vector_sum_col, const ITensorInfo *vector_sum_row,
391  int32_t a_offset, int32_t b_offset)
392 {
393  ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(mm_result, vector_sum_col, vector_sum_row, a_offset, b_offset));
394  return Status{};
395 }
396 
398 {
399  ARM_COMPUTE_UNUSED(info);
402 
403  auto vector_sum_col = tensors.get_const_tensor(TensorType::ACL_SRC_0);
404  auto vector_sum_row = tensors.get_const_tensor(TensorType::ACL_SRC_1);
405  auto mm_result = tensors.get_tensor(TensorType::ACL_DST);
406 
407  // Check if input is a 3D reinterpretation
408  const bool reinterpret_as_3d = vector_sum_row != nullptr
409  && mm_result->info()->num_dimensions() > 1
410  && mm_result->info()->tensor_shape().y() != vector_sum_row->info()->tensor_shape().x();
411 
412  run_offset_contribution(window, mm_result, vector_sum_col, vector_sum_row, _a_offset, _b_offset, _k_offset, _slide_vector_sum_col, reinterpret_as_3d);
413 }
414 
416 {
417  return "CpuGemmLowpOffsetContributionKernel";
418 }
419 } // namespace kernels
420 } // namespace cpu
421 } // namespace arm_compute
virtual size_t num_dimensions() const =0
The number of dimensions of the tensor (rank)
Window calculate_max_window(const ValidRegion &valid_region, const Steps &steps, bool skip_border, BorderSize border_size)
const Window & window() const
The maximum window the kernel can be executed on.
Definition: IKernel.cpp:28
#define ARM_COMPUTE_RETURN_ON_ERROR(status)
Checks if a status contains an error and returns it.
Definition: Error.h:204
Store the tensor&#39;s metadata.
Definition: ITensorInfo.h:40
#define ARM_COMPUTE_ERROR_THROW_ON(status)
Definition: Error.h:455
Status class.
Definition: Error.h:52
Status validate_arguments(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *dst, const PadStrideInfo &conv_info)
#define ARM_COMPUTE_RETURN_ERROR_ON(cond)
If the condition is true, an error is returned.
Definition: Error.h:296
Copyright (c) 2017-2022 Arm Limited.
1 channel, 1 S32 per channel
const ITensor * get_const_tensor(int id) const
Get constant tensor of a given id.
Definition: ITensorPack.cpp:54
static constexpr size_t DimX
Alias for dimension 0 also known as X dimension.
Definition: Window.h:43
#define ARM_COMPUTE_UNUSED(...)
To avoid unused variables warnings.
Definition: Error.h:152
virtual const TensorShape & tensor_shape() const =0
Size for each dimension of the tensor.
Class to describe a number of elements in each dimension.
Definition: Steps.h:40
void configure(ITensorInfo *mm_result, ITensorInfo *vector_sum_col, ITensorInfo *vector_sum_row, int32_t k, int32_t a_offset, int32_t b_offset)
Initialise the kernel&#39;s input and output.
virtual ITensorInfo * info() const =0
Interface to be implemented by the child class to return the tensor&#39;s metadata.
static Status validate(const ITensorInfo *mm_result, const ITensorInfo *vector_sum_col, const ITensorInfo *vector_sum_row, int32_t a_offset, int32_t b_offset)
Static function to check if given info will lead to a valid configuration.
#define ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(k)
Definition: Validate.h:915
void run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info) override
Execute the kernel on the passed window.
static constexpr size_t DimY
Alias for dimension 1 also known as Y dimension.
Definition: Window.h:45
ScaleKernelInfo info(interpolation_policy, default_border_mode, PixelValue(), sampling_policy, false)
ITensor * get_tensor(int id)
Get tensor of a given id from the pac.
Definition: ITensorPack.cpp:64
Information about executing thread and CPU.
Definition: CPPTypes.h:179
static constexpr size_t DimZ
Alias for dimension 2 also known as Z dimension.
Definition: Window.h:47
unsigned int num_dimensions() const
Returns the effective dimensionality of the tensor.
Definition: Dimensions.h:143
#define ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(t, c,...)
Definition: Validate.h:788
#define ARM_COMPUTE_RETURN_ERROR_ON_MSG(cond, msg)
If the condition is true, an error is returned.
Definition: Error.h:244
Tensor packing service.
Definition: ITensorPack.h:39
#define ARM_COMPUTE_ERROR_ON_NULLPTR(...)
Definition: Validate.h:157
void execute_window_loop(const Window &w, L &&lambda_function, Ts &&... iterators)
Iterate through the passed window, automatically adjusting the iterators and calling the lambda_funct...
Definition: Helpers.inl:77
Describe a multidimensional execution window.
Definition: Window.h:39
#define ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(f, s)
Definition: Validate.h:201