1 /*
2 * Copyright (c) 2017-2021 Arm Limited.
3 *
4 * SPDX-License-Identifier: MIT
5 *
6 * Permission is hereby granted, free of charge, to any person obtaining a copy
7 * of this software and associated documentation files (the "Software"), to
8 * deal in the Software without restriction, including without limitation the
9 * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
10 * sell copies of the Software, and to permit persons to whom the Software is
11 * furnished to do so, subject to the following conditions:
12 *
13 * The above copyright notice and this permission notice shall be included in all
14 * copies or substantial portions of the Software.
15 *
16 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
17 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
18 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
19 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
20 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
21 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
22 * SOFTWARE.
23 */
24 #include "src/gpu/cl/operators/ClGemmConv2d.h"
25
26 #include "arm_compute/core/CL/ICLTensor.h"
27 #include "arm_compute/core/PixelValue.h"
28 #include "arm_compute/core/Size2D.h"
29 #include "arm_compute/core/TensorInfo.h"
30 #include "arm_compute/core/Utils.h"
31 #include "arm_compute/core/Validate.h"
32 #include "arm_compute/core/utils/misc/ShapeCalculator.h"
33 #include "arm_compute/core/utils/quantization/AsymmHelpers.h"
34 #include "arm_compute/runtime/CL/CLScheduler.h"
35 #include "src/core/helpers/AutoConfiguration.h"
36 #include "src/core/helpers/MemoryHelpers.h"
37 #include "src/gpu/cl/kernels/ClActivationKernel.h"
38 #include "src/gpu/cl/kernels/ClCol2ImKernel.h"
39 #include "src/gpu/cl/kernels/ClIm2ColKernel.h"
40 #include "src/gpu/cl/kernels/ClWeightsReshapeKernel.h"
41 #include "src/gpu/cl/operators/ClGemm.h"
42 #include "src/gpu/cl/operators/ClGemmLowpMatrixMultiplyCore.h"
43 #include "src/gpu/cl/utils/ClAuxTensorHandler.h"
44
45 #include "src/common/utils/Log.h"
46 #include "support/Cast.h"
47
48 namespace arm_compute
49 {
50 using namespace experimental;
51 using namespace misc::shape_calculator;
52 using namespace utils::cast;
53 namespace opencl
54 {
ClGemmConv2d()55 ClGemmConv2d::ClGemmConv2d()
56 : _weights_reshape_kernel(nullptr), _im2col_kernel(nullptr), _mm_gemm(nullptr), _mm_gemmlowp(nullptr), _col2im_kernel(nullptr), _activation_kernel(nullptr), _im2col_output(), _weights_reshaped(),
57 _gemm_output(), _skip_im2col(false), _skip_col2im(false), _is_quantized(false), _fuse_activation(true), _append_bias(false), _is_prepared(false), _use_post_ops(false), _aux_mem(AuxTensorIdx::Count)
58 {
59 }
60 ClGemmConv2d::~ClGemmConv2d() = default;
61
configure_mm(const ClCompileContext & compile_context,const ITensorInfo * src,ITensorInfo * weights,ITensorInfo * biases,ITensorInfo * dst,const GEMMLowpOutputStageInfo & gemmlowp_output_stage,int gemm_3d_depth,const ActivationLayerInfo & act_info,const experimental::PostOpList<ITensorInfo * > & post_ops)62 void ClGemmConv2d::configure_mm(const ClCompileContext &compile_context, const ITensorInfo *src, ITensorInfo *weights, ITensorInfo *biases, ITensorInfo *dst,
63 const GEMMLowpOutputStageInfo &gemmlowp_output_stage,
64 int gemm_3d_depth, const ActivationLayerInfo &act_info, const experimental::PostOpList<ITensorInfo *> &post_ops)
65 {
66 ARM_COMPUTE_ERROR_ON_NULLPTR(src, weights);
67 ARM_COMPUTE_ERROR_THROW_ON(validate_mm(src, weights, biases, dst, gemmlowp_output_stage, gemm_3d_depth, _skip_im2col, act_info));
68
69 const GEMMInfo &gemm_info = GEMMInfo(false, // is_a_reshaped
70 false, // is_b_reshaped
71 true, // reshape_b_only_on_first_run
72 gemm_3d_depth, // depth_output_gemm3d
73 _skip_im2col, // reinterpret_input_as_3d
74 false, // retain_internal_weights
75 gemmlowp_output_stage, // gemmlowp_output_stage
76 false, // fast_math
77 false, // fp_mixed_precision
78 true, // broadcast_bias
79 act_info, // activation_info
80 post_ops // post ops
81 );
82
83 TensorInfo tmp_src{ *src };
84 if(_is_quantized)
85 {
86 ARM_COMPUTE_ERROR_ON_MSG(post_ops.size() > 0, "ClGemmConv2d quantized types do not support post ops");
87 // Since we need negative offsets for computing convolution, we need to change QuantizationInfo()
88 // Extract and negate input and weights offset
89 const QuantizationInfo input_quantization_info = src->quantization_info();
90 const QuantizationInfo weights_quantization_info = weights->quantization_info();
91
92 tmp_src.set_quantization_info(QuantizationInfo(input_quantization_info.uniform().scale, -input_quantization_info.uniform().offset));
93 weights->set_quantization_info(QuantizationInfo(weights_quantization_info.uniform().scale, -weights_quantization_info.uniform().offset));
94
95 _mm_gemmlowp = std::make_unique<ClGemmLowpMatrixMultiplyCore>();
96 _mm_gemmlowp->configure(compile_context, &tmp_src, weights, biases, dst, gemm_info);
97
98 // Revert back QuantizatioInfo as weights could be used in other convolution layers
99 weights->set_quantization_info(weights_quantization_info);
100
101 auto mm_mem_req = _mm_gemmlowp->workspace();
102 for(unsigned int cont = 0; cont < mm_mem_req.size(); ++cont)
103 {
104 _aux_mem[cont] = mm_mem_req[cont];
105 }
106 }
107 else
108 {
109 // Configure matrix multiply function
110 _mm_gemm = std::make_unique<ClGemm>();
111 _mm_gemm->configure(compile_context, &tmp_src, weights, biases, dst, 1.0f, 1.0f, gemm_info);
112 auto mm_mem_req = _mm_gemm->workspace();
113 for(unsigned int cont = 0; cont < mm_mem_req.size(); ++cont)
114 {
115 _aux_mem[cont] = mm_mem_req[cont];
116 }
117 }
118 }
119
validate_mm(const ITensorInfo * src,const ITensorInfo * weights,const ITensorInfo * biases,const ITensorInfo * dst,const GEMMLowpOutputStageInfo & gemmlowp_output_stage,int gemm_3d_depth,bool skip_im2col,const ActivationLayerInfo & act_info,const experimental::PostOpList<ITensorInfo * > & post_ops)120 Status ClGemmConv2d::validate_mm(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst,
121 const GEMMLowpOutputStageInfo &gemmlowp_output_stage, int gemm_3d_depth, bool skip_im2col, const ActivationLayerInfo &act_info, const experimental::PostOpList<ITensorInfo *> &post_ops)
122 {
123 const bool is_quantized = is_data_type_quantized_asymmetric(src->data_type());
124
125 const GEMMInfo &gemm_info = GEMMInfo(false, // is_a_reshaped
126 false, // is_b_reshaped
127 true, // reshape_b_only_on_first_run
128 gemm_3d_depth, // depth_output_gemm3d
129 skip_im2col, // reinterpret_input_as_3d
130 false, // retain_internal_weights
131 gemmlowp_output_stage, // gemmlowp_output_stage
132 false, // fast_math
133 false, // fp_mixed_precision
134 true, // broadcast_bias
135 act_info, // activation_info
136 post_ops // post ops
137 );
138
139 if(is_quantized)
140 {
141 ARM_COMPUTE_RETURN_ERROR_ON_MSG(post_ops.size() > 0, "ClGemmConv2d quantized types do not support post ops");
142 // Since we need negative offsets for computing convolution, we need to change QuantizationInfo()
143 // Extract and negate input and weights offset
144 const QuantizationInfo input_quantization_info = src->quantization_info();
145 const QuantizationInfo weights_quantization_info = weights->quantization_info();
146
147 std::unique_ptr<ITensorInfo> src_qa = src->clone();
148 std::unique_ptr<ITensorInfo> weights_qa = weights->clone();
149 src_qa->set_quantization_info(QuantizationInfo(input_quantization_info.uniform().scale, -input_quantization_info.uniform().offset));
150 weights_qa->set_quantization_info(QuantizationInfo(weights_quantization_info.uniform().scale, -weights_quantization_info.uniform().offset));
151
152 // Perform validation step on GEMMLowp
153 return ClGemmLowpMatrixMultiplyCore::validate(src_qa.get(), weights_qa.get(), biases, dst, gemm_info);
154 }
155 else
156 {
157 // Perform validation step on Matrix multiply function
158 return ClGemm::validate(src, weights, biases, dst, 1.0f, 1.0f, gemm_info);
159 }
160 }
161
configure(const CLCompileContext & compile_context,ITensorInfo * src,ITensorInfo * weights,ITensorInfo * biases,ITensorInfo * dst,const Conv2dInfo & conv2d_info,const WeightsInfo & weights_info)162 void ClGemmConv2d::configure(const CLCompileContext &compile_context, ITensorInfo *src, ITensorInfo *weights, ITensorInfo *biases, ITensorInfo *dst,
163 const Conv2dInfo &conv2d_info, const WeightsInfo &weights_info)
164 {
165 ARM_COMPUTE_ERROR_ON_NULLPTR(src, weights, dst);
166
167 ARM_COMPUTE_ERROR_THROW_ON(ClGemmConv2d::validate(src, weights, biases, dst,
168 conv2d_info,
169 weights_info));
170 ARM_COMPUTE_LOG_PARAMS(src, weights, biases, dst, conv2d_info, weights_info);
171
172 const DataType data_type = src->data_type();
173 const DataLayout data_layout = src->data_layout();
174 const int idx_width = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
175 const int idx_height = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
176 const int idx_kernels = get_data_layout_dimension_index(data_layout, DataLayoutDimension::BATCHES);
177
178 const unsigned int kernel_width = weights->dimension(idx_width);
179 const unsigned int kernel_height = weights->dimension(idx_height);
180 const unsigned int num_kernels = weights->dimension(idx_kernels);
181
182 const UniformQuantizationInfo iq_info = src->quantization_info().uniform();
183 const UniformQuantizationInfo oq_info = dst->quantization_info().uniform();
184
185 _is_prepared = weights_info.retain_internal_weights();
186 _is_quantized = is_data_type_quantized_asymmetric(src->data_type());
187 _skip_im2col = (data_layout == DataLayout::NHWC && kernel_width == 1 && kernel_height == 1 && conv2d_info.conv_info.stride().first == 1 && conv2d_info.conv_info.stride().second == 1);
188 _skip_col2im = data_layout == DataLayout::NHWC;
189
190 // Only for quantize there are few cases where we cannot fuse the activation function in GEMM
191 _fuse_activation = true;
192 _use_post_ops = conv2d_info.post_ops.size() > 0;
193
194 const ITensorInfo *gemm_input_to_use = src;
195 ITensorInfo *gemm_output_to_use = dst;
196
197 // Get parameters from conv_info
198 unsigned int stride_x = 0;
199 unsigned int stride_y = 0;
200 std::tie(stride_x, stride_y) = conv2d_info.conv_info.stride();
201
202 // Get convolved dimensions
203 unsigned int conv_w = 0;
204 unsigned int conv_h = 0;
205 std::tie(conv_w, conv_h) = scaled_dimensions(src->dimension(idx_width),
206 src->dimension(idx_height),
207 kernel_width,
208 kernel_height,
209 conv2d_info.conv_info,
210 conv2d_info.dilation);
211
212 unsigned int mat_weights_cols = num_kernels / conv2d_info.num_groups;
213
214 ITensorInfo *biases_to_use = biases;
215 _append_bias = false;
216
217 _weights_reshape_kernel = std::make_unique<kernels::ClWeightsReshapeKernel>();
218 if(conv2d_info.num_groups != 1 && biases != nullptr)
219 {
220 // num_groups != 1 can only be for NCHW
221 // Since it is missing an utility function to reshape the biases, we append the biases into the weights tensor
222 biases_to_use = nullptr;
223 _append_bias = true;
224 _weights_reshape_kernel->configure(compile_context, weights, biases, &_weights_reshaped, conv2d_info.num_groups);
225 }
226 else
227 {
228 _weights_reshape_kernel->configure(compile_context, weights, nullptr, &_weights_reshaped, conv2d_info.num_groups);
229 }
230
231 // Create tensor to store im2col reshaped inputs
232 if(!_skip_im2col)
233 {
234 // Configure and tune im2col. im2col output shape is auto-initialized
235 _im2col_kernel = std::make_unique<opencl::kernels::ClIm2ColKernel>();
236
237 // Set the GPU target for im2col
238 _im2col_kernel->set_target(CLScheduler::get().target());
239 _im2col_kernel->configure(compile_context, src, &_im2col_output, Size2D(kernel_width, kernel_height), conv2d_info.conv_info, _append_bias, conv2d_info.dilation, conv2d_info.num_groups);
240
241 // Set quantization info
242 _im2col_output.set_quantization_info(src->quantization_info());
243 CLScheduler::get().tune_kernel_static(*_im2col_kernel);
244
245 // Update GEMM input
246 gemm_input_to_use = &_im2col_output;
247 }
248
249 // Create GEMM output tensor
250 if(!_skip_col2im)
251 {
252 TensorShape shape_gemm;
253
254 // If we cannot skip col2im it means we run im2col as well
255 shape_gemm = _im2col_output.tensor_shape();
256 shape_gemm.set(0, mat_weights_cols);
257 shape_gemm.set(1, conv_w * conv_h);
258
259 _gemm_output = TensorInfo(shape_gemm, 1, data_type);
260 _gemm_output.set_quantization_info(dst->quantization_info()).set_data_layout(src->data_layout());
261
262 // Update GEMM output
263 gemm_output_to_use = &_gemm_output;
264 }
265
266 GEMMLowpOutputStageInfo gemmlowp_output_stage;
267 gemmlowp_output_stage.type = GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT;
268 gemmlowp_output_stage.gemmlowp_offset = 0;
269
270 // Configure output stage for quantized case
271 if(_is_quantized)
272 {
273 const auto output_quant_info = (dst->total_size() == 0) ? iq_info : oq_info;
274 const bool is_quantized_per_channel = is_data_type_quantized_per_channel(weights->data_type());
275 const unsigned int num_filters = (is_quantized_per_channel) ? num_kernels : 1;
276
277 gemmlowp_output_stage.is_quantized_per_channel = is_quantized_per_channel;
278
279 gemmlowp_output_stage.gemmlowp_multipliers.resize(num_filters);
280 gemmlowp_output_stage.gemmlowp_shifts.resize(num_filters);
281 quantization::compute_quantized_multipliers_and_shifts(src, weights, dst,
282 gemmlowp_output_stage.gemmlowp_multipliers.data(),
283 gemmlowp_output_stage.gemmlowp_shifts.data());
284 gemmlowp_output_stage.gemmlowp_multiplier = gemmlowp_output_stage.gemmlowp_multipliers[0];
285 gemmlowp_output_stage.gemmlowp_shift = gemmlowp_output_stage.gemmlowp_shifts[0];
286
287 PixelValue min_val{};
288 PixelValue max_val{};
289 std::tie(min_val, max_val) = get_min_max(dst->data_type());
290
291 auto min_activation = min_val.get<int32_t>();
292 auto max_activation = max_val.get<int32_t>();
293
294 const std::set<ActivationLayerInfo::ActivationFunction> supported_acts = { ActivationLayerInfo::ActivationFunction::RELU,
295 ActivationLayerInfo::ActivationFunction::BOUNDED_RELU,
296 ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU
297 };
298
299 if(conv2d_info.act_info.enabled())
300 {
301 if(supported_acts.count(conv2d_info.act_info.activation()) != 0)
302 {
303 std::tie(min_activation, max_activation) = get_quantized_activation_min_max(conv2d_info.act_info, data_type, output_quant_info);
304 }
305 else
306 {
307 _fuse_activation = false;
308 }
309 }
310
311 // Set the GEMMLowp output stage info
312 gemmlowp_output_stage.gemmlowp_offset = output_quant_info.offset;
313 gemmlowp_output_stage.gemmlowp_min_bound = min_activation;
314 gemmlowp_output_stage.gemmlowp_max_bound = max_activation;
315 }
316
317 // Configure and tune GEMM
318 // In case of NHWC, we need to run GEMM3D (gemm_3d_depth != 0) in order to avoid reshaping the output matrix
319 const unsigned int gemm_3d_depth = (data_layout == DataLayout::NHWC) ? conv_h : 0;
320
321 configure_mm(compile_context, gemm_input_to_use, &_weights_reshaped, biases_to_use, gemm_output_to_use, gemmlowp_output_stage, gemm_3d_depth, conv2d_info.act_info, conv2d_info.post_ops);
322
323 if(!_skip_col2im)
324 {
325 ARM_COMPUTE_ERROR_ON_MSG(conv2d_info.post_ops.size() > 0, "ClGemmConv2d does not support post ops with col2im operation"); // Post ops must be performed after every other op
326 // Set the GPU target for col2im
327 _col2im_kernel = std::make_unique<opencl::kernels::ClCol2ImKernel>();
328 _col2im_kernel->set_target(CLScheduler::get().target());
329 // Configure and tune Col2Im
330 _col2im_kernel->configure(compile_context, gemm_output_to_use, dst, Size2D(conv_w, conv_h), conv2d_info.num_groups);
331 CLScheduler::get().tune_kernel_static(*_col2im_kernel.get());
332 }
333
334 ARM_COMPUTE_ERROR_ON_MSG((dst->dimension(idx_width) != conv_w) || (dst->dimension(idx_height) != conv_h),
335 "Output shape does not match the expected one");
336
337 // Disable running of activation kernel if post ops are used
338 if(!_fuse_activation && !_use_post_ops)
339 {
340 _activation_kernel = std::make_unique<opencl::kernels::ClActivationKernel>();
341 _activation_kernel->configure(compile_context, dst, nullptr, conv2d_info.act_info);
342 }
343
344 _aux_mem[Im2ColOutput] = MemoryInfo(offset_int_vec(Im2ColOutput), MemoryLifetime::Temporary, _im2col_output.total_size());
345 _aux_mem[WeightsReshaped] = MemoryInfo(offset_int_vec(WeightsReshaped), MemoryLifetime::Persistent, _weights_reshaped.total_size());
346 _aux_mem[GemmOutput] = MemoryInfo(offset_int_vec(GemmOutput), MemoryLifetime::Temporary, _gemm_output.total_size());
347 }
348
validate(const ITensorInfo * src,const ITensorInfo * weights,const ITensorInfo * biases,const ITensorInfo * dst,const Conv2dInfo & conv2d_info,const WeightsInfo & weights_info)349 Status ClGemmConv2d::validate(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const Conv2dInfo &conv2d_info,
350 const WeightsInfo &weights_info)
351 {
352 ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src, weights, dst);
353 ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights_info.are_reshaped(), "Weights already reshaped are not supported!");
354 ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::F16, DataType::F32);
355 const bool is_quantized_per_channel = is_data_type_quantized_per_channel(weights->data_type());
356
357 if(!is_quantized_per_channel)
358 {
359 ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, weights);
360 }
361 ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(src, weights);
362 ARM_COMPUTE_RETURN_ERROR_ON_MSG((conv2d_info.num_groups != 1) && (src->data_layout() != DataLayout::NCHW), "Grouping (num_groups != 1) with NHWC data layout is not supported");
363 ARM_COMPUTE_RETURN_ERROR_ON_MSG((conv2d_info.num_groups != 1) && (src->data_type() == DataType::QASYMM8), "Grouping (num_groups != 1) is not supported with QASYMM8");
364 ARM_COMPUTE_RETURN_ERROR_ON(((src->dimension(2) / weights->dimension(2)) != conv2d_info.num_groups) && (src->data_layout() == DataLayout::NCHW));
365
366 const DataLayout data_layout = src->data_layout();
367 const DataType data_type = src->data_type();
368 const int idx_width = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
369 const int idx_height = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
370 const int idx_channel = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL);
371 const int idx_kernels = get_data_layout_dimension_index(data_layout, DataLayoutDimension::BATCHES);
372
373 const unsigned int kernel_width = weights->dimension(idx_width);
374 const unsigned int kernel_height = weights->dimension(idx_height);
375 const unsigned int num_kernels = weights->dimension(idx_kernels);
376
377 TensorInfo im2col_reshaped_info{};
378 TensorInfo info_gemm{};
379 TensorInfo weights_reshaped_info{};
380 const ITensorInfo *gemm_input_to_use = src;
381 const ITensorInfo *gemm_output_to_use = dst;
382 const ITensorInfo *weights_to_use = weights;
383 const bool is_quantized = is_data_type_quantized_asymmetric(data_type);
384 const bool skip_im2col = (data_layout == DataLayout::NHWC && kernel_width == 1 && kernel_height == 1 && conv2d_info.conv_info.stride().first == 1
385 && conv2d_info.conv_info.stride().second == 1);
386 const bool skip_col2im = data_layout == DataLayout::NHWC;
387 bool fuse_activation = true;
388 bool use_post_ops = conv2d_info.post_ops.size() > 0;
389
390 ARM_COMPUTE_RETURN_ERROR_ON((weights->dimension(idx_channel) * conv2d_info.num_groups) != src->dimension(idx_channel));
391 ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4);
392 ARM_COMPUTE_RETURN_ERROR_ON_MSG(!skip_im2col
393 && conv2d_info.post_ops.size() > 0,
394 "ClGemmConv2d does not support post ops with col2im or im2col operation"); // Post ops must be performed after every other op
395
396 // Validate biases
397 if(biases != nullptr)
398 {
399 if(is_quantized)
400 {
401 ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::S32);
402 }
403 else
404 {
405 ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, biases);
406 }
407 ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(idx_kernels));
408 ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
409 }
410
411 if(conv2d_info.act_info.enabled())
412 {
413 ARM_COMPUTE_ERROR_ON(conv2d_info.act_info.b() > conv2d_info.act_info.a());
414 }
415
416 // Get convolved dimensions
417 unsigned int conv_w = 0;
418 unsigned int conv_h = 0;
419
420 std::tie(conv_w, conv_h) = scaled_dimensions(src->dimension(idx_width),
421 src->dimension(idx_height),
422 kernel_width,
423 kernel_height,
424 conv2d_info.conv_info,
425 conv2d_info.dilation);
426
427 unsigned int mat_weights_cols = num_kernels / conv2d_info.num_groups;
428
429 const ITensorInfo *biases_to_use = biases;
430 bool append_bias = false;
431
432 if(conv2d_info.num_groups != 1 && biases != nullptr)
433 {
434 // num_groups != 1 can only be for NCHW
435 // Since it is missing an utility function to reshape the biases, we append the biases into the weights tensor
436 biases_to_use = nullptr;
437 append_bias = true;
438 weights_reshaped_info = TensorInfo(compute_weights_reshaped_shape(*weights, true, conv2d_info.num_groups), 1, data_type);
439 }
440 else
441 {
442 weights_reshaped_info = TensorInfo(compute_weights_reshaped_shape(*weights, false, conv2d_info.num_groups), 1, data_type);
443 }
444
445 weights_to_use = &weights_reshaped_info;
446
447 if(!skip_im2col)
448 {
449 const Size2D kernel_dims(kernel_width, kernel_height);
450
451 // Output tensor auto initialization if not yet initialized
452 TensorShape expected_output_shape = compute_im2col_conv_shape(src, kernel_dims, conv2d_info.conv_info, append_bias, conv2d_info.dilation, conv2d_info.num_groups == 1, conv2d_info.num_groups);
453
454 auto_init_if_empty(im2col_reshaped_info, src->clone()->set_tensor_shape(expected_output_shape));
455
456 ARM_COMPUTE_RETURN_ON_ERROR(opencl::kernels::ClIm2ColKernel::validate(src, &im2col_reshaped_info, kernel_dims, conv2d_info.conv_info, append_bias, conv2d_info.dilation, conv2d_info.num_groups));
457 gemm_input_to_use = &im2col_reshaped_info;
458 }
459
460 // Create GEMM output tensor
461 if(!skip_col2im)
462 {
463 TensorShape shape_gemm;
464
465 shape_gemm = gemm_input_to_use->tensor_shape();
466 shape_gemm.set(0, mat_weights_cols);
467 shape_gemm.set(1, conv_w * conv_h);
468
469 info_gemm = TensorInfo(shape_gemm, 1, data_type);
470 info_gemm.set_quantization_info(dst->quantization_info()).set_data_layout(src->data_layout());
471 gemm_output_to_use = &info_gemm;
472 }
473
474 GEMMLowpOutputStageInfo gemmlowp_output_stage;
475 gemmlowp_output_stage.type = GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT;
476 gemmlowp_output_stage.gemmlowp_offset = 0;
477 gemmlowp_output_stage.is_quantized_per_channel = is_quantized_per_channel;
478
479 if(is_quantized)
480 {
481 const UniformQuantizationInfo iq_info = src->quantization_info().uniform();
482 const UniformQuantizationInfo oq_info = dst->quantization_info().uniform();
483 const auto output_quant_info = (dst->total_size() == 0) ? iq_info : oq_info;
484 const unsigned int num_filters = (is_quantized_per_channel) ? num_kernels : 1;
485
486 gemmlowp_output_stage.gemmlowp_multipliers.resize(num_filters);
487 gemmlowp_output_stage.gemmlowp_shifts.resize(num_filters);
488 quantization::compute_quantized_multipliers_and_shifts(src, weights, dst,
489 gemmlowp_output_stage.gemmlowp_multipliers.data(),
490 gemmlowp_output_stage.gemmlowp_shifts.data());
491 gemmlowp_output_stage.gemmlowp_multiplier = gemmlowp_output_stage.gemmlowp_multipliers[0];
492 gemmlowp_output_stage.gemmlowp_shift = gemmlowp_output_stage.gemmlowp_shifts[0];
493
494 int min_activation = 0;
495 int max_activation = 0;
496
497 const std::set<ActivationLayerInfo::ActivationFunction> supported_acts = { ActivationLayerInfo::ActivationFunction::RELU,
498 ActivationLayerInfo::ActivationFunction::BOUNDED_RELU,
499 ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU
500 };
501
502 if(conv2d_info.act_info.enabled())
503 {
504 if(supported_acts.count(conv2d_info.act_info.activation()) != 0)
505 {
506 std::tie(min_activation, max_activation) = get_quantized_activation_min_max(conv2d_info.act_info, data_type, output_quant_info);
507 }
508 else
509 {
510 fuse_activation = false;
511 }
512 }
513
514 // Set the GEMMLowp output stage info
515 gemmlowp_output_stage.gemmlowp_offset = output_quant_info.offset;
516 gemmlowp_output_stage.gemmlowp_min_bound = min_activation;
517 gemmlowp_output_stage.gemmlowp_max_bound = max_activation;
518 }
519
520 // In case of NHWC, we need to run GEMM3D (gemm_3d_depth != 0) in order to avoid reshaping the output matrix
521 const unsigned int gemm_3d_depth = (data_layout == DataLayout::NHWC) ? conv_h : 0;
522
523 ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(gemm_input_to_use, weights_to_use, biases_to_use, gemm_output_to_use, gemmlowp_output_stage, gemm_3d_depth, skip_im2col, conv2d_info.act_info,
524 conv2d_info.post_ops));
525
526 // Validate Col2Im
527 if(!skip_col2im)
528 {
529 ARM_COMPUTE_RETURN_ON_ERROR(kernels::ClCol2ImKernel::validate(gemm_output_to_use, dst, Size2D(conv_w, conv_h), conv2d_info.num_groups));
530 }
531
532 // Validate Activation Layer
533 // Disable running (thus validation) of activation kernel if post ops are used
534 if(!fuse_activation && !use_post_ops)
535 {
536 ARM_COMPUTE_RETURN_ON_ERROR(kernels::ClActivationKernel::validate(dst, nullptr, conv2d_info.act_info));
537 }
538
539 return Status{};
540 }
541
run(ITensorPack & tensors)542 void ClGemmConv2d::run(ITensorPack &tensors)
543 {
544 prepare(tensors);
545
546 auto src = tensors.get_const_tensor(ACL_SRC_0);
547 auto biases = tensors.get_const_tensor(ACL_SRC_2);
548 auto dst = tensors.get_tensor(ACL_DST);
549 auto gemm_input_to_use = src;
550 auto gemm_output_to_use = dst;
551
552 CLAuxTensorHandler im2col_output(offset_int_vec(Im2ColOutput), _im2col_output, tensors, false);
553 CLAuxTensorHandler gemm_output(offset_int_vec(GemmOutput), _gemm_output, tensors, false);
554 CLAuxTensorHandler weights_reshaped(offset_int_vec(WeightsReshaped), _weights_reshaped, tensors, false);
555
556 // Run im2col
557 if(!_skip_im2col)
558 {
559 ITensorPack pack =
560 {
561 { TensorType::ACL_SRC, src },
562 { TensorType::ACL_DST, im2col_output.get() }
563 };
564 CLScheduler::get().enqueue_op(*_im2col_kernel, pack, false);
565 gemm_input_to_use = im2col_output.get();
566 }
567 if(!_skip_col2im)
568 {
569 gemm_output_to_use = gemm_output.get();
570 }
571 ITensorPack pack_mm = tensors;
572 pack_mm.add_const_tensor(TensorType::ACL_SRC_0, gemm_input_to_use);
573 pack_mm.add_const_tensor(TensorType::ACL_SRC_1, weights_reshaped.get());
574 if(!_append_bias)
575 {
576 pack_mm.add_const_tensor(TensorType::ACL_SRC_2, biases);
577 }
578 pack_mm.add_tensor(TensorType::ACL_DST, gemm_output_to_use);
579 // Runs ClGemm or ClGemmLowpMatrixMultiplyCore functions
580 if(_is_quantized)
581 {
582 // Run gemmlowp
583 _mm_gemmlowp->run(pack_mm);
584 }
585 else
586 {
587 // Run gemm
588 _mm_gemm->run(pack_mm);
589 }
590
591 // Reshape output matrix
592 if(!_skip_col2im)
593 {
594 ITensorPack pack =
595 {
596 { TensorType::ACL_SRC, gemm_output_to_use },
597 { TensorType::ACL_DST, dst }
598 };
599 CLScheduler::get().enqueue_op(*_col2im_kernel.get(), pack, false);
600 }
601
602 //Run Activation Layer if we cannot fuse in GEMM
603 // Disable running of activation kernel if post ops are used
604 if(!_fuse_activation && !_use_post_ops)
605 {
606 ITensorPack pack =
607 {
608 { TensorType::ACL_SRC, dst },
609 { TensorType::ACL_DST, dst }
610 };
611 CLScheduler::get().enqueue_op(*_activation_kernel.get(), pack, false);
612 }
613 }
614
prepare(ITensorPack & tensors)615 void ClGemmConv2d::prepare(ITensorPack &tensors)
616 {
617 if(!_is_prepared)
618 {
619 // Run weights reshaping and mark original weights tensor as unused
620 ICLTensor *weights_reshaped_p = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(offset_int_vec(WeightsReshaped)));
621 CLAuxTensorHandler weights_reshaped(_weights_reshaped, *weights_reshaped_p);
622 auto weights = tensors.get_const_tensor(TensorType::ACL_SRC_1);
623 ITensorPack pack =
624 {
625 { TensorType::ACL_SRC, weights },
626 { TensorType::ACL_DST, weights_reshaped.get() }
627 };
628
629 if(_append_bias)
630 {
631 const auto biases = tensors.get_const_tensor(TensorType::ACL_SRC_2);
632 pack.add_const_tensor(TensorType::ACL_BIAS, biases);
633 }
634 CLScheduler::get().enqueue_op(*_weights_reshape_kernel.get(), pack, true);
635 tensors.add_const_tensor(TensorType::ACL_SRC_1, weights_reshaped.get());
636
637 // Prepare GEMM
638 _is_quantized ? _mm_gemmlowp->prepare(tensors) : _mm_gemm->prepare(tensors);
639 _is_prepared = true;
640 }
641 }
workspace() const642 experimental::MemoryRequirements ClGemmConv2d::workspace() const
643 {
644 return _aux_mem;
645 }
646 } // namespace opencl
647 } // namespace arm_compute
648