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Diffstat (limited to 'src/runtime/CL/functions/CLGEMMDeconvolutionLayer.cpp')
| -rw-r--r-- | src/runtime/CL/functions/CLGEMMDeconvolutionLayer.cpp | 373 |
1 files changed, 373 insertions, 0 deletions
diff --git a/src/runtime/CL/functions/CLGEMMDeconvolutionLayer.cpp b/src/runtime/CL/functions/CLGEMMDeconvolutionLayer.cpp new file mode 100644 index 0000000000..bcb91e052c --- /dev/null +++ b/src/runtime/CL/functions/CLGEMMDeconvolutionLayer.cpp @@ -0,0 +1,373 @@ +/* + * Copyright (c) 2019 ARM Limited. + * + * SPDX-License-Identifier: MIT + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to + * deal in the Software without restriction, including without limitation the + * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or + * sell copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + */ +#include "arm_compute/runtime/CL/functions/CLGEMMDeconvolutionLayer.h" + +#include "arm_compute/core/Helpers.h" +#include "arm_compute/core/Validate.h" +#include "arm_compute/core/utils/misc/ShapeCalculator.h" +#include "arm_compute/core/utils/quantization/AsymmHelpers.h" +#include "arm_compute/runtime/CL/CLScheduler.h" +#include "utils/TypePrinter.h" + +#include <memory> +#include <tuple> + +namespace arm_compute +{ +namespace +{ +std::pair<Coordinates, Coordinates> compute_start_end_slice_coordinates(const ITensorInfo &output_info, const PadStrideInfo &deconv_info, bool is_nchw) +{ + Coordinates start; + Coordinates end; + + if(is_nchw) + { + start.set(0, deconv_info.pad_left()); + start.set(1, deconv_info.pad_top()); + end.set(0, output_info.dimension(0) - deconv_info.pad_right()); + end.set(1, output_info.dimension(1) - deconv_info.pad_bottom()); + } + else + { + start.set(0, 0); + start.set(1, deconv_info.pad_left()); + start.set(2, deconv_info.pad_top()); + + end.set(0, output_info.dimension(0)); + end.set(1, output_info.dimension(1) - deconv_info.pad_right()); + end.set(2, output_info.dimension(2) - deconv_info.pad_bottom()); + } + + return { start, end }; +} +} // namespace + +CLGEMMDeconvolutionLayer::CLGEMMDeconvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager) // NOLINT + : _memory_group(std::move(memory_manager)), + _mm_gemm(), + _mm_gemmlowp(), + _gemmlowp_output_stage(), + _permute_input_to_nhwc(), + _permute_weights_to_nhwc(), + _reshape_weights(), + _transpose_weights(), + _deconv_reshape(), + _slice_gemm(), + _gemmlowp_final(), + _reshaped_weights(), + _reshaped_weights_t(), + _permuted_input(), + _permuted_weights(), + _gemm_output(), + _slice_gemm_input(), + _original_weights(), + _is_prepared(false), + _padded_input(false), + _is_nchw(false), + _is_quantized(false) +{ +} + +Status CLGEMMDeconvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *bias, const ITensorInfo *output, const PadStrideInfo &deconv_info) +{ + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32, DataType::F16, DataType::QASYMM8); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input, weights); + + DataLayout data_layout = input->data_layout(); + const bool padded_input = deconv_info.pad_bottom() > 0 || deconv_info.pad_left() > 0 || deconv_info.pad_right() > 0 || deconv_info.pad_top() > 0; + const bool is_nchw = input->data_layout() == DataLayout::NCHW; + const bool is_quantized = is_data_type_quantized_asymmetric(input->data_type()); + + const size_t idx_w = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); + const size_t idx_h = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); + const size_t idx_b = get_data_layout_dimension_index(data_layout, DataLayoutDimension::BATCHES); + + ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(idx_w) != deconv_info.stride().first); + ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(idx_h) != deconv_info.stride().second); + + TensorShape nhwc_weights_shape = weights->tensor_shape(); + TensorShape nhwc_input_shape = input->tensor_shape(); + + if(is_nchw) + { + permute(nhwc_weights_shape, PermutationVector(2, 0, 1)); + permute(nhwc_input_shape, PermutationVector(2, 0, 1)); + + TensorInfo nhwc_input_info = input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(nhwc_input_shape).set_data_layout(DataLayout::NCHW); + + TensorInfo nhwc_weights_info = weights->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(nhwc_weights_shape).set_data_layout(DataLayout::NCHW); + + CLPermute::validate(weights, &nhwc_weights_info, PermutationVector(2, 0, 1)); + CLPermute::validate(input, &nhwc_input_info, PermutationVector(2, 0, 1)); + } + + const TensorShape reshaped_shape = TensorShape(nhwc_weights_shape[0], nhwc_weights_shape[1] * nhwc_weights_shape[2] * nhwc_weights_shape[3]); + const TensorInfo reshaped_info = weights->clone()->set_tensor_shape(reshaped_shape).set_data_layout(DataLayout::NCHW).set_is_resizable(true); + ARM_COMPUTE_RETURN_ON_ERROR(CLReshapeLayer::validate(weights, &reshaped_info)); + + TensorShape transposed_shape(reshaped_shape[1], reshaped_shape[0]); + const TensorInfo reshaped_t_info = reshaped_info.clone()->set_is_resizable(true).set_tensor_shape(transposed_shape); + ARM_COMPUTE_RETURN_ON_ERROR(CLTranspose::validate(&reshaped_info, &reshaped_t_info)); + + TensorShape gemm_output_shape(weights->dimension(idx_w) * weights->dimension(idx_h) * weights->dimension(idx_b), + input->dimension(idx_w), + input->dimension(idx_h), + input->dimension(idx_b)); + + TensorInfo gemm_output_info = reshaped_t_info.clone()->set_tensor_shape(gemm_output_shape).set_is_resizable(true); + GEMMInfo gemm_info(false, false, true, input->dimension(idx_h), true); + + if(is_quantized) + { + ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMLowpMatrixMultiplyCore::validate(&input->clone()->set_tensor_shape(nhwc_input_shape), &reshaped_t_info, nullptr, &gemm_output_info.set_data_type(DataType::S32), + gemm_info)); + } + else + { + ARM_COMPUTE_RETURN_ON_ERROR(CLGEMM::validate(&input->clone()->set_tensor_shape(nhwc_input_shape).set_is_resizable(true), &reshaped_t_info, nullptr, &gemm_output_info, 1.0f, 0.0f, gemm_info)); + } + + auto out_dims = deconvolution_output_dimensions(input->dimension(idx_w), input->dimension(idx_h), weights->dimension(idx_w), weights->dimension(idx_h), + 0, 0, deconv_info.stride().first, deconv_info.stride().second); + const TensorShape deconv_shape = misc::shape_calculator::compute_deconvolution_output_shape(out_dims, *input, *weights); + TensorInfo col2im_output_info = gemm_output_info.clone()->set_tensor_shape(deconv_shape).set_is_resizable(true); + + if(padded_input && is_quantized) + { + const auto start_end = compute_start_end_slice_coordinates(col2im_output_info, deconv_info, is_nchw); + ARM_COMPUTE_RETURN_ON_ERROR(CLDeconvolutionReshapeOutputKernel::validate(&gemm_output_info, bias, &col2im_output_info, input, weights, deconv_info)); + ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint::validate(&col2im_output_info, nullptr, + &col2im_output_info.clone()->set_is_resizable(true).set_data_type(DataType::QASYMM8))); + ARM_COMPUTE_RETURN_ON_ERROR(CLSlice::validate(&col2im_output_info.clone()->set_is_resizable(true).set_data_type(DataType::QASYMM8), output, start_end.first, start_end.second)); + } + else if(padded_input) + { + const auto start_end = compute_start_end_slice_coordinates(col2im_output_info, deconv_info, is_nchw); + ARM_COMPUTE_RETURN_ON_ERROR(CLDeconvolutionReshapeOutputKernel::validate(&gemm_output_info, bias, &col2im_output_info, input, weights, deconv_info)); + ARM_COMPUTE_RETURN_ON_ERROR(CLSlice::validate(&col2im_output_info, output, start_end.first, start_end.second)); + } + else if(is_quantized) + { + ARM_COMPUTE_RETURN_ON_ERROR(CLDeconvolutionReshapeOutputKernel::validate(&gemm_output_info, bias, &col2im_output_info, input, weights, deconv_info)); + ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint::validate(&col2im_output_info, nullptr, output)); + } + else + { + ARM_COMPUTE_RETURN_ON_ERROR(CLDeconvolutionReshapeOutputKernel::validate(&gemm_output_info, bias, output, input, weights, deconv_info)); + } + + return Status{}; +} + +void CLGEMMDeconvolutionLayer::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *bias, ICLTensor *output, const PadStrideInfo &deconv_info) +{ + ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output); + ARM_COMPUTE_ERROR_THROW_ON(CLGEMMDeconvolutionLayer::validate(input->info(), + weights->info(), + bias != nullptr ? bias->info() : nullptr, + output->info(), + deconv_info)); + + _original_weights = weights; + _padded_input = deconv_info.pad_bottom() > 0 || deconv_info.pad_left() > 0 || deconv_info.pad_right() > 0 || deconv_info.pad_top() > 0; + _is_nchw = input->info()->data_layout() == DataLayout::NCHW; + _is_quantized = is_data_type_quantized_asymmetric(input->info()->data_type()); + + const ICLTensor *input_to_use = input; + const ICLTensor *weights_to_use = weights; + + // If the data layout is NCHW, transform everything in NHWC. Another alternative could be to + // do an outer product in NCHW and then an accumulation through a reduction. This would have two + // drawbacks: first, the outer product is less efficient than a full GEMM. Second, the reduction + // might be slower than GEMM. + if(_is_nchw) + { + _memory_group.manage(&_permuted_input); + _permute_input_to_nhwc.configure(input, &_permuted_input, PermutationVector(2U, 0U, 1U)); + + _permute_weights_to_nhwc.configure(weights, &_permuted_weights, PermutationVector(2U, 0U, 1U)); + + input_to_use = &_permuted_input; + weights_to_use = &_permuted_weights; + } + + // Reshape the input weights. The weights will be reshaped only once during the call to prepare() + _reshaped_weights.allocator()->init(TensorInfo(TensorShape(weights_to_use->info()->dimension(0), + weights_to_use->info()->dimension(1) * weights_to_use->info()->dimension(2) * weights_to_use->info()->dimension(3)), + 1, + input->info()->data_type(), weights->info()->quantization_info())); + + _reshape_weights.configure(weights_to_use, &_reshaped_weights); + _transpose_weights.configure(&_reshaped_weights, &_reshaped_weights_t); + + const size_t idx_h = get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::HEIGHT); + GEMMInfo gemm_info(false, false, true, input->info()->dimension(idx_h), true); + + // Configure output stage for asymmetric quantized types + if(_is_quantized) + { + _mm_gemmlowp.configure(input_to_use, &_reshaped_weights_t, nullptr, &_gemm_output, gemm_info); + } + else + { + _mm_gemm.configure(input_to_use, &_reshaped_weights_t, nullptr, &_gemm_output, 1.f, 0.0f, gemm_info); + } + + if(_is_nchw) + { + _permuted_input.allocator()->allocate(); + } + + ICLTensor *deconv_reshape_output = nullptr; + ICLTensor *slice_output = nullptr; + ICLTensor *output_stage_output = nullptr; + + if(_padded_input && _is_quantized) + { + _memory_group.manage(&_slice_gemm_input); + _memory_group.manage(&_gemmlowp_final); + deconv_reshape_output = &_gemmlowp_final; + output_stage_output = &_slice_gemm_input; + slice_output = output; + } + else if(_padded_input) + { + _memory_group.manage(&_slice_gemm_input); + deconv_reshape_output = &_slice_gemm_input; + slice_output = output; + } + else if(_is_quantized) + { + _memory_group.manage(&_gemmlowp_final); + deconv_reshape_output = &_gemmlowp_final; + output_stage_output = output; + } + else + { + deconv_reshape_output = output; + } + + // Configure a Col2Im call to reshape the output of GEMM + _deconv_reshape.configure(&_gemm_output, bias, deconv_reshape_output, input->info(), weights->info(), deconv_info); + _gemm_output.allocator()->allocate(); + + if(_is_quantized) + { + float multiplier = input->info()->quantization_info().scale * weights->info()->quantization_info().scale / _gemmlowp_final.info()->quantization_info().scale; + int output_multiplier(0); + int output_shift(0); + quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift); + _gemmlowp_output_stage.configure(&_gemmlowp_final, nullptr, output_stage_output, output_multiplier, output_shift, _gemmlowp_final.info()->quantization_info().offset); + _gemmlowp_final.allocator()->allocate(); + } + + // If the input was padded, the output needs to be sliced. + if(_padded_input) + { + const auto start_end = compute_start_end_slice_coordinates(*deconv_reshape_output->info(), deconv_info, _is_nchw); + _slice_gemm.configure(&_slice_gemm_input, slice_output, start_end.first, start_end.second); + _slice_gemm_input.allocator()->allocate(); + } +} + +void CLGEMMDeconvolutionLayer::run() +{ + prepare(); + + MemoryGroupResourceScope scope_mg(_memory_group); + + if(_is_nchw) + { + _permute_input_to_nhwc.run(); + } + + if(_is_quantized) + { + _mm_gemmlowp.run(); + } + else + { + _mm_gemm.run(); + } + + CLScheduler::get().enqueue(_deconv_reshape, false); + + if(_is_quantized) + { + _gemmlowp_output_stage.run(); + } + + if(_padded_input) + { + _slice_gemm.run(); + } +} + +void CLGEMMDeconvolutionLayer::prepare() +{ + if(!_is_prepared) + { + ARM_COMPUTE_ERROR_ON(!_original_weights->is_used()); + + if(_is_nchw) + { + _permuted_weights.allocator()->allocate(); + _permute_weights_to_nhwc.run(); + } + + _reshaped_weights.allocator()->allocate(); + _reshape_weights.run(); + + if(_is_nchw) + { + _permuted_weights.allocator()->free(); + } + + _reshaped_weights_t.allocator()->allocate(); + _transpose_weights.run(); + + // Prepare gemm + if(!_is_quantized) + { + _mm_gemm.prepare(); + } + else + { + _mm_gemmlowp.prepare(); + } + + // Free resources + if(!_reshaped_weights_t.is_used()) + { + _reshaped_weights_t.allocator()->free(); + } + + _original_weights->mark_as_unused(); + _is_prepared = true; + } +} +} // namespace arm_compute |