arm_compute v19.05
diff --git a/src/runtime/CL/functions/CLGEMMDeconvolutionLayer.cpp b/src/runtime/CL/functions/CLGEMMDeconvolutionLayer.cpp
new file mode 100644
index 0000000..bcb91e0
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+++ b/src/runtime/CL/functions/CLGEMMDeconvolutionLayer.cpp
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+/*
+ * 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