DirectConv3d support refine
- Decouple data support of CpuDirectConv3dKernel
- Update documentation for Conv3d
Signed-off-by: Sheri Zhang <sheri.zhang@arm.com>
Change-Id: I1d94aa28f821f45a1a3d39cc3335c8faeee89f0d
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/6453
Reviewed-by: Giorgio Arena <giorgio.arena@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
diff --git a/src/cpu/kernels/conv3d/neon/list.h b/src/cpu/kernels/conv3d/neon/list.h
new file mode 100644
index 0000000..b24785a
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+++ b/src/cpu/kernels/conv3d/neon/list.h
@@ -0,0 +1,176 @@
+/*
+ * Copyright (c) 2021 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.
+ */
+#ifndef SRC_CORE_NEON_KERNELS_CONV3D_LIST_H
+#define SRC_CORE_NEON_KERNELS_CONV3D_LIST_H
+
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/utils/misc/Traits.h"
+#include "arm_compute/runtime/FunctionDescriptors.h"
+#include "src/core/NEON/wrapper/wrapper.h"
+#include "src/core/helpers/WindowHelpers.h"
+
+namespace arm_compute
+{
+namespace cpu
+{
+template <typename T>
+void directconv3d_float_neon_ndhwc(const ITensor *src0, const ITensor *src1, const ITensor *src2, ITensor *dst, const Conv3dInfo &conv_info, const Window &window)
+{
+ const ITensor *src = src0;
+ const ITensor *weights = src1;
+ const ITensor *biases = src2;
+
+ using vtype = wrapper::traits::neon_bitvector<T, wrapper::traits::BitWidth::W128>;
+ using vector_type = typename vtype::type;
+ using tag_type = typename vtype::tag_type;
+ constexpr int num_elems_read_per_iteration = 16 / sizeof(T);
+
+ // Scalar quantities (N D H W Cin)
+ const int element_size = src->info()->element_size();
+ const int input_stride_w = src->info()->strides_in_bytes().y() / element_size;
+ const int input_stride_h = src->info()->strides_in_bytes().z() / element_size;
+ const int input_stride_d = src->info()->strides_in_bytes()[3] / element_size;
+ const int input_stride_n = src->info()->strides_in_bytes()[4] / element_size;
+ const int input_dim_w = src->info()->dimension(1);
+ const int input_dim_h = src->info()->dimension(2);
+ const int input_dim_d = src->info()->dimension(3);
+
+ // Kernel info (D H W Cin Cout)
+ const unsigned int kernel_stride_w = weights->info()->strides_in_bytes()[2] / element_size;
+ const unsigned int kernel_stride_h = weights->info()->strides_in_bytes()[3] / element_size;
+ const unsigned int kernel_stride_d = weights->info()->strides_in_bytes()[4] / element_size;
+ const int kernel_dim_w = weights->info()->dimension(2);
+ const int kernel_dim_h = weights->info()->dimension(3);
+ const int kernel_dim_d = weights->info()->dimension(4);
+
+ // Convolution padding and stride
+ const int conv_pad_top = conv_info.padding.top;
+ const int conv_pad_left = conv_info.padding.left;
+ const int conv_pad_front = conv_info.padding.front;
+ const int conv_stride_w = conv_info.stride.width;
+ const int conv_stride_h = conv_info.stride.height;
+ const int conv_stride_d = conv_info.stride.depth;
+
+ // Setup input window for the output iterator
+ Window window_out = window;
+ window_out.set(Window::DimX, Window::Dimension(0, 1, 1));
+
+ // Setup input window for the weights iterator
+ Window window_w = calculate_max_window(*weights->info(), Steps());
+ window_w.set(Window::DimY, Window::Dimension(0, 1, 1));
+ window_w.set(Window::DimZ, Window::Dimension(0, 1, 1));
+ window_w.set(Window::DimW, Window::Dimension(0, 1, 1));
+ window_w.set(4, Window::Dimension(0, 1, 1));
+
+ Iterator out(dst, window_out);
+ Iterator wei(weights, window_w);
+
+ const T *biases_ptr = nullptr;
+ if(biases != nullptr)
+ {
+ biases_ptr = reinterpret_cast<T *>(biases->buffer() + biases->info()->offset_first_element_in_bytes());
+ }
+ execute_window_loop(window_out, [&](const Coordinates & id)
+ {
+ // We are computing the theoretical input starting points
+ const int in_w_start_t = static_cast<int>(id.y()) * conv_stride_w - conv_pad_left;
+ const int in_h_start_t = static_cast<int>(id.z()) * conv_stride_h - conv_pad_top;
+ const int in_d_start_t = static_cast<int>(id[3]) * conv_stride_d - conv_pad_front;
+ const int in_w_end_t = in_w_start_t + kernel_dim_w;
+ const int in_h_end_t = in_h_start_t + kernel_dim_h;
+ const int in_d_end_t = in_d_start_t + kernel_dim_d;
+
+ // We are computing the valid initial and ending input points by checking the borders
+ const int in_w_start = std::max(in_w_start_t, 0);
+ const int in_h_start = std::max(in_h_start_t, 0);
+ const int in_d_start = std::max(in_d_start_t, 0);
+ const int in_w_end = std::min(in_w_end_t, input_dim_w);
+ const int in_h_end = std::min(in_h_end_t, input_dim_h);
+ const int in_d_end = std::min(in_d_end_t, input_dim_d);
+
+ // We use the input points to select the valid weight points to use
+ const int wei_w_start = in_w_start - in_w_start_t;
+ const int wei_h_start = in_h_start - in_h_start_t;
+ const int wei_d_start = in_d_start - in_d_start_t;
+ const int wei_w_end = kernel_dim_w - (in_w_end_t - in_w_end);
+ const int wei_h_end = kernel_dim_h - (in_h_end_t - in_h_end);
+ const int wei_d_end = kernel_dim_d - (in_d_end_t - in_d_end);
+
+ const int index_c_out_end = weights->info()->dimension(0);
+ const int index_c_in_end = weights->info()->dimension(1);
+ const T *const in_ptr_start = reinterpret_cast<const T *>(src->buffer() + src->info()->offset_first_element_in_bytes()) + id[4] * input_stride_n;
+
+ execute_window_loop(window_w, [&](const Coordinates & id_w)
+ {
+ /*
+ * This is the loop in the weights, and it goes along OFM (output feature map)
+ */
+ const auto weights_ptr_start = reinterpret_cast<const T *>(wei.ptr());
+ T out_temp = static_cast<T>(0);
+ T *out_ptr = reinterpret_cast<T *>(out.ptr());
+ for(int index_wei_d = wei_d_start, index_in_d = in_d_start; index_wei_d < wei_d_end; ++index_wei_d, ++index_in_d)
+ {
+ const auto in_ptr_d = in_ptr_start + index_in_d * input_stride_d;
+ const auto weights_ptr_d = weights_ptr_start + index_wei_d * kernel_stride_d;
+ for(int index_wei_h = wei_h_start, index_in_h = in_h_start; index_wei_h < wei_h_end; ++index_wei_h, ++index_in_h)
+ {
+ const T *const in_ptr_row = in_ptr_d + index_in_h * input_stride_h;
+ const T *const weights_ptr_row = weights_ptr_d + index_wei_h * kernel_stride_h;
+ for(int index_wei_w = wei_w_start, index_in_w = in_w_start; index_wei_w < wei_w_end; ++index_wei_w, ++index_in_w)
+ {
+ const T *in_ptr_mover = in_ptr_row + index_in_w * input_stride_w;
+ const T *weights_ptr_mover = weights_ptr_row + index_wei_w * kernel_stride_w;
+ int index_c_in = 0;
+ vector_type out_temp_vec = wrapper::vdup_n(static_cast<T>(0), tag_type());
+ vector_type w_vec = wrapper::vdup_n(static_cast<T>(0), tag_type());
+ for(; index_c_in <= index_c_in_end - num_elems_read_per_iteration;
+ index_c_in += num_elems_read_per_iteration, in_ptr_mover += num_elems_read_per_iteration)
+ {
+ const auto src_vec = wrapper::vloadq(in_ptr_mover);
+ //Load Cin weights
+ for(unsigned int k = 0; k < num_elems_read_per_iteration; ++k, weights_ptr_mover += index_c_out_end)
+ {
+ w_vec = wrapper::vsetlane(*weights_ptr_mover, w_vec, k);
+ }
+ out_temp_vec = wrapper::vmla(out_temp_vec, w_vec, src_vec);
+ }
+ out_temp += vreduce(out_temp_vec);
+ for(; index_c_in < index_c_in_end; ++index_c_in, ++in_ptr_mover, weights_ptr_mover += index_c_out_end)
+ {
+ const auto src_val = *(in_ptr_mover);
+ const auto w_val = *(weights_ptr_mover);
+ out_temp += src_val * w_val;
+ }
+ }
+ }
+ }
+ *(reinterpret_cast<T *>(out_ptr + id_w[0])) = (biases_ptr != nullptr) ? out_temp + biases_ptr[id_w[0]] : out_temp;
+ },
+ wei);
+ },
+ out);
+}
+} // namespace cpu
+} // namespace arm_compute
+#endif // SRC_CORE_NEON_KERNELS_CONV3D_LIST_H
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