Add support for dilation > 1 in assembly DepthwiseConvolution
* Resolve COMPMID-5689
Change-Id: I81a3791ad054db59562b76d1c729f2b2168aee8b
Signed-off-by: Pablo Marquez Tello <pablo.tello@arm.com>
Signed-off-by: Andrew Mundy <andrew.mundy@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/8919
Reviewed-by: Jakub Sujak <jakub.sujak@arm.com>
Reviewed-by: Viet-Hoa Do <viet-hoa.do@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Benchmark: Arm Jenkins <bsgcomp@arm.com>
diff --git a/src/core/NEON/kernels/arm_conv/depthwise/depthwise_planar.hpp b/src/core/NEON/kernels/arm_conv/depthwise/depthwise_planar.hpp
index f3160fb..2b2e6f3 100644
--- a/src/core/NEON/kernels/arm_conv/depthwise/depthwise_planar.hpp
+++ b/src/core/NEON/kernels/arm_conv/depthwise/depthwise_planar.hpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2022 Arm Limited.
+ * Copyright (c) 2022-2023 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -332,18 +332,12 @@
}
void execute_internal(
- unsigned int batches,
- unsigned int input_height,
- unsigned int input_width,
- unsigned int n_input_channels,
- const PaddingValues &padding,
+ const DepthwiseArgs &args,
const void *input,
size_t ld_input_col,
size_t ld_input_row,
size_t ld_input_batch,
const void *parameters,
- unsigned int output_height,
- unsigned int output_width,
void *output,
size_t ld_output_col,
size_t ld_output_row,
@@ -359,7 +353,7 @@
this->initialise_working_space(thread_working_space);
auto ws = reinterpret_cast<WorkspaceType *>(thread_working_space);
- const auto n_output_channels = n_input_channels * this->m_args.channel_multiplier;
+ const auto n_output_channels = args.input_channels * args.channel_multiplier;
const auto vl = get_vector_length<TAccum>(m_strat->get_vl_type());
// Get typed pointers
@@ -368,23 +362,23 @@
auto weights = reinterpret_cast<const TWeight *>(parameters);
// Iterate over batches
- for (; batches; batches--)
+ for (auto batches = args.n_batches; batches; batches--)
{
// NOTE: Other loop orderings are possible and it would be worth
// investigating them.
// Within a batch, stripe threads across rows.
for (auto start_output_i = thread_id * m_strat->get_output_rows();
- start_output_i < output_height;
+ start_output_i < args.output_rows;
start_output_i += n_threads * m_strat->get_output_rows())
{
// Determine what (if any padding) is required on the top/bottom of
// this row of the convolution.
- const int start_input_i = start_output_i * this->m_args.stride_rows - padding.top;
+ const int start_input_i = start_output_i * args.stride_rows - args.padding.top;
const unsigned int input_pad_top = start_input_i < 0 ? -start_input_i : 0;
const unsigned int input_i = start_input_i < 0 ? 0 : start_input_i;
- const unsigned int valid_input_rows = input_i > input_height ? 0 : input_height - input_i;
- const unsigned int valid_output_rows = output_height - start_output_i;
+ const unsigned int valid_input_rows = input_i > args.input_rows ? 0 : args.input_rows - input_i;
+ const unsigned int valid_output_rows = args.output_rows - start_output_i;
auto inptr_row = input_batch + input_i*ld_input_row;
auto outptr_row = output_batch + start_output_i * ld_output_row;
@@ -392,10 +386,10 @@
// Execute the kernel
this->execute_kernel(
inptr_row, ld_input_row, ld_input_col, vl,
- input_pad_top, valid_input_rows, padding.left, input_width,
+ input_pad_top, valid_input_rows, args.padding.left, args.input_cols,
weights, this->m_bias,
outptr_row, ld_output_row, ld_output_col, vl,
- valid_output_rows, output_width,
+ valid_output_rows, args.output_cols,
0 /* first channel */, n_output_channels,
ws
);