Fix GEMMLowp/Batched MatMul mismatches on CPU
- Fixes Column Offset matrix is not being iterated through in y dimension
Resolves : COMPMID-5795
Signed-off-by: Mohammed Suhail Munshi <MohammedSuhail.Munshi@arm.com>
Change-Id: I0190474be404b4f0e171855739cfd0a48cbed5bc
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/9020
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Gunes Bayir <gunes.bayir@arm.com>
Reviewed-by: SiCong Li <sicong.li@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Benchmark: Arm Jenkins <bsgcomp@arm.com>
diff --git a/src/cpu/kernels/CpuGemmLowpOffsetContributionOutputStageKernel.cpp b/src/cpu/kernels/CpuGemmLowpOffsetContributionOutputStageKernel.cpp
index 89aa364..c69af55 100644
--- a/src/cpu/kernels/CpuGemmLowpOffsetContributionOutputStageKernel.cpp
+++ b/src/cpu/kernels/CpuGemmLowpOffsetContributionOutputStageKernel.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2019-2021 Arm Limited.
+ * Copyright (c) 2019-2021, 2023 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -475,9 +475,18 @@
template <typename T>
void run_offset_contribution_output_stage(const Window &window,
const ITensor *mm_result, const ITensor *vector_sum_col, const ITensor *vector_sum_row, const ITensor *bias, ITensor *output,
- int32_t a_offset, int32_t b_offset, int32_t k_offset, bool slide_vector_sum_col,
+ int32_t a_offset, int32_t b_offset, int32_t k_offset, bool is_vector_sum_col_batched,
GEMMLowpOutputStageInfo output_stage, bool is_gemm3d, bool is_bounded_relu, bool is_fixed_point)
{
+ // Semantics of XYZW Explained for each tensor
+ //
+ // | Tensor | XYZW when is_gemm3d == false | XYZW when is_gemm3d == true |
+ // -------------------------------------------------------------------------------------------------------------------
+ // | mm_result | x -> width, y -> height, z -> batch | x -> width, y -> height, z -> depth, w -> batch |
+ // | collapsed window | x -> width, y -> height, z -> batch | x -> width, y -> height, z -> depth * batch |
+ // | vector_sum_row | x -> height, y -> batch | x -> height * depth, y -> batch |
+ // | Vector_sum_col | x -> width, y -> batch | x -> width, y -> batch |
+
using ExactTagType = typename wrapper::traits::neon_bitvector_tag_t<T, wrapper::traits::BitWidth::W128>;
using Typer = VectorTyper<T>;
@@ -517,8 +526,8 @@
const size_t sum_row_stride_y = vector_sum_row->info()->strides_in_bytes().y();
- // Offset in case vector_sum_col is batched
- const int vector_sum_col_batch_offset = slide_vector_sum_col ? vector_sum_col->info()->strides_in_bytes().z() : 0;
+ // Offset in case vector_sum_col is batched in y dimension
+ const int vector_sum_col_stride_batch = is_vector_sum_col_batched ? vector_sum_col->info()->strides_in_bytes().y() : 0;
if(bias != nullptr)
{
@@ -526,7 +535,7 @@
execute_window_loop(collapsed_window, [&](const Coordinates & id)
{
const int batch_id = id.z() / depth_input;
- const auto vector_sum_col_ptr = reinterpret_cast<const int32_t *>(vector_sum_col_it.ptr() + batch_id * vector_sum_col_batch_offset);
+ const auto vector_sum_col_ptr = reinterpret_cast<const int32_t *>(vector_sum_col_it.ptr() + batch_id * vector_sum_col_stride_batch);
const auto vector_sum_row_ptr = reinterpret_cast<const int32_t *>(vector_sum_row_it.ptr() + batch_id * sum_row_stride_y)
+ id.y() + (id.z() % depth_input) * height_input;
run_offset_contribution_output_stage_window<Typer>(vector_sum_col_ptr, vector_sum_row_ptr, reinterpret_cast<const int32_t *>(bias_it.ptr()),
@@ -544,7 +553,7 @@
execute_window_loop(collapsed_window, [&](const Coordinates & id)
{
const int batch_id = id.z() / depth_input;
- const auto vector_sum_col_ptr = reinterpret_cast<const int32_t *>(vector_sum_col_it.ptr() + batch_id * vector_sum_col_batch_offset);
+ const auto vector_sum_col_ptr = reinterpret_cast<const int32_t *>(vector_sum_col_it.ptr() + batch_id * vector_sum_col_stride_batch);
const auto vector_sum_row_ptr = reinterpret_cast<const int32_t *>(vector_sum_row_it.ptr() + batch_id * sum_row_stride_y)
+ id.y() + (id.z() % depth_input) * height_input;
run_offset_contribution_output_stage_window<Typer>(vector_sum_col_ptr, vector_sum_row_ptr, nullptr, mm_result_it, out_it,
@@ -603,8 +612,8 @@
Iterator vector_sum_col_it = get_vector_sum_col_it(collapsed_window, vector_sum_col);
- // Offset in case vector_sum_col is batched
- const int vector_sum_col_batch_offset = slide_vector_sum_col ? vector_sum_col->info()->strides_in_bytes().z() : 0;
+ // Offset in case vector_sum_col is batched in y dimension
+ const int vector_sum_col_stride_batch = is_vector_sum_col_batched ? vector_sum_col->info()->strides_in_bytes().y() : 0;
if(bias != nullptr)
{
@@ -612,7 +621,7 @@
execute_window_loop(collapsed_window, [&](const Coordinates & id)
{
const int batch_id = id.z() / depth_input;
- const auto vector_sum_col_ptr = reinterpret_cast<const int32_t *>(vector_sum_col_it.ptr() + batch_id * vector_sum_col_batch_offset);
+ const auto vector_sum_col_ptr = reinterpret_cast<const int32_t *>(vector_sum_col_it.ptr() + batch_id * vector_sum_col_stride_batch);
run_offset_contribution_output_stage_window<Typer>(vector_sum_col_ptr, nullptr, reinterpret_cast<const int32_t *>(bias_it.ptr()), mm_result_it,
out_it,
result_offset_s32, result_shift_s32,
@@ -627,7 +636,7 @@
execute_window_loop(collapsed_window, [&](const Coordinates & id)
{
const int batch_id = id.z() / depth_input;
- const auto vector_sum_col_ptr = reinterpret_cast<const int32_t *>(vector_sum_col_it.ptr() + batch_id * vector_sum_col_batch_offset);
+ const auto vector_sum_col_ptr = reinterpret_cast<const int32_t *>(vector_sum_col_it.ptr() + batch_id * vector_sum_col_stride_batch);
run_offset_contribution_output_stage_window<Typer>(vector_sum_col_ptr, nullptr, nullptr, mm_result_it, out_it,
result_offset_s32, result_shift_s32,
min_vec, max_vec, a_offset, b_offset, k_offset,
@@ -670,7 +679,7 @@
void run_offset_contribution_output_stage_symm(const Window &window,
const ITensor *mm_result, const ITensor *vector_sum_col, const ITensor *vector_sum_row, const ITensor *bias, ITensor *output,
- int32_t a_offset, int32_t b_offset, int32_t k_offset, bool slide_vector_sum_col,
+ int32_t a_offset, int32_t b_offset, int32_t k_offset, bool is_vector_sum_col_batched,
GEMMLowpOutputStageInfo output_stage, bool is_gemm3d, bool is_bounded_relu, bool is_fixed_point)
{
ARM_COMPUTE_UNUSED(vector_sum_row, b_offset, k_offset);
@@ -705,8 +714,8 @@
Iterator vector_sum_col_it = get_vector_sum_col_it(collapsed_window, vector_sum_col);
- // Offset in case vector_sum_col is batched
- const int vector_sum_col_batch_offset = slide_vector_sum_col ? vector_sum_col->info()->strides_in_bytes().z() : 0;
+ // Offset in case vector_sum_col is batched in y dimension
+ const int vector_sum_col_stride_batch = is_vector_sum_col_batched ? vector_sum_col->info()->strides_in_bytes().y() : 0;
if(bias != nullptr)
{
@@ -714,7 +723,7 @@
execute_window_loop(collapsed_window, [&](const Coordinates & id)
{
const int batch_id = id.z() / depth_input;
- const auto vector_sum_col_ptr = reinterpret_cast<const int32_t *>(vector_sum_col_it.ptr() + batch_id * vector_sum_col_batch_offset);
+ const auto vector_sum_col_ptr = reinterpret_cast<const int32_t *>(vector_sum_col_it.ptr() + batch_id * vector_sum_col_stride_batch);
run_offset_contribution_output_stage_window_symm(vector_sum_col_ptr, reinterpret_cast<const int32_t *>(bias_it.ptr()), mm_result_it, out_it,
result_multipliers, result_shifts,
result_offset_s32, min_s8, max_s8,
@@ -728,7 +737,7 @@
execute_window_loop(collapsed_window, [&](const Coordinates & id)
{
const int batch_id = id.z() / depth_input;
- const auto vector_sum_col_ptr = reinterpret_cast<const int32_t *>(vector_sum_col_it.ptr() + batch_id * vector_sum_col_batch_offset);
+ const auto vector_sum_col_ptr = reinterpret_cast<const int32_t *>(vector_sum_col_it.ptr() + batch_id * vector_sum_col_stride_batch);
run_offset_contribution_output_stage_window_symm(vector_sum_col_ptr, nullptr, mm_result_it, out_it,
result_multipliers, result_shifts,
result_offset_s32, min_s8, max_s8,
@@ -792,6 +801,7 @@
{
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(vector_sum_col, 1, DataType::S32);
ARM_COMPUTE_RETURN_ERROR_ON(vector_sum_col->dimension(0) != mm_result->dimension(0));
+ ARM_COMPUTE_RETURN_ERROR_ON(vector_sum_col->num_dimensions() > 2);
}
// If b_offset == 0, vector_sum_row can be a nullptr
@@ -827,6 +837,9 @@
"vector_sum_col tensor must have the same number of batches of vector_sum_row_shape or the number of batches must be set to 1");
}
}
+
+ // Check Tensor Rank of vector_sum_row
+ ARM_COMPUTE_RETURN_ERROR_ON(vector_sum_row->num_dimensions() > 2);
}
if(output->total_size() != 0)
@@ -860,7 +873,7 @@
// Check if vector_sum_col_shape should be slidden or not
// Don't slide vector_sum_col_shape along the y dimension if vector_sum_col_shape has just 1 dimension and vector_sum_row_shape more than 1
// This scenario can happen when the the matrix multiplication is used to perform a convolution operation
- _slide_vector_sum_col = vector_sum_col->tensor_shape().num_dimensions() > 1;
+ _is_vector_sum_col_batched = vector_sum_col->tensor_shape().num_dimensions() > 1;
}
// Output auto inizialitation if not yet initialized
@@ -919,19 +932,19 @@
if(is_symm)
{
- run_offset_contribution_output_stage_symm(window, mm_result, vector_sum_col, vector_sum_row, bias, dst, _a_offset, _b_offset, _k_offset, _slide_vector_sum_col, _output_stage,
+ run_offset_contribution_output_stage_symm(window, mm_result, vector_sum_col, vector_sum_row, bias, dst, _a_offset, _b_offset, _k_offset, _is_vector_sum_col_batched, _output_stage,
reinterpret_as_3d, is_bounded_relu, is_fixed_point);
}
else
{
if(is_signed)
{
- run_offset_contribution_output_stage<int8_t>(window, mm_result, vector_sum_col, vector_sum_row, bias, dst, _a_offset, _b_offset, _k_offset, _slide_vector_sum_col, _output_stage,
+ run_offset_contribution_output_stage<int8_t>(window, mm_result, vector_sum_col, vector_sum_row, bias, dst, _a_offset, _b_offset, _k_offset, _is_vector_sum_col_batched, _output_stage,
reinterpret_as_3d, is_bounded_relu, is_fixed_point);
}
else
{
- run_offset_contribution_output_stage<uint8_t>(window, mm_result, vector_sum_col, vector_sum_row, bias, dst, _a_offset, _b_offset, _k_offset, _slide_vector_sum_col, _output_stage,
+ run_offset_contribution_output_stage<uint8_t>(window, mm_result, vector_sum_col, vector_sum_row, bias, dst, _a_offset, _b_offset, _k_offset, _is_vector_sum_col_batched, _output_stage,
reinterpret_as_3d, is_bounded_relu, is_fixed_point);
}
}