COMPMID-759 - CLGEMM optimization for McVail benchmarks
This patch introduces an optimization for CLGEMM on Bifrost
architectures which can bring to 40% of FMA utilization on
config 3 of McVail. The new CLGEMM does not require any reshape of
matrix A and matrix B.
This patch also adds the auto-config in CLConvolutionLayer and CLGEMM
and extends the interface for NEGEMM and CLGEMM.
Change-Id: Ibb354eda45e9ca64b14a99700fb21dff5989dda9
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/113716
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Michalis Spyrou <michalis.spyrou@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
diff --git a/src/runtime/CL/functions/CLGEMM.cpp b/src/runtime/CL/functions/CLGEMM.cpp
index ca0228f..be2527f 100644
--- a/src/runtime/CL/functions/CLGEMM.cpp
+++ b/src/runtime/CL/functions/CLGEMM.cpp
@@ -39,14 +39,17 @@
using namespace arm_compute;
CLGEMM::CLGEMM(std::shared_ptr<IMemoryManager> memory_manager)
- : _memory_group(std::move(memory_manager)), _interleave_kernel(), _transpose_kernel(), _mm_kernel(), _ma_kernel(), _tmp_a(), _tmp_b(), _is_interleaved_transposed(false), _run_addition(false)
+ : _memory_group(std::move(memory_manager)), _interleave_kernel(), _transpose_kernel(), _mm_kernel(), _ma_kernel(), _tmp_a(), _tmp_b(), _is_interleaved_transposed(false), _run_addition(false),
+ _is_first_run(true), _reshape_b_only_on_first_run(false)
{
}
-void CLGEMM::configure(const ICLTensor *a, const ICLTensor *b, const ICLTensor *c, ICLTensor *output, float alpha, float beta)
+void CLGEMM::configure(const ICLTensor *a, const ICLTensor *b, const ICLTensor *c, ICLTensor *output, float alpha, float beta, const GEMMInfo &gemm_info)
{
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(a, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(a, b, output);
+ ARM_COMPUTE_ERROR_ON_MSG(gemm_info.is_a_reshaped(), "Matrix A already reshaped is not supported");
+ ARM_COMPUTE_ERROR_ON_MSG(gemm_info.is_b_reshaped(), "Matrix B already reshaped is not supported");
if(c != nullptr)
{
@@ -60,7 +63,11 @@
ARM_COMPUTE_ERROR_ON_MSG(a->info()->dimension(0) != b->info()->dimension(1), "The product AB is defined only if the number of columns in A is equal to the number of rows in B");
// If the input tensor has less than 16 rows, we run a special version of GEMM without reshaping the input tensors
- _is_interleaved_transposed = a->info()->dimension(1) > 16;
+ // For Bifrost architectures we do not reshape the input matrices
+ _is_interleaved_transposed = (a->info()->dimension(1) > 16 && CLScheduler::get().target() != GPUTarget::BIFROST);
+
+ // Check if we need to reshape the matrix B only on the first run
+ _reshape_b_only_on_first_run = gemm_info.reshape_b_only_on_first_run();
const ICLTensor *matrix_a = a;
const ICLTensor *matrix_b = b;
@@ -73,31 +80,17 @@
matrix_a = &_tmp_a;
matrix_b = &_tmp_b;
- TensorShape shape_tmp_a = a->info()->tensor_shape();
- TensorShape shape_tmp_b = b->info()->tensor_shape();
-
- shape_tmp_a.set(0, a->info()->dimension(0) * 4);
- shape_tmp_a.set(1, std::ceil(a->info()->dimension(1) / 4.0f));
-
- const unsigned int transpose_w = max_cl_vector_width / data_size_from_type(b->info()->data_type());
- shape_tmp_b.set(0, b->info()->dimension(1) * transpose_w);
- shape_tmp_b.set(1, std::ceil(b->info()->dimension(0) / static_cast<float>(transpose_w)));
-
- TensorInfo info_a(shape_tmp_a, 1, a->info()->data_type(), a->info()->fixed_point_position());
- _tmp_a.allocator()->init(info_a);
-
- TensorInfo info_b(shape_tmp_b, 1, b->info()->data_type(), b->info()->fixed_point_position());
- _tmp_b.allocator()->init(info_b);
-
- // Manage intermediate buffers
- _memory_group.manage(&_tmp_a);
- _memory_group.manage(&_tmp_b);
+ // _tmp_a and _tmp_n will be auto configured in _interleave_kernel and in _transpose_kernel
// Configure interleave kernel
_interleave_kernel.configure(a, &_tmp_a);
// Configure transpose kernel
_transpose_kernel.configure(b, &_tmp_b);
+
+ // Manage intermediate buffers
+ _memory_group.manage(&_tmp_a);
+ _memory_group.manage(&_tmp_b);
}
_mm_kernel.configure(matrix_a, matrix_b, output, alpha, _is_interleaved_transposed);
@@ -126,8 +119,18 @@
// Run interleave kernel
CLScheduler::get().enqueue(_interleave_kernel, false);
- // Run transpose kernel
- CLScheduler::get().enqueue(_transpose_kernel, false);
+ if(_is_first_run)
+ {
+ // Run transpose kernel
+ CLScheduler::get().enqueue(_transpose_kernel, false);
+
+ _is_first_run = false;
+ }
+ else if(!_reshape_b_only_on_first_run)
+ {
+ // Run transpose kernel
+ CLScheduler::get().enqueue(_transpose_kernel, false);
+ }
}
// Run matrix multiply kernel