arm_compute v17.09
Change-Id: I4bf8f4e6e5f84ce0d5b6f5ba570d276879f42a81
diff --git a/src/runtime/CL/functions/CLGEMM.cpp b/src/runtime/CL/functions/CLGEMM.cpp
index 7408054..a81d113 100644
--- a/src/runtime/CL/functions/CLGEMM.cpp
+++ b/src/runtime/CL/functions/CLGEMM.cpp
@@ -38,20 +38,18 @@
using namespace arm_compute;
-CLGEMM::CLGEMM()
- : _interleave_kernel(), _transpose_kernel(), _mm_kernel(), _ma_kernel(), _tmp_a(), _tmp_b(), _run_vector_matrix_multiplication(false), _run_addition(false)
+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)
{
}
void CLGEMM::configure(const ICLTensor *a, const ICLTensor *b, const ICLTensor *c, ICLTensor *output, float alpha, float beta)
{
- ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(a, 1, DataType::F32, DataType::F16);
- ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(b, 1, DataType::F32, DataType::F16);
- ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::F32, DataType::F16);
+ 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);
if(c != nullptr)
{
- ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(c, 1, DataType::F32, DataType::F16);
ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(a, c);
ARM_COMPUTE_ERROR_ON_MSG(a->info()->dimension(1) != c->info()->dimension(1), "The C matrix must have the same number of rows as the matrix A");
ARM_COMPUTE_ERROR_ON_MSG(b->info()->dimension(0) != c->info()->dimension(0), "The C matrix must have the same number of columns as the matrix C");
@@ -59,13 +57,18 @@
ARM_COMPUTE_ERROR_ON_MSG(c->info()->dimension(1) != output->info()->dimension(1), "The C matrix must have the same number of columns as the output matrix");
}
- ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(a, b, output);
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");
- // Check if the first input tensor is a vector. If so, all the kernels for reshaping the tensors can be skipped
- if(a->info()->dimension(1) != 1)
+ // 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;
+
+ const ICLTensor *matrix_a = a;
+ const ICLTensor *matrix_b = b;
+
+ if(_is_interleaved_transposed)
{
- _run_vector_matrix_multiplication = false;
+ matrix_a = &_tmp_a;
+ matrix_b = &_tmp_b;
TensorShape shape_tmp_a = a->info()->tensor_shape();
TensorShape shape_tmp_b = b->info()->tensor_shape();
@@ -73,27 +76,20 @@
shape_tmp_a.set(0, a->info()->dimension(0) * 4);
shape_tmp_a.set(1, std::ceil(a->info()->dimension(1) / 4.0f));
- if(DataType::F32 == a->info()->data_type())
- {
- shape_tmp_b.set(0, b->info()->dimension(1) * 4);
- shape_tmp_b.set(1, std::ceil(b->info()->dimension(0) / 4.0f));
- }
- else if(DataType::F16 == a->info()->data_type())
- {
- shape_tmp_b.set(0, b->info()->dimension(1) * 8);
- shape_tmp_b.set(1, std::ceil(b->info()->dimension(0) / 8.0f));
- }
- else
- {
- ARM_COMPUTE_ERROR("DataType not supported");
- }
+ 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());
+ 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());
+ 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);
+
// Configure interleave kernel
_interleave_kernel.configure(a, &_tmp_a);
@@ -101,19 +97,17 @@
_transpose_kernel.configure(b, &_tmp_b);
// Configure matrix multiply kernel
- _mm_kernel.configure(&_tmp_a, &_tmp_b, output, alpha);
+ _mm_kernel.set_target(CLScheduler::get().target());
+ }
+ _mm_kernel.configure(matrix_a, matrix_b, output, alpha, _is_interleaved_transposed);
+
+ if(_is_interleaved_transposed)
+ {
// Allocate intermediate tensors
_tmp_a.allocator()->allocate();
_tmp_b.allocator()->allocate();
}
- else // The first input tensor is a vector
- {
- _run_vector_matrix_multiplication = true;
-
- // Configure the matrix multiply kernel
- _mm_kernel.configure(a, b, output, alpha);
- }
// Configure matrix addition kernel
if(beta != 0 && c != nullptr)
@@ -125,7 +119,9 @@
void CLGEMM::run()
{
- if(!_run_vector_matrix_multiplication)
+ _memory_group.acquire();
+
+ if(_is_interleaved_transposed)
{
// Run interleave kernel
CLScheduler::get().enqueue(_interleave_kernel, false);
@@ -142,4 +138,6 @@
{
CLScheduler::get().enqueue(_ma_kernel);
}
+
+ _memory_group.release();
}