Revert "Implement memory injection in CpuDirectGemmConv2d"
This reverts commit b3be45759bdd0749ae3a16fe470820f0d9830ea9.
Resolves: COMPMID-4548
Change-Id: I46e0d8c67ddf988af3ce38f83177cda412db916c
Signed-off-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/5775
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Sheri Zhang <sheri.zhang@arm.com>
diff --git a/src/core/helpers/MemoryHelpers.h b/src/core/helpers/MemoryHelpers.h
index dfa8e60..6756a90 100644
--- a/src/core/helpers/MemoryHelpers.h
+++ b/src/core/helpers/MemoryHelpers.h
@@ -56,13 +56,12 @@
continue;
}
- const auto alignment = req.alignment;
- const auto aux_info = TensorInfo{ TensorShape(req.size + alignment), 1, DataType::U8 };
+ const auto aux_info = TensorInfo{ TensorShape(req.size), 1, DataType::U8 };
workspace_memory.emplace_back(req.slot, std::make_unique<TensorType>());
auto aux_tensor = workspace_memory.back().second.get();
ARM_COMPUTE_ERROR_ON_NULLPTR(aux_tensor);
- aux_tensor->allocator()->init(aux_info, alignment);
+ aux_tensor->allocator()->init(aux_info);
if(req.lifetime == experimental::MemoryLifetime::Temporary)
{
@@ -83,14 +82,5 @@
return workspace_memory;
}
-
-template <typename TensorType>
-WorkspaceData<TensorType> manage_workspace(const experimental::MemoryRequirements &mem_reqs,
- MemoryGroup &mgroup,
- ITensorPack &run_pack)
-{
- ITensorPack dummy_prep_pack{};
- return manage_workspace<TensorType>(mem_reqs, mgroup, run_pack, dummy_prep_pack);
-}
} // namespace arm_compute
#endif /* SRC_COMMON_MEMORY_HELPERS_H */
diff --git a/src/runtime/NEON/functions/NEGEMM.cpp b/src/runtime/NEON/functions/NEGEMM.cpp
index b526874..7318c3e 100644
--- a/src/runtime/NEON/functions/NEGEMM.cpp
+++ b/src/runtime/NEON/functions/NEGEMM.cpp
@@ -38,7 +38,6 @@
#include "src/core/NEON/kernels/NEGEMMMatrixMultiplyKernel.h"
#include "src/core/NEON/kernels/NEGEMMTranspose1xWKernel.h"
#include "src/core/helpers/AutoConfiguration.h"
-#include "src/core/helpers/MemoryHelpers.h"
#include "src/runtime/cpu/operators/internal/CpuGemmAssemblyDispatch.h"
#include <cmath>
@@ -47,14 +46,6 @@
namespace arm_compute
{
-using WorkspaceDataType = WorkspaceData<Tensor>;
-
-struct NEGEMM::AsmGlueTensors
-{
- ITensorPack tensors{};
- WorkspaceDataType ws{};
-};
-
namespace
{
cpu::AsmGemmInfo init_assembly_metadata(const GEMMInfo &info)
@@ -72,7 +63,7 @@
NEGEMM::NEGEMM(std::shared_ptr<IMemoryManager> memory_manager, IWeightsManager *weights_manager)
: _memory_group(memory_manager), _weights_manager(weights_manager), _interleave_kernel(), _transpose_kernel(), _mm_kernel(), _asm_glue(std::make_unique<cpu::CpuGemmAssemblyDispatch>()), _ma_kernel(),
_alpha_scale_func(nullptr), _add_bias(), _activation_func(), _tmp_a(), _tmp_b(), _tmp_d(), _original_b(nullptr), _run_vector_matrix_multiplication(false), _run_alpha_scale(false),
- _run_addition(false), _run_bias_addition(false), _run_activation(false), _reshape_b_only_on_first_run(false), _is_prepared(false), _asm_glue_tensors(std::make_unique<AsmGlueTensors>())
+ _run_addition(false), _run_bias_addition(false), _run_activation(false), _reshape_b_only_on_first_run(false), _is_prepared(false)
{
}
@@ -103,7 +94,7 @@
_asm_glue->configure(a->info(), b->info(), c_info_to_use, d->info(), asm_info);
ARM_COMPUTE_ERROR_ON(!_asm_glue->is_configured());
- _asm_glue_tensors->tensors =
+ _asm_glue_tensors =
{
{ ACL_SRC_0, a },
{ ACL_SRC_1, b },
@@ -111,8 +102,6 @@
{ ACL_DST, d },
};
- _asm_glue_tensors->ws = manage_workspace<Tensor>(_asm_glue->workspace(), _memory_group, _asm_glue_tensors->tensors);
-
// Scale product by alpha
if(_run_alpha_scale)
{
@@ -334,7 +323,7 @@
if(_asm_glue->is_configured())
{
- _asm_glue->run(_asm_glue_tensors->tensors);
+ _asm_glue->run(_asm_glue_tensors);
if(_run_alpha_scale)
{
_alpha_scale_func.run();
@@ -388,7 +377,7 @@
ARM_COMPUTE_ERROR_ON(!_original_b->is_used());
}
- _asm_glue->prepare(_asm_glue_tensors->tensors);
+ _asm_glue->prepare(_asm_glue_tensors);
if(!original_b_managed_by_weights_manager)
{
_original_b->mark_as_unused();
diff --git a/src/runtime/NEON/functions/NEGEMMConv2d.cpp b/src/runtime/NEON/functions/NEGEMMConv2d.cpp
index 790543a..94ceb6d 100644
--- a/src/runtime/NEON/functions/NEGEMMConv2d.cpp
+++ b/src/runtime/NEON/functions/NEGEMMConv2d.cpp
@@ -26,37 +26,24 @@
#include "arm_compute/core/utils/misc/ShapeCalculator.h"
#include "arm_compute/core/utils/quantization/AsymmHelpers.h"
#include "arm_compute/runtime/NEON/NEScheduler.h"
-#include "src/core/helpers/MemoryHelpers.h"
#include "src/runtime/cpu/operators/CpuGemmDirectConv2d.h"
#include <set>
namespace arm_compute
{
-using OperatorType = cpu::CpuGemmDirectConv2d;
-using WorkspaceDataType = WorkspaceData<Tensor>;
+using OperatorType = cpu::CpuGemmDirectConv2d;
struct NEGEMMConv2d::Impl
{
ITensorPack tensors{};
- MemoryGroup mg{};
std::unique_ptr<OperatorType> op{ nullptr };
- WorkspaceDataType ws{};
-
- void allocate_and_add_workspace()
- {
- if(op)
- {
- ws = manage_workspace<Tensor>(op->workspace(), mg, tensors);
- }
- }
};
NEGEMMConv2d::NEGEMMConv2d(const std::shared_ptr<IMemoryManager> &memory_manager)
: _impl(std::make_unique<Impl>())
{
- _impl->op = std::make_unique<OperatorType>();
- _impl->mg = MemoryGroup(memory_manager);
+ _impl->op = std::make_unique<OperatorType>(memory_manager);
}
NEGEMMConv2d::~NEGEMMConv2d() = default;
@@ -68,9 +55,7 @@
_impl->tensors.add_const_tensor(TensorType::ACL_SRC_2, biases);
_impl->tensors.add_tensor(TensorType::ACL_DST, output);
- _impl->op->configure(input->info(), weights->info(), ((biases) ? biases->info() : nullptr), output->info(), info);
-
- _impl->allocate_and_add_workspace();
+ _impl->op->configure(input->info(), weights->info(), biases->info(), output->info(), info);
}
Status NEGEMMConv2d::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const Conv2dInfo &info)
diff --git a/src/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.cpp b/src/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.cpp
index d42e656..cc0f20e 100644
--- a/src/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.cpp
+++ b/src/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.cpp
@@ -42,17 +42,10 @@
#include "src/core/NEON/kernels/NEGEMMLowpOffsetContributionOutputStageKernel.h"
#include "src/core/NEON/kernels/NEGEMMLowpReductionKernel.h"
#include "src/core/NEON/kernels/NEGEMMTranspose1xWKernel.h"
-#include "src/core/helpers/MemoryHelpers.h"
#include "src/runtime/cpu/operators/internal/CpuGemmAssemblyDispatch.h"
namespace arm_compute
{
-using WorkspaceDataType = WorkspaceData<Tensor>;
-struct NEGEMMLowpMatrixMultiplyCore::AsmGlueTensors
-{
- ITensorPack tensors{};
- WorkspaceDataType ws{};
-};
namespace
{
cpu::AsmGemmInfo init_assembly_metadata(const GEMMInfo &info)
@@ -73,11 +66,11 @@
NEGEMMLowpMatrixMultiplyCore::~NEGEMMLowpMatrixMultiplyCore() = default;
NEGEMMLowpMatrixMultiplyCore::NEGEMMLowpMatrixMultiplyCore(std::shared_ptr<IMemoryManager> memory_manager, IWeightsManager *weights_manager)
- : _memory_group(memory_manager), _weights_manager(weights_manager), _asm_glue(std::make_unique<cpu::CpuGemmAssemblyDispatch>(weights_manager)), _mm_kernel(), _mtx_a_reshape_kernel(),
+ : _memory_group(memory_manager), _weights_manager(weights_manager), _asm_glue(std::make_unique<cpu::CpuGemmAssemblyDispatch>(memory_manager, weights_manager)), _mm_kernel(), _mtx_a_reshape_kernel(),
_mtx_b_reshape_kernel(), _mtx_a_reduction_kernel(), _mtx_b_reduction_kernel(), _offset_contribution_kernel(), _offset_contribution_output_stage_kernel(), _activation_func(),
_convert_to_signed_asymm(), _convert_from_signed_asymm(), _vector_sum_col(), _vector_sum_row(), _tmp_a(), _tmp_b(), _mm_result_s32(), _signed_a(), _signed_output(), _original_b(nullptr), _a_offset(0),
_b_offset(0), _run_vector_matrix_multiplication(false), _assembly_path(false), _fused_assembly_path(false), _reshape_b_only_on_first_run(false), _is_prepared(false), _fuse_output_stage(false),
- _run_activation(false), _flip_signedness(false), _asm_glue_tensors(std::make_unique<AsmGlueTensors>())
+ _run_activation(false), _flip_signedness(false)
{
}
@@ -156,24 +149,18 @@
auto c_info_to_use = c == nullptr ? nullptr : c->info();
_asm_glue->configure(a_to_use->info(), b->info(), c_info_to_use, output->info(), asm_info);
_fused_assembly_path = _asm_glue->is_configured();
- _asm_glue_tensors->tensors.add_const_tensor(TensorType::ACL_SRC_2, c);
- _asm_glue_tensors->tensors.add_tensor(TensorType::ACL_DST, output);
+ _asm_glue_tensors.add_const_tensor(TensorType::ACL_SRC_2, c);
+ _asm_glue_tensors.add_tensor(TensorType::ACL_DST, output);
}
else
{
auto output_to_use = (_fuse_output_stage ? &_mm_result_s32 : output);
_asm_glue->configure(a_to_use->info(), b->info(), nullptr, output_to_use->info(), asm_info);
- _asm_glue_tensors->tensors.add_tensor(TensorType::ACL_DST, output_to_use);
+ _asm_glue_tensors.add_tensor(TensorType::ACL_DST, output_to_use);
}
_assembly_path = _asm_glue->is_configured();
- _asm_glue_tensors->tensors.add_const_tensor(TensorType::ACL_SRC_0, a_to_use);
- _asm_glue_tensors->tensors.add_const_tensor(TensorType::ACL_SRC_1, b);
-
- if(_assembly_path)
- {
- _asm_glue_tensors->ws = manage_workspace<Tensor>(_asm_glue->workspace(), _memory_group, _asm_glue_tensors->tensors);
- }
-
+ _asm_glue_tensors.add_const_tensor(TensorType::ACL_SRC_0, a_to_use);
+ _asm_glue_tensors.add_const_tensor(TensorType::ACL_SRC_1, b);
break;
}
default:
@@ -533,7 +520,7 @@
// Run GEMM
if(_asm_glue->is_configured())
{
- _asm_glue->run(_asm_glue_tensors->tensors);
+ _asm_glue->run(_asm_glue_tensors);
}
else
{
@@ -603,7 +590,7 @@
ARM_COMPUTE_ERROR_ON(!_original_b->is_used());
}
- _asm_glue->prepare(_asm_glue_tensors->tensors);
+ _asm_glue->prepare(_asm_glue_tensors);
if(!original_b_managed_by_weights_manager)
{
_original_b->mark_as_unused();
diff --git a/src/runtime/cpu/operators/CpuGemmDirectConv2d.cpp b/src/runtime/cpu/operators/CpuGemmDirectConv2d.cpp
index 7b7b68a..e50099d 100644
--- a/src/runtime/cpu/operators/CpuGemmDirectConv2d.cpp
+++ b/src/runtime/cpu/operators/CpuGemmDirectConv2d.cpp
@@ -53,13 +53,11 @@
ActivationLayerInfo::ActivationFunction::BOUNDED_RELU,
ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU
};
-
PixelValue type_min{};
PixelValue type_max{};
-
std::tie(type_min, type_max) = get_min_max(data_type);
- int32_t min_activation = type_min.get<int32_t>();
- int32_t max_activation = type_max.get<int32_t>();
+ int32_t min_activation = type_min.get<int32_t>();
+ int32_t max_activation = type_max.get<int32_t>();
if(supported_acts.count(act.activation()) != 0)
{
std::tie(min_activation, max_activation) = get_quantized_activation_min_max(act, data_type, uoqinfo);
@@ -89,8 +87,8 @@
}
} // namespace
-CpuGemmDirectConv2d::CpuGemmDirectConv2d()
- : _gemm_asm_func(std::make_unique<CpuGemmAssemblyDispatch>()),
+CpuGemmDirectConv2d::CpuGemmDirectConv2d(const std::shared_ptr<IMemoryManager> &memory_manager)
+ : _gemm_asm_func(std::make_unique<CpuGemmAssemblyDispatch>(memory_manager)),
_activation_func(std::make_unique<CpuActivation>()),
_weights_permute_func(std::make_unique<CpuPermute>()),
_permuted_weights_info(),
@@ -165,8 +163,6 @@
}
void CpuGemmDirectConv2d::run(ITensorPack &tensors)
{
- import_workspace_memory(tensors);
-
prepare(tensors);
_gemm_asm_func->run(tensors);
@@ -174,14 +170,22 @@
{
_activation_func->run(tensors);
}
+}
- free_imported_workspace_memory();
+void CpuGemmDirectConv2d::allocate_permuted_weights()
+{
+ // TODO: This function will be removed when memory injection is implemeted.
+ ARM_COMPUTE_ERROR_ON(_permuted_weights == nullptr);
+ _permuted_weights->allocator()->free();
+ _permuted_weights->allocator()->init(_permuted_weights_info);
+ _permuted_weights->allocator()->allocate();
}
void CpuGemmDirectConv2d::prepare(ITensorPack &tensors)
{
if(!_is_prepared)
{
+ allocate_permuted_weights();
ITensorPack permute_tensors
{
{ TensorType::ACL_SRC, tensors.get_const_tensor(TensorType::ACL_SRC_1) },
@@ -198,41 +202,5 @@
}
}
-experimental::MemoryRequirements CpuGemmDirectConv2d::workspace() const
-{
- experimental::MemoryRequirements req = _gemm_asm_func->workspace();
-
- auto index = static_cast<std::underlying_type<TensorType>::type>(TensorType::ACL_INT_0);
-
- if(req.size() > 0)
- {
- index = req.back().slot + 1;
-
- constexpr auto max_index = static_cast<std::underlying_type<TensorType>::type>(TensorType::ACL_INT_4);
- ARM_COMPUTE_UNUSED(max_index); // in order to prevent build error with assertion is disabled.
- ARM_COMPUTE_ERROR_ON(index > max_index);
- }
-
- req.emplace_back(index, _permuted_weights_info.total_size(), 0);
-
- return req;
-}
-
-void CpuGemmDirectConv2d::import_workspace_memory(ITensorPack &tensors)
-{
- auto imported_tensor = tensors.get_tensor(workspace().back().slot);
-
- ARM_COMPUTE_ERROR_ON_NULLPTR(imported_tensor);
-
- auto imported_memory = imported_tensor->buffer();
- _permuted_weights->allocator()->init(_permuted_weights_info);
- _permuted_weights->allocator()->import_memory(imported_memory);
-}
-
-void CpuGemmDirectConv2d::free_imported_workspace_memory()
-{
- _permuted_weights->allocator()->free();
-}
-
} // namespace cpu
} // namespace arm_compute
\ No newline at end of file
diff --git a/src/runtime/cpu/operators/CpuGemmDirectConv2d.h b/src/runtime/cpu/operators/CpuGemmDirectConv2d.h
index 305a076..6aa17c2 100644
--- a/src/runtime/cpu/operators/CpuGemmDirectConv2d.h
+++ b/src/runtime/cpu/operators/CpuGemmDirectConv2d.h
@@ -48,7 +48,7 @@
{
public:
/** Constructor */
- CpuGemmDirectConv2d();
+ CpuGemmDirectConv2d(const std::shared_ptr<IMemoryManager> &memory_manager = nullptr);
ARM_COMPUTE_DISALLOW_COPY_ALLOW_MOVE(CpuGemmDirectConv2d);
/** Destructor */
~CpuGemmDirectConv2d();
@@ -80,16 +80,15 @@
void configure(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, ITensorInfo *dst, const Conv2dInfo &info);
/** Static function to check if given info will lead to a valid configuration of @ref CpuGemmDirectConv2d
*
- * Similar to @ref CpuGemmDirectConv2d::configure()
+ * Similar to CpuGemmDirectConv2d::configure()
*
* @return a status
*/
static Status validate(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const Conv2dInfo &info);
// Inherited methods overridden:
- void run(ITensorPack &tensors) override;
- void prepare(ITensorPack &constants) override;
- experimental::MemoryRequirements workspace() const override;
+ void run(ITensorPack &tensors) override;
+ void prepare(ITensorPack &constants) override;
private:
std::unique_ptr<CpuGemmAssemblyDispatch> _gemm_asm_func;
@@ -101,13 +100,11 @@
bool _is_prepared{ false };
bool _run_activation{ false };
- /** Function to import workspace tensors
+ /** Function to allocated a tensor for permuted weights
*
- * @param[in] tensors Tensor pack includes workspace tensors
+ * @note This function will be removed when memory injection is properly implemented.
*/
- void import_workspace_memory(ITensorPack &tensors);
- /** Function free used workspace tensors */
- void free_imported_workspace_memory();
+ void allocate_permuted_weights();
};
} // namespace cpu
} // namespace arm_compute
diff --git a/src/runtime/cpu/operators/internal/CpuGemmAssemblyDispatch.cpp b/src/runtime/cpu/operators/internal/CpuGemmAssemblyDispatch.cpp
index 53d71a3..ea3742f 100644
--- a/src/runtime/cpu/operators/internal/CpuGemmAssemblyDispatch.cpp
+++ b/src/runtime/cpu/operators/internal/CpuGemmAssemblyDispatch.cpp
@@ -204,11 +204,11 @@
}
private:
- Tensor _output{};
- int _ldb{};
- const TypeInput *_in1_ptr{};
- int _multi_stride_b{};
- size_t _B_pretranspose_size{};
+ Tensor _output{};
+ int _ldb{};
+ const TypeInput *_in1_ptr{};
+ int _multi_stride_b{};
+ size_t _B_pretranspose_size{};
std::shared_ptr<arm_gemm::GemmCommon<TypeInput, TypeOutput>> _gemm_kernel_asm{ nullptr };
};
@@ -240,7 +240,7 @@
*/
void configure(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, ITensorInfo *d,
arm_gemm::GemmArgs args, const AsmGemmInfo &gemm_info,
- IWeightsManager *weights_manager, const OutputStage &os = {});
+ MemoryGroup &memory_group, IWeightsManager *weights_manager, const OutputStage &os = {});
/** Set requantization shifts to be used
*
@@ -265,42 +265,13 @@
bool is_configured() const override;
private:
- static constexpr size_t _workspace_alignment{ 4096 };
- /** Function to get the memory requirements */
- experimental::MemoryRequirements get_workspace() const override
- {
- experimental::MemoryRequirements req{};
- const auto size = _gemm_kernel_asm->get_working_size();
- if(size > 0)
- {
- req.emplace_back(TensorType::ACL_INT, size, _workspace_alignment);
- }
- return req;
- }
-
- /** Function to import workspace tensors
+ /** Allocate a workspace tensor.
*
- * @param[in] tensors Tensor pack includes workspace tensors
+ * @param[in] workspace_size Size to allocate.
+ * @param[in] memory_group Tensor memory group.
+ * @param[in] alignment Workspace memory alignment.
*/
- void import_workspace(ITensorPack &tensors)
- {
- const auto size = _gemm_kernel_asm->get_working_size();
-
- if(size > 0)
- {
- auto imported_tensor = tensors.get_tensor(TensorType::ACL_INT);
- ARM_COMPUTE_ERROR_ON_NULLPTR(imported_tensor);
- const size_t workspace_size = _gemm_kernel_asm->get_working_size();
- _workspace.allocator()->init(TensorInfo(TensorShape{ (workspace_size + _workspace_alignment) }, 1, DataType::S8), _workspace_alignment);
- _workspace.allocator()->import_memory(imported_tensor->buffer());
- }
- }
- /** Function free used workspace tensors */
- void free_imported_workspace()
- {
- _workspace.allocator()->free();
- }
-
+ void allocate_workspace(size_t workspace_size, MemoryGroup &memory_group, size_t alignment);
/** Configure the indirect buffer
*
* @param[in] a Input tensor containing the Matrix A.
@@ -339,8 +310,8 @@
/** Indirect buffer */
std::unique_ptr<const TypeInput *const *, free_delete> _indirect_arg{};
std::unique_ptr<const TypeInput *, free_delete> _indirect_buf{};
- std::vector<TypeInput> _indirect_pad{};
- arm_gemm::ConvolutionParameters _cp{};
+ std::vector<TypeInput> _indirect_pad{};
+ arm_gemm::ConvolutionParameters _cp{};
bool is_weight_managed()
{
@@ -363,9 +334,6 @@
};
template <typename TypeInput, typename TypeOutput, class OutputStage>
-constexpr size_t Fallback<TypeInput, TypeOutput, OutputStage>::_workspace_alignment;
-
-template <typename TypeInput, typename TypeOutput, class OutputStage>
std::tuple<bool, const int32_t *, const int32_t *, const int32_t *>
Fallback<TypeInput, TypeOutput, OutputStage>::set_requantize_data(const std::vector<int32_t> &shifts, const std::vector<int32_t> &multipliers)
{
@@ -502,7 +470,7 @@
template <typename TypeInput, typename TypeOutput, class OutputStage>
void Fallback<TypeInput, TypeOutput, OutputStage>::configure(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, ITensorInfo *d,
arm_gemm::GemmArgs args, const AsmGemmInfo &gemm_info,
- IWeightsManager *weights_manager, const OutputStage &os)
+ MemoryGroup &memory_group, IWeightsManager *weights_manager, const OutputStage &os)
{
ARM_COMPUTE_UNUSED(c);
arm_gemm::GemmConfig gemm_cfg;
@@ -524,6 +492,13 @@
auto acl_gemm_wrapper = std::make_unique<kernel::CpuGemmAssemblyWrapperKernel<TypeInput, TypeOutput>>();
ARM_COMPUTE_ERROR_ON(acl_gemm_wrapper == nullptr);
acl_gemm_wrapper->configure(_gemm_kernel_asm.get(), gemm_cfg.filter);
+ const size_t workspace_size = _gemm_kernel_asm->get_working_size();
+ if(workspace_size > 0)
+ {
+ // Allocate workspace
+ const unsigned int alignment = 4096;
+ allocate_workspace(workspace_size, memory_group, alignment);
+ }
//if we disable this code below in brackets then ConvLayer deadlocks when threads > 1 and
//the shapes are In=1x1x1024 Weights=1x1x1024x1001 Biases=1001 Out=1x1x1001
@@ -612,6 +587,15 @@
}
template <typename TypeInput, typename TypeOutput, class OutputStage>
+void Fallback<TypeInput, TypeOutput, OutputStage>::allocate_workspace(size_t workspace_size, MemoryGroup &memory_group, size_t alignment)
+{
+ ARM_COMPUTE_ERROR_ON_MSG(workspace_size == 0, "size cannot be 0");
+ _workspace.allocator()->init(TensorInfo(TensorShape{ (workspace_size + alignment) }, 1, DataType::S8), alignment);
+ memory_group.manage(&_workspace);
+ _workspace.allocator()->allocate();
+}
+
+template <typename TypeInput, typename TypeOutput, class OutputStage>
bool Fallback<TypeInput, TypeOutput, OutputStage>::is_configured() const
{
return _optimised_kernel != nullptr;
@@ -625,10 +609,6 @@
auto c = tensors.get_const_tensor(TensorType::ACL_SRC_2);
auto d = tensors.get_tensor(TensorType::ACL_DST);
- ARM_COMPUTE_ERROR_ON_NULLPTR(a, b, d);
-
- import_workspace(tensors);
-
int lda = a->info()->strides_in_bytes().y() / sizeof(TypeInput);
int ldb = 0;
const int ldd = d->info()->strides_in_bytes().y() / sizeof(TypeOutput);
@@ -704,11 +684,10 @@
bias, 0);
// Schedule
NEScheduler::get().schedule(_optimised_kernel.get(), scheduling_hint);
- free_imported_workspace();
}
template <typename TypeInput, typename TypeOutput>
-void create_arm_gemm(std::unique_ptr<CpuGemmAssemblyDispatch::IFallback> &arm_gemm,
+void create_arm_gemm(std::unique_ptr<CpuGemmAssemblyDispatch::IFallback> &arm_gemm, MemoryGroup &memory_group,
const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, ITensorInfo *d, arm_gemm::Activation activation, const AsmGemmInfo &info,
IWeightsManager *weights_manager)
{
@@ -720,12 +699,12 @@
// Create arm_gemm fallback
auto fallback = std::make_unique<Fallback<TypeInput, TypeOutput>>();
- fallback->configure(a, b, c, d, args, info, weights_manager);
+ fallback->configure(a, b, c, d, args, info, memory_group, weights_manager);
arm_gemm = std::move(fallback);
}
template <typename TypeInput, typename TypeOutput>
-void create_arm_gemm_quant(std::unique_ptr<CpuGemmAssemblyDispatch::IFallback> &arm_gemm,
+void create_arm_gemm_quant(std::unique_ptr<CpuGemmAssemblyDispatch::IFallback> &arm_gemm, MemoryGroup &memory_group,
const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, ITensorInfo *d, arm_gemm::Activation activation, const AsmGemmInfo &info,
IWeightsManager *weights_manager)
{
@@ -765,14 +744,14 @@
}
// Configure fallback
- fallback->configure(a, b, c, d, args, info, weights_manager, gemm_requant_info);
+ fallback->configure(a, b, c, d, args, info, memory_group, weights_manager, gemm_requant_info);
arm_gemm = std::move(fallback);
}
} //namespace
-CpuGemmAssemblyDispatch::CpuGemmAssemblyDispatch(IWeightsManager *weights_manager)
- : _arm_gemm(nullptr), _weights_manager(weights_manager)
+CpuGemmAssemblyDispatch::CpuGemmAssemblyDispatch(std::shared_ptr<IMemoryManager> memory_manager, IWeightsManager *weights_manager)
+ : _arm_gemm(nullptr), _memory_group(std::move(memory_manager)), _weights_manager(weights_manager)
{
}
@@ -827,40 +806,40 @@
switch(a->data_type())
{
case DataType::F32:
- create_arm_gemm<float, float>(_arm_gemm, a, b, c, d, act, info, _weights_manager);
+ create_arm_gemm<float, float>(_arm_gemm, _memory_group, a, b, c, d, act, info, _weights_manager);
break;
#ifdef __aarch64__
case DataType::U8:
case DataType::QASYMM8:
if(d->data_type() == DataType::S32)
{
- create_arm_gemm<uint8_t, uint32_t>(_arm_gemm, a, b, c, d, act, info, _weights_manager);
+ create_arm_gemm<uint8_t, uint32_t>(_arm_gemm, _memory_group, a, b, c, d, act, info, _weights_manager);
}
else
{
- create_arm_gemm_quant<uint8_t, uint8_t>(_arm_gemm, a, b, c, d, act, info, _weights_manager);
+ create_arm_gemm_quant<uint8_t, uint8_t>(_arm_gemm, _memory_group, a, b, c, d, act, info, _weights_manager);
}
break;
case DataType::S8:
case DataType::QASYMM8_SIGNED:
if(d->data_type() == DataType::S32)
{
- create_arm_gemm<int8_t, int32_t>(_arm_gemm, a, b, c, d, act, info, _weights_manager);
+ create_arm_gemm<int8_t, int32_t>(_arm_gemm, _memory_group, a, b, c, d, act, info, _weights_manager);
}
else
{
- create_arm_gemm_quant<int8_t, int8_t>(_arm_gemm, a, b, c, d, act, info, _weights_manager);
+ create_arm_gemm_quant<int8_t, int8_t>(_arm_gemm, _memory_group, a, b, c, d, act, info, _weights_manager);
}
break;
#endif /* __aarch64__ */
#if defined(__ARM_FEATURE_BF16_VECTOR_ARITHMETIC) || defined(ARM_COMPUTE_FORCE_BF16)
case DataType::BFLOAT16:
- create_arm_gemm<bfloat16, float>(_arm_gemm, a, b, c, d, act, info, _weights_manager);
+ create_arm_gemm<bfloat16, float>(_arm_gemm, _memory_group, a, b, c, d, act, info, _weights_manager);
break;
#endif /* defined(__ARM_FEATURE_BF16_VECTOR_ARITHMETIC) || defined(ARM_COMPUTE_FORCE_BF16) */
#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
case DataType::F16:
- create_arm_gemm<float16_t, float16_t>(_arm_gemm, a, b, c, d, act, info, _weights_manager);
+ create_arm_gemm<float16_t, float16_t>(_arm_gemm, _memory_group, a, b, c, d, act, info, _weights_manager);
break;
#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
default:
@@ -881,13 +860,10 @@
void CpuGemmAssemblyDispatch::run(ITensorPack &tensors)
{
+ MemoryGroupResourceScope scope_mg(_memory_group);
+
ARM_COMPUTE_ERROR_ON(_arm_gemm == nullptr);
_arm_gemm->run(tensors);
}
-
-experimental::MemoryRequirements CpuGemmAssemblyDispatch::workspace() const
-{
- return is_configured() ? _arm_gemm->get_workspace() : experimental::MemoryRequirements{};
-}
} // namespace cpu
} // namespace arm_compute
diff --git a/src/runtime/cpu/operators/internal/CpuGemmAssemblyDispatch.h b/src/runtime/cpu/operators/internal/CpuGemmAssemblyDispatch.h
index 154def6..ffc097c 100644
--- a/src/runtime/cpu/operators/internal/CpuGemmAssemblyDispatch.h
+++ b/src/runtime/cpu/operators/internal/CpuGemmAssemblyDispatch.h
@@ -24,6 +24,7 @@
#ifndef ARM_COMPUTE_CPU_INTERNAL_CPU_GEMM_ASSEMBLY_DISPATCH_H
#define ARM_COMPUTE_CPU_INTERNAL_CPU_GEMM_ASSEMBLY_DISPATCH_H
+#include "arm_compute/runtime/IMemoryManager.h"
#include "arm_compute/runtime/IWeightsManager.h"
#include "arm_compute/runtime/MemoryGroup.h"
#include "arm_compute/runtime/Tensor.h"
@@ -61,7 +62,7 @@
{
public:
/** Constructor */
- CpuGemmAssemblyDispatch(IWeightsManager *weights_manager = nullptr);
+ CpuGemmAssemblyDispatch(std::shared_ptr<IMemoryManager> memory_manager = nullptr, IWeightsManager *weights_manager = nullptr);
/** Defautl destructor */
~CpuGemmAssemblyDispatch() = default;
@@ -70,11 +71,10 @@
class IFallback
{
public:
- virtual void run(ITensorPack &tensors) = 0;
- virtual void prepare(ITensorPack &tensors) = 0;
- virtual bool is_configured() const = 0;
- virtual ~IFallback() = default;
- virtual experimental::MemoryRequirements get_workspace() const = 0;
+ virtual void run(ITensorPack &tensors) = 0;
+ virtual void prepare(ITensorPack &tensors) = 0;
+ virtual bool is_configured() const = 0;
+ virtual ~IFallback() = default;
};
public:
@@ -113,12 +113,12 @@
bool is_configured() const;
// Inherited methods overridden:
- void prepare(ITensorPack &tensors) override;
- void run(ITensorPack &tensors) override;
- experimental::MemoryRequirements workspace() const override;
+ void prepare(ITensorPack &tensors) override;
+ void run(ITensorPack &tensors) override;
private:
std::unique_ptr<IFallback> _arm_gemm; /**< Interface for the arm_gemm fallback */
+ MemoryGroup _memory_group; /**< Function memory group */
IWeightsManager *_weights_manager; /**< Pointer to the weights manager */
};
} // namespace cpu