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KernelGenerator.cc
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/*
* Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "KernelGenerator.h"
#include <arm_compute/runtime/CL/CLFunctions.h> // Include all ARM Compute CL functions
#include <arm_compute/runtime/CL/CLFunctionsEx.h> // Include all ARM Compute EX CL functions
#include <AclActivationBuilder.h>
#include <AclFunction.h>
#include <Convert.h>
#include <Swizzle.h>
#include "ir/Index.h"
#include "ir/DataType.h"
#include "ir/InternalType.h"
#include "exec/NopFunction.h"
#include "exec/FunctionSequence.h"
#include "util/logging.h"
#include "AclKernelGen.h"
namespace onert::backend::acl_cl
{
using ::onert::backend::acl_common::asAclFunction;
using ActivationBuilder = ::onert::backend::acl_common::AclActivationBuilder<
::arm_compute::ICLTensor, ::arm_compute::CLActivationLayer, acl_common::AclFunction>;
KernelGenerator::KernelGenerator(
const ir::Graph &graph, const std::shared_ptr<TensorBuilder> &tensor_builder,
const std::shared_ptr<acl_common::AclTensorRegistry<TensorManager>> &tensor_reg)
: basic::KernelGeneratorBase{graph}, _ctx(graph.operands()), _operations_ctx(graph.operations()),
_tensor_builder(tensor_builder), _tensor_reg(tensor_reg)
{
// DO NOTHING
}
std::unique_ptr<exec::FunctionSequence> KernelGenerator::generate(ir::OperationIndex ind)
{
auto ret = std::make_unique<exec::FunctionSequence>();
ret->enableDynamicShapeInferer(false);
const auto &op = _graph.operations().at(ind);
op.accept(*this);
ret->append(releaseFunction());
return ret;
}
void KernelGenerator::visit(const ir::operation::BatchToSpaceND &node)
{
const auto ofm_index{node.getOutputs().at(0)};
const auto ifm_index{node.getInputs().at(ir::operation::BatchToSpaceND::Input::INPUT)};
const auto block_size_index{
node.getInputs().at(ir::operation::BatchToSpaceND::Input::BLOCK_SIZE)};
const auto NNApiInputs = 2;
if (node.getInputs().size() != NNApiInputs)
{
const auto crops_index{node.getInputs().at(ir::operation::BatchToSpaceND::Input::CROPS_DATA)};
if (!_ctx.at(crops_index).isConstant())
{
throw std::runtime_error("Non-constant crops NYI for acl_cl backend BatchToSpaceND");
}
auto crops = _ctx.at(crops_index).asVector<int32_t>();
for (auto &&crop : crops)
{
if (crop != 0)
{
throw std::runtime_error("Non-zero crops NYI for acl_cl backend BatchToSpaceND");
}
}
}
auto ofm_tensor = _tensor_reg->getAclTensor(ofm_index);
auto ifm_tensor = _tensor_reg->getAclTensor(ifm_index);
if (!_ctx.at(block_size_index).data())
throw std::runtime_error("ACL CL does not support dynamic block size for BatchToSpaceND");
auto block = _ctx.at(block_size_index).asVector<int32_t>();
int32_t height = block[0];
int32_t width = block[1];
auto fn = acl_common::generateLayer<arm_compute::CLBatchToSpaceLayer>(
ifm_tensor->handle(), width, height, ofm_tensor->handle());
_return_fn = asAclFunction(std::move(fn));
}
void KernelGenerator::visit(const ir::operation::BinaryArithmetic &node)
{
const auto ofm_index{node.getOutputs().at(0)};
const auto lhs_index{node.getInputs().at(ir::operation::BinaryArithmetic::Input::LHS)};
const auto rhs_index{node.getInputs().at(ir::operation::BinaryArithmetic::Input::RHS)};
const auto activation = node.param().activation;
auto ofm_tensor = _tensor_reg->getAclTensor(ofm_index);
auto lhs_tensor = _tensor_reg->getAclTensor(lhs_index);
auto rhs_tensor = _tensor_reg->getAclTensor(rhs_index);
const auto act_info = acl_common::asActivationLayerInfo(activation);
std::unique_ptr<arm_compute::IFunction> fn;
switch (node.param().arithmetic_type)
{
case ir::operation::BinaryArithmetic::ArithmeticType::ADD:
{
arm_compute::CLArithmeticAddition::validate(lhs_tensor->info(), rhs_tensor->info(),
ofm_tensor->info(),
arm_compute::ConvertPolicy::SATURATE, act_info)
.throw_if_error();
fn = acl_common::generateLayer<arm_compute::CLArithmeticAddition>(
lhs_tensor->handle(), rhs_tensor->handle(), ofm_tensor->handle(),
arm_compute::ConvertPolicy::SATURATE, act_info);
break;
}
case ir::operation::BinaryArithmetic::ArithmeticType::SUB:
{
arm_compute::CLArithmeticSubtraction::validate(lhs_tensor->info(), rhs_tensor->info(),
ofm_tensor->info(),
arm_compute::ConvertPolicy::SATURATE, act_info)
.throw_if_error();
fn = acl_common::generateLayer<arm_compute::CLArithmeticSubtraction>(
lhs_tensor->handle(), rhs_tensor->handle(), ofm_tensor->handle(),
arm_compute::ConvertPolicy::SATURATE, act_info);
break;
}
case ir::operation::BinaryArithmetic::ArithmeticType::MUL:
{
arm_compute::CLPixelWiseMultiplication::validate(
lhs_tensor->info(), rhs_tensor->info(), ofm_tensor->info(), 1.0,
arm_compute::ConvertPolicy::SATURATE, arm_compute::RoundingPolicy::TO_NEAREST_EVEN,
act_info)
.throw_if_error();
fn = acl_common::generateLayer<arm_compute::CLPixelWiseMultiplication>(
lhs_tensor->handle(), rhs_tensor->handle(), ofm_tensor->handle(), 1.0, // scale
arm_compute::ConvertPolicy::SATURATE, arm_compute::RoundingPolicy::TO_NEAREST_EVEN,
act_info);
break;
}
case ir::operation::BinaryArithmetic::ArithmeticType::DIV:
{
arm_compute::CLArithmeticDivision::validate(lhs_tensor->info(), rhs_tensor->info(),
ofm_tensor->info(), act_info)
.throw_if_error();
fn = acl_common::generateLayer<arm_compute::CLArithmeticDivision>(
lhs_tensor->handle(), rhs_tensor->handle(), ofm_tensor->handle(), act_info);
break;
}
default:
assert(false && "The BinaryArithmetic operation supports only binary arithmetic operations");
break;
}
_return_fn = asAclFunction(std::move(fn));
}
void KernelGenerator::visit(const ir::operation::Conv2D &node)
{
using ir::operation::Conv2D;
const auto ofm_index{node.getOutputs().at(0)};
const auto ifm_index{node.getInputs().at(Conv2D::Input::INPUT)};
const auto ker_index{node.getInputs().at(Conv2D::Input::KERNEL)};
const auto bias_index{node.getInputs().at(Conv2D::Input::BIAS)};
const auto ifm_shape = _ctx.at(ifm_index).shape().asFeature();
const auto ofm_shape = _ctx.at(ofm_index).shape().asFeature();
// Kernel format is [depth_out, kernel_height, kernel_width, depth_in].
const auto &ker_shape = _ctx.at(ker_index).shape();
const auto ker_height = ker_shape.dim(1);
const auto ker_width = ker_shape.dim(2);
const auto stride = node.param().stride;
const auto padding =
ir::calculatePadding(node.param().padding, ifm_shape, ofm_shape, stride, ker_width, ker_height);
const auto activation = node.param().activation;
auto ofm_tensor = _tensor_reg->getAclTensor(ofm_index);
auto ifm_tensor = _tensor_reg->getAclTensor(ifm_index);
auto ker_tensor = _tensor_reg->getAclTensor(ker_index);
auto bias_tensor = _tensor_reg->getAclTensor(bias_index);
const auto conv_info = acl_common::asPadStrideInfo(padding, stride);
const auto act_info = acl_common::asActivationLayerInfo(activation);
auto fn = acl_common::generateLayer<arm_compute::CLConvolutionLayer>(
_tensor_builder->acl_tensor_manager()->internal_buffer_manager(), ifm_tensor->handle(),
ker_tensor->handle(), bias_tensor->handle(), ofm_tensor->handle(), conv_info,
::arm_compute::WeightsInfo(), ::arm_compute::Size2D(1U, 1U), act_info);
_return_fn = asAclFunction(std::move(fn));
}
void KernelGenerator::visit(const ir::operation::DepthwiseConv2D &node)
{
using ir::operation::DepthwiseConv2D;
const auto ofm_index{node.getOutputs().at(0)};
const auto ifm_index{node.getInputs().at(DepthwiseConv2D::Input::INPUT)};
const auto ker_index{node.getInputs().at(DepthwiseConv2D::Input::KERNEL)};
const auto bias_index{node.getInputs().at(DepthwiseConv2D::Input::BIAS)};
const auto ifm_shape = _ctx.at(ifm_index).shape().asFeature();
const auto ofm_shape = _ctx.at(ofm_index).shape().asFeature();
// Kernel format is [1, kernel_height, kernel_width, depth_out].
const auto &ker_shape = _ctx.at(ker_index).shape();
const auto ker_height = ker_shape.dim(1);
const auto ker_width = ker_shape.dim(2);
const auto stride = node.param().stride;
const auto dilation = node.param().dilation;
const auto padding =
ir::calculatePadding(node.param().padding, ifm_shape, ofm_shape, stride, ker_width, ker_height,
dilation.width_factor, dilation.height_factor);
const auto multiplier = node.param().multiplier;
const auto activation = node.param().activation;
auto ofm_tensor = _tensor_reg->getAclTensor(ofm_index);
auto ifm_tensor = _tensor_reg->getAclTensor(ifm_index);
auto ker_tensor = _tensor_reg->getAclTensor(ker_index);
auto bias_tensor = _tensor_reg->getAclTensor(bias_index);
const auto conv_info = acl_common::asPadStrideInfo(padding, stride);
const auto act_info = acl_common::asActivationLayerInfo(activation);
const auto dilation_info = acl_common::asDilation(dilation.width_factor, dilation.height_factor);
auto fn = acl_common::generateLayer<arm_compute::CLDepthwiseConvolutionLayer>(
ifm_tensor->handle(), ker_tensor->handle(), bias_tensor->handle(), ofm_tensor->handle(),
conv_info, multiplier, act_info, dilation_info);
_return_fn = asAclFunction(std::move(fn));
}
void KernelGenerator::visit(const ir::operation::Concat &node)
{
const auto ofm_index{node.getOutputs().at(0)};
std::vector<ir::OperandIndex> input_indexes;
for (const auto &input : node.getInputs())
input_indexes.emplace_back(input);
const auto axis = node.param().axis;
// Concat elimination check
bool eliminated = _tensor_builder->areSubTensorsOf(ofm_index, node.getInputs());
if (eliminated)
{
// If concat eliminated, return a NOP IFunction
VERBOSE(acl_cl_KernelGenerator_Concat) << "Concat eliminated" << std::endl;
_return_fn = std::make_unique<exec::NopFunction>();
return;
}
auto output_tensor = _tensor_reg->getAclTensor(ofm_index);
std::vector<const ::arm_compute::ICLTensor *> input_tensors;
for (const auto &ifm_ind : input_indexes)
input_tensors.emplace_back(_tensor_reg->getAclTensor(ifm_ind)->handle());
std::unique_ptr<::arm_compute::IFunction> fn;
if (input_indexes.size() < 2)
{
::arm_compute::ICLTensor *input_tesor =
_tensor_reg->getAclTensor(input_indexes.at(0))->handle();
fn = acl_common::generateLayer<arm_compute::CLCopy>(input_tesor, output_tensor->handle());
}
else
{
const auto rank = _ctx.at(ofm_index).shape().rank();
const auto fixed_axis = acl_common::ToARMComputeAxis(rank, axis).value();
fn = acl_common::generateLayer<::arm_compute::CLConcatenateLayer>(
input_tensors, output_tensor->handle(), fixed_axis);
}
_return_fn = asAclFunction(std::move(fn));
}
void KernelGenerator::visit(const ir::operation::FullyConnected &node)
{
const auto output_index{node.getOutputs().at(0)};
auto output_tensor = _tensor_reg->getAclTensor(output_index);
const auto activation = node.param().activation;
if (node.param().weights_format == ir::FullyConnectedWeightsFormat::Shuffled16x1Float32)
throw std::runtime_error(
"KernelGenerator(acl_cl): FullyConnected 16x1Float32 weights is not supported.");
auto fn = acl_common::kernelGenFullyConnected<acl_common::AclFunction, ::arm_compute::ICLTensor,
::arm_compute::CLFullyConnectedReshapingLayer>(
node, _ctx, _tensor_builder, _tensor_reg);
_return_fn = std::make_unique<exec::FunctionSequence>(
std::move(fn), ActivationBuilder::generate(activation, output_tensor->handle()));
}
void KernelGenerator::visit(const ir::operation::Reduce &node)
{
const auto output_index{node.getOutputs().at(0)};
const auto input_index{node.getInputs().at(ir::operation::Reduce::Input::INPUT)};
const auto axes_index{node.getInputs().at(ir::operation::Reduce::Input::AXES)};
const auto keep_dims{node.param().keep_dims};
const auto reduce_type = node.param().reduce_type;
auto output_tensor = _tensor_reg->getAclTensor(output_index);
auto input_tensor = _tensor_reg->getAclTensor(input_index);
// Convert to ACL axes taking into account negative values and possible duplicates.
const auto &axes = _ctx.at(axes_index);
const auto input_rank = _ctx.at(input_index).shape().rank();
std::unique_ptr<arm_compute::IFunction> fn;
if (reduce_type == ir::operation::Reduce::ReduceType::MEAN)
{
const auto acl_axes = acl_common::asCoordinates(axes, input_rank);
fn = acl_common::generateLayer<arm_compute::CLReduceMean>(input_tensor->handle(), acl_axes,
keep_dims, output_tensor->handle());
}
else
{
const auto acl_axes = acl_common::asSet(axes, input_rank);
fn = acl_common::generateLayer<arm_compute::CLReduceOperation>(
_tensor_builder->acl_tensor_manager()->internal_buffer_manager(), input_tensor->handle(),
output_tensor->handle(), acl_axes, keep_dims, acl_common::convertReduceType(reduce_type));
}
_return_fn = asAclFunction(std::move(fn));
}
void KernelGenerator::visit(const ir::operation::Reshape &node)
{
const auto output_index{node.getOutputs().at(0)};
const auto input_index{node.getInputs().at(ir::operation::Reshape::Input::INPUT)};
auto output_tensor = _tensor_reg->getAclTensor(output_index);
auto input_tensor = _tensor_reg->getAclTensor(input_index);
auto fn = acl_common::generateLayer<arm_compute::CLReshapeLayer>(input_tensor->handle(),
output_tensor->handle());
_return_fn = asAclFunction(std::move(fn));
}
void KernelGenerator::visit(const ir::operation::Squeeze &node)
{
// Squeeze is identical to reshape except that it has an optional dimensions input.
// In addition, optional dims_index is ignored since output tensor already has squeezed shape
// by freezer and toco
// TODO Support multi-layout for frontend and backend
const auto output_index{node.getOutputs().at(0)};
const auto input_index{node.getInputs().at(ir::operation::Squeeze::Input::INPUT)};
const auto dims{node.param().dims};
const auto ndim{node.param().ndim};
(void)dims;
(void)ndim;
auto output_tensor = _tensor_reg->getAclTensor(output_index);
auto input_tensor = _tensor_reg->getAclTensor(input_index);
auto fn = acl_common::generateLayer<arm_compute::CLReshapeLayer>(input_tensor->handle(),
output_tensor->handle());
_return_fn = asAclFunction(std::move(fn));
}
void KernelGenerator::visit(const ir::operation::Softmax &node)
{
const auto output_index{node.getOutputs().at(0)};
const auto input_index{node.getInputs().at(ir::operation::Softmax::Input::INPUT)};
const auto beta = node.param().beta;
auto output_tensor = _tensor_reg->getAclTensor(output_index);
auto input_tensor = _tensor_reg->getAclTensor(input_index);
auto fn = acl_common::generateLayer<arm_compute::CLSoftmaxLayer>(
_tensor_builder->acl_tensor_manager()->internal_buffer_manager(), input_tensor->handle(),
output_tensor->handle(), beta);
_return_fn = asAclFunction(std::move(fn));
}
void KernelGenerator::visit(const ir::operation::Slice &node)
{
const auto output_index{node.getOutputs().at(0)};
const auto input_index{node.getInputs().at(ir::operation::Slice::Input::INPUT)};
const auto begins_index{node.getInputs().at(ir::operation::Slice::Input::BEGINS)};
const auto sizes_index{node.getInputs().at(ir::operation::Slice::Input::SIZES)};
auto outputData_tensor = _tensor_reg->getAclTensor(output_index);
auto inputData_tensor = _tensor_reg->getAclTensor(input_index);
// Set initializers for indices data such as order of inputData
int input_rank = _ctx.at(input_index).shape().rank();
std::vector<int32_t> starts;
std::vector<int32_t> ends;
starts.resize(input_rank, 0);
ends.resize(input_rank, 0);
{
assert(_ctx.at(begins_index).data());
assert(_ctx.at(sizes_index).data());
auto beginData_base = _ctx.at(begins_index).data()->base();
auto sizeData_base = _ctx.at(sizes_index).data()->base();
[[maybe_unused]] const int beginData_size = _ctx.at(begins_index).shape().num_elements();
[[maybe_unused]] const int sizeData_size = _ctx.at(sizes_index).shape().num_elements();
using ir::DataType;
assert(_ctx.at(begins_index).typeInfo().type() == DataType::INT32);
assert(_ctx.at(sizes_index).typeInfo().type() == DataType::INT32);
assert(beginData_size == input_rank);
assert(sizeData_size == input_rank);
assert(beginData_base != nullptr);
for (int n = 0; n < input_rank; ++n)
{
auto axis = ::onert::backend::acl_common::ToARMComputeAxis(input_rank, n).value();
int32_t begin_value = *(reinterpret_cast<const int32_t *>(beginData_base) + n);
starts[axis] = begin_value;
int32_t size_value = *(reinterpret_cast<const int32_t *>(sizeData_base) + n);
ends[axis] = begin_value + size_value;
}
}
::arm_compute::Coordinates starts_set;
::arm_compute::Coordinates ends_set;
for (size_t i = 0; i < starts.size(); ++i)
{
starts_set.set(i, starts[i]);
ends_set.set(i, ends[i]);
}
auto fn = acl_common::generateLayer<arm_compute::CLSlice>(
inputData_tensor->handle(), outputData_tensor->handle(), starts_set, ends_set);
_return_fn = asAclFunction(std::move(fn));
}
void KernelGenerator::visit(const ir::operation::StridedSlice &node)
{
const auto output_index{node.getOutputs().at(0)};
const auto input_index{node.getInputs().at(ir::operation::StridedSlice::Input::INPUT)};
const auto starts_index{node.getInputs().at(ir::operation::StridedSlice::Input::STARTS)};
const auto ends_index{node.getInputs().at(ir::operation::StridedSlice::Input::ENDS)};
const auto strides_index{node.getInputs().at(ir::operation::StridedSlice::Input::STRIDES)};
auto outputData_tensor = _tensor_reg->getAclTensor(output_index);
auto inputData_tensor = _tensor_reg->getAclTensor(input_index);
// Set initializers for indices data such as order of inputData
int input_rank = _ctx.at(input_index).shape().rank();
std::vector<int32_t> starts;
std::vector<int32_t> ends;
std::vector<int32_t> strides;
starts.resize(input_rank, 0);
ends.resize(input_rank, 0);
strides.resize(input_rank, 0);
{
assert(_ctx.at(starts_index).data());
assert(_ctx.at(ends_index).data());
assert(_ctx.at(strides_index).data());
auto startData_base = _ctx.at(starts_index).data()->base();
auto endData_base = _ctx.at(ends_index).data()->base();
auto stridesData_base = _ctx.at(strides_index).data()->base();
[[maybe_unused]] const int startData_size = _ctx.at(starts_index).shape().num_elements();
[[maybe_unused]] const int endData_size = _ctx.at(ends_index).shape().num_elements();
[[maybe_unused]] const int stridesData_size = _ctx.at(strides_index).shape().num_elements();
using ir::DataType;
assert(_ctx.at(starts_index).typeInfo().type() == DataType::INT32);
assert(_ctx.at(ends_index).typeInfo().type() == DataType::INT32);
assert(_ctx.at(strides_index).typeInfo().type() == DataType::INT32);
assert(startData_size == input_rank);
assert(endData_size == input_rank);
assert(stridesData_size == input_rank);
assert(startData_base != nullptr);
for (int n = 0; n < input_rank; ++n)
{
auto axis = ::onert::backend::acl_common::ToARMComputeAxis(input_rank, n).value();
int32_t start_value = *(reinterpret_cast<const int32_t *>(startData_base) + n);
starts[axis] = start_value;
int32_t end_value = *(reinterpret_cast<const int32_t *>(endData_base) + n);
ends[axis] = end_value;
int32_t strides_value = *(reinterpret_cast<const int32_t *>(stridesData_base) + n);
strides[axis] = strides_value;
}
}
// Set mask bits such as order of inputData
const auto begin_mask = acl_common::ReorderBits<int32_t>(node.param().begin_mask, input_rank);
const auto end_mask = acl_common::ReorderBits<int32_t>(node.param().end_mask, input_rank);
const auto shrink_axis_mask =
acl_common::ReorderBits<int32_t>(node.param().shrink_axis_mask, input_rank);
::arm_compute::Coordinates starts_set;
::arm_compute::Coordinates ends_set;
::arm_compute::BiStrides strides_set;
for (size_t i = 0; i < starts.size(); ++i)
{
starts_set.set(i, starts[i]);
ends_set.set(i, ends[i]);
strides_set.set(i, strides[i]);
}
// Disable applied dim_correction
if (inputData_tensor->num_dimensions() != inputData_tensor->info()->num_dimensions())
{
// This means that high dimension's value is 1 and input tensor is applied dim_correction
acl_common::disableDimCorrection(inputData_tensor);
}
auto fn = acl_common::generateLayer<arm_compute::CLStridedSlice>(
inputData_tensor->handle(), outputData_tensor->handle(), starts_set, ends_set, strides_set,
begin_mask, end_mask, shrink_axis_mask);
// Revert disabling applied dim_correction
if (inputData_tensor->dimension(0) == 1)
{
acl_common::enableDimCorrection(inputData_tensor);
}
_return_fn = asAclFunction(std::move(fn));
}
void KernelGenerator::visit(const ir::operation::Transpose &node)
{
const auto ofm_idx{node.getOutputs().at(0)};
const auto ifm_idx{node.getInputs().at(ir::operation::Transpose::Input::INPUT)};
const auto perm_idx{node.getInputs().at(ir::operation::Transpose::Input::PERMUTATION)};
const auto rank = _ctx.at(ifm_idx).shape().rank();
auto ofm_tensor = _tensor_reg->getAclTensor(ofm_idx);
auto ifm_tensor = _tensor_reg->getAclTensor(ifm_idx);
const auto &perms = _ctx.at(perm_idx);
std::vector<int32_t> pv;
if (perms.shape() == ir::Shape{0})
{
pv.resize(rank);
std::iota(pv.begin(), pv.end(), 0);
std::reverse(pv.begin(), pv.end());
}
else
{
pv = _ctx.at(perm_idx).asVector<int32_t>();
}
std::unique_ptr<arm_compute::IFunction> fn;
if (rank == 1)
{
fn = acl_common::generateLayer<arm_compute::CLCopy>(ifm_tensor->handle(), ofm_tensor->handle());
}
else if (rank == 2)
{
assert(pv.size() == 2 && pv.at(0) == 1 && pv.at(1) == 0);
fn = acl_common::generateLayer<arm_compute::CLTranspose>(ifm_tensor->handle(),
ofm_tensor->handle());
}
else
{
auto backend_pv = acl_common::getARMComputePermutationVector(rank, pv);
fn = acl_common::generateLayer<arm_compute::CLPermute>(ifm_tensor->handle(),
ofm_tensor->handle(), backend_pv);
}
_return_fn = asAclFunction(std::move(fn));
}
void KernelGenerator::visit(const ir::operation::ElementwiseActivation &node)
{
const auto ofm_index{node.getOutputs().at(0)};
const auto ifm_index{node.getInputs().at(ir::operation::ElementwiseActivation::Input::INPUT)};
auto ofm_tensor = _tensor_reg->getAclTensor(ofm_index);
auto ifm_tensor = _tensor_reg->getAclTensor(ifm_index);
const ::arm_compute::ActivationLayerInfo act_info =
acl_common::asActivationLayerInfo(node.param().op_type, node.param().alpha, node.param().beta);
auto fn = acl_common::generateLayer<arm_compute::CLActivationLayer>(
ifm_tensor->handle(), ofm_tensor->handle(), act_info);
_return_fn = asAclFunction(std::move(fn));
}
void KernelGenerator::visit(const ir::operation::ElementwiseBinary &node)
{
const auto output_index{node.getOutputs().at(0)};
const auto lhs_index{node.getInputs().at(ir::operation::ElementwiseBinary::Input::LHS)};
const auto rhs_index{node.getInputs().at(ir::operation::ElementwiseBinary::Input::RHS)};
auto output_tensor = _tensor_reg->getAclTensor(output_index);
auto lhs_tensor = _tensor_reg->getAclTensor(lhs_index);
auto rhs_tensor = _tensor_reg->getAclTensor(rhs_index);
std::unique_ptr<arm_compute::IFunction> fn;
switch (node.param().op_type)
{
case ir::operation::ElementwiseBinary::ElementwiseBinaryType::LOGICAL_AND:
{
fn = acl_common::generateLayer<arm_compute::CLBinaryLogicalOp>(
lhs_tensor->handle(), rhs_tensor->handle(), output_tensor->handle(),
arm_compute::BinaryLogicalOperation::AND);
break;
}
case ir::operation::ElementwiseBinary::ElementwiseBinaryType::LOGICAL_OR:
{
fn = acl_common::generateLayer<arm_compute::CLBitwiseOr>(
lhs_tensor->handle(), rhs_tensor->handle(), output_tensor->handle());
break;
}
case ir::operation::ElementwiseBinary::ElementwiseBinaryType::MAX:
{
fn = acl_common::generateLayer<arm_compute::CLElementwiseMax>(
lhs_tensor->handle(), rhs_tensor->handle(), output_tensor->handle());
break;
}
case ir::operation::ElementwiseBinary::ElementwiseBinaryType::MIN:
{
fn = acl_common::generateLayer<arm_compute::CLElementwiseMin>(
lhs_tensor->handle(), rhs_tensor->handle(), output_tensor->handle());
break;
}
default:
{
std::string err_msg("acl_cl KernelGenerator : " + node.name() +
"is not elementwise-binary operations");
assert(false && err_msg.c_str());
break;
}
}
_return_fn = asAclFunction(std::move(fn));
}
void KernelGenerator::visit(const ir::operation::ElementwiseUnary &node)
{
const auto output_index{node.getOutputs().at(0)};
const auto input_index{node.getInputs().at(ir::operation::ElementwiseUnary::Input::INPUT)};
auto output_tensor = _tensor_reg->getAclTensor(output_index);
auto input_tensor = _tensor_reg->getAclTensor(input_index);
std::unique_ptr<arm_compute::IFunction> fn;
switch (node.param().op_type)
{
case ir::operation::ElementwiseUnary::Type::ABS:
{
const ::arm_compute::ActivationLayerInfo act_info{
::arm_compute::ActivationLayerInfo::ActivationFunction::ABS};
fn = acl_common::generateLayer<arm_compute::CLActivationLayer>(
input_tensor->handle(), output_tensor->handle(), act_info);
break;
}
case ir::operation::ElementwiseUnary::Type::CAST:
{
if (input_tensor->data_type() == output_tensor->data_type())
{
fn = acl_common::generateLayer<arm_compute::CLCopy>(input_tensor->handle(),
output_tensor->handle());
}
else if (_ctx.at(input_index).typeInfo().type() == ir::DataType::BOOL8)
{
fn = acl_common::generateLayer<arm_compute::CLCastBool>(input_tensor->handle(),
output_tensor->handle());
}
else
{
// TODO Support converting float to int32 as round down
fn = acl_common::generateLayer<arm_compute::CLCast>(
input_tensor->handle(), output_tensor->handle(), arm_compute::ConvertPolicy::SATURATE);
}
break;
}
case ir::operation::ElementwiseUnary::Type::DEQUANTIZE:
{
fn = acl_common::generateLayer<arm_compute::CLDequantizationLayer>(input_tensor->handle(),
output_tensor->handle());
break;
}
case ir::operation::ElementwiseUnary::Type::EXP:
{
fn = acl_common::generateLayer<arm_compute::CLExpLayer>(input_tensor->handle(),
output_tensor->handle());
break;
}
case ir::operation::ElementwiseUnary::Type::FLOOR:
{
fn = acl_common::generateLayer<arm_compute::CLFloor>(input_tensor->handle(),
output_tensor->handle());
break;
}
case ir::operation::ElementwiseUnary::Type::LOGICAL_NOT:
{
fn = acl_common::generateLayer<arm_compute::CLBitwiseNot>(input_tensor->handle(),
output_tensor->handle());
break;
}
case ir::operation::ElementwiseUnary::Type::NEG:
{
fn = acl_common::generateLayer<arm_compute::CLNeg>(input_tensor->handle(),
output_tensor->handle());
break;
}
case ir::operation::ElementwiseUnary::Type::RSQRT:
{
fn = acl_common::generateLayer<arm_compute::CLRsqrtLayer>(input_tensor->handle(),
output_tensor->handle());
break;
}
case ir::operation::ElementwiseUnary::Type::SQRT:
{
const ::arm_compute::ActivationLayerInfo act_info{
::arm_compute::ActivationLayerInfo::ActivationFunction::SQRT};
fn = acl_common::generateLayer<arm_compute::CLActivationLayer>(
input_tensor->handle(), output_tensor->handle(), act_info);
break;
}
default:
{
throw std::runtime_error("acl_cl KernelGenerator : " + node.name() + "is not supported yet");
break;
}
}
auto acl_fn = asAclFunction(std::move(fn));
_return_fn = std::move(acl_fn);
}
void KernelGenerator::visit(const ir::operation::ExpandDims &node)
{
const auto output_index{node.getOutputs().at(0)};
const auto input_index{node.getInputs().at(ir::operation::ExpandDims::Input::INPUT)};
auto output_tensor = _tensor_reg->getAclTensor(output_index);
auto input_tensor = _tensor_reg->getAclTensor(input_index);
auto fn = acl_common::generateLayer<arm_compute::CLReshapeLayer>(input_tensor->handle(),
output_tensor->handle());
_return_fn = asAclFunction(std::move(fn));
}
void KernelGenerator::visit(const ir::operation::InstanceNorm &node)
{
const auto ofm_index{node.getOutputs().at(0)};
const auto ifm_index{node.getInputs().at(ir::operation::InstanceNorm::Input::INPUT)};
const auto gamma_index{node.getInputs().at(ir::operation::InstanceNorm::Input::GAMMA)};
const auto beta_index{node.getInputs().at(ir::operation::InstanceNorm::Input::BETA)};
auto ofm_tensor = _tensor_reg->getAclTensor(ofm_index);
auto ifm_tensor = _tensor_reg->getAclTensor(ifm_index);
auto gamma_tensor = _tensor_reg->getAclTensor(gamma_index);
auto beta_tensor = _tensor_reg->getAclTensor(beta_index);
auto epsilon = node.param().epsilon;
auto activation = node.param().activation;
auto fn = acl_common::generateLayer<arm_compute::CLInstanceNormalizationLayerEx>(
ifm_tensor->handle(), ofm_tensor->handle(), gamma_tensor->handle(), beta_tensor->handle(),
epsilon);
_return_fn = std::make_unique<exec::FunctionSequence>(
asAclFunction(std::move(fn)), ActivationBuilder::generate(activation, ofm_tensor->handle()));
}
void KernelGenerator::visit(const ir::operation::LSTM &node)
{
_return_fn = acl_common::kernelGenLSTM<acl_common::AclFunction, ::arm_compute::ICLTensor,
::arm_compute::CLLSTMLayer>(node, _ctx, _tensor_reg);
}
void KernelGenerator::visit(const ir::operation::Comparison &node)
{
const auto output_index{node.getOutputs().at(0)};
const auto input0_index{node.getInputs().at(ir::operation::Comparison::Input::INPUT0)};
const auto input1_index{node.getInputs().at(ir::operation::Comparison::Input::INPUT1)};
const auto comparison_type = node.param().comparison_type;
auto output_tensor = _tensor_reg->getAclTensor(output_index);
auto input0_tensor = _tensor_reg->getAclTensor(input0_index);
auto input1_tensor = _tensor_reg->getAclTensor(input1_index);
auto fn = acl_common::generateLayer<arm_compute::CLComparison>(
input0_tensor->handle(), input1_tensor->handle(), output_tensor->handle(),
(arm_compute::ComparisonOperation)comparison_type);
_return_fn = asAclFunction(std::move(fn));
}
void KernelGenerator::visit(const ir::operation::OneHot &node)
{
const auto output_idx{node.getOutputs().at(0)};
const auto indices_idx{node.getInputs().at(ir::operation::OneHot::Input::INDICES)};
const auto depth_idx{node.getInputs().at(ir::operation::OneHot::Input::DEPTH)};
const auto onvalue_idx{node.getInputs().at(ir::operation::OneHot::Input::ON_VALUE)};
const auto offvalue_idx{node.getInputs().at(ir::operation::OneHot::Input::OFF_VALUE)};
const auto depth = _ctx.at(depth_idx).asScalar<int32_t>();
assert(depth > 0);
auto output_tensor = _tensor_reg->getAclTensor(output_idx);
auto indices_tensor = _tensor_reg->getAclTensor(indices_idx);
auto onvalue_tensor = _tensor_reg->getAclTensor(onvalue_idx);
const size_t output_rank = _ctx.at(output_idx).shape().rank();
int32_t axis = node.param().axis == -1 ? output_rank - 1 : node.param().axis;
axis = acl_common::ToARMComputeAxis(output_rank, axis).value();
if (output_tensor->num_dimensions() != output_tensor->info()->num_dimensions())
{
// This means that high dimension's value is 1 and output_tensor is applied dim_correction
acl_common::disableDimCorrection(output_tensor);
}
std::unique_ptr<::arm_compute::IFunction> fn;
const auto &offvalue = _ctx.at(offvalue_idx);
if (offvalue.isConstant())
{
fn = acl_common::generateLayer<arm_compute::CLOneHot>(
indices_tensor->handle(), onvalue_tensor->handle(), output_tensor->handle(),
acl_common::asPixelValue(offvalue), static_cast<uint32_t>(depth), axis);
}
else
{
auto offvalue_tensor = _tensor_reg->getAclTensor(offvalue_idx);
fn = acl_common::generateLayer<arm_compute::CLOneHot>(
indices_tensor->handle(), onvalue_tensor->handle(), offvalue_tensor->handle(),
output_tensor->handle(), static_cast<uint32_t>(depth), axis);
}
if (output_tensor->dimension(0) == 1)
{
acl_common::enableDimCorrection(output_tensor);
}
_return_fn = asAclFunction(std::move(fn));
}
void KernelGenerator::visit(const ir::operation::Pack &node)
{
const auto output_index{node.getOutputs().at(0)};
auto axis{node.param().axis};
const auto output_rank = _ctx.at(output_index).shape().rank();
std::vector<ir::OperandIndex> input_indexes;
for (const auto &input_index : node.getInputs())
input_indexes.emplace_back(input_index);
auto output = _tensor_reg->getAclTensor(output_index)->handle();
std::vector<arm_compute::ICLTensor *> inputs;
for (const auto &input_index : input_indexes)
inputs.emplace_back(_tensor_reg->getAclTensor(input_index)->handle());
if (axis < 0)
axis += output_rank;
axis = acl_common::ToARMComputeAxis(output_rank, axis).value();
// Disable applied dim_correction
for (const auto &input_index : input_indexes)
{
const auto &input_tensor = _tensor_reg->getAclTensor(input_index);
if (input_tensor->num_dimensions() != input_tensor->info()->num_dimensions())
{
// This means that high dimension's value is 1 and input tensor is applied dim_correction
acl_common::disableDimCorrection(input_tensor);
}
}
auto fn = acl_common::generateLayer<arm_compute::CLStackLayer>(inputs, axis, output);
// Revert disabling applied dim_correction
for (const auto &input_index : input_indexes)
{
const auto &input_tensor = _tensor_reg->getAclTensor(input_index);
if (input_tensor->dimension(0) == 1)
{
acl_common::enableDimCorrection(input_tensor);
}
}
_return_fn = asAclFunction(std::move(fn));
}
void KernelGenerator::visit(const ir::operation::Pool2D &node)
{
auto raw_fn = acl_common::kernelGenPool2D<::arm_compute::CLPoolingLayer>(
node, _ctx, _tensor_reg, acl_common::convertPoolType(node.param().op_type));
const auto ofm_index{node.getOutputs().at(0)};
auto ofm_tensor = _tensor_reg->getAclTensor(ofm_index);
const auto activation = node.param().activation;
_return_fn = std::make_unique<exec::FunctionSequence>(
asAclFunction(std::move(raw_fn)),
ActivationBuilder::generate(activation, ofm_tensor->handle()));
}
void KernelGenerator::visit(const ir::operation::ResizeBilinear &node)
{
const auto ofm_index{node.getOutputs().at(0)};
const auto ifm_index{node.getInputs().at(ir::operation::ResizeBilinear::Input::INPUT)};
auto ofm_tensor = _tensor_reg->getAclTensor(ofm_index);
auto ifm_tensor = _tensor_reg->getAclTensor(ifm_index);
auto fn = acl_common::generateLayer<arm_compute::CLScale>(
ifm_tensor->handle(), ofm_tensor->handle(),
::arm_compute::ScaleKernelInfo{
::arm_compute::InterpolationPolicy::BILINEAR, ::arm_compute::BorderMode::REPLICATE,
::arm_compute::PixelValue(0.f), ::arm_compute::SamplingPolicy::TOP_LEFT});
_return_fn = asAclFunction(std::move(fn));
}
void KernelGenerator::visit(const ir::operation::ResizeNearestNeighbor &node)
{
const auto ofm_index{node.getOutputs().at(0)};
const auto ifm_index{node.getInputs().at(ir::operation::ResizeNearestNeighbor::Input::INPUT)};
auto ofm_tensor = _tensor_reg->getAclTensor(ofm_index);
auto ifm_tensor = _tensor_reg->getAclTensor(ifm_index);
auto fn = acl_common::generateLayer<arm_compute::CLScale>(
ifm_tensor->handle(), ofm_tensor->handle(),
::arm_compute::ScaleKernelInfo{
::arm_compute::InterpolationPolicy::NEAREST_NEIGHBOR, ::arm_compute::BorderMode::REPLICATE,
::arm_compute::PixelValue(0.f), ::arm_compute::SamplingPolicy::TOP_LEFT});
_return_fn = asAclFunction(std::move(fn));
}
void KernelGenerator::visit(const ir::operation::RNN &node)
{
const auto output_index{node.getOutputs().at(ir::operation::RNN::Output::OUTPUT)};
const auto hidden_state_out_index{
node.getOutputs().at(ir::operation::RNN::Output::HIDDEN_STATE_OUT)};
const auto input_index{node.getInputs().at(ir::operation::RNN::Input::INPUT)};
const auto weights_index{node.getInputs().at(ir::operation::RNN::Input::WEIGHTS)};
const auto recurrent_weights_index{
node.getInputs().at(ir::operation::RNN::Input::RECURRENT_WEIGHTS)};
const auto bias_index{node.getInputs().at(ir::operation::RNN::Input::BIAS)};
const auto hidden_state_in_index{node.getInputs().at(ir::operation::RNN::Input::HIDDEN_STATE_IN)};
const auto activation = node.param().activation;
auto output_tensor = _tensor_reg->getAclTensor(output_index);
auto hidden_state_out_tensor = _tensor_reg->getAclTensor(hidden_state_out_index);
auto input_tensor = _tensor_reg->getAclTensor(input_index);
auto weights_tensor = _tensor_reg->getAclTensor(weights_index);
auto recurrent_weights_tensor = _tensor_reg->getAclTensor(recurrent_weights_index);
auto bias_tensor = _tensor_reg->getAclTensor(bias_index);
auto hidden_state_in_tensor = _tensor_reg->getAclTensor(hidden_state_in_index);
auto act_info = ::onert::backend::acl_common::asActivationLayerInfo(activation);
auto copy_layer = acl_common::generateLayer<arm_compute::CLCopy>(
hidden_state_in_tensor->handle(), hidden_state_out_tensor->handle());
_return_fn = asAclFunction(std::move(copy_layer));
auto fn = acl_common::generateLayer<arm_compute::CLRNNLayer>(
_tensor_builder->acl_tensor_manager()->internal_buffer_manager(), input_tensor->handle(),
weights_tensor->handle(), recurrent_weights_tensor->handle(), bias_tensor->handle(),
hidden_state_out_tensor->handle(), output_tensor->handle(), act_info);
_return_fn = asAclFunction(std::move(fn));
}
void KernelGenerator::visit(const ir::operation::SpaceToBatchND &node)
{
const auto ofm_index{node.getOutputs().at(0)};
const auto ifm_index{node.getInputs().at(ir::operation::SpaceToBatchND::Input::INPUT)};
const auto block_size_index{
node.getInputs().at(ir::operation::SpaceToBatchND::Input::BLOCK_SIZE)};