CLGEMMConvolutionLayer Class Reference

Basic function to compute the convolution layer. More...

#include <CLGEMMConvolutionLayer.h>

Collaboration diagram for CLGEMMConvolutionLayer:

Public Member Functions
	CLGEMMConvolutionLayer (std::shared_ptr< IMemoryManager > memory_manager=nullptr, IWeightsManager *weights_manager=nullptr)
	Constructor. More...
	CLGEMMConvolutionLayer (const CLGEMMConvolutionLayer &)=delete
	Prevent instances of this class from being copied (As this class contains pointers) More...
	CLGEMMConvolutionLayer (CLGEMMConvolutionLayer &&)=default
	Default move constructor. More...
CLGEMMConvolutionLayer &	operator= (const CLGEMMConvolutionLayer &)=delete
	Prevent instances of this class from being copied (As this class contains pointers) More...
CLGEMMConvolutionLayer &	operator= (CLGEMMConvolutionLayer &&)=default
	Default move assignment operator. More...
	~CLGEMMConvolutionLayer ()
	Default destructor. More...
void	configure (const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info=WeightsInfo(), const Size2D &dilation=Size2D(1U, 1U), const ActivationLayerInfo &act_info=ActivationLayerInfo(), unsigned int num_groups=1)
	Set the input and output tensors. More...
void	configure (const CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info=WeightsInfo(), const Size2D &dilation=Size2D(1U, 1U), const ActivationLayerInfo &act_info=ActivationLayerInfo(), unsigned int num_groups=1)
	Set the input and output tensors. More...
void	run () override
	Run the kernels contained in the function. More...
void	prepare () override
	Prepare the function for executing. More...
Public Member Functions inherited from IFunction
virtual	~IFunction ()=default
	Destructor. More...

Static Public Member Functions
static Status	validate (const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info=WeightsInfo(), const Size2D &dilation=Size2D(1U, 1U), const ActivationLayerInfo &act_info=ActivationLayerInfo(), unsigned int num_groups=1)
	Static function to check if given info will lead to a valid configuration of CLGEMMConvolutionLayer. More...

Detailed Description

Basic function to compute the convolution layer.

This function calls the following OpenCL kernels/functions:

1. opencl::ClGemmConv2d

Definition at line 47 of file CLGEMMConvolutionLayer.h.

Constructor & Destructor Documentation

CLGEMMConvolutionLayer() [1/3]

CLGEMMConvolutionLayer	(	std::shared_ptr< IMemoryManager >	memory_manager = nullptr,
		IWeightsManager *	weights_manager = nullptr
	)

Constructor.

Parameters

[in]	memory_manager	(Optional) Memory manager.
[in]	weights_manager	(Optional) Weights manager.

Definition at line 62 of file CLGEMMConvolutionLayer.cpp.

    : _impl(std::make_unique<Impl>())
{
    _impl->memory_group    = MemoryGroup(memory_manager);
    _impl->weights_manager = weights_manager;
}

CLGEMMConvolutionLayer() [2/3]

CLGEMMConvolutionLayer ( const CLGEMMConvolutionLayer &  )

delete

Prevent instances of this class from being copied (As this class contains pointers)

CLGEMMConvolutionLayer() [3/3]

CLGEMMConvolutionLayer ( CLGEMMConvolutionLayer &&  )

default

Default move constructor.

~CLGEMMConvolutionLayer()

~CLGEMMConvolutionLayer ( )

default

Default destructor.

Member Function Documentation

configure() [1/2]

void configure	(	const CLCompileContext &	compile_context,
		const ICLTensor *	input,
		const ICLTensor *	weights,
		const ICLTensor *	biases,
		ICLTensor *	output,
		const PadStrideInfo &	conv_info,
		const WeightsInfo &	weights_info = WeightsInfo(),
		const Size2D &	dilation = Size2D(1U, 1U),
		const ActivationLayerInfo &	act_info = ActivationLayerInfo(),
		unsigned int	num_groups = 1
	)

Set the input and output tensors.

Parameters

[in]	compile_context	The compile context to be used.
[in]	input	Source tensor. 3 lower dimensions represent a single input [width, height, IFM], while every optional dimension from 4 and above represent a batch of inputs. Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32.
[in]	weights	Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported: Same as input or QASYMM8/QSYMM8_PER_CHANNEL when input is QASYMM8 or QASYMM8_SIGNED/QSYMM8_PER_CHANNEL when input is QASYMM8_SIGNED.
[in]	biases	Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. Data type supported: Should match input data type, except for input of quantized type where biases should be of S32 type.
[out]	output	Destination tensor. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs. Data types supported: Same as input.
[in]	conv_info	Contains padding and stride information described in PadStrideInfo.
[in]	weights_info	Specifies if the weights tensor has been reshaped with CLWeightsReshapeKernel. If this is not part of the fully connected layer the weights tensor has also been transposed with CLGEMMReshapeRHSMatrixKernel. Data type supported: Same as input.
[in]	dilation	(Optional) Dilation, in elements, across x and y. Defaults to (1, 1).
[in]	act_info	(Optional) Activation layer information in case of a fused activation.
[in]	num_groups	(Optional) Number of groups when performing a grouped convolution. num_groups != 1 is only supported for NCHW data layout

Definition at line 86 of file CLGEMMConvolutionLayer.cpp.

{
    ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
    _impl->weights               = weights;
    _impl->op                    = std::make_unique<opencl::ClGemmConv2d>();
    const Conv2dInfo conv2d_info = Conv2dInfo(conv_info, dilation, act_info, false, num_groups);
    _impl->op->configure(compile_context, input->info(), weights->info(),
                         (biases != nullptr ? biases->info() : nullptr), output->info(), conv2d_info, weights_info);
 
    _impl->run_pack  = {{TensorType::ACL_SRC_0, input},
                        {TensorType::ACL_SRC_1, weights},
                        {TensorType::ACL_SRC_2, biases},
                        {TensorType::ACL_DST, output}};
    _impl->prep_pack = {
        {TensorType::ACL_SRC_1, weights},
        {TensorType::ACL_SRC_2, biases},
    };
    _impl->aux_mem_req = _impl->op->workspace();
    _impl->workspace_tensors =
        manage_workspace<CLTensor>(_impl->aux_mem_req, _impl->memory_group, _impl->run_pack, _impl->prep_pack);
}

References arm_compute::ACL_DST, arm_compute::ACL_SRC_0, arm_compute::ACL_SRC_1, arm_compute::ACL_SRC_2, arm_compute::test::validation::act_info, ARM_COMPUTE_ERROR_ON_NULLPTR, arm_compute::test::validation::conv_info, ITensor::info(), arm_compute::test::validation::input, arm_compute::test::validation::num_groups, and arm_compute::test::validation::weights_info.

configure() [2/2]

void configure	(	const ICLTensor *	input,
		const ICLTensor *	weights,
		const ICLTensor *	biases,
		ICLTensor *	output,
		const PadStrideInfo &	conv_info,
		const WeightsInfo &	weights_info = WeightsInfo(),
		const Size2D &	dilation = Size2D(1U, 1U),
		const ActivationLayerInfo &	act_info = ActivationLayerInfo(),
		unsigned int	num_groups = 1
	)

Set the input and output tensors.

Valid data layouts:

• NHWC
• NCHW

Valid data type configurations:

src0	src1	src2	dst
F16	F16	F16	F16
F32	F32	F32	F32
QASYMM8	QASYMM8	S32	QASYMM8
QASYMM8	QSYMM8_PER_CHANNEL	S32	QASYMM8
QASYMM8_SIGNED	QASYMM8_SIGNED	S32	QASYMM8_SIGNED
QASYMM8_SIGNED	QSYMM8_PER_CHANNEL	S32	QASYMM8_SIGNED

Parameters

[in]	input	Source tensor. 3 lower dimensions represent a single input [width, height, IFM], while every optional dimension from 4 and above represent a batch of inputs. Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32.
[in]	weights	Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported: Same as input or QASYMM8/QSYMM8_PER_CHANNEL when input is QASYMM8 or QASYMM8_SIGNED/QSYMM8_PER_CHANNEL when input is QASYMM8_SIGNED.
[in]	biases	Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. Data type supported: Should match input data type, except for input of quantized type where biases should be of S32 type.
[out]	output	Destination tensor. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs. Data types supported: Same as input.
[in]	conv_info	Contains padding and stride information described in PadStrideInfo.
[in]	weights_info	Specifies if the weights tensor has been reshaped with CLWeightsReshapeKernel. If this is not part of the fully connected layer the weights tensor has also been transposed with CLGEMMReshapeRHSMatrixKernel. Data type supported: Same as input.
[in]	dilation	(Optional) Dilation, in elements, across x and y. Defaults to (1, 1).
[in]	act_info	(Optional) Activation layer information in case of a fused activation.
[in]	num_groups	(Optional) Number of groups when performing a grouped convolution. num_groups != 1 is only supported for NCHW data layout

Definition at line 72 of file CLGEMMConvolutionLayer.cpp.

{
    configure(CLKernelLibrary::get().get_compile_context(), input, weights, biases, output, conv_info, weights_info,
              dilation, act_info, num_groups);
}

References arm_compute::test::validation::act_info, arm_compute::test::validation::conv_info, CLKernelLibrary::get(), arm_compute::test::validation::input, arm_compute::test::validation::num_groups, and arm_compute::test::validation::weights_info.

operator=() [1/2]

CLGEMMConvolutionLayer& operator= ( CLGEMMConvolutionLayer &&  )

default

Default move assignment operator.

operator=() [2/2]

CLGEMMConvolutionLayer& operator= ( const CLGEMMConvolutionLayer &  )

delete

Prevent instances of this class from being copied (As this class contains pointers)

prepare()

void prepare ( )

overridevirtual

Prepare the function for executing.

Any one off pre-processing step required by the function is handled here

Note

Prepare stage might not need all the function's buffers' backing memory to be available in order to execute

Reimplemented from IFunction.

Definition at line 138 of file CLGEMMConvolutionLayer.cpp.

{
    if (!_impl->is_prepared)
    {
        _impl->op->prepare(_impl->prep_pack);
        auto has_reshape =
            std::find_if(_impl->aux_mem_req.begin(), _impl->aux_mem_req.end(),
                         [](const MemoryInfo &m) -> bool { return m.lifetime == MemoryLifetime::Persistent; });
 
        if (has_reshape != std::end(_impl->aux_mem_req))
        {
            _impl->weights->mark_as_unused();
        }
        else
        {
            // Pack the B matrix to be used as the underlying GEMM performs no reshapes
            _impl->run_pack.add_const_tensor(ACL_SRC_1, _impl->weights);
        }
        release_temporaries(_impl->aux_mem_req, _impl->workspace_tensors);
        _impl->is_prepared = true;
    }
}

References arm_compute::ACL_SRC_1, arm_compute::mlgo::parser::end(), and arm_compute::release_temporaries().

Referenced by CLGEMMConvolutionLayer::run().

run()

void run ( )

overridevirtual

Run the kernels contained in the function.

For CPU kernels:

• Multi-threading is used for the kernels which are parallelisable.
• By default std::thread::hardware_concurrency() threads are used.

Note

CPPScheduler::set_num_threads() can be used to manually set the number of threads

For OpenCL kernels:

• All the kernels are enqueued on the queue associated with CLScheduler.
• The queue is then flushed.

Note

The function will not block until the kernels are executed. It is the user's responsibility to wait.

Will call prepare() on first run if hasn't been done

Implements IFunction.

Definition at line 131 of file CLGEMMConvolutionLayer.cpp.

{
    prepare();
    MemoryGroupResourceScope scope_mg(_impl->memory_group);
    _impl->op->run(_impl->run_pack);
}

References CLGEMMConvolutionLayer::prepare().

validate()

Status validate ( const ITensorInfo *  input, const ITensorInfo *  weights, const ITensorInfo *  biases, const ITensorInfo *  output, const PadStrideInfo &  conv_info, const WeightsInfo &  weights_info = WeightsInfo(), const Size2D &  dilation = Size2D(1U, 1U), const ActivationLayerInfo &  act_info = ActivationLayerInfo(), unsigned int  num_groups = 1  )

static

Static function to check if given info will lead to a valid configuration of CLGEMMConvolutionLayer.

Parameters

[in]	input	Source tensor. 3 lower dimensions represent a single input [width, height, IFM], while every optional dimension from 4 and above represent a batch of inputs. Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32.
[in]	weights	Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported: Same as input or QASYMM8/QSYMM8_PER_CHANNEL when input is QASYMM8 or QASYMM8_SIGNED/QSYMM8_PER_CHANNEL when input is QASYMM8_SIGNED.
[in]	biases	Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. Data type supported: Should match input data type, except for input of quantized type where biases should be of S32 type.
[out]	output	Destination tensor. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs. Data types supported: Same as input.
[in]	conv_info	Contains padding and stride information described in PadStrideInfo.
[in]	weights_info	Specifies if the weights tensor has been reshaped with CLWeightsReshapeKernel. If this is not part of the fully connected layer the weights tensor has also been transposed with CLGEMMReshapeRHSMatrixKernel. Data type supported: Same as input.
[in]	dilation	(Optional) Dilation, in elements, across x and y. Defaults to (1, 1).
[in]	act_info	(Optional) Activation layer information in case of a fused activation.
[in]	num_groups	(Optional) Number of groups when performing a grouped convolution. num_groups != 1 is only supported for NCHW data layout

Returns

a status

Definition at line 117 of file CLGEMMConvolutionLayer.cpp.

{
    const Conv2dInfo conv2d_info = Conv2dInfo(conv_info, dilation, act_info, false, num_groups);
    return opencl::ClGemmConv2d::validate(input, weights, biases, output, conv2d_info, weights_info);
}

References arm_compute::test::validation::act_info, arm_compute::test::validation::conv_info, arm_compute::test::validation::input, arm_compute::test::validation::num_groups, ClGemmConv2d::validate(), and arm_compute::test::validation::weights_info.

---

The documentation for this class was generated from the following files:

• arm_compute/runtime/CL/functions/CLGEMMConvolutionLayer.h
• src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp