Deeploy.Targets.Snitch.Platform.SnitchClusterEngine

class Deeploy.Targets.Snitch.Platform.SnitchClusterEngine(name: str, Mapping={'Add': AddLayer(maps=[ AddParser [ _SnitchAddTemplate(int8_t, int8_t) -> int32_t ] ]), 'Gather': GatherLayer(maps=[ GatherParser [ NodeTemplate(int8_t, int32_t) -> int8_t NodeTemplate(int16_t, int32_t) -> int16_t NodeTemplate(int32_t, int32_t) -> int32_t NodeTemplate(int64_t, int32_t) -> int64_t NodeTemplate(float32_t, int8_t) -> float32_t NodeTemplate(float32_t, uint8_t) -> float32_t NodeTemplate(float32_t, int16_t) -> float32_t NodeTemplate(float32_t, uint16_t) -> float32_t NodeTemplate(float32_t, int32_t) -> float32_t NodeTemplate(float32_t, uint32_t) -> float32_t NodeTemplate(float32_t, int64_t) -> float32_t NodeTemplate(float32_t, uint64_t) -> float32_t ] ]), 'Gemm': GEMMLayer(maps=[ SnitchGEMMParser [ SnitchGemmTemplate(int8_t, int8_t, int32_t) -> int32_t NodeTemplate(float32_t, float32_t, float32_t) -> float32_t ] ]), 'MatMul': MatMulLayer(maps=[ MatMulParser [ _MatMulTemplate(int8_t, int8_t) -> int32_t NodeTemplate(float32_t, float32_t) -> float32_t ] ]), 'Pad': PadLayer(maps=[ Pad1DParser [ _Pad1DTemplate(int8_t) -> int8_t _Pad1DTemplate(int16_t) -> int16_t _Pad1DTemplate(int32_t) -> int32_t _Pad1DTemplate(int64_t) -> int64_t ] Pad2DParser [ _Pad2DTemplate(int8_t) -> int8_t _Pad2DTemplate(int16_t) -> int16_t _Pad2DTemplate(int32_t) -> int32_t _Pad2DTemplate(int64_t) -> int64_t NodeTemplate(float32_t, float32_t, float32_t) -> float32_t ] ]), 'RQGemm': RQGEMMLayer(maps=[ SnitchRQGEMMParser [ SnitchRqGemmTemplate(int8_t, int8_t, int32_t, int32_t, int32_t) -> int8_t ] ]), 'RQIntegerDiv': RQIntegerDivLayer(maps=[ RQIntegerDivParser [ NodeTemplate(int32_t, int32_t, int32_t, int32_t, int32_t) -> int8_t ] ]), 'RequantizedAdd': AddLayer(maps=[ RQAddParser [ RQAddTemplate(int8_t, int8_t) -> int8_t ] ]), 'Softmax': SoftmaxLayer(maps=[ SoftmaxParser [ iSoftmaxPreAllocatedBuffTemplate(int8_t) -> uint8_t iSoftmaxPreAllocatedBuffTemplate(uint8_t) -> uint8_t FloatSoftmaxTemplate(float32_t) -> float32_t ] ]), 'Unsqueeze': ReshapeLayer(maps=[ UnsqueezeParser [ _ReshapeTemplate(int8_t, int32_t) -> int8_t _ReshapeTemplate(uint8_t, int32_t) -> uint8_t _ReshapeTemplate(int16_t, int32_t) -> int16_t _ReshapeTemplate(uint16_t, int32_t) -> uint16_t _ReshapeTemplate(int32_t, int32_t) -> int32_t _ReshapeTemplate(uint32_t, int32_t) -> uint32_t _ReshapeTemplate(int64_t, int32_t) -> int64_t _ReshapeTemplate(uint64_t, int32_t) -> uint64_t _ReshapeTemplate(float32_t, int8_t) -> float32_t _ReshapeTemplate(float32_t, uint8_t) -> float32_t _ReshapeTemplate(float32_t, int16_t) -> float32_t _ReshapeTemplate(float32_t, uint16_t) -> float32_t _ReshapeTemplate(float32_t, int32_t) -> float32_t _ReshapeTemplate(float32_t, uint32_t) -> float32_t _ReshapeTemplate(float32_t, int64_t) -> float32_t _ReshapeTemplate(float32_t, uint64_t) -> float32_t ] ]), 'iLayerNorm': LayerNormLayer(maps=[ iLayerNormParser [ _iLayerNormTemplate(int8_t, int32_t, int32_t) -> int8_t NodeTemplate(float32_t, float32_t, float32_t) -> float32_t, float32_t, float32_t ] ]), 'iNoNorm': iNoNormLayer(maps=[ iNoNormParser [ _iNoNormTemplate(int8_t, int8_t, int32_t) -> int8_t ] ]), 'iSoftmax': SoftmaxLayer(maps=[ iSoftmaxParser [ iSoftmaxPreAllocatedBuffTemplate(int8_t) -> uint8_t iSoftmaxPreAllocatedBuffTemplate(uint8_t) -> uint8_t FloatSoftmaxTemplate(float32_t) -> float32_t ] ])}, initCode='', includeList=['snrt.h', 'DeeploySnitchMath.h'])

Bases: DeploymentEngine

Methods

__init__(name: str, Mapping={'Add': AddLayer(maps=[ AddParser [ _SnitchAddTemplate(int8_t, int8_t) -> int32_t ] ]), 'Gather': GatherLayer(maps=[ GatherParser [ NodeTemplate(int8_t, int32_t) -> int8_t NodeTemplate(int16_t, int32_t) -> int16_t NodeTemplate(int32_t, int32_t) -> int32_t NodeTemplate(int64_t, int32_t) -> int64_t NodeTemplate(float32_t, int8_t) -> float32_t NodeTemplate(float32_t, uint8_t) -> float32_t NodeTemplate(float32_t, int16_t) -> float32_t NodeTemplate(float32_t, uint16_t) -> float32_t NodeTemplate(float32_t, int32_t) -> float32_t NodeTemplate(float32_t, uint32_t) -> float32_t NodeTemplate(float32_t, int64_t) -> float32_t NodeTemplate(float32_t, uint64_t) -> float32_t ] ]), 'Gemm': GEMMLayer(maps=[ SnitchGEMMParser [ SnitchGemmTemplate(int8_t, int8_t, int32_t) -> int32_t NodeTemplate(float32_t, float32_t, float32_t) -> float32_t ] ]), 'MatMul': MatMulLayer(maps=[ MatMulParser [ _MatMulTemplate(int8_t, int8_t) -> int32_t NodeTemplate(float32_t, float32_t) -> float32_t ] ]), 'Pad': PadLayer(maps=[ Pad1DParser [ _Pad1DTemplate(int8_t) -> int8_t _Pad1DTemplate(int16_t) -> int16_t _Pad1DTemplate(int32_t) -> int32_t _Pad1DTemplate(int64_t) -> int64_t ] Pad2DParser [ _Pad2DTemplate(int8_t) -> int8_t _Pad2DTemplate(int16_t) -> int16_t _Pad2DTemplate(int32_t) -> int32_t _Pad2DTemplate(int64_t) -> int64_t NodeTemplate(float32_t, float32_t, float32_t) -> float32_t ] ]), 'RQGemm': RQGEMMLayer(maps=[ SnitchRQGEMMParser [ SnitchRqGemmTemplate(int8_t, int8_t, int32_t, int32_t, int32_t) -> int8_t ] ]), 'RQIntegerDiv': RQIntegerDivLayer(maps=[ RQIntegerDivParser [ NodeTemplate(int32_t, int32_t, int32_t, int32_t, int32_t) -> int8_t ] ]), 'RequantizedAdd': AddLayer(maps=[ RQAddParser [ RQAddTemplate(int8_t, int8_t) -> int8_t ] ]), 'Softmax': SoftmaxLayer(maps=[ SoftmaxParser [ iSoftmaxPreAllocatedBuffTemplate(int8_t) -> uint8_t iSoftmaxPreAllocatedBuffTemplate(uint8_t) -> uint8_t FloatSoftmaxTemplate(float32_t) -> float32_t ] ]), 'Unsqueeze': ReshapeLayer(maps=[ UnsqueezeParser [ _ReshapeTemplate(int8_t, int32_t) -> int8_t _ReshapeTemplate(uint8_t, int32_t) -> uint8_t _ReshapeTemplate(int16_t, int32_t) -> int16_t _ReshapeTemplate(uint16_t, int32_t) -> uint16_t _ReshapeTemplate(int32_t, int32_t) -> int32_t _ReshapeTemplate(uint32_t, int32_t) -> uint32_t _ReshapeTemplate(int64_t, int32_t) -> int64_t _ReshapeTemplate(uint64_t, int32_t) -> uint64_t _ReshapeTemplate(float32_t, int8_t) -> float32_t _ReshapeTemplate(float32_t, uint8_t) -> float32_t _ReshapeTemplate(float32_t, int16_t) -> float32_t _ReshapeTemplate(float32_t, uint16_t) -> float32_t _ReshapeTemplate(float32_t, int32_t) -> float32_t _ReshapeTemplate(float32_t, uint32_t) -> float32_t _ReshapeTemplate(float32_t, int64_t) -> float32_t _ReshapeTemplate(float32_t, uint64_t) -> float32_t ] ]), 'iLayerNorm': LayerNormLayer(maps=[ iLayerNormParser [ _iLayerNormTemplate(int8_t, int32_t, int32_t) -> int8_t NodeTemplate(float32_t, float32_t, float32_t) -> float32_t, float32_t, float32_t ] ]), 'iNoNorm': iNoNormLayer(maps=[ iNoNormParser [ _iNoNormTemplate(int8_t, int8_t, int32_t) -> int8_t ] ]), 'iSoftmax': SoftmaxLayer(maps=[ iSoftmaxParser [ iSoftmaxPreAllocatedBuffTemplate(int8_t) -> uint8_t iSoftmaxPreAllocatedBuffTemplate(uint8_t) -> uint8_t FloatSoftmaxTemplate(float32_t) -> float32_t ] ])}, initCode='', includeList=['snrt.h', 'DeeploySnitchMath.h']) → None

Instantiate a new engine

Parameters:

name (str) – Name of this compute engine; must be unique per deployemnt
Mapping (Dict[str, Union[ONNXLayer, Callable[[gs.Node], Any]]]) – Mapping between operator names and ONNXLayer implementations
initCode (str) – Static initialization code for this engine
includeList (List[str]) – List of header files to be included with #include directives

`__init__`(name[, Mapping])	Instantiate a new engine
`canExecute`(node)	Return whether this accelerator can execute an operator

Attributes

`name`	Name of this compute engine; must be unique per deployemnt
`Mapping`	Mapping between operator names and ONNXLayer implementations
`includeList`	List of header files to be included with #include directives

canExecute(node: Node) → bool

Return whether this accelerator can execute an operator

Parameters:: node (gs.Node) – Operator to be checked
Returns:: True if operator can be run on this Engine, False otherwise
Return type:: bool

name

Name of this compute engine; must be unique per deployemnt

Type:: str

Mapping: Mapping between operator names and ONNXLayer implementations

includeList

List of header files to be included with #include directives

Type:: List[str]