backends/xnnpack/serialization/runtime_schema.fbs - platform/external/executorch - Git at Google

 // Copyright (c) Meta Platforms, Inc. and affiliates.

 namespace fb_xnnpack;

 // Update after any BC breaking changes
 file_identifier "XN00";

 // datatype for xnn-values
 enum XNNDatatype : short {
   /// Invalid data type. Valid Values never have this datatype.
   xnn_datatype_invalid = 0,
   /// IEEE754 single-precision floating-point.
   xnn_datatype_fp32 = 1,
   /// IEEE754 half-precision floating-point.
   xnn_datatype_fp16 = 2,
   /// Quantized 8-bit signed integer with shared per-Value quantization parameters.
   xnn_datatype_qint8 = 3,
   /// Quantized 8-bit unsigned integer with shared per-Value quantization parameters.
   xnn_datatype_quint8 = 4,
   /// Quantized 32-bit signed integer with shared per-Value quantization parameters.
   xnn_datatype_qint32 = 5,
   /// Quantized 8-bit signed integer with shared per-channel quantization parameters.
   xnn_datatype_qcint8 = 6,
   /// Quantized 32-bit signed integer with shared per-channel quantization parameters.
   xnn_datatype_qcint32 = 7,
   /// Quantized 4-bit signed integer with shared per-channel quantization parameters.
   xnn_datatype_qcint4 = 8,
   /// Dynamically quantized 8-bit signed integer with per-batch quantization parameters.
   xnn_datatype_qdint8 = 9,
   /// Quantized 4-bit signed integer with shared blockwise quantization parameters.
   xnn_datatype_qbint4 = 10,
 }

 // type of quantization
 union XNNQuantParams {
   PerChannelQuant,
   PerTensorQuant,
   PerTokenDynamicQuant,
   PerChannelGroupQuant,
 }

 // taken from executorch
 // Data buffer abstraction.
 table Buffer {
   storage:[ubyte] (force_align: 16);
 }

 table PerChannelQuant {
   scale:[float];
   channel_dim:int;
 }

 table PerTokenDynamicQuant {
   num_nonbatch_dims:int;
 }

 table PerTensorQuant {
   scale:float;
   zero_point:int;
 }

 table PerChannelGroupQuant {
   scale:[float];
   channel_dim:int;
   group_size:int;
   scale_bf16:[ushort];
 }

 table XNNTensorValue {
   // type of the tensor elements.
   datatype:XNNDatatype;
   // number of dimensions in the shape.
   num_dims:uint;
   // pointer to an array of @a num_dims shape dimensions. If num_dims is 0, this pointer can be NULL.
   // XNNPACK does not keep any pointers to this array after the function returns.
   dims:[uint];
   // Index to the program's constant buffer table, value 0 is reserved to indicate non constant
   constant_buffer_idx:uint;
   // external ID for the Value. The ID must be within the range of reserved Value IDs specified on
   // the Subgraph creation. If the external ID is XNN_INVALID_VALUE_ID, an internal ID will be
   // created for the Value.
   external_id:uint;
   // binary features of the Value. Supported values are any combination of XNN_VALUE_FLAG_EXTERNAL_INPUT
   // and XNN_VALUE_FLAG_EXTERNAL_OUTPUT.
   flags:uint;
   // pointer to the variable that will be initialized with the Value ID upon successful return. If a
   // valid @a external_id was provided, the variable will be initialized with the @a external_id value.
   id_out:uint;
   // does this value need to be quantized dynamically at runtime?
   // if we are quantizing at runtime, this field points to a target dtype
   dq_datatype:XNNDatatype = xnn_datatype_invalid;
 }

 table XNNQuantizedTensorValue {
   // Base Tensor Value
   tensor_value:XNNTensorValue;
   // Quantization parameters
   quant_params:XNNQuantParams;
 }

 union XNodeUnion {
   XNNAdd: _XNNNode2x1,
   XNNFullyConnected,
   XNNSoftmax: _XNNNode1x1,
   XNNSigmoid: _XNNNode1x1,
   XNNStaticTranspose,
   XNNClamp: _XNNNode1x1,
   XNNConv2d: _XNNNodeConv,
   XNNDiv: _XNNNode2x1,
   XNNStaticResizeBilinear2D,
   XNNStaticConstantPad,
   XNNAvgPooling2d: _XNNPooling2D,
   XNNMinimum: _XNNNode2x1,
   XNNDepthwiseConv2d: _XNNNodeConv,
   XNNMaxPooling2d: _XNNPooling2D,
   XNNMultiply: _XNNNode2x1,
   XNNSubtract: _XNNNode2x1,
   XNNFloor: _XNNNode1x1,
   XNNConvert: _XNNNode1x1,
   XNNGlobalAvgPooling2d: _XNNNode1x1,
   XNNStaticReshape,
   XNNArgMaxPooling2d,
   XNNSquareRoot: _XNNNode1x1,
   XNNCeiling: _XNNNode1x1,
   XNNHardswish: _XNNNode1x1,
   XNNLeakyReLU,
   XNNMaximum: _XNNNode2x1,
   XNNNegate: _XNNNode1x1,
   XNNSquare: _XNNNode1x1,
   XNNELU,
   XNNAbs: _XNNNode1x1,
   XNNPReLU: _XNNNode2x1,
   XNNConcatenate2: _XNNCat,
   XNNConcatenate3: _XNNCat,
   XNNConcatenate4: _XNNCat,
   XNNStaticSlice,
   XNNScaledDotProductAttention,
   XNNBatchMatrixMultiply: _XNNNode2x1,
 }

 union XValueUnion {
   XNNTensorValue,
   XNNQuantizedTensorValue,
 }

 table OutputMinMax {
   output_min:float;
   output_max:float;
 }

 table XNode {
   xnode_union:XNodeUnion;
   // An int which can be linked back to the node in the origin graph
   debug_handle:uint;
   output_min_max:OutputMinMax;
 }

 table XValue {
   xvalue_union:XValueUnion;
 }

 table XNNStaticTranspose {
   num_dims:uint;
   perm:[uint];
   input_id:uint;
   output_id:uint;
   flags:uint;
 }

 table XNNStaticResizeBilinear2D {
   new_height:uint;
   new_width:uint;
   input_id:uint;
   output_id:uint;
   flags:uint;
 }

 table XNNStaticConstantPad {
   pre_paddings:[uint];
   post_paddings:[uint];
   padding_value:float;
   input_id:uint;
   output_id:uint;
   flags:uint;
 }

 // A node with two input and one output
 // Not meant to be used directly
 table _XNNNode2x1 {
   input1_id:uint;
   input2_id:uint;
   output_id:uint;
   flags:uint;
 }

 // A node with one input and one output
 // Not meant to be used directly
 table _XNNNode1x1 {
   input_id:uint;
   output_id:uint;
   flags:uint;
 }

 table _XNNCat {
   axis: uint;
   input1_id: uint;
   input2_id: uint;
   input3_id: uint;
   input4_id: uint;
   output_id: uint;
   flags: uint;
 }

 table XNNELU {
   alpha:float;
   input_id:uint;
   output_id:uint;
   flags:uint;
 }

 table XNNFullyConnected {
   input1_id:uint;
   filter_id:uint;
   bias_id:uint;
   output_id:uint;
   flags:uint;
 }

 table _XNNNodeConv {
   padding_top:uint;
   padding_right:uint;
   padding_bottom:uint;
   padding_left:uint;
   kernel_height:uint;
   kernel_width:uint;
   subsampling_height:uint;
   subsampling_width:uint;
   dilation_height:uint;
   dilation_width:uint;
   group_input_channels:uint;
   group_output_channels:uint;
   groups:uint;
   adjustment_height:uint;
   adjustment_width:uint;
   input1_id:uint;
   filter_id:uint;
   bias_id:uint;
   output_id:uint;
   flags:uint;
 }

 table _XNNPooling2D {
   padding_top: uint;
   padding_right: uint;
   padding_bottom: uint;
   padding_left: uint;
   pooling_height: uint;
   pooling_width: uint;
   stride_height: uint;
   stride_width: uint;
   dilation_height: uint;
   dilation_width: uint;
   input_id: uint;
   output_id: uint;
   flags: uint;
 }

 table XNNStaticReshape {
   num_dims:uint;
   new_shape:[uint];
   input_id: uint;
   output_id: uint;
   flags: uint;
 }

 table XNNStaticSlice {
   num_dims:uint;
   offsets:[uint];
   sizes:[uint];
   input_id:uint;
   output_id:uint;
   flags:uint;
 }

 table XNNScaledDotProductAttention {
     query_id:uint;
     key_id:uint;
     value_id:uint;
     scale_id:uint;
     mask_id:uint;
     output_id:uint;
     flags:uint;
 }

 table XNNArgMaxPooling2d {
   padding_top: uint;
   padding_right: uint;
   padding_bottom: uint;
   padding_left: uint;
   pooling_height: uint;
   pooling_width: uint;
   input_id: uint;
   output_value_id: uint;
   output_index_id: uint;
   flags: uint;
 }

 table XNNLeakyReLU {
   negative_slope: float;
   input_id: uint;
   output_id: uint;
   flags: uint;
 }

 // Describes data offsets for constant data
 table ConstantDataOffset {
   // Constant data offsets are relative to the constant data base offset provided
   // in the XNNPACKHeader.
   offset: uint64;

   // The size in bytes of valid data starting at the offset. The constant data
   // may be followed by padding before the next piece of constant data
   size: uint64;
 }

 table XNNGraph {
   // Schema version.
   version:string;
   xnodes:[XNode];
   xvalues:[XValue];

   // Number of external inputs/outputs
   num_externs:uint;

   // Ids of external inputs
   input_ids:[uint];

   // Ids of external outputs
   output_ids:[uint];

   // Tables of constant data, used for constant Values (e.g.
   // data field of weight tensors). Each constant is assigned an index into the table
   // which are each individually aligned. 0 index is reserved to be pointed to by non-constant
   // Tensors. Exactly one of constant_buffer and constant_data must be non-empty
   constant_buffer:[Buffer];

   // the list index is memory buffer id, the value is the memory buffer size.
   mem_buffer_sizes: [uint];

   // List of the constant data that follows the XNNGraph in this file. Each constant data is assigned an index into
   // the table. 0 index is reserved to be pointed to by non-constant Tensor. Exactly one of constant_buffer and
   // constant_data must be non-empty
   constant_data:[ConstantDataOffset];
 }

 root_type XNNGraph;
	// Copyright (c) Meta Platforms, Inc. and affiliates.

	namespace fb_xnnpack;

	// Update after any BC breaking changes
	file_identifier "XN00";

	// datatype for xnn-values
	enum XNNDatatype : short {
	/// Invalid data type. Valid Values never have this datatype.
	xnn_datatype_invalid = 0,
	/// IEEE754 single-precision floating-point.
	xnn_datatype_fp32 = 1,
	/// IEEE754 half-precision floating-point.
	xnn_datatype_fp16 = 2,
	/// Quantized 8-bit signed integer with shared per-Value quantization parameters.
	xnn_datatype_qint8 = 3,
	/// Quantized 8-bit unsigned integer with shared per-Value quantization parameters.
	xnn_datatype_quint8 = 4,
	/// Quantized 32-bit signed integer with shared per-Value quantization parameters.
	xnn_datatype_qint32 = 5,
	/// Quantized 8-bit signed integer with shared per-channel quantization parameters.
	xnn_datatype_qcint8 = 6,
	/// Quantized 32-bit signed integer with shared per-channel quantization parameters.
	xnn_datatype_qcint32 = 7,
	/// Quantized 4-bit signed integer with shared per-channel quantization parameters.
	xnn_datatype_qcint4 = 8,
	/// Dynamically quantized 8-bit signed integer with per-batch quantization parameters.
	xnn_datatype_qdint8 = 9,
	/// Quantized 4-bit signed integer with shared blockwise quantization parameters.
	xnn_datatype_qbint4 = 10,
	}

	// type of quantization
	union XNNQuantParams {
	PerChannelQuant,
	PerTensorQuant,
	PerTokenDynamicQuant,
	PerChannelGroupQuant,
	}

	// taken from executorch
	// Data buffer abstraction.
	table Buffer {
	storage:[ubyte] (force_align: 16);
	}

	table PerChannelQuant {
	scale:[float];
	channel_dim:int;
	}

	table PerTokenDynamicQuant {
	num_nonbatch_dims:int;
	}

	table PerTensorQuant {
	scale:float;
	zero_point:int;
	}

	table PerChannelGroupQuant {
	scale:[float];
	channel_dim:int;
	group_size:int;
	scale_bf16:[ushort];
	}

	table XNNTensorValue {
	// type of the tensor elements.
	datatype:XNNDatatype;
	// number of dimensions in the shape.
	num_dims:uint;
	// pointer to an array of @a num_dims shape dimensions. If num_dims is 0, this pointer can be NULL.
	// XNNPACK does not keep any pointers to this array after the function returns.
	dims:[uint];
	// Index to the program's constant buffer table, value 0 is reserved to indicate non constant
	constant_buffer_idx:uint;
	// external ID for the Value. The ID must be within the range of reserved Value IDs specified on
	// the Subgraph creation. If the external ID is XNN_INVALID_VALUE_ID, an internal ID will be
	// created for the Value.
	external_id:uint;
	// binary features of the Value. Supported values are any combination of XNN_VALUE_FLAG_EXTERNAL_INPUT
	// and XNN_VALUE_FLAG_EXTERNAL_OUTPUT.
	flags:uint;
	// pointer to the variable that will be initialized with the Value ID upon successful return. If a
	// valid @a external_id was provided, the variable will be initialized with the @a external_id value.
	id_out:uint;
	// does this value need to be quantized dynamically at runtime?
	// if we are quantizing at runtime, this field points to a target dtype
	dq_datatype:XNNDatatype = xnn_datatype_invalid;
	}

	table XNNQuantizedTensorValue {
	// Base Tensor Value
	tensor_value:XNNTensorValue;
	// Quantization parameters
	quant_params:XNNQuantParams;
	}

	union XNodeUnion {
	XNNAdd: _XNNNode2x1,
	XNNFullyConnected,
	XNNSoftmax: _XNNNode1x1,
	XNNSigmoid: _XNNNode1x1,
	XNNStaticTranspose,
	XNNClamp: _XNNNode1x1,
	XNNConv2d: _XNNNodeConv,
	XNNDiv: _XNNNode2x1,
	XNNStaticResizeBilinear2D,
	XNNStaticConstantPad,
	XNNAvgPooling2d: _XNNPooling2D,
	XNNMinimum: _XNNNode2x1,
	XNNDepthwiseConv2d: _XNNNodeConv,
	XNNMaxPooling2d: _XNNPooling2D,
	XNNMultiply: _XNNNode2x1,
	XNNSubtract: _XNNNode2x1,
	XNNFloor: _XNNNode1x1,
	XNNConvert: _XNNNode1x1,
	XNNGlobalAvgPooling2d: _XNNNode1x1,
	XNNStaticReshape,
	XNNArgMaxPooling2d,
	XNNSquareRoot: _XNNNode1x1,
	XNNCeiling: _XNNNode1x1,
	XNNHardswish: _XNNNode1x1,
	XNNLeakyReLU,
	XNNMaximum: _XNNNode2x1,
	XNNNegate: _XNNNode1x1,
	XNNSquare: _XNNNode1x1,
	XNNELU,
	XNNAbs: _XNNNode1x1,
	XNNPReLU: _XNNNode2x1,
	XNNConcatenate2: _XNNCat,
	XNNConcatenate3: _XNNCat,
	XNNConcatenate4: _XNNCat,
	XNNStaticSlice,
	XNNScaledDotProductAttention,
	XNNBatchMatrixMultiply: _XNNNode2x1,
	}

	union XValueUnion {
	XNNTensorValue,
	XNNQuantizedTensorValue,
	}

	table OutputMinMax {
	output_min:float;
	output_max:float;
	}

	table XNode {
	xnode_union:XNodeUnion;
	// An int which can be linked back to the node in the origin graph
	debug_handle:uint;
	output_min_max:OutputMinMax;
	}

	table XValue {
	xvalue_union:XValueUnion;
	}

	table XNNStaticTranspose {
	num_dims:uint;
	perm:[uint];
	input_id:uint;
	output_id:uint;
	flags:uint;
	}

	table XNNStaticResizeBilinear2D {
	new_height:uint;
	new_width:uint;
	input_id:uint;
	output_id:uint;
	flags:uint;
	}

	table XNNStaticConstantPad {
	pre_paddings:[uint];
	post_paddings:[uint];
	padding_value:float;
	input_id:uint;
	output_id:uint;
	flags:uint;
	}

	// A node with two input and one output
	// Not meant to be used directly
	table _XNNNode2x1 {
	input1_id:uint;
	input2_id:uint;
	output_id:uint;
	flags:uint;
	}

	// A node with one input and one output
	// Not meant to be used directly
	table _XNNNode1x1 {
	input_id:uint;
	output_id:uint;
	flags:uint;
	}

	table _XNNCat {
	axis: uint;
	input1_id: uint;
	input2_id: uint;
	input3_id: uint;
	input4_id: uint;
	output_id: uint;
	flags: uint;
	}

	table XNNELU {
	alpha:float;
	input_id:uint;
	output_id:uint;
	flags:uint;
	}

	table XNNFullyConnected {
	input1_id:uint;
	filter_id:uint;
	bias_id:uint;
	output_id:uint;
	flags:uint;
	}

	table _XNNNodeConv {
	padding_top:uint;
	padding_right:uint;
	padding_bottom:uint;
	padding_left:uint;
	kernel_height:uint;
	kernel_width:uint;
	subsampling_height:uint;
	subsampling_width:uint;
	dilation_height:uint;
	dilation_width:uint;
	group_input_channels:uint;
	group_output_channels:uint;
	groups:uint;
	adjustment_height:uint;
	adjustment_width:uint;
	input1_id:uint;
	filter_id:uint;
	bias_id:uint;
	output_id:uint;
	flags:uint;
	}

	table _XNNPooling2D {
	padding_top: uint;
	padding_right: uint;
	padding_bottom: uint;
	padding_left: uint;
	pooling_height: uint;
	pooling_width: uint;
	stride_height: uint;
	stride_width: uint;
	dilation_height: uint;
	dilation_width: uint;
	input_id: uint;
	output_id: uint;
	flags: uint;
	}

	table XNNStaticReshape {
	num_dims:uint;
	new_shape:[uint];
	input_id: uint;
	output_id: uint;
	flags: uint;
	}

	table XNNStaticSlice {
	num_dims:uint;
	offsets:[uint];
	sizes:[uint];
	input_id:uint;
	output_id:uint;
	flags:uint;
	}

	table XNNScaledDotProductAttention {
	query_id:uint;
	key_id:uint;
	value_id:uint;
	scale_id:uint;
	mask_id:uint;
	output_id:uint;
	flags:uint;
	}

	table XNNArgMaxPooling2d {
	padding_top: uint;
	padding_right: uint;
	padding_bottom: uint;
	padding_left: uint;
	pooling_height: uint;
	pooling_width: uint;
	input_id: uint;
	output_value_id: uint;
	output_index_id: uint;
	flags: uint;
	}

	table XNNLeakyReLU {
	negative_slope: float;
	input_id: uint;
	output_id: uint;
	flags: uint;
	}

	// Describes data offsets for constant data
	table ConstantDataOffset {
	// Constant data offsets are relative to the constant data base offset provided
	// in the XNNPACKHeader.
	offset: uint64;

	// The size in bytes of valid data starting at the offset. The constant data
	// may be followed by padding before the next piece of constant data
	size: uint64;
	}

	table XNNGraph {
	// Schema version.
	version:string;
	xnodes:[XNode];
	xvalues:[XValue];

	// Number of external inputs/outputs
	num_externs:uint;

	// Ids of external inputs
	input_ids:[uint];

	// Ids of external outputs
	output_ids:[uint];

	// Tables of constant data, used for constant Values (e.g.
	// data field of weight tensors). Each constant is assigned an index into the table
	// which are each individually aligned. 0 index is reserved to be pointed to by non-constant
	// Tensors. Exactly one of constant_buffer and constant_data must be non-empty
	constant_buffer:[Buffer];

	// the list index is memory buffer id, the value is the memory buffer size.
	mem_buffer_sizes: [uint];

	// List of the constant data that follows the XNNGraph in this file. Each constant data is assigned an index into
	// the table. 0 index is reserved to be pointed to by non-constant Tensor. Exactly one of constant_buffer and
	// constant_data must be non-empty
	constant_data:[ConstantDataOffset];
	}

	root_type XNNGraph;