Transformation - Streamline

Transformation (Streamline)

class finn.transformation.streamline.Streamline

Bases: Transformation

Apply the streamlining transform, see arXiv:1709.04060.

apply(model)

finn.transformation.streamline.absorb

class finn.transformation.streamline.absorb.Absorb1BitMulIntoConv

Bases: Transformation

Absorb bipolar or binary multiplications into the preceding convolution.

apply(model)

class finn.transformation.streamline.absorb.Absorb1BitMulIntoMatMul

Bases: Transformation

Absorb bipolar or binary multiplications into the preceding matrix multiply.

apply(model)

class finn.transformation.streamline.absorb.AbsorbAddIntoMultiThreshold

Bases: Transformation

Absorb preceding Add ops into MultiThreshold by updating the threshold values. Only scalar/1D add vectors can be absorbed.

apply(model)

class finn.transformation.streamline.absorb.AbsorbConsecutiveTransposes

Bases: Transformation

Remove (Transpose -> Transpose) patterns when the input and output of the pattern have the same layout.

apply(model)

are_opposite_permutations(perms1, perms2)

class finn.transformation.streamline.absorb.AbsorbMulIntoMultiThreshold

Bases: Transformation

Absorb preceding Mul ops into MultiThreshold by updating the threshold values. Only positive scalar/1D mul vectors can be absorbed.

apply(model)

class finn.transformation.streamline.absorb.AbsorbScalarMulAddIntoTopK

Bases: Transformation

Remove mul/add node prior to topk node if the op is scalar. Note that the TopK output probabilities will change, but the indices won’t.

apply(model)

class finn.transformation.streamline.absorb.AbsorbSignBiasIntoMultiThreshold

Bases: Transformation

Absorb scalar bias originating from signed int export back into MultiThreshold and re-evaluate the output datatype.

apply(model)

class finn.transformation.streamline.absorb.AbsorbTransposeIntoFlatten

Bases: Transformation

Absorb transpose node into succeeding flatten node, if H=W=1 and the first dimension stays the same. Can also be applied if flatten is implemented implicitly by a reshape node with shape [1, -1] and the first input dimension is 1

apply(model)

class finn.transformation.streamline.absorb.AbsorbTransposeIntoMultiThreshold

Bases: Transformation

For (NCHWTranspose -> MultiThreshold) move Transpose past MultiThreshold and set its data_layout mode to NHWC.

apply(model)

class finn.transformation.streamline.absorb.AbsorbTransposeIntoResize

Bases: Transformation

For (NCHWTranspose -> Resize) move Transpose past Resize and change the Resize node’s attributes accordingly.

apply(model)

class finn.transformation.streamline.absorb.FactorOutMulSignMagnitude

Bases: Transformation

Split multiply-by-constant nodes into two multiply-by-constant nodes, where the first node is a bipolar vector (of signs) and the second is a vector of magnitudes.

apply(model)

finn.transformation.streamline.collapse_repeated

class finn.transformation.streamline.collapse_repeated.CollapseRepeatedAdd

Bases: CollapseRepeatedOp

Collapse repeated adder node into a single operation.

class finn.transformation.streamline.collapse_repeated.CollapseRepeatedMul

Bases: CollapseRepeatedOp

Collapse repeated multiplier node into a single operation.

class finn.transformation.streamline.collapse_repeated.CollapseRepeatedOp(op_name, make_collapsed_param_fxn)

Bases: Transformation

Collapse repeated consecutive operations with constant parameters into a single operation. make_collapsed_param_fxn must take two tensors and return a tensor which gives the equivalent result using a single op.

apply(model)

finn.transformation.streamline.reorder

class finn.transformation.streamline.reorder.MakeMaxPoolNHWC

Bases: Transformation

Convert (MaxPool, NHWCTranspose) into (NHWCTranspose, MaxPoolNHWC) and (NCHWTranspose, MaxPool) into (MaxPoolNHWC, NCHWTranspose).

apply(model)

class finn.transformation.streamline.reorder.MakeScaleResizeNHWC

Bases: Transformation

Converts the inputs and outputs for all scales Resize and Upsample nodes from NCHW to NHWC.

apply(model)

class finn.transformation.streamline.reorder.MoveAddPastConv

Bases: Transformation

Move scalar and channelwise add operations past conv operations. We want to have adds next to each other such that they can be collapsed into a single add.

apply(model)

class finn.transformation.streamline.reorder.MoveAddPastFork: Bases: MoveOpPastFork

class finn.transformation.streamline.reorder.MoveAddPastJoinAdd

Bases: MoveIdenticalOpPastJoinOp

are_producers_identical(model, producers): Checks only op_types Should be overwritten for additional checks

move_node(model, n, producers): We use the base move_node method to move the first producer past the join node (and delete the rest)

class finn.transformation.streamline.reorder.MoveAddPastJoinConcat: Bases: MoveAffinePastJoinConcat

class finn.transformation.streamline.reorder.MoveAddPastMul

Bases: Transformation

Move add operations past multiply operations on linear segments of the graph. The aim is to have them next to each other such that they can be collapsed into a single add.

apply(model)

class finn.transformation.streamline.reorder.MoveAffinePastJoinConcat(linear_ops=['Mul', 'Add'])

Bases: MoveIdenticalOpPastJoinOp

Applies to scalar linear or channelwise affine ops with the same parameter value

are_producers_channelwise_ops(channel_dim, model, producers)

are_producers_identical_scalar_ops(model, producers)

move_node(model, n, producers)

Should be overwritten for some operations

Returns:: bool: whether moving the node was successful

class finn.transformation.streamline.reorder.MoveFlattenPastAffine

Bases: Transformation

Moves a node that implements a (1, -1) reshape past a MatMul, Mul or Add node.

apply(model)

class finn.transformation.streamline.reorder.MoveFlattenPastTopK

Bases: Transformation

Move flatten node past a succeeding topk node, if the “axis” attribute in topk is set to -1 and the data layout before the flatten is NHWC with H=W=1

apply(model)

class finn.transformation.streamline.reorder.MoveIdenticalOpPastJoinOp(identical_op_list, join_node_list)

Bases: Transformation

Move multiple identical operations on different branches past the common join node. It assumes the shape to be preserved by the join op in the default move_node() method

apply(model)

are_producers_identical(model, producers): Checks only op_types Should be overwritten for additional checks

move_node(model, n, producers)

Should be overwritten for some operations

Returns:: bool: whether moving the node was successful

class finn.transformation.streamline.reorder.MoveLinearPastFork: Bases: MoveOpPastFork

class finn.transformation.streamline.reorder.MoveMaxPoolPastMultiThreshold

Bases: Transformation

Move MaxPool nodes past MultiThreshold nodes on linear segments of the graph.

apply(model)

class finn.transformation.streamline.reorder.MoveMulPastDWConv

Bases: Transformation

Move channelwise mul operations past depthwise conv operations. We want to have muls next to each other such that they can be collapsed into a single mul.

apply(model)

class finn.transformation.streamline.reorder.MoveMulPastFork: Bases: MoveOpPastFork

class finn.transformation.streamline.reorder.MoveMulPastJoinAdd

Bases: MoveIdenticalOpPastJoinOp

are_producers_identical(model, producers): Checks only op_types Should be overwritten for additional checks

class finn.transformation.streamline.reorder.MoveMulPastJoinConcat: Bases: MoveAffinePastJoinConcat

class finn.transformation.streamline.reorder.MoveMulPastMaxPool

Bases: Transformation

Move non-negative scalar or channelwise mul operations past max pool operations. We want to have muls next to each other such that they can be collapsed into a single mul.

apply(model)

class finn.transformation.streamline.reorder.MoveOpPastFork(op_name_list)

Bases: Transformation

Move node operations past graph forks. Used when a node before a fork can be merged with nodes in the branches

apply(model)

class finn.transformation.streamline.reorder.MoveScalarAddPastMatMul

Bases: Transformation

Move scalar add operations past matmul operations. We want to have adds next to each other such that they can be collapsed into a single add.

apply(model)

class finn.transformation.streamline.reorder.MoveScalarLinearPastInvariants

Bases: Transformation

Move scalar linear operations (mul, add) past functions which are invariant to them. Specifically, matches and transforms the following patterns: f(x*C) -> f(x) * C f(x+C) -> f(x) + C where x is a dynamic input, C is a constant tensor. Known f which obey this property are: Reshape, Flatten, Transpose, GlobalAveragePool

SUPPORTED_INVARIANTS = {'Flatten', 'GlobalAveragePool', 'Reshape', 'Slice', 'Squeeze', 'Transpose', 'Unsqueeze'}

apply(model)

class finn.transformation.streamline.reorder.MoveScalarLinearPastSplit

Bases: Transformation

Move scalar Mul and Add nodes past channel split operation.

apply(model)

class finn.transformation.streamline.reorder.MoveScalarMulPastConv

Bases: Transformation

Move scalar mul operations past conv operations. We want to have muls next to each other such that they can be collapsed into a single mul.

apply(model)

class finn.transformation.streamline.reorder.MoveScalarMulPastConvTranspose

Bases: Transformation

Move scalar mul operations past ConvTranspose operations. We want to have muls next to each other such that they can be collapsed into a single mul.

apply(model)

class finn.transformation.streamline.reorder.MoveScalarMulPastMatMul

Bases: Transformation

Move scalar mul operations past matmul operations. We want to have muls next to each other such that they can be collapsed into a single mul.

apply(model)

class finn.transformation.streamline.reorder.MoveTransposePastFork: Bases: MoveOpPastFork

class finn.transformation.streamline.reorder.MoveTransposePastJoinAdd

Bases: MoveIdenticalOpPastJoinOp

are_producers_identical(model, producers): Checks only op_types Should be overwritten for additional checks

class finn.transformation.streamline.reorder.MoveTransposePastJoinConcat

Bases: MoveIdenticalOpPastJoinOp

are_producers_identical(model, producers): Checks only op_types Should be overwritten for additional checks

move_node(model, n, producers)

Should be overwritten for some operations

Returns:: bool: whether moving the node was successful

class finn.transformation.streamline.reorder.MoveTransposePastScalarMul

Bases: Transformation

Moves a Transpose node past a scalar Mul node

apply(model)

class finn.transformation.streamline.reorder.MoveTransposePastSplit

Bases: Transformation

apply(model)

finn.transformation.streamline.reorder.permute_shape(shape, perm)

finn.transformation.streamline.round_thresholds

class finn.transformation.streamline.round_thresholds.RoundAndClipThresholds

Bases: Transformation

For MultiThreshold, Thresholding, MVAU, and VVAU nodes operating on integer inp/accumulators, round up (ceil) threshold values to the nearest integer and clip to valid range. Type-casts thresholds (back) to the float32 container type (this is separate from the quantization annotation). Runs InferDataTypes() afterward to propagate any changes to the quantization data types.

apply(model: ModelWrapper)

finn.transformation.streamline.sign_to_thres

class finn.transformation.streamline.sign_to_thres.ConvertSignToThres

Bases: Transformation

Convert Sign node instances to MultiThreshold with threshold at 0.

apply(model)