MLBEDSW-3148: Refactor Operation
- op.type is now an enum instead of a string
- Removed unused operator codes
- Refactored some attributes like npu_block_type, fused_activation_function
- Refactored operator index calculation
- Refactored a number of operator sets
Change-Id: I641f65ee375794b7aec42abc0664251ae37d78e8
Signed-off-by: Louis Verhaard <louis.verhaard@arm.com>
diff --git a/ethosu/vela/mark_tensors.py b/ethosu/vela/mark_tensors.py
index c4496cd..206d836 100644
--- a/ethosu/vela/mark_tensors.py
+++ b/ethosu/vela/mark_tensors.py
@@ -18,10 +18,11 @@
from . import rewrite_graph
from . import weight_compressor
from .errors import OperatorError
+from .operation import CustomType
+from .operation import Op
from .tensor import MemType
from .tensor import TensorFormat
from .tensor import TensorPurpose
-from .tflite_mapping import custom_prefix
def purpose_from_list(lst):
@@ -59,72 +60,53 @@
(
set(
(
- "Relu",
- "Relu6",
- "Mul",
- "Add",
- "Sub",
- "Rsqrt",
- "Abs",
- "Cast",
- "Exp",
- "Floor",
- "FloorDiv",
- "FloorMod",
- "SquaredDifference",
- "AddN",
- "BiasAdd",
- "RealDiv",
- "Maximum",
- "Minimum",
- "Sigmoid",
- "Tanh",
- "FusedBatchNorm",
- "AvgPool",
- "MaxPool",
- "Squeeze",
- "Softmax",
- "LRN",
- "Assign",
- "BatchMatMul",
- "ZerosLike",
- "ExtractImagePatches",
- "MulAct",
- "AddAct",
- "SubAct",
- "DivAct",
- "AvgPoolAct",
- "MaxPoolAct",
- "LeakyRelu",
- "CLZ",
- "SHL",
- "SHR",
- "ReduceSum",
+ Op.Relu,
+ Op.Relu6,
+ Op.Rsqrt,
+ Op.Abs,
+ Op.Cast,
+ Op.Exp,
+ Op.Floor,
+ Op.FloorDiv,
+ Op.FloorMod,
+ Op.SquaredDifference,
+ Op.AddN,
+ Op.Maximum,
+ Op.Minimum,
+ Op.Sigmoid,
+ Op.Tanh,
+ Op.AvgPool,
+ Op.MaxPool,
+ Op.Squeeze,
+ Op.Softmax,
+ Op.LRN,
+ Op.BatchMatMul,
+ Op.ZerosLike,
+ Op.Mul,
+ Op.Add,
+ Op.Sub,
+ Op.Div,
+ Op.LeakyRelu,
+ Op.CLZ,
+ Op.SHL,
+ Op.SHR,
+ Op.ReduceSum,
)
),
all_fm,
),
(
- set(
- (
- "Conv2D",
- "DepthwiseConv2dNative",
- "MatMul",
- "Conv2DBiasAct",
- "DepthwiseConv2dBiasAct",
- "FullyConnectedAct",
- )
- ),
+ set((Op.Conv2D, Op.MatMul, Op.Conv2DBias, Op.DepthwiseConv2DBias, Op.FullyConnected,)),
purpose_from_list([TensorPurpose.FeatureMap, TensorPurpose.Weights, TensorPurpose.FeatureMap]),
),
(
- set(("Conv2DBackpropInputSwitchedBias",)),
+ set((Op.Conv2DBackpropInputSwitchedBias,)),
purpose_from_list(
[TensorPurpose.FeatureMap, TensorPurpose.Weights, TensorPurpose.FeatureMap, TensorPurpose.FeatureMap]
),
),
(
- set(("QuantizedConv2D", "QuantizedMatMul")),
+ set((Op.QuantizedConv2D, Op.QuantizedMatMul)),
purpose_from_list(
[
TensorPurpose.FeatureMap,
@@ -139,66 +121,39 @@
(
set(
(
- "Reshape",
- "Min",
- "Max",
- "Mean",
- "Pad",
- "MirrorPad",
- "ArgMax",
- "ArgMin",
- "ExpandDims",
- "ResizeNearestNeighbor",
- "ResizeBilinear",
- "Tile",
- "Transpose",
- "Mfcc",
+ Op.Reshape,
+ Op.Min,
+ Op.Max,
+ Op.Mean,
+ Op.Pad,
+ Op.MirrorPad,
+ Op.ArgMax,
+ Op.ArgMin,
+ Op.ExpandDims,
+ Op.ResizeNearestNeighbor,
+ Op.ResizeBilinear,
+ Op.Tile,
+ Op.Transpose,
)
),
purpose_from_list([TensorPurpose.FeatureMap, TensorPurpose.FeatureMap]),
),
(
- set(("QuantizedReshape", "QuantizedResizeBilinear")),
+ set((Op.QuantizedReshape,)),
purpose_from_list(
[TensorPurpose.FeatureMap, TensorPurpose.FeatureMap, TensorPurpose.FeatureMap, TensorPurpose.FeatureMap]
),
),
(
- set(("QuantizedBiasAdd", "QuantizedAdd", "QuantizedMul")),
- purpose_from_list(
- [
- TensorPurpose.FeatureMap,
- TensorPurpose.FeatureMap,
- TensorPurpose.FeatureMap,
- TensorPurpose.FeatureMap,
- TensorPurpose.FeatureMap,
- TensorPurpose.FeatureMap,
- ]
- ),
- ),
- (
- set(
- (
- "Dequantize",
- "Quantize",
- "QuantizeV2",
- "QuantizedRelu",
- "QuantizedRelu1",
- "QuantizedRelu6",
- "QuantizedAvgPool",
- "QuantizedMaxPool",
- "Slice",
- "SplitV",
- )
- ),
+ set((Op.Dequantize, Op.Quantize, Op.QuantizedAvgPool, Op.QuantizedMaxPool, Op.Slice, Op.SplitV,)),
purpose_from_list([TensorPurpose.FeatureMap, TensorPurpose.FeatureMap, TensorPurpose.FeatureMap]),
),
(
- set(("BatchToSpaceND", "SpaceToBatchND", "DepthToSpaceND", "SpaceToDepthND")),
+ set((Op.BatchToSpaceND, Op.SpaceToBatchND, Op.DepthToSpace, Op.SpaceToDepth)),
purpose_from_list([TensorPurpose.FeatureMap, TensorPurpose.FeatureMap, TensorPurpose.FeatureMap]),
),
(
- set(("BlockLSTM",)),
+ set((Op.BlockLSTM,)),
purpose_from_list(
[
TensorPurpose.FeatureMap,
@@ -213,33 +168,18 @@
]
),
),
- (set(("SplitSliceRead",)), purpose_from_list([TensorPurpose.FeatureMap, TensorPurpose.FeatureMap])),
- (set(("Shape", "ConcatSliceWrite", "AudioSpectrogram")), purpose_from_list([TensorPurpose.FeatureMap])),
+ (set((Op.SplitSliceRead,)), purpose_from_list([TensorPurpose.FeatureMap, TensorPurpose.FeatureMap])),
+ (set((Op.Shape, Op.ConcatSliceWrite)), purpose_from_list([TensorPurpose.FeatureMap])),
(
- set(("StridedSlice",)),
+ set((Op.StridedSlice,)),
purpose_from_list(
[TensorPurpose.FeatureMap, TensorPurpose.FeatureMap, TensorPurpose.FeatureMap, TensorPurpose.FeatureMap]
),
),
- (set(("Fill", "Pack", "Range")), all_parameter),
- (
- set(("Requantize",)),
- purpose_from_list(
- [
- TensorPurpose.FeatureMap,
- TensorPurpose.FeatureMap,
- TensorPurpose.FeatureMap,
- TensorPurpose.FeatureMap,
- TensorPurpose.FeatureMap,
- ]
- ),
- ),
- (set(("Placeholder", "SubgraphInput", "Const", "VariableV2")), purpose_from_list([])),
- (set(("FakeQuantWithMinMaxArgs", "FakeQuantWithMinMaxVars")), input0_from_output_rest_parameter),
- (
- set(("Square", "Sqrt", "Log", "Less", "Enter", "Exit", "Identity", "StopGradient", "Merge", "Switch")),
- inputs_from_output,
- ),
+ (set((Op.Fill, Op.Pack, Op.Range)), all_parameter),
+ (set((Op.Placeholder, Op.SubgraphInput, Op.Const,)), purpose_from_list([])),
+ (set((Op.FakeQuantWithMinMaxArgs,)), input0_from_output_rest_parameter),
+ (set((Op.Square, Op.Sqrt, Op.Log, Op.Less, Op.Identity,)), inputs_from_output,),
(None, all_fm),
]
@@ -247,8 +187,6 @@
for ops, input_purpose in tensor_purposes:
if ops is None:
continue
- for op in ops:
- assert len(op) > 1, "string literal has been decomposed"
def mark_tensor_purpose(nng, arch, verbose_tensor_purpose=False):
@@ -260,7 +198,7 @@
tens.mem_area = arch.tensor_storage_mem_area[tens.purpose]
tens.mem_type = arch.tensor_storage_mem_type[tens.purpose]
- if len(tens.ops) == 1 and tens.ops[0].type == "Const":
+ if len(tens.ops) == 1 and tens.ops[0].type == Op.Const:
tens.mem_area = (
arch.permanent_storage_mem_area
) # special case constants, as they must be in permanent storage
@@ -288,11 +226,11 @@
purpose = input_purpose(op, idx) if tens.purpose == TensorPurpose.Unknown else tens.purpose
mark_tensor_helper(tens, purpose)
- if op.type == "Reshape":
+ if op.type == Op.Reshape:
# Reshape's input and output point to same data
op.outputs[0].mem_area = op.inputs[0].mem_area
- if op.type.startswith(custom_prefix) and op.attrs.get("custom_type", "") == "ExistingNpuOp":
+ if op.type == Op.Custom and op.attrs.get("custom_type") == CustomType.ExistingNpuOp:
scratch_tensor = None
if len(op.inputs) >= 3:
@@ -301,7 +239,7 @@
scratch_tensor.purpose = TensorPurpose.Scratch
if scratch_tensor is None:
- raise OperatorError(op, "Scratch tensor not found.")
+ OperatorError(op, "Scratch tensor not found.")
break
@@ -318,21 +256,6 @@
return nng
-reshape_operations = set(
- (
- "Reshape",
- "QuantizedReshape",
- "ExpandDims",
- "Squeeze",
- "BatchToSpaceND",
- "SpaceToBatchND",
- "DepthToSpaceND",
- "SpaceToDepthND",
- "Placeholder",
- )
-)
-
-
def mark_tensor_format(nng, arch, verbose_tensor_format=False):
formats_for_tensor = {}
@@ -375,8 +298,9 @@
if src_tens is not None:
op = tens.find_npu_op()
if op is not None:
- npu_block_type = op.attrs["npu_block_type"]
- weight_compressor.compress_weights(arch, nng, tens, npu_block_type, 16, 16, op.get_dilation_h_w())
+ weight_compressor.compress_weights(
+ arch, nng, tens, op.type.npu_block_type, 16, 16, op.get_dilation_h_w()
+ )
# Alias compressed weights back into source tensor
src_tens.copy_compressed_weight_info(tens)