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/supported_operators.py b/ethosu/vela/supported_operators.py
index 6ae072f..3d4a09f 100644
--- a/ethosu/vela/supported_operators.py
+++ b/ethosu/vela/supported_operators.py
@@ -20,6 +20,7 @@
from .data_type import BaseType
from .data_type import DataType
from .operation import get_slice_offsets
+from .operation import Op
# Custom decorator function to allow formatting docstrings containing "{}"
@@ -37,18 +38,18 @@
class SupportedOperators:
# Categorised lists of supported operators
- npu_pre_ops = set(("QuantizedResizeBilinear", "SplitSliceRead",))
- convolution_ops = set(("Conv2DBiasAct", "Conv2D", "QuantizedConv2D",))
- depthwise_convolution_ops = set(("DepthwiseConv2dBiasAct", "DepthwiseConv2dNative", "QuantizedDepthwiseConv2D",))
- transpose_convolution_ops = set(("Conv2DBackpropInput",))
- max_pooling_ops = set(("QuantizedMaxPool", "MaxPool", "MaxPoolAct",))
- avg_pooling_ops = set(("QuantizedAvgPool", "AvgPool", "AvgPoolAct",))
- pooling_ops = set(("ReduceSum",)) | max_pooling_ops | avg_pooling_ops
- resizing_ops = set(("ResizeBilinear",))
- fc_vector_products = set(("QuantizedMatMul", "MatMul", "FullyConnectedAct",))
+ npu_pre_ops = set((Op.SplitSliceRead,))
+ convolution_ops = set((Op.Conv2DBias, Op.Conv2D, Op.QuantizedConv2D,))
+ depthwise_convolution_ops = set((Op.DepthwiseConv2DBias,))
+ transpose_convolution_ops = set((Op.Conv2DBackpropInput,))
+ max_pooling_ops = Op.op_set(Op.is_maxpool_op)
+ avg_pooling_ops = Op.op_set(Op.is_avgpool_op)
+ pooling_ops = set((Op.ReduceSum,)) | max_pooling_ops | avg_pooling_ops
+ resizing_ops = set((Op.ResizeBilinear,))
+ fc_vector_products = set((Op.QuantizedMatMul, Op.MatMul, Op.FullyConnected,))
mac_main_ops = (
# RNN/LSTM/GRU
- set(("BlockLSTM",))
+ set((Op.BlockLSTM,))
# convolutions
| convolution_ops
# depth-wise convolutions
@@ -62,45 +63,29 @@
# FC layers
| fc_vector_products
)
- unary_elem_wise_main_ops = set(("LeakyRelu", "Abs", "CLZ",))
- binary_elem_wise_min_max_ops = set(("Minimum", "Maximum",))
- binary_elem_wise_shift_ops = set(("SHL", "SHR",))
- binary_elem_wise_add_mul_sub = set(
- ("AddAct", "MulAct", "SubAct", "QuantizedAdd", "QuantizedSub", "QuantizedMul", "Mul", "Add", "Sub",)
- )
+ unary_elem_wise_main_ops = Op.op_set(Op.is_unary_elementwise_op)
+ binary_elem_wise_min_max_ops = set((Op.Minimum, Op.Maximum,))
+ binary_elem_wise_shift_ops = set((Op.SHL, Op.SHR,))
+ binary_elem_wise_add_mul_sub = set((Op.Add, Op.Mul, Op.Sub,))
binary_elem_wise_main_ops = binary_elem_wise_min_max_ops | binary_elem_wise_add_mul_sub | binary_elem_wise_shift_ops
elem_wise_main_ops = binary_elem_wise_main_ops | unary_elem_wise_main_ops
supported_int32_tensor_ops = (
- set(("Requantize", "ReduceSum", "CLZ",)) | binary_elem_wise_add_mul_sub | binary_elem_wise_shift_ops
+ set((Op.ReduceSum, Op.CLZ,)) | binary_elem_wise_add_mul_sub | binary_elem_wise_shift_ops
)
- activation_ops = set(
- (
- "QuantizedRelu",
- "QuantizedRelu1",
- "QuantizedRelu6",
- "Relu",
- "Relu6",
- "ReluN1To1",
- "Sigmoid",
- "Tanh",
- "Softmax",
- )
- )
+ activation_ops = set((Op.Relu, Op.Relu6, Op.ReluN1To1, Op.Sigmoid, Op.Tanh, Op.Softmax,))
npu_post_ops = (
- # concatenation write direction
- set(("ConcatSliceWrite",))
- # bias add and batch norm
- | set(("QuantizedBiasAdd", "Requantize", "QuantizedBatchNorm", "BiasAdd", "FusedBatchNorm",))
- # Quantization
- | set(("Quantize",))
# activation functions
- | activation_ops
+ activation_ops
+ # concatenation write direction
+ | set((Op.ConcatSliceWrite,))
+ # Quantization
+ | set((Op.Quantize,))
)
- split_ops = set(("Split", "SplitV", "StridedSlice", "Slice", "UnpackReshaped", "Unpack",))
- concat_ops = set(("Concat", "ConcatV2", "QuantizedConcat", "ConcatTFLite", "PackReshaped", "Pack",))
- memory_only_ops = set(("Squeeze", "Reshape", "QuantizedReshape", "ExpandDims",)) | concat_ops | split_ops
- shapeless_input_ops = set(("Split", "SplitV",)) | binary_elem_wise_main_ops
- supported_fused_activations = set(("Relu", "Relu6", "ReluN1To1", "Tanh", "Sigmoid", "LUT",))
+ split_ops = set((Op.Split, Op.SplitV, Op.StridedSlice, Op.Slice, Op.UnpackReshaped, Op.Unpack,))
+ concat_ops = set((Op.Concat, Op.ConcatTFLite, Op.PackReshaped, Op.Pack,))
+ memory_only_ops = set((Op.Squeeze, Op.Reshape, Op.QuantizedReshape, Op.ExpandDims,)) | concat_ops | split_ops
+ shapeless_input_ops = binary_elem_wise_main_ops | set((Op.Split, Op.SplitV,))
+ supported_fused_activations = set((Op.Relu, Op.Relu6, Op.ReluN1To1, Op.Tanh, Op.Sigmoid, Op.LUT,))
supported_operators = npu_pre_ops | mac_main_ops | elem_wise_main_ops | npu_post_ops | memory_only_ops
supported_dtypes = set((DataType.uint8, DataType.int8, DataType.int16, DataType.int32))
# Defined ranges for allowed values:
@@ -233,7 +218,7 @@
@docstring_format_args([supported_fused_activations])
def constraint_faf(cls, op):
"The fused activation function (if present) must be one of type: {}"
- faf = op.attrs.get("fused_activation_function")
+ faf = op.activation
valid = (faf is None) or (faf in cls.supported_fused_activations)
extra = "fused_activation_function={}".format(faf)
return valid, extra
@@ -300,7 +285,7 @@
@classmethod
def check_depthwise_convolution_restrictions(cls, op):
# check depth
- ifm_tensor, _, _, ofm_tensor = op.get_ifm_ifm2_weights_ofm()
+ ifm_tensor, ofm_tensor = op.get_ifm_ofm()
if op.attrs["depth_multiplier"] > 1 and not (
(ifm_tensor.shape[3] == 1) and (ofm_tensor.shape[3] == op.attrs["depth_multiplier"])
):
@@ -337,9 +322,9 @@
return False
# check data type
- ifm_tensor, _, _, ofm_tensor = op.get_ifm_ifm2_weights_ofm()
+ ifm_tensor, ofm_tensor = op.get_ifm_ofm()
if ifm_tensor.dtype != ofm_tensor.dtype:
- if op.type != "ReduceSum":
+ if op.type != Op.ReduceSum:
return False
# TODO: else check ReduceSum restrictions.
@@ -365,7 +350,7 @@
@classmethod
def check_resize_restrictions(cls, op):
# check unsupported upscaling factor
- if op.type == "ResizeBilinear":
+ if op.type == Op.ResizeBilinear:
if op.inputs[0].shape[1] == 1 and op.inputs[0].shape[2] == 1:
return True
if op.inputs[0].shape == op.outputs[0].shape:
@@ -424,10 +409,10 @@
ifm_tensor.dtype == ofm_tensor.dtype or ofm_tensor.dtype == DataType.int32
):
return False
- elif op.type in cls.binary_elem_wise_shift_ops | set(("CLZ")):
+ elif op.type in cls.binary_elem_wise_shift_ops:
if ifm_tensor.dtype != DataType.int32 or ifm2_tensor.dtype != DataType.int32:
return False
- if op.type in ("CLZ", "SHL") and ofm_tensor.dtype != DataType.int32:
+ if op.type in (Op.CLZ, Op.SHL) and ofm_tensor.dtype != DataType.int32:
return False
# check batch size
@@ -438,7 +423,7 @@
return False
# negative alpha values are not supported
- if op.type == "LeakyRelu" and op.attrs["alpha"] < 0:
+ if op.type == Op.LeakyRelu and op.attrs["alpha"] < 0:
return False
# check if ifm or ifm2 has ofm shape
@@ -452,7 +437,7 @@
@classmethod
def check_memory_only_restrictions(cls, op):
- if op.type == "StridedSlice":
+ if op.type == Op.StridedSlice:
if len(op.inputs) != 4:
warn_cpu(op, "has {} input tensors, only 4 inputs are supported".format(len(op.inputs)))
return False
@@ -493,7 +478,7 @@
),
)
return False
- if op.type == "SplitV":
+ if op.type == Op.SplitV:
# check that maximum one size is set to -1, indicating that size should be inferred
sizes = op.inputs[1].values
num_to_be_inferred = 0
@@ -504,7 +489,7 @@
if num_to_be_inferred > 1:
print("Warning:", op.type, "has more than one size to be inferred, which is illegal, placing on CPU")
return False
- if op.type.find("Concat") != -1:
+ if op.type in set((Op.Concat, Op.ConcatTFLite,)):
axis = op.attrs.get("axis", None)
if axis is None:
print("Warning:", op.type, "invalid or missing axis, placing on CPU")
@@ -554,7 +539,7 @@
@classmethod
def check_activation_ops(cls, op):
- if op.type == "Softmax":
+ if op.type == Op.Softmax:
ifm_tensor = op.inputs[0]
ofm_tensor = op.outputs[0]