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>

4 files changed, 41 insertions, 39 deletions

diff --git a/ethosu/vela/test/test_lut.py b/ethosu/vela/test/test_lut.py
index ee1a40fe..44ee0afb 100644
--- a/ethosu/vela/test/test_lut.py
+++ b/ethosu/vela/test/test_lut.py
@@ -26,6 +26,7 @@ from ethosu.vela import pass_packing
 from ethosu.vela.data_type import DataType
 from ethosu.vela.high_level_command_stream import DMA
 from ethosu.vela.nn_graph import Graph
+from ethosu.vela.operation import Op
 from ethosu.vela.rewrite_graph import verify_graph_health
 from ethosu.vela.tensor import create_const_tensor
 from ethosu.vela.tensor import TensorPurpose
@@ -94,28 +95,28 @@ def test_optimize_high_level_cmd_stream_2K():
     arch = testutil.create_arch()
     shape = [1, 1, 1, 1]
     # u8 LUT op, should lead to DMA
-    op0 = testutil.create_elemwise_op("AddAct", "op0", shape, shape, shape)
+    op0 = testutil.create_elemwise_op(Op.Add, "op0", shape, shape, shape)
     set_256_lut(op0, "lut0")
     # u8 LUT op, should lead to DMA
-    op1 = testutil.create_elemwise_op("AddAct", "op1", shape, shape, shape)
+    op1 = testutil.create_elemwise_op(Op.Add, "op1", shape, shape, shape)
     set_256_lut(op1, "lut1")
     # u8 LUT op with different LUT, should lead to DMA
-    op2 = testutil.create_elemwise_op("AddAct", "op2", shape, shape, shape)
+    op2 = testutil.create_elemwise_op(Op.Add, "op2", shape, shape, shape)
     set_256_lut(op2, "lut2")
     # u8 LUT op with same LUT as in op1, should not lead to DMA
-    op3 = testutil.create_elemwise_op("AddAct", "op3", shape, shape, shape)
+    op3 = testutil.create_elemwise_op(Op.Add, "op3", shape, shape, shape)
     set_256_lut(op3, "lut1")
     # u8 LUT op with same LUT as in op2, should not lead to DMA
-    op4 = testutil.create_elemwise_op("AddAct", "op4", shape, shape, shape)
+    op4 = testutil.create_elemwise_op(Op.Add, "op4", shape, shape, shape)
     set_256_lut(op4, "lut2")
     # 2K LUT op, should lead to DMA, and will overwrite all previous LUTs in SHRAM
-    op5_2K = testutil.create_elemwise_op("AddAct", "op5", shape, shape, shape)
+    op5_2K = testutil.create_elemwise_op(Op.Add, "op5", shape, shape, shape)
     set_2K_lut(op5_2K, "lut5")
     # Another 2K LUT op, should lead to DMA, and will overwrite the previous LUT in SHRAM
-    op6_2K = testutil.create_elemwise_op("AddAct", "op6", shape, shape, shape)
+    op6_2K = testutil.create_elemwise_op(Op.Add, "op6", shape, shape, shape)
     set_2K_lut(op6_2K, "lut6")
     # u8 LUT op with same LUT as in op1, should lead to DMA
-    op7 = testutil.create_elemwise_op("AddAct", "op7", shape, shape, shape)
+    op7 = testutil.create_elemwise_op(Op.Add, "op7", shape, shape, shape)
     set_256_lut(op7, "lut1")
 
     op_list = [op0, op1, op2, op3, op4, op5_2K, op6_2K, op7]
@@ -149,28 +150,28 @@ def test_optimize_high_level_cmd_stream_1K():
     arch = testutil.create_arch()
     shape = [1, 1, 1, 1]
     # u8 LUT op, should lead to DMA
-    op0 = testutil.create_elemwise_op("AddAct", "op0", shape, shape, shape)
+    op0 = testutil.create_elemwise_op(Op.Add, "op0", shape, shape, shape)
     set_256_lut(op0, "lut0")
     # u8 LUT op, should lead to DMA
-    op1 = testutil.create_elemwise_op("AddAct", "op1", shape, shape, shape)
+    op1 = testutil.create_elemwise_op(Op.Add, "op1", shape, shape, shape)
     set_256_lut(op1, "lut1")
     # 1K LUT op with different LUT, should lead to DMA
-    op2_1K = testutil.create_elemwise_op("AddAct", "op2", shape, shape, shape)
+    op2_1K = testutil.create_elemwise_op(Op.Add, "op2", shape, shape, shape)
     set_1K_lut(op2_1K, "lut2")
     # u8 LUT op with same LUT as in op1, should not lead to DMA
-    op3 = testutil.create_elemwise_op("AddAct", "op3", shape, shape, shape)
+    op3 = testutil.create_elemwise_op(Op.Add, "op3", shape, shape, shape)
     set_256_lut(op3, "lut1")
     # 1K LUT op with same LUT as in op2, should not lead to DMA
-    op4_1K = testutil.create_elemwise_op("AddAct", "op4", shape, shape, shape)
+    op4_1K = testutil.create_elemwise_op(Op.Add, "op4", shape, shape, shape)
     set_1K_lut(op4_1K, "lut2")
     # 1K LUT op, should lead to DMA, and will overwrite lut2
-    op5_2K = testutil.create_elemwise_op("AddAct", "op5", shape, shape, shape)
+    op5_2K = testutil.create_elemwise_op(Op.Add, "op5", shape, shape, shape)
     set_1K_lut(op5_2K, "lut5")
     # u8 LUT op, lut0 should still be present, should not lead to DMA
-    op6 = testutil.create_elemwise_op("AddAct", "op6", shape, shape, shape)
+    op6 = testutil.create_elemwise_op(Op.Add, "op6", shape, shape, shape)
     set_256_lut(op6, "lut0")
     # 1K LUT op with same LUT as in op2, should lead to DMA
-    op7 = testutil.create_elemwise_op("AddAct", "op7", shape, shape, shape)
+    op7 = testutil.create_elemwise_op(Op.Add, "op7", shape, shape, shape)
     set_1K_lut(op7, "lut2")
 
     op_list = [op0, op1, op2_1K, op3, op4_1K, op5_2K, op6, op7]
diff --git a/ethosu/vela/test/test_supported_operators.py b/ethosu/vela/test/test_supported_operators.py
index 53c20927..20d448d7 100644
--- a/ethosu/vela/test/test_supported_operators.py
+++ b/ethosu/vela/test/test_supported_operators.py
@@ -19,6 +19,7 @@
 import numpy as np
 
 from ethosu.vela.data_type import DataType
+from ethosu.vela.operation import Op
 from ethosu.vela.supported_operators import SupportedOperators
 from ethosu.vela.tensor import create_const_tensor
 from ethosu.vela.tensor import QuantizationParameters
@@ -35,7 +36,7 @@ def create_strided_slice_op(in_shape, out_shape, start_offsets, end_offsets):
     in3 = create_const_tensor("strides", [len(end_offsets)], DataType.uint8, len(end_offsets) * [1])
     out = Tensor(out_shape, DataType.uint8, "out")
     attrs = {"ellipsis_mask": 0, "new_axis_mask": 0, "shrink_axis_mask": 0, "begin_mask": 0, "end_mask": 0}
-    return testutil.create_op("StridedSlice", [in0, in1, in2, in3], out, attrs=attrs)
+    return testutil.create_op(Op.StridedSlice, [in0, in1, in2, in3], out, attrs=attrs)
 
 
 def create_strided_slice():
@@ -93,21 +94,21 @@ def test_constraint_tens_defined_shape():
     # Tensors cannot have None in them
     inp = Tensor([1, 8, None, 8], DataType.uint8, "in")
     out = Tensor([1, 8, 8, 8], DataType.uint8, "out")
-    op = testutil.create_op("Relu", [inp], out)
+    op = testutil.create_op(Op.Relu, [inp], out)
     assert not support.is_operator_supported(op)
 
 
 def test_constraint_tens_shapeless():
     # Shapeless input is allowed if its of a certain type:
-    op = testutil.create_elemwise_op("Mul", "scalar_mul", [1, 8, 8, 8], [], [1, 8, 8, 8])
+    op = testutil.create_elemwise_op(Op.Mul, "scalar_mul", [1, 8, 8, 8], [], [1, 8, 8, 8])
     assert support.is_operator_supported(op)
     # Shapeless output is not allowed at all:
-    op = testutil.create_elemwise_op("Mul", "scalar_mul", [1, 8, 8, 8], [1, 8, 8, 8], [])
+    op = testutil.create_elemwise_op(Op.Mul, "scalar_mul", [1, 8, 8, 8], [1, 8, 8, 8], [])
     assert not support.is_operator_supported(op)
     # Invalid shapeless input due to op type:
     inp = Tensor([], DataType.uint8, "in")
     out = Tensor([1, 8, 8, 8], DataType.uint8, "out")
-    op = testutil.create_op("Relu", [inp], out)
+    op = testutil.create_op(Op.Relu, [inp], out)
     assert not support.is_operator_supported(op)
 
 
@@ -115,7 +116,7 @@ def test_constraint_tens_shape_size():
     # Tensors cannot be > 4D
     inp = Tensor([1, 1, 8, 8, 8], DataType.uint8, "in")
     out = Tensor([1, 1, 8, 8, 8], DataType.uint8, "out")
-    op = testutil.create_op("Relu", [inp], out)
+    op = testutil.create_op(Op.Relu, [inp], out)
     assert not support.is_operator_supported(op)
 
 
@@ -123,14 +124,14 @@ def test_constraint_tens_dtype():
     # Tensors can only be of type uint8, int8, int16 (and int32)
     inp = Tensor([1, 8, 8, 8], DataType.float32, "in")
     out = Tensor([1, 8, 8, 8], DataType.float32, "out")
-    op = testutil.create_op("Relu", [inp], out)
+    op = testutil.create_op(Op.Relu, [inp], out)
     assert not support.is_operator_supported(op)
     # For int32, only select op types are allowed:
-    op = testutil.create_elemwise_op("Mul", "scalar_mul", [1, 8, 8, 8], [], [1, 8, 8, 8], DataType.int32)
+    op = testutil.create_elemwise_op(Op.Mul, "scalar_mul", [1, 8, 8, 8], [], [1, 8, 8, 8], DataType.int32)
     assert support.is_operator_supported(op)
     inp = Tensor([1, 8, 8, 8], DataType.int32, "in")
     out = Tensor([1, 8, 8, 8], DataType.int32, "out")
-    op = testutil.create_op("Relu", [inp], out)
+    op = testutil.create_op(Op.Relu, [inp], out)
     assert not support.is_operator_supported(op)
 
 
@@ -138,11 +139,11 @@ def test_constraint_tens_dimension():
     # Tensors can only have values in the inclusive range of 1-65535
     inp = Tensor([1, 8, 8, 0], DataType.uint8, "in")
     out = Tensor([1, 8, 8, 0], DataType.uint8, "out")
-    op = testutil.create_op("Relu", [inp], out)
+    op = testutil.create_op(Op.Relu, [inp], out)
     assert not support.is_operator_supported(op)
     inp = Tensor([1, 8, 8, 65536], DataType.uint8, "in")
     out = Tensor([1, 8, 8, 65536], DataType.uint8, "out")
-    op = testutil.create_op("Relu", [inp], out)
+    op = testutil.create_op(Op.Relu, [inp], out)
     assert not support.is_operator_supported(op)
 
 
@@ -150,13 +151,14 @@ def test_constraint_faf():
     # Fused activation functions, if set, must be a valid op type
     inp = Tensor([1, 8, 8, 8], DataType.uint8, "in")
     out = Tensor([1, 8, 8, 8], DataType.uint8, "out")
-    op = testutil.create_op("Relu", [inp], out, attrs={"fused_activation_function": "Conv2D"})
+    op = testutil.create_op(Op.Relu, [inp], out)
+    op.activation = Op.Conv2D
     assert not support.is_operator_supported(op)
 
 
 def test_constraint_tens_quant_scale():
     # Quantization scale cannot be infinit
-    op = testutil.create_elemwise_op("Mul", "scalar_mul", [1, 8, 8, 8], [], [1, 8, 8, 8])
+    op = testutil.create_elemwise_op(Op.Mul, "scalar_mul", [1, 8, 8, 8], [], [1, 8, 8, 8])
     op.inputs[0].quantization = QuantizationParameters()
     op.inputs[0].quantization.scale_f32 = np.inf
     assert not support.is_operator_supported(op)
diff --git a/ethosu/vela/test/test_tflite_reader.py b/ethosu/vela/test/test_tflite_reader.py
index d63c0007..23abb4a0 100644
--- a/ethosu/vela/test/test_tflite_reader.py
+++ b/ethosu/vela/test/test_tflite_reader.py
@@ -20,6 +20,7 @@ from unittest.mock import patch
 
 import pytest
 
+from ethosu.vela.operation import Op
 from ethosu.vela.tflite_reader import TFLiteSubgraph
 
 
@@ -41,13 +42,13 @@ class TestTFLiteSubgraph:
 
     parse_op_testdata = [
         # op_type, opt_serializer, inputs, output, expected
-        ("FullyConnected", None, [0, 1, 2], 3, 3),  # FC
-        ("FullyConnected", None, [0, 1, -1], 3, 3),  # FC disabled Bias
-        ("FullyConnected", None, [0, 1], 3, 3),  # FC no Bias
-        ("Conv2D", None, [2, 1, 3], 0, 3),  # Conv2D
-        ("Conv2DBackprop", None, [0, 1, 2, 3], 4, 4),  # TransposeConv
-        ("Conv2DBackprop", None, [0, 1, 2], 4, 4),  # TransposeConv no Bias
-        pytest.param("Conv2D", None, [0, -1, 1], 3, 3, marks=pytest.mark.xfail),  # Conv2D no Weights
+        (Op.FullyConnected, None, [0, 1, 2], 3, 3),  # FC
+        (Op.FullyConnected, None, [0, 1, -1], 3, 3),  # FC disabled Bias
+        (Op.FullyConnected, None, [0, 1], 3, 3),  # FC no Bias
+        (Op.Conv2D, None, [2, 1, 3], 0, 3),  # Conv2D
+        (Op.Conv2DBackpropInput, None, [0, 1, 2, 3], 4, 4),  # TransposeConv
+        (Op.Conv2DBackpropInput, None, [0, 1, 2], 4, 4),  # TransposeConv no Bias
+        pytest.param(Op.Conv2D, None, [0, -1, 1], 3, 3, marks=pytest.mark.xfail),  # Conv2D no Weights
     ]
 
     @pytest.mark.parametrize("op_type, opt_serializer, inputs, output, expected", parse_op_testdata)
@@ -56,7 +57,7 @@ class TestTFLiteSubgraph:
             # Mock a TFLiteSubGraph
             sg = TFLiteSubgraph(None, None)
             sg.graph = MagicMock()
-            sg.graph.operator_codes = [(op_type, opt_serializer)]
+            sg.graph.operator_codes = [(op_type, opt_serializer, "")]
 
             # Mock a couple of tensors
             sg.tensors = [MagicMock() for _ in range(5)]
diff --git a/ethosu/vela/test/testutil.py b/ethosu/vela/test/testutil.py
index 13b6bf40..adb874a0 100644
--- a/ethosu/vela/test/testutil.py
+++ b/ethosu/vela/test/testutil.py
@@ -20,7 +20,6 @@ import numpy as np
 from ethosu.vela import architecture_features
 from ethosu.vela.data_type import DataType
 from ethosu.vela.nn_graph import Subgraph
-from ethosu.vela.operation import NpuBlockType
 from ethosu.vela.operation import Operation
 from ethosu.vela.tensor import create_const_tensor
 from ethosu.vela.tensor import Tensor
@@ -52,7 +51,6 @@ def create_elemwise_op(type, name, ifm_shape, ifm2_shape, ofm_shape, datatype=Da
     op.add_input_tensor(create_const_tensor(name + "_ifm2", ifm2_shape, datatype, np.zeros(ifm2_shape), np_type))
     ofm = Tensor(ofm_shape, datatype, name + "_ofm")
     op.set_output_tensor(ofm)
-    op.attrs["npu_block_type"] = NpuBlockType.ElementWise
     return op