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>

1 files changed, 5 insertions, 7 deletions

diff --git a/ethosu/vela/insert_dma.py b/ethosu/vela/insert_dma.py
index 99b46c07..56d68d13 100644
--- a/ethosu/vela/insert_dma.py
+++ b/ethosu/vela/insert_dma.py
@@ -17,6 +17,7 @@
 # Insert DMA operations into the graph for transfering weights.
 from . import rewrite_graph
 from .operation import NpuBlockType
+from .operation import Op
 from .operation import Operation
 from .tensor import MemArea
 from .tensor import MemType
@@ -24,9 +25,6 @@ from .tensor import TensorPurpose
 from .weight_compressor import compress_weights
 
 
-binary_elementwise_op = set(("AddAct", "MulAct", "SubAct", "Maximum", "Minimum"))
-
-
 def weights_fit_sram(arch, op, tens, nng):
     if tens.purpose != TensorPurpose.Weights:
         return True
@@ -57,7 +55,7 @@ def weights_fit_sram(arch, op, tens, nng):
 
 
 def insert_dma_cmd(op, arch, nng):
-    if op.type == "DMA" or not op.run_on_npu:
+    if op.type == Op.DMA or not op.run_on_npu:
         return op
 
     is_lut_used = any(inp.purpose == TensorPurpose.LUT for inp in op.inputs)
@@ -76,14 +74,14 @@ def insert_dma_cmd(op, arch, nng):
             ) or tens.purpose == TensorPurpose.LUT:
                 if tens.purpose in (TensorPurpose.Weights, TensorPurpose.LUT) or (
                     tens.purpose == TensorPurpose.FeatureMap
-                    and op.type in binary_elementwise_op
+                    and op.type.is_binary_elementwise_op()
                     and tens.shape != []
                     and tens.shape != op.outputs[0].shape
                     and tens.storage_size() > max_ifm_shram_avail
                 ):
                     only_vector_product_consumers = True
                     for oper in tens.consumers():
-                        if oper is None or oper.attrs.get("npu_block_type") != NpuBlockType.VectorProduct:
+                        if oper is None or oper.type.npu_block_type != NpuBlockType.VectorProduct:
                             only_vector_product_consumers = False
                             break
 
@@ -95,7 +93,7 @@ def insert_dma_cmd(op, arch, nng):
                     ) or tens.purpose == TensorPurpose.LUT:
                         # Insert a DMA command here, as well as a new tensor situated in SRAM of the same size.
                         new_tens = tens.clone_into_fast_storage(arch)
-                        dma_cmd = Operation("DMA", tens.ops[0].name + "_dma")
+                        dma_cmd = Operation(Op.DMA, tens.ops[0].name + "_dma")
                         dma_cmd.inputs = [tens]
                         dma_cmd.set_output_tensor(new_tens)
                         dma_cmd.attrs["source"] = tens.mem_area