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, 12 insertions, 14 deletions

diff --git a/ethosu/vela/high_level_command_stream_generator.py b/ethosu/vela/high_level_command_stream_generator.py
index 8486dadc..dc52ae52 100644
--- a/ethosu/vela/high_level_command_stream_generator.py
+++ b/ethosu/vela/high_level_command_stream_generator.py
@@ -25,6 +25,7 @@ from .nn_graph import PassPlacement
 from .nn_graph import SchedulingStrategy
 from .numeric_util import round_up_divide
 from .operation import NpuBlockType
+from .operation import Op
 from .tensor import TensorPurpose
 
 
@@ -39,7 +40,7 @@ def match_tensor(source, derived):
     if source == derived:
         return True
     ops = derived.ops
-    return ops != [] and len(ops) == 1 and ops[0].type == "SplitSliceRead" and source == ops[0].inputs[0]
+    return ops != [] and len(ops) == 1 and ops[0].type == Op.SplitSliceRead and source == ops[0].inputs[0]
 
 
 def generate_high_level_command_stream_for_pass(strat, passes, block_configs, idx):
@@ -56,8 +57,8 @@ def generate_high_level_command_stream_for_pass(strat, passes, block_configs, id
             ps.ifm_tensor, ps.ifm2_tensor = ps.ifm2_tensor, ps.ifm_tensor
 
         for op in ps.ops:
-            if op.type == "SplitSliceRead":
-                ps.primary_op.attrs["fused_memory_function"] = op.type
+            if op.type == Op.SplitSliceRead:
+                ps.primary_op.memory_function = op.type
                 assert len(op.inputs) == 1
                 if match_tensor(ps.ifm_tensor, op.inputs[0]):
                     split_offsets[0] = op.attrs["split_start"]
@@ -68,10 +69,10 @@ def generate_high_level_command_stream_for_pass(strat, passes, block_configs, id
     else:
         ifm_idx = 0
         for op in ps.ops:
-            if op.type == "SplitSliceRead":
+            if op.type == Op.SplitSliceRead:
                 assert ifm_idx < 2
                 split_offsets[ifm_idx] = op.attrs["split_start"]
-                ps.primary_op.attrs["fused_memory_function"] = op.type
+                ps.primary_op.memory_function = op.type
                 ifm_idx += 1
 
     ifm_tensor = ps.ifm_tensor
@@ -89,19 +90,16 @@ def generate_high_level_command_stream_for_pass(strat, passes, block_configs, id
     if ps.primary_op is not None:
         strides = ps.primary_op.attrs.get("strides", None)
         skirt = ps.primary_op.attrs.get("skirt", None)
-        if ps.primary_op.type == "Conv2DBackpropInputSwitchedBias":
+        if ps.primary_op.type == Op.Conv2DBackpropInputSwitchedBias:
             upscaling = ofm_tensor.shape[-3] // ifm_tensor.shape[-3]
-        elif ps.primary_op.type == "ResizeBilinear":
+        elif ps.primary_op.type == Op.ResizeBilinear:
             upscaling = round_up_divide(ofm_tensor.shape[-3], ifm_tensor.shape[-3])
 
     concat_axis = 0
     concat_offset = 0
 
-    # Fusable activation functions
-    activation_ops = set(("Sigmoid", "Tanh", "Relu", "Relu6", "ReluN1To1"))
-
     for op in ps.ops:
-        if op.type == "ConcatSliceWrite":
+        if op.type == Op.ConcatSliceWrite:
             concat_axis = op.attrs["concat_axis"]
             concat_start = op.attrs["concat_start"]
             concat_end = op.attrs["concat_end"]
@@ -109,9 +107,9 @@ def generate_high_level_command_stream_for_pass(strat, passes, block_configs, id
             ofm_start[concat_axis] = concat_start
             ofm_end[concat_axis] = concat_end
             concat_offset = concat_start
-            ps.primary_op.attrs["fused_memory_function"] = op.type
-        elif op.type in activation_ops:
-            ps.primary_op.attrs["fused_activation_function"] = op.type
+            ps.primary_op.memory_function = op.type
+        elif op.type.is_relu_op() or op.type in (Op.Tanh, Op.Sigmoid):
+            ps.primary_op.activation = op.type
 
     if strat == SchedulingStrategy.WeightStream:
         ofm_step = block_config[-1]