MLBEDSW-3774 Remove SplitSliceRead
Removed SplitSliceRead from subgraph during
graph optimisation.
Signed-off-by: Patrik Gustavsson <patrik.gustavsson@arm.com>
Change-Id: I9315d4c2a6767828dd2b4e66823d73b10ebee99c
diff --git a/ethosu/vela/graph_optimiser.py b/ethosu/vela/graph_optimiser.py
index eb93106..50368b8 100644
--- a/ethosu/vela/graph_optimiser.py
+++ b/ethosu/vela/graph_optimiser.py
@@ -164,10 +164,8 @@
# If start offset is not a multiple of 16 in the C-dimension, NHCWB16 need to be avoided in the input
if (offset_start[-1] % 16) != 0:
inp.avoid_NHCWB16 = True
- else:
- offset_start = full_shape(4, offset_start, 0)
- new_op.attrs["split_start"] = offset_start
+ new_op.read_offsets[0] = Shape4D.from_list(offset_start, 0)
new_op.run_on_npu = True
new_op.set_output_tensor(tens)
new_op.ifm_shapes.append(Shape4D(inp.shape))
@@ -177,6 +175,45 @@
return tens
+def remove_SplitSliceRead(op, arch):
+
+ if op.type == Op.SplitSliceRead:
+ # Check if it is possible to put the SplitSliceRead on the tensor consumer, or if an avgpool need to be inserted
+ if (
+ len(op.ofm.consumer_list) == 1
+ and op.ofm.consumer_list[0] is not None
+ and op.ofm.consumer_list[0].run_on_npu
+ and op.ofm.consumer_list[0].type != Op.Reshape
+ and op.ofm_shapes[0] == Shape4D.from_list(op.ofm.shape)
+ ):
+ # SplitSliceRead can be performed by tensor consumer
+ cons_op = op.ofm.consumer_list[0]
+ if cons_op.ifm == op.ofm:
+ cons_op.read_offsets[0] = op.read_offsets[0]
+ cons_op.set_input_tensor(op.ifm, cons_op.type.info.indices.ifms[0])
+ cons_op.ifm_shapes[0] = op.ifm_shapes[0]
+ elif cons_op.type.is_binary_elementwise_op() and cons_op.ifm2 == op.ofm:
+ cons_op.read_offsets[1] = op.read_offsets[0]
+ cons_op.set_input_tensor(op.ifm, cons_op.type.info.indices.ifms[1])
+ cons_op.ifm_shapes[1] = op.ifm_shapes[0]
+
+ op.ofm.consumer_list.remove(cons_op)
+ op.ofm.ops = []
+ op.ifm.consumer_list.remove(op)
+ else:
+ avgpool_op = create_avgpool_nop(op.name + "_avgpool")
+ avgpool_op.add_input_tensor(op.ifm)
+ avgpool_op.outputs = [op.ofm]
+ op.ofm.ops.remove(op)
+ op.ofm.ops.append(avgpool_op)
+ avgpool_op.ifm_shapes.append(op.ifm_shapes[0])
+ avgpool_op.ofm_shapes.append(op.ofm_shapes[0])
+ avgpool_op.read_offsets[0] = op.read_offsets[0]
+
+ op.ifm.consumer_list.remove(op)
+ DebugDatabase.add_optimised(op, avgpool_op)
+
+
def insert_copy_op_after_tens(tens):
tens_cons_list_copy = tens.consumer_list.copy()
@@ -202,7 +239,7 @@
if not op.run_on_npu or op.type != Op.Reshape:
return op
- # For the memory operators we want to remove, tensors are removed.
+ # For the Reshape operators we want to remove, tensors are removed.
# But in order to to do this, they cannot be outputs of the sg,
# this need to be fixed prior to the removal.
# Solution is to add a avgpool NOP, to maintain the original tensor.
@@ -1295,6 +1332,12 @@
[fuse_activation_function_with_prev, optimise_pad, add_padding_fields],
)
+ # Removal of SplitSliceRead, need to be done after optimisation has been performed,
+ # since ifm/ofm_shapes are of importance to this function
+ for sg in nng.subgraphs:
+ rewrite_graph.visit_graph_post_order(sg.output_tensors, arch, [], [remove_SplitSliceRead])
+ sg.refresh_after_modification()
+
# Post-optimisation operator debug tracing, and checking that no undesired reshapes are left in the graph
for sg in nng.subgraphs:
rewrite_graph.visit_graph_post_order(sg.output_tensors, arch, [], [check_reshapes, _record_optimised])