MLBEDSW-5102 Update removal of memory only operators
Memory only operators such as Reshape, Squeeze and ExpandDims are
removed in the graph optimiser step.
- Added semantic check that memory only operators have same
quantisation parameters on ifm/ofm.
- Added support for the ExpandDims operator.
- Addition and cleanup of related unit tests.
- Removed TOSA from the generated SUPPORTED_OPS.md documentation.
Signed-off-by: Jonas Ohlsson <jonas.ohlsson@arm.com>
Change-Id: If848d8afc58c18806e10997ed94e4dae83f30879
diff --git a/ethosu/vela/tflite_graph_optimiser.py b/ethosu/vela/tflite_graph_optimiser.py
index 15b82c7..b48cc7a 100644
--- a/ethosu/vela/tflite_graph_optimiser.py
+++ b/ethosu/vela/tflite_graph_optimiser.py
@@ -30,10 +30,11 @@
from .debug_database import DebugDatabase
from .errors import UnsupportedFeatureError
from .ethos_u55_regs.ethos_u55_regs import resampling_mode
-from .graph_optimiser_util import bypass_reshape_and_squeeze_ops
+from .graph_optimiser_util import bypass_memory_only_ops
from .graph_optimiser_util import calc_explicit_padding
from .graph_optimiser_util import convert_depthwise_to_conv
from .graph_optimiser_util import fix_sg_input_output
+from .graph_optimiser_util import memory_only_ops
from .graph_optimiser_util import move_splitsliceread_to_consumer
from .graph_optimiser_util import needed_total_padding
from .graph_optimiser_util import set_ifm_ofm_op_shapes
@@ -190,7 +191,7 @@
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 not in (Op.Reshape, Op.Squeeze)
+ and op.ofm.consumer_list[0].type not in memory_only_ops
and op.ofm_shapes[0] == Shape4D.from_list(op.ofm.shape)
):
# SplitSliceRead can be performed by tensor consumer
@@ -211,27 +212,6 @@
DebugDatabase.add_optimised(op, avgpool_op)
-def insert_copy_op_after_tens(tens):
- tens_cons_list_copy = tens.consumer_list.copy()
-
- # Create a avg_pool nop op with ifm as input
- copy_tens = tens.clone()
- copy_op = create_avgpool_nop(tens.name + "_avgpool")
- copy_op.add_input_tensor(tens)
- copy_op.set_output_tensor(copy_tens)
- copy_op.set_ifm_ofm_shapes()
- copy_op.run_on_npu = True
-
- # Set copy_ifm consumers
- for tens_cons in tens_cons_list_copy:
- if tens_cons is not None:
- for ifm_idx, cons_inp in enumerate(tens_cons.inputs):
- if cons_inp == tens:
- tens_cons.set_input_tensor(copy_tens, ifm_idx)
-
- DebugDatabase.add_optimised(tens.ops[0], copy_op)
-
-
def calc_padding_and_skirt(padding_type, kernel, input_shape, explicit_padding):
k_w, k_h = kernel.dilated_wh()
s_x, s_y = kernel.stride
@@ -985,19 +965,9 @@
return op
-def remove_reshape_and_squeeze_ops(op, arch):
- if op.run_on_npu and op.type in (Op.Reshape, Op.Squeeze):
- ofm = op.ofm
- ifm = op.ifm
-
- # Check if quantization is the same in the input and output for the reshape ops
- if not check_quantized_tens_scaling_equal(ifm, ofm):
- # TODO Both tensors are needed, since quantisation properties currently are linked to Tensors.
- # In order to remove this reshape either quantization properties need to be moved to Operator,
- # or the reshape need to be replace with a NOP.
- return
-
- bypass_reshape_and_squeeze_ops(op)
+def remove_memory_only_ops(op, arch):
+ if op.run_on_npu and op.type in memory_only_ops:
+ bypass_memory_only_ops(op)
def fuse_activation_function_with_prev(op, arch, nng):
@@ -1463,9 +1433,9 @@
nng, sg, arch, [], [fix_sg_input_output], rewrite_unsupported=False,
)
- # Removal of reshapes and squeeze
+ # Removal of memory only operators
for sg in nng.subgraphs:
- rewrite_graph.visit_graph_post_order(sg.output_tensors, arch, [], [remove_reshape_and_squeeze_ops])
+ rewrite_graph.visit_graph_post_order(sg.output_tensors, arch, [], [remove_memory_only_ops])
sg.refresh_after_modification()
# Rewrite of operators