MLBEDSW-2306 Added more supported mem-cfgs
Additional supported memory configurations:
-Permanent_storage = DRAM
-Tensor arena either in DRAM or SRAM
Signed-off-by: Patrik Gustavsson <patrik.gustavsson@arm.com>
Change-Id: I20beb7151e306bfdba540e7c0b2a7b478b4d94e1
diff --git a/ethosu/vela/live_range.py b/ethosu/vela/live_range.py
index 2a35a11..8fe3d57 100644
--- a/ethosu/vela/live_range.py
+++ b/ethosu/vela/live_range.py
@@ -18,7 +18,7 @@
# Can work with either a pass packed subgraph or a scheduled subgraph.
from .high_level_command_stream_generator import calc_allowed_ofm_ifm_overlap_for_cascaded_pass
from .nn_graph import PassPlacement
-from .tensor import MemArea
+from .tensor import MemType
from .tensor import Tensor
@@ -220,6 +220,7 @@
def extract_live_ranges_from_cascaded_passes(
sg,
target_mem_area,
+ target_mem_type_set,
mark_output_tensors_overlapping_with_input_tensors=False,
use_ifm_ofm_overlap=True,
ignore_subgraph_input_output_tensors=False,
@@ -236,8 +237,8 @@
lr_graph.ignore_tensors.update(sg.input_tensors)
lr_graph.ignore_tensors.update(sg.output_tensors)
- def tensor_should_be_ignored(tens, target_mem_area):
- if tens.mem_area != target_mem_area:
+ def tensor_should_be_ignored(tens, target_mem_area, target_mem_type_set):
+ if tens.mem_area != target_mem_area or tens.mem_type not in target_mem_type_set:
return True
if tens in lr_graph.ignore_tensors:
return True
@@ -247,9 +248,24 @@
return True
return False
+ def merge_memory_op_ranges(sg, lr_graph, tensor_should_be_ignored, target_mem_area, target_mem_type_set):
+ for ps in sg.passes:
+ if ps.placement == PassPlacement.MemoryOnly:
+ # For memory only passes, e.g. Reshape. Add input and output tensor to the same LiveRange
+ input_tensor = ps.inputs[0]
+ output_tensor = ps.outputs[0]
+ # If the input or output tensor is tied to a Cpu tensor, i.e. a subgraph input
+ # or output, fuse the live-range with the Cpu tensors' live-range instead.
+ input_tensor = input_tensor.cpu_tensor if input_tensor.cpu_tensor is not None else input_tensor
+ output_tensor = output_tensor.cpu_tensor if output_tensor.cpu_tensor is not None else output_tensor
+ if not tensor_should_be_ignored(input_tensor, target_mem_area, target_mem_type_set) and not (
+ tensor_should_be_ignored(output_tensor, target_mem_area, target_mem_type_set)
+ ):
+ lr_graph.fuse_ranges(input_tensor, output_tensor)
+
# Merge only memory operations in the NPU subgraphs
if sg.placement == PassPlacement.Npu:
- merge_memory_op_ranges(sg, lr_graph, tensor_should_be_ignored, target_mem_area)
+ merge_memory_op_ranges(sg, lr_graph, tensor_should_be_ignored, target_mem_area, target_mem_type_set)
for cps in sg.cascaded_passes:
cps.time = lr_graph.current_time
@@ -259,19 +275,21 @@
is_element_wise = cps.is_element_wise
for tens in cps.inputs:
- if tensor_should_be_ignored(tens, target_mem_area):
+ if tensor_should_be_ignored(tens, target_mem_area, target_mem_type_set):
continue
rng = lr_graph.get_or_create_range(tens)
rng.mark_usage(time_for_pass)
cps_primary_op = cps.passes[0].primary_op
- if cps_primary_op and cps_primary_op.type == "NpuOp" and target_mem_area in set((MemArea.Sram, MemArea.Dram)):
+
+ if cps_primary_op and cps_primary_op.type == "NpuOp" and MemType.Permanent_CPU not in target_mem_type_set:
# If the primary-op is an NpuOp that means this is where an Npu subgraph
# is called. Go into said subgraph and extract live ranges before continuing.
npu_sg = cps_primary_op.attrs["subgraph"]
lr_graph = extract_live_ranges_from_cascaded_passes(
npu_sg,
target_mem_area,
+ target_mem_type_set,
mark_output_tensors_overlapping_with_input_tensors,
use_ifm_ofm_overlap,
False,
@@ -282,13 +300,13 @@
cps.time = time_for_pass
for tens in cps.intermediates:
- if tensor_should_be_ignored(tens, target_mem_area):
+ if tensor_should_be_ignored(tens, target_mem_area, target_mem_type_set):
continue
rng = lr_graph.get_or_create_range(tens)
rng.mark_usage(time_for_pass)
for tens in cps.outputs:
- if tensor_should_be_ignored(tens, target_mem_area):
+ if tensor_should_be_ignored(tens, target_mem_area, target_mem_type_set):
continue
rng = lr_graph.get_or_create_range(tens)
output_time = time_for_pass
@@ -303,8 +321,8 @@
if (
ifm_tensor is not None
and ofm_tensor is not None
- and not tensor_should_be_ignored(ifm_tensor, target_mem_area)
- and not tensor_should_be_ignored(ofm_tensor, target_mem_area)
+ and not tensor_should_be_ignored(ifm_tensor, target_mem_area, target_mem_type_set)
+ and not tensor_should_be_ignored(ofm_tensor, target_mem_area, target_mem_type_set)
):
lr_graph.allowed_overlaps[(ifm_tensor, ofm_tensor)] = calc_allowed_ofm_ifm_overlap_for_cascaded_pass(
cps
@@ -318,7 +336,7 @@
end_time = max(end_time, rng.end_time)
for tens in sg.output_tensors:
- if tensor_should_be_ignored(tens, target_mem_area):
+ if tensor_should_be_ignored(tens, target_mem_area, target_mem_type_set):
continue
rng = lr_graph.get_or_create_range(tens)
rng.mark_usage(end_time)