MLBEDSW-6563: networks failing with memory area exceeded in vela
- For allocations that have a hard memory limit the Hill Climb allocator
should be given more attempts to find a solution that would fit
- The fix is to use a memory limit when there is a hard constraint, and
a minimum iteration count, reset on every improvement, when there is a soft
constraint
- Added maximum number iterations CLI option
Signed-off-by: Tim Hall <tim.hall@arm.com>
Change-Id: I19ff53a0b68412de280263626778a3102cbe52fa
diff --git a/ethosu/vela/tensor_allocation.py b/ethosu/vela/tensor_allocation.py
index ab65740..1ffae4c 100644
--- a/ethosu/vela/tensor_allocation.py
+++ b/ethosu/vela/tensor_allocation.py
@@ -66,9 +66,11 @@
return total_sz
-def hillclimb_allocate_live_ranges(live_ranges: LiveRangeGraph, alloc_granularity: int) -> int:
+def hillclimb_allocate_live_ranges(
+ live_ranges: LiveRangeGraph, alloc_granularity: int, max_iterations: int, mem_limit: int
+) -> int:
# Allocates using the hill climb allocator
- addresses = hillclimb_allocation.allocate_live_ranges(live_ranges.lrs)
+ addresses = hillclimb_allocation.allocate_live_ranges(live_ranges.lrs, max_iterations, mem_limit)
# The result is a list containing the allocated addresses
total_sz = 0
for lr, address in zip(live_ranges.lrs, addresses):
@@ -144,7 +146,10 @@
memory_hist = memory_usage_histogram(lrs.lrs)
min_mem_usage_for_alloc = max(memory_hist)
- print("Start Time - End Time: Start Addr - End Addr: Tensor Size: Memory Usage: Tensor Purpose: Tensor Name")
+ print(
+ f"{'Start Time':>10s} - {'End Time':>10s}: {'Start Addr':>10s} - {'End Addr':>10s}: {'Tensor Size':>11s}:"
+ f" {'Memory Usage':>12s}: {'Purpose':12s}: Name"
+ )
for start_time, end_time, size, start_addr, end_addr, purpose, name in sorted(
(
lr.start_time,
@@ -159,7 +164,7 @@
):
print(
f"{start_time:10d} - {end_time:10d}: {start_addr:#10x} - {end_addr:#10x}: {size:11d}:"
- f" {memory_hist[start_time]:12d}: {purpose.display_name():15s}: {name:s}"
+ f" {memory_hist[start_time]:12d}: {purpose.display_name():12s}: {name:s}"
)
alloc_overhead_fraction = (actual_mem_usage_for_alloc - min_mem_usage_for_alloc) / min_mem_usage_for_alloc
@@ -194,6 +199,7 @@
tensor_allocator=TensorAllocator.Greedy,
lr_graph=None,
cpu_tensor_alignment=Tensor.AllocationQuantum,
+ hillclimb_max_iterations=None,
):
# Allocates addresses to tensors, returns False if tensors could not be fit within max_size
lrs = live_range.extract_live_ranges_from_cascaded_passes(
@@ -207,12 +213,14 @@
if lrs.ranges:
tens_alloc = tensor_allocator
if tens_alloc == TensorAllocator.Greedy:
- total_sz = greedy_allocate_live_ranges(sg, arch, lrs, mem_area, cpu_tensor_alignment)
+ total_sz = greedy_allocate_live_ranges(lrs, cpu_tensor_alignment)
verify_allocation(lrs, cpu_tensor_alignment)
elif tens_alloc == TensorAllocator.LinearAlloc:
total_sz = linear_allocate_live_ranges(lrs, cpu_tensor_alignment)
elif tens_alloc == TensorAllocator.HillClimb:
- total_sz = hillclimb_allocate_live_ranges(lrs, cpu_tensor_alignment)
+ mem_type = MemType.Scratch_fast if MemType.Scratch_fast in mem_type_set else list(mem_type_set)[0]
+ mem_size = arch.mem_type_size(mem_type)
+ total_sz = hillclimb_allocate_live_ranges(lrs, cpu_tensor_alignment, hillclimb_max_iterations, mem_size)
else:
assert 0
return lrs, total_sz
@@ -228,6 +236,7 @@
verbose_allocation=False,
lr_graph=None,
cpu_tensor_alignment=Tensor.AllocationQuantum,
+ hillclimb_max_iterations=None,
max_size=None,
dry_test=False,
):
@@ -240,6 +249,7 @@
tensor_allocator=tensor_allocator,
lr_graph=lr_graph,
cpu_tensor_alignment=cpu_tensor_alignment,
+ hillclimb_max_iterations=hillclimb_max_iterations,
)
if lrs.ranges: