Update version of Black to 22.3.0
Update version of Black to 22.3.0 due to updated dependencies.
Updates to fix reported issues due to new version.
Signed-off-by: Jonas Ohlsson <jonas.ohlsson@arm.com>
Change-Id: I60056aae452093ce8dcea1f499ecced22b25eef1
diff --git a/ethosu/vela/scheduler.py b/ethosu/vela/scheduler.py
index fe2d711..a19d053 100644
--- a/ethosu/vela/scheduler.py
+++ b/ethosu/vela/scheduler.py
@@ -113,7 +113,13 @@
self.full_weight_transfer_cycles = 0
def copy(self):
- res = SchedulerOpInfo(self.block_config, self.weights_size, self.stripe_input, self.stripe_input2, self.stripe,)
+ res = SchedulerOpInfo(
+ self.block_config,
+ self.weights_size,
+ self.stripe_input,
+ self.stripe_input2,
+ self.stripe,
+ )
res.cascade = self.cascade
return res
@@ -135,7 +141,10 @@
"""Contains options for the Scheduler"""
def __init__(
- self, optimization_strategy, sram_target, verbose_schedule,
+ self,
+ optimization_strategy,
+ sram_target,
+ verbose_schedule,
):
self.optimization_strategy = optimization_strategy
self.optimization_sram_limit = sram_target
@@ -175,15 +184,28 @@
)
self.ifm_ublock = arch.ifm_ublock
- self.ifm = SchedulerTensor(ps.ifm_shapes[0], ps.ifm_tensor.dtype, ps.ifm_tensor.mem_area, ps.ifm_tensor.format,)
+ self.ifm = SchedulerTensor(
+ ps.ifm_shapes[0],
+ ps.ifm_tensor.dtype,
+ ps.ifm_tensor.mem_area,
+ ps.ifm_tensor.format,
+ )
self.ifm2 = None
if ps.ifm2_tensor:
self.ifm2 = SchedulerTensor(
- ps.ifm_shapes[1], ps.ifm2_tensor.dtype, ps.ifm2_tensor.mem_area, ps.ifm2_tensor.format,
+ ps.ifm_shapes[1],
+ ps.ifm2_tensor.dtype,
+ ps.ifm2_tensor.mem_area,
+ ps.ifm2_tensor.format,
)
- self.ofm = SchedulerTensor(ps.ofm_shapes[0], ps.ofm_tensor.dtype, ps.ofm_tensor.mem_area, ps.ofm_tensor.format,)
+ self.ofm = SchedulerTensor(
+ ps.ofm_shapes[0],
+ ps.ofm_tensor.dtype,
+ ps.ofm_tensor.mem_area,
+ ps.ofm_tensor.format,
+ )
# Input volume width and height required to produce the smallest possible stripe
self.min_stripe_input_w, self.min_stripe_input_h = self._calculate_min_stripe_input()
@@ -481,7 +503,11 @@
lr_graph = live_range.LiveRangeGraph()
for mem_area, mem_type_set in memories_list:
live_range.extract_live_ranges_from_cascaded_passes(
- self.nng.get_root_subgraph(), mem_area, mem_type_set, lr_graph, Tensor.AllocationQuantum,
+ self.nng.get_root_subgraph(),
+ mem_area,
+ mem_type_set,
+ lr_graph,
+ Tensor.AllocationQuantum,
)
# Populate time-array with memory used by live ranges
@@ -923,7 +949,11 @@
return best_schedule
def optimize_schedule(
- self, schedule: Schedule, max_sched: Schedule, max_template: Schedule, options: SchedulerOptions,
+ self,
+ schedule: Schedule,
+ max_sched: Schedule,
+ max_template: Schedule,
+ options: SchedulerOptions,
) -> Schedule:
"""Extracts sub-schedules based on the cascades and optimizes them and applies them to the final schedule"""
sram_limit = options.optimization_sram_limit
@@ -994,7 +1024,11 @@
lr_graph = live_range.LiveRangeGraph()
for mem_area, mem_type_set in memories_list:
live_range.extract_live_ranges_from_cascaded_passes(
- self.nng.get_root_subgraph(), mem_area, mem_type_set, lr_graph, Tensor.AllocationQuantum,
+ self.nng.get_root_subgraph(),
+ mem_area,
+ mem_type_set,
+ lr_graph,
+ Tensor.AllocationQuantum,
)
max_mem_usage = lr_graph.get_temporal_memory_usage(fast_storage_mem_area)
@@ -1252,7 +1286,14 @@
cascaded_passes = []
for idx, ps in enumerate(sg.passes):
cps = CascadedPass(
- ps.name, SchedulingStrategy.WeightStream, ps.inputs, [], ps.outputs, [ps], ps.placement, False,
+ ps.name,
+ SchedulingStrategy.WeightStream,
+ ps.inputs,
+ [],
+ ps.outputs,
+ [ps],
+ ps.placement,
+ False,
)
cps.time = idx