MLBEDSW-4034: New Scheduler Size or Performance Optimisation
- Merged dev/scheduler at 83639f90e8c828f70de6e29142355a940224959b
Signed-off-by: Tim Hall <tim.hall@arm.com>
Change-Id: I0050529d4b42da93768c7264296434dd877fb5b4
diff --git a/ethosu/vela/operation.py b/ethosu/vela/operation.py
index a5a58e8..6bd955d 100644
--- a/ethosu/vela/operation.py
+++ b/ethosu/vela/operation.py
@@ -53,13 +53,23 @@
Kernel information for NPU operations
"""
- def __init__(self, w: int, h: int, stride_x: int = 1, stride_y: int = 1, dilation_x: int = 1, dilation_y: int = 1):
+ def __init__(
+ self,
+ w: int,
+ h: int,
+ stride_x: int = 1,
+ stride_y: int = 1,
+ dilation_x: int = 1,
+ dilation_y: int = 1,
+ valid_padding=False,
+ ):
assert stride_x > 0 and stride_y > 0
assert dilation_x > 0 and dilation_y > 0
self.width = w
self.height = h
self.stride = PointXY(stride_x, stride_y)
self.dilation = PointXY(dilation_x, dilation_y)
+ self.valid_padding = valid_padding
def elements_wh(self) -> int:
return self.width * self.height
@@ -70,6 +80,9 @@
def area_height(self) -> int:
return (self.height - 1) * self.dilation.y + 1
+ def dilation(self) -> PointXY:
+ return self.dilation
+
def dilated_wh(self) -> Tuple[int, int]:
"""Returns the dilated kernel width/height"""
return self.dilation.x * (self.width - 1) + 1, self.dilation.y * (self.height - 1) + 1
@@ -149,7 +162,6 @@
Cumsum = OperatorInfo()
Custom = OperatorInfo() # Custom 3rd party operator, only used in CPU subgraphs
CustomNpuOp = OperatorInfo() # NPU custom operator, only used in CPU subgraphs
- DMA = OperatorInfo()
Delegate = OperatorInfo()
Densify = OperatorInfo()
DepthToSpace = OperatorInfo()
@@ -422,6 +434,7 @@
"ofm_shapes",
"rescale",
"read_offsets",
+ "read_shapes",
"rounding_mode",
"low_precision_scaling",
"write_offset",
@@ -455,6 +468,7 @@
# (which overrides the ofm tensor's scale)
self.rescale = None
self.read_offsets: List[Shape4D] = [None, None] # offset for [ifm, ifm2]
+ self.read_shapes: List[Shape4D] = [None, None] # read shape for [ifm, ifm2]
self.rounding_mode: Optional[NpuRoundingMode] = None
# The Mean operator (implemented as a depthwise convolution) requires scaling
# to be calculated differently in one case. In that case, this is set to True.
@@ -482,6 +496,7 @@
res.scheduled_pass = self.scheduled_pass
res.op_index = None # not relevant as not part of input network
res.read_offsets = list(self.read_offsets)
+ res.read_shapes = list(self.read_shapes)
res.rounding_mode = self.rounding_mode
res.low_precision_scaling = self.low_precision_scaling
@@ -788,3 +803,13 @@
self.ifm_shapes.append(Shape4D(full_shape(4, ifm2_tensor.shape, 1)))
if ofm_tensor is not None:
self.ofm_shapes.append(Shape4D(full_shape(4, ofm_tensor.shape, 1)))
+
+ def has_scaling(self):
+ scaled = True
+ for tensor in [self.ifm, self.ifm2, self.ofm]:
+ if tensor is not None:
+ if tensor.quantization is None:
+ scaled = False
+ break
+
+ return scaled