vela: Refactor operators to use Kernel objects

 - Normalise kernel availability by requiring all operators offer a kernel
   describing how much data they consume from the source, per OFM element,
   regardless of whether kernels are relevant to the operation.

Signed-off-by: Tim Hall <tim.hall@arm.com>
Change-Id: Idbcff64879fc2eccf292b6208a7d2038eb388017

1 files changed, 42 insertions, 0 deletions

diff --git a/ethosu/vela/operation.py b/ethosu/vela/operation.py
index 6e5b4820..cc52ff4b 100644
--- a/ethosu/vela/operation.py
+++ b/ethosu/vela/operation.py
@@ -18,6 +18,11 @@
 from collections import namedtuple
 from enum import Enum
 
+from .numeric_util import full_shape
+
+PointXY = namedtuple("PointXY", "x y")
+PointXYZ = namedtuple("PointXYZ", "x y z")
+
 
 class NpuBlockType(Enum):
     Default = 0
@@ -29,6 +34,26 @@ class NpuBlockType(Enum):
     ReduceSum = 6
 
 
+class Kernel:
+    def __init__(self, w, h, sx=1, sy=1, dx=1, dy=1):
+        assert sx > 0 and sy > 0
+        assert dx > 0 and dy > 0
+        self.width = w
+        self.height = h
+        self.stride = PointXY(sx, sy)
+        self.dilation = PointXY(dx, dy)
+        self.upscale = 1
+
+    def elements_wh(self):
+        return self.width * self.height
+
+    def area_width(self):
+        return (self.width - 1) * self.dilation.x + 1
+
+    def area_height(self):
+        return (self.height - 1) * self.dilation.y + 1
+
+
 # Classifies operators of type Custom
 class CustomType(Enum):
     ThirdPartyOp = 0  # Third party custom op
@@ -330,6 +355,7 @@ class Operation:
         "memory_function",
         "forced_output_quantization",
         "activation_lut",
+        "_kernel",
     )
 
     def __init__(self, op_type, name):
@@ -350,6 +376,7 @@ class Operation:
         self.scheduled_pass = None
         self.op_index = None  # input network operator index
         self.activation_lut = None
+        self._kernel = None
 
     def clone(self, suffix="_clone"):
         res = Operation(self.type, self.name + suffix)
@@ -372,6 +399,21 @@ class Operation:
 
     __repr__ = __str__
 
+    @property
+    def kernel(self):
+        strides = self.attrs.get("strides", (1, 1, 1, 1))
+        dilation = self.attrs.get("dilation", (1, 1, 1, 1))
+        weights = self.weights
+        if weights and self.type.npu_block_type in (NpuBlockType.ConvolutionDepthWise, NpuBlockType.ConvolutionMxN):
+            weight_shape = full_shape(4, weights.shape, 1)
+            k_h = weight_shape[-4]
+            k_w = weight_shape[-3]
+        else:
+            k_h = self.attrs.get("filter_height", 1)
+            k_w = self.attrs.get("filter_width", 1)
+        self._kernel = Kernel(k_w, k_h, strides[2], strides[1], dilation[2], dilation[1])
+        return self._kernel
+
     def get_ifm_ifm2_weights_ofm(self):
         return self.ifm, self.ifm2, self.weights, self.ofm