Main conformance: Add local bound flag for convolutions

Adds a local_bound flag to convolution operations.

If the local_bound flag is true then the output accuracy
for floating-point is measured relative to the
input tensor data local to the particular convolution output.

If the local_bound flag is false then the output accuracy
for floating-point is measured relative to the whole input tensor.
This accuracy measure is the default and more approapriate for
implementations with non local optimizations (such as
transform based fast convolutions).

Signed-off-by: Dominic Symes <dominic.symes@arm.com>
Change-Id: I64e3e4981a63e26e6391149e28d5d71e7ef5560a

1 files changed, 29 insertions, 5 deletions

diff --git a/chapters/appendix_a.adoc b/chapters/appendix_a.adoc
index d4235e8..ba3b6bb 100644
--- a/chapters/appendix_a.adoc
+++ b/chapters/appendix_a.adoc
@@ -37,7 +37,10 @@ This function takes the following arguments:
 
 * S is the test set number which identifies which generator is used
 * KS is the kernel size
-* p is the parameter number of 0 for the first input (usually data) and 1 for the second input (usually weights)
+* p is the parameter number of:
+** 0 for the first input (usually data)
+** 1 for the second input (usually weights)
+** 2 for the third input if present (usually bias)
 * k is the index within the kernel in the range 0 \<= k < KS
 * i is the index within the tensor to write
 
@@ -61,6 +64,7 @@ The aim of this generator is to check that sum of products with zero gives zero
 | p | tosa_mi_data(S, KS, p, k, i) =
 | 0 | set_data(2*S, i) < 0 ? 0.0 : set_data(2*S+1, i)
 | 1 | set_data(2*S, i) < 0 ? set_data(2*S+1, i) : 0.0
+| 2 | 0.0
 |===
 
 ==== Test set S=1
@@ -70,8 +74,9 @@ The aim of this test set is to check values with large exponents.
 [cols="1,9"]
 |===
 | p | tosa_mi_data(S, KS, p, k, i) =
-| 0 | (B/sqrt(KS))*(0.75 + 0.25*set_data(2*S+0, i))
-| 1 | (B/sqrt(KS))*(0.75 + 0.25*set_data(2*S+1, i))
+| 0 | (B/sqrt(KS+1))*(0.75 + 0.25*set_data(3*S+0, i))
+| 1 | (B/sqrt(KS+1))*(0.75 + 0.25*set_data(3*S+1, i))
+| 2 | (B*B/(KS+1))*(0.75 + 0.25*set_data(3*S+2, i))
 |===
 
 ==== Test set S=2
@@ -85,6 +90,7 @@ If the implementation changes the order of the sum, then the test data must also
 | p | tosa_mi_data(S, KS, p, k, i) =
 | 0 | (k==0) ? 1.0 : set_data(2*S+0, i)/sqrt(KS)
 | 1 | (k==0) ? 1.0 : set_data(2*S+1, i)/sqrt(KS)
+| 2 | 0.0
 |===
 
 ==== Test set S=3
@@ -98,6 +104,7 @@ If the implementation changes the order of the sum, then the test data must also
 | p | tosa_mi_data(S, KS, p, k, i) =
 | 0 | (k==0) ? 16.0 : exp(2*set_data(2*S+0, 2*i+0)) * set_data(2*S+0, 2*i+1)
 | 1 | (k==0) ? 16.0 : exp(2*set_data(2*S+1, 2*i+0)) * set_data(2*S+1, 2*i+1)
+| 2 | 0.0
 |===
 
 ==== Test set S=4
@@ -109,6 +116,7 @@ The aim of this test set is to check a mixture of zero and non-zero products.
 | p | tosa_mi_data(S, KS, p, k, i) =
 | 0 | (k==KS/2) ? +0.5 : (set_data(2*S, i) < 0 ? 0.0 : (B/sqrt(KS))*set_data(2*S+1, i))
 | 1 | (k==KS/2) ? -0.5 : (set_data(2*S, i) < 0 ? (B/sqrt(KS))*set_data(2*S+1, i) : 0.0)
+| 2 | 0.0
 |===
 
 ==== Test set S=5
@@ -118,8 +126,9 @@ The aim of this test set is to check signed inputs of large range.
 [cols="1,9"]
 |===
 | p | tosa_mi_data(S, KS, p, k, i) =
-| 0 | (B/sqrt(KS))*set_data(2*S+0, i)
-| 1 | (B/sqrt(KS))*set_data(2*S+1, i)
+| 0 | (B/sqrt(KS+1))*set_data(3*S+0, i)
+| 1 | (B/sqrt(KS+1))*set_data(3*S+1, i)
+| 2 | (B*B/(KS+1))*set_data(3*S+2, i)
 |===
 
 === Main Inference operator test data
@@ -145,6 +154,9 @@ for (0 <= n < N, 0 <= iy < IH, 0 <= ix < IW, 0 <= ic < IC) {
 for (0 <= oc < OC, 0 <= ky < KH, 0 <= kx < KW, 0 <= ic < IC) {
   weight[oc, ky, kx, ic] = tosa_mi_data(S, KS, 1, (ky*KW+kx)*IC+ic, ((oc*KH+ky)*KW+kx)*IC+ic);
 }
+for (0 <= oc < OC) {
+  bias[oc] = tosa_mi_data(S, KS, 2, oc)
+}
 ----
 
 ==== CONV3D
@@ -162,6 +174,9 @@ for (0 <= n < N, 0 <= id < UD, 0 <= iy < IH, 0 <= ix < IW, 0 <= ic < IC) {
 for (0 <= oc < OC, 0 <= kd < KD, 0 <= ky < KH, 0 <= kx < KW, 0 <= ic < IC) {
   weight[oc, kd, ky, kx, ic] = tosa_mi_data(S, KS, 1, ((kd*KH+ky)*KW+kx)*IC+ic, (((oc*KD+kd)*KH+ky)*KW+kx)*IC+ic);
 }
+for (0 <= oc < OC) {
+  bias[oc] = tosa_mi_data(S, KS, 2, oc)
+}
 ----
 
 ==== DEPTHWISE_CONV2D
@@ -179,6 +194,9 @@ for (0 <= n < N, 0 <= iy < IH, 0 <= ix < IW, 0 <= c < C) {
 for (0 <= ky < KH, 0 <= kx < KW, 0 <= c < C, 0 <= m < M) {
   weight[ky, kx,  c, m] = tosa_mi_data(S, KS, 1, (ky*KW+kx), ((ky*KW+kx)*C+c)*M+m);
 }
+for (0 <= oc < C*M) {
+  bias[oc] = tosa_mi_data(S, KS, 2, oc)
+}
 ----
 
 ==== FULLY_CONNECTED
@@ -196,6 +214,9 @@ for (0 <= n < N, 0 <= ic < IC) {
 for (0 <= oc < OC, 0 <= ic < IC) {
   weight[oc, ic] = tosa_mi_data(S, KS, 1, ic, oc*IC+ic);
 }
+for (0 <= oc < OC) {
+  bias[oc] = tosa_mi_data(S, KS, 2, oc)
+}
 ----
 
 ==== MATMUL
@@ -230,6 +251,9 @@ for (0 <= n < N, 0 <= iy < IH, 0 <= ix < IW, 0 <= ic < IC) {
 for (0 <= oc < OC, 0 <= ky < KH, 0 <= kx < KW, 0 <= ic < IC) {
   weight[oc, ky, kx, ic] = tosa_mi_data(S, KS, 1, (ky*KW+kx)*IC+ic, ((oc*KH+ky)*KW+kx)*IC+ic);
 }
+for (0 <= oc < OC) {
+  bias[oc] = tosa_mi_data(S, KS, 2, oc)
+}
 ----
 
 ==== FFT2D