Remove aint8 type

The aint8 type represented an asymmetrically quantized range.
With the change to move scaling fully into the RESCALE operator,
the aint8 and int8 types didn't have significant differences in
their use with operators. Unifying to a single data type makes
things simpler.

Signed-off-by: Eric Kunze <eric.kunze@arm.com>
Change-Id: I3ee89cbdafdc61293ef1f1bec52398d744e716a1

1 files changed, 46 insertions, 45 deletions

diff --git a/chapters/tensor_ops.adoc b/chapters/tensor_ops.adoc
index 7d84ae6..76f39ca 100644
--- a/chapters/tensor_ops.adoc
+++ b/chapters/tensor_ops.adoc
@@ -1,7 +1,7 @@
 //
 // This confidential and proprietary software may be used only as
 // authorised by a licensing agreement from ARM Limited
-// (C) COPYRIGHT 2020 ARM Limited
+// (C) COPYRIGHT 2020-2021 ARM Limited
 // ALL RIGHTS RESERVED
 // The entire notice above must be reproduced on all authorised
 // copies and copies may only be made to the extent permitted
@@ -53,7 +53,7 @@ for_each ( left_index in left_shape, right_index in right_shape )
 |===
 |Profile|Mode|in_t|out_t
 
-|Any|signed 8|aint8|int32
+|Any|signed 8|int8|int32
 |Any|signed 16|int16|int32
 |MI, MT|float|float|int32
 |===
@@ -87,15 +87,15 @@ This performs an average pooling over the given input tensor. A sliding window o
 
 [source,c]
 ----
-assert(in_t == aint8_t || input_zp == 0) // Zero point only for asymmetric int8
-assert(out_t == aint8_t || output_zp == 0) // Zero point only for asymmetric int8
+assert(in_t == int8_t || input_zp == 0) // Zero point only for int8
+assert(out_t == int8_t || output_zp == 0) // Zero point only for int8
 pad=concat([0,0],pad,[0,0])
-for_each ( 0<=n<N, 0<=oy<H, 0<=ox<W, 0<=c<C ) {
+for_each ( 0 <= n < N, 0 <= oy < H, 0 <= ox < W, 0 <= c < C ) {
     acc_t acc = 0;
     int count = 0;
     iy = oy * stride_y - pad_top
     ix = ox * stride_x - pad_left
-    for_each ( 0<=ky<kernel_y, 0<=kx<kernel_x) {
+    for_each ( 0 <= ky < kernel_y, 0 <= kx < kernel_x) {
         y = iy + ky
         x = ix + kx
         in_t value  = tensor_read<in_t>(input, [N,IH,IW,IC], [n,y,x,c], input_zp, pad)
@@ -117,7 +117,7 @@ for_each ( 0<=n<N, 0<=oy<H, 0<=ox<W, 0<=c<C ) {
 |===
 |Profile|Mode|in_t|acc_t|out_t
 
-|Any|signed 8|aint8|int32_t|aint8
+|Any|signed 8|int8|int32_t|int8
 |Any|signed 16|int16|int32_t|int16
 |MI, MT|float|float|float|float
 |===
@@ -153,14 +153,14 @@ Performs a 2D convolution over the given tensor input, using the weight tensor.
 
 [source,c]
 ----
-assert(in_t == aint8_t || input_zp == 0) // Zero point only for asymmetric int8
-assert(weight_t == aint8_t || weight_zp == 0)
-pad=concat([0,0],pad,[0,0])
-for_each (0<=n<N, 0<=oy<H, 0<=ox<W; 0<=oc<OC) {
+assert(in_t == int8_t || input_zp == 0) // Zero point only for int8
+assert(weight_t == int8_t || weight_zp == 0)
+pad=concat([0,0], pad, [0,0])
+for_each (0 <= n < N, 0 <= oy < H, 0 <= ox < W; 0 <= oc < OC) {
     acc_t acc = 0
     iy = oy * stride_y - pad_top
     ix = ox * stride_x - pad_left
-    for_each (0<=ky<KH, 0<=kx<KW, 0<=ic<IC) {
+    for_each (0 <= ky < KH, 0 <= kx < KW, 0 <= ic < IC) {
         y = iy + ky * dilation_y
         x = ix + kx * dilation_x
         in_t value  = tensor_read<in_t>(input, [N,IH,IW,IC], [n,y,x,ic], input_zp, pad)
@@ -177,8 +177,8 @@ for_each (0<=n<N, 0<=oy<H, 0<=ox<W; 0<=oc<OC) {
 |===
 |Profile|Mode|in_t|weight_t|acc_t
 
-|Any|signed 8x8|aint8|int8,aint8|int32
-|Any|signed 8x4|aint8|int4|int32
+|Any|signed 8x8|int8|int8|int32
+|Any|signed 8x4|int8|int4|int32
 |Any|signed 16x8|int16|int8|int48
 |MI, MT|float|float|float|float
 |===
@@ -214,15 +214,15 @@ Performs a 3D convolution over the given input tensor.
 
 [source,c]
 ----
-assert(in_t == aint8_t || input_zp == 0) // Zero point only for asymmetric int8
-assert(weight_t == aint8_t || weight_zp == 0)
-pad=concat([0,0],pad,[0,0])
-for_each (0<=n<N, 0<=od<D, 0<=oy<H, 0<=ox<W; 0<=oc<OC) {
+assert(in_t == int8_t || input_zp == 0) // Zero point only for int8
+assert(weight_t == int8_t || weight_zp == 0)
+pad=concat([0,0], pad, [0,0])
+for_each (0 <= n < N, 0 <= od < D, 0 <= oy < H, 0 <= ox < W; 0 <= oc < OC) {
     acc_t acc = 0
     id = od * stride_d - pad_d0
     iy = oy * stride_y - pad_top
     ix = ox * stride_x - pad_left
-    for_each (0<=kd<KD, 0<=ky<KH, 0<=kx<KW, 0<=ic<IC) {
+    for_each (0 <= kd < KD, 0 <= ky < KH, 0 <= kx < KW, 0 <= ic < IC) {
         d = id + kd * dilation_d
         y = iy + ky * dilation_y
         x = ix + kx * dilation_x
@@ -240,8 +240,8 @@ for_each (0<=n<N, 0<=od<D, 0<=oy<H, 0<=ox<W; 0<=oc<OC) {
 |===
 |Profile|Mode|in_t|weight_t|acc_t
 
-|Any|signed 8x8|aint8|int8,aint8|int32
-|Any|signed 8x4|aint8|int4|int32
+|Any|signed 8x8|int8|int8|int32
+|Any|signed 8x4|int8|int4|int32
 |Any|signed 16x8|int16|int8|int48
 |MI, MT|float|float|float|float
 |===
@@ -278,14 +278,14 @@ Performs 2D convolutions separately over each channel of the given tensor input,
 
 [source,c]
 ----
-assert(in_t==aint8_t || input_zp==0) // Zero point only for asymmetric int8
-assert(weight_t==aint8_t || weight_zp==0)
-pad=concat([0,0],pad,[0,0])
+assert(in_t == int8_t || input_zp == 0) // Zero point only for int8
+assert(weight_t == int8_t || weight_zp == 0)
+pad=concat([0,0], pad, [0,0])
 for_each (0 <= n<N, 0 <= oy < H, 0 <= ox < W; 0 <= c < (C * M), 0 <= m < M) {
     acc_t acc = 0
     iy = oy * stride_y - pad_top
     ix = ox * stride_x - pad_left
-    for_each (0<=ky<KH, 0<=kx<KW) {
+    for_each (0 <= ky < KH, 0 <= kx < KW) {
         y = iy + ky * dilation_y
         x = ix + kx * dilation_x
         in_t value  = tensor_read<in_t>(input, [N,H,W,C], [n,y,x,c], input_zp, pad)
@@ -302,8 +302,8 @@ for_each (0 <= n<N, 0 <= oy < H, 0 <= ox < W; 0 <= c < (C * M), 0 <= m < M) {
 |===
 |Profile|Mode|in_t|weight_t|acc_t
 
-|Any|signed 8x8|aint8|int8,aint8|int32
-|Any|signed 8x4|aint8|int4|int32
+|Any|signed 8x8|int8|int8|int32
+|Any|signed 8x4|int8|int4|int32
 |Any|signed 16x8|int16|int8|int48
 |MI, MT|float|float|float|float
 |===
@@ -336,11 +336,11 @@ Performs a fully connected network.
 
 [source,c]
 ----
-assert(in_t == aint8_t || input_zp == 0) // Zero point only for asymmetric int8
-assert(weight_t == aint8_t || weight_zp == 0)
-for_each (0<=n<N, 0<=oc<OC) {
+assert(in_t == int8_t || input_zp == 0) // Zero point only for int8
+assert(weight_t == int8_t || weight_zp == 0)
+for_each (0 <= n < N, 0 <= oc < OC) {
     acc_t acc = 0
-    for_each (0<=ic<IC) {
+    for_each (0 <= ic < IC) {
         in_t value  = tensor_read<in_t>(input, [N,IC], [n,ic], input_zp)
         weight_t weight = tensor_read<weight_t>(weight, [OC,IC], [oc,ic], weight_zp)
         acc = apply_add<acc_t>(acc, value * weight)
@@ -355,8 +355,8 @@ for_each (0<=n<N, 0<=oc<OC) {
 |===
 |Profile|Mode|in_t|weight_t|acc_t
 
-|Any|signed 8x8|aint8|int8,aint8|int32
-|Any|signed 8x4|aint8|int4|int32
+|Any|signed 8x8|int8|int8|int32
+|Any|signed 8x4|int8|int4|int32
 |Any|signed 16x8 |int16|int8|int48
 |MI, MT|float|float|float|float
 |===
@@ -387,10 +387,10 @@ Performs a two dimensional matrix multiplication. This allows both inputs to be
 
 [source,c]
 ----
-assert(in_t==aint8_t || (A_zp==0 && B_zp==0)) // Zero point only for asymmetric int8
-for_each (0<=m<M, 0<=n<N) {
+assert(in_t == int8_t || (A_zp == 0 && B_zp == 0)) // Zero point only for int8
+for_each (0 <= m < M, 0 <= n < N) {
     acc_t acc = 0
-    for_each (0<=k<K) {
+    for_each (0 <= k < K) {
         in_t value1 = tensor_read<in_t>(A, [M,K], [m,k], A_zp)
         in_t value2 = tensor_read<in_t>(B, [K,N], [k,n], B_zp)
         acc = apply_add<acc_t>(acc, value1 * value2)
@@ -404,7 +404,7 @@ for_each (0<=m<M, 0<=n<N) {
 |===
 |Profile|Mode|in_t|acc_t
 
-|Any|signed 8x8|aint8|int32
+|Any|signed 8x8|int8|int32
 |Any|signed 16x16|int16|int48
 |MI, MT|float|float|float
 |===
@@ -432,8 +432,8 @@ None
 
 [source,c]
 ----
-pad=concat([0,0],pad,[0,0])
-for_each ( 0<=n<N, 0<=oy<H, 0<=ox<W, 0<=c<C ) {
+pad=concat([0,0], pad, [0,0])
+for_each (0 <= n < N, 0 <= oy < H, 0 <= ox < W, 0 <= c < C ) {
     in_t acc = minimum_value<in_t>;
     iy = oy * stride_y - pad_top
     ix = ox * stride_x - pad_left
@@ -452,7 +452,7 @@ for_each ( 0<=n<N, 0<=oy<H, 0<=ox<W, 0<=c<C ) {
 |===
 |Profile|Mode|in_t|out_t
 
-|Any|signed 8|aint8|aint8
+|Any|signed 8|int8|int8
 |Any|16-bit|int16|int16
 |MI, MT|float|float|float
 |===
@@ -488,12 +488,13 @@ Performs a 2D transposed convolution over the given tensor input, using the weig
 
 [source,c]
 ----
-assert(in_t==aint8_t  || input_zp==0) // Zero point only for asymmetric int8
-assert(weight_t == aint8_t || weight_zp == 0)
+assert(in_t == int8_t  || input_zp == 0) // Zero point only allowed for int8
+assert(weight_t == int8_t || weight_zp == 0)
 for_each (index in out_shape) {
     tensor_write<acc_t>(output, [N,OH,OW,OC], index, bias[index[3]])
 }
-for_each (0<=n<N, 0<=iy<IH, 0<=ix<IW, 0<=oc<OC, 0<=ic<IC, 0<=ky<KH,  0<=kx<KW) {
+for_each (0 <= n < N, 0 <= iy < IH, 0 <= ix < IW, 0 <= oc < OC,
+          0 <= ic < IC, 0 <= ky < KH,  0 <= kx < KW) {
     oy = iy * stride_y - out_pad_top  + ky
     ox = ix * stride_x - out_pad_left + kx
     if (oy>=0 && oy<OH && ox>=0 && ox<OW) {
@@ -511,8 +512,8 @@ for_each (0<=n<N, 0<=iy<IH, 0<=ix<IW, 0<=oc<OC, 0<=ic<IC, 0<=ky<KH,  0<=kx<KW) {
 |===
 |Profile|Mode|in_t|weight_t|acc_t
 
-|Any|signed 8x8|aint8|int8,aint8|int32
-|Any|signed 8x4|aint8|int4|int32
+|Any|signed 8x8|int8|int8|int32
+|Any|signed 8x4|int8|int4|int32
 |Any|signed 16x8|int16|int8|int48
 |MI, MT|float|float|float|float
 |===