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-rw-r--r--chapters/tensor_ops.adoc293
1 files changed, 12 insertions, 281 deletions
diff --git a/chapters/tensor_ops.adoc b/chapters/tensor_ops.adoc
index b3de433..3de5150 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 2020-2023 ARM Limited
+// (C) COPYRIGHT 2020-2024 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
@@ -17,34 +17,7 @@ include::{generated}/operators/ARGMAX.adoc[]
[source,c++]
----
-ERROR_IF(axis < 0 || axis >= rank(shape1));
-if (axis == 0) {
- left_shape = [];
-} else {
- left_shape = shape1[0:axis - 1];
-}
-if (axis == rank(shape1)-1) {
- right_shape = [];
-} else {
- right_shape = shape1[axis+1:rank(shape1) - 1];
-}
-ERROR_IF(flatten(left_shape, right_shape) != shape);
-for_each(left_index in left_shape) {
- for_each(right_index in right_shape) {
- in_t max_value = minimum_s<in_t>;
- out_t max_index = 0;
- for (i = 0; i < shape[axis]; i++) {
- dim_t index = flatten(left_index, [i], right_index);
- in_t value = tensor_read<in_t>(input, shape1, index);
- if (apply_max_s<in_t>(value, max_value) != max_value) {
- max_value = value;
- max_index = i;
- }
- }
- dim_t index = flatten(left_index, right_index);
- tensor_write<out_t>(output, shape, index, max_index);
- }
-}
+include::{pseudocode}/operators/ARGMAX.tosac[lines=10..-1]
----
==== AVG_POOL2D
@@ -57,47 +30,7 @@ include::{generated}/operators/AVG_POOL2D.adoc[]
[source,c++]
----
-ERROR_IF(in_out_t != i8_t && input_zp != 0); // Zero point only for int8_t
-ERROR_IF(in_out_t != i8_t && output_zp != 0); // Zero point only for int8_t
-ERROR_IF(kernel_y < 1 || kernel_x < 1); // kernel size must be >= 1
-ERROR_IF(stride_y < 1 || stride_x < 1);
-ERROR_IF(pad_top < 0 || pad_bottom < 0 || pad_left < 0 || pad_right < 0);
-// Padding must be less than kernel size to avoid
-// a divide-by-zero.
-ERROR_IF(pad_right >= kernel_x || pad_left >= kernel_x);
-ERROR_IF(pad_top >= kernel_y || pad_bottom >= kernel_y);
-ERROR_IF(OH != idiv_check(IH + pad_top + pad_bottom - kernel_y, stride_y) + 1);
-ERROR_IF(OW != idiv_check(IW + pad_left + pad_right - kernel_x, stride_x) + 1);
-
-for_each(0 <= n < N, 0 <= oy < OH, 0 <= ox < OW, 0 <= c < C ) {
- in_out_t output_val;
- acc_t acc = 0;
- int count = 0;
- index_t iy = oy * stride_y - pad_top;
- index_t ix = ox * stride_x - pad_left;
- for_each(0 <= ky < kernel_y, 0 <= kx < kernel_x) {
- index_t y = iy + ky;
- index_t x = ix + kx;
- // Only values from the input tensor are used to calculate the
- // average, padding does not count
- if (0 <= y < IH and 0 <= x < IW) {
- count++;
- acc_t value = sign_extend<acc_t>(tensor_read<in_out_t>(input, [N,IH,IW,C], [n,y,x,c]));
- value = apply_sub_s<acc_t>(value, sign_extend<acc_t>(input_zp));
- acc = apply_add_s<acc_t>(acc, value);
- }
- }
- if (is_float(in_out_t)) {
- output_val = acc / static_cast<in_out_t>(count);
- } else {
- scale_t scale = reciprocal_scale(count);
- acc = apply_scale_32(acc, scale.multiplier, scale.shift, false);
- acc = apply_add_s<acc_t>(acc, sign_extend<acc_t>(output_zp));
- acc = apply_clip_s<acc_t>(acc, minimum_s<in_out_t>, maximum_s<in_out_t>);
- output_val = static_cast<in_out_t>(acc);
- }
- tensor_write<in_out_t>(output, [N,OH,OW,C], [n,oy,ox,c], output_val);
-}
+include::{pseudocode}/operators/AVG_POOL2D.tosac[lines=10..-1]
----
==== CONV2D
@@ -108,37 +41,7 @@ include::{generated}/operators/CONV2D.adoc[]
[source,c++]
----
-ERROR_IF(in_t != i8_t && input_zp != 0); // Zero point only for int8_t
-ERROR_IF(weight_t != int8_t && weight_zp != 0);
-ERROR_IF(pad_top < 0 || pad_bottom < 0 || pad_left < 0 || pad_right < 0);
-ERROR_IF(stride_y < 1 || stride_x < 1);
-ERROR_IF(dilation_y < 1 || dilation_x < 1);
-ERROR_IF(OH != idiv_check(IH - 1 + pad_top + pad_bottom - (KH - 1) * dilation_y, stride_y) + 1);
-ERROR_IF(OW != idiv_check(IW - 1 + pad_left + pad_right - (KW - 1) * dilation_x, stride_x) + 1);
-ERROR_IF(BC != OC && BC != 1);
-
-for_each(0 <= n < N, 0 <= oy < OH, 0 <= ox < OW; 0 <= oc < OC) {
- out_t acc = 0;
- index_t iy = oy * stride_y - pad_top;
- index_t ix = ox * stride_x - pad_left;
- for_each(0 <= ky < KH, 0 <= kx < KW, 0 <= ic < IC) {
- index_t y = iy + ky * dilation_y;
- index_t x = ix + kx * dilation_x;
- if (0 <= y < IH && 0 <= x < IW) {
- out_t value = static_cast<out_t>(tensor_read<in_t>(input,
- [N,IH,IW,IC],
- [n,y,x,ic]));
- out_t weight = static_cast<out_t>(tensor_read<weight_t>(weight,
- [OC,KH,KW,IC],
- [oc,ky,kx,ic]));
- value = apply_sub_s<out_t>(value, static_cast<out_t>(input_zp));
- weight = apply_sub_s<out_t>(weight, static_cast<out_t>(weight_zp));
- acc = apply_add_s<out_t>(acc, apply_mul_s<out_t>(value, weight));
- }
- }
- acc = apply_add_s<out_t>(acc, bias[(BC == 1) ? 0 : oc]);
- tensor_write<out_t>(output, [N,OH,OW,OC], [n,oy,ox,oc], acc);
-}
+include::{pseudocode}/operators/CONV2D.tosac[lines=10..-1]
----
==== CONV3D
@@ -149,40 +52,7 @@ include::{generated}/operators/CONV3D.adoc[]
[source,c++]
----
-ERROR_IF(in_t != i8_t && input_zp != 0); // Zero point only for int8_t
-ERROR_IF(weight_t != i8_t && weight_zp != 0);
-ERROR_IF(pad_d0 < 0 || pad_d1 < 0 || pad_top < 0 || pad_bottom < 0 || pad_left < 0 || pad_right < 0);
-ERROR_IF(stride_d < 1 || stride_y < 1 || stride_x < 1);
-ERROR_IF(dilation_d < 1 || dilation_y < 1 || dilation_x < 1);
-ERROR_IF(OD != idiv_check(ID - 1 + pad_d0 + pad_d1 - (KD - 1) * dilation_d, stride_d) + 1);
-ERROR_IF(OH != idiv_check(IH - 1 + pad_top + pad_bottom - (KH - 1) * dilation_y, stride_y) + 1);
-ERROR_IF(OW != idiv_check(IW - 1 + pad_left + pad_right - (KW - 1) * dilation_x, stride_x) + 1);
-ERROR_IF(BC != OC && BC != 1);
-
-for_each(0 <= n < N, 0 <= od < OD, 0 <= oy < OH, 0 <= ox < OW; 0 <= oc < OC) {
- out_t acc = 0;
- index_t id = od * stride_d - pad_d0;
- index_t iy = oy * stride_y - pad_top;
- index_t ix = ox * stride_x - pad_left;
- for_each(0 <= kd < KD, 0 <= ky < KH, 0 <= kx < KW, 0 <= ic < IC) {
- index_t d = id + kd * dilation_d;
- index_t y = iy + ky * dilation_y;
- index_t x = ix + kx * dilation_x;
- if (0 <= x < IW && 0 <= y < IH && 0 <= d < ID) {
- out_t value = static_cast<out_t>(tensor_read<in_t>(input,
- [N,ID,IH,IW,IC],
- [n,d,y,x,ic]));
- out_t weight = static_cast<out_t>(tensor_read<weight_t>(weight,
- [OC,KD,KH,KW,IC],
- [oc,kd,ky,kx,ic]));
- value = apply_sub_s<out_t>(value, static_cast<out_t>(input_zp));
- weight = apply_sub_s<out_t>(weight, static_cast<out_t>(weight_zp));
- acc = apply_add_s<out_t>(acc, apply_mul_s<out_t>(value, weight));
- }
- }
- acc = apply_add_s<out_t>(acc, bias[(BC == 1) ? 0 : oc]);
- tensor_write<out_t>(output, [N,OD,OH,OW,OC], [n,od,oy,ox,oc], acc);
-}
+include::{pseudocode}/operators/CONV3D.tosac[lines=10..-1]
----
==== DEPTHWISE_CONV2D
@@ -193,37 +63,7 @@ include::{generated}/operators/DEPTHWISE_CONV2D.adoc[]
[source,c++]
----
-ERROR_IF(in_t != i8_t && input_zp != 0); // Zero point only for int8_t
-ERROR_IF(weight_t != i8_t && weight_zp != 0);
-ERROR_IF(pad_top < 0 || pad_bottom < 0 || pad_left < 0 || pad_right < 0);
-ERROR_IF(stride_y < 1 || stride_x < 1);
-ERROR_IF(dilation_y < 1 || dilation_x < 1);
-ERROR_IF(OH != idiv_check(IH - 1 + pad_top + pad_bottom - (KH - 1) * dilation_y, stride_y) + 1);
-ERROR_IF(OW != idiv_check(IW - 1 + pad_left + pad_right - (KW - 1) * dilation_x, stride_x) + 1);
-ERROR_IF(BC != C*M && BC != 1);
-
-for_each(0 <= n < N, 0 <= oy < OH, 0 <= ox < OW; 0 <= c < C, 0 <= m < M) {
- out_t acc = 0;
- index_t iy = oy * stride_y - pad_top;
- index_t ix = ox * stride_x - pad_left;
- for_each(0 <= ky < KH, 0 <= kx < KW) {
- index_t y = iy + ky * dilation_y;
- index_t x = ix + kx * dilation_x;
- if (0 <= y < IH && 0 <= x < IW) {
- out_t value = static_cast<out_t>(tensor_read<in_t>(input,
- [N,IH,IW,C],
- [n,y,x,c]));
- out_t weight = static_cast<out_t>(tensor_read<weight_t>(weight,
- [KH,KW,C,M],
- [ky,kx,c,m]));
- value = apply_sub_s<out_t>(value, static_cast<out_t>input_zp);
- weight = apply_sub_s<out_t>(weight, static_cast<out_t>weight_zp);
- acc = apply_add_s<out_t>(acc, apply_mul_s<out_t>(value, weight));
- }
- }
- acc = apply_add_s<out_t>(acc, bias[(BC == 1) ? 0 : (c * M) + m]);
- tensor_write<out_t>(output, [N,OH,OW,C * M], [n,oy,ox,c * M + m], acc);
-}
+include::{pseudocode}/operators/DEPTHWISE_CONV2D.tosac[lines=10..-1]
----
==== FFT2D
@@ -247,28 +87,7 @@ include::{generated}/operators/FFT2D.adoc[]
[source,c++]
----
-ERROR_IF(!power_of_two(H));
-ERROR_IF(!power_of_two(W));
-
-float sign_val = 1.0;
-
-if (inverse) {
- sign_val = -1.0;
-}
-
-for_each(0 <= n < N, 0 <= oy < H, 0 <= ox < W) {
- in_out_t sum_real = 0.0;
- in_out_t sum_imag = 0.0;
- for_each(0 <= iy < H, 0 <= ix < W) {
- in_out_t val_real = tensor_read<in_out_t>(input_real, [N,H,W], [n,iy,ix]);
- in_out_t val_imag = tensor_read<in_out_t>(input_imag, [N,H,W], [n,iy,ix]);
- float_t a = sign_val * 2 * pi() * ((iy * oy) / H + (ix * ox) / W);
- sum_real += val_real * cos(a) + val_imag * sin(a);
- sum_imag += -val_real * sin(a) + val_imag * cos(a);
- }
- tensor_write<in_out_t>(output_real, [N,H,W], [n,oy,ox], sum_real);
- tensor_write<in_out_t>(output_imag, [N,H,W], [n,oy,ox], sum_imag);
-}
+include::{pseudocode}/operators/FFT2D.tosac[lines=10..-1]
----
==== FULLY_CONNECTED
@@ -279,22 +98,7 @@ include::{generated}/operators/FULLY_CONNECTED.adoc[]
[source,c++]
----
-ERROR_IF(in_t != i8_t && input_zp != 0); // Zero point only for int8_t
-ERROR_IF(weight_t != i8_t && weight_zp != 0);
-ERROR_IF(BC != OC && BC != 1);
-
-for_each(0 <= n < N, 0 <= oc < OC) {
- out_t acc = 0;
- for_each(0 <= ic < IC) {
- out_t value = static_cast<out_t>(tensor_read<in_t>(input, [N,IC], [n,ic]));
- out_t weight = static_cast<out_t>(tensor_read<weight_t>(weight, [OC,IC], [oc,ic]));
- value = apply_sub_s<out_t>(value, static_cast<out_t>(input_zp));
- weight = apply_sub_s<out_t>(weight, static_cast<out_t>(weight_zp));
- acc = apply_add_s<out_t>(acc, apply_mul_s<out_t>(value, weight));
- }
- acc = apply_add_s<out_t>(acc, bias[(BC == 1) ? 0 : oc]);
- tensor_write<out_t>(output, [N,OC], [n,oc], acc);
-}
+include::{pseudocode}/operators/FULLY_CONNECTED.tosac[lines=10..-1]
----
==== MATMUL
@@ -305,18 +109,7 @@ include::{generated}/operators/MATMUL.adoc[]
[source,c++]
----
-ERROR_IF(in_t != i8_t && (A_zp != 0 || B_zp != 0)); // Zero point only for int8_t
-for_each(0 <= n < N, 0 <= h < H, 0 <= w < W) {
- out_t acc = 0;
- for_each(0 <= c < C) {
- out_t value1 = static_cast<out_t>(tensor_read<in_t>(A, [N,H,C], [n,h,c]));
- out_t value2 = static_cast<out_t>(tensor_read<in_t>(B, [N,C,W], [n,c,w]));
- value1 = apply_sub_s<out_t>(value1, static_cast<out_t>(A_zp));
- value2 = apply_sub_s<out_t>(value2, static_cast<out_t>(B_zp));
- acc = apply_add_s<out_t>(acc, apply_mul_s<out_t>(value1 * value2));
- }
- tensor_write<out_t>(output, [N,H,W], [n,h,w], acc);
-}
+include::{pseudocode}/operators/MATMUL.tosac[lines=10..-1]
----
==== MAX_POOL2D
@@ -327,30 +120,7 @@ include::{generated}/operators/MAX_POOL2D.adoc[]
[source,c++]
----
-ERROR_IF(kernel_y < 1 || kernel_x < 1); // kernel size must be >= 1
-ERROR_IF(stride_y < 1 || stride_x < 1);
-ERROR_IF(pad_top < 0 || pad_bottom < 0 || pad_left < 0 || pad_right < 0);
-// Padding must be less than kernel size, otherwise no
-// input values will be used.
-ERROR_IF(pad_right >= kernel_x || pad_left >= kernel_x);
-ERROR_IF(pad_top >= kernel_y || pad_bottom >= kernel_y);
-ERROR_IF(OH != idiv_check(IH + pad_top + pad_bottom - kernel_y, stride_y) + 1);
-ERROR_IF(OW != idiv_check(IW + pad_left + pad_right - kernel_x, stride_x) + 1);
-
-for_each(0 <= n < N, 0 <= oy < H, 0 <= ox < W, 0 <= c < C ) {
- in_out_t acc = minimum_value<in_out_t>;
- index_t iy = oy * stride_y - pad_top;
- index_t ix = ox * stride_x - pad_left;
- for_each( 0 <= ky < kernel_y, 0 <= kx < kernel_x ) {
- index_t y = iy + ky;
- index_t x = ix + kx;
- if (y >= 0 && y < IH && x >= 0 && x < IW) {
- in_out_t value = tensor_read<in_out_t>(input, [N,IH,IW,C], [n,y,x,c]);
- acc = apply_max_s<in_out_t>(acc, value);
- }
- }
- tensor_write<in_out_t>(output, [N,OH,OW,C], [n,oy,ox,c], acc);
-}
+include::{pseudocode}/operators/MAX_POOL2D.tosac[lines=10..-1]
----
==== RFFT2D
@@ -368,21 +138,7 @@ include::{generated}/operators/RFFT2D.adoc[]
[source,c++]
----
-ERROR_IF(!power_of_two(H));
-ERROR_IF(!power_of_two(W));
-
-for_each(0 <= n < N, 0 <= oy < H, 0 <= ox < W/2 + 1) {
- in_out_t sum_real = 0.0;
- in_out_t sum_imag = 0.0;
- for_each(0 <= iy < H, 0 <= ix < W) {
- in_out_t val_real = tensor_read<in_out_t>(input_real, [N,H,W], [n,iy,ix]);
- float_t a = 2 * pi() * ((iy * oy) / H + (ix * ox) / W);
- sum_real += val_real * cos(a);
- sum_imag += -val_real * sin(a);
- }
- tensor_write<in_out_t>(output_real, [N,H,W], [n,oy,ox], sum_real);
- tensor_write<in_out_t>(output_imag, [N,H,W], [n,oy,ox], sum_imag);
-}
+include::{pseudocode}/operators/RFFT2D.tosac[lines=10..-1]
----
==== TRANSPOSE_CONV2D
@@ -393,30 +149,5 @@ include::{generated}/operators/TRANSPOSE_CONV2D.adoc[]
[source,c++]
----
-ERROR_IF(in_t != i8_t && input_zp != 0); // Zero point only allowed for int8_t
-ERROR_IF(weight_t != i8_t && weight_zp != 0);
-ERROR_IF(out_pad_top <= -KH || out_pad_bottom <= -KH);
-ERROR_IF(out_pad_left <= -KW || out_pad_right <= -KW);
-ERROR_IF(stride_y < 1 || stride_x < 1);
-ERROR_IF(OH != (IH - 1) * stride_y + out_pad_top + out_pad_bottom + KH);
-ERROR_IF(OW != (IW - 1) * stride_x + out_pad_left + out_pad_right + KW);
-ERROR_IF(BC != OC && BC != 1);
-
-for_each(index in [N, OH, OW, OC]) {
- tensor_write<out_t>(output, [N,OH,OW,OC], index, bias[(BC == 1) ? 0 : 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) {
- index_t oy = iy * stride_y + out_pad_top + ky;
- index_t ox = ix * stride_x + out_pad_left + kx;
- if (oy >= 0 && oy < OH && ox >= 0 && ox < OW) {
- out_t acc = static_cast<out_t>(tensor_read<out_t>(output, [N,OH,OW,OC], [n,oy,ox,oc]));
- out_t value = static_cast<out_t>(tensor_read<in_t>(input, [N,IH,IW,IC], [n,iy,ix,ic]));
- out_t weight = static_cast<out_t>(tensor_read<weight_t>(weight, [OC,KH,KW,IC], [oc,ky,kx,ic]));
- value = apply_sub_s<out_t>(value, static_cast<out_t>(input_zp));
- weight = apply_sub_s<out_t>(weight, static_cast<out_t>(weight_zp));
- acc = apply_add_s<out_t>(acc, apply_mul_s<out_t>(value, weight));
- tensor_write<out_t>(output, [N,OH,OW,OC], [n,oy,ox,oc], acc);
- }
-}
+include::{pseudocode}/operators/TRANSPOSE_CONV2D.tosac[lines=10..-1]
----
generated by cgit v1.2.1 (git 2.23.0) at 2024-05-19 04:24:14 +0000