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-rw-r--r--chapters/image.adoc4
-rw-r--r--chapters/pseudocode.adoc7
-rw-r--r--chapters/tensor_ops.adoc62
3 files changed, 46 insertions, 27 deletions
diff --git a/chapters/image.adoc b/chapters/image.adoc
index 16e83b5..039595e 100644
--- a/chapters/image.adoc
+++ b/chapters/image.adoc
@@ -67,8 +67,8 @@ input position (IH-1,IW-1).
[source,c++]
----
// Derive the output dimensions from the input dimensions
-OH = floor(((IH-1)*(1<<shift) - offset_y)/stride_y)) + 1 + border_y
-OW = floor(((IW-1)*(1<<shift) - offset_x)/stride_x)) + 1 + border_x
+OH = idiv((IH-1)*(1<<shift) - offset_y, stride_y) + 1 + border_y;
+OW = idiv((IW-1)*(1<<shift) - offset_x, stride_x) + 1 + border_x;
// Ensure the image size is supported by GPU APIs and that for integer
// implementations, position * stride does not overflow int32_t.
ERROR_IF(max(OH,OW,IH,IW) >= 16384);
diff --git a/chapters/pseudocode.adoc b/chapters/pseudocode.adoc
index 238aa33..3f885c7 100644
--- a/chapters/pseudocode.adoc
+++ b/chapters/pseudocode.adoc
@@ -185,6 +185,13 @@ int idiv(int input1, int input2) {
return input1 / input2; // Integer divide that truncates towards zero
}
+// Integer division that checks input1 is a multiple of input2
+
+int idiv_check(int input1, int input2) {
+ ERROR_IF(input1 % input2 != 0); // input1 must be a multiple of input2
+ return input1 / input2; // exact quotient without rounding
+}
+
int length(in_t input)
return number of elements in input list
diff --git a/chapters/tensor_ops.adoc b/chapters/tensor_ops.adoc
index 9a4ab88..7f39e81 100644
--- a/chapters/tensor_ops.adoc
+++ b/chapters/tensor_ops.adoc
@@ -80,13 +80,16 @@ When calculating the average, only the number of valid input tensor values, but
|Attribute|int*|pad|[4]|[pad_top, pad_bottom, pad_left, pad_right]
|Attribute|in_out_t|input_zp|-|Input tensor zero point. Must be zero for non-int8 types.
|Attribute|in_out_t|output_zp|-|Output tensor zero point. Must be zero for non-int8 types.
-|Output|in_out_t*|output|[N,H,W,C]|Output tensor 4D
+|Output|in_out_t*|output|[N,OH,OW,C]|Output tensor 4D
|===
*Operation Function:*
[source,c++]
----
+// Derive output dimensions from input dimensions and padding
+OH = idiv_check(IH + pad_top + pad_bottom - kernel_y, stride_y) + 1;
+OW = idiv_check(IW + pad_left + pad_right - kernel_x, stride_x) + 1;
ERROR_IF(in_out_t != int8_t && input_zp != 0); // Zero point only for int8_t
ERROR_IF(in_out_t != int8_t && output_zp != 0); // Zero point only for int8_t
ERROR_IF(kernel_y < 1 || kernel_x < 1); // kernel size must be >= 1
@@ -96,12 +99,8 @@ ERROR_IF(pad_top < 0 || pad_bottom < 0 || pad_left < 0 || pad_right < 0);
// a divide-by-zero.
ERROR_IF(pad_right >= kernel_x || pad_left >= kernel_x);
ERROR_IF(pad_top >= kernel_y || pad_bottom >= kernel_y);
-// Output shape must match expected shape given the input shape
-// and arguments provided
-ERROR_IF(H != idiv((IH + pad_top + pad_bottom + stride_y - kernel_y), stride_y));
-ERROR_IF(W != idiv((IW + pad_left + pad_right + stride_x - kernel_x), stride_x));
-for_each(0 <= n < N, 0 <= oy < H, 0 <= ox < W, 0 <= c < C ) {
+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;
@@ -126,7 +125,7 @@ for_each(0 <= n < N, 0 <= oy < H, 0 <= ox < W, 0 <= c < C ) {
acc = apply_scale_32(acc, scale.multiplier, scale.shift, false);
output_val = (in_out_t)apply_clip<acc_t>(acc + output_zp, minimum<in_out_t>, maximum<in_out_t>)
}
- tensor_write<in_out_t>(output, [N,H,W,C], [n,oy,ox,c], output_val);
+ tensor_write<in_out_t>(output, [N,OH,OW,C], [n,oy,ox,c], output_val);
}
----
@@ -156,20 +155,23 @@ Performs a 2D convolution over the given tensor input, using the weight tensor.
|Attribute|int*|dilation|[2]|[dilation_y, dilation_x]
|Attribute|in_t|input_zp|-|Input tensor zero point. Must be zero for non-int8 types.
|Attribute|weight_t|weight_zp|-|Weight zero point. Must be zero for non-int8 types.
-|Output|out_t*|output|[N,H,W,OC]|Output tensor
+|Output|out_t*|output|[N,OH,OW,OC]|Output tensor
|===
*Operation Function*
[source,c++]
----
+// Derive output dimensions from input dimensions and padding
+OH = idiv_check(IH-1 + pad_top + pad_bottom - (KH-1)*dilation_y, stride_y) + 1;
+OW = idiv_check(IW-1 + pad_left + pad_right - (KW-1)*dilation_x, stride_x) + 1;
ERROR_IF(in_t != int8_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);
pad = flatten([0,0], pad, [0,0]);
-for_each(0 <= n < N, 0 <= oy < H, 0 <= ox < W; 0 <= oc < OC) {
+for_each(0 <= n < N, 0 <= oy < OH, 0 <= ox < OW; 0 <= oc < OC) {
out_t acc = 0;
iy = oy * stride_y - pad_top;
ix = ox * stride_x - pad_left;
@@ -185,7 +187,7 @@ for_each(0 <= n < N, 0 <= oy < H, 0 <= ox < W; 0 <= oc < OC) {
}
}
acc = apply_add<out_t>(acc, bias[oc]);
- tensor_write<out_t>(output, [N,H,W,OC], [n,oy,ox,oc], acc);
+ tensor_write<out_t>(output, [N,OH,OW,OC], [n,oy,ox,oc], acc);
}
----
@@ -217,20 +219,24 @@ Performs a 3D convolution over the given input tensor.
|Attribute|int*|dilation|[3]|[dilation_d, dilation_y, dilation_x]
|Attribute|in_t|input_zp|-|Input tensor zero point. Must be zero for non-int8 types.
|Attribute|weight_t|weight_zp|-|Weight zero point. Must be zero for non-int8 types.
-|Output|out_t*|output|[N,D,H,W,OC]|Output tensor
+|Output|out_t*|output|[N,OD,OH,OW,OC]|Output tensor
|===
*Operation Function*
[source,c++]
----
+// Derive output dimensions from input dimensions and padding
+OD = idiv_check(ID-1 + pad_d0 + pad_d1 - (KD-1)*dilation_d, stride_d) + 1;
+OH = idiv_check(IH-1 + pad_top + pad_bottom - (KH-1)*dilation_y, stride_y) + 1;
+OW = idiv_check(IW-1 + pad_left + pad_right - (KW-1)*dilation_x, stride_x) + 1;
ERROR_IF(in_t != int8_t && input_zp != 0); // Zero point only for int8_t
ERROR_IF(weight_t != int8_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);
pad = flatten([0,0], pad, [0,0]);
-for_each(0 <= n < N, 0 <= od < D, 0 <= oy < H, 0 <= ox < W; 0 <= oc < OC) {
+for_each(0 <= n < N, 0 <= od < OD, 0 <= oy < OH, 0 <= ox < OW; 0 <= oc < OC) {
out_t acc = 0;
id = od * stride_d - pad_d0;
iy = oy * stride_y - pad_top;
@@ -248,7 +254,7 @@ for_each(0 <= n < N, 0 <= od < D, 0 <= oy < H, 0 <= ox < W; 0 <= oc < OC) {
}
}
acc = apply_add<out_t>(acc, bias[oc]);
- tensor_write<out_t>(output, [N,D,H,W,OC], [n,od,oy,ox,oc], acc);
+ tensor_write<out_t>(output, [N,OD,OH,OW,OC], [n,od,oy,ox,oc], acc);
}
----
@@ -281,20 +287,23 @@ Performs 2D convolutions separately over each channel of the given tensor input,
|Attribute|int*|dilation|[2]|[dilation_y, dilation_x]
|Attribute|in_t|input_zp|-|Input tensor zero point. Must be zero for non-int8 types.
|Attribute|weight_t|weight_zp|-|Weight zero point. Must be zero for non-int8 types.
-|Output|out_t*|output|[N,H,W,C*M]|Output tensor
+|Output|out_t*|output|[N,OH,OW,C*M]|Output tensor
|===
*Operation Function*
[source,c++]
----
+// Derive output dimensions from input dimensions and padding
+OH = idiv_check(IH-1 + pad_top + pad_bottom - (KH-1)*dilation_y, stride_y) + 1;
+OW = idiv_check(IW-1 + pad_left + pad_right - (KW-1)*dilation_x, stride_x) + 1;
ERROR_IF(in_t != int8_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);
pad = flatten([0,0], pad, [0,0]);
-for_each(0 <= n<N, 0 <= oy < H, 0 <= ox < W; 0 <= c < C, 0 <= m < M) {
+for_each(0 <= n<N, 0 <= oy < OH, 0 <= ox < OW; 0 <= c < C, 0 <= m < M) {
out_t acc = 0;
iy = oy * stride_y - pad_top;
ix = ox * stride_x - pad_left;
@@ -302,7 +311,7 @@ for_each(0 <= n<N, 0 <= oy < H, 0 <= ox < W; 0 <= c < C, 0 <= m < M) {
y = iy + ky * dilation_y;
x = ix + kx * dilation_x;
if (0 <= y < IH && 0 <= x < IW) {
- out_t value = tensor_read<in_t>(input, [N,H,W,C], [n,y,x,c]);
+ out_t value = tensor_read<in_t>(input, [N,IH,IW,C], [n,y,x,c]);
out_t weight = tensor_read<weight_t>(weight, [KH,KW,C,M], [ky,kx,c,m]);
value = value - input_zp;
weight = weight - weight_zp;
@@ -310,7 +319,7 @@ for_each(0 <= n<N, 0 <= oy < H, 0 <= ox < W; 0 <= c < C, 0 <= m < M) {
}
}
acc = apply_add<out_t>(acc, bias[(c * M) + m]);
- tensor_write<out_t>(output, [N,H,W,C * M], [n,oy,ox,c * M + m], acc);
+ tensor_write<out_t>(output, [N,OH,OW,C * M], [n,oy,ox,c * M + m], acc);
}
----
@@ -428,13 +437,16 @@ This performs a max pooling over the given input tensor. A sliding window of siz
|Attribute|int*|kernel|[2]|[kernel_y, kernel_x]
|Attribute|int*|stride|[2]|[stride_y, stride_x]
|Attribute|int*|pad|[4]|[pad_top, pad_bottom, pad_left, pad_right]
-|Output|in_out_t*|output|[N,H,W,C]|Output tensor 4D
+|Output|in_out_t*|output|[N,OH,OW,C]|Output tensor 4D
|===
*Operation Function:*
[source,c++]
----
+// Derive output dimensions from input dimensions and padding
+OH = idiv_check(IH + pad_top + pad_bottom - kernel_y, stride_y) + 1;
+OW = idiv_check(IW + pad_left + pad_right - kernel_x, stride_x) + 1;
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);
@@ -442,10 +454,6 @@ ERROR_IF(pad_top < 0 || pad_bottom < 0 || pad_left < 0 || pad_right < 0);
// 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);
-// Output shape must match expected shape given the input shape
-// and arguments provided
-ERROR_IF(H != idiv((IH + pad_top + pad_bottom + stride_y - kernel_y), stride_y));
-ERROR_IF(W != idiv((IW + pad_left + pad_right + stride_x - kernel_x), stride_x));
for_each(0 <= n < N, 0 <= oy < H, 0 <= ox < W, 0 <= c < C ) {
in_out_t acc = minimum_value<in_out_t>;
@@ -459,7 +467,7 @@ for_each(0 <= n < N, 0 <= oy < H, 0 <= ox < W, 0 <= c < C ) {
acc = apply_max(acc, value);
}
}
- tensor_write<in_out_t>(output, [N,H,W,C], [n,oy,ox,c], acc);
+ tensor_write<in_out_t>(output, [N,OH,OW,C], [n,oy,ox,c], acc);
}
----
@@ -485,7 +493,7 @@ Performs a 2D transposed convolution over the given tensor input, using the weig
|Input|in_t*|input|[N,IH,IW,IC]|Input tensor
|Input (MT profile) Attribute (BI/MI profiles)|weight_t*|weight|[OC,KH,KW,IC]|Weight kernel size KH x KW
|Input (MT profile) Attribute (BI/MI profiles)|out_t*|bias|[OC]|Per output channel bias data.
-|Attribute|int*|out_pad|[2]|[out_pad_top, out_pad_left]
+|Attribute|int*|out_pad|[4]|[out_pad_top, out_pad_bottom, out_pad_left, out_pad_right]
|Attribute|int*|stride|[2]|[stride_y, stride_x]
|Attribute|int*|out_shape|[4]|[N,OH,OW,OC]
|Attribute|in_t|input_zp|-|Input tensor zero point. Must be zero for non-int8 types.
@@ -497,9 +505,13 @@ Performs a 2D transposed convolution over the given tensor input, using the weig
[source,c++]
----
+// Derive output dimensions from input dimensions and padding
+OH = (IH-1)*stride_y - out_pad_top - out_pad_bottom + KH;
+OW = (IW-1)*stride_x - out_pad_left - out_pad_right + KW;
ERROR_IF(in_t != int8_t && input_zp != 0); // Zero point only allowed for int8_t
ERROR_IF(weight_t != int8_t && weight_zp != 0);
-ERROR_IF(out_pad_top < 0 || out_pad_left < 0);
+ERROR_IF(out_pad_top < 0 || out_pad_bottom < 0);
+ERROR_IF(out_pad_left < 0 || out_pad_right < 0);
ERROR_IF(stride_y < 1 || stride_x < 1);
for_each(index in out_shape) {
tensor_write<out_t>(output, [N,OH,OW,OC], index, bias[index[3]])
generated by cgit v1.2.1 (git 2.23.0) at 2024-05-09 22:19:01 +0000