Move quantization parameters into argument tables

The TOSA specification doesn't have quantized types.
Without those types, it is cleaner to move effects of quantization
such as the zero points into the standard list of arguments, rather
than existing as a separate table.

Change-Id: I18ea2959e9d3e4a3e9a0ead7a8ca319bb4426a47

9 files changed, 19 insertions, 133 deletions

diff --git a/chapters/data_layout.adoc b/chapters/data_layout.adoc
index 4368474..bc8d853 100644
--- a/chapters/data_layout.adoc
+++ b/chapters/data_layout.adoc
@@ -68,17 +68,10 @@ Returns a new tensor with the padding included.
 |Input|in_t*|input1|shape1|Input tensor
 |Attribute|int|padding|[rank(input1),2]|Amount of padding to be done
 |Attribute|in_t|pad_const|-|Constant value to be used as padding
+|Attribute|in_t|input1_zp|-|Input tensor zero point. Must be zero for non-int8 types.
 |Output|in_t*|output|shape|Output tensor of same type as the input tensor
 |===
 
-*Quantization Parameters:*
-
-|===
-|Argument|Type|Name|Shape|Description
-
-|Attribute|in_t|input1_zp|-|Input tensor zero point
-|===
-
 *Operation Function:*
 
 [source,c++]
diff --git a/chapters/ewise_binary.adoc b/chapters/ewise_binary.adoc
index 99eb597..f44f7f5 100644
--- a/chapters/ewise_binary.adoc
+++ b/chapters/ewise_binary.adoc
@@ -271,10 +271,6 @@ Axis of size 1 will be broadcast, as necessary. Rank of input tensors must match
 |Output|in_t*|output|shape|Output tensor with broadcast shape if necessary
 |===
 
-*Quantization Parameters:*
-
-None
-
 *Operation Function:*
 
 [source,c++]
@@ -586,10 +582,6 @@ Axis of size 1 will be broadcast, as necessary. Rank of input tensors must match
 |Output|in_t*|output|shape|Output tensor of same type as the input tensors, with broadcast shape if necessary
 |===
 
-*Quantization Parameters:*
-
-Only supported with floating-point values.
-
 *Supported Data Types:*
 
 |===
@@ -662,10 +654,6 @@ An int16_t to int16_t table lookup can be constructed in TOSA as follows:
 |Output|out_t*|output|shape|Output tensor
 |===
 
-*Quantization Parameters:*
-
-None
-
 *Operation Function:*
 
 [source,c++]
diff --git a/chapters/ewise_ternary.adoc b/chapters/ewise_ternary.adoc
index e7f8c2e..ecf40d1 100644
--- a/chapters/ewise_ternary.adoc
+++ b/chapters/ewise_ternary.adoc
@@ -24,10 +24,6 @@ Elementwise select of the output based on a condition.
 |Output|in_t*|output|shape|Output tensor of same type as input2 and input3, with broadcast shape if necessary
 |===
 
-*Quantization Parameters:*
-
-None
-
 *Operation Function:*
 
 [source,c++]
diff --git a/chapters/ewise_unary.adoc b/chapters/ewise_unary.adoc
index 036c6e3..2dc01df 100644
--- a/chapters/ewise_unary.adoc
+++ b/chapters/ewise_unary.adoc
@@ -56,10 +56,6 @@ Elementwise bitwise NOT of input tensor.
 |Output|in_t*|output|shape|Output tensor of same type, size as the input tensor
 |===
 
-*Quantization Parameters:*
-
-None
-
 *Operation Function:*
 
 [source,c++]
@@ -210,10 +206,6 @@ Elementwise logical NOT of input.
 |Output|in_t*|output|shape|Output tensor of same type, size as the input tensor
 |===
 
-*Quantization Parameters:*
-
-None
-
 *Operation Function:*
 
 [source,c++]
@@ -243,18 +235,11 @@ Elementwise negation operation
 |Argument|Type|Name|Shape|Description
 
 |Input|in_t*|input1|shape|Input tensor
+|Attribute|in_t|input1_zp|-|Input 1 zero point. Must be zero for non-int8 types.
+|Attribute|in_t|output_zp|-|Output zero point. Must be zero for non-int8 types.
 |Output|in_t*|output|shape|Output tensor of same type, size as the input tensor
 |===
 
-*Quantization Parameters:*
-
-|===
-|Argument|Type|Name|Shape|Description
-
-|Attribute|in_t|input1_zp|-|Input 1 zero point
-|Attribute|in_t|output_zp|-|Output zero point
-|===
-
 *Operation Function:*
 
 [source,c++]
diff --git a/chapters/image.adoc b/chapters/image.adoc
index b8789ff..2491ea5 100644
--- a/chapters/image.adoc
+++ b/chapters/image.adoc
@@ -45,10 +45,6 @@ from the strides.
 |Output|out_t*|output|[N,OH,OW,C]|Output tensor
 |===
 
-*Quantization Parameters:*
-
-None
-
 *Operation Function*
 
 [source,c++]
diff --git a/chapters/reduction.adoc b/chapters/reduction.adoc
index c39fecf..b687896 100644
--- a/chapters/reduction.adoc
+++ b/chapters/reduction.adoc
@@ -148,10 +148,6 @@ Reduce a tensor along the given axis with a minimum operation
 |Output|in_t*|output|shape|Output tensor. Same rank as the input tensor.
 |===
 
-*Quantization Parameters:*
-
-Quantization is ignored when doing the REDUCE_MIN operation. The input and output must maintain the same parameters.
-
 *Operation Function:*
 
 [source,c]
diff --git a/chapters/scatter_gather.adoc b/chapters/scatter_gather.adoc
index 6fedb45..63f30dc 100644
--- a/chapters/scatter_gather.adoc
+++ b/chapters/scatter_gather.adoc
@@ -24,10 +24,6 @@ N is the number of batches, W the number of indices in each batch, K the range o
 |Output|value_t*|output|[N,W,C]|3D output tensor
 |===
 
-*Quantization Parameters:*
-
-None
-
 *Operation Function:*
 
 [source,c++]
diff --git a/chapters/tensor_ops.adoc b/chapters/tensor_ops.adoc
index 16b0341..0b78c8f 100644
--- a/chapters/tensor_ops.adoc
+++ b/chapters/tensor_ops.adoc
@@ -23,10 +23,6 @@ This returns the index with the largest value across the given axis of the input
 |Output|out_t*|output|shape|Output tensor, with rank = rank(shape1)-1
 |===
 
-*Quantization Parameters:*
-
-None
-
 *Operation Function:*
 
 [source,c++]
@@ -82,18 +78,11 @@ When calculating the average, only the number of valid input tensor values, but
 |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]
+|Attribute|in_t|input_zp|-|Input tensor zero point. Must be zero for non-int8 types.
+|Attribute|in_t|output_zp|-|Output tensor zero point. Must be zero for non-int8 types.
 |Output|in_t*|output|[N,H,W,C]|Output tensor 4D
 |===
 
-*Quantization Parameters:*
-
-|===
-|Argument|Type|Name|Shape|Description
-
-|Attribute|in_t|input_zp|-|Input tensor zero point
-|Attribute|in_t|output_zp|-|Output tensor zero point
-|===
-
 *Operation Function:*
 
 [source,c++]
@@ -161,18 +150,11 @@ Performs a 2D convolution over the given tensor input, using the weight tensor.
 |Attribute|int*|pad|[4]|[pad_top, pad_bottom, pad_left, pad_right]
 |Attribute|int*|stride|[2]|[stride_y, stride_x]
 |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|acc_t*|output|[N,H,W,OC]|Output tensor
 |===
 
-*Quantization Parameters:*
-
-|===
-|Argument|Type|Name|Shape|Description
-
-|Attribute|in_t|input_zp|-|Input tensor zero point
-|Attribute|weight_t|weight_zp|-|Weight zero point
-|===
-
 *Operation Function*
 
 [source,c++]
@@ -227,18 +209,11 @@ Performs a 3D convolution over the given input tensor.
 |Attribute|int*|pad|[6]|[pad_d0, pad_d1, pad_top, pad_bottom, pad_left, pad_right]
 |Attribute|int*|stride|[3]|[stride_d, stride_y, stride_x]
 |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|acc_t*|output|[N,D,H,W,OC]|Output tensor
 |===
 
-*Quantization Parameters:*
-
-|===
-|Argument|Type|Name|Shape|Description
-
-|Attribute|in_t|input_zp|-|Input tensor zero point
-|Attribute|weight_t|weight_zp|-|Weight zero point
-|===
-
 *Operation Function*
 
 [source,c++]
@@ -296,18 +271,11 @@ Performs 2D convolutions separately over each channel of the given tensor input,
 |Attribute|int*|pad|[4]|[pad_top, pad_bottom, pad_left, pad_right]
 |Attribute|int*|stride|[2]|[stride_y, stride_x]
 |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|acc_t*|output|[N,H,W,C*M]|Output tensor
 |===
 
-*Quantization Parameters:*
-
-|===
-|Argument|Type|Name|Shape|Description
-
-|Attribute|in_t|input_zp|-|Input tensor zero point
-|Attribute|weight_t|weight_zp|-|Weight zero point
-|===
-
 *Operation Function*
 
 [source,c++]
@@ -359,18 +327,11 @@ Performs a fully connected network.
 |Input|in_t*|input|[N,IC]|Input tensor
 |Attribute|weight_t*|weight|[OC,IC]|Weights
 |Attribute|acc_t*|bias|[OC]|Per output channel bias data.
+|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|acc_t*|output|[N,OC]|Output tensor
 |===
 
-*Quantization Parameters:*
-
-|===
-|Argument|Type|Name|Shape|Description
-
-|Attribute|in_t|input_zp|-|Input tensor zero point
-|Attribute|weight_t|weight_zp|-|Weight zero point
-|===
-
 *Operation Function*
 
 [source,c++]
@@ -410,18 +371,11 @@ Performs two dimensional matrix multiplications. This allows both inputs to be a
 
 |Input|in_t*|A|[N,H,C]|Input tensor A, N matrices of size HxC
 |Input|in_t*|B|[N,C,W]|Input tensor B, N matrices of size CxW
+|Attribute|in_t|A_zp|-|Input tensor A zero point. Must be zero for non-int8 types.
+|Attribute|in_t|B_zp|-|Input tensor B zero point. Must be zero for non-int8 types.
 |Output|acc_t*|output|[N,H,W]|Output tensor, N matrices of size HxW
 |===
 
-*Quantization Parameters:*
-
-|===
-|Argument|Type|Name|Shape|Description
-
-|Attribute|in_t|A_zp|-|Input tensor A zero point
-|Attribute|in_t|B_zp|-|Input tensor B zero point
-|===
-
 *Operation Function*
 
 [source,c++]
@@ -463,10 +417,6 @@ This performs a max pooling over the given input tensor. A sliding window of siz
 |Output|in_t*|output|[N,H,W,C]|Output tensor 4D
 |===
 
-*Quantization Parameters:*
-
-None
-
 *Operation Function:*
 
 [source,c++]
@@ -521,18 +471,11 @@ Performs a 2D transposed convolution over the given tensor input, using the weig
 |Attribute|int*|out_pad|[2]|[out_pad_top, out_pad_left]
 |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.
+|Attribute|weight_t|weight_zp|-|Weight zero point. Must be zero for non-int8 types.
 |Output|acc_t*|output|[N,OH,OW,OC]|Output tensor
 |===
 
-*Quantization Parameters:*
-
-|===
-|Argument|Type|Name|Shape|Description
-
-|Attribute|in_t|input_zp|-|Input tensor zero point
-|Attribute|weight_t|weight_zp|-|Weight zero point
-|===
-
 *Operation Function*
 
 [source,c++]
diff --git a/chapters/type_conversion.adoc b/chapters/type_conversion.adoc
index b947b73..8619fc3 100644
--- a/chapters/type_conversion.adoc
+++ b/chapters/type_conversion.adoc
@@ -82,15 +82,8 @@ Rescale quantized values into a new domain. This function scales by factor: mult
 
 |Input|in_t*|input|shape|Input tensor from 1 to 4 dims
 |Output|out_t*|output|shape|Output tensor with the same shape as input
-|===
-
-*Quantization Parameters:*
-
-|===
-|Argument|Type|Name|Shape|Description
-
-|Attribute|in_t|input_zp|-|Input tensor zero point
-|Attribute|out_t|output_zp|-|Output tensor zero point
+|Attribute|in_t|input_zp|-|Input tensor zero point. Must be zero for non-int8 types.
+|Attribute|out_t|output_zp|-|Output tensor zero point. Must be zero for non-int8 types.
 |Input (MT profile) Attribute (BI/MI profiles)|mul_t|multiplier[NC]|-|Scaling multiplier array
 |Input (MT profile) Attribute (BI/MI profiles)|uint6_t|shift[NC] |-|Scaling shift array
 |Attribute|bool_t|scale32|-|if (scale32) mul_t=int32_t else mul_t=int16_t