The MLIR TOSA dialect implements the TOSA specification. This document describes the decision process for how TOSA expresses operators in high level dialects.
TOSA was developed after parallel efforts to rationalize the top-down picture from multiple high-level frameworks, as well as a bottom-up view of different hardware target concerns (CPU, GPU and NPU), and reflects a set of choices that attempt to manage both sets of requirements.
TOSA endeavors to provide an operator set that tries to fulfil the following expressiveness goals at the tensor level of abstraction :
This is driven by the top-down perspective, needing to express as much of multiple high level frameworks fully in TOSA, as possible. This was originally done from an operator frequency analysis done upon dozens of high level networks in different frameworks, to select the most frequently occurring ones and establish a common set of tensor-level operators that could express them.
TOSA categorizes its operator set into classes and attempts to address major functional operations at the tensor level, including compute, reduction, elementwise transformations, comparison and control flow.
This takes the bottom-up approach - keep the TOSA operator set minimal in order to bound the design of hardware, operator kernels, code generation strategies and associated considerations that effect the executability of TOSA content.
In this regard TOSA seeks to avoid creating compound operators, instead leaving it to compiler backend to fuse multiple TOSA ops if required. This choice also benefits the numerical precision goal, since it is easier to fuse the numerical functionality of successive operators, than to split the numerical functionality of a compound operator.
TOSA began as a means to address operator-level numerical precision for code generation and hardware development. It therefore incorporates precision detail into the operator set.
In this regard, TOSA operators are best understood as a combination of the visible quantization information embedded within an operation, together with the functional information about how that information is used, as described in the specification of the operation.
The general basis of selection of the operator set that constitutes TOSA is described in the TOSA specification document under Section 1.3 Operator Selection. Explanation of the thinking behind some operators is listed here:
Several neural networks express conditional control flow at the tensor level. A survey of multiple high level frameworks indicated that conditional if and a loop construct are common in all major frameworks, with some variation. Since TOSA endeavors to be complete in expressing tensor level functionality including control flow, it implements these constructs.
The COND_IF and WHILE_LOOP operators implement such structured control flow forms and should be lowerable to corresponding ops in the scf dialect. Since the dialect seeks to remain isomorphic with an external, serialized form, the decision was to keep these ops in the dialect (as opposed to deferring completely to scf), and this may be re-evaluated if this turns out to not yield the expected value.
The TOSA specification describes each operator in functional detail. It is expected that compilers that use TOSA will use its builders to construct the operators so that the quantization information for the operator is correctly generated.
The functional steps described in the pseudocode of the specification enables the construction of code generation for that operation, or decisions on the design of underlying hardware. The functional pseudocode also describes how the quantization parameters are utilized within the operation.
TOSA uses the quantization parameters embedded in the input and output tensors to construct the quantization attributes that sit within the operator. Once these attributes are constructed, the quantization information within the tensors are no longer necessary for code generation.
This enables the tensors to be subsequently interpreted simply as contiguous buffers containing raw data, with no ‘meta information’ in the form of the quantization_type. Precision related manipulation of the input or output are instead described by the operator itself which describes, for example, when the zero point is applied, or when the scale multiplication is done.
However, TOSA does not eliminate the existing MLIR QuantOps quantization type information within the tensors; this leaves the choice of how to handle quantization information, to later backend code generation steps.
Maintaining the ability to overlap these different representations of quantization parameters (i.e. tensor-carried vs op-carried) is an important capability when considering progressive lowering between uses that expect one scheme vs the other.