MODEL-SPECIFIC ASIC COMPILATION BY MODIFYING TEMPLATE MODELS
Embodiments herein describe using template files for translating an existing (or base) AI model into a new AI model for a model-specific chipset. That is, instead of requiring a developer to use an AI framework to prepare new code for the new AI model, a compiler can receive a template file which indicates a base AI model (e.g., an AI model that has already been executed on the model-specific chipset) and structural parameters for the new AI model. The compiler can use the structural parameters to modify compilation data corresponding to the base AI model. The compiler can then use the modified compilation data to create code for the new AI model that executes on the model-specific chipset.
1 . A method, comprising:
receiving a template file identifying a base artificial intelligence (AI) model and structural parameters corresponding to a second AI model, wherein the base AI model has been previously compiled to execute on a model-specific chipset;
modifying compilation data corresponding to the base AI model using the structural parameters;
creating, at a compiler, executable code for the second AI model using the modified compilation data; and
executing the second AI model on the model-specific chipset using the executable code.
2 . The method of claim 1 , further comprising, after receiving the template file but before modifying the compilation data:
identifying code corresponding to the base AI model stored in a library,
wherein modifying the compilation data comprises:
replacing values in the code for the base AI model with values in the structural parameters.
3 . The method of claim 2 , wherein the library stores code for a plurality of base AI models that are supported by the compiler, wherein the plurality of base AI models have each been previously executed on the model-specific chipset.
4 . The method of claim 1 , further comprising, after receiving the template file but before modifying the compilation data:
identifying an intermediate representation (IR) corresponding to the base AI model that is stored in a library,
wherein modifying the compilation data comprises:
replacing values in the IR with values in the structural parameters to generate a modified IR.
5 . The method of claim 4 , wherein the modified IR has values for various arguments to perform operations that are part of the second AI model.
6 . The method of claim 4 , wherein the library stores IRs for a plurality of base AI models that are supported by the compiler, wherein the plurality of base AI models have each been previously executed on the model-specific chipset, wherein the IRs each comprises values of arguments that configure the model-specific chipset to perform operations for a respective base AI model.
7 . The method of claim 1 , wherein the template file does not include software code for a programming language.
8 . The method of claim 7 , wherein data in the template file uses a JavaScript Object Notation (JSON) format.
9 . The method of claim 1 , wherein structural parameters change an architecture or structure of the base AI model to convert the base AI model into the second AI model.
10 . The method of claim 1 , wherein the base AI model and the second AI model are different types of transformer models, wherein the model-specific chipset can is optimized to execute only transformer models.
11 . A non-transitory computer readable medium having program instructions embodied therewith, the program instructions executable by a processor to perform an operation, the operation comprising:
receiving a template file identifying a base artificial intelligence (AI) model and structural parameters corresponding to a second AI model, wherein the base AI model has been previously compiled to execute on a model-specific chipset;
modifying compilation data corresponding to the base AI model using the structural parameters;
creating, at a compiler, executable code for the second AI model using the modified compilation data; and
executing the second AI model on the model-specific chipset using the executable code.
12 . The non-transitory computer readable medium of claim 11 , wherein the operation further comprises, after receiving the template file but before modifying the compilation data:
identifying code corresponding to the base AI model stored in a library,
wherein modifying the compilation data comprises:
replacing values in the code for the base AI model with values in the structural parameters.
13 . The non-transitory computer readable medium of claim 12 , wherein the library stores code for a plurality of base AI models that are supported by the compiler, wherein the plurality of base AI models have each been previously executed on the model-specific chipset.
14 . The non-transitory computer readable medium of claim 11 , wherein the operation further comprises, after receiving the template file but before modifying the compilation data:
identifying an intermediate representation (IR) corresponding to the base AI model that is stored in a library,
wherein modifying the compilation data comprises:
replacing values in the IR with values in the structural parameters to generate a modified IR.
15 . The non-transitory computer readable medium of claim 14 , wherein the modified IR has values for various arguments to perform operations that are part of the second AI model,
wherein the library stores IRs for a plurality of base AI models that are supported by the compiler, wherein the plurality of base AI models have each been previously executed on the model-specific chipset, wherein the IRs each comprises values of arguments that configure the model-specific chipset to perform operations for a respective base AI model.
16 . The non-transitory computer readable medium of claim 11 , wherein the base AI model and the second AI model are different types of transformer models, wherein the model-specific chipset is optimized to execute only transformer models.
17 . A system, comprising:
one or more processors; and
one or more memories storing a compiler which, when executed by the one or more processors, performs an operation comprising:
receiving a template file identifying a base artificial intelligence (AI) model and structural parameters corresponding to a second AI model, wherein the base AI model has been previously compiled to execute on a model-specific chipset;
modifying compilation data corresponding to the base AI model using the structural parameters; and
creating executable code for the second AI model using the modified compilation data, wherein the executable code, when executed on the model-specific chipset, performs the second AI model.
18 . The system of claim 17 , wherein the operation further comprises, after receiving the template file but before modifying the compilation data:
identifying code corresponding to the base AI model stored in a library,
wherein modifying the compilation data comprises:
replacing values in the code for the base AI model with values in the structural parameters,
wherein the library stores code for a plurality of base AI models that are supported by the compiler, wherein the plurality of base AI models have each been previously executed on the model-specific chipset.
19 . The system of claim 17 , wherein the operation further comprises, after receiving the template file but before modifying the compilation data:
identifying an intermediate representation (IR) corresponding to the base AI model that is stored in a library,
wherein modifying the compilation data comprises:
replacing values in the IR with values in the structural parameters to generate a modified IR,
wherein the modified IR has values for various arguments to perform operations that are part of the second AI model, and
wherein the library stores IRs for a plurality of base AI models that are supported by the compiler, wherein the plurality of base AI models have each been previously executed on the model-specific chipset, wherein the IRs each comprises values of arguments that configure the model-specific chipset to perform operations for a respective base AI model.
20 . The system of claim 17 , wherein the base AI model and the second AI model are different types of transformer models, wherein the model-specific chipset is optimized to execute only transformer models.