Understanding Model Transformation and Intermediate Representations

Model transformation involves re-expressing a neural network's structure and parameters into a format suitable for execution in a target environment. A core aspect of this process is interpreting the model’s computation graph—its directed acyclic graph (DAG) of operations—and adapting it to meet the constraints and optimizations required by the destination platform. This may involve restructuring nodes, fusing operations, or modifying data flow paths to ensure correctness and efficiency in inference.

To enable cross-framework compatibility and efficient optimization, modern AI systems use an Intermediate Representation (IR) as a bridge between training frameworks (like PyTorch or TensorFlow) and inference engines. The IR abstracts away framework-specific details, allowing a unified infrastructure for graph analysis, transformation, and hardware-specific code generation.

Core Components of a Computation Graph

A computation graph is fundamentally composed of two elements: tensors and operators. These form the building blocks for expressing any deep learning model.

Tensors: Multi-Dimensional Data Containers

In machine learning, a tensor represents multi-dimensional arrays. Its dimensionality, known as rank, determines its structure:

• Rank-0: Scalar (single value)
• Rank-1: Vector (one axis)
• Rank-3: RGB image (batch, channel, height, width)

The shape of a tensor, such as [3, 2, 5], defines its size along each dimension. Tensors can hold various data types including floating-point numbers, integers, and strings.

Operators: Functional Building Blocks

An operator performs a specific transformation on one or more input tensors to produce output tensors. Examples include:

• Arithmetic operations (Add, Multiply)
• Data reshaping (Reshape, Transpose)
• Neural network layers (Convolution, Pooling)
• Control flow constructs (If, Loop)

Each operator has a defined signature: number of inputs, outputs, and associated parameters (e.g., kernel size for convolution). Complex models are formed by composing these primitive operators into a connected graph.

Differences Between Training Frameworks and Inference Engines

While both training frameworks and inference engines rely on computation graphs, their design goals lead to significant differences:

These distinctions guide how IRs are designed: inference-focused IRs eliminate gradient-related nodes, simplify control flow, and prioritize runtime performance over flexibility.

Defining a Custom IR Using Schema Languages

To build a portable and efficient IR, schema definition languages like FlatBuffers or Protocol Buffers are commonly used. They allow structured, language-agnostic serialization of the computation graph while supporting fast parsing with minimal overhead.

Data Type and Layout Specification

A key part of the IR is defining tensor metadata. Below is an example using FlatBuffers syntax:

enum DataType : byte {
  INVALID = 0,
  FLOAT32 = 1,
  FLOAT64 = 2,
  INT32 = 3,
  UINT8 = 4,
  INT16 = 5,
  INT8 = 6
}

enum DataLayout : byte {
  UNKNOWN,
  ND,      // Generic N-D
  NCHW,    // Batch-Channel-Height-Width
  NHWC,    // Batch-Height-Width-Channel
  NC4HW4,  // Optimized for certain accelerators
  NC1HWC0  // Used in specialized chips like Ascend
}

Tensor Structure Definition

The Tensor table captures essential properties:

table Tensor {
  dims: [int];         // Shape vector
  layout: DataLayout;
  dtype: DataType;
  name: string;
}

Operator Representation

Operators must be generalized across frameworks. Since different frameworks may implement the same logical operation differently, a custom IR defines canonical forms.

enum OpType : int {
  Constant,
  Concatenate,
  Conv2D,
  DepthwiseConv2D,
  TransposeConv,
  MatrixMultiply,
  MaxPool,
  Relu,
  Add
}

Each operator has associated parameters encapsulated via a union:

union OpParameter {
  Conv2DParams,
  Pool2DParams,
  AddParams
}

table Op {
  type: OpType;
  param: OpParameter;
  inputs: [int];   // Index references to tensors
  outputs: [int];
  name: string;
}

Graph-Level Structure

The entire model is represented as a Model or Net structure that includes all operators, tensors, and execution order:

table SubGraph {
  name: string;
  inputs: [int];
  outputs: [int];
  nodes: [Op];
  tensors: [string];
}

table Net {
  name: string;
  inputs: [string];
  outputs: [string];
  ops: [Op];
  subgraphs: [SubGraph];  // For control flow branches
}

The root type declaration specifies the entry point for deserialization:

root_type Net;

Workflow for IR-Based Model Conversion

1. Parse Source Model: Read trained models from frameworks (e.g., ONNX, TorchScript, SavedModel) using appropriate parsers.
2. Lower to IR: Map framework-specific operators to canonical IR operators. Handle mismatches through decomposition or fusion.
3. Optimize Graph: Apply transformations such as:
   • Constant folding
   • Dead node elimination
   • Operator fusion (e.g., Conv + Bias + ReLU)
   • Memory layout optimization (NCHW vs NHWC)
4. Serialize IR: Export the optimized graph into a binary or text format using FlatBuffers/Protobuf for deployment.
5. Runtime Execution: Load the IR into the inference engine, allocate memory, schedule operators, and execute.

Example: Defining a Simple CNN in FlatBuffers

Below is a complete schema snippet for a minimal network with convolution and pooling layers:

namespace CustomIR;

table Conv2DParams {
  kernel_h: int = 1;
  kernel_w: int = 1;
  stride_h: int = 1;
  stride_w: int = 1;
  pad_type: string = "VALID";
}

table Pool2DParams {
  pool_size_h: int = 2;
  pool_size_w: int = 2;
  stride_h: int = 2;
  stride_w: int = 2;
}

union OpParameter {
  Conv2DParams,
  Pool2DParams
}

enum OpType : int {
  Conv2D,
  MaxPool2D
}

table Op {
  type: OpType;
  param: OpParameter;
  inputs: [int];
  outputs: [int];
  name: string;
}

table Net {
  ops: [Op];
  tensor_names: [string];
  input_names: [string];
  output_names: [string];
}

root_type Net;

This schema enables consistent representation of convolutional networks and serves as a foundation for further optimization and backend code generation.