Quantization is one of those things where everyone unanimously agrees “it’s important”, but the details are often fuzzy; I mean, no one really wants to think about it in their compiler’s IR, right?

In most stacks, quantization lives in an (awkward?) place:

  • High-level frameworks (like PyTorch) expose “quantized layers” or post-training quantization APIs.
  • Backends (like TensorRT, ONNX Runtime) and kernels really care about bit-widths, scales, zero points, and data layouts.
  • The glue in between is often a mishmash of ad-hoc passes, custom operators, and brittle assumptions.

MLIR is actually a sweet spot to make quantization less cursed:

You can make quantization a first-class citizen of your IR: visible in types, explicit in ops, and modular in passes.

In this post, I want to walk through:

  1. What “quantization” means in this context,
  2. How you can represent quantized tensors in MLIR types,
  3. Where to insert quantize and dequantize ops in your IR,
  4. How this plays with dialects like Linalg.
  5. Some design considerations worth arguing about.

1. Quick mental model: What are we even quantizing?

Most ML inference quantization schemes boil down to this:

  • Store values in low-precision integers (e.g., int8, int4) to save memory and bandwidth.
  • Interpret them using a scale (and sometimes a zero point) to map back to real numbers.

The classic affine mapping is:

real_value = scale * (quantized_value - zero_point)
  • Here, quantized_value is the low-precision integer stored in memory.
  • scale is a floating-point number that determines the “step size” between quantized levels.
  • zero_point is an integer offset that allows representing zero exactly.

So if you want this integrated in the IR, we need to answer:

  • Where do we store scale and zero_point?
  • Are they attached to types, attributes, or explicit ops?
  • Where in the IR do we cross between quantized and real-valued representations?

2. Quantized types: putting scale and zero point in the type system

MLIR’s type system is rich enough that quantization metadata does not have to float around in comments or attributes. You can make it part of the type.

A conceptual example:

// An 8-bit signed quantized element type
!qint = !quant.uniform<i8:f32, scale=0.02, zero_point=-5>

// A quantized tensor type
!qtensor = tensor<128x128x64x!qint>

Here, !quant.uniform<i8:f32, scale=0.02, zero_point=-5> defines a quantized integer type with:

  • i8 as the storage type (8-bit integer) in hardware,
  • f32 as the real-valued type it maps to (logical real domain),
  • scale and zero_point as parameters.

And, we get some nice properties:

  • Passes can easily query quantized vs real types.
  • Implement patterns that operate on quantized data whose semantics are clear. For example, “Fuse two operations that both use the same quantization parameters.”
  • Lowering to backend-specific Instruction Set Architectures (ISAs) can directly leverage this type information.

We can also define different quantization schemes (e.g., symmetric, asymmetric, per-channel) by extending the type system further.

E.g. for per-channel quantization

// Per-channel quantization along the C dimension
!qconv_weight = tensor<64x3x3x3x!quant.uniform<i8:f32,
                                               scales = [0.01, 0.02, ...],
                                               zero_points = [0, 0, ...],
                                               axis = 0>>

Per-channel quantization allows each output channel to have its own scale and zero point, which is common in convolutional weights.

3. quantize and dequantize ops: where do we switch domains?

At some point, we need to convert between real-valued tensors and quantized tensors. This is where quantize and dequantize ops come in.

%f = ... : tensor<...xf32>

// Float to quantized
%q = "quant.quantize" %f : tensor<...xf32> to tensor<...x!qint>

// Quantized to float
%f2 = "quant.dequantize" %q : tensor<...x!qint> to tensor<...xf32>

The big question is: Where do we place these ops in the IR?

Two common patterns:

  1. Early quantization, late dequantization:
  • Insert quantize ops right after the model is loaded or after a preprocessing/calibration phase.
  • Keep the rest of the model in quantized form as long as possible.
  • Insert dequantize ops only at the very end, before outputting results, or when a non-quantized op is needed.

Pros:

  • Maximum performance gain from quantization.
  • Lets you propagate quantized types through most of the graph.

Cons:

  • More complex handling of quantized ops, i.e. IR transformations need to be quantization-aware.
  • Some transformations need variant handling for quantized ops.
  • Potentially more numerical error accumulation.
  1. Late quantization (backend-specific):
  • Keep the model in floating-point form (f32/ f16) through most of the IR.
  • Let the backend do a late “quantized lowering” where it introduces quantized types and quantize/dequantize ops as needed.

Pros:

  • Simpler IR transformations, as most ops remain in floating-point.
  • Quantization logic can be centralized in backend lowering.

Cons:

  • Harder to do global optimizations that are quantization-aware. (e.g. fusing quantized ops, layout transformations driven by int8 kernels)
  • We are effectively doing quantization “too late” to fully exploit its benefits.

In practice, a hybrid approach could be used:

  • A high-level quantization pass which decides which tensors/ops should be quantized and inserts quantize/dequantize ops accordingly.
  • Later lowerings that replace generic quantized ops with backend-specific implementations. (e.g. linalg.matmul on quantized types lowering to int8 GEMM kernels)

4. Quantization and linalg: generic ops, concrete kernels

MLIR’s linalg dialect is a great fit for quantization because it provides high-level, generic operations that can be specialized for quantized types.

For example,

//linalg.matmul on quantized types
linalg.matmul ins (%A_q, %B_q : tensor<MxKx!qint>, tensor<KxNx!qint>) 
               outs (%C_q : tensor<MxNx!qint>)

But what does this mean semantically?

  • Are we interpreting it as an integer matrix multiplication followed by scaling?
  • Are we expecting hardware-specific instructions (e.g. INT8 GEMM)?
  • Are scales/zero points baked into the type or explicitly passed as rescale ops before/after the matmul?

A reasonable pattern is

  1. Keep linalg ops element-type-generic: they operate on whatever types are given (quantized or real).
  2. Add passes that:
    • Lower quantized linalg ops into int kernels + explicit rescaling.
    // Pseudocode lowering
%acc "linalg.matmul" ins(%A_int8, %B_int8) : tensor<MxKxi8>, tensor<KxNxi8> -> tensor<MxNxi32>
%out = "quant.rescale" (acc) 
{scale = ..., zero_point = ...} : tensor<MxNxi32> to tensor<MxNxi8>
    • Or directly lower to backend-specific quantized kernels that understand the quantization parameters.
  3. Let backend-specific passes choose instructions based on the quantization parameters encoded in types.

The key idea:

linalg gives you structured loops; quantization adds semantic constraints.

The compiler’s job is to bridge the two: ensuring that quantized semantics are respected while still leveraging the high-level structure of linalg ops.

5. Quantization-friendly fusion and vectorization

Quantization graphs are full of patterns like:

  • quantize -> conv -> relu -> dequantize
  • quantize -> matmul -> add_bias -> activation -> dequantize

To make these fast, you want to

  • Fuse quantized ops together while staying in the quantized domain.
  • Avoid spurious dequantize/quantize hops when ops can be fused.
  • Vectorize int8/uint8 arithmetic to leverage SIMD instructions.

In MLIR terms, this means:

  • Rewrite patterns like
%q = quant.quantize %f : tensor<...xf32> to tensor<...x!qint>
%y = "mydialect.conv"(%q, %w) : tensor<...x!qint>, tensor<...x!qint> -> tensor<...x!qint>
%z = "mydialect.relu"(%y) : tensor<...x!qint> -> tensor<...x!qint>
%out = quant.dequantize %z : tensor<...x!qint> to tensor<...xf32>

into a single fused op that stays in the quantized domain:

%out = "mydialect.fused_conv_relu"(%f, %w) :
    tensor<...xf32>, tensor<...x!qint> -> tensor<...xf32>
  • Teach vector lowering and target-specific backends to handle quantized types efficiently.
%vA = vector.transfer_read %A_q ... : tensor<...x!qint> to vector<...xi8>
%vB = vector.transfer_read %B_q ... : tensor<...x!qint> to vector<...xi8>
%VAcc = "vector.dot_qi8"(%vA, %vB) : vector<...xi8>, vector<...xi8> -> vector<...xi32>

Again, whether you use actual quant.* dialect types or domain-specific ones, the pattern is

  • Make quantization explicit in types and ops.
  • Enable fusion and vectorization passes to recognize and optimize quantized patterns.
  • Only dequantize when absolutely necessary.

6. Where to put the “q”: design considerations

Zooming out, a plausible high-level storyline for an MLIR-based quantization pipeline could be:

  1. Model Import: Load a floating-point model (e.g., from ONNX, TensorFlow).
  2. Quantization Pass: Analyze the model, decide which tensors/ops to quantize, attach parameters to types, insert quantize/dequantize ops.
  3. Quantized Optimization Passes: Fuse quantized ops, fold unnecessary conversions, vectorize quantized computations.
  4. Structured Lowering: Lower linalg and other high-level ops to quantized kernels, respecting quantization semantics.
  5. Backend Lowering:Final mapping to LLVM or target-specific codegen, leveraging quantized instructions.

7. Design questions to ponder

None of this is set in stone, quantization is a moving target with many open questions:

  • How much should the type system know?
  • Do you always carry scales and zero points in types, or if that is too heavyweight, can you use attributes or side tables?
  • Static vs Dynamic Quantization Parameters
  • Some schemes use fixed scales/zero points, others compute them on-the-fly (e.g., per-batch). How to represent this in types/ops?
  • Interplay with mixed precision
  • Where do fp16/bfloat16 mixed precision and quantization interact?
  • Verification and correctness How to ensure that quantized computations respect numerical properties (e.g., range, overflow)?

These are exciting questions for compiler and MLIR designers to explore.