add-inference-backend

name	add-inference-backend
description	Add a new hardware inference backend to AutoRound for deploying quantized models (e.g., CUDA/Marlin, Triton, CPU, HPU, ARK). Use when implementing QuantLinear kernels, registering backend capabilities, or enabling quantized model inference on a new hardware platform.

Adding a New Inference Backend to AutoRound

Overview

This skill guides you through adding a new inference backend for running quantized models on a specific hardware platform. A backend defines how quantized weights are unpacked and computed at inference time. AutoRound automatically selects the best available backend based on hardware, quantization config, and priority.

Prerequisites

Before starting, determine:

1. Target hardware: CPU (Intel/AMD), CUDA GPU, Intel XPU, Habana HPU, etc.
2. Supported quantization configs: Which bit-widths, group sizes, and data types your backend handles
3. Kernel implementation: Triton, CUDA C++, PyTorch native, or external library (e.g., GPTQModel Marlin)
4. Packing format: How quantized weights are stored in memory

Step 1: Register Backend Info

Edit auto_round/inference/backend.py to register your backend's capabilities:

BackendInfos["auto_round:your_backend"] = BackendInfo(
    device=["cuda"],  # Supported devices
    sym=[True, False],  # Symmetric and/or asymmetric
    packing_format=["auto_round"],  # Compatible packing formats
    bits=[2, 4, 8],  # Supported bit-widths
    group_size=[32, 64, 128, -1],  # Supported group sizes (-1 = per-channel)
    compute_dtype=["float16", "bfloat16"],  # Compute precision
    data_type=["int"],  # Quantization data types
    act_bits=[16, 32],  # Activation bit-widths (16 = WxA16)
    priority=2,  # Higher = preferred (0-5 typical range)
    checkers=[your_feature_checker],  # Validation functions (optional)
    alias=["your_backend_short"],  # Alternative names (optional)
    requirements=["some_package>=1.0"],  # Required packages (optional)
    systems=["linux"],  # OS restriction (optional)
)

BackendInfo Fields Reference

Field	Type	Description
device	list[str]	Hardware targets: "cpu", "cuda", "xpu", "hpu"
sym	list[bool]	True for symmetric, False for asymmetric
packing_format	list[str]	How weights are packed: "auto_round", "auto_gptq", etc.
bits	list[int]	Supported weight bit-widths
group_size	list[int]	Group sizes; -1 means per-channel
compute_dtype	list[str]	Compute precision during inference
data_type	list[str]	Quantization data types: "int", "nv_fp", "mx_fp"
act_bits	list[int]	Activation bits: [16, 32] for weight-only, [8] for W8A8
priority	int	Selection priority (higher wins when multiple backends match)
checkers	list[Callable]	Functions to validate layer compatibility
alias	list[str]	Alternative names for CLI/API usage
requirements	list[str]	pip-installable dependency specifications
systems	list[str]	OS names: "linux", ,

Checker Functions

Use these pre-built checkers or create your own:

# Require in_features and out_features divisible by 32
from auto_round.inference.backend import feature_multiply_checker_32

# Require in_features divisible by group_size
from auto_round.inference.backend import in_feature_checker_group_size


# Custom checker
def your_feature_checker(in_feature, out_feature, config):
    """Check if layer dimensions are compatible with your backend."""
    return in_feature % 64 == 0 and out_feature % 64 == 0 and config["group_size"] in [64, 128]

Step 2: Implement QuantLinear Module

Create auto_round_extension/your_device/qlinear_your_backend.py:

import torch
import torch.nn as nn

QUANT_TYPE = "your_backend"


class QuantLinear(nn.Module):
    """Quantized linear layer for your backend.

    Stores packed quantized weights and performs dequantize-then-matmul
    (or fused quantized matmul) at inference time.
    """

    QUANT_TYPE = QUANT_TYPE

    def __init__(self, bits, group_size, in_features, out_features, bias=True, sym=True, **kwargs):
        super().__init__()
        self.bits = bits
        self.group_size = group_size
        self.in_features = in_features
        self.out_features = out_features
        self.sym = sym

        # Register packed weight buffers
        # Example: INT4 packed into INT32
        pack_factor = 32 // bits
        self.register_buffer(
            "qweight",
            torch.zeros(in_features // pack_factor, out_features, dtype=torch.int32),
        )
        self.register_buffer(
            "scales",
            torch.zeros(
                (in_features // group_size, out_features),
                dtype=torch.float16,
            ),
        )
        if not sym:
            self.register_buffer(
                "qzeros",
                torch.zeros(
                    (in_features // group_size, out_features // pack_factor),
                    dtype=torch.int32,
                ),
            )
        if bias:
            self.register_buffer("bias", torch.zeros(out_features, dtype=torch.float16))
        else:
            self.bias = 

     ():
        
        weight = ._dequantize()
        out = torch.matmul(x, weight.T)
         .bias   :
            out += .bias
         out

     ():
        
        
        
        ...


     ():
        
        ...

Step 3: Wire Up QuantLinear Import Logic

Register your backend in the explicit import logic in auto_round/inference/backend.py. In this repository, backend loading is not a generic directory scan; dynamic_import_inference_linear() maps backend keys to specific QuantLinear implementations.

Add a new backend key in BackendInfos[...] if needed, and make sure dynamic_import_inference_linear() returns your QuantLinear class for that backend:

if backend == "auto_round:your_backend":
    from auto_round_extension.your_device.qlinear_your_backend import QuantLinear

    return QuantLinear

If your backend fits an existing branch pattern, you can also reuse that logic, but contributors should update the explicit import mapping rather than rely on implicit auto-discovery.

Step 4: Add Extension __init__.py

Create auto_round_extension/your_device/__init__.py if the directory is new:

# Auto-Round extension for YourDevice backend

Step 5: Test

Unit test for the QuantLinear

def test_your_backend_qlinear():
    from auto_round_extension.your_device.qlinear_your_backend import QuantLinear

    ql = QuantLinear(bits=4, group_size=128, in_features=256, out_features=512)
    x = torch.randn(1, 256, dtype=torch.float16, device="cuda")
    out = ql(x)
    assert out.shape == (1, 512)

End-to-end test

def test_your_backend_e2e(tiny_opt_model_path, dataloader):
    ar = AutoRound(
        tiny_opt_model_path,
        bits=4,
        group_size=128,
        dataset=dataloader,
        iters=2,
        nsamples=2,
    )
    compressed_model, _ = ar.quantize()
    ar.save_quantized(output_dir="./tmp_backend_test", format="auto_round")

    # Load and verify inference with your backend
    from transformers import AutoModelForCausalLM, AutoTokenizer

    model = AutoModelForCausalLM.from_pretrained("./tmp_backend_test")
    tokenizer = AutoTokenizer.from_pretrained("./tmp_backend_test")
    inputs = tokenizer("Hello", return_tensors="pt").to("cuda")
    outputs = model.generate(**inputs, max_new_tokens=10)
    assert outputs.shape[1] > inputs["input_ids"].shape[1]

Reference: Existing Backend Implementations

Backend Key	Device	Extension Dir	Key Patterns
auto_gptq:exllamav2	CUDA	cuda/	Marlin kernels via GPTQModel, priority=3
auto_round:triton_*	CUDA	triton/	Triton JIT-compiled kernels
auto_round:torch_*	CPU/CUDA	torch/	Pure PyTorch fallback
auto_round:ark	ARK	ark/	ARK accelerator kernels
HPU backends	HPU	hpu/	Habana Gaudi optimized

Key Registration Points

What	Where	Mechanism
Backend capabilities	auto_round/inference/backend.py	BackendInfos["name"] dict
QuantLinear module	auto_round_extension/<device>/qlinear_*.py	QUANT_TYPE class attr
QuantLinear import wiring	auto_round/inference/backend.py	dynamic_import_inference_linear()
Feature checkers	auto_round/inference/backend.py	functools.partial wrappers