new architecture for auto_round

[n1ck-guo]

Description

• Compressor:
  Main entry point responsible for orchestrating the workflow, invoking different algorithms, and handling model persistence. Supports block-wise or layer-wise quantization strategies. Primary subclasses include TuneCompressor and ZeroShotCompressor.
• Calibration: Handles the calibration process (Work in Progress)
• Context: Manages shared configurations and model states throughout the quantization pipeline, providing centralized control to prevent cross-module dependencies
  • ModelContext: Handles model loading and tracks model states and relevant configurations
  • CompressContext: Stores shared compression settings such as low_cpu_mem_usage, enable_torch_compile, etc.
• Algorithms: Concrete quantization and weight transformation implementations
  • Quantization: Various quantization algorithms, including AutoRound, RTN, OptRTN, etc.
  • Transform: Weight transformation algorithms such as Hadamard transform

Usage of new api:

from auto_round.algorithms.rotation import HadamardConfig 

quant_cfg  = AutoRoundConfig(bits=4, group_size=128, iters=200)
had_cfg_1  = HadamardConfig(hadamard_type="hadamard",        block_size=32)
had_cfg_2  = HadamardConfig(hadamard_type="random_hadamard", block_size=64, random_seed=True)

compressor = Compressor(
    config=[quant_cfg, had_cfg_1, had_cfg_2], 
    model="facebook/opt-125m",
    scheme="MXFP4",
    format="auto_round",
)

model, layer_config = compressor.quantize_and_save(
    output_dir="./output",
)

Type of Change

• Bug fix
• New feature
• Documentation update
• Performance improvement
• Code refactoring
• Other (please specify):

Related Issues

Fixes or relates to #

Checklist Before Submitting

• My code has been tested locally.
• Documentation has been updated as needed.
• New or updated tests are included where applicable.

[Copilot]

Pull request overview

Refactors AutoRound toward a new “context + compressor + algorithm” architecture, introducing new compressors_new/ and context/ modules and updating scheme parsing/export helpers to support the new flow.

Changes:

• Added new context singletons (ModelContext, CompressContext) and a new compressors_new implementation path.
• Expanded scheme parsing to reconcile bits/data_type and support user overrides + AutoScheme integration.
• Added new calibration utilities and algorithm scaffolding for quantization backends (AutoRound/RTN).

Reviewed changes

Copilot reviewed 26 out of 26 changed files in this pull request and generated 18 comments.

Show a summary per file

File	Description
auto_round/utils/model.py	Avoids runtime import cycles via TYPE_CHECKING for QuantizationScheme.
auto_round/schemes.py	Adds scheme override + parsing helpers and bits/dtype reconciliation.
auto_round/formats.py	Switches divisibility checks to global supported-layer constants.
auto_round/context/model_context.py	Introduces model lifecycle/loading + AMP setup and forward-hook management.
auto_round/context/compress_context.py	Introduces device/device_map and memory-usage knobs as shared context.
auto_round/context/base.py	Adds simple singleton context base.
auto_round/context/init.py	Package init for new context module.
auto_round/compressors_new/utils.py	New utility module (layer config, gguf mapping, caching helpers, forward helpers).
auto_round/compressors_new/shard_writer.py	New shard-based saver with optional safetensors support.
auto_round/compressors_new/config.py	Introduces extra/legacy config dataclasses for the new compressor path.
auto_round/compressors_new/base.py	New “BaseCompressor” implementation wiring contexts, formats, caching, quant loop.
auto_round/compressors_new/init.py	Package init for compressors_new.
auto_round/compressors/utils.py	Extends legacy layer-config resolution to include safetensors-only tensors and skip missing modules.
auto_round/calibration/utils.py	Adds helpers for “early stop” caching and input reshaping for block tuning.
auto_round/calibration/init.py	Package init for calibration.
auto_round/algorithms/quantization/rtn/rtn.py	Adds placeholder RTN quantization module file.
auto_round/algorithms/quantization/rtn/config.py	Adds RTN algorithm config stub.
auto_round/algorithms/quantization/rtn/init.py	Package init for RTN quantization.
auto_round/algorithms/quantization/base.py	Adds base quantization class stub.
auto_round/algorithms/quantization/auto_round/quantize.py	Adds new AutoRound quantizer implementation (algorithm object).
auto_round/algorithms/quantization/auto_round/config.py	Adds new AutoRound algorithm config.
auto_round/algorithms/quantization/auto_round/init.py	Package init for AutoRound quantization algorithm.
auto_round/algorithms/quantization/init.py	Package init for quantization algorithms.
auto_round/algorithms/base.py	Adds base algorithm stub.
auto_round/algorithms/alg_config.py	Adds base algorithm config stub.
auto_round/algorithms/init.py	Package init for algorithms.

[wenhuach21]

If there is already an algorithm folder, what is the purpose of the compressor folder?

[wenhuach21]

I’d prefer using rotation, but it’s up to you.