Add quantization functionality to Oumi -- Yuzhang #1799

42Shawn · 2025-07-09T02:16:50Z

Description

PR Description

Overview

This PR enhances the quantization functionality in Oumi by implementing a comprehensive AWQ (Activation-aware Weight Quantization) pipeline with robust fallback mechanisms and improved user experience.

Key Features

🔧 Enhanced AWQ Support

Complete AWQ quantization implementation with calibration dataset support
Calibration-based quantization using the pileval dataset
Configurable parameters for AWQ group size, zero point, and calibration sample count
Intelligent fallback to BitsAndBytes when AutoAWQ is unavailable

📦 Multi-Format Support

GGUF Format: Optimized for llama.cpp and CPU inference
Safetensors Format: Compatible with HuggingFace transformers
PyTorch Format: Native PyTorch serialization for research workflows

🛠️ GGUF Conversion Pipeline

Multiple conversion methods: llama.cpp scripts, llama-cpp-python, and fallback methods
Enhanced script discovery across different installations
Robust error handling with informative error messages
Automatic attempts to install missing dependencies

🎯 Developer Experience

Rich logging with emoji indicators for better UX
Simulation mode for testing without quantization dependencies
Improved model size calculation for HuggingFace models
Configurable temporary file cleanup with safety checks

Usage Examples

AWQ Quantization

oumi quantize --method awq_q4_0 --model "oumi-ai/HallOumi-8B" --output halloumi_awq4bit.pytorch

Expected Result:
✅ Model quantized successfully!
📁 Output saved to: halloumi_awq4bit.pytorch
📊 Original size: 15.0 GB
📉 Output size: 5.4 GB
🗜️ Compression ratio: 2.80x

Other Example Commonds

oumi quantize --method awq_q4_0 --model "meta-llama/Llama-2-7b-hf" --output model.pytorch
oumi quantize --method awq_q4_0 --model "Qwen/Qwen3-14B" --output Qwen3-14B_awq4bit.pytorch

Configuration File

model:
  model_name: "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
method: "awq_q4_0"
output_path: "tinyllama_quantized.pytorch"
output_format: "pytorch"
awq_group_size: 128
calibration_samples: 512

PyTorch Format Output

oumi quantize --method awq_q4_0 --model "TinyLlama/TinyLlama-1.1B-Chat-v1.0" --output model.pytorch

Implementation Details

Core Quantization Methods

AWQ Quantization: awq_q4_0, awq_q4_1, awq_q8_0, awq_f16
BitsAndBytes: bnb_4bit, bnb_8bit with fallback support
Direct GGUF: q4_0, q4_1, q5_0, q5_1, q8_0, f16, f32

Fallback Strategy

Primary: AutoAWQ with CUDA acceleration
Secondary: BitsAndBytes fallback for macOS/CPU systems
Tertiary: Simulation mode for testing without dependencies

Testing

The implementation includes comprehensive error handling and simulation modes:

Simulation Mode: Creates realistic mock outputs when dependencies are missing
Fallback Testing: Validates BitsAndBytes fallback on systems without AutoAWQ
Error Recovery: Graceful handling of conversion failures with informative messages

Dependencies

Required

torch (already required by Oumi)
transformers (already required by Oumi)
safetensors (already required by Oumi)

Optional (with fallbacks)

autoawq - For AWQ quantization (Linux/Windows with CUDA)
bitsandbytes - Fallback quantization (macOS/CPU systems)
llama-cpp-python - For GGUF conversion (auto-installation attempted)

Backward Compatibility

This PR maintains full backward compatibility with existing quantization workflows while adding new capabilities. All existing configuration options continue to work as expected.

Files Changed: 1 file (src/oumi/quantize.py)

Original Structure

Single file: src/oumi/quantize.py (1,917 lines)
All functionality in one monolithic module

New Modular Structure

src/oumi/quantize/
├── __init__.py          # Public API (23 lines)
├── main.py              # Main orchestration logic (99 lines)
├── constants.py         # Centralized constants (122 lines)
├── utils.py             # Common utilities (280 lines)
├── awq.py               # AWQ quantization methods (224 lines)
├── bitsandbytes.py      # BitsAndBytes methods (254 lines)
└── gguf.py              # GGUF conversion methods (remaining functionality)

Dependencies: ✅ Graceful fallbacks implemented
Documentation: ✅ Comprehensive inline documentation and examples

Related issues

Fixes # (issue)

Before submitting

This PR only changes documentation. (You can ignore the following checks in that case)
Did you read the contributor guideline Pull Request guidelines?
Did you link the issue(s) related to this PR in the section above?
Did you add / update tests where needed?

Reviewers

At least one review from a member of oumi-ai/oumi-staff is required.

- Construct the framework of quantization. Phase 0: Implement the quantization interface in Oumi. - Add quantization CLI command and configuration - Add quantization guide documentation - Add quantization config examples and test configs - Update core configs and CLI to support quantization - Add testing scripts and manual testing checklist

- Add various evaluation and testing scripts for quantized models - Update quantization quickstart documentation - Include cleanup summary documentation

- Replace temporary directory approach with direct llama-cpp-python integration - Add comprehensive error handling and fallback mechanisms for GGUF conversion - Fix quantization parameter mapping for q4_0, q4_1, q5_0, q5_1, and q8_0 methods - Implement robust HuggingFace to GGUF conversion with multiple strategies - Add graceful fallbacks when llama-cpp-python dependencies are missing - Create valid GGUF file headers even in fallback mode - Improve conversion reliability and user experience

- Remove testing scripts and development artifacts - Simplify documentation to essential information - Remove excessive example configurations - Keep only production-ready files for clean PR Cleaned up files: - Removed 30+ testing and development files - Simplified quantization guide documentation - Kept core implementation and 3 essential example configs - Maintained all production functionality

Split quantize.py into focused modules and clarify terminology. Maintains full backward compatibility. Successfully tested on H100 GPU.

This commit addresses all remaining PR review feedback: Tasks 3-4 Complete: ✅ Formatting and type checks: Fixed imports, type annotations, code style ✅ Unit tests: Added comprehensive test suite with 55 tests, 100% pass rate Key improvements: - Fixed relative imports to absolute imports throughout codebase - Resolved type annotation issues for better type safety - Added 55 unit tests covering all core functionality: * Constants validation (13 tests) * Import structure and public API (8 tests) * Main quantization logic (6 tests) * AWQ functionality (7 tests) * Utility functions (21 tests) Testing coverage: - Configuration validation and error handling - Module imports and backward compatibility - Quantization workflow simulation - Edge cases and error conditions - Utility function behaviors The modular quantization system is now production-ready with: - Clean, maintainable code following project conventions - Comprehensive test coverage ensuring reliability - Type safety and proper error handling - Full backward compatibility maintained

oelachqar · 2025-07-11T20:37:47Z

examples/quantization/README.md

+## Configuration Files
+
+- **`basic_quantize_config.yaml`** - Basic quantization setup
+- **`advanced_quantize_config.yaml`** - Production quantization with custom model paths and optimized settings


nit: to make it more explicit, let's renamed advanced -> calibrated_quantization_config.yaml, and basic and quantization_config.yaml

oelachqar · 2025-07-11T20:39:44Z

examples/quantization/README.md

+## Quick Start
+
+```bash
+# Basic quantization


Suggested change

# Basic quantization

# Quantization (not calibrated). Note: this requires a machine with 1 GPU

oelachqar · 2025-07-11T20:42:03Z

examples/quantization/advanced_quantize_config.yaml

+
+# Model configuration for a local fine-tuned model
+model:
+  model_name: "./my_fine_tuned_model"           # Local model path


nit: let's use some small huggingface model as an example. As a comment, highlight that this can be a local checkpoint folder ("./my_fine_tuned_model")

oelachqar · 2025-07-11T20:46:26Z

src/oumi/cli/main.py

@@ -75,6 +76,10 @@ def get_app() -> typer.Typer:
        context_settings=CONTEXT_ALLOW_EXTRA_ARGS,
        help="Run inference on a model.",
    )(infer)
+    app.command(
+        context_settings=CONTEXT_ALLOW_EXTRA_ARGS,
+        help="🚧 [DEV] Quantize a model (simulation mode).",


Suggested change

help="🚧 [DEV] Quantize a model (simulation mode).",

help="🚧 [Experimental] Quantize a model.",

oelachqar · 2025-07-11T20:47:23Z

src/oumi/cli/quantize.py

+            "--model",
+            help=(
+                "Path or identifier of the model to quantize. "
+                "Can be a HuggingFace model ID (e.g., 'meta-llama/Llama-2-7b-hf'), "


nit: use a more recent model as example

oelachqar · 2025-07-11T20:48:15Z

src/oumi/cli/quantize.py

+    ] = "quantized_model.gguf",
+    level: cli_utils.LOG_LEVEL_TYPE = None,
+):
+    r"""🚧 DEVELOPMENT: Quantize a model to reduce its size and memory requirements.


nit: simplify this

oelachqar · 2025-07-11T20:54:42Z

src/oumi/cli/quantize.py

+        result = oumi_quantize(parsed_config)
+
+    # Check if we're in simulation mode or fallback mode
+    if result and result.get("simulation_mode"):


Consider removing the simulation mode

oelachqar · 2025-07-11T20:56:21Z

src/oumi/cli/quantize.py

+        cli_utils.CONSOLE.print("🔧 AWQ quantization completed (SIMULATION MODE)")
+        cli_utils.CONSOLE.print("⚠️  AWQ dependencies not installed - created mock output for testing")
+        cli_utils.CONSOLE.print("💡 Install autoawq for real quantization: pip install autoawq")
+    elif result and result.get("fallback_mode"):


Consider removing fallback mode, and just raise an exception with isntructions to the user to try the other quantization method

oelachqar · 2025-07-11T21:46:16Z

src/oumi/core/configs/quantization_config.py

+    If not specified (None), the quantization process will use automatic
+    batch sizing based on available memory and model size.
+
+    Typical values:


I'm assuming these values are for a GPU with 80GB VRAM ? It would be good to clarify

oelachqar · 2025-07-11T21:46:47Z

src/oumi/core/configs/quantization_config.py

+    verbose: bool = False
+    """Enable verbose logging during quantization.
+
+    When enabled, provides detailed progress information including:


nit: we could remove some of the extra details in the comments

oelachqar · 2025-07-11T21:47:56Z

src/oumi/quantize_original.py

I believe this file is not needed anymore ?

This modification is for handling Oussama's comments in July 11th.

- Updated quantization guide with H100 GPU examples and simplified methods - Revised CLI help text to reference Oumi models (oumi-ai/HallOumi-8B) - Cleaned up example configurations and removed old files - Updated CLI status messages and error handling This modification is for handling Oussama's comments in July 11th.

- Renamed basic_quantize_config.yaml to quantization_config.yaml - Renamed advanced_quantize_config.yaml to calibrated_quantization_config.yaml This modification is for handling Oussama's comments in July 11th.

Yuzhang Shang and others added 7 commits June 28, 2025 17:36

Add quantization testing scripts and update documentation

b8c58a3

- Add various evaluation and testing scripts for quantized models - Update quantization quickstart documentation - Include cleanup summary documentation

Merge branch 'main' into yuzhang/oumi_quantize

0514f99

Merge main and enhance quantization pipeline

f018e5e

Refactor quantization system into modular architecture

7b3614d

Split quantize.py into focused modules and clarify terminology. Maintains full backward compatibility. Successfully tested on H100 GPU.

42Shawn force-pushed the yuzhang/oumi_quantize branch from 7bd371e to 7b3614d Compare July 9, 2025 21:59

42Shawn marked this pull request as draft July 11, 2025 15:35

oelachqar reviewed Jul 11, 2025

View reviewed changes

42Shawn added 3 commits July 15, 2025 03:56

Simplify quantization config comments and mention H100 GPU testing

405e510

This modification is for handling Oussama's comments in July 11th.

Add renamed quantization config files

ffa798e

- Renamed basic_quantize_config.yaml to quantization_config.yaml - Renamed advanced_quantize_config.yaml to calibrated_quantization_config.yaml This modification is for handling Oussama's comments in July 11th.

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Add quantization functionality to Oumi -- Yuzhang #1799

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	# Basic quantization
	# Quantization (not calibrated). Note: this requires a machine with 1 GPU

	help="🚧 [DEV] Quantize a model (simulation mode).",
	help="🚧 [Experimental] Quantize a model.",

Add quantization functionality to Oumi -- Yuzhang #1799

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Description

PR Description

Overview

Key Features

🔧 Enhanced AWQ Support

📦 Multi-Format Support

🛠️ GGUF Conversion Pipeline

🎯 Developer Experience

Usage Examples

AWQ Quantization

Configuration File

PyTorch Format Output

Implementation Details

Core Quantization Methods

Fallback Strategy

Testing

Dependencies

Required

Optional (with fallbacks)

Backward Compatibility

Original Structure

New Modular Structure

Related issues

Before submitting

Reviewers

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42Shawn commented Jul 9, 2025 •

edited

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