Release Note

New Features (Version 0.5.1)

• Quark for PyTorch

  • Export Modifications:

    • Ignore the configuration of preprocessing algorithms when exporting Json-safetensors format

    • Remove sub-directory in the exporting path.

• Quark for ONNX

  • ONNX Quantizer Enhancements:

    • Supported compatibility with onnxruntime version 1.19.

New Features (Version 0.5.0)

• Quark for PyTorch

  • Model Support:

    • Provided more examples of LLM models quantization:

      • INT/OCP_FP8E4M3: Llama-3.1, gpt-j-6b, Qwen1.5-MoE-A2.7B, phi-2, Phi-3-mini, Phi-3.5-mini-instruct, Mistral-7B-v0.1

      • OCP_FP8E4M3: mistralai/Mixtral-8x7B-v0.1, hpcai-tech/grok-1, CohereForAI/c4ai-command-r-plus-08-2024, CohereForAI/c4ai-command-r-08-2024, CohereForAI/c4ai-command-r-plus, CohereForAI/c4ai-command-r-v01, databricks/dbrx-instruct, deepseek-ai/deepseek-moe-16b-chat

    • Provided more examples of diffusion model quantization:

      • Supported models: SDXL, SDXL-Turbo, SD1.5, Controlnet-Canny-SDXL, Controlnet-Depth-SDXL, Controlnet-Canny-SD1.5

      • Supported schemes: FP8, W8, W8A8 with and without SmoothQuant

  • PyTorch Quantizer Enhancements:

    • Supported more CNN models for graph mode quantization.

  • Data Types:

    • Supported BFP16, MXFP8_E5M2.

    • Supported MX6 and MX9. (experimental)

  • Advanced Quantization Algorithms:

    • Supported Rotation for Llama models.

    • Supported SmoothQuant and AWQ for models with GQA and MQA (e.g., LLaMA-3-8B, QWen2-7B).

    • Provided scripts for generating AWQ configuration automatically.(experimental)

    • Supported trained quantization thresholds (TQT) and learned step size quantization (LSQ) for better QAT results. (experimental)

  • Export Capabilities:

    • Supported reloading function of Json-Safetensors export format.

    • Enhanced quantization configuration in Json-Safetensors export format.

• Quark for ONNX

  • ONNX Quantizer Enhancements:

    • Supported compatibility with onnxruntime version 1.18.

    • Enhanced quantization support for LLM models.

  • Quantization Strategy:

    • Supported dynamic quantization.

  • Custom operations:

    • Optimized “BFPFixNeuron” to support running on GPU.

  • Advanced Quantization Algorithms:

    • Improved AdaQuant to support BFP data types.

New Features (Version 0.2.0)

• Quark for PyTorch

  • PyTorch Quantizer Enhancements:

    • Post Training Quantization (PTQ) and Quantization-Aware Training (QAT) are now supported in FX graph mode.

    • Introduced quantization support of the following modules: torch.nn.Conv2d.

  • Data Types:

    • OCP Microscaling (MX) is supported. Valid element data types include INT8, FP8_E4M3, FP4, FP6_E3M2, and FP6_E2M3.

  • Export Capabilities:

    • Quantized models can now be exported in GGUF format. The exported GGUF model is runnable with llama.cpp. Only Llama2 is supported for now.

    • Introduced Quark’s native Json-Safetensors export format, which is identical to AutoFP8 and AutoAWQ when used for FP8 and AWQ quantization.

  • Model Support:

    • Added support for SDXL model quantization in eager mode, including fp8 per-channel and per-tensor quantization.

    • Added support for PTQ and QAT of CNN models in graph mode, including architectures like ResNet.

  • Integration with other toolkits:

    • Provided the integrated example with APL(AMD Pytorch-light,internal project name), supporting the invocation of APL’s INT-K, BFP16, and BRECQ.

    • Introduced the experimental Quark extension interface, enabling seamless integration of Brevitas for Stable Diffusion and Imagenet classification model quantization.

• Quark for ONNX

  • ONNX Quantizer Enhancements:

    • Multiple optimization and refinement strategies for different deployment backends.

    • Supported automatic mixing precision to balance accuracy and performance.

  • Quantization Strategy:

    • Supported symmetric and asymmetric quantization.

    • Supported float scale, INT16 scale and power-of-two scale.

    • Supported static quantization and weight-only quantization.

  • Quantization Granularity:

    • Supported for per-tensor and per-channel granularity.

  • Data Types:

    • Multiple data types are supported, including INT32/UINT32, Float16, Bfloat16, INT16/UINT16, INT8/UINT8 and BFP.

  • Calibration Methods:

    • MinMax, Entropy and Percentile for float scale.

    • MinMax for INT16 scale.

    • NonOverflow and MinMSE for power-of-two scale.

  • Custom operations:

    • “BFPFixNeuron” which supports block floating-point data type. It can run on the CPU on Windows, and on both the CPU and GPU on Linux.

    • “VitisQuantizeLinear” and “VitisDequantizeLinear” which support INT32/UINT32, Float16, Bfloat16, INT16/UINT16 quantization.

    • “VitisInstanceNormalization” and “VitisLSTM” which have customized Bfloat16 kernels.

    • All custom operations support running on the CPU on both Linux and Windows.

  • Advanced Quantization Algorithms:

    • Supported CLE, BiasCorrection, AdaQuant, AdaRound and SmoothQuant.

  • Operating System Support:

    • Linux and Windows.

New Features (Version 0.1.0)

• Quark for PyTorch

  • Pytorch Quantizer Enhancements:

    • Eager mode is supported.

    • Post Training Quantization (PTQ) is now available.

    • Automatic in-place replacement of nn.module operations.

    • Quantization of the following modules is supported: torch.nn.linear.

    • The customizable calibration process is introduced.

  • Quantization Strategy:

    • Symmetric and asymmetric quantization are supported.

    • Weight-only, dynamic, and static quantization modes are available.

  • Quantization Granularity:

    • Support for per-tensor, per-channel, and per-group granularity.

  • Data Types:

    • Multiple data types are supported, including float16, bfloat16, int4, uint4, int8, and fp8 (e4m3fn).

  • Calibration Methods:

    • MinMax, Percentile, and MSE calibration methods are now supported.

  • Large Language Model Support:

    • FP8 KV-cache quantization for large language models(LLMs).

  • Advanced Quantization Algorithms:

    • Support SmoothQuant, AWQ(uint4), and GPTQ(uint4) for LLMs. (Note: AWQ/GPTQ/SmoothQuant algorithms are currently limited to single GPU usage.)

  • Export Capabilities:

    • Export of Q/DQ quantized models to ONNX and vLLM-adopted JSON-safetensors format now supported.

  • Operating System Support:

    • Linux (supports ROCM and CUDA)

    • Windows (support CPU only).