Release Notes

Release Notes#

Release 0.9#

  • AMD Quark for PyTorch

    • New Features

      • OCP MXFP4 fake quantization and dequantization kernels

        • Efficient kernels are added to Quark’s torch/kernel/hw_emulation/csrc for OCP MXFP4 quantization and dequantization. They are useful to simulate OCP MXFP4 workload on hardware that does not support natively this data type (e.g. MI300X GPUs).

    • Quantized models can be reloaded with no memory overhead

      • The method ModelImporter.import_model_info used to reload a quantized model checkpoint now supports using a non-quantized backbone placed on torch.device("meta") (reference) device, avoiding the memory overhead of instantiating the non-quantized model on device. More details are available here <https://quark.docs.amd.com/latest/pytorch/export/quark_export_hf.html#loading-quantized-models-saved-in-hugging-face-format-safetensors-format>_.

      from quark.torch.export.api import ModelImporter
from transformers import AutoConfig, AutoModelForCausalLM
import torch

model_importer = ModelImporter(
   model_info_dir="./opt-125m-quantized",
   saved_format="safetensors"
)

# We only need the backbone/architecture of the original model,
# not its weights, as weights are loaded from the quantized checkpoint.
config = AutoConfig.from_pretrained("facebook/opt-125m")
with torch.device("meta"):
   original_model = AutoModelForCausalLM.from_config(config)

quantized_model = model_importer.import_model_info(original_model)

    • Deprecations and breaking changes

      • Some quantization schemes in AMD Quark LLM PTQ example are deprecated (reference <https://quark.docs.amd.com/latest/pytorch/example_quark_torch_llm_ptq.html>_):

        • w_mx_fp4_a_mx_fp4_sym is deprecated in favor of: w_mxfp4_a_mxfp4,

        • w_mx_fp6_e3m2_sym in favor of w_mxfp6_e3m2,

        • w_mx_fp6_e2m3_sym in favor of w_mxfp6_e2m3,

        • w_mx_int8_per_group_sym in favor of w_mxint8,

        • w_mxfp4_a_mxfp4_sym in favor of w_mxfp4_a_mxfp4,

        • w_mx_fp6_e2m3_a_mx_fp6_e2m3 in favor of w_mxfp6_e2m3_a_mxfp6_e2m3,

        • w_mx_fp6_e3m2_a_mx_fp6_e3m2 in favor of w_mxfp6_e3m2_a_mxfp6_e3m2,

        • w_mx_fp4_a_mx_fp6_sym in favor of w_mxfp4_a_mxfp6,

        • w_mx_fp8_a_mx_fp8 in favor of w_mxfp8_a_mxfp8.

    • Bug fixes and minor improvements

      • Fake quantization methods for FP4 and FP6 are made compatible with CUDA Graph.

      • A summary of replaced modules for quantization is displayed when calling ModelQuantizer.quantize_model for easier inspection.

    • Model Support:

      • Support Gemma2 in OGA flow.

    • Quantization and Export:

      • Support quantization and export of models in MXFP settings, e.g. MXFP4, MXFP6.

      • Support sequential quantization, e.g. W-A-MXFP4+Scale-FP8e4m3.

      • Support more models with FP8 attention: OPT, LLaMA, Phi, Mixtral.

    • Algorithms:

      • Support GPTQ for MXFP4 Quantization.

      • QAT Enhancements using huggingface Trainer.

      • Fix AWQ implementation for qkv-packed MHA model (e.g., microsoft/Phi-3-mini-4k-instruct) and raise warning to users if using incorrect or unknown AWQ configurations.

    • Performance:

      • Speedup model export.

      • Accelerate FP8 inference acceleration.

      • Tensor parallelism for evaluation of quantized model.

      • Multi-device quantization as well as export.

    • FX Graph quantization:

      • Improve efficiency of power-of-2 scale quantization for less memory and faster computation.

      • Support channel-wise power-of-2 quantization by using per-channel MSE/NON-overflow observer.

      • Support Conv’s Bias for int32 power-of-2 quantization, where bias’s scale = weight’s scale * activation’s scale.

      • Support export of INT16/INT32 quantization model to ONNX format and the corresponding ONNXRuntime.

  • AMD Quark for ONNX

    • New Features:

      • Introduced an encrypted mode for scenarios demanding high model confidentiality.

      • Supported fixing the shape of all tensors.

      • Supported quantization with int16 bias.

    • Enhancements:

      • Supported compatibility with ONNX Runtime version 1.21.x and 1.22.0.

      • Reduced CPU/GPU memory usage to prevent OOM.

      • Improved auto search efficiency by utilizing a cached datareader.

      • Enhanced multi-platform support: now supports Windows (CPU/CUDA) and Linux (CPU/CUDA/ROCm).

    • Examples:

      • Provided quantization examples of TIMM models.

    • Documentation:

      • Added specifications for all custom operators.

      • Improved FAQ documentation.

    • Custom Operations:

      • Renamed custom operation types and updated their domain to the com.amd.quark:

        • BFPFixNeuron → BFPQuantizeDequantize.

        • MXFixNeuron → MXQuantizeDequantize.

        • VitisQuantFormat and VitisQuantType → ExtendedQuantFormat and ExtendedQuantType.

    • Bug fixes and minor improvements

      • Fixed the issue where extremely large or small values caused -inf/inf during scale calculation.

Release 0.8.2#

New Features#

AMD Quark for PyTorch

  • Added support for ONNX Runtime 1.22.0

Release 0.8.1#

Bug Fixes and Enhancements#

AMD Quark for ONNX

  • Fixed BFP Kernel compilation issue for GCC 13

Release 0.8#

  • AMD Quark for PyTorch

    • Model Support:

      • Supported SD3.0 quantization with W-INT4, W-INT8-A-INT8, and W-FP8-A-FP8.

      • Supported FLUX.1 quantization with W-INT4, W-INT8-A-INT8, and W-FP8-A-FP8.

      • Supported DLRM embedding-bag UINT4 weight quantization.

    • Quantization Enhancement:

      • Supported fp8 attention quantization of Llama Family.

      • Integrated SmoothQuant algorithm for SDXL.

      • Enabled quantization for all SDXL components (UNet, VAE, text_encoder, text_encoder_2), supporting both W-INT8-A-INT8 and W-FP8-A-FP8 formats.

    • Model Export:

      • Exported diffusion models (SDXL, SDXL-Turbo and SD1.5) to ONNX format via optimum.

    • Model Evaluation:

      • Added Rouge and Meteor evaluation metrics for LLMs.

      • Supported evaluating ONNX models exported using torch.onnx.export for LLMs.

      • Supported offline evaluation mode (evaluation without generation) for LLMs.

  • AMD Quark for ONNX

    • Model Support:

      • Provided more ONNX quantization examples of detection models such as yolov7/yolov8.

    • Data Types:

      • Supported Microexponents (MX) data types, including MX4, MX6 and MX9.

      • Enhanced BFloat16 with more implementation formats suitable for deployment.

    • ONNX Quantizer Enhancements:

      • Supported compatibility with ONNX Runtime version 1.20.0 and 1.20.1.

      • Supported quantization with excluding subgraphs.

      • Enhanced mixed precision to support quantizing a model with any two data types.

    • Documentation Enhancements:

      • Supported Best Practice for Quark ONNX.

      • Supported documentation of converting from FP32/FP16 to BF16.

      • Supported documentation of XINT8, A8W8 and A16W8 quantization.

    • Custom Operations:

      • Optimized the customized “QuantizeLinear” and “DequantizeLinear” to support running on GPU.

    • Advanced Quantization Algorithms:

      • Supported Quarot Rotation R1 algorithm.

      • Improved AdaQuant algorithm to support Microexponents and Microscaling data types.

      • Added auto-search algorithm to automatically find the optimal quantized model with the best accuracy within the search space.

      • Enhanced the LLM quantization by using EMA algorithm.

    • Model Evaluation:

      • Supported evaluation of L2/PSNR/VMAF/COS.

Release 0.7#

New Features#

PyTorch

  • Added quantization error statistics collection tool.

  • Added support for reloading quantized models using load_state_dict.

  • Added support for W8A8 quantization for the Llama-3.1-8B-Instruct example.

  • Added option of saving metrics to CSV in examples.

  • Added support for HuggingFace integration.

  • Added support for more models

    • Added support for Gemma2 quantization using the OGA flow.

    • Added support for Llama-3.2 with FP8 quantization (weight, activation and KV-Cache) for the vision and language components.

    • Added support for Stable Diffusion v1-5 and Stable Diffusion XL Base 1.0

ONNX

  • Added a tool to replace BFloat16 QDQ with Cast op.

  • Added support for rouge and meteor evaluation metrics.

  • Added a feature to fuse Gelu ops into a single Gelu op.

  • Added the HQQ algorithm for MatMulNBits.

  • Added a tool to convert opset version.

  • Added support for fast fine-tuning BF16 quantized models.

  • Added U8U8_AAWA and some other built-in configurations.

Bug Fixes and Enhancements#

PyTorch

  • Enhanced LLM examples to support layer group size customization.

  • Decoupled model inference from LLM evaluation harness.

  • Fixed OOM issues when quantizing the entire SDXL pipeline.

  • Fixed LLM eval bugs caused by export and multi-gpu usage.

  • Fixed QAT functionality.

  • Addressed AWQ preparation issues.

  • Fixed mismatching QDQ implementation compared to Torch.

  • Enhanced readability and added docstring for graph quantization.

  • Fixed config retrieval by name pattern.

  • Supported more Torch versions for auto config rotation.

  • Refactored dataloader of algorithms.

  • Fixed accuracy issues with Qwen2-MOE.

  • Fixed upscaling of scales during the export of quantized models.

  • Added support for reloading per-layer quantization config.

  • Fixed misleading code in ModelQuantizer._do_calibration for weight-only quantization.

  • Implemented transpose scales for per-group quantization for int8/uint8.

  • Implemented export and load for compressed models.

  • Fixed auto config rotation compatibility for more PyTorch versions.

  • Fixed bug in input of get_config in exporter.

  • Fixed bug in input of the eval_model function.

  • Refactored LLM PTQ examples.

  • Fixed infer_pack_shape function.

  • Documented smoothquant alpha and warned users about possible undesired values.

  • Fixed slightly misleading code in ModelQuantizer._do_calibration.

  • Aligned ONNX mean 2 GAP.

ONNX

  • Refactored documentation for LLM evaluations.

  • Fixed NaN issues caused by overflow for BF16 quantization.

  • Fixed an issue where trying to fast fine-tune the MatMul layers without weights.

  • Updated ONNX unit tests to use temporary paths.

  • Removed generated model “sym_shape_infer_temp.onnx” on infer_shape failure.

  • Fixed error in mixed-precision weights calculation.

  • Fixed a bug when simplifying Llama2-7b without kv_cache.

  • Fixed import path and add parent directory to system path in BFP quantize_model.py example.

Release 0.6#

  • AMD Quark for PyTorch

    • Model Support:

      • Provided more examples of LLM PTQ, such as Llama3.2 and Llama3.2-Vision models (only quantizing the language part).

      • Provided examples of Phi and ChatGLM for LLM QAT.

      • Provided examples of LLM pruning for Qwen2.5, Llama, OPT, CohereForAI/c4ai-command models.

      • Provided an example of YOLO-NAS, a detection model PTQ/QAT, which can partially quantize the model using your configuration under FX mode.

      • Provided an example of SDXL v1.0 with weight INT8 activation INT8 under Eager Mode.

      • Supported more models for rotation, such as Qwen models under Eager Mode.

    • PyTorch Quantizer Enhancements:

      • Supported partially quantizing the model by your config under FX mode.

      • Supported quantization of ConvTranspose2d in Eager Mode and FX mode.

      • Advanced Quantization Algorithms: Improved rotation by auto-generating configurations.

      • Optimized Configuration with DataTypeSpec for ease of use.

      • Accelerated in-place replacement under Eager Mode.

      • Supported loading configuration from a file of algorithms and pre-optimizations under Eager Mode.

    • Evaluation:

      • Provided LLM evaluation method of quantized models on benchmark tasks: Open LLM Leaderboard and more such.

    • Export Capabilities:

      • Integrated the export configurations into the Quark format export content, standardizing the pack method for per-group quantization.

    • PyTorch Pruning:

      • Supported LLM pruning algorithm.

  • AMD Quark for ONNX

    • Model Support:

      • Provided more ONNX quantization examples of LLM models such as Llama2.

    • Data Types:

      • Supported int4 and uint4 data types.

      • Supported Microscaling (MX) data types with int8, fp8_e4m3fn, fp8_e5m2, fp6_e3m2, fp6_e2m3, and fp4 elements.

    • ONNX Quantizer Enhancements:

      • Supported compatibility with ONNX Runtime version 1.19.

      • Supported MatMulNBits quantization for LLM models.

      • Supported fast fine-tuning on the MatMul operator.

      • Supported quantizing specified operators.

      • Supported quantization type alignment of element-wise operators.

      • Supported ONNX graph cleaning for Ryzen AI workflow.

      • Supported int32 bias quantization for Ryzen AI workflow.

      • Enhanced support for Windows systems and ROCm GPU.

      • Optimized the quantization of FP16 models to save memory.

      • Optimized the custom operator compilation process.

      • Optimized the default parameters for auto mixed precision.

    • Advanced Quantization Algorithms:

      • Supported GPTQ for both QDQ format and MatMulNBits format.

Release 0.5.1#

  • AMD Quark for PyTorch

    • Export Modifications:

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

      • Remove sub-directory in the exporting path.

  • AMD Quark for ONNX

    • ONNX Quantizer Enhancements:

      • Supported compatibility with onnxruntime version 1.19.

Release 0.5.0#

  • AMD 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 (for example, 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.

  • AMD 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.

Release 0.2.0#

  • AMD Quark for PyTorch

  • AMD 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.

Release 0.1.0#

  • AMD 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 (supports CPU only).

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