Weight Only Quantization (WOQ)

Introduction
Supported Framework Model Matrix
Examples

Introduction

As large language models (LLMs) become more prevalent, there is a growing need for new and improved quantization methods that can meet the computational demands of these modern architectures while maintaining the accuracy. Compared to normal quantization like W8A8, weight only quantization is probably a better trade-off to balance the performance and the accuracy, since we will see below that the bottleneck of deploying LLMs is the memory bandwidth and normally weight only quantization could lead to better accuracy.

Model inference: Roughly speaking , two key steps are required to get the model’s result. The first one is moving the model from the memory to the cache piece by piece, in which, memory bandwidth $B$ and parameter count $P$ are the key factors, theoretically the time cost is $P*4 /B$. The second one is computation, in which, the device’s computation capacity $C$ measured in FLOPS and the forward FLOPs $F$ play the key roles, theoretically the cost is $F/C$.

Text generation: The most famous application of LLMs is text generation, which predicts the next token/word based on the inputs/context. To generate a sequence of texts, we need to predict them one by one. In this scenario, $F\approx P$ if some operations like bmm are ignored and past key values have been saved. However, the $C/B$ of the modern device could be to 100X, that makes the memory bandwidth as the bottleneck in this scenario.

Besides, as mentioned in many papers[1][2], activation quantization is the main reason to cause the accuracy drop. So for text generation task, weight only quantization is a preferred option in most cases.

Theoretically, round-to-nearest (RTN) is the most straightforward way to quantize weight using scale maps. However, when the number of bits is small (e.g. 3), the MSE loss is larger than expected. A group size is introduced to reduce elements using the same scale to improve accuracy.

There are many excellent works for weight only quantization to improve its accuracy performance, such as AWQ[3], GPTQ[4]. Neural compressor integrates these popular algorithms in time to help customers leverage them and deploy them to their own tasks.

Supported Framework Model Matrix

Algorithms/Framework	PyTorch	ONNX Runtime
RTN	✔	✔
AWQ	✔	✔
GPTQ	✔	✔
TEQ	✔	stay tuned

Note: To get the validated accuracy results on popular models, please refer to PyTorch Models with Torch 2.0.1+cpu in WOQ Mode

RTN: A quantification method that we can think of very intuitively. It does not require additional datasets and is a very fast quantization method. Generally speaking, RTN will convert the weight into a uniformly distributed integer data type, but some algorithms, such as Qlora, propose a non-uniform NF4 data type and prove its theoretical optimality.

GPTQ: A new one-shot weight quantization method based on approximate second-order information, that is both highly-accurate and highly efficient[4]. The weights of each column are updated based on the fixed-scale pseudo-quantization error and the inverse of the Hessian matrix calculated from the activations. The updated columns sharing the same scale may generate a new max/min value, so the scale needs to be saved for restoration.

AWQ: Proved that protecting only 1% of salient weights can greatly reduce quantization error. the salient weight channels are selected by observing the distribution of activation and weight per channel. The salient weights are also quantized after multiplying a big scale factor before quantization for preserving.

TEQ: A trainable equivalent transformation that preserves the FP32 precision in weight-only quantization. It is inspired by AWQ while providing a new solution to search for the optimal per-channel scaling factor between activations and weights.

Examples

Quantization Capability:

| Config | Capability | | :—: | :—:| | dtype | [’int’, ‘nf4’, ‘fp4’] | | bits | [1, …, 8] | | group_size | [-1, 1, …, $C_{in}$] | | scheme | [’asym’, ‘sym’] | | algorithm | [’RTN’, ‘AWQ’, ‘GPTQ’] |

Notes:

group_size = -1 refers to per output channel quantization. Taking a linear layer (input channel = $C_{in}$, output channel = $C_{out}$) for instance, when group size = -1, quantization will calculate total $C_{out}$ quantization parameters. Otherwise, when group_size = gs quantization parameters are calculate with every $gs$ elements along with the input channel, leading to total $C_{out} \times (C_{in} / gs)$ quantization parameters.
4-bit NormalFloat(NF4) is proposed in QLoRA[5]. ‘fp4’ includes fp4_e2m1 and fp4_e2m1_bnb. By default, fp4 refers to fp4_e2m1_bnb.

GPTQ arguments: | gptq_args | default value | comments | |:———-:|:————-:|:——————————————————————-:| | actorder | False | Whether to sort Hessian’s diagonal values to rearrange channel-wise quantization order| | percdamp | 0.01 | Percentage of Hessian’s diagonal values’ average, which will be added to Hessian’s diagonal to increase numerical stability| | nsamples | 128 | Calibration samples’ size | | pad_max_length | 2048 | Whether to align calibration data to a fixed length. This value should not exceed model’s acceptable sequence length. Please refer to model’s config json to find out this value.| | use_max_length | False | Whether to align all calibration data to fixed length, which equals to pad_max_length. | | block_size | 128 | Execute GPTQ quantization per block, block shape = [$C_{out}$, block_size] |

Note: Neural compressor provides Unsigned integer for asymmetric quantization and Signed integer for symmetric quantization. Please follow the below section to compress the low bit data type for saving.

Export Compressed Model

To support low memory inference, Neural Compressor implemented WeightOnlyLinear, a torch.nn.Module, to compress the fake quantized fp32 model. Since torch does not provide flexible data type storage, WeightOnlyLinear combines low bits data into a long date type, such as torch.int8 and torch.int32. Low bits data includes weights and zero points. When using WeightOnlyLinear for inference, it will restore the compressed data to float32 and run torch linear function.

User code:

conf = PostTrainingQuantConfig(
    approach="weight_only",
    op_type_dict={
        ".*": {  # re.match
            "weight": {
                "bits": 8,  # 1-8 bit
                "group_size": -1,  # -1 (per-channel)
                "scheme": "sym",
                "algorithm": "RTN",
            },
        },
    },
    recipes={
        # 'rtn_args':{'enable_full_range': True, 'enable_mse_search': True},
        # 'gptq_args':{'percdamp': 0.01, 'actorder':True, 'block_size': 128, 'nsamples': 128, 'use_full_length': False},
        # 'awq_args':{'enable_auto_scale': True, 'enable_mse_search': True, 'n_blocks': 5},
    },
)
q_model = quantization.fit(model, conf, eval_func=eval_func)
q_model.save("saved_results")
compressed_model = q_model.export_compressed_model(
    compression_dtype=torch.int32,
    compression_dim=1,
    scale_dtype=torch.float16,
)
torch.save(compressed_model.state_dict(), "compressed_model.pt")

The saved_results folder contains two files: best_model.pt and qconfig.json, and the generated q_model is a fake quantized model.

Reference

[1]. Xiao, Guangxuan, et al. “Smoothquant: Accurate and efficient post-training quantization for large language models.” arXiv preprint arXiv:2211.10438 (2022).

[2]. Wei, Xiuying, et al. “Outlier suppression: Pushing the limit of low-bit transformer language models.” arXiv preprint arXiv:2209.13325 (2022).

[3]. Lin, Ji, et al. “AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration.” arXiv preprint arXiv:2306.00978 (2023).

[4]. Frantar, Elias, et al. “Gptq: Accurate post-training quantization for generative pre-trained transformers.” arXiv preprint arXiv:2210.17323 (2022).

[5]. Dettmers, Tim, et al. “Qlora: Efficient finetuning of quantized llms.” arXiv preprint arXiv:2305.14314 (2023).