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f5247aca01
* HQQ model serialization attempt * fix hqq dispatch and unexpected keys * style * remove check_old_param * revert to check HQQLinear in quantizer_hqq.py * revert to check HQQLinear in quantizer_hqq.py * update HqqConfig default params * make ci happy * make ci happy * revert to HQQLinear check in quantizer_hqq.py * check hqq_min version 0.2.0 * set axis=1 as default in quantization_config.py * validate_env with hqq>=0.2.0 version message * deprecated hqq kwargs message * make ci happy * remove run_expected_keys_check hack + bump to 0.2.1 min hqq version * fix unexpected_keys hqq update * add pre_quantized check * add update_expected_keys to base quantizerr * ci base.py fix? * ci base.py fix? * fix "quantization typo" src/transformers/utils/quantization_config.py Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * fix post merge --------- Co-authored-by: Marc Sun <marc@huggingface.co> Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
70 lines
2.7 KiB
Markdown
Executable File
70 lines
2.7 KiB
Markdown
Executable File
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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rendered properly in your Markdown viewer.
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-->
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# HQQ
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Half-Quadratic Quantization (HQQ) implements on-the-fly quantization via fast robust optimization. It doesn't require calibration data and can be used to quantize any model.
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Please refer to the <a href="https://github.com/mobiusml/hqq/">official package</a> for more details.
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For installation, we recommend you use the following approach to get the latest version and build its corresponding CUDA kernels:
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```
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pip install hqq
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```
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To quantize a model, you need to create an [`HqqConfig`]. There are two ways of doing it:
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``` Python
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from transformers import AutoModelForCausalLM, AutoTokenizer, HqqConfig
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# Method 1: all linear layers will use the same quantization config
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quant_config = HqqConfig(nbits=8, group_size=64)
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```
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``` Python
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# Method 2: each linear layer with the same tag will use a dedicated quantization config
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q4_config = {'nbits':4, 'group_size':64}
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q3_config = {'nbits':3, 'group_size':32}
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quant_config = HqqConfig(dynamic_config={
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'self_attn.q_proj':q4_config,
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'self_attn.k_proj':q4_config,
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'self_attn.v_proj':q4_config,
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'self_attn.o_proj':q4_config,
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'mlp.gate_proj':q3_config,
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'mlp.up_proj' :q3_config,
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'mlp.down_proj':q3_config,
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})
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```
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The second approach is especially interesting for quantizing Mixture-of-Experts (MoEs) because the experts are less affected by lower quantization settings.
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Then you simply quantize the model as follows
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``` Python
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model = transformers.AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.float16,
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device_map="cuda",
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quantization_config=quant_config
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)
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```
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## Optimized Runtime
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HQQ supports various backends, including pure PyTorch and custom dequantization CUDA kernels. These backends are suitable for older gpus and peft/QLoRA training.
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For faster inference, HQQ supports 4-bit fused kernels (TorchAO and Marlin), reaching up to 200 tokens/sec on a single 4090.
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For more details on how to use the backends, please refer to https://github.com/mobiusml/hqq/?tab=readme-ov-file#backend
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