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awq-py : remove (#5768)
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Georgi Gerganov
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awq-py/README.md
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awq-py/README.md
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# AWQ: Activation-aware Weight Quantization for LLM - version apply to llamacpp
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[[Paper](https://arxiv.org/abs/2306.00978)][[Original Repo](https://github.com/mit-han-lab/llm-awq)][[Easy-to-use Repo](https://github.com/casper-hansen/AutoAWQ)]
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**Supported models:**
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- [X] LLaMA
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- [x] LLaMA 2
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- [X] MPT
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- [X] Mistral AI v0.1
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- [ ] Bloom
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- [ ] Mixtral MoE
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**TODO:**
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- [x] Update version work with both MPT and MPT-AWQ model
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- [ ] Add OPT model
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- [ ] Add Bloom model
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- [ ] Add Mixtral MoE
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- [ ] Support w3, w2
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## Contents
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- [Install](##Install)
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- [Convert](##Convert)
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- [Quantize](##Quantize)
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- [Test](##Test)
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- [Benchmark](##Benchmark)
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- [Results](##Results)
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## Install
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Install requirements
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```bash
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pip install -r requirements.txt
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```
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Get the pre-computed AWQ search results for multiple model families, including LLaMA, LLaMA2, MPT, OPT
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```bash
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git clone https://huggingface.co/datasets/mit-han-lab/awq-model-zoo awq_cache
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```
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## Convert
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Example for llama model
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```bash
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# For llama7b and llama2 models
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python convert.py models/llama-7b/ --awq-path awq_cache/llama-7b-w4-g128.pt --outfile models/llama_7b_fp16.gguf
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# For mistral and mpt models
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python convert-hf-to-gguf.py models/mpt-7b/ --awq-path awq_cache/mpt-7b-w4-g128.pt --outfile models/mpt_7b_fp16.gguf
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```
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## Quantize
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```bash
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# We only benchmark and confirm the results on q4_0, q4_1, and q2_k types.
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./quantize models/llama_7b_fp16.gguf models/llama_7b_q4_0.gguf q4_0
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```
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## Test
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```bash
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# For all models.
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./build/bin/main -m models/llama_7b_q4_0.gguf -n 128 --prompt "Once upon a time"
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```
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## Benchmark
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The perplexity measurements in table above are done against the `wikitext2` test dataset (https://paperswithcode.com/dataset/wikitext-2), with context length of 512.
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```bash
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# For llama and llama2, and mistral models.
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./perplexity -m models/llama_7b_q4_0.gguf -f datasets/wikitext-2-raw/wiki.test.raw
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```
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## Results
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Results are run on OpenBLAS (CPU) and CuBLAS (GPU) for fair comparison
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We use three types of llamacpp quantization methods to work with our version, including q4_0, q4_1, and q2_k
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### Llama 7B (Build with OpenBLAS)
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| Model | Measure | F16 | Q4_0 | Q4_1 | Q2_K |
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|-----------:|--------------|-------:|-------:|-------:|-------:|
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|Llama 7B | perplexity | 5.9066 | 6.1214 | 6.0643 | 6.5808 |
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|Llama 7B | file size | 12.9G | 3.5G | 3.9G | 2.7G |
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|Llama 7B | bits/weight | 16.0 | 4.5 | 5.0 | 2.6 |
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|AWQ-LLama 7B| perplexity | 5.9175 | 6.0252 | 5.9987 | 6.3692 |
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|AWQ-LLama 7B| file size | 12.9G | 3.5G | 3.9G | 2.7G |
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|AWQ-LLama 7B| bits/weight | 16.0 | 4.5 | 5.0 | 2.6 |
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### Llama2 7B (Build with CuBLAS)
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| Model | Measure | F16 | Q4_0 | Q4_1 | Q2_K |
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|------------:|--------------|-------:|-------:|-------:|-------:|
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|Llama2 7B | perplexity | 5.8664 | 6.0260 | 6.0656 | 6.4496 |
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|Llama2 7B | file size | 12.9G | 3.5G | 3.9G | 2.7G |
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|Llama2 7B | bits/weight | 16.0 | 4.5 | 5.0 | 2.6 |
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|AWQ-LLama2 7B| perplexity | 5.8801 | 6.0054 | 5.9849 | 6.3650 |
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|AWQ-LLama2 7B| file size | 12.9G | 3.5G | 3.9G | 2.7G |
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|AWQ-LLama2 7B| bits/weight | 16.0 | 4.5 | 5.0 | 2.6 |
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### Mistral 7B v0.1 (Build with CuBLAS)
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| Model | Measure | F16 | Q4_0 | Q4_1 | Q2_K |
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|-------------:|--------------|-------:|-------:|-------:|-------:|
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|Mistral 7B | perplexity | 5.6931 | 5.8202 | 5.8268 | 6.1645 |
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|Mistral 7B | file size | 14.5G | 4.1G | 4.5G | 3.1G |
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|Mistral 7B | bits/weight | 16.0 | 4.5 | 5.0 | 2.6 |
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|AWQ-Mistral 7B| perplexity | 5.6934 | 5.8020 | 5.7691 | 6.0426 |
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|AWQ-Mistral 7B| file size | 14.5G | 4.1G | 4.5G | 3.1G |
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|AWQ-Mistral 7B| bits/weight | 16.0 | 4.5 | 5.0 | 2.6 |
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### MPT 7B (Build with OpenBLAS)
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| Model | Measure | F16 | Q4_0 | Q4_1 | Q2_K |
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|---------:|--------------|-------:|-------:|-------:|--------:|
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|MPT 7B | perplexity | 8.4369 | 8.7956 | 8.6265 | 11.4913 |
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|MPT 7B | file size | 13.7G | 3.9G | 4.3G | 2.8G |
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|MPT 7B | bits/weight | 16.0 | 4.5 | 5.0 | 2.6 |
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|AWQ-MPT 7B| perplexity | 8.4944 | 8.7053 | 8.6750 | 10.2873|
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|AWQ-MPT 7B| file size | 13.7G | 3.9G | 4.3G | 2.8G |
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|AWQ-MPT 7B| bits/weight | 16.0 | 4.5 | 5.0 | 2.6 |
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@ -1,254 +0,0 @@
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"""
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Implements the AWQ for llama.cpp use cases.
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Original paper: https://arxiv.org/abs/2306.00978
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This code is based on versions of the AWQ implementation found in the following repositories:
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* https://github.com/mit-han-lab/llm-awq
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* https://github.com/casper-hansen/AutoAWQ
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"""
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import os
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import torch
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import torch.nn as nn
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from transformers import AutoModelForCausalLM, AutoConfig
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from transformers.models.bloom.modeling_bloom import BloomGelu
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from transformers.models.llama.modeling_llama import LlamaRMSNorm
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from transformers.activations import GELUActivation
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class ScaledActivation(nn.Module):
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"""
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ScaledActivation module wraps an existing activation function and applies a
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scale factor to its output.
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Args:
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module (nn.Module): The activation function to be scaled.
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scales (torch.Tensor): A tensor of size (num_features,) containing the initial
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scale factors for each feature.
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Returns:
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torch.Tensor: The scaled output of the activation function.
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"""
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def __init__(self, module, scales):
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super().__init__()
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self.act = module
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self.scales = nn.Parameter(scales.data)
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def forward(self, x):
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return self.act(x) / self.scales.view(1, 1, -1).to(x.device)
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def set_op_by_name(layer, name, new_module):
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"""
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Set the new module for given module's name.
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Args:
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layer (nn.Module): The layer in which to replace the submodule.
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name (str): The path to the submodule to be replaced, using dot notation
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to access nested modules.
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new_module (nn.Module): The new module to replace the existing one.
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"""
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levels = name.split(".")
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if len(levels) > 1:
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mod_ = layer
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for l_idx in range(len(levels) - 1):
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if levels[l_idx].isdigit():
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mod_ = mod_[int(levels[l_idx])]
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else:
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mod_ = getattr(mod_, levels[l_idx])
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setattr(mod_, levels[-1], new_module)
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else:
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setattr(layer, name, new_module)
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def get_op_by_name(module, op_name):
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"""
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Retrieves a submodule within a given layer based on its name.
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Args:
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module (nn.Module): The layer containing the submodule to find.
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op_name (str): The name of the submodule.
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Returns:
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nn.Module: The requested submodule found within the given layer.
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Raises:
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ValueError: If the specified submodule cannot be found within the layer.
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"""
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for name, m in module.named_modules():
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if name == op_name:
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return m
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raise ValueError(f"Cannot find op {op_name} in module {module}")
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@torch.no_grad()
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def scale_ln_fcs(ln, fcs, scales):
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"""
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Scales the weights of a LayerNorm and a list of fully-connected layers proportionally.
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Args:
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ln (nn.LayerNorm): The LayerNorm module to be scaled.
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fcs (List[nn.Linear]): A list of fully-connected layers to be scaled.
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scales (torch.Tensor): A 1D tensor of size (num_features,).
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"""
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if not isinstance(fcs, list):
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fcs = [fcs]
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scales = scales.to(ln.weight.device)
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ln.weight.div_(scales)
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if hasattr(ln, "bias") and ln.bias is not None:
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ln.bias.div_(scales)
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for fc in fcs:
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fc.weight.mul_(scales.view(1, -1))
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for p in ln.parameters():
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assert torch.isnan(p).sum() == 0
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for fc in fcs:
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for p in fc.parameters():
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assert torch.isnan(p).sum() == 0
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@torch.no_grad()
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def scale_fc_fc(fc1, fc2, scales):
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"""
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Scales the weights of two fully-connected layers in a specific pattern.
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Args:
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fc1 (nn.Linear): The first fully-connected layer to be scaled.
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fc2 (nn.Linear): The second fully-connected layer to be scaled.
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scales (torch.Tensor): A 1D tensor of size (num_features,).
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"""
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assert isinstance(fc1, nn.Linear)
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assert isinstance(fc2, nn.Linear)
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scales = scales.to(fc1.weight.device)
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fc1.weight[-scales.size(0):].div_(scales.view(-1, 1))
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if fc1.bias is not None:
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fc1.bias.div_(scales.view(-1))
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fc2.weight.mul_(scales.view(1, -1))
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for p in fc1.parameters():
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assert torch.isnan(p).sum() == 0
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for p in fc2.parameters():
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assert torch.isnan(p).sum() == 0
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@torch.no_grad()
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def scale_gelu_fc(gelu, fc, scales):
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"""
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Scales the weight of a GELU activation and a fully-connected layer proportionally.
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Args:
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gelu (Union[nn.GELU, BloomGelu, GELUActivation]): The GELU activation module to be scaled.
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fc (nn.Linear): The fully-connected layer to be scaled.
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scales (torch.Tensor): A 1D tensor of size (num_features,).
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Raises:
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TypeError: If the `gelu` module is not of type `nn.GELU`, `BloomGelu`, or `GELUActivation`.
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TypeError: If the `fc` module is not of type `nn.Linear`.
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"""
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assert isinstance(gelu, (nn.GELU, BloomGelu, GELUActivation))
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assert isinstance(fc, nn.Linear)
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fc.weight.mul_(scales.view(1, -1).to(fc.weight.device))
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for p in fc.parameters():
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assert torch.isnan(p).sum() == 0
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def apply_scale(module, scales_list, input_feat_dict=None):
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"""
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Applies different scaling strategies to layers based on their type and hierarchy within a given module.
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Args:
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module (nn.Module): The module containing the layers to be scaled.
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scales_list (List[Tuple[str, List[str], torch.Tensor]]): A list of tuples containing:
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* prev_op_name (str): The name of the preceding operation or module,
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relative to which the layers to be scaled are located.
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* layer_names (List[str]): A list of names of the layers to be scaled, relative to the preceding operation.
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* scales (torch.Tensor): A 1D tensor of size (num_features,) containing the scaling factors for each feature.
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input_feat_dict (Optional[Dict[str, torch.Tensor]]): A dictionary mapping layer names to their corresponding
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input features (optional).
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"""
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for prev_op_name, layer_names, scales in scales_list:
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prev_op = get_op_by_name(module, prev_op_name)
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layers = [get_op_by_name(module, name) for name in layer_names]
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prev_op.cuda()
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for layer in layers:
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layer.cuda()
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scales.cuda()
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if isinstance(prev_op, nn.Linear):
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assert len(layers) == 1
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scale_fc_fc(prev_op, layers[0], scales)
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elif isinstance(prev_op, (nn.LayerNorm, LlamaRMSNorm)) or "rmsnorm" in str(prev_op.__class__).lower():
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scale_ln_fcs(prev_op, layers, scales)
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elif isinstance(prev_op, (nn.GELU, BloomGelu, GELUActivation)):
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new_module = ScaledActivation(prev_op, scales)
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set_op_by_name(module, prev_op_name, new_module)
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scale_gelu_fc(prev_op, layers[0], scales)
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else:
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raise NotImplementedError(f"prev_op {type(prev_op)} not supported yet!")
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# apply the scaling to input feat if given; prepare it for clipping
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if input_feat_dict is not None:
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for layer_name in layer_names:
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inp = input_feat_dict[layer_name]
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inp.div_(scales.view(1, -1).to(inp.device))
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prev_op.cpu()
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for layer in layers:
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layer.cpu()
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scales.cpu()
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@torch.no_grad()
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def apply_clip(module, clip_list):
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"""
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Applies element-wise clipping to the weight of a specific layer within a given module.
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Args:
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module (nn.Module): The module containing the layer to be clipped.
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clip_list (List[Tuple[str, torch.Tensor]]): A list of tuples containing:
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* name (str): The name of the layer to be clipped, relative to the root of the module.
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* max_val (torch.Tensor): A 1D or 2D tensor defining the upper bound for each element of the layer's weight.
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"""
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for name, max_val in clip_list:
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layer = get_op_by_name(module, name)
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layer.cuda()
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max_val = max_val.to(layer.weight.device)
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org_shape = layer.weight.shape
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layer.weight.data = layer.weight.data.reshape(*max_val.shape[:2], -1)
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layer.weight.data = torch.clamp(layer.weight.data, -max_val, max_val)
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layer.weight.data = layer.weight.data.reshape(org_shape)
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layer.cpu()
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def add_scale_weights(model_path, scale_path, tmp_path):
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"""
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Adds pre-computed Activation Weight Quantization (AWQ) results to a model,
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including scaling factors and clipping bounds.
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Args:
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model_path (str): Path to the pre-trained model to be equipped with AWQ.
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scale_path (str): Path to the AWQ scale factors (.pt file).
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tmp_path (str): Path to the temporary directory where the equipped model will be saved.
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"""
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config = AutoConfig.from_pretrained(model_path, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(
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model_path, config=config, trust_remote_code=True
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)
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model.eval()
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awq_results = torch.load(str(scale_path), map_location="cpu")
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apply_scale(model, awq_results["scale"])
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apply_clip(model, awq_results["clip"])
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model.save_pretrained(str(tmp_path))
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os.system(f"cp {str(model_path)}/tokenizer* {str(tmp_path)}")
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@ -1,2 +0,0 @@
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torch>=2.1.1
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transformers>=4.32.0
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