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awq-py/README.md
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# AWQ: Activation-aware Weight Quantization for LLM - version apply to llamacpp
[[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)]
**Supported models:**
- [X] LLaMA
- [x] LLaMA 2
- [X] MPT
- [X] Mistral AI v0.1
- [ ] Bloom
- [ ] Mixtral MoE
**TODO:**
- [x] Update version work with both MPT and MPT-AWQ model
- [ ] Add OPT model
- [ ] Add Bloom model
- [ ] Add Mixtral MoE
- [ ] Support w3, w2
## Contents
- [Install](##Install)
- [Convert](##Convert)
- [Quantize](##Quantize)
- [Test](##Test)
- [Benchmark](##Benchmark)
- [Results](##Results)
## Install
Install requirements
```bash
pip install -r requirements.txt
```
Get the pre-computed AWQ search results for multiple model families, including LLaMA, LLaMA2, MPT, OPT
```bash
git clone https://huggingface.co/datasets/mit-han-lab/awq-model-zoo awq_cache
```
## Convert
Example for llama model
```bash
# For llama7b and llama2 models
python convert.py models/llama-7b/ --awq-path awq_cache/llama-7b-w4-g128.pt --outfile models/llama_7b_fp16.gguf
# For mistral and mpt models
python convert-hf-to-gguf.py models/mpt-7b/ --awq-path awq_cache/mpt-7b-w4-g128.pt --outfile models/mpt_7b_fp16.gguf
```
## Quantize
```bash
# We only benchmark and confirm the results on q4_0, q4_1, and q2_k types.
./quantize models/llama_7b_fp16.gguf models/llama_7b_q4_0.gguf q4_0
```
## Test
```bash
# For all models.
./build/bin/main -m models/llama_7b_q4_0.gguf -n 128 --prompt "Once upon a time"
```
## Benchmark
The perplexity measurements in table above are done against the `wikitext2` test dataset (https://paperswithcode.com/dataset/wikitext-2), with context length of 512.
```bash
# For llama and llama2, and mistral models.
./perplexity -m models/llama_7b_q4_0.gguf -f datasets/wikitext-2-raw/wiki.test.raw
```
## Results
Results are run on OpenBLAS (CPU) and CuBLAS (GPU) for fair comparison
We use three types of llamacpp quantization methods to work with our version, including q4_0, q4_1, and q2_k
### Llama 7B (Build with OpenBLAS)
| Model | Measure | F16 | Q4_0 | Q4_1 | Q2_K |
|-----------:|--------------|-------:|-------:|-------:|-------:|
|Llama 7B | perplexity | 5.9066 | 6.1214 | 6.0643 | 6.5808 |
|Llama 7B | file size | 12.9G | 3.5G | 3.9G | 2.7G |
|Llama 7B | bits/weight | 16.0 | 4.5 | 5.0 | 2.6 |
|AWQ-LLama 7B| perplexity | 5.9175 | 6.0252 | 5.9987 | 6.3692 |
|AWQ-LLama 7B| file size | 12.9G | 3.5G | 3.9G | 2.7G |
|AWQ-LLama 7B| bits/weight | 16.0 | 4.5 | 5.0 | 2.6 |
### Llama2 7B (Build with CuBLAS)
| Model | Measure | F16 | Q4_0 | Q4_1 | Q2_K |
|------------:|--------------|-------:|-------:|-------:|-------:|
|Llama2 7B | perplexity | 5.8664 | 6.0260 | 6.0656 | 6.4496 |
|Llama2 7B | file size | 12.9G | 3.5G | 3.9G | 2.7G |
|Llama2 7B | bits/weight | 16.0 | 4.5 | 5.0 | 2.6 |
|AWQ-LLama2 7B| perplexity | 5.8801 | 6.0054 | 5.9849 | 6.3650 |
|AWQ-LLama2 7B| file size | 12.9G | 3.5G | 3.9G | 2.7G |
|AWQ-LLama2 7B| bits/weight | 16.0 | 4.5 | 5.0 | 2.6 |
### Mistral 7B v0.1 (Build with CuBLAS)
| Model | Measure | F16 | Q4_0 | Q4_1 | Q2_K |
|-------------:|--------------|-------:|-------:|-------:|-------:|
|Mistral 7B | perplexity | 5.6931 | 5.8202 | 5.8268 | 6.1645 |
|Mistral 7B | file size | 14.5G | 4.1G | 4.5G | 3.1G |
|Mistral 7B | bits/weight | 16.0 | 4.5 | 5.0 | 2.6 |
|AWQ-Mistral 7B| perplexity | 5.6934 | 5.8020 | 5.7691 | 6.0426 |
|AWQ-Mistral 7B| file size | 14.5G | 4.1G | 4.5G | 3.1G |
|AWQ-Mistral 7B| bits/weight | 16.0 | 4.5 | 5.0 | 2.6 |
### MPT 7B (Build with OpenBLAS)
| Model | Measure | F16 | Q4_0 | Q4_1 | Q2_K |
|---------:|--------------|-------:|-------:|-------:|--------:|
|MPT 7B | perplexity | 8.4369 | 8.7956 | 8.6265 | 11.4913 |
|MPT 7B | file size | 13.7G | 3.9G | 4.3G | 2.8G |
|MPT 7B | bits/weight | 16.0 | 4.5 | 5.0 | 2.6 |
|AWQ-MPT 7B| perplexity | 8.4944 | 8.7053 | 8.6750 | 10.2873|
|AWQ-MPT 7B| file size | 13.7G | 3.9G | 4.3G | 2.8G |
|AWQ-MPT 7B| bits/weight | 16.0 | 4.5 | 5.0 | 2.6 |

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awq-py/awq/apply_awq.py
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"""
Implements the AWQ for llama.cpp use cases.
Original paper: https://arxiv.org/abs/2306.00978
This code is based on versions of the AWQ implementation found in the following repositories:
* https://github.com/mit-han-lab/llm-awq
* https://github.com/casper-hansen/AutoAWQ
"""
import os
import torch
import torch.nn as nn
from transformers import AutoModelForCausalLM, AutoConfig
from transformers.models.bloom.modeling_bloom import BloomGelu
from transformers.models.llama.modeling_llama import LlamaRMSNorm
from transformers.activations import GELUActivation
class ScaledActivation(nn.Module):
"""
ScaledActivation module wraps an existing activation function and applies a
scale factor to its output.
Args:
module (nn.Module): The activation function to be scaled.
scales (torch.Tensor): A tensor of size (num_features,) containing the initial
scale factors for each feature.
Returns:
torch.Tensor: The scaled output of the activation function.
"""
def __init__(self, module, scales):
super().__init__()
self.act = module
self.scales = nn.Parameter(scales.data)
def forward(self, x):
return self.act(x) / self.scales.view(1, 1, -1).to(x.device)
def set_op_by_name(layer, name, new_module):
"""
Set the new module for given module's name.
Args:
layer (nn.Module): The layer in which to replace the submodule.
name (str): The path to the submodule to be replaced, using dot notation
to access nested modules.
new_module (nn.Module): The new module to replace the existing one.
"""
levels = name.split(".")
if len(levels) > 1:
mod_ = layer
for l_idx in range(len(levels) - 1):
if levels[l_idx].isdigit():
mod_ = mod_[int(levels[l_idx])]
else:
mod_ = getattr(mod_, levels[l_idx])
setattr(mod_, levels[-1], new_module)
else:
setattr(layer, name, new_module)
def get_op_by_name(module, op_name):
"""
Retrieves a submodule within a given layer based on its name.
Args:
module (nn.Module): The layer containing the submodule to find.
op_name (str): The name of the submodule.
Returns:
nn.Module: The requested submodule found within the given layer.
Raises:
ValueError: If the specified submodule cannot be found within the layer.
"""
for name, m in module.named_modules():
if name == op_name:
return m
raise ValueError(f"Cannot find op {op_name} in module {module}")
@torch.no_grad()
def scale_ln_fcs(ln, fcs, scales):
"""
Scales the weights of a LayerNorm and a list of fully-connected layers proportionally.
Args:
ln (nn.LayerNorm): The LayerNorm module to be scaled.
fcs (List[nn.Linear]): A list of fully-connected layers to be scaled.
scales (torch.Tensor): A 1D tensor of size (num_features,).
"""
if not isinstance(fcs, list):
fcs = [fcs]
scales = scales.to(ln.weight.device)
ln.weight.div_(scales)
if hasattr(ln, "bias") and ln.bias is not None:
ln.bias.div_(scales)
for fc in fcs:
fc.weight.mul_(scales.view(1, -1))
for p in ln.parameters():
assert torch.isnan(p).sum() == 0
for fc in fcs:
for p in fc.parameters():
assert torch.isnan(p).sum() == 0
@torch.no_grad()
def scale_fc_fc(fc1, fc2, scales):
"""
Scales the weights of two fully-connected layers in a specific pattern.
Args:
fc1 (nn.Linear): The first fully-connected layer to be scaled.
fc2 (nn.Linear): The second fully-connected layer to be scaled.
scales (torch.Tensor): A 1D tensor of size (num_features,).
"""
assert isinstance(fc1, nn.Linear)
assert isinstance(fc2, nn.Linear)
scales = scales.to(fc1.weight.device)
fc1.weight[-scales.size(0):].div_(scales.view(-1, 1))
if fc1.bias is not None:
fc1.bias.div_(scales.view(-1))
fc2.weight.mul_(scales.view(1, -1))
for p in fc1.parameters():
assert torch.isnan(p).sum() == 0
for p in fc2.parameters():
assert torch.isnan(p).sum() == 0
@torch.no_grad()
def scale_gelu_fc(gelu, fc, scales):
"""
Scales the weight of a GELU activation and a fully-connected layer proportionally.
Args:
gelu (Union[nn.GELU, BloomGelu, GELUActivation]): The GELU activation module to be scaled.
fc (nn.Linear): The fully-connected layer to be scaled.
scales (torch.Tensor): A 1D tensor of size (num_features,).
Raises:
TypeError: If the `gelu` module is not of type `nn.GELU`, `BloomGelu`, or `GELUActivation`.
TypeError: If the `fc` module is not of type `nn.Linear`.
"""
assert isinstance(gelu, (nn.GELU, BloomGelu, GELUActivation))
assert isinstance(fc, nn.Linear)
fc.weight.mul_(scales.view(1, -1).to(fc.weight.device))
for p in fc.parameters():
assert torch.isnan(p).sum() == 0
def apply_scale(module, scales_list, input_feat_dict=None):
"""
Applies different scaling strategies to layers based on their type and hierarchy within a given module.
Args:
module (nn.Module): The module containing the layers to be scaled.
scales_list (List[Tuple[str, List[str], torch.Tensor]]): A list of tuples containing:
* prev_op_name (str): The name of the preceding operation or module,
relative to which the layers to be scaled are located.
* layer_names (List[str]): A list of names of the layers to be scaled, relative to the preceding operation.
* scales (torch.Tensor): A 1D tensor of size (num_features,) containing the scaling factors for each feature.
input_feat_dict (Optional[Dict[str, torch.Tensor]]): A dictionary mapping layer names to their corresponding
input features (optional).
"""
for prev_op_name, layer_names, scales in scales_list:
prev_op = get_op_by_name(module, prev_op_name)
layers = [get_op_by_name(module, name) for name in layer_names]
prev_op.cuda()
for layer in layers:
layer.cuda()
scales.cuda()
if isinstance(prev_op, nn.Linear):
assert len(layers) == 1
scale_fc_fc(prev_op, layers[0], scales)
elif isinstance(prev_op, (nn.LayerNorm, LlamaRMSNorm)) or "rmsnorm" in str(prev_op.__class__).lower():
scale_ln_fcs(prev_op, layers, scales)
elif isinstance(prev_op, (nn.GELU, BloomGelu, GELUActivation)):
new_module = ScaledActivation(prev_op, scales)
set_op_by_name(module, prev_op_name, new_module)
scale_gelu_fc(prev_op, layers[0], scales)
else:
raise NotImplementedError(f"prev_op {type(prev_op)} not supported yet!")
# apply the scaling to input feat if given; prepare it for clipping
if input_feat_dict is not None:
for layer_name in layer_names:
inp = input_feat_dict[layer_name]
inp.div_(scales.view(1, -1).to(inp.device))
prev_op.cpu()
for layer in layers:
layer.cpu()
scales.cpu()
@torch.no_grad()
def apply_clip(module, clip_list):
"""
Applies element-wise clipping to the weight of a specific layer within a given module.
Args:
module (nn.Module): The module containing the layer to be clipped.
clip_list (List[Tuple[str, torch.Tensor]]): A list of tuples containing:
* name (str): The name of the layer to be clipped, relative to the root of the module.
* max_val (torch.Tensor): A 1D or 2D tensor defining the upper bound for each element of the layer's weight.
"""
for name, max_val in clip_list:
layer = get_op_by_name(module, name)
layer.cuda()
max_val = max_val.to(layer.weight.device)
org_shape = layer.weight.shape
layer.weight.data = layer.weight.data.reshape(*max_val.shape[:2], -1)
layer.weight.data = torch.clamp(layer.weight.data, -max_val, max_val)
layer.weight.data = layer.weight.data.reshape(org_shape)
layer.cpu()
def add_scale_weights(model_path, scale_path, tmp_path):
"""
Adds pre-computed Activation Weight Quantization (AWQ) results to a model,
including scaling factors and clipping bounds.
Args:
model_path (str): Path to the pre-trained model to be equipped with AWQ.
scale_path (str): Path to the AWQ scale factors (.pt file).
tmp_path (str): Path to the temporary directory where the equipped model will be saved.
"""
config = AutoConfig.from_pretrained(model_path, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_path, config=config, trust_remote_code=True
)
model.eval()
awq_results = torch.load(str(scale_path), map_location="cpu")
apply_scale(model, awq_results["scale"])
apply_clip(model, awq_results["clip"])
model.save_pretrained(str(tmp_path))
os.system(f"cp {str(model_path)}/tokenizer* {str(tmp_path)}")

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awq-py/requirements.txt
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torch>=2.1.1
transformers>=4.32.0