mirror of
https://github.com/huggingface/transformers.git
synced 2026-10-29 09:14:55 +08:00
Cyril Vallez
d8f6d3790a
* update everywhere * style * pipelines * switch it everywhere in tests * switch it everywhere in docs * switch in converters everywhere * update in examples * update in model docstrings * style * warnings * style * Update configuration_utils.py * fix * Update configuration_utils.py * fixes and add first test * add pipeline tests * Update test_pipelines_common.py * add config test * Update test_modeling_common.py * add new ones * post rebase * add new * post rebase adds
79 lines
2.3 KiB
Python
79 lines
2.3 KiB
Python
import json
|
|
from typing import Any
|
|
|
|
import torch
|
|
|
|
from transformers import AutoModelForCausalLM, AutoTokenizer
|
|
from transformers.quantizers import HfQuantizer, register_quantization_config, register_quantizer
|
|
from transformers.utils.quantization_config import QuantizationConfigMixin
|
|
|
|
|
|
@register_quantization_config("custom")
|
|
class CustomConfig(QuantizationConfigMixin):
|
|
def __init__(self):
|
|
self.quant_method = "custom"
|
|
self.bits = 8
|
|
|
|
def to_dict(self) -> dict[str, Any]:
|
|
output = {
|
|
"num_bits": self.bits,
|
|
}
|
|
return output
|
|
|
|
def __repr__(self):
|
|
config_dict = self.to_dict()
|
|
return f"{self.__class__.__name__} {json.dumps(config_dict, indent=2, sort_keys=True)}\n"
|
|
|
|
def to_diff_dict(self) -> dict[str, Any]:
|
|
config_dict = self.to_dict()
|
|
|
|
default_config_dict = CustomConfig().to_dict()
|
|
|
|
serializable_config_dict = {}
|
|
|
|
for key, value in config_dict.items():
|
|
if value != default_config_dict[key]:
|
|
serializable_config_dict[key] = value
|
|
|
|
return serializable_config_dict
|
|
|
|
|
|
@register_quantizer("custom")
|
|
class CustomQuantizer(HfQuantizer):
|
|
def __init__(self, quantization_config: QuantizationConfigMixin, **kwargs):
|
|
super().__init__(quantization_config, **kwargs)
|
|
self.quantization_config = quantization_config
|
|
self.scale_map = {}
|
|
self.device = kwargs.get("device", "cuda" if torch.cuda.is_available() else "cpu")
|
|
self.dtype = kwargs.get("dtype", torch.float32)
|
|
|
|
def _process_model_before_weight_loading(self, model, **kwargs):
|
|
return True
|
|
|
|
def _process_model_after_weight_loading(self, model, **kwargs):
|
|
return True
|
|
|
|
def is_serializable(self) -> bool:
|
|
return True
|
|
|
|
def is_trainable(self) -> bool:
|
|
return False
|
|
|
|
|
|
model_8bit = AutoModelForCausalLM.from_pretrained(
|
|
"facebook/opt-350m", quantization_config=CustomConfig(), dtype="auto"
|
|
)
|
|
|
|
tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m")
|
|
input_text = "once there is"
|
|
inputs = tokenizer(input_text, return_tensors="pt")
|
|
output = model_8bit.generate(
|
|
**inputs,
|
|
max_length=100,
|
|
num_return_sequences=1,
|
|
no_repeat_ngram_size=2,
|
|
)
|
|
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
|
|
|
|
print(generated_text)
|