使用场景:当 Post-training static quantization 没有效果时使用; 特注提醒:上面说的使用场景只是经验选择,不表示一定适用,具体问题还需要具体分析。 3.2.1 dynamic quantization dynamic quantization 是 3种方式中最简单的一个。 import torch.quantization quantized_model = torch.quantization.quantize_dynamic( model,...
from transformers import AutoModelForSpeechSeq2Seqmodel_id = "openai/whisper-large-v3"quanto_config = QuantoConfig(weights="int8")model = AutoModelForSpeechSeq2Seq.from_pretrained( model_id, torch_dtype=torch.float16, device_map="cuda", quantization_config=quanto_config)你可查阅此 ...
import torch import torchvision from torch.quantization import QuantStub, DeQuantStub, quantize, prepare, convert # 定义一个示例模型 model = torchvision.models.resnet18() # 创建QuantStub和DeQuantStub对象 quant_stub = QuantStub() dequant_stub = DeQuantStub() # 将模型和量化/反量化层包装在prepar...
from transformers import AutoModelForCausalLM, AutoTokenizer, QuantoConfig model_id = "facebook/opt-125m" tokenizer = AutoTokenizer.from_pretrained(model_id) quantization_config = QuantoConfig(weights="int8") quantized_model = AutoModelForCausalLM.from_pretrained( model_id, quantization_config= qua...
Objective: My primary goal is to accelerate my model's performance using int8 + fp16 quantization. To achieve this, I first need to quantize the model and then calibrate it. As far as I understand, there are two quantization methods avai...
model_id="openai/whisper-large-v3"quanto_config=QuantoConfig(weights="int8")model=AutoModelForSpeechSeq2Seq.from_pretrained(model_id,torch_dtype=torch.float16,device_map="cuda",quantization_config=quanto_config) 你可查阅此 notebook,以详细了解如何在中正确使用! notebook https://colab.research.goo...
with torch.inference_mode(): for _ in range(10): x = torch.rand(1, 2, 28, 28) model_prepared(x) # quantize model_quantized = quantize_fx.convert_fx(model_prepared) PS:直观对比EAGER模式和FX模式的代码量,可以看出FX模式真香! 感知量化[Quantization-aware Training (QAT)] PTQ方法适用于大型...
model=m, qconfig_spec={nn.LSTM, nn.Linear}, dtype=torch.qint8, inplace=False ) ## FX MODE from torch.quantization import quantize_fx qconfig_dict = {"": torch.quantization.default_dynamic_qconfig} # An empty key denotes the default applied to all modules ...
example_inputs) # no calibration needed when we only have dynamic/weight_only quantization # quantize model_quantized_dynamic = quantize_fx.convert_fx(model_prepared) 正如你所看到的,只需要通过模型传递一个示例输入来校准量化层,所以代码十分简单,看看我们的模型对比: ...
API Example: importtorch# define a floating point model where some layers could be statically quantizedclassM(torch.nn.Module):def__init__(self):super(M, self).__init__()# QuantStub converts tensors from floating point to quantizedself.quant = torch.quantization.QuantStub() ...