quantize each layer: 100%|████████████████████████████████████████████████████████████████████████████████████████████
一般称之为simulator quantize,也就是说模型可以通过quantize->dequantize这种fake quantize来模拟量化的过程和量化误差,计算的时候使用的FP32算子,但是计算的输入的input和weight都是经过量化反量化操作得来的。
一般称之为simulator quantize,也就是说模型可以通过quantize->dequantize这种fake quantize来模拟量化的过程和量化误差,计算的时候使用的FP32算子,但是计算的输入的input和weight都是经过量化反量化操作得来的。
# 设置4块卡可见CUDA_VISIBLE_DEVICES=0,1,6,7 torchrun\# 单机--standalone\# 一个结点--nnodes=1\# 4块卡--nproc_per_node=4\# 运行的脚本train_qlora.py\# 配置文件--train_args_file train_args/qlora/baichuan-7b-sft-qlora_alex.json# 特别强调:如果是nohup 跑,需要将 CUDA_VISIBLE_DEVICES...
Convert: Quantize weights and replace floating point operations with their dynamic quantized counterparts. Quantization aware training: Fuse modules: The first step is calling torch.quantization.fuse_modules() to fuse convolution layers with batch norm and optionally Relu operations. Prepare modules: ...
"AtenLinalgCrossDynamic_basic" Crashing tests "FakeQuantizePerTensorAffineModule_basic", "FakeQuantizePerTensorAffineDynamicShapeModule_basic", Lower Priority: Failure - onnx_export "AdaptiveAvgPool1dGeneralDynamic_basic", "AdaptiveAvgPool1dNonUnitOutputSizeDynamicModule_basic", "AdaptiveAvgPool1dStaticLa...
int8 if quant_min == -128 else torch.uint8 zero_point = torch.tensor(zero_point, dtype=zero_point_dtype) # ONNX requires zero_point to be tensor return g.op("DequantizeLinear", g.op("QuantizeLinear", inputs, scale, zero_point), scale, zero_point) ...
Since the path to the blob storage mounted on the computing cluster is dynamic, the YAML recipe must be modified dynamically. Here's an example of how to adjust the configuration using Jinja templates to ensure the paths are set correctly at runtime: ...
tensor([]).to(dtype)) return g.op("npu::NPUMultiHeadAttention", query, key, value, query_weight, key_weight, value_weight, attn_mask, out_proj_weight, query_bias, key_bias, value_bias, out_proj_bias, dropout_mask, attn_head_num_i=attn_head_num, attn_dim_per...
from torch.quantization.quantize_fximportprepare_fx,convert_fx float_model.eval()# 因为是PTQ,所以就推理模式就够了 qconfig=get_default_qconfig("fbgemm")# 指定量化细节配置 qconfig_dict={"":qconfig}# 指定量化选项 defcalibrate(model,data_loader):# 校准功能函数 ...