Hi, Are the quantized models differentiable(i.e. can we get gradients using backprop using these) or is it not possible due to actual INT8 quantization? Pl reply ASAP

Hi, may I ask for more details on what this function does?

   def _initialize_calib_parameters(self):
        """ 
        set parameters for feeding calibration data
        """
        self.calib_size = int(self.raw_input.shape[0])
        self.calib_batch_size = int(self.raw_input.shape[0])
        while True:
            numel = (2*(self.raw_input.numel()+self.raw_out.numel()) /
                     self.calib_size*self.calib_batch_size)  # number of parameters on GPU
            self.parallel_eq_n = int((15*1024*1024*1024/4)//numel)
            if self.parallel_eq_n <= 1:
                self.calib_need_batching = True
                self.calib_batch_size //= 2
            else:
                break

I am adapting your code for 1DConvolution and the self.parallel_eq_n is multiplying by the output channels of the weights and causing memory issues. If you could provide further details it would be really helpful. Thankyou. Ed

作者您好，非常感谢您的突出贡献。我在运行您的代码时遇到几个问题需要请教一下：

1. 为什么我的实验运行结果会集中于几个数字，比如在对vit_tiny_patch16_224运行时的消融实验结果是：

我的数据集训练和验证数据集都是将32张图片分成4类（文件夹），每一类中分别有8张图片。这样做是否正确，如果不正确请您指教 2. 在linear.py中class PTQSLQuantlinear 是在哪用到的？basePTQ? 希望有时间的话您能够回复一下，谢谢

Dear author, thank you for your outstanding contribution. But I'm having some problems running the program: I used the dataset Imagenet2012 in your program and did not change any parameters (I found the α of PTQ4ViT =0.01 in the program), but there is a little difference between the result of several attempts and the original result. For example, in the result of PTQ4ViT, your ViT-B/224 accuracy rate of w8a8 is 84.25, but mine is 84.148. Your ViT-B/224 accuracy rate of w6a6 is 81.65, but mine is 81.844, and so on. I use an Nvidia RTX 3090-24G. Did I do something wrong? We look forward to your reply. Thanks again!

First of all, thank you for the great work and the official code.

I have one question.

Where is the code implementation for constraining the scaling factors for post-softmax and post-gelu, i.e., ∆R2 = 2m∆R1, where m is an unsigned integer, in order for an efficient process.

I really appreciate for providing the code once again.

Hi! Thanks for your great work! There was a problem when I was trying to load the quantized model 'vit_base_patch16_384.pth' (you've provided) into the net created by timm.create_model. The error is as follows.

RuntimeError: Error(s) in loading state_dict for VisionTransformer: Missing key(s) in state_dict: "cls_token", "pos_embed", "patch_embed.proj.weight", "patch_embed.proj.bias", "blocks.0.norm1.weight", "blocks.0.norm1.bias", "blocks.0.attn.qkv.weight", "blocks.0.attn.qkv.bias", "blocks.0.attn.proj.weight", "blocks.0.attn.proj.bias", "blocks.0.norm2.weight", "blocks.0.norm2.bias", "blocks.0.mlp.fc1.weight", "blocks.0.mlp.fc1.bias", "blocks.0.mlp.fc2.weight", "blocks.0.mlp.fc2.bias".......

Would you please provide the correct method to load your quantized model? Thank you a lot~