as title.

there is a bug: https://github.com/yhhhli/BRECQ/blob/da93abc4f7e3ef437b356a2df8a5ecd8c326556e/main_imagenet.py#L173

args.batchsize should be args.workers

Hi, Thanks for the release of your code. But I have one problem regarding the detail of the implementation. In quant_block.py, take the following code of ResNet-18 and ResNet-34 for example. The disable_act_quant is set True for conv2, which disables the quantization of the output of conv2.

class QuantBasicBlock(BaseQuantBlock):
    """
    Implementation of Quantized BasicBlock used in ResNet-18 and ResNet-34.
    """
    def __init__(self, basic_block: BasicBlock, weight_quant_params: dict = {}, act_quant_params: dict = {}):
        super().__init__(act_quant_params)
        self.conv1 = QuantModule(basic_block.conv1, weight_quant_params, act_quant_params)
        self.conv1.activation_function = basic_block.relu1
        self.conv2 = QuantModule(basic_block.conv2, weight_quant_params, act_quant_params, disable_act_quant=True)

        # modify the activation function to ReLU
        self.activation_function = basic_block.relu2

        if basic_block.downsample is None:
            self.downsample = None
        else:
            self.downsample = QuantModule(basic_block.downsample[0], weight_quant_params, act_quant_params,
                                          disable_act_quant=True)
        # copying all attributes in original block
        self.stride = basic_block.stride

It will cause a boost in accuracy, the following is the result I get use the your code and the same ImageNet dataset you used in the paper. [1] and [2] denotes the modification I did to the original code.

[1]: quant_block.py→QuantBasicBlock→__init__→self.conv2=QuantModule(... , disable_act_quant=True) self.downsample = QuantModule(basic_block.downsample[0], weight_quant_params, act_quant_params, disable_act_quant=True). Change from True to False; [2]: quant_block.py→QuantInvertedResidual→__init__→self.conv=nn.Sequential(..., QuantModule(... , disable_act_quant=True), change from True to False

But I do not think it is applicable for most of NPUs, which do quantization of every output of conv layer. So why not quantize the activation of the last conv layer in a block? Is there any particular reason for this? Also, for the methods you compared with in your paper, have you checked whether they do the same thing as you do or not?

Hi, Very impressive coding.

There is a question about the quantization of activation values.

In the code:

disable act quantization is designed for convolution before elemental-wise operation,

in that case, we apply activation function and quantization after ele-wise op.

Why can it be replaced like this？

Thanks

On my eyes, your code is just running with single gpu while I need to test this code with multi-gpu for other implementations. I just want to check that you have ran your code using data parallel and distributed data parallel.

https://github.com/yhhhli/BRECQ/blob/2888b29de0a88ece561ae2443defc758444e41c1/quant/block_recon.py#L91

What is the purpose for setting retain_graph=True?

Greetings,

Really appreciate your open source contribution.

However, it seems the accuracy mentioned in the paper cannot be reproduced applying the standard Imagenet. For instance, with the full precision model, I have tested Resnet 18 (70.186%), MobileNetv2(71.618%), which is slightly lower than the results from your paper (71.08, 72.49 respectively).

Have you utilized any preprocessing techniques other than imagenet.build_imagenet_data?

Thanks

Got an error:

Traceback (most recent call last):
  File "main_imagenet.py", line 198, in <module>
    print('Quantized accuracy before brecq: {}'.format(validate_model(test_loader, qnn)))
  File "/home/xxxx/anaconda3/envs/torch/lib/python3.7/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context
    return func(*args, **kwargs)
  File "main_imagenet.py", line 108, in validate_model
    acc1, acc5 = accuracy(output, target, topk=(1, 5))
  File "main_imagenet.py", line 77, in accuracy
    correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
RuntimeError: view size is not compatible with input tensor's size and stride (at least one dimension spans across two contiguous subspaces). Use .reshape(...) instead.

So suggest replacing .view with .reshape in accuracy() function.

Hi, nice idea for quantizaton But it seems that the paper(not include the appendix) did not point that it is channel-wise quantization. however, the code showed it is. As we know, it is of course that channel-wise quntization would outperform layer-wise quantization. So, maybe it's hard to say that the performance of your method is close to QAT

Hello, thank you for an interesting paper and nice code.

I have two questions concerning implementation details.

1. Does the "one-by-one" block reconstruction mentioned in the paper mean that input to each block comes from already quantized preceding blocks, i.e. each block may correct quantization errors coming from previous blocks? Or maybe input to each block is collected from the full-precision model?
2. Am I correct in my understanding that in block-wise reconstruction objective you use gradients for each object in calibration sample independently (i.e. no gradient averaging or smth, like in Adam mentioned on the paper)? Besides, what is happening here in data_utils.py, why do you add 1.0 to the gradients?

cached_grads = cached_grads.abs() + 1.0
# scaling to make sure its mean is 1
# cached_grads = cached_grads * torch.sqrt(cached_grads.numel() / cached_grads.pow(2).sum())

Thank you for your time and consideration!

Hi,

So I tried running your code on CIFAR-10 with a pre-trained ResNet50 model. I've attached the code below. My accuracy however does not come nearly as close to the float model which is around 93% but after quanitzation: I get:

• Accuracy of the network on the 10000 test images: 10.0 % top5: 52.28 %

Please help me with this. The code is inside the zip file.

main_cifar.zip s

如文中所提出的Fisher-diag方式来估计Hessian矩阵，需要计算每一层pre-activation的梯度。但在实际代码运行时，save_grad_data中的cur_grad = get_grad(cali_data[i * batch_size:(i + 1) * batch_size])在执行到第二个batch的时候会报错Trying to backward through the graph a second time，第一个batch的数据并不会报错。不知道作者是否遇到过类似的情况？

[email protected]:/path_to/BRECQ# python main_imagenet.py --data_path /path_to/IMAGENET_2012/ --arch resnet18 --n_bits_w 2 --channel_wise --n_bits_a 4 --act_quant --test_before_calibration You are using fake SyncBatchNorm2d who is actually the official BatchNorm2d ==> Using Pytorch Dataset Downloading: "https://github.com/yhhhli/BRECQ/releases/download/v1.0/resnet18_imagenet.pth.tar" to /root/.cache/torch/hub/checkpoints/resnet18_imagenet.pth.tar 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 44.6M/44.6M [00:27<00:00, 1.70MB/s] Traceback (most recent call last): File "main_imagenet.py", line 178, in cnn.cuda() File "/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py", line 680, in cuda return self._apply(lambda t: t.cuda(device)) File "/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py", line 570, in _apply module._apply(fn) File "/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py", line 593, in _apply param_applied = fn(param) File "/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py", line 680, in return self._apply(lambda t: t.cuda(device)) RuntimeError: CUDA error: out of memory CUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect. For debugging consider passing CUDA_LAUNCH_BLOCKING=1.