AITemplate

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can we get stable diffusion example work with xformers?

10

Hi, I was wondering ... is there any easy way to use xformers with the AITemplate in stable diffusion? Since it lowers memory consumption, we can infer with resolution north...

batrlatom

[FEATURE] Support for NVIDIA T4 (Turing Architecture)

2

Hello 🙋🏻‍♂️ It is very cool to see MetaAI going into inference optimization! This will help the community and companies to much long term speaking! While reading through the [announcement...

philschmid

automatically parsing Pytorch module to AIT module, not define AIT module layer by layer

4

I referred official tutorial but I can't find the automatic conversion from PyTorch module to AIT module like ONNX->TensorRT. https://facebookincubator.github.io/AITemplate/tutorial/how_to_infer_pt.html#define-a-pytorch-module Is there any parser or converter building AIT module from...

khlee369

Support arbitrary width & height in stable diffusion example

3

It would be nice to be able to use different heights and widths up to 1024x1024.

Suhail

I built a torchserve docker image on top of AITemplate docker. The demo code works fine in my docker. However, when I pack the AITemplate SD model to torchserve archiver...

ericlormul

When I compile a model that had `nn.Conv2dBiasFewChannels` I get this error: ```text Profile: conv2d_bias_few_channels_1: NI == 1 && HI == 384 && WI == 384 && CI == 4...

dashesy

Compile issue: Tensor conv2d_bias_64_1 not in outputs for op avg_pool2d_53

3

## summary I use AITemplate to re-construct a diffusion model which is slightly different than the one in examples, but error occurs while call compile_model(). Since it says some conv2d_bias...

howellyoung-s

Future plans to support FP32？

1

hg123-we

nn.Conv2d output mismatch with torch.nn.Conv2d

6

This is a full repro: ```python import torch import numpy as np from collections import OrderedDict from aitemplate.testing import detect_target from aitemplate.frontend import nn, Tensor from aitemplate.compiler import compile_model def...

dashesy

ops.conv2d(group=256) outputs NaN and Inf

3

This is the UnitTest ```python import unittest import torch from aitemplate.compiler import compile_model, ops from aitemplate.frontend import Tensor from aitemplate.testing import detect_target class ConvGroupTestCase(unittest.TestCase): def test_fp16(self): groups = 256 #...

dashesy