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#include <stdio.h>
char str[] = "#include <stdio.h>%cchar str[] = %c%s%c;%cint main() {%c    printf(str, 10, 34, str, 34, 10, 10, 10);%c}";
int main() {
	printf(str, 10, 34, str, 34, 10, 10, 10);
}

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from __future__ import print_function
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable

def sample_gumbel(shape, eps=1e-20):
    U = torch.rand(shape).cuda()
    return -Variable(torch.log(-torch.log(U + eps) + eps))

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Retrospective on SparseTIR artifact

Once SparseTIR was accepted to ASPLOS 2023, we began constructing the sparsetir-artifact repository for artifact evaluation. Though we already have lots of benchmarking scripts, we found it still not a trivial job to put them together and evaluate in a unified manner. While preparing our artifact, we also found some problems with our profiler and bugs in existing implementations. We carefully addressed these issues and standardized the settings for all baselines. We are writing this post to document the challenges we faced and the lessons we learned from creating the artifact. We aim to provide insight that will benefit researchers and engineers working in related fields.

Notes on performance difference

If you previously read our manuscript on ArXiv, you may have noticed that there are some discrepancies in the reported performance between SparseTIRv3 and our [camera-ready ver

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import os
import sys
import glob
import logging

logging.basicConfig(level=logging.INFO)


def fix_cite_format(line_number: int, line: str):
    out = ""

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import dgl
import dgl.ops as ops
import numpy as np
import torch as th
import torch.nn as nn

class FFN(nn.Module):
    def __init__(self, d_feat, d_ffn, dropout=0.1):
        super().__init__()
        self.linear_0 = nn.Linear(d_feat, d_ffn)

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import tvm
from tvm import tir
from tvm.script import ty
from tvm.tir.schedule.schedule import Schedule


@tvm.script.tir
def ell_spmm(indices_: ty.handle, a_data: ty.handle, b: ty.handle, c: ty.handle) -> None:
    mb = tir.var('int32')
    n = tir.var('int32')

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import tvm
from tvm import tir
from tvm.script import ty

@tvm.script.tir
def csr_spmm(indptr_: ty.handle, indices_: ty.handle, a_data: ty.handle, b: ty.handle, c: ty.handle) -> None:
    m = tir.var('int32')
    n = tir.var('int32')
    k = tir.var('int32')
    nnz = tir.var('int32')

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prog = "prog = {:c}{}{:c}{:c}print(prog.format(34, prog, 34, 10))"
print(prog.format(34, prog, 34, 10))

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"""Training graphsage w/ fp16.

Usage:

python train_full.py --gpu 0 --fp16 --dataset

Note that GradScaler is not acitvated because the model successfully converges
without gradient scaling.

DGL's Message Passing APIs are not compatible with fp16 yet, hence we disabled

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"""
Usage:

    python convert.py xxx.rst xxx.py

To convert markdown to sphinx_gallery `.py`, use pandoc to generate a `.rst` text in advance:

    pandoc xxx.md --output xxx.rst
    python convert.py xxx.rst xxx.py