Chillee

import torch
from triton.testing import do_bench
import torch._inductor.config as config

config.max_autotune_gemm_backends = "cutlass"

torch.set_default_device('cuda')

a = torch.randn(4224, 8192, dtype=torch.bfloat16)
b = torch.randn(2048, 8192, dtype=torch.bfloat16).t()

Chillee

import torch
from torch.utils.flop_counter import FlopCounterMode
from triton.testing import do_bench

def get_flops_achieved(f):
    flop_counter = FlopCounterMode(display=False)
    with flop_counter:
        f()
    total_flops = flop_counter.get_total_flops()
    ms_per_iter = do_bench(f)

NicolasHug

ameya98

# Add this in a Google Colab cell to install the correct version of Pytorch Geometric.
import torch

def format_pytorch_version(version):
  return version.split('+')[0]

TORCH_version = torch.__version__
TORCH = format_pytorch_version(TORCH_version)

def format_cuda_version(version):

Keno

AyeGill

using Catlab.GAT
using Catlab.Theories
using Flux

struct EucSpc
    dim::Int
end

struct ParaEucFun
    domdim::Int

unixpickle

import torch
import torch.nn.functional as F


def maml_grad(model, inputs, outputs, lr, batch=1):
    """
    Update a model's gradient using MAML.

    The gradient will point in the direction that
    improves the total loss across all inner-loop

andy-thomason

Genomics - A programmer's guide.

Andy Thomason is a Senior Programmer at Genomics PLC. He has been witing graphics systems, games and compilers since the '70s and specialises in code performance.

https://www.genomicsplc.com

rxwei

See the official Differentiable Programming Manifesto instead.

simoninithomas