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.
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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) |
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# 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): |
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using Catlab.GAT | |
using Catlab.Theories | |
using Flux | |
struct EucSpc | |
dim::Int | |
end | |
struct ParaEucFun | |
domdim::Int |
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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 |
See the official Differentiable Programming Manifesto instead.
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