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a3f2e4d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
from bitblas import tvm
from tvm import te
from tvm.tir import IndexMap
from tvm.contrib.dlpack import to_pytorch_func
import torch
def apply_transform_on_input(input: torch.Tensor, index_map: IndexMap) -> torch.Tensor:
dtype = str(input.dtype).split(".")[1]
inp = te.placeholder(input.shape, name="inp", dtype=dtype)
args = [inp]
arg = args[-1]
def fcompute(*args):
warp_i, warp_j = args[-2:]
spatial_args = args[:-2]
permutate_i, permutate_j = index_map.map_indices([warp_i, warp_j])
new_index = (*spatial_args, permutate_i, permutate_j)
return arg[new_index]
out = te.compute(
input.shape,
fcompute,
name="permutate",
)
args.append(out)
func = te.create_prim_func(args)
rt_mod = tvm.build(func, target="llvm", name="permutate")
output = torch.zeros_like(input)
torch_func = to_pytorch_func(rt_mod)
torch_func(input, output)
return output
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