mirror of https://github.com/llvm/torch-mlir
37 lines
1.0 KiB
Python
37 lines
1.0 KiB
Python
# -*- Python -*-
|
|
# This file is licensed under a pytorch-style license
|
|
# See frontends/pytorch/LICENSE for license information.
|
|
|
|
import sys
|
|
import numpy as np
|
|
import torch
|
|
import torch_mlir
|
|
|
|
import npcomp
|
|
from npcomp.compiler.pytorch.backend.refjit import *
|
|
from npcomp.compiler.utils import logging
|
|
|
|
logging.enable()
|
|
|
|
lhs = torch.ones((4, 6, 1))
|
|
rhs = torch.ones((1, 1, 3)) * 0.6
|
|
bias = torch.ones((1, 1, 3)) * 0.2
|
|
threshold = torch.tensor((0.75, 0.25, 0.10))
|
|
|
|
mb = torch_mlir.ModuleBuilder()
|
|
with mb.capture_function("mul_maximum", [lhs, rhs, threshold, bias]) as f:
|
|
result = torch.maximum(lhs * rhs, threshold)
|
|
result = result + bias
|
|
f.returns([result])
|
|
|
|
backend = CompilerBackend()
|
|
jit_module = backend.load(backend.compile(mb.module))
|
|
|
|
jit_result = jit_module.mul_maximum(lhs.numpy(), rhs.numpy(), threshold.numpy(),
|
|
bias.numpy())
|
|
|
|
print(f"PyTorch Result = {result.numpy()}", file=sys.stderr)
|
|
print(f"JIT Result = {jit_result}", file=sys.stderr)
|
|
|
|
np.testing.assert_allclose(result.numpy(), jit_result)
|