mirror of https://github.com/llvm/torch-mlir
45 lines
1.3 KiB
Python
45 lines
1.3 KiB
Python
import torch
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from torch_mlir_e2e_test.torchscript.framework import TestUtils
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from torch_mlir_e2e_test.torchscript.registry import register_test_case
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from torch_mlir_e2e_test.torchscript.annotations import annotate_args, export
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# ==============================================================================
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class UniformModule(torch.nn.Module):
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def __init__(self):
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super().__init__()
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@export
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@annotate_args([
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None,
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([-1, -1, -1], torch.float64, True),
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([-1, -1, -1], torch.float64, True),
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([-1, -1, -1], torch.float64, True),
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])
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def forward(self, x, y, z):
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a = torch.ops.aten.uniform_(x, 1.0, 10.0)
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b = torch.ops.aten.uniform_(y, -20.0, -5.0)
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c = torch.ops.aten.uniform_(z, -15.0, 3.0)
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std = torch.cat([
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torch.flatten(torch.std(a)),
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torch.flatten(torch.std(b)),
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torch.flatten(torch.std(c))
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])
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mean = torch.cat([
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torch.flatten(torch.mean(a)),
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torch.flatten(torch.mean(b)),
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torch.flatten(torch.mean(c))
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])
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return std, mean
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@register_test_case(module_factory=lambda: UniformModule())
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def UniformModule_basic(module, tu: TestUtils):
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module.forward(
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tu.rand(256, 512, 8).double(),
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tu.rand(512, 1024, 4).double(),
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tu.rand(512, 256, 4).double())
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