mirror of https://github.com/llvm/torch-mlir
138 lines
4.1 KiB
Python
138 lines
4.1 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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# ==============================================================================
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class UniformStaticModule(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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([256, 512, 8], torch.float64, True),
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([512, 1024, 4], torch.float64, True),
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([512, 256, 4], 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: UniformStaticModule())
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def UniformStaticModule_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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# ==============================================================================
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class BernoulliModule(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.float32, True),
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])
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def forward(self, a):
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x = torch.bernoulli(a)
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mean = torch.mean(x)
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std = torch.std(x)
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return mean, std
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@register_test_case(module_factory=lambda: BernoulliModule())
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def BernoulliModule_basic(module, tu: TestUtils):
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module.forward(tu.rand(256, 512, 64))
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class BernoulliFloatModule(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, a, b, c):
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x = torch.ops.aten.bernoulli_(a, 0.4)
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y = torch.ops.aten.bernoulli_(b, 0.7)
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z = torch.ops.aten.bernoulli_(c, 0.5)
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mean = torch.cat([
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torch.flatten(torch.mean(x)),
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torch.flatten(torch.mean(y)),
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torch.flatten(torch.mean(z))
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])
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std = torch.cat([
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torch.flatten(torch.std(x)),
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torch.flatten(torch.std(y)),
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torch.flatten(torch.std(z))
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])
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return mean, std
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@register_test_case(module_factory=lambda: BernoulliFloatModule())
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def BernoulliFloatModule_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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