mirror of https://github.com/llvm/torch-mlir
397 lines
10 KiB
Python
397 lines
10 KiB
Python
# Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
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# See https://llvm.org/LICENSE.txt for license information.
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# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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# Also available under a BSD-style license. See LICENSE.
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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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# TODO: Support scalar !torch.int/!torch.float variants. Add support to
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# ReduceOpVariants to implement them in terms of the tensor-only variants +
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# torch.prim.NumToTensor.
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# TODO: This is pretty verbose. Can we have a helper to reduce
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# the boilerplate?
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# ==============================================================================
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class ElementwiseUnaryModule(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], torch.float32, True),
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])
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def forward(self, a):
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return torch.tanh(a)
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@register_test_case(module_factory=lambda: ElementwiseUnaryModule())
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def ElementwiseUnaryModule_basic(module, tu: TestUtils):
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module.forward(tu.rand(3, 4))
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# ==============================================================================
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class ElementwiseBinaryModule(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], torch.float32, True),
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([-1], torch.float32, True),
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])
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def forward(self, a, b):
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return a * b
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@register_test_case(module_factory=lambda: ElementwiseBinaryModule())
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def ElementwiseBinaryModule_basic(module, tu: TestUtils):
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module.forward(tu.rand(3, 4), tu.rand(4))
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# ==============================================================================
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class ElementwiseTernaryModule(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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([-1, -1], torch.float32, True),
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([-1], torch.float32, True),
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])
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def forward(self, a, b, c):
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return torch.lerp(a, b, c)
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@register_test_case(module_factory=lambda: ElementwiseTernaryModule())
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def ElementwiseTernaryModule_basic(module, tu: TestUtils):
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module.forward(tu.rand(3, 4, 5), tu.rand(4, 5), tu.rand(5))
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# ==============================================================================
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# Addition is an interesting special case of a binary op, because under the hood
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# it carries a third scalar "alpha" parameter, which needs special handling.
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class ElementwiseAddModule(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], torch.float32, True),
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([], torch.float32, True),
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])
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def forward(self, a, b):
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return a + b
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@register_test_case(module_factory=lambda: ElementwiseAddModule())
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def ElementwiseAddModule_basic(module, tu: TestUtils):
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module.forward(tu.rand(4), tu.rand())
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# ==============================================================================
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class ElementwiseUnsqueezeBroadcastModule(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], torch.float32, True),
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([], torch.float32, True),
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])
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def forward(self, a, b):
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return a * b.unsqueeze(0)
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@register_test_case(
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module_factory=lambda: ElementwiseUnsqueezeBroadcastModule())
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def ElementwiseUnsqueezeBroadcastModule_basic(module, tu: TestUtils):
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module.forward(tu.rand(4), tu.rand())
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# ==============================================================================
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class ElementwiseUnsqueezeNegDimsModule(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], torch.float32, True),
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])
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def forward(self, a):
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# As mentioned in `unsqueeze` docstring,
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# valid dim values are [-input.dim()-1, input.dim()+1).
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# This tests the lower bound
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return torch.unsqueeze(a, -3)
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@register_test_case(
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module_factory=lambda: ElementwiseUnsqueezeNegDimsModule())
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def ElementwiseUnsqueezeNegDimsModule_basic(module, tu: TestUtils):
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module.forward(tu.rand(4, 3))
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# ==============================================================================
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class ElementwiseFlattenBroadcastModule(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], torch.float32, True),
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([], torch.float32, True),
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])
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def forward(self, a, b):
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return a * b.flatten(-1, -1)
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@register_test_case(module_factory=lambda: ElementwiseFlattenBroadcastModule())
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def ElementwiseFlattenBroadcastModule_basic(module, tu: TestUtils):
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module.forward(tu.rand(6), tu.rand())
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# ==============================================================================
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class ElementwiseReluModule(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], torch.float32, True),
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])
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def forward(self, x):
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return torch.relu(x)
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@register_test_case(module_factory=lambda: ElementwiseReluModule())
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def ElementwiseReluModule_basic(module, tu: TestUtils):
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module.forward(tu.rand(4, 2) - 0.5)
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# ==============================================================================
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class ElementwiseGeluModule(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.gelu = torch.nn.GELU()
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@export
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@annotate_args([
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None,
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([-1, -1], torch.float32, True),
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])
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def forward(self, x):
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return self.gelu(x)
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@register_test_case(module_factory=lambda: ElementwiseGeluModule())
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def ElementwiseGeluModule_basic(module, tu: TestUtils):
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module.forward(2*tu.rand(5, 3) - 0.5)
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# ==============================================================================
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class ElementwiseSigmoidModule(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], torch.float32, True),
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])
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def forward(self, x):
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return torch.sigmoid(x)
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@register_test_case(module_factory=lambda: ElementwiseSigmoidModule())
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def ElementwiseSigmoidModule_basic(module, tu: TestUtils):
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module.forward(tu.rand(3, 5))
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# ==============================================================================
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class ElementwiseMinimumModule(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], torch.float32, True),
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([-1, -1], torch.float32, True),
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])
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def forward(self, x, y):
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return torch.minimum(x, y)
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@register_test_case(module_factory=lambda: ElementwiseMinimumModule())
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def ElementwiseMinimumModule_basic(module, tu: TestUtils):
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module.forward(tu.rand(3, 5), tu.rand(3, 5))
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module.forward(tu.nans(3, 5), tu.rand(3, 5))
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# ==============================================================================
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class ElementwiseMaximumModule(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], torch.float32, True),
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([-1, -1], torch.float32, True),
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])
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def forward(self, x, y):
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return torch.maximum(x, y)
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@register_test_case(module_factory=lambda: ElementwiseMaximumModule())
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def ElementwiseMaximumModule_basic(module, tu: TestUtils):
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module.forward(tu.rand(3, 5), tu.rand(3, 5))
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module.forward(tu.nans(3, 5), tu.rand(3, 5))
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# ==============================================================================
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class ElementwiseClampModule(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], torch.float32, True),
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])
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def forward(self, x):
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# TODO: It would be great to return all of these, so they get checked
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# individually, but RefBackend doesn't support multiple returns.
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# Instead, multiply them together, which has some chance of propagating
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# all the values.
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float_min = torch.clamp(x, min=-2.0)
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int_min = torch.clamp(x, min=-3)
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float_max = torch.clamp(x, max=2.0)
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int_max = torch.clamp(x, max=3)
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both = torch.clamp(x, min=-5, max=5)
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return float_min * int_min * float_max * int_max * both
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@register_test_case(module_factory=lambda: ElementwiseClampModule())
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def ElementwiseClampModule_basic(module, tu: TestUtils):
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module.forward(tu.rand(3, 5, low=-10, high=10))
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class RsubModule(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], torch.float32, True),
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])
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def forward(self, x):
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return torch.rsub(x, 3.0, alpha=1.0)
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@register_test_case(module_factory=lambda: RsubModule())
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def RsubModule_basic(module, tu: TestUtils):
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module.forward(tu.rand(3, 4))
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class RsubModule_noalpha(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], torch.float32, True),
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])
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def forward(self, x):
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return torch.rsub(x, 2.0)
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@register_test_case(module_factory=lambda: RsubModule_noalpha())
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def RsubModule_noalpha_basic(module, tu: TestUtils):
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module.forward(tu.rand(3, 4))
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class ElementwiseLogModule(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], torch.float32, True),
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])
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def forward(self, a):
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return torch.log(a)
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@register_test_case(module_factory=lambda: ElementwiseLogModule())
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def ElementwiseLogModule_basic(module, tu: TestUtils):
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module.forward(tu.rand(3, 4))
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class ElementwiseSqrtModule(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], torch.float32, True),
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])
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def forward(self, a):
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return torch.sqrt(a)
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@register_test_case(module_factory=lambda: ElementwiseSqrtModule())
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def ElementwiseSqrtModule_basic(module, tu: TestUtils):
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module.forward(tu.rand(3, 4))
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class ElementwiseFloorModule(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], torch.float32, True),
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])
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def forward(self, a):
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return torch.floor(a)
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@register_test_case(module_factory=lambda: ElementwiseFloorModule())
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def ElementwiseFloorModule_basic(module, tu: TestUtils):
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module.forward(tu.rand(3, 4))
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