mirror of https://github.com/llvm/torch-mlir
70 lines
2.3 KiB
Python
70 lines
2.3 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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# RUN: %PYTHON %s | FileCheck %s
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import torch
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from framework import run_test
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from torch_mlir.eager_mode.torch_mlir_dispatch import normalize_args_kwargs
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# CHECK: PASS - should_normalize
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@run_test
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def should_normalize():
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target = torch.ops.aten.max_pool2d_with_indices.default
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input = torch.randn((1, 3, 32, 32))
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kwargs = {"kernel_size": [3, 3]}
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golden = {
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"kernel_size": [3, 3],
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# This is due to the schema for max_pool2d_with_indices defining
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# the stride arg as int[2] stride=[].
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"stride": [],
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"padding": [0, 0],
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"dilation": [1, 1],
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"ceil_mode": False,
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}
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new_kwargs = normalize_args_kwargs(target, (input,), kwargs)
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assert torch.allclose(new_kwargs["input"], input)
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for k, v in new_kwargs.items():
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if k == "input": continue
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assert v == golden[k]
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# CHECK: FAIL - shouldnt_normalize1
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# CHECK: Errors: missing a required argument: 'kernel_size'
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@run_test
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def shouldnt_normalize1():
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target = torch.ops.aten.max_pool2d_with_indices.default
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args = (torch.randn((1, 3, 32, 32)),)
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kwargs = {"stride": []}
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normalize_args_kwargs(target, args, kwargs)
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# This next two tests are XPASS because of https://github.com/pytorch/pytorch/issues/75342
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# I.e., they should fail but in fact they pass because of the upstream bug.
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# The reason for the bug is a fast path branch in operator_schemas.normalize_function
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# that doesn't do rigorous type checking, and hence lets type mistmatches slip through.
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# TODO(max): change these to FAIL when the upstream bug is fixed.
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# CHECK: XPASS - shouldnt_normalize2
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@run_test(XPASS=True)
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def shouldnt_normalize2():
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target = torch.ops.aten.max_pool2d_with_indices.default
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args = (torch.randn((1, 3, 32, 32)),)
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kwargs = {"kernel_size": []}
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normalize_args_kwargs(target, args, kwargs)
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# CHECK: XPASS - shouldnt_normalize3
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@run_test(XPASS=True)
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def shouldnt_normalize3():
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target = torch.ops.aten.max_pool2d_with_indices.default
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args = (torch.randn((1, 3, 32, 32)),)
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kwargs = {"kernel_size": [3, 3], "padding": None}
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normalize_args_kwargs(target, args, kwargs)
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