# Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions. # See https://llvm.org/LICENSE.txt for license information. # SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception import torch from torch_mlir.torchscript.e2e_test.framework import TestUtils from torch_mlir.torchscript.e2e_test.registry import register_test_case from torch_mlir.torchscript.annotations import annotate_args, export # ============================================================================== class MmModule(torch.nn.Module): def __init__(self): super().__init__() @export @annotate_args([ None, ([-1, -1], torch.float32), ([-1, -1], torch.float32), ]) def forward(self, lhs, rhs): return torch.mm(lhs, rhs) @register_test_case(module_factory=lambda: MmModule()) def MmModule_basic(module, tu: TestUtils): module.forward(tu.rand(4, 4), tu.rand(4, 4)) @register_test_case(module_factory=lambda: MmModule()) def MmModule_chained(module, tu: TestUtils): res = module.forward(tu.rand(4, 4), tu.rand(4, 4)) module.forward(res, res) # ============================================================================== class TanhModule(torch.nn.Module): def __init__(self): super().__init__() @export @annotate_args([ None, ([2, 3, -1], torch.float32), ]) def forward(self, x): return torch.tanh(x) @register_test_case(module_factory=lambda: TanhModule()) def TanhModule_basic(module, tu: TestUtils): module.forward(tu.rand(2, 3, 1)) # ============================================================================== class MmTanhModule(torch.nn.Module): def __init__(self): super().__init__() @export @annotate_args([ None, ([-1, -1], torch.float32), ([-1, -1], torch.float32), ]) def forward(self, lhs, rhs): return torch.tanh(self.matmul(lhs, rhs)) def matmul(self, lhs, rhs): return torch.mm(lhs, rhs) @register_test_case(module_factory=lambda: MmTanhModule()) def MmTanhModule_basic(module, tu: TestUtils): module.forward(tu.rand(4, 2), tu.rand(2, 4))