mirror of https://github.com/llvm/torch-mlir
59 lines
2.3 KiB
Python
59 lines
2.3 KiB
Python
# 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
|
|
# Also available under a BSD-style license. See LICENSE.
|
|
|
|
# RUN: %PYTHON %s | FileCheck %s
|
|
|
|
import torch
|
|
|
|
import torch_mlir
|
|
|
|
class TanhModule(torch.nn.Module):
|
|
def __init__(self):
|
|
super().__init__()
|
|
def forward(self, x):
|
|
return torch.ops.aten.tanh(x)
|
|
|
|
tanh_example_input = torch.ones(2, 3)
|
|
|
|
# Simplest case: One example argument.
|
|
print(torch_mlir.compile(TanhModule(), tanh_example_input))
|
|
# CHECK-LABEL: @forward
|
|
# CHECK: torch.aten.tanh %{{.*}} : !torch.vtensor<[2,3],f32> -> !torch.vtensor<[2,3],f32>
|
|
|
|
# Use a TensorPlaceholder to represent dynamic axes.
|
|
placeholder = torch_mlir.TensorPlaceholder.like(tanh_example_input, dynamic_axes=[1])
|
|
print(torch_mlir.compile(TanhModule(), placeholder))
|
|
# CHECK-LABEL: @forward
|
|
# CHECK: torch.aten.tanh %{{.*}} : !torch.vtensor<[2,?],f32> -> !torch.vtensor<[2,?],f32>
|
|
|
|
# Explicitly construct a TensorPlaceholder.
|
|
placeholder = torch_mlir.TensorPlaceholder([-1, 2], torch.float32)
|
|
print(torch_mlir.compile(TanhModule(), placeholder))
|
|
# CHECK-LABEL: @forward
|
|
# CHECK: torch.aten.tanh %{{.*}} : !torch.vtensor<[?,2],f32> -> !torch.vtensor<[?,2],f32>
|
|
|
|
# Basic smoke test for the raw output type.
|
|
print(torch_mlir.compile(TanhModule(), tanh_example_input, output_type=torch_mlir.OutputType.RAW))
|
|
# CHECK: torch.nn_module {
|
|
# CHECK: } : !torch.nn.Module<"{{.*}}.TanhModule">
|
|
|
|
class MmModule(torch.nn.Module):
|
|
def __init__(self):
|
|
super().__init__()
|
|
def forward(self, lhs, rhs ):
|
|
return torch.ops.aten.mm(lhs, rhs)
|
|
|
|
# N > 1 inputs.
|
|
mm_example_inputs = [torch.ones(2, 3), torch.ones(3, 4)]
|
|
print(torch_mlir.compile(MmModule(), mm_example_inputs))
|
|
# CHECK-LABEL: @forward
|
|
# CHECK: torch.aten.mm %{{.*}}, %{{.*}} : !torch.vtensor<[2,3],f32>, !torch.vtensor<[3,4],f32> -> !torch.vtensor<[2,4],f32>
|
|
|
|
# Mixes Tensor's and TensorPlaceholder's.
|
|
mm_dynamic_inputs = [mm_example_inputs[0], torch_mlir.TensorPlaceholder.like(mm_example_inputs[1], dynamic_axes=[1])]
|
|
print(torch_mlir.compile(MmModule(), mm_dynamic_inputs))
|
|
# CHECK-LABEL: @forward
|
|
# CHECK: torch.aten.mm %{{.*}}, %{{.*}} : !torch.vtensor<[2,3],f32>, !torch.vtensor<[3,?],f32> -> !torch.vtensor<[2,?],f32>
|