mirror of https://github.com/llvm/torch-mlir
59 lines
2.1 KiB
Python
59 lines
2.1 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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from typing import Optional
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import torch
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import torch.export
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import torch.nn as nn
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from torch_mlir import fx
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def run(f):
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print(f"{f.__name__}")
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print("-" * len(f.__name__))
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f()
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print()
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@run
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# CHECK-LABEL: test_import_frozen_exported_program
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# CHECK: func.func @main(%[[ARG0:[a-zA-Z0-9]+]]: !torch.vtensor<[3,4],f32>) -> !torch.vtensor<[3,4],f32>
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# CHECK-DAG: %[[a:.+]] = torch.vtensor.literal(dense_resource<torch_tensor_1_4_torch.float32> : tensor<1x4xf32>) : !torch.vtensor<[1,4],f32>
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# CHECK-DAG: %[[b:.+]] = torch.vtensor.literal(dense_resource<torch_tensor_3_1_torch.float32> : tensor<3x1xf32>) : !torch.vtensor<[3,1],f32>
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# CHECK-DAG: %[[p:.+]] = torch.vtensor.literal(dense<{{.*>+}} : tensor<1x1xf32>) : !torch.vtensor<[1,1],f32>
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# CHECK-DAG: %[[tanh:.+]] = torch.aten.tanh %[[ARG0]]
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# CHECK-DAG: %[[mul_a:.+]] = torch.aten.mul.Tensor %[[tanh]], %[[a]]
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# CHECK-DAG: %[[mul_b:.+]] = torch.aten.mul.Tensor %[[mul_a]], %[[b]]
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# CHECK-DAG: %[[mul_p:.+]] = torch.aten.mul.Tensor %[[mul_b]], %[[p]]
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# CHECK: return %[[mul_p]]
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#
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# Validate dialect resources exist.
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# CHECK: dialect_resources:
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# CHECK-DAG: torch_tensor_1_4_torch.float32
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# CHECK-DAG: torch_tensor_3_1_torch.float32
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def test_import_frozen_exported_program():
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# Tests the basic structural premises of import_frozen_exported_program,
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# namely that free tensors (buffers) and parameters are treated as
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# literals and frozen.
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@torch._dynamo.assume_constant_result
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def get_a():
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return torch.randn(1, 4)
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class Basic(nn.Module):
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def __init__(self):
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super().__init__()
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self.b = torch.randn(3, 1)
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self.p = nn.Parameter(torch.randn(1, 1))
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def forward(self, x):
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return torch.tanh(x) * get_a() * self.b * self.p
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m = fx.export_and_import(Basic(), torch.randn(3, 4))
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print(m)
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