mirror of https://github.com/llvm/torch-mlir
59 lines
1.6 KiB
Python
59 lines
1.6 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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import torch
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from torch import nn
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from npcomp_torchscript.e2e_test.framework import TestUtils
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from npcomp_torchscript.e2e_test.registry import register_test_case
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from npcomp_torchscript.annotations import annotate_args, export
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# ==============================================================================
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class QuantizedMLP(nn.Module):
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def __init__(self):
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super().__init__()
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torch.random.manual_seed(0)
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self.layers = nn.Sequential(
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nn.Linear(16, 8),
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nn.Tanh(),
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nn.Linear(8, 4),
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)
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self.quantize = torch.quantization.QuantStub()
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self.dequantize = torch.quantization.DeQuantStub()
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@export
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@export
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@annotate_args([
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None,
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([1, 16], torch.float32, True),
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])
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def forward(self, x):
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x = self.quantize(x)
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x = self.layers(x)
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x = self.dequantize(x)
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return x
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def get_mlp_input():
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return 2 * torch.rand((1, 16)) - 1
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def get_quantized_mlp():
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model = QuantizedMLP()
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model.eval()
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model.qconfig = torch.quantization.default_qconfig
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torch.quantization.prepare(model, inplace=True)
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torch.manual_seed(0)
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for _ in range(32):
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model(get_mlp_input())
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torch.quantization.convert(model, inplace=True)
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return model
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@register_test_case(module_factory=get_quantized_mlp)
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def QuantizedMLP_basic(module, tu: TestUtils):
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module.forward(get_mlp_input())
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