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54 lines
1.6 KiB
Python
54 lines
1.6 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Test quantized RNN modules for cuda."""
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import copy
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import pytest
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import torch
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import torch.nn as nn
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from modelopt.torch.quantization import set_quantizer_attribute
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from modelopt.torch.quantization.nn import QuantModuleRegistry
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@pytest.mark.parametrize(
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("original_cls", "bidirectional", "bias"),
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[
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(nn.LSTM, True, True),
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(nn.LSTM, False, False),
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],
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)
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def test_no_quant_proj(original_cls, bidirectional, bias):
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rnn_object = original_cls(
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8,
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8,
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2,
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bidirectional=bidirectional,
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bias=bias,
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proj_size=4,
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).cuda()
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rnn_object_original = copy.deepcopy(rnn_object)
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quant_rnn_object = QuantModuleRegistry.convert(rnn_object)
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set_quantizer_attribute(quant_rnn_object, lambda name: True, {"enable": False})
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test_input = torch.randn((3, 2, 8), device="cuda")
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out1 = quant_rnn_object(test_input)[0]
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out2 = rnn_object_original(test_input)[0]
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assert torch.allclose(out1, out2, atol=1e-5)
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