wehub-resource-sync
95 lines
3.2 KiB
Python
95 lines
3.2 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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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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import os
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import tempfile
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from unittest import TestCase
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import numpy as np
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import paddle
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from huggingface_hub import hf_hub_download
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from parameterized import parameterized
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from paddlenlp.utils import load_torch
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from tests.testing_utils import require_package
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class SerializationTest(TestCase):
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@parameterized.expand(
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[
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"float32",
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"float16",
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"bfloat16",
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]
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)
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@require_package("torch")
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def test_simple_load(self, dtype: str):
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import torch
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# torch "normal_kernel_cpu" not implemented for 'Char', 'Int', 'Long', so only support float
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dtype_mapping = {
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"float32": torch.float32,
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"float16": torch.float16,
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"bfloat16": torch.bfloat16, # test bfloat16
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}
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dtype = dtype_mapping[dtype]
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with tempfile.TemporaryDirectory() as tempdir:
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weight_file_path = os.path.join(tempdir, "pytorch_model.bin")
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torch.save(
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{
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"a": torch.randn(2, 3, dtype=dtype),
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"b": torch.randn(3, 4, dtype=dtype),
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"a_parameter": torch.nn.Parameter(torch.randn(2, 3, dtype=dtype)), # test torch.nn.Parameter
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"b_parameter": torch.nn.Parameter(torch.randn(3, 4, dtype=dtype)),
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},
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weight_file_path,
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)
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numpy_data = load_torch(weight_file_path)
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torch_data = torch.load(weight_file_path)
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for key, arr in numpy_data.items():
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assert np.allclose(
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paddle.to_tensor(arr).cast("float32").cpu().numpy(),
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torch_data[key].detach().cpu().to(torch.float32).numpy(),
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)
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@parameterized.expand(
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[
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"hf-internal-testing/tiny-random-codegen",
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"hf-internal-testing/tiny-random-Data2VecTextModel",
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"hf-internal-testing/tiny-random-SwinModel",
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]
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)
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@require_package("torch")
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def test_load_bert_model(self, repo_id):
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import torch
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with tempfile.TemporaryDirectory() as tempdir:
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weight_file = hf_hub_download(
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repo_id=repo_id,
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filename="pytorch_model.bin",
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cache_dir=tempdir,
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library_name="PaddleNLP",
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)
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torch_weight = torch.load(weight_file)
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torch_weight = {key: value for key, value in torch_weight.items()}
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paddle_weight = load_torch(weight_file)
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for key, arr in paddle_weight.items():
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assert np.allclose(
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arr,
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torch_weight[key].numpy(),
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)
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