86 lines
2.6 KiB
Python
86 lines
2.6 KiB
Python
# coding=utf-8
|
|
# Copyright 2020 The HuggingFace Team. All rights reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
import os
|
|
import unittest
|
|
|
|
from transformers import is_torch_available
|
|
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
|
|
|
|
|
|
if is_torch_available():
|
|
import torch
|
|
|
|
from transformers import GPT2LMHeadModel
|
|
|
|
|
|
@require_torch
|
|
@require_sentencepiece
|
|
@require_tokenizers
|
|
class MegatronGPT2IntegrationTest(unittest.TestCase):
|
|
@slow
|
|
@unittest.skip("Model is not available.")
|
|
def test_inference_no_head(self):
|
|
directory = "nvidia/megatron-gpt2-345m/"
|
|
if "MYDIR" in os.environ:
|
|
directory = os.path.join(os.environ["MYDIR"], directory)
|
|
model = GPT2LMHeadModel.from_pretrained(directory)
|
|
model.to(torch_device)
|
|
model.half()
|
|
|
|
input_ids = torch.tensor(
|
|
[[101, 7110, 1005, 1056, 2023, 11333, 17413, 1029, 102]],
|
|
device=torch_device,
|
|
dtype=torch.long,
|
|
)
|
|
|
|
with torch.no_grad():
|
|
output = model(input_ids).logits
|
|
|
|
expected_shape = torch.Size((1, 9, 50257))
|
|
self.assertEqual(output.shape, expected_shape)
|
|
|
|
expected_diag = torch.tensor(
|
|
[
|
|
4.9414,
|
|
-0.2920,
|
|
-1.2148,
|
|
-4.0273,
|
|
-0.5161,
|
|
-5.2109,
|
|
-1.2412,
|
|
-1.8301,
|
|
-1.7734,
|
|
-4.7148,
|
|
-0.2317,
|
|
-1.0811,
|
|
-2.1777,
|
|
0.4141,
|
|
-3.7969,
|
|
-4.0586,
|
|
-2.5332,
|
|
-3.3809,
|
|
4.3867,
|
|
],
|
|
device=torch_device,
|
|
dtype=torch.half,
|
|
)
|
|
|
|
for i in range(19):
|
|
r, c = 8 * i // 17, 2792 * i # along the diagonal
|
|
computed, expected = output[0, r, c], expected_diag[i]
|
|
msg = f"row={r} col={c} computed={computed} expected={expected}"
|
|
self.assertAlmostEqual(computed, expected, delta=1e-4, msg=msg)
|