codingutf8 2020 the huggingface team 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 orglicenseslicense2 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 coding utf 8 2020 the huggingface team 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 along the diagonal
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  
            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)