File size: 2,892 Bytes
b386992 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 |
# Copyright (c) 2025, NVIDIA CORPORATION. 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
from functools import partial
from pathlib import Path
import fiddle as fdl
import pytest
import yaml
from lightning.pytorch.loggers import TensorBoardLogger
from nemo import lightning as nl
from nemo.collections import llm
from nemo.collections.nlp.modules.common.tokenizer_utils import get_nmt_tokenizer
from nemo.lightning import io
from nemo.utils.import_utils import safe_import
te, HAVE_TE = safe_import("transformer_engine")
ARTIFACTS_DIR = os.path.join(os.path.dirname(os.path.realpath(__file__)), "artifacts")
def dummy_extra(a, b, c=5):
return a + b + c
@pytest.fixture
def partial_function_with_pos_and_key_args():
return partial(dummy_extra, 10, c=15)
class TestLoad:
def test_reload_ckpt(self, tmpdir, partial_function_with_pos_and_key_args):
trainer = nl.Trainer(
devices=1,
accelerator="cpu",
strategy=nl.MegatronStrategy(),
logger=TensorBoardLogger("tb_logs", name="my_model"),
)
tokenizer = get_nmt_tokenizer("megatron", "GPT2BPETokenizer")
model = llm.GPTModel(
llm.GPTConfig(
num_layers=2,
hidden_size=1024,
ffn_hidden_size=4096,
num_attention_heads=8,
),
tokenizer=tokenizer,
)
ckpt = io.TrainerContext(model, trainer, extra={"dummy": partial_function_with_pos_and_key_args})
ckpt.io_dump(tmpdir, yaml_attrs=["model"])
loaded = io.load_context(tmpdir)
assert loaded.model.config.seq_length == ckpt.model.config.seq_length
assert loaded.model.__io__.tokenizer.vocab_file.startswith(str(tmpdir))
assert loaded.model.__io__.tokenizer.merges_file.startswith(str(tmpdir))
loaded_func = loaded.extra["dummy"]
assert loaded_func(b=2) == partial_function_with_pos_and_key_args(b=2)
config = io.load_context(tmpdir, build=False)
assert isinstance(config, fdl.Config)
assert config.model.config.seq_length == ckpt.model.config.seq_length
assert config.model.tokenizer.vocab_file.startswith(str(tmpdir))
assert config.model.tokenizer.merges_file.startswith(str(tmpdir))
assert config.extra["dummy"] == fdl.Partial(dummy_extra, 10, c=15)
|