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# 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
import tarfile
import tempfile
import typing
import numpy as np
import pytest
import torch
from PIL import Image
from pytriton.model_config import Tensor
from nemo.deploy.utils import (
NEMO1,
NEMO2,
broadcast_list,
cast_output,
ndarray2img,
nemo_checkpoint_version,
str_list2numpy,
str_ndarray2list,
typedict2tensor,
)
class TestTypedict2Tensor:
class SampleTypedict:
int_field: int
float_field: float
bool_field: bool
str_field: str
int_list: typing.List[int]
float_list: typing.List[float]
bool_list: typing.List[bool]
str_list: typing.List[str]
def test_typedict2tensor_basic(self):
tensors = typedict2tensor(self.SampleTypedict)
assert len(tensors) == 8
assert all(isinstance(t, Tensor) for t in tensors)
# Check int field
int_tensor = next(t for t in tensors if t.name == "int_field")
assert int_tensor.dtype == np.int32
assert int_tensor.shape == (1,)
# Check float field
float_tensor = next(t for t in tensors if t.name == "float_field")
assert float_tensor.dtype == np.float32
assert float_tensor.shape == (1,)
# Check bool field
bool_tensor = next(t for t in tensors if t.name == "bool_field")
assert bool_tensor.dtype == np.bool_
assert bool_tensor.shape == (1,)
# Check str field
str_tensor = next(t for t in tensors if t.name == "str_field")
assert str_tensor.dtype == bytes
assert str_tensor.shape == (1,)
def test_typedict2tensor_with_overwrite(self):
overwrite_kwargs = {"optional": True}
tensors = typedict2tensor(self.SampleTypedict, overwrite_kwargs=overwrite_kwargs)
assert all(t.optional for t in tensors)
def test_typedict2tensor_list_types(self):
tensors = typedict2tensor(self.SampleTypedict)
# Check int list
int_list_tensor = next(t for t in tensors if t.name == "int_list")
assert int_list_tensor.dtype == np.int32
assert int_list_tensor.shape == (1,)
# Check float list
float_list_tensor = next(t for t in tensors if t.name == "float_list")
assert float_list_tensor.dtype == np.float32
assert float_list_tensor.shape == (1,)
# Check bool list
bool_list_tensor = next(t for t in tensors if t.name == "bool_list")
assert bool_list_tensor.dtype == np.bool_
assert bool_list_tensor.shape == (1,)
# Check str list
str_list_tensor = next(t for t in tensors if t.name == "str_list")
assert str_list_tensor.dtype == bytes
assert str_list_tensor.shape == (1,)
class TestNemoCheckpointVersion:
def test_nemo2_checkpoint_dir(self):
with tempfile.TemporaryDirectory() as tmpdir:
# Create NEMO 2.0 structure
os.makedirs(os.path.join(tmpdir, "context"))
os.makedirs(os.path.join(tmpdir, "weights"))
assert nemo_checkpoint_version(tmpdir) == NEMO2
def test_nemo1_checkpoint_dir(self):
with tempfile.TemporaryDirectory() as tmpdir:
# Create NEMO 1.0 structure (no context/weights dirs)
assert nemo_checkpoint_version(tmpdir) == NEMO1
def test_nemo2_checkpoint_tar(self):
with tempfile.TemporaryDirectory() as tmpdir:
tar_path = os.path.join(tmpdir, "checkpoint.tar")
with tarfile.open(tar_path, "w") as tar:
# Create NEMO 2.0 structure in tar
context_info = tarfile.TarInfo("context")
context_info.type = tarfile.DIRTYPE
tar.addfile(context_info)
weights_info = tarfile.TarInfo("weights")
weights_info.type = tarfile.DIRTYPE
tar.addfile(weights_info)
assert nemo_checkpoint_version(tar_path) == NEMO2
def test_nemo1_checkpoint_tar(self):
with tempfile.TemporaryDirectory() as tmpdir:
tar_path = os.path.join(tmpdir, "checkpoint.tar")
with tarfile.open(tar_path, "w") as tar:
# Create empty tar (NEMO 1.0)
pass
assert nemo_checkpoint_version(tar_path) == NEMO1
class TestStringConversions:
def test_str_list2numpy(self):
input_list = ["hello", "world", "test"]
result = str_list2numpy(input_list)
assert isinstance(result, np.ndarray)
assert result.shape == (3, 1)
assert all(isinstance(x, bytes) for x in result.flatten())
def test_str_ndarray2list(self):
input_array = np.array([b"hello", b"world", b"test"]).reshape(3, 1)
result = str_ndarray2list(input_array)
assert isinstance(result, list)
assert result == ["hello", "world", "test"]
def test_str_conversion_roundtrip(self):
input_list = ["hello", "world", "test"]
numpy_array = str_list2numpy(input_list)
output_list = str_ndarray2list(numpy_array)
assert input_list == output_list
class TestImageConversions:
def test_ndarray2img(self):
# Create a test image array
img_array = np.random.randint(0, 255, size=(2, 100, 100, 3), dtype=np.uint8)
result = ndarray2img(img_array)
assert isinstance(result, list)
assert len(result) == 2
assert all(isinstance(img, Image.Image) for img in result)
assert all(img.size == (100, 100) for img in result)
class TestCastOutput:
def test_cast_tensor(self):
input_tensor = torch.tensor([1, 2, 3])
result = cast_output(input_tensor, np.int32)
assert isinstance(result, np.ndarray)
assert result.dtype == np.int32
assert result.shape == (3, 1)
def test_cast_numpy(self):
input_array = np.array([1, 2, 3])
result = cast_output(input_array, np.float32)
assert isinstance(result, np.ndarray)
assert result.dtype == np.float32
assert result.shape == (3, 1)
def test_cast_string(self):
input_list = ["hello", "world"]
result = cast_output(input_list, bytes)
assert isinstance(result, np.ndarray)
assert result.shape == (2, 1)
def test_cast_1d_to_2d(self):
input_array = np.array([1, 2, 3])
result = cast_output(input_array, np.int32)
assert result.ndim == 2
assert result.shape == (3, 1)
class TestBroadcastList:
def test_broadcast_list_no_distributed(self):
with pytest.raises(RuntimeError, match="Distributed environment is not initialized"):
broadcast_list(["test"])
def test_broadcast_list_distributed(self, monkeypatch):
# Mock distributed environment
monkeypatch.setattr(torch.distributed, "is_initialized", lambda: True)
monkeypatch.setattr(torch.distributed, "get_rank", lambda: 0)
# Mock broadcast_object_list
def mock_broadcast_object_list(object_list, src, group=None):
if src == 0:
object_list[0] = ["test"]
monkeypatch.setattr(torch.distributed, "broadcast_object_list", mock_broadcast_object_list)
result = broadcast_list(["test"])
assert result == ["test"]
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