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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 pytest
import torch
import torch.distributed as dist
import torch.multiprocessing as mp
from nemo.deploy.nlp.hf_deployable import HuggingFaceLLMDeploy
from nemo.deploy.utils import broadcast_list
@pytest.mark.run_only_on('GPU')
@pytest.mark.unit
def test_hf_generate():
"""Tests HF deployable class's generate function."""
hf_deployable = HuggingFaceLLMDeploy(
hf_model_id_path="/home/TestData/llm/models/llama3.2-1B-hf/",
task="text-generation",
trust_remote_code=True,
device_map=None,
tp_plan=None,
)
output = hf_deployable.generate(
text_inputs=["What is the color of a banana? ", "Tell me a joke."],
max_length=32,
do_sample=True,
)
assert len(output) == 2, "Output should have to be a list."
assert len(output[0]) > 0, "First list in the output should have more than 0 elements."
assert len(output[1]) > 0, "Second list in the output should have more than 0 elements."
# Test output_logits and output_scores
output = hf_deployable.generate(
text_inputs=["What is the color of a banana? ", "Tell me a joke."],
max_length=32,
do_sample=True,
output_logits=True,
output_scores=True,
return_dict_in_generate=True,
)
assert "logits" in output, "Output should have logits."
assert "scores" in output, "Output should have scores."
assert "sentences" in output, "Output should have sentences."
assert len(output["sentences"]) == 2, "Output should have 2 sentences."
@pytest.mark.run_only_on('GPU')
@pytest.mark.unit
@pytest.mark.skip(reason="will be enabled later.")
def test_hf_multigpu_generate():
"""Tests HF deployable class's generate function with multiple GPUs."""
mp.spawn(_run_generate, nprocs=2)
def _run_generate(rank):
"""Code to run generate in each rank."""
os.environ['WORLD_SIZE'] = '2'
os.environ['MASTER_ADDR'] = 'localhost'
os.environ['MASTER_PORT'] = '12355'
if rank == 0:
os.environ['RANK'] = str(rank)
dist.init_process_group("nccl", rank=rank, world_size=2)
_hf_generate_ranks()
dist.destroy_process_group()
else:
os.environ['RANK'] = str(rank)
dist.init_process_group("nccl", rank=rank, world_size=2)
_hf_generate_ranks()
dist.destroy_process_group()
def _hf_generate_ranks():
"""Generate by Ranks"""
torch.cuda.set_device(dist.get_rank())
hf_deployable = HuggingFaceLLMDeploy(
hf_model_id_path="/home/TestData/llm/models/llama3.2-1B-hf/",
task="text-generation",
trust_remote_code=True,
device_map=None,
tp_plan=None,
)
if dist.get_rank() == 0:
temperature = 1.0
top_k = 1
top_p = 0.0
num_tokens_to_generate = 32
output_logits = False
output_scores = False
prompts = ["What is the color of a banana? ", "Tell me a joke."]
dist.broadcast(torch.tensor([0], dtype=torch.long, device="cuda"), src=0)
broadcast_list(prompts, src=0)
broadcast_list(
data=[
temperature,
top_k,
top_p,
num_tokens_to_generate,
output_logits,
output_scores,
],
src=0,
)
output = hf_deployable.generate(
text_inputs=prompts,
max_length=num_tokens_to_generate,
do_sample=True,
temperature=temperature,
top_k=top_k,
top_p=top_p,
output_logits=output_logits,
output_scores=output_scores,
)
dist.broadcast(torch.tensor([1], dtype=torch.long, device="cuda"), src=0)
else:
hf_deployable.generate_other_ranks()
dist.barrier()
if dist.get_rank() == 0:
assert len(output) == 2, "Output should have to be a lists."
assert len(output[0]) > 0, "First list in the output should have more than 0 elements."
assert len(output[1]) > 0, "Second list in the output should have more than 0 elements."
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