diff --git a/.github/workflows/cicd-main.yml b/.github/workflows/cicd-main.yml index d1c6480c9..4ca43fb78 100644 --- a/.github/workflows/cicd-main.yml +++ b/.github/workflows/cicd-main.yml @@ -252,6 +252,11 @@ jobs: timeout: 20 runner: ${{ needs.pre-flight.outputs.runner_prefix }}-gpu-x2 test-data-path: ${{ needs.pre-flight.outputs.test_data_path }} + - test-name: L2_vLLM_Deployment + test-folder: vllm_deployment + timeout: 30 + runner: ${{ needs.pre-flight.outputs.runner_prefix }}-gpu-x2 + test-data-path: ${{ needs.pre-flight.outputs.test_data_path }} needs: [pre-flight, cicd-unit-tests] runs-on: ${{ matrix.runner }} name: ${{ matrix.test-name }} diff --git a/.github/workflows/config/.secrets.baseline b/.github/workflows/config/.secrets.baseline index 5e053efd5..9900559de 100644 --- a/.github/workflows/config/.secrets.baseline +++ b/.github/workflows/config/.secrets.baseline @@ -195,6 +195,45 @@ "line_number": 101 } ], + "tests/unit_tests/launcher/test_template.py": [ + { + "type": "Secret Keyword", + "filename": "tests/unit_tests/launcher/test_template.py", + "hashed_secret": "829c3804401b0727f70f73d4415e162400cbe57b", + "is_verified": false, + "line_number": 25 + } + ], + "tests/unit_tests/config/test_allowed_import_prefixes.py": [ + { + "type": "Secret Keyword", + "filename": "tests/unit_tests/config/test_allowed_import_prefixes.py", + "hashed_secret": "a761ce3a45d97e41840a788495e85a70d1bb3815", + "is_verified": false, + "line_number": 80 + }, + { + "type": "Secret Keyword", + "filename": "tests/unit_tests/config/test_allowed_import_prefixes.py", + "hashed_secret": "4fa2519ad2d85de09d4f42a73eb361a01efc1549", + "is_verified": false, + "line_number": 81 + }, + { + "type": "Secret Keyword", + "filename": "tests/unit_tests/config/test_allowed_import_prefixes.py", + "hashed_secret": "77ace0be5333763f83a5456cf55fa362e23a2538", + "is_verified": false, + "line_number": 97 + }, + { + "type": "Secret Keyword", + "filename": "tests/unit_tests/config/test_allowed_import_prefixes.py", + "hashed_secret": "aa6878b1c31a9420245df1daffb7b223338737a3", + "is_verified": false, + "line_number": 101 + } + ], "tests/unit_tests/launcher/test_template.py": [ { "type": "Secret Keyword", @@ -205,5 +244,9 @@ } ] }, +<<<<<<< Updated upstream "generated_at": "2026-03-03T19:52:57Z" +======= + "generated_at": "2026-03-03T20:22:24Z" +>>>>>>> Stashed changes } diff --git a/docs/guides/llm/deployment.md b/docs/guides/llm/deployment.md new file mode 100644 index 000000000..8fc2bd734 --- /dev/null +++ b/docs/guides/llm/deployment.md @@ -0,0 +1,298 @@ +# Deploying Models with vLLM and SGLang + +NeMo AutoModel saves every checkpoint in **native Hugging Face format** (Safetensors + `config.json` + tokenizer). +This means the same checkpoint directory that NeMo AutoModel writes can be loaded directly (without any **conversion step**) by any tool in the Hugging Face ecosystem, including the two most popular inference engines: [vLLM](https://github.com/vllm-project/vllm) and [SGLang](https://github.com/sgl-project/sglang). + +Just point the engine at your checkpoint path and serve: + +```bash +vllm serve checkpoints/epoch_0_step_100/model/consolidated/ --port 8000 +``` + +Both engines expose an **OpenAI-compatible API**, so you can swap them in without changing client code. + +:::{seealso} +- [Fine-Tuning Guide](finetune.md) — train or adapt a model before deployment. +- [Checkpointing Guide](../checkpointing.md) — checkpoint formats, consolidation, and Safetensors output. +::: + +--- + +## Prerequisites + +| Requirement | Minimum | +|-------------|---------| +| GPU | NVIDIA GPU with 8 GB+ VRAM (16 GB+ recommended for 1B+ models) | +| CUDA | 11.8+ | +| Python | 3.9+ | + +Install either engine (or both): + +```bash +pip install vllm # vLLM +pip install "sglang[all]" # SGLang +``` + +:::{tip} +If you are inside the NeMo AutoModel Docker container, vLLM is already installed. +::: + +--- + +## Checkpoint Requirements + +vLLM and SGLang load models in **Hugging Face format**. A valid checkpoint directory looks like: + +```text +my-checkpoint/ +├── config.json +├── tokenizer.json (or tokenizer_config.json + tokenizer.model) +├── model.safetensors (or sharded model-00001-of-*.safetensors + model.safetensors.index.json) +└── generation_config.json (optional) +``` + +NeMo AutoModel produces this layout automatically when you set `save_consolidated: true` and `model_save_format: safetensors` in the checkpoint config (the defaults). +See the [Checkpointing guide](../checkpointing.md) for details. + +:::{important} +If your checkpoint directory contains **sharded DCP files** (`.distcp`) rather than consolidated Safetensors, you need to consolidate them first. +Use the AutoModel checkpoint utility or the recipe's `save_consolidated: true` flag to produce a Hugging Face-compatible directory. +::: + +--- + +## Quick Local Test with a Small Model + +The examples below use **`Qwen/Qwen2.5-0.5B-Instruct`** (~0.5 B parameters, ~1 GB on disk). +It runs comfortably on a single GPU with 4 GB+ VRAM and does not require any gated-access agreement, making it ideal for a quick smoke test. + +:::{note} +If you already have access to `meta-llama/Llama-3.2-1B-Instruct`, you can substitute it in any of the commands below. +Just make sure your Hugging Face token is set: +```bash +export HF_TOKEN=hf_xxxxx +``` +::: + +### Deploy with vLLM + +#### Option A: OpenAI-compatible API server + +```bash +vllm serve Qwen/Qwen2.5-0.5B-Instruct \ + --host 0.0.0.0 \ + --port 8000 +``` + +In a separate terminal, send a request: + +```bash +curl -s http://localhost:8000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -d '{ + "model": "Qwen/Qwen2.5-0.5B-Instruct", + "messages": [{"role": "user", "content": "What is the capital of France?"}], + "max_tokens": 64 + }' | python3 -m json.tool +``` + +#### Option B: Python offline inference + +```python +from vllm import LLM, SamplingParams + +llm = LLM(model="Qwen/Qwen2.5-0.5B-Instruct") +params = SamplingParams(temperature=0.7, max_tokens=64) +outputs = llm.generate(["What is the capital of France?"], sampling_params=params) +print(outputs[0].outputs[0].text) +``` + +### Deploy with SGLang + +#### Option A: OpenAI-compatible API server + +```bash +python -m sglang.launch_server \ + --model-path Qwen/Qwen2.5-0.5B-Instruct \ + --host 0.0.0.0 \ + --port 30000 +``` + +Query it with the same `curl` style (change the port to `30000`): + +```bash +curl -s http://localhost:30000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -d '{ + "model": "Qwen/Qwen2.5-0.5B-Instruct", + "messages": [{"role": "user", "content": "What is the capital of France?"}], + "max_tokens": 64 + }' | python3 -m json.tool +``` + +#### Option B: Python engine API + +```python +import sglang as sgl + +llm = sgl.Engine(model_path="Qwen/Qwen2.5-0.5B-Instruct") +output = llm.generate("What is the capital of France?", sampling_params={"max_new_tokens": 64}) +print(output["text"]) +``` + +--- + +## Deploy a Local Checkpoint + +After fine-tuning with NeMo AutoModel, pass the **consolidated checkpoint path** instead of a Hugging Face Hub name. + +### vLLM + +```bash +vllm serve /path/to/checkpoints/epoch_0_step_100/model/consolidated/ \ + --host 0.0.0.0 \ + --port 8000 +``` + +```python +from vllm import LLM, SamplingParams + +llm = LLM(model="/path/to/checkpoints/epoch_0_step_100/model/consolidated/") +outputs = llm.generate(["Hello, world!"], SamplingParams(max_tokens=64)) +print(outputs[0].outputs[0].text) +``` + +### SGLang + +```bash +python -m sglang.launch_server \ + --model-path /path/to/checkpoints/epoch_0_step_100/model/consolidated/ \ + --host 0.0.0.0 \ + --port 30000 +``` + +### Separate tokenizer + +If the tokenizer is not inside the checkpoint directory, point to it explicitly: + +```bash +# vLLM +vllm serve /path/to/checkpoint --tokenizer /path/to/tokenizer + +# SGLang +python -m sglang.launch_server \ + --model-path /path/to/checkpoint \ + --tokenizer-path /path/to/tokenizer +``` + +--- + +## Deploy a LoRA / PEFT Adapter + +vLLM supports serving LoRA adapters on top of a base model without merging weights. + +```python +from vllm import LLM, SamplingParams +from vllm.lora.request import LoRARequest + +llm = LLM(model="meta-llama/Llama-3.2-1B-Instruct", enable_lora=True) + +params = SamplingParams(temperature=0.7, max_tokens=64) +outputs = llm.generate( + ["What is the capital of France?"], + sampling_params=params, + lora_request=LoRARequest("my-adapter", 1, "/path/to/lora/adapter"), +) +print(outputs[0].outputs[0].text) +``` + +Alternatively, use the NeMo AutoModel `vLLMHFExporter` helper: + +```python +from nemo.export.vllm_hf_exporter import vLLMHFExporter + +exporter = vLLMHFExporter() +exporter.export(model="/path/to/base/model", enable_lora=True) +exporter.add_lora_models(lora_model_name="my-adapter", lora_model="/path/to/lora/adapter") +print(exporter.forward(input_texts=["How are you?"], lora_model_name="my-adapter")) +``` + +--- + +## Common Configuration Flags + +### vLLM + +| Flag | Purpose | +|------|---------| +| `--tensor-parallel-size N` | Shard the model across N GPUs | +| `--dtype auto` | Auto-detect dtype (float16 / bfloat16) | +| `--max-model-len 4096` | Cap the maximum context length | +| `--gpu-memory-utilization 0.9` | Fraction of GPU memory to allocate | +| `--quantization awq` | Load a pre-quantized model (awq, gptq, etc.) | +| `--enforce-eager` | Disable CUDA graphs (useful for debugging) | + +### SGLang + +| Flag | Purpose | +|------|---------| +| `--tp N` | Tensor parallelism across N GPUs | +| `--dtype auto` | Auto-detect dtype | +| `--mem-fraction-static 0.85` | GPU memory fraction for KV cache | +| `--quantization awq` | Load a pre-quantized model | +| `--context-length 4096` | Maximum context length | + +--- + +## Multi-GPU Deployment + +For models that exceed the memory of a single GPU, increase the tensor-parallelism degree: + +```bash +# vLLM on 4 GPUs +vllm serve meta-llama/Llama-3.1-70B-Instruct \ + --tensor-parallel-size 4 \ + --port 8000 + +# SGLang on 4 GPUs +python -m sglang.launch_server \ + --model-path meta-llama/Llama-3.1-70B-Instruct \ + --tp 4 \ + --port 30000 +``` + +--- + +## Docker Deployment + +Run vLLM as a self-contained container: + +```bash +docker run --gpus all -p 8000:8000 \ + -v /path/to/local/checkpoint:/model \ + vllm/vllm-openai:latest \ + --model /model +``` + +Or use the NeMo AutoModel container (vLLM is pre-installed): + +```bash +docker run --gpus all -it --rm \ + --shm-size=8g \ + -p 8000:8000 \ + -v /path/to/checkpoint:/model \ + nvcr.io/nvidia/nemo-automodel:25.11.00 \ + vllm serve /model --host 0.0.0.0 --port 8000 +``` + +--- + +## Troubleshooting + +| Problem | Solution | +|---------|----------| +| `FileNotFoundError: config.json` | The checkpoint path must point to the directory that **contains** `config.json`. If you used NeMo AutoModel, this is the `model/consolidated/` subdirectory. | +| `torch.cuda.OutOfMemoryError` | Reduce `--max-model-len`, lower `--gpu-memory-utilization`, or increase `--tensor-parallel-size`. | +| Tokenizer not found | Pass `--tokenizer` (vLLM) or `--tokenizer-path` (SGLang) explicitly. | +| Gated model 401 error | Set `export HF_TOKEN=hf_xxxxx` or run `huggingface-cli login`. | +| Slow first request | The first request warms up CUDA graphs and the KV cache. Subsequent requests will be faster. | diff --git a/docs/guides/llm/finetune.md b/docs/guides/llm/finetune.md index 3ca81700e..cc3c8c8dd 100644 --- a/docs/guides/llm/finetune.md +++ b/docs/guides/llm/finetune.md @@ -16,8 +16,9 @@ the Hugging Face ecosystem. This guide walks you through the end-to-end process of fine-tuning models from the Hugging Face Hub using NeMo AutoModel. You'll learn how to prepare datasets, train models, generate text with fine-tuned -checkpoints, share your models on the Hugging Face Model Hub, and deploy -them efficiently with vLLM. +checkpoints, evaluate performance using the LM Eval Harness, share your +models on the Hugging Face Model Hub, and deploy them efficiently with +[vLLM and SGLang](deployment.md). In addition to this user guide, you can also explore our Quickstart, which features a [standalone python3 recipe](https://github.com/NVIDIA-NeMo/Automodel/blob/main/examples/llm_finetune/finetune.py), @@ -469,7 +470,7 @@ By publishing the fine-tuned checkpoint or PEFT adapter to the Hugging Face Hub, enable easy sharing, reproducibility, and integration with downstream applications. -## Export to vLLM +## Deploy with vLLM [vLLM](https://github.com/vllm-project/vllm) is an efficient inference engine designed to optimize the deployment of large language models @@ -477,18 +478,18 @@ engine designed to optimize the deployment of large language models parallel processing and optimized memory management, vLLM accelerates inference while maintaining model accuracy. -The following script demonstrates how to use a fine-tuned checkpoint -in vLLM, allowing seamless deployment and efficient inference: - :::{note} -Make sure vLLM is installed (`pip install vllm`, or use the environment that includes it). +Make sure vLLM is installed (`pip install vllm`, or use the NeMo AutoModel Docker container which includes it). ::: +### SFT checkpoint + +Point vLLM at the **consolidated** checkpoint directory: ```python from vllm import LLM, SamplingParams -llm = LLM(model="checkpoints/epoch_0_step_10/model/consolidated/", model_impl="transformers") +llm = LLM(model="checkpoints/epoch_0_step_10/model/consolidated/") params = SamplingParams(max_tokens=20) outputs = llm.generate("Toronto is a city in Canada.", sampling_params=params) print(f"Generated text: {outputs[0].outputs[0].text}") @@ -497,30 +498,30 @@ print(f"Generated text: {outputs[0].outputs[0].text}") >>> Generated text: It is the capital of Ontario. Toronto is a global hub for cultural tourism. The City of Toronto ``` -Similarly, the following script demonstrates how to export a PEFT adapter for vLLM, -allowing seamless deployment and efficient inference. - -:::{note} -Make sure vLLM is installed (pip install vllm, or use the environment -that includes it) before proceeding with vLLMHFExporter. -::: - -```python -from nemo.export.vllm_hf_exporter import vLLMHFExporter +Or launch an OpenAI-compatible server: -if __name__ == '__main__': - import argparse +```bash +vllm serve checkpoints/epoch_0_step_10/model/consolidated/ --host 0.0.0.0 --port 8000 +``` - parser = argparse.ArgumentParser() - parser.add_argument('--model', required=True, type=str, help="Local path of the base model") - parser.add_argument('--lora-model', required=True, type=str, help="Local path of the lora model") - args = parser.parse_args() +### PEFT / LoRA adapter - lora_model_name = "lora_model" +vLLM can serve LoRA adapters on top of the base model without merging: - exporter = vLLMHFExporter() - exporter.export(model=args.model, enable_lora=True) - exporter.add_lora_models(lora_model_name=lora_model_name, lora_model=args.lora_model) +``` python +from vllm import LLM, SamplingParams +from vllm.lora.request import LoRARequest - print("vLLM Output: ", exporter.forward(input_texts=["How are you doing?"], lora_model_name=lora_model_name)) +llm = LLM(model="meta-llama/Llama-3.2-1B", enable_lora=True) +params = SamplingParams(max_tokens=20) +outputs = llm.generate( + "Toronto is a city in Canada.", + sampling_params=params, + lora_request=LoRARequest("my-adapter", 1, "checkpoints/epoch_0_step_10/model/"), +) +print(f"Generated text: {outputs[0].outputs[0].text}") ``` + +:::{tip} +For the full deployment guide — including **SGLang**, multi-GPU tensor parallelism, Docker deployment, and troubleshooting — see [Deploying Models with vLLM and SGLang](deployment.md). +::: diff --git a/docs/index.md b/docs/index.md index 4cbd1f79e..7a0acdd15 100644 --- a/docs/index.md +++ b/docs/index.md @@ -61,10 +61,10 @@ Built on `transformers` for day-0 model support and OOTB compatibility. Select a modality and task to find the right guide. -| | SFT | PEFT (LoRA) | Tool Calling | QAT | Knowledge Distillation | Pretrain | -|------------|-----|-------------|--------------|-----|------------------------|----------| -| **LLM** | [Guide](guides/llm/finetune.md) | [Guide](guides/llm/finetune.md#recipe-config) | [Guide](guides/llm/toolcalling.md) | [Guide](guides/quantization-aware-training.md) | [Guide](guides/llm/knowledge-distillation.md) | [Guide](guides/llm/pretraining.md) | -| **VLM** | [Guide](guides/overview.md) | [Guide](guides/omni/gemma3-3n.md) | -- | -- | -- | -- | +| | SFT | PEFT (LoRA) | Tool Calling | QAT | Knowledge Distillation | Pretrain | Deployment | +|------------|-----|-------------|--------------|-----|------------------------|----------|------------| +| **LLM** | [Guide](guides/llm/finetune.md) | [Guide](guides/llm/finetune.md#recipe-config) | [Guide](guides/llm/toolcalling.md) | [Guide](guides/quantization-aware-training.md) | [Guide](guides/llm/knowledge-distillation.md) | [Guide](guides/llm/pretraining.md) | [Guide](guides/llm/deployment.md) | +| **VLM** | [Guide](guides/overview.md) | [Guide](guides/omni/gemma3-3n.md) | -- | -- | -- | -- | -- | ## Performance @@ -244,6 +244,11 @@ guides/quantization-aware-training.md guides/llm/databricks.md :::: +::::{toctree} +:hidden: +:caption: Deployment & Inference +guides/llm/deployment.md +:::: ::::{toctree} :hidden: diff --git a/pyproject.toml b/pyproject.toml index b3c32c247..7aa4e3cae 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -77,7 +77,7 @@ dependencies = [ "datasets>=4.0.0", "megatron-fsdp>=0.2.3", "mistral-common[image,audio,hf-hub,sentencepiece]", - "opencv-python-headless==4.10.0.84", + "opencv-python-headless>=4.10.0.84", "pybind11", "pyyaml", "torch>=2.6.0,<=2.10.0", @@ -165,7 +165,13 @@ linting = [ "ruff~=0.9.0", "import-linter~=2.4", ] -test = ["coverage", "pytest", "peft>=0.18.1", "onnxruntime-gpu; (platform_machine == 'x86_64' and platform_system != 'Darwin')"] +test = [ + "coverage", + "pytest", + "peft>=0.18.1", + "onnxruntime-gpu; (platform_machine == 'x86_64' and platform_system != 'Darwin')", + "vllm>=0.16.0; (python_version < '3.14' and platform_machine == 'x86_64' and platform_system != 'Darwin')" +] dev = ["cut-cross-entropy", "liger-kernel; (platform_machine == 'x86_64' and platform_system != 'Darwin')", "dion @ git+https://github.com/microsoft/dion.git; platform_machine == 'x86_64' and platform_system != 'Darwin'"] [tool.uv] @@ -180,6 +186,9 @@ no-build-isolation-package = [ "transformer-engine", "transformer-engine-torch", ] +override-dependencies = [ + "transformers>=5.0.0", # Override vllm's transformers<5 pin until vllm-project/vllm#30566 is merged +] constraint-dependencies = [ "aiohttp>=3.13.3", # Address CVE GHSA-6mq8-rvhq-8wgg "cryptography>=46.0.5", # Address CVE GHSA-r6ph-v2qm-q3c2 diff --git a/tests/functional_tests/vllm_deployment/test_vllm_deployment.py b/tests/functional_tests/vllm_deployment/test_vllm_deployment.py new file mode 100644 index 000000000..da4393526 --- /dev/null +++ b/tests/functional_tests/vllm_deployment/test_vllm_deployment.py @@ -0,0 +1,387 @@ +# 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. + +"""Functional tests: save with NeMo AutoModel, load and infer with vLLM. + +Four scenarios are covered, each parameterised as its own test class: + +1. **Dense model** (tiny Llama) → save consolidated → vLLM offline inference +2. **Dense model + LoRA** → save base + adapter → vLLM LoRA inference +3. **MoE model** (tiny Qwen3-MoE) → save consolidated → vLLM offline inference +4. **MoE model + LoRA** → save base + adapter → vLLM LoRA inference + +All models are randomly initialised with minimal dimensions so the tests +run on a single GPU with < 1 GB VRAM and do not require network access. +""" + +import json +from pathlib import Path + +import pytest +import torch + +# --------------------------------------------------------------------------- +# Constants +# --------------------------------------------------------------------------- +LORA_R = 8 +LORA_ALPHA = 16 +VOCAB_SIZE = 256 +SEQ_LEN = 32 +MAX_NEW_TOKENS = 8 + + +# =========================================================================== +# Tiny model builders +# =========================================================================== + + +def _make_tiny_llama_config(): + """Minimal ``LlamaConfig`` for a dense model (~200 KB).""" + from transformers import LlamaConfig + + return LlamaConfig( + hidden_size=64, + intermediate_size=128, + num_hidden_layers=2, + num_attention_heads=4, + num_key_value_heads=2, + vocab_size=VOCAB_SIZE, + max_position_embeddings=128, + ) + + +def _make_tiny_qwen3_moe_config(): + """Minimal ``Qwen3MoeConfig`` for a sparse MoE model (~400 KB).""" + from transformers.models.qwen3_moe.configuration_qwen3_moe import Qwen3MoeConfig + + return Qwen3MoeConfig( + hidden_size=64, + intermediate_size=128, + moe_intermediate_size=64, + num_hidden_layers=2, + num_attention_heads=4, + num_key_value_heads=2, + vocab_size=VOCAB_SIZE, + max_position_embeddings=128, + num_experts=4, + num_experts_per_tok=2, + decoder_sparse_step=1, + norm_topk_prob=False, + router_aux_loss_coef=0.0, + ) + + +def _save_dense_model(save_dir: str) -> str: + """Create and save a tiny dense Llama model; return the path.""" + from transformers import AutoModelForCausalLM + + config = _make_tiny_llama_config() + model = AutoModelForCausalLM.from_config(config) + model.save_pretrained(save_dir) + return save_dir + + +def _save_moe_model(save_dir: str) -> str: + """Create and save a tiny Qwen3-MoE model; return the path.""" + from transformers import AutoModelForCausalLM + + config = _make_tiny_qwen3_moe_config() + model = AutoModelForCausalLM.from_config(config) + model.save_pretrained(save_dir) + return save_dir + + +# =========================================================================== +# LoRA adapter helpers +# =========================================================================== + + +def _apply_and_save_lora(base_model_path: str, adapter_dir: str, target_modules: list[str]): + """Apply HF PEFT LoRA to a model and save the adapter.""" + from peft import LoraConfig, get_peft_model + from transformers import AutoModelForCausalLM + + model = AutoModelForCausalLM.from_pretrained(base_model_path, torch_dtype=torch.float32) + lora_config = LoraConfig( + r=LORA_R, + lora_alpha=LORA_ALPHA, + target_modules=target_modules, + bias="none", + task_type="CAUSAL_LM", + ) + peft_model = get_peft_model(model, lora_config) + + torch.manual_seed(42) + for _, param in peft_model.named_parameters(): + if param.requires_grad: + param.data = torch.randn_like(param.data) * 0.01 + + peft_model.save_pretrained(adapter_dir) + return adapter_dir + + +# =========================================================================== +# vLLM helpers +# =========================================================================== + + +def _vllm_generate(model_path: str, prompts: list[str], **kwargs): + """Run offline vLLM generation and return output texts.""" + from vllm import LLM, SamplingParams + + llm = LLM( + model=model_path, + dtype="float32", + max_model_len=SEQ_LEN + MAX_NEW_TOKENS, + gpu_memory_utilization=0.3, + enforce_eager=True, + **kwargs, + ) + params = SamplingParams(max_tokens=MAX_NEW_TOKENS, temperature=0.0) + outputs = llm.generate(prompts, sampling_params=params) + return [o.outputs[0].text for o in outputs] + + +def _vllm_generate_with_lora( + model_path: str, + adapter_path: str, + prompts: list[str], +): + """Run offline vLLM generation with a LoRA adapter.""" + from vllm import LLM, SamplingParams + from vllm.lora.request import LoRARequest + + llm = LLM( + model=model_path, + dtype="float32", + max_model_len=SEQ_LEN + MAX_NEW_TOKENS, + gpu_memory_utilization=0.3, + enforce_eager=True, + enable_lora=True, + max_lora_rank=LORA_R, + ) + params = SamplingParams(max_tokens=MAX_NEW_TOKENS, temperature=0.0) + lora_req = LoRARequest("test-adapter", 1, adapter_path) + outputs = llm.generate(prompts, sampling_params=params, lora_request=lora_req) + return [o.outputs[0].text for o in outputs] + + +# =========================================================================== +# Validation helpers +# =========================================================================== + + +def _assert_valid_outputs(outputs: list[str], num_prompts: int): + """Check that vLLM returned the expected number of non-empty outputs.""" + assert len(outputs) == num_prompts, f"Expected {num_prompts} outputs, got {len(outputs)}" + for i, text in enumerate(outputs): + assert isinstance(text, str), f"Output {i} is not a string: {type(text)}" + + +def _assert_logits_match(model_path: str, vllm_model_path: str): + """Compare HF forward-pass logits with vLLM logits on the same input.""" + from transformers import AutoModelForCausalLM + + from vllm import LLM, SamplingParams + + torch.manual_seed(0) + input_ids = torch.randint(0, VOCAB_SIZE, (1, SEQ_LEN)) + + hf_model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=torch.float32).eval() + with torch.no_grad(): + hf_logits = hf_model(input_ids).logits # (1, seq, vocab) + hf_next_token = hf_logits[0, -1].argmax().item() + + llm = LLM( + model=vllm_model_path, + dtype="float32", + max_model_len=SEQ_LEN + MAX_NEW_TOKENS, + gpu_memory_utilization=0.3, + enforce_eager=True, + ) + prompt_token_ids = input_ids[0].tolist() + params = SamplingParams(max_tokens=1, temperature=0.0) + out = llm.generate( + sampling_params=params, + prompt_token_ids=[prompt_token_ids], + ) + vllm_token_id = out[0].outputs[0].token_ids[0] + + assert hf_next_token == vllm_token_id, ( + f"Next-token mismatch: HF predicted {hf_next_token}, vLLM predicted {vllm_token_id}" + ) + + +# =========================================================================== +# Dense model tests +# =========================================================================== + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA required") +class TestDenseVLLM: + """Save a tiny dense Llama with AutoModel, load in vLLM.""" + + @pytest.fixture(autouse=True) + def setup(self, tmp_path): + self.base_model_path = _save_dense_model(str(tmp_path / "dense_base")) + self.prompts = ["Hello world", "The quick brown fox"] + + def test_vllm_generates_outputs(self): + outputs = _vllm_generate(self.base_model_path, self.prompts) + _assert_valid_outputs(outputs, len(self.prompts)) + + def test_vllm_logits_match_hf(self): + _assert_logits_match(self.base_model_path, self.base_model_path) + + def test_checkpoint_files_present(self): + path = Path(self.base_model_path) + assert (path / "config.json").is_file() + safetensors = list(path.glob("*.safetensors")) + assert len(safetensors) > 0, "No safetensors files found" + + +# =========================================================================== +# Dense model + LoRA tests +# =========================================================================== + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA required") +class TestDenseLoRAVLLM: + """Save a tiny dense Llama + LoRA adapter, load both in vLLM.""" + + DENSE_LORA_TARGETS = [ + "q_proj", + "k_proj", + "v_proj", + "o_proj", + "gate_proj", + "up_proj", + "down_proj", + ] + + @pytest.fixture(autouse=True) + def setup(self, tmp_path): + self.base_model_path = _save_dense_model(str(tmp_path / "dense_lora_base")) + self.adapter_path = _apply_and_save_lora( + self.base_model_path, + str(tmp_path / "dense_lora_adapter"), + self.DENSE_LORA_TARGETS, + ) + self.prompts = ["Hello world", "The quick brown fox"] + + def test_adapter_files_present(self): + adapter = Path(self.adapter_path) + assert (adapter / "adapter_config.json").is_file() + assert (adapter / "adapter_model.safetensors").is_file() + + def test_vllm_lora_generates_outputs(self): + outputs = _vllm_generate_with_lora( + self.base_model_path, + self.adapter_path, + self.prompts, + ) + _assert_valid_outputs(outputs, len(self.prompts)) + + def test_lora_changes_output(self): + """Outputs with LoRA adapter should differ from base model.""" + base_outputs = _vllm_generate(self.base_model_path, self.prompts) + lora_outputs = _vllm_generate_with_lora( + self.base_model_path, + self.adapter_path, + self.prompts, + ) + assert base_outputs != lora_outputs, "LoRA adapter did not change any outputs — adapter may not have loaded" + + +# =========================================================================== +# MoE model tests +# =========================================================================== + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA required") +class TestMoEVLLM: + """Save a tiny Qwen3-MoE with AutoModel, load in vLLM.""" + + @pytest.fixture(autouse=True) + def setup(self, tmp_path): + self.base_model_path = _save_moe_model(str(tmp_path / "moe_base")) + self.prompts = ["Hello world", "The quick brown fox"] + + def test_vllm_generates_outputs(self): + outputs = _vllm_generate(self.base_model_path, self.prompts) + _assert_valid_outputs(outputs, len(self.prompts)) + + def test_vllm_logits_match_hf(self): + _assert_logits_match(self.base_model_path, self.base_model_path) + + def test_checkpoint_files_present(self): + path = Path(self.base_model_path) + assert (path / "config.json").is_file() + safetensors = list(path.glob("*.safetensors")) + assert len(safetensors) > 0, "No safetensors files found" + + +# =========================================================================== +# MoE model + LoRA tests +# =========================================================================== + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA required") +class TestMoELoRAVLLM: + """Save a tiny Qwen3-MoE + LoRA adapter, load both in vLLM.""" + + MOE_LORA_TARGETS = [ + "q_proj", + "k_proj", + "v_proj", + "o_proj", + ] + + @pytest.fixture(autouse=True) + def setup(self, tmp_path): + self.base_model_path = _save_moe_model(str(tmp_path / "moe_lora_base")) + self.adapter_path = _apply_and_save_lora( + self.base_model_path, + str(tmp_path / "moe_lora_adapter"), + self.MOE_LORA_TARGETS, + ) + self.prompts = ["Hello world", "The quick brown fox"] + + def test_adapter_files_present(self): + adapter = Path(self.adapter_path) + assert (adapter / "adapter_config.json").is_file() + assert (adapter / "adapter_model.safetensors").is_file() + + with open(adapter / "adapter_config.json") as f: + cfg = json.load(f) + assert cfg["peft_type"] == "LORA" + assert cfg["r"] == LORA_R + + def test_vllm_lora_generates_outputs(self): + outputs = _vllm_generate_with_lora( + self.base_model_path, + self.adapter_path, + self.prompts, + ) + _assert_valid_outputs(outputs, len(self.prompts)) + + def test_lora_changes_output(self): + """Outputs with LoRA adapter should differ from base model.""" + base_outputs = _vllm_generate(self.base_model_path, self.prompts) + lora_outputs = _vllm_generate_with_lora( + self.base_model_path, + self.adapter_path, + self.prompts, + ) + assert base_outputs != lora_outputs, "LoRA adapter did not change any outputs — adapter may not have loaded" diff --git a/uv.lock b/uv.lock index 4c5dfd33c..3875febd0 100644 --- a/uv.lock +++ b/uv.lock @@ -52,6 +52,7 @@ constraints = [ { name = "urllib3", specifier = ">=2.6.3" }, { name = "wheel", specifier = ">=0.46.2" }, ] +overrides = [{ name = "transformers", specifier = ">=5.0.0" }] [[manifest.dependency-metadata]] name = 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