mirror of
https://github.com/NVIDIA/Model-Optimizer.git
synced 2026-10-19 05:49:19 +08:00
Chenhan D. Yu
0246041b01
### What does this PR do? Type of change: New feature, new example, bug fix Adds a vLLM-based synthetic data generation pipeline for speculative decoding draft model training, along with dataset preparation scripts for NVIDIA's Nemotron Post-Training dataset collections. **Data synthesis pipeline** (`tools/launcher/common/vllm/query.sh` + `common/query.py`): - Launch a vLLM server and run multi-turn inference to synthesize training data from input conversation skeletons - Fork-safe OpenAI client: reinitializes HTTP connection pool after `datasets.map()` forks worker processes, preventing 400 errors from corrupted connections - Clear Docker `ENTRYPOINT` so vLLM containers (which default to `vllm serve`) work correctly under NeMo Run's executor - `--max-tokens` argument to bound generation length - Local file loading support (`--data /path/to/file.jsonl`) - Re-raise connection errors so `datasets.map()` halts the shard instead of silently producing empty rows - Map `developer` role to `system` (OpenAI format compatibility) **Multi-turn reasoning trace handling** (`common/query.py`): - Strip `<think>...</think>` blocks from intermediate assistant turns before re-feeding to the model; preserve the full trace only on the final turn **Nemotron dataset preparation** (`examples/dataset/`): - `make_nemotron_ptv2_dataset.py` — prepares [nvidia/Nemotron-Post-Training-Dataset-v2](https://huggingface.co/datasets/nvidia/Nemotron-Post-Training-Dataset-v2) (~3.3M rows generate, ~1.9M rows train) - `make_nemotron_ptv3_dataset.py` — prepares the [Nemotron PTv3 collection](https://huggingface.co/collections/nvidia/nemotron-post-training-v3) of 16 datasets (~3.4M rows generate, ~3.9M rows train) - Both support `generate` mode (strips assistant turns for synthesis input) and `train` mode (normalizes to clean OpenAI format for SFT) - `conversation_utils.py` — shared utilities: `strip_assistant_turns`, `normalize_messages`, `make_augment_fn`, `AugmentationSpec` - `augmentations.yaml` — 12 language-redirect variants + style/format hints, cycled across dataset rows - Scripts live in `examples/dataset/` (not under `speculative_decoding/`) to signal reusability beyond speculative decoding **Bug fixes**: - `strip_assistant_turns()`: return `{"messages": []}` when no user turns remain (system-only rows were previously passed through instead of being filtered) - `concatenate_datasets()`: guard against empty parts list - SSH tunnel user precedence: explicit `user` arg now correctly overrides `slurm_config.user` ### Usage ```bash # Prepare PTv3 input conversations for synthesis (~3.4M rows): python examples/dataset/make_nemotron_ptv3_dataset.py --output-dir /tmp/ptv3_gen # Launch vLLM server + synthesize responses: bash tools/launcher/common/vllm/query.sh \ --model /path/to/model \ --tensor-parallel-size 4 \ -- \ --data /tmp/ptv3_gen/default.jsonl \ --save /tmp/ptv3_responses \ --num-shards 10 --num-proc 4 --max-tokens 4096 # Prepare PTv2 for direct SFT training (~1.9M rows): python examples/dataset/make_nemotron_ptv2_dataset.py --mode train --output-dir /tmp/ptv2_train ``` ### Testing Tested end-to-end on an NVIDIA GB10 node (119 GiB GPU memory) with `vllm/vllm-openai:qwen3_5-cu130` container and `Qwen/Qwen3.5-4B`: - vLLM server starts correctly with cleared Docker entrypoint - `datasets.map(num_proc=4)` runs without connection errors (fork-safe client) - Multi-turn synthesis produces correct assistant responses with thinking traces handled ### Before your PR is "*Ready for review*" Make sure you read and follow [Contributor guidelines](https://github.com/NVIDIA/Model-Optimizer/blob/main/CONTRIBUTING.md) and your commits are signed (`git commit -s -S`). Make sure you read and follow the [Security Best Practices](https://github.com/NVIDIA/Model-Optimizer/blob/main/SECURITY.md#security-coding-practices-for-contributors) (e.g. avoiding hardcoded `trust_remote_code=True`, `torch.load(..., weights_only=False)`, `pickle`, etc.). - Is this change backward compatible?: ✅ - If you copied code from any other sources or added a new PIP dependency, did you follow guidance in `CONTRIBUTING.md`: N/A - Did you write any new necessary tests?: N/A (data synthesis scripts; tested manually) - Did you update [Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?: N/A <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit * **New Features** * Added dataset generation and augmentation capabilities for Nemotron post-training datasets (v2 and v3) * Enhanced query functionality with thinking-block filtering and improved client management for robust parallel processing * Added support for local dataset file paths alongside HuggingFace Hub datasets * **Bug Fixes** * Fixed SLURM executor user resolution and Docker container entrypoint configuration * Improved error handling for connection failures during dataset synthesis * **Documentation** * Updated dataset preparation guide with new generation modes and augmentation configuration details <!-- end of auto-generated comment: release notes by coderabbit.ai --> Signed-off-by: Chenhan D. Yu <5185878+ChenhanYu@users.noreply.github.com> Signed-off-by: Chenhan Yu <chenhany@nvidia.com>
42 lines
1.6 KiB
Python
42 lines
1.6 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2023-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Utility functions."""
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import hashlib
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import json
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from pathlib import Path
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import aiohttp
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async def download_file(url: str, destination: Path) -> None:
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"""Download a file from a URL to a specified destination."""
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destination.parent.mkdir(parents=True, exist_ok=True)
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async with aiohttp.ClientSession() as session, session.get(url) as response:
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if response.status != 200:
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msg = f"Failed to download {url}: {response.status}"
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raise RuntimeError(msg)
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content = await response.read()
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destination.write_bytes(content)
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print(f"Downloaded {url} to {destination}")
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def id_for_conversation(conversation: list) -> str:
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"""Generate a unique ID for a conversation based on its content."""
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json_str = json.dumps(conversation, sort_keys=True, separators=(",", ":"), ensure_ascii=False)
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json_bytes = json_str.encode("utf-8")
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return hashlib.sha256(json_bytes).hexdigest()
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