[Model] Support Hy3 preview

[stevenkuang-tencent]

Purpose

Support Hy3-preview model

Test Plan

Test model, reasoning parser and tool parser.

Test Result

All pass.

Hy3-preview model

Hy3 preview is a Mixture-of-Experts model with integrated fast and slow thinking, developed by the Tencent HunYuan team. With 295B total parameters, 21B activated parameters, and 3.8B MTP layer parameters. Hy3 preview is the first model trained after our infrastructure rebuild, and the most intelligent HunYuan model to date, achieving significant improvements in reasoning, instruction following, context learning, coding, agent capabilities, and inference performance.

[claude]

Claude Code Review

This pull request is from a fork — automated review is disabled. A repository maintainer can comment @claude review to run a one-time review.

[github-actions]

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[mergify]

Documentation preview: https://vllm--40681.org.readthedocs.build/en/40681/

[gemini-code-assist]

Code Review

This pull request introduces support for the HY V3 model, featuring a Mixture-of-Experts (MoE) architecture and a Multi-Token Predictor (MTP) for speculative decoding. It also implements specialized reasoning and tool call parsers to handle the model's specific output formats. Feedback focuses on improving the robustness of the implementation by ensuring deterministic initialization of MoE biases, preventing side effects from in-place tensor modifications in the MTP module, and fixing potential indexing errors during weight loading. Additionally, improvements are suggested for the parsers to handle streaming tokens more reliably and to use standard JSON escaping for tool calls.

[gemini-code-assist]

Initializing expert_bias with torch.empty leaves the parameter with uninitialized values. If this weight is not present in the checkpoint, it will contain random noise which can negatively impact the MoE routing logic. It should be initialized to zeros to ensure deterministic behavior when the bias is not provided.

Suggested change

	self.expert_bias = nn.Parameter(torch.empty(config.num_experts))
	self.expert_bias = nn.Parameter(torch.zeros(config.num_experts))

[gemini-code-assist]

This in-place modification of inputs_embeds can lead to side effects if the tensor is shared with other parts of the model or reused in subsequent speculative decoding steps. It is safer to clone the tensor before modification.

Suggested change

	inputs_embeds[positions == 0] = 0
	inputs_embeds = inputs_embeds.clone()
	inputs_embeds[positions == 0] = 0

[gemini-code-assist]

Indexing a 0-dimensional tensor (scalar) with [0] will raise an IndexError. You should check the number of dimensions before indexing, similar to the implementation in hy_v3.py.

Suggested change

	loaded_weight = loaded_weight[0]
	loaded_weight = (loaded_weight if loaded_weight.dim() == 0 else loaded_weight[0])

[gemini-code-assist]

Using find on delta_text is fragile for streaming because the end_token (</think>) might be split across multiple chunks. If the tag is split, this logic will fail to detect it. The BaseThinkingReasoningParser already provides robust handling for split tokens; overriding it with this manual check re-introduces the bug.

[gemini-code-assist]

Manual JSON escaping is incomplete and error-prone. This implementation misses several characters that must be escaped according to the JSON specification (e.g., control characters U+0000 through U+001F). This can result in invalid JSON being produced. It is recommended to use json.dumps for the value and then strip the surrounding quotes if a partial string is needed.

            escaped_val = json.dumps(partial_value, ensure_ascii=False)[1:-1]

[mergify]

Hi @stevenkuang-tencent, the pre-commit checks have failed. Please run:

uv pip install pre-commit>=4.5.1
pre-commit install
pre-commit run --all-files

Then, commit the changes and push to your branch.

For future commits, pre-commit will run automatically on changed files before each commit.

Tip

Is mypy failing?

mypy is run differently in CI. If the failure is related to this check, please use the following command to run it locally:

# For mypy (substitute "3.10" with the failing version if needed)
pre-commit run --hook-stage manual mypy-3.10

[andyluo7]

AMD MI300X validation — works end-to-end ✅

Built this branch (stevenkuang-tencent:feature/support_hy_v3, head ff89db9) editable from rocm/vllm-dev:nightly (vLLM 0.19.2rc1, ROCm 7.2.1) on a single 8× AMD Instinct MI300X (gfx942) node and ran tencent/Hy3-preview at TP=8.

Build

docker run -it --device=/dev/kfd --device=/dev/dri --network=host --ipc=host --shm-size=128g \
  --group-add video --cap-add SYS_PTRACE --security-opt seccomp=unconfined \
  -v /path/to/work:/work -w /work -e PYTHONPATH=/work/build/vllm \
  rocm/vllm-dev:nightly bash

git clone https://github.com/stevenkuang-tencent/vllm.git -b feature/support_hy_v3
cd vllm
pip uninstall -y vllm
SETUPTOOLS_SCM_PRETEND_VERSION=0.20.0.dev0 VLLM_TARGET_DEVICE=rocm \
  pip install --editable . --no-build-isolation

Build took ~28 min (cmake -j 64 / ninja -j 64, all 9 ROCm offload arches gfx90a;gfx942;gfx950;gfx1100;gfx1101;gfx1200;gfx1201;gfx1150;gfx1151).

Note on PYTHONPATH=/work/build/vllm: The base image ships an empty /app/vllm directory that wins as a namespace package over the editable install when cwd is /app, which makes from vllm import SamplingParams fail in subprocesses (e.g. _run_in_subprocess for HYV3MTPModel inspection). Setting PYTHONPATH (or cd to a non-/app workdir) avoids this. Not a vLLM bug, but worth flagging for AMD/ROCm reproducers.

Functional validation

Both HYV3ForCausalLM and HYV3MTPModel register correctly. Server boots at TP=8 in ~6 min.

reasoning_effort: "high" correctly produces a separate reasoning field via --reasoning-parser hy_v3. Output is well-formed and self-consistent (sample: 17 × 24 solved 3 different ways, all returning 408).

Performance (MI300X, TP=8, BF16, gpu-mem-util=0.90)

VRAM at idle after load: 92–93 % per GPU = ~178 GB / GPU (model + KV + buffers, comfortably within MI300X's 192 GB / GPU).

Workload	No MTP	With MTP ({"method":"mtp","num_speculative_tokens":1})	Δ
Single-stream short (~100 tok)	26.9 tok/s	81.5 tok/s	+203 %
Single-stream long (512 tok)	58.3 tok/s	68.5 tok/s	+18 %
Concurrent c=4 (16 reqs, 128 tok each)	195.9 tok/s	181.8 tok/s	−7 %
Concurrent c=8 (32 reqs, 128 tok each)	313.9 tok/s	383.7 tok/s	+22 %

MTP spec-decoding metrics on MI300X (during decode-heavy workload):

• Mean acceptance length: 1.67–1.73 (out of theoretical max 2.0 for num_speculative_tokens=1)
• Per-position acceptance rate: 67–72 %
• Accepted throughput up to ~126 tok/s in the steady-state window

Smoke output

$ curl ... -d '{"messages":[{"role":"user","content":"Hello! Can you briefly introduce yourself?"}], "chat_template_kwargs":{"reasoning_effort":"no_think"}}'
"Hello! I'm Kepler, an AI assistant developed by Linshen (DataWave). I'm here to help you with tasks like answering questions, creating content, analyzing information, and more. I can support both Chinese and English communication. How can I assist you today? 😊"

Summary

The feature/support_hy_v3 branch works on AMD MI300X (gfx942) at TP=8 with no AMD-specific changes needed. MTP path also works (good acceptance rate of ~70 %) and gives the expected single-stream latency wins. Happy to extend this to MI355X (gfx950) or to a longer-context bench if useful for the PR review.

cc @stevenkuang-tencent — let me know if there's a specific config or workload you'd like AMD numbers on before merge.