[Model] Add native RWKV7 serving, fused execution, and quantization

[123123213weqw]

Summary

This PR adds native vLLM serving support for non-hybrid, linear-recurrent RWKV7 causal-LM checkpoints and carries the implementation through batched serving, recurrent-state integration, fused execution boundaries, and 8-bit/4-bit inference.

• Adds RWKV7Config and RWKV7ForCausalLM to the native config and model registries.
• Integrates attention-shift, recurrent-WKV, and FFN-shift states with the existing Mamba state-cache plumbing.
• Supports V1 dynamic batching, batched prefill/decode, chunked prefill, pipeline parallel state transport, and CUDA-graph padded batches.
• Adds an opaque rwkv7_block_forward custom-op boundary and fail-closed auto/torch/triton dispatch. The automatic path remains on the reference Torch implementation until long-horizon fused-kernel parity is proven.
• Adds online dense INT8 inference with per-output-channel weights, dynamic per-token activations, and vLLM's selected scaled-matmul backend (Cutlass on the tested RTX 4080).
• Enables TorchAO quantization of untied ParallelLMHead layers and provides a packed INT4 load-time path.
• Declares the BitsAndBytes packed-module mapping contract for RWKV7.
• Keeps recurrent key/value and low-rank control projections in the model dtype because their error feeds recurrent state; receptance, output, feed-forward, and untied LM-head projections remain quantizable.
• Adds correctness tests, quantization documentation, and a fresh-process benchmark with throughput, model-memory, deterministic-repeat, and greedy-token-drift reporting.

Relationship to #48686 and #49875

This is the direct-fork continuation of #48686 and the recreated successor to #49875, whose nested-fork head repository was removed. It is not an independent competing implementation. The original author is credited in the baseline commit.

Please do not merge both #48686 and this PR. The current PR keeps the work on one review branch because the original fork branch cannot be updated by this contributor.

Duplicate-work check

The duplicate-work review was repeated on July 28, 2026:

• Reviewed issue Supporting RWKV models #3583 and all comments.
• Searched open PRs for RWKV7 quantization, RWKV7 INT8, online INT8 dense, and TorchAO ParallelLMHead.
• No open PR implements RWKV7 INT8/INT4 serving or TorchAO ParallelLMHead support.
• Generic online-quantization PRs [Quantization] add hadamard transform + online quantization support to humming #42997, [Quark] Support online block-diagonal rotations in dense GEMM layers #32272, and [Bugfix] Support non-gated MoE in online quantization and Marlin MoE tile padding #48028 address different Humming, Quark rotation, and MoE-padding work.

The only overlapping RWKV7 PR is the acknowledged original #48686.

Quantization design

RWKV recurrent state magnifies error differently from a Transformer KV cache. Quantizing k_proj, v_proj, or the small decay/gate/interpolation low-rank projections caused non-finite logits on the tested 1.5B checkpoint. Those numerically sensitive projections now remain FP16/BF16. Large read/output/FFN projections and the untied output head use the selected quantization method.

For online INT8, FP16/BF16 weights are quantized per output channel during loading. Activations are quantized dynamically per token and dispatched through vLLM's INT8 scaled-matmul kernel selection. No pre-quantized checkpoint is required.

For INT4, TorchAO uses Int4WeightOnlyConfig(group_size=128, int4_packing_format="tile_packed_to_4d") with BF16 activations.

Validation

Unit and integration tests

Environment: RTX 4080, Python 3.12, PyTorch 2.11.0+cu130.

PYTHONPATH=<current-main-source-tree> \
python -m pytest -q \
  tests/model_executor/test_rwkv7.py \
  tests/quantization/test_online.py::test_online_int8_linear_quantizes_per_output_channel \
  tests/quantization/test_torchao.py::test_torchao_quantizes_parallel_lm_head

Result: 27 passed, 2 skipped. The skipped tests are opt-in external-FLA parity tests.

PYTHONPATH=<current-main-source-tree> \
python -m pytest -q tests/quantization/test_online.py -k online_int8

Result: 2 passed. This covers per-channel quantization/idempotence plus dense-linear and LM-head dispatch.

pre-commit run --files \
  benchmarks/benchmark_rwkv7_quantization.py \
  docs/features/quantization/online.md \
  docs/features/quantization/torchao.md \
  tests/model_executor/test_rwkv7.py \
  tests/quantization/test_online.py \
  tests/quantization/test_torchao.py \
  vllm/model_executor/layers/quantization/online/base.py \
  vllm/model_executor/layers/quantization/online/int8.py \
  vllm/model_executor/layers/quantization/torchao.py \
  vllm/model_executor/models/rwkv7.py

Result: passed, including Ruff, formatting, markdownlint, mypy, SPDX, forbidden-import, and configuration checks.

RTX 4080 end-to-end serving benchmark

Checkpoint: reconstructed 1.5B HF-format RWKV7 checkpoint, verified tensor-by-tensor against the original safetensors (795/795, no SHA-256 mismatches).

Each setting ran in a fresh process with Torch compilation and CUDA graphs enabled, chunked prefill enabled, eight concurrent prompts, 64 generated tokens per prompt, three warmups, three measured repetitions, and deterministic algorithms enabled. Both quantized modes produced identical tokens across their three repetitions.

Mode	Baseline dtype	Output tok/s	Speed vs baseline	Model memory	Memory reduction	Repeatable	Greedy token agreement
FP16 baseline	FP16	543.90	1.000x	2.85 GiB	-	yes	100%
Online INT8	FP16	607.27	1.117x	1.78 GiB	37.54%	yes	33.98%
BF16 baseline	BF16	529.45	1.000x	2.85 GiB	-	yes	100%
TorchAO INT4	BF16	665.56	1.257x	1.28 GiB	55.09%	yes	1.95%

Commands:

python benchmarks/benchmark_rwkv7_quantization.py \
  --model <rwkv7-1.5b> --tokenizer <tokenizer> \
  --settings fp16 online-int8 --dtype half \
  --max-num-batched-tokens 32 --warmup-runs 3 --repeats 3 \
  --max-tokens 64 --no-enforce-eager

python benchmarks/benchmark_rwkv7_quantization.py \
  --model <rwkv7-1.5b> --tokenizer <tokenizer> \
  --settings fp16 torchao-int4 --dtype bfloat16 \
  --max-num-batched-tokens 32 --warmup-runs 3 --repeats 3 \
  --max-tokens 64 --no-enforce-eager

Greedy token agreement is reported because this change affects numerical output. It is a strict token-by-token drift indicator: once one greedy token changes, subsequent context also changes. It is not presented as a downstream task-accuracy score, and this PR makes no task-accuracy claim for the quantized checkpoints.

The end-to-end run used the current RWKV7 Python implementation with a v0.20.1 binary-compatible runtime tree on the 4080. Current-main unit tests used a separately synchronized current-main source tree; the host's installed binary extension does not expose every current-main custom operator.

AI assistance

AI assistance was used during development, testing, and PR preparation. The human submitter reviewed the changed lines and test evidence and is responsible for the contribution.

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[mergify]

Documentation preview: https://vllm--50077.org.readthedocs.build/en/50077/

[chatgpt-codex-connector]

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[chatgpt-codex-connector]

Preserve tied embeddings on the final pipeline stage

When pipeline_parallel_size > 1 and tie_word_embeddings is enabled, the final stage receives a PPMissingLayer here because only the first stage constructs embed_tokens; RWKV7ForCausalLM.__init__ then assigns that placeholder as lm_head, so logits computation cannot access the tied embedding weight. Construct the embedding on the last stage as well when weights are tied, as the other pipeline-parallel causal models do.

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[chatgpt-codex-connector]

Keep the model-loading registry alphabetized

Move this entry to the R section: it is currently inserted between the Mamba and FalconMamba entries, while the required model-testing guide says entries in each registry section must remain alphabetized. Leaving it here makes the newly added required loading registry violate the repository's model-specific test convention.

AGENTS.md reference: AGENTS.md:L99-L100

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This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @123123213weqw.

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