[feature] support pcp + mtp in full graph

[zhangsicheng5]

1. support pcp + mtp in full graph
2. pcp/dcp related mtp bugfix
3. support pcp + mtpx

• vLLM version: v0.12.0
• vLLM main: vllm-project/vllm@ad32e3e

[github-actions]

👋 Hi! Thank you for contributing to the vLLM Ascend project. The following points will speed up your PR merge:‌‌

• A PR should do only one thing, smaller PRs enable faster reviews.
• Every PR should include unit tests and end-to-end tests ‌to ensure it works and is not broken by other future PRs.
• Write the commit message by fulfilling the PR description to help reviewer and future developers understand.

If CI fails, you can run linting and testing checks locally according Contributing and Testing.

[gemini-code-assist]

Code Review

This pull request introduces support for PCP (Prefill Context Parallelism) and MTP (Multi-Token Prediction) in full graph mode, along with several related bug fixes. The changes correctly generalize PCP-only logic to accommodate DCP (Decode Context Parallelism) as well. A notable improvement is the handling of variable query lengths in speculative decoding batches, which replaces assumptions of fixed lengths with more robust logic. However, I've identified one critical issue in the implementation that needs to be addressed.

[Ronald1995]

is query_lens_d a device tensor? if so, you call query_lens_d.sum().item() will incur cpu blocking, please fix it.

[zhangsicheng5]

This is a host tensor, refer to model_runner_v1.py: self.query_lens = torch.from_numpy(num_scheduled_tokens), so it will not influence host device sync. The _d is abbreviation of _decode.

[github-actions]

This pull request has conflicts, please resolve those before we can evaluate the pull request.