[Bugfix] Fix TurboQuant cache dtype propagation and FP8 store on Ampere

[luckymrwang]

Purpose

Fix two initialization failures when serving a model with
--kv-cache-dtype turboquant_k8v4, particularly on NVIDIA Ampere GPUs.

1. Preserve the TurboQuant cache dtype in GPUModelRunner

TQFullAttentionSpec reports KVQuantMode.NONE, but it still uses the TurboQuant-specific packed KV cache layout.

GPUModelRunner._reshape_kv_cache_tensors() treated every spec with KVQuantMode.NONE as an unquantized or skipped layer and passed "auto" to TurboQuantAttentionBackend.get_kv_cache_shape(). This caused initialization
to fail with:

ValueError: Unknown TurboQuant cache dtype: 'auto'.

This change excludes TQFullAttentionSpec from that fallback and preserves the configured TurboQuant dtype. It also aligns this path with the existing behavior in vllm/v1/worker/gpu/attn_utils.py.

2. Use constexpr control flow for the FP8 type selection

The TurboQuant store kernel selected the FP8 type using a Python conditional expression:

k_fp8 = k_vals.to(tl.float8e4b15) if FP8_E4B15 else k_vals.to(tl.float8e4nv)

On SM80, Triton could still attempt to compile the float8e4nv conversion, which is only valid on newer architectures.
The selection is now expressed as a tl.constexpr branch so that only the architecture-compatible conversion is compiled.
This builds on #39988, which added the BF16-to-FP32 intermediate conversion. That conversion is retained; this PR addresses the separate FP8 type-selection issue that remains on Ampere.

Related to #40124.
Related to #40069.
Follow-up to #39988.

Test Plan

Run an OpenAI-compatible vLLM server on an NVIDIA A800 (SM80):

vllm serve Qwen3.6-27B \
    --host 0.0.0.0 \
    --served-model-name qwen \
    --gpu-memory-utilization 0.95 \
    --kv-cache-dtype turboquant_k8v4

After the server becomes ready, send completion requests with approximately 10K-token and 32K-token prompts.

Test Result

Before this change:

1. KV cache initialization failed with:

ValueError: Unknown TurboQuant cache dtype: 'auto'.

2. After preserving the configured cache dtype, the TurboQuant Triton store kernel failed to compile on SM80 because the incompatible float8e4nv conversion branch was selected or compiled.

After this change:

• The model initialized successfully with turboquant_k8v4.
• An approximately 10K-token request completed successfully with HTTP 200.
• An approximately 32K-token request completed successfully with HTTP 200.
• git diff --check passed.

Test environment:

• GPU: NVIDIA A800, SM80
• Model: Qwen3.6-27B
• KV cache dtype: turboquant_k8v4

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• [ x ] The test results, such as pasting the results comparison before and after, or e2e results
• (Optional) The necessary documentation update, such as updating supported_models.md and examples for a new model.

[claude]

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[mergify]

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

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork