int4_per_token_head kv cache + kv token reporting fix

[lesj0610]

fork pr first. i wanted to get this up somewhere before touching upstream.

this started because gemma4 made the bad kv-token reporting really obvious on my side, but the patch itself is meant to stay on the common path. i didn't want to solve this with a gemma4-only branch.

what's in here

• adds a first int4_per_token_head kv-cache path on the v1 + triton path
• uses int4_per_token_head as the actual dtype name instead of q4
• fixes the reported GPU KV cache size token count so it goes through the same group-fit logic init already uses, instead of the old rough heuristic
• adds focused tests for the int4 path and the reporting side

some notes

• gemma4 is what made me look at this, but this isn't meant to be a gemma4-specific patch
• padded kv-page handling still assumes the backend block dimension is leading. if that is not true, it still raises NotImplementedError
• the padded-stride setup is mirrored in attn_utils.py and gpu_model_runner.py for now. not ideal, yeah, but both raw-kv paths needed the same physical layout and i didn't want to widen this patch more than necessary
• cp handling in the token report now comes from the same kv-spec/group-fit path used during init, so i dropped the old extra multiplier heuristic instead of stacking another one on top
• i used Codex and Claude Code during iteration, but i went back through the patch and the test runs myself before posting this

checks i actually ran

• python3 -m py_compile on the touched files
• pytest -q tests/kernels/attention/test_triton_int4_kv_cache.py
• pytest -q tests/v1/core/test_kv_cache_utils.py -k 'get_kv_cache_config_one_worker or estimate_token_capacity_for_kv_cache_config or get_max_concurrency_for_kv_cache_config'

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[chatgpt-codex-connector]

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[chatgpt-codex-connector]

Route int4 KV cache away from FlashAttention backend

Adding "int4_per_token_head" to CacheDType makes this value pass selector validation, but CUDA backend selection still prefers FlashAttention before Triton. FlashAttention currently treats any quantized KV dtype as FP8-capable (flash_attn.py supports_kv_cache_dtype), then later calls get_fp8_dtype_for_flashattn(self.kv_cache_dtype) which raises for "int4_per_token_head" (flash_attn.py lines 744-746 / 378-384). In practice, users on CUDA with FlashAttention available will hit a runtime failure unless they manually force Triton, so int4 is exposed as supported but is not actually runnable in the default backend path.

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[lesj0610]

good catch. i fixed this in 9740ba545 by tightening FlashAttentionBackend.supports_kv_cache_dtype() so flash only accepts fp8-backed quantized kv cache. int4_per_token_head now gets rejected during backend validation instead of slipping through and failing later. i also added a selector regression test for fp8 vs int4_per_token_head.

[lesj0610]

Closing per request; keeping only vllm-project#39460.