[WS1][kernels] Batch-invariant attention (standard softmax)

[Flink-ddd]

Part of WS1 — Full Batch-Invariant Forward Chain (epic: #)

Why

Flash-style attention parallelizes over the KV dimension and merges partial results, and the number of KV splits depends on sequence length and batch — so the online-softmax accumulation order changes with batch configuration, breaking invariance. Because attention mixes information across positions, even tiny drift here spreads to every later token.

Scope

Provide a batch-invariant standard-softmax attention for the forward chain.

• Start from a deterministic masked-softmax + logsumexp (LSE) kernel with a fixed max / sum-exp order, then extend to the full attention output (P x V).
• Avoid split-KV / variable KV-partitioning; use a single-SM whole-sequence reduction, or a dual-kernel design with an identical, fixed accumulation order regardless of split count.
• Causal mask + padding mask support; variable-length sequence coverage.
• Keep prefill and decode on a consistent reduction path (coordinate with the KV-cache consistency issue); expose the hooks that issue needs.
• Accumulate softmax statistics in FP32.
• Validate against the [WS1] Ground-truth harness + numerical contract for batch-invariant ops #108 harness across batch=1/N, chunked-prefill on/off, padding layouts.

Out of scope

• Non-standard attention variants (linear / sliding-window / MoE routing) — standard softmax only for this gate.
• Multi-GPU / sequence-parallel attention (WS2).
• FP8 attention.
• Full FlashAttention replacement / maximum throughput in the first deterministic baseline.

Acceptance criteria

• The same query / sequence produces bitwise-identical (or within [WS1] Ground-truth harness + numerical contract for batch-invariant ops #108 tolerance) attention output regardless of batch size, batch position, and chunked-prefill on/off.
• No split-KV path whose split count varies with batch / seq config; the accumulation order is pinned and documented.
• Softmax + LSE outputs are invariant across the sweep; tests cover both fixed-length and variable-length sequences.
• Decode-stage output matches the prefill path for the same effective context (handoff with the KV-cache issue).
• The attention backward path passes the shared gradient-invariance check from the WS1 backward-consistency issue.

Notes

• Tightly coupled with the KV-cache path-consistency issue — agree on the shared reduction contract early.
• Existing attention kernels ([FEAT][kernels]: implement Prefix-Shared Fused Attention for GRPO LogProb computation #65 prefix-shared fused attention, [FEAT][kernels]: implement pure Triton FlashAttention as a universal fallback #41 Triton FlashAttention fallback) are reference / audit starting points, not the invariant path itself.
• Single-SM whole-sequence is simplest to prove correct but may cost throughput on long sequences; note the tradeoff if choosing the dual-kernel route.

Planned PRs

• Deterministic masked-softmax + LSE kernel (fixed max / sum-exp order)
• Causal + padding mask tests; variable-length coverage
• Deterministic P x V aggregation (single-SM or fixed-order dual-kernel)
• Prefill/decode handoff hooks (coordinate with the KV-cache issue)
• Wire through the [WS1] Ground-truth harness + numerical contract for batch-invariant ops #108 harness sweep; benchmark + tradeoff note