Add SDPA attention implementation#512
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Flash-attn errors out at head_size > 256, so head_size=512 models cannot train without materializing the full O(S²) attention matrix via the backup path. Add `AttentionImplementation.sdpa` using `torch.nested` to bridge the packed-varlen layout to SDPA's batched signature, pinning the EFFICIENT backend. K/V are manually repeat_interleaved to match Q heads because the fused kernels reject broadcasted GQA inputs. Auto-fallback: flash when bf16/fp16 + head_size <= 256 + flash is available; backup for windowed attention (the sdpa path does not support sliding window); sdpa otherwise. Tests: SDPA equivalence check parallel to flash, gated on CUDA + bf16; two head_size=320 cases exercising the SDPA-only regime; refactored parametrization from `_build_test_cases` plus single-use variant lists into a few inline for-loops at module level. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The SDPA path uses `nested_tensor_from_jagged + is_causal=True` which has no viable backend on CPU (math rejects nested + is_causal; the fused EFFICIENT/Flash backends are CUDA-only). Auto previously routed CPU runs through SDPA and they would crash; route them to backup. Also widens the SDPA branch to fp32 explicitly: the EFFICIENT backend engages on CUDA across bf16/fp16/fp32, and benchmarking confirms it beats backup on memory at every length and matches it on time at seq_len >= 4096 (backup grows quadratically; SDPA stays near constant). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The previous attempt routed CPU and windowed configurations to backup because the nested + is_causal=True form has no viable backend on CPU and cannot express sliding window. SDPA actually works fine in those cases when given an explicit attn_mask: backup's preprocessing already builds the combined causal+document mask (and threads sliding window into it), so the SDPA path can reuse it as-is. CUDA without a window keeps the nested + is_causal path so EFFICIENT runs without materializing the mask. CUDA with a window and CPU runs both fall through to dense + attn_mask, which lets MATH engage on CPU and reuses the windowed mask on CUDA. Auto-fallback simplifies to flash-or-sdpa: SDPA now covers every case backup used to (CPU, windowed without flash, head_size > 256). Verified on H100 bf16 head_size=512 that the dense + attn_mask form also engages EFFICIENT (peak 323 MiB vs 319 MiB for is_causal — the 4 MiB delta is the mask itself). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
jlamypoirier
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…on call The CUDA-no-window and dense-mask paths shared the K/V expansion, the SDPA call signature (dropout + scale), and the (B, H, S, D) layout requirement. Lift those out: rebind query/key/value to either nested-jagged or unsqueeze(0)'d 4D tensors in the per-path setup, build an `sdpa_args` dict that adds `is_causal=...` for nested or `attn_mask=...` for dense, then make a single SDPA call that works for both. The unwrap branches on `output.is_nested`. Also drops the explicit EFFICIENT_ATTENTION pin from the nested path — nested + is_causal=True has no other viable backend (MATH and Flash both reject it), so the auto pick lands on EFFICIENT or the call errors out either way. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The nested path floors per-call wall around 6 ms because SDPA's nested dispatch pulls `max_seqlen` / `min_seqlen` to host (5 cudaMemcpyAsync DtoH + cudaStreamSynchronize per call). Sync count is fixed regardless of num_docs, so the path stays much faster than dense+mask in varlen training; the comment just makes the cost discoverable. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
PyTorch's nested SDPA dispatch reads `max_seqlen` and `min_seqlen` to host on every call (5 cudaMemcpyAsync DtoH + cudaStreamSynchronize per call) when they aren't supplied. Both are trivially derivable from the Python `lengths` list at preprocessing time, so we compute them as plain ints, thread them through `BlockModelInput` / kwargs, and pass them to `nested_tensor_from_jagged`. While doing this, drop the `torch.full((1,), ..., device=...)` wrap on `max_lengths` — the value was always a Python int, and flash accepts an int directly (verified). The auto-device-move on the `Document` base class only moves Tensor fields, so plain ints pass through to_kwargs untouched. Sync events per call (Llama-7B-shape, 4 docs × 4096): before: 5 cudaStreamSynchronize + 5 Memcpy DtoH after: 0 Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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Summary
Flash-attn caps at
head_size = 256;head_size = 512models (e.g. Gemma 4's full-attention layers) currently force thebackuppath, which materializes the full O(S²) attention matrix and OOMs above ~8K context on H100. AddAttentionImplementation.sdpaso those models can train.The implementation has two CUDA-aware paths sharing the rest of the layer:
torch.nested.nested_tensor_from_jagged(values, cu_seqlens, min_seqlen=..., max_seqlen=...)+is_causal=Trueunder EFFICIENT. Each document becomes its own batch element, so cross-document attention is excluded by structure rather than by mask. Pre-computedmin_seqlen/max_seqlenare passed in to keep the dispatch sync-free (otherwise it reads them to host on every call — 5 host barriers per layer).(1, H, total, D)+attn_mask, reusing backup's preprocessed causal+document mask. MATH cannot accept nested +is_causal=True, so the mask path is the only viable form on CPU; on CUDA-with-window the mask is needed because nested +is_causalcannot express sliding window. Per a cluster probe, EFFICIENT also engages on CUDA with explicitattn_mask— only ~4 MiB extra overis_causalfor the mask itself.K/V are manually
repeat_interleaved across query heads in both paths because SDPA's fused kernels reject broadcasted GQA inputs.Auto-fallback simplifies to
flashforbf16/fp16+head_size ≤ 256+ flash available, otherwisesdpa. SDPA now covers every previously-backupcase (CPU, windowed without flash, head_size > 256);backupremains as an explicitimplementation: backupoption but the auto path no longer reaches it.To pre-compute the seqlens for SDPA, the data preprocessor's
max_lengthsis changed from a 1-element device tensor to a Pythonint(flash acceptsintnatively, verified),min_lengthsis added symmetrically, and areturn_min_sequence_lengthsflag is added toLengthPreprocessingConfig. Plain ints sail throughDocument.to_device_since it only movesTensorfields.get_preprocessing_configbranches by impl: flash needs cu_seqlens + max_seqlens; sdpa-CUDA-no-window needs cu_seqlens + max + min; sdpa-windowed / sdpa-CPU / backup all need document_index (mask is built inpreprocessand shared).Tests: SDPA equivalence check parallel to flash via a small
_check_packedclosure (CUDA bf16); twohead_size=320cases that exercise the SDPA-only regime; windowed cases now exercise SDPA too. Parametrization refactored from_build_test_cases+ single-use variant lists into inline for-loops at module level.Benchmark — H100 bf16, 20 iters after 10 warmup, fwd+bwd wall
Llama-7B-shape (32 heads MHA, head_size=128):
Gemma-4 full-attn (16/8 GQA, head_size=512):
Multi-document varlen — the typical training case — is where nested+is_causal pulls ahead of mask by 2.6×–7×: nested processes each doc as its own batch element (4×4K² attention work) while mask materializes the full 16K² matrix even though same-doc cross-attention is then masked out. Backup OOMs above ~8K at these widths.
Sync events per nested SDPA call (profiled): 0 with pre-computed seqlens; 5 without. Pure wall-clock impact in synthetic bench is ~0.1–1 ms/call, but in a real training loop those 5 host barriers per layer × 30 layers × 8 microbatches = 1200 syncs/step would have prevented host-GPU overlap; with them gone, the nested path's ~6 ms of Python wrapping overhead can hide behind GPU compute.
Test plan
pytest -v -n 4 tests/layers/test_attention.py(CPU): 56 passedpytest -v -n 8 tests/layers/test_attention.py(CUDA): 56 passed; all SDPA equivalence checks run, including windowed cases🤖 Generated with Claude Code