From 2d425b6cfd9057387c47990ee52680ae158beb37 Mon Sep 17 00:00:00 2001 From: yiliu30 Date: Wed, 29 Jul 2026 11:14:22 +0000 Subject: [PATCH 01/42] test: update sparse bf16 validation status --- .../ark/SPARSE_BF16_STATUS_20260729.md | 169 +++++++++++++++++ .../ark/SPARSE_BF16_STATUS_20260729_CN.md | 169 +++++++++++++++++ .../test/test_sage_sparse_prefill_e2e.py | 175 +++++++++++++++++- 3 files changed, 504 insertions(+), 9 deletions(-) create mode 100644 auto_round_extension/ark/SPARSE_BF16_STATUS_20260729.md create mode 100644 auto_round_extension/ark/SPARSE_BF16_STATUS_20260729_CN.md diff --git a/auto_round_extension/ark/SPARSE_BF16_STATUS_20260729.md b/auto_round_extension/ark/SPARSE_BF16_STATUS_20260729.md new file mode 100644 index 0000000000..dc5208b971 --- /dev/null +++ b/auto_round_extension/ark/SPARSE_BF16_STATUS_20260729.md @@ -0,0 +1,169 @@ +# Sparse BF16 Status 2026-07-29 + +## Scope + +This note captures the current sparse attention status on Wednesday, July 29, 2026 after rebuilding with oneAPI 2025 and rerunning focused accuracy and performance checks on XPU 7. + +Environment: + +- `ONEAPI_ROOT=/home/yiliu4/intel/oneapi` +- Python: `/home/yiliu4/workspace/auto-round-prefill-clean-sparse-pr/auto_round_extension/ark/ark-torch-212/bin/python` +- device: `XPU_MASK=7` +- native BF16 sparse enabled with `SAGE_ATTN_XPU_SPARSE_BF16_NATIVE=1` +- rebuilt extension: `auto_round_kernel/xbuild_bf16_v2/auto_round_kernel_xpu.cpython-313-x86_64-linux-gnu.so` + +Kernel config under test: + +- `head_dim=128` +- `q_tile_override=256` +- `sparse_q_block_tokens=256` +- `sparse_k_block_tokens=64` +- layout: `HND` + +## Accuracy Status + +### Fixed-LUT sparse kernel + +`fixed-LUT sparse` means the sparse mask is explicitly provided by `lut` and `valid_block_num`, and the kernel is checked against an exact dense BF16 reference with the same mask. + +Target correctness case: + +- shape: `B=1, Hq=32, Hkv=8, Sq=512, Skv=768, D=128` +- sparse pattern: first Q tile uses K blocks `[0, 1, 2]`, second Q tile uses `[9, 10, 11]` +- reference: explicit BF16 `Q @ K^T`, add exact mask, softmax, then `P @ V` + +Result: + +- `bf16_sparse` vs exact BF16 reference: + - `max_diff=0.003906` + - `mean_diff=0.000127` + +Comparison on the same case: + +- `int8_sparse` vs exact BF16 reference: + - `max_diff=3.826172` + - `mean_diff=0.344735` +- `int8_sparse` vs `bf16_sparse`: + - `max_diff=3.826172` + - `mean_diff=0.344735` + +Conclusion: + +- `bf16_sparse` is accurate for the exact fixed-LUT sparse kernel path. +- `int8_sparse` is materially less accurate than `bf16_sparse` on the same case. + +### Heuristic top-k end-to-end path + +The `sparge_sage2_attn_meansim_topk_xpu_bf16` path is heuristic. It should not be treated as an exact correctness reference for a handcrafted LUT mask. + +Because of that, the BF16 sparse test now treats: + +- exact correctness: `sage_sparse_bf16` vs explicit masked BF16 reference +- heuristic top-k path: benchmark/perf path, not exact reference + +## Test Harness Status + +File updated: + +- `auto_round_kernel/wrapper/test/test_sage_sparse_prefill_e2e.py` + +Current test harness behavior: + +- force-loads the rebuilt BF16-capable extension from `xbuild_bf16_v2` +- uses explicit masked BF16 math as the BF16 sparse correctness reference +- no longer treats heuristic BF16 top-k output as an exact oracle for the fixed-LUT kernel + +Verified run: + +```bash +source benchmarks/source_env_xpu67_oneapi2025.sh +export XPU_MASK=7 +export SAGE_ATTN_XPU_SPARSE_BF16_NATIVE=1 +${ARK_BENCH_PYTHON} auto_round_kernel/wrapper/test/test_sage_sparse_prefill_e2e.py +``` + +Observed result: + +- full script exits successfully under oneAPI 2025 +- INT8 sparse cases pass +- BF16 sparse exact-reference cases pass + +## Performance Status + +### Medium-size exact sparse case + +Shape: + +- `B=1, Hq=32, Hkv=8, Sq=512, Skv=768, D=128` + +Latency: + +- dense BF16 with exact mask: `0.105 ms` +- `bf16_sparse`: `0.096 ms` +- `int8_sparse`: `0.090 ms` +- INT8 Q/K quantization only: `0.027 ms` + +Speedup vs dense BF16 exact-mask baseline: + +- `bf16_sparse`: `1.093x` +- `int8_sparse`: `1.175x` +- `int8_sparse + quant`: `0.902x` + +Conclusion: + +- kernel-only, `int8_sparse` is slightly faster +- including quantization, this small/medium case does not favor the INT8 path +- `bf16_sparse` is the better accuracy point here + +### Long-sequence benchmark + +Shape: + +- `B=1, Hq=40, Hkv=40, S=75000, D=128` +- layout: `HND` +- `topk=0.5` +- `q_tile_override=256` +- `sparse_q_block_tokens=256` +- `sparse_k_block_tokens=64` + +Sparse selection stats: + +- `selected_ratio=0.502557` +- `selected_blocks_per_row=588.997` + +Sparse timings: + +- `preprocess=127.809 ms` +- `int8_sparse_kernel=390.990 ms` +- `int8_sparse_e2e=501.369 ms` +- `bf16_sparse_kernel=804.864 ms` +- `bf16_sparse_e2e=919.896 ms` + +Dense BF16 baselines on the same shape: + +- `dense_torch_sdpa_bf16=2154.809 ms` +- `dense_ark_sdpa_bf16=2494.633 ms` +- `dense_ark_sagev1_bf16=10848.612 ms` + +Speedup vs dense torch BF16 SDPA: + +- `int8_sparse_e2e`: about `4.30x` +- `bf16_sparse_e2e`: about `2.34x` + +Speedup vs dense ARK BF16 SDPA: + +- `int8_sparse_e2e`: about `4.98x` +- `bf16_sparse_e2e`: about `2.71x` + +Conclusion: + +- at `75k / 40 / 128`, both sparse paths are substantially faster than dense BF16 +- `int8_sparse` remains the fastest path +- `bf16_sparse` is slower than `int8_sparse` but still much faster than dense BF16 + +## Current Summary + +- BF16 sparse fixed-LUT kernel correctness is in good shape after rebuilding with oneAPI 2025. +- The previous BF16 mismatch was caused by stale extension loading and an unreliable masked-XPU reference path in the test harness. +- For long sequences, BF16 sparse is a valid accuracy-oriented sparse path with meaningful speedup over dense BF16. +- INT8 sparse is still the fastest sparse option, but it gives up substantial accuracy relative to BF16 sparse on the exact fixed-LUT check. diff --git a/auto_round_extension/ark/SPARSE_BF16_STATUS_20260729_CN.md b/auto_round_extension/ark/SPARSE_BF16_STATUS_20260729_CN.md new file mode 100644 index 0000000000..8581393545 --- /dev/null +++ b/auto_round_extension/ark/SPARSE_BF16_STATUS_20260729_CN.md @@ -0,0 +1,169 @@ +# Sparse BF16 状态 2026-07-29 + +## 范围 + +本文档记录了 2026 年 7 月 29 日（星期三）在 oneAPI 2025 环境下重新构建并在 XPU 7 上复测后的 sparse attention 当前状态。 + +环境： + +- `ONEAPI_ROOT=/home/yiliu4/intel/oneapi` +- Python: `/home/yiliu4/workspace/auto-round-prefill-clean-sparse-pr/auto_round_extension/ark/ark-torch-212/bin/python` +- 设备：`XPU_MASK=7` +- 原生 BF16 sparse 打开：`SAGE_ATTN_XPU_SPARSE_BF16_NATIVE=1` +- 重建后的扩展：`auto_round_kernel/xbuild_bf16_v2/auto_round_kernel_xpu.cpython-313-x86_64-linux-gnu.so` + +本次测试的 kernel 配置： + +- `head_dim=128` +- `q_tile_override=256` +- `sparse_q_block_tokens=256` +- `sparse_k_block_tokens=64` +- layout：`HND` + +## 精度状态 + +### 固定 LUT sparse kernel + +这里的 `fixed-LUT sparse` 指 sparse mask 由 `lut` 和 `valid_block_num` 显式给出，然后用相同 mask 的精确 dense BF16 参考结果进行校验。 + +目标正确性用例： + +- shape：`B=1, Hq=32, Hkv=8, Sq=512, Skv=768, D=128` +- sparse pattern：第一个 Q tile 使用 K blocks `[0, 1, 2]`，第二个 Q tile 使用 `[9, 10, 11]` +- 参考实现：显式 BF16 `Q @ K^T`，加精确 mask，softmax，然后 `P @ V` + +结果： + +- `bf16_sparse` 对比精确 BF16 参考： + - `max_diff=0.003906` + - `mean_diff=0.000127` + +同一用例下的对比： + +- `int8_sparse` 对比精确 BF16 参考： + - `max_diff=3.826172` + - `mean_diff=0.344735` +- `int8_sparse` 对比 `bf16_sparse`： + - `max_diff=3.826172` + - `mean_diff=0.344735` + +结论： + +- `bf16_sparse` 在精确 fixed-LUT sparse kernel 路径上是准确的。 +- 同一用例下，`int8_sparse` 的精度明显差于 `bf16_sparse`。 + +### 启发式 top-k 端到端路径 + +`sparge_sage2_attn_meansim_topk_xpu_bf16` 是启发式路径，不应该作为手工构造 LUT mask 的精确正确性参考。 + +因此当前 BF16 sparse 测试的含义是： + +- 精确正确性：`sage_sparse_bf16` 对比显式 masked BF16 参考 +- 启发式 top-k 路径：用于 benchmark / perf，不作为精确 oracle + +## 测试状态 + +更新文件： + +- `auto_round_kernel/wrapper/test/test_sage_sparse_prefill_e2e.py` + +当前测试脚本行为： + +- 强制加载 `xbuild_bf16_v2` 中带 BF16 sparse 能力的扩展 +- 使用显式 masked BF16 数学结果作为 BF16 sparse 正确性参考 +- 不再把启发式 BF16 top-k 输出当成 fixed-LUT kernel 的精确标准答案 + +验证命令： + +```bash +source benchmarks/source_env_xpu67_oneapi2025.sh +export XPU_MASK=7 +export SAGE_ATTN_XPU_SPARSE_BF16_NATIVE=1 +${ARK_BENCH_PYTHON} auto_round_kernel/wrapper/test/test_sage_sparse_prefill_e2e.py +``` + +观察结果： + +- 整个脚本在 oneAPI 2025 下成功退出 +- INT8 sparse 用例通过 +- BF16 sparse 精确参考用例通过 + +## 性能状态 + +### 中等尺寸精确 sparse 用例 + +Shape： + +- `B=1, Hq=32, Hkv=8, Sq=512, Skv=768, D=128` + +时延： + +- 带精确 mask 的 dense BF16：`0.105 ms` +- `bf16_sparse`：`0.096 ms` +- `int8_sparse`：`0.090 ms` +- 仅 INT8 Q/K 量化：`0.027 ms` + +相对 dense BF16 精确 mask 基线的加速比： + +- `bf16_sparse`：`1.093x` +- `int8_sparse`：`1.175x` +- `int8_sparse + quant`：`0.902x` + +结论： + +- 只看 kernel，`int8_sparse` 略快 +- 如果把量化时间算进去，这个中等尺寸用例并不偏向 INT8 路径 +- 这里 `bf16_sparse` 是更好的精度点 + +### 长序列 benchmark + +Shape： + +- `B=1, Hq=40, Hkv=40, S=75000, D=128` +- layout：`HND` +- `topk=0.5` +- `q_tile_override=256` +- `sparse_q_block_tokens=256` +- `sparse_k_block_tokens=64` + +sparse 选择统计： + +- `selected_ratio=0.502557` +- `selected_blocks_per_row=588.997` + +sparse 时延： + +- `preprocess=127.809 ms` +- `int8_sparse_kernel=390.990 ms` +- `int8_sparse_e2e=501.369 ms` +- `bf16_sparse_kernel=804.864 ms` +- `bf16_sparse_e2e=919.896 ms` + +同一 shape 下的 dense BF16 基线： + +- `dense_torch_sdpa_bf16=2154.809 ms` +- `dense_ark_sdpa_bf16=2494.633 ms` +- `dense_ark_sagev1_bf16=10848.612 ms` + +相对 dense torch BF16 SDPA 的加速比： + +- `int8_sparse_e2e`：约 `4.30x` +- `bf16_sparse_e2e`：约 `2.34x` + +相对 dense ARK BF16 SDPA 的加速比： + +- `int8_sparse_e2e`：约 `4.98x` +- `bf16_sparse_e2e`：约 `2.71x` + +结论： + +- 在 `75k / 40 / 128` 下，两条 sparse 路径都明显快于 dense BF16 +- `int8_sparse` 仍然是最快路径 +- `bf16_sparse` 虽然慢于 `int8_sparse`，但仍然显著快于 dense BF16 + +## 当前结论 + +- 在 oneAPI 2025 重新构建后，BF16 sparse fixed-LUT kernel 的正确性状态是好的。 +- 之前 BF16 mismatch 的主要原因是旧扩展被错误加载，以及测试里使用了不可靠的 masked XPU 参考路径。 +- 对长序列来说，BF16 sparse 是一个兼顾精度的 sparse 路径，并且相对 dense BF16 有明显加速。 +- `int8_sparse` 仍然是最快的 sparse 方案，但在精确 fixed-LUT 检查里，相比 `bf16_sparse` 牺牲了明显精度。 diff --git a/auto_round_extension/ark/auto_round_kernel/wrapper/test/test_sage_sparse_prefill_e2e.py b/auto_round_extension/ark/auto_round_kernel/wrapper/test/test_sage_sparse_prefill_e2e.py index 27ab86385c..60c9a87922 100644 --- a/auto_round_extension/ark/auto_round_kernel/wrapper/test/test_sage_sparse_prefill_e2e.py +++ b/auto_round_extension/ark/auto_round_kernel/wrapper/test/test_sage_sparse_prefill_e2e.py @@ -15,8 +15,20 @@ from auto_round_kernel.xpu_loader import ensure_xpu_lib -def ensure_sparse_binding() -> None: - ensure_xpu_lib(required_symbols=("sage_sparse",)) +def ensure_sparse_binding(*, required_symbols: tuple[str, ...] = ("sage_sparse",)) -> None: + bf16_ext = ( + REPO_PARENT / "auto_round_kernel" / "xbuild_bf16_v2" / "auto_round_kernel_xpu.cpython-313-x86_64-linux-gnu.so" + ) + search_roots = ( + REPO_PARENT / "auto_round_kernel", + REPO_PARENT / "auto_round_kernel" / "xbuild", + REPO_PARENT / "auto_round_kernel" / "xbuild_diffuser", + ) + ensure_xpu_lib( + required_symbols=required_symbols, + ext_path=bf16_ext if bf16_ext.is_file() else None, + search_roots=search_roots, + ) def quantize_qk(tensor: torch.Tensor, block_size: int) -> tuple[torch.Tensor, torch.Tensor]: @@ -43,7 +55,7 @@ def quantize_qk(tensor: torch.Tensor, block_size: int) -> tuple[torch.Tensor, to def build_sparse_metadata_and_mask( batch: int, heads: int, - seq_len: int, + seq_len_q: int, quant_block_size: int, query_tile_tokens: int, per_query_tile_selection: list[list[int]], @@ -51,17 +63,19 @@ def build_sparse_metadata_and_mask( is_causal: bool = False, sparse_q_block_tokens: int | None = None, sparse_k_block_tokens: int | None = None, + seq_len_kv: int | None = None, ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + seq_len_kv = seq_len_q if seq_len_kv is None else seq_len_kv q_block_tokens = quant_block_size if sparse_q_block_tokens is None else sparse_q_block_tokens k_block_tokens = quant_block_size if sparse_k_block_tokens is None else sparse_k_block_tokens - q_blocks = (seq_len + q_block_tokens - 1) // q_block_tokens - kv_blocks = (seq_len + k_block_tokens - 1) // k_block_tokens - active_query_tiles = (seq_len + query_tile_tokens - 1) // query_tile_tokens + q_blocks = (seq_len_q + q_block_tokens - 1) // q_block_tokens + kv_blocks = (seq_len_kv + k_block_tokens - 1) // k_block_tokens + active_query_tiles = (seq_len_q + query_tile_tokens - 1) // query_tile_tokens assert len(per_query_tile_selection) == active_query_tiles lut = torch.zeros((batch, heads, q_blocks, kv_blocks), dtype=torch.int32, device=device) valid = torch.zeros((batch, heads, q_blocks), dtype=torch.int32, device=device) - mask = torch.full((batch, 1, seq_len, seq_len), -1.0e9, dtype=torch.float32, device=device) + mask = torch.full((batch, 1, seq_len_q, seq_len_kv), -1.0e9, dtype=torch.float32, device=device) q_blocks_per_query_tile = max(1, query_tile_tokens // q_block_tokens) for qblk in range(q_blocks): @@ -75,11 +89,11 @@ def build_sparse_metadata_and_mask( for qtile, selected_blocks in enumerate(per_query_tile_selection): q_start = qtile * query_tile_tokens - q_end = min(q_start + query_tile_tokens, seq_len) + q_end = min(q_start + query_tile_tokens, seq_len_q) for qt in range(q_start, q_end): for selected in selected_blocks: k_start = selected * k_block_tokens - k_end = min(k_start + k_block_tokens, seq_len) + k_end = min(k_start + k_block_tokens, seq_len_kv) if not is_causal: mask[:, :, qt : qt + 1, k_start:k_end] = 0.0 else: @@ -98,6 +112,27 @@ def _clamp_selected_blocks(kv_blocks: int, per_query_tile_selection: list[list[i return clamped +def bf16_sparse_reference( + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attn_mask: torch.Tensor | None, + *, + scale: float, + enable_gqa: bool, +) -> torch.Tensor: + if enable_gqa and query.shape[1] != key.shape[1]: + repeat = query.shape[1] // key.shape[1] + key = key.repeat_interleave(repeat, dim=1) + value = value.repeat_interleave(repeat, dim=1) + + scores = torch.matmul(query.float(), key.float().transpose(-1, -2)) * scale + if attn_mask is not None: + scores = scores + attn_mask.float() + probs = torch.softmax(scores, dim=-1) + return torch.matmul(probs, value.float()).to(query.dtype) + + def run_case( head_dim: int, block_size: int = 64, @@ -201,6 +236,114 @@ def run_case( ) +def run_case_bf16( + head_dim: int, + *, + seq_len_q: int = 256, + seq_len_kv: int = 256, + num_heads_q: int = 4, + num_heads_kv: int | None = None, + is_causal: bool = False, + q_tile_override: int = 0, + sparse_q_block_tokens: int | None = None, + sparse_k_block_tokens: int | None = None, +) -> None: + ensure_sparse_binding(required_symbols=("sage_sparse", "sage_sparse_bf16")) + device = torch.device("xpu") + batch = 1 + num_heads_kv = num_heads_q if num_heads_kv is None else num_heads_kv + scale = 1.0 / math.sqrt(head_dim) + query_tile_tokens = q_tile_override or (64 if head_dim == 64 else 256) + k_block_tokens = 64 if sparse_k_block_tokens is None else sparse_k_block_tokens + kv_blocks = (seq_len_kv + k_block_tokens - 1) // k_block_tokens + active_query_tiles = (seq_len_q + query_tile_tokens - 1) // query_tile_tokens + if not is_causal: + per_query_tile_selection = [list(range(max(1, min(kv_blocks, 3)))) for _ in range(active_query_tiles)] + if active_query_tiles > 1 and kv_blocks > 3: + per_query_tile_selection[-1] = list(range(kv_blocks - 3, kv_blocks)) + case_name = "bf16_prefill" + else: + per_query_tile_selection = [list(range(min(kv_blocks, idx + 1))) for idx in range(active_query_tiles)] + case_name = "bf16_prefill_causal" + per_query_tile_selection = _clamp_selected_blocks(kv_blocks, per_query_tile_selection) + + torch.manual_seed(6026 + head_dim + num_heads_q + (num_heads_kv * 13) + seq_len_q + seq_len_kv) + query = torch.randn((batch, num_heads_q, seq_len_q, head_dim), dtype=torch.bfloat16, device=device) + key = torch.randn((batch, num_heads_kv, seq_len_kv, head_dim), dtype=torch.bfloat16, device=device) + value = torch.randn((batch, num_heads_kv, seq_len_kv, head_dim), dtype=torch.bfloat16, device=device) + + effective_sparse_q_block_tokens = 64 if sparse_q_block_tokens is None else sparse_q_block_tokens + effective_sparse_k_block_tokens = 64 if sparse_k_block_tokens is None else sparse_k_block_tokens + lut, valid, dense_mask = build_sparse_metadata_and_mask( + batch, + num_heads_q, + seq_len_q, + 64, + query_tile_tokens, + per_query_tile_selection, + device, + is_causal=is_causal, + sparse_q_block_tokens=effective_sparse_q_block_tokens, + sparse_k_block_tokens=effective_sparse_k_block_tokens, + seq_len_kv=seq_len_kv, + ) + + dense_out = bf16_sparse_reference( + query, + key, + value, + dense_mask, + scale=scale, + enable_gqa=num_heads_q != num_heads_kv, + ) + sparse_out = ark.sage_sparse_bf16( + query, + key, + value, + lut, + valid, + is_causal=is_causal, + scale=scale, + q_tile_override=q_tile_override, + sparse_q_block_tokens=sparse_q_block_tokens, + sparse_k_block_tokens=sparse_k_block_tokens, + tensor_layout="HND", + ) + e2e_out = ark.sparge_sage2_attn_meansim_topk_xpu_bf16( + query, + key, + value, + is_causal=is_causal, + scale=scale, + smooth_k=True, + simthreshd1=-1.0, + topk=0.5, + attention_sink=False, + tensor_layout="HND", + q_tile_override=q_tile_override, + sparse_q_block_tokens=sparse_q_block_tokens, + sparse_k_block_tokens=sparse_k_block_tokens, + ) + torch.xpu.synchronize() + + diff = (dense_out.float() - sparse_out.float()).abs() + e2e_diff = (e2e_out.float() - sparse_out.float()).abs() + max_diff = float(diff.max().cpu()) + mean_diff = float(diff.mean().cpu()) + e2e_max_diff = float(e2e_diff.max().cpu()) + e2e_mean_diff = float(e2e_diff.mean().cpu()) + print( + f"[sage_sparse_bf16][{case_name}] D={head_dim} Hq={num_heads_q} Hkv={num_heads_kv} " + f"Sq={seq_len_q} Skv={seq_len_kv} max_diff={max_diff:.6f} mean_diff={mean_diff:.6f} " + f"e2e_max_diff={e2e_max_diff:.6f} e2e_mean_diff={e2e_mean_diff:.6f}" + ) + if max_diff > 2e-2 or mean_diff > 2e-3: + raise RuntimeError( + f"sage_sparse_bf16 mismatch for D={head_dim}, Hq={num_heads_q}, Hkv={num_heads_kv}, " + f"Sq={seq_len_q}, Skv={seq_len_kv}, causal={is_causal}" + ) + + def run_multi_row_tile_case() -> None: device = torch.device("xpu") batch = 1 @@ -341,6 +484,20 @@ def main() -> None: ) run_case(64, is_causal=True) run_case(128, is_causal=True) + run_case_bf16(64) + run_case_bf16(64, seq_len_q=192, seq_len_kv=256, num_heads_q=16, num_heads_kv=4) + run_case_bf16(128, q_tile_override=64, num_heads_q=32, num_heads_kv=8) + run_case_bf16( + 128, + seq_len_q=512, + seq_len_kv=768, + num_heads_q=32, + num_heads_kv=8, + q_tile_override=256, + sparse_q_block_tokens=256, + sparse_k_block_tokens=64, + ) + run_case_bf16(128, is_causal=True, q_tile_override=64) if __name__ == "__main__": From ddaad538be3f3d3cd590dfb6fd3cd65288421076 Mon Sep 17 00:00:00 2001 From: yiliu30 Date: Thu, 30 Jul 2026 03:34:50 +0000 Subject: [PATCH 02/42] feat: add bf16 sparse mode to flux example --- .../ark/SPARSE_BF16_STATUS_20260729.md | 36 +++++++++ .../ark/SPARSE_BF16_STATUS_20260729_CN.md | 36 +++++++++ .../ark/examples/flux_sparse_patch.py | 76 ++++++++++++++----- auto_round_extension/ark/examples/run_flux.py | 33 ++++++-- .../ark/examples/wan_sparse_patch.py | 13 +++- 5 files changed, 168 insertions(+), 26 deletions(-) diff --git a/auto_round_extension/ark/SPARSE_BF16_STATUS_20260729.md b/auto_round_extension/ark/SPARSE_BF16_STATUS_20260729.md index dc5208b971..87902eafa6 100644 --- a/auto_round_extension/ark/SPARSE_BF16_STATUS_20260729.md +++ b/auto_round_extension/ark/SPARSE_BF16_STATUS_20260729.md @@ -167,3 +167,39 @@ Conclusion: - The previous BF16 mismatch was caused by stale extension loading and an unreliable masked-XPU reference path in the test harness. - For long sequences, BF16 sparse is a valid accuracy-oriented sparse path with meaningful speedup over dense BF16. - INT8 sparse is still the fastest sparse option, but it gives up substantial accuracy relative to BF16 sparse on the exact fixed-LUT check. + +## July 30 Rerun Summary + +On Thursday, July 30, 2026, the `75k / topk=0.5` case was rerun on an idle `XPU 7` with oneAPI 2025 and the BF16-capable sparse extension explicitly loaded from `auto_round_kernel/xbuild_bf16_v2/auto_round_kernel_xpu.cpython-313-x86_64-linux-gnu.so`. + +Shape and config: + +- `B=1, Hq=40, Hkv=40, S=75000, D=128` +- layout: `HND` +- `topk=0.5` +- `q_tile_override=256` +- `sparse_q_block_tokens=256` +- `sparse_k_block_tokens=64` +- warmup `2`, iters `3` + +Sparse selection stats: + +- `selected_ratio=0.502557` +- `selected_blocks_per_row=588.997` + +Latency and throughput summary: + +| Mode | Latency (ms) | Speedup vs Torch BF16 | Speedup vs SageV1 BF16 | Baseline TFLOPS | Effective TFLOPS | +|---|---:|---:|---:|---:|---:| +| `dense_torch_sdpa_bf16` | 1263.297 | 1.000 | 0.595 | 91.190 | 91.190 | +| `dense_sagev1_bf16` | 751.410 | 1.681 | 1.000 | 153.312 | 153.312 | +| `sparse_qtile256_row64k_kernel_only` | 398.381 | 3.305 | 1.944 | 289.171 | 145.325 | +| `sparse_qtile256_row64k_e2e` | 514.271 | 2.560 | 1.506 | 224.007 | 112.576 | +| `sparse_bf16_qtile256_row64k_kernel_only` | 812.091 | 1.556 | 0.925 | 141.856 | 71.291 | +| `sparse_bf16_qtile256_row64k_e2e` | 928.958 | 1.360 | 0.809 | 124.010 | 62.322 | + +Notes: + +- `kernel_only` is sparse kernel time after preprocess. +- `e2e` includes preprocess plus kernel. +- On this rerun, BF16 sparse is faster than dense torch BF16 SDPA, but still slower than dense `sagev1_bf16`. diff --git a/auto_round_extension/ark/SPARSE_BF16_STATUS_20260729_CN.md b/auto_round_extension/ark/SPARSE_BF16_STATUS_20260729_CN.md index 8581393545..a6cb5fde5c 100644 --- a/auto_round_extension/ark/SPARSE_BF16_STATUS_20260729_CN.md +++ b/auto_round_extension/ark/SPARSE_BF16_STATUS_20260729_CN.md @@ -167,3 +167,39 @@ sparse 时延： - 之前 BF16 mismatch 的主要原因是旧扩展被错误加载，以及测试里使用了不可靠的 masked XPU 参考路径。 - 对长序列来说，BF16 sparse 是一个兼顾精度的 sparse 路径，并且相对 dense BF16 有明显加速。 - `int8_sparse` 仍然是最快的 sparse 方案，但在精确 fixed-LUT 检查里，相比 `bf16_sparse` 牺牲了明显精度。 + +## 2026-07-30 复测摘要 + +在 2026 年 7 月 30 日（星期四），对 `75k / topk=0.5` 用例进行了重新测试。测试使用空闲的 `XPU 7`、oneAPI 2025，并且显式加载了带 BF16 sparse 能力的扩展 `auto_round_kernel/xbuild_bf16_v2/auto_round_kernel_xpu.cpython-313-x86_64-linux-gnu.so`。 + +Shape 和配置： + +- `B=1, Hq=40, Hkv=40, S=75000, D=128` +- layout：`HND` +- `topk=0.5` +- `q_tile_override=256` +- `sparse_q_block_tokens=256` +- `sparse_k_block_tokens=64` +- warmup `2`，iters `3` + +sparse 选择统计： + +- `selected_ratio=0.502557` +- `selected_blocks_per_row=588.997` + +时延和吞吐摘要： + +| Mode | Latency (ms) | Speedup vs Torch BF16 | Speedup vs SageV1 BF16 | Baseline TFLOPS | Effective TFLOPS | +|---|---:|---:|---:|---:|---:| +| `dense_torch_sdpa_bf16` | 1263.297 | 1.000 | 0.595 | 91.190 | 91.190 | +| `dense_sagev1_bf16` | 751.410 | 1.681 | 1.000 | 153.312 | 153.312 | +| `sparse_qtile256_row64k_kernel_only` | 398.381 | 3.305 | 1.944 | 289.171 | 145.325 | +| `sparse_qtile256_row64k_e2e` | 514.271 | 2.560 | 1.506 | 224.007 | 112.576 | +| `sparse_bf16_qtile256_row64k_kernel_only` | 812.091 | 1.556 | 0.925 | 141.856 | 71.291 | +| `sparse_bf16_qtile256_row64k_e2e` | 928.958 | 1.360 | 0.809 | 124.010 | 62.322 | + +说明： + +- `kernel_only` 表示 preprocess 之后仅 kernel 的时间。 +- `e2e` 表示 preprocess 加 kernel 的总时间。 +- 这次复测中，BF16 sparse 比 dense torch BF16 SDPA 更快，但仍然慢于 dense `sagev1_bf16`。 diff --git a/auto_round_extension/ark/examples/flux_sparse_patch.py b/auto_round_extension/ark/examples/flux_sparse_patch.py index 6695bcbb92..abe9bfcc0e 100644 --- a/auto_round_extension/ark/examples/flux_sparse_patch.py +++ b/auto_round_extension/ark/examples/flux_sparse_patch.py @@ -20,6 +20,19 @@ from wan_sparse_patch import _parse_bool_env, _parse_optional_int_env, ensure_ark_sparse_binding +def _parse_sparse_kernel_env(name: str = "FLUX_SPARSE_KERNEL", default: str = "int8") -> str: + value = os.getenv(name, default).strip().lower() + aliases = { + "int8": "int8", + "qks8": "int8", + "bf16": "bf16", + } + normalized = aliases.get(value) + if normalized is None: + raise ValueError(f"{name} must be one of: int8, bf16") + return normalized + + def _normalize_attention_mask( attn_mask: torch.Tensor | None, batch: int, @@ -90,6 +103,7 @@ def __init__( q_tile_override: int, sparse_q_block_tokens: int | None, sparse_k_block_tokens: int | None, + sparse_kernel: str, ): self.original_processor = original_processor self.stats = stats @@ -100,6 +114,7 @@ def __init__( self.q_tile_override = q_tile_override self.sparse_q_block_tokens = sparse_q_block_tokens self.sparse_k_block_tokens = sparse_k_block_tokens + self.sparse_kernel = sparse_kernel self._warned_runtime_fallback = False @staticmethod @@ -192,22 +207,40 @@ def __call__( seq_kv=key.shape[1], device=query.device, ) - hidden_states, sparsity = ark.sparge_sage2_attn_meansim_topk_xpu( - query, - key, - value, - attn_mask=mask, - is_causal=False, - smooth_k=self.smooth_k, - simthreshd1=-1.0, - topk=self.topk, - attention_sink=self.attention_sink, - tensor_layout="NHD", - q_tile_override=self.q_tile_override, - sparse_q_block_tokens=self.sparse_q_block_tokens, - sparse_k_block_tokens=self.sparse_k_block_tokens, - return_sparsity=True, - ) + if self.sparse_kernel == "bf16": + hidden_states, sparsity = ark.sparge_sage2_attn_meansim_topk_xpu_bf16( + query, + key, + value, + attn_mask=mask, + is_causal=False, + smooth_k=self.smooth_k, + simthreshd1=-1.0, + topk=self.topk, + attention_sink=self.attention_sink, + tensor_layout="NHD", + q_tile_override=self.q_tile_override, + sparse_q_block_tokens=self.sparse_q_block_tokens, + sparse_k_block_tokens=self.sparse_k_block_tokens, + return_sparsity=True, + ) + else: + hidden_states, sparsity = ark.sparge_sage2_attn_meansim_topk_xpu( + query, + key, + value, + attn_mask=mask, + is_causal=False, + smooth_k=self.smooth_k, + simthreshd1=-1.0, + topk=self.topk, + attention_sink=self.attention_sink, + tensor_layout="NHD", + q_tile_override=self.q_tile_override, + sparse_q_block_tokens=self.sparse_q_block_tokens, + sparse_k_block_tokens=self.sparse_k_block_tokens, + return_sparsity=True, + ) self.stats.sparse_calls += 1 self.stats.sparse_sparsity_sum += float(sparsity) @@ -270,8 +303,15 @@ def patch_flux_sparse_attention( q_tile_override: int = 0, sparse_q_block_tokens: int | None = None, sparse_k_block_tokens: int | None = None, + sparse_kernel: str = "int8", ): - ensure_ark_sparse_binding() + ensure_ark_sparse_binding( + required_symbols=( + ("sage_sparse_bf16", "sage_dynamic_quant_layout") + if sparse_kernel == "bf16" + else ("sage_sparse", "sage_dynamic_quant_layout") + ) + ) originals: list[tuple[FluxAttention, object]] = [] stats = FluxSparseAttentionStats() @@ -289,6 +329,7 @@ def patch_flux_sparse_attention( q_tile_override=q_tile_override, sparse_q_block_tokens=sparse_q_block_tokens, sparse_k_block_tokens=sparse_k_block_tokens, + sparse_kernel=sparse_kernel, ) ) originals.append((module, original)) @@ -310,4 +351,5 @@ def patch_flux_sparse_attention_from_env(transformer): q_tile_override=int(os.getenv("FLUX_SPARSE_Q_TILE_OVERRIDE", "0")), sparse_q_block_tokens=_parse_optional_int_env("FLUX_SPARSE_Q_BLOCK_TOKENS"), sparse_k_block_tokens=_parse_optional_int_env("FLUX_SPARSE_K_BLOCK_TOKENS"), + sparse_kernel=_parse_sparse_kernel_env(), ) diff --git a/auto_round_extension/ark/examples/run_flux.py b/auto_round_extension/ark/examples/run_flux.py index 0c1bd3544c..0bc89fec53 100644 --- a/auto_round_extension/ark/examples/run_flux.py +++ b/auto_round_extension/ark/examples/run_flux.py @@ -24,6 +24,19 @@ def env_flag(name, default="0"): return os.getenv(name, default).lower() not in {"0", "false", "no", "off"} +def env_sparse_kernel(name="FLUX_SPARSE_KERNEL", default="int8"): + value = os.getenv(name, default).strip().lower() + aliases = { + "int8": "int8", + "qks8": "int8", + "bf16": "bf16", + } + normalized = aliases.get(value) + if normalized is None: + raise ValueError(f"{name} must be one of: int8, bf16") + return normalized + + benchmark_enabled = env_flag("FLUX_BENCHMARK_ENABLE", "0") profiler_enabled = env_flag("FLUX_PROFILER_ENABLE", "0") benchmark_scope = os.getenv("FLUX_BENCHMARK_SCOPE", "full").strip().lower() @@ -53,11 +66,15 @@ def env_flag(name, default="0"): max_sequence_length = int(os.getenv("FLUX_MAX_SEQUENCE_LENGTH", "512")) seed = int(os.getenv("FLUX_SEED", "0")) use_sparse = os.getenv("FLUX_USE_SPARSE", "1").lower() not in {"0", "false", "no", "off"} +sparse_kernel = env_sparse_kernel() -output_file = ( - f"flux_output_{height}x{width}_{num_inference_steps}steps_" - f"{guidance_scale}gs_sparse{os.getenv('FLUX_SPARSE_TOPK', '0.5')}.png" -) +if use_sparse: + output_file = ( + f"flux_output_{height}x{width}_{num_inference_steps}steps_" + f"{guidance_scale}gs_sparse_{sparse_kernel}_topk{os.getenv('FLUX_SPARSE_TOPK', '0.5')}.png" + ) +else: + output_file = f"flux_output_{height}x{width}_{num_inference_steps}steps_{guidance_scale}gs_dense.png" output_path = os.getenv("FLUX_OUTPUT", output_file) prompt = os.getenv("FLUX_PROMPT", "A cat holding a sign that says hello world") @@ -195,7 +212,8 @@ def sparse_patch_context(): if not use_sparse: return contextlib.nullcontext(None) print( - f"[flux_sparse] enabled attention sparse patch: topk={os.getenv('FLUX_SPARSE_TOPK', '0.5')} " + f"[flux_sparse] enabled attention sparse patch: kernel={sparse_kernel}" + f" topk={os.getenv('FLUX_SPARSE_TOPK', '0.5')} " f"smooth_k={os.getenv('FLUX_SPARSE_SMOOTH_K', '1')}" f" q_tile={os.getenv('FLUX_SPARSE_Q_TILE_OVERRIDE', '0')}" f" q_block={os.getenv('FLUX_SPARSE_Q_BLOCK_TOKENS', 'default')}" @@ -524,6 +542,7 @@ def run_block_benchmark(current_run_tag): "max_sequence_length": max_sequence_length, "seed": seed, "use_sparse": use_sparse, + "sparse_kernel": sparse_kernel if use_sparse else None, "cpu_offload_enabled": cpu_offload_enabled, "block_kind": block_state["block_kind"], "block_index": block_state["block_index"], @@ -543,7 +562,7 @@ def run_block_benchmark(current_run_tag): return result -run_tag = "sparse" if use_sparse else "dense" +run_tag = f"sparse_{sparse_kernel}" if use_sparse else "dense" def maybe_run_with_profiler(run_callable, current_run_tag, record_label="flux_generate"): @@ -615,6 +634,7 @@ def run_benchmark(run_callable, current_run_tag, benchmark_scope): "max_sequence_length": max_sequence_length, "seed": seed, "use_sparse": use_sparse, + "sparse_kernel": sparse_kernel if use_sparse else None, "sparse_topk": os.getenv("FLUX_SPARSE_TOPK", "0.5"), "sparse_smooth_k": os.getenv("FLUX_SPARSE_SMOOTH_K", "1"), "output_path": output_path, @@ -704,6 +724,7 @@ def run_denoising_benchmark(current_run_tag): "max_sequence_length": max_sequence_length, "seed": seed, "use_sparse": use_sparse, + "sparse_kernel": sparse_kernel if use_sparse else None, "sparse_topk": os.getenv("FLUX_SPARSE_TOPK", "0.5"), "sparse_smooth_k": os.getenv("FLUX_SPARSE_SMOOTH_K", "1"), "output_path": output_path, diff --git a/auto_round_extension/ark/examples/wan_sparse_patch.py b/auto_round_extension/ark/examples/wan_sparse_patch.py index 2e61ca6cdc..6418209521 100644 --- a/auto_round_extension/ark/examples/wan_sparse_patch.py +++ b/auto_round_extension/ark/examples/wan_sparse_patch.py @@ -36,10 +36,17 @@ def _parse_optional_int_env(name: str) -> int | None: return None if parsed == 0 else parsed -def ensure_ark_sparse_binding() -> None: +def ensure_ark_sparse_binding( + *, required_symbols: tuple[str, ...] = ("sage_sparse", "sage_dynamic_quant_layout") +) -> None: ensure_xpu_lib( - required_symbols=("sage_sparse", "sage_dynamic_quant_layout"), - search_roots=(KERNEL_DIR, KERNEL_DIR / "xbuild", KERNEL_DIR / "xbuild_diffuser"), + required_symbols=required_symbols, + search_roots=( + KERNEL_DIR, + KERNEL_DIR / "xbuild", + KERNEL_DIR / "xbuild_bf16_v2", + KERNEL_DIR / "xbuild_diffuser", + ), ) From 2e2ecb5481be5e8d1226f630f7801936c1b8d984 Mon Sep 17 00:00:00 2001 From: yiliu30 Date: Wed, 5 Aug 2026 06:18:46 +0000 Subject: [PATCH 03/42] feat: add bf16 sparse kernel sources --- .../auto_round_kernel/sdpa_sparse_bf16.cpp | 229 +++++++ .../stla/xe_sparse_sdpa_fwd_mainloop.hpp | 634 ++++++++++++++++++ 2 files changed, 863 insertions(+) create mode 100644 auto_round_extension/ark/auto_round_kernel/sdpa_sparse_bf16.cpp create mode 100644 auto_round_extension/ark/auto_round_kernel/wrapper/include/stla/xe_sparse_sdpa_fwd_mainloop.hpp diff --git a/auto_round_extension/ark/auto_round_kernel/sdpa_sparse_bf16.cpp b/auto_round_extension/ark/auto_round_kernel/sdpa_sparse_bf16.cpp new file mode 100644 index 0000000000..1384aba2ce --- /dev/null +++ b/auto_round_extension/ark/auto_round_kernel/sdpa_sparse_bf16.cpp @@ -0,0 +1,229 @@ +// Copyright (c) 2026 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#if defined(ARK_XPU) && defined(ARK_SYCL_TLA) + +#include +#include +#include "bestla/bestla.h" + +#include +#include "sycl_tla_sdpa_sparse.hpp" + +namespace ark { + +namespace detail = sparse_detail; + +namespace { + +using KernelLauncher = int (*)(detail::Options const& options); + +int launch_prefill_kernel_bf16_128_sparse_sdpa(detail::Options const& options) { + return detail::launch_sparse_sdpa_prefill_kernel_128(options); +} + +int launch_prefill_kernel_bf16_128_sparse_sdpa_qtile64(detail::Options const& options) { + return detail::launch_sparse_sdpa_prefill_kernel_128_qtile64< + cute::bfloat16_t, cute::bfloat16_t, cute::bfloat16_t>( + options); +} + +int launch_prefill_kernel_bf16_64_sparse_sdpa(detail::Options const& options) { + return detail::launch_sparse_sdpa_prefill_kernel_64(options); +} + +int launch_prefill_kernel_bf16_64_sparse_sdpa_qtile64(detail::Options const& options) { + return detail::launch_sparse_sdpa_prefill_kernel_64_qtile64< + cute::bfloat16_t, cute::bfloat16_t, cute::bfloat16_t>(options); +} + +KernelLauncher select_sparse_bf16_prefill_launcher(int head_dim, int q_tile_override) { + switch (head_dim) { + case 128: + if (q_tile_override == 64) return launch_prefill_kernel_bf16_128_sparse_sdpa_qtile64; + if (q_tile_override != 0 && q_tile_override != 256) return nullptr; + return launch_prefill_kernel_bf16_128_sparse_sdpa; + case 64: + if (q_tile_override == 64) return launch_prefill_kernel_bf16_64_sparse_sdpa_qtile64; + if (q_tile_override != 0 && q_tile_override != 128) return nullptr; + return launch_prefill_kernel_bf16_64_sparse_sdpa; + default: + return nullptr; + } +} + +detail::Options make_common_options(void* Q_ptr, void* K_ptr, void* V_ptr, void* O_ptr, void* mask, int q_stride_s, + int q_stride_d, int q_stride_h, int q_stride_b, int k_stride_s, int k_stride_d, + int k_stride_h, int k_stride_b, int v_stride_d, int v_stride_s, int v_stride_h, + int v_stride_b, int o_stride_s, int o_stride_d, int o_stride_h, int o_stride_b, + int batch, int num_heads_q, int num_heads_kv, int seq_len_q, int seq_len_kv, + int head_dim, float softmax_scale, bool is_causal) { + if (q_stride_d != 1 || k_stride_d != 1 || v_stride_d != 1 || o_stride_d != 1) { + throw std::invalid_argument("make_common_options: head-dim stride must be 1 for Q/K/V/O"); + } + detail::Options options; + options.q = Q_ptr; + options.k = K_ptr; + options.v = V_ptr; + options.mask = mask; + options.o = O_ptr; + options.use_tensor_strides = true; + options.q_stride_s = q_stride_s; + options.q_stride_d = q_stride_d; + options.q_stride_h = q_stride_h; + options.q_stride_b = q_stride_b; + options.k_stride_s = k_stride_s; + options.k_stride_d = k_stride_d; + options.k_stride_h = k_stride_h; + options.k_stride_b = k_stride_b; + options.v_stride_d = v_stride_d; + options.v_stride_s = v_stride_s; + options.v_stride_h = v_stride_h; + options.v_stride_b = v_stride_b; + options.o_stride_s = o_stride_s; + options.o_stride_d = o_stride_d; + options.o_stride_h = o_stride_h; + options.o_stride_b = o_stride_b; + options.batch = batch; + options.num_heads_q = num_heads_q; + options.num_heads_kv = num_heads_kv; + options.seq_len_qo = seq_len_q; + options.seq_len_kv = seq_len_kv; + options.head_size_qk = head_dim; + options.head_size_vo = head_dim; + options.softmax_scale = softmax_scale; + options.is_causal = is_causal; + return options; +} + +void sparse_bf16_prefill(sycl::queue* q, void* Q_ptr, void* K_ptr, void* V_ptr, void* O_ptr, void* mask, void* lut, + void* valid_block_num, int num_q_blocks, int num_k_blocks, int q_tile_override, + int q_stride_s, int q_stride_d, int q_stride_h, int q_stride_b, int k_stride_s, + int k_stride_d, int k_stride_h, int k_stride_b, int v_stride_d, int v_stride_s, + int v_stride_h, int v_stride_b, int o_stride_s, int o_stride_d, int o_stride_h, + int o_stride_b, int batch, int num_heads_q, int num_heads_kv, int seq_len_q, int seq_len_kv, + int head_dim, float softmax_scale, bool is_causal, int sparse_q_block_size = 0) { + const int effective_q_tile_override = + (head_dim == 128 && q_tile_override == 0) ? 64 : ((head_dim == 64 && q_tile_override == 0) ? 64 : q_tile_override); + detail::Options options = + make_common_options(Q_ptr, K_ptr, V_ptr, O_ptr, mask, q_stride_s, q_stride_d, q_stride_h, q_stride_b, + k_stride_s, k_stride_d, k_stride_h, k_stride_b, v_stride_d, v_stride_s, v_stride_h, + v_stride_b, o_stride_s, o_stride_d, o_stride_h, o_stride_b, batch, num_heads_q, + num_heads_kv, seq_len_q, seq_len_kv, head_dim, softmax_scale, is_causal); + options.scale_block_size = 0; + options.sparse_q_block_size = sparse_q_block_size; + options.q_tile_override = effective_q_tile_override; + options.qscale = nullptr; + options.kscale = nullptr; + options.lut = static_cast(lut); + options.valid_block_num = static_cast(valid_block_num); + options.num_q_blocks = num_q_blocks; + options.num_k_blocks = num_k_blocks; + compat::set_default_queue(*q); + + KernelLauncher launcher = select_sparse_bf16_prefill_launcher(head_dim, effective_q_tile_override); + if (launcher == nullptr) { + throw std::runtime_error("Unsupported sparse_bf16_prefill config"); + } + + launcher(options); +} + +} // namespace + +void sdpa_impl_bf16_sparse_d64( + sycl::queue* q, void* Q_ptr, void* K_ptr, void* V_ptr, void* O_ptr, void* mask, void* lut, void* valid_block_num, + int num_q_blocks, int num_k_blocks, int q_tile_override, int q_stride_s, int q_stride_d, int q_stride_h, + int q_stride_b, int k_stride_s, int k_stride_d, int k_stride_h, int k_stride_b, int v_stride_d, int v_stride_s, + int v_stride_h, int v_stride_b, int o_stride_s, int o_stride_d, int o_stride_h, int o_stride_b, int batch, + int num_heads_q, int num_heads_kv, int seq_len_q, int seq_len_kv, int head_dim, float softmax_scale, + bool is_causal) { + if (mask && is_causal) { + throw std::invalid_argument("sdpa_impl_bf16_sparse_d64: mask and is_causal cannot both be set"); + } + if (seq_len_q <= 0 || seq_len_kv <= 0) { + throw std::invalid_argument("sdpa_impl_bf16_sparse_d64: seq_len_q and seq_len_kv must be greater than 0"); + } + if (lut == nullptr || valid_block_num == nullptr) { + throw std::invalid_argument("sdpa_impl_bf16_sparse_d64: lut and valid_block_num must be provided"); + } + if (num_q_blocks <= 0 || num_k_blocks <= 0) { + throw std::invalid_argument("sdpa_impl_bf16_sparse_d64: num_q_blocks and num_k_blocks must be greater than 0"); + } + if (head_dim != 64) { + throw std::invalid_argument("sdpa_impl_bf16_sparse_d64: head_dim must be 64"); + } + + sparse_bf16_prefill(q, Q_ptr, K_ptr, V_ptr, O_ptr, mask, lut, valid_block_num, num_q_blocks, num_k_blocks, + q_tile_override, q_stride_s, q_stride_d, q_stride_h, q_stride_b, k_stride_s, k_stride_d, + k_stride_h, k_stride_b, v_stride_d, v_stride_s, v_stride_h, v_stride_b, o_stride_s, o_stride_d, + o_stride_h, o_stride_b, batch, num_heads_q, num_heads_kv, seq_len_q, seq_len_kv, head_dim, + softmax_scale, is_causal, /*sparse_q_block_size=*/64); +} + +void sdpa_impl_bf16_sparse_row_linear( + sycl::queue* q, void* Q_ptr, void* K_ptr, void* V_ptr, void* O_ptr, void* mask, void* lut, void* valid_block_num, + int num_q_blocks, int num_k_blocks, int q_tile_override, int q_stride_s, int q_stride_d, int q_stride_h, + int q_stride_b, int k_stride_s, int k_stride_d, int k_stride_h, int k_stride_b, int v_stride_d, int v_stride_s, + int v_stride_h, int v_stride_b, int o_stride_s, int o_stride_d, int o_stride_h, int o_stride_b, int batch, + int num_heads_q, int num_heads_kv, int seq_len_q, int seq_len_kv, int head_dim, float softmax_scale, + bool is_causal) { + if (q_tile_override != 0 && q_tile_override != 64) { + throw std::invalid_argument("sdpa_impl_bf16_sparse_row_linear: q_tile_override must be 0 or 64"); + } + if (mask && is_causal) { + throw std::invalid_argument("sdpa_impl_bf16_sparse_row_linear: mask and is_causal cannot both be set"); + } + if (seq_len_q <= 0 || seq_len_kv <= 0) { + throw std::invalid_argument("sdpa_impl_bf16_sparse_row_linear: seq_len_q and seq_len_kv must be greater than 0"); + } + if (lut == nullptr || valid_block_num == nullptr) { + throw std::invalid_argument("sdpa_impl_bf16_sparse_row_linear: lut and valid_block_num must be provided"); + } + if (num_q_blocks <= 0 || num_k_blocks <= 0) { + throw std::invalid_argument( + "sdpa_impl_bf16_sparse_row_linear: num_q_blocks and num_k_blocks must be greater than 0"); + } + + sparse_bf16_prefill(q, Q_ptr, K_ptr, V_ptr, O_ptr, mask, lut, valid_block_num, num_q_blocks, num_k_blocks, 64, + q_stride_s, q_stride_d, q_stride_h, q_stride_b, k_stride_s, k_stride_d, k_stride_h, + k_stride_b, v_stride_d, v_stride_s, v_stride_h, v_stride_b, o_stride_s, o_stride_d, o_stride_h, + o_stride_b, batch, num_heads_q, num_heads_kv, seq_len_q, seq_len_kv, head_dim, softmax_scale, + is_causal, /*sparse_q_block_size=*/64); +} + +void sdpa_impl_bf16_sparse_qtile256_row64k( + sycl::queue* q, void* Q_ptr, void* K_ptr, void* V_ptr, void* O_ptr, void* mask, void* lut, void* valid_block_num, + int num_q_blocks, int num_k_blocks, int q_tile_override, int q_stride_s, int q_stride_d, int q_stride_h, + int q_stride_b, int k_stride_s, int k_stride_d, int k_stride_h, int k_stride_b, int v_stride_d, int v_stride_s, + int v_stride_h, int v_stride_b, int o_stride_s, int o_stride_d, int o_stride_h, int o_stride_b, int batch, + int num_heads_q, int num_heads_kv, int seq_len_q, int seq_len_kv, int head_dim, float softmax_scale, + bool is_causal) { + if (q_tile_override != 256) { + throw std::invalid_argument("sdpa_impl_bf16_sparse_qtile256_row64k: q_tile_override must be 256"); + } + if (head_dim != 128) { + throw std::invalid_argument("sdpa_impl_bf16_sparse_qtile256_row64k: head_dim must be 128"); + } + + sparse_bf16_prefill(q, Q_ptr, K_ptr, V_ptr, O_ptr, mask, lut, valid_block_num, num_q_blocks, num_k_blocks, + q_tile_override, q_stride_s, q_stride_d, q_stride_h, q_stride_b, k_stride_s, k_stride_d, + k_stride_h, k_stride_b, v_stride_d, v_stride_s, v_stride_h, v_stride_b, o_stride_s, o_stride_d, + o_stride_h, o_stride_b, batch, num_heads_q, num_heads_kv, seq_len_q, seq_len_kv, head_dim, + softmax_scale, is_causal, /*sparse_q_block_size=*/256); +} + +} // namespace ark + +#endif // ARK_XPU && ARK_SYCL_TLA diff --git a/auto_round_extension/ark/auto_round_kernel/wrapper/include/stla/xe_sparse_sdpa_fwd_mainloop.hpp b/auto_round_extension/ark/auto_round_kernel/wrapper/include/stla/xe_sparse_sdpa_fwd_mainloop.hpp new file mode 100644 index 0000000000..45563462a8 --- /dev/null +++ b/auto_round_extension/ark/auto_round_kernel/wrapper/include/stla/xe_sparse_sdpa_fwd_mainloop.hpp @@ -0,0 +1,634 @@ +/*************************************************************************************************** + * Copyright (C) 2026 Intel Corporation, All rights reserved. + * SPDX-License-Identifier: BSD-3-Clause + **************************************************************************************************/ + +#pragma once + +#include "cute/util/print_tensor.hpp" +#include "cutlass/cutlass.h" +#include "cutlass/gemm/dispatch_policy.hpp" + +#include "cute/algorithm/functional.hpp" +#include "cute/algorithm/gemm.hpp" +#include "cute/algorithm/subgroup_algorithms.hpp" +#include "cute/atom/mma_atom.hpp" +#include "flash_attention_v2/collective/fmha_fusion.hpp" +#include + +namespace cutlass::sdpa { + +template +class XeDefault {}; + +} // namespace cutlass::sdpa + +namespace cutlass::fmha::collective { + +using namespace cute; + +template +struct SPARSESDPAFwdMainloop { + static_assert(cutlass::detail::dependent_false, "Could not find a mainloop specialization."); +}; + +template +struct SPARSESDPAFwdMainloop, CausalMask_, FullMask_, CachedKV_, PagedKV_, + TiledMMAQK_, TiledMMAPV_, VTiles_, TensorQ_, TensorK_, TensorV_, TensorK_cache_, + TensorV_cache_, TiledCopyQ_, TiledCopyK_, TiledCopyV_, TiledCopyK_cache_, + TiledCopyV_cache_> { + using TiledMMAQK = TiledMMAQK_; + using TiledMMAPV = TiledMMAPV_; + using TileShapeQK = decltype(TiledMMAQK{}.tile_mnk()); + using TileShapePV = decltype(TiledMMAPV{}.tile_mnk()); + static constexpr int VTiles = VTiles_; + using SubgroupLayoutQK = decltype(TiledMMAQK{}.get_atom_layout_mnk()); + using SGPerWG = decltype(product(take<1, 4>(shape(typename TiledMMAQK::ThrLayoutVMNK{})))); + + using TensorQ = TensorQ_; + using TensorK = TensorK_; + using TensorV = TensorV_; + + using TensorQ2D = decltype(TensorQ_{}(append>(make_coord(_, _), 0))); + using TensorK2D = decltype(TensorK_{}(append>(make_coord(_, _), 0))); + using TensorV2D = decltype(TensorV_{}(append>(make_coord(_, _), 0))); + + using TiledCopyQ = + conditional_t, decltype(make_block_2d_copy_A(TiledMMAQK{}, TensorQ2D{})), TiledCopyQ_>; + using TiledCopyK = + conditional_t, decltype(make_block_2d_copy_B(TiledMMAQK{}, TensorK2D{})), TiledCopyK_>; + using TiledCopyV = + conditional_t, decltype(make_block_2d_copy_B(TiledMMAPV{}, TensorV2D{})), TiledCopyV_>; + using TensorK_cache = TensorK_cache_; + using TensorV_cache = TensorV_cache_; + using TensorK_cache2D = decltype(TensorK_cache_{}(append>(make_coord(_, _), 0))); + using TensorV_cache2D = decltype(TensorV_cache_{}(append>(make_coord(_, _), 0))); + using TiledCopyK_cache = + conditional_t, decltype(make_block_2d_copy_B(TiledMMAQK{}, TensorK_cache2D{})), + TiledCopyK_cache_>; + using TiledCopyV_cache = + conditional_t, decltype(make_block_2d_copy_B(TiledMMAPV{}, TensorV_cache2D{})), + TiledCopyV_cache_>; + + template + using FragC = decltype(TiledMMA{}.get_slice(0).partition_sg_fragment_C( + make_identity_tensor(select<0, 1>(TiledMMA{}.tile_mnk())))); + + using FragS = FragC; + using FragSRow = decltype(reduce<1>(FragS{}, sycl::plus{})); + using FragSCol = decltype(reduce<0>(FragS{}, sycl::plus{})); + using ElementS = typename TiledMMAQK::ValTypeD; + using ElementM = typename TiledMMAQK::ValTypeA; + + using SingleFragA = FragC; + using FragA = expand_sg_fragment_t; + using FragARow = decltype(reduce<1>(FragA{}, sycl::plus{})); + using ElementA = typename TiledMMAPV::ValTypeD; + + static constexpr bool CausalMask = CausalMask_; + static constexpr bool CachedKV = CachedKV_; + static constexpr bool PagedKV = PagedKV_; + + struct Arguments { + float const scale; + float const* mask = nullptr; + int scale_block_size = 0; + float const* qscale = nullptr; + float const* kscale = nullptr; + float const* vscale = nullptr; + int const* lut = nullptr; + int const* valid_block_num = nullptr; + int num_q_blocks = 0; + int num_k_blocks = 0; + int sparse_q_block_size = 0; + bool canonical_nhd_k = false; + int const* ptr_page_table = nullptr; + int page_size = 0; + int const* num_pages_per_seq = nullptr; + }; + + using Params = Arguments; + struct EmptySharedStorage {}; + using SharedStorage = EmptySharedStorage; + + Params params; + SharedStorage& shared_storage; + + SPARSESDPAFwdMainloop(Params const& params_, SharedStorage& shared_storage_) + : params(params_), shared_storage(shared_storage_) {} + + static constexpr Params to_underlying_arguments(Arguments const& args, void* /* workspace */) { + constexpr double kLog2e = 1.4426950408889634074; + float val = args.scale * static_cast(kLog2e); + return Params{val, args.mask, args.scale_block_size, args.qscale, args.kscale, args.vscale, args.lut, + args.valid_block_num, args.num_q_blocks, args.num_k_blocks, args.sparse_q_block_size, + args.canonical_nhd_k, args.ptr_page_table, args.page_size, args.num_pages_per_seq}; + } + + CUTLASS_HOST_DEVICE static bool can_implement(Arguments const&) { return true; } + + CUTLASS_DEVICE + int get_physical_k_tile(int K, int l_coord, int seq_len_kv_cache) { + int next_page_logical_idx = K * get<1>(TileShapeQK{}) / params.page_size; + int tiles_per_page = params.page_size / get<1>(TileShapeQK{}); + int batch_offset = + params.num_pages_per_seq ? params.num_pages_per_seq[l_coord] : l_coord * (seq_len_kv_cache / params.page_size); + + return params.ptr_page_table[batch_offset + next_page_logical_idx] * tiles_per_page + K % tiles_per_page; + } + + CUTLASS_DEVICE + static int logical_block_from_delta_row(int const* row, int valid_blocks, int idx) { + int logical_block = 0; + for (int i = 0; i <= idx && i < valid_blocks; ++i) { + logical_block += row[i]; + } + return logical_block; + } + + template + CUTLASS_DEVICE void operator()(TensorQ2D const& Q_2D, TensorK2D const& K_2D, TensorV2D const& V_2D, FragA& tArA, + FragARow& tA_max, FragARow& tA_sum, QVCoord blk_qv, int blk_k0, int blk_k1, + int total_blk, int thr_id, int seq_len, int seq_len_kv_cache, int l_coord, + [[maybe_unused]] float* scaleQ, [[maybe_unused]] float* scaleK, + [[maybe_unused]] float* scaleV, int full_tile_offset, int discard_seq_coord, + int const* lut_rows_base = nullptr, int const* valid_blocks_base = nullptr, + int sparse_q_rows_in_tile = 1, + TensorK_cache2D const& K_cache_2D = TensorK_cache2D{}, + TensorV_cache2D const& V_cache_2D = TensorV_cache2D{}) { + using namespace sycl::ext::oneapi::this_work_item; + + auto tile_shape_v = make_shape(get<1>(TileShapePV{}) * C{}, get<2>(TileShapePV{})); + + Tensor cQ = make_identity_tensor(Q_2D.shape()); + Tensor cK = make_identity_tensor(K_2D.shape()); + Tensor cV = make_identity_tensor(V_2D.shape()); + Tensor cK_cache = make_identity_tensor(K_cache_2D.shape()); + Tensor cV_cache = make_identity_tensor(V_cache_2D.shape()); + Tensor cP = make_identity_tensor(take<0, 2>(TileShapeQK{})); + + Tensor gQ = local_tile(cQ, TileShapeQK{}, append(blk_qv, _), Step<_1, X, _1>{}); + Tensor gK = local_tile(cK, TileShapeQK{}, make_coord(_, _, _), Step{}); + Tensor gV = local_tile(cV, tile_shape_v, make_coord(get<1>(blk_qv), _)); + Tensor gV_split = local_tile(gV, TileShapePV{}, make_coord(_, _, 0), Step{}); + + Tensor gK_cache = local_tile(cK_cache, TileShapeQK{}, make_coord(_, _, _), Step{}); + Tensor gV_cache = local_tile(cV_cache, tile_shape_v, make_coord(get<1>(blk_qv), _)); + Tensor gV_cache_split = local_tile(gV_cache, TileShapePV{}, make_coord(_, _, 0), Step{}); + + TiledCopyQ copy_q{Q_2D}; + TiledCopyK copy_k{K_2D}; + TiledCopyV copy_v{V_2D}; + TiledCopyK_cache copy_k_cache{K_cache_2D}; + TiledCopyV_cache copy_v_cache{V_cache_2D}; + + TiledMMAQK mma_qk{}; + TiledMMAPV mma_pv{}; + + auto thr_copy_q = copy_q.get_slice(thr_id); + auto thr_copy_k = copy_k.get_slice(thr_id); + auto thr_copy_v = copy_v.get_slice(thr_id); + auto thr_copy_k_cache = copy_k_cache.get_slice(thr_id); + auto thr_copy_v_cache = copy_v_cache.get_slice(thr_id); + auto thr_mma_qk = mma_qk.get_slice(thr_id); + auto thr_mma_pv = mma_pv.get_slice(thr_id); + + auto tQgQ = thr_copy_q.partition_S(gQ); + auto tKgK = thr_copy_k.partition_S(gK); + auto tVgV = thr_copy_v.partition_S(gV_split); + auto tKgK_cache = thr_copy_k_cache.partition_S(gK_cache); + auto tVgV_cache = thr_copy_v_cache.partition_S(gV_cache_split); + + auto tQrQ = thr_copy_q.partition_sg_fragment_D(gQ(_, _, 0)); + auto tSrQ = thr_mma_qk.partition_sg_fragment_A(gQ(_, _, 0)); + auto tKrK = thr_copy_k.partition_sg_fragment_D(gK(_, _, 0, 0)); + auto tSrK = thr_mma_qk.partition_sg_fragment_B(gK(_, _, 0, 0)); + auto tSrS = thr_mma_qk.partition_sg_fragment_C(cP); + auto tArP = thr_mma_pv.partition_sg_fragment_A(cP); + auto tVrV = thr_copy_v.partition_sg_fragment_D(gV_split(_, _, 0, 0)); + auto tArV = thr_mma_pv.partition_sg_fragment_B(gV_split(_, _, 0, 0)); + + auto prefetch_q = make_block_2d_prefetch(copy_q); + auto prefetch_k = make_block_2d_prefetch(copy_k); + auto prefetch_v = make_block_2d_prefetch(copy_v); + auto prefetch_k_cache = make_block_2d_prefetch(copy_k_cache); + auto prefetch_v_cache = make_block_2d_prefetch(copy_v_cache); + + auto pQgQ = prefetch_q.get_slice(thr_id).partition_S(gQ); + auto pKgK = prefetch_k.get_slice(thr_id).partition_S(gK); + auto pVgV = prefetch_v.get_slice(thr_id).partition_S(gV_split); + auto pKgK_cache = prefetch_k_cache.get_slice(thr_id).partition_S(gK_cache); + auto pVgV_cache = prefetch_v_cache.get_slice(thr_id).partition_S(gV_cache_split); + + int kblocks_cache = ceil_div(seq_len_kv_cache, get<1>(TileShapeQK{})); + for (int D = 0; D < size<3>(pQgQ); D++) { + prefetch(prefetch_q, pQgQ(_, _, _, D)); + } + if (lut_rows_base == nullptr) { + for (int D = 0; D < size<4>(pKgK); D++) { + CUTLASS_PRAGMA_UNROLL + for (int K = 0; K < Stages; K++) { + if (K < kblocks_cache) { + if constexpr (PagedKV) { + int physical_K_tile = get_physical_k_tile(K, l_coord, seq_len_kv_cache); + prefetch(prefetch_k_cache, pKgK_cache(_, _, _, physical_K_tile, D)); + } else { + prefetch(prefetch_k_cache, pKgK_cache(_, _, _, K, D)); + } + } else { + prefetch(prefetch_k, pKgK(_, _, _, K - kblocks_cache, D)); + } + } + } + } + if (blk_k0 == 0) { + clear(tArA); + fill(tA_max, cutlass::platform::numeric_limits::lowest()); + clear(tA_sum); + } + + bool check_remainder_k = (seq_len % get<1>(TileShapeQK{}) != 0); + int q_sg_tile = get<0>(shape_div(TileShapeQK{}, shape(SubgroupLayoutQK{}))); + int sparse_q_block_size = params.sparse_q_block_size > 0 ? params.sparse_q_block_size : params.scale_block_size; + int q_blocks_per_wg_tile = + sparse_q_block_size > 0 ? cute::max(1, int(get<0>(TileShapeQK{})) / sparse_q_block_size) : 1; + int sg_rows_per_sparse_q_block = + sparse_q_block_size > 0 ? cute::max(1, sparse_q_block_size / q_sg_tile) : 1; + int subgroup_q_row_in_tile = get_sub_group_id() / sg_rows_per_sparse_q_block; + subgroup_q_row_in_tile = cute::min(subgroup_q_row_in_tile, q_blocks_per_wg_tile - 1); + + auto prefetch_sparse_k_block = [&](int logical_block) { + if (logical_block < 0 || logical_block >= total_blk) return; + for (int D = 0; D < size<4>(pKgK); D++) { + if constexpr (CachedKV) { + if (logical_block < kblocks_cache) { + int physical_block = logical_block; + if constexpr (PagedKV) { + physical_block = get_physical_k_tile(logical_block, l_coord, seq_len_kv_cache); + } + prefetch(prefetch_k_cache, pKgK_cache(_, _, _, physical_block, D)); + } else { + prefetch(prefetch_k, pKgK(_, _, _, logical_block - kblocks_cache, D)); + } + } else { + prefetch(prefetch_k, pKgK(_, _, _, logical_block - kblocks_cache, D)); + } + } + }; + + auto mainloop_body = [&](auto cached_k, int K, bool first_block, bool subgroup_selected, int sparse_prefetch_block, + auto& copy_k_cur, auto& copy_v_cur, auto& prefetch_v_cur, auto& tKgK_cur, + auto& tVgV_cur, auto& pVgV_cur) { + barrier_arrive(ScopeWorkgroup); + constexpr bool is_cache = decltype(cached_k)::value; + + int k_idx; + if constexpr (is_cache) { + k_idx = K; + if constexpr (PagedKV) { + k_idx = get_physical_k_tile(K, l_coord, seq_len_kv_cache); + } + } else { + k_idx = K - kblocks_cache; + } + + clear(tSrS); + CUTLASS_PRAGMA_UNROLL + for (int D = 0; D < size<4>(tKgK); D++) { + copy(copy_q, tQgQ(_, _, _, D), tQrQ); + copy(copy_k_cur, tKgK_cur(_, _, _, k_idx, D), tKrK); + reorder(tQrQ, tSrQ); + reorder(tKrK, tSrK); + cute::gemm(mma_qk, tSrQ, tSrK, tSrS); + } + + CUTLASS_PRAGMA_UNROLL + for (int VV = 0; VV < VTiles; VV++) { + prefetch(prefetch_v_cur, pVgV_cur(_, _, _, VV, k_idx)); + } + + if (subgroup_selected) { + if constexpr (!is_cache && CausalMask) { + if (K == total_blk - 1) { + Tensor cPgP = make_identity_tensor(make_shape(seq_len, seq_len)); + Tensor gP = local_tile(cPgP, take<0, 2>(TileShapeQK{}), make_coord(get<0>(blk_qv), K)); + auto cS_thread = thr_mma_qk.partition_C(gP); + CUTLASS_PRAGMA_UNROLL + for (int i = 0; i < tSrS.size(); ++i) { + int row_idx = get<0>(cS_thread(i)); + int col_idx = get<1>(cS_thread(i)); + if (col_idx - seq_len_kv_cache - full_tile_offset > row_idx - discard_seq_coord) { + tSrS(i) = ElementS(-INFINITY); + } + } + } + } else if constexpr (FullMask_) { + Tensor cPgP = make_identity_tensor(make_shape(seq_len, seq_len)); + Tensor gP = local_tile(cPgP, take<0, 2>(TileShapeQK{}), make_coord(get<0>(blk_qv), K)); + auto cS_thread = thr_mma_qk.partition_C(gP); + int row_idx_begin = get<0>(cS_thread(0)); + int row_idx_end = row_idx_begin + q_sg_tile; + int col_idx_begin = get<1>(cS_thread(0)); + int col_idx_end = col_idx_begin + get<1>(TileShapeQK{}); + if (row_idx_end <= seq_len && col_idx_end <= seq_len) { + CUTLASS_PRAGMA_UNROLL + for (int i = 0; i < tSrS.size(); ++i) { + int row_idx = get<0>(cS_thread(i)); + int col_idx = get<1>(cS_thread(i)); + tSrS(i) += ElementS(params.mask[col_idx + row_idx * seq_len]); + } + } else { + CUTLASS_PRAGMA_UNROLL + for (int i = 0; i < tSrS.size(); ++i) { + int row_idx = get<0>(cS_thread(i)); + int col_idx = get<1>(cS_thread(i)); + tSrS(i) += + (row_idx < seq_len && col_idx < seq_len) ? ElementS(params.mask[col_idx + row_idx * seq_len]) + : ElementS(-INFINITY); + } + } + } + + if constexpr (!is_cache) { + if (check_remainder_k && K == total_blk - 1) { + FragSCol k_rem_mask; + int k_val = get<0>(tKgK_cur(0, 0, 0, k_idx, 0)) + kblocks_cache * get<1>(TileShapeQK{}); + int k = k_val + get_sub_group().get_local_id()[0]; + CUTLASS_PRAGMA_UNROLL + for (int i = 0; i < k_rem_mask.size(); i++, k += intel::sg_size) { + k_rem_mask(i) = (k < seq_len) ? ElementS(sycl::nan(0u)) : ElementS(-INFINITY); + } + CUTLASS_PRAGMA_UNROLL + for (int i = 0; i < tSrS.size(); i++) { + tSrS(i) = sycl::fmin(tSrS(i), broadcast<1>(k_rem_mask, tSrS, i)); + } + } + } + + auto rescale = softmax(first_block, tSrS, tA_max, tA_sum); + reorder(tSrS, tArP); + + CUTLASS_PRAGMA_UNROLL + for (int VV = 0; VV < VTiles; VV++) { + copy(copy_v_cur, tVgV_cur(_, _, _, VV, k_idx), tVrV); + reorder(tVrV, tArV); + if (!first_block) { + CUTLASS_PRAGMA_UNROLL + for (int i = 0; i < tArA.size() / VTiles; i++) { + tArA(_, _, _, VV)(i) *= broadcast<0>(rescale, tArA, i); + } + } + cute::gemm(mma_pv, tArP, tArV, tArA(_, _, _, VV)); + } + } + + if (lut_rows_base == nullptr) { + int K_next = K + Stages; + for (int D = 0; D < size<4>(pKgK); D++) { + if constexpr (is_cache) { + bool is_cache_next = K_next < kblocks_cache; + int physical_K_next = K_next; + if constexpr (PagedKV) { + if (is_cache_next) { + physical_K_next = get_physical_k_tile(K_next, l_coord, seq_len_kv_cache); + } + } + if (is_cache_next) { + prefetch(prefetch_k_cache, pKgK_cache(_, _, _, physical_K_next, D)); + } else { + prefetch(prefetch_k, pKgK(_, _, _, K_next - kblocks_cache, D)); + } + } else { + prefetch(prefetch_k, pKgK(_, _, _, K_next - kblocks_cache, D)); + } + } + } else if (sparse_prefetch_block < total_blk) { + prefetch_sparse_k_block(sparse_prefetch_block); + } + + barrier_wait(ScopeWorkgroup); + }; + + if (lut_rows_base != nullptr && valid_blocks_base != nullptr) { + bool subgroup_started = false; + if (sparse_q_rows_in_tile == 1) { + int const* row_ptr = lut_rows_base; + int row_valid = valid_blocks_base[0]; + int row_pos = 0; + int row_cur_block = row_valid > 0 ? logical_block_from_delta_row(row_ptr, row_valid, 0) : total_blk; + int prefetch_pos = row_pos; + int prefetch_cur_block = row_cur_block; + + auto advance_single_sparse_block = [&](int& frontier_pos, int& frontier_cur_block) { + if (frontier_cur_block >= total_blk) return; + frontier_pos += 1; + if (frontier_pos < row_valid) { + frontier_cur_block += row_ptr[frontier_pos]; + } else { + frontier_cur_block = total_blk; + } + }; + + auto pop_single_sparse_block = [&](int& frontier_pos, int& frontier_cur_block) { + int block = frontier_cur_block; + advance_single_sparse_block(frontier_pos, frontier_cur_block); + return block; + }; + + for (int stage = 0; stage < Stages; ++stage) { + int sparse_prefetch_block = pop_single_sparse_block(prefetch_pos, prefetch_cur_block); + if (sparse_prefetch_block >= total_blk) break; + prefetch_sparse_k_block(sparse_prefetch_block); + } + + while (row_cur_block < total_blk) { + int next_block = row_cur_block; + bool subgroup_selected = (subgroup_q_row_in_tile == 0); + bool first_selected_block = !subgroup_started; + int sparse_prefetch_block = pop_single_sparse_block(prefetch_pos, prefetch_cur_block); + int K = next_block; + if constexpr (CachedKV) { + if (K < kblocks_cache) { + if (K >= blk_k0 && K < blk_k1) { + mainloop_body(std::bool_constant{}, K, first_selected_block, subgroup_selected, + sparse_prefetch_block, copy_k_cache, copy_v_cache, prefetch_v_cache, tKgK_cache, + tVgV_cache, pVgV_cache); + subgroup_started = true; + } + } else { + K += kblocks_cache; + if (K >= (blk_k0 > kblocks_cache ? blk_k0 : kblocks_cache) && K < blk_k1) { + mainloop_body(std::bool_constant{}, K, first_selected_block, subgroup_selected, + sparse_prefetch_block, copy_k, copy_v, prefetch_v, tKgK, tVgV, pVgV); + subgroup_started = true; + } + } + } else if (K >= blk_k0 && K < blk_k1) { + mainloop_body(std::bool_constant{}, K, first_selected_block, subgroup_selected, sparse_prefetch_block, + copy_k, copy_v, prefetch_v, tKgK, tVgV, pVgV); + subgroup_started = true; + } + advance_single_sparse_block(row_pos, row_cur_block); + } + } else { + static constexpr int kMaxSparseRowsPerTile = cute::max(1, int(get<0>(TileShapeQK{})) / 64); + int const* row_ptrs[kMaxSparseRowsPerTile]; + int active_rows[kMaxSparseRowsPerTile]; + int active_row_count = 0; + bool subgroup_started_rows[kMaxSparseRowsPerTile]; + int row_valid[kMaxSparseRowsPerTile]; + int row_pos[kMaxSparseRowsPerTile]; + int row_cur_block[kMaxSparseRowsPerTile]; + + for (int row = 0; row < kMaxSparseRowsPerTile; ++row) { + row_ptrs[row] = lut_rows_base + row * params.num_k_blocks; + subgroup_started_rows[row] = false; + if (row < sparse_q_rows_in_tile) { + row_valid[row] = valid_blocks_base[row]; + row_pos[row] = 0; + row_cur_block[row] = + row_valid[row] > 0 ? logical_block_from_delta_row(row_ptrs[row], row_valid[row], 0) : total_blk; + if (row_valid[row] > 0) { + active_rows[active_row_count++] = row; + } + } else { + row_valid[row] = 0; + row_pos[row] = 0; + row_cur_block[row] = total_blk; + } + } + + auto find_sparse_block = [&](int* frontier_pos, int* frontier_cur_block) { + int block = total_blk; + for (int active = 0; active < active_row_count; ++active) { + int row = active_rows[active]; + if (frontier_pos[row] < row_valid[row]) { + block = cute::min(block, frontier_cur_block[row]); + } + } + return block; + }; + + auto advance_sparse_block = [&](int block, int* frontier_pos, int* frontier_cur_block) { + if (block >= total_blk) return; + for (int active = 0; active < active_row_count; ++active) { + int row = active_rows[active]; + if (frontier_pos[row] < row_valid[row] && frontier_cur_block[row] == block) { + frontier_pos[row] += 1; + if (frontier_pos[row] < row_valid[row]) { + frontier_cur_block[row] += row_ptrs[row][frontier_pos[row]]; + } else { + frontier_cur_block[row] = total_blk; + } + } + } + }; + + auto pop_sparse_block = [&](int* frontier_pos, int* frontier_cur_block) { + int block = find_sparse_block(frontier_pos, frontier_cur_block); + advance_sparse_block(block, frontier_pos, frontier_cur_block); + return block; + }; + + int prefetch_pos[kMaxSparseRowsPerTile]; + int prefetch_cur_block[kMaxSparseRowsPerTile]; + for (int row = 0; row < kMaxSparseRowsPerTile; ++row) { + prefetch_pos[row] = row_pos[row]; + prefetch_cur_block[row] = row_cur_block[row]; + } + + for (int stage = 0; stage < Stages; ++stage) { + int sparse_prefetch_block = pop_sparse_block(prefetch_pos, prefetch_cur_block); + if (sparse_prefetch_block >= total_blk) break; + prefetch_sparse_k_block(sparse_prefetch_block); + } + + int next_block = find_sparse_block(row_pos, row_cur_block); + while (next_block < total_blk) { + int selected_row = subgroup_q_row_in_tile; + bool subgroup_selected = subgroup_q_row_in_tile < sparse_q_rows_in_tile && + row_pos[selected_row] < row_valid[selected_row] && + row_cur_block[selected_row] == next_block; + bool first_selected_block = subgroup_selected ? !subgroup_started_rows[selected_row] : false; + int sparse_prefetch_block = pop_sparse_block(prefetch_pos, prefetch_cur_block); + int K = next_block; + if constexpr (CachedKV) { + if (K < kblocks_cache) { + if (K >= blk_k0 && K < blk_k1) { + mainloop_body(std::bool_constant{}, K, first_selected_block, subgroup_selected, + sparse_prefetch_block, copy_k_cache, copy_v_cache, prefetch_v_cache, tKgK_cache, + tVgV_cache, pVgV_cache); + if (subgroup_selected) subgroup_started_rows[selected_row] = true; + } + } else { + K += kblocks_cache; + if (K >= (blk_k0 > kblocks_cache ? blk_k0 : kblocks_cache) && K < blk_k1) { + mainloop_body(std::bool_constant{}, K, first_selected_block, subgroup_selected, + sparse_prefetch_block, copy_k, copy_v, prefetch_v, tKgK, tVgV, pVgV); + if (subgroup_selected) subgroup_started_rows[selected_row] = true; + } + } + } else if (K >= blk_k0 && K < blk_k1) { + mainloop_body(std::bool_constant{}, K, first_selected_block, subgroup_selected, sparse_prefetch_block, + copy_k, copy_v, prefetch_v, tKgK, tVgV, pVgV); + if (subgroup_selected) subgroup_started_rows[selected_row] = true; + } + + advance_sparse_block(next_block, row_pos, row_cur_block); + next_block = find_sparse_block(row_pos, row_cur_block); + } + } + } else { + if constexpr (CachedKV) { + for (int K = blk_k0; K < kblocks_cache; K++) { + mainloop_body(std::bool_constant{}, K, K == blk_k0, true, total_blk, copy_k_cache, copy_v_cache, + prefetch_v_cache, tKgK_cache, tVgV_cache, pVgV_cache); + } + } + for (int K = (blk_k0 > kblocks_cache ? blk_k0 : kblocks_cache); K < blk_k1; K++) { + mainloop_body(std::bool_constant{}, K, + K == (blk_k0 > kblocks_cache ? blk_k0 : kblocks_cache), true, total_blk, copy_k, copy_v, + prefetch_v, tKgK, tVgV, pVgV); + } + } + } + + CUTLASS_DEVICE + FragSRow softmax(bool first_block, FragS& tS, FragSRow& tS_max, FragSRow& tS_sum) { + auto tS_bmax = reduce<1>(tS, sycl::maximum{}); + FragSRow rescale; + CUTLASS_PRAGMA_UNROLL + for (int i = 0; i < tS_max.size(); i++) { + ElementS new_max = sycl::max(tS_max(i), params.scale * tS_bmax(i)); + rescale(i) = sycl::native::exp2(tS_max(i) - new_max); + tS_max(i) = new_max; + } + + CUTLASS_PRAGMA_UNROLL + for (int i = 0; i < tS.size(); i++) { + tS(i) = sycl::native::exp2(params.scale * tS(i) - broadcast<0>(tS_max, tS, i)); + } + + if (!first_block) { + CUTLASS_PRAGMA_UNROLL + for (int i = 0; i < tS_sum.size(); i++) { + tS_sum(i) *= rescale(i); + } + } + + auto tS_bsum = reduce<1>(tS, sycl::plus{}); + CUTLASS_PRAGMA_UNROLL + for (int i = 0; i < tS_sum.size(); i++) { + tS_sum(i) += tS_bsum(i); + } + return rescale; + } +}; + +} // namespace cutlass::fmha::collective From 0850714f1873fbfa2be3a22288edd14a8df08c7c Mon Sep 17 00:00:00 2001 From: yiliu30 Date: Thu, 6 Aug 2026 09:55:22 +0800 Subject: [PATCH 04/42] chore: ignore XPU build artifact directories Add ark-xbuild*, ark-xbuild-builtin*, and xbuild_bf16* to the ark .gitignore. These are local CMake build outputs (hundreds of MB each) and must not be tracked. --- auto_round_extension/ark/.gitignore | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/auto_round_extension/ark/.gitignore b/auto_round_extension/ark/.gitignore index f0294d5764..17e27ad2a0 100644 --- a/auto_round_extension/ark/.gitignore +++ b/auto_round_extension/ark/.gitignore @@ -1,5 +1,8 @@ build xbuild +ark-xbuild* +ark-xbuild-builtin* +xbuild_bf16* *.csv *.so -*.pyc \ No newline at end of file +*.pyc From 95f2c0603415617a0d921a53030b0fe66936ce34 Mon Sep 17 00:00:00 2001 From: yiliu30 Date: Thu, 6 Aug 2026 09:55:31 +0800 Subject: [PATCH 05/42] feat: wire native BF16 sparse SDPA kernel and launchers - sdpa_sparse_bf16.cpp: route BF16 sparse prefill through the sparse SAGE launchers (launch_sparse_sage_prefill_kernel_{128,64}). - sycl_tla_common.hpp: declare the BF16 sparse sdpa entry points (d64, row_linear, qtile256_row64k). - ark.cpp: expose the low-level BF16 sparse binding via pybind. - __init__.py: export sage_sparse_bf16 and the BF16 e2e entry point. - CMakeLists.txt: add the BF16 sparse kernel sources to the build. --- .../ark/auto_round_kernel/CMakeLists.txt | 21 +++++++ .../ark/auto_round_kernel/__init__.py | 2 + .../ark/auto_round_kernel/ark.cpp | 60 +++++++++++++++++++ .../auto_round_kernel/sdpa_sparse_bf16.cpp | 16 ++--- .../wrapper/include/sycl_tla_common.hpp | 24 ++++++++ 5 files changed, 113 insertions(+), 10 deletions(-) diff --git a/auto_round_extension/ark/auto_round_kernel/CMakeLists.txt b/auto_round_extension/ark/auto_round_kernel/CMakeLists.txt index 6cdf56e12f..cf55ee0c23 100755 --- a/auto_round_extension/ark/auto_round_kernel/CMakeLists.txt +++ b/auto_round_extension/ark/auto_round_kernel/CMakeLists.txt @@ -171,6 +171,18 @@ if(ARK_XPU AND ARK_SYCL_TLA) target_compile_definitions(${PY_NAME} PRIVATE ARK_SYCL_TLA=1 CUTLASS_ENABLE_SYCL=1 SYCL_INTEL_TARGET=1) target_include_directories(${PY_NAME} PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/wrapper/include) target_include_directories(${PY_NAME} PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/generated/sdpa) + # sycl-tla may provision oneMKL through an ExternalProject. Wire its staged + # headers into this module directly so parallel builds do not compile before + # oneMKL has been downloaded and installed. + if(TARGET onemkl_project) + add_dependencies(${PY_NAME} onemkl_project) + target_include_directories(${PY_NAME} SYSTEM PRIVATE ${CMAKE_BINARY_DIR}/deps/oneMKL/include) + elseif(TARGET MKL::MKL) + target_link_libraries(${PY_NAME} PRIVATE MKL::MKL) + if(DEFINED ENV{MKLROOT} AND EXISTS "$ENV{MKLROOT}/include") + target_include_directories(${PY_NAME} SYSTEM PRIVATE "$ENV{MKLROOT}/include") + endif() + endif() # Use SYSTEM include directories for sycl-tla to suppress warnings/errors from third-party headers # (e.g., std::common_type specialization issues in traits.hpp) foreach(_inc_dir IN LISTS _sycl_tla_include_dirs) @@ -197,6 +209,15 @@ if(ARK_UT) if(ARK_XPU AND ARK_SYCL_TLA) target_compile_definitions(${TEST_NAME} PRIVATE ARK_SYCL_TLA=1 CUTLASS_ENABLE_SYCL=1 SYCL_INTEL_TARGET=1) target_include_directories(${TEST_NAME} PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/generated/sdpa) + if(TARGET onemkl_project) + add_dependencies(${TEST_NAME} onemkl_project) + target_include_directories(${TEST_NAME} SYSTEM PRIVATE ${CMAKE_BINARY_DIR}/deps/oneMKL/include) + elseif(TARGET MKL::MKL) + target_link_libraries(${TEST_NAME} PRIVATE MKL::MKL) + if(DEFINED ENV{MKLROOT} AND EXISTS "$ENV{MKLROOT}/include") + target_include_directories(${TEST_NAME} SYSTEM PRIVATE "$ENV{MKLROOT}/include") + endif() + endif() foreach(_inc_dir IN LISTS _sycl_tla_include_dirs) if(EXISTS "${_inc_dir}") target_include_directories(${TEST_NAME} SYSTEM PRIVATE "${_inc_dir}") diff --git a/auto_round_extension/ark/auto_round_kernel/__init__.py b/auto_round_extension/ark/auto_round_kernel/__init__.py index b66e180d82..93e5244e29 100644 --- a/auto_round_extension/ark/auto_round_kernel/__init__.py +++ b/auto_round_extension/ark/auto_round_kernel/__init__.py @@ -1292,9 +1292,11 @@ def sageattn( _slice_sequence_native_layout, _to_hnd, sage_sparse, + sage_sparse_bf16, sparge_block_map_to_mask, sparge_preprocess_topk, sparge_sage2_attn_meansim_topk_xpu, + sparge_sage2_attn_meansim_topk_xpu_bf16, ) diff --git a/auto_round_extension/ark/auto_round_kernel/ark.cpp b/auto_round_extension/ark/auto_round_kernel/ark.cpp index d9c9c7c03f..20cd9d6974 100755 --- a/auto_round_extension/ark/auto_round_kernel/ark.cpp +++ b/auto_round_extension/ark/auto_round_kernel/ark.cpp @@ -478,6 +478,65 @@ static void sage_sparse(torch_ptr stream, torch_ptr Q, torch_ptr K, torch_ptr V, throw std::invalid_argument("ark::sage_sparse: unsupported head_dim; supported values are 64 and 128"); } +static void sage_sparse_bf16(torch_ptr stream, torch_ptr Q, torch_ptr K, torch_ptr V, torch_ptr O, torch_ptr mask, + torch_ptr lut, torch_ptr valid_block_num, int num_q_blocks, int num_k_blocks, + int q_tile_override, int q_stride_s, int q_stride_d, int q_stride_h, int q_stride_b, + int k_stride_s, int k_stride_d, int k_stride_h, int k_stride_b, int v_stride_d, + int v_stride_s, int v_stride_h, int v_stride_b, int o_stride_s, int o_stride_d, + int o_stride_h, int o_stride_b, int batch, int num_heads_q, int num_heads_kv, + int seq_len_q, int seq_len_kv, int head_dim, float softmax_scale, bool is_causal) { + if (mask && is_causal) { + throw std::invalid_argument("ark::sage_sparse_bf16: mask and is_causal cannot both be set"); + } + if (!lut || !valid_block_num) { + throw std::invalid_argument("ark::sage_sparse_bf16: lut and valid_block_num must be provided"); + } + auto matches_block_size = [](int seq_len, int num_blocks, int block_size) { + return block_size > 0 && num_blocks == ((seq_len + block_size - 1) / block_size); + }; + const bool key_block_is_64 = matches_block_size(seq_len_kv, num_k_blocks, 64); + if (!key_block_is_64) { + throw std::invalid_argument("ark::sage_sparse_bf16: only key block size 64 is supported"); + } + if (head_dim == 64) { + if (!matches_block_size(seq_len_q, num_q_blocks, 64)) { + throw std::invalid_argument("ark::sage_sparse_bf16: head_dim=64 requires query block size 64"); + } + ark::sdpa_impl_bf16_sparse_d64( + (sycl::queue*)stream, (void*)Q, (void*)K, (void*)V, (void*)O, (void*)mask, (void*)lut, + (void*)valid_block_num, num_q_blocks, num_k_blocks, q_tile_override, q_stride_s, q_stride_d, q_stride_h, + q_stride_b, k_stride_s, k_stride_d, k_stride_h, k_stride_b, v_stride_d, v_stride_s, v_stride_h, + v_stride_b, o_stride_s, o_stride_d, o_stride_h, o_stride_b, batch, num_heads_q, num_heads_kv, seq_len_q, + seq_len_kv, head_dim, softmax_scale, is_causal); + return; + } + if (head_dim == 128) { + if (matches_block_size(seq_len_q, num_q_blocks, 256)) { + if (q_tile_override != 256) { + throw std::invalid_argument("ark::sage_sparse_bf16: head_dim=128 query block size 256 requires q_tile_override=256"); + } + ark::sdpa_impl_bf16_sparse_qtile256_row64k( + (sycl::queue*)stream, (void*)Q, (void*)K, (void*)V, (void*)O, (void*)mask, (void*)lut, + (void*)valid_block_num, num_q_blocks, num_k_blocks, q_tile_override, q_stride_s, q_stride_d, q_stride_h, + q_stride_b, k_stride_s, k_stride_d, k_stride_h, k_stride_b, v_stride_d, v_stride_s, v_stride_h, + v_stride_b, o_stride_s, o_stride_d, o_stride_h, o_stride_b, batch, num_heads_q, num_heads_kv, seq_len_q, + seq_len_kv, head_dim, softmax_scale, is_causal); + return; + } + if (matches_block_size(seq_len_q, num_q_blocks, 64)) { + ark::sdpa_impl_bf16_sparse_row_linear( + (sycl::queue*)stream, (void*)Q, (void*)K, (void*)V, (void*)O, (void*)mask, (void*)lut, + (void*)valid_block_num, num_q_blocks, num_k_blocks, q_tile_override, q_stride_s, q_stride_d, q_stride_h, + q_stride_b, k_stride_s, k_stride_d, k_stride_h, k_stride_b, v_stride_d, v_stride_s, v_stride_h, + v_stride_b, o_stride_s, o_stride_d, o_stride_h, o_stride_b, batch, num_heads_q, num_heads_kv, seq_len_q, + seq_len_kv, head_dim, softmax_scale, is_causal); + return; + } + throw std::invalid_argument("ark::sage_sparse_bf16: head_dim=128 supports query block sizes 64 and 256 only"); + } + throw std::invalid_argument("ark::sage_sparse_bf16: unsupported head_dim; supported values are 64 and 128"); +} + static void moe_gemm_wrapper(torch_ptr stream, torch_ptr activations, torch_ptr weights, torch_ptr scales, torch_ptr outputs, int dtype, int N, int K, torch_ptr num_tokens_per_expert, int num_experts) { @@ -784,6 +843,7 @@ PYBIND11_MODULE(PY_NAME, m) { pybind11::arg("head_dim"), pybind11::arg("softmax_scale"), pybind11::arg("is_causal"), pybind11::arg("tensor_layout"), pybind11::arg("lse") = 0); m.def("sage_sparse", &ark::sage_sparse); + m.def("sage_sparse_bf16", &ark::sage_sparse_bf16); // Low-level SAGE PVi8 API: input Q/K/V are pre-quantized int8 with qscale/kscale/vscale. m.def("sage_pvi8", &ark::sage_pvi8, pybind11::arg("stream"), pybind11::arg("Q"), pybind11::arg("K"), pybind11::arg("V"), pybind11::arg("O"), pybind11::arg("mask"), diff --git a/auto_round_extension/ark/auto_round_kernel/sdpa_sparse_bf16.cpp b/auto_round_extension/ark/auto_round_kernel/sdpa_sparse_bf16.cpp index 1384aba2ce..5ea82a35d3 100644 --- a/auto_round_extension/ark/auto_round_kernel/sdpa_sparse_bf16.cpp +++ b/auto_round_extension/ark/auto_round_kernel/sdpa_sparse_bf16.cpp @@ -30,22 +30,19 @@ namespace { using KernelLauncher = int (*)(detail::Options const& options); int launch_prefill_kernel_bf16_128_sparse_sdpa(detail::Options const& options) { - return detail::launch_sparse_sdpa_prefill_kernel_128(options); + return detail::launch_sparse_sage_prefill_kernel_128( + options); } int launch_prefill_kernel_bf16_128_sparse_sdpa_qtile64(detail::Options const& options) { - return detail::launch_sparse_sdpa_prefill_kernel_128_qtile64< + return detail::launch_sparse_sage_prefill_kernel_128_qtile64< cute::bfloat16_t, cute::bfloat16_t, cute::bfloat16_t>( options); } int launch_prefill_kernel_bf16_64_sparse_sdpa(detail::Options const& options) { - return detail::launch_sparse_sdpa_prefill_kernel_64(options); -} - -int launch_prefill_kernel_bf16_64_sparse_sdpa_qtile64(detail::Options const& options) { - return detail::launch_sparse_sdpa_prefill_kernel_64_qtile64< - cute::bfloat16_t, cute::bfloat16_t, cute::bfloat16_t>(options); + return detail::launch_sparse_sage_prefill_kernel_64( + options); } KernelLauncher select_sparse_bf16_prefill_launcher(int head_dim, int q_tile_override) { @@ -55,8 +52,7 @@ KernelLauncher select_sparse_bf16_prefill_launcher(int head_dim, int q_tile_over if (q_tile_override != 0 && q_tile_override != 256) return nullptr; return launch_prefill_kernel_bf16_128_sparse_sdpa; case 64: - if (q_tile_override == 64) return launch_prefill_kernel_bf16_64_sparse_sdpa_qtile64; - if (q_tile_override != 0 && q_tile_override != 128) return nullptr; + if (q_tile_override != 0 && q_tile_override != 64 && q_tile_override != 128) return nullptr; return launch_prefill_kernel_bf16_64_sparse_sdpa; default: return nullptr; diff --git a/auto_round_extension/ark/auto_round_kernel/wrapper/include/sycl_tla_common.hpp b/auto_round_extension/ark/auto_round_kernel/wrapper/include/sycl_tla_common.hpp index ee417c114f..c6d6fa9e00 100644 --- a/auto_round_extension/ark/auto_round_kernel/wrapper/include/sycl_tla_common.hpp +++ b/auto_round_extension/ark/auto_round_kernel/wrapper/include/sycl_tla_common.hpp @@ -255,6 +255,30 @@ void sdpa_impl_qks8_sparse_qtile256_row64k_pvhalf( int o_stride_s, int o_stride_d, int o_stride_h, int o_stride_b, int batch, int num_heads_q, int num_heads_kv, int seq_len_q, int seq_len_kv, int head_dim, float softmax_scale, bool is_causal, BTLA_DTYPE pv_dtype = BTLA_DTYPE::F16); + +void sdpa_impl_bf16_sparse_d64( + sycl::queue* q, void* Q_ptr, void* K_ptr, void* V_ptr, void* O_ptr, void* mask, void* lut, void* valid_block_num, + int num_q_blocks, int num_k_blocks, int q_tile_override, int q_stride_s, int q_stride_d, int q_stride_h, + int q_stride_b, int k_stride_s, int k_stride_d, int k_stride_h, int k_stride_b, int v_stride_d, int v_stride_s, + int v_stride_h, int v_stride_b, int o_stride_s, int o_stride_d, int o_stride_h, int o_stride_b, int batch, + int num_heads_q, int num_heads_kv, int seq_len_q, int seq_len_kv, int head_dim, float softmax_scale, + bool is_causal); + +void sdpa_impl_bf16_sparse_row_linear( + sycl::queue* q, void* Q_ptr, void* K_ptr, void* V_ptr, void* O_ptr, void* mask, void* lut, void* valid_block_num, + int num_q_blocks, int num_k_blocks, int q_tile_override, int q_stride_s, int q_stride_d, int q_stride_h, + int q_stride_b, int k_stride_s, int k_stride_d, int k_stride_h, int k_stride_b, int v_stride_d, int v_stride_s, + int v_stride_h, int v_stride_b, int o_stride_s, int o_stride_d, int o_stride_h, int o_stride_b, int batch, + int num_heads_q, int num_heads_kv, int seq_len_q, int seq_len_kv, int head_dim, float softmax_scale, + bool is_causal); + +void sdpa_impl_bf16_sparse_qtile256_row64k( + sycl::queue* q, void* Q_ptr, void* K_ptr, void* V_ptr, void* O_ptr, void* mask, void* lut, void* valid_block_num, + int num_q_blocks, int num_k_blocks, int q_tile_override, int q_stride_s, int q_stride_d, int q_stride_h, + int q_stride_b, int k_stride_s, int k_stride_d, int k_stride_h, int k_stride_b, int v_stride_d, int v_stride_s, + int v_stride_h, int v_stride_b, int o_stride_s, int o_stride_d, int o_stride_h, int o_stride_b, int batch, + int num_heads_q, int num_heads_kv, int seq_len_q, int seq_len_kv, int head_dim, float softmax_scale, + bool is_causal); #endif // ARK_SYCL_TLA } // namespace ark From f96ec416556de5d52a316351d740dc39d7d796aa Mon Sep 17 00:00:00 2001 From: yiliu30 Date: Thu, 6 Aug 2026 09:55:51 +0800 Subject: [PATCH 06/42] fix: BF16 sparse native kernel element-aware MMA and softmax scale Two device-level bugs made the native BF16 sparse kernel unusable: - SparseSageConfig hardcoded the Q*K MMA to an int8 DPAS (XE_DPAS_TT<..., int32_t, int8_t>) regardless of ElementQ. Feeding bf16 fragments into the int8 DPAS hung the device; INT8 worked only because its fragments genuinely are int8. The MMA is now ElementQ-aware: int8 keeps the int32/int8 DPAS, while bf16/fp16 use a float/ DPAS. - The mainloop applied the softmax scale only inside the scale_block_size != 0 branches. The native BF16 path uses scale_block_size 0 (no quantization), so the 1/sqrt(d) scale was never applied and the softmax ran on unscaled scores. Added the else branch that multiplies by params.scale. --- .../include/stla/xe_sparse_sagev1_fwd_mainloop.hpp | 7 +++++++ .../wrapper/include/sycl_tla_sdpa_sparse.hpp | 14 ++++++++++++-- 2 files changed, 19 insertions(+), 2 deletions(-) diff --git a/auto_round_extension/ark/auto_round_kernel/wrapper/include/stla/xe_sparse_sagev1_fwd_mainloop.hpp b/auto_round_extension/ark/auto_round_kernel/wrapper/include/stla/xe_sparse_sagev1_fwd_mainloop.hpp index 0124fd21de..c88496b7f8 100644 --- a/auto_round_extension/ark/auto_round_kernel/wrapper/include/stla/xe_sparse_sagev1_fwd_mainloop.hpp +++ b/auto_round_extension/ark/auto_round_kernel/wrapper/include/stla/xe_sparse_sagev1_fwd_mainloop.hpp @@ -521,6 +521,13 @@ struct SPARSESAGEV1FwdMainloop, CausalMask_, FullMask_, CUTLASS_PRAGMA_UNROLL for (int i = 0; i < tPrS.size(); i++) tPrS(i) *= _scale; } + } else { + // Native FP16/BF16 Q/K path (scale_block_size == 0): there is no + // qscale/kscale dequantization, so apply the raw softmax scale here. + // INT8 gets it via dq_scale above; without this branch the softmax + // runs on unscaled scores and the output is numerically wrong. + CUTLASS_PRAGMA_UNROLL + for (int i = 0; i < tPrS.size(); i++) tPrS(i) *= params.scale; } } else { float _scale = dq_scale * scaleK[scalek_idx]; diff --git a/auto_round_extension/ark/auto_round_kernel/wrapper/include/sycl_tla_sdpa_sparse.hpp b/auto_round_extension/ark/auto_round_kernel/wrapper/include/sycl_tla_sdpa_sparse.hpp index a8b4d2d10c..3c935e56d1 100644 --- a/auto_round_extension/ark/auto_round_kernel/wrapper/include/sycl_tla_sdpa_sparse.hpp +++ b/auto_round_extension/ark/auto_round_kernel/wrapper/include/sycl_tla_sdpa_sparse.hpp @@ -937,8 +937,18 @@ template struct SparseSageConfig { static constexpr int SGTileQ = get<0>(shape_div(TileShapeQK{}, shape(SubgroupLayoutQK{})))(); - using MMAOperation = - cute::conditional_t, XE_DPAS_TT, MMAOperation_>; + // The Q*K MMA must match ElementQ. The sparse mainloop dequantizes INT8 Q/K with + // qscale/kscale, so INT8 keeps an int32/int8 DPAS. Native BF16/FP16 Q/K must use a + // float/ DPAS; reusing the int8 DPAS on bf16 fragments hangs the device. + using MMAOperation = cute::conditional_t< + is_void_v, + typename cute::conditional_t< + cute::is_same_v || cute::is_same_v, + XE_DPAS_TT, + cute::conditional_t, + XE_DPAS_TT, + XE_DPAS_TT>>, + MMAOperation_>; using MMAOperationPV = cute::conditional_t, XE_DPAS_TT, MMAOperation_>; using SubgroupLayoutPV = From f9e8612232b092271d703c1c72ea4a2f37908bb6 Mon Sep 17 00:00:00 2001 From: yiliu30 Date: Thu, 6 Aug 2026 09:56:08 +0800 Subject: [PATCH 07/42] fix: sparse BF16 LUT crash, lossless round-trip, and preprocess speed - _block_map_lut_torch: replace the boolean-mask assignment (filled_matrix[~block_map] = 10_000_000) with torch.where. On the XPU backend that assignment silently misses entries, leaving 0s that become -1 LUT entries and drive _lut_to_block_map's scatter_ out of bounds (ScatterGatherKernels assert). Replace the same unsafe pattern (final_tile_map[~mask] = True) with |= in sparse_attention and the triton preprocess. - _lut_to_block_map: scatter True at the recovered block positions instead of scattering the valid mask. Masked-out tail indices all point at slot 0, so their trailing False writes were silently dropping a selected block at position 0; the round-trip is now lossless (empty rows cleared explicitly). - sage_sparse_bf16: route to the native BF16 sparse kernel when available, keeping the dense-mask fallback for unsupported configs. The fallback materializes a [B,Hq,Sq,Skv] mask and OOMs at long sequences. - _fill_block_map_torch: vectorize the O(k_blocks) python loop (thousands of sequential kernel launches at long sequences) into a gather/cumsum/scatter. Preprocess at seq 75600 drops from ~1170 ms to ~450 ms. --- .../sparge_preprocess_triton.py | 5 +- .../ark/auto_round_kernel/sparse_attention.py | 349 +++++++++++++++++- 2 files changed, 333 insertions(+), 21 deletions(-) diff --git a/auto_round_extension/ark/auto_round_kernel/sparge_preprocess_triton.py b/auto_round_extension/ark/auto_round_kernel/sparge_preprocess_triton.py index 5306cf4e84..d18eab1d71 100644 --- a/auto_round_extension/ark/auto_round_kernel/sparge_preprocess_triton.py +++ b/auto_round_extension/ark/auto_round_kernel/sparge_preprocess_triton.py @@ -387,8 +387,9 @@ def _run_triton_xpu_preprocess(ctx: Any) -> dict[str, Any]: .contiguous() ) final_tile_map = torch.zeros_like(pooled_prob, dtype=torch.bool) - final_tile_map[~sim_k_expand] = True - final_tile_map[~sim_q_expand] = True + # Boolean-mask assignment (`t[mask] = val`) is unreliable on XPU; use |= instead. + final_tile_map |= ~sim_k_expand + final_tile_map |= ~sim_q_expand final_tile_map = _fill_block_map_triton(final_tile_map, num_to_select, sorted_prob.indices) if causal_mask is not None: final_tile_map &= causal_mask.view(1, 1, ctx.num_q_tiles, num_k_route_blocks) diff --git a/auto_round_extension/ark/auto_round_kernel/sparse_attention.py b/auto_round_extension/ark/auto_round_kernel/sparse_attention.py index 0d5932747c..71a537931a 100644 --- a/auto_round_extension/ark/auto_round_kernel/sparse_attention.py +++ b/auto_round_extension/ark/auto_round_kernel/sparse_attention.py @@ -199,6 +199,164 @@ def sage_sparse( return O +def sage_sparse_bf16( + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + lut: torch.Tensor, + valid_block_num: torch.Tensor, + attn_mask: torch.Tensor | None = None, + dropout_p: float = 0.0, + is_causal: bool = False, + scale: float | None = None, + enable_gqa: bool = False, + q_tile_override: int = 0, + sparse_q_block_tokens: int | None = None, + sparse_k_block_tokens: int | None = None, + tensor_layout: str = "HND", +) -> torch.Tensor: + """Low-level sparse BF16 attention routed by sparse LUT metadata.""" + del dropout_p, enable_gqa + if query.device.type != "xpu": + raise NotImplementedError("sage_sparse_bf16 is only supported on XPU") + if query.dtype != torch.bfloat16 or key.dtype != torch.bfloat16: + raise ValueError(f"Q/K must be bfloat16, got Q={query.dtype}, K={key.dtype}") + if value.dtype != torch.bfloat16: + raise ValueError(f"V must be bfloat16, got {value.dtype}") + if lut.dtype != torch.int32 or valid_block_num.dtype != torch.int32: + raise ValueError("lut and valid_block_num must be int32 tensors") + if lut.device != query.device or valid_block_num.device != query.device: + raise ValueError("lut and valid_block_num must be on the same XPU device as Q/K/V") + + B, Hq, Sq, D = _validate_attention_tensor(query, "Q", tensor_layout) + Bk, Hkv, Skv, Dk = _validate_attention_tensor(key, "K", tensor_layout) + Bv, Hkv2, Skv2, Dv = _validate_attention_tensor(value, "V", tensor_layout) + + if Bk != B or Bv != B: + raise ValueError("Batch size mismatch between Q/K/V") + if Hkv2 != Hkv or Skv2 != Skv or Dv != Dk: + raise ValueError("K/V shape mismatch") + if Dk != D: + raise ValueError("Head dim mismatch between Q and K/V") + _validate_gqa_head_config(Hq, Hkv, op_name="sage_sparse_bf16") + if D not in (64, 128): + raise ValueError(f"Unsupported head_dim={D}; supported: 64, 128") + effective_sparse_q_block_tokens = 64 if sparse_q_block_tokens is None else int(sparse_q_block_tokens) + effective_sparse_k_block_tokens = 64 if sparse_k_block_tokens is None else int(sparse_k_block_tokens) + if effective_sparse_q_block_tokens <= 0 or effective_sparse_k_block_tokens <= 0: + raise ValueError( + "sparse_q_block_tokens and sparse_k_block_tokens must be positive when provided; " + f"got {effective_sparse_q_block_tokens} and {effective_sparse_k_block_tokens}" + ) + + q_sparse_blocks = (Sq + effective_sparse_q_block_tokens - 1) // effective_sparse_q_block_tokens + kv_sparse_blocks = (Skv + effective_sparse_k_block_tokens - 1) // effective_sparse_k_block_tokens + if tuple(lut.shape) != (B, Hq, q_sparse_blocks, kv_sparse_blocks): + raise ValueError(f"lut must have shape {(B, Hq, q_sparse_blocks, kv_sparse_blocks)}, got {tuple(lut.shape)}") + if tuple(valid_block_num.shape) != (B, Hq, q_sparse_blocks): + raise ValueError( + f"valid_block_num must have shape {(B, Hq, q_sparse_blocks)}, got {tuple(valid_block_num.shape)}" + ) + if torch.any(valid_block_num < 0).item(): + raise ValueError("valid_block_num entries must be non-negative") + if torch.any(valid_block_num > kv_sparse_blocks).item(): + raise ValueError(f"valid_block_num entries must be <= {kv_sparse_blocks}") + + if D == 64 and q_tile_override not in (0, 64, 128): + raise ValueError( + f"q_tile_override must be one of {{0, 64, 128}} for sage_sparse_bf16 with head_dim=64, got {q_tile_override}" + ) + + # Preferred path: the native XPU sparse kernel. The dense fallback below is + # numerically correct but materializes a full [B, Hq, Sq, Skv] mask, which OOMs + # at long sequence lengths. Unsupported configs fall through to the fallback. + if query.device.type == "xpu": + try: + lib = get_lib(query) + if hasattr(lib, "sage_sparse_bf16"): + effective_q_tile_override = _resolve_sparse_prefill_q_tile_override( + head_dim=D, + quant_block_size=64, + q_tile_override=q_tile_override, + sparse_q_block_tokens=effective_sparse_q_block_tokens, + sparse_k_block_tokens=effective_sparse_k_block_tokens, + tensor_layout=tensor_layout, + ) + stream = get_stream(query) + O = _empty_attention_output( + B, Hq, Sq, D, dtype=value.dtype, device=query.device, tensor_layout=tensor_layout + ) + lib.sage_sparse_bf16( + stream, + query.data_ptr(), + key.data_ptr(), + value.data_ptr(), + O.data_ptr(), + attn_mask.data_ptr() if attn_mask is not None else 0, + lut.data_ptr(), + valid_block_num.data_ptr(), + q_sparse_blocks, + kv_sparse_blocks, + effective_q_tile_override, + *_attention_strides_qko(query, tensor_layout), + *_attention_strides_qko(key, tensor_layout), + *_attention_strides_v(value, tensor_layout), + *_attention_strides_qko(O, tensor_layout), + B, + Hq, + Hkv, + Sq, + Skv, + D, + float(scale) if scale is not None else 1.0 / (D**0.5), + bool(is_causal), + ) + return O + except (NotImplementedError, RuntimeError, ValueError): + # Unsupported native sparse config; fall through to the dense fallback. + pass + + block_map = _lut_to_block_map(lut, valid_block_num) + sparse_mask = sparge_block_map_to_mask( + block_map, + quant_block_size=64, + q_block_tokens=effective_sparse_q_block_tokens, + k_block_tokens=effective_sparse_k_block_tokens, + seq_len_q=Sq, + seq_len_kv=Skv, + is_causal=is_causal, + ) + normalized_mask = _normalize_sparse_mask(attn_mask, B, Sq, Skv, query.device) + if normalized_mask is not None: + sparse_mask = torch.minimum(sparse_mask, normalized_mask.expand(-1, Hq, -1, -1)) + + query_hnd = _to_hnd(query, tensor_layout) + key_hnd = _to_hnd(key, tensor_layout) + value_hnd = _to_hnd(value, tensor_layout) + kv_head_index = _kv_head_index_for_q_heads(Hq, Hkv, query.device) + outputs: list[torch.Tensor] = [] + effective_scale = float(scale) if scale is not None else 1.0 / (D**0.5) + for q_head in range(Hq): + kv_head = int(kv_head_index[q_head].item()) + query_head = torch.empty((B, 1, Sq, D), dtype=query_hnd.dtype, device=query.device) + key_head = torch.empty((B, 1, Skv, D), dtype=key_hnd.dtype, device=key.device) + value_head = torch.empty((B, 1, Skv, D), dtype=value_hnd.dtype, device=value.device) + query_head.copy_(query_hnd[:, q_head : q_head + 1]) + key_head.copy_(key_hnd[:, kv_head : kv_head + 1]) + value_head.copy_(value_hnd[:, kv_head : kv_head + 1]) + head_out = _dense_sagev1( + query_head, + key_head, + value_head, + attn_mask=sparse_mask[:, q_head : q_head + 1].contiguous(), + is_causal=False, + scale=effective_scale, + tensor_layout="HND", + ) + outputs.append(head_out) + return _from_hnd(torch.cat(outputs, dim=1), tensor_layout) + + def sage_sparse_row_linear( query: torch.Tensor, key: torch.Tensor, @@ -616,19 +774,25 @@ def _build_block_causal_mask( def _fill_block_map_torch( final_map: torch.Tensor, num_to_select: torch.Tensor, sorted_indices: torch.Tensor ) -> torch.Tensor: + """Fill ``final_map`` with the top ``num_to_select`` NEW blocks in sorted order. + + The naive implementation looped over every K block (``for rank in range(k_blocks)``), + launching a handful of elementwise kernels per iteration. At long sequences that is + thousands of sequential kernel launches and dominates the torch preprocess. This + version is fully vectorized: for a block at sorted ``rank r`` it is the + ``(r - #already_filled_before_r)``-th *new* block, and it is selected iff that index + is below the per-row quota. + """ k_blocks = final_map.shape[-1] - filled = final_map.clone() - column_ids = torch.arange(k_blocks, device=final_map.device).view(1, 1, 1, k_blocks) target_new = torch.maximum(num_to_select, torch.ones_like(num_to_select)) - added = torch.zeros_like(num_to_select) - for rank in range(k_blocks): - idx_match = column_ids == sorted_indices[..., rank : rank + 1] - is_new = idx_match & ~filled - should_add = (added < target_new).unsqueeze(-1) - newly_selected = should_add & is_new - filled |= newly_selected - added = added + newly_selected.any(dim=-1).to(added.dtype) - return filled + already = torch.gather(final_map, -1, sorted_indices) # is the block at this rank already filled? + already_cum = torch.cumsum(already.to(torch.int64), dim=-1) - already.to(torch.int64) + rank = torch.arange(k_blocks, device=final_map.device).view(1, 1, 1, k_blocks) + new_idx = rank - already_cum # 0-based index among new blocks at this rank + add_new = (new_idx < target_new.unsqueeze(-1)) & ~already + chosen = torch.zeros_like(final_map) + chosen.scatter_(-1, sorted_indices, add_new) + return final_map | chosen def _block_map_lut_torch(block_map: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: @@ -637,8 +801,13 @@ def _block_map_lut_torch(block_map: torch.Tensor) -> tuple[torch.Tensor, torch.T one_matrix = torch.ones(block_map.shape, dtype=torch.int32, device=block_map.device) cum_matrix = torch.cumsum(one_matrix, dim=-1) masked_cum_matrix = cum_matrix * block_map.to(torch.int32) - filled_matrix = masked_cum_matrix.clone() - filled_matrix[~block_map] = 10_000_000 + # NOTE: do not use `filled_matrix[~block_map] = 10_000_000` here — boolean-mask + # in-place assignment silently misses entries on the XPU backend (torch-xpu-ops), + # leaving 0s in the mask that become -1 in the LUT after sort and drive the + # ScatterGatherKernels out-of-bounds assert downstream. Use torch.where instead. + filled_matrix = torch.where( + block_map, masked_cum_matrix, torch.full_like(masked_cum_matrix, 10_000_000) + ) lut = torch.sort(filled_matrix, dim=-1)[0] - 1 lut[..., 1:] = lut[..., 1:] - lut[..., :-1] invalid_mask = torch.arange(num_k_blocks, device=block_map.device).view( @@ -648,6 +817,27 @@ def _block_map_lut_torch(block_map: torch.Tensor) -> tuple[torch.Tensor, torch.T return lut.to(torch.int32).contiguous(), valid_block_num.to(torch.int32).contiguous() +def _lut_to_block_map(lut: torch.Tensor, valid_block_num: torch.Tensor) -> torch.Tensor: + if lut.dtype != torch.int32 or valid_block_num.dtype != torch.int32: + raise ValueError("lut and valid_block_num must be int32 tensors") + if lut.shape[:-1] != valid_block_num.shape: + raise ValueError( + f"lut and valid_block_num shape mismatch: expected lut.shape[:-1] == valid_block_num.shape, " + f"got {tuple(lut.shape)} and {tuple(valid_block_num.shape)}" + ) + # Run-length encoded LUT: cumsum recovers the 0-indexed selected block positions. + logical_blocks = torch.cumsum(lut.to(torch.int64), dim=-1) + # Scatter True at every recovered position. The invalid tail of `logical_blocks` + # plateaus at the last selected position, so those duplicate indices do not add + # spurious blocks. Do NOT scatter the tail with False (e.g. `scatter_(-1, idx, mask)`): + # masked-out positions all index slot 0, and their trailing False writes clobber a + # selected block at position 0, silently dropping it. Empty rows are cleared below. + block_map = torch.zeros_like(lut, dtype=torch.bool) + block_map.scatter_(-1, logical_blocks, True) + block_map &= valid_block_num.unsqueeze(-1) > 0 + return block_map.contiguous() + + def _prefix_keep_cross_attn_enabled() -> bool: return os.getenv("SPARGE_KEEP_PREFIX_ON_CROSS_ATTN", "0").strip().lower() in {"1", "true", "yes"} @@ -1088,8 +1278,8 @@ def _sparge_preprocess_topk_torch_impl(ctx: _SpargePreprocessContext) -> dict[st .contiguous() ) tail_map = torch.zeros_like(tail_prob, dtype=torch.bool) - tail_map[~tail_sim_k_expand] = True - tail_map[~tail_sim_q_expand] = True + tail_map |= ~tail_sim_k_expand + tail_map |= ~tail_sim_q_expand tail_map = _fill_block_map_torch(tail_map, num_to_select_tail, tail_sorted_prob.indices) if ctx.cdfthreshd is not None: prefix_mass = pooled_prob[..., :prefix_route_blocks].sum(dim=-1, keepdim=True) @@ -1111,8 +1301,9 @@ def _sparge_preprocess_topk_torch_impl(ctx: _SpargePreprocessContext) -> dict[st .contiguous() ) final_tile_map = torch.zeros_like(pooled_prob, dtype=torch.bool) - final_tile_map[~sim_k_expand] = True - final_tile_map[~sim_q_expand] = True + # Boolean-mask assignment (`t[mask] = val`) is unreliable on XPU; use |= instead. + final_tile_map |= ~sim_k_expand + final_tile_map |= ~sim_q_expand final_tile_map = _fill_block_map_torch(final_tile_map, num_to_select, sorted_prob.indices) if ctx.cdfthreshd is not None: final_tile_map |= _select_blocks_for_cdf( @@ -1383,17 +1574,31 @@ def sparge_sage2_attn_meansim_topk_xpu( effective_query_tile_tokens = query_tile_tokens effective_q_tile_override = q_tile_override + if D == 64 and effective_query_tile_tokens is None and q_tile_override == 0: + effective_query_tile_tokens = 64 if effective_query_tile_tokens is None and q_tile_override in (64, 128, 256): effective_query_tile_tokens = q_tile_override elif effective_query_tile_tokens is not None: if q_tile_override == 0: - effective_q_tile_override = int(effective_query_tile_tokens) + effective_q_tile_override = 0 if D == 64 and int(effective_query_tile_tokens) == 64 else int( + effective_query_tile_tokens + ) elif q_tile_override != int(effective_query_tile_tokens): raise ValueError( "query_tile_tokens and q_tile_override must match when both are set; " f"got query_tile_tokens={effective_query_tile_tokens}, q_tile_override={q_tile_override}" ) - _validate_sparse_q_tile_override_for_head_dim(D, effective_q_tile_override) + if D == 64: + supported_q_tiles = (0, 64, 128) + elif D == 128: + supported_q_tiles = (0, 64, 256) + else: + raise ValueError(f"Unsupported head_dim={D}; supported: 64, 128") + if effective_q_tile_override not in supported_q_tiles: + raise ValueError( + f"q_tile_override={effective_q_tile_override} is not supported for BF16 sparse head_dim={D}; " + f"supported values: {', '.join(str(v) for v in supported_q_tiles)}" + ) normalized_mask = _normalize_sparse_mask(attn_mask, B, Sq, Skv, query.device) metadata = sparge_preprocess_topk( @@ -1438,3 +1643,109 @@ def sparge_sage2_attn_meansim_topk_xpu( if return_sparsity: return out, sparsity_ratio return out + + +def sparge_sage2_attn_meansim_topk_xpu_bf16( + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attn_mask: torch.Tensor | None = None, + dropout_p: float = 0.0, + is_causal: bool = False, + scale: float | None = None, + smooth_k: bool = True, + simthreshd1: float | torch.Tensor = -0.1, + cdfthreshd: float | torch.Tensor | None = None, + topk: float | torch.Tensor = 0.5, + pvthreshd: float | torch.Tensor = 50, + attention_sink: bool = False, + tensor_layout: str = "HND", + output_dtype: torch.dtype | None = None, + return_sparsity: bool = False, + return_metadata: bool = False, + k_quant_granularity: int = 64, + query_tile_tokens: int | None = None, + q_tile_override: int = 0, + sparse_q_block_tokens: int | None = None, + sparse_k_block_tokens: int | None = None, +) -> torch.Tensor | tuple[Any, ...]: + if query.device.type != "xpu": + raise NotImplementedError("sparge_sage2_attn_meansim_topk_xpu_bf16 is only supported on XPU") + if dropout_p != 0.0: + raise NotImplementedError("dropout_p must be 0.0 for sparge_sage2_attn_meansim_topk_xpu_bf16") + if attn_mask is not None and is_causal: + raise ValueError("attn_mask and is_causal cannot both be set") + if query.dtype != torch.bfloat16 or key.dtype != torch.bfloat16 or value.dtype != torch.bfloat16: + raise ValueError( + "sparge_sage2_attn_meansim_topk_xpu_bf16 requires bfloat16 Q/K/V, " + f"got Q={query.dtype}, K={key.dtype}, V={value.dtype}" + ) + if output_dtype is not None and output_dtype != torch.bfloat16: + raise ValueError(f"output_dtype must be torch.bfloat16 in the current implementation, got {output_dtype}") + if pvthreshd not in (None, 50): + warnings.warn("pvthreshd is not supported by the current ARK sparse kernel and is ignored", stacklevel=2) + + B, Hq, Sq, D = _validate_attention_tensor(query, "Q", tensor_layout, expected_dtype=query.dtype) + Bk, Hkv, Skv, Dk = _validate_attention_tensor(key, "K", tensor_layout, expected_dtype=key.dtype) + Bv, Hkv2, Skv2, Dv = _validate_attention_tensor(value, "V", tensor_layout, expected_dtype=value.dtype) + if Bk != B or Bv != B: + raise ValueError("Batch size mismatch between Q/K/V") + if Hkv2 != Hkv or Skv2 != Skv or Dv != Dk: + raise ValueError("K/V shape mismatch") + if Dk != D: + raise ValueError("Head dim mismatch between Q and K/V") + + effective_query_tile_tokens = query_tile_tokens + effective_q_tile_override = q_tile_override + if effective_query_tile_tokens is None and q_tile_override in (64, 128, 256): + effective_query_tile_tokens = q_tile_override + elif effective_query_tile_tokens is not None: + if q_tile_override == 0: + effective_q_tile_override = int(effective_query_tile_tokens) + elif q_tile_override != int(effective_query_tile_tokens): + raise ValueError( + "query_tile_tokens and q_tile_override must match when both are set; " + f"got query_tile_tokens={effective_query_tile_tokens}, q_tile_override={q_tile_override}" + ) + _validate_sparse_q_tile_override_for_head_dim(D, effective_q_tile_override) + + normalized_mask = _normalize_sparse_mask(attn_mask, B, Sq, Skv, query.device) + metadata = sparge_preprocess_topk( + query, + key, + is_causal=is_causal, + smooth_k=smooth_k, + simthreshd1=simthreshd1, + topk=topk, + cdfthreshd=cdfthreshd, + attention_sink=attention_sink, + quant_block_size=64, + tensor_layout=tensor_layout, + k_quant_granularity=k_quant_granularity, + query_tile_tokens=effective_query_tile_tokens, + sparse_q_block_tokens=sparse_q_block_tokens, + sparse_k_block_tokens=sparse_k_block_tokens, + ) + _get_xpu_sparse_kernel_backend() + out = sage_sparse_bf16( + query, + key, + value, + metadata["lut"], + metadata["valid_block_num"], + attn_mask=normalized_mask, + is_causal=is_causal, + scale=scale, + q_tile_override=effective_q_tile_override, + sparse_q_block_tokens=metadata["sparse_q_block_tokens"], + sparse_k_block_tokens=metadata["sparse_k_block_tokens"], + tensor_layout=tensor_layout, + ) + sparsity_ratio = metadata["stats"]["sparsity_ratio"] + if return_metadata and return_sparsity: + return out, sparsity_ratio, metadata + if return_metadata: + return out, metadata + if return_sparsity: + return out, sparsity_ratio + return out From c94a1b80cfed91ffa399afafad2c93289922e020 Mon Sep 17 00:00:00 2001 From: yiliu30 Date: Thu, 6 Aug 2026 09:56:17 +0800 Subject: [PATCH 08/42] feat: BF16 sparse SDPA benchmark support and repro script - bench_sparse_topk.py: add --dtype bf16, the sparse_bf16_kernel_only / sparse_bf16_e2e modes, and baseline/effective TFLOPS columns. The binding loader also accepts the ark-xbuild artifact. - tools/repro_sparse_bf16_sdpa_bench.sh: one-command repro that sources oneAPI 2026.1, pins a device, and runs the BF16 sparse benchmark against the rebuilt ark-xbuild extension. --- .../ark/benchmarks/bench_sparse_topk.py | 192 +++++++++++++++++- .../ark/tools/repro_sparse_bf16_sdpa_bench.sh | 45 ++++ 2 files changed, 227 insertions(+), 10 deletions(-) create mode 100755 auto_round_extension/ark/tools/repro_sparse_bf16_sdpa_bench.sh diff --git a/auto_round_extension/ark/benchmarks/bench_sparse_topk.py b/auto_round_extension/ark/benchmarks/bench_sparse_topk.py index 2813ed48b6..63eafd4646 100644 --- a/auto_round_extension/ark/benchmarks/bench_sparse_topk.py +++ b/auto_round_extension/ark/benchmarks/bench_sparse_topk.py @@ -23,13 +23,13 @@ from auto_round_kernel.xpu_loader import load_xpu_lib -def _load_sparse_binding(ext_path: Path) -> None: +def _load_sparse_binding(ext_path: Path, *, required_symbols: tuple[str, ...]) -> None: module_name = "auto_round_kernel._bench.auto_round_kernel_xpu" print(f"Loading XPU extension from {ext_path.resolve()} as {module_name}") - load_xpu_lib(ext_path, required_symbols=("sage_sparse",), module_name=module_name) + load_xpu_lib(ext_path, required_symbols=required_symbols, module_name=module_name) -def _resolve_sparse_binding(args: argparse.Namespace) -> Path | None: +def _resolve_sparse_binding(args: argparse.Namespace, *, required_symbols: tuple[str, ...]) -> Path | None: local_kernel_dir = REPO_ROOT / "auto_round_extension" / "ark" / "auto_round_kernel" current_file = getattr(getattr(ark, "xpu_lib", None), "__file__", None) if args.xpu_so is not None: @@ -37,9 +37,11 @@ def _resolve_sparse_binding(args: argparse.Namespace) -> Path | None: if args.xbuild_dir is not None: candidates = sorted(args.xbuild_dir.resolve().glob("auto_round_kernel_xpu*.so")) if not candidates: - raise RuntimeError(f"Unable to locate built XPU extension with sage_sparse in {args.xbuild_dir}") + raise RuntimeError( + f"Unable to locate built XPU extension with required symbols {required_symbols} in {args.xbuild_dir}" + ) return candidates[-1] - if current_file is not None and hasattr(ark.xpu_lib, "sage_sparse"): + if current_file is not None and all(hasattr(ark.xpu_lib, symbol) for symbol in required_symbols): try: current_path = Path(current_file).resolve() if current_path.is_relative_to(local_kernel_dir.resolve()): @@ -47,22 +49,38 @@ def _resolve_sparse_binding(args: argparse.Namespace) -> Path | None: return None except Exception: pass - candidates = sorted((local_kernel_dir / "xbuild").glob("auto_round_kernel_xpu*.so")) + build_roots = [root for root in local_kernel_dir.iterdir() if root.is_dir() and "build" in root.name] + candidates: list[Path] = [] + for root in build_roots: + candidates.extend(sorted(root.glob("auto_round_kernel_xpu*.so"))) if not candidates: - raise RuntimeError("Unable to locate built XPU extension with sage_sparse in auto_round_kernel/xbuild") + raise RuntimeError( + f"Unable to locate built XPU extension with required symbols {required_symbols} under {local_kernel_dir}" + ) + candidates.sort(key=lambda path: path.stat().st_mtime) return candidates[-1] def ensure_sparse_binding(args: argparse.Namespace) -> None: - ext_path = _resolve_sparse_binding(args) + required_symbols = ("sage_sparse", "sage_sparse_bf16") if args.dtype == "bf16" else ("sage_sparse",) + ext_path = _resolve_sparse_binding(args, required_symbols=required_symbols) if ext_path is not None: - _load_sparse_binding(ext_path) + _load_sparse_binding(ext_path, required_symbols=required_symbols) def is_xpu_available() -> bool: return hasattr(torch, "xpu") and torch.xpu.is_available() +def resolve_dtype(name: str) -> torch.dtype: + normalized = name.strip().lower() + if normalized == "fp16": + return torch.float16 + if normalized == "bf16": + return torch.bfloat16 + raise ValueError(f"Unsupported dtype={name!r}; supported values: fp16, bf16") + + def bench(fn, warmup: int, iters: int) -> float: for _ in range(warmup): out = fn() @@ -501,6 +519,8 @@ def summarize_speedups(rows: list[dict[str, object]]) -> list[dict[str, object]] "dense_sagev1", "sparse_kernel_only", "sparse_e2e", + "sparse_bf16_kernel_only", + "sparse_bf16_e2e", "sparse_qtile256_row64k_kernel_only", "sparse_qtile256_row64k_e2e", }: @@ -548,7 +568,7 @@ def write_csv(rows: list[dict[str, object]], output_csv: Path) -> None: def run_single_benchmark(args: argparse.Namespace, *, seq_len: int, tensor_layout: str) -> list[dict[str, object]]: device = torch.device("xpu") - dtype = torch.float16 + dtype = resolve_dtype(args.dtype) scale = 1.0 / math.sqrt(args.head_dim) enable_gqa = args.num_heads_q // args.num_heads_kv > 1 q_hnd_src, k_hnd_src, v_hnd_src = build_inputs( @@ -710,6 +730,49 @@ def run_single_benchmark(args: argparse.Namespace, *, seq_len: int, tensor_layou note=f"preprocess failed before e2e benchmark: {preprocess_note}", ) ) + if dtype == torch.bfloat16: + rows.append( + make_row( + mode="sparse_bf16_kernel_only", + batch=args.batch, + num_heads_q=args.num_heads_q, + num_heads_kv=args.num_heads_kv, + seq_len=seq_len, + tensor_layout=tensor_layout, + head_dim=args.head_dim, + dtype=dtype, + is_causal=args.causal, + warmup=args.warmup, + iters=args.iters, + requested_topk=topk, + selected_ratio=None, + selected_blocks_per_row=None, + latency_ms=None, + status=status, + note=f"preprocess failed before bf16 kernel benchmark: {preprocess_note}", + ) + ) + rows.append( + make_row( + mode="sparse_bf16_e2e", + batch=args.batch, + num_heads_q=args.num_heads_q, + num_heads_kv=args.num_heads_kv, + seq_len=seq_len, + tensor_layout=tensor_layout, + head_dim=args.head_dim, + dtype=dtype, + is_causal=args.causal, + warmup=args.warmup, + iters=args.iters, + requested_topk=topk, + selected_ratio=None, + selected_blocks_per_row=None, + latency_ms=None, + status=status, + note=f"preprocess failed before bf16 e2e benchmark: {preprocess_note}", + ) + ) continue # try: @@ -891,6 +954,109 @@ def run_single_benchmark(args: argparse.Namespace, *, seq_len: int, tensor_layou selected_blocks_per_row=selected_blocks_per_row, ) ) + if dtype == torch.bfloat16: + rows.append( + try_benchmark( + "sparse_bf16_kernel_only", + lambda preprocess=preprocess: ( + hnd_to_nhd( + ark.sage_sparse_bf16( + nhd_to_hnd(q_nhd_src), + nhd_to_hnd(k_nhd_src), + nhd_to_hnd(v_nhd_src), + preprocess["lut"], + preprocess["valid_block_num"], + is_causal=args.causal, + scale=scale, + q_tile_override=args.q_tile_override, + sparse_q_block_tokens=preprocess["sparse_q_block_tokens"], + sparse_k_block_tokens=preprocess["sparse_k_block_tokens"], + tensor_layout="HND", + ) + ) + if tensor_layout == "HND" + else ark.sage_sparse_bf16( + q, + k, + v, + preprocess["lut"], + preprocess["valid_block_num"], + is_causal=args.causal, + scale=scale, + q_tile_override=args.q_tile_override, + sparse_q_block_tokens=preprocess["sparse_q_block_tokens"], + sparse_k_block_tokens=preprocess["sparse_k_block_tokens"], + tensor_layout=tensor_layout, + ) + ), + batch=args.batch, + num_heads_q=args.num_heads_q, + num_heads_kv=args.num_heads_kv, + seq_len=seq_len, + tensor_layout=tensor_layout, + head_dim=args.head_dim, + dtype=dtype, + is_causal=args.causal, + warmup=args.warmup, + iters=args.iters, + requested_topk=topk, + selected_ratio=selected_ratio, + selected_blocks_per_row=selected_blocks_per_row, + ) + ) + rows.append( + try_benchmark( + "sparse_bf16_e2e", + lambda topk=topk: ( + hnd_to_nhd( + ark.sparge_sage2_attn_meansim_topk_xpu_bf16( + nhd_to_hnd(q_nhd_src), + nhd_to_hnd(k_nhd_src), + nhd_to_hnd(v_nhd_src), + is_causal=args.causal, + scale=scale, + smooth_k=True, + simthreshd1=-1.0, + topk=topk, + attention_sink=False, + tensor_layout="HND", + q_tile_override=args.q_tile_override, + sparse_q_block_tokens=args.sparse_q_block_tokens, + sparse_k_block_tokens=args.sparse_k_block_tokens, + ) + ) + if tensor_layout == "HND" + else ark.sparge_sage2_attn_meansim_topk_xpu_bf16( + q, + k, + v, + is_causal=args.causal, + scale=scale, + smooth_k=True, + simthreshd1=-1.0, + topk=topk, + attention_sink=False, + tensor_layout=tensor_layout, + q_tile_override=args.q_tile_override, + sparse_q_block_tokens=args.sparse_q_block_tokens, + sparse_k_block_tokens=args.sparse_k_block_tokens, + ) + ), + batch=args.batch, + num_heads_q=args.num_heads_q, + num_heads_kv=args.num_heads_kv, + seq_len=seq_len, + tensor_layout=tensor_layout, + head_dim=args.head_dim, + dtype=dtype, + is_causal=args.causal, + warmup=args.warmup, + iters=args.iters, + requested_topk=topk, + selected_ratio=selected_ratio, + selected_blocks_per_row=selected_blocks_per_row, + ) + ) del preprocess empty_xpu_cache() @@ -921,6 +1087,12 @@ def parse_args() -> argparse.Namespace: parser.add_argument("--num-heads-kv", type=int, default=40) parser.add_argument("--seq-len", type=int, nargs="+", default=[32768, 75600]) parser.add_argument("--head-dim", type=int, default=128) + parser.add_argument( + "--dtype", + choices=("fp16", "bf16"), + default="fp16", + help="Input/output dtype used for dense SDPA, dense SAGE, and sparse attention benchmarks.", + ) parser.add_argument("--topk", type=float, nargs="+", default=[0.5, 0.25, 0.125]) parser.add_argument("--quant-block-size", type=int, default=64) parser.add_argument( diff --git a/auto_round_extension/ark/tools/repro_sparse_bf16_sdpa_bench.sh b/auto_round_extension/ark/tools/repro_sparse_bf16_sdpa_bench.sh new file mode 100755 index 0000000000..d26c692b48 --- /dev/null +++ b/auto_round_extension/ark/tools/repro_sparse_bf16_sdpa_bench.sh @@ -0,0 +1,45 @@ +#!/usr/bin/env bash + +set -euo pipefail + +SCRIPT_PATH="$(readlink -f "$0")" +REPO_ROOT="/home/yiliu4/workspace/auto-round/auto_round_extension/ark" +PYTHON_BIN="${REPO_ROOT}/.venv/bin/python" +OUTPUT_CSV="${OUTPUT_CSV:-/tmp/bench_sparse_topk_bf16_dev6.csv}" +ZE_AFFINITY_MASK_VALUE="${ZE_AFFINITY_MASK_VALUE:-6}" +XPU_SO="${XPU_SO:-}" + +cd "${REPO_ROOT}" + +if [[ "${1:-}" == "--inner" ]]; then + shift +elif command -v sg >/dev/null 2>&1; then + current_groups="$(id -nG || true)" + if [[ ! " ${current_groups} " =~ [[:space:]]render[[:space:]] ]] || [[ ! " ${current_groups} " =~ [[:space:]]video[[:space:]] ]]; then + exec sg render -c "sg video -c '${SCRIPT_PATH} --inner'" + fi +fi + +# oneAPI's setvars.sh is not compatible with `set -u` in this environment. +set +u +source /opt/intel/oneapi/setvars.sh --force >/dev/null 2>&1 +set -u + +export MKLROOT=/opt/intel/oneapi/mkl/2026.1 +export ZE_AFFINITY_MASK="${ZE_AFFINITY_MASK_VALUE}" +export SAGE_ATTN_SPARSE_PREPROCESS_BACKEND=torch + +if [[ -z "${XPU_SO}" ]]; then + XPU_SO="$(find "${REPO_ROOT}/auto_round_kernel/ark-xbuild" -maxdepth 1 -name 'auto_round_kernel_xpu*.so' | sort | tail -n 1)" +fi + +echo "XPU process snapshot:" +xpu-smi ps || true +echo +echo "Running sparse BF16 SDPA benchmark on device ${ZE_AFFINITY_MASK}..." +echo "Using XPU extension: ${XPU_SO}" + +exec "${PYTHON_BIN}" benchmarks/bench_sparse_topk.py \ + --dtype bf16 \ + --xpu-so "${XPU_SO}" \ + --output-csv "${OUTPUT_CSV}" From e34327a619293b108645bdb025d01c8fa86f4cf8 Mon Sep 17 00:00:00 2001 From: yiliu30 Date: Thu, 6 Aug 2026 09:56:27 +0800 Subject: [PATCH 09/42] test: BF16 sparse preprocess and LUT regression tests - run_lut_roundtrip_case: the LUT must be non-negative and the block_map -> lut -> block_map round-trip lossless (regression for the XPU boolean-mask assignment bug that produced -1 LUT entries). - run_fill_block_map_case: the vectorized _fill_block_map_torch must match the reference loop implementation (regression for the preprocess speedup). - test_sage_sparse_prefill_e2e: include ark-xbuild in the binding search roots. --- .../test/test_sage_sparse_prefill_e2e.py | 8 +- .../test/test_sparge_preprocess_topk_e2e.py | 81 ++++++++++++++++++- 2 files changed, 83 insertions(+), 6 deletions(-) diff --git a/auto_round_extension/ark/auto_round_kernel/wrapper/test/test_sage_sparse_prefill_e2e.py b/auto_round_extension/ark/auto_round_kernel/wrapper/test/test_sage_sparse_prefill_e2e.py index 60c9a87922..7d372a2858 100644 --- a/auto_round_extension/ark/auto_round_kernel/wrapper/test/test_sage_sparse_prefill_e2e.py +++ b/auto_round_extension/ark/auto_round_kernel/wrapper/test/test_sage_sparse_prefill_e2e.py @@ -16,17 +16,15 @@ def ensure_sparse_binding(*, required_symbols: tuple[str, ...] = ("sage_sparse",)) -> None: - bf16_ext = ( - REPO_PARENT / "auto_round_kernel" / "xbuild_bf16_v2" / "auto_round_kernel_xpu.cpython-313-x86_64-linux-gnu.so" - ) search_roots = ( REPO_PARENT / "auto_round_kernel", - REPO_PARENT / "auto_round_kernel" / "xbuild", REPO_PARENT / "auto_round_kernel" / "xbuild_diffuser", + REPO_PARENT / "auto_round_kernel" / "xbuild", + REPO_PARENT / "auto_round_kernel" / "xbuild_bf16_v2", + REPO_PARENT / "auto_round_kernel" / "ark-xbuild", ) ensure_xpu_lib( required_symbols=required_symbols, - ext_path=bf16_ext if bf16_ext.is_file() else None, search_roots=search_roots, ) diff --git a/auto_round_extension/ark/auto_round_kernel/wrapper/test/test_sparge_preprocess_topk_e2e.py b/auto_round_extension/ark/auto_round_kernel/wrapper/test/test_sparge_preprocess_topk_e2e.py index 02426f3913..5476207284 100644 --- a/auto_round_extension/ark/auto_round_kernel/wrapper/test/test_sparge_preprocess_topk_e2e.py +++ b/auto_round_extension/ark/auto_round_kernel/wrapper/test/test_sparge_preprocess_topk_e2e.py @@ -16,7 +16,16 @@ def ensure_sparse_binding() -> None: - ensure_xpu_lib(required_symbols=("sage_sparse",)) + ensure_xpu_lib( + required_symbols=("sage_sparse",), + search_roots=( + REPO_PARENT / "auto_round_kernel", + REPO_PARENT / "auto_round_kernel" / "xbuild_diffuser", + REPO_PARENT / "auto_round_kernel" / "xbuild", + REPO_PARENT / "auto_round_kernel" / "xbuild_bf16_v2", + REPO_PARENT / "auto_round_kernel" / "ark-xbuild", + ), + ) def _to_layout(tensor: torch.Tensor, tensor_layout: str) -> torch.Tensor: @@ -264,6 +273,74 @@ def run_case( assert torch.equal(crafted_meta["block_map"], crafted_meta["raw_block_map"]) +def run_lut_roundtrip_case(*, num_heads_q: int = 40, q_blocks: int = 8, k_blocks: int = 32) -> None: + """Regression test for the XPU boolean-mask assignment bug. + + torch-xpu-ops' boolean-mask in-place assignment (`tensor[mask] = value`) silently + misses entries on 4-D int64/bool tensors of these sizes, which used to leave 0s in + ``_block_map_lut_torch``'s ``filled_matrix`` and produce **-1** LUT entries. Those + -1s then drove the ``scatter_`` in ``_lut_to_block_map`` out of bounds + (``ScatterGatherKernels.cpp:233``). The LUT must be non-negative and the + block_map -> lut -> block_map round-trip must be lossless. + """ + from auto_round_kernel.sparse_attention import _block_map_lut_torch, _lut_to_block_map + + device = torch.device("xpu") + torch.manual_seed(1234) + block_map = torch.rand(1, num_heads_q, q_blocks, k_blocks, device=device) < 0.5 + lut, valid_block_num = _block_map_lut_torch(block_map) + torch.xpu.synchronize() + + assert lut.dtype == torch.int32 + assert valid_block_num.dtype == torch.int32 + assert tuple(lut.shape) == tuple(block_map.shape) + # Regression: on XPU this was -1 because the masked assignment silently failed. + assert int(lut.min().item()) >= 0, f"lut must be non-negative, got min={int(lut.min().item())}" + assert int(valid_block_num.max().item()) <= k_blocks + + reconstructed = _lut_to_block_map(lut, valid_block_num) + torch.xpu.synchronize() + assert torch.equal(reconstructed, block_map), "block_map -> lut -> block_map round-trip must be lossless" + + +def run_fill_block_map_case() -> None: + """Regression test for the vectorized ``_fill_block_map_torch``. + + The naive implementation looped ``for rank in range(k_blocks)`` (thousands of + sequential kernel launches at long sequences); the vectorized rewrite must be + exactly equivalent. Runs on CPU against a reference loop implementation. + """ + from auto_round_kernel.sparse_attention import _fill_block_map_torch + + def reference_loop(final_map, num_to_select, sorted_indices): + k_blocks = final_map.shape[-1] + filled = final_map.clone() + column_ids = torch.arange(k_blocks, device=final_map.device).view(1, 1, 1, k_blocks) + target_new = torch.maximum(num_to_select, torch.ones_like(num_to_select)) + added = torch.zeros_like(num_to_select) + for rank in range(k_blocks): + idx_match = column_ids == sorted_indices[..., rank : rank + 1] + is_new = idx_match & ~filled + should_add = (added < target_new).unsqueeze(-1) + newly_selected = should_add & is_new + filled |= newly_selected + added = added + newly_selected.any(dim=-1).to(added.dtype) + return filled + + for shape in [(1, 4, 8, 32), (1, 40, 8, 32), (1, 40, 128, 512), (2, 16, 64, 128)]: + b, h, q, k = shape + for seed in range(4): + torch.manual_seed(seed) + probs = torch.rand(shape) + _, sorted_indices = torch.sort(probs, dim=-1, descending=True) + num_to_select = torch.randint(0, k + 1, (b, h, q), dtype=torch.int64) + final_map = torch.rand(shape) < 0.1 # some blocks already forced + assert torch.equal( + _fill_block_map_torch(final_map, num_to_select, sorted_indices), + reference_loop(final_map, num_to_select, sorted_indices), + ), f"vectorized _fill_block_map_torch differs from reference at shape={shape} seed={seed}" + + def run_causal_decoupled_wrapper_case(*, tensor_layout: str) -> None: device = torch.device("xpu") batch = 1 @@ -410,6 +487,8 @@ def main() -> None: num_heads_kv=1, ) run_causal_decoupled_wrapper_case(tensor_layout=tensor_layout) + run_lut_roundtrip_case() + run_fill_block_map_case() if __name__ == "__main__": From 9c86dda62d7dc70d5362e61cdee8044df40f3924 Mon Sep 17 00:00:00 2001 From: yiliu30 Date: Thu, 6 Aug 2026 09:56:35 +0800 Subject: [PATCH 10/42] feat: include ark-xbuild in wan sparse binding search roots --- auto_round_extension/ark/examples/wan_sparse_patch.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/auto_round_extension/ark/examples/wan_sparse_patch.py b/auto_round_extension/ark/examples/wan_sparse_patch.py index 6418209521..0d82071f84 100644 --- a/auto_round_extension/ark/examples/wan_sparse_patch.py +++ b/auto_round_extension/ark/examples/wan_sparse_patch.py @@ -43,9 +43,10 @@ def ensure_ark_sparse_binding( required_symbols=required_symbols, search_roots=( KERNEL_DIR, + KERNEL_DIR / "xbuild_diffuser", KERNEL_DIR / "xbuild", KERNEL_DIR / "xbuild_bf16_v2", - KERNEL_DIR / "xbuild_diffuser", + KERNEL_DIR / "ark-xbuild", ), ) From 743cece1a6d119ea5447bf030cfa285cc56c9fbe Mon Sep 17 00:00:00 2001 From: yiliu30 Date: Thu, 6 Aug 2026 10:09:13 +0800 Subject: [PATCH 11/42] refactor: remove dense-mask fallback from sage_sparse_bf16 sage_sparse_bf16 now uses the native XPU sparse kernel exclusively. The dense-mask fallback (rebuild the full block map, then run per-head dense sagev1) is removed: it materialized a [B, Hq, Sq, Skv] mask and OOM'd at long sequences, and the native kernel now covers every supported config. Unsupported configs raise instead of silently degrading to the slow path. --- .../ark/auto_round_kernel/sparse_attention.py | 129 ++++++------------ 1 file changed, 43 insertions(+), 86 deletions(-) diff --git a/auto_round_extension/ark/auto_round_kernel/sparse_attention.py b/auto_round_extension/ark/auto_round_kernel/sparse_attention.py index 71a537931a..2342a10707 100644 --- a/auto_round_extension/ark/auto_round_kernel/sparse_attention.py +++ b/auto_round_extension/ark/auto_round_kernel/sparse_attention.py @@ -267,94 +267,51 @@ def sage_sparse_bf16( f"q_tile_override must be one of {{0, 64, 128}} for sage_sparse_bf16 with head_dim=64, got {q_tile_override}" ) - # Preferred path: the native XPU sparse kernel. The dense fallback below is - # numerically correct but materializes a full [B, Hq, Sq, Skv] mask, which OOMs - # at long sequence lengths. Unsupported configs fall through to the fallback. - if query.device.type == "xpu": - try: - lib = get_lib(query) - if hasattr(lib, "sage_sparse_bf16"): - effective_q_tile_override = _resolve_sparse_prefill_q_tile_override( - head_dim=D, - quant_block_size=64, - q_tile_override=q_tile_override, - sparse_q_block_tokens=effective_sparse_q_block_tokens, - sparse_k_block_tokens=effective_sparse_k_block_tokens, - tensor_layout=tensor_layout, - ) - stream = get_stream(query) - O = _empty_attention_output( - B, Hq, Sq, D, dtype=value.dtype, device=query.device, tensor_layout=tensor_layout - ) - lib.sage_sparse_bf16( - stream, - query.data_ptr(), - key.data_ptr(), - value.data_ptr(), - O.data_ptr(), - attn_mask.data_ptr() if attn_mask is not None else 0, - lut.data_ptr(), - valid_block_num.data_ptr(), - q_sparse_blocks, - kv_sparse_blocks, - effective_q_tile_override, - *_attention_strides_qko(query, tensor_layout), - *_attention_strides_qko(key, tensor_layout), - *_attention_strides_v(value, tensor_layout), - *_attention_strides_qko(O, tensor_layout), - B, - Hq, - Hkv, - Sq, - Skv, - D, - float(scale) if scale is not None else 1.0 / (D**0.5), - bool(is_causal), - ) - return O - except (NotImplementedError, RuntimeError, ValueError): - # Unsupported native sparse config; fall through to the dense fallback. - pass - - block_map = _lut_to_block_map(lut, valid_block_num) - sparse_mask = sparge_block_map_to_mask( - block_map, + # Native XPU sparse kernel only. The historical dense-mask fallback (rebuild the + # full block map, then run per-head dense sagev1) is removed: it materialized a + # [B, Hq, Sq, Skv] mask and OOM'd at long sequences, and the native kernel now + # covers every supported config. + lib = get_lib(query) + if not hasattr(lib, "sage_sparse_bf16"): + raise RuntimeError("Loaded XPU extension does not expose sage_sparse_bf16") + effective_q_tile_override = _resolve_sparse_prefill_q_tile_override( + head_dim=D, quant_block_size=64, - q_block_tokens=effective_sparse_q_block_tokens, - k_block_tokens=effective_sparse_k_block_tokens, - seq_len_q=Sq, - seq_len_kv=Skv, - is_causal=is_causal, + q_tile_override=q_tile_override, + sparse_q_block_tokens=effective_sparse_q_block_tokens, + sparse_k_block_tokens=effective_sparse_k_block_tokens, + tensor_layout=tensor_layout, ) - normalized_mask = _normalize_sparse_mask(attn_mask, B, Sq, Skv, query.device) - if normalized_mask is not None: - sparse_mask = torch.minimum(sparse_mask, normalized_mask.expand(-1, Hq, -1, -1)) - - query_hnd = _to_hnd(query, tensor_layout) - key_hnd = _to_hnd(key, tensor_layout) - value_hnd = _to_hnd(value, tensor_layout) - kv_head_index = _kv_head_index_for_q_heads(Hq, Hkv, query.device) - outputs: list[torch.Tensor] = [] - effective_scale = float(scale) if scale is not None else 1.0 / (D**0.5) - for q_head in range(Hq): - kv_head = int(kv_head_index[q_head].item()) - query_head = torch.empty((B, 1, Sq, D), dtype=query_hnd.dtype, device=query.device) - key_head = torch.empty((B, 1, Skv, D), dtype=key_hnd.dtype, device=key.device) - value_head = torch.empty((B, 1, Skv, D), dtype=value_hnd.dtype, device=value.device) - query_head.copy_(query_hnd[:, q_head : q_head + 1]) - key_head.copy_(key_hnd[:, kv_head : kv_head + 1]) - value_head.copy_(value_hnd[:, kv_head : kv_head + 1]) - head_out = _dense_sagev1( - query_head, - key_head, - value_head, - attn_mask=sparse_mask[:, q_head : q_head + 1].contiguous(), - is_causal=False, - scale=effective_scale, - tensor_layout="HND", - ) - outputs.append(head_out) - return _from_hnd(torch.cat(outputs, dim=1), tensor_layout) + stream = get_stream(query) + O = _empty_attention_output( + B, Hq, Sq, D, dtype=value.dtype, device=query.device, tensor_layout=tensor_layout + ) + lib.sage_sparse_bf16( + stream, + query.data_ptr(), + key.data_ptr(), + value.data_ptr(), + O.data_ptr(), + attn_mask.data_ptr() if attn_mask is not None else 0, + lut.data_ptr(), + valid_block_num.data_ptr(), + q_sparse_blocks, + kv_sparse_blocks, + effective_q_tile_override, + *_attention_strides_qko(query, tensor_layout), + *_attention_strides_qko(key, tensor_layout), + *_attention_strides_v(value, tensor_layout), + *_attention_strides_qko(O, tensor_layout), + B, + Hq, + Hkv, + Sq, + Skv, + D, + float(scale) if scale is not None else 1.0 / (D**0.5), + bool(is_causal), + ) + return O def sage_sparse_row_linear( From c6bd14a52ce4ba2f2b000af38e85955a0d8396cf Mon Sep 17 00:00:00 2001 From: yiliu30 Date: Thu, 6 Aug 2026 10:36:02 +0800 Subject: [PATCH 12/42] docs: add documented BF16 sparse benchmark runner script tools/run_bf16_sparse_bench.sh documents and runs the exact BF16 sparse SDPA benchmark command: sources oneAPI 2026.1, pins a GPU via ZE_AFFINITY_MASK, uses the rebuilt ark-xbuild extension, and saves a timestamped CSV + log under benchmarks/results/. Includes env overrides and prerequisites in the header. --- .../ark/tools/run_bf16_sparse_bench.sh | 91 +++++++++++++++++++ 1 file changed, 91 insertions(+) create mode 100755 auto_round_extension/ark/tools/run_bf16_sparse_bench.sh diff --git a/auto_round_extension/ark/tools/run_bf16_sparse_bench.sh b/auto_round_extension/ark/tools/run_bf16_sparse_bench.sh new file mode 100755 index 0000000000..fb5e1b5e65 --- /dev/null +++ b/auto_round_extension/ark/tools/run_bf16_sparse_bench.sh @@ -0,0 +1,91 @@ +#!/usr/bin/env bash +# +# run_bf16_sparse_bench.sh +# ======================== +# Documented reproduce command for the BF16 sparse SDPA benchmark on Intel XPU. +# +# WHAT IT DOES +# Runs benchmarks/bench_sparse_topk.py --dtype bf16 against the rebuilt +# XPU extension (auto_round_kernel/ark-xbuild), pinning a GPU device, and +# saves the CSV + a readable log under benchmarks/results/. +# +# USAGE +# ./tools/run_bf16_sparse_bench.sh +# +# ENV OVERRIDES +# ZE_AFFINITY_MASK_VALUE GPU device index to run on (default: 6) +# OUTPUT_DIR where CSV + log are written (default: benchmarks/results) +# XPU_SO explicit path to the built auto_round_kernel_xpu*.so +# (default: newest under auto_round_kernel/ark-xbuild) +# +# PREREQUISITES +# - oneAPI 2026.1 runtime (sourced from /opt/intel/oneapi/setvars.sh) +# - Python venv at .venv/bin/python (PyTorch 2.13.0+xpu) +# - a rebuilt extension with BF16 sparse symbols under auto_round_kernel/ark-xbuild/ +# - a free GPU (pick via ZE_AFFINITY_MASK_VALUE; check `xpu-smi ps` first) +# +# BENCHMARK CONFIG (bench_sparse_topk.py defaults) +# batch=1 num_heads_q/kv=40 head_dim=128 dtype=bf16 qtile=256 +# seq_len=[32768, 75600] tensor_layout=[HND, NHD] topk=[0.5, 0.25, 0.125] +# warmup=2 iters=3 +# +# NOTE +# First call per process pays a ~30s SYCL JIT for the sparse kernels; the +# full sweep above takes roughly 15-25 minutes on one GPU. + +set -euo pipefail + +SCRIPT_PATH="$(readlink -f "$0")" +REPO_ROOT="$(cd "$(dirname "${SCRIPT_PATH}")/.." && pwd)" +PYTHON_BIN="${REPO_ROOT}/.venv/bin/python" +OUTPUT_DIR="${OUTPUT_DIR:-${REPO_ROOT}/benchmarks/results}" +ZE_AFFINITY_MASK_VALUE="${ZE_AFFINITY_MASK_VALUE:-6}" +XPU_SO="${XPU_SO:-}" + +cd "${REPO_ROOT}" + +# Re-exec under the render/video groups if this shell does not already have them. +if [[ "${1:-}" == "--inner" ]]; then + shift +elif command -v sg >/dev/null 2>&1; then + current_groups="$(id -nG || true)" + if [[ ! " ${current_groups} " =~ [[:space:]]render[[:space:]] ]] || [[ ! " ${current_groups} " =~ [[:space:]]video[[:space:]] ]]; then + exec sg render -c "sg video -c '${SCRIPT_PATH} --inner'" + fi +fi + +# oneAPI's setvars.sh is not compatible with `set -u` in this environment. +set +u +source /opt/intel/oneapi/setvars.sh --force >/dev/null 2>&1 +set -u + +export MKLROOT=/opt/intel/oneapi/mkl/2026.1 +export ZE_AFFINITY_MASK="${ZE_AFFINITY_MASK_VALUE}" +export SAGE_ATTN_SPARSE_PREPROCESS_BACKEND=torch + +if [[ -z "${XPU_SO}" ]]; then + XPU_SO="$(find "${REPO_ROOT}/auto_round_kernel/ark-xbuild" -maxdepth 1 -name 'auto_round_kernel_xpu*.so' | sort | tail -n 1)" +fi +if [[ -z "${XPU_SO}" ]]; then + echo "error: no auto_round_kernel_xpu*.so found under auto_round_kernel/ark-xbuild" >&2 + exit 1 +fi + +mkdir -p "${OUTPUT_DIR}" +stamp="$(date +%Y%m%d_%H%M%S)" +out_csv="${OUTPUT_DIR}/bf16_sparse_${stamp}.csv" +out_log="${OUTPUT_DIR}/bf16_sparse_${stamp}.log" + +echo "XPU process snapshot:" +xpu-smi ps || true +echo +echo "Running BF16 sparse SDPA benchmark on device ${ZE_AFFINITY_MASK}..." +echo "Using XPU extension: ${XPU_SO}" +echo "CSV: ${out_csv}" +echo "Log: ${out_log}" +echo + +"${PYTHON_BIN}" benchmarks/bench_sparse_topk.py \ + --dtype bf16 \ + --xpu-so "${XPU_SO}" \ + --output-csv "${out_csv}" 2>&1 | tee "${out_log}" From 625d483110bd2547fca689273f0360213c69e575 Mon Sep 17 00:00:00 2001 From: yiliu30 Date: Thu, 6 Aug 2026 11:39:12 +0800 Subject: [PATCH 13/42] fix: work around triton SPV_INTEL_predicated_io on Intel XPU triton 3.7.x enables has_predicated_io for non-LTS Intel GPU drivers and emits the SPV_INTEL_predicated_io SPIR-V extension. Some level-zero loaders (e.g. 1.13.35563) reject that extension at kernel load time, aborting every triton kernel launch with 'InvalidModule: ... unknown extension'. Since it is not a catchable Python exception, the torch preprocess fallback never ran and the whole benchmark died. Add auto_round_kernel/_xpu_triton_compat.py which patches the intel triton backend to force has_predicated_io off (standard predicated loads instead), and apply it when the triton preprocess module loads. The triton-xpu preprocess backend now works and is faster than torch (seq 32768/50% e2e: 327ms -> 206ms). --- .../auto_round_kernel/_xpu_triton_compat.py | 49 +++++++++++++++++++ .../sparge_preprocess_triton.py | 4 ++ 2 files changed, 53 insertions(+) create mode 100644 auto_round_extension/ark/auto_round_kernel/_xpu_triton_compat.py diff --git a/auto_round_extension/ark/auto_round_kernel/_xpu_triton_compat.py b/auto_round_extension/ark/auto_round_kernel/_xpu_triton_compat.py new file mode 100644 index 0000000000..f86e7f5763 --- /dev/null +++ b/auto_round_extension/ark/auto_round_kernel/_xpu_triton_compat.py @@ -0,0 +1,49 @@ +# # Copyright (C) 2026 Intel Corporation +# # SPDX-License-Identifier: Apache-2.0 + +"""Workarounds for the Intel XPU triton backend on this platform. + +Triton 3.7.x enables ``has_predicated_io`` for non-LTS Intel GPU drivers and then +emits the ``SPV_INTEL_predicated_io`` SPIR-V extension in its JIT kernels. Some +level-zero loaders (e.g. 1.13.35563) reject that extension at kernel-load time:: + + InvalidModule: Invalid SPIR-V module: input SPIR-V module uses unknown + extension 'SPV_INTEL_predicated_io' + +which aborts any triton kernel launch and kills the process (it is not a +catchable Python exception, so the torch preprocess fallback never runs). + +The workaround forces ``has_predicated_io`` off so triton emits standard +(non-extension) predicated loads/stores. This disables a minor codegen +optimization on every XPU driver, but the ark triton preprocess kernels are small +and the trade-off is only safe correctness on the affected drivers. +""" + +import logging + +logger = logging.getLogger(__name__) + +_APPLIED = False + + +def apply_xpu_triton_workarounds() -> None: + """Patch triton's Intel compiler so JIT kernels avoid the rejected SPIR-V extension.""" + global _APPLIED + if _APPLIED: + return + try: + import triton.backends.intel.compiler as _intel_compiler + except ImportError: + # No intel triton backend (e.g. stock triton); nothing to patch. + return + + _orig_parse_target = _intel_compiler.XPUBackend.parse_target + + def _patched_parse_target(self, tgt_prop): + dev_prop = _orig_parse_target(self, tgt_prop) + dev_prop["has_predicated_io"] = False + return dev_prop + + _intel_compiler.XPUBackend.parse_target = _patched_parse_target + _APPLIED = True + logger.info("Applied XPU triton workaround: has_predicated_io forced off (SPV_INTEL_predicated_io)") diff --git a/auto_round_extension/ark/auto_round_kernel/sparge_preprocess_triton.py b/auto_round_extension/ark/auto_round_kernel/sparge_preprocess_triton.py index 5306cf4e84..4e9e710f4a 100644 --- a/auto_round_extension/ark/auto_round_kernel/sparge_preprocess_triton.py +++ b/auto_round_extension/ark/auto_round_kernel/sparge_preprocess_triton.py @@ -10,6 +10,10 @@ import triton import triton.language as tl +from ._xpu_triton_compat import apply_xpu_triton_workarounds + +apply_xpu_triton_workarounds() + logger = logging.getLogger(__name__) _TRITON_FALLBACK_WARNING_LOGGED = False From cf191c45ab60909e80b33cf0d211e7ce844b40de Mon Sep 17 00:00:00 2001 From: yiliu30 Date: Thu, 6 Aug 2026 11:39:12 +0800 Subject: [PATCH 14/42] docs: add sparse SAGE v1 benchmark runner script tools/run_sparse_sagev1_bench.sh documents and runs the sparse SAGE v1 (fp16 / INT8) benchmark on main: sources oneAPI, pins a GPU, uses the rebuilt extension, and saves timestamped CSV + log under benchmarks/results/. Includes env overrides and a note on the triton preprocess backend. --- .../ark/tools/run_sparse_sagev1_bench.sh | 111 ++++++++++++++++++ 1 file changed, 111 insertions(+) create mode 100755 auto_round_extension/ark/tools/run_sparse_sagev1_bench.sh diff --git a/auto_round_extension/ark/tools/run_sparse_sagev1_bench.sh b/auto_round_extension/ark/tools/run_sparse_sagev1_bench.sh new file mode 100755 index 0000000000..41c6701b43 --- /dev/null +++ b/auto_round_extension/ark/tools/run_sparse_sagev1_bench.sh @@ -0,0 +1,111 @@ +#!/usr/bin/env bash +# +# run_sparse_sagev1_bench.sh +# ========================== +# Documented reproduce command for the sparse SAGE v1 SDPA benchmark on Intel XPU. +# +# This runs the INT8 sparse path (fp16 Q/K/V inputs, int8 quantized routing) that +# lives on the `main` branch: dense torch SDPA + dense sagev1 + the sparse +# kernel-only and sparse e2e modes, across top-k selections. +# +# WHAT IT DOES +# Runs benchmarks/bench_sparse_topk.py (fp16 / INT8 sparse) against the rebuilt +# XPU extension, pinning a GPU device, and saves a timestamped CSV + log under +# benchmarks/results/. +# +# USAGE +# ./tools/run_sparse_sagev1_bench.sh +# +# ENV OVERRIDES +# ZE_AFFINITY_MASK_VALUE GPU device index to run on (default: 6) +# OUTPUT_DIR where CSV + log are written (default: benchmarks/results) +# XPU_SO explicit path to the built auto_round_kernel_xpu*.so +# (default: newest under auto_round_kernel/ark-xbuild) +# BENCH_ARGS extra args passed to bench_sparse_topk.py +# (e.g. "--seq-len 32768 --topk 0.5 --iters 3 --tensor-layout HND") +# +# PREREQUISITES +# - oneAPI 2026.1 runtime (sourced from /opt/intel/oneapi/setvars.sh) +# - Python venv at .venv/bin/python (PyTorch 2.13.0+xpu) +# - a rebuilt extension with the sage_sparse binding under +# auto_round_kernel/ark-xbuild/ (on `main`: rebuild after `git checkout main`) +# - a free GPU (pick via ZE_AFFINITY_MASK_VALUE; check `xpu-smi ps` first) +# +# IMPORTANT +# The default preprocess backend is triton-xpu (via "auto"), which is faster +# than torch. On some level-zero drivers triton's JIT emitted SPIR-V that the +# runtime rejected ("InvalidModule: ... unknown extension +# 'SPV_INTEL_predicated_io'); that is fixed by auto_round_kernel/_xpu_triton_compat.py +# (forces has_predicated_io off), applied when the triton preprocess loads. +# If you still hit the SPIR-V error, force the torch backend: +# export SAGE_ATTN_SPARSE_PREPROCESS_BACKEND=torch +# +# BENCHMARK CONFIG (bench_sparse_topk.py defaults on `main`) +# dtype=fp16 batch=1 num_heads_q/kv=40 head_dim=128 qtile=256 +# seq_len=[32768, 75600] tensor_layout=[HND, NHD] topk=[0.5, 0.25, 0.125] +# warmup=2 iters=3 +# +# NOTE +# First call per process pays a ~30s SYCL JIT for the sparse kernels; the full +# sweep takes roughly 15-25 minutes on one GPU. + +set -euo pipefail + +SCRIPT_PATH="$(readlink -f "$0")" +REPO_ROOT="$(cd "$(dirname "${SCRIPT_PATH}")/.." && pwd)" +PYTHON_BIN="${REPO_ROOT}/.venv/bin/python" +OUTPUT_DIR="${OUTPUT_DIR:-${REPO_ROOT}/benchmarks/results}" +ZE_AFFINITY_MASK_VALUE="${ZE_AFFINITY_MASK_VALUE:-6}" +XPU_SO="${XPU_SO:-}" +BENCH_ARGS="${BENCH_ARGS:-}" + +cd "${REPO_ROOT}" + +# Re-exec under the render/video groups if this shell does not already have them. +if [[ "${1:-}" == "--inner" ]]; then + shift +elif command -v sg >/dev/null 2>&1; then + current_groups="$(id -nG || true)" + if [[ ! " ${current_groups} " =~ [[:space:]]render[[:space:]] ]] || [[ ! " ${current_groups} " =~ [[:space:]]video[[:space:]] ]]; then + exec sg render -c "sg video -c '${SCRIPT_PATH} --inner'" + fi +fi + +# oneAPI's setvars.sh is not compatible with `set -u` in this environment. +set +u +source /opt/intel/oneapi/setvars.sh --force >/dev/null 2>&1 +set -u + +export MKLROOT=/opt/intel/oneapi/mkl/2026.1 +export ZE_AFFINITY_MASK="${ZE_AFFINITY_MASK_VALUE}" +# Required: the triton-xpu preprocess backend is rejected by the runtime on this +# machine (SPV_INTEL_predicated_io); force the torch backend. +# export SAGE_ATTN_SPARSE_PREPROCESS_BACKEND=torch + +if [[ -z "${XPU_SO}" ]]; then + XPU_SO="$(find "${REPO_ROOT}/auto_round_kernel/ark-xbuild" -maxdepth 1 -name 'auto_round_kernel_xpu*.so' | sort | tail -n 1)" +fi +if [[ -z "${XPU_SO}" ]]; then + echo "error: no auto_round_kernel_xpu*.so found under auto_round_kernel/ark-xbuild" >&2 + exit 1 +fi + +mkdir -p "${OUTPUT_DIR}" +stamp="$(date +%Y%m%d_%H%M%S)" +out_csv="${OUTPUT_DIR}/sparse_sagev1_${stamp}.csv" +out_log="${OUTPUT_DIR}/sparse_sagev1_${stamp}.log" + +echo "XPU process snapshot:" +xpu-smi ps || true +echo +echo "Running sparse SAGE v1 SDPA benchmark on device ${ZE_AFFINITY_MASK}..." +echo "Using XPU extension: ${XPU_SO}" +echo "CSV: ${out_csv}" +echo "Log: ${out_log}" +echo + +# shellcheck disable=SC2086 +"${PYTHON_BIN}" benchmarks/bench_sparse_topk.py \ + --xpu-so "${XPU_SO}" \ + --output-csv "${out_csv}" \ + ${BENCH_ARGS} 2>&1 | tee "${out_log}" From de2eca03c2402ed32f72ca05e2de87f0fa6af8a9 Mon Sep 17 00:00:00 2001 From: yiliu30 Date: Thu, 6 Aug 2026 11:39:12 +0800 Subject: [PATCH 15/42] fix: work around triton SPV_INTEL_predicated_io on Intel XPU triton 3.7.x enables has_predicated_io for non-LTS Intel GPU drivers and emits the SPV_INTEL_predicated_io SPIR-V extension. Some level-zero loaders (e.g. 1.13.35563) reject that extension at kernel load time, aborting every triton kernel launch with 'InvalidModule: ... unknown extension'. Since it is not a catchable Python exception, the torch preprocess fallback never ran and the whole benchmark died. Add auto_round_kernel/_xpu_triton_compat.py which patches the intel triton backend to force has_predicated_io off (standard predicated loads instead), and apply it when the triton preprocess module loads. The triton-xpu preprocess backend now works and is faster than torch (seq 32768/50% e2e: 327ms -> 206ms). --- .../auto_round_kernel/_xpu_triton_compat.py | 49 +++++++++++++++++++ .../sparge_preprocess_triton.py | 4 ++ 2 files changed, 53 insertions(+) create mode 100644 auto_round_extension/ark/auto_round_kernel/_xpu_triton_compat.py diff --git a/auto_round_extension/ark/auto_round_kernel/_xpu_triton_compat.py b/auto_round_extension/ark/auto_round_kernel/_xpu_triton_compat.py new file mode 100644 index 0000000000..f86e7f5763 --- /dev/null +++ b/auto_round_extension/ark/auto_round_kernel/_xpu_triton_compat.py @@ -0,0 +1,49 @@ +# # Copyright (C) 2026 Intel Corporation +# # SPDX-License-Identifier: Apache-2.0 + +"""Workarounds for the Intel XPU triton backend on this platform. + +Triton 3.7.x enables ``has_predicated_io`` for non-LTS Intel GPU drivers and then +emits the ``SPV_INTEL_predicated_io`` SPIR-V extension in its JIT kernels. Some +level-zero loaders (e.g. 1.13.35563) reject that extension at kernel-load time:: + + InvalidModule: Invalid SPIR-V module: input SPIR-V module uses unknown + extension 'SPV_INTEL_predicated_io' + +which aborts any triton kernel launch and kills the process (it is not a +catchable Python exception, so the torch preprocess fallback never runs). + +The workaround forces ``has_predicated_io`` off so triton emits standard +(non-extension) predicated loads/stores. This disables a minor codegen +optimization on every XPU driver, but the ark triton preprocess kernels are small +and the trade-off is only safe correctness on the affected drivers. +""" + +import logging + +logger = logging.getLogger(__name__) + +_APPLIED = False + + +def apply_xpu_triton_workarounds() -> None: + """Patch triton's Intel compiler so JIT kernels avoid the rejected SPIR-V extension.""" + global _APPLIED + if _APPLIED: + return + try: + import triton.backends.intel.compiler as _intel_compiler + except ImportError: + # No intel triton backend (e.g. stock triton); nothing to patch. + return + + _orig_parse_target = _intel_compiler.XPUBackend.parse_target + + def _patched_parse_target(self, tgt_prop): + dev_prop = _orig_parse_target(self, tgt_prop) + dev_prop["has_predicated_io"] = False + return dev_prop + + _intel_compiler.XPUBackend.parse_target = _patched_parse_target + _APPLIED = True + logger.info("Applied XPU triton workaround: has_predicated_io forced off (SPV_INTEL_predicated_io)") diff --git a/auto_round_extension/ark/auto_round_kernel/sparge_preprocess_triton.py b/auto_round_extension/ark/auto_round_kernel/sparge_preprocess_triton.py index d18eab1d71..420529f0b9 100644 --- a/auto_round_extension/ark/auto_round_kernel/sparge_preprocess_triton.py +++ b/auto_round_extension/ark/auto_round_kernel/sparge_preprocess_triton.py @@ -10,6 +10,10 @@ import triton import triton.language as tl +from ._xpu_triton_compat import apply_xpu_triton_workarounds + +apply_xpu_triton_workarounds() + logger = logging.getLogger(__name__) _TRITON_FALLBACK_WARNING_LOGGED = False From 31fad6e30ddf60cf1c918423e13b3a08cedf5219 Mon Sep 17 00:00:00 2001 From: yiliu30 Date: Thu, 6 Aug 2026 13:17:38 +0800 Subject: [PATCH 16/42] docs: default BF16 benchmark runner to triton preprocess backend run_bf16_sparse_bench.sh now uses the triton_xpu preprocess backend by default (override via SPARGE_PREPROCESS_BACKEND=torch), enabled by the SPV_INTEL_predicated_io workaround in _xpu_triton_compat. This lifts BF16 sparse e2e speedup vs dense torch SDPA to 1.1-1.3x at 50% selection and ~4x at 12.5%. 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Native-precision path applies the softmax scale directly and carries no INT8 dequant baggage, unlike the shared sparse-SAGE path. --- .../ark/auto_round_kernel/ark.cpp | 93 +++++ .../auto_round_kernel/sdpa_sparse_sdpa.cpp | 330 ++++++++++++++++++ .../wrapper/include/sycl_tla_common.hpp | 48 +++ .../wrapper/include/sycl_tla_sdpa_sparse.hpp | 166 +++++++++ 4 files changed, 637 insertions(+) create mode 100644 auto_round_extension/ark/auto_round_kernel/sdpa_sparse_sdpa.cpp diff --git a/auto_round_extension/ark/auto_round_kernel/ark.cpp b/auto_round_extension/ark/auto_round_kernel/ark.cpp index 20cd9d6974..57dd6ecb7d 100755 --- a/auto_round_extension/ark/auto_round_kernel/ark.cpp +++ b/auto_round_extension/ark/auto_round_kernel/ark.cpp @@ -537,6 +537,98 @@ static void sage_sparse_bf16(torch_ptr stream, torch_ptr Q, torch_ptr K, torch_p throw std::invalid_argument("ark::sage_sparse_bf16: unsupported head_dim; supported values are 64 and 128"); } +static void sage_sparse_sdpa(torch_ptr stream, torch_ptr Q, torch_ptr K, torch_ptr V, torch_ptr O, torch_ptr mask, + torch_ptr lut, torch_ptr valid_block_num, int num_q_blocks, int num_k_blocks, + int q_tile_override, int q_stride_s, int q_stride_d, int q_stride_h, int q_stride_b, + int k_stride_s, int k_stride_d, int k_stride_h, int k_stride_b, int v_stride_d, + int v_stride_s, int v_stride_h, int v_stride_b, int o_stride_s, int o_stride_d, + int o_stride_h, int o_stride_b, int q_dtype, int batch, int num_heads_q, + int num_heads_kv, int seq_len_q, int seq_len_kv, int head_dim, float softmax_scale, + bool is_causal) { + if (q_dtype != (int)FlashAttnDtype::FP16 && q_dtype != (int)FlashAttnDtype::BF16) { + throw std::invalid_argument("ark::sage_sparse_sdpa: q_dtype must be FP16 or BF16"); + } + if (mask && is_causal) { + throw std::invalid_argument("ark::sage_sparse_sdpa: mask and is_causal cannot both be set"); + } + if (!lut || !valid_block_num) { + throw std::invalid_argument("ark::sage_sparse_sdpa: lut and valid_block_num must be provided"); + } + auto matches_block_size = [](int seq_len, int num_blocks, int block_size) { + return block_size > 0 && num_blocks == ((seq_len + block_size - 1) / block_size); + }; + const bool key_block_is_64 = matches_block_size(seq_len_kv, num_k_blocks, 64); + if (!key_block_is_64) { + throw std::invalid_argument("ark::sage_sparse_sdpa: only key block size 64 is supported"); + } + const bool is_bf16 = (q_dtype == (int)FlashAttnDtype::BF16); + if (head_dim == 64) { + if (!matches_block_size(seq_len_q, num_q_blocks, 64)) { + throw std::invalid_argument("ark::sage_sparse_sdpa: head_dim=64 requires query block size 64"); + } + if (is_bf16) { + ark::sdpa_impl_bf16_sparse_sdpa_d64( + (sycl::queue*)stream, (void*)Q, (void*)K, (void*)V, (void*)O, (void*)mask, (void*)lut, + (void*)valid_block_num, num_q_blocks, num_k_blocks, q_tile_override, q_stride_s, q_stride_d, q_stride_h, + q_stride_b, k_stride_s, k_stride_d, k_stride_h, k_stride_b, v_stride_d, v_stride_s, v_stride_h, + v_stride_b, o_stride_s, o_stride_d, o_stride_h, o_stride_b, batch, num_heads_q, num_heads_kv, seq_len_q, + seq_len_kv, head_dim, softmax_scale, is_causal); + } else { + ark::sdpa_impl_fp16_sparse_sdpa_d64( + (sycl::queue*)stream, (void*)Q, (void*)K, (void*)V, (void*)O, (void*)mask, (void*)lut, + (void*)valid_block_num, num_q_blocks, num_k_blocks, q_tile_override, q_stride_s, q_stride_d, q_stride_h, + q_stride_b, k_stride_s, k_stride_d, k_stride_h, k_stride_b, v_stride_d, v_stride_s, v_stride_h, + v_stride_b, o_stride_s, o_stride_d, o_stride_h, o_stride_b, batch, num_heads_q, num_heads_kv, seq_len_q, + seq_len_kv, head_dim, softmax_scale, is_causal); + } + return; + } + if (head_dim == 128) { + if (matches_block_size(seq_len_q, num_q_blocks, 256)) { + if (q_tile_override != 256) { + throw std::invalid_argument( + "ark::sage_sparse_sdpa: head_dim=128 query block size 256 requires q_tile_override=256"); + } + if (is_bf16) { + ark::sdpa_impl_bf16_sparse_sdpa_qtile256_row64k( + (sycl::queue*)stream, (void*)Q, (void*)K, (void*)V, (void*)O, (void*)mask, (void*)lut, + (void*)valid_block_num, num_q_blocks, num_k_blocks, q_tile_override, q_stride_s, q_stride_d, q_stride_h, + q_stride_b, k_stride_s, k_stride_d, k_stride_h, k_stride_b, v_stride_d, v_stride_s, v_stride_h, + v_stride_b, o_stride_s, o_stride_d, o_stride_h, o_stride_b, batch, num_heads_q, num_heads_kv, seq_len_q, + seq_len_kv, head_dim, softmax_scale, is_causal); + } else { + ark::sdpa_impl_fp16_sparse_sdpa_qtile256_row64k( + (sycl::queue*)stream, (void*)Q, (void*)K, (void*)V, (void*)O, (void*)mask, (void*)lut, + (void*)valid_block_num, num_q_blocks, num_k_blocks, q_tile_override, q_stride_s, q_stride_d, q_stride_h, + q_stride_b, k_stride_s, k_stride_d, k_stride_h, k_stride_b, v_stride_d, v_stride_s, v_stride_h, + v_stride_b, o_stride_s, o_stride_d, o_stride_h, o_stride_b, batch, num_heads_q, num_heads_kv, seq_len_q, + seq_len_kv, head_dim, softmax_scale, is_causal); + } + return; + } + if (matches_block_size(seq_len_q, num_q_blocks, 64)) { + if (is_bf16) { + ark::sdpa_impl_bf16_sparse_sdpa_row_linear( + (sycl::queue*)stream, (void*)Q, (void*)K, (void*)V, (void*)O, (void*)mask, (void*)lut, + (void*)valid_block_num, num_q_blocks, num_k_blocks, q_tile_override, q_stride_s, q_stride_d, q_stride_h, + q_stride_b, k_stride_s, k_stride_d, k_stride_h, k_stride_b, v_stride_d, v_stride_s, v_stride_h, + v_stride_b, o_stride_s, o_stride_d, o_stride_h, o_stride_b, batch, num_heads_q, num_heads_kv, seq_len_q, + seq_len_kv, head_dim, softmax_scale, is_causal); + } else { + ark::sdpa_impl_fp16_sparse_sdpa_row_linear( + (sycl::queue*)stream, (void*)Q, (void*)K, (void*)V, (void*)O, (void*)mask, (void*)lut, + (void*)valid_block_num, num_q_blocks, num_k_blocks, q_tile_override, q_stride_s, q_stride_d, q_stride_h, + q_stride_b, k_stride_s, k_stride_d, k_stride_h, k_stride_b, v_stride_d, v_stride_s, v_stride_h, + v_stride_b, o_stride_s, o_stride_d, o_stride_h, o_stride_b, batch, num_heads_q, num_heads_kv, seq_len_q, + seq_len_kv, head_dim, softmax_scale, is_causal); + } + return; + } + throw std::invalid_argument("ark::sage_sparse_sdpa: head_dim=128 supports query block sizes 64 and 256 only"); + } + throw std::invalid_argument("ark::sage_sparse_sdpa: unsupported head_dim; supported values are 64 and 128"); +} + static void moe_gemm_wrapper(torch_ptr stream, torch_ptr activations, torch_ptr weights, torch_ptr scales, torch_ptr outputs, int dtype, int N, int K, torch_ptr num_tokens_per_expert, int num_experts) { @@ -844,6 +936,7 @@ PYBIND11_MODULE(PY_NAME, m) { pybind11::arg("tensor_layout"), pybind11::arg("lse") = 0); m.def("sage_sparse", &ark::sage_sparse); m.def("sage_sparse_bf16", &ark::sage_sparse_bf16); + m.def("sage_sparse_sdpa", &ark::sage_sparse_sdpa); // Low-level SAGE PVi8 API: input Q/K/V are pre-quantized int8 with qscale/kscale/vscale. m.def("sage_pvi8", &ark::sage_pvi8, pybind11::arg("stream"), pybind11::arg("Q"), pybind11::arg("K"), pybind11::arg("V"), pybind11::arg("O"), pybind11::arg("mask"), diff --git a/auto_round_extension/ark/auto_round_kernel/sdpa_sparse_sdpa.cpp b/auto_round_extension/ark/auto_round_kernel/sdpa_sparse_sdpa.cpp new file mode 100644 index 0000000000..2a69823c51 --- /dev/null +++ b/auto_round_extension/ark/auto_round_kernel/sdpa_sparse_sdpa.cpp @@ -0,0 +1,330 @@ +// Copyright (c) 2026 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +// Independent native-precision sparse SDPA path (BF16 + FP16). This dispatches to +// the sparse SDPA mainloop (SPARSESDPAFwdMainloop via SparseSDPAConfig), separate +// from the INT8-centric sparse SAGE path in sdpa_sparse.cpp / sdpa_sparse_bf16.cpp. + +#if defined(ARK_XPU) && defined(ARK_SYCL_TLA) + +#include +#include +#include "bestla/bestla.h" + +#include +#include "sycl_tla_sdpa_sparse.hpp" + +namespace ark { + +namespace detail = sparse_detail; + +namespace { + +using KernelLauncher = int (*)(detail::Options const& options); + +int launch_prefill_kernel_bf16_128_sparse_sdpa(detail::Options const& options) { + return detail::launch_sparse_sdpa_prefill_kernel_128(options); +} + +int launch_prefill_kernel_bf16_128_sparse_sdpa_qtile64(detail::Options const& options) { + return detail::launch_sparse_sdpa_prefill_kernel_128_qtile64< + cute::bfloat16_t, cute::bfloat16_t, cute::bfloat16_t>(options); +} + +int launch_prefill_kernel_bf16_64_sparse_sdpa(detail::Options const& options) { + return detail::launch_sparse_sdpa_prefill_kernel_64(options); +} + +int launch_prefill_kernel_f16_128_sparse_sdpa(detail::Options const& options) { + return detail::launch_sparse_sdpa_prefill_kernel_128(options); +} + +int launch_prefill_kernel_f16_128_sparse_sdpa_qtile64(detail::Options const& options) { + return detail::launch_sparse_sdpa_prefill_kernel_128_qtile64(options); +} + +int launch_prefill_kernel_f16_64_sparse_sdpa(detail::Options const& options) { + return detail::launch_sparse_sdpa_prefill_kernel_64(options); +} + +KernelLauncher select_sparse_sdpa_prefill_launcher(BTLA_DTYPE q_dtype, int head_dim, int q_tile_override) { + switch (head_dim) { + case 128: + if (q_tile_override == 64) { + return q_dtype == BTLA_DTYPE::BF16 ? launch_prefill_kernel_bf16_128_sparse_sdpa_qtile64 + : launch_prefill_kernel_f16_128_sparse_sdpa_qtile64; + } + if (q_tile_override != 0 && q_tile_override != 256) return nullptr; + return q_dtype == BTLA_DTYPE::BF16 ? launch_prefill_kernel_bf16_128_sparse_sdpa + : launch_prefill_kernel_f16_128_sparse_sdpa; + case 64: + if (q_tile_override != 0 && q_tile_override != 64 && q_tile_override != 128) return nullptr; + return q_dtype == BTLA_DTYPE::BF16 ? launch_prefill_kernel_bf16_64_sparse_sdpa + : launch_prefill_kernel_f16_64_sparse_sdpa; + default: + return nullptr; + } +} + +detail::Options make_common_options(void* Q_ptr, void* K_ptr, void* V_ptr, void* O_ptr, void* mask, int q_stride_s, + int q_stride_d, int q_stride_h, int q_stride_b, int k_stride_s, int k_stride_d, + int k_stride_h, int k_stride_b, int v_stride_d, int v_stride_s, int v_stride_h, + int v_stride_b, int o_stride_s, int o_stride_d, int o_stride_h, int o_stride_b, + int batch, int num_heads_q, int num_heads_kv, int seq_len_q, int seq_len_kv, + int head_dim, float softmax_scale, bool is_causal) { + if (q_stride_d != 1 || k_stride_d != 1 || v_stride_d != 1 || o_stride_d != 1) { + throw std::invalid_argument("make_common_options: head-dim stride must be 1 for Q/K/V/O"); + } + detail::Options options; + options.q = Q_ptr; + options.k = K_ptr; + options.v = V_ptr; + options.mask = mask; + options.o = O_ptr; + options.use_tensor_strides = true; + options.q_stride_s = q_stride_s; + options.q_stride_d = q_stride_d; + options.q_stride_h = q_stride_h; + options.q_stride_b = q_stride_b; + options.k_stride_s = k_stride_s; + options.k_stride_d = k_stride_d; + options.k_stride_h = k_stride_h; + options.k_stride_b = k_stride_b; + options.v_stride_d = v_stride_d; + options.v_stride_s = v_stride_s; + options.v_stride_h = v_stride_h; + options.v_stride_b = v_stride_b; + options.o_stride_s = o_stride_s; + options.o_stride_d = o_stride_d; + options.o_stride_h = o_stride_h; + options.o_stride_b = o_stride_b; + options.batch = batch; + options.num_heads_q = num_heads_q; + options.num_heads_kv = num_heads_kv; + options.seq_len_qo = seq_len_q; + options.seq_len_kv = seq_len_kv; + options.head_size_qk = head_dim; + options.head_size_vo = head_dim; + options.softmax_scale = softmax_scale; + options.is_causal = is_causal; + return options; +} + +void sparse_sdpa_prefill(sycl::queue* q, void* Q_ptr, void* K_ptr, void* V_ptr, void* O_ptr, void* mask, void* lut, + void* valid_block_num, int num_q_blocks, int num_k_blocks, int q_tile_override, + int q_stride_s, int q_stride_d, int q_stride_h, int q_stride_b, int k_stride_s, + int k_stride_d, int k_stride_h, int k_stride_b, int v_stride_d, int v_stride_s, + int v_stride_h, int v_stride_b, int o_stride_s, int o_stride_d, int o_stride_h, + int o_stride_b, int batch, int num_heads_q, int num_heads_kv, int seq_len_q, int seq_len_kv, + int head_dim, float softmax_scale, bool is_causal, BTLA_DTYPE q_dtype, + int sparse_q_block_size = 0) { + const int effective_q_tile_override = + (head_dim == 128 && q_tile_override == 0) ? 64 : ((head_dim == 64 && q_tile_override == 0) ? 64 : q_tile_override); + detail::Options options = + make_common_options(Q_ptr, K_ptr, V_ptr, O_ptr, mask, q_stride_s, q_stride_d, q_stride_h, q_stride_b, + k_stride_s, k_stride_d, k_stride_h, k_stride_b, v_stride_d, v_stride_s, v_stride_h, + v_stride_b, o_stride_s, o_stride_d, o_stride_h, o_stride_b, batch, num_heads_q, + num_heads_kv, seq_len_q, seq_len_kv, head_dim, softmax_scale, is_causal); + // Native-precision path: no INT8 dequant scales, softmax scale applied directly. + options.scale_block_size = 0; + options.sparse_q_block_size = sparse_q_block_size; + options.q_tile_override = effective_q_tile_override; + options.qscale = nullptr; + options.kscale = nullptr; + options.vscale = nullptr; + options.lut = static_cast(lut); + options.valid_block_num = static_cast(valid_block_num); + options.num_q_blocks = num_q_blocks; + options.num_k_blocks = num_k_blocks; + compat::set_default_queue(*q); + + KernelLauncher launcher = select_sparse_sdpa_prefill_launcher(q_dtype, head_dim, effective_q_tile_override); + if (launcher == nullptr) { + throw std::runtime_error("Unsupported sparse_sdpa_prefill config"); + } + + launcher(options); +} + +} // namespace + +void sdpa_impl_bf16_sparse_sdpa_d64( + sycl::queue* q, void* Q_ptr, void* K_ptr, void* V_ptr, void* O_ptr, void* mask, void* lut, void* valid_block_num, + int num_q_blocks, int num_k_blocks, int q_tile_override, int q_stride_s, int q_stride_d, int q_stride_h, + int q_stride_b, int k_stride_s, int k_stride_d, int k_stride_h, int k_stride_b, int v_stride_d, int v_stride_s, + int v_stride_h, int v_stride_b, int o_stride_s, int o_stride_d, int o_stride_h, int o_stride_b, int batch, + int num_heads_q, int num_heads_kv, int seq_len_q, int seq_len_kv, int head_dim, float softmax_scale, + bool is_causal) { + if (mask && is_causal) { + throw std::invalid_argument("sdpa_impl_bf16_sparse_sdpa_d64: mask and is_causal cannot both be set"); + } + if (seq_len_q <= 0 || seq_len_kv <= 0) { + throw std::invalid_argument("sdpa_impl_bf16_sparse_sdpa_d64: seq_len_q and seq_len_kv must be greater than 0"); + } + if (lut == nullptr || valid_block_num == nullptr) { + throw std::invalid_argument("sdpa_impl_bf16_sparse_sdpa_d64: lut and valid_block_num must be provided"); + } + if (num_q_blocks <= 0 || num_k_blocks <= 0) { + throw std::invalid_argument("sdpa_impl_bf16_sparse_sdpa_d64: num_q_blocks and num_k_blocks must be greater than 0"); + } + if (head_dim != 64) { + throw std::invalid_argument("sdpa_impl_bf16_sparse_sdpa_d64: head_dim must be 64"); + } + + sparse_sdpa_prefill(q, Q_ptr, K_ptr, V_ptr, O_ptr, mask, lut, valid_block_num, num_q_blocks, num_k_blocks, + q_tile_override, q_stride_s, q_stride_d, q_stride_h, q_stride_b, k_stride_s, k_stride_d, + k_stride_h, k_stride_b, v_stride_d, v_stride_s, v_stride_h, v_stride_b, o_stride_s, o_stride_d, + o_stride_h, o_stride_b, batch, num_heads_q, num_heads_kv, seq_len_q, seq_len_kv, head_dim, + softmax_scale, is_causal, BTLA_DTYPE::BF16, /*sparse_q_block_size=*/64); +} + +void sdpa_impl_fp16_sparse_sdpa_d64( + sycl::queue* q, void* Q_ptr, void* K_ptr, void* V_ptr, void* O_ptr, void* mask, void* lut, void* valid_block_num, + int num_q_blocks, int num_k_blocks, int q_tile_override, int q_stride_s, int q_stride_d, int q_stride_h, + int q_stride_b, int k_stride_s, int k_stride_d, int k_stride_h, int k_stride_b, int v_stride_d, int v_stride_s, + int v_stride_h, int v_stride_b, int o_stride_s, int o_stride_d, int o_stride_h, int o_stride_b, int batch, + int num_heads_q, int num_heads_kv, int seq_len_q, int seq_len_kv, int head_dim, float softmax_scale, + bool is_causal) { + if (mask && is_causal) { + throw std::invalid_argument("sdpa_impl_fp16_sparse_sdpa_d64: mask and is_causal cannot both be set"); + } + if (seq_len_q <= 0 || seq_len_kv <= 0) { + throw std::invalid_argument("sdpa_impl_fp16_sparse_sdpa_d64: seq_len_q and seq_len_kv must be greater than 0"); + } + if (lut == nullptr || valid_block_num == nullptr) { + throw std::invalid_argument("sdpa_impl_fp16_sparse_sdpa_d64: lut and valid_block_num must be provided"); + } + if (num_q_blocks <= 0 || num_k_blocks <= 0) { + throw std::invalid_argument("sdpa_impl_fp16_sparse_sdpa_d64: num_q_blocks and num_k_blocks must be greater than 0"); + } + if (head_dim != 64) { + throw std::invalid_argument("sdpa_impl_fp16_sparse_sdpa_d64: head_dim must be 64"); + } + + sparse_sdpa_prefill(q, Q_ptr, K_ptr, V_ptr, O_ptr, mask, lut, valid_block_num, num_q_blocks, num_k_blocks, + q_tile_override, q_stride_s, q_stride_d, q_stride_h, q_stride_b, k_stride_s, k_stride_d, + k_stride_h, k_stride_b, v_stride_d, v_stride_s, v_stride_h, v_stride_b, o_stride_s, o_stride_d, + o_stride_h, o_stride_b, batch, num_heads_q, num_heads_kv, seq_len_q, seq_len_kv, head_dim, + softmax_scale, is_causal, BTLA_DTYPE::F16, /*sparse_q_block_size=*/64); +} + +void sdpa_impl_bf16_sparse_sdpa_row_linear( + sycl::queue* q, void* Q_ptr, void* K_ptr, void* V_ptr, void* O_ptr, void* mask, void* lut, void* valid_block_num, + int num_q_blocks, int num_k_blocks, int q_tile_override, int q_stride_s, int q_stride_d, int q_stride_h, + int q_stride_b, int k_stride_s, int k_stride_d, int k_stride_h, int k_stride_b, int v_stride_d, int v_stride_s, + int v_stride_h, int v_stride_b, int o_stride_s, int o_stride_d, int o_stride_h, int o_stride_b, int batch, + int num_heads_q, int num_heads_kv, int seq_len_q, int seq_len_kv, int head_dim, float softmax_scale, + bool is_causal) { + if (q_tile_override != 0 && q_tile_override != 64) { + throw std::invalid_argument("sdpa_impl_bf16_sparse_sdpa_row_linear: q_tile_override must be 0 or 64"); + } + if (mask && is_causal) { + throw std::invalid_argument("sdpa_impl_bf16_sparse_sdpa_row_linear: mask and is_causal cannot both be set"); + } + if (seq_len_q <= 0 || seq_len_kv <= 0) { + throw std::invalid_argument( + "sdpa_impl_bf16_sparse_sdpa_row_linear: seq_len_q and seq_len_kv must be greater than 0"); + } + if (lut == nullptr || valid_block_num == nullptr) { + throw std::invalid_argument("sdpa_impl_bf16_sparse_sdpa_row_linear: lut and valid_block_num must be provided"); + } + if (num_q_blocks <= 0 || num_k_blocks <= 0) { + throw std::invalid_argument( + "sdpa_impl_bf16_sparse_sdpa_row_linear: num_q_blocks and num_k_blocks must be greater than 0"); + } + + sparse_sdpa_prefill(q, Q_ptr, K_ptr, V_ptr, O_ptr, mask, lut, valid_block_num, num_q_blocks, num_k_blocks, 64, + q_stride_s, q_stride_d, q_stride_h, q_stride_b, k_stride_s, k_stride_d, k_stride_h, + k_stride_b, v_stride_d, v_stride_s, v_stride_h, v_stride_b, o_stride_s, o_stride_d, o_stride_h, + o_stride_b, batch, num_heads_q, num_heads_kv, seq_len_q, seq_len_kv, head_dim, softmax_scale, + is_causal, BTLA_DTYPE::BF16, /*sparse_q_block_size=*/64); +} + +void sdpa_impl_fp16_sparse_sdpa_row_linear( + sycl::queue* q, void* Q_ptr, void* K_ptr, void* V_ptr, void* O_ptr, void* mask, void* lut, void* valid_block_num, + int num_q_blocks, int num_k_blocks, int q_tile_override, int q_stride_s, int q_stride_d, int q_stride_h, + int q_stride_b, int k_stride_s, int k_stride_d, int k_stride_h, int k_stride_b, int v_stride_d, int v_stride_s, + int v_stride_h, int v_stride_b, int o_stride_s, int o_stride_d, int o_stride_h, int o_stride_b, int batch, + int num_heads_q, int num_heads_kv, int seq_len_q, int seq_len_kv, int head_dim, float softmax_scale, + bool is_causal) { + if (q_tile_override != 0 && q_tile_override != 64) { + throw std::invalid_argument("sdpa_impl_fp16_sparse_sdpa_row_linear: q_tile_override must be 0 or 64"); + } + if (mask && is_causal) { + throw std::invalid_argument("sdpa_impl_fp16_sparse_sdpa_row_linear: mask and is_causal cannot both be set"); + } + if (seq_len_q <= 0 || seq_len_kv <= 0) { + throw std::invalid_argument( + "sdpa_impl_fp16_sparse_sdpa_row_linear: seq_len_q and seq_len_kv must be greater than 0"); + } + if (lut == nullptr || valid_block_num == nullptr) { + throw std::invalid_argument("sdpa_impl_fp16_sparse_sdpa_row_linear: lut and valid_block_num must be provided"); + } + if (num_q_blocks <= 0 || num_k_blocks <= 0) { + throw std::invalid_argument( + "sdpa_impl_fp16_sparse_sdpa_row_linear: num_q_blocks and num_k_blocks must be greater than 0"); + } + + sparse_sdpa_prefill(q, Q_ptr, K_ptr, V_ptr, O_ptr, mask, lut, valid_block_num, num_q_blocks, num_k_blocks, 64, + q_stride_s, q_stride_d, q_stride_h, q_stride_b, k_stride_s, k_stride_d, k_stride_h, + k_stride_b, v_stride_d, v_stride_s, v_stride_h, v_stride_b, o_stride_s, o_stride_d, o_stride_h, + o_stride_b, batch, num_heads_q, num_heads_kv, seq_len_q, seq_len_kv, head_dim, softmax_scale, + is_causal, BTLA_DTYPE::F16, /*sparse_q_block_size=*/64); +} + +void sdpa_impl_bf16_sparse_sdpa_qtile256_row64k( + sycl::queue* q, void* Q_ptr, void* K_ptr, void* V_ptr, void* O_ptr, void* mask, void* lut, void* valid_block_num, + int num_q_blocks, int num_k_blocks, int q_tile_override, int q_stride_s, int q_stride_d, int q_stride_h, + int q_stride_b, int k_stride_s, int k_stride_d, int k_stride_h, int k_stride_b, int v_stride_d, int v_stride_s, + int v_stride_h, int v_stride_b, int o_stride_s, int o_stride_d, int o_stride_h, int o_stride_b, int batch, + int num_heads_q, int num_heads_kv, int seq_len_q, int seq_len_kv, int head_dim, float softmax_scale, + bool is_causal) { + if (q_tile_override != 256) { + throw std::invalid_argument("sdpa_impl_bf16_sparse_sdpa_qtile256_row64k: q_tile_override must be 256"); + } + if (head_dim != 128) { + throw std::invalid_argument("sdpa_impl_bf16_sparse_sdpa_qtile256_row64k: head_dim must be 128"); + } + + sparse_sdpa_prefill(q, Q_ptr, K_ptr, V_ptr, O_ptr, mask, lut, valid_block_num, num_q_blocks, num_k_blocks, + q_tile_override, q_stride_s, q_stride_d, q_stride_h, q_stride_b, k_stride_s, k_stride_d, + k_stride_h, k_stride_b, v_stride_d, v_stride_s, v_stride_h, v_stride_b, o_stride_s, o_stride_d, + o_stride_h, o_stride_b, batch, num_heads_q, num_heads_kv, seq_len_q, seq_len_kv, head_dim, + softmax_scale, is_causal, BTLA_DTYPE::BF16, /*sparse_q_block_size=*/256); +} + +void sdpa_impl_fp16_sparse_sdpa_qtile256_row64k( + sycl::queue* q, void* Q_ptr, void* K_ptr, void* V_ptr, void* O_ptr, void* mask, void* lut, void* valid_block_num, + int num_q_blocks, int num_k_blocks, int q_tile_override, int q_stride_s, int q_stride_d, int q_stride_h, + int q_stride_b, int k_stride_s, int k_stride_d, int k_stride_h, int k_stride_b, int v_stride_d, int v_stride_s, + int v_stride_h, int v_stride_b, int o_stride_s, int o_stride_d, int o_stride_h, int o_stride_b, int batch, + int num_heads_q, int num_heads_kv, int seq_len_q, int seq_len_kv, int head_dim, float softmax_scale, + bool is_causal) { + if (q_tile_override != 256) { + throw std::invalid_argument("sdpa_impl_fp16_sparse_sdpa_qtile256_row64k: q_tile_override must be 256"); + } + if (head_dim != 128) { + throw std::invalid_argument("sdpa_impl_fp16_sparse_sdpa_qtile256_row64k: head_dim must be 128"); + } + + sparse_sdpa_prefill(q, Q_ptr, K_ptr, V_ptr, O_ptr, mask, lut, valid_block_num, num_q_blocks, num_k_blocks, + q_tile_override, q_stride_s, q_stride_d, q_stride_h, q_stride_b, k_stride_s, k_stride_d, + k_stride_h, k_stride_b, v_stride_d, v_stride_s, v_stride_h, v_stride_b, o_stride_s, o_stride_d, + o_stride_h, o_stride_b, batch, num_heads_q, num_heads_kv, seq_len_q, seq_len_kv, head_dim, + softmax_scale, is_causal, BTLA_DTYPE::F16, /*sparse_q_block_size=*/256); +} + +} // namespace ark + +#endif // ARK_XPU && ARK_SYCL_TLA diff --git a/auto_round_extension/ark/auto_round_kernel/wrapper/include/sycl_tla_common.hpp b/auto_round_extension/ark/auto_round_kernel/wrapper/include/sycl_tla_common.hpp index c6d6fa9e00..215213b0e0 100644 --- a/auto_round_extension/ark/auto_round_kernel/wrapper/include/sycl_tla_common.hpp +++ b/auto_round_extension/ark/auto_round_kernel/wrapper/include/sycl_tla_common.hpp @@ -279,6 +279,54 @@ void sdpa_impl_bf16_sparse_qtile256_row64k( int v_stride_h, int v_stride_b, int o_stride_s, int o_stride_d, int o_stride_h, int o_stride_b, int batch, int num_heads_q, int num_heads_kv, int seq_len_q, int seq_len_kv, int head_dim, float softmax_scale, bool is_causal); + +void sdpa_impl_bf16_sparse_sdpa_d64( + sycl::queue* q, void* Q_ptr, void* K_ptr, void* V_ptr, void* O_ptr, void* mask, void* lut, void* valid_block_num, + int num_q_blocks, int num_k_blocks, int q_tile_override, int q_stride_s, int q_stride_d, int q_stride_h, + int q_stride_b, int k_stride_s, int k_stride_d, int k_stride_h, int k_stride_b, int v_stride_d, int v_stride_s, + int v_stride_h, int v_stride_b, int o_stride_s, int o_stride_d, int o_stride_h, int o_stride_b, int batch, + int num_heads_q, int num_heads_kv, int seq_len_q, int seq_len_kv, int head_dim, float softmax_scale, + bool is_causal); + +void sdpa_impl_fp16_sparse_sdpa_d64( + sycl::queue* q, void* Q_ptr, void* K_ptr, void* V_ptr, void* O_ptr, void* mask, void* lut, void* valid_block_num, + int num_q_blocks, int num_k_blocks, int q_tile_override, int q_stride_s, int q_stride_d, int q_stride_h, + int q_stride_b, int k_stride_s, int k_stride_d, int k_stride_h, int k_stride_b, int v_stride_d, int v_stride_s, + int v_stride_h, int v_stride_b, int o_stride_s, int o_stride_d, int o_stride_h, int o_stride_b, int batch, + int num_heads_q, int num_heads_kv, int seq_len_q, int seq_len_kv, int head_dim, float softmax_scale, + bool is_causal); + +void sdpa_impl_bf16_sparse_sdpa_row_linear( + sycl::queue* q, void* Q_ptr, void* K_ptr, void* V_ptr, void* O_ptr, void* mask, void* lut, void* valid_block_num, + int num_q_blocks, int num_k_blocks, int q_tile_override, int q_stride_s, int q_stride_d, int q_stride_h, + int q_stride_b, int k_stride_s, int k_stride_d, int k_stride_h, int k_stride_b, int v_stride_d, int v_stride_s, + int v_stride_h, int v_stride_b, int o_stride_s, int o_stride_d, int o_stride_h, int o_stride_b, int batch, + int num_heads_q, int num_heads_kv, int seq_len_q, int seq_len_kv, int head_dim, float softmax_scale, + bool is_causal); + +void sdpa_impl_fp16_sparse_sdpa_row_linear( + sycl::queue* q, void* Q_ptr, void* K_ptr, void* V_ptr, void* O_ptr, void* mask, void* lut, void* valid_block_num, + int num_q_blocks, int num_k_blocks, int q_tile_override, int q_stride_s, int q_stride_d, int q_stride_h, + int q_stride_b, int k_stride_s, int k_stride_d, int k_stride_h, int k_stride_b, int v_stride_d, int v_stride_s, + int v_stride_h, int v_stride_b, int o_stride_s, int o_stride_d, int o_stride_h, int o_stride_b, int batch, + int num_heads_q, int num_heads_kv, int seq_len_q, int seq_len_kv, int head_dim, float softmax_scale, + bool is_causal); + +void sdpa_impl_bf16_sparse_sdpa_qtile256_row64k( + sycl::queue* q, void* Q_ptr, void* K_ptr, void* V_ptr, void* O_ptr, void* mask, void* lut, void* valid_block_num, + int num_q_blocks, int num_k_blocks, int q_tile_override, int q_stride_s, int q_stride_d, int q_stride_h, + int q_stride_b, int k_stride_s, int k_stride_d, int k_stride_h, int k_stride_b, int v_stride_d, int v_stride_s, + int v_stride_h, int v_stride_b, int o_stride_s, int o_stride_d, int o_stride_h, int o_stride_b, int batch, + int num_heads_q, int num_heads_kv, int seq_len_q, int seq_len_kv, int head_dim, float softmax_scale, + bool is_causal); + +void sdpa_impl_fp16_sparse_sdpa_qtile256_row64k( + sycl::queue* q, void* Q_ptr, void* K_ptr, void* V_ptr, void* O_ptr, void* mask, void* lut, void* valid_block_num, + int num_q_blocks, int num_k_blocks, int q_tile_override, int q_stride_s, int q_stride_d, int q_stride_h, + int q_stride_b, int k_stride_s, int k_stride_d, int k_stride_h, int k_stride_b, int v_stride_d, int v_stride_s, + int v_stride_h, int v_stride_b, int o_stride_s, int o_stride_d, int o_stride_h, int o_stride_b, int batch, + int num_heads_q, int num_heads_kv, int seq_len_q, int seq_len_kv, int head_dim, float softmax_scale, + bool is_causal); #endif // ARK_SYCL_TLA } // namespace ark diff --git a/auto_round_extension/ark/auto_round_kernel/wrapper/include/sycl_tla_sdpa_sparse.hpp b/auto_round_extension/ark/auto_round_kernel/wrapper/include/sycl_tla_sdpa_sparse.hpp index 3c935e56d1..1e5e690830 100644 --- a/auto_round_extension/ark/auto_round_kernel/wrapper/include/sycl_tla_sdpa_sparse.hpp +++ b/auto_round_extension/ark/auto_round_kernel/wrapper/include/sycl_tla_sdpa_sparse.hpp @@ -35,6 +35,7 @@ #if defined(ARK_SDPA_ENABLE_SPARSE) #include "stla/xe_sparse_fmha_fwd_epilogue.hpp" #include "stla/xe_sparse_sagev1_fwd_mainloop.hpp" +#include "stla/xe_sparse_sdpa_fwd_mainloop.hpp" #include "stla/xe_sparse_sage_fwd_kernel.hpp" #endif #include "flash_attention_v2/collective/fmha_fusion.hpp" @@ -1048,6 +1049,122 @@ struct SparseSageConfig { return run(options); } }; + +// Independent native-precision sparse path built on the dense SDPA mainloop +// (SPARSESDPAFwdMainloop). Unlike SparseSageConfig it does not dequantize INT8 +// Q/K: Q/K/V are bf16/fp16 and the softmax applies `params.scale` directly. +template , typename StrideK = Stride, + typename StrideV = Stride<_1, int, int, int>, typename StrideO = Stride, + typename GmemTiledCopyQ = void, typename GmemTiledCopyK = void, typename GmemTiledCopyV = void, + typename GmemTiledCopyO = void> +struct SparseSDPAConfig { + static constexpr int SGTileQ = get<0>(shape_div(TileShapeQK{}, shape(SubgroupLayoutQK{})))(); + // Native-precision Q*K MMA: bf16 -> float/bf16 DPAS, fp16 -> float/half DPAS. + using MMAOperation = cute::conditional_t< + is_void_v, + typename cute::conditional_t< + cute::is_same_v || cute::is_same_v, + XE_DPAS_TT, + XE_DPAS_TT>, + MMAOperation_>; + using MMAOperationPV = cute::conditional_t, + XE_DPAS_TT, MMAOperation_>; + using SubgroupLayoutPV = + cute::conditional_t, + decltype(cutlass::fmha::collective::get_sg_layout_pv(SubgroupLayoutQK{})), SubgroupLayoutPV_>; + + template + static int run(const Options& options) { + cutlass::KernelHardwareInfo hw_info; + hw_info.sm_count = cutlass::KernelHardwareInfo::query_device_multiprocessor_count(hw_info.device_id); + using ProblemShapeType = cutlass::fmha::kernel::SparseSageProblemShape; + using TiledMMAQK = typename TiledMMAHelper, Layout, SubgroupLayoutQK>::TiledMMA; + using TiledMMAPV = + typename TiledMMAHelper, Layout, SubgroupLayoutPV>::TiledMMA; + static_assert(get<0>(TileShapeOutput{}) == get<0>(TileShapePV{}), + "Output tile and P*V tile have different sizes in Q dimension"); + constexpr int VTiles = get<1>(TileShapeOutput{}) / get<1>(TileShapePV{}); + auto make_dummy_tensor = [&](auto val, auto stride) { + return make_tensor(make_gmem_ptr(&val), make_layout(repeat>(1), stride)); + }; + using TensorQ = decltype(make_dummy_tensor(ElementQ{}, StrideQ{})); + using TensorK = decltype(make_dummy_tensor(ElementK{}, StrideK{})); + using TensorV = decltype(make_dummy_tensor(ElementV{}, StrideV{})); + using TensorO = decltype(make_dummy_tensor(ElementO{}, StrideO{})); + using TensorK_cache = TensorK; + using TensorV_cache = TensorV; + using GmemTiledCopyK_cache = GmemTiledCopyK; + using GmemTiledCopyV_cache = GmemTiledCopyV; + using MainloopDispatchPolicy = cutlass::sdpa::XeDefault; + if constexpr (Causal) { + using CollectiveMainloop = + cutlass::fmha::collective::SPARSESDPAFwdMainloop; + using CollectiveEpilogue = + cutlass::fmha::collective::SparseFMHAFwdEpilogue; + using FMHAKernel = cutlass::fmha::kernel::XeSparseSageFwdKernel; + SageKernelRunner runner; + CUTLASS_CHECK(runner.run(options, hw_info)); + } else { + if (options.mask) { + using CollectiveMainloop = + cutlass::fmha::collective::SPARSESDPAFwdMainloop; + using CollectiveEpilogue = + cutlass::fmha::collective::SparseFMHAFwdEpilogue; + using FMHAKernel = cutlass::fmha::kernel::XeSparseSageFwdKernel; + SageKernelRunner runner; + CUTLASS_CHECK(runner.run(options, hw_info)); + } else { + using CollectiveMainloop = + cutlass::fmha::collective::SPARSESDPAFwdMainloop; + using CollectiveEpilogue = + cutlass::fmha::collective::SparseFMHAFwdEpilogue; + using FMHAKernel = cutlass::fmha::kernel::XeSparseSageFwdKernel; + SageKernelRunner runner; + CUTLASS_CHECK(runner.run(options, hw_info)); + } + } + return 0; + } + + static int run(const Options& options) { + if (options.use_paged_kv || options.varlen) { + throw std::runtime_error("Sparse SDPA does not support paged KV or varlen in v1"); + } + if (options.block_K != nullptr || options.block_V != nullptr) { + if (options.seq_len_kv_cache <= 0 || options.seq_len_qo != 1) { + throw std::runtime_error("Sparse SDPA only supports block_K/block_V for seq_len_q == 1 cached decode in v1"); + } + } else if (options.seq_len_kv_cache > 0) { + throw std::runtime_error("Sparse SDPA cached decode requires block_K/block_V cache tensors"); + } + if (options.lut == nullptr || options.valid_block_num == nullptr) { + throw std::runtime_error("Sparse SDPA requires lut and valid_block_num"); + } + if (options.seq_len_kv_cache > 0) { + return run(options); + } + return run(options); + } +}; #endif // ARK_SDPA_ENABLE_SPARSE // ======================================================================== @@ -1188,6 +1305,55 @@ inline int launch_sparse_sage_prefill_kernel_64(Options const& options) { SubgroupLayoutPV, PipelineStages, false, ElementQ, ElementK, ElementV, ElementO>::run(options); } + +template +inline int launch_sparse_sdpa_prefill_kernel_128(Options const& options) { + constexpr int PipelineStages = 2; + using ShapeQK = Shape<_256, _64, _32>; + using ShapePV = Shape<_256, _32, _64>; + using ShapeOut = Shape<_256, _128>; + using SubgroupLayoutQK = Layout>; + using ShapeQK1 = Shape<_256, _64, _32>; + using ShapePV1 = Shape<_256, _32, _64>; + using ShapeOut1 = Shape<_256, _128>; + using SubgroupLayoutQK1 = Layout>; + return options.is_causal ? SparseSDPAConfig::run(options) + : SparseSDPAConfig::run(options); +} + +template +inline int launch_sparse_sdpa_prefill_kernel_128_qtile64(Options const& options) { + constexpr int PipelineStages = 2; + using ShapeQK = Shape<_64, _64, _32>; + using ShapePV = Shape<_64, _32, _64>; + using ShapeOut = Shape<_64, _128>; + using SubgroupLayoutQK = Layout>; + using ShapeQK1 = Shape<_64, _64, _32>; + using ShapePV1 = Shape<_64, _32, _64>; + using ShapeOut1 = Shape<_64, _128>; + using SubgroupLayoutQK1 = Layout>; + return options.is_causal ? SparseSDPAConfig::run(options) + : SparseSDPAConfig::run(options); +} + +template +inline int launch_sparse_sdpa_prefill_kernel_64(Options const& options) { + constexpr int PipelineStages = 2; + using ShapeQK = Shape<_128, _64, _32>; + using ShapePV = Shape<_128, _32, _64>; + using ShapeOut = Shape<_128, _64>; + using SubgroupLayoutQK = Layout>; + using SubgroupLayoutPV = void; + return options.is_causal + ? SparseSDPAConfig::run(options) + : SparseSDPAConfig::run(options); +} #endif // ARK_SDPA_ENABLE_SPARSE #if defined(ARK_SDPA_ENABLE_DENSE) From 2127635a4c3c77e940acc43b98b4cd2ef76f43d2 Mon Sep 17 00:00:00 2001 From: yiliu30 Date: Thu, 6 Aug 2026 15:58:27 +0800 Subject: [PATCH 18/42] feat: sparse-SDPA Python wrappers, switch sage_sparse_bf16, benchmark modes Add sage_sparse_sdpa and sparge_sage2_attn_meansim_topk_xpu_sdpa (bf16+fp16), route sage_sparse_bf16 through the sparse-SDPA path, and add sparse_sdpa_* modes to the benchmark. --- .../ark/auto_round_kernel/__init__.py | 2 + .../ark/auto_round_kernel/sparse_attention.py | 183 ++++++++++++++++-- .../ark/benchmarks/bench_sparse_topk.py | 111 ++++++++++- 3 files changed, 280 insertions(+), 16 deletions(-) diff --git a/auto_round_extension/ark/auto_round_kernel/__init__.py b/auto_round_extension/ark/auto_round_kernel/__init__.py index 93e5244e29..b625cfd741 100644 --- a/auto_round_extension/ark/auto_round_kernel/__init__.py +++ b/auto_round_extension/ark/auto_round_kernel/__init__.py @@ -1293,10 +1293,12 @@ def sageattn( _to_hnd, sage_sparse, sage_sparse_bf16, + sage_sparse_sdpa, sparge_block_map_to_mask, sparge_preprocess_topk, sparge_sage2_attn_meansim_topk_xpu, sparge_sage2_attn_meansim_topk_xpu_bf16, + sparge_sage2_attn_meansim_topk_xpu_sdpa, ) diff --git a/auto_round_extension/ark/auto_round_kernel/sparse_attention.py b/auto_round_extension/ark/auto_round_kernel/sparse_attention.py index 2342a10707..fff58426a3 100644 --- a/auto_round_extension/ark/auto_round_kernel/sparse_attention.py +++ b/auto_round_extension/ark/auto_round_kernel/sparse_attention.py @@ -215,14 +215,60 @@ def sage_sparse_bf16( sparse_k_block_tokens: int | None = None, tensor_layout: str = "HND", ) -> torch.Tensor: - """Low-level sparse BF16 attention routed by sparse LUT metadata.""" + """Low-level sparse BF16 attention routed by sparse LUT metadata. + + Dispatched through the independent native-precision sparse-SDPA path + (``sage_sparse_sdpa``); kept as a bf16 convenience alias. + """ + if query.dtype != torch.bfloat16 or key.dtype != torch.bfloat16 or value.dtype != torch.bfloat16: + raise ValueError(f"sage_sparse_bf16 requires bfloat16 Q/K/V, got Q={query.dtype}, K={key.dtype}, V={value.dtype}") + return sage_sparse_sdpa( + query, + key, + value, + lut, + valid_block_num, + attn_mask=attn_mask, + dropout_p=dropout_p, + is_causal=is_causal, + scale=scale, + enable_gqa=enable_gqa, + q_tile_override=q_tile_override, + sparse_q_block_tokens=sparse_q_block_tokens, + sparse_k_block_tokens=sparse_k_block_tokens, + tensor_layout=tensor_layout, + ) + + +def sage_sparse_sdpa( + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + lut: torch.Tensor, + valid_block_num: torch.Tensor, + attn_mask: torch.Tensor | None = None, + dropout_p: float = 0.0, + is_causal: bool = False, + scale: float | None = None, + enable_gqa: bool = False, + q_tile_override: int = 0, + sparse_q_block_tokens: int | None = None, + sparse_k_block_tokens: int | None = None, + tensor_layout: str = "HND", +) -> torch.Tensor: + """Low-level native-precision sparse attention (BF16/FP16) routed by sparse LUT metadata. + + Independent path built on the dense-SDPA sparse mainloop (SparseSDPAConfig). + Unlike ``sage_sparse_bf16`` it does not reuse the INT8-centric SAGE mainloop; + Q/K/V are native BF16/FP16 and the softmax scale is applied directly. + """ del dropout_p, enable_gqa if query.device.type != "xpu": - raise NotImplementedError("sage_sparse_bf16 is only supported on XPU") - if query.dtype != torch.bfloat16 or key.dtype != torch.bfloat16: - raise ValueError(f"Q/K must be bfloat16, got Q={query.dtype}, K={key.dtype}") - if value.dtype != torch.bfloat16: - raise ValueError(f"V must be bfloat16, got {value.dtype}") + raise NotImplementedError("sage_sparse_sdpa is only supported on XPU") + if query.dtype not in (torch.bfloat16, torch.float16): + raise ValueError(f"Q/K/V must be bfloat16 or float16, got {query.dtype}") + if key.dtype != query.dtype or value.dtype != query.dtype: + raise ValueError(f"Q/K/V dtypes must match, got Q={query.dtype}, K={key.dtype}, V={value.dtype}") if lut.dtype != torch.int32 or valid_block_num.dtype != torch.int32: raise ValueError("lut and valid_block_num must be int32 tensors") if lut.device != query.device or valid_block_num.device != query.device: @@ -238,7 +284,7 @@ def sage_sparse_bf16( raise ValueError("K/V shape mismatch") if Dk != D: raise ValueError("Head dim mismatch between Q and K/V") - _validate_gqa_head_config(Hq, Hkv, op_name="sage_sparse_bf16") + _validate_gqa_head_config(Hq, Hkv, op_name="sage_sparse_sdpa") if D not in (64, 128): raise ValueError(f"Unsupported head_dim={D}; supported: 64, 128") effective_sparse_q_block_tokens = 64 if sparse_q_block_tokens is None else int(sparse_q_block_tokens) @@ -264,16 +310,12 @@ def sage_sparse_bf16( if D == 64 and q_tile_override not in (0, 64, 128): raise ValueError( - f"q_tile_override must be one of {{0, 64, 128}} for sage_sparse_bf16 with head_dim=64, got {q_tile_override}" + f"q_tile_override must be one of {{0, 64, 128}} for sage_sparse_sdpa with head_dim=64, got {q_tile_override}" ) - # Native XPU sparse kernel only. The historical dense-mask fallback (rebuild the - # full block map, then run per-head dense sagev1) is removed: it materialized a - # [B, Hq, Sq, Skv] mask and OOM'd at long sequences, and the native kernel now - # covers every supported config. lib = get_lib(query) - if not hasattr(lib, "sage_sparse_bf16"): - raise RuntimeError("Loaded XPU extension does not expose sage_sparse_bf16") + if not hasattr(lib, "sage_sparse_sdpa"): + raise RuntimeError("Loaded XPU extension does not expose sage_sparse_sdpa") effective_q_tile_override = _resolve_sparse_prefill_q_tile_override( head_dim=D, quant_block_size=64, @@ -286,7 +328,8 @@ def sage_sparse_bf16( O = _empty_attention_output( B, Hq, Sq, D, dtype=value.dtype, device=query.device, tensor_layout=tensor_layout ) - lib.sage_sparse_bf16( + q_dtype = 1 if query.dtype == torch.bfloat16 else 0 # FlashAttnDtype: 0=FP16, 1=BF16 + lib.sage_sparse_sdpa( stream, query.data_ptr(), key.data_ptr(), @@ -302,6 +345,7 @@ def sage_sparse_bf16( *_attention_strides_qko(key, tensor_layout), *_attention_strides_v(value, tensor_layout), *_attention_strides_qko(O, tensor_layout), + q_dtype, B, Hq, Hkv, @@ -1602,6 +1646,115 @@ def sparge_sage2_attn_meansim_topk_xpu( return out +def sparge_sage2_attn_meansim_topk_xpu_sdpa( + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attn_mask: torch.Tensor | None = None, + dropout_p: float = 0.0, + is_causal: bool = False, + scale: float | None = None, + smooth_k: bool = True, + simthreshd1: float | torch.Tensor = -0.1, + cdfthreshd: float | torch.Tensor | None = None, + topk: float | torch.Tensor = 0.5, + pvthreshd: float | torch.Tensor = 50, + attention_sink: bool = False, + tensor_layout: str = "HND", + output_dtype: torch.dtype | None = None, + return_sparsity: bool = False, + return_metadata: bool = False, + k_quant_granularity: int = 64, + query_tile_tokens: int | None = None, + q_tile_override: int = 0, + sparse_q_block_tokens: int | None = None, + sparse_k_block_tokens: int | None = None, +) -> torch.Tensor | tuple[Any, ...]: + """E2E sparse attention on the independent native-precision (BF16/FP16) SDPA path.""" + if query.device.type != "xpu": + raise NotImplementedError("sparge_sage2_attn_meansim_topk_xpu_sdpa is only supported on XPU") + if dropout_p != 0.0: + raise NotImplementedError("dropout_p must be 0.0 for sparge_sage2_attn_meansim_topk_xpu_sdpa") + if attn_mask is not None and is_causal: + raise ValueError("attn_mask and is_causal cannot both be set") + if query.dtype not in (torch.bfloat16, torch.float16): + raise ValueError( + "sparge_sage2_attn_meansim_topk_xpu_sdpa requires bfloat16 or float16 Q/K/V, " + f"got Q={query.dtype}, K={key.dtype}, V={value.dtype}" + ) + if key.dtype != query.dtype or value.dtype != query.dtype: + raise ValueError(f"Q/K/V dtypes must match, got Q={query.dtype}, K={key.dtype}, V={value.dtype}") + if output_dtype is not None and output_dtype != query.dtype: + raise ValueError(f"output_dtype must match Q dtype in the current implementation, got {output_dtype}") + if pvthreshd not in (None, 50): + warnings.warn("pvthreshd is not supported by the current ARK sparse kernel and is ignored", stacklevel=2) + + B, Hq, Sq, D = _validate_attention_tensor(query, "Q", tensor_layout, expected_dtype=query.dtype) + Bk, Hkv, Skv, Dk = _validate_attention_tensor(key, "K", tensor_layout, expected_dtype=key.dtype) + Bv, Hkv2, Skv2, Dv = _validate_attention_tensor(value, "V", tensor_layout, expected_dtype=value.dtype) + if Bk != B or Bv != B: + raise ValueError("Batch size mismatch between Q/K/V") + if Hkv2 != Hkv or Skv2 != Skv or Dv != Dk: + raise ValueError("K/V shape mismatch") + if Dk != D: + raise ValueError("Head dim mismatch between Q and K/V") + + effective_query_tile_tokens = query_tile_tokens + effective_q_tile_override = q_tile_override + if effective_query_tile_tokens is None and q_tile_override in (64, 128, 256): + effective_query_tile_tokens = q_tile_override + elif effective_query_tile_tokens is not None: + if q_tile_override == 0: + effective_q_tile_override = int(effective_query_tile_tokens) + elif q_tile_override != int(effective_query_tile_tokens): + raise ValueError( + "query_tile_tokens and q_tile_override must match when both are set; " + f"got query_tile_tokens={effective_query_tile_tokens}, q_tile_override={q_tile_override}" + ) + _validate_sparse_q_tile_override_for_head_dim(D, effective_q_tile_override) + + normalized_mask = _normalize_sparse_mask(attn_mask, B, Sq, Skv, query.device) + metadata = sparge_preprocess_topk( + query, + key, + is_causal=is_causal, + smooth_k=smooth_k, + simthreshd1=simthreshd1, + topk=topk, + cdfthreshd=cdfthreshd, + attention_sink=attention_sink, + quant_block_size=64, + tensor_layout=tensor_layout, + k_quant_granularity=k_quant_granularity, + query_tile_tokens=effective_query_tile_tokens, + sparse_q_block_tokens=sparse_q_block_tokens, + sparse_k_block_tokens=sparse_k_block_tokens, + ) + _get_xpu_sparse_kernel_backend() + out = sage_sparse_sdpa( + query, + key, + value, + metadata["lut"], + metadata["valid_block_num"], + attn_mask=normalized_mask, + is_causal=is_causal, + scale=scale, + q_tile_override=effective_q_tile_override, + sparse_q_block_tokens=metadata["sparse_q_block_tokens"], + sparse_k_block_tokens=metadata["sparse_k_block_tokens"], + tensor_layout=tensor_layout, + ) + sparsity_ratio = metadata["stats"]["sparsity_ratio"] + if return_metadata and return_sparsity: + return out, sparsity_ratio, metadata + if return_metadata: + return out, metadata + if return_sparsity: + return out, sparsity_ratio + return out + + def sparge_sage2_attn_meansim_topk_xpu_bf16( query: torch.Tensor, key: torch.Tensor, diff --git a/auto_round_extension/ark/benchmarks/bench_sparse_topk.py b/auto_round_extension/ark/benchmarks/bench_sparse_topk.py index 63eafd4646..ceb34e5588 100644 --- a/auto_round_extension/ark/benchmarks/bench_sparse_topk.py +++ b/auto_round_extension/ark/benchmarks/bench_sparse_topk.py @@ -62,7 +62,10 @@ def _resolve_sparse_binding(args: argparse.Namespace, *, required_symbols: tuple def ensure_sparse_binding(args: argparse.Namespace) -> None: - required_symbols = ("sage_sparse", "sage_sparse_bf16") if args.dtype == "bf16" else ("sage_sparse",) + if args.dtype == "bf16": + required_symbols = ("sage_sparse", "sage_sparse_bf16", "sage_sparse_sdpa") + else: + required_symbols = ("sage_sparse", "sage_sparse_sdpa") ext_path = _resolve_sparse_binding(args, required_symbols=required_symbols) if ext_path is not None: _load_sparse_binding(ext_path, required_symbols=required_symbols) @@ -523,6 +526,10 @@ def summarize_speedups(rows: list[dict[str, object]]) -> list[dict[str, object]] "sparse_bf16_e2e", "sparse_qtile256_row64k_kernel_only", "sparse_qtile256_row64k_e2e", + "sparse_sdpa_bf16_kernel_only", + "sparse_sdpa_bf16_e2e", + "sparse_sdpa_fp16_kernel_only", + "sparse_sdpa_fp16_e2e", }: row["baseline_tflops"] = flops_to_tflops(dense_flops, float(latency_ms)) work_ratio = 1.0 @@ -954,6 +961,108 @@ def run_single_benchmark(args: argparse.Namespace, *, seq_len: int, tensor_layou selected_blocks_per_row=selected_blocks_per_row, ) ) + rows.append( + try_benchmark( + "sparse_sdpa_fp16_kernel_only" if dtype == torch.float16 else "sparse_sdpa_bf16_kernel_only", + lambda preprocess=preprocess: ( + hnd_to_nhd( + ark.sage_sparse_sdpa( + nhd_to_hnd(q_nhd_src), + nhd_to_hnd(k_nhd_src), + nhd_to_hnd(v_nhd_src), + preprocess["lut"], + preprocess["valid_block_num"], + is_causal=args.causal, + scale=scale, + q_tile_override=args.q_tile_override, + sparse_q_block_tokens=preprocess["sparse_q_block_tokens"], + sparse_k_block_tokens=preprocess["sparse_k_block_tokens"], + tensor_layout="HND", + ) + ) + if tensor_layout == "HND" + else ark.sage_sparse_sdpa( + q, + k, + v, + preprocess["lut"], + preprocess["valid_block_num"], + is_causal=args.causal, + scale=scale, + q_tile_override=args.q_tile_override, + sparse_q_block_tokens=preprocess["sparse_q_block_tokens"], + sparse_k_block_tokens=preprocess["sparse_k_block_tokens"], + tensor_layout=tensor_layout, + ) + ), + batch=args.batch, + num_heads_q=args.num_heads_q, + num_heads_kv=args.num_heads_kv, + seq_len=seq_len, + tensor_layout=tensor_layout, + head_dim=args.head_dim, + dtype=dtype, + is_causal=args.causal, + warmup=args.warmup, + iters=args.iters, + requested_topk=topk, + selected_ratio=selected_ratio, + selected_blocks_per_row=selected_blocks_per_row, + ) + ) + rows.append( + try_benchmark( + "sparse_sdpa_fp16_e2e" if dtype == torch.float16 else "sparse_sdpa_bf16_e2e", + lambda topk=topk: ( + hnd_to_nhd( + ark.sparge_sage2_attn_meansim_topk_xpu_sdpa( + nhd_to_hnd(q_nhd_src), + nhd_to_hnd(k_nhd_src), + nhd_to_hnd(v_nhd_src), + is_causal=args.causal, + scale=scale, + smooth_k=True, + simthreshd1=-1.0, + topk=topk, + attention_sink=False, + tensor_layout="HND", + q_tile_override=args.q_tile_override, + sparse_q_block_tokens=args.sparse_q_block_tokens, + sparse_k_block_tokens=args.sparse_k_block_tokens, + ) + ) + if tensor_layout == "HND" + else ark.sparge_sage2_attn_meansim_topk_xpu_sdpa( + q, + k, + v, + is_causal=args.causal, + scale=scale, + smooth_k=True, + simthreshd1=-1.0, + topk=topk, + attention_sink=False, + tensor_layout=tensor_layout, + q_tile_override=args.q_tile_override, + sparse_q_block_tokens=args.sparse_q_block_tokens, + sparse_k_block_tokens=args.sparse_k_block_tokens, + ) + ), + batch=args.batch, + num_heads_q=args.num_heads_q, + num_heads_kv=args.num_heads_kv, + seq_len=seq_len, + tensor_layout=tensor_layout, + head_dim=args.head_dim, + dtype=dtype, + is_causal=args.causal, + warmup=args.warmup, + iters=args.iters, + requested_topk=topk, + selected_ratio=selected_ratio, + selected_blocks_per_row=selected_blocks_per_row, + ) + ) if dtype == torch.bfloat16: rows.append( try_benchmark( From 952ef649734c42726492b38298ebf50d6054a316 Mon Sep 17 00:00:00 2001 From: yiliu30 Date: Thu, 6 Aug 2026 15:58:27 +0800 Subject: [PATCH 19/42] test: sparse-SDPA correctness tests; fix XPU softmax reference Add run_case_sdpa/run_case_sdpa_full (bf16+fp16 vs dense ref + sparse-SAGE parity + topk=1.0 dense gate). Compute the dense reference on CPU because XPU torch.softmax is numerically broken on large masked tensors (sums to <1), which also un-breaks the existing bf16 qtile256 test case. --- .../test/test_sage_sparse_prefill_e2e.py | 224 +++++++++++++++++- 1 file changed, 215 insertions(+), 9 deletions(-) diff --git a/auto_round_extension/ark/auto_round_kernel/wrapper/test/test_sage_sparse_prefill_e2e.py b/auto_round_extension/ark/auto_round_kernel/wrapper/test/test_sage_sparse_prefill_e2e.py index 7d372a2858..6626731533 100644 --- a/auto_round_extension/ark/auto_round_kernel/wrapper/test/test_sage_sparse_prefill_e2e.py +++ b/auto_round_extension/ark/auto_round_kernel/wrapper/test/test_sage_sparse_prefill_e2e.py @@ -119,16 +119,22 @@ def bf16_sparse_reference( scale: float, enable_gqa: bool, ) -> torch.Tensor: - if enable_gqa and query.shape[1] != key.shape[1]: - repeat = query.shape[1] // key.shape[1] - key = key.repeat_interleave(repeat, dim=1) - value = value.repeat_interleave(repeat, dim=1) - - scores = torch.matmul(query.float(), key.float().transpose(-1, -2)) * scale - if attn_mask is not None: - scores = scores + attn_mask.float() + # Run the reference on CPU: torch-xpu's softmax is numerically broken on large + # masked tensors (sums to <1, drops visible entries), which corrupts the reference. + q = query.float().cpu() + k = key.float().cpu() + v = value.float().cpu() + mask = None if attn_mask is None else attn_mask.float().cpu() + if enable_gqa and q.shape[1] != k.shape[1]: + repeat = q.shape[1] // k.shape[1] + k = k.repeat_interleave(repeat, dim=1) + v = v.repeat_interleave(repeat, dim=1) + + scores = torch.matmul(q, k.transpose(-1, -2)) * scale + if mask is not None: + scores = scores + mask probs = torch.softmax(scores, dim=-1) - return torch.matmul(probs, value.float()).to(query.dtype) + return torch.matmul(probs, v).to(query.dtype).to(query.device) def run_case( @@ -342,6 +348,188 @@ def run_case_bf16( ) +def run_case_sdpa( + dtype: torch.dtype, + head_dim: int, + *, + seq_len_q: int = 256, + seq_len_kv: int = 256, + num_heads_q: int = 4, + num_heads_kv: int | None = None, + is_causal: bool = False, + q_tile_override: int = 0, + sparse_q_block_tokens: int | None = None, + sparse_k_block_tokens: int | None = None, +) -> None: + """Validate the independent sparse-SDPA path (bf16/fp16) against the dense + reference and, for bf16, against the sparse-SAGE path (sage_sparse_bf16).""" + ensure_sparse_binding(required_symbols=("sage_sparse", "sage_sparse_bf16", "sage_sparse_sdpa")) + device = torch.device("xpu") + batch = 1 + num_heads_kv = num_heads_q if num_heads_kv is None else num_heads_kv + scale = 1.0 / math.sqrt(head_dim) + query_tile_tokens = q_tile_override or (64 if head_dim == 64 else 256) + k_block_tokens = 64 if sparse_k_block_tokens is None else sparse_k_block_tokens + kv_blocks = (seq_len_kv + k_block_tokens - 1) // k_block_tokens + active_query_tiles = (seq_len_q + query_tile_tokens - 1) // query_tile_tokens + if not is_causal: + per_query_tile_selection = [list(range(max(1, min(kv_blocks, 3)))) for _ in range(active_query_tiles)] + if active_query_tiles > 1 and kv_blocks > 3: + per_query_tile_selection[-1] = list(range(kv_blocks - 3, kv_blocks)) + case_name = f"{dtype}_prefill" + else: + per_query_tile_selection = [list(range(min(kv_blocks, idx + 1))) for idx in range(active_query_tiles)] + case_name = f"{dtype}_prefill_causal" + per_query_tile_selection = _clamp_selected_blocks(kv_blocks, per_query_tile_selection) + + torch.manual_seed(9026 + head_dim + num_heads_q + (num_heads_kv * 13) + seq_len_q + seq_len_kv) + query = torch.randn((batch, num_heads_q, seq_len_q, head_dim), dtype=dtype, device=device) + key = torch.randn((batch, num_heads_kv, seq_len_kv, head_dim), dtype=dtype, device=device) + value = torch.randn((batch, num_heads_kv, seq_len_kv, head_dim), dtype=dtype, device=device) + + effective_sparse_q_block_tokens = 64 if sparse_q_block_tokens is None else sparse_q_block_tokens + effective_sparse_k_block_tokens = 64 if sparse_k_block_tokens is None else sparse_k_block_tokens + lut, valid, dense_mask = build_sparse_metadata_and_mask( + batch, + num_heads_q, + seq_len_q, + 64, + query_tile_tokens, + per_query_tile_selection, + device, + is_causal=is_causal, + sparse_q_block_tokens=effective_sparse_q_block_tokens, + sparse_k_block_tokens=effective_sparse_k_block_tokens, + seq_len_kv=seq_len_kv, + ) + + dense_out = bf16_sparse_reference( + query, + key, + value, + dense_mask, + scale=scale, + enable_gqa=num_heads_q != num_heads_kv, + ) + sparse_out = ark.sage_sparse_sdpa( + query, + key, + value, + lut, + valid, + is_causal=is_causal, + scale=scale, + q_tile_override=q_tile_override, + sparse_q_block_tokens=sparse_q_block_tokens, + sparse_k_block_tokens=sparse_k_block_tokens, + tensor_layout="HND", + ) + torch.xpu.synchronize() + + diff = (dense_out.float() - sparse_out.float()).abs() + max_diff = float(diff.max().cpu()) + mean_diff = float(diff.mean().cpu()) + print( + f"[sage_sparse_sdpa][{case_name}] D={head_dim} Hq={num_heads_q} Hkv={num_heads_kv} " + f"Sq={seq_len_q} Skv={seq_len_kv} max_diff={max_diff:.6f} mean_diff={mean_diff:.6f}" + ) + if max_diff > 2e-2 or mean_diff > 2e-3: + raise RuntimeError( + f"sage_sparse_sdpa mismatch for dtype={dtype}, D={head_dim}, Hq={num_heads_q}, Hkv={num_heads_kv}, " + f"Sq={seq_len_q}, Skv={seq_len_kv}, causal={is_causal}" + ) + + if dtype == torch.bfloat16: + sage_out = ark.sage_sparse_bf16( + query, + key, + value, + lut, + valid, + is_causal=is_causal, + scale=scale, + q_tile_override=q_tile_override, + sparse_q_block_tokens=sparse_q_block_tokens, + sparse_k_block_tokens=sparse_k_block_tokens, + tensor_layout="HND", + ) + torch.xpu.synchronize() + sd_diff = (sparse_out.float() - sage_out.float()).abs() + sd_max = float(sd_diff.max().cpu()) + sd_mean = float(sd_diff.mean().cpu()) + print(f" sparse-SDPA vs sparse-SAGE: max={sd_max:.6f} mean={sd_mean:.6f}") + if sd_max > 5e-3 or sd_mean > 5e-4: + raise RuntimeError( + f"sage_sparse_sdpa vs sage_sparse_bf16 mismatch for D={head_dim}, Hq={num_heads_q}, " + f"Hkv={num_heads_kv}, Sq={seq_len_q}, Skv={seq_len_kv}" + ) + + +def run_case_sdpa_full( + dtype: torch.dtype, + head_dim: int, + *, + seq_len_q: int = 256, + seq_len_kv: int = 256, + num_heads_q: int = 4, + q_tile_override: int = 0, + sparse_q_block_tokens: int | None = None, + sparse_k_block_tokens: int | None = None, +) -> None: + """Dense gate: selecting every KV block must make sparse-SDPA match the dense reference.""" + ensure_sparse_binding(required_symbols=("sage_sparse", "sage_sparse_bf16", "sage_sparse_sdpa")) + device = torch.device("xpu") + batch = 1 + scale = 1.0 / math.sqrt(head_dim) + query_tile_tokens = q_tile_override or (64 if head_dim == 64 else 256) + k_block_tokens = 64 if sparse_k_block_tokens is None else sparse_k_block_tokens + kv_blocks = (seq_len_kv + k_block_tokens - 1) // k_block_tokens + active_query_tiles = (seq_len_q + query_tile_tokens - 1) // query_tile_tokens + per_query_tile_selection = [list(range(kv_blocks)) for _ in range(active_query_tiles)] + + torch.manual_seed(77 + head_dim + num_heads_q) + query = torch.randn((batch, num_heads_q, seq_len_q, head_dim), dtype=dtype, device=device) + key = torch.randn((batch, num_heads_q, seq_len_kv, head_dim), dtype=dtype, device=device) + value = torch.randn((batch, num_heads_q, seq_len_kv, head_dim), dtype=dtype, device=device) + + lut, valid, dense_mask = build_sparse_metadata_and_mask( + batch, + num_heads_q, + seq_len_q, + 64, + query_tile_tokens, + per_query_tile_selection, + device, + is_causal=False, + sparse_q_block_tokens=64 if sparse_q_block_tokens is None else sparse_q_block_tokens, + sparse_k_block_tokens=k_block_tokens, + seq_len_kv=seq_len_kv, + ) + + dense_out = bf16_sparse_reference(query, key, value, dense_mask, scale=scale, enable_gqa=False) + sparse_out = ark.sage_sparse_sdpa( + query, + key, + value, + lut, + valid, + is_causal=False, + scale=scale, + q_tile_override=q_tile_override, + sparse_q_block_tokens=sparse_q_block_tokens, + sparse_k_block_tokens=sparse_k_block_tokens, + tensor_layout="HND", + ) + torch.xpu.synchronize() + + diff = (dense_out.float() - sparse_out.float()).abs() + max_diff = float(diff.max().cpu()) + mean_diff = float(diff.mean().cpu()) + print(f"[sage_sparse_sdpa][{dtype}_all_selected] D={head_dim} max_diff={max_diff:.6f} mean_diff={mean_diff:.6f}") + if max_diff > 2e-2 or mean_diff > 2e-3: + raise RuntimeError(f"sage_sparse_sdpa all-selected mismatch for dtype={dtype}, D={head_dim}") + + def run_multi_row_tile_case() -> None: device = torch.device("xpu") batch = 1 @@ -496,6 +684,24 @@ def main() -> None: sparse_k_block_tokens=64, ) run_case_bf16(128, is_causal=True, q_tile_override=64) + # Independent sparse-SDPA path (bf16 + fp16) + for dtype in (torch.bfloat16, torch.float16): + run_case_sdpa(dtype, 64) + run_case_sdpa(dtype, 128, q_tile_override=64, num_heads_q=32, num_heads_kv=8) + run_case_sdpa( + dtype, + 128, + seq_len_q=512, + seq_len_kv=768, + num_heads_q=32, + num_heads_kv=8, + q_tile_override=256, + sparse_q_block_tokens=256, + sparse_k_block_tokens=64, + ) + run_case_sdpa(dtype, 128, is_causal=True, q_tile_override=64) + run_case_sdpa_full(dtype, 64) + run_case_sdpa_full(dtype, 128, q_tile_override=64) if __name__ == "__main__": From 1da474b35a166ead17a7e1ba399f85e8b4b2ab88 Mon Sep 17 00:00:00 2001 From: yiliu30 Date: Thu, 6 Aug 2026 19:05:52 +0800 Subject: [PATCH 20/42] refactor: separate native-precision sparse SDPA from INT8 sparse SAGE path The BF16 native sparse attention path previously ran through the INT8-centric SAGE kernel structures (sage_sparse_bf16 -> SparseSageConfig -> SPARSESAGEV1FwdMainloop). Remove that coupling so each dtype has one clean path: - INT8: sage_sparse / sparge_sage2_attn_meansim_topk_xpu - BF16/FP16: sage_sparse_sdpa / sparge_sage2_attn_meansim_topk_xpu_sdpa Changes: - delete sdpa_sparse_bf16.cpp (superseded by sdpa_sparse_sdpa.cpp) - drop sage_sparse_bf16 pybind + Python alias and sparge_sage2_attn_meansim_topk_xpu_bf16, plus __init__ exports - remove sdpa_impl_bf16_sparse_* declarations from sycl_tla_common.hpp - hardcode SparseSageConfig MMA to int32/int8 DPAS; static_assert ElementQ==int8 - remove the native softmax-scale branch from the INT8 SAGE mainloop - guard the scale_block_size division in XeSparseSageFwdKernel (0 on native path) - align _query_tile_tokens_for_head_dim(64) default to 64 with the e2e wrappers - drop bf16-only cases from the prefill e2e test and bf16-only bench modes - switch the flux example to the native sparse-SDPA entry points - gitignore generated benchmarks/results/ Co-Authored-By: Claude --- auto_round_extension/ark/.gitignore | 1 + .../ark/auto_round_kernel/__init__.py | 2 - .../ark/auto_round_kernel/ark.cpp | 60 ----- .../auto_round_kernel/sdpa_sparse_bf16.cpp | 225 ------------------ .../auto_round_kernel/sdpa_sparse_sdpa.cpp | 2 +- .../ark/auto_round_kernel/sparse_attention.py | 153 +----------- .../stla/xe_sparse_sage_fwd_kernel.hpp | 11 +- .../stla/xe_sparse_sagev1_fwd_mainloop.hpp | 7 - .../wrapper/include/sycl_tla_common.hpp | 24 -- .../wrapper/include/sycl_tla_sdpa_sparse.hpp | 19 +- .../test/test_sage_sparse_prefill_e2e.py | 155 +----------- .../ark/benchmarks/bench_sparse_topk.py | 153 +----------- .../ark/examples/flux_sparse_patch.py | 4 +- 13 files changed, 30 insertions(+), 786 deletions(-) delete mode 100644 auto_round_extension/ark/auto_round_kernel/sdpa_sparse_bf16.cpp diff --git a/auto_round_extension/ark/.gitignore b/auto_round_extension/ark/.gitignore index 17e27ad2a0..8e16b488bb 100644 --- a/auto_round_extension/ark/.gitignore +++ b/auto_round_extension/ark/.gitignore @@ -6,3 +6,4 @@ xbuild_bf16* *.csv *.so *.pyc +benchmarks/results/ diff --git a/auto_round_extension/ark/auto_round_kernel/__init__.py b/auto_round_extension/ark/auto_round_kernel/__init__.py index b625cfd741..12529d4dbe 100644 --- a/auto_round_extension/ark/auto_round_kernel/__init__.py +++ b/auto_round_extension/ark/auto_round_kernel/__init__.py @@ -1292,12 +1292,10 @@ def sageattn( _slice_sequence_native_layout, _to_hnd, sage_sparse, - sage_sparse_bf16, sage_sparse_sdpa, sparge_block_map_to_mask, sparge_preprocess_topk, sparge_sage2_attn_meansim_topk_xpu, - sparge_sage2_attn_meansim_topk_xpu_bf16, sparge_sage2_attn_meansim_topk_xpu_sdpa, ) diff --git a/auto_round_extension/ark/auto_round_kernel/ark.cpp b/auto_round_extension/ark/auto_round_kernel/ark.cpp index 57dd6ecb7d..ac085f8a43 100755 --- a/auto_round_extension/ark/auto_round_kernel/ark.cpp +++ b/auto_round_extension/ark/auto_round_kernel/ark.cpp @@ -478,65 +478,6 @@ static void sage_sparse(torch_ptr stream, torch_ptr Q, torch_ptr K, torch_ptr V, throw std::invalid_argument("ark::sage_sparse: unsupported head_dim; supported values are 64 and 128"); } -static void sage_sparse_bf16(torch_ptr stream, torch_ptr Q, torch_ptr K, torch_ptr V, torch_ptr O, torch_ptr mask, - torch_ptr lut, torch_ptr valid_block_num, int num_q_blocks, int num_k_blocks, - int q_tile_override, int q_stride_s, int q_stride_d, int q_stride_h, int q_stride_b, - int k_stride_s, int k_stride_d, int k_stride_h, int k_stride_b, int v_stride_d, - int v_stride_s, int v_stride_h, int v_stride_b, int o_stride_s, int o_stride_d, - int o_stride_h, int o_stride_b, int batch, int num_heads_q, int num_heads_kv, - int seq_len_q, int seq_len_kv, int head_dim, float softmax_scale, bool is_causal) { - if (mask && is_causal) { - throw std::invalid_argument("ark::sage_sparse_bf16: mask and is_causal cannot both be set"); - } - if (!lut || !valid_block_num) { - throw std::invalid_argument("ark::sage_sparse_bf16: lut and valid_block_num must be provided"); - } - auto matches_block_size = [](int seq_len, int num_blocks, int block_size) { - return block_size > 0 && num_blocks == ((seq_len + block_size - 1) / block_size); - }; - const bool key_block_is_64 = matches_block_size(seq_len_kv, num_k_blocks, 64); - if (!key_block_is_64) { - throw std::invalid_argument("ark::sage_sparse_bf16: only key block size 64 is supported"); - } - if (head_dim == 64) { - if (!matches_block_size(seq_len_q, num_q_blocks, 64)) { - throw std::invalid_argument("ark::sage_sparse_bf16: head_dim=64 requires query block size 64"); - } - ark::sdpa_impl_bf16_sparse_d64( - (sycl::queue*)stream, (void*)Q, (void*)K, (void*)V, (void*)O, (void*)mask, (void*)lut, - (void*)valid_block_num, num_q_blocks, num_k_blocks, q_tile_override, q_stride_s, q_stride_d, q_stride_h, - q_stride_b, k_stride_s, k_stride_d, k_stride_h, k_stride_b, v_stride_d, v_stride_s, v_stride_h, - v_stride_b, o_stride_s, o_stride_d, o_stride_h, o_stride_b, batch, num_heads_q, num_heads_kv, seq_len_q, - seq_len_kv, head_dim, softmax_scale, is_causal); - return; - } - if (head_dim == 128) { - if (matches_block_size(seq_len_q, num_q_blocks, 256)) { - if (q_tile_override != 256) { - throw std::invalid_argument("ark::sage_sparse_bf16: head_dim=128 query block size 256 requires q_tile_override=256"); - } - ark::sdpa_impl_bf16_sparse_qtile256_row64k( - (sycl::queue*)stream, (void*)Q, (void*)K, (void*)V, (void*)O, (void*)mask, (void*)lut, - (void*)valid_block_num, num_q_blocks, num_k_blocks, q_tile_override, q_stride_s, q_stride_d, q_stride_h, - q_stride_b, k_stride_s, k_stride_d, k_stride_h, k_stride_b, v_stride_d, v_stride_s, v_stride_h, - v_stride_b, o_stride_s, o_stride_d, o_stride_h, o_stride_b, batch, num_heads_q, num_heads_kv, seq_len_q, - seq_len_kv, head_dim, softmax_scale, is_causal); - return; - } - if (matches_block_size(seq_len_q, num_q_blocks, 64)) { - ark::sdpa_impl_bf16_sparse_row_linear( - (sycl::queue*)stream, (void*)Q, (void*)K, (void*)V, (void*)O, (void*)mask, (void*)lut, - (void*)valid_block_num, num_q_blocks, num_k_blocks, q_tile_override, q_stride_s, q_stride_d, q_stride_h, - q_stride_b, k_stride_s, k_stride_d, k_stride_h, k_stride_b, v_stride_d, v_stride_s, v_stride_h, - v_stride_b, o_stride_s, o_stride_d, o_stride_h, o_stride_b, batch, num_heads_q, num_heads_kv, seq_len_q, - seq_len_kv, head_dim, softmax_scale, is_causal); - return; - } - throw std::invalid_argument("ark::sage_sparse_bf16: head_dim=128 supports query block sizes 64 and 256 only"); - } - throw std::invalid_argument("ark::sage_sparse_bf16: unsupported head_dim; supported values are 64 and 128"); -} - static void sage_sparse_sdpa(torch_ptr stream, torch_ptr Q, torch_ptr K, torch_ptr V, torch_ptr O, torch_ptr mask, torch_ptr lut, torch_ptr valid_block_num, int num_q_blocks, int num_k_blocks, int q_tile_override, int q_stride_s, int q_stride_d, int q_stride_h, int q_stride_b, @@ -935,7 +876,6 @@ PYBIND11_MODULE(PY_NAME, m) { pybind11::arg("head_dim"), pybind11::arg("softmax_scale"), pybind11::arg("is_causal"), pybind11::arg("tensor_layout"), pybind11::arg("lse") = 0); m.def("sage_sparse", &ark::sage_sparse); - m.def("sage_sparse_bf16", &ark::sage_sparse_bf16); m.def("sage_sparse_sdpa", &ark::sage_sparse_sdpa); // Low-level SAGE PVi8 API: input Q/K/V are pre-quantized int8 with qscale/kscale/vscale. m.def("sage_pvi8", &ark::sage_pvi8, pybind11::arg("stream"), pybind11::arg("Q"), pybind11::arg("K"), diff --git a/auto_round_extension/ark/auto_round_kernel/sdpa_sparse_bf16.cpp b/auto_round_extension/ark/auto_round_kernel/sdpa_sparse_bf16.cpp deleted file mode 100644 index 5ea82a35d3..0000000000 --- a/auto_round_extension/ark/auto_round_kernel/sdpa_sparse_bf16.cpp +++ /dev/null @@ -1,225 +0,0 @@ -// Copyright (c) 2026 Intel Corporation -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#if defined(ARK_XPU) && defined(ARK_SYCL_TLA) - -#include -#include -#include "bestla/bestla.h" - -#include -#include "sycl_tla_sdpa_sparse.hpp" - -namespace ark { - -namespace detail = sparse_detail; - -namespace { - -using KernelLauncher = int (*)(detail::Options const& options); - -int launch_prefill_kernel_bf16_128_sparse_sdpa(detail::Options const& options) { - return detail::launch_sparse_sage_prefill_kernel_128( - options); -} - -int launch_prefill_kernel_bf16_128_sparse_sdpa_qtile64(detail::Options const& options) { - return detail::launch_sparse_sage_prefill_kernel_128_qtile64< - cute::bfloat16_t, cute::bfloat16_t, cute::bfloat16_t>( - options); -} - -int launch_prefill_kernel_bf16_64_sparse_sdpa(detail::Options const& options) { - return detail::launch_sparse_sage_prefill_kernel_64( - options); -} - -KernelLauncher select_sparse_bf16_prefill_launcher(int head_dim, int q_tile_override) { - switch (head_dim) { - case 128: - if (q_tile_override == 64) return launch_prefill_kernel_bf16_128_sparse_sdpa_qtile64; - if (q_tile_override != 0 && q_tile_override != 256) return nullptr; - return launch_prefill_kernel_bf16_128_sparse_sdpa; - case 64: - if (q_tile_override != 0 && q_tile_override != 64 && q_tile_override != 128) return nullptr; - return launch_prefill_kernel_bf16_64_sparse_sdpa; - default: - return nullptr; - } -} - -detail::Options make_common_options(void* Q_ptr, void* K_ptr, void* V_ptr, void* O_ptr, void* mask, int q_stride_s, - int q_stride_d, int q_stride_h, int q_stride_b, int k_stride_s, int k_stride_d, - int k_stride_h, int k_stride_b, int v_stride_d, int v_stride_s, int v_stride_h, - int v_stride_b, int o_stride_s, int o_stride_d, int o_stride_h, int o_stride_b, - int batch, int num_heads_q, int num_heads_kv, int seq_len_q, int seq_len_kv, - int head_dim, float softmax_scale, bool is_causal) { - if (q_stride_d != 1 || k_stride_d != 1 || v_stride_d != 1 || o_stride_d != 1) { - throw std::invalid_argument("make_common_options: head-dim stride must be 1 for Q/K/V/O"); - } - detail::Options options; - options.q = Q_ptr; - options.k = K_ptr; - options.v = V_ptr; - options.mask = mask; - options.o = O_ptr; - options.use_tensor_strides = true; - options.q_stride_s = q_stride_s; - options.q_stride_d = q_stride_d; - options.q_stride_h = q_stride_h; - options.q_stride_b = q_stride_b; - options.k_stride_s = k_stride_s; - options.k_stride_d = k_stride_d; - options.k_stride_h = k_stride_h; - options.k_stride_b = k_stride_b; - options.v_stride_d = v_stride_d; - options.v_stride_s = v_stride_s; - options.v_stride_h = v_stride_h; - options.v_stride_b = v_stride_b; - options.o_stride_s = o_stride_s; - options.o_stride_d = o_stride_d; - options.o_stride_h = o_stride_h; - options.o_stride_b = o_stride_b; - options.batch = batch; - options.num_heads_q = num_heads_q; - options.num_heads_kv = num_heads_kv; - options.seq_len_qo = seq_len_q; - options.seq_len_kv = seq_len_kv; - options.head_size_qk = head_dim; - options.head_size_vo = head_dim; - options.softmax_scale = softmax_scale; - options.is_causal = is_causal; - return options; -} - -void sparse_bf16_prefill(sycl::queue* q, void* Q_ptr, void* K_ptr, void* V_ptr, void* O_ptr, void* mask, void* lut, - void* valid_block_num, int num_q_blocks, int num_k_blocks, int q_tile_override, - int q_stride_s, int q_stride_d, int q_stride_h, int q_stride_b, int k_stride_s, - int k_stride_d, int k_stride_h, int k_stride_b, int v_stride_d, int v_stride_s, - int v_stride_h, int v_stride_b, int o_stride_s, int o_stride_d, int o_stride_h, - int o_stride_b, int batch, int num_heads_q, int num_heads_kv, int seq_len_q, int seq_len_kv, - int head_dim, float softmax_scale, bool is_causal, int sparse_q_block_size = 0) { - const int effective_q_tile_override = - (head_dim == 128 && q_tile_override == 0) ? 64 : ((head_dim == 64 && q_tile_override == 0) ? 64 : q_tile_override); - detail::Options options = - make_common_options(Q_ptr, K_ptr, V_ptr, O_ptr, mask, q_stride_s, q_stride_d, q_stride_h, q_stride_b, - k_stride_s, k_stride_d, k_stride_h, k_stride_b, v_stride_d, v_stride_s, v_stride_h, - v_stride_b, o_stride_s, o_stride_d, o_stride_h, o_stride_b, batch, num_heads_q, - num_heads_kv, seq_len_q, seq_len_kv, head_dim, softmax_scale, is_causal); - options.scale_block_size = 0; - options.sparse_q_block_size = sparse_q_block_size; - options.q_tile_override = effective_q_tile_override; - options.qscale = nullptr; - options.kscale = nullptr; - options.lut = static_cast(lut); - options.valid_block_num = static_cast(valid_block_num); - options.num_q_blocks = num_q_blocks; - options.num_k_blocks = num_k_blocks; - compat::set_default_queue(*q); - - KernelLauncher launcher = select_sparse_bf16_prefill_launcher(head_dim, effective_q_tile_override); - if (launcher == nullptr) { - throw std::runtime_error("Unsupported sparse_bf16_prefill config"); - } - - launcher(options); -} - -} // namespace - -void sdpa_impl_bf16_sparse_d64( - sycl::queue* q, void* Q_ptr, void* K_ptr, void* V_ptr, void* O_ptr, void* mask, void* lut, void* valid_block_num, - int num_q_blocks, int num_k_blocks, int q_tile_override, int q_stride_s, int q_stride_d, int q_stride_h, - int q_stride_b, int k_stride_s, int k_stride_d, int k_stride_h, int k_stride_b, int v_stride_d, int v_stride_s, - int v_stride_h, int v_stride_b, int o_stride_s, int o_stride_d, int o_stride_h, int o_stride_b, int batch, - int num_heads_q, int num_heads_kv, int seq_len_q, int seq_len_kv, int head_dim, float softmax_scale, - bool is_causal) { - if (mask && is_causal) { - throw std::invalid_argument("sdpa_impl_bf16_sparse_d64: mask and is_causal cannot both be set"); - } - if (seq_len_q <= 0 || seq_len_kv <= 0) { - throw std::invalid_argument("sdpa_impl_bf16_sparse_d64: seq_len_q and seq_len_kv must be greater than 0"); - } - if (lut == nullptr || valid_block_num == nullptr) { - throw std::invalid_argument("sdpa_impl_bf16_sparse_d64: lut and valid_block_num must be provided"); - } - if (num_q_blocks <= 0 || num_k_blocks <= 0) { - throw std::invalid_argument("sdpa_impl_bf16_sparse_d64: num_q_blocks and num_k_blocks must be greater than 0"); - } - if (head_dim != 64) { - throw std::invalid_argument("sdpa_impl_bf16_sparse_d64: head_dim must be 64"); - } - - sparse_bf16_prefill(q, Q_ptr, K_ptr, V_ptr, O_ptr, mask, lut, valid_block_num, num_q_blocks, num_k_blocks, - q_tile_override, q_stride_s, q_stride_d, q_stride_h, q_stride_b, k_stride_s, k_stride_d, - k_stride_h, k_stride_b, v_stride_d, v_stride_s, v_stride_h, v_stride_b, o_stride_s, o_stride_d, - o_stride_h, o_stride_b, batch, num_heads_q, num_heads_kv, seq_len_q, seq_len_kv, head_dim, - softmax_scale, is_causal, /*sparse_q_block_size=*/64); -} - -void sdpa_impl_bf16_sparse_row_linear( - sycl::queue* q, void* Q_ptr, void* K_ptr, void* V_ptr, void* O_ptr, void* mask, void* lut, void* valid_block_num, - int num_q_blocks, int num_k_blocks, int q_tile_override, int q_stride_s, int q_stride_d, int q_stride_h, - int q_stride_b, int k_stride_s, int k_stride_d, int k_stride_h, int k_stride_b, int v_stride_d, int v_stride_s, - int v_stride_h, int v_stride_b, int o_stride_s, int o_stride_d, int o_stride_h, int o_stride_b, int batch, - int num_heads_q, int num_heads_kv, int seq_len_q, int seq_len_kv, int head_dim, float softmax_scale, - bool is_causal) { - if (q_tile_override != 0 && q_tile_override != 64) { - throw std::invalid_argument("sdpa_impl_bf16_sparse_row_linear: q_tile_override must be 0 or 64"); - } - if (mask && is_causal) { - throw std::invalid_argument("sdpa_impl_bf16_sparse_row_linear: mask and is_causal cannot both be set"); - } - if (seq_len_q <= 0 || seq_len_kv <= 0) { - throw std::invalid_argument("sdpa_impl_bf16_sparse_row_linear: seq_len_q and seq_len_kv must be greater than 0"); - } - if (lut == nullptr || valid_block_num == nullptr) { - throw std::invalid_argument("sdpa_impl_bf16_sparse_row_linear: lut and valid_block_num must be provided"); - } - if (num_q_blocks <= 0 || num_k_blocks <= 0) { - throw std::invalid_argument( - "sdpa_impl_bf16_sparse_row_linear: num_q_blocks and num_k_blocks must be greater than 0"); - } - - sparse_bf16_prefill(q, Q_ptr, K_ptr, V_ptr, O_ptr, mask, lut, valid_block_num, num_q_blocks, num_k_blocks, 64, - q_stride_s, q_stride_d, q_stride_h, q_stride_b, k_stride_s, k_stride_d, k_stride_h, - k_stride_b, v_stride_d, v_stride_s, v_stride_h, v_stride_b, o_stride_s, o_stride_d, o_stride_h, - o_stride_b, batch, num_heads_q, num_heads_kv, seq_len_q, seq_len_kv, head_dim, softmax_scale, - is_causal, /*sparse_q_block_size=*/64); -} - -void sdpa_impl_bf16_sparse_qtile256_row64k( - sycl::queue* q, void* Q_ptr, void* K_ptr, void* V_ptr, void* O_ptr, void* mask, void* lut, void* valid_block_num, - int num_q_blocks, int num_k_blocks, int q_tile_override, int q_stride_s, int q_stride_d, int q_stride_h, - int q_stride_b, int k_stride_s, int k_stride_d, int k_stride_h, int k_stride_b, int v_stride_d, int v_stride_s, - int v_stride_h, int v_stride_b, int o_stride_s, int o_stride_d, int o_stride_h, int o_stride_b, int batch, - int num_heads_q, int num_heads_kv, int seq_len_q, int seq_len_kv, int head_dim, float softmax_scale, - bool is_causal) { - if (q_tile_override != 256) { - throw std::invalid_argument("sdpa_impl_bf16_sparse_qtile256_row64k: q_tile_override must be 256"); - } - if (head_dim != 128) { - throw std::invalid_argument("sdpa_impl_bf16_sparse_qtile256_row64k: head_dim must be 128"); - } - - sparse_bf16_prefill(q, Q_ptr, K_ptr, V_ptr, O_ptr, mask, lut, valid_block_num, num_q_blocks, num_k_blocks, - q_tile_override, q_stride_s, q_stride_d, q_stride_h, q_stride_b, k_stride_s, k_stride_d, - k_stride_h, k_stride_b, v_stride_d, v_stride_s, v_stride_h, v_stride_b, o_stride_s, o_stride_d, - o_stride_h, o_stride_b, batch, num_heads_q, num_heads_kv, seq_len_q, seq_len_kv, head_dim, - softmax_scale, is_causal, /*sparse_q_block_size=*/256); -} - -} // namespace ark - -#endif // ARK_XPU && ARK_SYCL_TLA diff --git a/auto_round_extension/ark/auto_round_kernel/sdpa_sparse_sdpa.cpp b/auto_round_extension/ark/auto_round_kernel/sdpa_sparse_sdpa.cpp index 2a69823c51..b569a49f2a 100644 --- a/auto_round_extension/ark/auto_round_kernel/sdpa_sparse_sdpa.cpp +++ b/auto_round_extension/ark/auto_round_kernel/sdpa_sparse_sdpa.cpp @@ -14,7 +14,7 @@ // Independent native-precision sparse SDPA path (BF16 + FP16). This dispatches to // the sparse SDPA mainloop (SPARSESDPAFwdMainloop via SparseSDPAConfig), separate -// from the INT8-centric sparse SAGE path in sdpa_sparse.cpp / sdpa_sparse_bf16.cpp. +// from the INT8-centric sparse SAGE path in sdpa_sparse.cpp. #if defined(ARK_XPU) && defined(ARK_SYCL_TLA) diff --git a/auto_round_extension/ark/auto_round_kernel/sparse_attention.py b/auto_round_extension/ark/auto_round_kernel/sparse_attention.py index fff58426a3..fc1da94539 100644 --- a/auto_round_extension/ark/auto_round_kernel/sparse_attention.py +++ b/auto_round_extension/ark/auto_round_kernel/sparse_attention.py @@ -199,47 +199,6 @@ def sage_sparse( return O -def sage_sparse_bf16( - query: torch.Tensor, - key: torch.Tensor, - value: torch.Tensor, - lut: torch.Tensor, - valid_block_num: torch.Tensor, - attn_mask: torch.Tensor | None = None, - dropout_p: float = 0.0, - is_causal: bool = False, - scale: float | None = None, - enable_gqa: bool = False, - q_tile_override: int = 0, - sparse_q_block_tokens: int | None = None, - sparse_k_block_tokens: int | None = None, - tensor_layout: str = "HND", -) -> torch.Tensor: - """Low-level sparse BF16 attention routed by sparse LUT metadata. - - Dispatched through the independent native-precision sparse-SDPA path - (``sage_sparse_sdpa``); kept as a bf16 convenience alias. - """ - if query.dtype != torch.bfloat16 or key.dtype != torch.bfloat16 or value.dtype != torch.bfloat16: - raise ValueError(f"sage_sparse_bf16 requires bfloat16 Q/K/V, got Q={query.dtype}, K={key.dtype}, V={value.dtype}") - return sage_sparse_sdpa( - query, - key, - value, - lut, - valid_block_num, - attn_mask=attn_mask, - dropout_p=dropout_p, - is_causal=is_causal, - scale=scale, - enable_gqa=enable_gqa, - q_tile_override=q_tile_override, - sparse_q_block_tokens=sparse_q_block_tokens, - sparse_k_block_tokens=sparse_k_block_tokens, - tensor_layout=tensor_layout, - ) - - def sage_sparse_sdpa( query: torch.Tensor, key: torch.Tensor, @@ -259,8 +218,8 @@ def sage_sparse_sdpa( """Low-level native-precision sparse attention (BF16/FP16) routed by sparse LUT metadata. Independent path built on the dense-SDPA sparse mainloop (SparseSDPAConfig). - Unlike ``sage_sparse_bf16`` it does not reuse the INT8-centric SAGE mainloop; - Q/K/V are native BF16/FP16 and the softmax scale is applied directly. + Unlike the INT8 ``sage_sparse`` path it does not reuse the INT8-centric SAGE + mainloop; Q/K/V are native BF16/FP16 and the softmax scale is applied directly. """ del dropout_p, enable_gqa if query.device.type != "xpu": @@ -558,8 +517,10 @@ def _normalize_per_head_hparam( def _query_tile_tokens_for_head_dim(head_dim: int) -> int: + # Keep the preprocess default aligned with the sparse e2e wrappers: for head_dim 64 + # the wrappers default to 64-token query tiles, for head_dim 128 to 64 as well. if head_dim == 64: - return 128 + return 64 if head_dim == 128: return 64 raise ValueError(f"Unsupported head_dim={head_dim}; supported: 64, 128") @@ -1755,107 +1716,3 @@ def sparge_sage2_attn_meansim_topk_xpu_sdpa( return out -def sparge_sage2_attn_meansim_topk_xpu_bf16( - query: torch.Tensor, - key: torch.Tensor, - value: torch.Tensor, - attn_mask: torch.Tensor | None = None, - dropout_p: float = 0.0, - is_causal: bool = False, - scale: float | None = None, - smooth_k: bool = True, - simthreshd1: float | torch.Tensor = -0.1, - cdfthreshd: float | torch.Tensor | None = None, - topk: float | torch.Tensor = 0.5, - pvthreshd: float | torch.Tensor = 50, - attention_sink: bool = False, - tensor_layout: str = "HND", - output_dtype: torch.dtype | None = None, - return_sparsity: bool = False, - return_metadata: bool = False, - k_quant_granularity: int = 64, - query_tile_tokens: int | None = None, - q_tile_override: int = 0, - sparse_q_block_tokens: int | None = None, - sparse_k_block_tokens: int | None = None, -) -> torch.Tensor | tuple[Any, ...]: - if query.device.type != "xpu": - raise NotImplementedError("sparge_sage2_attn_meansim_topk_xpu_bf16 is only supported on XPU") - if dropout_p != 0.0: - raise NotImplementedError("dropout_p must be 0.0 for sparge_sage2_attn_meansim_topk_xpu_bf16") - if attn_mask is not None and is_causal: - raise ValueError("attn_mask and is_causal cannot both be set") - if query.dtype != torch.bfloat16 or key.dtype != torch.bfloat16 or value.dtype != torch.bfloat16: - raise ValueError( - "sparge_sage2_attn_meansim_topk_xpu_bf16 requires bfloat16 Q/K/V, " - f"got Q={query.dtype}, K={key.dtype}, V={value.dtype}" - ) - if output_dtype is not None and output_dtype != torch.bfloat16: - raise ValueError(f"output_dtype must be torch.bfloat16 in the current implementation, got {output_dtype}") - if pvthreshd not in (None, 50): - warnings.warn("pvthreshd is not supported by the current ARK sparse kernel and is ignored", stacklevel=2) - - B, Hq, Sq, D = _validate_attention_tensor(query, "Q", tensor_layout, expected_dtype=query.dtype) - Bk, Hkv, Skv, Dk = _validate_attention_tensor(key, "K", tensor_layout, expected_dtype=key.dtype) - Bv, Hkv2, Skv2, Dv = _validate_attention_tensor(value, "V", tensor_layout, expected_dtype=value.dtype) - if Bk != B or Bv != B: - raise ValueError("Batch size mismatch between Q/K/V") - if Hkv2 != Hkv or Skv2 != Skv or Dv != Dk: - raise ValueError("K/V shape mismatch") - if Dk != D: - raise ValueError("Head dim mismatch between Q and K/V") - - effective_query_tile_tokens = query_tile_tokens - effective_q_tile_override = q_tile_override - if effective_query_tile_tokens is None and q_tile_override in (64, 128, 256): - effective_query_tile_tokens = q_tile_override - elif effective_query_tile_tokens is not None: - if q_tile_override == 0: - effective_q_tile_override = int(effective_query_tile_tokens) - elif q_tile_override != int(effective_query_tile_tokens): - raise ValueError( - "query_tile_tokens and q_tile_override must match when both are set; " - f"got query_tile_tokens={effective_query_tile_tokens}, q_tile_override={q_tile_override}" - ) - _validate_sparse_q_tile_override_for_head_dim(D, effective_q_tile_override) - - normalized_mask = _normalize_sparse_mask(attn_mask, B, Sq, Skv, query.device) - metadata = sparge_preprocess_topk( - query, - key, - is_causal=is_causal, - smooth_k=smooth_k, - simthreshd1=simthreshd1, - topk=topk, - cdfthreshd=cdfthreshd, - attention_sink=attention_sink, - quant_block_size=64, - tensor_layout=tensor_layout, - k_quant_granularity=k_quant_granularity, - query_tile_tokens=effective_query_tile_tokens, - sparse_q_block_tokens=sparse_q_block_tokens, - sparse_k_block_tokens=sparse_k_block_tokens, - ) - _get_xpu_sparse_kernel_backend() - out = sage_sparse_bf16( - query, - key, - value, - metadata["lut"], - metadata["valid_block_num"], - attn_mask=normalized_mask, - is_causal=is_causal, - scale=scale, - q_tile_override=effective_q_tile_override, - sparse_q_block_tokens=metadata["sparse_q_block_tokens"], - sparse_k_block_tokens=metadata["sparse_k_block_tokens"], - tensor_layout=tensor_layout, - ) - sparsity_ratio = metadata["stats"]["sparsity_ratio"] - if return_metadata and return_sparsity: - return out, sparsity_ratio, metadata - if return_metadata: - return out, metadata - if return_sparsity: - return out, sparsity_ratio - return out diff --git a/auto_round_extension/ark/auto_round_kernel/wrapper/include/stla/xe_sparse_sage_fwd_kernel.hpp b/auto_round_extension/ark/auto_round_kernel/wrapper/include/stla/xe_sparse_sage_fwd_kernel.hpp index 90e0e32b9a..fe3d4a3702 100644 --- a/auto_round_extension/ark/auto_round_kernel/wrapper/include/stla/xe_sparse_sage_fwd_kernel.hpp +++ b/auto_round_extension/ark/auto_round_kernel/wrapper/include/stla/xe_sparse_sage_fwd_kernel.hpp @@ -245,9 +245,16 @@ class XeSparseSageFwdKernel { auto dcV = const_cast(p.V + offset_v); auto dcK_cache = const_cast(p.K_cache + offset_k_cache); auto dcV_cache = const_cast(p.V_cache + offset_v_cache); - int seq_q_pad = (seq_len_qo + params.mainloop.scale_block_size - 1) / params.mainloop.scale_block_size; + // scale_block_size is 0 on the native-precision sparse-SDPA path (no INT8 dequant); + // guard the pad computation so it does not divide by zero. The pads are only used + // inside the scale_block_size ? ... : nullptr ternaries below. + int seq_q_pad = params.mainloop.scale_block_size > 0 + ? (seq_len_qo + params.mainloop.scale_block_size - 1) / params.mainloop.scale_block_size + : 0; int seq_kv_total = seq_len_kv + seq_len_kv_cache; - int seq_kv_pad = (seq_kv_total + params.mainloop.scale_block_size - 1) / params.mainloop.scale_block_size; + int seq_kv_pad = params.mainloop.scale_block_size > 0 + ? (seq_kv_total + params.mainloop.scale_block_size - 1) / params.mainloop.scale_block_size + : 0; auto scaleQ = params.mainloop.scale_block_size ? (float*)params.mainloop.qscale + (idx_b * s.num_heads_q * seq_q_pad + head_q * seq_q_pad) : nullptr; diff --git a/auto_round_extension/ark/auto_round_kernel/wrapper/include/stla/xe_sparse_sagev1_fwd_mainloop.hpp b/auto_round_extension/ark/auto_round_kernel/wrapper/include/stla/xe_sparse_sagev1_fwd_mainloop.hpp index c88496b7f8..0124fd21de 100644 --- a/auto_round_extension/ark/auto_round_kernel/wrapper/include/stla/xe_sparse_sagev1_fwd_mainloop.hpp +++ b/auto_round_extension/ark/auto_round_kernel/wrapper/include/stla/xe_sparse_sagev1_fwd_mainloop.hpp @@ -521,13 +521,6 @@ struct SPARSESAGEV1FwdMainloop, CausalMask_, FullMask_, CUTLASS_PRAGMA_UNROLL for (int i = 0; i < tPrS.size(); i++) tPrS(i) *= _scale; } - } else { - // Native FP16/BF16 Q/K path (scale_block_size == 0): there is no - // qscale/kscale dequantization, so apply the raw softmax scale here. - // INT8 gets it via dq_scale above; without this branch the softmax - // runs on unscaled scores and the output is numerically wrong. - CUTLASS_PRAGMA_UNROLL - for (int i = 0; i < tPrS.size(); i++) tPrS(i) *= params.scale; } } else { float _scale = dq_scale * scaleK[scalek_idx]; diff --git a/auto_round_extension/ark/auto_round_kernel/wrapper/include/sycl_tla_common.hpp b/auto_round_extension/ark/auto_round_kernel/wrapper/include/sycl_tla_common.hpp index 215213b0e0..1d80f29d74 100644 --- a/auto_round_extension/ark/auto_round_kernel/wrapper/include/sycl_tla_common.hpp +++ b/auto_round_extension/ark/auto_round_kernel/wrapper/include/sycl_tla_common.hpp @@ -256,30 +256,6 @@ void sdpa_impl_qks8_sparse_qtile256_row64k_pvhalf( int seq_len_q, int seq_len_kv, int head_dim, float softmax_scale, bool is_causal, BTLA_DTYPE pv_dtype = BTLA_DTYPE::F16); -void sdpa_impl_bf16_sparse_d64( - sycl::queue* q, void* Q_ptr, void* K_ptr, void* V_ptr, void* O_ptr, void* mask, void* lut, void* valid_block_num, - int num_q_blocks, int num_k_blocks, int q_tile_override, int q_stride_s, int q_stride_d, int q_stride_h, - int q_stride_b, int k_stride_s, int k_stride_d, int k_stride_h, int k_stride_b, int v_stride_d, int v_stride_s, - int v_stride_h, int v_stride_b, int o_stride_s, int o_stride_d, int o_stride_h, int o_stride_b, int batch, - int num_heads_q, int num_heads_kv, int seq_len_q, int seq_len_kv, int head_dim, float softmax_scale, - bool is_causal); - -void sdpa_impl_bf16_sparse_row_linear( - sycl::queue* q, void* Q_ptr, void* K_ptr, void* V_ptr, void* O_ptr, void* mask, void* lut, void* valid_block_num, - int num_q_blocks, int num_k_blocks, int q_tile_override, int q_stride_s, int q_stride_d, int q_stride_h, - int q_stride_b, int k_stride_s, int k_stride_d, int k_stride_h, int k_stride_b, int v_stride_d, int v_stride_s, - int v_stride_h, int v_stride_b, int o_stride_s, int o_stride_d, int o_stride_h, int o_stride_b, int batch, - int num_heads_q, int num_heads_kv, int seq_len_q, int seq_len_kv, int head_dim, float softmax_scale, - bool is_causal); - -void sdpa_impl_bf16_sparse_qtile256_row64k( - sycl::queue* q, void* Q_ptr, void* K_ptr, void* V_ptr, void* O_ptr, void* mask, void* lut, void* valid_block_num, - int num_q_blocks, int num_k_blocks, int q_tile_override, int q_stride_s, int q_stride_d, int q_stride_h, - int q_stride_b, int k_stride_s, int k_stride_d, int k_stride_h, int k_stride_b, int v_stride_d, int v_stride_s, - int v_stride_h, int v_stride_b, int o_stride_s, int o_stride_d, int o_stride_h, int o_stride_b, int batch, - int num_heads_q, int num_heads_kv, int seq_len_q, int seq_len_kv, int head_dim, float softmax_scale, - bool is_causal); - void sdpa_impl_bf16_sparse_sdpa_d64( sycl::queue* q, void* Q_ptr, void* K_ptr, void* V_ptr, void* O_ptr, void* mask, void* lut, void* valid_block_num, int num_q_blocks, int num_k_blocks, int q_tile_override, int q_stride_s, int q_stride_d, int q_stride_h, diff --git a/auto_round_extension/ark/auto_round_kernel/wrapper/include/sycl_tla_sdpa_sparse.hpp b/auto_round_extension/ark/auto_round_kernel/wrapper/include/sycl_tla_sdpa_sparse.hpp index 1e5e690830..0073bd6a8d 100644 --- a/auto_round_extension/ark/auto_round_kernel/wrapper/include/sycl_tla_sdpa_sparse.hpp +++ b/auto_round_extension/ark/auto_round_kernel/wrapper/include/sycl_tla_sdpa_sparse.hpp @@ -938,18 +938,13 @@ template struct SparseSageConfig { static constexpr int SGTileQ = get<0>(shape_div(TileShapeQK{}, shape(SubgroupLayoutQK{})))(); - // The Q*K MMA must match ElementQ. The sparse mainloop dequantizes INT8 Q/K with - // qscale/kscale, so INT8 keeps an int32/int8 DPAS. Native BF16/FP16 Q/K must use a - // float/ DPAS; reusing the int8 DPAS on bf16 fragments hangs the device. - using MMAOperation = cute::conditional_t< - is_void_v, - typename cute::conditional_t< - cute::is_same_v || cute::is_same_v, - XE_DPAS_TT, - cute::conditional_t, - XE_DPAS_TT, - XE_DPAS_TT>>, - MMAOperation_>; + static_assert(cute::is_same_v, + "SparseSageConfig is INT8-only; use SparseSDPAConfig for native-precision BF16/FP16"); + // INT8-only sparse SAGE path: Q/K are int8 and dequantized via qscale/kscale, + // so the Q*K MMA is always an int32/int8 DPAS. Native-precision BF16/FP16 sparse + // uses the separate SparseSDPAConfig (float/ DPAS). + using MMAOperation = cute::conditional_t, + XE_DPAS_TT, MMAOperation_>; using MMAOperationPV = cute::conditional_t, XE_DPAS_TT, MMAOperation_>; using SubgroupLayoutPV = diff --git a/auto_round_extension/ark/auto_round_kernel/wrapper/test/test_sage_sparse_prefill_e2e.py b/auto_round_extension/ark/auto_round_kernel/wrapper/test/test_sage_sparse_prefill_e2e.py index 6626731533..a08bccc10b 100644 --- a/auto_round_extension/ark/auto_round_kernel/wrapper/test/test_sage_sparse_prefill_e2e.py +++ b/auto_round_extension/ark/auto_round_kernel/wrapper/test/test_sage_sparse_prefill_e2e.py @@ -240,114 +240,6 @@ def run_case( ) -def run_case_bf16( - head_dim: int, - *, - seq_len_q: int = 256, - seq_len_kv: int = 256, - num_heads_q: int = 4, - num_heads_kv: int | None = None, - is_causal: bool = False, - q_tile_override: int = 0, - sparse_q_block_tokens: int | None = None, - sparse_k_block_tokens: int | None = None, -) -> None: - ensure_sparse_binding(required_symbols=("sage_sparse", "sage_sparse_bf16")) - device = torch.device("xpu") - batch = 1 - num_heads_kv = num_heads_q if num_heads_kv is None else num_heads_kv - scale = 1.0 / math.sqrt(head_dim) - query_tile_tokens = q_tile_override or (64 if head_dim == 64 else 256) - k_block_tokens = 64 if sparse_k_block_tokens is None else sparse_k_block_tokens - kv_blocks = (seq_len_kv + k_block_tokens - 1) // k_block_tokens - active_query_tiles = (seq_len_q + query_tile_tokens - 1) // query_tile_tokens - if not is_causal: - per_query_tile_selection = [list(range(max(1, min(kv_blocks, 3)))) for _ in range(active_query_tiles)] - if active_query_tiles > 1 and kv_blocks > 3: - per_query_tile_selection[-1] = list(range(kv_blocks - 3, kv_blocks)) - case_name = "bf16_prefill" - else: - per_query_tile_selection = [list(range(min(kv_blocks, idx + 1))) for idx in range(active_query_tiles)] - case_name = "bf16_prefill_causal" - per_query_tile_selection = _clamp_selected_blocks(kv_blocks, per_query_tile_selection) - - torch.manual_seed(6026 + head_dim + num_heads_q + (num_heads_kv * 13) + seq_len_q + seq_len_kv) - query = torch.randn((batch, num_heads_q, seq_len_q, head_dim), dtype=torch.bfloat16, device=device) - key = torch.randn((batch, num_heads_kv, seq_len_kv, head_dim), dtype=torch.bfloat16, device=device) - value = torch.randn((batch, num_heads_kv, seq_len_kv, head_dim), dtype=torch.bfloat16, device=device) - - effective_sparse_q_block_tokens = 64 if sparse_q_block_tokens is None else sparse_q_block_tokens - effective_sparse_k_block_tokens = 64 if sparse_k_block_tokens is None else sparse_k_block_tokens - lut, valid, dense_mask = build_sparse_metadata_and_mask( - batch, - num_heads_q, - seq_len_q, - 64, - query_tile_tokens, - per_query_tile_selection, - device, - is_causal=is_causal, - sparse_q_block_tokens=effective_sparse_q_block_tokens, - sparse_k_block_tokens=effective_sparse_k_block_tokens, - seq_len_kv=seq_len_kv, - ) - - dense_out = bf16_sparse_reference( - query, - key, - value, - dense_mask, - scale=scale, - enable_gqa=num_heads_q != num_heads_kv, - ) - sparse_out = ark.sage_sparse_bf16( - query, - key, - value, - lut, - valid, - is_causal=is_causal, - scale=scale, - q_tile_override=q_tile_override, - sparse_q_block_tokens=sparse_q_block_tokens, - sparse_k_block_tokens=sparse_k_block_tokens, - tensor_layout="HND", - ) - e2e_out = ark.sparge_sage2_attn_meansim_topk_xpu_bf16( - query, - key, - value, - is_causal=is_causal, - scale=scale, - smooth_k=True, - simthreshd1=-1.0, - topk=0.5, - attention_sink=False, - tensor_layout="HND", - q_tile_override=q_tile_override, - sparse_q_block_tokens=sparse_q_block_tokens, - sparse_k_block_tokens=sparse_k_block_tokens, - ) - torch.xpu.synchronize() - - diff = (dense_out.float() - sparse_out.float()).abs() - e2e_diff = (e2e_out.float() - sparse_out.float()).abs() - max_diff = float(diff.max().cpu()) - mean_diff = float(diff.mean().cpu()) - e2e_max_diff = float(e2e_diff.max().cpu()) - e2e_mean_diff = float(e2e_diff.mean().cpu()) - print( - f"[sage_sparse_bf16][{case_name}] D={head_dim} Hq={num_heads_q} Hkv={num_heads_kv} " - f"Sq={seq_len_q} Skv={seq_len_kv} max_diff={max_diff:.6f} mean_diff={mean_diff:.6f} " - f"e2e_max_diff={e2e_max_diff:.6f} e2e_mean_diff={e2e_mean_diff:.6f}" - ) - if max_diff > 2e-2 or mean_diff > 2e-3: - raise RuntimeError( - f"sage_sparse_bf16 mismatch for D={head_dim}, Hq={num_heads_q}, Hkv={num_heads_kv}, " - f"Sq={seq_len_q}, Skv={seq_len_kv}, causal={is_causal}" - ) - - def run_case_sdpa( dtype: torch.dtype, head_dim: int, @@ -361,9 +253,9 @@ def run_case_sdpa( sparse_q_block_tokens: int | None = None, sparse_k_block_tokens: int | None = None, ) -> None: - """Validate the independent sparse-SDPA path (bf16/fp16) against the dense - reference and, for bf16, against the sparse-SAGE path (sage_sparse_bf16).""" - ensure_sparse_binding(required_symbols=("sage_sparse", "sage_sparse_bf16", "sage_sparse_sdpa")) + """Validate the independent native-precision sparse-SDPA path (bf16/fp16) + against a dense reference built from the same block selection.""" + ensure_sparse_binding(required_symbols=("sage_sparse", "sage_sparse_sdpa")) device = torch.device("xpu") batch = 1 num_heads_kv = num_heads_q if num_heads_kv is None else num_heads_kv @@ -439,31 +331,6 @@ def run_case_sdpa( f"Sq={seq_len_q}, Skv={seq_len_kv}, causal={is_causal}" ) - if dtype == torch.bfloat16: - sage_out = ark.sage_sparse_bf16( - query, - key, - value, - lut, - valid, - is_causal=is_causal, - scale=scale, - q_tile_override=q_tile_override, - sparse_q_block_tokens=sparse_q_block_tokens, - sparse_k_block_tokens=sparse_k_block_tokens, - tensor_layout="HND", - ) - torch.xpu.synchronize() - sd_diff = (sparse_out.float() - sage_out.float()).abs() - sd_max = float(sd_diff.max().cpu()) - sd_mean = float(sd_diff.mean().cpu()) - print(f" sparse-SDPA vs sparse-SAGE: max={sd_max:.6f} mean={sd_mean:.6f}") - if sd_max > 5e-3 or sd_mean > 5e-4: - raise RuntimeError( - f"sage_sparse_sdpa vs sage_sparse_bf16 mismatch for D={head_dim}, Hq={num_heads_q}, " - f"Hkv={num_heads_kv}, Sq={seq_len_q}, Skv={seq_len_kv}" - ) - def run_case_sdpa_full( dtype: torch.dtype, @@ -477,7 +344,7 @@ def run_case_sdpa_full( sparse_k_block_tokens: int | None = None, ) -> None: """Dense gate: selecting every KV block must make sparse-SDPA match the dense reference.""" - ensure_sparse_binding(required_symbols=("sage_sparse", "sage_sparse_bf16", "sage_sparse_sdpa")) + ensure_sparse_binding(required_symbols=("sage_sparse", "sage_sparse_sdpa")) device = torch.device("xpu") batch = 1 scale = 1.0 / math.sqrt(head_dim) @@ -670,20 +537,6 @@ def main() -> None: ) run_case(64, is_causal=True) run_case(128, is_causal=True) - run_case_bf16(64) - run_case_bf16(64, seq_len_q=192, seq_len_kv=256, num_heads_q=16, num_heads_kv=4) - run_case_bf16(128, q_tile_override=64, num_heads_q=32, num_heads_kv=8) - run_case_bf16( - 128, - seq_len_q=512, - seq_len_kv=768, - num_heads_q=32, - num_heads_kv=8, - q_tile_override=256, - sparse_q_block_tokens=256, - sparse_k_block_tokens=64, - ) - run_case_bf16(128, is_causal=True, q_tile_override=64) # Independent sparse-SDPA path (bf16 + fp16) for dtype in (torch.bfloat16, torch.float16): run_case_sdpa(dtype, 64) diff --git a/auto_round_extension/ark/benchmarks/bench_sparse_topk.py b/auto_round_extension/ark/benchmarks/bench_sparse_topk.py index ceb34e5588..757cd6de65 100644 --- a/auto_round_extension/ark/benchmarks/bench_sparse_topk.py +++ b/auto_round_extension/ark/benchmarks/bench_sparse_topk.py @@ -62,10 +62,7 @@ def _resolve_sparse_binding(args: argparse.Namespace, *, required_symbols: tuple def ensure_sparse_binding(args: argparse.Namespace) -> None: - if args.dtype == "bf16": - required_symbols = ("sage_sparse", "sage_sparse_bf16", "sage_sparse_sdpa") - else: - required_symbols = ("sage_sparse", "sage_sparse_sdpa") + required_symbols = ("sage_sparse", "sage_sparse_sdpa") ext_path = _resolve_sparse_binding(args, required_symbols=required_symbols) if ext_path is not None: _load_sparse_binding(ext_path, required_symbols=required_symbols) @@ -522,8 +519,6 @@ def summarize_speedups(rows: list[dict[str, object]]) -> list[dict[str, object]] "dense_sagev1", "sparse_kernel_only", "sparse_e2e", - "sparse_bf16_kernel_only", - "sparse_bf16_e2e", "sparse_qtile256_row64k_kernel_only", "sparse_qtile256_row64k_e2e", "sparse_sdpa_bf16_kernel_only", @@ -737,49 +732,6 @@ def run_single_benchmark(args: argparse.Namespace, *, seq_len: int, tensor_layou note=f"preprocess failed before e2e benchmark: {preprocess_note}", ) ) - if dtype == torch.bfloat16: - rows.append( - make_row( - mode="sparse_bf16_kernel_only", - batch=args.batch, - num_heads_q=args.num_heads_q, - num_heads_kv=args.num_heads_kv, - seq_len=seq_len, - tensor_layout=tensor_layout, - head_dim=args.head_dim, - dtype=dtype, - is_causal=args.causal, - warmup=args.warmup, - iters=args.iters, - requested_topk=topk, - selected_ratio=None, - selected_blocks_per_row=None, - latency_ms=None, - status=status, - note=f"preprocess failed before bf16 kernel benchmark: {preprocess_note}", - ) - ) - rows.append( - make_row( - mode="sparse_bf16_e2e", - batch=args.batch, - num_heads_q=args.num_heads_q, - num_heads_kv=args.num_heads_kv, - seq_len=seq_len, - tensor_layout=tensor_layout, - head_dim=args.head_dim, - dtype=dtype, - is_causal=args.causal, - warmup=args.warmup, - iters=args.iters, - requested_topk=topk, - selected_ratio=None, - selected_blocks_per_row=None, - latency_ms=None, - status=status, - note=f"preprocess failed before bf16 e2e benchmark: {preprocess_note}", - ) - ) continue # try: @@ -1063,109 +1015,6 @@ def run_single_benchmark(args: argparse.Namespace, *, seq_len: int, tensor_layou selected_blocks_per_row=selected_blocks_per_row, ) ) - if dtype == torch.bfloat16: - rows.append( - try_benchmark( - "sparse_bf16_kernel_only", - lambda preprocess=preprocess: ( - hnd_to_nhd( - ark.sage_sparse_bf16( - nhd_to_hnd(q_nhd_src), - nhd_to_hnd(k_nhd_src), - nhd_to_hnd(v_nhd_src), - preprocess["lut"], - preprocess["valid_block_num"], - is_causal=args.causal, - scale=scale, - q_tile_override=args.q_tile_override, - sparse_q_block_tokens=preprocess["sparse_q_block_tokens"], - sparse_k_block_tokens=preprocess["sparse_k_block_tokens"], - tensor_layout="HND", - ) - ) - if tensor_layout == "HND" - else ark.sage_sparse_bf16( - q, - k, - v, - preprocess["lut"], - preprocess["valid_block_num"], - is_causal=args.causal, - scale=scale, - q_tile_override=args.q_tile_override, - sparse_q_block_tokens=preprocess["sparse_q_block_tokens"], - sparse_k_block_tokens=preprocess["sparse_k_block_tokens"], - tensor_layout=tensor_layout, - ) - ), - batch=args.batch, - num_heads_q=args.num_heads_q, - num_heads_kv=args.num_heads_kv, - seq_len=seq_len, - tensor_layout=tensor_layout, - head_dim=args.head_dim, - dtype=dtype, - is_causal=args.causal, - warmup=args.warmup, - iters=args.iters, - requested_topk=topk, - selected_ratio=selected_ratio, - selected_blocks_per_row=selected_blocks_per_row, - ) - ) - rows.append( - try_benchmark( - "sparse_bf16_e2e", - lambda topk=topk: ( - hnd_to_nhd( - ark.sparge_sage2_attn_meansim_topk_xpu_bf16( - nhd_to_hnd(q_nhd_src), - nhd_to_hnd(k_nhd_src), - nhd_to_hnd(v_nhd_src), - is_causal=args.causal, - scale=scale, - smooth_k=True, - simthreshd1=-1.0, - topk=topk, - attention_sink=False, - tensor_layout="HND", - q_tile_override=args.q_tile_override, - sparse_q_block_tokens=args.sparse_q_block_tokens, - sparse_k_block_tokens=args.sparse_k_block_tokens, - ) - ) - if tensor_layout == "HND" - else ark.sparge_sage2_attn_meansim_topk_xpu_bf16( - q, - k, - v, - is_causal=args.causal, - scale=scale, - smooth_k=True, - simthreshd1=-1.0, - topk=topk, - attention_sink=False, - tensor_layout=tensor_layout, - q_tile_override=args.q_tile_override, - sparse_q_block_tokens=args.sparse_q_block_tokens, - sparse_k_block_tokens=args.sparse_k_block_tokens, - ) - ), - batch=args.batch, - num_heads_q=args.num_heads_q, - num_heads_kv=args.num_heads_kv, - seq_len=seq_len, - tensor_layout=tensor_layout, - head_dim=args.head_dim, - dtype=dtype, - is_causal=args.causal, - warmup=args.warmup, - iters=args.iters, - requested_topk=topk, - selected_ratio=selected_ratio, - selected_blocks_per_row=selected_blocks_per_row, - ) - ) del preprocess empty_xpu_cache() diff --git a/auto_round_extension/ark/examples/flux_sparse_patch.py b/auto_round_extension/ark/examples/flux_sparse_patch.py index abe9bfcc0e..2e90ec387b 100644 --- a/auto_round_extension/ark/examples/flux_sparse_patch.py +++ b/auto_round_extension/ark/examples/flux_sparse_patch.py @@ -208,7 +208,7 @@ def __call__( device=query.device, ) if self.sparse_kernel == "bf16": - hidden_states, sparsity = ark.sparge_sage2_attn_meansim_topk_xpu_bf16( + hidden_states, sparsity = ark.sparge_sage2_attn_meansim_topk_xpu_sdpa( query, key, value, @@ -307,7 +307,7 @@ def patch_flux_sparse_attention( ): ensure_ark_sparse_binding( required_symbols=( - ("sage_sparse_bf16", "sage_dynamic_quant_layout") + ("sage_sparse_sdpa", "sage_dynamic_quant_layout") if sparse_kernel == "bf16" else ("sage_sparse", "sage_dynamic_quant_layout") ) From 333e242666c55d7150fab7256e4eab34ec8d3479 Mon Sep 17 00:00:00 2001 From: yiliu30 Date: Thu, 6 Aug 2026 19:05:57 +0800 Subject: [PATCH 21/42] docs: refresh sparse BF16 handoff after int8/bf16 path separation Bring the handoff in sync with the refactored state: one native sage_sparse_sdpa path per precision, the deleted sdpa_sparse_bf16.cpp, the re-configure requirement for the GLOB source list, the torch preprocess backend caveat, and the confirmed full-sweep benchmark numbers. Co-Authored-By: Claude --- .../ark/SPARSE_BF16_HANDOFF_20260805.md | 158 ++++++++++++++++++ .../ark/SPARSE_BF16_HANDOFF_20260805_CN.md | 153 +++++++++++++++++ 2 files changed, 311 insertions(+) create mode 100644 auto_round_extension/ark/SPARSE_BF16_HANDOFF_20260805.md create mode 100644 auto_round_extension/ark/SPARSE_BF16_HANDOFF_20260805_CN.md diff --git a/auto_round_extension/ark/SPARSE_BF16_HANDOFF_20260805.md b/auto_round_extension/ark/SPARSE_BF16_HANDOFF_20260805.md new file mode 100644 index 0000000000..53859d42e0 --- /dev/null +++ b/auto_round_extension/ark/SPARSE_BF16_HANDOFF_20260805.md @@ -0,0 +1,158 @@ +# Sparse BF16 Handoff (updated 2026-08-06) + +> **Note:** This is a refresh of the 2026-08-05 handoff. The earlier version described a +> pre-refactor state and reported the BF16 benchmark as failing; both are stale. The BF16 +> sparse path has been cleanly separated from the INT8 sparse path, and recent benchmark +> runs complete successfully. + +## Scope + +Handoff for the `sparse BF16` work in `auto_round_extension/ark` after the INT8/BF16 +path-separation refactor on branch `feat/sparse-bf16-prefill-v2`. + +Covers: + +- the final architecture (two clean, per-dtype sparse attention paths) +- the public API +- build/verify/reproduce steps +- current benchmark status and known caveats + +## Goal + +Two independent sparse SDPA paths, one per precision: + +- **INT8** — `sage_sparse` / `sparge_sage2_attn_meansim_topk_xpu` +- **BF16 / FP16** — `sage_sparse_sdpa` / `sparge_sage2_attn_meansim_topk_xpu_sdpa` + +Previously the BF16 native path was mixed into the INT8-centric SAGE kernel path +(`sage_sparse_bf16` routed native Q/K through `SparseSageConfig` / +`SPARSESAGEV1FwdMainloop`). The refactor removed that coupling. + +## Current Environment + +- repo: `/home/yiliu4/workspace/auto-round/auto_round_extension/ark` +- Python: `.venv/bin/python` +- PyTorch: `2.13.0+xpu`, `torch.version.xpu=20260000` +- oneAPI compiler: `2026.1.1` (`/opt/intel/oneapi/compiler/2026.1`), MKL `2026.1` +- CMake XPU build target: `intel_gpu_bmg_g21`, `ARK_SYCL_TLA=ON`, icx + +## Architecture (post-refactor) + +**INT8 path:** `sparse_attention.py:sage_sparse` → pybind `ark.cpp:sage_sparse` → +`sdpa_sparse.cpp` (`sdpa_impl_qks8_sparse_{d64,row_linear,qtile256_row64k}_pvhalf`) → +`SparseSageConfig` → `SPARSESAGEV1FwdMainloop` +(`wrapper/include/stla/xe_sparse_sagev1_fwd_mainloop.hpp`). + +- Q/K are `int8_t` (dequantized via qscale/kscale); V/PV is fp16/bf16. +- Q*K MMA is hardcoded to int32/int8 DPAS; `SparseSageConfig` carries a + `static_assert` that `ElementQ == int8_t` (INT8-only). + +**Native BF16/FP16 path:** `sparse_attention.py:sage_sparse_sdpa` → pybind +`ark.cpp:sage_sparse_sdpa` → `sdpa_sparse_sdpa.cpp` +(`sdpa_impl_{bf16,fp16}_sparse_sdpa_{d64,row_linear,qtile256_row64k}`) → +`SparseSDPAConfig` → `SPARSESDPAFwdMainloop` +(`wrapper/include/stla/xe_sparse_sdpa_fwd_mainloop.hpp`). + +- Q/K/V native precision; `scale_block_size = 0`, no dequant scales; softmax scale applied directly. +- Head dim 64/128, q_tile_override 0/64/256 (d64 / row_linear / qtile256_row64k). + +**Shared:** `sparge_preprocess_topk` (torch or triton preprocess backend), +`XeSparseSageFwdKernel` (`xe_sparse_sage_fwd_kernel.hpp`), `SparseFMHAFwdEpilogue`. + +**Removed by the refactor:** + +- `auto_round_kernel/sdpa_sparse_bf16.cpp` (superseded by `sdpa_sparse_sdpa.cpp`) +- `sage_sparse_bf16` (pybind binding + Python alias; was an alias of `sage_sparse_sdpa`) +- `sparge_sage2_attn_meansim_topk_xpu_bf16` (e2e wrapper) +- `sdpa_impl_bf16_sparse_{d64,row_linear,qtile256_row64k}` declarations +- native softmax-scale branch in the INT8 SAGE mainloop + +**Follow-up fix (verification pass):** `_query_tile_tokens_for_head_dim(64)` now returns +64 (was 128) so the preprocess None-default for head_dim 64 matches the sparse e2e +wrappers, fixing a pre-existing `tile_block_map` metadata mismatch that failed +`test_sparge_preprocess_topk_e2e.py`. + +## Public API Status + +BF16/FP16 sparse is exposed as: + +- `sage_sparse_sdpa(query, key, value, lut, valid_block_num, ...)` +- `sparge_sage2_attn_meansim_topk_xpu_sdpa(query, key, value, ...)` (end-to-end) + +INT8 sparse remains `sage_sparse` / `sparge_sage2_attn_meansim_topk_xpu`. +There is no `sage_sparse_bf16` anymore. + +## Build Status + +The XPU extension is rebuilt into `auto_round_kernel/ark-xbuild`: + +```bash +source /opt/intel/oneapi/setvars.sh --force >/dev/null 2>&1 +export MKLROOT=/opt/intel/oneapi/mkl/2026.1 +cmake -S auto_round_kernel -B auto_round_kernel/ark-xbuild \ + -DCMAKE_BUILD_TYPE=Release -DCMAKE_CXX_COMPILER=icx \ + -DARK_XPU=ON -DARK_SYCL_TLA=ON -DARK_DNNL=OFF -DARK_JOINT_MATRIX=OFF \ + -DDPCPP_SYCL_TARGET=intel_gpu_bmg_g21 +cmake --build auto_round_kernel/ark-xbuild -j 16 +``` + +**Important:** `auto_round_kernel/CMakeLists.txt` collects sources with +`file(GLOB SRCS .../*.cpp)` and has **no** `CONFIGURE_DEPENDS`. After adding/removing a +`.cpp` (e.g. the deleted `sdpa_sparse_bf16.cpp`), you MUST re-run `cmake -S ... -B ...` +before `cmake --build`, otherwise the stale build references the removed file and the old +`.so` still exports the old symbols. + +## Reproduce / Verify + +```bash +cd /home/yiliu4/workspace/auto-round/auto_round_extension/ark +# correctness (INT8 + BF16 + FP16). Set torch preprocess backend to avoid an XPU +# scatter/gather OOB assertion that the triton preprocess backend triggers on this host. +export SAGE_ATTN_SPARSE_PREPROCESS_BACKEND=torch +.venv/bin/python auto_round_kernel/wrapper/test/test_sage_sparse_prefill_e2e.py +.venv/bin/python auto_round_kernel/wrapper/test/test_sparge_preprocess_topk_e2e.py + +# benchmark (script already exports SAGE_ATTN_SPARSE_PREPROCESS_BACKEND=torch, ZE_AFFINITY_MASK=6) +./tools/repro_sparse_bf16_sdpa_bench.sh +# CSV default: /tmp/bench_sparse_topk_bf16_dev6.csv +``` + +## Benchmark Status (2026-08-06) + +The BF16 sparse benchmark **completes successfully** (`status: ok`). Full sweep results +are in `benchmarks/results/bf16_sparse_20260806_full.csv`. Native-path +(`sparse_sdpa_bf16_*` via `sage_sparse_sdpa`) speedups vs dense torch SDPA: + +| case | kernel-only | e2e | +|---|---|---| +| seq 32768, topk 0.5 | 1.40–1.44x | 0.85–0.87x | +| seq 32768, topk 0.125 | 5.38–5.67x | 1.56–1.57x | +| seq 75600, topk 0.5 | 1.51–1.53x | 1.15x | +| seq 75600, topk 0.125 | 5.71–5.83x | 2.64–2.65x | + +The e2e mode is preprocess-bound at moderate sizes (topk 0.5 @ seq 32768) but wins at +larger seq_len / lower topk. These match the earlier mixed-path `sparse_bf16_*` numbers, +so the separation did not regress performance. + +## Known Caveats + +- **Preprocess backend:** use `SAGE_ATTN_SPARSE_PREPROCESS_BACKEND=torch`. The triton + preprocess backend hits a PyTorch XPU scatter/gather "index out of bounds" assert on + this host (`ScatterGatherKernels.cpp:233`). The repro script already sets `torch`; + `tools/run_bf16_sparse_bench.sh` defaults to `triton_xpu` and should be overridden. +- **`scale_block_size == 0` on the native path:** `xe_sparse_sage_fwd_kernel.hpp` + guards the `seq_*_pad` division by `scale_block_size` to avoid div-by-zero UB; pads are + only used inside `scale_block_size ? ... : nullptr` ternaries. +- **INT8-only SAGE config:** `SparseSageConfig` enforces `ElementQ == int8_t` at compile + time. Use `SparseSDPAConfig` for native-precision BF16/FP16. +- **Stale docs:** `SPARSE_BF16_HANDOFF_20260805.md` (this file) supersedes the + pre-refactor handoff; `SPARSE_BF16_STATUS_20260729.md` / `SPARSE_BF16_BENCH_20260805.md` + are historical and refer to older builds/symbols. + +## Summary + +The BF16 native sparse path is fully separated from the INT8 SAGE path. The build wiring, +public API, correctness tests, and benchmark modes are in place; BF16 sparse completes and +outperforms dense torch SDPA. The main runtime caveat is the preprocess backend choice +(torch, not triton). Remaining work is any follow-up perf tuning or integration into the +Flux/Wan sparse examples via `sage_sparse_sdpa`. diff --git a/auto_round_extension/ark/SPARSE_BF16_HANDOFF_20260805_CN.md b/auto_round_extension/ark/SPARSE_BF16_HANDOFF_20260805_CN.md new file mode 100644 index 0000000000..5c8b7b0129 --- /dev/null +++ b/auto_round_extension/ark/SPARSE_BF16_HANDOFF_20260805_CN.md @@ -0,0 +1,153 @@ +# Sparse BF16 交接文档（2026-08-06 更新） + +> **说明：** 本文档是 2026-08-05 交接文档的更新版。旧版描述的是重构前的状态，并且认为 +> BF16 benchmark 是失败的；这两点都已过时。BF16 sparse 路径已经从 INT8 sparse 路径中 +> 干净地分离出来，且最近的 benchmark 运行可以成功完成。 + +## 范围 + +交接 `auto_round_extension/ark` 中 `sparse BF16` 的工作，以 `feat/sparse-bf16-prefill-v2` +分支上的 INT8/BF16 路径分离重构之后的状态为准。 + +内容包括： + +- 最终架构（两条按精度分离的 sparse attention 路径） +- 公开 API +- 构建 / 验证 / 复现步骤 +- 当前 benchmark 状态与已知注意事项 + +## 目标 + +两条相互独立的 sparse SDPA 路径，每种精度一条： + +- **INT8** —— `sage_sparse` / `sparge_sage2_attn_meansim_topk_xpu` +- **BF16 / FP16** —— `sage_sparse_sdpa` / `sparge_sage2_attn_meansim_topk_xpu_sdpa` + +此前 BF16 原生路径被混进了以 INT8 为中心的 SAGE kernel 路径（`sage_sparse_bf16` 把原生 +Q/K 路由到 `SparseSageConfig` / `SPARSESAGEV1FwdMainloop`）。本次重构已移除该耦合。 + +## 当前环境 + +- 仓库：`/home/yiliu4/workspace/auto-round/auto_round_extension/ark` +- Python：`.venv/bin/python` +- PyTorch：`2.13.0+xpu`，`torch.version.xpu=20260000` +- oneAPI 编译器：`2026.1.1`（`/opt/intel/oneapi/compiler/2026.1`），MKL `2026.1` +- CMake XPU 构建目标：`intel_gpu_bmg_g21`，`ARK_SYCL_TLA=ON`，icx + +## 架构（重构后） + +**INT8 路径：** `sparse_attention.py:sage_sparse` → pybind `ark.cpp:sage_sparse` → +`sdpa_sparse.cpp`（`sdpa_impl_qks8_sparse_{d64,row_linear,qtile256_row64k}_pvhalf`）→ +`SparseSageConfig` → `SPARSESAGEV1FwdMainloop` +（`wrapper/include/stla/xe_sparse_sagev1_fwd_mainloop.hpp`）。 + +- Q/K 为 `int8_t`（通过 qscale/kscale 反量化）；V/PV 为 fp16/bf16。 +- Q*K MMA 固定为 int32/int8 DPAS；`SparseSageConfig` 内 `static_assert` 强制 + `ElementQ == int8_t`（仅支持 INT8）。 + +**BF16/FP16 原生路径：** `sparse_attention.py:sage_sparse_sdpa` → pybind +`ark.cpp:sage_sparse_sdpa` → `sdpa_sparse_sdpa.cpp` +（`sdpa_impl_{bf16,fp16}_sparse_sdpa_{d64,row_linear,qtile256_row64k}`）→ +`SparseSDPAConfig` → `SPARSESDPAFwdMainloop` +（`wrapper/include/stla/xe_sparse_sdpa_fwd_mainloop.hpp`）。 + +- Q/K/V 为原生精度；`scale_block_size = 0`，无反量化 scale；softmax scale 直接应用。 +- head_dim 64/128，q_tile_override 0/64/256（d64 / row_linear / qtile256_row64k）。 + +**共享部分：** `sparge_preprocess_topk`（torch 或 triton 的 preprocess 后端）、 +`XeSparseSageFwdKernel`（`xe_sparse_sage_fwd_kernel.hpp`）、`SparseFMHAFwdEpilogue`。 + +**本次重构移除的内容：** + +- `auto_round_kernel/sdpa_sparse_bf16.cpp`（由 `sdpa_sparse_sdpa.cpp` 取代） +- `sage_sparse_bf16`（pybind 绑定 + Python 别名；原先只是 `sage_sparse_sdpa` 的别名） +- `sparge_sage2_attn_meansim_topk_xpu_bf16`（端到端封装） +- `sdpa_impl_bf16_sparse_{d64,row_linear,qtile256_row64k}` 声明 +- INT8 SAGE mainloop 中的原生 softmax-scale 分支 + +**验证阶段的修复：** `_query_tile_tokens_for_head_dim(64)` 现在返回 64（原来返回 128），使 +head_dim 64 的 preprocess 默认值与 sparse e2e 封装保持一致，修复了导致 +`test_sparge_preprocess_topk_e2e.py` 失败的既有 `tile_block_map` 元数据不一致问题。 + +## 公开 API 状态 + +BF16/FP16 sparse 对外暴露为： + +- `sage_sparse_sdpa(query, key, value, lut, valid_block_num, ...)` +- `sparge_sage2_attn_meansim_topk_xpu_sdpa(query, key, value, ...)`（端到端） + +INT8 sparse 仍为 `sage_sparse` / `sparge_sage2_attn_meansim_topk_xpu`。不再有 +`sage_sparse_bf16`。 + +## 构建状态 + +XPU 扩展重建到 `auto_round_kernel/ark-xbuild`： + +```bash +source /opt/intel/oneapi/setvars.sh --force >/dev/null 2>&1 +export MKLROOT=/opt/intel/oneapi/mkl/2026.1 +cmake -S auto_round_kernel -B auto_round_kernel/ark-xbuild \ + -DCMAKE_BUILD_TYPE=Release -DCMAKE_CXX_COMPILER=icx \ + -DARK_XPU=ON -DARK_SYCL_TLA=ON -DARK_DNNL=OFF -DARK_JOINT_MATRIX=OFF \ + -DDPCPP_SYCL_TARGET=intel_gpu_bmg_g21 +cmake --build auto_round_kernel/ark-xbuild -j 16 +``` + +**重要：** `auto_round_kernel/CMakeLists.txt` 通过 `file(GLOB SRCS .../*.cpp)` 收集源文件， +且**没有** `CONFIGURE_DEPENDS`。新增/删除 `.cpp`（例如已删除的 `sdpa_sparse_bf16.cpp`）后， +**必须**先重新运行 `cmake -S ... -B ...` 再 `cmake --build`，否则旧构建仍会引用已删除的 +文件，且旧的 `.so` 仍然导出旧符号。 + +## 复现 / 验证 + +```bash +cd /home/yiliu4/workspace/auto-round/auto_round_extension/ark +# 正确性（INT8 + BF16 + FP16）。设置 torch preprocess 后端，避免 triton 后端在本机 +# 触发 XPU scatter/gather 越界断言。 +export SAGE_ATTN_SPARSE_PREPROCESS_BACKEND=torch +.venv/bin/python auto_round_kernel/wrapper/test/test_sage_sparse_prefill_e2e.py +.venv/bin/python auto_round_kernel/wrapper/test/test_sparge_preprocess_topk_e2e.py + +# benchmark（脚本已设置 SAGE_ATTN_SPARSE_PREPROCESS_BACKEND=torch、ZE_AFFINITY_MASK=6） +./tools/repro_sparse_bf16_sdpa_bench.sh +# CSV 默认输出：/tmp/bench_sparse_topk_bf16_dev6.csv +``` + +## Benchmark 状态（2026-08-06） + +BF16 sparse benchmark **可以成功跑完**（`status: ok`）。完整结果见 +`benchmarks/results/bf16_sparse_20260806_full.csv`。原生路径（`sparse_sdpa_bf16_*`， +经 `sage_sparse_sdpa`）相对 dense torch SDPA 的加速比： + +| 用例 | 仅 kernel | e2e | +|---|---|---| +| seq 32768、topk 0.5 | 1.40–1.44x | 0.85–0.87x | +| seq 32768、topk 0.125 | 5.38–5.67x | 1.56–1.57x | +| seq 75600、topk 0.5 | 1.51–1.53x | 1.15x | +| seq 75600、topk 0.125 | 5.71–5.83x | 2.64–2.65x | + +在中等规模（seq 32768、topk 0.5）下 e2e 主要受 preprocess 开销影响；在更大 seq_len 或 +更低 topk 下则更快。这些数字与此前混合路径 `sparse_bf16_*` 一致，说明分离没有造成 +性能回退。 + +## 已知注意事项 + +- **Preprocess 后端：** 使用 `SAGE_ATTN_SPARSE_PREPROCESS_BACKEND=torch`。triton + preprocess 后端在本机会触发 PyTorch XPU scatter/gather 越界断言 + （`ScatterGatherKernels.cpp:233`）。复现脚本已设置 `torch`；`tools/run_bf16_sparse_bench.sh` + 默认是 `triton_xpu`，需要覆盖该变量。 +- **原生路径 `scale_block_size == 0`：** `xe_sparse_sage_fwd_kernel.hpp` 中对 + `seq_*_pad` 除以 `scale_block_size` 做了保护，避免除零 UB；pad 只在 + `scale_block_size ? ... : nullptr` 三元表达式中使用。 +- **仅 INT8 的 SAGE 配置：** `SparseSageConfig` 在编译期强制 `ElementQ == int8_t`。 + 原生精度 BF16/FP16 请使用 `SparseSDPAConfig`。 +- **过时文档：** 本文档（`SPARSE_BF16_HANDOFF_20260805.md`）取代重构前的交接文档； + `SPARSE_BF16_STATUS_20260729.md` / `SPARSE_BF16_BENCH_20260805.md` 属于历史记录， + 指向旧构建和旧符号。 + +## 总结 + +BF16 原生 sparse 路径已与 INT8 SAGE 路径完全分离。构建接线、公开 API、正确性测试和 +benchmark 模式均已就位；BF16 sparse 可以跑通并且快于 dense torch SDPA。主要运行时注意 +事项是 preprocess 后端的选择（用 torch，不要用 triton）。后续工作可继续在 Flux/Wan +sparse 示例中通过 `sage_sparse_sdpa` 做性能调优或集成。 From e39a2acb22827313bf33e726e8e2708b3502b354 Mon Sep 17 00:00:00 2001 From: yiliu30 Date: Mon, 10 Aug 2026 17:04:31 +0800 Subject: [PATCH 22/42] chore: add bf16-sparse FLUX.1-dev output image reference 512x512 render (50 steps) with the native bf16 sparse attention patch at topk 0.5 (avg sparsity 50%, all 2850 attention calls through the sparse path). Reference for the FLUX sparse-attention investigation. 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z4LLo?ap|`uvcA1<+gB!r>hc7{#D5Q-GA6uMCq6%JGj@KL{A=rkgq z7~y8!!$=}JTBw2|q0AN&Lq7<};M0-Sx7i8omSLRt?%v&c z8nnEJEPFx7D%=(%C>LVH# Date: Tue, 11 Aug 2026 08:02:28 +0000 Subject: [PATCH 23/42] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- .../ark/auto_round_kernel/sparse_attention.py | 14 ++++---------- .../ark/tools/repro_sparse_bf16_sdpa_bench.sh | 3 +++ .../ark/tools/run_bf16_sparse_bench.sh | 3 +++ 3 files changed, 10 insertions(+), 10 deletions(-) diff --git a/auto_round_extension/ark/auto_round_kernel/sparse_attention.py b/auto_round_extension/ark/auto_round_kernel/sparse_attention.py index fc1da94539..6ff52a3245 100644 --- a/auto_round_extension/ark/auto_round_kernel/sparse_attention.py +++ b/auto_round_extension/ark/auto_round_kernel/sparse_attention.py @@ -284,9 +284,7 @@ def sage_sparse_sdpa( tensor_layout=tensor_layout, ) stream = get_stream(query) - O = _empty_attention_output( - B, Hq, Sq, D, dtype=value.dtype, device=query.device, tensor_layout=tensor_layout - ) + O = _empty_attention_output(B, Hq, Sq, D, dtype=value.dtype, device=query.device, tensor_layout=tensor_layout) q_dtype = 1 if query.dtype == torch.bfloat16 else 0 # FlashAttnDtype: 0=FP16, 1=BF16 lib.sage_sparse_sdpa( stream, @@ -767,9 +765,7 @@ def _block_map_lut_torch(block_map: torch.Tensor) -> tuple[torch.Tensor, torch.T # in-place assignment silently misses entries on the XPU backend (torch-xpu-ops), # leaving 0s in the mask that become -1 in the LUT after sort and drive the # ScatterGatherKernels out-of-bounds assert downstream. Use torch.where instead. - filled_matrix = torch.where( - block_map, masked_cum_matrix, torch.full_like(masked_cum_matrix, 10_000_000) - ) + filled_matrix = torch.where(block_map, masked_cum_matrix, torch.full_like(masked_cum_matrix, 10_000_000)) lut = torch.sort(filled_matrix, dim=-1)[0] - 1 lut[..., 1:] = lut[..., 1:] - lut[..., :-1] invalid_mask = torch.arange(num_k_blocks, device=block_map.device).view( @@ -1542,8 +1538,8 @@ def sparge_sage2_attn_meansim_topk_xpu( effective_query_tile_tokens = q_tile_override elif effective_query_tile_tokens is not None: if q_tile_override == 0: - effective_q_tile_override = 0 if D == 64 and int(effective_query_tile_tokens) == 64 else int( - effective_query_tile_tokens + effective_q_tile_override = ( + 0 if D == 64 and int(effective_query_tile_tokens) == 64 else int(effective_query_tile_tokens) ) elif q_tile_override != int(effective_query_tile_tokens): raise ValueError( @@ -1714,5 +1710,3 @@ def sparge_sage2_attn_meansim_topk_xpu_sdpa( if return_sparsity: return out, sparsity_ratio return out - - diff --git a/auto_round_extension/ark/tools/repro_sparse_bf16_sdpa_bench.sh b/auto_round_extension/ark/tools/repro_sparse_bf16_sdpa_bench.sh index d26c692b48..fb5f372ce1 100755 --- a/auto_round_extension/ark/tools/repro_sparse_bf16_sdpa_bench.sh +++ b/auto_round_extension/ark/tools/repro_sparse_bf16_sdpa_bench.sh @@ -1,5 +1,8 @@ #!/usr/bin/env bash +# # Copyright (C) 2026 Intel Corporation +# # SPDX-License-Identifier: Apache-2.0 + set -euo pipefail SCRIPT_PATH="$(readlink -f "$0")" diff --git a/auto_round_extension/ark/tools/run_bf16_sparse_bench.sh b/auto_round_extension/ark/tools/run_bf16_sparse_bench.sh index e2253a011f..93c4b65ca2 100755 --- a/auto_round_extension/ark/tools/run_bf16_sparse_bench.sh +++ b/auto_round_extension/ark/tools/run_bf16_sparse_bench.sh @@ -1,4 +1,7 @@ #!/usr/bin/env bash +# # Copyright (C) 2026 Intel Corporation +# # SPDX-License-Identifier: Apache-2.0 + # # run_bf16_sparse_bench.sh # ======================== From 0ca035fb045f0d3f0e4add984333e9c9a36aa245 Mon Sep 17 00:00:00 2001 From: yiliu30 Date: Tue, 11 Aug 2026 16:06:48 +0800 Subject: [PATCH 24/42] clean Signed-off-by: yiliu30 --- .../ark/SPARSE_BF16_HANDOFF_20260805_CN.md | 153 ------------- .../ark/SPARSE_BF16_STATUS_20260729.md | 205 ------------------ .../ark/SPARSE_BF16_STATUS_20260729_CN.md | 205 ------------------ 3 files changed, 563 deletions(-) delete mode 100644 auto_round_extension/ark/SPARSE_BF16_HANDOFF_20260805_CN.md delete mode 100644 auto_round_extension/ark/SPARSE_BF16_STATUS_20260729.md delete mode 100644 auto_round_extension/ark/SPARSE_BF16_STATUS_20260729_CN.md diff --git a/auto_round_extension/ark/SPARSE_BF16_HANDOFF_20260805_CN.md b/auto_round_extension/ark/SPARSE_BF16_HANDOFF_20260805_CN.md deleted file mode 100644 index 5c8b7b0129..0000000000 --- a/auto_round_extension/ark/SPARSE_BF16_HANDOFF_20260805_CN.md +++ /dev/null @@ -1,153 +0,0 @@ -# Sparse BF16 交接文档（2026-08-06 更新） - -> **说明：** 本文档是 2026-08-05 交接文档的更新版。旧版描述的是重构前的状态，并且认为 -> BF16 benchmark 是失败的；这两点都已过时。BF16 sparse 路径已经从 INT8 sparse 路径中 -> 干净地分离出来，且最近的 benchmark 运行可以成功完成。 - -## 范围 - -交接 `auto_round_extension/ark` 中 `sparse BF16` 的工作，以 `feat/sparse-bf16-prefill-v2` -分支上的 INT8/BF16 路径分离重构之后的状态为准。 - -内容包括： - -- 最终架构（两条按精度分离的 sparse attention 路径） -- 公开 API -- 构建 / 验证 / 复现步骤 -- 当前 benchmark 状态与已知注意事项 - -## 目标 - -两条相互独立的 sparse SDPA 路径，每种精度一条： - -- **INT8** —— `sage_sparse` / `sparge_sage2_attn_meansim_topk_xpu` -- **BF16 / FP16** —— `sage_sparse_sdpa` / `sparge_sage2_attn_meansim_topk_xpu_sdpa` - -此前 BF16 原生路径被混进了以 INT8 为中心的 SAGE kernel 路径（`sage_sparse_bf16` 把原生 -Q/K 路由到 `SparseSageConfig` / `SPARSESAGEV1FwdMainloop`）。本次重构已移除该耦合。 - -## 当前环境 - -- 仓库：`/home/yiliu4/workspace/auto-round/auto_round_extension/ark` -- Python：`.venv/bin/python` -- PyTorch：`2.13.0+xpu`，`torch.version.xpu=20260000` -- oneAPI 编译器：`2026.1.1`（`/opt/intel/oneapi/compiler/2026.1`），MKL `2026.1` -- CMake XPU 构建目标：`intel_gpu_bmg_g21`，`ARK_SYCL_TLA=ON`，icx - -## 架构（重构后） - -**INT8 路径：** `sparse_attention.py:sage_sparse` → pybind `ark.cpp:sage_sparse` → -`sdpa_sparse.cpp`（`sdpa_impl_qks8_sparse_{d64,row_linear,qtile256_row64k}_pvhalf`）→ -`SparseSageConfig` → `SPARSESAGEV1FwdMainloop` -（`wrapper/include/stla/xe_sparse_sagev1_fwd_mainloop.hpp`）。 - -- Q/K 为 `int8_t`（通过 qscale/kscale 反量化）；V/PV 为 fp16/bf16。 -- Q*K MMA 固定为 int32/int8 DPAS；`SparseSageConfig` 内 `static_assert` 强制 - `ElementQ == int8_t`（仅支持 INT8）。 - -**BF16/FP16 原生路径：** `sparse_attention.py:sage_sparse_sdpa` → pybind -`ark.cpp:sage_sparse_sdpa` → `sdpa_sparse_sdpa.cpp` -（`sdpa_impl_{bf16,fp16}_sparse_sdpa_{d64,row_linear,qtile256_row64k}`）→ -`SparseSDPAConfig` → `SPARSESDPAFwdMainloop` -（`wrapper/include/stla/xe_sparse_sdpa_fwd_mainloop.hpp`）。 - -- Q/K/V 为原生精度；`scale_block_size = 0`，无反量化 scale；softmax scale 直接应用。 -- head_dim 64/128，q_tile_override 0/64/256（d64 / row_linear / qtile256_row64k）。 - -**共享部分：** `sparge_preprocess_topk`（torch 或 triton 的 preprocess 后端）、 -`XeSparseSageFwdKernel`（`xe_sparse_sage_fwd_kernel.hpp`）、`SparseFMHAFwdEpilogue`。 - -**本次重构移除的内容：** - -- `auto_round_kernel/sdpa_sparse_bf16.cpp`（由 `sdpa_sparse_sdpa.cpp` 取代） -- `sage_sparse_bf16`（pybind 绑定 + Python 别名；原先只是 `sage_sparse_sdpa` 的别名） -- `sparge_sage2_attn_meansim_topk_xpu_bf16`（端到端封装） -- `sdpa_impl_bf16_sparse_{d64,row_linear,qtile256_row64k}` 声明 -- INT8 SAGE mainloop 中的原生 softmax-scale 分支 - -**验证阶段的修复：** `_query_tile_tokens_for_head_dim(64)` 现在返回 64（原来返回 128），使 -head_dim 64 的 preprocess 默认值与 sparse e2e 封装保持一致，修复了导致 -`test_sparge_preprocess_topk_e2e.py` 失败的既有 `tile_block_map` 元数据不一致问题。 - -## 公开 API 状态 - -BF16/FP16 sparse 对外暴露为： - -- `sage_sparse_sdpa(query, key, value, lut, valid_block_num, ...)` -- `sparge_sage2_attn_meansim_topk_xpu_sdpa(query, key, value, ...)`（端到端） - -INT8 sparse 仍为 `sage_sparse` / `sparge_sage2_attn_meansim_topk_xpu`。不再有 -`sage_sparse_bf16`。 - -## 构建状态 - -XPU 扩展重建到 `auto_round_kernel/ark-xbuild`： - -```bash -source /opt/intel/oneapi/setvars.sh --force >/dev/null 2>&1 -export MKLROOT=/opt/intel/oneapi/mkl/2026.1 -cmake -S auto_round_kernel -B auto_round_kernel/ark-xbuild \ - -DCMAKE_BUILD_TYPE=Release -DCMAKE_CXX_COMPILER=icx \ - -DARK_XPU=ON -DARK_SYCL_TLA=ON -DARK_DNNL=OFF -DARK_JOINT_MATRIX=OFF \ - -DDPCPP_SYCL_TARGET=intel_gpu_bmg_g21 -cmake --build auto_round_kernel/ark-xbuild -j 16 -``` - -**重要：** `auto_round_kernel/CMakeLists.txt` 通过 `file(GLOB SRCS .../*.cpp)` 收集源文件， -且**没有** `CONFIGURE_DEPENDS`。新增/删除 `.cpp`（例如已删除的 `sdpa_sparse_bf16.cpp`）后， -**必须**先重新运行 `cmake -S ... -B ...` 再 `cmake --build`，否则旧构建仍会引用已删除的 -文件，且旧的 `.so` 仍然导出旧符号。 - -## 复现 / 验证 - -```bash -cd /home/yiliu4/workspace/auto-round/auto_round_extension/ark -# 正确性（INT8 + BF16 + FP16）。设置 torch preprocess 后端，避免 triton 后端在本机 -# 触发 XPU scatter/gather 越界断言。 -export SAGE_ATTN_SPARSE_PREPROCESS_BACKEND=torch -.venv/bin/python auto_round_kernel/wrapper/test/test_sage_sparse_prefill_e2e.py -.venv/bin/python auto_round_kernel/wrapper/test/test_sparge_preprocess_topk_e2e.py - -# benchmark（脚本已设置 SAGE_ATTN_SPARSE_PREPROCESS_BACKEND=torch、ZE_AFFINITY_MASK=6） -./tools/repro_sparse_bf16_sdpa_bench.sh -# CSV 默认输出：/tmp/bench_sparse_topk_bf16_dev6.csv -``` - -## Benchmark 状态（2026-08-06） - -BF16 sparse benchmark **可以成功跑完**（`status: ok`）。完整结果见 -`benchmarks/results/bf16_sparse_20260806_full.csv`。原生路径（`sparse_sdpa_bf16_*`， -经 `sage_sparse_sdpa`）相对 dense torch SDPA 的加速比： - -| 用例 | 仅 kernel | e2e | -|---|---|---| -| seq 32768、topk 0.5 | 1.40–1.44x | 0.85–0.87x | -| seq 32768、topk 0.125 | 5.38–5.67x | 1.56–1.57x | -| seq 75600、topk 0.5 | 1.51–1.53x | 1.15x | -| seq 75600、topk 0.125 | 5.71–5.83x | 2.64–2.65x | - -在中等规模（seq 32768、topk 0.5）下 e2e 主要受 preprocess 开销影响；在更大 seq_len 或 -更低 topk 下则更快。这些数字与此前混合路径 `sparse_bf16_*` 一致，说明分离没有造成 -性能回退。 - -## 已知注意事项 - -- **Preprocess 后端：** 使用 `SAGE_ATTN_SPARSE_PREPROCESS_BACKEND=torch`。triton - preprocess 后端在本机会触发 PyTorch XPU scatter/gather 越界断言 - （`ScatterGatherKernels.cpp:233`）。复现脚本已设置 `torch`；`tools/run_bf16_sparse_bench.sh` - 默认是 `triton_xpu`，需要覆盖该变量。 -- **原生路径 `scale_block_size == 0`：** `xe_sparse_sage_fwd_kernel.hpp` 中对 - `seq_*_pad` 除以 `scale_block_size` 做了保护，避免除零 UB；pad 只在 - `scale_block_size ? ... : nullptr` 三元表达式中使用。 -- **仅 INT8 的 SAGE 配置：** `SparseSageConfig` 在编译期强制 `ElementQ == int8_t`。 - 原生精度 BF16/FP16 请使用 `SparseSDPAConfig`。 -- **过时文档：** 本文档（`SPARSE_BF16_HANDOFF_20260805.md`）取代重构前的交接文档； - `SPARSE_BF16_STATUS_20260729.md` / `SPARSE_BF16_BENCH_20260805.md` 属于历史记录， - 指向旧构建和旧符号。 - -## 总结 - -BF16 原生 sparse 路径已与 INT8 SAGE 路径完全分离。构建接线、公开 API、正确性测试和 -benchmark 模式均已就位；BF16 sparse 可以跑通并且快于 dense torch SDPA。主要运行时注意 -事项是 preprocess 后端的选择（用 torch，不要用 triton）。后续工作可继续在 Flux/Wan -sparse 示例中通过 `sage_sparse_sdpa` 做性能调优或集成。 diff --git a/auto_round_extension/ark/SPARSE_BF16_STATUS_20260729.md b/auto_round_extension/ark/SPARSE_BF16_STATUS_20260729.md deleted file mode 100644 index 87902eafa6..0000000000 --- a/auto_round_extension/ark/SPARSE_BF16_STATUS_20260729.md +++ /dev/null @@ -1,205 +0,0 @@ -# Sparse BF16 Status 2026-07-29 - -## Scope - -This note captures the current sparse attention status on Wednesday, July 29, 2026 after rebuilding with oneAPI 2025 and rerunning focused accuracy and performance checks on XPU 7. - -Environment: - -- `ONEAPI_ROOT=/home/yiliu4/intel/oneapi` -- Python: `/home/yiliu4/workspace/auto-round-prefill-clean-sparse-pr/auto_round_extension/ark/ark-torch-212/bin/python` -- device: `XPU_MASK=7` -- native BF16 sparse enabled with `SAGE_ATTN_XPU_SPARSE_BF16_NATIVE=1` -- rebuilt extension: `auto_round_kernel/xbuild_bf16_v2/auto_round_kernel_xpu.cpython-313-x86_64-linux-gnu.so` - -Kernel config under test: - -- `head_dim=128` -- `q_tile_override=256` -- `sparse_q_block_tokens=256` -- `sparse_k_block_tokens=64` -- layout: `HND` - -## Accuracy Status - -### Fixed-LUT sparse kernel - -`fixed-LUT sparse` means the sparse mask is explicitly provided by `lut` and `valid_block_num`, and the kernel is checked against an exact dense BF16 reference with the same mask. - -Target correctness case: - -- shape: `B=1, Hq=32, Hkv=8, Sq=512, Skv=768, D=128` -- sparse pattern: first Q tile uses K blocks `[0, 1, 2]`, second Q tile uses `[9, 10, 11]` -- reference: explicit BF16 `Q @ K^T`, add exact mask, softmax, then `P @ V` - -Result: - -- `bf16_sparse` vs exact BF16 reference: - - `max_diff=0.003906` - - `mean_diff=0.000127` - -Comparison on the same case: - -- `int8_sparse` vs exact BF16 reference: - - `max_diff=3.826172` - - `mean_diff=0.344735` -- `int8_sparse` vs `bf16_sparse`: - - `max_diff=3.826172` - - `mean_diff=0.344735` - -Conclusion: - -- `bf16_sparse` is accurate for the exact fixed-LUT sparse kernel path. -- `int8_sparse` is materially less accurate than `bf16_sparse` on the same case. - -### Heuristic top-k end-to-end path - -The `sparge_sage2_attn_meansim_topk_xpu_bf16` path is heuristic. It should not be treated as an exact correctness reference for a handcrafted LUT mask. - -Because of that, the BF16 sparse test now treats: - -- exact correctness: `sage_sparse_bf16` vs explicit masked BF16 reference -- heuristic top-k path: benchmark/perf path, not exact reference - -## Test Harness Status - -File updated: - -- `auto_round_kernel/wrapper/test/test_sage_sparse_prefill_e2e.py` - -Current test harness behavior: - -- force-loads the rebuilt BF16-capable extension from `xbuild_bf16_v2` -- uses explicit masked BF16 math as the BF16 sparse correctness reference -- no longer treats heuristic BF16 top-k output as an exact oracle for the fixed-LUT kernel - -Verified run: - -```bash -source benchmarks/source_env_xpu67_oneapi2025.sh -export XPU_MASK=7 -export SAGE_ATTN_XPU_SPARSE_BF16_NATIVE=1 -${ARK_BENCH_PYTHON} auto_round_kernel/wrapper/test/test_sage_sparse_prefill_e2e.py -``` - -Observed result: - -- full script exits successfully under oneAPI 2025 -- INT8 sparse cases pass -- BF16 sparse exact-reference cases pass - -## Performance Status - -### Medium-size exact sparse case - -Shape: - -- `B=1, Hq=32, Hkv=8, Sq=512, Skv=768, D=128` - -Latency: - -- dense BF16 with exact mask: `0.105 ms` -- `bf16_sparse`: `0.096 ms` -- `int8_sparse`: `0.090 ms` -- INT8 Q/K quantization only: `0.027 ms` - -Speedup vs dense BF16 exact-mask baseline: - -- `bf16_sparse`: `1.093x` -- `int8_sparse`: `1.175x` -- `int8_sparse + quant`: `0.902x` - -Conclusion: - -- kernel-only, `int8_sparse` is slightly faster -- including quantization, this small/medium case does not favor the INT8 path -- `bf16_sparse` is the better accuracy point here - -### Long-sequence benchmark - -Shape: - -- `B=1, Hq=40, Hkv=40, S=75000, D=128` -- layout: `HND` -- `topk=0.5` -- `q_tile_override=256` -- `sparse_q_block_tokens=256` -- `sparse_k_block_tokens=64` - -Sparse selection stats: - -- `selected_ratio=0.502557` -- `selected_blocks_per_row=588.997` - -Sparse timings: - -- `preprocess=127.809 ms` -- `int8_sparse_kernel=390.990 ms` -- `int8_sparse_e2e=501.369 ms` -- `bf16_sparse_kernel=804.864 ms` -- `bf16_sparse_e2e=919.896 ms` - -Dense BF16 baselines on the same shape: - -- `dense_torch_sdpa_bf16=2154.809 ms` -- `dense_ark_sdpa_bf16=2494.633 ms` -- `dense_ark_sagev1_bf16=10848.612 ms` - -Speedup vs dense torch BF16 SDPA: - -- `int8_sparse_e2e`: about `4.30x` -- `bf16_sparse_e2e`: about `2.34x` - -Speedup vs dense ARK BF16 SDPA: - -- `int8_sparse_e2e`: about `4.98x` -- `bf16_sparse_e2e`: about `2.71x` - -Conclusion: - -- at `75k / 40 / 128`, both sparse paths are substantially faster than dense BF16 -- `int8_sparse` remains the fastest path -- `bf16_sparse` is slower than `int8_sparse` but still much faster than dense BF16 - -## Current Summary - -- BF16 sparse fixed-LUT kernel correctness is in good shape after rebuilding with oneAPI 2025. -- The previous BF16 mismatch was caused by stale extension loading and an unreliable masked-XPU reference path in the test harness. -- For long sequences, BF16 sparse is a valid accuracy-oriented sparse path with meaningful speedup over dense BF16. -- INT8 sparse is still the fastest sparse option, but it gives up substantial accuracy relative to BF16 sparse on the exact fixed-LUT check. - -## July 30 Rerun Summary - -On Thursday, July 30, 2026, the `75k / topk=0.5` case was rerun on an idle `XPU 7` with oneAPI 2025 and the BF16-capable sparse extension explicitly loaded from `auto_round_kernel/xbuild_bf16_v2/auto_round_kernel_xpu.cpython-313-x86_64-linux-gnu.so`. - -Shape and config: - -- `B=1, Hq=40, Hkv=40, S=75000, D=128` -- layout: `HND` -- `topk=0.5` -- `q_tile_override=256` -- `sparse_q_block_tokens=256` -- `sparse_k_block_tokens=64` -- warmup `2`, iters `3` - -Sparse selection stats: - -- `selected_ratio=0.502557` -- `selected_blocks_per_row=588.997` - -Latency and throughput summary: - -| Mode | Latency (ms) | Speedup vs Torch BF16 | Speedup vs SageV1 BF16 | Baseline TFLOPS | Effective TFLOPS | -|---|---:|---:|---:|---:|---:| -| `dense_torch_sdpa_bf16` | 1263.297 | 1.000 | 0.595 | 91.190 | 91.190 | -| `dense_sagev1_bf16` | 751.410 | 1.681 | 1.000 | 153.312 | 153.312 | -| `sparse_qtile256_row64k_kernel_only` | 398.381 | 3.305 | 1.944 | 289.171 | 145.325 | -| `sparse_qtile256_row64k_e2e` | 514.271 | 2.560 | 1.506 | 224.007 | 112.576 | -| `sparse_bf16_qtile256_row64k_kernel_only` | 812.091 | 1.556 | 0.925 | 141.856 | 71.291 | -| `sparse_bf16_qtile256_row64k_e2e` | 928.958 | 1.360 | 0.809 | 124.010 | 62.322 | - -Notes: - -- `kernel_only` is sparse kernel time after preprocess. -- `e2e` includes preprocess plus kernel. -- On this rerun, BF16 sparse is faster than dense torch BF16 SDPA, but still slower than dense `sagev1_bf16`. diff --git a/auto_round_extension/ark/SPARSE_BF16_STATUS_20260729_CN.md b/auto_round_extension/ark/SPARSE_BF16_STATUS_20260729_CN.md deleted file mode 100644 index a6cb5fde5c..0000000000 --- a/auto_round_extension/ark/SPARSE_BF16_STATUS_20260729_CN.md +++ /dev/null @@ -1,205 +0,0 @@ -# Sparse BF16 状态 2026-07-29 - -## 范围 - -本文档记录了 2026 年 7 月 29 日（星期三）在 oneAPI 2025 环境下重新构建并在 XPU 7 上复测后的 sparse attention 当前状态。 - -环境： - -- `ONEAPI_ROOT=/home/yiliu4/intel/oneapi` -- Python: `/home/yiliu4/workspace/auto-round-prefill-clean-sparse-pr/auto_round_extension/ark/ark-torch-212/bin/python` -- 设备：`XPU_MASK=7` -- 原生 BF16 sparse 打开：`SAGE_ATTN_XPU_SPARSE_BF16_NATIVE=1` -- 重建后的扩展：`auto_round_kernel/xbuild_bf16_v2/auto_round_kernel_xpu.cpython-313-x86_64-linux-gnu.so` - -本次测试的 kernel 配置： - -- `head_dim=128` -- `q_tile_override=256` -- `sparse_q_block_tokens=256` -- `sparse_k_block_tokens=64` -- layout：`HND` - -## 精度状态 - -### 固定 LUT sparse kernel - -这里的 `fixed-LUT sparse` 指 sparse mask 由 `lut` 和 `valid_block_num` 显式给出，然后用相同 mask 的精确 dense BF16 参考结果进行校验。 - -目标正确性用例： - -- shape：`B=1, Hq=32, Hkv=8, Sq=512, Skv=768, D=128` -- sparse pattern：第一个 Q tile 使用 K blocks `[0, 1, 2]`，第二个 Q tile 使用 `[9, 10, 11]` -- 参考实现：显式 BF16 `Q @ K^T`，加精确 mask，softmax，然后 `P @ V` - -结果： - -- `bf16_sparse` 对比精确 BF16 参考： - - `max_diff=0.003906` - - `mean_diff=0.000127` - -同一用例下的对比： - -- `int8_sparse` 对比精确 BF16 参考： - - `max_diff=3.826172` - - `mean_diff=0.344735` -- `int8_sparse` 对比 `bf16_sparse`： - - `max_diff=3.826172` - - `mean_diff=0.344735` - -结论： - -- `bf16_sparse` 在精确 fixed-LUT sparse kernel 路径上是准确的。 -- 同一用例下，`int8_sparse` 的精度明显差于 `bf16_sparse`。 - -### 启发式 top-k 端到端路径 - -`sparge_sage2_attn_meansim_topk_xpu_bf16` 是启发式路径，不应该作为手工构造 LUT mask 的精确正确性参考。 - -因此当前 BF16 sparse 测试的含义是： - -- 精确正确性：`sage_sparse_bf16` 对比显式 masked BF16 参考 -- 启发式 top-k 路径：用于 benchmark / perf，不作为精确 oracle - -## 测试状态 - -更新文件： - -- `auto_round_kernel/wrapper/test/test_sage_sparse_prefill_e2e.py` - -当前测试脚本行为： - -- 强制加载 `xbuild_bf16_v2` 中带 BF16 sparse 能力的扩展 -- 使用显式 masked BF16 数学结果作为 BF16 sparse 正确性参考 -- 不再把启发式 BF16 top-k 输出当成 fixed-LUT kernel 的精确标准答案 - -验证命令： - -```bash -source benchmarks/source_env_xpu67_oneapi2025.sh -export XPU_MASK=7 -export SAGE_ATTN_XPU_SPARSE_BF16_NATIVE=1 -${ARK_BENCH_PYTHON} auto_round_kernel/wrapper/test/test_sage_sparse_prefill_e2e.py -``` - -观察结果： - -- 整个脚本在 oneAPI 2025 下成功退出 -- INT8 sparse 用例通过 -- BF16 sparse 精确参考用例通过 - -## 性能状态 - -### 中等尺寸精确 sparse 用例 - -Shape： - -- `B=1, Hq=32, Hkv=8, Sq=512, Skv=768, D=128` - -时延： - -- 带精确 mask 的 dense BF16：`0.105 ms` -- `bf16_sparse`：`0.096 ms` -- `int8_sparse`：`0.090 ms` -- 仅 INT8 Q/K 量化：`0.027 ms` - -相对 dense BF16 精确 mask 基线的加速比： - -- `bf16_sparse`：`1.093x` -- `int8_sparse`：`1.175x` -- `int8_sparse + quant`：`0.902x` - -结论： - -- 只看 kernel，`int8_sparse` 略快 -- 如果把量化时间算进去，这个中等尺寸用例并不偏向 INT8 路径 -- 这里 `bf16_sparse` 是更好的精度点 - -### 长序列 benchmark - -Shape： - -- `B=1, Hq=40, Hkv=40, S=75000, D=128` -- layout：`HND` -- `topk=0.5` -- `q_tile_override=256` -- `sparse_q_block_tokens=256` -- `sparse_k_block_tokens=64` - -sparse 选择统计： - -- `selected_ratio=0.502557` -- `selected_blocks_per_row=588.997` - -sparse 时延： - -- `preprocess=127.809 ms` -- `int8_sparse_kernel=390.990 ms` -- `int8_sparse_e2e=501.369 ms` -- `bf16_sparse_kernel=804.864 ms` -- `bf16_sparse_e2e=919.896 ms` - -同一 shape 下的 dense BF16 基线： - -- `dense_torch_sdpa_bf16=2154.809 ms` -- `dense_ark_sdpa_bf16=2494.633 ms` -- `dense_ark_sagev1_bf16=10848.612 ms` - -相对 dense torch BF16 SDPA 的加速比： - -- `int8_sparse_e2e`：约 `4.30x` -- `bf16_sparse_e2e`：约 `2.34x` - -相对 dense ARK BF16 SDPA 的加速比： - -- `int8_sparse_e2e`：约 `4.98x` -- `bf16_sparse_e2e`：约 `2.71x` - -结论： - -- 在 `75k / 40 / 128` 下，两条 sparse 路径都明显快于 dense BF16 -- `int8_sparse` 仍然是最快路径 -- `bf16_sparse` 虽然慢于 `int8_sparse`，但仍然显著快于 dense BF16 - -## 当前结论 - -- 在 oneAPI 2025 重新构建后，BF16 sparse fixed-LUT kernel 的正确性状态是好的。 -- 之前 BF16 mismatch 的主要原因是旧扩展被错误加载，以及测试里使用了不可靠的 masked XPU 参考路径。 -- 对长序列来说，BF16 sparse 是一个兼顾精度的 sparse 路径，并且相对 dense BF16 有明显加速。 -- `int8_sparse` 仍然是最快的 sparse 方案，但在精确 fixed-LUT 检查里，相比 `bf16_sparse` 牺牲了明显精度。 - -## 2026-07-30 复测摘要 - -在 2026 年 7 月 30 日（星期四），对 `75k / topk=0.5` 用例进行了重新测试。测试使用空闲的 `XPU 7`、oneAPI 2025，并且显式加载了带 BF16 sparse 能力的扩展 `auto_round_kernel/xbuild_bf16_v2/auto_round_kernel_xpu.cpython-313-x86_64-linux-gnu.so`。 - -Shape 和配置： - -- `B=1, Hq=40, Hkv=40, S=75000, D=128` -- layout：`HND` -- `topk=0.5` -- `q_tile_override=256` -- `sparse_q_block_tokens=256` -- `sparse_k_block_tokens=64` -- warmup `2`，iters `3` - -sparse 选择统计： - -- `selected_ratio=0.502557` -- `selected_blocks_per_row=588.997` - -时延和吞吐摘要： - -| Mode | Latency (ms) | Speedup vs Torch BF16 | Speedup vs SageV1 BF16 | Baseline TFLOPS | Effective TFLOPS | -|---|---:|---:|---:|---:|---:| -| `dense_torch_sdpa_bf16` | 1263.297 | 1.000 | 0.595 | 91.190 | 91.190 | -| `dense_sagev1_bf16` | 751.410 | 1.681 | 1.000 | 153.312 | 153.312 | -| `sparse_qtile256_row64k_kernel_only` | 398.381 | 3.305 | 1.944 | 289.171 | 145.325 | -| `sparse_qtile256_row64k_e2e` | 514.271 | 2.560 | 1.506 | 224.007 | 112.576 | -| `sparse_bf16_qtile256_row64k_kernel_only` | 812.091 | 1.556 | 0.925 | 141.856 | 71.291 | -| `sparse_bf16_qtile256_row64k_e2e` | 928.958 | 1.360 | 0.809 | 124.010 | 62.322 | - -说明： - -- `kernel_only` 表示 preprocess 之后仅 kernel 的时间。 -- `e2e` 表示 preprocess 加 kernel 的总时间。 -- 这次复测中，BF16 sparse 比 dense torch BF16 SDPA 更快，但仍然慢于 dense `sagev1_bf16`。 From a559dc82fcf2d851a388428bf0a0d501b3b862bd Mon Sep 17 00:00:00 2001 From: yiliu30 Date: Tue, 11 Aug 2026 16:18:43 +0800 Subject: [PATCH 25/42] chore: drop dev handoff and generated images from bf16 sparse branch The bf16 sparse feature is source-only for the formal PR: remove the dev handoff note (no _CN counterpart) and the generated benchmark/render PNGs. Co-Authored-By: Claude --- .../ark/SPARSE_BF16_HANDOFF_20260805.md | 158 ------------------ .../ark/bf16_sparse_speedup_vs_dense_sdpa.png | Bin 157588 -> 0 bytes .../examples/flux_sparse_bf16_topk0.5_512.png | Bin 343922 -> 0 bytes 3 files changed, 158 deletions(-) delete mode 100644 auto_round_extension/ark/SPARSE_BF16_HANDOFF_20260805.md delete mode 100644 auto_round_extension/ark/bf16_sparse_speedup_vs_dense_sdpa.png delete mode 100644 auto_round_extension/ark/examples/flux_sparse_bf16_topk0.5_512.png diff --git a/auto_round_extension/ark/SPARSE_BF16_HANDOFF_20260805.md b/auto_round_extension/ark/SPARSE_BF16_HANDOFF_20260805.md deleted file mode 100644 index 53859d42e0..0000000000 --- a/auto_round_extension/ark/SPARSE_BF16_HANDOFF_20260805.md +++ /dev/null @@ -1,158 +0,0 @@ -# Sparse BF16 Handoff (updated 2026-08-06) - -> **Note:** This is a refresh of the 2026-08-05 handoff. The earlier version described a -> pre-refactor state and reported the BF16 benchmark as failing; both are stale. The BF16 -> sparse path has been cleanly separated from the INT8 sparse path, and recent benchmark -> runs complete successfully. - -## Scope - -Handoff for the `sparse BF16` work in `auto_round_extension/ark` after the INT8/BF16 -path-separation refactor on branch `feat/sparse-bf16-prefill-v2`. - -Covers: - -- the final architecture (two clean, per-dtype sparse attention paths) -- the public API -- build/verify/reproduce steps -- current benchmark status and known caveats - -## Goal - -Two independent sparse SDPA paths, one per precision: - -- **INT8** — `sage_sparse` / `sparge_sage2_attn_meansim_topk_xpu` -- **BF16 / FP16** — `sage_sparse_sdpa` / `sparge_sage2_attn_meansim_topk_xpu_sdpa` - -Previously the BF16 native path was mixed into the INT8-centric SAGE kernel path -(`sage_sparse_bf16` routed native Q/K through `SparseSageConfig` / -`SPARSESAGEV1FwdMainloop`). The refactor removed that coupling. - -## Current Environment - -- repo: `/home/yiliu4/workspace/auto-round/auto_round_extension/ark` -- Python: `.venv/bin/python` -- PyTorch: `2.13.0+xpu`, `torch.version.xpu=20260000` -- oneAPI compiler: `2026.1.1` (`/opt/intel/oneapi/compiler/2026.1`), MKL `2026.1` -- CMake XPU build target: `intel_gpu_bmg_g21`, `ARK_SYCL_TLA=ON`, icx - -## Architecture (post-refactor) - -**INT8 path:** `sparse_attention.py:sage_sparse` → pybind `ark.cpp:sage_sparse` → -`sdpa_sparse.cpp` (`sdpa_impl_qks8_sparse_{d64,row_linear,qtile256_row64k}_pvhalf`) → -`SparseSageConfig` → `SPARSESAGEV1FwdMainloop` -(`wrapper/include/stla/xe_sparse_sagev1_fwd_mainloop.hpp`). - -- Q/K are `int8_t` (dequantized via qscale/kscale); V/PV is fp16/bf16. -- Q*K MMA is hardcoded to int32/int8 DPAS; `SparseSageConfig` carries a - `static_assert` that `ElementQ == int8_t` (INT8-only). - -**Native BF16/FP16 path:** `sparse_attention.py:sage_sparse_sdpa` → pybind -`ark.cpp:sage_sparse_sdpa` → `sdpa_sparse_sdpa.cpp` -(`sdpa_impl_{bf16,fp16}_sparse_sdpa_{d64,row_linear,qtile256_row64k}`) → -`SparseSDPAConfig` → `SPARSESDPAFwdMainloop` -(`wrapper/include/stla/xe_sparse_sdpa_fwd_mainloop.hpp`). - -- Q/K/V native precision; `scale_block_size = 0`, no dequant scales; softmax scale applied directly. -- Head dim 64/128, q_tile_override 0/64/256 (d64 / row_linear / qtile256_row64k). - -**Shared:** `sparge_preprocess_topk` (torch or triton preprocess backend), -`XeSparseSageFwdKernel` (`xe_sparse_sage_fwd_kernel.hpp`), `SparseFMHAFwdEpilogue`. - -**Removed by the refactor:** - -- `auto_round_kernel/sdpa_sparse_bf16.cpp` (superseded by `sdpa_sparse_sdpa.cpp`) -- `sage_sparse_bf16` (pybind binding + Python alias; was an alias of `sage_sparse_sdpa`) -- `sparge_sage2_attn_meansim_topk_xpu_bf16` (e2e wrapper) -- `sdpa_impl_bf16_sparse_{d64,row_linear,qtile256_row64k}` declarations -- native softmax-scale branch in the INT8 SAGE mainloop - -**Follow-up fix (verification pass):** `_query_tile_tokens_for_head_dim(64)` now returns -64 (was 128) so the preprocess None-default for head_dim 64 matches the sparse e2e -wrappers, fixing a pre-existing `tile_block_map` metadata mismatch that failed -`test_sparge_preprocess_topk_e2e.py`. - -## Public API Status - -BF16/FP16 sparse is exposed as: - -- `sage_sparse_sdpa(query, key, value, lut, valid_block_num, ...)` -- `sparge_sage2_attn_meansim_topk_xpu_sdpa(query, key, value, ...)` (end-to-end) - -INT8 sparse remains `sage_sparse` / `sparge_sage2_attn_meansim_topk_xpu`. -There is no `sage_sparse_bf16` anymore. - -## Build Status - -The XPU extension is rebuilt into `auto_round_kernel/ark-xbuild`: - -```bash -source /opt/intel/oneapi/setvars.sh --force >/dev/null 2>&1 -export MKLROOT=/opt/intel/oneapi/mkl/2026.1 -cmake -S auto_round_kernel -B auto_round_kernel/ark-xbuild \ - -DCMAKE_BUILD_TYPE=Release -DCMAKE_CXX_COMPILER=icx \ - -DARK_XPU=ON -DARK_SYCL_TLA=ON -DARK_DNNL=OFF -DARK_JOINT_MATRIX=OFF \ - -DDPCPP_SYCL_TARGET=intel_gpu_bmg_g21 -cmake --build auto_round_kernel/ark-xbuild -j 16 -``` - -**Important:** `auto_round_kernel/CMakeLists.txt` collects sources with -`file(GLOB SRCS .../*.cpp)` and has **no** `CONFIGURE_DEPENDS`. After adding/removing a -`.cpp` (e.g. the deleted `sdpa_sparse_bf16.cpp`), you MUST re-run `cmake -S ... -B ...` -before `cmake --build`, otherwise the stale build references the removed file and the old -`.so` still exports the old symbols. - -## Reproduce / Verify - -```bash -cd /home/yiliu4/workspace/auto-round/auto_round_extension/ark -# correctness (INT8 + BF16 + FP16). Set torch preprocess backend to avoid an XPU -# scatter/gather OOB assertion that the triton preprocess backend triggers on this host. -export SAGE_ATTN_SPARSE_PREPROCESS_BACKEND=torch -.venv/bin/python auto_round_kernel/wrapper/test/test_sage_sparse_prefill_e2e.py -.venv/bin/python auto_round_kernel/wrapper/test/test_sparge_preprocess_topk_e2e.py - -# benchmark (script already exports SAGE_ATTN_SPARSE_PREPROCESS_BACKEND=torch, ZE_AFFINITY_MASK=6) -./tools/repro_sparse_bf16_sdpa_bench.sh -# CSV default: /tmp/bench_sparse_topk_bf16_dev6.csv -``` - -## Benchmark Status (2026-08-06) - -The BF16 sparse benchmark **completes successfully** (`status: ok`). Full sweep results -are in `benchmarks/results/bf16_sparse_20260806_full.csv`. Native-path -(`sparse_sdpa_bf16_*` via `sage_sparse_sdpa`) speedups vs dense torch SDPA: - -| case | kernel-only | e2e | -|---|---|---| -| seq 32768, topk 0.5 | 1.40–1.44x | 0.85–0.87x | -| seq 32768, topk 0.125 | 5.38–5.67x | 1.56–1.57x | -| seq 75600, topk 0.5 | 1.51–1.53x | 1.15x | -| seq 75600, topk 0.125 | 5.71–5.83x | 2.64–2.65x | - -The e2e mode is preprocess-bound at moderate sizes (topk 0.5 @ seq 32768) but wins at -larger seq_len / lower topk. These match the earlier mixed-path `sparse_bf16_*` numbers, -so the separation did not regress performance. - -## Known Caveats - -- **Preprocess backend:** use `SAGE_ATTN_SPARSE_PREPROCESS_BACKEND=torch`. The triton - preprocess backend hits a PyTorch XPU scatter/gather "index out of bounds" assert on - this host (`ScatterGatherKernels.cpp:233`). The repro script already sets `torch`; - `tools/run_bf16_sparse_bench.sh` defaults to `triton_xpu` and should be overridden. -- **`scale_block_size == 0` on the native path:** `xe_sparse_sage_fwd_kernel.hpp` - guards the `seq_*_pad` division by `scale_block_size` to avoid div-by-zero UB; pads are - only used inside `scale_block_size ? ... : nullptr` ternaries. -- **INT8-only SAGE config:** `SparseSageConfig` enforces `ElementQ == int8_t` at compile - time. Use `SparseSDPAConfig` for native-precision BF16/FP16. -- **Stale docs:** `SPARSE_BF16_HANDOFF_20260805.md` (this file) supersedes the - pre-refactor handoff; `SPARSE_BF16_STATUS_20260729.md` / `SPARSE_BF16_BENCH_20260805.md` - are historical and refer to older builds/symbols. - -## Summary - -The BF16 native sparse path is fully separated from the INT8 SAGE path. The build wiring, -public API, correctness tests, and benchmark modes are in place; BF16 sparse completes and -outperforms dense torch SDPA. 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z4LLo?ap|`uvcA1<+gB!r>hc7{#D5Q-GA6uMCq6%JGj@KL{A=rkgq z7~y8!!$=}JTBw2|q0AN&Lq7<};M0-Sx7i8omSLRt?%v&c z8nnEJEPFx7D%=(%C>LVH# Date: Fri, 14 Aug 2026 16:52:36 +0800 Subject: [PATCH 26/42] fix: use k32 sparse sdpa tiles --- .../stla/xe_sparse_sdpa_fwd_mainloop.hpp | 135 +++++++++--------- .../wrapper/include/sycl_tla_sdpa_sparse.hpp | 16 +-- 2 files changed, 73 insertions(+), 78 deletions(-) diff --git a/auto_round_extension/ark/auto_round_kernel/wrapper/include/stla/xe_sparse_sdpa_fwd_mainloop.hpp b/auto_round_extension/ark/auto_round_kernel/wrapper/include/stla/xe_sparse_sdpa_fwd_mainloop.hpp index 45563462a8..a89d36aabe 100644 --- a/auto_round_extension/ark/auto_round_kernel/wrapper/include/stla/xe_sparse_sdpa_fwd_mainloop.hpp +++ b/auto_round_extension/ark/auto_round_kernel/wrapper/include/stla/xe_sparse_sdpa_fwd_mainloop.hpp @@ -263,21 +263,31 @@ struct SPARSESDPAFwdMainloop, CausalMask_, Full int subgroup_q_row_in_tile = get_sub_group_id() / sg_rows_per_sparse_q_block; subgroup_q_row_in_tile = cute::min(subgroup_q_row_in_tile, q_blocks_per_wg_tile - 1); - auto prefetch_sparse_k_block = [&](int logical_block) { - if (logical_block < 0 || logical_block >= total_blk) return; - for (int D = 0; D < size<4>(pKgK); D++) { - if constexpr (CachedKV) { - if (logical_block < kblocks_cache) { - int physical_block = logical_block; - if constexpr (PagedKV) { - physical_block = get_physical_k_tile(logical_block, l_coord, seq_len_kv_cache); + int sparse_route_blocks = params.num_k_blocks > 0 ? params.num_k_blocks : total_blk; + int k_tiles_per_sparse_route_block = cute::max(1, ceil_div(total_blk, sparse_route_blocks)); + auto route_block_to_k_tile = [&](int route_block, int micro_tile) { + return route_block * k_tiles_per_sparse_route_block + micro_tile; + }; + + auto prefetch_sparse_k_block = [&](int route_block) { + if (route_block < 0 || route_block >= sparse_route_blocks) return; + for (int micro_tile = 0; micro_tile < k_tiles_per_sparse_route_block; ++micro_tile) { + int logical_block = route_block_to_k_tile(route_block, micro_tile); + if (logical_block >= total_blk) break; + for (int D = 0; D < size<4>(pKgK); D++) { + if constexpr (CachedKV) { + if (logical_block < kblocks_cache) { + int physical_block = logical_block; + if constexpr (PagedKV) { + physical_block = get_physical_k_tile(logical_block, l_coord, seq_len_kv_cache); + } + prefetch(prefetch_k_cache, pKgK_cache(_, _, _, physical_block, D)); + } else { + prefetch(prefetch_k, pKgK(_, _, _, logical_block - kblocks_cache, D)); } - prefetch(prefetch_k_cache, pKgK_cache(_, _, _, physical_block, D)); } else { prefetch(prefetch_k, pKgK(_, _, _, logical_block - kblocks_cache, D)); } - } else { - prefetch(prefetch_k, pKgK(_, _, _, logical_block - kblocks_cache, D)); } } }; @@ -408,30 +418,58 @@ struct SPARSESDPAFwdMainloop, CausalMask_, Full prefetch(prefetch_k, pKgK(_, _, _, K_next - kblocks_cache, D)); } } - } else if (sparse_prefetch_block < total_blk) { + } else if (sparse_prefetch_block < sparse_route_blocks) { prefetch_sparse_k_block(sparse_prefetch_block); } barrier_wait(ScopeWorkgroup); }; + auto run_sparse_route_block = [&](int route_block, bool subgroup_selected, auto& subgroup_started, + int sparse_prefetch_block) { + if (route_block < 0 || route_block >= sparse_route_blocks) return; + for (int micro_tile = 0; micro_tile < k_tiles_per_sparse_route_block; ++micro_tile) { + int K = route_block_to_k_tile(route_block, micro_tile); + if (K >= total_blk) break; + bool first_selected_block = subgroup_selected ? !subgroup_started : false; + if constexpr (CachedKV) { + if (K < kblocks_cache) { + if (K >= blk_k0 && K < blk_k1) { + mainloop_body(std::bool_constant{}, K, first_selected_block, subgroup_selected, + sparse_prefetch_block, copy_k_cache, copy_v_cache, prefetch_v_cache, tKgK_cache, + tVgV_cache, pVgV_cache); + if (subgroup_selected) subgroup_started = true; + } + } else if (K >= (blk_k0 > kblocks_cache ? blk_k0 : kblocks_cache) && K < blk_k1) { + mainloop_body(std::bool_constant{}, K, first_selected_block, subgroup_selected, + sparse_prefetch_block, copy_k, copy_v, prefetch_v, tKgK, tVgV, pVgV); + if (subgroup_selected) subgroup_started = true; + } + } else if (K >= blk_k0 && K < blk_k1) { + mainloop_body(std::bool_constant{}, K, first_selected_block, subgroup_selected, sparse_prefetch_block, + copy_k, copy_v, prefetch_v, tKgK, tVgV, pVgV); + if (subgroup_selected) subgroup_started = true; + } + } + }; + if (lut_rows_base != nullptr && valid_blocks_base != nullptr) { bool subgroup_started = false; if (sparse_q_rows_in_tile == 1) { int const* row_ptr = lut_rows_base; int row_valid = valid_blocks_base[0]; int row_pos = 0; - int row_cur_block = row_valid > 0 ? logical_block_from_delta_row(row_ptr, row_valid, 0) : total_blk; + int row_cur_block = row_valid > 0 ? logical_block_from_delta_row(row_ptr, row_valid, 0) : sparse_route_blocks; int prefetch_pos = row_pos; int prefetch_cur_block = row_cur_block; auto advance_single_sparse_block = [&](int& frontier_pos, int& frontier_cur_block) { - if (frontier_cur_block >= total_blk) return; + if (frontier_cur_block >= sparse_route_blocks) return; frontier_pos += 1; if (frontier_pos < row_valid) { frontier_cur_block += row_ptr[frontier_pos]; } else { - frontier_cur_block = total_blk; + frontier_cur_block = sparse_route_blocks; } }; @@ -443,37 +481,15 @@ struct SPARSESDPAFwdMainloop, CausalMask_, Full for (int stage = 0; stage < Stages; ++stage) { int sparse_prefetch_block = pop_single_sparse_block(prefetch_pos, prefetch_cur_block); - if (sparse_prefetch_block >= total_blk) break; + if (sparse_prefetch_block >= sparse_route_blocks) break; prefetch_sparse_k_block(sparse_prefetch_block); } - while (row_cur_block < total_blk) { + while (row_cur_block < sparse_route_blocks) { int next_block = row_cur_block; bool subgroup_selected = (subgroup_q_row_in_tile == 0); - bool first_selected_block = !subgroup_started; int sparse_prefetch_block = pop_single_sparse_block(prefetch_pos, prefetch_cur_block); - int K = next_block; - if constexpr (CachedKV) { - if (K < kblocks_cache) { - if (K >= blk_k0 && K < blk_k1) { - mainloop_body(std::bool_constant{}, K, first_selected_block, subgroup_selected, - sparse_prefetch_block, copy_k_cache, copy_v_cache, prefetch_v_cache, tKgK_cache, - tVgV_cache, pVgV_cache); - subgroup_started = true; - } - } else { - K += kblocks_cache; - if (K >= (blk_k0 > kblocks_cache ? blk_k0 : kblocks_cache) && K < blk_k1) { - mainloop_body(std::bool_constant{}, K, first_selected_block, subgroup_selected, - sparse_prefetch_block, copy_k, copy_v, prefetch_v, tKgK, tVgV, pVgV); - subgroup_started = true; - } - } - } else if (K >= blk_k0 && K < blk_k1) { - mainloop_body(std::bool_constant{}, K, first_selected_block, subgroup_selected, sparse_prefetch_block, - copy_k, copy_v, prefetch_v, tKgK, tVgV, pVgV); - subgroup_started = true; - } + run_sparse_route_block(next_block, subgroup_selected, subgroup_started, sparse_prefetch_block); advance_single_sparse_block(row_pos, row_cur_block); } } else { @@ -493,19 +509,19 @@ struct SPARSESDPAFwdMainloop, CausalMask_, Full row_valid[row] = valid_blocks_base[row]; row_pos[row] = 0; row_cur_block[row] = - row_valid[row] > 0 ? logical_block_from_delta_row(row_ptrs[row], row_valid[row], 0) : total_blk; + row_valid[row] > 0 ? logical_block_from_delta_row(row_ptrs[row], row_valid[row], 0) : sparse_route_blocks; if (row_valid[row] > 0) { active_rows[active_row_count++] = row; } } else { row_valid[row] = 0; row_pos[row] = 0; - row_cur_block[row] = total_blk; + row_cur_block[row] = sparse_route_blocks; } } auto find_sparse_block = [&](int* frontier_pos, int* frontier_cur_block) { - int block = total_blk; + int block = sparse_route_blocks; for (int active = 0; active < active_row_count; ++active) { int row = active_rows[active]; if (frontier_pos[row] < row_valid[row]) { @@ -516,7 +532,7 @@ struct SPARSESDPAFwdMainloop, CausalMask_, Full }; auto advance_sparse_block = [&](int block, int* frontier_pos, int* frontier_cur_block) { - if (block >= total_blk) return; + if (block >= sparse_route_blocks) return; for (int active = 0; active < active_row_count; ++active) { int row = active_rows[active]; if (frontier_pos[row] < row_valid[row] && frontier_cur_block[row] == block) { @@ -524,7 +540,7 @@ struct SPARSESDPAFwdMainloop, CausalMask_, Full if (frontier_pos[row] < row_valid[row]) { frontier_cur_block[row] += row_ptrs[row][frontier_pos[row]]; } else { - frontier_cur_block[row] = total_blk; + frontier_cur_block[row] = sparse_route_blocks; } } } @@ -545,40 +561,19 @@ struct SPARSESDPAFwdMainloop, CausalMask_, Full for (int stage = 0; stage < Stages; ++stage) { int sparse_prefetch_block = pop_sparse_block(prefetch_pos, prefetch_cur_block); - if (sparse_prefetch_block >= total_blk) break; + if (sparse_prefetch_block >= sparse_route_blocks) break; prefetch_sparse_k_block(sparse_prefetch_block); } int next_block = find_sparse_block(row_pos, row_cur_block); - while (next_block < total_blk) { + while (next_block < sparse_route_blocks) { int selected_row = subgroup_q_row_in_tile; bool subgroup_selected = subgroup_q_row_in_tile < sparse_q_rows_in_tile && row_pos[selected_row] < row_valid[selected_row] && row_cur_block[selected_row] == next_block; - bool first_selected_block = subgroup_selected ? !subgroup_started_rows[selected_row] : false; int sparse_prefetch_block = pop_sparse_block(prefetch_pos, prefetch_cur_block); - int K = next_block; - if constexpr (CachedKV) { - if (K < kblocks_cache) { - if (K >= blk_k0 && K < blk_k1) { - mainloop_body(std::bool_constant{}, K, first_selected_block, subgroup_selected, - sparse_prefetch_block, copy_k_cache, copy_v_cache, prefetch_v_cache, tKgK_cache, - tVgV_cache, pVgV_cache); - if (subgroup_selected) subgroup_started_rows[selected_row] = true; - } - } else { - K += kblocks_cache; - if (K >= (blk_k0 > kblocks_cache ? blk_k0 : kblocks_cache) && K < blk_k1) { - mainloop_body(std::bool_constant{}, K, first_selected_block, subgroup_selected, - sparse_prefetch_block, copy_k, copy_v, prefetch_v, tKgK, tVgV, pVgV); - if (subgroup_selected) subgroup_started_rows[selected_row] = true; - } - } - } else if (K >= blk_k0 && K < blk_k1) { - mainloop_body(std::bool_constant{}, K, first_selected_block, subgroup_selected, sparse_prefetch_block, - copy_k, copy_v, prefetch_v, tKgK, tVgV, pVgV); - if (subgroup_selected) subgroup_started_rows[selected_row] = true; - } + run_sparse_route_block(next_block, subgroup_selected, subgroup_started_rows[selected_row], + sparse_prefetch_block); advance_sparse_block(next_block, row_pos, row_cur_block); next_block = find_sparse_block(row_pos, row_cur_block); diff --git a/auto_round_extension/ark/auto_round_kernel/wrapper/include/sycl_tla_sdpa_sparse.hpp b/auto_round_extension/ark/auto_round_kernel/wrapper/include/sycl_tla_sdpa_sparse.hpp index 0073bd6a8d..2233151b14 100644 --- a/auto_round_extension/ark/auto_round_kernel/wrapper/include/sycl_tla_sdpa_sparse.hpp +++ b/auto_round_extension/ark/auto_round_kernel/wrapper/include/sycl_tla_sdpa_sparse.hpp @@ -1171,12 +1171,12 @@ template ; - using ShapePV = Shape<_256, _32, _32>; + using ShapeQK = Shape<_256, _64, _32>; + using ShapePV = Shape<_256, _32, _64>; using ShapeOut = Shape<_256, _128>; using SubgroupLayoutQK = Layout>; - using ShapeQK1 = Shape<_256, _32, _32>; - using ShapePV1 = Shape<_256, _32, _32>; + using ShapeQK1 = Shape<_256, _64, _32>; + using ShapePV1 = Shape<_256, _32, _64>; using ShapeOut1 = Shape<_256, _128>; using SubgroupLayoutQK1 = Layout>; return options.is_causal ? FMHAConfig inline int launch_sparse_sdpa_prefill_kernel_128(Options const& options) { constexpr int PipelineStages = 2; - using ShapeQK = Shape<_256, _64, _32>; - using ShapePV = Shape<_256, _32, _64>; + using ShapeQK = Shape<_256, _32, _32>; + using ShapePV = Shape<_256, _32, _32>; using ShapeOut = Shape<_256, _128>; using SubgroupLayoutQK = Layout>; - using ShapeQK1 = Shape<_256, _64, _32>; - using ShapePV1 = Shape<_256, _32, _64>; + using ShapeQK1 = Shape<_256, _32, _32>; + using ShapePV1 = Shape<_256, _32, _32>; using ShapeOut1 = Shape<_256, _128>; using SubgroupLayoutQK1 = Layout>; return options.is_causal ? SparseSDPAConfig Date: Fri, 14 Aug 2026 16:52:41 +0800 Subject: [PATCH 27/42] bench: add ark sdpa baseline --- .../ark/benchmarks/bench_sparse_topk.py | 35 +++++++++++++++++-- 1 file changed, 32 insertions(+), 3 deletions(-) diff --git a/auto_round_extension/ark/benchmarks/bench_sparse_topk.py b/auto_round_extension/ark/benchmarks/bench_sparse_topk.py index 757cd6de65..bbd4a95468 100644 --- a/auto_round_extension/ark/benchmarks/bench_sparse_topk.py +++ b/auto_round_extension/ark/benchmarks/bench_sparse_topk.py @@ -494,18 +494,22 @@ def hnd_to_nhd(x: torch.Tensor) -> torch.Tensor: def summarize_speedups(rows: list[dict[str, object]]) -> list[dict[str, object]]: torch_row = next((row for row in rows if row["mode"] == "dense_torch_sdpa" and row["status"] == "ok"), None) sage_row = next((row for row in rows if row["mode"] == "dense_sagev1" and row["status"] == "ok"), None) + ark_row = next((row for row in rows if row["mode"] == "dense_ark_sdpa" and row["status"] == "ok"), None) torch_ms = None if torch_row is None else float(torch_row["latency_ms"]) sage_ms = None if sage_row is None else float(sage_row["latency_ms"]) + ark_ms = None if ark_row is None else float(ark_row["latency_ms"]) for row in rows: latency_ms = row["latency_ms"] if latency_ms is None: row["speedup_vs_torch"] = None row["speedup_vs_sagev1"] = None + row["speedup_vs_ark"] = None row["baseline_tflops"] = None row["effective_tflops"] = None continue row["speedup_vs_torch"] = (torch_ms / latency_ms) if torch_ms is not None else None row["speedup_vs_sagev1"] = (sage_ms / latency_ms) if sage_ms is not None else None + row["speedup_vs_ark"] = (ark_ms / latency_ms) if ark_ms is not None else None batch = int(row["batch"]) num_heads_q = int(row["num_heads_q"]) seq_len = int(row["seq_len"]) @@ -517,6 +521,7 @@ def summarize_speedups(rows: list[dict[str, object]]) -> list[dict[str, object]] if mode in { "dense_torch_sdpa", "dense_sagev1", + "dense_ark_sdpa", "sparse_kernel_only", "sparse_e2e", "sparse_qtile256_row64k_kernel_only", @@ -536,9 +541,9 @@ def summarize_speedups(rows: list[dict[str, object]]) -> list[dict[str, object]] def print_summary(rows: list[dict[str, object]]) -> None: print( - "| layout | seq_len | mode | pattern | gqa_group | topk | selected_ratio | blocks/row | latency (ms) | baseline_tflops | effective_tflops | status | speedup_vs_torch | speedup_vs_sagev1 |" + "| layout | seq_len | mode | pattern | gqa_group | topk | selected_ratio | blocks/row | latency (ms) | baseline_tflops | effective_tflops | status | speedup_vs_torch | speedup_vs_sagev1 | speedup_vs_ark |" ) - print("|---|---|---|---|---|---|---|---|---|---|---|---|---|---|") + print("|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|") for row in rows: topk = "-" if row["requested_topk"] is None else f"{float(row['requested_topk']):.3f}" ratio = "-" if row["selected_ratio"] is None else f"{float(row['selected_ratio']):.6f}" @@ -548,10 +553,11 @@ def print_summary(rows: list[dict[str, object]]) -> None: effective_tflops = "-" if row.get("effective_tflops") is None else f"{float(row['effective_tflops']):.3f}" sp_torch = "-" if row.get("speedup_vs_torch") is None else f"{float(row['speedup_vs_torch']):.3f}" sp_sage = "-" if row.get("speedup_vs_sagev1") is None else f"{float(row['speedup_vs_sagev1']):.3f}" + sp_ark = "-" if row.get("speedup_vs_ark") is None else f"{float(row['speedup_vs_ark']):.3f}" print( f"| {row['tensor_layout']} | {row['seq_len']} | {row['mode']} | {row['attention_pattern']} | {row['gqa_group_size']} | {topk} | {ratio} | " f"{blocks} | {latency} | {baseline_tflops} | {effective_tflops} | {row['status']} | {sp_torch} | " - f"{sp_sage} |" + f"{sp_sage} | {sp_ark} |" ) if row["note"]: print(f"note[{row['mode']}]: {row['note']}") @@ -663,6 +669,29 @@ def run_single_benchmark(args: argparse.Namespace, *, seq_len: int, tensor_layou iters=args.iters, ) ) + rows.append( + try_benchmark( + "dense_ark_sdpa", + lambda: ark.sdpa( + q, + k, + v, + is_causal=args.causal, + scale=scale, + tensor_layout=tensor_layout, + ), + batch=args.batch, + num_heads_q=args.num_heads_q, + num_heads_kv=args.num_heads_kv, + seq_len=seq_len, + tensor_layout=tensor_layout, + head_dim=args.head_dim, + dtype=dtype, + is_causal=args.causal, + warmup=args.warmup, + iters=args.iters, + ) + ) for topk in args.topk: preprocess = None From ee565a16527480e634b8e858ad2a787cfa743902 Mon Sep 17 00:00:00 2001 From: yiliu30 Date: Fri, 14 Aug 2026 16:52:52 +0800 Subject: [PATCH 28/42] tools: add sparse bf16 sweep helpers --- .../ark/examples/flux_gen_bf16_sweep.py | 167 ++++++++++++++++++ .../ark/tools/bench_sparse_bf16_fp16_sweep.sh | 110 ++++++++++++ .../ark/tools/sweep_flux_bf16_topk.sh | 105 +++++++++++ 3 files changed, 382 insertions(+) create mode 100644 auto_round_extension/ark/examples/flux_gen_bf16_sweep.py create mode 100755 auto_round_extension/ark/tools/bench_sparse_bf16_fp16_sweep.sh create mode 100755 auto_round_extension/ark/tools/sweep_flux_bf16_topk.sh diff --git a/auto_round_extension/ark/examples/flux_gen_bf16_sweep.py b/auto_round_extension/ark/examples/flux_gen_bf16_sweep.py new file mode 100644 index 0000000000..597890add2 --- /dev/null +++ b/auto_round_extension/ark/examples/flux_gen_bf16_sweep.py @@ -0,0 +1,167 @@ +# Copyright (C) 2026 Intel Corporation +# SPDX-License-Identifier: Apache-2.0 + +import contextlib +import csv +import os +import sys +import time +from pathlib import Path + +import torch +from diffusers import FluxPipeline +from flux_sparse_patch import patch_flux_sparse_attention_from_env + +dtype = torch.bfloat16 + +MODEL_DEFAULT = "/mnt/disk3/models/black-forest-labs/FLUX.1-dev" +PROMPT_DEFAULT = "A cat holding a sign that says hello world" + + +def env_flag(name, default="1"): + return os.getenv(name, default).lower() not in {"0", "false", "no", "off"} + + +def sync_xpu(): + if hasattr(torch, "xpu") and hasattr(torch.xpu, "synchronize"): + torch.xpu.synchronize() + + +def main(): + model_id = os.getenv("FLUX_MODEL", MODEL_DEFAULT) + topks = os.getenv("FLUX_SPARSE_TOPKS", "0.5 0.25 0.125").split() + run_dense = env_flag("FLUX_RUN_DENSE", "1") + prompt = os.getenv("FLUX_PROMPT", PROMPT_DEFAULT) + height = int(os.getenv("FLUX_HEIGHT", "512")) + width = int(os.getenv("FLUX_WIDTH", "512")) + steps = int(os.getenv("FLUX_STEPS", "50")) + seed = int(os.getenv("FLUX_SEED", "0")) + guidance_scale = float(os.getenv("FLUX_GUIDANCE_SCALE", "3.5")) + max_sequence_length = int(os.getenv("FLUX_MAX_SEQUENCE_LENGTH", "512")) + + default_out = Path("benchmarks/results") / f"flux_bf16_{time.strftime('%Y%m%d_%H%M%S')}" + out_dir = Path(os.getenv("FLUX_OUTPUT_DIR", str(default_out))) + out_dir.mkdir(parents=True, exist_ok=True) + + device_id = os.getenv("ZE_AFFINITY_MASK_VALUE", "") + dev_suffix = f"_dev{device_id}" if device_id else "" + + print(f"[flux_sweep] model={model_id}", flush=True) + print(f"[flux_sweep] topks={topks} run_dense={run_dense} size={height}x{width} steps={steps} seed={seed}", flush=True) + print(f"[flux_sweep] out_dir={out_dir}", flush=True) + print(f"[flux_sweep] kernel={os.getenv('FLUX_SPARSE_KERNEL', '?')} " + f"q_tile={os.getenv('FLUX_SPARSE_Q_TILE_OVERRIDE', '0')} " + f"q_block={os.getenv('FLUX_SPARSE_Q_BLOCK_TOKENS', 'default')} " + f"k_block={os.getenv('FLUX_SPARSE_K_BLOCK_TOKENS', 'default')}", flush=True) + + pipe = FluxPipeline.from_pretrained(model_id, torch_dtype=dtype) + # Offload-only: pipe.to(device) would load the whole ~54 GB model onto the + # 24.4 GB device and OOM. + # + # Use sequential (block-level) offload, NOT enable_model_cpu_offload(): + # model-level offload pulls the whole ~46 GB transformer onto the device, + # peaking at ~24 GB (the full 24.4 GB device) during every transformer + # forward. The sparse BF16 preprocess (triton_xpu) then has <0.4 GB of + # headroom, and its kernel launch flakily OOMs / resets the GPU + # (UR_RESULT_ERROR_OUT_OF_DEVICE_MEMORY / OUT_OF_RESOURCES / DEVICE_LOST). + # Sequential offload keeps only a few blocks resident (peak ~0.16 GB), so + # the triton_xpu sparse path always has room. + pipe.enable_sequential_cpu_offload() + + common_kwargs = dict( + height=height, + width=width, + guidance_scale=guidance_scale, + num_inference_steps=steps, + max_sequence_length=max_sequence_length, + generator=torch.Generator("cpu").manual_seed(seed), + ) + + rows = [] + + def do_run(tag, topk, png_name): + if topk is None: + ctx = contextlib.nullcontext(None) + os.environ.pop("FLUX_SPARSE_TOPK", None) + else: + os.environ["FLUX_SPARSE_TOPK"] = str(topk) + ctx = patch_flux_sparse_attention_from_env(pipe.transformer) + + sync_xpu() + t0 = time.perf_counter() + with ctx as stats: + image = pipe(prompt, output_type="pil", **common_kwargs).images[0] + sync_xpu() + wall_s = time.perf_counter() - t0 + + png_path = out_dir / png_name + image.save(png_path) + + row = { + "tag": tag, + "topk": "" if topk is None else topk, + "wall_s": round(wall_s, 3), + "sparsity": "", + "calls": "", + "sparse_calls": "", + "runtime_fallbacks": "", + "unsupported_fallbacks": "", + "png": str(png_path), + } + if topk is not None: + if stats is None or stats.sparse_calls == 0: + raise RuntimeError( + f"topk={topk}: sparse path did not run (sparse_calls=0) — check FLUX_SPARSE_KERNEL / qtile256 config" + ) + if stats.runtime_fallbacks or stats.unsupported_fallbacks: + raise RuntimeError( + f"topk={topk}: silent fallback detected — runtime={stats.runtime_fallbacks}, " + f"unsupported={stats.unsupported_fallbacks}" + ) + row["sparsity"] = round(float(stats.avg_sparsity), 4) + row["calls"] = stats.total_calls + row["sparse_calls"] = stats.sparse_calls + row["runtime_fallbacks"] = stats.runtime_fallbacks + row["unsupported_fallbacks"] = stats.unsupported_fallbacks + print( + f"[flux_sweep] DONE tag={tag} topk={topk} wall={wall_s:.3f}s " + f"sparsity={stats.avg_sparsity:.4f} sparse_calls={stats.sparse_calls} " + f"runtime_fallbacks={stats.runtime_fallbacks}", flush=True + ) + else: + print(f"[flux_sweep] DONE tag={tag} wall={wall_s:.3f}s", flush=True) + rows.append(row) + return row + + if run_dense: + do_run("dense", None, f"flux_dense_512{dev_suffix}.png") + + for topk in topks: + do_run("sparse_bf16_qtile256", topk, f"flux_bf16_qtile256_topk{topk}_512{dev_suffix}.png") + + # CSV + markdown summary (device-suffixed so parallel instances sharing + # FLUX_OUTPUT_DIR do not clobber each other). + csv_path = out_dir / f"sweep_summary{dev_suffix}.csv" + fieldnames = ["tag", "topk", "wall_s", "sparsity", "calls", "sparse_calls", + "runtime_fallbacks", "unsupported_fallbacks", "png"] + with open(csv_path, "w", newline="", encoding="utf-8") as fh: + writer = csv.DictWriter(fh, fieldnames=fieldnames) + writer.writeheader() + writer.writerows(rows) + + md_path = out_dir / f"SWEEP_SUMMARY{dev_suffix}.md" + with open(md_path, "w", encoding="utf-8") as fh: + fh.write("| tag | topk | wall_s | sparsity | calls | sparse_calls | runtime_fallbacks | png |\n") + fh.write("|---|---|---|---|---|---|---|---|\n") + for r in rows: + fh.write( + f"| {r['tag']} | {r['topk']} | {r['wall_s']} | {r['sparsity']} | {r['calls']} " + f"| {r['sparse_calls']} | {r['runtime_fallbacks']} | `{r['png']}` |\n" + ) + + print(f"[flux_sweep] summary: {csv_path}", flush=True) + print(f"[flux_sweep] summary: {md_path}", flush=True) + + +if __name__ == "__main__": + main() diff --git a/auto_round_extension/ark/tools/bench_sparse_bf16_fp16_sweep.sh b/auto_round_extension/ark/tools/bench_sparse_bf16_fp16_sweep.sh new file mode 100755 index 0000000000..29159a2eac --- /dev/null +++ b/auto_round_extension/ark/tools/bench_sparse_bf16_fp16_sweep.sh @@ -0,0 +1,110 @@ +#!/usr/bin/env bash +# Copyright (C) 2026 Intel Corporation +# SPDX-License-Identifier: Apache-2.0 +# +# bench_sparse_bf16_fp16_sweep.sh +# =============================== +# Run the sparse BF16 and FP16 kernel benchmark sweep (both HND and NHD layouts) +# against the rebuilt ARK XPU extension, and save CSVs under benchmarks/results/. +# +# USAGE +# ./tools/bench_sparse_bf16_fp16_sweep.sh +# +# ENV OVERRIDES (all optional) +# DTYPES dtypes to sweep, space-separated (default: "bf16 fp16") +# SEQ_LENS seq lengths, space-separated (default: "75000") +# TOPKS topk values, space-separated (default: "0.5 0.25 0.125") +# LAYOUTS tensor layouts, space-separated (default: "HND NHD") +# HEADS num heads q (kv = q) (default: 40) +# HEAD_DIM head dim (default: 128) +# Q_TILE q_tile_override (default: 256) +# WARMUP warmup iters (default: 2) +# ITERS measured iters (default: 3) +# ZE_AFFINITY_MASK_VALUE GPU device index (default: 6) +# XPU_SO explicit path to the built .so (default: newest under ark-xbuild) +# OUTPUT_DIR where CSVs are written (default: benchmarks/results) +# SPARGE_PREPROCESS_BACKEND sparse preprocess backend (default: triton_xpu); torch | triton_xpu | auto +# +# PREREQUISITES +# - oneAPI 2026.1 runtime (sourced from /opt/intel/oneapi/setvars.sh) +# - venv at .venv/bin/python with torch.xpu +# - rebuilt extension under auto_round_kernel/ark-xbuild/ +# - a free GPU (pick via ZE_AFFINITY_MASK_VALUE; check `xpu-smi ps` first) + +set -uo pipefail + +SCRIPT_PATH="$(readlink -f "$0")" +REPO_ROOT="$(cd "$(dirname "${SCRIPT_PATH}")/.." && pwd)" +PYTHON_BIN="${REPO_ROOT}/.venv/bin/python" +BENCH="${REPO_ROOT}/benchmarks/bench_sparse_topk.py" + +DTYPES="${DTYPES:-bf16 fp16}" +SEQ_LENS="${SEQ_LENS:-75000}" +TOPKS="${TOPKS:-0.5 0.25 0.125}" +LAYOUTS="${LAYOUTS:-HND NHD}" +HEADS="${HEADS:-40}" +HEAD_DIM="${HEAD_DIM:-128}" +Q_TILE="${Q_TILE:-256}" +WARMUP="${WARMUP:-2}" +ITERS="${ITERS:-3}" +ZE_AFFINITY_MASK_VALUE="${ZE_AFFINITY_MASK_VALUE:-6}" +XPU_SO="${XPU_SO:-}" +OUTPUT_DIR="${OUTPUT_DIR:-${REPO_ROOT}/benchmarks/results}" + +cd "${REPO_ROOT}" + +# Re-exec under the render/video groups if this shell does not already have them. +if [[ "${1:-}" == "--inner" ]]; then + shift +elif command -v sg >/dev/null 2>&1; then + current_groups="$(id -nG || true)" + if [[ ! " ${current_groups} " =~ [[:space:]]render[[:space:]] ]] || [[ ! " ${current_groups} " =~ [[:space:]]video[[:space:]] ]]; then + exec sg render -c "sg video -c '${SCRIPT_PATH} --inner'" + fi +fi + +set +u +source /opt/intel/oneapi/setvars.sh --force >/dev/null 2>&1 +set -u + +export MKLROOT=/opt/intel/oneapi/mkl/2026.1 +export ZE_AFFINITY_MASK="${ZE_AFFINITY_MASK_VALUE}" +export SAGE_ATTN_SPARSE_PREPROCESS_BACKEND="${SPARGE_PREPROCESS_BACKEND:-triton_xpu}" # auto | torch | triton_xpu + +if [[ -z "${XPU_SO}" ]]; then + XPU_SO="$(find "${REPO_ROOT}/auto_round_kernel/ark-xbuild" -maxdepth 1 -name 'auto_round_kernel_xpu*.so' | sort | tail -n 1)" +fi +if [[ -z "${XPU_SO}" ]]; then + echo "error: no auto_round_kernel_xpu*.so found under auto_round_kernel/ark-xbuild" >&2 + exit 1 +fi + +mkdir -p "${OUTPUT_DIR}" +stamp="$(date +%Y%m%d_%H%M%S)" + +echo "XPU process snapshot:" +xpu-smi ps || true +echo "Using XPU extension: ${XPU_SO}" +echo "dtypes=${DTYPES} seq=${SEQ_LENS} topk=${TOPKS} layouts=${LAYOUTS} heads=${HEADS} head_dim=${HEAD_DIM} q_tile=${Q_TILE}" +echo + +for dtype in ${DTYPES}; do + out_csv="${OUTPUT_DIR}/bench_sparse_${dtype}_${stamp}.csv" + echo "========== dtype=${dtype} -> ${out_csv} ==========" + "${PYTHON_BIN}" "${BENCH}" \ + --dtype "${dtype}" \ + --seq-len ${SEQ_LENS} \ + --topk ${TOPKS} \ + --tensor-layout ${LAYOUTS} \ + --head-dim "${HEAD_DIM}" \ + --num-heads-q "${HEADS}" --num-heads-kv "${HEADS}" \ + --q-tile-override "${Q_TILE}" \ + --sparse-q-block-tokens "${Q_TILE}" \ + --sparse-k-block-tokens 64 \ + --warmup "${WARMUP}" --iters "${ITERS}" \ + --xpu-so "${XPU_SO}" \ + --output-csv "${out_csv}" + echo +done + +echo "SWEEP_DONE: CSVs written under ${OUTPUT_DIR}" diff --git a/auto_round_extension/ark/tools/sweep_flux_bf16_topk.sh b/auto_round_extension/ark/tools/sweep_flux_bf16_topk.sh new file mode 100755 index 0000000000..babc5083a4 --- /dev/null +++ b/auto_round_extension/ark/tools/sweep_flux_bf16_topk.sh @@ -0,0 +1,105 @@ +#!/usr/bin/env bash +# Copyright (C) 2026 Intel Corporation +# SPDX-License-Identifier: Apache-2.0 +# +# sweep_flux_bf16_topk.sh +# ======================= +# One-GPU FLUX.1-dev BF16-sparse topk sweep. Runs examples/flux_gen_bf16_sweep.py +# with the perf-tuned qtile256 config (q_tile=256, q_block=256, k_block=64) and +# saves PNGs + a summary under a shared FLUX_OUTPUT_DIR. +# +# To use both XPU devices, launch two instances concurrently with disjoint +# topk subsets pointing at the SAME FLUX_OUTPUT_DIR, e.g.: +# STAMP=$(date +%Y%m%d_%H%M%S) +# OUT=benchmarks/results/flux_bf16_${STAMP} +# ZE_AFFINITY_MASK_VALUE=5 FLUX_RUN_DENSE=1 FLUX_SPARSE_TOPKS="0.5 0.25" \ +# FLUX_OUTPUT_DIR=$OUT ./tools/sweep_flux_bf16_topk.sh & +# ZE_AFFINITY_MASK_VALUE=6 FLUX_RUN_DENSE=0 FLUX_SPARSE_TOPKS="0.125" \ +# FLUX_OUTPUT_DIR=$OUT ./tools/sweep_flux_bf16_topk.sh & +# wait +# +# ENV OVERRIDES (all optional) +# ZE_AFFINITY_MASK_VALUE XPU device index (default: 6) +# FLUX_MODEL model dir (diffusers format) (default: /mnt/disk3/models/black-forest-labs/FLUX.1-dev) +# FLUX_SPARSE_TOPKS space-separated topk list (default: "0.5 0.25 0.125") +# FLUX_RUN_DENSE 1/0 include a dense baseline generation (default: 1) +# FLUX_OUTPUT_DIR results dir (default: benchmarks/results/flux_bf16_) +# FLUX_HEIGHT / FLUX_WIDTH generation size (keep 512) (default: 512) +# FLUX_STEPS / FLUX_SEED / FLUX_PROMPT (defaults: 50 / 0 / "A cat ...") +# SPARGE_PREPROCESS_BACKEND torch | triton_xpu | auto (default: triton_xpu) +# XPU_SO explicit path to the built auto_round_kernel_xpu*.so +# (default: newest under auto_round_kernel/ark-xbuild) +# +# PREREQUISITES +# - oneAPI 2026.1 runtime (sourced from /opt/intel/oneapi/setvars.sh) +# - venv at .venv/bin/python with torch.xpu + diffusers +# - rebuilt extension with BF16 sparse symbols under auto_round_kernel/ark-xbuild/ +# - a free GPU (pick via ZE_AFFINITY_MASK_VALUE; check `xpu-smi ps` first) + +set -euo pipefail + +SCRIPT_PATH="$(readlink -f "$0")" +REPO_ROOT="$(cd "$(dirname "${SCRIPT_PATH}")/.." && pwd)" +PYTHON_BIN="${REPO_ROOT}/.venv/bin/python" + +ZE_AFFINITY_MASK_VALUE="${ZE_AFFINITY_MASK_VALUE:-6}" +FLUX_MODEL="${FLUX_MODEL:-/mnt/disk3/models/black-forest-labs/FLUX.1-dev}" +FLUX_SPARSE_TOPKS="${FLUX_SPARSE_TOPKS:-0.5 0.25 0.125}" +FLUX_RUN_DENSE="${FLUX_RUN_DENSE:-1}" +FLUX_HEIGHT="${FLUX_HEIGHT:-512}" +FLUX_WIDTH="${FLUX_WIDTH:-512}" +FLUX_STEPS="${FLUX_STEPS:-50}" +FLUX_SEED="${FLUX_SEED:-0}" +SPARGE_PREPROCESS_BACKEND="${SPARGE_PREPROCESS_BACKEND:-triton_xpu}" +XPU_SO="${XPU_SO:-}" + +cd "${REPO_ROOT}" + +# Re-exec under the render/video groups if this shell does not already have them. +if [[ "${1:-}" == "--inner" ]]; then + shift +elif command -v sg >/dev/null 2>&1; then + current_groups="$(id -nG || true)" + if [[ ! " ${current_groups} " =~ [[:space:]]render[[:space:]] ]] || [[ ! " ${current_groups} " =~ [[:space:]]video[[:space:]] ]]; then + exec sg render -c "sg video -c '${SCRIPT_PATH} --inner'" + fi +fi + +# oneAPI's setvars.sh is not compatible with `set -u` in this environment. +set +u +source /opt/intel/oneapi/setvars.sh --force >/dev/null 2>&1 +set -u + +export MKLROOT=/opt/intel/oneapi/mkl/2026.1 +export ZE_AFFINITY_MASK="${ZE_AFFINITY_MASK_VALUE}" +export SAGE_ATTN_SPARSE_PREPROCESS_BACKEND="${SPARGE_PREPROCESS_BACKEND}" + +# qtile256 perf config is fixed for this sweep; kernel is env-overridable (bf16 default, int8 for SAGE). +export FLUX_SPARSE_KERNEL="${FLUX_SPARSE_KERNEL:-bf16}" +export FLUX_SPARSE_Q_TILE_OVERRIDE=256 +export FLUX_SPARSE_Q_BLOCK_TOKENS=256 +export FLUX_SPARSE_K_BLOCK_TOKENS=64 + +if [[ -z "${XPU_SO}" ]]; then + XPU_SO="$(find "${REPO_ROOT}/auto_round_kernel/ark-xbuild" -maxdepth 1 -name 'auto_round_kernel_xpu*.so' | sort | tail -n 1)" +fi +if [[ -z "${XPU_SO}" ]]; then + echo "error: no auto_round_kernel_xpu*.so found under auto_round_kernel/ark-xbuild" >&2 + exit 1 +fi + +if [[ -z "${FLUX_OUTPUT_DIR:-}" ]]; then + FLUX_OUTPUT_DIR="${REPO_ROOT}/benchmarks/results/flux_bf16_$(date +%Y%m%d_%H%M%S)" +fi +export FLUX_OUTPUT_DIR="${FLUX_OUTPUT_DIR}" +mkdir -p "${FLUX_OUTPUT_DIR}" + +echo "XPU process snapshot:" +xpu-smi ps || true +echo "Using XPU extension: ${XPU_SO}" +echo "device=${ZE_AFFINITY_MASK_VALUE} topks='${FLUX_SPARSE_TOPKS}' run_dense=${FLUX_RUN_DENSE}" +echo "qtile256 config: FLUX_SPARSE_Q_TILE_OVERRIDE=256 FLUX_SPARSE_Q_BLOCK_TOKENS=256 FLUX_SPARSE_K_BLOCK_TOKENS=64" +echo "output dir: ${FLUX_OUTPUT_DIR}" +echo + +"${PYTHON_BIN}" examples/flux_gen_bf16_sweep.py From 9b1a95123ed53f7b4b3a0bceba7f29c089425a54 Mon Sep 17 00:00:00 2001 From: yiliu30 Date: Fri, 14 Aug 2026 16:54:16 +0800 Subject: [PATCH 29/42] docs: record sparse bf16 profiling results --- .../results/SPARSE_BF16_HANDOFF_20260814.md | 171 ++++++++++ .../SPARSE_BF16_HANDOFF_20260814_CN.md | 156 ++++++++++ .../results/SPARSE_BF16_K32_AB_20260814.md | 258 +++++++++++++++ .../results/SPARSE_BF16_K32_AB_20260814_CN.md | 193 ++++++++++++ .../results/UNITRACE_USAGE_GUIDE_20260814.md | 294 ++++++++++++++++++ .../ab_bf16_sdpa_k32_20260814_092404.csv | 16 + .../results/ab_k32_bf16_20260814_091351.csv | 16 + .../results/ab_k64_bf16_20260814_090508.csv | 16 + .../SWEEP_SUMMARY_dev5.md | 3 + .../flux_bf16_qtile256_topk0.9_512_dev5.png | Bin 0 -> 305961 bytes .../sweep_summary_dev5.csv | 2 + .../SWEEP_SUMMARY.md | 71 +++++ .../bf16_k32_qtile256_topk_0p5_0p3_0p1.csv | 31 ++ .../fp16_k32_qtile256_topk_0p5_0p3_0p1.csv | 31 ++ .../k32_compute.metrics.2889557 | 7 + .../results/unitrace_k32_stalls_20260814.csv | 8 + .../k32_stalls.metrics.2892853 | 7 + .../results/unitrace_k32_verbose_20260814.csv | 8 + .../k32_verbose.3726840 | 19 ++ .../unitrace_k64_compute_real_20260814.csv | 8 + .../k64_compute_real.2948755 | 32 ++ .../k64_compute_real.metrics.2948755 | 7 + .../unitrace_k64_stalls_real_20260814.csv | 8 + .../k64_stalls_real.metrics.2952213 | 7 + 24 files changed, 1369 insertions(+) create mode 100644 auto_round_extension/ark/benchmarks/results/SPARSE_BF16_HANDOFF_20260814.md create mode 100644 auto_round_extension/ark/benchmarks/results/SPARSE_BF16_HANDOFF_20260814_CN.md create mode 100644 auto_round_extension/ark/benchmarks/results/SPARSE_BF16_K32_AB_20260814.md create mode 100644 auto_round_extension/ark/benchmarks/results/SPARSE_BF16_K32_AB_20260814_CN.md create mode 100644 auto_round_extension/ark/benchmarks/results/UNITRACE_USAGE_GUIDE_20260814.md create mode 100644 auto_round_extension/ark/benchmarks/results/ab_bf16_sdpa_k32_20260814_092404.csv create mode 100644 auto_round_extension/ark/benchmarks/results/ab_k32_bf16_20260814_091351.csv create mode 100644 auto_round_extension/ark/benchmarks/results/ab_k64_bf16_20260814_090508.csv create mode 100644 auto_round_extension/ark/benchmarks/results/flux_bf16_topk09_k32_20260814_112445/SWEEP_SUMMARY_dev5.md create mode 100644 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/dev/null +++ b/auto_round_extension/ark/benchmarks/results/SPARSE_BF16_HANDOFF_20260814.md @@ -0,0 +1,171 @@ +# Handoff: BF16 native sparse SDPA (sparge) — 2026-08-14 + +Status snapshot for a developer picking up this work. Everything here was verified on +**Intel Battlemage XPU (24.4 GB, `intel_gpu_bmg_g21`)**, oneAPI 2026.1, PyTorch 2.13.0+xpu. + +## 1. Where the code lives + +- **Branch `feat/sparse-bf16-prefill-v2`** — all sparse work (INT8 SAGE **and** native BF16/FP16 + sparse SDPA). **`main` only has the INT8 SAGE path** (empty `ark.cpp`/`sparse_attention.py` were + false alarms; check paths against the repo root — `main` has a working int8 `sparge_sage2_attn_meansim_topk_xpu` + in `auto_round_extension/ark/auto_round_kernel/sparse_attention.py`, 1440 lines). +- Switching branches requires a **rebuild** every time (the `.so` in `auto_round_kernel/ark-xbuild/` + only matches the branch it was built on). + +### Key changes in this handoff +- BF16 sparse SDPA now has a separate native BF16/FP16 path (`SparseSDPAConfig`) instead of reusing + the INT8 SAGE DPAS path. +- `benchmarks/bench_sparse_topk.py` now includes dense `ark.sdpa` as a baseline and reports + `speedup_vs_ark`. +- The BF16 sparse selected-block microtile was A/B tested at K64 vs K32. K32 is the current + experimental direction and the locally rebuilt `.so` is back on K32. +- `xe_sparse_sdpa_fwd_mainloop.hpp` supports expanding one 64-token sparse route block into multiple + physical K microtiles, which is required for K32 while keeping the existing 64-token LUT format. +- Unitrace profiles show K64 regresses because of compiler-reported spill plus SEND/SBID and + memory-write pressure. +- A FLUX.1-dev sparse BF16 topk 0.9 K32 image was generated successfully with zero fallbacks. + +### Two separate paths (do not mix) + +| path | wrapper | config | DPAS | +|---|---|---|---| +| INT8 sparse | `sparge_sage2_attn_meansim_topk_xpu` / `sage_sparse` | `SparseSageConfig` | `XE_DPAS_TT<8, int, int8, int8, int>` (+ `static_assert(ElementQ==int8_t)`) | +| BF16/FP16 sparse | `sparge_sage2_attn_meansim_topk_xpu_sdpa` / `sage_sparse_sdpa` | `SparseSDPAConfig` | `XE_DPAS_TT<8, float, bf16, bf16, float>` | + +**Using the int8 DPAS on bf16 fragments hangs the device** (fixed by the split; see +`SPARSE_BF16_DPAS_HANG.md`). + +### Key files +- `auto_round_kernel/sdpa_sparse_sdpa.cpp` — BF16 launcher. `sdpa_sparse.cpp` — INT8 launcher. +- `auto_round_kernel/sparse_attention.py` — Python wrappers + `sparge_preprocess_topk`. +- `auto_round_kernel/sparge_preprocess_triton.py` — triton_xpu preprocess (+ torch fallback). +- `wrapper/include/stla/xe_sparse_sdpa_fwd_mainloop.hpp`, `sycl_tla_sdpa_sparse.hpp` — kernels. +- `auto_round_kernel/sdpa.cpp` — **dense** SYCL-TLA SDPA → Python `ark.sdpa(...)` + (`xe_sdpa_fwd_mainloop.hpp`). + +## 2. Correctness — verified + +- **Repo e2e tests pass** (`test_sage_sparse_prefill_e2e.py`, `test_sparge_preprocess_topk_e2e.py`). +- **topk=1 (all blocks selected) == dense SDPA**: max diff **0.0039** (bf16 rounding) vs an exact + CPU fp32 reference. This is the "dense gate" test. +- **INT8 == BF16 kernels**: on identical Q/K/V they select the *same* blocks (bit-identical LUT) + and produce outputs within 0.008; verified across separate processes (bit-identical). +- **Preprocess + kernel are deterministic** (same QKV → identical results, in-process and + cross-process). + +## 3. Performance (bench `benchmarks/bench_sparse_topk.py`, seq 75000, 40h×128) + +New mode `dense_ark_sdpa` (dense SYCL-TLA SDPA via `ark.sdpa`) added as a third baseline; every +row gets a `speedup_vs_ark` column. Full tables in `BENCH_SPARSE_ARK_SDPA_20260813.md`. + +| mode | topk | vs torch | vs sagev1 | vs ark_sdpa | +|---|---|---|---|---| +| dense_ark_sdpa | — | 1.5–1.6× | 0.82–0.87× | 1.0 | +| INT8 sparse qtile256 (kernel) | 0.5 / 0.25 / 0.125 | 3.5 / 6.9 / 13.2× | — | 2.2 / 4.3 / 8.2× | +| BF16 sparse (kernel) | 0.5 / 0.25 / 0.125 | 1.5 / 2.9 / 5.6× | — | 0.92 / 1.8 / 3.5× | + +Notes: `dense_ark_sdpa` is faster than torch SDPA but slower than `dense_sagev1`; the BF16 sparse +kernel only beats the dense ark kernel at topk ≤ 0.25. The 1.5×-not-2× ceiling at topk 0.5 is the +**SBID scoreboard stall** (memory-SEND concurrency bound; INT8 avoids it because its K is half the +size) — earlier profiling details in `SPARSE_BF16_KERNEL_OPTIMIZATION_JOURNEY.md` / +`SPARSE_SDPA_KERNEL_OPTIMIZATION_OPTIONS.md`. + +**2026-08-14 A/B update:** changing the BF16 sparse qtile256 selected-block microtile from K64 to +K32 improved kernel-only latency by **1.69–1.77×**. At topk 0.5, BF16 sparse improved from +1511 ms / **0.91× vs ark** to 863 ms / **1.59× vs ark**. Correctness e2e still passed. See +`SPARSE_BF16_K32_AB_20260814.md`. + +**2026-08-14 unitrace update:** profiling K32 vs K64 at topk 0.5 confirmed the K64 regression is +compiler-reported spill plus memory/SEND scoreboard pressure, not lower occupancy. Kernel properties +from `unitrace --device-timing --verbose` showed `Spill Memory Per Thread = 0 B` for K32 and +`3840 B` for K64, with the same `Private Memory Per Thread = 2432 B` and register file size 256. +Aggregated `SPARSESDPAFwdMainloop` GPU time was 1684.6 ms (K32) vs 3072.4 ms (K64), XVE active +64.7% vs 39.9%, XVE stall 35.1% vs 59.9%, and SBID stall 31.4% vs 55.7%. SEND instructions rose +4.4× and GPU memory writes rose ~5.0× with K64. SLM read/write was 0/0 in both profiles because the +spill is private/scratch traffic, not SLM-backed storage. Raw profiles are in +`unitrace_k32_compute_20260814/`, `unitrace_k32_stalls_20260814/`, +`unitrace_k32_verbose_20260814/`, `unitrace_k64_compute_real_20260814/`, and +`unitrace_k64_stalls_real_20260814/`. + +## 4. FLUX.1-dev end-to-end — the critical gotcha + +**Use `enable_sequential_cpu_offload()`, NOT `enable_model_cpu_offload()`.** + +- `enable_model_cpu_offload()` streams at the *component* level: the whole ~46 GB transformer gets + pulled onto the 24.4 GB device (peak ~24 GB, ~350 MB headroom). The sparse launch then flakily + OOMs (`OUT_OF_DEVICE_MEMORY` / `OUT_OF_RESOURCES`), and can **reset the GPU** + (`dmesg: exec queue reset` on `0000:ba:00.0` = dev5; the `DEVICE_LOST` error). +- Sequential offload keeps the peak at **~0.16 GB** → triton_xpu sparse runs reliably (0 fallbacks). + Cost: ~4 s/step vs ~1 s/step (50-step generation ≈ 4–5 min). + +### FLUX image-quality findings +- bf16 sparse and int8 sparse produce **identical images** at the same config (verified, 0.0 diff). +- `topk=0.9` drops **12.5–16.7%** of blocks (route-block rounding + sim threshold), not 10% → + images sit ~15–18 from dense. Attention-mass coverage: topk 0.9 keeps **91.5%** of the mass, + topk 0.5 keeps **64.6%** — the renormalized output shifting is inherent to topk-sparse on + FLUX's spread-out attention. +- **Latest K32 BF16 sparse topk 0.9 run:** saved PNG at + `benchmarks/results/flux_bf16_topk09_k32_20260814_112445/flux_bf16_qtile256_topk0.9_512_dev5.png`. + Config: BF16 sparse qtile256, selected-block K32, `topk=0.9`, 512×512, 50 steps, seed 0, + `ZE_AFFINITY_MASK=5`. Result: wall **238.884 s**, sparsity **0.125**, calls **2850**, + sparse_calls **2850**, runtime_fallbacks **0**. +- **Open question: run-to-run non-determinism.** Same config (bf16 qtile256 topk0.5, seed 0) gave + images differing by **48.5** across two runs (means 97.6 vs 101.3), while topk 0.9 was + deterministic (0.0). QKV, preprocess and kernel are all verified deterministic → suspected + diffusion-trajectory amplification of a tiny perturbation elsewhere, not a sparse-kernel bug. + Worth re-checking before trusting single-run image comparisons. + +### Dev tooling (untracked) +- `examples/flux_gen_bf16_sweep.py` — offload-only runner (loop over topk, saves PNGs + summary). +- `tools/sweep_flux_bf16_topk.sh` — one-GPU wrapper (qtile256 env). **Note: `FLUX_SPARSE_KERNEL` + is now env-overridable (default bf16)**; earlier it hardcoded bf16 and silently made an "int8" + run bf16. `main`'s `flux_sparse_patch.py` is int8-only and ignores the block-token envs. +- `tools/bench_sparse_bf16_fp16_sweep.sh` — the bench sweep driver. + +## 5. Current repo state (uncommitted) + +- **Branch**: `feat/sparse-bf16-prefill-v2`. +- **Modified (tracked)**: `benchmarks/bench_sparse_topk.py` (added `dense_ark_sdpa` mode + + `speedup_vs_ark` column), sparse SDPA headers for K32 route-block expansion — not yet committed. +- **Untracked**: `examples/flux_gen_bf16_sweep.py`, `tools/sweep_flux_bf16_topk.sh`, + `tools/bench_sparse_bf16_fp16_sweep.sh`. +- `.so`: `auto_round_kernel/ark-xbuild/auto_round_kernel_xpu.cpython-313-x86_64-linux-gnu.so` + (feature-branch build). A stale **main** `.so` copy sits in `auto_round_kernel/xbuild/` (harmless; + feature's loader uses `ark-xbuild`). +- `benchmarks/results/` is gitignored → all result CSVs, PNGs, and docs below are local-only. + +## 6. How to run + +```bash +# rebuild (after oneAPI: source /opt/intel/oneapi/setvars.sh --force) +cd auto_round_extension/ark/auto_round_kernel/ark-xbuild && cmake --build . -j 16 + +# correctness tests +export ZE_AFFINITY_MASK=5 SAGE_ATTN_SPARSE_PREPROCESS_BACKEND=torch +.venv/bin/python auto_round_kernel/wrapper/test/test_sage_sparse_prefill_e2e.py +.venv/bin/python auto_round_kernel/wrapper/test/test_sparge_preprocess_topk_e2e.py + +# FLUX sweep (bf16 qtile256; set FLUX_SPARSE_KERNEL=int8 for SAGE) +export ZE_AFFINITY_MASK=5 SAGE_ATTN_SPARSE_PREPROCESS_BACKEND=triton_xpu +export FLUX_SPARSE_KERNEL=bf16 FLUX_SPARSE_TOPKS="0.9 0.5" FLUX_RUN_DENSE=0 +export FLUX_OUTPUT_DIR=benchmarks/results/flux_ +.venv/bin/python examples/flux_gen_bf16_sweep.py + +# bench (needs feature branch — main lacks bf16 symbols) +export ZE_AFFINITY_MASK=5 SAGE_ATTN_SPARSE_PREPROCESS_BACKEND=triton_xpu +.venv/bin/python benchmarks/bench_sparse_topk.py --dtype bf16 --seq-len 75000 \ + --topk 0.5 0.25 0.125 --tensor-layout HND NHD --q-tile-override 256 \ + --sparse-q-block-tokens 256 --sparse-k-block-tokens 64 --warmup 2 --iters 3 \ + --output-csv benchmarks/results/bench_.csv +``` + +## 7. Open items / suggestions + +1. **Resolve the topk-0.5 cross-run non-determinism** before trusting single-run image diffs + (capture QKV per run; all sparse ops verified deterministic). +2. **Clean up the K32 BF16 sparse SDPA experiment** (`SPARSE_BF16_K32_AB_20260814.md`) into a final + implementation. The unitrace A/B says to keep the selected-block microtile at K32 unless a later + compiler/assembly pass removes the K64 write/SBID pressure. +3. Consider making the FLUX runner's `_512` filename suffix derive from the actual size (cosmetic; + the 1024×1024 run is mislabeled `_512`). +4. Decide whether to commit the bench `dense_ark_sdpa` change + dev tooling (currently uncommitted). diff --git a/auto_round_extension/ark/benchmarks/results/SPARSE_BF16_HANDOFF_20260814_CN.md b/auto_round_extension/ark/benchmarks/results/SPARSE_BF16_HANDOFF_20260814_CN.md new file mode 100644 index 0000000000..26a9abd9e4 --- /dev/null +++ b/auto_round_extension/ark/benchmarks/results/SPARSE_BF16_HANDOFF_20260814_CN.md @@ -0,0 +1,156 @@ +# 交接文档：BF16 原生稀疏 SDPA（sparge）— 2026-08-14 + +供后续开发接手的状态快照。以下内容均在 **Intel Battlemage XPU（24.4 GB, `intel_gpu_bmg_g21`）**、 +oneAPI 2026.1、PyTorch 2.13.0+xpu 上验证。 + +## 1. 代码位置 + +- **分支 `feat/sparse-bf16-prefill-v2`** — 全部稀疏工作（INT8 SAGE **与** 原生 BF16/FP16 稀疏 SDPA）。 + **`main` 只有 INT8 SAGE 路径**（此前认为 `ark.cpp`/`sparse_attention.py` 为空是路径写错导致的误判； + 以仓库根为相对路径检查：`main` 有可用的 int8 `sparge_sage2_attn_meansim_topk_xpu`， + 位于 `auto_round_extension/ark/auto_round_kernel/sparse_attention.py`，1440 行）。 +- 切换分支后 **每次都要重新编译**（`auto_round_kernel/ark-xbuild/` 里的 `.so` 只匹配编译它的分支）。 + +### 本交接中的关键改动 +- BF16 稀疏 SDPA 已拆成独立的原生 BF16/FP16 路径（`SparseSDPAConfig`），不再复用 INT8 SAGE + DPAS 路径。 +- `benchmarks/bench_sparse_topk.py` 已加入稠密 `ark.sdpa` 基线，并输出 `speedup_vs_ark`。 +- BF16 稀疏已选中 block microtile 做了 K64 vs K32 A/B。K32 是当前实验方向，本地重新编译后的 + `.so` 已恢复为 K32。 +- `xe_sparse_sdpa_fwd_mainloop.hpp` 支持把一个 64-token 稀疏路由 block 展开为多个物理 K microtile； + 这让 K32 能继续沿用现有 64-token LUT 格式。 +- Unitrace profile 显示 K64 回退来自 SEND/SBID 与 memory-write 压力。 +- 已成功生成一张 FLUX.1-dev sparse BF16 topk 0.9 K32 图片，且无 fallback。 + +### 两条独立路径（勿混用） + +| 路径 | 封装函数 | 配置 | DPAS | +|---|---|---|---| +| INT8 稀疏 | `sparge_sage2_attn_meansim_topk_xpu` / `sage_sparse` | `SparseSageConfig` | `XE_DPAS_TT<8, int, int8, int8, int>`（含 `static_assert(ElementQ==int8_t)`） | +| BF16/FP16 稀疏 | `sparge_sage2_attn_meansim_topk_xpu_sdpa` / `sage_sparse_sdpa` | `SparseSDPAConfig` | `XE_DPAS_TT<8, float, bf16, bf16, float>` | + +**在 bf16 片段上复用 int8 DPAS 会导致设备挂起**（已通过拆分修复；见 `SPARSE_BF16_DPAS_HANG.md`）。 + +### 关键文件 +- `auto_round_kernel/sdpa_sparse_sdpa.cpp` — BF16 启动器；`sdpa_sparse.cpp` — INT8 启动器。 +- `auto_round_kernel/sparse_attention.py` — Python 封装 + `sparge_preprocess_topk`。 +- `auto_round_kernel/sparge_preprocess_triton.py` — triton_xpu 预处理（含 torch 回退）。 +- `wrapper/include/stla/xe_sparse_sdpa_fwd_mainloop.hpp`、`sycl_tla_sdpa_sparse.hpp` — 内核。 +- `auto_round_kernel/sdpa.cpp` — **稠密** SYCL-TLA SDPA → Python `ark.sdpa(...)` + （`xe_sdpa_fwd_mainloop.hpp`）。 + +## 2. 正确性 — 已验证 + +- **仓库 e2e 测试通过**（`test_sage_sparse_prefill_e2e.py`、`test_sparge_preprocess_topk_e2e.py`）。 +- **topk=1（全 block 选中）== 稠密 SDPA**：与精确 CPU fp32 参考的最大误差 **0.0039**（bf16 舍入）。 + 即"稠密门"测试。 +- **INT8 == BF16 内核**：相同 Q/K/V 下选中的 block 完全一致（LUT 逐位相同），输出误差 <0.008； + 跨进程验证逐位一致。 +- **预处理与内核均确定**（相同 QKV → 结果一致，进程内与跨进程均一致）。 + +## 3. 性能（bench `benchmarks/bench_sparse_topk.py`，seq 75000，40 头 × 128） + +新增 `dense_ark_sdpa`（经 `ark.sdpa` 调用的稠密 SYCL-TLA SDPA）作为第三个稠密基线；每行新增 +`speedup_vs_ark` 列。完整表格见 `BENCH_SPARSE_ARK_SDPA_20260813.md`。 + +| 模式 | topk | vs torch | vs sagev1 | vs ark_sdpa | +|---|---|---|---|---| +| dense_ark_sdpa | — | 1.5–1.6× | 0.82–0.87× | 1.0 | +| INT8 稀疏 qtile256（内核） | 0.5 / 0.25 / 0.125 | 3.5 / 6.9 / 13.2× | — | 2.2 / 4.3 / 8.2× | +| BF16 稀疏（内核） | 0.5 / 0.25 / 0.125 | 1.5 / 2.9 / 5.6× | — | 0.92 / 1.8 / 3.5× | + +说明：`dense_ark_sdpa` 快于 torch SDPA 但慢于 `dense_sagev1`；BF16 稀疏内核仅在 topk ≤ 0.25 时 +快于稠密 ark 内核。topk 0.5 处"1.5× 而非 2×"的上限源于 **SBID 分数板停顿**（内存 SEND 并发瓶颈； +INT8 因 K 尺寸减半而无此问题）——早期剖析见 `SPARSE_BF16_KERNEL_OPTIMIZATION_JOURNEY.md` / +`SPARSE_SDPA_KERNEL_OPTIMIZATION_OPTIONS.md`。 + +**2026-08-14 A/B 更新：** 将 BF16 稀疏 qtile256 已选中 block 的 microtile 从 K64 改为 K32 后， +kernel-only 延迟提升 **1.69–1.77×**。topk 0.5 从 1511 ms / **0.91× vs ark** 提升到 +863 ms / **1.59× vs ark**。正确性 e2e 仍通过。见 `SPARSE_BF16_K32_AB_20260814.md`。 + +**2026-08-14 unitrace 更新：** 在 topk 0.5 下 profile K32 与 K64，确认 K64 回退主要是 +memory/SEND scoreboard 压力，而不是 occupancy 下降。聚合 `SPARSESDPAFwdMainloop` GPU time 为 +1684.6 ms（K32）对 3072.4 ms（K64），XVE active 64.7% 对 39.9%，XVE stall 35.1% 对 59.9%， +SBID stall 31.4% 对 55.7%。K64 的 SEND 指令增加 4.4×，GPU memory write 增加约 5.0×。 +两边 SLM read/write 都是 0/0，因此 unitrace 没显示 SLM-backed spill；巨大的 LSC write 差异是当前 +spill/pressure proxy。原始 profile 在 `unitrace_k32_compute_20260814/`、 +`unitrace_k32_stalls_20260814/`、`unitrace_k64_compute_real_20260814/`、 +`unitrace_k64_stalls_real_20260814/`。 + +## 4. FLUX.1-dev 端到端 — 关键坑 + +**必须用 `enable_sequential_cpu_offload()`，不要用 `enable_model_cpu_offload()`。** + +- `enable_model_cpu_offload()` 按*组件*粒度搬运：整个约 46 GB 的 transformer 会被拉到 24.4 GB + 设备上（峰值约 24 GB，仅剩约 350 MB 余量）。稀疏启动会偶发 OOM + （`OUT_OF_DEVICE_MEMORY` / `OUT_OF_RESOURCES`），甚至**重置 GPU** + （`dmesg: exec queue reset` on `0000:ba:00.0` = dev5；即 `DEVICE_LOST` 错误）。 +- Sequential offload 使峰值降到 **约 0.16 GB** → triton_xpu 稀疏稳定运行（0 回退）。 + 代价：每步约 4 s 对 1 s（50 步生成约 4–5 分钟）。 + +### FLUX 图像质量结论 +- 相同配置下，bf16 稀疏与 int8 稀疏**产出完全相同图像**（已验证，0.0 差异）。 +- `topk=0.9` 实际丢弃 **12.5–16.7%** 的 block（路由块取整 + sim 阈值），而非 10% → 图像与稠密 + 相差约 15–18。注意力质量覆盖率：topk 0.9 保留 **91.5%**、topk 0.5 保留 **64.6%** —— + 归一化后输出的偏移是 FLUX 注意力分散下的 topk 稀疏固有现象。 +- **最新 K32 BF16 稀疏 topk 0.9 运行：** PNG 已保存到 + `benchmarks/results/flux_bf16_topk09_k32_20260814_112445/flux_bf16_qtile256_topk0.9_512_dev5.png`。 + 配置：BF16 sparse qtile256、已选中 block K32、`topk=0.9`、512×512、50 steps、seed 0、 + `ZE_AFFINITY_MASK=5`。结果：wall **238.884 s**、sparsity **0.125**、calls **2850**、 + sparse_calls **2850**、runtime_fallbacks **0**。 +- **待解决：运行间非确定性。** 相同配置（bf16 qtile256 topk0.5，seed 0）两次运行图像相差 + **48.5**（均值 97.6 对 101.3），而 topk 0.9 完全确定（0.0）。QKV、预处理、内核均已验证确定 → + 疑似扩散轨迹对其它微小扰动的放大，而非稀疏内核缺陷。做单次图像对比前建议复查。 + +### 开发工具（未跟踪） +- `examples/flux_gen_bf16_sweep.py` — offload-only 运行器（遍历 topk，保存 PNG + 汇总）。 +- `tools/sweep_flux_bf16_topk.sh` — 单 GPU 封装（qtile256 环境变量）。**注意：`FLUX_SPARSE_KERNEL` + 现已可通过环境变量覆盖（默认 bf16）**；此前写死 bf16，导致"int8"运行实际是 bf16。 + `main` 的 `flux_sparse_patch.py` 仅 int8，且忽略 block-token 环境变量。 +- `tools/bench_sparse_bf16_fp16_sweep.sh` — bench 扫描驱动。 + +## 5. 当前仓库状态（未提交） + +- **分支**：`feat/sparse-bf16-prefill-v2`。 +- **已修改（跟踪）**：`benchmarks/bench_sparse_topk.py`（新增 `dense_ark_sdpa` 模式 + + `speedup_vs_ark` 列）、稀疏 SDPA 头文件中的 K32 route-block expansion —— 尚未提交。 +- **未跟踪**：`examples/flux_gen_bf16_sweep.py`、`tools/sweep_flux_bf16_topk.sh`、 + `tools/bench_sparse_bf16_fp16_sweep.sh`。 +- `.so`：`auto_round_kernel/ark-xbuild/auto_round_kernel_xpu.cpython-313-x86_64-linux-gnu.so` + （特性分支构建）。`auto_round_kernel/xbuild/` 里有一份**过期的 main** `.so`（无碍； + 特性分支的 loader 用 `ark-xbuild`）。 +- `benchmarks/results/` 已 gitignore → 下面的结果 CSV、PNG、文档均为本地。 + +## 6. 如何运行 + +```bash +# 重新编译（先 source /opt/intel/oneapi/setvars.sh --force） +cd auto_round_extension/ark/auto_round_kernel/ark-xbuild && cmake --build . -j 16 + +# 正确性测试 +export ZE_AFFINITY_MASK=5 SAGE_ATTN_SPARSE_PREPROCESS_BACKEND=torch +.venv/bin/python auto_round_kernel/wrapper/test/test_sage_sparse_prefill_e2e.py +.venv/bin/python auto_round_kernel/wrapper/test/test_sparge_preprocess_topk_e2e.py + +# FLUX 扫描（bf16 qtile256；SAGE 时设 FLUX_SPARSE_KERNEL=int8） +export ZE_AFFINITY_MASK=5 SAGE_ATTN_SPARSE_PREPROCESS_BACKEND=triton_xpu +export FLUX_SPARSE_KERNEL=bf16 FLUX_SPARSE_TOPKS="0.9 0.5" FLUX_RUN_DENSE=0 +export FLUX_OUTPUT_DIR=benchmarks/results/flux_ +.venv/bin/python examples/flux_gen_bf16_sweep.py + +# bench（需要特性分支 —— main 缺少 bf16 符号） +export ZE_AFFINITY_MASK=5 SAGE_ATTN_SPARSE_PREPROCESS_BACKEND=triton_xpu +.venv/bin/python benchmarks/bench_sparse_topk.py --dtype bf16 --seq-len 75000 \ + --topk 0.5 0.25 0.125 --tensor-layout HND NHD --q-tile-override 256 \ + --sparse-q-block-tokens 256 --sparse-k-block-tokens 64 --warmup 2 --iters 3 \ + --output-csv benchmarks/results/bench_.csv +``` + +## 7. 待办 / 建议 + +1. **定位 topk-0.5 的跨运行非确定性**，再做单次图像对比（逐次抓取 QKV；稀疏算子已全部验证确定）。 +2. **将 K32 BF16 稀疏 SDPA 实验整理成最终实现**（`SPARSE_BF16_K32_AB_20260814.md`）。unitrace + A/B 说明应保持已选中 block microtile 为 K32，除非后续 compiler/assembly 优化能消除 K64 的 + write/SBID 压力。 +3. 考虑让 FLUX 运行器文件名里的 `_512` 后缀跟随实际尺寸（1024×1024 运行被误标为 `_512`，纯外观问题）。 +4. 决定是否提交 bench 的 `dense_ark_sdpa` 改动及开发工具（当前均未提交）。 diff --git a/auto_round_extension/ark/benchmarks/results/SPARSE_BF16_K32_AB_20260814.md b/auto_round_extension/ark/benchmarks/results/SPARSE_BF16_K32_AB_20260814.md new file mode 100644 index 0000000000..164da22c26 --- /dev/null +++ b/auto_round_extension/ark/benchmarks/results/SPARSE_BF16_K32_AB_20260814.md @@ -0,0 +1,258 @@ +# BF16 sparse SDPA K64 vs K32 A/B — 2026-08-14 + +## Summary + +The BF16 sparse SDPA qtile256 path was tested with the selected-block K tile changed from 64 tokens +to 32 tokens, matching the dense `ark.sdpa` head_dim=128 K tile size. This is an experimental patch, +not yet a cleaned-up final implementation. + +Result: **K32 is substantially faster**. At topk 0.5, BF16 sparse changes from slower than dense +`ark.sdpa` to clearly faster. + +## Test setup + +- Branch: `feat/sparse-bf16-prefill-v2` +- Device: Intel Battlemage XPU, `ZE_AFFINITY_MASK=5` +- Benchmark: `benchmarks/bench_sparse_topk.py` +- Shape: `seq_len=75000`, HND, 40 heads, head_dim 128 +- Sparse config: `q_tile_override=256`, `sparse_q_block_tokens=256`, `sparse_k_block_tokens=64` +- Benchmark options: `--warmup 1 --iters 2` +- Baseline CSV: `benchmarks/results/ab_k64_bf16_20260814_090508.csv` +- K32 CSV: `benchmarks/results/ab_bf16_sdpa_k32_20260814_092404.csv` + +## Result table + +Only `sparse_sdpa_bf16_kernel_only` is compared below. + +| topk | K64 latency | K32 latency | K32 speedup | K64 vs ark | K32 vs ark | effective TFLOPS K64 -> K32 | +|---|---:|---:|---:|---:|---:|---:| +| 1.0 | 2986.014 ms | 1691.257 ms | 1.766x | 0.460x | 0.812x | 38.6 -> 68.1 | +| 0.5 | 1511.047 ms | 862.649 ms | 1.752x | 0.910x | 1.593x | 38.3 -> 67.1 | +| 0.25 | 766.744 ms | 455.005 ms | 1.685x | 1.792x | 3.020x | 38.1 -> 64.2 | + +Dense `ark.sdpa` in the same run was ~1374 ms. With K64, BF16 sparse at topk 0.5 was slower than +dense ark (`0.91x`). With K32, BF16 sparse at topk 0.5 became faster (`1.59x`). + +## Patch shape + +The A/B changed `launch_sparse_sdpa_prefill_kernel_128` in +`auto_round_kernel/wrapper/include/sycl_tla_sdpa_sparse.hpp`: + +```cpp +// K64 original +using ShapeQK = Shape<_256, _64, _32>; +using ShapePV = Shape<_256, _32, _64>; + +// K32 experiment +using ShapeQK = Shape<_256, _32, _32>; +using ShapePV = Shape<_256, _32, _32>; +``` + +Because the routing LUT still uses 64-token sparse K blocks, the experiment also changed +`auto_round_kernel/wrapper/include/stla/xe_sparse_sdpa_fwd_mainloop.hpp` so each sparse route block +can expand into multiple physical K microtiles. For the K32 experiment, one 64-token routed block +runs as two 32-token mainloop iterations. + +## Tile meaning + +For QK: + +```cpp +using ShapeQK = Shape<_256, _32, _32>; +``` + +This means: + +```text +M = 256 query tokens per workgroup tile +N = 32 key tokens per physical K microtile +K = 32 head-dim reduction chunk +``` + +The QK output score tile is therefore `256 x 32`. Since head_dim is 128, each physical tile still +does four reduction chunks over the feature dimension: + +```text +head_dim 128 / reduction chunk 32 = 4 QK reduction chunks +``` + +For PV: + +```cpp +using ShapePV = Shape<_256, _32, _32>; +``` + +This means: + +```text +M = 256 query rows / probability rows +N = 32 value/output head-dim columns per tile +K = 32 selected key/value tokens reduced per PV tile +``` + +So one PV microtile consumes `P[256, 32] x V[32, 32] -> O[256, 32]`. The output head_dim 128 is +covered as four 32-column output chunks. + +Pseudocode for one selected 64-token sparse route block: + +```cpp +// K64 original: +// ShapeQK = Shape<_256, _64, _32> +// ShapePV = Shape<_256, _32, _64> +for each selected_route_block_64_tokens { + scores[256][64] = 0; + for d = 0; d < 128; d += 32 { + scores[256][64] += Q[256][d:d+32] * transpose(K[64][d:d+32]); + } + softmax_update(scores[256][64]); + O[256][0:32] += P[256][64] * V[64][0:32]; // repeated for output-dim chunks +} + +// K32 experiment: +// ShapeQK = Shape<_256, _32, _32> +// ShapePV = Shape<_256, _32, _32> +for each selected_route_block_64_tokens { + for micro_tile = 0; micro_tile < 2; ++micro_tile { + key_start = selected_route_block_64_tokens * 64 + micro_tile * 32; + + scores[256][32] = 0; + for d = 0; d < 128; d += 32 { + scores[256][32] += Q[256][d:d+32] * transpose(K[key_start:key_start+32][d:d+32]); + } + softmax_update(scores[256][32]); + O[256][0:32] += P[256][32] * V[key_start:key_start+32][0:32]; // repeated for output-dim chunks + } +} +``` + +The head-dim reduction loop count is the same in both cases (`128 / 32 = 4`). The difference is the +physical selected-key span: + +```text +K64: one route block -> one 256x64 score tile +K32: one route block -> two 256x32 score tiles +``` + +K32 adds one extra microtile iteration per 64-token route block, but each physical tile is smaller. +The unitrace result below shows that smaller tile wins because it greatly reduces SEND/SBID and +memory-write pressure. + +## Correctness + +After rebuilding, `auto_round_kernel/wrapper/test/test_sage_sparse_prefill_e2e.py` passed. +Relevant BF16 SDPA all-selected result: + +```text +[sage_sparse_sdpa][torch.bfloat16_all_selected] D=128 max_diff=0.001953 mean_diff=0.000111 +``` + +## Unitrace profile + +The profile below uses `topk=0.5`, `seq_len=75000`, HND, head_dim 128, `q_tile_override=256`, +`sparse_q_block_tokens=256`, and the same 64-token sparse routing blocks. The profiler adds large +wall-time overhead, so the table uses the included `SPARSESDPAFwdMainloop` kernel GPU metrics rather +than benchmark wall time. + +Files: + +- K32 ComputeBasic: `benchmarks/results/unitrace_k32_compute_20260814/k32_compute.metrics.2889557` +- K32 VectorEngineStalls: `benchmarks/results/unitrace_k32_stalls_20260814/k32_stalls.metrics.2892853` +- K64 ComputeBasic: `benchmarks/results/unitrace_k64_compute_real_20260814/k64_compute_real.metrics.2948755` +- K64 VectorEngineStalls: `benchmarks/results/unitrace_k64_stalls_real_20260814/k64_stalls_real.metrics.2952213` +- K32 kernel properties: `benchmarks/results/unitrace_k32_verbose_20260814/k32_verbose.3726840` +- K64 kernel properties: `benchmarks/results/unitrace_k64_compute_real_20260814/k64_compute_real.2948755` + +Commands: + +```bash +# K32 kernel properties. Run after building the K32 variant. +source /opt/intel/oneapi/setvars.sh --force +export ZE_AFFINITY_MASK=5 +export ZE_FLAT_DEVICE_HIERARCHY=FLAT +export SAGE_ATTN_SPARSE_PREPROCESS_BACKEND=triton_xpu +unitrace \ + --device-timing --verbose \ + --include-kernels SPARSESDPAFwdMainloop \ + --output k32_verbose \ + .venv/bin/python benchmarks/bench_sparse_topk.py --dtype bf16 --seq-len 75000 \ + --topk 0.5 --tensor-layout HND --q-tile-override 256 \ + --sparse-q-block-tokens 256 --warmup 0 --iters 1 \ + --output-csv benchmarks/results/unitrace_k32_verbose_20260814.csv +``` + +```bash +# K64 ComputeBasic. Run after temporarily changing launch_sparse_sdpa_prefill_kernel_128 +# back to ShapeQK=Shape<_256,_64,_32>, ShapePV=Shape<_256,_32,_64> and rebuilding. +source /opt/intel/oneapi/setvars.sh --force +export ZE_AFFINITY_MASK=5 +export ZE_FLAT_DEVICE_HIERARCHY=FLAT +export SAGE_ATTN_SPARSE_PREPROCESS_BACKEND=triton_xpu +unitrace \ + --device-timing --kernel-submission --metric-query --group ComputeBasic \ + --include-kernels SPARSESDPAFwdMainloop \ + --output k64_compute_real \ + .venv/bin/python benchmarks/bench_sparse_topk.py --dtype bf16 --seq-len 75000 \ + --topk 0.5 --tensor-layout HND --q-tile-override 256 \ + --sparse-q-block-tokens 256 --warmup 0 --iters 1 \ + --output-csv benchmarks/results/unitrace_k64_compute_real_20260814.csv +``` + +```bash +# K32/K64 stall pass: use the same build-specific setup as above and change only the group/output. +unitrace \ + --device-timing --kernel-submission --metric-query --group VectorEngineStalls \ + --include-kernels SPARSESDPAFwdMainloop \ + --output _stalls \ + .venv/bin/python benchmarks/bench_sparse_topk.py --dtype bf16 --seq-len 75000 \ + --topk 0.5 --tensor-layout HND --q-tile-override 256 \ + --sparse-q-block-tokens 256 --warmup 0 --iters 1 \ + --output-csv benchmarks/results/_stalls.csv +``` + +Kernel properties from `unitrace --device-timing --verbose`: + +| variant | SIMD | SLM / WG | private memory / thread | spill memory / thread | register file size / thread | +|---|---:|---:|---:|---:|---:| +| K32 | 16 | 0 | 2432 B | 0 B | 256 | +| K64 | 16 | 0 | 2432 B | 3840 B | 256 | + +ComputeBasic aggregate over the two profiled `SPARSESDPAFwdMainloop` rows: + +| metric | K32 | K64 | K64/K32 | +|---|---:|---:|---:| +| GPU time | 1684.6 ms | 3072.4 ms | 1.824x | +| XVE active | 64.718% | 39.934% | 0.617x | +| XVE stall | 35.149% | 59.939% | 1.705x | +| thread occupancy | 99.828% | 99.843% | 1.000x | +| SEND instructions | 18.593G | 81.568G | 4.387x | +| GPU memory read | 43.462 GB | 73.245 GB | 1.685x | +| GPU memory write | 1.633 GB | 8.135 GB | 4.981x | +| LSC byte read | 9653.807 GB | 13389.615 GB | 1.387x | +| LSC byte write | 72.008 MB | 2333.083 GB | 32400.183x | +| SLM read/write | 0 / 0 | 0 / 0 | n/a | + +VectorEngineStalls aggregate: + +| stall metric | K32 | K64 | delta | +|---|---:|---:|---:| +| SBID | 31.449% | 55.730% | +24.281 pp | +| ALU write | 29.562% | 16.532% | -13.030 pp | +| instruction fetch | 12.329% | 3.638% | -8.691 pp | +| pipe stall | 4.748% | 0.891% | -3.857 pp | +| control | 4.316% | 1.310% | -3.006 pp | +| barrier | 0.240% | 0.531% | +0.291 pp | + +## Interpretation + +This confirms the K64 selected-block microtile is a major performance issue for BF16 sparse SDPA. +The kernel properties pass shows that K64 does cause compiler-reported spill memory: +`Spill Memory Per Thread` is **3840 B** for K64 and **0 B** for K32. SLM remains 0 because this is +private/scratch spill traffic, not SLM-backed storage. + +The runtime metrics match that diagnosis. K64 is memory/SEND scoreboard limited: SBID stall rises +from 31.4% to 55.7%, XVE active drops from 64.7% to 39.9%, SEND instructions rise 4.4x, and memory +writes rise ~5.0x. The large LSC write delta is now best interpreted as spill/scratch pressure plus +the extra memory traffic created by the larger K64 live state. + +Recommendation: keep the selected-block BF16 sparse SDPA path on K32 and treat K64 as a regression +case unless compiler/offline assembly data proves the write/SBID pressure can be removed another way. diff --git a/auto_round_extension/ark/benchmarks/results/SPARSE_BF16_K32_AB_20260814_CN.md b/auto_round_extension/ark/benchmarks/results/SPARSE_BF16_K32_AB_20260814_CN.md new file mode 100644 index 0000000000..024eefdcbf --- /dev/null +++ b/auto_round_extension/ark/benchmarks/results/SPARSE_BF16_K32_AB_20260814_CN.md @@ -0,0 +1,193 @@ +# BF16 稀疏 SDPA K64 vs K32 A/B — 2026-08-14 + +## 摘要 + +本次测试将 BF16 稀疏 SDPA qtile256 路径的已选中 block 的 K tile 从 64 token 改为 32 token， +与稠密 `ark.sdpa` 在 head_dim=128 下的 K tile 大小保持一致。这是实验性补丁，尚未整理成最终实现。 + +结论：**K32 明显更快**。在 topk 0.5 下，BF16 稀疏从慢于稠密 `ark.sdpa` 变为明显快于它。 + +## 测试设置 + +- 分支：`feat/sparse-bf16-prefill-v2` +- 设备：Intel Battlemage XPU，`ZE_AFFINITY_MASK=5` +- Benchmark：`benchmarks/bench_sparse_topk.py` +- 形状：`seq_len=75000`，HND，40 头，head_dim 128 +- 稀疏配置：`q_tile_override=256`、`sparse_q_block_tokens=256`、`sparse_k_block_tokens=64` +- Benchmark 参数：`--warmup 1 --iters 2` +- 基线 CSV：`benchmarks/results/ab_k64_bf16_20260814_090508.csv` +- K32 CSV：`benchmarks/results/ab_bf16_sdpa_k32_20260814_092404.csv` + +## 结果表 + +下表仅比较 `sparse_sdpa_bf16_kernel_only`。 + +| topk | K64 延迟 | K32 延迟 | K32 加速 | K64 vs ark | K32 vs ark | effective TFLOPS K64 -> K32 | +|---|---:|---:|---:|---:|---:|---:| +| 1.0 | 2986.014 ms | 1691.257 ms | 1.766x | 0.460x | 0.812x | 38.6 -> 68.1 | +| 0.5 | 1511.047 ms | 862.649 ms | 1.752x | 0.910x | 1.593x | 38.3 -> 67.1 | +| 0.25 | 766.744 ms | 455.005 ms | 1.685x | 1.792x | 3.020x | 38.1 -> 64.2 | + +同次运行中稠密 `ark.sdpa` 约为 1374 ms。K64 时，BF16 稀疏 topk 0.5 慢于稠密 ark +（`0.91x`）；K32 时，BF16 稀疏 topk 0.5 变为更快（`1.59x`）。 + +## 补丁形态 + +A/B 修改了 `auto_round_kernel/wrapper/include/sycl_tla_sdpa_sparse.hpp` 中的 +`launch_sparse_sdpa_prefill_kernel_128`： + +```cpp +// K64 原始配置 +using ShapeQK = Shape<_256, _64, _32>; +using ShapePV = Shape<_256, _32, _64>; + +// K32 实验配置 +using ShapeQK = Shape<_256, _32, _32>; +using ShapePV = Shape<_256, _32, _32>; +``` + +由于路由 LUT 仍以 64-token 稀疏 K block 为单位，实验还修改了 +`auto_round_kernel/wrapper/include/stla/xe_sparse_sdpa_fwd_mainloop.hpp`，使每个稀疏路由 block +可以展开为多个物理 K microtile。在 K32 实验中，一个 64-token 路由 block 会执行两次 32-token +mainloop。 + +## Tile 含义 + +对于 QK： + +```cpp +using ShapeQK = Shape<_256, _32, _32>; +``` + +含义是： + +```text +M = 256 每个 workgroup tile 的 query token 数 +N = 32 每个物理 K microtile 的 key token 数 +K = 32 head-dim reduction chunk +``` + +因此 QK 输出的 score tile 是 `256 x 32`。由于 head_dim 是 128，每个物理 tile 仍然需要在特征维上做 +4 个 reduction chunk： + +```text +head_dim 128 / reduction chunk 32 = 4 个 QK reduction chunk +``` + +对于 PV： + +```cpp +using ShapePV = Shape<_256, _32, _32>; +``` + +含义是： + +```text +M = 256 query 行 / probability 行 +N = 32 每个 tile 的 value/output head-dim 列数 +K = 32 每个 PV tile reduction 的 selected key/value token 数 +``` + +所以一个 PV microtile 计算 `P[256, 32] x V[32, 32] -> O[256, 32]`。output head_dim 128 会拆成 +4 个 32-column output chunk。 + +一个被选中的 64-token 稀疏路由 block 的伪代码： + +```cpp +// K64 原始配置： +// ShapeQK = Shape<_256, _64, _32> +// ShapePV = Shape<_256, _32, _64> +for each selected_route_block_64_tokens { + scores[256][64] = 0; + for d = 0; d < 128; d += 32 { + scores[256][64] += Q[256][d:d+32] * transpose(K[64][d:d+32]); + } + softmax_update(scores[256][64]); + O[256][0:32] += P[256][64] * V[64][0:32]; // 对 output-dim chunk 重复 +} + +// K32 实验配置： +// ShapeQK = Shape<_256, _32, _32> +// ShapePV = Shape<_256, _32, _32> +for each selected_route_block_64_tokens { + for micro_tile = 0; micro_tile < 2; ++micro_tile { + key_start = selected_route_block_64_tokens * 64 + micro_tile * 32; + + scores[256][32] = 0; + for d = 0; d < 128; d += 32 { + scores[256][32] += Q[256][d:d+32] * transpose(K[key_start:key_start+32][d:d+32]); + } + softmax_update(scores[256][32]); + O[256][0:32] += P[256][32] * V[key_start:key_start+32][0:32]; // 对 output-dim chunk 重复 + } +} +``` + +两种配置的 head-dim reduction loop 次数相同（`128 / 32 = 4`）。差别在物理 selected-key 跨度： + +```text +K64: 一个 route block -> 一个 256x64 score tile +K32: 一个 route block -> 两个 256x32 score tile +``` + +K32 对每个 64-token route block 多了一次 microtile iteration，但每个物理 tile 更小。下面的 unitrace +结果显示，更小的 tile 胜出，因为它显著降低了 SEND/SBID 与 memory-write 压力。 + +## 正确性 + +重新编译后，`auto_round_kernel/wrapper/test/test_sage_sparse_prefill_e2e.py` 通过。 +相关 BF16 SDPA 全选结果： + +```text +[sage_sparse_sdpa][torch.bfloat16_all_selected] D=128 max_diff=0.001953 mean_diff=0.000111 +``` + +## Unitrace profile + +下面 profile 使用 `topk=0.5`、`seq_len=75000`、HND、head_dim 128、`q_tile_override=256`、 +`sparse_q_block_tokens=256`，稀疏路由 block 仍为 64 token。Profiler 会显著拉长墙钟时间，因此下表 +使用被 `--include-kernels SPARSESDPAFwdMainloop` 捕获的 kernel GPU metrics，而不是 benchmark 墙钟时间。 + +文件： + +- K32 ComputeBasic：`benchmarks/results/unitrace_k32_compute_20260814/k32_compute.metrics.2889557` +- K32 VectorEngineStalls：`benchmarks/results/unitrace_k32_stalls_20260814/k32_stalls.metrics.2892853` +- K64 ComputeBasic：`benchmarks/results/unitrace_k64_compute_real_20260814/k64_compute_real.metrics.2948755` +- K64 VectorEngineStalls：`benchmarks/results/unitrace_k64_stalls_real_20260814/k64_stalls_real.metrics.2952213` + +ComputeBasic，对两个 `SPARSESDPAFwdMainloop` 行聚合： + +| metric | K32 | K64 | K64/K32 | +|---|---:|---:|---:| +| GPU time | 1684.6 ms | 3072.4 ms | 1.824x | +| XVE active | 64.718% | 39.934% | 0.617x | +| XVE stall | 35.149% | 59.939% | 1.705x | +| thread occupancy | 99.828% | 99.843% | 1.000x | +| SEND instructions | 18.593G | 81.568G | 4.387x | +| GPU memory read | 43.462 GB | 73.245 GB | 1.685x | +| GPU memory write | 1.633 GB | 8.135 GB | 4.981x | +| LSC byte read | 9653.807 GB | 13389.615 GB | 1.387x | +| LSC byte write | 72.008 MB | 2333.083 GB | 32400.183x | +| SLM read/write | 0 / 0 | 0 / 0 | n/a | + +VectorEngineStalls 聚合： + +| stall metric | K32 | K64 | delta | +|---|---:|---:|---:| +| SBID | 31.449% | 55.730% | +24.281 pp | +| ALU write | 29.562% | 16.532% | -13.030 pp | +| instruction fetch | 12.329% | 3.638% | -8.691 pp | +| pipe stall | 4.748% | 0.891% | -3.857 pp | +| control | 4.316% | 1.310% | -3.006 pp | +| barrier | 0.240% | 0.531% | +0.291 pp | + +## 解读 + +该 A/B 确认 K64 的已选中 block microtile 是 BF16 稀疏 SDPA 的主要性能问题。这个结果并不能单独证明 +存在 register spill；unitrace 也没有直接给出 GRF spill 计数器。但它明确显示 K64 被 memory/SEND +scoreboard 限制：SBID stall 从 31.4% 升到 55.7%，XVE active 从 64.7% 降到 39.9%，SEND 指令增加 +4.4x，memory write 增加约 5.0x。SLM 流量仍为 0，因此这些指标没有显示 SLM-backed spill。巨大的 LSC +write 差异是这里能采到的最佳 spill/pressure proxy，符合 K64 带来更高 scratch/memory 压力的判断。 + +建议：BF16 稀疏 SDPA 的已选中 block 路径保持 K32；除非后续 compiler/offline assembly 数据证明可以 +用其它方式消除 write/SBID 压力，否则 K64 应视为性能回退。 diff --git a/auto_round_extension/ark/benchmarks/results/UNITRACE_USAGE_GUIDE_20260814.md b/auto_round_extension/ark/benchmarks/results/UNITRACE_USAGE_GUIDE_20260814.md new file mode 100644 index 0000000000..b813717b0c --- /dev/null +++ b/auto_round_extension/ark/benchmarks/results/UNITRACE_USAGE_GUIDE_20260814.md @@ -0,0 +1,294 @@ +# Unitrace Usage Guide for Intel GPU Kernels — 2026-08-14 + +This is a general unitrace workflow for profiling SYCL/Level Zero kernels on Intel GPUs. It applies +to sparse SDPA and most other GPU kernels. + +## 1. Environment + +Use the project venv and source oneAPI before profiling: + +```bash +source /opt/intel/oneapi/setvars.sh --force +export ZE_AFFINITY_MASK=5 +export ZE_FLAT_DEVICE_HIERARCHY=FLAT +``` + +`ZE_AFFINITY_MASK` chooses the GPU. `ZE_FLAT_DEVICE_HIERARCHY=FLAT` is recommended for hardware +metric profiling. + +Useful checks: + +```bash +unitrace --device-list +unitrace --metric-list +``` + +## 2. Find Hot Kernels + +Start with device timing. This has lower overhead than full metric collection and identifies which +kernels matter. + +```bash +unitrace \ + --device-timing --verbose \ + --output timing \ + +``` + +Use `--include-kernels` once you know a stable kernel-name substring: + +```bash +unitrace \ + --device-timing --verbose \ + --include-kernels "" \ + --output timing_filtered \ + +``` + +Important: the command examples do not use `--` before the application command. + +## 3. Kernel Properties: Spill, Private Memory, SLM, Registers + +The key mode for spill information is: + +```bash +unitrace \ + --device-timing --verbose \ + --include-kernels "" \ + --output kernel_props \ + +``` + +Look for the `=== Kernel Properties ===` table: + +```text +Kernel, Compiled, SIMD, Number of Arguments, SLM Per Work Group, +Private Memory Per Thread, Spill Memory Per Thread, Register File Size Per Thread +``` + +Key fields: + +| field | meaning | +|---|---| +| `SLM Per Work Group` | Shared local memory allocated per workgroup. | +| `Private Memory Per Thread` | Per-thread private memory allocation. | +| `Spill Memory Per Thread` | Compiler-reported spill/scratch memory per thread. This is the direct spill signal. | +| `Register File Size Per Thread` | Register file allocation reported by runtime/compiler metadata. | + +Example interpretation: + +```text +Spill Memory Per Thread = 0 -> no compiler-reported spill +Spill Memory Per Thread = 3840 -> compiler-reported private/scratch spill +``` + +SLM counters being zero does not rule out spill. Spill can be private/scratch memory and appear as +LSC/GPU memory traffic, not SLM traffic. + +## 4. Compute Overview: ComputeBasic + +Use `ComputeBasic` for the first hardware-counter pass. + +```bash +unitrace \ + --device-timing --kernel-submission --metric-query --group ComputeBasic \ + --include-kernels "" \ + --output compute_basic \ + +``` + +Key metrics: + +| metric | what it tells you | +|---|---| +| `GpuTime[ns]` | GPU time under the profiling pass. Use ratios more than absolute time. | +| `XVE_ACTIVE[%]` | Whether execution units are actively doing work. | +| `XVE_STALL[%]` | Time with threads loaded but no execution pipe active. | +| `XVE_THREADS_OCCUPANCY_ALL[%]` | Thread-slot occupancy. Low occupancy can indicate resource pressure. | +| `XVE_INST_EXECUTED_SEND_ALL` | SEND instruction pressure, usually memory/message traffic. | +| `GPU_MEMORY_BYTE_READ/WRITE` | Device-memory bytes. | +| `LOAD_STORE_CACHE_BYTE_READ/WRITE` | LSC traffic, useful as scratch/spill pressure proxy when spill is reported. | +| `L3_READ/WRITE/MISS/HIT` | L3 behavior and locality. | +| `SLM_BYTE_READ/WRITE` | SLM traffic only, not private/scratch spill traffic. | + +Use this pass to classify the broad bottleneck: compute active, memory/SEND pressure, occupancy, or +cache behavior. + +## 5. Stall Breakdown: VectorEngineStalls + +Use this when `XVE_STALL[%]` is high. + +```bash +unitrace \ + --device-timing --kernel-submission --metric-query --group VectorEngineStalls \ + --include-kernels "" \ + --output vector_stalls \ + +``` + +Common stall meanings: + +| metric | likely meaning | +|---|---| +| `XVE_STALL_SBID[%]` | Scoreboard waits, often memory latency or dependent SEND results. | +| `XVE_STALL_ALUWR[%]` | ALU writeback/dependency pressure. | +| `XVE_STALL_INSTFETCH[%]` | Instruction fetch/code-size pressure. | +| `XVE_STALL_PIPESTALL[%]` | Structural hazards, often register pressure, accumulator hazards, or SEND holds. | +| `XVE_STALL_BARRIER[%]` | Synchronization/load imbalance. | +| `XVE_STALL_CONTROL[%]` | Control-flow overhead. | + +High SBID plus high SEND/LSC/memory traffic usually points to memory-message latency or scratch/spill +pressure. Confirm spill with the kernel properties pass. + +## 6. Memory Deep Dive: MemoryProfile + +Use this when ComputeBasic suggests memory/SEND pressure. + +```bash +unitrace \ + --device-timing --kernel-submission --metric-query --group MemoryProfile \ + --include-kernels "" \ + --output memory_profile \ + +``` + +Useful metrics: + +| metric | what it tells you | +|---|---| +| `GPU_MEMORY_BYTE_READ/WRITE` | DRAM/device-memory traffic. | +| `LOAD_STORE_CACHE_BYTE_READ/WRITE` | LSC traffic. | +| `XVE_LOAD_STORE_CACHE_READ_MESSAGE_COUNT` | LSC read message count. | +| `XVE_LOAD_STORE_CACHE_WRITE_MESSAGE_COUNT` | LSC write message count. | +| `XVE_LOAD_STORE_CACHE_REGISTER_REQUEST_COUNT` | Payload/register transactions sent to LSC. | +| `XVE_LOAD_STORE_CACHE_REGISTER_RESPONSE_COUNT` | Payload/register responses from LSC. | +| `GPU_MEMORY_REQUEST_QUEUE_FULL[%]` | Memory request queue pressure. | +| `L3_BUSY[%]`, `L3_STALL[%]`, `L3_HIT`, `L3_MISS` | L3 saturation and locality. | + +MemoryProfile still does not label individual messages as spill/fill. Combine it with +`Spill Memory Per Thread` from the kernel properties pass. + +## 7. Timeline and Submission + +Use kernel submission timing to separate launch/queue overhead from device execution: + +```bash +unitrace \ + --device-timing --kernel-submission \ + --include-kernels "" \ + --output submission \ + +``` + +Use Chrome trace when host/device sequencing matters: + +```bash +unitrace \ + --chrome-kernel-logging --chrome-sycl-logging --chrome-device-logging \ + --output trace \ + +``` + +Open the generated JSON in Perfetto. + +## 8. Instruction-Level Stall Sampling + +For instruction-level stall analysis, unitrace supports `--stall-sampling`, but it is only useful +when shader/debug dumps are available. + +Typical flow: + +```bash +# Build or run with shader dumps/debug info enabled where supported. +export IGC_ShaderDumpEnable=1 +export IGC_DumpToCustomDir=./shader_dump + +unitrace \ + --stall-sampling \ + --include-kernels "" \ + --output stall_sampling \ + +``` + +This is the path to correlate stalls to instruction regions. For spill proof, kernel properties are +usually simpler; assembly dumps are useful when you need to inspect actual scratch send +instructions. + +## 9. Output Files + +`--output-dir-path` may not always place every output where expected with some unitrace builds. +If files appear in the current directory, move them after the run: + +```bash +mkdir -p +find . -maxdepth 1 -type f -name '*' -exec mv -t {} + +``` + +Common output patterns: + +```text +. timing/log output +.metrics. metric-query CSV-style output +.json Chrome trace, when enabled +``` + +## 10. Practical Analysis Pattern + +Use this order for most kernels: + +1. `--device-timing --verbose`: identify hot kernels and check spill/private/SLM/register properties. +2. `--metric-query --group ComputeBasic`: broad compute, occupancy, SEND, memory and L3 counters. +3. `--metric-query --group VectorEngineStalls`: dominant stall reason. +4. `--metric-query --group MemoryProfile`: deeper memory/LSC/L3 analysis if SEND or memory pressure is high. +5. Chrome trace or stall sampling only if timeline or instruction-level evidence is needed. + +Minimal command set: + +```bash +# Properties and spill +unitrace --device-timing --verbose --include-kernels "" --output props + +# Broad counters +unitrace --device-timing --kernel-submission --metric-query --group ComputeBasic \ + --include-kernels "" --output basic + +# Stall reason +unitrace --device-timing --kernel-submission --metric-query --group VectorEngineStalls \ + --include-kernels "" --output stalls + +# Memory detail +unitrace --device-timing --kernel-submission --metric-query --group MemoryProfile \ + --include-kernels "" --output memory +``` + +## 11. Reading Results Safely + +- Metric profiling adds overhead. Use ratios, percentages, and relative A/B comparisons more than + profiled wall time. +- Run the same benchmark shape for A/B comparisons. +- Use `--include-kernels` to reduce unrelated noise. +- The benchmark may call the target kernel multiple times even with `--iters 1`; aggregate rows by + kernel name or compare matching rows consistently. +- `Spill Memory Per Thread` is the direct unitrace spill field. `LSC` and `GPU_MEMORY` counters are + runtime symptoms, not direct proof by themselves. +- `SLM_BYTE_READ/WRITE = 0` only means no SLM traffic. It does not rule out private/scratch spill. + +## 12. Sparse SDPA Example + +For the BF16 sparse SDPA K32/K64 A/B, the important result came from combining: + +```text +unitrace --device-timing --verbose +unitrace --metric-query --group ComputeBasic +unitrace --metric-query --group VectorEngineStalls +``` + +Observed: + +| variant | spill/thread | XVE active | XVE stall | SBID stall | SEND instructions | GPU memory write | +|---|---:|---:|---:|---:|---:|---:| +| K32 | 0 B | 64.7% | 35.1% | 31.4% | 18.6G | 1.63 GB | +| K64 | 3840 B | 39.9% | 59.9% | 55.7% | 81.6G | 8.14 GB | + +Conclusion: K64 has compiler-reported private/scratch spill and much higher SEND/SBID and memory +pressure. K32 avoids the spill and is the better selected-block microtile for this kernel. diff --git a/auto_round_extension/ark/benchmarks/results/ab_bf16_sdpa_k32_20260814_092404.csv b/auto_round_extension/ark/benchmarks/results/ab_bf16_sdpa_k32_20260814_092404.csv new file mode 100644 index 0000000000..feaa6ee7fe --- /dev/null +++ b/auto_round_extension/ark/benchmarks/results/ab_bf16_sdpa_k32_20260814_092404.csv @@ -0,0 +1,16 @@ +mode,batch,num_heads_q,num_heads_kv,gqa_group_size,attention_pattern,seq_len,tensor_layout,head_dim,dtype,causal,requested_topk,selected_ratio,selected_blocks_per_row,warmup,iters,latency_ms,status,note,speedup_vs_torch,speedup_vs_sagev1,speedup_vs_ark,baseline_tflops,effective_tflops +dense_torch_sdpa,1,40,40,1,mha,75000,HND,128,bfloat16,False,,,,1,2,2198.8499785074964,ok,,1.0,0.5424874441849272,0.6248773510755263,52.39102309207733,52.39102309207733 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cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, 4, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, void, void, void, void, void>, cutlass::fmha::collective::SparseFMHAFwdEpilogue, false, false, false, false, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, 4, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, void, void, void, void, void>, cute::tuple, cute::C<128> >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, void>, cutlass::fmha::kernel::XeFHMAIndividualTileScheduler>[SIMD16 {1; 293; 40} {256; 1; 1}]",13,0,842606440,2025281475,2403,0,99.999992,0,0,11720,0,39767077175,10940,64.705299,62135142400,84126985600,9296625280,152226166576,0.384969,35.176994,99.842812,113099407360,15.795247,750080,0,0,0,4826988181696,36003840,103929039779,101765613576,0,1814872248,343078599,2139381298,24764234,0.001012,2992412,2992412,22036784640,815173120,26.153116,0.967442,87156825,3276645,0.000000,48126,187520,0.005092,100.000008,1,0.000000,1,0.000000,0,3.391425,5.848985,19.174847,25.961508,69.804741,654237,21,3,17,602325112931510,2400,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 - -"cutlass::fmha::kernel::XeSparseSageFwdKernel, cutlass::fmha::collective::SPARSESDPAFwdMainloop, false, false, false, false, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, 4, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, void, void, void, void, void>, cutlass::fmha::collective::SparseFMHAFwdEpilogue, false, false, false, false, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, 4, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, void, void, void, void, void>, cute::tuple, cute::C<128> >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, void>, cutlass::fmha::kernel::XeFHMAIndividualTileScheduler>[SIMD16 {1; 293; 40} {256; 1; 1}]",19,0,842014472,2023946209,2403,0,100.000008,0,0,11720,0,39770894776,10940,64.731178,62135142400,84126985600,9296625280,152226167064,0.372979,35.120743,99.812370,113099407360,15.818870,750080,0,0,0,4826818373952,36003840,103926386533,101781278068,0,1798988877,340708340,2121071765,24764324,0.001402,3058609,3001843,21425017856,817649152,25.444952,0.971063,84775206,3340930,0.000000,48180,187520,0.005096,100.000008,1,0.446112,1,0.000000,0,3.396352,5.853045,19.187498,25.978636,69.850800,650048,36,4,18,602326139770625,2400,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 diff --git a/auto_round_extension/ark/benchmarks/results/unitrace_k32_stalls_20260814.csv b/auto_round_extension/ark/benchmarks/results/unitrace_k32_stalls_20260814.csv deleted file mode 100644 index 0f65211098..0000000000 --- a/auto_round_extension/ark/benchmarks/results/unitrace_k32_stalls_20260814.csv +++ /dev/null @@ -1,8 +0,0 @@ -mode,batch,num_heads_q,num_heads_kv,gqa_group_size,attention_pattern,seq_len,tensor_layout,head_dim,dtype,causal,requested_topk,selected_ratio,selected_blocks_per_row,warmup,iters,latency_ms,status,note,speedup_vs_torch,speedup_vs_sagev1,speedup_vs_ark,baseline_tflops,effective_tflops -dense_torch_sdpa,1,40,40,1,mha,75000,HND,128,bfloat16,False,,,,0,1,3050.2180999610573,ok,,1.0,3.1489193746993007,0.8734807393059724,37.767791097125404,37.767791097125404 -dense_sagev1,1,40,40,1,mha,75000,HND,128,bfloat16,False,,,,0,1,9604.890872025862,ok,,0.31756926139002617,1.0,0.27739063321981167,11.993889523046922,11.993889523046922 -dense_ark_sdpa,1,40,40,1,mha,75000,HND,128,bfloat16,False,,,,0,1,2664.3067609984428,ok,,1.1448449347544332,3.6050243960745916,1.0,43.2382643344076,43.2382643344076 -sparse_qtile256_row64k_kernel_only,1,40,40,1,mha,75000,HND,128,bfloat16,False,0.5,0.5025568148726252,588.9965870307167,0,1,28501.629570964724,ok,,0.1070190773606989,0.3369944461635481,0.09347910281286634,4.04187415716598,2.031271402541311 -sparse_qtile256_row64k_e2e,1,40,40,1,mha,75000,HND,128,bfloat16,False,0.5,0.5025568148726252,588.9965870307167,0,1,803.9296750212088,ok,,3.794135475693926,11.947426709646551,3.3141042603361477,143.29611604020073,72.01443966078138 -sparse_sdpa_bf16_kernel_only,1,40,40,1,mha,75000,HND,128,bfloat16,False,0.5,0.5025568148726252,588.9965870307167,0,1,25396.277436986566,ok,,0.12010492906014597,0.37820073811437876,0.10490934222974768,4.5360978704786605,2.2796468977382536 -sparse_sdpa_bf16_e2e,1,40,40,1,mha,75000,HND,128,bfloat16,False,0.5,0.5025568148726252,588.9965870307167,0,1,1023.8845960702747,ok,,2.979064351263767,9.38083345417048,2.6021553319819426,112.51268008342339,56.544014135507915 diff --git a/auto_round_extension/ark/benchmarks/results/unitrace_k32_stalls_20260814/k32_stalls.metrics.2892853 b/auto_round_extension/ark/benchmarks/results/unitrace_k32_stalls_20260814/k32_stalls.metrics.2892853 deleted file mode 100644 index e4feee2459..0000000000 --- a/auto_round_extension/ark/benchmarks/results/unitrace_k32_stalls_20260814/k32_stalls.metrics.2892853 +++ /dev/null @@ -1,7 +0,0 @@ - -=== Device #0 Metrics === - -Kernel,GlobalInstanceId,SubDeviceId,GpuTime[ns],GpuCoreClocks[cycles],AvgGpuCoreFrequencyMHz[MHz],ResultUncertainty[%],GPU_BUSY[%],IA_VERTEX[events],GPGPU_THREADGROUP_COUNT[events],ASYNC_GPGPU_THREADGROUP_COUNT[events],RASTERIZER_SAMPLE_OUTPUT[events],XVE_STALL_ALUWR[%],XVE_STALL_BARRIER[%],XVE_STALL_CONTROL[%],XVE_STALL_INSTFETCH[%],XVE_STALL_OTHER[%],XVE_STALL_PIPESTALL[%],XVE_STALL_PS_DEPENDENCY[%],XVE_STALL_SBID[%],XVE_STALL_SENDWR[%],THREAD_DISPATCH_QUEUE0_ACTIVE[%],THREAD_DISPATCH_QUEUE0_STALL[%],THREAD_DISPATCH_QUEUE1_ACTIVE[%],THREAD_DISPATCH_QUEUE1_STALL[%],THREADGROUP_DISPATCH_QUEUE0_RESOURCE_STALL[%],THREADGROUP_DISPATCH_QUEUE1_RESOURCE_STALL[%],XVE_ACTIVE[%],XVE_SHARED_FUNCTION_ACCESS_HOLD[%],XVE_STALL[%],XVE_THREADS_OCCUPANCY_ALL[%],XVE_MULTIPLE_PIPE_ACTIVE[%],XVE_PIPE_ALU0_AND_ALU1_ACTIVE[%],XVE_PIPE_ALU0_AND_ALU2_ACTIVE[%],THREADGROUP_DISPATCH_QUEUE0_INPUT_AVAILABLE[%],THREADGROUP_DISPATCH_QUEUE1_INPUT_AVAILABLE[%],QueryBeginTime[ns],CoreFrequencyMHz[MHz],CoreFrequencyChanged,QuerySplitOccurred,ReportId,ReportsCount,OverrunOccured,MidQueryTimer,MidQueryProgramming,MidQueryMarker,MidQueryCtxSwitch,MidQueryC6,MidQueryFreqChange,MidQueryMmioTrigger,ReportError,ReportLost,ReportInconsistent,ReportCtxSwitchLost,ReportWithoutWorkload,ReportContextMismatch,ReportQueryModeMismatch -"cutlass::fmha::kernel::XeSparseSageFwdKernel, cutlass::fmha::collective::SPARSESDPAFwdMainloop, false, false, false, false, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, 4, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, void, void, void, void, void>, cutlass::fmha::collective::SparseFMHAFwdEpilogue, false, false, false, false, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, 4, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, void, void, void, void, void>, cute::tuple, cute::C<128> >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, void>, cutlass::fmha::kernel::XeFHMAIndividualTileScheduler>[SIMD16 {1; 293; 40} {256; 1; 1}]",13,0,841596288,2022810523,2403,0,100.000000,0,0,11720,0,29.554045,0.240841,4.315089,12.326859,0.000001,4.746475,0.000000,31.465960,0.000076,0.000000,0.000000,0.005099,0.000000,0.000000,0.000005,64.755737,0.370239,35.093006,99.824524,15.837934,3.402867,5.854964,0.000000,0.000063,602413782604062,2400,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 - -"cutlass::fmha::kernel::XeSparseSageFwdKernel, cutlass::fmha::collective::SPARSESDPAFwdMainloop, false, false, false, false, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, 4, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, void, void, void, void, void>, cutlass::fmha::collective::SparseFMHAFwdEpilogue, false, false, false, false, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, 4, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, void, void, void, void, void>, cute::tuple, cute::C<128> >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, void>, cutlass::fmha::kernel::XeFHMAIndividualTileScheduler>[SIMD16 {1; 293; 40} {256; 1; 1}]",19,0,840939216,2021361718,2403,0,99.999992,0,0,11720,0,29.570486,0.239372,4.317172,12.330883,0.000001,4.749287,0.000000,31.431385,0.000075,0.000000,0.000000,0.005102,0.000000,0.000000,0.000005,64.799057,0.367478,35.085705,99.860268,15.851206,3.403399,5.862384,0.000000,0.000064,602414807449687,2400,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 diff --git a/auto_round_extension/ark/benchmarks/results/unitrace_k32_verbose_20260814.csv b/auto_round_extension/ark/benchmarks/results/unitrace_k32_verbose_20260814.csv deleted file mode 100644 index 93451c8eb2..0000000000 --- a/auto_round_extension/ark/benchmarks/results/unitrace_k32_verbose_20260814.csv +++ /dev/null @@ -1,8 +0,0 @@ -mode,batch,num_heads_q,num_heads_kv,gqa_group_size,attention_pattern,seq_len,tensor_layout,head_dim,dtype,causal,requested_topk,selected_ratio,selected_blocks_per_row,warmup,iters,latency_ms,status,note,speedup_vs_torch,speedup_vs_sagev1,speedup_vs_ark,baseline_tflops,effective_tflops -dense_torch_sdpa,1,40,40,1,mha,75000,HND,128,bfloat16,False,,,,0,1,3051.9098619697616,ok,,1.0,3.1640525778102258,0.875786425847173,37.74685531690234,37.74685531690234 -dense_sagev1,1,40,40,1,mha,75000,HND,128,bfloat16,False,,,,0,1,9656.403266009875,ok,,0.316050373819034,1.0,0.2767926272746347,11.929907733399977,11.929907733399977 -dense_ark_sdpa,1,40,40,1,mha,75000,HND,128,bfloat16,False,,,,0,1,2672.8212300222367,ok,,1.1418308967653519,3.6128129923337737,1.0,43.1005256565706,43.1005256565706 -sparse_qtile256_row64k_kernel_only,1,40,40,1,mha,75000,HND,128,bfloat16,False,0.5,0.5025568148726252,588.9965870307167,0,1,28692.606869037263,ok,,0.10636572256747906,0.33654673868027946,0.09315365600002448,4.0149715404324064,2.017751309163948 -sparse_qtile256_row64k_e2e,1,40,40,1,mha,75000,HND,128,bfloat16,False,0.5,0.5025568148726252,588.9965870307167,0,1,804.5742539688945,ok,,3.7931985107837556,12.001879526191251,3.3220317662881245,143.18131537484388,71.95674580405438 -sparse_sdpa_bf16_kernel_only,1,40,40,1,mha,75000,HND,128,bfloat16,False,0.5,0.5025568148726252,588.9965870307167,0,1,25407.54995297175,ok,,0.12011822736228844,0.38006038692764355,0.1051979130207167,4.534085349167082,2.2786354914380436 -sparse_sdpa_bf16_e2e,1,40,40,1,mha,75000,HND,128,bfloat16,False,0.5,0.5025568148726252,588.9965870307167,0,1,1022.452419041656,ok,,2.9848918200323835,9.444354657658119,2.614127738606624,112.6702796673677,56.62321688044021 diff --git a/auto_round_extension/ark/benchmarks/results/unitrace_k32_verbose_20260814/k32_verbose.3726840 b/auto_round_extension/ark/benchmarks/results/unitrace_k32_verbose_20260814/k32_verbose.3726840 deleted file mode 100644 index c8b9a16ec4..0000000000 --- a/auto_round_extension/ark/benchmarks/results/unitrace_k32_verbose_20260814/k32_verbose.3726840 +++ /dev/null @@ -1,19 +0,0 @@ - -=== Device Timing Summary === - - Total Execution Time (ns): 79051466957 - Total Device Time for L0 backend (ns): 1686731870 - -== L0 Backend == - - Kernel, Calls, Time (ns), Time (%), Average (ns), Min (ns), Max (ns) -"cutlass::fmha::kernel::XeSparseSageFwdKernel, cutlass::fmha::collective::SPARSESDPAFwdMainloop, false, false, false, false, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, 4, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, void, void, void, void, void>, cutlass::fmha::collective::SparseFMHAFwdEpilogue, false, false, false, false, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, 4, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, void, void, void, void, void>, cute::tuple, cute::C<128> >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, void>, cutlass::fmha::kernel::XeFHMAIndividualTileScheduler>[SIMD16 {1; 293; 40} {256; 1; 1}]", 2, 1685721458, 99.940094, 842860729, 842572604, 843148854 - "zeCommandListAppendMemoryCopy(D2D)[54943360]", 6, 991873, 0.058804, 165312, 58541, 278020 - "zeCommandListAppendMemoryCopy(D2M)[1]", 8, 11821, 0.000701, 1477, 1197, 2604 - "zeCommandListAppendMemoryCopy(D2M)[8]", 3, 6718, 0.000398, 2239, 1510, 2656 - - -=== Kernel Properties === - - Kernel, Compiled, SIMD, Number of Arguments, SLM Per Work Group, Private Memory Per Thread, Spill Memory Per Thread, Register File Size Per Thread -"cutlass::fmha::kernel::XeSparseSageFwdKernel, cutlass::fmha::collective::SPARSESDPAFwdMainloop, false, false, false, false, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, 4, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, void, void, void, void, void>, cutlass::fmha::collective::SparseFMHAFwdEpilogue, false, false, false, false, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, 4, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, void, void, void, void, void>, cute::tuple, cute::C<128> >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, void>, cutlass::fmha::kernel::XeFHMAIndividualTileScheduler>[SIMD16 {1; 293; 40} {256; 1; 1}]", JIT, 16, 2, 0, 2432, 0, 256 diff --git a/auto_round_extension/ark/benchmarks/results/unitrace_k64_compute_real_20260814.csv b/auto_round_extension/ark/benchmarks/results/unitrace_k64_compute_real_20260814.csv deleted file mode 100644 index c98ac6ac5a..0000000000 --- a/auto_round_extension/ark/benchmarks/results/unitrace_k64_compute_real_20260814.csv +++ /dev/null @@ -1,8 +0,0 @@ -mode,batch,num_heads_q,num_heads_kv,gqa_group_size,attention_pattern,seq_len,tensor_layout,head_dim,dtype,causal,requested_topk,selected_ratio,selected_blocks_per_row,warmup,iters,latency_ms,status,note,speedup_vs_torch,speedup_vs_sagev1,speedup_vs_ark,baseline_tflops,effective_tflops -dense_torch_sdpa,1,40,40,1,mha,75000,HND,128,bfloat16,False,,,,0,1,3052.225488005206,ok,,1.0,3.129175918210046,0.8672042433799233,37.74295197151027,37.74295197151027 -dense_sagev1,1,40,40,1,mha,75000,HND,128,bfloat16,False,,,,0,1,9550.950494012795,ok,,0.31957295663070967,1.0,0.2771350240596196,12.06162675350641,12.06162675350641 -dense_ark_sdpa,1,40,40,1,mha,75000,HND,128,bfloat16,False,,,,0,1,2646.902894950472,ok,,1.1531308888693925,3.6083494079942477,1.0,43.522563755462436,43.522563755462436 -sparse_qtile256_row64k_kernel_only,1,40,40,1,mha,75000,HND,128,bfloat16,False,0.5,0.5025568148726252,588.9965870307167,0,1,28671.261675073765,ok,,0.1064559182150938,0.3331192956296097,0.09231902400903541,4.017960608275311,2.019253485578516 -sparse_qtile256_row64k_e2e,1,40,40,1,mha,75000,HND,128,bfloat16,False,0.5,0.5025568148726252,588.9965870307167,0,1,804.9391260137782,ok,,3.79187119791337,11.865432037464796,3.2883267931805875,143.11641250499747,71.92412842450827 -sparse_sdpa_bf16_kernel_only,1,40,40,1,mha,75000,HND,128,bfloat16,False,0.5,0.5025568148726252,588.9965870307167,0,1,30556.331855943426,ok,,0.09988847818497318,0.31256862044306755,0.08662371214677164,3.7700860346426945,1.8946824293657984 -sparse_sdpa_bf16_e2e,1,40,40,1,mha,75000,HND,128,bfloat16,False,0.5,0.5025568148726252,588.9965870307167,0,1,1721.1301509523764,ok,,1.7733844743329124,5.54923199081013,1.5378865412855762,66.9327650417691,33.63751721000927 diff --git a/auto_round_extension/ark/benchmarks/results/unitrace_k64_compute_real_20260814/k64_compute_real.2948755 b/auto_round_extension/ark/benchmarks/results/unitrace_k64_compute_real_20260814/k64_compute_real.2948755 deleted file mode 100644 index aca4758a37..0000000000 --- a/auto_round_extension/ark/benchmarks/results/unitrace_k64_compute_real_20260814/k64_compute_real.2948755 +++ /dev/null @@ -1,32 +0,0 @@ - -=== Device Timing Summary === - - Total Execution Time (ns): 84973767741 - Total Device Time for L0 backend (ns): 3078355254 - -== L0 Backend == - - Kernel, Calls, Time (ns), Time (%), Average (ns), Min (ns), Max (ns) -"cutlass::fmha::kernel::XeSparseSageFwdKernel, cutlass::fmha::collective::SPARSESDPAFwdMainloop, false, false, false, false, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, 4, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, void, void, void, void, void>, cutlass::fmha::collective::SparseFMHAFwdEpilogue, false, false, false, false, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, 4, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, void, void, void, void, void>, cute::tuple, cute::C<128> >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, void>, cutlass::fmha::kernel::XeFHMAIndividualTileScheduler>", 2, 3077343124, 99.967125, 1538671562, 1537868437, 1539474687 - "zeCommandListAppendMemoryCopy(D2D)", 6, 993123, 0.032261, 165520, 58437, 278125 - "zeCommandListAppendMemoryCopy(D2M)", 11, 19007, 0.000617, 1727, 1197, 2760 - - -=== Kernel Properties === - - Kernel, Compiled, SIMD, Number of Arguments, SLM Per Work Group, Private Memory Per Thread, Spill Memory Per Thread, Register File Size Per Thread -"cutlass::fmha::kernel::XeSparseSageFwdKernel, cutlass::fmha::collective::SPARSESDPAFwdMainloop, false, false, false, false, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, 4, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, void, void, void, void, void>, cutlass::fmha::collective::SparseFMHAFwdEpilogue, false, false, false, false, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, 4, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, void, void, void, void, void>, cute::tuple, cute::C<128> >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, void>, cutlass::fmha::kernel::XeFHMAIndividualTileScheduler>", JIT, 16, 2, 0, 2432, 3840, 256 - - - -=== Kernel Submission Summary === - - Total Execution Time (ns): 84973767741 - Total Device Time for L0 backend (ns): 3078355254 - -== L0 Backend == - - Kernel, Calls, Append (ns), Append (%), Submit (ns), Submit (%), Execute (ns), Execute (%) -"cutlass::fmha::kernel::XeSparseSageFwdKernel, cutlass::fmha::collective::SPARSESDPAFwdMainloop, false, false, false, false, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, 4, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, void, void, void, void, void>, cutlass::fmha::collective::SparseFMHAFwdEpilogue, false, false, false, false, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, 4, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, void, void, void, void, void>, cute::tuple, cute::C<128> >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, void>, cutlass::fmha::kernel::XeFHMAIndividualTileScheduler>", 2, 0, -nan, 317707, 0.033428, 3077343124, 99.967125 - "zeCommandListAppendMemoryCopy(D2D)", 6, 0, -nan, 611043591, 64.292694, 993123, 0.032261 - "zeCommandListAppendMemoryCopy(D2M)", 11, 0, -nan, 339047807, 35.673882, 19007, 0.000617 diff --git a/auto_round_extension/ark/benchmarks/results/unitrace_k64_compute_real_20260814/k64_compute_real.metrics.2948755 b/auto_round_extension/ark/benchmarks/results/unitrace_k64_compute_real_20260814/k64_compute_real.metrics.2948755 deleted file mode 100644 index 76557fbf99..0000000000 --- a/auto_round_extension/ark/benchmarks/results/unitrace_k64_compute_real_20260814/k64_compute_real.metrics.2948755 +++ /dev/null @@ -1,7 +0,0 @@ - -=== Device #0 Metrics === - -Kernel,GlobalInstanceId,SubDeviceId,GpuTime[ns],GpuCoreClocks[cycles],AvgGpuCoreFrequencyMHz[MHz],ResultUncertainty[%],GPU_BUSY[%],IA_VERTEX[events],GPGPU_THREADGROUP_COUNT[events],ASYNC_GPGPU_THREADGROUP_COUNT[events],RASTERIZER_SAMPLE_OUTPUT[events],ICACHE_HIT[events],ICACHE_MISS[events],XVE_ACTIVE[%],XVE_INST_EXECUTED_ALU0_ALL[events],XVE_INST_EXECUTED_ALU1_ALL[events],XVE_INST_EXECUTED_SEND_ALL[events],XVE_INST_ISSUED_ALL[events],XVE_SHARED_FUNCTION_ACCESS_HOLD[%],XVE_STALL[%],XVE_THREADS_OCCUPANCY_ALL[%],XVE_INST_EXECUTED_ALU2_ALL[events],XVE_MULTIPLE_PIPE_ACTIVE[%],LOAD_STORE_CACHE_PARTIAL_WRITE_COUNT[events],SLM_BANK_CONFLICT_COUNT[events],SLM_BYTE_READ[bytes],SLM_BYTE_WRITE[bytes],LOAD_STORE_CACHE_BYTE_READ[bytes],LOAD_STORE_CACHE_BYTE_WRITE[bytes],LOAD_STORE_CACHE_ACCESS[events],LOAD_STORE_CACHE_HIT[events],L3_ATOMIC_ACCESS[events],L3_HIT[events],L3_MISS[events],L3_READ[events],L3_WRITE[events],L3_STALL[%],COMPRESSOR_INPUT[events],COMPRESSOR_OUTPUT[events],GPU_MEMORY_BYTE_READ[bytes],GPU_MEMORY_BYTE_WRITE[bytes],GPU_MEMORY_BYTE_READ_RATE[GBpS],GPU_MEMORY_BYTE_WRITE_RATE[GBpS],GPU_MEMORY_L3_READ[events],GPU_MEMORY_L3_WRITE[events],GPU_MEMORY_REQUEST_QUEUE_FULL[%],TLB_MISS[events],ASYNC_GPGPU_THREAD_EXIT_COUNT[messages],GPGPU_DISPATCH[%],COMMAND_PARSER_COMPUTE_ENGINE_BUSY[%],COMMAND_PARSER_COMPUTE_ENGINE_DISPATCH_KERNEL_COUNT[events],COMMAND_PARSER_COPY_ENGINE_BUSY[%],COMMAND_PARSER_FLUSH_COUNT[events],COMMAND_PARSER_RENDER_ENGINE_BUSY[%],COMMAND_PARSER_RENDER_ENGINE_DISPATCH_KERNEL_COUNT[events],XVE_PIPE_ALU0_AND_ALU1_ACTIVE[%],XVE_PIPE_ALU0_AND_ALU2_ACTIVE[%],XVE_INST_EXECUTED_ALU0_ALL_UTILIZATION[%],XVE_INST_EXECUTED_ALU1_ALL_UTILIZATION[%],XVE_INST_EXECUTED_ALU2_ALL_UTILIZATION[%],HOST_TO_GPUMEM_TRANSACTION_READ[events],HOST_TO_GPUMEM_TRANSACTION_WRITE[events],SYSMEM_TRANSACTION_READ[events],SYSMEM_TRANSACTION_WRITE[events],QueryBeginTime[ns],CoreFrequencyMHz[MHz],CoreFrequencyChanged,QuerySplitOccurred,ReportId,ReportsCount,OverrunOccured,MidQueryTimer,MidQueryProgramming,MidQueryMarker,MidQueryCtxSwitch,MidQueryC6,MidQueryFreqChange,MidQueryMmioTrigger,ReportError,ReportLost,ReportInconsistent,ReportCtxSwitchLost,ReportWithoutWorkload,ReportContextMismatch,ReportQueryModeMismatch -"cutlass::fmha::kernel::XeSparseSageFwdKernel, cutlass::fmha::collective::SPARSESDPAFwdMainloop, false, false, false, false, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, 4, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, void, void, void, void, void>, cutlass::fmha::collective::SparseFMHAFwdEpilogue, false, false, false, false, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, 4, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, void, void, void, void, void>, cute::tuple, cute::C<128> >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, void>, cutlass::fmha::kernel::XeFHMAIndividualTileScheduler>[SIMD16 {1; 293; 40} {256; 1; 1}]",13,0,1535408316,3690568189,2403,0,100.000000,0,0,11720,0,31072289842,14180,39.954498,48230240000,66374007680,40784238720,131183357152,19.292183,59.890968,99.816544,113099407360,4.077200,750080,0,0,0,6694842331072,1166541660160,151338661503,138265715260,0,14187776782,643149139,11119708210,3717068770,0.000555,2993279,2993279,37791607808,4113692672,24.613394,2.679218,150374771,17856300,0.000000,44749,187520,0.002795,100.000000,1,0.000000,1,0.000000,0,1.724919,1.626167,8.167821,11.240480,38.306911,1127998,21,3,17,603328285516718,2400,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 - -"cutlass::fmha::kernel::XeSparseSageFwdKernel, cutlass::fmha::collective::SPARSESDPAFwdMainloop, false, false, false, false, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, 4, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, void, void, void, void, void>, cutlass::fmha::collective::SparseFMHAFwdEpilogue, false, false, false, false, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, 4, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, void, void, void, void, void>, cute::tuple, cute::C<128> >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, void>, cutlass::fmha::kernel::XeFHMAIndividualTileScheduler>[SIMD16 {1; 293; 40} {256; 1; 1}]",19,0,1537011892,3694509142,2403,0,100.000008,0,0,11720,0,31047649164,14180,39.913635,48230240000,66374007680,40784238720,131183357448,19.328482,59.987267,99.869186,113099407360,4.067187,750080,0,0,0,6694772599744,1166541660160,151337571951,138324432787,0,14157620305,609852603,11089353225,3683883768,0.000838,3057878,3001145,35453591040,4021094400,23.066570,2.616177,141190751,17473185,0.000000,44461,187520,0.002792,100.000000,1,0.371961,1,0.000000,0,1.716085,1.622224,8.159108,11.228489,38.266045,1127154,37,4,27,603330005450781,2400,0,0,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0 diff --git a/auto_round_extension/ark/benchmarks/results/unitrace_k64_stalls_real_20260814.csv b/auto_round_extension/ark/benchmarks/results/unitrace_k64_stalls_real_20260814.csv deleted file mode 100644 index 3c384e960d..0000000000 --- a/auto_round_extension/ark/benchmarks/results/unitrace_k64_stalls_real_20260814.csv +++ /dev/null @@ -1,8 +0,0 @@ -mode,batch,num_heads_q,num_heads_kv,gqa_group_size,attention_pattern,seq_len,tensor_layout,head_dim,dtype,causal,requested_topk,selected_ratio,selected_blocks_per_row,warmup,iters,latency_ms,status,note,speedup_vs_torch,speedup_vs_sagev1,speedup_vs_ark,baseline_tflops,effective_tflops -dense_torch_sdpa,1,40,40,1,mha,75000,HND,128,bfloat16,False,,,,0,1,3054.134353995323,ok,,1.0,3.139249184469624,0.8705397637467008,37.7193622308393,37.7193622308393 -dense_sagev1,1,40,40,1,mha,75000,HND,128,bfloat16,False,,,,0,1,9587.68878004048,ok,,0.31854750650160635,1.0,0.27730827105200906,12.015408785464729,12.015408785464729 -dense_ark_sdpa,1,40,40,1,mha,75000,HND,128,bfloat16,False,,,,0,1,2658.7453989777714,ok,,1.1487126052647125,3.6060951092672258,1.0,43.328706857110824,43.328706857110824 -sparse_qtile256_row64k_kernel_only,1,40,40,1,mha,75000,HND,128,bfloat16,False,0.5,0.5025568148726252,588.9965870307167,0,1,28159.330382943153,ok,,0.1084590546885054,0.3404799989792369,0.09441791984472199,4.091006371010145,2.055963131438476 -sparse_qtile256_row64k_e2e,1,40,40,1,mha,75000,HND,128,bfloat16,False,0.5,0.5025568148726252,588.9965870307167,0,1,805.575530976057,ok,,3.7912451862767624,11.901663359143713,3.3004296887671893,143.00335048709903,71.86730833691017 -sparse_sdpa_bf16_kernel_only,1,40,40,1,mha,75000,HND,128,bfloat16,False,0.5,0.5025568148726252,588.9965870307167,0,1,30696.65548496414,ok,,0.09949404277906763,0.31233659265377395,0.08661352049509369,3.752851839394274,1.886021267094859 -sparse_sdpa_bf16_e2e,1,40,40,1,mha,75000,HND,128,bfloat16,False,0.5,0.5025568148726252,588.9965870307167,0,1,1720.7675460958853,ok,,1.7748674775536122,5.571751281451836,1.5450927145912243,66.94686929758075,33.644605399886125 diff --git a/auto_round_extension/ark/benchmarks/results/unitrace_k64_stalls_real_20260814/k64_stalls_real.metrics.2952213 b/auto_round_extension/ark/benchmarks/results/unitrace_k64_stalls_real_20260814/k64_stalls_real.metrics.2952213 deleted file mode 100644 index c48b429095..0000000000 --- a/auto_round_extension/ark/benchmarks/results/unitrace_k64_stalls_real_20260814/k64_stalls_real.metrics.2952213 +++ /dev/null @@ -1,7 +0,0 @@ - -=== Device #0 Metrics === - -Kernel,GlobalInstanceId,SubDeviceId,GpuTime[ns],GpuCoreClocks[cycles],AvgGpuCoreFrequencyMHz[MHz],ResultUncertainty[%],GPU_BUSY[%],IA_VERTEX[events],GPGPU_THREADGROUP_COUNT[events],ASYNC_GPGPU_THREADGROUP_COUNT[events],RASTERIZER_SAMPLE_OUTPUT[events],XVE_STALL_ALUWR[%],XVE_STALL_BARRIER[%],XVE_STALL_CONTROL[%],XVE_STALL_INSTFETCH[%],XVE_STALL_OTHER[%],XVE_STALL_PIPESTALL[%],XVE_STALL_PS_DEPENDENCY[%],XVE_STALL_SBID[%],XVE_STALL_SENDWR[%],THREAD_DISPATCH_QUEUE0_ACTIVE[%],THREAD_DISPATCH_QUEUE0_STALL[%],THREAD_DISPATCH_QUEUE1_ACTIVE[%],THREAD_DISPATCH_QUEUE1_STALL[%],THREADGROUP_DISPATCH_QUEUE0_RESOURCE_STALL[%],THREADGROUP_DISPATCH_QUEUE1_RESOURCE_STALL[%],XVE_ACTIVE[%],XVE_SHARED_FUNCTION_ACCESS_HOLD[%],XVE_STALL[%],XVE_THREADS_OCCUPANCY_ALL[%],XVE_MULTIPLE_PIPE_ACTIVE[%],XVE_PIPE_ALU0_AND_ALU1_ACTIVE[%],XVE_PIPE_ALU0_AND_ALU2_ACTIVE[%],THREADGROUP_DISPATCH_QUEUE0_INPUT_AVAILABLE[%],THREADGROUP_DISPATCH_QUEUE1_INPUT_AVAILABLE[%],QueryBeginTime[ns],CoreFrequencyMHz[MHz],CoreFrequencyChanged,QuerySplitOccurred,ReportId,ReportsCount,OverrunOccured,MidQueryTimer,MidQueryProgramming,MidQueryMarker,MidQueryCtxSwitch,MidQueryC6,MidQueryFreqChange,MidQueryMmioTrigger,ReportError,ReportLost,ReportInconsistent,ReportCtxSwitchLost,ReportWithoutWorkload,ReportContextMismatch,ReportQueryModeMismatch -"cutlass::fmha::kernel::XeSparseSageFwdKernel, cutlass::fmha::collective::SPARSESDPAFwdMainloop, false, false, false, false, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, 4, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, void, void, void, void, void>, cutlass::fmha::collective::SparseFMHAFwdEpilogue, false, false, false, false, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, 4, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, void, void, void, void, void>, cute::tuple, cute::C<128> >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, void>, cutlass::fmha::kernel::XeFHMAIndividualTileScheduler>[SIMD16 {1; 293; 40} {256; 1; 1}]",13,0,1534833404,3689186584,2403,0,100.000000,0,0,11720,0,16.455626,0.512216,1.308606,3.631864,0.000000,0.887532,0.000000,55.635029,3.059207,0.000000,0.000000,0.002796,0.000000,0.000000,0.000003,39.953323,19.185026,59.893566,99.811523,4.085039,1.716491,1.629522,0.000000,0.000035,603420934757447,2400,0,0,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0 - -"cutlass::fmha::kernel::XeSparseSageFwdKernel, cutlass::fmha::collective::SPARSESDPAFwdMainloop, false, false, false, false, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, 4, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, void, void, void, void, void>, cutlass::fmha::collective::SparseFMHAFwdEpilogue, false, false, false, false, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, 4, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, void, void, void, void, void>, cute::tuple, cute::C<128> >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, void>, cutlass::fmha::kernel::XeFHMAIndividualTileScheduler>[SIMD16 {1; 293; 40} {256; 1; 1}]",19,0,1536064296,3692231472,2403,0,100.000000,0,0,11720,0,16.608208,0.549755,1.310681,3.644871,0.000000,0.893470,0.000000,55.824959,3.042835,0.000000,0.000000,0.002793,0.000000,0.000000,0.000003,39.910118,19.170021,59.959267,99.842361,4.083643,1.704670,1.628649,0.000000,0.000035,603422654691614,2400,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 From b47dbe4384c9897b81e69c376ecd182242c62077 Mon Sep 17 00:00:00 2001 From: yiliu30 Date: Tue, 18 Aug 2026 14:25:42 +0800 Subject: [PATCH 36/42] fix Signed-off-by: yiliu30 --- .azure-pipelines/nightly-test-cpu.yml | 2 +- .../include/stla/xe_sparse_sage_fwd_kernel.hpp | 12 +++--------- .../wrapper/include/sycl_tla_s8_gemm.hpp | 2 +- 3 files changed, 5 insertions(+), 11 deletions(-) diff --git a/.azure-pipelines/nightly-test-cpu.yml b/.azure-pipelines/nightly-test-cpu.yml index 9485a59959..e20ecc9c08 100644 --- a/.azure-pipelines/nightly-test-cpu.yml +++ b/.azure-pipelines/nightly-test-cpu.yml @@ -89,7 +89,7 @@ stages: artifact: patterns: '*_coverage/.coverage.*' path: $(DOWNLOAD_PATH) - + - task: UsePythonVersion@0 inputs: versionSpec: '3.12' diff --git a/auto_round_extension/ark/auto_round_kernel/wrapper/include/stla/xe_sparse_sage_fwd_kernel.hpp b/auto_round_extension/ark/auto_round_kernel/wrapper/include/stla/xe_sparse_sage_fwd_kernel.hpp index fe3d4a3702..ff5b5d3d56 100644 --- a/auto_round_extension/ark/auto_round_kernel/wrapper/include/stla/xe_sparse_sage_fwd_kernel.hpp +++ b/auto_round_extension/ark/auto_round_kernel/wrapper/include/stla/xe_sparse_sage_fwd_kernel.hpp @@ -245,16 +245,10 @@ class XeSparseSageFwdKernel { auto dcV = const_cast(p.V + offset_v); auto dcK_cache = const_cast(p.K_cache + offset_k_cache); auto dcV_cache = const_cast(p.V_cache + offset_v_cache); - // scale_block_size is 0 on the native-precision sparse-SDPA path (no INT8 dequant); - // guard the pad computation so it does not divide by zero. The pads are only used - // inside the scale_block_size ? ... : nullptr ternaries below. - int seq_q_pad = params.mainloop.scale_block_size > 0 - ? (seq_len_qo + params.mainloop.scale_block_size - 1) / params.mainloop.scale_block_size - : 0; + int seq_q_pad = (seq_len_qo + params.mainloop.scale_block_size - 1) / +params.mainloop.scale_block_size; int seq_kv_total = seq_len_kv + seq_len_kv_cache; - int seq_kv_pad = params.mainloop.scale_block_size > 0 - ? (seq_kv_total + params.mainloop.scale_block_size - 1) / params.mainloop.scale_block_size - : 0; + int seq_kv_pad = (seq_kv_total + params.mainloop.scale_block_size - 1) / params.mainloop.scale_block_size; auto scaleQ = params.mainloop.scale_block_size ? (float*)params.mainloop.qscale + (idx_b * s.num_heads_q * seq_q_pad + head_q * seq_q_pad) : nullptr; diff --git a/auto_round_extension/ark/auto_round_kernel/wrapper/include/sycl_tla_s8_gemm.hpp b/auto_round_extension/ark/auto_round_kernel/wrapper/include/sycl_tla_s8_gemm.hpp index ade33c96c8..890c328341 100644 --- a/auto_round_extension/ark/auto_round_kernel/wrapper/include/sycl_tla_s8_gemm.hpp +++ b/auto_round_extension/ark/auto_round_kernel/wrapper/include/sycl_tla_s8_gemm.hpp @@ -153,7 +153,7 @@ void igemm_kblock_device_impl(TiledMMA const& mma, const int8_t* a, const int8_t tFrC(i) += static_cast(tCrC(i)) * sb; } } - + CUTE_UNROLL for (int i = 0; i < size(tFrC); ++i) { From 5a5a97d0b2498a06038d3dc01c32005aef3b071c Mon Sep 17 00:00:00 2001 From: yiliu30 Date: Tue, 18 Aug 2026 14:30:27 +0800 Subject: [PATCH 37/42] revert Signed-off-by: yiliu30 --- .../wrapper/include/sycl_tla_sdpa_sparse.hpp | 8 ++++---- docs/step_by_step.md | 4 ++-- docs/step_by_step_CN.md | 4 ++-- 3 files changed, 8 insertions(+), 8 deletions(-) diff --git a/auto_round_extension/ark/auto_round_kernel/wrapper/include/sycl_tla_sdpa_sparse.hpp b/auto_round_extension/ark/auto_round_kernel/wrapper/include/sycl_tla_sdpa_sparse.hpp index 2233151b14..5a0f7aead1 100644 --- a/auto_round_extension/ark/auto_round_kernel/wrapper/include/sycl_tla_sdpa_sparse.hpp +++ b/auto_round_extension/ark/auto_round_kernel/wrapper/include/sycl_tla_sdpa_sparse.hpp @@ -1171,12 +1171,12 @@ template ; - using ShapePV = Shape<_256, _32, _64>; + using ShapeQK = Shape<_256, _32, _32>; + using ShapePV = Shape<_256, _32, _32>; using ShapeOut = Shape<_256, _128>; using SubgroupLayoutQK = Layout>; - using ShapeQK1 = Shape<_256, _64, _32>; - using ShapePV1 = Shape<_256, _32, _64>; + using ShapeQK1 = Shape<_256, _32, _32>; + using ShapePV1 = Shape<_256, _32, _32>; using ShapeOut1 = Shape<_256, _128>; using SubgroupLayoutQK1 = Layout>; return options.is_causal ? FMHAConfig **Auto-enabled by default.** As of v0.13, when you pass `--iters 0 --disable_opt_rtn` together with a supported INT WOQ or MXFP scheme, the CLI automatically takes the model-free path. This is **bit-exactly equivalent** to the regular `--iters 0 --disable_opt_rtn` flow but uses far less memory. Use `--disable_model_free` to opt out and force the original flow. +> **Auto-enabled by default.** As of v0.13, when you pass `--iters 0 --disable_opt_rtn` together with a supported INT WOQ or MXFP scheme, the CLI automatically takes the model-free path. This is **bit-exactly equivalent** to the regular `--iters 0 --disable_opt_rtn` flow but uses far less memory. Use `--disable_model_free` to opt out and force the original flow. > When using `--model_free` explicitly, INT WOQ always uses **plain RTN** (opt_rtn is disabled for INT WOQ to preserve accuracy); MXFP schemes use **optimized RTN (opt_rtn) by default** — pass `--disable_opt_rtn` to use plain RTN for MXFP. **Key features:** @@ -1155,4 +1155,4 @@ Randomness in quantization may affect tuning results for some models, set `enabl Some VLMs require manual support. -Mamba is not supported. +Mamba is not supported. \ No newline at end of file diff --git a/docs/step_by_step_CN.md b/docs/step_by_step_CN.md index 7e78b7257f..5c1575ad80 100755 --- a/docs/step_by_step_CN.md +++ b/docs/step_by_step_CN.md @@ -605,7 +605,7 @@ ar.quantize_and_save(output_dir, format="auto_round") 免模型架构量化模式（Model-Free Mode）可以**无需将完整模型加载到内存中**即可执行无标定数据的 WOQ 量化。它直接下载 safetensors 文件，逐分片地对每个 Linear 权重张量进行量化并保存打包结果。当您需要快速、无标定数据的量化且资源有限时，该模式非常实用。 -> **默认自动启用。** 自 v0.13 起，当您同时传入 `--iters 0 --disable_opt_rtn` 与一个受支持的 INT WOQ 或 MXFP scheme 时，CLI 会自动走免模型路径。该路径与原始 `--iters 0 --disable_opt_rtn` 流程**位级（bit-exact）等价**，但内存占用大幅降低。如需关闭自动路由、强制使用原始流程，可加 `--disable_model_free`。 +> **默认自动启用。** 自 v0.13 起，当您同时传入 `--iters 0 --disable_opt_rtn` 与一个受支持的 INT WOQ 或 MXFP scheme 时，CLI 会自动走免模型路径。该路径与原始 `--iters 0 --disable_opt_rtn` 流程**位级（bit-exact）等价**，但内存占用大幅降低。如需关闭自动路由、强制使用原始流程，可加 `--disable_model_free`。 > 显式传入 `--model_free` 时，INT WOQ 始终使用**原始 RTN**（opt_rtn 对 INT WOQ 已禁用以保持精度）；MXFP scheme 默认启用**优化版 RTN（opt_rtn）**，如需原始 RTN，加上 `--disable_opt_rtn` 即可。 **主要特性：** - **无需模型对象** — 仅需 `config.json` 和 safetensors 文件 @@ -1114,4 +1114,4 @@ CUDA_VISIBLE_DEVICES=0,1 auto-round "your_model_path" --eval --tasks lambada_ope 部分视觉语言模型（VLM）需要手动适配。 -暂不支持 Mamba 架构的模型。 +暂不支持 Mamba 架构的模型。 \ No newline at end of file From 4e2970133329225c5b2cece46f1b895610f1d85d Mon Sep 17 00:00:00 2001 From: yiliu30 Date: Tue, 18 Aug 2026 14:35:02 +0800 Subject: [PATCH 38/42] fix: consolidate xpu triton workaround --- .../auto_round_kernel/_xpu_triton_compat.py | 49 ------------------- .../sparge_preprocess_triton.py | 10 ++-- .../ark/tools/run_sparse_sagev1_bench.sh | 4 +- 3 files changed, 7 insertions(+), 56 deletions(-) delete mode 100644 auto_round_extension/ark/auto_round_kernel/_xpu_triton_compat.py diff --git a/auto_round_extension/ark/auto_round_kernel/_xpu_triton_compat.py b/auto_round_extension/ark/auto_round_kernel/_xpu_triton_compat.py deleted file mode 100644 index f86e7f5763..0000000000 --- a/auto_round_extension/ark/auto_round_kernel/_xpu_triton_compat.py +++ /dev/null @@ -1,49 +0,0 @@ -# # Copyright (C) 2026 Intel Corporation -# # SPDX-License-Identifier: Apache-2.0 - -"""Workarounds for the Intel XPU triton backend on this platform. - -Triton 3.7.x enables ``has_predicated_io`` for non-LTS Intel GPU drivers and then -emits the ``SPV_INTEL_predicated_io`` SPIR-V extension in its JIT kernels. Some -level-zero loaders (e.g. 1.13.35563) reject that extension at kernel-load time:: - - InvalidModule: Invalid SPIR-V module: input SPIR-V module uses unknown - extension 'SPV_INTEL_predicated_io' - -which aborts any triton kernel launch and kills the process (it is not a -catchable Python exception, so the torch preprocess fallback never runs). - -The workaround forces ``has_predicated_io`` off so triton emits standard -(non-extension) predicated loads/stores. This disables a minor codegen -optimization on every XPU driver, but the ark triton preprocess kernels are small -and the trade-off is only safe correctness on the affected drivers. -""" - -import logging - -logger = logging.getLogger(__name__) - -_APPLIED = False - - -def apply_xpu_triton_workarounds() -> None: - """Patch triton's Intel compiler so JIT kernels avoid the rejected SPIR-V extension.""" - global _APPLIED - if _APPLIED: - return - try: - import triton.backends.intel.compiler as _intel_compiler - except ImportError: - # No intel triton backend (e.g. stock triton); nothing to patch. - return - - _orig_parse_target = _intel_compiler.XPUBackend.parse_target - - def _patched_parse_target(self, tgt_prop): - dev_prop = _orig_parse_target(self, tgt_prop) - dev_prop["has_predicated_io"] = False - return dev_prop - - _intel_compiler.XPUBackend.parse_target = _patched_parse_target - _APPLIED = True - logger.info("Applied XPU triton workaround: has_predicated_io forced off (SPV_INTEL_predicated_io)") diff --git a/auto_round_extension/ark/auto_round_kernel/sparge_preprocess_triton.py b/auto_round_extension/ark/auto_round_kernel/sparge_preprocess_triton.py index f2de9dc4c7..2fe1091f80 100644 --- a/auto_round_extension/ark/auto_round_kernel/sparge_preprocess_triton.py +++ b/auto_round_extension/ark/auto_round_kernel/sparge_preprocess_triton.py @@ -10,17 +10,17 @@ import triton import triton.language as tl -from ._xpu_triton_compat import apply_xpu_triton_workarounds - -apply_xpu_triton_workarounds() - logger = logging.getLogger(__name__) _APPLIED_TRITON_PREDICATED_CHECK = False def _apply_xpu_triton_workarounds() -> None: - """Patch triton's Intel compiler so JIT kernels avoid the rejected SPIR-V extension.""" + """Patch triton's Intel compiler so JIT kernels avoid the rejected SPIR-V extension. + + Triton 3.7.x can emit SPV_INTEL_predicated_io for non-LTS Intel GPU drivers, + but some level-zero loaders reject that extension at kernel-load time. + """ global _APPLIED_TRITON_PREDICATED_CHECK if _APPLIED_TRITON_PREDICATED_CHECK: return diff --git a/auto_round_extension/ark/tools/run_sparse_sagev1_bench.sh b/auto_round_extension/ark/tools/run_sparse_sagev1_bench.sh index bf92761f0a..1f3e7c0cd0 100755 --- a/auto_round_extension/ark/tools/run_sparse_sagev1_bench.sh +++ b/auto_round_extension/ark/tools/run_sparse_sagev1_bench.sh @@ -38,8 +38,8 @@ # The default preprocess backend is triton-xpu (via "auto"), which is faster # than torch. On some level-zero drivers triton's JIT emitted SPIR-V that the # runtime rejected ("InvalidModule: ... unknown extension -# 'SPV_INTEL_predicated_io'); that is fixed by auto_round_kernel/_xpu_triton_compat.py -# (forces has_predicated_io off), applied when the triton preprocess loads. +# 'SPV_INTEL_predicated_io'); that is fixed by _apply_xpu_triton_workarounds() +# in auto_round_kernel/sparge_preprocess_triton.py when the triton preprocess loads. # If you still hit the SPIR-V error, force the torch backend: # export SAGE_ATTN_SPARSE_PREPROCESS_BACKEND=torch # From b131143b8f9e98c3b16cd46df0d95dd0fdea83ba Mon Sep 17 00:00:00 2001 From: yiliu30 Date: Tue, 18 Aug 2026 14:40:38 +0800 Subject: [PATCH 39/42] clean Signed-off-by: yiliu30 --- .../wrapper/include/stla/xe_sparse_sage_fwd_kernel.hpp | 3 +-- .../wrapper/include/sycl_tla_sdpa_sparse.hpp | 7 ++----- 2 files changed, 3 insertions(+), 7 deletions(-) diff --git a/auto_round_extension/ark/auto_round_kernel/wrapper/include/stla/xe_sparse_sage_fwd_kernel.hpp b/auto_round_extension/ark/auto_round_kernel/wrapper/include/stla/xe_sparse_sage_fwd_kernel.hpp index ff5b5d3d56..90e0e32b9a 100644 --- a/auto_round_extension/ark/auto_round_kernel/wrapper/include/stla/xe_sparse_sage_fwd_kernel.hpp +++ b/auto_round_extension/ark/auto_round_kernel/wrapper/include/stla/xe_sparse_sage_fwd_kernel.hpp @@ -245,8 +245,7 @@ class XeSparseSageFwdKernel { auto dcV = const_cast(p.V + offset_v); auto dcK_cache = const_cast(p.K_cache + offset_k_cache); auto dcV_cache = const_cast(p.V_cache + offset_v_cache); - int seq_q_pad = (seq_len_qo + params.mainloop.scale_block_size - 1) / -params.mainloop.scale_block_size; + int seq_q_pad = (seq_len_qo + params.mainloop.scale_block_size - 1) / params.mainloop.scale_block_size; int seq_kv_total = seq_len_kv + seq_len_kv_cache; int seq_kv_pad = (seq_kv_total + params.mainloop.scale_block_size - 1) / params.mainloop.scale_block_size; auto scaleQ = params.mainloop.scale_block_size diff --git a/auto_round_extension/ark/auto_round_kernel/wrapper/include/sycl_tla_sdpa_sparse.hpp b/auto_round_extension/ark/auto_round_kernel/wrapper/include/sycl_tla_sdpa_sparse.hpp index 5a0f7aead1..b7683fbc2c 100644 --- a/auto_round_extension/ark/auto_round_kernel/wrapper/include/sycl_tla_sdpa_sparse.hpp +++ b/auto_round_extension/ark/auto_round_kernel/wrapper/include/sycl_tla_sdpa_sparse.hpp @@ -940,11 +940,8 @@ struct SparseSageConfig { static constexpr int SGTileQ = get<0>(shape_div(TileShapeQK{}, shape(SubgroupLayoutQK{})))(); static_assert(cute::is_same_v, "SparseSageConfig is INT8-only; use SparseSDPAConfig for native-precision BF16/FP16"); - // INT8-only sparse SAGE path: Q/K are int8 and dequantized via qscale/kscale, - // so the Q*K MMA is always an int32/int8 DPAS. Native-precision BF16/FP16 sparse - // uses the separate SparseSDPAConfig (float/ DPAS). - using MMAOperation = cute::conditional_t, - XE_DPAS_TT, MMAOperation_>; + using MMAOperation = + cute::conditional_t, XE_DPAS_TT, MMAOperation_>; using MMAOperationPV = cute::conditional_t, XE_DPAS_TT, MMAOperation_>; using SubgroupLayoutPV = From 308ba36f79885bddf5b30e7dab49bb9004366f52 Mon Sep 17 00:00:00 2001 From: yiliu30 Date: Tue, 18 Aug 2026 14:41:33 +0800 Subject: [PATCH 40/42] update Signed-off-by: yiliu30 --- docs/step_by_step.md | 2 +- docs/step_by_step_CN.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/step_by_step.md b/docs/step_by_step.md index 4c5f11add9..5dc2f37198 100755 --- a/docs/step_by_step.md +++ b/docs/step_by_step.md @@ -1155,4 +1155,4 @@ Randomness in quantization may affect tuning results for some models, set `enabl Some VLMs require manual support. -Mamba is not supported. \ No newline at end of file +Mamba is not supported. diff --git a/docs/step_by_step_CN.md b/docs/step_by_step_CN.md index 5c1575ad80..3c4421ae03 100755 --- a/docs/step_by_step_CN.md +++ b/docs/step_by_step_CN.md @@ -1114,4 +1114,4 @@ CUDA_VISIBLE_DEVICES=0,1 auto-round "your_model_path" --eval --tasks lambada_ope 部分视觉语言模型（VLM）需要手动适配。 -暂不支持 Mamba 架构的模型。 \ No newline at end of file +暂不支持 Mamba 架构的模型。 From 7d400d403f03dd2166a1c10664c507f070ae9c65 Mon Sep 17 00:00:00 2001 From: yiliu30 Date: Tue, 18 Aug 2026 15:34:36 +0800 Subject: [PATCH 41/42] refactor: rename sparse sdpa api --- .../ark/auto_round_kernel/__init__.py | 2 +- .../ark/auto_round_kernel/ark.cpp | 34 +++++++++---------- .../ark/auto_round_kernel/sparse_attention.py | 16 ++++----- .../test/test_sage_sparse_prefill_e2e.py | 16 ++++----- .../ark/benchmarks/bench_sparse_topk.py | 6 ++-- .../ark/examples/flux_sparse_patch.py | 2 +- 6 files changed, 38 insertions(+), 38 deletions(-) diff --git a/auto_round_extension/ark/auto_round_kernel/__init__.py b/auto_round_extension/ark/auto_round_kernel/__init__.py index 72f3d3436a..579212d19b 100644 --- a/auto_round_extension/ark/auto_round_kernel/__init__.py +++ b/auto_round_extension/ark/auto_round_kernel/__init__.py @@ -2146,8 +2146,8 @@ def sageattn( _sequence_mean_native_layout, _slice_sequence_native_layout, _to_hnd, + block_sparse_sdpa, sage_sparse, - sage_sparse_sdpa, sparge_block_map_to_mask, sparge_preprocess_topk, sparge_sage2_attn_meansim_topk_xpu, diff --git a/auto_round_extension/ark/auto_round_kernel/ark.cpp b/auto_round_extension/ark/auto_round_kernel/ark.cpp index 2ccde451e7..feca265346 100755 --- a/auto_round_extension/ark/auto_round_kernel/ark.cpp +++ b/auto_round_extension/ark/auto_round_kernel/ark.cpp @@ -507,34 +507,34 @@ static void sage_sparse(torch_ptr stream, torch_ptr Q, torch_ptr K, torch_ptr V, throw std::invalid_argument("ark::sage_sparse: unsupported head_dim; supported values are 64 and 128"); } -static void sage_sparse_sdpa(torch_ptr stream, torch_ptr Q, torch_ptr K, torch_ptr V, torch_ptr O, torch_ptr mask, - torch_ptr lut, torch_ptr valid_block_num, int num_q_blocks, int num_k_blocks, - int q_tile_override, int q_stride_s, int q_stride_d, int q_stride_h, int q_stride_b, - int k_stride_s, int k_stride_d, int k_stride_h, int k_stride_b, int v_stride_d, - int v_stride_s, int v_stride_h, int v_stride_b, int o_stride_s, int o_stride_d, - int o_stride_h, int o_stride_b, int q_dtype, int batch, int num_heads_q, - int num_heads_kv, int seq_len_q, int seq_len_kv, int head_dim, float softmax_scale, - bool is_causal) { +static void block_sparse_sdpa(torch_ptr stream, torch_ptr Q, torch_ptr K, torch_ptr V, torch_ptr O, torch_ptr mask, + torch_ptr lut, torch_ptr valid_block_num, int num_q_blocks, int num_k_blocks, + int q_tile_override, int q_stride_s, int q_stride_d, int q_stride_h, int q_stride_b, + int k_stride_s, int k_stride_d, int k_stride_h, int k_stride_b, int v_stride_d, + int v_stride_s, int v_stride_h, int v_stride_b, int o_stride_s, int o_stride_d, + int o_stride_h, int o_stride_b, int q_dtype, int batch, int num_heads_q, + int num_heads_kv, int seq_len_q, int seq_len_kv, int head_dim, float softmax_scale, + bool is_causal) { if (q_dtype != (int)FlashAttnDtype::FP16 && q_dtype != (int)FlashAttnDtype::BF16) { - throw std::invalid_argument("ark::sage_sparse_sdpa: q_dtype must be FP16 or BF16"); + throw std::invalid_argument("ark::block_sparse_sdpa: q_dtype must be FP16 or BF16"); } if (mask && is_causal) { - throw std::invalid_argument("ark::sage_sparse_sdpa: mask and is_causal cannot both be set"); + throw std::invalid_argument("ark::block_sparse_sdpa: mask and is_causal cannot both be set"); } if (!lut || !valid_block_num) { - throw std::invalid_argument("ark::sage_sparse_sdpa: lut and valid_block_num must be provided"); + throw std::invalid_argument("ark::block_sparse_sdpa: lut and valid_block_num must be provided"); } auto matches_block_size = [](int seq_len, int num_blocks, int block_size) { return block_size > 0 && num_blocks == ((seq_len + block_size - 1) / block_size); }; const bool key_block_is_64 = matches_block_size(seq_len_kv, num_k_blocks, 64); if (!key_block_is_64) { - throw std::invalid_argument("ark::sage_sparse_sdpa: only key block size 64 is supported"); + throw std::invalid_argument("ark::block_sparse_sdpa: only key block size 64 is supported"); } const bool is_bf16 = (q_dtype == (int)FlashAttnDtype::BF16); if (head_dim == 64) { if (!matches_block_size(seq_len_q, num_q_blocks, 64)) { - throw std::invalid_argument("ark::sage_sparse_sdpa: head_dim=64 requires query block size 64"); + throw std::invalid_argument("ark::block_sparse_sdpa: head_dim=64 requires query block size 64"); } if (is_bf16) { ark::sdpa_impl_bf16_sparse_sdpa_d64( @@ -557,7 +557,7 @@ static void sage_sparse_sdpa(torch_ptr stream, torch_ptr Q, torch_ptr K, torch_p if (matches_block_size(seq_len_q, num_q_blocks, 256)) { if (q_tile_override != 256) { throw std::invalid_argument( - "ark::sage_sparse_sdpa: head_dim=128 query block size 256 requires q_tile_override=256"); + "ark::block_sparse_sdpa: head_dim=128 query block size 256 requires q_tile_override=256"); } if (is_bf16) { ark::sdpa_impl_bf16_sparse_sdpa_qtile256_row64k( @@ -594,9 +594,9 @@ static void sage_sparse_sdpa(torch_ptr stream, torch_ptr Q, torch_ptr K, torch_p } return; } - throw std::invalid_argument("ark::sage_sparse_sdpa: head_dim=128 supports query block sizes 64 and 256 only"); + throw std::invalid_argument("ark::block_sparse_sdpa: head_dim=128 supports query block sizes 64 and 256 only"); } - throw std::invalid_argument("ark::sage_sparse_sdpa: unsupported head_dim; supported values are 64 and 128"); + throw std::invalid_argument("ark::block_sparse_sdpa: unsupported head_dim; supported values are 64 and 128"); } static void moe_gemm_wrapper(torch_ptr stream, torch_ptr activations, torch_ptr weights, torch_ptr scales, @@ -1471,7 +1471,7 @@ PYBIND11_MODULE(PY_NAME, m) { pybind11::arg("head_dim"), pybind11::arg("softmax_scale"), pybind11::arg("is_causal"), pybind11::arg("tensor_layout"), pybind11::arg("lse") = 0); m.def("sage_sparse", &ark::sage_sparse); - m.def("sage_sparse_sdpa", &ark::sage_sparse_sdpa); + m.def("block_sparse_sdpa", &ark::block_sparse_sdpa); // Low-level SAGE PVi8 API: input Q/K/V are pre-quantized int8 with qscale/kscale/vscale. m.def("sage_pvi8", &ark::sage_pvi8, pybind11::arg("stream"), pybind11::arg("Q"), pybind11::arg("K"), pybind11::arg("V"), pybind11::arg("O"), pybind11::arg("mask"), diff --git a/auto_round_extension/ark/auto_round_kernel/sparse_attention.py b/auto_round_extension/ark/auto_round_kernel/sparse_attention.py index 6ff52a3245..e45febf887 100644 --- a/auto_round_extension/ark/auto_round_kernel/sparse_attention.py +++ b/auto_round_extension/ark/auto_round_kernel/sparse_attention.py @@ -199,7 +199,7 @@ def sage_sparse( return O -def sage_sparse_sdpa( +def block_sparse_sdpa( query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, @@ -223,7 +223,7 @@ def sage_sparse_sdpa( """ del dropout_p, enable_gqa if query.device.type != "xpu": - raise NotImplementedError("sage_sparse_sdpa is only supported on XPU") + raise NotImplementedError("block_sparse_sdpa is only supported on XPU") if query.dtype not in (torch.bfloat16, torch.float16): raise ValueError(f"Q/K/V must be bfloat16 or float16, got {query.dtype}") if key.dtype != query.dtype or value.dtype != query.dtype: @@ -243,7 +243,7 @@ def sage_sparse_sdpa( raise ValueError("K/V shape mismatch") if Dk != D: raise ValueError("Head dim mismatch between Q and K/V") - _validate_gqa_head_config(Hq, Hkv, op_name="sage_sparse_sdpa") + _validate_gqa_head_config(Hq, Hkv, op_name="block_sparse_sdpa") if D not in (64, 128): raise ValueError(f"Unsupported head_dim={D}; supported: 64, 128") effective_sparse_q_block_tokens = 64 if sparse_q_block_tokens is None else int(sparse_q_block_tokens) @@ -269,12 +269,12 @@ def sage_sparse_sdpa( if D == 64 and q_tile_override not in (0, 64, 128): raise ValueError( - f"q_tile_override must be one of {{0, 64, 128}} for sage_sparse_sdpa with head_dim=64, got {q_tile_override}" + f"q_tile_override must be one of {{0, 64, 128}} for block_sparse_sdpa with head_dim=64, got {q_tile_override}" ) lib = get_lib(query) - if not hasattr(lib, "sage_sparse_sdpa"): - raise RuntimeError("Loaded XPU extension does not expose sage_sparse_sdpa") + if not hasattr(lib, "block_sparse_sdpa"): + raise RuntimeError("Loaded XPU extension does not expose block_sparse_sdpa") effective_q_tile_override = _resolve_sparse_prefill_q_tile_override( head_dim=D, quant_block_size=64, @@ -286,7 +286,7 @@ def sage_sparse_sdpa( stream = get_stream(query) O = _empty_attention_output(B, Hq, Sq, D, dtype=value.dtype, device=query.device, tensor_layout=tensor_layout) q_dtype = 1 if query.dtype == torch.bfloat16 else 0 # FlashAttnDtype: 0=FP16, 1=BF16 - lib.sage_sparse_sdpa( + lib.block_sparse_sdpa( stream, query.data_ptr(), key.data_ptr(), @@ -1688,7 +1688,7 @@ def sparge_sage2_attn_meansim_topk_xpu_sdpa( sparse_k_block_tokens=sparse_k_block_tokens, ) _get_xpu_sparse_kernel_backend() - out = sage_sparse_sdpa( + out = block_sparse_sdpa( query, key, value, diff --git a/auto_round_extension/ark/auto_round_kernel/wrapper/test/test_sage_sparse_prefill_e2e.py b/auto_round_extension/ark/auto_round_kernel/wrapper/test/test_sage_sparse_prefill_e2e.py index b37c9445b3..f258a5a053 100644 --- a/auto_round_extension/ark/auto_round_kernel/wrapper/test/test_sage_sparse_prefill_e2e.py +++ b/auto_round_extension/ark/auto_round_kernel/wrapper/test/test_sage_sparse_prefill_e2e.py @@ -255,7 +255,7 @@ def run_case_sdpa( ) -> None: """Validate the independent native-precision sparse-SDPA path (bf16/fp16) against a dense reference built from the same block selection.""" - ensure_sparse_binding(required_symbols=("sage_sparse", "sage_sparse_sdpa")) + ensure_sparse_binding(required_symbols=("sage_sparse", "block_sparse_sdpa")) device = torch.device("xpu") batch = 1 num_heads_kv = num_heads_q if num_heads_kv is None else num_heads_kv @@ -303,7 +303,7 @@ def run_case_sdpa( scale=scale, enable_gqa=num_heads_q != num_heads_kv, ) - sparse_out = ark.sage_sparse_sdpa( + sparse_out = ark.block_sparse_sdpa( query, key, value, @@ -322,12 +322,12 @@ def run_case_sdpa( max_diff = float(diff.max().cpu()) mean_diff = float(diff.mean().cpu()) print( - f"[sage_sparse_sdpa][{case_name}] D={head_dim} Hq={num_heads_q} Hkv={num_heads_kv} " + f"[block_sparse_sdpa][{case_name}] D={head_dim} Hq={num_heads_q} Hkv={num_heads_kv} " f"Sq={seq_len_q} Skv={seq_len_kv} max_diff={max_diff:.6f} mean_diff={mean_diff:.6f}" ) if max_diff > 2e-2 or mean_diff > 2e-3: raise RuntimeError( - f"sage_sparse_sdpa mismatch for dtype={dtype}, D={head_dim}, Hq={num_heads_q}, Hkv={num_heads_kv}, " + f"block_sparse_sdpa mismatch for dtype={dtype}, D={head_dim}, Hq={num_heads_q}, Hkv={num_heads_kv}, " f"Sq={seq_len_q}, Skv={seq_len_kv}, causal={is_causal}" ) @@ -344,7 +344,7 @@ def run_case_sdpa_full( sparse_k_block_tokens: int | None = None, ) -> None: """Dense gate: selecting every KV block must make sparse-SDPA match the dense reference.""" - ensure_sparse_binding(required_symbols=("sage_sparse", "sage_sparse_sdpa")) + ensure_sparse_binding(required_symbols=("sage_sparse", "block_sparse_sdpa")) device = torch.device("xpu") batch = 1 scale = 1.0 / math.sqrt(head_dim) @@ -374,7 +374,7 @@ def run_case_sdpa_full( ) dense_out = bf16_sparse_reference(query, key, value, dense_mask, scale=scale, enable_gqa=False) - sparse_out = ark.sage_sparse_sdpa( + sparse_out = ark.block_sparse_sdpa( query, key, value, @@ -392,9 +392,9 @@ def run_case_sdpa_full( diff = (dense_out.float() - sparse_out.float()).abs() max_diff = float(diff.max().cpu()) mean_diff = float(diff.mean().cpu()) - print(f"[sage_sparse_sdpa][{dtype}_all_selected] D={head_dim} max_diff={max_diff:.6f} mean_diff={mean_diff:.6f}") + print(f"[block_sparse_sdpa][{dtype}_all_selected] D={head_dim} max_diff={max_diff:.6f} mean_diff={mean_diff:.6f}") if max_diff > 2e-2 or mean_diff > 2e-3: - raise RuntimeError(f"sage_sparse_sdpa all-selected mismatch for dtype={dtype}, D={head_dim}") + raise RuntimeError(f"block_sparse_sdpa all-selected mismatch for dtype={dtype}, D={head_dim}") def run_multi_row_tile_case() -> None: diff --git a/auto_round_extension/ark/benchmarks/bench_sparse_topk.py b/auto_round_extension/ark/benchmarks/bench_sparse_topk.py index bbd4a95468..9efe91e070 100644 --- a/auto_round_extension/ark/benchmarks/bench_sparse_topk.py +++ b/auto_round_extension/ark/benchmarks/bench_sparse_topk.py @@ -62,7 +62,7 @@ def _resolve_sparse_binding(args: argparse.Namespace, *, required_symbols: tuple def ensure_sparse_binding(args: argparse.Namespace) -> None: - required_symbols = ("sage_sparse", "sage_sparse_sdpa") + required_symbols = ("sage_sparse", "block_sparse_sdpa") ext_path = _resolve_sparse_binding(args, required_symbols=required_symbols) if ext_path is not None: _load_sparse_binding(ext_path, required_symbols=required_symbols) @@ -947,7 +947,7 @@ def run_single_benchmark(args: argparse.Namespace, *, seq_len: int, tensor_layou "sparse_sdpa_fp16_kernel_only" if dtype == torch.float16 else "sparse_sdpa_bf16_kernel_only", lambda preprocess=preprocess: ( hnd_to_nhd( - ark.sage_sparse_sdpa( + ark.block_sparse_sdpa( nhd_to_hnd(q_nhd_src), nhd_to_hnd(k_nhd_src), nhd_to_hnd(v_nhd_src), @@ -962,7 +962,7 @@ def run_single_benchmark(args: argparse.Namespace, *, seq_len: int, tensor_layou ) ) if tensor_layout == "HND" - else ark.sage_sparse_sdpa( + else ark.block_sparse_sdpa( q, k, v, diff --git a/auto_round_extension/ark/examples/flux_sparse_patch.py b/auto_round_extension/ark/examples/flux_sparse_patch.py index 2e90ec387b..ab512c9aae 100644 --- a/auto_round_extension/ark/examples/flux_sparse_patch.py +++ b/auto_round_extension/ark/examples/flux_sparse_patch.py @@ -307,7 +307,7 @@ def patch_flux_sparse_attention( ): ensure_ark_sparse_binding( required_symbols=( - ("sage_sparse_sdpa", "sage_dynamic_quant_layout") + ("block_sparse_sdpa", "sage_dynamic_quant_layout") if sparse_kernel == "bf16" else ("sage_sparse", "sage_dynamic_quant_layout") ) From ace6cdf026f34509cf17b14ed0dba5ca7e5a92fb Mon Sep 17 00:00:00 2001 From: yiliu30 Date: Tue, 18 Aug 2026 23:29:06 +0800 Subject: [PATCH 42/42] update Signed-off-by: yiliu30 --- .../auto_round_kernel/wrapper/include/sycl_tla_sdpa_sparse.hpp | 2 -- 1 file changed, 2 deletions(-) diff --git a/auto_round_extension/ark/auto_round_kernel/wrapper/include/sycl_tla_sdpa_sparse.hpp b/auto_round_extension/ark/auto_round_kernel/wrapper/include/sycl_tla_sdpa_sparse.hpp index b7683fbc2c..32f49abbfd 100644 --- a/auto_round_extension/ark/auto_round_kernel/wrapper/include/sycl_tla_sdpa_sparse.hpp +++ b/auto_round_extension/ark/auto_round_kernel/wrapper/include/sycl_tla_sdpa_sparse.hpp @@ -938,8 +938,6 @@ template struct SparseSageConfig { static constexpr int SGTileQ = get<0>(shape_div(TileShapeQK{}, shape(SubgroupLayoutQK{})))(); - static_assert(cute::is_same_v, - "SparseSageConfig is INT8-only; use SparseSDPAConfig for native-precision BF16/FP16"); using MMAOperation = cute::conditional_t, XE_DPAS_TT, MMAOperation_>; using MMAOperationPV = cute::conditional_t,

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>, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, void, void, void, void, void>, cute::tuple, cute::C<128> >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, void>, cutlass::fmha::kernel::XeFHMAIndividualTileScheduler>[SIMD16 {1; 293; 40} {256; 1; 1}]",13,0,842606440,2025281475,2403,0,99.999992,0,0,11720,0,39767077175,10940,64.705299,62135142400,84126985600,9296625280,152226166576,0.384969,35.176994,99.842812,113099407360,15.795247,750080,0,0,0,4826988181696,36003840,103929039779,101765613576,0,1814872248,343078599,2139381298,24764234,0.001012,2992412,2992412,22036784640,815173120,26.153116,0.967442,87156825,3276645,0.000000,48126,187520,0.005092,100.000008,1,0.000000,1,0.000000,0,3.391425,5.848985,19.174847,25.961508,69.804741,654237,21,3,17,602325112931510,2400,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 + +"cutlass::fmha::kernel::XeSparseSageFwdKernel, cutlass::fmha::collective::SPARSESDPAFwdMainloop, false, false, false, false, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, 4, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, void, void, void, void, void>, cutlass::fmha::collective::SparseFMHAFwdEpilogue, false, false, false, false, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, 4, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, void, void, void, void, void>, cute::tuple, cute::C<128> >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, void>, cutlass::fmha::kernel::XeFHMAIndividualTileScheduler>[SIMD16 {1; 293; 40} {256; 1; 1}]",19,0,842014472,2023946209,2403,0,100.000008,0,0,11720,0,39770894776,10940,64.731178,62135142400,84126985600,9296625280,152226167064,0.372979,35.120743,99.812370,113099407360,15.818870,750080,0,0,0,4826818373952,36003840,103926386533,101781278068,0,1798988877,340708340,2121071765,24764324,0.001402,3058609,3001843,21425017856,817649152,25.444952,0.971063,84775206,3340930,0.000000,48180,187520,0.005096,100.000008,1,0.446112,1,0.000000,0,3.396352,5.853045,19.187498,25.978636,69.850800,650048,36,4,18,602326139770625,2400,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 diff --git a/auto_round_extension/ark/benchmarks/results/unitrace_k32_stalls_20260814.csv b/auto_round_extension/ark/benchmarks/results/unitrace_k32_stalls_20260814.csv new file mode 100644 index 0000000000..0f65211098 --- /dev/null +++ b/auto_round_extension/ark/benchmarks/results/unitrace_k32_stalls_20260814.csv @@ -0,0 +1,8 @@ +mode,batch,num_heads_q,num_heads_kv,gqa_group_size,attention_pattern,seq_len,tensor_layout,head_dim,dtype,causal,requested_topk,selected_ratio,selected_blocks_per_row,warmup,iters,latency_ms,status,note,speedup_vs_torch,speedup_vs_sagev1,speedup_vs_ark,baseline_tflops,effective_tflops +dense_torch_sdpa,1,40,40,1,mha,75000,HND,128,bfloat16,False,,,,0,1,3050.2180999610573,ok,,1.0,3.1489193746993007,0.8734807393059724,37.767791097125404,37.767791097125404 +dense_sagev1,1,40,40,1,mha,75000,HND,128,bfloat16,False,,,,0,1,9604.890872025862,ok,,0.31756926139002617,1.0,0.27739063321981167,11.993889523046922,11.993889523046922 +dense_ark_sdpa,1,40,40,1,mha,75000,HND,128,bfloat16,False,,,,0,1,2664.3067609984428,ok,,1.1448449347544332,3.6050243960745916,1.0,43.2382643344076,43.2382643344076 +sparse_qtile256_row64k_kernel_only,1,40,40,1,mha,75000,HND,128,bfloat16,False,0.5,0.5025568148726252,588.9965870307167,0,1,28501.629570964724,ok,,0.1070190773606989,0.3369944461635481,0.09347910281286634,4.04187415716598,2.031271402541311 +sparse_qtile256_row64k_e2e,1,40,40,1,mha,75000,HND,128,bfloat16,False,0.5,0.5025568148726252,588.9965870307167,0,1,803.9296750212088,ok,,3.794135475693926,11.947426709646551,3.3141042603361477,143.29611604020073,72.01443966078138 +sparse_sdpa_bf16_kernel_only,1,40,40,1,mha,75000,HND,128,bfloat16,False,0.5,0.5025568148726252,588.9965870307167,0,1,25396.277436986566,ok,,0.12010492906014597,0.37820073811437876,0.10490934222974768,4.5360978704786605,2.2796468977382536 +sparse_sdpa_bf16_e2e,1,40,40,1,mha,75000,HND,128,bfloat16,False,0.5,0.5025568148726252,588.9965870307167,0,1,1023.8845960702747,ok,,2.979064351263767,9.38083345417048,2.6021553319819426,112.51268008342339,56.544014135507915 diff --git a/auto_round_extension/ark/benchmarks/results/unitrace_k32_stalls_20260814/k32_stalls.metrics.2892853 b/auto_round_extension/ark/benchmarks/results/unitrace_k32_stalls_20260814/k32_stalls.metrics.2892853 new file mode 100644 index 0000000000..e4feee2459 --- /dev/null +++ b/auto_round_extension/ark/benchmarks/results/unitrace_k32_stalls_20260814/k32_stalls.metrics.2892853 @@ -0,0 +1,7 @@ + +=== Device #0 Metrics === + +Kernel,GlobalInstanceId,SubDeviceId,GpuTime[ns],GpuCoreClocks[cycles],AvgGpuCoreFrequencyMHz[MHz],ResultUncertainty[%],GPU_BUSY[%],IA_VERTEX[events],GPGPU_THREADGROUP_COUNT[events],ASYNC_GPGPU_THREADGROUP_COUNT[events],RASTERIZER_SAMPLE_OUTPUT[events],XVE_STALL_ALUWR[%],XVE_STALL_BARRIER[%],XVE_STALL_CONTROL[%],XVE_STALL_INSTFETCH[%],XVE_STALL_OTHER[%],XVE_STALL_PIPESTALL[%],XVE_STALL_PS_DEPENDENCY[%],XVE_STALL_SBID[%],XVE_STALL_SENDWR[%],THREAD_DISPATCH_QUEUE0_ACTIVE[%],THREAD_DISPATCH_QUEUE0_STALL[%],THREAD_DISPATCH_QUEUE1_ACTIVE[%],THREAD_DISPATCH_QUEUE1_STALL[%],THREADGROUP_DISPATCH_QUEUE0_RESOURCE_STALL[%],THREADGROUP_DISPATCH_QUEUE1_RESOURCE_STALL[%],XVE_ACTIVE[%],XVE_SHARED_FUNCTION_ACCESS_HOLD[%],XVE_STALL[%],XVE_THREADS_OCCUPANCY_ALL[%],XVE_MULTIPLE_PIPE_ACTIVE[%],XVE_PIPE_ALU0_AND_ALU1_ACTIVE[%],XVE_PIPE_ALU0_AND_ALU2_ACTIVE[%],THREADGROUP_DISPATCH_QUEUE0_INPUT_AVAILABLE[%],THREADGROUP_DISPATCH_QUEUE1_INPUT_AVAILABLE[%],QueryBeginTime[ns],CoreFrequencyMHz[MHz],CoreFrequencyChanged,QuerySplitOccurred,ReportId,ReportsCount,OverrunOccured,MidQueryTimer,MidQueryProgramming,MidQueryMarker,MidQueryCtxSwitch,MidQueryC6,MidQueryFreqChange,MidQueryMmioTrigger,ReportError,ReportLost,ReportInconsistent,ReportCtxSwitchLost,ReportWithoutWorkload,ReportContextMismatch,ReportQueryModeMismatch +"cutlass::fmha::kernel::XeSparseSageFwdKernel, cutlass::fmha::collective::SPARSESDPAFwdMainloop, false, false, false, false, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, 4, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, void, void, void, void, void>, cutlass::fmha::collective::SparseFMHAFwdEpilogue, false, false, false, false, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, 4, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, void, void, void, void, void>, cute::tuple, cute::C<128> >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, void>, cutlass::fmha::kernel::XeFHMAIndividualTileScheduler>[SIMD16 {1; 293; 40} {256; 1; 1}]",13,0,841596288,2022810523,2403,0,100.000000,0,0,11720,0,29.554045,0.240841,4.315089,12.326859,0.000001,4.746475,0.000000,31.465960,0.000076,0.000000,0.000000,0.005099,0.000000,0.000000,0.000005,64.755737,0.370239,35.093006,99.824524,15.837934,3.402867,5.854964,0.000000,0.000063,602413782604062,2400,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 + +"cutlass::fmha::kernel::XeSparseSageFwdKernel, cutlass::fmha::collective::SPARSESDPAFwdMainloop, false, false, false, false, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, 4, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, void, void, void, void, void>, cutlass::fmha::collective::SparseFMHAFwdEpilogue, false, false, false, false, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, 4, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, void, void, void, void, void>, cute::tuple, cute::C<128> >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, void>, cutlass::fmha::kernel::XeFHMAIndividualTileScheduler>[SIMD16 {1; 293; 40} {256; 1; 1}]",19,0,840939216,2021361718,2403,0,99.999992,0,0,11720,0,29.570486,0.239372,4.317172,12.330883,0.000001,4.749287,0.000000,31.431385,0.000075,0.000000,0.000000,0.005102,0.000000,0.000000,0.000005,64.799057,0.367478,35.085705,99.860268,15.851206,3.403399,5.862384,0.000000,0.000064,602414807449687,2400,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 diff --git a/auto_round_extension/ark/benchmarks/results/unitrace_k32_verbose_20260814.csv b/auto_round_extension/ark/benchmarks/results/unitrace_k32_verbose_20260814.csv new file mode 100644 index 0000000000..93451c8eb2 --- /dev/null +++ b/auto_round_extension/ark/benchmarks/results/unitrace_k32_verbose_20260814.csv @@ -0,0 +1,8 @@ +mode,batch,num_heads_q,num_heads_kv,gqa_group_size,attention_pattern,seq_len,tensor_layout,head_dim,dtype,causal,requested_topk,selected_ratio,selected_blocks_per_row,warmup,iters,latency_ms,status,note,speedup_vs_torch,speedup_vs_sagev1,speedup_vs_ark,baseline_tflops,effective_tflops +dense_torch_sdpa,1,40,40,1,mha,75000,HND,128,bfloat16,False,,,,0,1,3051.9098619697616,ok,,1.0,3.1640525778102258,0.875786425847173,37.74685531690234,37.74685531690234 +dense_sagev1,1,40,40,1,mha,75000,HND,128,bfloat16,False,,,,0,1,9656.403266009875,ok,,0.316050373819034,1.0,0.2767926272746347,11.929907733399977,11.929907733399977 +dense_ark_sdpa,1,40,40,1,mha,75000,HND,128,bfloat16,False,,,,0,1,2672.8212300222367,ok,,1.1418308967653519,3.6128129923337737,1.0,43.1005256565706,43.1005256565706 +sparse_qtile256_row64k_kernel_only,1,40,40,1,mha,75000,HND,128,bfloat16,False,0.5,0.5025568148726252,588.9965870307167,0,1,28692.606869037263,ok,,0.10636572256747906,0.33654673868027946,0.09315365600002448,4.0149715404324064,2.017751309163948 +sparse_qtile256_row64k_e2e,1,40,40,1,mha,75000,HND,128,bfloat16,False,0.5,0.5025568148726252,588.9965870307167,0,1,804.5742539688945,ok,,3.7931985107837556,12.001879526191251,3.3220317662881245,143.18131537484388,71.95674580405438 +sparse_sdpa_bf16_kernel_only,1,40,40,1,mha,75000,HND,128,bfloat16,False,0.5,0.5025568148726252,588.9965870307167,0,1,25407.54995297175,ok,,0.12011822736228844,0.38006038692764355,0.1051979130207167,4.534085349167082,2.2786354914380436 +sparse_sdpa_bf16_e2e,1,40,40,1,mha,75000,HND,128,bfloat16,False,0.5,0.5025568148726252,588.9965870307167,0,1,1022.452419041656,ok,,2.9848918200323835,9.444354657658119,2.614127738606624,112.6702796673677,56.62321688044021 diff --git a/auto_round_extension/ark/benchmarks/results/unitrace_k32_verbose_20260814/k32_verbose.3726840 b/auto_round_extension/ark/benchmarks/results/unitrace_k32_verbose_20260814/k32_verbose.3726840 new file mode 100644 index 0000000000..c8b9a16ec4 --- /dev/null +++ b/auto_round_extension/ark/benchmarks/results/unitrace_k32_verbose_20260814/k32_verbose.3726840 @@ -0,0 +1,19 @@ + +=== Device Timing Summary === + + Total Execution Time (ns): 79051466957 + Total Device Time for L0 backend (ns): 1686731870 + +== L0 Backend == + + Kernel, Calls, Time (ns), Time (%), Average (ns), Min (ns), Max (ns) +"cutlass::fmha::kernel::XeSparseSageFwdKernel, cutlass::fmha::collective::SPARSESDPAFwdMainloop, false, false, false, false, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, 4, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, void, void, void, void, void>, cutlass::fmha::collective::SparseFMHAFwdEpilogue, false, false, false, false, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, 4, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, void, void, void, void, void>, cute::tuple, cute::C<128> >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, void>, cutlass::fmha::kernel::XeFHMAIndividualTileScheduler>[SIMD16 {1; 293; 40} {256; 1; 1}]", 2, 1685721458, 99.940094, 842860729, 842572604, 843148854 + "zeCommandListAppendMemoryCopy(D2D)[54943360]", 6, 991873, 0.058804, 165312, 58541, 278020 + "zeCommandListAppendMemoryCopy(D2M)[1]", 8, 11821, 0.000701, 1477, 1197, 2604 + "zeCommandListAppendMemoryCopy(D2M)[8]", 3, 6718, 0.000398, 2239, 1510, 2656 + + +=== Kernel Properties === + + Kernel, Compiled, SIMD, Number of Arguments, SLM Per Work Group, Private Memory Per Thread, Spill Memory Per Thread, Register File Size Per Thread +"cutlass::fmha::kernel::XeSparseSageFwdKernel, cutlass::fmha::collective::SPARSESDPAFwdMainloop, false, false, false, false, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, 4, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, void, void, void, void, void>, cutlass::fmha::collective::SparseFMHAFwdEpilogue, false, false, false, false, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, 4, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, void, void, void, void, void>, cute::tuple, cute::C<128> >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, void>, cutlass::fmha::kernel::XeFHMAIndividualTileScheduler>[SIMD16 {1; 293; 40} {256; 1; 1}]", JIT, 16, 2, 0, 2432, 0, 256 diff --git a/auto_round_extension/ark/benchmarks/results/unitrace_k64_compute_real_20260814.csv b/auto_round_extension/ark/benchmarks/results/unitrace_k64_compute_real_20260814.csv new file mode 100644 index 0000000000..c98ac6ac5a --- /dev/null +++ b/auto_round_extension/ark/benchmarks/results/unitrace_k64_compute_real_20260814.csv @@ -0,0 +1,8 @@ +mode,batch,num_heads_q,num_heads_kv,gqa_group_size,attention_pattern,seq_len,tensor_layout,head_dim,dtype,causal,requested_topk,selected_ratio,selected_blocks_per_row,warmup,iters,latency_ms,status,note,speedup_vs_torch,speedup_vs_sagev1,speedup_vs_ark,baseline_tflops,effective_tflops +dense_torch_sdpa,1,40,40,1,mha,75000,HND,128,bfloat16,False,,,,0,1,3052.225488005206,ok,,1.0,3.129175918210046,0.8672042433799233,37.74295197151027,37.74295197151027 +dense_sagev1,1,40,40,1,mha,75000,HND,128,bfloat16,False,,,,0,1,9550.950494012795,ok,,0.31957295663070967,1.0,0.2771350240596196,12.06162675350641,12.06162675350641 +dense_ark_sdpa,1,40,40,1,mha,75000,HND,128,bfloat16,False,,,,0,1,2646.902894950472,ok,,1.1531308888693925,3.6083494079942477,1.0,43.522563755462436,43.522563755462436 +sparse_qtile256_row64k_kernel_only,1,40,40,1,mha,75000,HND,128,bfloat16,False,0.5,0.5025568148726252,588.9965870307167,0,1,28671.261675073765,ok,,0.1064559182150938,0.3331192956296097,0.09231902400903541,4.017960608275311,2.019253485578516 +sparse_qtile256_row64k_e2e,1,40,40,1,mha,75000,HND,128,bfloat16,False,0.5,0.5025568148726252,588.9965870307167,0,1,804.9391260137782,ok,,3.79187119791337,11.865432037464796,3.2883267931805875,143.11641250499747,71.92412842450827 +sparse_sdpa_bf16_kernel_only,1,40,40,1,mha,75000,HND,128,bfloat16,False,0.5,0.5025568148726252,588.9965870307167,0,1,30556.331855943426,ok,,0.09988847818497318,0.31256862044306755,0.08662371214677164,3.7700860346426945,1.8946824293657984 +sparse_sdpa_bf16_e2e,1,40,40,1,mha,75000,HND,128,bfloat16,False,0.5,0.5025568148726252,588.9965870307167,0,1,1721.1301509523764,ok,,1.7733844743329124,5.54923199081013,1.5378865412855762,66.9327650417691,33.63751721000927 diff --git a/auto_round_extension/ark/benchmarks/results/unitrace_k64_compute_real_20260814/k64_compute_real.2948755 b/auto_round_extension/ark/benchmarks/results/unitrace_k64_compute_real_20260814/k64_compute_real.2948755 new file mode 100644 index 0000000000..aca4758a37 --- /dev/null +++ b/auto_round_extension/ark/benchmarks/results/unitrace_k64_compute_real_20260814/k64_compute_real.2948755 @@ -0,0 +1,32 @@ + +=== Device Timing Summary === + + Total Execution Time (ns): 84973767741 + Total Device Time for L0 backend (ns): 3078355254 + +== L0 Backend == + + Kernel, Calls, Time (ns), Time (%), Average (ns), Min (ns), Max (ns) +"cutlass::fmha::kernel::XeSparseSageFwdKernel, cutlass::fmha::collective::SPARSESDPAFwdMainloop, false, false, false, false, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, 4, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, void, void, void, void, void>, cutlass::fmha::collective::SparseFMHAFwdEpilogue, false, false, false, false, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, 4, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, void, void, void, void, void>, cute::tuple, cute::C<128> >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, void>, cutlass::fmha::kernel::XeFHMAIndividualTileScheduler>", 2, 3077343124, 99.967125, 1538671562, 1537868437, 1539474687 + "zeCommandListAppendMemoryCopy(D2D)", 6, 993123, 0.032261, 165520, 58437, 278125 + "zeCommandListAppendMemoryCopy(D2M)", 11, 19007, 0.000617, 1727, 1197, 2760 + + +=== Kernel Properties === + + Kernel, Compiled, SIMD, Number of Arguments, SLM Per Work Group, Private Memory Per Thread, Spill Memory Per Thread, Register File Size Per Thread +"cutlass::fmha::kernel::XeSparseSageFwdKernel, cutlass::fmha::collective::SPARSESDPAFwdMainloop, false, false, false, false, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, 4, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, void, void, void, void, void>, cutlass::fmha::collective::SparseFMHAFwdEpilogue, false, false, false, false, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, 4, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, void, void, void, void, void>, cute::tuple, cute::C<128> >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, void>, cutlass::fmha::kernel::XeFHMAIndividualTileScheduler>", JIT, 16, 2, 0, 2432, 3840, 256 + + + +=== Kernel Submission Summary === + + Total Execution Time (ns): 84973767741 + Total Device Time for L0 backend (ns): 3078355254 + +== L0 Backend == + + Kernel, Calls, Append (ns), Append (%), Submit (ns), Submit (%), Execute (ns), Execute (%) +"cutlass::fmha::kernel::XeSparseSageFwdKernel, cutlass::fmha::collective::SPARSESDPAFwdMainloop, false, false, false, false, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, 4, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, void, void, void, void, void>, cutlass::fmha::collective::SparseFMHAFwdEpilogue, false, false, false, false, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, 4, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, void, void, void, void, void>, cute::tuple, cute::C<128> >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, void>, cutlass::fmha::kernel::XeFHMAIndividualTileScheduler>", 2, 0, -nan, 317707, 0.033428, 3077343124, 99.967125 + "zeCommandListAppendMemoryCopy(D2D)", 6, 0, -nan, 611043591, 64.292694, 993123, 0.032261 + "zeCommandListAppendMemoryCopy(D2M)", 11, 0, -nan, 339047807, 35.673882, 19007, 0.000617 diff --git a/auto_round_extension/ark/benchmarks/results/unitrace_k64_compute_real_20260814/k64_compute_real.metrics.2948755 b/auto_round_extension/ark/benchmarks/results/unitrace_k64_compute_real_20260814/k64_compute_real.metrics.2948755 new file mode 100644 index 0000000000..76557fbf99 --- /dev/null +++ b/auto_round_extension/ark/benchmarks/results/unitrace_k64_compute_real_20260814/k64_compute_real.metrics.2948755 @@ -0,0 +1,7 @@ + +=== Device #0 Metrics === + +Kernel,GlobalInstanceId,SubDeviceId,GpuTime[ns],GpuCoreClocks[cycles],AvgGpuCoreFrequencyMHz[MHz],ResultUncertainty[%],GPU_BUSY[%],IA_VERTEX[events],GPGPU_THREADGROUP_COUNT[events],ASYNC_GPGPU_THREADGROUP_COUNT[events],RASTERIZER_SAMPLE_OUTPUT[events],ICACHE_HIT[events],ICACHE_MISS[events],XVE_ACTIVE[%],XVE_INST_EXECUTED_ALU0_ALL[events],XVE_INST_EXECUTED_ALU1_ALL[events],XVE_INST_EXECUTED_SEND_ALL[events],XVE_INST_ISSUED_ALL[events],XVE_SHARED_FUNCTION_ACCESS_HOLD[%],XVE_STALL[%],XVE_THREADS_OCCUPANCY_ALL[%],XVE_INST_EXECUTED_ALU2_ALL[events],XVE_MULTIPLE_PIPE_ACTIVE[%],LOAD_STORE_CACHE_PARTIAL_WRITE_COUNT[events],SLM_BANK_CONFLICT_COUNT[events],SLM_BYTE_READ[bytes],SLM_BYTE_WRITE[bytes],LOAD_STORE_CACHE_BYTE_READ[bytes],LOAD_STORE_CACHE_BYTE_WRITE[bytes],LOAD_STORE_CACHE_ACCESS[events],LOAD_STORE_CACHE_HIT[events],L3_ATOMIC_ACCESS[events],L3_HIT[events],L3_MISS[events],L3_READ[events],L3_WRITE[events],L3_STALL[%],COMPRESSOR_INPUT[events],COMPRESSOR_OUTPUT[events],GPU_MEMORY_BYTE_READ[bytes],GPU_MEMORY_BYTE_WRITE[bytes],GPU_MEMORY_BYTE_READ_RATE[GBpS],GPU_MEMORY_BYTE_WRITE_RATE[GBpS],GPU_MEMORY_L3_READ[events],GPU_MEMORY_L3_WRITE[events],GPU_MEMORY_REQUEST_QUEUE_FULL[%],TLB_MISS[events],ASYNC_GPGPU_THREAD_EXIT_COUNT[messages],GPGPU_DISPATCH[%],COMMAND_PARSER_COMPUTE_ENGINE_BUSY[%],COMMAND_PARSER_COMPUTE_ENGINE_DISPATCH_KERNEL_COUNT[events],COMMAND_PARSER_COPY_ENGINE_BUSY[%],COMMAND_PARSER_FLUSH_COUNT[events],COMMAND_PARSER_RENDER_ENGINE_BUSY[%],COMMAND_PARSER_RENDER_ENGINE_DISPATCH_KERNEL_COUNT[events],XVE_PIPE_ALU0_AND_ALU1_ACTIVE[%],XVE_PIPE_ALU0_AND_ALU2_ACTIVE[%],XVE_INST_EXECUTED_ALU0_ALL_UTILIZATION[%],XVE_INST_EXECUTED_ALU1_ALL_UTILIZATION[%],XVE_INST_EXECUTED_ALU2_ALL_UTILIZATION[%],HOST_TO_GPUMEM_TRANSACTION_READ[events],HOST_TO_GPUMEM_TRANSACTION_WRITE[events],SYSMEM_TRANSACTION_READ[events],SYSMEM_TRANSACTION_WRITE[events],QueryBeginTime[ns],CoreFrequencyMHz[MHz],CoreFrequencyChanged,QuerySplitOccurred,ReportId,ReportsCount,OverrunOccured,MidQueryTimer,MidQueryProgramming,MidQueryMarker,MidQueryCtxSwitch,MidQueryC6,MidQueryFreqChange,MidQueryMmioTrigger,ReportError,ReportLost,ReportInconsistent,ReportCtxSwitchLost,ReportWithoutWorkload,ReportContextMismatch,ReportQueryModeMismatch +"cutlass::fmha::kernel::XeSparseSageFwdKernel, cutlass::fmha::collective::SPARSESDPAFwdMainloop, false, false, false, false, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, 4, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, void, void, void, void, void>, cutlass::fmha::collective::SparseFMHAFwdEpilogue, false, false, false, false, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, 4, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, void, void, void, void, void>, cute::tuple, cute::C<128> >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, void>, cutlass::fmha::kernel::XeFHMAIndividualTileScheduler>[SIMD16 {1; 293; 40} {256; 1; 1}]",13,0,1535408316,3690568189,2403,0,100.000000,0,0,11720,0,31072289842,14180,39.954498,48230240000,66374007680,40784238720,131183357152,19.292183,59.890968,99.816544,113099407360,4.077200,750080,0,0,0,6694842331072,1166541660160,151338661503,138265715260,0,14187776782,643149139,11119708210,3717068770,0.000555,2993279,2993279,37791607808,4113692672,24.613394,2.679218,150374771,17856300,0.000000,44749,187520,0.002795,100.000000,1,0.000000,1,0.000000,0,1.724919,1.626167,8.167821,11.240480,38.306911,1127998,21,3,17,603328285516718,2400,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 + +"cutlass::fmha::kernel::XeSparseSageFwdKernel, cutlass::fmha::collective::SPARSESDPAFwdMainloop, false, false, false, false, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, 4, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, void, void, void, void, void>, cutlass::fmha::collective::SparseFMHAFwdEpilogue, false, false, false, false, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, 4, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, void, void, void, void, void>, cute::tuple, cute::C<128> >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, void>, cutlass::fmha::kernel::XeFHMAIndividualTileScheduler>[SIMD16 {1; 293; 40} {256; 1; 1}]",19,0,1537011892,3694509142,2403,0,100.000008,0,0,11720,0,31047649164,14180,39.913635,48230240000,66374007680,40784238720,131183357448,19.328482,59.987267,99.869186,113099407360,4.067187,750080,0,0,0,6694772599744,1166541660160,151337571951,138324432787,0,14157620305,609852603,11089353225,3683883768,0.000838,3057878,3001145,35453591040,4021094400,23.066570,2.616177,141190751,17473185,0.000000,44461,187520,0.002792,100.000000,1,0.371961,1,0.000000,0,1.716085,1.622224,8.159108,11.228489,38.266045,1127154,37,4,27,603330005450781,2400,0,0,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0 diff --git a/auto_round_extension/ark/benchmarks/results/unitrace_k64_stalls_real_20260814.csv b/auto_round_extension/ark/benchmarks/results/unitrace_k64_stalls_real_20260814.csv new file mode 100644 index 0000000000..3c384e960d --- /dev/null +++ b/auto_round_extension/ark/benchmarks/results/unitrace_k64_stalls_real_20260814.csv @@ -0,0 +1,8 @@ +mode,batch,num_heads_q,num_heads_kv,gqa_group_size,attention_pattern,seq_len,tensor_layout,head_dim,dtype,causal,requested_topk,selected_ratio,selected_blocks_per_row,warmup,iters,latency_ms,status,note,speedup_vs_torch,speedup_vs_sagev1,speedup_vs_ark,baseline_tflops,effective_tflops +dense_torch_sdpa,1,40,40,1,mha,75000,HND,128,bfloat16,False,,,,0,1,3054.134353995323,ok,,1.0,3.139249184469624,0.8705397637467008,37.7193622308393,37.7193622308393 +dense_sagev1,1,40,40,1,mha,75000,HND,128,bfloat16,False,,,,0,1,9587.68878004048,ok,,0.31854750650160635,1.0,0.27730827105200906,12.015408785464729,12.015408785464729 +dense_ark_sdpa,1,40,40,1,mha,75000,HND,128,bfloat16,False,,,,0,1,2658.7453989777714,ok,,1.1487126052647125,3.6060951092672258,1.0,43.328706857110824,43.328706857110824 +sparse_qtile256_row64k_kernel_only,1,40,40,1,mha,75000,HND,128,bfloat16,False,0.5,0.5025568148726252,588.9965870307167,0,1,28159.330382943153,ok,,0.1084590546885054,0.3404799989792369,0.09441791984472199,4.091006371010145,2.055963131438476 +sparse_qtile256_row64k_e2e,1,40,40,1,mha,75000,HND,128,bfloat16,False,0.5,0.5025568148726252,588.9965870307167,0,1,805.575530976057,ok,,3.7912451862767624,11.901663359143713,3.3004296887671893,143.00335048709903,71.86730833691017 +sparse_sdpa_bf16_kernel_only,1,40,40,1,mha,75000,HND,128,bfloat16,False,0.5,0.5025568148726252,588.9965870307167,0,1,30696.65548496414,ok,,0.09949404277906763,0.31233659265377395,0.08661352049509369,3.752851839394274,1.886021267094859 +sparse_sdpa_bf16_e2e,1,40,40,1,mha,75000,HND,128,bfloat16,False,0.5,0.5025568148726252,588.9965870307167,0,1,1720.7675460958853,ok,,1.7748674775536122,5.571751281451836,1.5450927145912243,66.94686929758075,33.644605399886125 diff --git a/auto_round_extension/ark/benchmarks/results/unitrace_k64_stalls_real_20260814/k64_stalls_real.metrics.2952213 b/auto_round_extension/ark/benchmarks/results/unitrace_k64_stalls_real_20260814/k64_stalls_real.metrics.2952213 new file mode 100644 index 0000000000..c48b429095 --- /dev/null +++ b/auto_round_extension/ark/benchmarks/results/unitrace_k64_stalls_real_20260814/k64_stalls_real.metrics.2952213 @@ -0,0 +1,7 @@ + +=== Device #0 Metrics === + +Kernel,GlobalInstanceId,SubDeviceId,GpuTime[ns],GpuCoreClocks[cycles],AvgGpuCoreFrequencyMHz[MHz],ResultUncertainty[%],GPU_BUSY[%],IA_VERTEX[events],GPGPU_THREADGROUP_COUNT[events],ASYNC_GPGPU_THREADGROUP_COUNT[events],RASTERIZER_SAMPLE_OUTPUT[events],XVE_STALL_ALUWR[%],XVE_STALL_BARRIER[%],XVE_STALL_CONTROL[%],XVE_STALL_INSTFETCH[%],XVE_STALL_OTHER[%],XVE_STALL_PIPESTALL[%],XVE_STALL_PS_DEPENDENCY[%],XVE_STALL_SBID[%],XVE_STALL_SENDWR[%],THREAD_DISPATCH_QUEUE0_ACTIVE[%],THREAD_DISPATCH_QUEUE0_STALL[%],THREAD_DISPATCH_QUEUE1_ACTIVE[%],THREAD_DISPATCH_QUEUE1_STALL[%],THREADGROUP_DISPATCH_QUEUE0_RESOURCE_STALL[%],THREADGROUP_DISPATCH_QUEUE1_RESOURCE_STALL[%],XVE_ACTIVE[%],XVE_SHARED_FUNCTION_ACCESS_HOLD[%],XVE_STALL[%],XVE_THREADS_OCCUPANCY_ALL[%],XVE_MULTIPLE_PIPE_ACTIVE[%],XVE_PIPE_ALU0_AND_ALU1_ACTIVE[%],XVE_PIPE_ALU0_AND_ALU2_ACTIVE[%],THREADGROUP_DISPATCH_QUEUE0_INPUT_AVAILABLE[%],THREADGROUP_DISPATCH_QUEUE1_INPUT_AVAILABLE[%],QueryBeginTime[ns],CoreFrequencyMHz[MHz],CoreFrequencyChanged,QuerySplitOccurred,ReportId,ReportsCount,OverrunOccured,MidQueryTimer,MidQueryProgramming,MidQueryMarker,MidQueryCtxSwitch,MidQueryC6,MidQueryFreqChange,MidQueryMmioTrigger,ReportError,ReportLost,ReportInconsistent,ReportCtxSwitchLost,ReportWithoutWorkload,ReportContextMismatch,ReportQueryModeMismatch +"cutlass::fmha::kernel::XeSparseSageFwdKernel, cutlass::fmha::collective::SPARSESDPAFwdMainloop, false, false, false, false, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, 4, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, void, void, void, void, void>, cutlass::fmha::collective::SparseFMHAFwdEpilogue, false, false, false, false, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, 4, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, void, void, void, void, void>, cute::tuple, cute::C<128> >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, void>, cutlass::fmha::kernel::XeFHMAIndividualTileScheduler>[SIMD16 {1; 293; 40} {256; 1; 1}]",13,0,1534833404,3689186584,2403,0,100.000000,0,0,11720,0,16.455626,0.512216,1.308606,3.631864,0.000000,0.887532,0.000000,55.635029,3.059207,0.000000,0.000000,0.002796,0.000000,0.000000,0.000003,39.953323,19.185026,59.893566,99.811523,4.085039,1.716491,1.629522,0.000000,0.000035,603420934757447,2400,0,0,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0 + +"cutlass::fmha::kernel::XeSparseSageFwdKernel, cutlass::fmha::collective::SPARSESDPAFwdMainloop, false, false, false, false, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, 4, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, void, void, void, void, void>, cutlass::fmha::collective::SparseFMHAFwdEpilogue, false, false, false, false, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, cute::TiledMMA >, cute::Layout, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<0>, cute::C<0> > >, cute::tuple, cute::C<16>, cute::C<2> >, cute::tuple, cute::C<16>, cute::C<8> > >, cute::Layout, cute::C<1> >, cute::Layout, cute::C<1> > > const>, 4, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, cute::Tensor >, cute::Layout, cute::tuple, int, int, int> > >, void, void, void, void, void>, cute::tuple, cute::C<128> >, cute::Tensor >, cute::Layout, cute::tuple, int, int> > >, void>, cutlass::fmha::kernel::XeFHMAIndividualTileScheduler>[SIMD16 {1; 293; 40} {256; 1; 1}]",19,0,1536064296,3692231472,2403,0,100.000000,0,0,11720,0,16.608208,0.549755,1.310681,3.644871,0.000000,0.893470,0.000000,55.824959,3.042835,0.000000,0.000000,0.002793,0.000000,0.000000,0.000003,39.910118,19.170021,59.959267,99.842361,4.083643,1.704670,1.628649,0.000000,0.000035,603422654691614,2400,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 From 7e82ad5060a0114d9f9b3a321d92799f5f7ddf80 Mon Sep 17 00:00:00 2001 From: yiliu30 Date: Fri, 14 Aug 2026 16:25:20 +0000 Subject: [PATCH 30/42] docs: record BF16 Flux sweep and offload fix --- .../results/SPARSE_BF16_K32_AB_20260814.md | 65 +++++++++++++++++++ .../results/SPARSE_BF16_K32_AB_20260814_CN.md | 62 ++++++++++++++++++ auto_round_extension/ark/examples/run_flux.py | 6 +- 3 files changed, 130 insertions(+), 3 deletions(-) diff --git a/auto_round_extension/ark/benchmarks/results/SPARSE_BF16_K32_AB_20260814.md b/auto_round_extension/ark/benchmarks/results/SPARSE_BF16_K32_AB_20260814.md index 164da22c26..03871e86c9 100644 --- a/auto_round_extension/ark/benchmarks/results/SPARSE_BF16_K32_AB_20260814.md +++ b/auto_round_extension/ark/benchmarks/results/SPARSE_BF16_K32_AB_20260814.md @@ -256,3 +256,68 @@ the extra memory traffic created by the larger K64 live state. Recommendation: keep the selected-block BF16 sparse SDPA path on K32 and treat K64 as a regression case unless compiler/offline assembly data proves the write/SBID pressure can be removed another way. + +## Latest apple-to-apple ARK SDPA comparison + +After pulling the latest K32 implementation, dense ARK SDPA and BF16 sparse SDPA were rerun on XPU 0 +and XPU 1 with the same 75k-token shape and launch configuration: + +- Commit: `9b1a9512` +- Shape: `seq_len=75000`, HND, 40 Q heads, 40 KV heads, head_dim 128 +- Sparse configuration: `topk=0.5`, `q_tile_override=256`, `sparse_q_block_tokens=256`, + `sparse_k_block_tokens=64` +- Benchmark options: `--warmup 1 --iters 2` +- Timing: mean of XPU 0 and XPU 1; sparse results include both kernel-only and end-to-end paths + +| path | mean latency | speedup vs dense ARK | +|---|---:|---:| +| dense ARK SDPA | 994.6 ms | 1.00x | +| BF16 sparse K32, kernel-only | 598.2 ms | 1.66x | +| BF16 sparse K32, end-to-end | 697.6 ms | 1.43x | + +The latest Unitrace kernel-property pass reported SIMD16, SLM 0, private memory 0 B/thread, +spill 0, and GRF 256 for dense ARK. BF16 sparse reported SIMD16, SLM 0, private memory +2176 B/thread, spill 0, and GRF 256. + +The current ComputeBasic and VectorEngineStalls counter passes could not create a metric query or +streamer because the host has `observation_paranoid=1`. No system policy was changed, so the latest +comparison uses same-shape unprofiled timing plus the kernel-property pass. The raw Unitrace output +and timing CSVs are in: + +`benchmarks/results/apple_to_apple_ark_sdpa_bf16_sparse_75k_xpu0_20260814_135844/` + +Timing CSVs: + +- `benchmarks/results/k32_rerun_bf16_xpu0_20260814_134727.csv` +- `benchmarks/results/k32_rerun_bf16_xpu1_20260814_134959.csv` + +## Flux BF16 sparse topk sweep + +The latest K32 BF16 sparse path was also run through the Flux example at 1024x1024 with the same +prompt, seed, 50 denoising steps, qtile 256, Q block 256, and K routing block 64. Each point ran +in parallel on a separate XPU. All runs completed with 2,850/2,850 sparse attention calls and zero +runtime fallbacks. + +| topk | XPU | observed sparsity | PNG | +|---:|---:|---:|---| +| 1.0 | 0 | 0.00% | `flux_topk1p0.png` | +| 0.9 | 1 | 11.11% | `flux_topk0p9.png` | +| 0.8 | 2 | 20.83% | `flux_topk0p8.png` | +| 0.7 | 3 | 30.56% | `flux_topk0p7.png` | +| 0.6 | 4 | 40.28% | `flux_topk0p6.png` | +| 0.5 | 5 | 50.00% | `flux_topk0p5.png` | +| 0.4 | 6 | 61.11% | `flux_topk0p4.png` | +| 0.3 | 7 | 70.83% | `flux_topk0p3.png` | +| 0.2 | 0 | 80.56% | `flux_topk0p2.png` | +| 0.1 | 1 | 90.28% | `flux_topk0p1.png` | +| 0.01 | 2 | 98.61% | `flux_topk0p01.png` | + +The main sweep artifacts are under: + +`benchmarks/results/flux_bf16_topk_sweep_1024_parallel_20260814_160126/` + +The additional low-topk artifacts are under: + +`benchmarks/results/flux_bf16_topk_sweep_1024_parallel_extra_20260814_160550/` + +Each directory contains `summary.csv`, the exact per-run commands, logs, status files, and PNGs. diff --git a/auto_round_extension/ark/benchmarks/results/SPARSE_BF16_K32_AB_20260814_CN.md b/auto_round_extension/ark/benchmarks/results/SPARSE_BF16_K32_AB_20260814_CN.md index 024eefdcbf..c93f32b4d1 100644 --- a/auto_round_extension/ark/benchmarks/results/SPARSE_BF16_K32_AB_20260814_CN.md +++ b/auto_round_extension/ark/benchmarks/results/SPARSE_BF16_K32_AB_20260814_CN.md @@ -191,3 +191,65 @@ write 差异是这里能采到的最佳 spill/pressure proxy，符合 K64 带来 建议：BF16 稀疏 SDPA 的已选中 block 路径保持 K32；除非后续 compiler/offline assembly 数据证明可以 用其它方式消除 write/SBID 压力，否则 K64 应视为性能回退。 + +## 最新 ARK SDPA 对比 + +拉取最新 K32 实现后，在 XPU 0 和 XPU 1 上使用相同的 75k-token shape 与 launch 配置，重新运行了 +dense ARK SDPA 和 BF16 sparse SDPA： + +- Commit：`9b1a9512` +- Shape：`seq_len=75000`、HND、40 个 Q heads、40 个 KV heads、head_dim 128 +- Sparse 配置：`topk=0.5`、`q_tile_override=256`、`sparse_q_block_tokens=256`、 + `sparse_k_block_tokens=64` +- Benchmark 选项：`--warmup 1 --iters 2` +- Timing：XPU 0 和 XPU 1 的均值；sparse 同时记录 kernel-only 和 end-to-end + +| 路径 | 平均延迟 | 相对 dense ARK 加速比 | +|---|---:|---:| +| dense ARK SDPA | 994.6 ms | 1.00x | +| BF16 sparse K32，kernel-only | 598.2 ms | 1.66x | +| BF16 sparse K32，end-to-end | 697.6 ms | 1.43x | + +最新 Unitrace kernel-property pass 报告：dense ARK 为 SIMD16、SLM 0、private memory 0 B/thread、 +spill 0、GRF 256；BF16 sparse 为 SIMD16、SLM 0、private memory 2176 B/thread、spill 0、GRF 256。 + +当前 ComputeBasic 和 VectorEngineStalls counter pass 因主机 `observation_paranoid=1`，无法创建 +metric query 或 streamer。没有修改系统策略，因此最新比较使用相同 shape 的 unprofiled timing 加 +kernel-property pass。原始 Unitrace 输出和 timing CSV 位于： + +`benchmarks/results/apple_to_apple_ark_sdpa_bf16_sparse_75k_xpu0_20260814_135844/` + +Timing CSV： + +- `benchmarks/results/k32_rerun_bf16_xpu0_20260814_134727.csv` +- `benchmarks/results/k32_rerun_bf16_xpu1_20260814_134959.csv` + +## Flux BF16 sparse topk sweep + +最新 K32 BF16 sparse 路径通过 Flux example 运行了 1024x1024 sweep，使用相同 prompt、seed、50 个 +denoising steps、qtile 256、Q block 256 和 K routing block 64。每个 topk 在独立 XPU 上并行运行。 +全部运行均完成了 2,850/2,850 次 sparse attention call，runtime fallback 为 0。 + +| topk | XPU | 实测 sparsity | PNG | +|---:|---:|---:|---| +| 1.0 | 0 | 0.00% | `flux_topk1p0.png` | +| 0.9 | 1 | 11.11% | `flux_topk0p9.png` | +| 0.8 | 2 | 20.83% | `flux_topk0p8.png` | +| 0.7 | 3 | 30.56% | `flux_topk0p7.png` | +| 0.6 | 4 | 40.28% | `flux_topk0p6.png` | +| 0.5 | 5 | 50.00% | `flux_topk0p5.png` | +| 0.4 | 6 | 61.11% | `flux_topk0p4.png` | +| 0.3 | 7 | 70.83% | `flux_topk0p3.png` | +| 0.2 | 0 | 80.56% | `flux_topk0p2.png` | +| 0.1 | 1 | 90.28% | `flux_topk0p1.png` | +| 0.01 | 2 | 98.61% | `flux_topk0p01.png` | + +主 sweep artifacts： + +`benchmarks/results/flux_bf16_topk_sweep_1024_parallel_20260814_160126/` + +新增低 topk artifacts： + +`benchmarks/results/flux_bf16_topk_sweep_1024_parallel_extra_20260814_160550/` + +每个目录包含 `summary.csv`、精确 per-run command、log、status 文件和 PNG。 diff --git a/auto_round_extension/ark/examples/run_flux.py b/auto_round_extension/ark/examples/run_flux.py index 0bc89fec53..57fcf902d6 100644 --- a/auto_round_extension/ark/examples/run_flux.py +++ b/auto_round_extension/ark/examples/run_flux.py @@ -54,10 +54,10 @@ def env_sparse_kernel(name="FLUX_SPARSE_KERNEL", default="int8"): model_id = os.getenv("FLUX_MODEL", "~/workspace/models/black-forest-labs/FLUX.1-dev/") pipe = FluxPipeline.from_pretrained(model_id, torch_dtype=dtype) -if benchmark_scope != "block": - pipe.to(device) if cpu_offload_enabled: - pipe.enable_model_cpu_offload() + pipe.enable_model_cpu_offload(device=device) +elif benchmark_scope != "block": + pipe.to(device) height = int(os.getenv("FLUX_HEIGHT", "1024")) width = int(os.getenv("FLUX_WIDTH", "1024")) From 58105f7acb24ca5079d04a842f940e787be00782 Mon Sep 17 00:00:00 2001 From: yiliu30 Date: Mon, 17 Aug 2026 02:50:40 +0000 Subject: [PATCH 31/42] fix: support causal qtile256 sparse attention --- .../stla/xe_sparse_sdpa_fwd_mainloop.hpp | 31 ++++++----- .../test/test_sage_sparse_prefill_e2e.py | 13 +++++ ...PARSE_BF16_INT8_APPLE_TO_APPLE_20260816.md | 53 +++++++++++++++++++ ...SE_BF16_INT8_APPLE_TO_APPLE_20260816_CN.md | 53 +++++++++++++++++++ 4 files changed, 138 insertions(+), 12 deletions(-) create mode 100644 auto_round_extension/ark/benchmarks/results/FLUX_SPARSE_BF16_INT8_APPLE_TO_APPLE_20260816.md create mode 100644 auto_round_extension/ark/benchmarks/results/FLUX_SPARSE_BF16_INT8_APPLE_TO_APPLE_20260816_CN.md diff --git a/auto_round_extension/ark/auto_round_kernel/wrapper/include/stla/xe_sparse_sdpa_fwd_mainloop.hpp b/auto_round_extension/ark/auto_round_kernel/wrapper/include/stla/xe_sparse_sdpa_fwd_mainloop.hpp index a89d36aabe..c1b2d1fae2 100644 --- a/auto_round_extension/ark/auto_round_kernel/wrapper/include/stla/xe_sparse_sdpa_fwd_mainloop.hpp +++ b/auto_round_extension/ark/auto_round_kernel/wrapper/include/stla/xe_sparse_sdpa_fwd_mainloop.hpp @@ -264,7 +264,13 @@ struct SPARSESDPAFwdMainloop, CausalMask_, Full subgroup_q_row_in_tile = cute::min(subgroup_q_row_in_tile, q_blocks_per_wg_tile - 1); int sparse_route_blocks = params.num_k_blocks > 0 ? params.num_k_blocks : total_blk; - int k_tiles_per_sparse_route_block = cute::max(1, ceil_div(total_blk, sparse_route_blocks)); + // The causal scheduler shortens total_blk for each Q subgroup. Deriving + // this ratio from total_blk would then collapse a 64-token route block to + // one K32 tile near the beginning of the sequence and drop half its keys. + // Keep the route-to-physical-tile mapping tied to the full K shape instead. + int k_route_block_tokens = cute::ceil_div(int(size<0>(K_2D)), sparse_route_blocks); + int k_tiles_per_sparse_route_block = + cute::max(1, ceil_div(k_route_block_tokens, get<1>(TileShapeQK{}))); auto route_block_to_k_tile = [&](int route_block, int micro_tile) { return route_block * k_tiles_per_sparse_route_block + micro_tile; }; @@ -325,17 +331,18 @@ struct SPARSESDPAFwdMainloop, CausalMask_, Full if (subgroup_selected) { if constexpr (!is_cache && CausalMask) { - if (K == total_blk - 1) { - Tensor cPgP = make_identity_tensor(make_shape(seq_len, seq_len)); - Tensor gP = local_tile(cPgP, take<0, 2>(TileShapeQK{}), make_coord(get<0>(blk_qv), K)); - auto cS_thread = thr_mma_qk.partition_C(gP); - CUTLASS_PRAGMA_UNROLL - for (int i = 0; i < tSrS.size(); ++i) { - int row_idx = get<0>(cS_thread(i)); - int col_idx = get<1>(cS_thread(i)); - if (col_idx - seq_len_kv_cache - full_tile_offset > row_idx - discard_seq_coord) { - tSrS(i) = ElementS(-INFINITY); - } + // K32 splits each logical 64-token sparse route block into two + // physical K tiles. The diagonal can therefore occur in any + // physical tile, rather than only in the final K tile. + Tensor cPgP = make_identity_tensor(make_shape(seq_len, seq_len)); + Tensor gP = local_tile(cPgP, take<0, 2>(TileShapeQK{}), make_coord(get<0>(blk_qv), K)); + auto cS_thread = thr_mma_qk.partition_C(gP); + CUTLASS_PRAGMA_UNROLL + for (int i = 0; i < tSrS.size(); ++i) { + int row_idx = get<0>(cS_thread(i)); + int col_idx = get<1>(cS_thread(i)); + if (col_idx - seq_len_kv_cache - full_tile_offset > row_idx - discard_seq_coord) { + tSrS(i) = ElementS(-INFINITY); } } } else if constexpr (FullMask_) { diff --git a/auto_round_extension/ark/auto_round_kernel/wrapper/test/test_sage_sparse_prefill_e2e.py b/auto_round_extension/ark/auto_round_kernel/wrapper/test/test_sage_sparse_prefill_e2e.py index a08bccc10b..b37c9445b3 100644 --- a/auto_round_extension/ark/auto_round_kernel/wrapper/test/test_sage_sparse_prefill_e2e.py +++ b/auto_round_extension/ark/auto_round_kernel/wrapper/test/test_sage_sparse_prefill_e2e.py @@ -553,6 +553,19 @@ def main() -> None: sparse_k_block_tokens=64, ) run_case_sdpa(dtype, 128, is_causal=True, q_tile_override=64) + # Regression coverage for causal masking across K32 qtile256 microtiles. + run_case_sdpa( + dtype, + 128, + seq_len_q=512, + seq_len_kv=512, + num_heads_q=8, + num_heads_kv=8, + is_causal=True, + q_tile_override=256, + sparse_q_block_tokens=256, + sparse_k_block_tokens=64, + ) run_case_sdpa_full(dtype, 64) run_case_sdpa_full(dtype, 128, q_tile_override=64) diff --git a/auto_round_extension/ark/benchmarks/results/FLUX_SPARSE_BF16_INT8_APPLE_TO_APPLE_20260816.md b/auto_round_extension/ark/benchmarks/results/FLUX_SPARSE_BF16_INT8_APPLE_TO_APPLE_20260816.md new file mode 100644 index 0000000000..8e13eb450c --- /dev/null +++ b/auto_round_extension/ark/benchmarks/results/FLUX_SPARSE_BF16_INT8_APPLE_TO_APPLE_20260816.md @@ -0,0 +1,53 @@ +# Flux Sparse Attention: BF16 vs INT8 Apples-to-Apples Comparison + +Date: 2026-08-16 + +## Scope + +This report compares the existing Flux BF16 sparse sweep with the INT8 sparse sweep generated on 2026-08-16. + +Both runs use the same effective configuration: + +- FLUX.1-dev, 1024×1024 output, 50 denoising steps, guidance scale 3.5, seed 0 +- Prompt: `A cat holding a sign that says hello world` +- qtile256, sparse Q block 256, sparse K block 64 +- BF16 pipeline tensors; BF16 sparse uses `sparge_sage2_attn_meansim_topk_xpu_sdpa`, while INT8 sparse uses `sparge_sage2_attn_meansim_topk_xpu` with INT8 Q/K quantization +- CPU model offload enabled; one process per XPU + +The BF16 runs were collected on 2026-08-14 and the INT8 runs on 2026-08-16. The table is therefore configuration-matched, but not a same-minute controlled performance experiment. The top-k-to-XPU assignment is the same for the common top-k values. + +## Common top-k results + +`INT8/BF16` is the wall-clock ratio; a value above 1 means INT8 took longer in these runs. `Mean RGB diff` is the mean absolute per-channel difference between the corresponding 1024×1024 PNGs, using the same prompt and seed. It is an image-difference diagnostic, not an image-quality score. + +| top-k | attention sparsity | BF16 time (s) | INT8 time (s) | INT8/BF16 | mean RGB diff | BF16 image | INT8 image | +|---:|---:|---:|---:|---:|---:|---|---| +| 1.0 | 0.00% | 119 | 173 | 1.45x | 1.357 | [PNG](flux_bf16_topk_sweep_1024_parallel_20260814_160126/flux_topk1p0.png) | [PNG](flux_int8_topk_sweep_20260816/flux_topk1p0.png) | +| 0.9 | 11.11% | 116 | 173 | 1.49x | 1.150 | [PNG](flux_bf16_topk_sweep_1024_parallel_20260814_160126/flux_topk0p9.png) | [PNG](flux_int8_topk_sweep_20260816/flux_topk0p9.png) | +| 0.8 | 20.83% | 117 | 168 | 1.44x | 6.168 | [PNG](flux_bf16_topk_sweep_1024_parallel_20260814_160126/flux_topk0p8.png) | [PNG](flux_int8_topk_sweep_20260816/flux_topk0p8.png) | +| 0.7 | 30.56% | 116 | 168 | 1.45x | 7.833 | [PNG](flux_bf16_topk_sweep_1024_parallel_20260814_160126/flux_topk0p7.png) | [PNG](flux_int8_topk_sweep_20260816/flux_topk0p7.png) | +| 0.6 | 40.28% | 113 | 169 | 1.50x | 3.121 | [PNG](flux_bf16_topk_sweep_1024_parallel_20260814_160126/flux_topk0p6.png) | [PNG](flux_int8_topk_sweep_20260816/flux_topk0p6.png) | +| 0.5 | 50.00% | 118 | 167 | 1.42x | 1.252 | [PNG](flux_bf16_topk_sweep_1024_parallel_20260814_160126/flux_topk0p5.png) | [PNG](flux_int8_topk_sweep_20260816/flux_topk0p5.png) | +| 0.4 | 61.11% | 114 | 167 | 1.46x | 9.763 | [PNG](flux_bf16_topk_sweep_1024_parallel_20260814_160126/flux_topk0p4.png) | [PNG](flux_int8_topk_sweep_20260816/flux_topk0p4.png) | +| 0.3 | 70.83% | 112 | 165 | 1.47x | 3.150 | [PNG](flux_bf16_topk_sweep_1024_parallel_20260814_160126/flux_topk0p3.png) | [PNG](flux_int8_topk_sweep_20260816/flux_topk0p3.png) | +| 0.2 | 80.56% | 111 | 116 | 1.05x | 10.759 | [PNG](flux_bf16_topk_sweep_1024_parallel_extra_20260814_160550/flux_topk0p2.png) | [PNG](flux_int8_topk_sweep_20260816/flux_topk0p2.png) | +| 0.1 | 90.28% | 111 | 116 | 1.05x | 17.252 | [PNG](flux_bf16_topk_sweep_1024_parallel_extra_20260814_160550/flux_topk0p1.png) | [PNG](flux_int8_topk_sweep_20260816/flux_topk0p1.png) | + +The BF16 links for top-k 0.2 and 0.1 are from the extra BF16 sweep directory; the directory name is `flux_bf16_topk_sweep_1024_parallel_extra_20260814_160550`. + +## Run integrity + +- Every common BF16 and INT8 run completed with exit code 0. +- Every run performed 2,850 sparse attention calls: 1,900 single-stream and 950 joint-stream calls. +- Both kernels reported zero unsupported fallbacks and zero runtime fallbacks. +- All generated files are valid 1024×1024 RGB PNGs. + +## BF16-only additional result + +BF16 also has a top-k 0.01 run: 111 seconds, 98.61% attention sparsity, with output at [flux_topk0p01.png](flux_bf16_topk_sweep_1024_parallel_extra_20260814_160550/flux_topk0p01.png). There is no matching INT8 top-k 0.01 run in the current sweep. + +## Raw records + +- [BF16 main summary](flux_bf16_topk_sweep_1024_parallel_20260814_160126/summary.csv) +- [BF16 extra summary](flux_bf16_topk_sweep_1024_parallel_extra_20260814_160550/summary.csv) +- [INT8 summary](flux_int8_topk_sweep_20260816/summary.csv) diff --git a/auto_round_extension/ark/benchmarks/results/FLUX_SPARSE_BF16_INT8_APPLE_TO_APPLE_20260816_CN.md b/auto_round_extension/ark/benchmarks/results/FLUX_SPARSE_BF16_INT8_APPLE_TO_APPLE_20260816_CN.md new file mode 100644 index 0000000000..f6da12d9c8 --- /dev/null +++ b/auto_round_extension/ark/benchmarks/results/FLUX_SPARSE_BF16_INT8_APPLE_TO_APPLE_20260816_CN.md @@ -0,0 +1,53 @@ +# Flux 稀疏注意力：BF16 与 INT8 的一对一对比 + +日期：2026-08-16 + +## 范围 + +本文对比已有的 Flux BF16 稀疏扫描和 2026-08-16 生成的 INT8 稀疏扫描。 + +两次运行使用相同的有效配置： + +- FLUX.1-dev，输出 1024×1024，50 个去噪步，guidance scale 3.5，seed 0 +- Prompt：`A cat holding a sign that says hello world` +- qtile256，稀疏 Q block 256，稀疏 K block 64 +- Flux pipeline 张量为 BF16；BF16 稀疏使用 `sparge_sage2_attn_meansim_topk_xpu_sdpa`，INT8 稀疏使用带 INT8 Q/K 量化的 `sparge_sage2_attn_meansim_topk_xpu` +- 启用 CPU model offload；每张 XPU 一个进程 + +BF16 运行于 2026-08-14 收集，INT8 运行于 2026-08-16 收集。因此下表是配置对齐的比较，但不是同一分钟内的严格性能实验。共同 top-k 的 XPU 分配相同。 + +## 共同 top-k 结果 + +`INT8/BF16` 是墙钟时间比值；大于 1 表示本次运行中 INT8 用时更长。`Mean RGB diff` 是相同 prompt 和 seed 下，对应 1024×1024 PNG 的逐通道绝对差均值。它是图像差异诊断值，不是图像质量评分。 + +| top-k | 注意力稀疏率 | BF16 用时（秒） | INT8 用时（秒） | INT8/BF16 | RGB 均值差 | BF16 图像 | INT8 图像 | +|---:|---:|---:|---:|---:|---:|---|---| +| 1.0 | 0.00% | 119 | 173 | 1.45x | 1.357 | [PNG](flux_bf16_topk_sweep_1024_parallel_20260814_160126/flux_topk1p0.png) | [PNG](flux_int8_topk_sweep_20260816/flux_topk1p0.png) | +| 0.9 | 11.11% | 116 | 173 | 1.49x | 1.150 | [PNG](flux_bf16_topk_sweep_1024_parallel_20260814_160126/flux_topk0p9.png) | [PNG](flux_int8_topk_sweep_20260816/flux_topk0p9.png) | +| 0.8 | 20.83% | 117 | 168 | 1.44x | 6.168 | [PNG](flux_bf16_topk_sweep_1024_parallel_20260814_160126/flux_topk0p8.png) | [PNG](flux_int8_topk_sweep_20260816/flux_topk0p8.png) | +| 0.7 | 30.56% | 116 | 168 | 1.45x | 7.833 | [PNG](flux_bf16_topk_sweep_1024_parallel_20260814_160126/flux_topk0p7.png) | [PNG](flux_int8_topk_sweep_20260816/flux_topk0p7.png) | +| 0.6 | 40.28% | 113 | 169 | 1.50x | 3.121 | [PNG](flux_bf16_topk_sweep_1024_parallel_20260814_160126/flux_topk0p6.png) | [PNG](flux_int8_topk_sweep_20260816/flux_topk0p6.png) | +| 0.5 | 50.00% | 118 | 167 | 1.42x | 1.252 | [PNG](flux_bf16_topk_sweep_1024_parallel_20260814_160126/flux_topk0p5.png) | [PNG](flux_int8_topk_sweep_20260816/flux_topk0p5.png) | +| 0.4 | 61.11% | 114 | 167 | 1.46x | 9.763 | [PNG](flux_bf16_topk_sweep_1024_parallel_20260814_160126/flux_topk0p4.png) | [PNG](flux_int8_topk_sweep_20260816/flux_topk0p4.png) | +| 0.3 | 70.83% | 112 | 165 | 1.47x | 3.150 | [PNG](flux_bf16_topk_sweep_1024_parallel_20260814_160126/flux_topk0p3.png) | [PNG](flux_int8_topk_sweep_20260816/flux_topk0p3.png) | +| 0.2 | 80.56% | 111 | 116 | 1.05x | 10.759 | [PNG](flux_bf16_topk_sweep_1024_parallel_extra_20260814_160550/flux_topk0p2.png) | [PNG](flux_int8_topk_sweep_20260816/flux_topk0p2.png) | +| 0.1 | 90.28% | 111 | 116 | 1.05x | 17.252 | [PNG](flux_bf16_topk_sweep_1024_parallel_extra_20260814_160550/flux_topk0p1.png) | [PNG](flux_int8_topk_sweep_20260816/flux_topk0p1.png) | + +BF16 的 top-k 0.2 和 0.1 链接来自额外 BF16 扫描目录：`flux_bf16_topk_sweep_1024_parallel_extra_20260814_160550`。 + +## 运行完整性 + +- 所有共同 BF16 和 INT8 运行均以 exit code 0 完成。 +- 每次运行均执行 2,850 次稀疏注意力：1,900 次 single-stream，950 次 joint-stream。 +- 两种 kernel 均报告 unsupported fallback 为 0、runtime fallback 为 0。 +- 所有输出文件均为有效的 1024×1024 RGB PNG。 + +## 仅 BF16 的额外结果 + +BF16 还运行了 top-k 0.01：111 秒，注意力稀疏率 98.61%，输出为 [flux_topk0p01.png](flux_bf16_topk_sweep_1024_parallel_extra_20260814_160550/flux_topk0p01.png)。当前扫描没有对应的 INT8 top-k 0.01 结果。 + +## 原始记录 + +- [BF16 主扫描汇总](flux_bf16_topk_sweep_1024_parallel_20260814_160126/summary.csv) +- [BF16 额外扫描汇总](flux_bf16_topk_sweep_1024_parallel_extra_20260814_160550/summary.csv) +- [INT8 扫描汇总](flux_int8_topk_sweep_20260816/summary.csv) From 1d0040e88730a1699269d1ce6b6de9d0f94f3f1c Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Tue, 18 Aug 2026 06:07:38 +0000 Subject: [PATCH 32/42] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- .../ark/examples/flux_gen_bf16_sweep.py | 38 ++++++++++++------- .../ark/tools/run_sparse_sagev1_bench.sh | 3 ++ 2 files changed, 28 insertions(+), 13 deletions(-) diff --git a/auto_round_extension/ark/examples/flux_gen_bf16_sweep.py b/auto_round_extension/ark/examples/flux_gen_bf16_sweep.py index 597890add2..c3be4e398b 100644 --- a/auto_round_extension/ark/examples/flux_gen_bf16_sweep.py +++ b/auto_round_extension/ark/examples/flux_gen_bf16_sweep.py @@ -47,12 +47,17 @@ def main(): dev_suffix = f"_dev{device_id}" if device_id else "" print(f"[flux_sweep] model={model_id}", flush=True) - print(f"[flux_sweep] topks={topks} run_dense={run_dense} size={height}x{width} steps={steps} seed={seed}", flush=True) + print( + f"[flux_sweep] topks={topks} run_dense={run_dense} size={height}x{width} steps={steps} seed={seed}", flush=True + ) print(f"[flux_sweep] out_dir={out_dir}", flush=True) - print(f"[flux_sweep] kernel={os.getenv('FLUX_SPARSE_KERNEL', '?')} " - f"q_tile={os.getenv('FLUX_SPARSE_Q_TILE_OVERRIDE', '0')} " - f"q_block={os.getenv('FLUX_SPARSE_Q_BLOCK_TOKENS', 'default')} " - f"k_block={os.getenv('FLUX_SPARSE_K_BLOCK_TOKENS', 'default')}", flush=True) + print( + f"[flux_sweep] kernel={os.getenv('FLUX_SPARSE_KERNEL', '?')} " + f"q_tile={os.getenv('FLUX_SPARSE_Q_TILE_OVERRIDE', '0')} " + f"q_block={os.getenv('FLUX_SPARSE_Q_BLOCK_TOKENS', 'default')} " + f"k_block={os.getenv('FLUX_SPARSE_K_BLOCK_TOKENS', 'default')}", + flush=True, + ) pipe = FluxPipeline.from_pretrained(model_id, torch_dtype=dtype) # Offload-only: pipe.to(device) would load the whole ~54 GB model onto the @@ -126,7 +131,8 @@ def do_run(tag, topk, png_name): print( f"[flux_sweep] DONE tag={tag} topk={topk} wall={wall_s:.3f}s " f"sparsity={stats.avg_sparsity:.4f} sparse_calls={stats.sparse_calls} " - f"runtime_fallbacks={stats.runtime_fallbacks}", flush=True + f"runtime_fallbacks={stats.runtime_fallbacks}", + flush=True, ) else: print(f"[flux_sweep] DONE tag={tag} wall={wall_s:.3f}s", flush=True) @@ -142,8 +148,17 @@ def do_run(tag, topk, png_name): # CSV + markdown summary (device-suffixed so parallel instances sharing # FLUX_OUTPUT_DIR do not clobber each other). csv_path = out_dir / f"sweep_summary{dev_suffix}.csv" - fieldnames = ["tag", "topk", "wall_s", "sparsity", "calls", "sparse_calls", - "runtime_fallbacks", "unsupported_fallbacks", "png"] + fieldnames = [ + "tag", + "topk", + "wall_s", + "sparsity", + "calls", + "sparse_calls", + "runtime_fallbacks", + "unsupported_fallbacks", + "png", + ] with open(csv_path, "w", newline="", encoding="utf-8") as fh: writer = csv.DictWriter(fh, fieldnames=fieldnames) writer.writeheader() @@ -153,11 +168,8 @@ def do_run(tag, topk, png_name): with open(md_path, "w", encoding="utf-8") as fh: fh.write("| tag | topk | wall_s | sparsity | calls | sparse_calls | runtime_fallbacks | png |\n") fh.write("|---|---|---|---|---|---|---|---|\n") - for r in rows: - fh.write( - f"| {r['tag']} | {r['topk']} | {r['wall_s']} | {r['sparsity']} | {r['calls']} " - f"| {r['sparse_calls']} | {r['runtime_fallbacks']} | `{r['png']}` |\n" - ) + fh.writelines(f"| {r['tag']} | {r['topk']} | {r['wall_s']} | {r['sparsity']} | {r['calls']} " + f"| {r['sparse_calls']} | {r['runtime_fallbacks']} | `{r['png']}` |\n" for r in rows) print(f"[flux_sweep] summary: {csv_path}", flush=True) print(f"[flux_sweep] summary: {md_path}", flush=True) diff --git a/auto_round_extension/ark/tools/run_sparse_sagev1_bench.sh b/auto_round_extension/ark/tools/run_sparse_sagev1_bench.sh index 41c6701b43..bf92761f0a 100755 --- a/auto_round_extension/ark/tools/run_sparse_sagev1_bench.sh +++ b/auto_round_extension/ark/tools/run_sparse_sagev1_bench.sh @@ -1,4 +1,7 @@ #!/usr/bin/env bash +# # Copyright (C) 2026 Intel Corporation +# # SPDX-License-Identifier: Apache-2.0 + # # run_sparse_sagev1_bench.sh # ========================== From fbf26b5716f69610ea6e172d4fb7ecbb62dd6deb Mon Sep 17 00:00:00 2001 From: yiliu30 Date: Tue, 18 Aug 2026 14:09:39 +0800 Subject: [PATCH 33/42] add shape analysis Signed-off-by: yiliu30 --- ...F16_SPARSE_TILE_SHAPE_ANALYSIS_20260818.md | 217 ++++++++++++++++++ 1 file changed, 217 insertions(+) create mode 100644 auto_round_extension/ark/benchmarks/BF16_SPARSE_TILE_SHAPE_ANALYSIS_20260818.md diff --git a/auto_round_extension/ark/benchmarks/BF16_SPARSE_TILE_SHAPE_ANALYSIS_20260818.md b/auto_round_extension/ark/benchmarks/BF16_SPARSE_TILE_SHAPE_ANALYSIS_20260818.md new file mode 100644 index 0000000000..56086fcf3f --- /dev/null +++ b/auto_round_extension/ark/benchmarks/BF16_SPARSE_TILE_SHAPE_ANALYSIS_20260818.md @@ -0,0 +1,217 @@ +# BF16 Sparse SDPA Tile Shape Analysis + +Date: 2026-08-18 + +## Scope + +This note summarizes the tile-shape discussion for the native BF16 sparse SDPA kernel. The question is whether reducing the workgroup Q tile from 256 to 64 while increasing the GEMM head-dimension tile from 32 to 128 can reach current performance. + +The analysis is based on: + +- Current ARK sparse BF16 SDPA implementation: + - `auto_round_kernel/wrapper/include/sycl_tla_sdpa_sparse.hpp` + - `auto_round_kernel/wrapper/include/stla/xe_sparse_sdpa_fwd_mainloop.hpp` + - `auto_round_kernel/sdpa_sparse_sdpa.cpp` +- SYCL-TLA tuning guide: + - `/home/yiliu4/workspace/sycl-tla/media/docs/cpp/cute/12_intel_performance_guide.md` + +## Current BF16 Sparse Tile + +For head_dim=128 with `q_tile_override=256`, the current native BF16 sparse SDPA path uses: + +```cpp +using ShapeQK = Shape<_256, _32, _32>; +using ShapePV = Shape<_256, _32, _32>; +using ShapeOut = Shape<_256, _128>; +using SubgroupLayoutQK = Layout>; +``` + +Interpretation: + +| Item | Value | +|---|---:| +| Q rows per workgroup | 256 | +| K-token columns per QK tile | 32 | +| Head-dim chunk per QK loop | 32 | +| Head-dim loops for head_dim=128 | 4 | +| QK score tile per workgroup | 256 x 32 | +| Output tile per workgroup | 256 x 128 | +| Subgroups per workgroup | 16 | +| Work-items per workgroup | 256 | +| Q rows per subgroup | 16 | + +The sparse API variant `sdpa_impl_bf16_sparse_sdpa_qtile256_row64k` passes `sparse_q_block_size=256`, so one sparse LUT row covers the full 256-token Q workgroup tile. The logical sparse K route block is 64 tokens, but the physical QK K-token tile is 32, so each selected K64 route block maps to two K32 micro-tiles. + +## Proposed Shape + +The proposed direction is to reduce M and increase the head-dimension K chunk: + +```cpp +using ShapeQK = Shape<_64, _32, _128>; +``` + +Here, `K=128` means the GEMM inner dimension, i.e. the head-dimension chunk, not the token-axis K tile. + +To keep the per-subgroup Q work comparable to the current kernel, the matching subgroup layout would likely be: + +```cpp +using SubgroupLayoutQK = Layout>; +``` + +This keeps: + +```text +Q rows per subgroup = 64 / 4 = 16 +``` + +which matches the current: + +```text +Q rows per subgroup = 256 / 16 = 16 +``` + +Keeping 16 subgroups for `M=64` would give only 4 Q rows per subgroup and would also change the DPAS M shape, so it is not an apples-to-apples comparison. + +## Apple-To-Apple Comparison + +| Config | ShapeQK | Q rows/WG | K-token tile | Head-dim chunk | Head-dim loops | Subgroups/WG | Q rows/SG | Main benefit | Main risk | +|---|---|---:|---:|---:|---:|---:|---:|---|---| +| Current | `Shape<_256, _32, _32>` | 256 | 32 | 32 | 4 | 16 | 16 | Large Q tile, stable fragments | More head-dim loop iterations | +| Proposed | `Shape<_64, _32, _128>` | 64 | 32 | 128 | 1 | 4 | 16 | Fewer head-dim loops | Larger Q/K fragments and 4x more workgroups | + +Current pseudocode: + +```cpp +for q_tile in Q step 256: + for selected k_token_tile in sparse_route: // physical token tile = 32 + S[256, 32] = 0 + + for d in head_dim step 32: // 4 loops + q_frag = load Q[256, 32] + k_frag = load K[32, 32] + S += q_frag @ k_frag.T + + P = softmax(S) + O[256, 128] += P @ V[32, 128] +``` + +Proposed pseudocode: + +```cpp +for q_tile in Q step 64: + for selected k_token_tile in sparse_route: // physical token tile = 32 + S[64, 32] = 0 + + for d in head_dim step 128: // 1 loop + q_frag = load Q[64, 128] + k_frag = load K[32, 128] + S += q_frag @ k_frag.T + + P = softmax(S) + O[64, 128] += P @ V[32, 128] +``` + +For the same 256-query region, the proposed shape launches four workgroups instead of one. The QK math is similar, but the execution shape is different: fewer head-dim loops per workgroup, more workgroups, less per-workgroup Q reuse, and larger Q/K fragments. + +## Coexisting Fragment Estimate + +The table below estimates logical fragment elements per subgroup. It is not an exact GRF count, but it shows the direction of register pressure. Prefetch fragments are excluded because `XE_PREFETCH_2D` has no destination register fragment. + +Assumptions: + +- Current: `ShapeQK = Shape<_256, _32, _32>`, `ShapePV = Shape<_256, _32, _32>`, `ShapeOut = Shape<_256, _128>`, 16 subgroups/WG. +- Proposed: `ShapeQK = Shape<_64, _32, _128>`, `ShapePV = Shape<_64, _32, _32>`, `ShapeOut = Shape<_64, _128>`, 4 subgroups/WG. +- Both keep 16 Q rows per subgroup. +- Copy fragments and MMA fragments are counted separately because both can be live around `reorder(copy_frag, mma_frag)` and `cute::gemm(...)`. + +### QK Phase + +| Fragment | Role | Current per SG | Proposed per SG | Change | +|---|---:|---:|---:|---:| +| `tQrQ` | Q copy fragment | `16 x 32 = 512` | `16 x 128 = 2048` | 4.0x | +| `tSrQ` | Q MMA fragment | `16 x 32 = 512` | `16 x 128 = 2048` | 4.0x | +| `tKrK` | K copy fragment | `32 x 32 = 1024` | `32 x 128 = 4096` | 4.0x | +| `tSrK` | K MMA fragment | `32 x 32 = 1024` | `32 x 128 = 4096` | 4.0x | +| `tSrS` | QK score accumulator | `16 x 32 = 512` | `16 x 32 = 512` | 1.0x | +| `tArA` | PV/O accumulator | `16 x 128 = 2048` | `16 x 128 = 2048` | 1.0x | +| `tA_max + tA_sum` | Softmax row state | `16 + 16 = 32` | `16 + 16 = 32` | 1.0x | +| **Approximate live sum** | | **5664** | **14880** | **2.63x** | + +### PV Phase + +| Fragment | Role | Current per SG | Proposed per SG | Change | +|---|---:|---:|---:|---:| +| `tSrS / tArP` | Score/probability fragment | `512 + 512` | `512 + 512` | 1.0x | +| `tVrV` | V copy fragment, one V tile | `32 x 32 = 1024` | `32 x 32 = 1024` | 1.0x | +| `tArV` | V MMA fragment, one V tile | `32 x 32 = 1024` | `32 x 32 = 1024` | 1.0x | +| `tArA` | Full output accumulator | `16 x 128 = 2048` | `16 x 128 = 2048` | 1.0x | +| `tA_max + tA_sum` | Softmax row state | `32` | `32` | 1.0x | +| **Approximate live sum** | | **5152** | **5152** | **1.0x** | + +The pressure increase is concentrated in the QK phase. Reducing M from 256 to 64 does not reduce per-subgroup Q rows if the subgroup layout is adjusted from 16 subgroups to 4 subgroups. Increasing the head-dim chunk from 32 to 128 makes the Q/K copy and MMA fragments 4x wider. + +## Why Subgroups Per Workgroup Change + +The subgroup count changes only if we intentionally preserve the same per-subgroup Q work. + +Current: + +```cpp +ShapeQK = Shape<_256, _32, _32>; +SubgroupLayoutQK = Layout>; +``` + +```text +16 subgroups/WG * 16 lanes/subgroup = 256 work-items/WG +Q rows/subgroup = 256 / 16 = 16 +``` + +Proposed apples-to-apples layout: + +```cpp +ShapeQK = Shape<_64, _32, _128>; +SubgroupLayoutQK = Layout>; +``` + +```text +4 subgroups/WG * 16 lanes/subgroup = 64 work-items/WG +Q rows/subgroup = 64 / 4 = 16 +``` + +If we kept 16 subgroups with `M=64`, each subgroup would cover only 4 Q rows. That would make the proposed kernel a different experiment: smaller per-subgroup work, different DPAS M behavior, and more overhead relative to useful compute. + +## Expected Performance Direction + +The SYCL-TLA performance guide gives the key tradeoff: + +- Increasing K can reduce K-loop and 2D block load issue overhead. +- Increasing K can also increase GRF pressure because larger copy/MMA fragments are live. +- Too much GRF pressure can cause compiler spill and erase the benefit. + +For this sparse BF16 SDPA kernel, `M64, headK128` is not expected to be a guaranteed win because: + +1. It reduces head-dim loops from 4 to 1. +2. It launches 4x more workgroups for the same Q region. +3. It makes Q/K copy and MMA fragments 4x wider per subgroup. +4. Attention already keeps score, softmax, probability, V, and output accumulator fragments live. +5. The estimated QK-phase live fragment footprint increases by about 2.6x per subgroup. + +## Recommendation + +Do not assume `Shape<_64, _32, _128>` will reach current performance. It is worth testing only as an A/B if profiling shows the current kernel is dominated by head-dim loop or 2D load issue overhead and unitrace/compiler reporting shows no spill. + +A safer first experiment is: + +```cpp +ShapeQK = Shape<_128, _32, _64>; +``` + +This reduces the head-dim loop count from 4 to 2 while taking a smaller GRF-risk step than `headK=128`. + +Validation should include: + +1. Correctness against dense SDPA. +2. Kernel timing on the same topk, layout, dtype, and sequence length. +3. `unitrace -d -v` to check GRF count and spill/private memory per thread. +4. ComputeBasic and stall metrics if timing changes materially. + From cbda4c48a87b393c3c32a2bccbd339171c1e8b33 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Tue, 18 Aug 2026 06:14:02 +0000 Subject: [PATCH 34/42] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- auto_round_extension/ark/examples/flux_gen_bf16_sweep.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/auto_round_extension/ark/examples/flux_gen_bf16_sweep.py b/auto_round_extension/ark/examples/flux_gen_bf16_sweep.py index c3be4e398b..904c8e7e76 100644 --- a/auto_round_extension/ark/examples/flux_gen_bf16_sweep.py +++ b/auto_round_extension/ark/examples/flux_gen_bf16_sweep.py @@ -168,8 +168,11 @@ def do_run(tag, topk, png_name): with open(md_path, "w", encoding="utf-8") as fh: fh.write("| tag | topk | wall_s | sparsity | calls | sparse_calls | runtime_fallbacks | png |\n") fh.write("|---|---|---|---|---|---|---|---|\n") - fh.writelines(f"| {r['tag']} | {r['topk']} | {r['wall_s']} | {r['sparsity']} | {r['calls']} " - f"| {r['sparse_calls']} | {r['runtime_fallbacks']} | `{r['png']}` |\n" for r in rows) + fh.writelines( + f"| {r['tag']} | {r['topk']} | {r['wall_s']} | {r['sparsity']} | {r['calls']} " + f"| {r['sparse_calls']} | {r['runtime_fallbacks']} | `{r['png']}` |\n" + for r in rows + ) print(f"[flux_sweep] summary: {csv_path}", flush=True) print(f"[flux_sweep] summary: {md_path}", flush=True) From c7620344ebe7c18914c6ec68c7bed18eb090e05b Mon Sep 17 00:00:00 2001 From: yiliu30 Date: Tue, 18 Aug 2026 14:20:02 +0800 Subject: [PATCH 35/42] chore: stop tracking ark benchmark results --- ...PARSE_BF16_INT8_APPLE_TO_APPLE_20260816.md | 53 --- ...SE_BF16_INT8_APPLE_TO_APPLE_20260816_CN.md | 53 --- .../results/SPARSE_BF16_HANDOFF_20260814.md | 171 ---------- .../SPARSE_BF16_HANDOFF_20260814_CN.md | 156 --------- .../results/SPARSE_BF16_K32_AB_20260814.md | 323 ------------------ .../results/SPARSE_BF16_K32_AB_20260814_CN.md | 255 -------------- .../results/UNITRACE_USAGE_GUIDE_20260814.md | 294 ---------------- .../ab_bf16_sdpa_k32_20260814_092404.csv | 16 - .../results/ab_k32_bf16_20260814_091351.csv | 16 - .../results/ab_k64_bf16_20260814_090508.csv | 16 - .../SWEEP_SUMMARY_dev5.md | 3 - .../flux_bf16_qtile256_topk0.9_512_dev5.png | Bin 305961 -> 0 bytes .../sweep_summary_dev5.csv | 2 - .../SWEEP_SUMMARY.md | 71 ---- .../bf16_k32_qtile256_topk_0p5_0p3_0p1.csv | 31 -- .../fp16_k32_qtile256_topk_0p5_0p3_0p1.csv | 31 -- .../k32_compute.metrics.2889557 | 7 - .../results/unitrace_k32_stalls_20260814.csv | 8 - .../k32_stalls.metrics.2892853 | 7 - .../results/unitrace_k32_verbose_20260814.csv | 8 - .../k32_verbose.3726840 | 19 -- .../unitrace_k64_compute_real_20260814.csv | 8 - .../k64_compute_real.2948755 | 32 -- .../k64_compute_real.metrics.2948755 | 7 - .../unitrace_k64_stalls_real_20260814.csv | 8 - .../k64_stalls_real.metrics.2952213 | 7 - 26 files changed, 1602 deletions(-) delete mode 100644 auto_round_extension/ark/benchmarks/results/FLUX_SPARSE_BF16_INT8_APPLE_TO_APPLE_20260816.md delete mode 100644 auto_round_extension/ark/benchmarks/results/FLUX_SPARSE_BF16_INT8_APPLE_TO_APPLE_20260816_CN.md delete mode 100644 auto_round_extension/ark/benchmarks/results/SPARSE_BF16_HANDOFF_20260814.md delete mode 100644 auto_round_extension/ark/benchmarks/results/SPARSE_BF16_HANDOFF_20260814_CN.md delete mode 100644 auto_round_extension/ark/benchmarks/results/SPARSE_BF16_K32_AB_20260814.md delete mode 100644 auto_round_extension/ark/benchmarks/results/SPARSE_BF16_K32_AB_20260814_CN.md delete mode 100644 auto_round_extension/ark/benchmarks/results/UNITRACE_USAGE_GUIDE_20260814.md delete mode 100644 auto_round_extension/ark/benchmarks/results/ab_bf16_sdpa_k32_20260814_092404.csv delete mode 100644 auto_round_extension/ark/benchmarks/results/ab_k32_bf16_20260814_091351.csv delete mode 100644 auto_round_extension/ark/benchmarks/results/ab_k64_bf16_20260814_090508.csv delete mode 100644 auto_round_extension/ark/benchmarks/results/flux_bf16_topk09_k32_20260814_112445/SWEEP_SUMMARY_dev5.md delete mode 100644 auto_round_extension/ark/benchmarks/results/flux_bf16_topk09_k32_20260814_112445/flux_bf16_qtile256_topk0.9_512_dev5.png delete mode 100644 auto_round_extension/ark/benchmarks/results/flux_bf16_topk09_k32_20260814_112445/sweep_summary_dev5.csv delete mode 100644 auto_round_extension/ark/benchmarks/results/k32_qtile256_sweep_20260814_163445/SWEEP_SUMMARY.md delete mode 100644 auto_round_extension/ark/benchmarks/results/k32_qtile256_sweep_20260814_163445/bf16_k32_qtile256_topk_0p5_0p3_0p1.csv delete mode 100644 auto_round_extension/ark/benchmarks/results/k32_qtile256_sweep_20260814_163445/fp16_k32_qtile256_topk_0p5_0p3_0p1.csv delete mode 100644 auto_round_extension/ark/benchmarks/results/unitrace_k32_compute_20260814/k32_compute.metrics.2889557 delete mode 100644 auto_round_extension/ark/benchmarks/results/unitrace_k32_stalls_20260814.csv delete mode 100644 auto_round_extension/ark/benchmarks/results/unitrace_k32_stalls_20260814/k32_stalls.metrics.2892853 delete mode 100644 auto_round_extension/ark/benchmarks/results/unitrace_k32_verbose_20260814.csv delete mode 100644 auto_round_extension/ark/benchmarks/results/unitrace_k32_verbose_20260814/k32_verbose.3726840 delete mode 100644 auto_round_extension/ark/benchmarks/results/unitrace_k64_compute_real_20260814.csv delete mode 100644 auto_round_extension/ark/benchmarks/results/unitrace_k64_compute_real_20260814/k64_compute_real.2948755 delete mode 100644 auto_round_extension/ark/benchmarks/results/unitrace_k64_compute_real_20260814/k64_compute_real.metrics.2948755 delete mode 100644 auto_round_extension/ark/benchmarks/results/unitrace_k64_stalls_real_20260814.csv delete mode 100644 auto_round_extension/ark/benchmarks/results/unitrace_k64_stalls_real_20260814/k64_stalls_real.metrics.2952213 diff --git a/auto_round_extension/ark/benchmarks/results/FLUX_SPARSE_BF16_INT8_APPLE_TO_APPLE_20260816.md b/auto_round_extension/ark/benchmarks/results/FLUX_SPARSE_BF16_INT8_APPLE_TO_APPLE_20260816.md deleted file mode 100644 index 8e13eb450c..0000000000 --- a/auto_round_extension/ark/benchmarks/results/FLUX_SPARSE_BF16_INT8_APPLE_TO_APPLE_20260816.md +++ /dev/null @@ -1,53 +0,0 @@ -# Flux Sparse Attention: BF16 vs INT8 Apples-to-Apples Comparison - -Date: 2026-08-16 - -## Scope - -This report compares the existing Flux BF16 sparse sweep with the INT8 sparse sweep generated on 2026-08-16. - -Both runs use the same effective configuration: - -- FLUX.1-dev, 1024×1024 output, 50 denoising steps, guidance scale 3.5, seed 0 -- Prompt: `A cat holding a sign that says hello world` -- qtile256, sparse Q block 256, sparse K block 64 -- BF16 pipeline tensors; BF16 sparse uses `sparge_sage2_attn_meansim_topk_xpu_sdpa`, while INT8 sparse uses `sparge_sage2_attn_meansim_topk_xpu` with INT8 Q/K quantization -- CPU model offload enabled; one process per XPU - -The BF16 runs were collected on 2026-08-14 and the INT8 runs on 2026-08-16. The table is therefore configuration-matched, but not a same-minute controlled performance experiment. The top-k-to-XPU assignment is the same for the common top-k values. - -## Common top-k results - -`INT8/BF16` is the wall-clock ratio; a value above 1 means INT8 took longer in these runs. `Mean RGB diff` is the mean absolute per-channel difference between the corresponding 1024×1024 PNGs, using the same prompt and seed. It is an image-difference diagnostic, not an image-quality score. - -| top-k | attention sparsity | BF16 time (s) | INT8 time (s) | INT8/BF16 | mean RGB diff | BF16 image | INT8 image | -|---:|---:|---:|---:|---:|---:|---|---| -| 1.0 | 0.00% | 119 | 173 | 1.45x | 1.357 | [PNG](flux_bf16_topk_sweep_1024_parallel_20260814_160126/flux_topk1p0.png) | [PNG](flux_int8_topk_sweep_20260816/flux_topk1p0.png) | -| 0.9 | 11.11% | 116 | 173 | 1.49x | 1.150 | [PNG](flux_bf16_topk_sweep_1024_parallel_20260814_160126/flux_topk0p9.png) | [PNG](flux_int8_topk_sweep_20260816/flux_topk0p9.png) | -| 0.8 | 20.83% | 117 | 168 | 1.44x | 6.168 | [PNG](flux_bf16_topk_sweep_1024_parallel_20260814_160126/flux_topk0p8.png) | [PNG](flux_int8_topk_sweep_20260816/flux_topk0p8.png) | -| 0.7 | 30.56% | 116 | 168 | 1.45x | 7.833 | [PNG](flux_bf16_topk_sweep_1024_parallel_20260814_160126/flux_topk0p7.png) | [PNG](flux_int8_topk_sweep_20260816/flux_topk0p7.png) | -| 0.6 | 40.28% | 113 | 169 | 1.50x | 3.121 | [PNG](flux_bf16_topk_sweep_1024_parallel_20260814_160126/flux_topk0p6.png) | [PNG](flux_int8_topk_sweep_20260816/flux_topk0p6.png) | -| 0.5 | 50.00% | 118 | 167 | 1.42x | 1.252 | [PNG](flux_bf16_topk_sweep_1024_parallel_20260814_160126/flux_topk0p5.png) | [PNG](flux_int8_topk_sweep_20260816/flux_topk0p5.png) | -| 0.4 | 61.11% | 114 | 167 | 1.46x | 9.763 | [PNG](flux_bf16_topk_sweep_1024_parallel_20260814_160126/flux_topk0p4.png) | [PNG](flux_int8_topk_sweep_20260816/flux_topk0p4.png) | -| 0.3 | 70.83% | 112 | 165 | 1.47x | 3.150 | [PNG](flux_bf16_topk_sweep_1024_parallel_20260814_160126/flux_topk0p3.png) | [PNG](flux_int8_topk_sweep_20260816/flux_topk0p3.png) | -| 0.2 | 80.56% | 111 | 116 | 1.05x | 10.759 | [PNG](flux_bf16_topk_sweep_1024_parallel_extra_20260814_160550/flux_topk0p2.png) | [PNG](flux_int8_topk_sweep_20260816/flux_topk0p2.png) | -| 0.1 | 90.28% | 111 | 116 | 1.05x | 17.252 | [PNG](flux_bf16_topk_sweep_1024_parallel_extra_20260814_160550/flux_topk0p1.png) | [PNG](flux_int8_topk_sweep_20260816/flux_topk0p1.png) | - -The BF16 links for top-k 0.2 and 0.1 are from the extra BF16 sweep directory; the directory name is `flux_bf16_topk_sweep_1024_parallel_extra_20260814_160550`. - -## Run integrity - -- Every common BF16 and INT8 run completed with exit code 0. -- Every run performed 2,850 sparse attention calls: 1,900 single-stream and 950 joint-stream calls. -- Both kernels reported zero unsupported fallbacks and zero runtime fallbacks. -- All generated files are valid 1024×1024 RGB PNGs. - -## BF16-only additional result - -BF16 also has a top-k 0.01 run: 111 seconds, 98.61% attention sparsity, with output at [flux_topk0p01.png](flux_bf16_topk_sweep_1024_parallel_extra_20260814_160550/flux_topk0p01.png). There is no matching INT8 top-k 0.01 run in the current sweep. - -## Raw records - -- [BF16 main summary](flux_bf16_topk_sweep_1024_parallel_20260814_160126/summary.csv) -- [BF16 extra summary](flux_bf16_topk_sweep_1024_parallel_extra_20260814_160550/summary.csv) -- [INT8 summary](flux_int8_topk_sweep_20260816/summary.csv) diff --git a/auto_round_extension/ark/benchmarks/results/FLUX_SPARSE_BF16_INT8_APPLE_TO_APPLE_20260816_CN.md b/auto_round_extension/ark/benchmarks/results/FLUX_SPARSE_BF16_INT8_APPLE_TO_APPLE_20260816_CN.md deleted file mode 100644 index f6da12d9c8..0000000000 --- a/auto_round_extension/ark/benchmarks/results/FLUX_SPARSE_BF16_INT8_APPLE_TO_APPLE_20260816_CN.md +++ /dev/null @@ -1,53 +0,0 @@ -# Flux 稀疏注意力：BF16 与 INT8 的一对一对比 - -日期：2026-08-16 - -## 范围 - -本文对比已有的 Flux BF16 稀疏扫描和 2026-08-16 生成的 INT8 稀疏扫描。 - -两次运行使用相同的有效配置： - -- FLUX.1-dev，输出 1024×1024，50 个去噪步，guidance scale 3.5，seed 0 -- Prompt：`A cat holding a sign that says hello world` -- qtile256，稀疏 Q block 256，稀疏 K block 64 -- Flux pipeline 张量为 BF16；BF16 稀疏使用 `sparge_sage2_attn_meansim_topk_xpu_sdpa`，INT8 稀疏使用带 INT8 Q/K 量化的 `sparge_sage2_attn_meansim_topk_xpu` -- 启用 CPU model offload；每张 XPU 一个进程 - -BF16 运行于 2026-08-14 收集，INT8 运行于 2026-08-16 收集。因此下表是配置对齐的比较，但不是同一分钟内的严格性能实验。共同 top-k 的 XPU 分配相同。 - -## 共同 top-k 结果 - -`INT8/BF16` 是墙钟时间比值；大于 1 表示本次运行中 INT8 用时更长。`Mean RGB diff` 是相同 prompt 和 seed 下，对应 1024×1024 PNG 的逐通道绝对差均值。它是图像差异诊断值，不是图像质量评分。 - -| top-k | 注意力稀疏率 | BF16 用时（秒） | INT8 用时（秒） | INT8/BF16 | RGB 均值差 | BF16 图像 | INT8 图像 | -|---:|---:|---:|---:|---:|---:|---|---| -| 1.0 | 0.00% | 119 | 173 | 1.45x | 1.357 | [PNG](flux_bf16_topk_sweep_1024_parallel_20260814_160126/flux_topk1p0.png) | [PNG](flux_int8_topk_sweep_20260816/flux_topk1p0.png) | -| 0.9 | 11.11% | 116 | 173 | 1.49x | 1.150 | [PNG](flux_bf16_topk_sweep_1024_parallel_20260814_160126/flux_topk0p9.png) | [PNG](flux_int8_topk_sweep_20260816/flux_topk0p9.png) | -| 0.8 | 20.83% | 117 | 168 | 1.44x | 6.168 | [PNG](flux_bf16_topk_sweep_1024_parallel_20260814_160126/flux_topk0p8.png) | [PNG](flux_int8_topk_sweep_20260816/flux_topk0p8.png) | -| 0.7 | 30.56% | 116 | 168 | 1.45x | 7.833 | [PNG](flux_bf16_topk_sweep_1024_parallel_20260814_160126/flux_topk0p7.png) | [PNG](flux_int8_topk_sweep_20260816/flux_topk0p7.png) | -| 0.6 | 40.28% | 113 | 169 | 1.50x | 3.121 | [PNG](flux_bf16_topk_sweep_1024_parallel_20260814_160126/flux_topk0p6.png) | [PNG](flux_int8_topk_sweep_20260816/flux_topk0p6.png) | -| 0.5 | 50.00% | 118 | 167 | 1.42x | 1.252 | [PNG](flux_bf16_topk_sweep_1024_parallel_20260814_160126/flux_topk0p5.png) | [PNG](flux_int8_topk_sweep_20260816/flux_topk0p5.png) | -| 0.4 | 61.11% | 114 | 167 | 1.46x | 9.763 | [PNG](flux_bf16_topk_sweep_1024_parallel_20260814_160126/flux_topk0p4.png) | [PNG](flux_int8_topk_sweep_20260816/flux_topk0p4.png) | -| 0.3 | 70.83% | 112 | 165 | 1.47x | 3.150 | [PNG](flux_bf16_topk_sweep_1024_parallel_20260814_160126/flux_topk0p3.png) | [PNG](flux_int8_topk_sweep_20260816/flux_topk0p3.png) | -| 0.2 | 80.56% | 111 | 116 | 1.05x | 10.759 | [PNG](flux_bf16_topk_sweep_1024_parallel_extra_20260814_160550/flux_topk0p2.png) | [PNG](flux_int8_topk_sweep_20260816/flux_topk0p2.png) | -| 0.1 | 90.28% | 111 | 116 | 1.05x | 17.252 | [PNG](flux_bf16_topk_sweep_1024_parallel_extra_20260814_160550/flux_topk0p1.png) | [PNG](flux_int8_topk_sweep_20260816/flux_topk0p1.png) | - -BF16 的 top-k 0.2 和 0.1 链接来自额外 BF16 扫描目录：`flux_bf16_topk_sweep_1024_parallel_extra_20260814_160550`。 - -## 运行完整性 - -- 所有共同 BF16 和 INT8 运行均以 exit code 0 完成。 -- 每次运行均执行 2,850 次稀疏注意力：1,900 次 single-stream，950 次 joint-stream。 -- 两种 kernel 均报告 unsupported fallback 为 0、runtime fallback 为 0。 -- 所有输出文件均为有效的 1024×1024 RGB PNG。 - -## 仅 BF16 的额外结果 - -BF16 还运行了 top-k 0.01：111 秒，注意力稀疏率 98.61%，输出为 [flux_topk0p01.png](flux_bf16_topk_sweep_1024_parallel_extra_20260814_160550/flux_topk0p01.png)。当前扫描没有对应的 INT8 top-k 0.01 结果。 - -## 原始记录 - -- [BF16 主扫描汇总](flux_bf16_topk_sweep_1024_parallel_20260814_160126/summary.csv) -- [BF16 额外扫描汇总](flux_bf16_topk_sweep_1024_parallel_extra_20260814_160550/summary.csv) -- [INT8 扫描汇总](flux_int8_topk_sweep_20260816/summary.csv) diff --git a/auto_round_extension/ark/benchmarks/results/SPARSE_BF16_HANDOFF_20260814.md b/auto_round_extension/ark/benchmarks/results/SPARSE_BF16_HANDOFF_20260814.md deleted file mode 100644 index 5b4dc56dac..0000000000 --- a/auto_round_extension/ark/benchmarks/results/SPARSE_BF16_HANDOFF_20260814.md +++ /dev/null @@ -1,171 +0,0 @@ -# Handoff: BF16 native sparse SDPA (sparge) — 2026-08-14 - -Status snapshot for a developer picking up this work. Everything here was verified on -**Intel Battlemage XPU (24.4 GB, `intel_gpu_bmg_g21`)**, oneAPI 2026.1, PyTorch 2.13.0+xpu. - -## 1. Where the code lives - -- **Branch `feat/sparse-bf16-prefill-v2`** — all sparse work (INT8 SAGE **and** native BF16/FP16 - sparse SDPA). **`main` only has the INT8 SAGE path** (empty `ark.cpp`/`sparse_attention.py` were - false alarms; check paths against the repo root — `main` has a working int8 `sparge_sage2_attn_meansim_topk_xpu` - in `auto_round_extension/ark/auto_round_kernel/sparse_attention.py`, 1440 lines). -- Switching branches requires a **rebuild** every time (the `.so` in `auto_round_kernel/ark-xbuild/` - only matches the branch it was built on). - -### Key changes in this handoff -- BF16 sparse SDPA now has a separate native BF16/FP16 path (`SparseSDPAConfig`) instead of reusing - the INT8 SAGE DPAS path. -- `benchmarks/bench_sparse_topk.py` now includes dense `ark.sdpa` as a baseline and reports - `speedup_vs_ark`. -- The BF16 sparse selected-block microtile was A/B tested at K64 vs K32. K32 is the current - experimental direction and the locally rebuilt `.so` is back on K32. -- `xe_sparse_sdpa_fwd_mainloop.hpp` supports expanding one 64-token sparse route block into multiple - physical K microtiles, which is required for K32 while keeping the existing 64-token LUT format. -- Unitrace profiles show K64 regresses because of compiler-reported spill plus SEND/SBID and - memory-write pressure. -- A FLUX.1-dev sparse BF16 topk 0.9 K32 image was generated successfully with zero fallbacks. - -### Two separate paths (do not mix) - -| path | wrapper | config | DPAS | -|---|---|---|---| -| INT8 sparse | `sparge_sage2_attn_meansim_topk_xpu` / `sage_sparse` | `SparseSageConfig` | `XE_DPAS_TT<8, int, int8, int8, int>` (+ `static_assert(ElementQ==int8_t)`) | -| BF16/FP16 sparse | `sparge_sage2_attn_meansim_topk_xpu_sdpa` / `sage_sparse_sdpa` | `SparseSDPAConfig` | `XE_DPAS_TT<8, float, bf16, bf16, float>` | - -**Using the int8 DPAS on bf16 fragments hangs the device** (fixed by the split; see -`SPARSE_BF16_DPAS_HANG.md`). - -### Key files -- `auto_round_kernel/sdpa_sparse_sdpa.cpp` — BF16 launcher. `sdpa_sparse.cpp` — INT8 launcher. -- `auto_round_kernel/sparse_attention.py` — Python wrappers + `sparge_preprocess_topk`. -- `auto_round_kernel/sparge_preprocess_triton.py` — triton_xpu preprocess (+ torch fallback). -- `wrapper/include/stla/xe_sparse_sdpa_fwd_mainloop.hpp`, `sycl_tla_sdpa_sparse.hpp` — kernels. -- `auto_round_kernel/sdpa.cpp` — **dense** SYCL-TLA SDPA → Python `ark.sdpa(...)` - (`xe_sdpa_fwd_mainloop.hpp`). - -## 2. Correctness — verified - -- **Repo e2e tests pass** (`test_sage_sparse_prefill_e2e.py`, `test_sparge_preprocess_topk_e2e.py`). -- **topk=1 (all blocks selected) == dense SDPA**: max diff **0.0039** (bf16 rounding) vs an exact - CPU fp32 reference. This is the "dense gate" test. -- **INT8 == BF16 kernels**: on identical Q/K/V they select the *same* blocks (bit-identical LUT) - and produce outputs within 0.008; verified across separate processes (bit-identical). -- **Preprocess + kernel are deterministic** (same QKV → identical results, in-process and - cross-process). - -## 3. Performance (bench `benchmarks/bench_sparse_topk.py`, seq 75000, 40h×128) - -New mode `dense_ark_sdpa` (dense SYCL-TLA SDPA via `ark.sdpa`) added as a third baseline; every -row gets a `speedup_vs_ark` column. Full tables in `BENCH_SPARSE_ARK_SDPA_20260813.md`. - -| mode | topk | vs torch | vs sagev1 | vs ark_sdpa | -|---|---|---|---|---| -| dense_ark_sdpa | — | 1.5–1.6× | 0.82–0.87× | 1.0 | -| INT8 sparse qtile256 (kernel) | 0.5 / 0.25 / 0.125 | 3.5 / 6.9 / 13.2× | — | 2.2 / 4.3 / 8.2× | -| BF16 sparse (kernel) | 0.5 / 0.25 / 0.125 | 1.5 / 2.9 / 5.6× | — | 0.92 / 1.8 / 3.5× | - -Notes: `dense_ark_sdpa` is faster than torch SDPA but slower than `dense_sagev1`; the BF16 sparse -kernel only beats the dense ark kernel at topk ≤ 0.25. The 1.5×-not-2× ceiling at topk 0.5 is the -**SBID scoreboard stall** (memory-SEND concurrency bound; INT8 avoids it because its K is half the -size) — earlier profiling details in `SPARSE_BF16_KERNEL_OPTIMIZATION_JOURNEY.md` / -`SPARSE_SDPA_KERNEL_OPTIMIZATION_OPTIONS.md`. - -**2026-08-14 A/B update:** changing the BF16 sparse qtile256 selected-block microtile from K64 to -K32 improved kernel-only latency by **1.69–1.77×**. At topk 0.5, BF16 sparse improved from -1511 ms / **0.91× vs ark** to 863 ms / **1.59× vs ark**. Correctness e2e still passed. See -`SPARSE_BF16_K32_AB_20260814.md`. - -**2026-08-14 unitrace update:** profiling K32 vs K64 at topk 0.5 confirmed the K64 regression is -compiler-reported spill plus memory/SEND scoreboard pressure, not lower occupancy. Kernel properties -from `unitrace --device-timing --verbose` showed `Spill Memory Per Thread = 0 B` for K32 and -`3840 B` for K64, with the same `Private Memory Per Thread = 2432 B` and register file size 256. -Aggregated `SPARSESDPAFwdMainloop` GPU time was 1684.6 ms (K32) vs 3072.4 ms (K64), XVE active -64.7% vs 39.9%, XVE stall 35.1% vs 59.9%, and SBID stall 31.4% vs 55.7%. SEND instructions rose -4.4× and GPU memory writes rose ~5.0× with K64. SLM read/write was 0/0 in both profiles because the -spill is private/scratch traffic, not SLM-backed storage. Raw profiles are in -`unitrace_k32_compute_20260814/`, `unitrace_k32_stalls_20260814/`, -`unitrace_k32_verbose_20260814/`, `unitrace_k64_compute_real_20260814/`, and -`unitrace_k64_stalls_real_20260814/`. - -## 4. FLUX.1-dev end-to-end — the critical gotcha - -**Use `enable_sequential_cpu_offload()`, NOT `enable_model_cpu_offload()`.** - -- `enable_model_cpu_offload()` streams at the *component* level: the whole ~46 GB transformer gets - pulled onto the 24.4 GB device (peak ~24 GB, ~350 MB headroom). The sparse launch then flakily - OOMs (`OUT_OF_DEVICE_MEMORY` / `OUT_OF_RESOURCES`), and can **reset the GPU** - (`dmesg: exec queue reset` on `0000:ba:00.0` = dev5; the `DEVICE_LOST` error). -- Sequential offload keeps the peak at **~0.16 GB** → triton_xpu sparse runs reliably (0 fallbacks). - Cost: ~4 s/step vs ~1 s/step (50-step generation ≈ 4–5 min). - -### FLUX image-quality findings -- bf16 sparse and int8 sparse produce **identical images** at the same config (verified, 0.0 diff). -- `topk=0.9` drops **12.5–16.7%** of blocks (route-block rounding + sim threshold), not 10% → - images sit ~15–18 from dense. Attention-mass coverage: topk 0.9 keeps **91.5%** of the mass, - topk 0.5 keeps **64.6%** — the renormalized output shifting is inherent to topk-sparse on - FLUX's spread-out attention. -- **Latest K32 BF16 sparse topk 0.9 run:** saved PNG at - `benchmarks/results/flux_bf16_topk09_k32_20260814_112445/flux_bf16_qtile256_topk0.9_512_dev5.png`. - Config: BF16 sparse qtile256, selected-block K32, `topk=0.9`, 512×512, 50 steps, seed 0, - `ZE_AFFINITY_MASK=5`. Result: wall **238.884 s**, sparsity **0.125**, calls **2850**, - sparse_calls **2850**, runtime_fallbacks **0**. -- **Open question: run-to-run non-determinism.** Same config (bf16 qtile256 topk0.5, seed 0) gave - images differing by **48.5** across two runs (means 97.6 vs 101.3), while topk 0.9 was - deterministic (0.0). QKV, preprocess and kernel are all verified deterministic → suspected - diffusion-trajectory amplification of a tiny perturbation elsewhere, not a sparse-kernel bug. - Worth re-checking before trusting single-run image comparisons. - -### Dev tooling (untracked) -- `examples/flux_gen_bf16_sweep.py` — offload-only runner (loop over topk, saves PNGs + summary). -- `tools/sweep_flux_bf16_topk.sh` — one-GPU wrapper (qtile256 env). **Note: `FLUX_SPARSE_KERNEL` - is now env-overridable (default bf16)**; earlier it hardcoded bf16 and silently made an "int8" - run bf16. `main`'s `flux_sparse_patch.py` is int8-only and ignores the block-token envs. -- `tools/bench_sparse_bf16_fp16_sweep.sh` — the bench sweep driver. - -## 5. Current repo state (uncommitted) - -- **Branch**: `feat/sparse-bf16-prefill-v2`. -- **Modified (tracked)**: `benchmarks/bench_sparse_topk.py` (added `dense_ark_sdpa` mode + - `speedup_vs_ark` column), sparse SDPA headers for K32 route-block expansion — not yet committed. -- **Untracked**: `examples/flux_gen_bf16_sweep.py`, `tools/sweep_flux_bf16_topk.sh`, - `tools/bench_sparse_bf16_fp16_sweep.sh`. -- `.so`: `auto_round_kernel/ark-xbuild/auto_round_kernel_xpu.cpython-313-x86_64-linux-gnu.so` - (feature-branch build). A stale **main** `.so` copy sits in `auto_round_kernel/xbuild/` (harmless; - feature's loader uses `ark-xbuild`). -- `benchmarks/results/` is gitignored → all result CSVs, PNGs, and docs below are local-only. - -## 6. How to run - -```bash -# rebuild (after oneAPI: source /opt/intel/oneapi/setvars.sh --force) -cd auto_round_extension/ark/auto_round_kernel/ark-xbuild && cmake --build . -j 16 - -# correctness tests -export ZE_AFFINITY_MASK=5 SAGE_ATTN_SPARSE_PREPROCESS_BACKEND=torch -.venv/bin/python auto_round_kernel/wrapper/test/test_sage_sparse_prefill_e2e.py -.venv/bin/python auto_round_kernel/wrapper/test/test_sparge_preprocess_topk_e2e.py - -# FLUX sweep (bf16 qtile256; set FLUX_SPARSE_KERNEL=int8 for SAGE) -export ZE_AFFINITY_MASK=5 SAGE_ATTN_SPARSE_PREPROCESS_BACKEND=triton_xpu -export FLUX_SPARSE_KERNEL=bf16 FLUX_SPARSE_TOPKS="0.9 0.5" FLUX_RUN_DENSE=0 -export FLUX_OUTPUT_DIR=benchmarks/results/flux_ -.venv/bin/python examples/flux_gen_bf16_sweep.py - -# bench (needs feature branch — main lacks bf16 symbols) -export ZE_AFFINITY_MASK=5 SAGE_ATTN_SPARSE_PREPROCESS_BACKEND=triton_xpu -.venv/bin/python benchmarks/bench_sparse_topk.py --dtype bf16 --seq-len 75000 \ - --topk 0.5 0.25 0.125 --tensor-layout HND NHD --q-tile-override 256 \ - --sparse-q-block-tokens 256 --sparse-k-block-tokens 64 --warmup 2 --iters 3 \ - --output-csv benchmarks/results/bench_.csv -``` - -## 7. Open items / suggestions - -1. **Resolve the topk-0.5 cross-run non-determinism** before trusting single-run image diffs - (capture QKV per run; all sparse ops verified deterministic). -2. **Clean up the K32 BF16 sparse SDPA experiment** (`SPARSE_BF16_K32_AB_20260814.md`) into a final - implementation. The unitrace A/B says to keep the selected-block microtile at K32 unless a later - compiler/assembly pass removes the K64 write/SBID pressure. -3. Consider making the FLUX runner's `_512` filename suffix derive from the actual size (cosmetic; - the 1024×1024 run is mislabeled `_512`). -4. Decide whether to commit the bench `dense_ark_sdpa` change + dev tooling (currently uncommitted). diff --git a/auto_round_extension/ark/benchmarks/results/SPARSE_BF16_HANDOFF_20260814_CN.md b/auto_round_extension/ark/benchmarks/results/SPARSE_BF16_HANDOFF_20260814_CN.md deleted file mode 100644 index 26a9abd9e4..0000000000 --- a/auto_round_extension/ark/benchmarks/results/SPARSE_BF16_HANDOFF_20260814_CN.md +++ /dev/null @@ -1,156 +0,0 @@ -# 交接文档：BF16 原生稀疏 SDPA（sparge）— 2026-08-14 - -供后续开发接手的状态快照。以下内容均在 **Intel Battlemage XPU（24.4 GB, `intel_gpu_bmg_g21`）**、 -oneAPI 2026.1、PyTorch 2.13.0+xpu 上验证。 - -## 1. 代码位置 - -- **分支 `feat/sparse-bf16-prefill-v2`** — 全部稀疏工作（INT8 SAGE **与** 原生 BF16/FP16 稀疏 SDPA）。 - **`main` 只有 INT8 SAGE 路径**（此前认为 `ark.cpp`/`sparse_attention.py` 为空是路径写错导致的误判； - 以仓库根为相对路径检查：`main` 有可用的 int8 `sparge_sage2_attn_meansim_topk_xpu`， - 位于 `auto_round_extension/ark/auto_round_kernel/sparse_attention.py`，1440 行）。 -- 切换分支后 **每次都要重新编译**（`auto_round_kernel/ark-xbuild/` 里的 `.so` 只匹配编译它的分支）。 - -### 本交接中的关键改动 -- BF16 稀疏 SDPA 已拆成独立的原生 BF16/FP16 路径（`SparseSDPAConfig`），不再复用 INT8 SAGE - DPAS 路径。 -- `benchmarks/bench_sparse_topk.py` 已加入稠密 `ark.sdpa` 基线，并输出 `speedup_vs_ark`。 -- BF16 稀疏已选中 block microtile 做了 K64 vs K32 A/B。K32 是当前实验方向，本地重新编译后的 - `.so` 已恢复为 K32。 -- `xe_sparse_sdpa_fwd_mainloop.hpp` 支持把一个 64-token 稀疏路由 block 展开为多个物理 K microtile； - 这让 K32 能继续沿用现有 64-token LUT 格式。 -- Unitrace profile 显示 K64 回退来自 SEND/SBID 与 memory-write 压力。 -- 已成功生成一张 FLUX.1-dev sparse BF16 topk 0.9 K32 图片，且无 fallback。 - -### 两条独立路径（勿混用） - -| 路径 | 封装函数 | 配置 | DPAS | -|---|---|---|---| -| INT8 稀疏 | `sparge_sage2_attn_meansim_topk_xpu` / `sage_sparse` | `SparseSageConfig` | `XE_DPAS_TT<8, int, int8, int8, int>`（含 `static_assert(ElementQ==int8_t)`） | -| BF16/FP16 稀疏 | `sparge_sage2_attn_meansim_topk_xpu_sdpa` / `sage_sparse_sdpa` | `SparseSDPAConfig` | `XE_DPAS_TT<8, float, bf16, bf16, float>` | - -**在 bf16 片段上复用 int8 DPAS 会导致设备挂起**（已通过拆分修复；见 `SPARSE_BF16_DPAS_HANG.md`）。 - -### 关键文件 -- `auto_round_kernel/sdpa_sparse_sdpa.cpp` — BF16 启动器；`sdpa_sparse.cpp` — INT8 启动器。 -- `auto_round_kernel/sparse_attention.py` — Python 封装 + `sparge_preprocess_topk`。 -- `auto_round_kernel/sparge_preprocess_triton.py` — triton_xpu 预处理（含 torch 回退）。 -- `wrapper/include/stla/xe_sparse_sdpa_fwd_mainloop.hpp`、`sycl_tla_sdpa_sparse.hpp` — 内核。 -- `auto_round_kernel/sdpa.cpp` — **稠密** SYCL-TLA SDPA → Python `ark.sdpa(...)` - （`xe_sdpa_fwd_mainloop.hpp`）。 - -## 2. 正确性 — 已验证 - -- **仓库 e2e 测试通过**（`test_sage_sparse_prefill_e2e.py`、`test_sparge_preprocess_topk_e2e.py`）。 -- **topk=1（全 block 选中）== 稠密 SDPA**：与精确 CPU fp32 参考的最大误差 **0.0039**（bf16 舍入）。 - 即"稠密门"测试。 -- **INT8 == BF16 内核**：相同 Q/K/V 下选中的 block 完全一致（LUT 逐位相同），输出误差 <0.008； - 跨进程验证逐位一致。 -- **预处理与内核均确定**（相同 QKV → 结果一致，进程内与跨进程均一致）。 - -## 3. 性能（bench `benchmarks/bench_sparse_topk.py`，seq 75000，40 头 × 128） - -新增 `dense_ark_sdpa`（经 `ark.sdpa` 调用的稠密 SYCL-TLA SDPA）作为第三个稠密基线；每行新增 -`speedup_vs_ark` 列。完整表格见 `BENCH_SPARSE_ARK_SDPA_20260813.md`。 - -| 模式 | topk | vs torch | vs sagev1 | vs ark_sdpa | -|---|---|---|---|---| -| dense_ark_sdpa | — | 1.5–1.6× | 0.82–0.87× | 1.0 | -| INT8 稀疏 qtile256（内核） | 0.5 / 0.25 / 0.125 | 3.5 / 6.9 / 13.2× | — | 2.2 / 4.3 / 8.2× | -| BF16 稀疏（内核） | 0.5 / 0.25 / 0.125 | 1.5 / 2.9 / 5.6× | — | 0.92 / 1.8 / 3.5× | - -说明：`dense_ark_sdpa` 快于 torch SDPA 但慢于 `dense_sagev1`；BF16 稀疏内核仅在 topk ≤ 0.25 时 -快于稠密 ark 内核。topk 0.5 处"1.5× 而非 2×"的上限源于 **SBID 分数板停顿**（内存 SEND 并发瓶颈； -INT8 因 K 尺寸减半而无此问题）——早期剖析见 `SPARSE_BF16_KERNEL_OPTIMIZATION_JOURNEY.md` / -`SPARSE_SDPA_KERNEL_OPTIMIZATION_OPTIONS.md`。 - -**2026-08-14 A/B 更新：** 将 BF16 稀疏 qtile256 已选中 block 的 microtile 从 K64 改为 K32 后， -kernel-only 延迟提升 **1.69–1.77×**。topk 0.5 从 1511 ms / **0.91× vs ark** 提升到 -863 ms / **1.59× vs ark**。正确性 e2e 仍通过。见 `SPARSE_BF16_K32_AB_20260814.md`。 - -**2026-08-14 unitrace 更新：** 在 topk 0.5 下 profile K32 与 K64，确认 K64 回退主要是 -memory/SEND scoreboard 压力，而不是 occupancy 下降。聚合 `SPARSESDPAFwdMainloop` GPU time 为 -1684.6 ms（K32）对 3072.4 ms（K64），XVE active 64.7% 对 39.9%，XVE stall 35.1% 对 59.9%， -SBID stall 31.4% 对 55.7%。K64 的 SEND 指令增加 4.4×，GPU memory write 增加约 5.0×。 -两边 SLM read/write 都是 0/0，因此 unitrace 没显示 SLM-backed spill；巨大的 LSC write 差异是当前 -spill/pressure proxy。原始 profile 在 `unitrace_k32_compute_20260814/`、 -`unitrace_k32_stalls_20260814/`、`unitrace_k64_compute_real_20260814/`、 -`unitrace_k64_stalls_real_20260814/`。 - -## 4. FLUX.1-dev 端到端 — 关键坑 - -**必须用 `enable_sequential_cpu_offload()`，不要用 `enable_model_cpu_offload()`。** - -- `enable_model_cpu_offload()` 按*组件*粒度搬运：整个约 46 GB 的 transformer 会被拉到 24.4 GB - 设备上（峰值约 24 GB，仅剩约 350 MB 余量）。稀疏启动会偶发 OOM - （`OUT_OF_DEVICE_MEMORY` / `OUT_OF_RESOURCES`），甚至**重置 GPU** - （`dmesg: exec queue reset` on `0000:ba:00.0` = dev5；即 `DEVICE_LOST` 错误）。 -- Sequential offload 使峰值降到 **约 0.16 GB** → triton_xpu 稀疏稳定运行（0 回退）。 - 代价：每步约 4 s 对 1 s（50 步生成约 4–5 分钟）。 - -### FLUX 图像质量结论 -- 相同配置下，bf16 稀疏与 int8 稀疏**产出完全相同图像**（已验证，0.0 差异）。 -- `topk=0.9` 实际丢弃 **12.5–16.7%** 的 block（路由块取整 + sim 阈值），而非 10% → 图像与稠密 - 相差约 15–18。注意力质量覆盖率：topk 0.9 保留 **91.5%**、topk 0.5 保留 **64.6%** —— - 归一化后输出的偏移是 FLUX 注意力分散下的 topk 稀疏固有现象。 -- **最新 K32 BF16 稀疏 topk 0.9 运行：** PNG 已保存到 - `benchmarks/results/flux_bf16_topk09_k32_20260814_112445/flux_bf16_qtile256_topk0.9_512_dev5.png`。 - 配置：BF16 sparse qtile256、已选中 block K32、`topk=0.9`、512×512、50 steps、seed 0、 - `ZE_AFFINITY_MASK=5`。结果：wall **238.884 s**、sparsity **0.125**、calls **2850**、 - sparse_calls **2850**、runtime_fallbacks **0**。 -- **待解决：运行间非确定性。** 相同配置（bf16 qtile256 topk0.5，seed 0）两次运行图像相差 - **48.5**（均值 97.6 对 101.3），而 topk 0.9 完全确定（0.0）。QKV、预处理、内核均已验证确定 → - 疑似扩散轨迹对其它微小扰动的放大，而非稀疏内核缺陷。做单次图像对比前建议复查。 - -### 开发工具（未跟踪） -- `examples/flux_gen_bf16_sweep.py` — offload-only 运行器（遍历 topk，保存 PNG + 汇总）。 -- `tools/sweep_flux_bf16_topk.sh` — 单 GPU 封装（qtile256 环境变量）。**注意：`FLUX_SPARSE_KERNEL` - 现已可通过环境变量覆盖（默认 bf16）**；此前写死 bf16，导致"int8"运行实际是 bf16。 - `main` 的 `flux_sparse_patch.py` 仅 int8，且忽略 block-token 环境变量。 -- `tools/bench_sparse_bf16_fp16_sweep.sh` — bench 扫描驱动。 - -## 5. 当前仓库状态（未提交） - -- **分支**：`feat/sparse-bf16-prefill-v2`。 -- **已修改（跟踪）**：`benchmarks/bench_sparse_topk.py`（新增 `dense_ark_sdpa` 模式 + - `speedup_vs_ark` 列）、稀疏 SDPA 头文件中的 K32 route-block expansion —— 尚未提交。 -- **未跟踪**：`examples/flux_gen_bf16_sweep.py`、`tools/sweep_flux_bf16_topk.sh`、 - `tools/bench_sparse_bf16_fp16_sweep.sh`。 -- `.so`：`auto_round_kernel/ark-xbuild/auto_round_kernel_xpu.cpython-313-x86_64-linux-gnu.so` - （特性分支构建）。`auto_round_kernel/xbuild/` 里有一份**过期的 main** `.so`（无碍； - 特性分支的 loader 用 `ark-xbuild`）。 -- `benchmarks/results/` 已 gitignore → 下面的结果 CSV、PNG、文档均为本地。 - -## 6. 如何运行 - -```bash -# 重新编译（先 source /opt/intel/oneapi/setvars.sh --force） -cd auto_round_extension/ark/auto_round_kernel/ark-xbuild && cmake --build . -j 16 - -# 正确性测试 -export ZE_AFFINITY_MASK=5 SAGE_ATTN_SPARSE_PREPROCESS_BACKEND=torch -.venv/bin/python auto_round_kernel/wrapper/test/test_sage_sparse_prefill_e2e.py -.venv/bin/python auto_round_kernel/wrapper/test/test_sparge_preprocess_topk_e2e.py - -# FLUX 扫描（bf16 qtile256；SAGE 时设 FLUX_SPARSE_KERNEL=int8） -export ZE_AFFINITY_MASK=5 SAGE_ATTN_SPARSE_PREPROCESS_BACKEND=triton_xpu -export FLUX_SPARSE_KERNEL=bf16 FLUX_SPARSE_TOPKS="0.9 0.5" FLUX_RUN_DENSE=0 -export FLUX_OUTPUT_DIR=benchmarks/results/flux_ -.venv/bin/python examples/flux_gen_bf16_sweep.py - -# bench（需要特性分支 —— main 缺少 bf16 符号） -export ZE_AFFINITY_MASK=5 SAGE_ATTN_SPARSE_PREPROCESS_BACKEND=triton_xpu -.venv/bin/python benchmarks/bench_sparse_topk.py --dtype bf16 --seq-len 75000 \ - --topk 0.5 0.25 0.125 --tensor-layout HND NHD --q-tile-override 256 \ - --sparse-q-block-tokens 256 --sparse-k-block-tokens 64 --warmup 2 --iters 3 \ - --output-csv benchmarks/results/bench_.csv -``` - -## 7. 待办 / 建议 - -1. **定位 topk-0.5 的跨运行非确定性**，再做单次图像对比（逐次抓取 QKV；稀疏算子已全部验证确定）。 -2. **将 K32 BF16 稀疏 SDPA 实验整理成最终实现**（`SPARSE_BF16_K32_AB_20260814.md`）。unitrace - A/B 说明应保持已选中 block microtile 为 K32，除非后续 compiler/assembly 优化能消除 K64 的 - write/SBID 压力。 -3. 考虑让 FLUX 运行器文件名里的 `_512` 后缀跟随实际尺寸（1024×1024 运行被误标为 `_512`，纯外观问题）。 -4. 决定是否提交 bench 的 `dense_ark_sdpa` 改动及开发工具（当前均未提交）。 diff --git a/auto_round_extension/ark/benchmarks/results/SPARSE_BF16_K32_AB_20260814.md b/auto_round_extension/ark/benchmarks/results/SPARSE_BF16_K32_AB_20260814.md deleted file mode 100644 index 03871e86c9..0000000000 --- a/auto_round_extension/ark/benchmarks/results/SPARSE_BF16_K32_AB_20260814.md +++ /dev/null @@ -1,323 +0,0 @@ -# BF16 sparse SDPA K64 vs K32 A/B — 2026-08-14 - -## Summary - -The BF16 sparse SDPA qtile256 path was tested with the selected-block K tile changed from 64 tokens -to 32 tokens, matching the dense `ark.sdpa` head_dim=128 K tile size. This is an experimental patch, -not yet a cleaned-up final implementation. - -Result: **K32 is substantially faster**. At topk 0.5, BF16 sparse changes from slower than dense -`ark.sdpa` to clearly faster. - -## Test setup - -- Branch: `feat/sparse-bf16-prefill-v2` -- Device: Intel Battlemage XPU, `ZE_AFFINITY_MASK=5` -- Benchmark: `benchmarks/bench_sparse_topk.py` -- Shape: `seq_len=75000`, HND, 40 heads, head_dim 128 -- Sparse config: `q_tile_override=256`, `sparse_q_block_tokens=256`, `sparse_k_block_tokens=64` -- Benchmark options: `--warmup 1 --iters 2` -- Baseline CSV: `benchmarks/results/ab_k64_bf16_20260814_090508.csv` -- K32 CSV: `benchmarks/results/ab_bf16_sdpa_k32_20260814_092404.csv` - -## Result table - -Only `sparse_sdpa_bf16_kernel_only` is compared below. - -| topk | K64 latency | K32 latency | K32 speedup | K64 vs ark | K32 vs ark | effective TFLOPS K64 -> K32 | -|---|---:|---:|---:|---:|---:|---:| -| 1.0 | 2986.014 ms | 1691.257 ms | 1.766x | 0.460x | 0.812x | 38.6 -> 68.1 | -| 0.5 | 1511.047 ms | 862.649 ms | 1.752x | 0.910x | 1.593x | 38.3 -> 67.1 | -| 0.25 | 766.744 ms | 455.005 ms | 1.685x | 1.792x | 3.020x | 38.1 -> 64.2 | - -Dense `ark.sdpa` in the same run was ~1374 ms. With K64, BF16 sparse at topk 0.5 was slower than -dense ark (`0.91x`). With K32, BF16 sparse at topk 0.5 became faster (`1.59x`). - -## Patch shape - -The A/B changed `launch_sparse_sdpa_prefill_kernel_128` in -`auto_round_kernel/wrapper/include/sycl_tla_sdpa_sparse.hpp`: - -```cpp -// K64 original -using ShapeQK = Shape<_256, _64, _32>; -using ShapePV = Shape<_256, _32, _64>; - -// K32 experiment -using ShapeQK = Shape<_256, _32, _32>; -using ShapePV = Shape<_256, _32, _32>; -``` - -Because the routing LUT still uses 64-token sparse K blocks, the experiment also changed -`auto_round_kernel/wrapper/include/stla/xe_sparse_sdpa_fwd_mainloop.hpp` so each sparse route block -can expand into multiple physical K microtiles. For the K32 experiment, one 64-token routed block -runs as two 32-token mainloop iterations. - -## Tile meaning - -For QK: - -```cpp -using ShapeQK = Shape<_256, _32, _32>; -``` - -This means: - -```text -M = 256 query tokens per workgroup tile -N = 32 key tokens per physical K microtile -K = 32 head-dim reduction chunk -``` - -The QK output score tile is therefore `256 x 32`. Since head_dim is 128, each physical tile still -does four reduction chunks over the feature dimension: - -```text -head_dim 128 / reduction chunk 32 = 4 QK reduction chunks -``` - -For PV: - -```cpp -using ShapePV = Shape<_256, _32, _32>; -``` - -This means: - -```text -M = 256 query rows / probability rows -N = 32 value/output head-dim columns per tile -K = 32 selected key/value tokens reduced per PV tile -``` - -So one PV microtile consumes `P[256, 32] x V[32, 32] -> O[256, 32]`. The output head_dim 128 is -covered as four 32-column output chunks. - -Pseudocode for one selected 64-token sparse route block: - -```cpp -// K64 original: -// ShapeQK = Shape<_256, _64, _32> -// ShapePV = Shape<_256, _32, _64> -for each selected_route_block_64_tokens { - scores[256][64] = 0; - for d = 0; d < 128; d += 32 { - scores[256][64] += Q[256][d:d+32] * transpose(K[64][d:d+32]); - } - softmax_update(scores[256][64]); - O[256][0:32] += P[256][64] * V[64][0:32]; // repeated for output-dim chunks -} - -// K32 experiment: -// ShapeQK = Shape<_256, _32, _32> -// ShapePV = Shape<_256, _32, _32> -for each selected_route_block_64_tokens { - for micro_tile = 0; micro_tile < 2; ++micro_tile { - key_start = selected_route_block_64_tokens * 64 + micro_tile * 32; - - scores[256][32] = 0; - for d = 0; d < 128; d += 32 { - scores[256][32] += Q[256][d:d+32] * transpose(K[key_start:key_start+32][d:d+32]); - } - softmax_update(scores[256][32]); - O[256][0:32] += P[256][32] * V[key_start:key_start+32][0:32]; // repeated for output-dim chunks - } -} -``` - -The head-dim reduction loop count is the same in both cases (`128 / 32 = 4`). The difference is the -physical selected-key span: - -```text -K64: one route block -> one 256x64 score tile -K32: one route block -> two 256x32 score tiles -``` - -K32 adds one extra microtile iteration per 64-token route block, but each physical tile is smaller. -The unitrace result below shows that smaller tile wins because it greatly reduces SEND/SBID and -memory-write pressure. - -## Correctness - -After rebuilding, `auto_round_kernel/wrapper/test/test_sage_sparse_prefill_e2e.py` passed. -Relevant BF16 SDPA all-selected result: - -```text -[sage_sparse_sdpa][torch.bfloat16_all_selected] D=128 max_diff=0.001953 mean_diff=0.000111 -``` - -## Unitrace profile - -The profile below uses `topk=0.5`, `seq_len=75000`, HND, head_dim 128, `q_tile_override=256`, -`sparse_q_block_tokens=256`, and the same 64-token sparse routing blocks. The profiler adds large -wall-time overhead, so the table uses the included `SPARSESDPAFwdMainloop` kernel GPU metrics rather -than benchmark wall time. - -Files: - -- K32 ComputeBasic: `benchmarks/results/unitrace_k32_compute_20260814/k32_compute.metrics.2889557` -- K32 VectorEngineStalls: `benchmarks/results/unitrace_k32_stalls_20260814/k32_stalls.metrics.2892853` -- K64 ComputeBasic: `benchmarks/results/unitrace_k64_compute_real_20260814/k64_compute_real.metrics.2948755` -- K64 VectorEngineStalls: `benchmarks/results/unitrace_k64_stalls_real_20260814/k64_stalls_real.metrics.2952213` -- K32 kernel properties: `benchmarks/results/unitrace_k32_verbose_20260814/k32_verbose.3726840` -- K64 kernel properties: `benchmarks/results/unitrace_k64_compute_real_20260814/k64_compute_real.2948755` - -Commands: - -```bash -# K32 kernel properties. Run after building the K32 variant. -source /opt/intel/oneapi/setvars.sh --force -export ZE_AFFINITY_MASK=5 -export ZE_FLAT_DEVICE_HIERARCHY=FLAT -export SAGE_ATTN_SPARSE_PREPROCESS_BACKEND=triton_xpu -unitrace \ - --device-timing --verbose \ - --include-kernels SPARSESDPAFwdMainloop \ - --output k32_verbose \ - .venv/bin/python benchmarks/bench_sparse_topk.py --dtype bf16 --seq-len 75000 \ - --topk 0.5 --tensor-layout HND --q-tile-override 256 \ - --sparse-q-block-tokens 256 --warmup 0 --iters 1 \ - --output-csv benchmarks/results/unitrace_k32_verbose_20260814.csv -``` - -```bash -# K64 ComputeBasic. Run after temporarily changing launch_sparse_sdpa_prefill_kernel_128 -# back to ShapeQK=Shape<_256,_64,_32>, ShapePV=Shape<_256,_32,_64> and rebuilding. -source /opt/intel/oneapi/setvars.sh --force -export ZE_AFFINITY_MASK=5 -export ZE_FLAT_DEVICE_HIERARCHY=FLAT -export SAGE_ATTN_SPARSE_PREPROCESS_BACKEND=triton_xpu -unitrace \ - --device-timing --kernel-submission --metric-query --group ComputeBasic \ - --include-kernels SPARSESDPAFwdMainloop \ - --output k64_compute_real \ - .venv/bin/python benchmarks/bench_sparse_topk.py --dtype bf16 --seq-len 75000 \ - --topk 0.5 --tensor-layout HND --q-tile-override 256 \ - --sparse-q-block-tokens 256 --warmup 0 --iters 1 \ - --output-csv benchmarks/results/unitrace_k64_compute_real_20260814.csv -``` - -```bash -# K32/K64 stall pass: use the same build-specific setup as above and change only the group/output. -unitrace \ - --device-timing --kernel-submission --metric-query --group VectorEngineStalls \ - --include-kernels SPARSESDPAFwdMainloop \ - --output _stalls \ - .venv/bin/python benchmarks/bench_sparse_topk.py --dtype bf16 --seq-len 75000 \ - --topk 0.5 --tensor-layout HND --q-tile-override 256 \ - --sparse-q-block-tokens 256 --warmup 0 --iters 1 \ - --output-csv benchmarks/results/_stalls.csv -``` - -Kernel properties from `unitrace --device-timing --verbose`: - -| variant | SIMD | SLM / WG | private memory / thread | spill memory / thread | register file size / thread | -|---|---:|---:|---:|---:|---:| -| K32 | 16 | 0 | 2432 B | 0 B | 256 | -| K64 | 16 | 0 | 2432 B | 3840 B | 256 | - -ComputeBasic aggregate over the two profiled `SPARSESDPAFwdMainloop` rows: - -| metric | K32 | K64 | K64/K32 | -|---|---:|---:|---:| -| GPU time | 1684.6 ms | 3072.4 ms | 1.824x | -| XVE active | 64.718% | 39.934% | 0.617x | -| XVE stall | 35.149% | 59.939% | 1.705x | -| thread occupancy | 99.828% | 99.843% | 1.000x | -| SEND instructions | 18.593G | 81.568G | 4.387x | -| GPU memory read | 43.462 GB | 73.245 GB | 1.685x | -| GPU memory write | 1.633 GB | 8.135 GB | 4.981x | -| LSC byte read | 9653.807 GB | 13389.615 GB | 1.387x | -| LSC byte write | 72.008 MB | 2333.083 GB | 32400.183x | -| SLM read/write | 0 / 0 | 0 / 0 | n/a | - -VectorEngineStalls aggregate: - -| stall metric | K32 | K64 | delta | -|---|---:|---:|---:| -| SBID | 31.449% | 55.730% | +24.281 pp | -| ALU write | 29.562% | 16.532% | -13.030 pp | -| instruction fetch | 12.329% | 3.638% | -8.691 pp | -| pipe stall | 4.748% | 0.891% | -3.857 pp | -| control | 4.316% | 1.310% | -3.006 pp | -| barrier | 0.240% | 0.531% | +0.291 pp | - -## Interpretation - -This confirms the K64 selected-block microtile is a major performance issue for BF16 sparse SDPA. -The kernel properties pass shows that K64 does cause compiler-reported spill memory: -`Spill Memory Per Thread` is **3840 B** for K64 and **0 B** for K32. SLM remains 0 because this is -private/scratch spill traffic, not SLM-backed storage. - -The runtime metrics match that diagnosis. K64 is memory/SEND scoreboard limited: SBID stall rises -from 31.4% to 55.7%, XVE active drops from 64.7% to 39.9%, SEND instructions rise 4.4x, and memory -writes rise ~5.0x. The large LSC write delta is now best interpreted as spill/scratch pressure plus -the extra memory traffic created by the larger K64 live state. - -Recommendation: keep the selected-block BF16 sparse SDPA path on K32 and treat K64 as a regression -case unless compiler/offline assembly data proves the write/SBID pressure can be removed another way. - -## Latest apple-to-apple ARK SDPA comparison - -After pulling the latest K32 implementation, dense ARK SDPA and BF16 sparse SDPA were rerun on XPU 0 -and XPU 1 with the same 75k-token shape and launch configuration: - -- Commit: `9b1a9512` -- Shape: `seq_len=75000`, HND, 40 Q heads, 40 KV heads, head_dim 128 -- Sparse configuration: `topk=0.5`, `q_tile_override=256`, `sparse_q_block_tokens=256`, - `sparse_k_block_tokens=64` -- Benchmark options: `--warmup 1 --iters 2` -- Timing: mean of XPU 0 and XPU 1; sparse results include both kernel-only and end-to-end paths - -| path | mean latency | speedup vs dense ARK | -|---|---:|---:| -| dense ARK SDPA | 994.6 ms | 1.00x | -| BF16 sparse K32, kernel-only | 598.2 ms | 1.66x | -| BF16 sparse K32, end-to-end | 697.6 ms | 1.43x | - -The latest Unitrace kernel-property pass reported SIMD16, SLM 0, private memory 0 B/thread, -spill 0, and GRF 256 for dense ARK. BF16 sparse reported SIMD16, SLM 0, private memory -2176 B/thread, spill 0, and GRF 256. - -The current ComputeBasic and VectorEngineStalls counter passes could not create a metric query or -streamer because the host has `observation_paranoid=1`. No system policy was changed, so the latest -comparison uses same-shape unprofiled timing plus the kernel-property pass. The raw Unitrace output -and timing CSVs are in: - -`benchmarks/results/apple_to_apple_ark_sdpa_bf16_sparse_75k_xpu0_20260814_135844/` - -Timing CSVs: - -- `benchmarks/results/k32_rerun_bf16_xpu0_20260814_134727.csv` -- `benchmarks/results/k32_rerun_bf16_xpu1_20260814_134959.csv` - -## Flux BF16 sparse topk sweep - -The latest K32 BF16 sparse path was also run through the Flux example at 1024x1024 with the same -prompt, seed, 50 denoising steps, qtile 256, Q block 256, and K routing block 64. Each point ran -in parallel on a separate XPU. All runs completed with 2,850/2,850 sparse attention calls and zero -runtime fallbacks. - -| topk | XPU | observed sparsity | PNG | -|---:|---:|---:|---| -| 1.0 | 0 | 0.00% | `flux_topk1p0.png` | -| 0.9 | 1 | 11.11% | `flux_topk0p9.png` | -| 0.8 | 2 | 20.83% | `flux_topk0p8.png` | -| 0.7 | 3 | 30.56% | `flux_topk0p7.png` | -| 0.6 | 4 | 40.28% | `flux_topk0p6.png` | -| 0.5 | 5 | 50.00% | `flux_topk0p5.png` | -| 0.4 | 6 | 61.11% | `flux_topk0p4.png` | -| 0.3 | 7 | 70.83% | `flux_topk0p3.png` | -| 0.2 | 0 | 80.56% | `flux_topk0p2.png` | -| 0.1 | 1 | 90.28% | `flux_topk0p1.png` | -| 0.01 | 2 | 98.61% | `flux_topk0p01.png` | - -The main sweep artifacts are under: - -`benchmarks/results/flux_bf16_topk_sweep_1024_parallel_20260814_160126/` - -The additional low-topk artifacts are under: - -`benchmarks/results/flux_bf16_topk_sweep_1024_parallel_extra_20260814_160550/` - -Each directory contains `summary.csv`, the exact per-run commands, logs, status files, and PNGs. diff --git a/auto_round_extension/ark/benchmarks/results/SPARSE_BF16_K32_AB_20260814_CN.md b/auto_round_extension/ark/benchmarks/results/SPARSE_BF16_K32_AB_20260814_CN.md deleted file mode 100644 index c93f32b4d1..0000000000 --- a/auto_round_extension/ark/benchmarks/results/SPARSE_BF16_K32_AB_20260814_CN.md +++ /dev/null @@ -1,255 +0,0 @@ -# BF16 稀疏 SDPA K64 vs K32 A/B — 2026-08-14 - -## 摘要 - -本次测试将 BF16 稀疏 SDPA qtile256 路径的已选中 block 的 K tile 从 64 token 改为 32 token， -与稠密 `ark.sdpa` 在 head_dim=128 下的 K tile 大小保持一致。这是实验性补丁，尚未整理成最终实现。 - -结论：**K32 明显更快**。在 topk 0.5 下，BF16 稀疏从慢于稠密 `ark.sdpa` 变为明显快于它。 - -## 测试设置 - -- 分支：`feat/sparse-bf16-prefill-v2` -- 设备：Intel Battlemage XPU，`ZE_AFFINITY_MASK=5` -- Benchmark：`benchmarks/bench_sparse_topk.py` -- 形状：`seq_len=75000`，HND，40 头，head_dim 128 -- 稀疏配置：`q_tile_override=256`、`sparse_q_block_tokens=256`、`sparse_k_block_tokens=64` -- Benchmark 参数：`--warmup 1 --iters 2` -- 基线 CSV：`benchmarks/results/ab_k64_bf16_20260814_090508.csv` -- K32 CSV：`benchmarks/results/ab_bf16_sdpa_k32_20260814_092404.csv` - -## 结果表 - -下表仅比较 `sparse_sdpa_bf16_kernel_only`。 - -| topk | K64 延迟 | K32 延迟 | K32 加速 | K64 vs ark | K32 vs ark | effective TFLOPS K64 -> K32 | -|---|---:|---:|---:|---:|---:|---:| -| 1.0 | 2986.014 ms | 1691.257 ms | 1.766x | 0.460x | 0.812x | 38.6 -> 68.1 | -| 0.5 | 1511.047 ms | 862.649 ms | 1.752x | 0.910x | 1.593x | 38.3 -> 67.1 | -| 0.25 | 766.744 ms | 455.005 ms | 1.685x | 1.792x | 3.020x | 38.1 -> 64.2 | - -同次运行中稠密 `ark.sdpa` 约为 1374 ms。K64 时，BF16 稀疏 topk 0.5 慢于稠密 ark -（`0.91x`）；K32 时，BF16 稀疏 topk 0.5 变为更快（`1.59x`）。 - -## 补丁形态 - -A/B 修改了 `auto_round_kernel/wrapper/include/sycl_tla_sdpa_sparse.hpp` 中的 -`launch_sparse_sdpa_prefill_kernel_128`： - -```cpp -// K64 原始配置 -using ShapeQK = Shape<_256, _64, _32>; -using ShapePV = Shape<_256, _32, _64>; - -// K32 实验配置 -using ShapeQK = Shape<_256, _32, _32>; -using ShapePV = Shape<_256, _32, _32>; -``` - -由于路由 LUT 仍以 64-token 稀疏 K block 为单位，实验还修改了 -`auto_round_kernel/wrapper/include/stla/xe_sparse_sdpa_fwd_mainloop.hpp`，使每个稀疏路由 block -可以展开为多个物理 K microtile。在 K32 实验中，一个 64-token 路由 block 会执行两次 32-token -mainloop。 - -## Tile 含义 - -对于 QK： - -```cpp -using ShapeQK = Shape<_256, _32, _32>; -``` - -含义是： - -```text -M = 256 每个 workgroup tile 的 query token 数 -N = 32 每个物理 K microtile 的 key token 数 -K = 32 head-dim reduction chunk -``` - -因此 QK 输出的 score tile 是 `256 x 32`。由于 head_dim 是 128，每个物理 tile 仍然需要在特征维上做 -4 个 reduction chunk： - -```text -head_dim 128 / reduction chunk 32 = 4 个 QK reduction chunk -``` - -对于 PV： - -```cpp -using ShapePV = Shape<_256, _32, _32>; -``` - -含义是： - -```text -M = 256 query 行 / probability 行 -N = 32 每个 tile 的 value/output head-dim 列数 -K = 32 每个 PV tile reduction 的 selected key/value token 数 -``` - -所以一个 PV microtile 计算 `P[256, 32] x V[32, 32] -> O[256, 32]`。output head_dim 128 会拆成 -4 个 32-column output chunk。 - -一个被选中的 64-token 稀疏路由 block 的伪代码： - -```cpp -// K64 原始配置： -// ShapeQK = Shape<_256, _64, _32> -// ShapePV = Shape<_256, _32, _64> -for each selected_route_block_64_tokens { - scores[256][64] = 0; - for d = 0; d < 128; d += 32 { - scores[256][64] += Q[256][d:d+32] * transpose(K[64][d:d+32]); - } - softmax_update(scores[256][64]); - O[256][0:32] += P[256][64] * V[64][0:32]; // 对 output-dim chunk 重复 -} - -// K32 实验配置： -// ShapeQK = Shape<_256, _32, _32> -// ShapePV = Shape<_256, _32, _32> -for each selected_route_block_64_tokens { - for micro_tile = 0; micro_tile < 2; ++micro_tile { - key_start = selected_route_block_64_tokens * 64 + micro_tile * 32; - - scores[256][32] = 0; - for d = 0; d < 128; d += 32 { - scores[256][32] += Q[256][d:d+32] * transpose(K[key_start:key_start+32][d:d+32]); - } - softmax_update(scores[256][32]); - O[256][0:32] += P[256][32] * V[key_start:key_start+32][0:32]; // 对 output-dim chunk 重复 - } -} -``` - -两种配置的 head-dim reduction loop 次数相同（`128 / 32 = 4`）。差别在物理 selected-key 跨度： - -```text -K64: 一个 route block -> 一个 256x64 score tile -K32: 一个 route block -> 两个 256x32 score tile -``` - -K32 对每个 64-token route block 多了一次 microtile iteration，但每个物理 tile 更小。下面的 unitrace -结果显示，更小的 tile 胜出，因为它显著降低了 SEND/SBID 与 memory-write 压力。 - -## 正确性 - -重新编译后，`auto_round_kernel/wrapper/test/test_sage_sparse_prefill_e2e.py` 通过。 -相关 BF16 SDPA 全选结果： - -```text -[sage_sparse_sdpa][torch.bfloat16_all_selected] D=128 max_diff=0.001953 mean_diff=0.000111 -``` - -## Unitrace profile - -下面 profile 使用 `topk=0.5`、`seq_len=75000`、HND、head_dim 128、`q_tile_override=256`、 -`sparse_q_block_tokens=256`，稀疏路由 block 仍为 64 token。Profiler 会显著拉长墙钟时间，因此下表 -使用被 `--include-kernels SPARSESDPAFwdMainloop` 捕获的 kernel GPU metrics，而不是 benchmark 墙钟时间。 - -文件： - -- K32 ComputeBasic：`benchmarks/results/unitrace_k32_compute_20260814/k32_compute.metrics.2889557` -- K32 VectorEngineStalls：`benchmarks/results/unitrace_k32_stalls_20260814/k32_stalls.metrics.2892853` -- K64 ComputeBasic：`benchmarks/results/unitrace_k64_compute_real_20260814/k64_compute_real.metrics.2948755` -- K64 VectorEngineStalls：`benchmarks/results/unitrace_k64_stalls_real_20260814/k64_stalls_real.metrics.2952213` - -ComputeBasic，对两个 `SPARSESDPAFwdMainloop` 行聚合： - -| metric | K32 | K64 | K64/K32 | -|---|---:|---:|---:| -| GPU time | 1684.6 ms | 3072.4 ms | 1.824x | -| XVE active | 64.718% | 39.934% | 0.617x | -| XVE stall | 35.149% | 59.939% | 1.705x | -| thread occupancy | 99.828% | 99.843% | 1.000x | -| SEND instructions | 18.593G | 81.568G | 4.387x | -| GPU memory read | 43.462 GB | 73.245 GB | 1.685x | -| GPU memory write | 1.633 GB | 8.135 GB | 4.981x | -| LSC byte read | 9653.807 GB | 13389.615 GB | 1.387x | -| LSC byte write | 72.008 MB | 2333.083 GB | 32400.183x | -| SLM read/write | 0 / 0 | 0 / 0 | n/a | - -VectorEngineStalls 聚合： - -| stall metric | K32 | K64 | delta | -|---|---:|---:|---:| -| SBID | 31.449% | 55.730% | +24.281 pp | -| ALU write | 29.562% | 16.532% | -13.030 pp | -| instruction fetch | 12.329% | 3.638% | -8.691 pp | -| pipe stall | 4.748% | 0.891% | -3.857 pp | -| control | 4.316% | 1.310% | -3.006 pp | -| barrier | 0.240% | 0.531% | +0.291 pp | - -## 解读 - -该 A/B 确认 K64 的已选中 block microtile 是 BF16 稀疏 SDPA 的主要性能问题。这个结果并不能单独证明 -存在 register spill；unitrace 也没有直接给出 GRF spill 计数器。但它明确显示 K64 被 memory/SEND -scoreboard 限制：SBID stall 从 31.4% 升到 55.7%，XVE active 从 64.7% 降到 39.9%，SEND 指令增加 -4.4x，memory write 增加约 5.0x。SLM 流量仍为 0，因此这些指标没有显示 SLM-backed spill。巨大的 LSC -write 差异是这里能采到的最佳 spill/pressure proxy，符合 K64 带来更高 scratch/memory 压力的判断。 - -建议：BF16 稀疏 SDPA 的已选中 block 路径保持 K32；除非后续 compiler/offline assembly 数据证明可以 -用其它方式消除 write/SBID 压力，否则 K64 应视为性能回退。 - -## 最新 ARK SDPA 对比 - -拉取最新 K32 实现后，在 XPU 0 和 XPU 1 上使用相同的 75k-token shape 与 launch 配置，重新运行了 -dense ARK SDPA 和 BF16 sparse SDPA： - -- Commit：`9b1a9512` -- Shape：`seq_len=75000`、HND、40 个 Q heads、40 个 KV heads、head_dim 128 -- Sparse 配置：`topk=0.5`、`q_tile_override=256`、`sparse_q_block_tokens=256`、 - `sparse_k_block_tokens=64` -- Benchmark 选项：`--warmup 1 --iters 2` -- Timing：XPU 0 和 XPU 1 的均值；sparse 同时记录 kernel-only 和 end-to-end - -| 路径 | 平均延迟 | 相对 dense ARK 加速比 | -|---|---:|---:| -| dense ARK SDPA | 994.6 ms | 1.00x | -| BF16 sparse K32，kernel-only | 598.2 ms | 1.66x | -| BF16 sparse K32，end-to-end | 697.6 ms | 1.43x | - -最新 Unitrace kernel-property pass 报告：dense ARK 为 SIMD16、SLM 0、private memory 0 B/thread、 -spill 0、GRF 256；BF16 sparse 为 SIMD16、SLM 0、private memory 2176 B/thread、spill 0、GRF 256。 - -当前 ComputeBasic 和 VectorEngineStalls counter pass 因主机 `observation_paranoid=1`，无法创建 -metric query 或 streamer。没有修改系统策略，因此最新比较使用相同 shape 的 unprofiled timing 加 -kernel-property pass。原始 Unitrace 输出和 timing CSV 位于： - -`benchmarks/results/apple_to_apple_ark_sdpa_bf16_sparse_75k_xpu0_20260814_135844/` - -Timing CSV： - -- `benchmarks/results/k32_rerun_bf16_xpu0_20260814_134727.csv` -- `benchmarks/results/k32_rerun_bf16_xpu1_20260814_134959.csv` - -## Flux BF16 sparse topk sweep - -最新 K32 BF16 sparse 路径通过 Flux example 运行了 1024x1024 sweep，使用相同 prompt、seed、50 个 -denoising steps、qtile 256、Q block 256 和 K routing block 64。每个 topk 在独立 XPU 上并行运行。 -全部运行均完成了 2,850/2,850 次 sparse attention call，runtime fallback 为 0。 - -| topk | XPU | 实测 sparsity | PNG | -|---:|---:|---:|---| -| 1.0 | 0 | 0.00% | `flux_topk1p0.png` | -| 0.9 | 1 | 11.11% | `flux_topk0p9.png` | -| 0.8 | 2 | 20.83% | `flux_topk0p8.png` | -| 0.7 | 3 | 30.56% | `flux_topk0p7.png` | -| 0.6 | 4 | 40.28% | `flux_topk0p6.png` | -| 0.5 | 5 | 50.00% | `flux_topk0p5.png` | -| 0.4 | 6 | 61.11% | `flux_topk0p4.png` | -| 0.3 | 7 | 70.83% | `flux_topk0p3.png` | -| 0.2 | 0 | 80.56% | `flux_topk0p2.png` | -| 0.1 | 1 | 90.28% | `flux_topk0p1.png` | -| 0.01 | 2 | 98.61% | `flux_topk0p01.png` | - -主 sweep artifacts： - -`benchmarks/results/flux_bf16_topk_sweep_1024_parallel_20260814_160126/` - -新增低 topk artifacts： - -`benchmarks/results/flux_bf16_topk_sweep_1024_parallel_extra_20260814_160550/` - -每个目录包含 `summary.csv`、精确 per-run command、log、status 文件和 PNG。 diff --git a/auto_round_extension/ark/benchmarks/results/UNITRACE_USAGE_GUIDE_20260814.md b/auto_round_extension/ark/benchmarks/results/UNITRACE_USAGE_GUIDE_20260814.md deleted file mode 100644 index b813717b0c..0000000000 --- a/auto_round_extension/ark/benchmarks/results/UNITRACE_USAGE_GUIDE_20260814.md +++ /dev/null @@ -1,294 +0,0 @@ -# Unitrace Usage Guide for Intel GPU Kernels — 2026-08-14 - -This is a general unitrace workflow for profiling SYCL/Level Zero kernels on Intel GPUs. It applies -to sparse SDPA and most other GPU kernels. - -## 1. Environment - -Use the project venv and source oneAPI before profiling: - -```bash -source /opt/intel/oneapi/setvars.sh --force -export ZE_AFFINITY_MASK=5 -export ZE_FLAT_DEVICE_HIERARCHY=FLAT -``` - -`ZE_AFFINITY_MASK` chooses the GPU. `ZE_FLAT_DEVICE_HIERARCHY=FLAT` is recommended for hardware -metric profiling. - -Useful checks: - -```bash -unitrace --device-list -unitrace --metric-list -``` - -## 2. Find Hot Kernels - -Start with device timing. This has lower overhead than full metric collection and identifies which -kernels matter. - -```bash -unitrace \ - --device-timing --verbose \ - --output timing \ - -``` - -Use `--include-kernels` once you know a stable kernel-name substring: - -```bash -unitrace \ - --device-timing --verbose \ - --include-kernels "" \ - --output timing_filtered \ - -``` - -Important: the command examples do not use `--` before the application command. - -## 3. Kernel Properties: Spill, Private Memory, SLM, Registers - -The key mode for spill information is: - -```bash -unitrace \ - --device-timing --verbose \ - --include-kernels "" \ - --output kernel_props \ - -``` - -Look for the `=== Kernel Properties ===` table: - -```text -Kernel, Compiled, SIMD, Number of Arguments, SLM Per Work Group, -Private Memory Per Thread, Spill Memory Per Thread, Register File Size Per Thread -``` - -Key fields: - -| field | meaning | -|---|---| -| `SLM Per Work Group` | Shared local memory allocated per workgroup. | -| `Private Memory Per Thread` | Per-thread private memory allocation. | -| `Spill Memory Per Thread` | Compiler-reported spill/scratch memory per thread. This is the direct spill signal. | -| `Register File Size Per Thread` | Register file allocation reported by runtime/compiler metadata. | - -Example interpretation: - -```text -Spill Memory Per Thread = 0 -> no compiler-reported spill -Spill Memory Per Thread = 3840 -> compiler-reported private/scratch spill -``` - -SLM counters being zero does not rule out spill. Spill can be private/scratch memory and appear as -LSC/GPU memory traffic, not SLM traffic. - -## 4. Compute Overview: ComputeBasic - -Use `ComputeBasic` for the first hardware-counter pass. - -```bash -unitrace \ - --device-timing --kernel-submission --metric-query --group ComputeBasic \ - --include-kernels "" \ - --output compute_basic \ - -``` - -Key metrics: - -| metric | what it tells you | -|---|---| -| `GpuTime[ns]` | GPU time under the profiling pass. Use ratios more than absolute time. | -| `XVE_ACTIVE[%]` | Whether execution units are actively doing work. | -| `XVE_STALL[%]` | Time with threads loaded but no execution pipe active. | -| `XVE_THREADS_OCCUPANCY_ALL[%]` | Thread-slot occupancy. Low occupancy can indicate resource pressure. | -| `XVE_INST_EXECUTED_SEND_ALL` | SEND instruction pressure, usually memory/message traffic. | -| `GPU_MEMORY_BYTE_READ/WRITE` | Device-memory bytes. | -| `LOAD_STORE_CACHE_BYTE_READ/WRITE` | LSC traffic, useful as scratch/spill pressure proxy when spill is reported. | -| `L3_READ/WRITE/MISS/HIT` | L3 behavior and locality. | -| `SLM_BYTE_READ/WRITE` | SLM traffic only, not private/scratch spill traffic. | - -Use this pass to classify the broad bottleneck: compute active, memory/SEND pressure, occupancy, or -cache behavior. - -## 5. Stall Breakdown: VectorEngineStalls - -Use this when `XVE_STALL[%]` is high. - -```bash -unitrace \ - --device-timing --kernel-submission --metric-query --group VectorEngineStalls \ - --include-kernels "" \ - --output vector_stalls \ - -``` - -Common stall meanings: - -| metric | likely meaning | -|---|---| -| `XVE_STALL_SBID[%]` | Scoreboard waits, often memory latency or dependent SEND results. | -| `XVE_STALL_ALUWR[%]` | ALU writeback/dependency pressure. | -| `XVE_STALL_INSTFETCH[%]` | Instruction fetch/code-size pressure. | -| `XVE_STALL_PIPESTALL[%]` | Structural hazards, often register pressure, accumulator hazards, or SEND holds. | -| `XVE_STALL_BARRIER[%]` | Synchronization/load imbalance. | -| `XVE_STALL_CONTROL[%]` | Control-flow overhead. | - -High SBID plus high SEND/LSC/memory traffic usually points to memory-message latency or scratch/spill -pressure. Confirm spill with the kernel properties pass. - -## 6. Memory Deep Dive: MemoryProfile - -Use this when ComputeBasic suggests memory/SEND pressure. - -```bash -unitrace \ - --device-timing --kernel-submission --metric-query --group MemoryProfile \ - --include-kernels "" \ - --output memory_profile \ - -``` - -Useful metrics: - -| metric | what it tells you | -|---|---| -| `GPU_MEMORY_BYTE_READ/WRITE` | DRAM/device-memory traffic. | -| `LOAD_STORE_CACHE_BYTE_READ/WRITE` | LSC traffic. | -| `XVE_LOAD_STORE_CACHE_READ_MESSAGE_COUNT` | LSC read message count. | -| `XVE_LOAD_STORE_CACHE_WRITE_MESSAGE_COUNT` | LSC write message count. | -| `XVE_LOAD_STORE_CACHE_REGISTER_REQUEST_COUNT` | Payload/register transactions sent to LSC. | -| `XVE_LOAD_STORE_CACHE_REGISTER_RESPONSE_COUNT` | Payload/register responses from LSC. | -| `GPU_MEMORY_REQUEST_QUEUE_FULL[%]` | Memory request queue pressure. | -| `L3_BUSY[%]`, `L3_STALL[%]`, `L3_HIT`, `L3_MISS` | L3 saturation and locality. | - -MemoryProfile still does not label individual messages as spill/fill. Combine it with -`Spill Memory Per Thread` from the kernel properties pass. - -## 7. Timeline and Submission - -Use kernel submission timing to separate launch/queue overhead from device execution: - -```bash -unitrace \ - --device-timing --kernel-submission \ - --include-kernels "" \ - --output submission \ - -``` - -Use Chrome trace when host/device sequencing matters: - -```bash -unitrace \ - --chrome-kernel-logging --chrome-sycl-logging --chrome-device-logging \ - --output trace \ - -``` - -Open the generated JSON in Perfetto. - -## 8. Instruction-Level Stall Sampling - -For instruction-level stall analysis, unitrace supports `--stall-sampling`, but it is only useful -when shader/debug dumps are available. - -Typical flow: - -```bash -# Build or run with shader dumps/debug info enabled where supported. -export IGC_ShaderDumpEnable=1 -export IGC_DumpToCustomDir=./shader_dump - -unitrace \ - --stall-sampling \ - --include-kernels "" \ - --output stall_sampling \ - -``` - -This is the path to correlate stalls to instruction regions. For spill proof, kernel properties are -usually simpler; assembly dumps are useful when you need to inspect actual scratch send -instructions. - -## 9. Output Files - -`--output-dir-path` may not always place every output where expected with some unitrace builds. -If files appear in the current directory, move them after the run: - -```bash -mkdir -p -find . -maxdepth 1 -type f -name '*' -exec mv -t {} + -``` - -Common output patterns: - -```text -. timing/log output -.metrics. metric-query CSV-style output -.json Chrome trace, when enabled -``` - -## 10. Practical Analysis Pattern - -Use this order for most kernels: - -1. `--device-timing --verbose`: identify hot kernels and check spill/private/SLM/register properties. -2. `--metric-query --group ComputeBasic`: broad compute, occupancy, SEND, memory and L3 counters. -3. `--metric-query --group VectorEngineStalls`: dominant stall reason. -4. `--metric-query --group MemoryProfile`: deeper memory/LSC/L3 analysis if SEND or memory pressure is high. -5. Chrome trace or stall sampling only if timeline or instruction-level evidence is needed. - -Minimal command set: - -```bash -# Properties and spill -unitrace --device-timing --verbose --include-kernels "" --output props - -# Broad counters -unitrace --device-timing --kernel-submission --metric-query --group ComputeBasic \ - --include-kernels "" --output basic - -# Stall reason -unitrace --device-timing --kernel-submission --metric-query --group VectorEngineStalls \ - --include-kernels "" --output stalls - -# Memory detail -unitrace --device-timing --kernel-submission --metric-query --group MemoryProfile \ - --include-kernels "" --output memory -``` - -## 11. Reading Results Safely - -- Metric profiling adds overhead. Use ratios, percentages, and relative A/B comparisons more than - profiled wall time. -- Run the same benchmark shape for A/B comparisons. -- Use `--include-kernels` to reduce unrelated noise. -- The benchmark may call the target kernel multiple times even with `--iters 1`; aggregate rows by - kernel name or compare matching rows consistently. -- `Spill Memory Per Thread` is the direct unitrace spill field. `LSC` and `GPU_MEMORY` counters are - runtime symptoms, not direct proof by themselves. -- `SLM_BYTE_READ/WRITE = 0` only means no SLM traffic. It does not rule out private/scratch spill. - -## 12. Sparse SDPA Example - -For the BF16 sparse SDPA K32/K64 A/B, the important result came from combining: - -```text -unitrace --device-timing --verbose -unitrace --metric-query --group ComputeBasic -unitrace --metric-query --group VectorEngineStalls -``` - -Observed: - -| variant | spill/thread | XVE active | XVE stall | SBID stall | SEND instructions | GPU memory write | -|---|---:|---:|---:|---:|---:|---:| -| K32 | 0 B | 64.7% | 35.1% | 31.4% | 18.6G | 1.63 GB | -| K64 | 3840 B | 39.9% | 59.9% | 55.7% | 81.6G | 8.14 GB | - -Conclusion: K64 has compiler-reported private/scratch spill and much higher SEND/SBID and memory -pressure. K32 avoids the spill and is the better selected-block microtile for this kernel. diff --git a/auto_round_extension/ark/benchmarks/results/ab_bf16_sdpa_k32_20260814_092404.csv b/auto_round_extension/ark/benchmarks/results/ab_bf16_sdpa_k32_20260814_092404.csv deleted file mode 100644 index feaa6ee7fe..0000000000 --- a/auto_round_extension/ark/benchmarks/results/ab_bf16_sdpa_k32_20260814_092404.csv +++ /dev/null @@ -1,16 +0,0 @@ -mode,batch,num_heads_q,num_heads_kv,gqa_group_size,attention_pattern,seq_len,tensor_layout,head_dim,dtype,causal,requested_topk,selected_ratio,selected_blocks_per_row,warmup,iters,latency_ms,status,note,speedup_vs_torch,speedup_vs_sagev1,speedup_vs_ark,baseline_tflops,effective_tflops -dense_torch_sdpa,1,40,40,1,mha,75000,HND,128,bfloat16,False,,,,1,2,2198.8499785074964,ok,,1.0,0.5424874441849272,0.6248773510755263,52.39102309207733,52.39102309207733 -dense_sagev1,1,40,40,1,mha,75000,HND,128,bfloat16,False,,,,1,2,1192.848504986614,ok,,1.843360635751622,1.0,1.1518743111453715,96.57554963468958,96.57554963468958 -dense_ark_sdpa,1,40,40,1,mha,75000,HND,128,bfloat16,False,,,,1,2,1374.0115499822423,ok,,1.6003140428738858,0.8681502750119025,1.0,83.84208997478139,83.84208997478139 -sparse_qtile256_row64k_kernel_only,1,40,40,1,mha,75000,HND,128,bfloat16,False,1.0,1.0,1172.0,1,2,1232.5617364840582,ok,,1.7839674179565415,0.9677799250764281,1.11476083453773,93.46387818967474,93.46387818967474 -sparse_qtile256_row64k_e2e,1,40,40,1,mha,75000,HND,128,bfloat16,False,1.0,1.0,1172.0,1,2,1403.636468516197,ok,,1.5665380800713522,0.8498272392762708,0.9788941658339273,82.07253272763671,82.07253272763671 -sparse_sdpa_bf16_kernel_only,1,40,40,1,mha,75000,HND,128,bfloat16,False,1.0,1.0,1172.0,1,2,1691.256666497793,ok,,1.3001279001967296,0.7053030616912401,0.8124204783343186,68.11503084186089,68.11503084186089 -sparse_sdpa_bf16_e2e,1,40,40,1,mha,75000,HND,128,bfloat16,False,1.0,1.0,1172.0,1,2,1855.3182344767265,ok,,1.1851605496281126,0.6429347175165584,0.7405799848508298,62.09177372338551,62.09177372338551 -sparse_qtile256_row64k_kernel_only,1,40,40,1,mha,75000,HND,128,bfloat16,False,0.5,0.5025568148726252,588.9965870307167,1,2,620.9528554463759,ok,,3.5410900509135095,1.9209968913487439,2.2127469709347345,185.521330628535,93.23500901160774 -sparse_qtile256_row64k_e2e,1,40,40,1,mha,75000,HND,128,bfloat16,False,0.5,0.5025568148726252,588.9965870307167,1,2,791.7039599851705,ok,,2.7773638754423855,1.5066850303602843,1.7355117814592957,145.5089349334034,73.12650687563928 -sparse_sdpa_bf16_kernel_only,1,40,40,1,mha,75000,HND,128,bfloat16,False,0.5,0.5025568148726252,588.9965870307167,1,2,862.6485769636929,ok,,2.548952188905125,1.3827745583087172,1.5927824918211992,133.54221298952947,67.11254921105966 -sparse_sdpa_bf16_e2e,1,40,40,1,mha,75000,HND,128,bfloat16,False,0.5,0.5025568148726252,588.9965870307167,1,2,1023.5971785150468,ok,,2.1481594758764504,1.1653495437698485,1.3423362029734673,112.54427270610786,56.559891223337694 -sparse_qtile256_row64k_kernel_only,1,40,40,1,mha,75000,HND,128,bfloat16,False,0.25,0.2534100571934443,296.99658703071674,1,2,318.0901705054566,ok,,6.9126624535849235,3.7500325869583935,4.319566202875395,362.1614582335037,91.77535584421337 -sparse_qtile256_row64k_e2e,1,40,40,1,mha,75000,HND,128,bfloat16,False,0.25,0.2534100571934443,296.99658703071674,1,2,487.6641205046326,ok,,4.508943524965783,2.4460452488328643,2.8175366860297646,236.2281643373549,59.86259263543146 -sparse_sdpa_bf16_kernel_only,1,40,40,1,mha,75000,HND,128,bfloat16,False,0.25,0.2534100571934443,296.99658703071674,1,2,455.0045364885591,ok,,4.832589133015788,2.6216189275655886,3.01977549627528,253.18428886235216,64.15944512109019 -sparse_sdpa_bf16_e2e,1,40,40,1,mha,75000,HND,128,bfloat16,False,0.25,0.2534100571934443,296.99658703071674,1,2,615.2271990431473,ok,,3.5740454614609556,1.9388747877886927,2.233340060581229,187.24789830353512,47.45050061845107 diff --git a/auto_round_extension/ark/benchmarks/results/ab_k32_bf16_20260814_091351.csv b/auto_round_extension/ark/benchmarks/results/ab_k32_bf16_20260814_091351.csv deleted file mode 100644 index 87c04adc39..0000000000 --- a/auto_round_extension/ark/benchmarks/results/ab_k32_bf16_20260814_091351.csv +++ /dev/null @@ -1,16 +0,0 @@ -mode,batch,num_heads_q,num_heads_kv,gqa_group_size,attention_pattern,seq_len,tensor_layout,head_dim,dtype,causal,requested_topk,selected_ratio,selected_blocks_per_row,warmup,iters,latency_ms,status,note,speedup_vs_torch,speedup_vs_sagev1,speedup_vs_ark,baseline_tflops,effective_tflops -dense_torch_sdpa,1,40,40,1,mha,75000,HND,128,bfloat16,False,,,,1,2,2198.8636924652383,ok,,1.0,0.5408843479418712,0.6250810260167103,52.390696337727256,52.390696337727256 -dense_sagev1,1,40,40,1,mha,75000,HND,128,bfloat16,False,,,,1,2,1189.3309545121156,ok,,1.8488240671136411,1.0,1.1556648447957822,96.86118028203266,96.86118028203266 -dense_ark_sdpa,1,40,40,1,mha,75000,HND,128,bfloat16,False,,,,1,2,1374.4679729570635,ok,,1.599792600284858,0.8653027774473059,1.0,83.81424832486708,83.81424832486708 -sparse_qtile256_row64k_kernel_only,1,40,40,1,mha,75000,HND,128,bfloat16,False,1.0,1.0,1172.0,1,2,1226.4589419937693,ok,,1.7928555267334902,0.9697274925312238,1.1206799721502998,93.92894947851035,93.92894947851035 -sparse_qtile256_row64k_e2e,1,40,40,1,mha,75000,HND,128,bfloat16,False,1.0,1.0,1172.0,1,2,1401.4715235098265,ok,,1.5689677996156737,0.848630125236916,0.9807320019709457,82.19931555333687,82.19931555333687 -sparse_sdpa_bf16_kernel_only,1,40,40,1,mha,75000,HND,128,bfloat16,False,1.0,1.0,1172.0,1,2,3097.4557574954815,ok,,0.7098934947316825,0.38397028000612227,0.4437409540494683,37.191814514615565,37.191814514615565 -sparse_sdpa_bf16_e2e,1,40,40,1,mha,75000,HND,128,bfloat16,False,1.0,1.0,1172.0,1,2,3262.153450981714,ok,,0.6740528076027543,0.3645846133186033,0.42133762056577395,35.3140959587084,35.3140959587084 -sparse_qtile256_row64k_kernel_only,1,40,40,1,mha,75000,HND,128,bfloat16,False,0.5,0.5025568148726252,588.9965870307167,1,2,620.1253090403043,ok,,3.5458376886248417,1.9178881061195592,2.216435860494337,185.76890560761282,93.35942950453526 -sparse_qtile256_row64k_e2e,1,40,40,1,mha,75000,HND,128,bfloat16,False,0.5,0.5025568148726252,588.9965870307167,1,2,791.9392834883183,ok,,2.7765559030986915,1.501795629171689,1.7355724127016838,145.46569718396762,73.10477745000057 -sparse_sdpa_bf16_kernel_only,1,40,40,1,mha,75000,HND,128,bfloat16,False,0.5,0.5025568148726252,588.9965870307167,1,2,1558.91097598942,ok,,1.410512676049156,0.7629242290485915,0.8816847107543823,73.89774129140639,37.13781348969048 -sparse_sdpa_bf16_e2e,1,40,40,1,mha,75000,HND,128,bfloat16,False,0.5,0.5025568148726252,588.9965870307167,1,2,1718.133609450888,ok,,1.2797978459707746,0.6922226234153137,0.7999773506533876,67.04950032193229,33.69618332059135 -sparse_qtile256_row64k_kernel_only,1,40,40,1,mha,75000,HND,128,bfloat16,False,0.25,0.2534100571934443,296.99658703071674,1,2,318.0500839953311,ok,,6.913576833067327,3.7394454972996476,4.321545700259084,362.20710446877644,91.78692305930448 -sparse_qtile256_row64k_e2e,1,40,40,1,mha,75000,HND,128,bfloat16,False,0.25,0.2534100571934443,296.99658703071674,1,2,487.35681897960603,ok,,4.51181476657917,2.4403699880556804,2.8202498034906527,236.37711736792312,59.90033883142689 -sparse_sdpa_bf16_kernel_only,1,40,40,1,mha,75000,HND,128,bfloat16,False,0.25,0.2534100571934443,296.99658703071674,1,2,786.9992540217936,ok,,2.793984468509226,1.5112224674093282,1.7464666782505003,146.37879186199316,37.09385801765496 -sparse_sdpa_bf16_e2e,1,40,40,1,mha,75000,HND,128,bfloat16,False,0.25,0.2534100571934443,296.99658703071674,1,2,947.5691915140487,ok,,2.3205310094050664,1.2551389019009516,1.4505199042625117,121.57423545602056,30.808133960159434 diff --git a/auto_round_extension/ark/benchmarks/results/ab_k64_bf16_20260814_090508.csv b/auto_round_extension/ark/benchmarks/results/ab_k64_bf16_20260814_090508.csv deleted file mode 100644 index cf2790e3a0..0000000000 --- a/auto_round_extension/ark/benchmarks/results/ab_k64_bf16_20260814_090508.csv +++ /dev/null @@ -1,16 +0,0 @@ -mode,batch,num_heads_q,num_heads_kv,gqa_group_size,attention_pattern,seq_len,tensor_layout,head_dim,dtype,causal,requested_topk,selected_ratio,selected_blocks_per_row,warmup,iters,latency_ms,status,note,speedup_vs_torch,speedup_vs_sagev1,speedup_vs_ark,baseline_tflops,effective_tflops -dense_torch_sdpa,1,40,40,1,mha,75000,HND,128,bfloat16,False,,,,1,2,2198.8340279785916,ok,,1.0,0.5418930146026886,0.6250399004918449,52.39140314101125,52.39140314101125 -dense_sagev1,1,40,40,1,mha,75000,HND,128,bfloat16,False,,,,1,2,1191.5328000322916,ok,,1.8453827103366365,1.0,1.1534378256381823,96.6821895266987,96.6821895266987 -dense_ark_sdpa,1,40,40,1,mha,75000,HND,128,bfloat16,False,,,,1,2,1374.3590020458214,ok,,1.5998978612614945,0.8669734750953854,1.0,83.82089383379264,83.82089383379264 -sparse_qtile256_row64k_kernel_only,1,40,40,1,mha,75000,HND,128,bfloat16,False,1.0,1.0,1172.0,1,2,1231.199766509235,ok,,1.7859279117740954,0.967781859974349,1.116276204260889,93.567269206541,93.567269206541 -sparse_qtile256_row64k_e2e,1,40,40,1,mha,75000,HND,128,bfloat16,False,1.0,1.0,1172.0,1,2,1401.3589860405773,ok,,1.5690726286996692,0.8502694968966288,0.9807329997069187,82.20591664773063,82.20591664773063 -sparse_sdpa_bf16_kernel_only,1,40,40,1,mha,75000,HND,128,bfloat16,False,1.0,1.0,1172.0,1,2,2986.013912013732,ok,,0.7363776903824686,0.3990379265275212,0.46026543832107275,38.57986044087467,38.57986044087467 -sparse_sdpa_bf16_e2e,1,40,40,1,mha,75000,HND,128,bfloat16,False,1.0,1.0,1172.0,1,2,3109.592208522372,ok,,0.7071133063532604,0.3831797612454428,0.4419740306395013,37.04665829952705,37.04665829952705 -sparse_qtile256_row64k_kernel_only,1,40,40,1,mha,75000,HND,128,bfloat16,False,0.5,0.5025568148726252,588.9965870307167,1,2,621.7157779610716,ok,,3.536719037740539,1.9165233411639406,2.21059051541696,185.29367290275394,93.12059807005808 -sparse_qtile256_row64k_e2e,1,40,40,1,mha,75000,HND,128,bfloat16,False,0.5,0.5025568148726252,588.9965870307167,1,2,790.3886099811643,ok,,2.781965732060577,1.5075277970676815,1.7388395843388653,145.75108819286416,73.2482026464249 -sparse_sdpa_bf16_kernel_only,1,40,40,1,mha,75000,HND,128,bfloat16,False,0.5,0.5025568148726252,588.9965870307167,1,2,1511.0474135144614,ok,,1.4551720934185948,0.7885475924683075,0.9095406204688681,76.23850778584287,38.31418164349503 -sparse_sdpa_bf16_e2e,1,40,40,1,mha,75000,HND,128,bfloat16,False,0.5,0.5025568148726252,588.9965870307167,1,2,1669.5242109708488,ok,,1.317042312731687,0.7136960292054708,0.8232039960933629,69.00169476009563,34.67727193944677 -sparse_qtile256_row64k_kernel_only,1,40,40,1,mha,75000,HND,128,bfloat16,False,0.25,0.2534100571934443,296.99658703071674,1,2,318.1397750158794,ok,,6.911534490991705,3.7453122608539537,4.319984830495409,362.1049898405504,91.76104618552544 -sparse_qtile256_row64k_e2e,1,40,40,1,mha,75000,HND,128,bfloat16,False,0.25,0.2534100571934443,296.99658703071674,1,2,487.69996053306386,ok,,4.508579466718084,2.4431677187956446,2.8180420612370463,236.21080443411262,59.85819346135797 -sparse_sdpa_bf16_kernel_only,1,40,40,1,mha,75000,HND,128,bfloat16,False,0.25,0.2534100571934443,296.99658703071674,1,2,766.7436815099791,ok,,2.867756306316525,1.554017109975733,1.7924621163349415,150.2457767544064,38.073790880407586 -sparse_sdpa_bf16_e2e,1,40,40,1,mha,75000,HND,128,bfloat16,False,0.25,0.2534100571934443,296.99658703071674,1,2,926.4499850105494,ok,,2.373397445684619,1.286127496692359,1.4834681032783132,124.34562239070922,31.51043128178405 diff --git a/auto_round_extension/ark/benchmarks/results/flux_bf16_topk09_k32_20260814_112445/SWEEP_SUMMARY_dev5.md b/auto_round_extension/ark/benchmarks/results/flux_bf16_topk09_k32_20260814_112445/SWEEP_SUMMARY_dev5.md deleted file mode 100644 index 35f42546a9..0000000000 --- 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