[Add] Add operator code and skill modifications for seer_attention and block_sparse_attn. #983
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| Original file line number | Diff line number | Diff line change | ||||
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| import math | ||||||
| import torch | ||||||
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| import tilelang | ||||||
| import tilelang.language as T | ||||||
| import torch.nn.functional as F | ||||||
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| def get_sparse_attn_mask_from_topk(x, topk, use_dense_for_last_block=False): | ||||||
| bsz, num_head, downsample_len, _ = x.shape | ||||||
| sparse_index = torch.topk(x, topk, dim=-1).indices | ||||||
| dense_mask = torch.full([bsz, num_head, downsample_len, downsample_len], False, dtype=torch.bool, device=x.device) | ||||||
| dense_mask.scatter_(-1, sparse_index, True) | ||||||
| if use_dense_for_last_block: | ||||||
| dense_mask[:, :, -2:, :] = True | ||||||
| dense_mask.tril_() | ||||||
| return dense_mask | ||||||
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| def get_sparse_attn_mask_from_threshold(x, threshold, use_dense_for_last_block=False): | ||||||
| dense_mask = x > threshold | ||||||
| if use_dense_for_last_block: | ||||||
| dense_mask[:, :, -2:, :] = True | ||||||
| dense_mask.tril_() | ||||||
| return dense_mask | ||||||
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| pass_configs = { | ||||||
| tilelang.PassConfigKey.TL_ASCEND_AUTO_CV_COMBINE: True, | ||||||
| tilelang.PassConfigKey.TL_ASCEND_AUTO_CV_SYNC: True, | ||||||
| tilelang.PassConfigKey.TL_ASCEND_AUTO_SYNC: True, | ||||||
| tilelang.PassConfigKey.TL_ASCEND_MEMORY_PLANNING: True, | ||||||
| } | ||||||
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| @tilelang.jit(out_idx=[4], workspace_idx=[5, 6, 7], pass_configs=pass_configs) | ||||||
| def blocksparse_flashattn_ascend(batch, heads, seq_q, seq_kv, dim, downsample_len, is_causal): | ||||||
| block_M = 64 | ||||||
| block_N = 64 | ||||||
| dtype = "float16" | ||||||
| accum_dtype = "float" | ||||||
| block_mask_dtype = "int8" | ||||||
| sm_scale = (1.0 / dim) ** 0.5 | ||||||
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| q_shape = [batch, heads, seq_q, dim] | ||||||
| kv_shape = [batch, heads, seq_kv, dim] | ||||||
| block_mask_shape = [batch, heads, downsample_len, downsample_len] | ||||||
| block_num = ((seq_q + block_M - 1) // block_M) * heads * batch | ||||||
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| @T.prim_func | ||||||
| def main( | ||||||
| Q: T.Tensor(q_shape, dtype), | ||||||
| K: T.Tensor(kv_shape, dtype), | ||||||
| V: T.Tensor(kv_shape, dtype), | ||||||
| BlockSparseMask: T.Tensor(block_mask_shape, block_mask_dtype), | ||||||
| Output: T.Tensor(q_shape, dtype), | ||||||
| workspace_1: T.Tensor([block_num, block_M, block_N], accum_dtype), | ||||||
| workspace_2: T.Tensor([block_num, block_M, block_N], dtype), | ||||||
| workspace_3: T.Tensor([block_num, block_M, dim], accum_dtype), | ||||||
| ): | ||||||
| with T.Kernel(block_num, is_npu=True) as (cid, vid): | ||||||
| bx = cid % ((seq_q + block_M - 1) // block_M) | ||||||
| by = cid // ((seq_q + block_M - 1) // block_M) % heads | ||||||
| bz = cid // ((seq_q + block_M - 1) // block_M) // heads % batch | ||||||
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| # L1 buffers (Cube core) | ||||||
| q_l1 = T.alloc_L1([block_M, dim], dtype) | ||||||
| k_l1 = T.alloc_L1([block_N, dim], dtype) | ||||||
| v_l1 = T.alloc_L1([block_N, dim], dtype) | ||||||
| acc_s_l1 = T.alloc_L1([block_M, block_N], dtype) | ||||||
| acc_s_l0c = T.alloc_L0C([block_M, block_N], accum_dtype) | ||||||
| acc_o_l0c = T.alloc_L0C([block_M, dim], accum_dtype) | ||||||
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| # UB buffers (Vector core) for online softmax | ||||||
| acc_o = T.alloc_ub([block_M // 2, dim], accum_dtype) | ||||||
| sumexp = T.alloc_ub([block_M // 2], accum_dtype) | ||||||
| m_i = T.alloc_ub([block_M // 2], accum_dtype) | ||||||
| acc_s_ub = T.alloc_ub([block_M // 2, block_N], accum_dtype) | ||||||
| m_i_prev = T.alloc_ub([block_M // 2], accum_dtype) | ||||||
| acc_s_ub_ = T.alloc_ub([block_M // 2, block_N], accum_dtype) | ||||||
| sumexp_i_ub = T.alloc_ub([block_M // 2], accum_dtype) | ||||||
| acc_s_half = T.alloc_ub([block_M // 2, block_N], dtype) | ||||||
| acc_o_ub = T.alloc_ub([block_M // 2, dim], accum_dtype) | ||||||
| acc_o_half = T.alloc_ub([block_M // 2, dim], dtype) | ||||||
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| # UB buffers for block-sparse mask and causal mask | ||||||
| mask_row = T.alloc_ub([downsample_len], block_mask_dtype) | ||||||
| col_idx = T.alloc_ub([block_N], "int32") | ||||||
| col_idx_f = T.alloc_ub([block_N], "float") | ||||||
| cmp_mask = T.alloc_ub([block_N // 8], "uint8") | ||||||
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| # === Cube: Load Q, run GEMM pipeline for all KV blocks === | ||||||
| T.copy(Q[bz, by, bx * block_M : (bx + 1) * block_M, :], q_l1) | ||||||
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| loop_range = (seq_kv + block_N - 1) // block_N | ||||||
| for k in T.serial(loop_range): | ||||||
| T.copy(K[bz, by, k * block_N : (k + 1) * block_N, :], k_l1) | ||||||
| T.gemm_v0(q_l1, k_l1, acc_s_l0c, transpose_B=True, init=True) | ||||||
| T.copy(acc_s_l0c, workspace_1[cid, :, :]) | ||||||
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| T.copy(workspace_2[cid, :, :], acc_s_l1) | ||||||
| T.copy(V[bz, by, k * block_N : (k + 1) * block_N, :], v_l1) | ||||||
| T.gemm_v0(acc_s_l1, v_l1, acc_o_l0c, init=True) | ||||||
| T.copy(acc_o_l0c, workspace_3[cid, :, :]) | ||||||
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| # === Vector: Online softmax + output accumulation === | ||||||
| T.tile.fill(acc_o, 0.0) | ||||||
| T.tile.fill(sumexp, 0.0) | ||||||
| T.tile.fill(m_i, -T.infinity(accum_dtype)) | ||||||
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| T.copy(BlockSparseMask[bz, by, bx, :], mask_row) | ||||||
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| past_len = seq_kv - seq_q | ||||||
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| for _k in T.serial(loop_range): | ||||||
| T.tile.fill(acc_s_ub, 0.0) | ||||||
| T.copy(m_i, m_i_prev) | ||||||
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| # Load S = Q@K^T from Cube workspace | ||||||
| T.copy(workspace_1[cid, vid * block_M // 2 : vid * block_M // 2 + block_M // 2, :], acc_s_ub_) | ||||||
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| # Block sparsity mask | ||||||
| if mask_row[_k] == 0: | ||||||
| T.tile.fill(acc_s_ub_, -T.infinity(accum_dtype)) | ||||||
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| # Element-level causal mask via compare+select (float32) | ||||||
| elif is_causal: | ||||||
| T.tile.createvecindex(col_idx, _k * block_N) | ||||||
| T.copy(col_idx, col_idx_f) | ||||||
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| for h_i in range(block_M // 2): | ||||||
| q_pos = bx * block_M + vid * (block_M // 2) + h_i + past_len | ||||||
| T.tile.compare(cmp_mask, col_idx_f, T.float32(q_pos), "LE") | ||||||
| T.tile.select(acc_s_ub_[h_i, :], cmp_mask, acc_s_ub_[h_i, :], -T.infinity(accum_dtype), "VSEL_TENSOR_SCALAR_MODE") | ||||||
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| T.tile.add(acc_s_ub, acc_s_ub, acc_s_ub_) | ||||||
| T.tile.mul(acc_s_ub, acc_s_ub, sm_scale) | ||||||
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| T.reduce_max(acc_s_ub, m_i, dim=-1) | ||||||
| T.tile.max(m_i, m_i, m_i_prev) | ||||||
| T.tile.sub(m_i_prev, m_i_prev, m_i) | ||||||
| T.tile.exp(m_i_prev, m_i_prev) | ||||||
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| for h_i in range(block_M // 2): | ||||||
| T.tile.sub(acc_s_ub[h_i, :], acc_s_ub[h_i, :], m_i[h_i]) | ||||||
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| T.tile.exp(acc_s_ub, acc_s_ub) | ||||||
| T.reduce_sum(acc_s_ub, sumexp_i_ub, dim=-1) | ||||||
| T.tile.mul(sumexp, sumexp, m_i_prev) | ||||||
| T.tile.add(sumexp, sumexp, sumexp_i_ub) | ||||||
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| for h_i in range(block_M // 2): | ||||||
| T.tile.mul(acc_o[h_i, :], acc_o[h_i, :], m_i_prev[h_i]) | ||||||
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| T.copy(acc_s_ub, acc_s_half) | ||||||
| T.copy(acc_s_half, workspace_2[cid, vid * block_M // 2 : vid * block_M // 2 + block_M // 2, :]) | ||||||
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| T.copy(workspace_3[cid, vid * block_M // 2 : vid * block_M // 2 + block_M // 2, :], acc_o_ub) | ||||||
| T.tile.add(acc_o, acc_o, acc_o_ub) | ||||||
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| # Final normalization: acc_o /= sumexp | ||||||
| for h_i in range(block_M // 2): | ||||||
| T.tile.div(acc_o[h_i, :], acc_o[h_i, :], sumexp[h_i]) | ||||||
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| T.copy(acc_o, acc_o_half) | ||||||
| T.copy(acc_o_half, Output[bz, by, bx * block_M + vid * block_M // 2 : bx * block_M + vid * block_M // 2 + block_M // 2, :]) | ||||||
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| return main | ||||||
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| def test_topk_sparse_attention(): | ||||||
| BATCH, N_HEADS, SEQ_LEN, D_HEAD = 4, 2, 256, 64 | ||||||
| TOPK = 2 | ||||||
| BLOCK = 64 | ||||||
| torch.manual_seed(0) | ||||||
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| q = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device="npu", dtype=torch.float16) | ||||||
| k = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device="npu", dtype=torch.float16) | ||||||
| v = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device="npu", dtype=torch.float16) | ||||||
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| sm_scale = 1.0 / (D_HEAD**0.5) | ||||||
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| downsample_factor = BLOCK | ||||||
| downsample_len = math.ceil(SEQ_LEN / downsample_factor) | ||||||
| x_ds = torch.randn([BATCH, N_HEADS, downsample_len, downsample_len], device="npu", dtype=torch.float16) | ||||||
| x_ds[:, :, :, 0] = 100 | ||||||
| block_mask = get_sparse_attn_mask_from_topk(x_ds, topk=TOPK) | ||||||
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| kernel = blocksparse_flashattn_ascend(BATCH, N_HEADS, SEQ_LEN, SEQ_LEN, D_HEAD, downsample_len, is_causal=True) | ||||||
| torch.npu.synchronize() | ||||||
| tilelang_output = kernel(q, k, v, block_mask.to(torch.int8)) | ||||||
| torch.npu.synchronize() | ||||||
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| # Reference with FULL element-level causal + block sparsity | ||||||
| full_mask = torch.kron(block_mask.float(), torch.ones(BLOCK, BLOCK, device="npu")) | ||||||
| full_mask = full_mask[..., :SEQ_LEN, :SEQ_LEN].bool() | ||||||
| full_mask = full_mask & torch.tril(torch.ones_like(full_mask)) | ||||||
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| attn = torch.einsum("bhsd,bhtd->bhst", q.float(), k.float()) * sm_scale | ||||||
| attn = attn.masked_fill(~full_mask, float("-inf")) | ||||||
| attn = F.softmax(attn, dim=-1) | ||||||
| ref_output = torch.einsum("bhst,bhtd->bhsd", attn, v.float()).to(torch.float16) | ||||||
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| torch.testing.assert_close(tilelang_output, ref_output, atol=1e-2, rtol=1e-2) | ||||||
| print("Pass topk sparse attention test with qlen == klen") | ||||||
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| def test_topk_sparse_attention_qlen_lt_klen(): | ||||||
| BATCH, N_HEADS = 1, 1 | ||||||
| Q_LEN, K_LEN, D_HEAD = 128, 256, 64 | ||||||
| TOPK = 1 | ||||||
| BLOCK = 64 | ||||||
| torch.manual_seed(0) | ||||||
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| q = torch.randn(BATCH, N_HEADS, Q_LEN, D_HEAD, device="npu", dtype=torch.float16) | ||||||
| k = torch.randn(BATCH, N_HEADS, K_LEN, D_HEAD, device="npu", dtype=torch.float16) | ||||||
| v = torch.randn(BATCH, N_HEADS, K_LEN, D_HEAD, device="npu", dtype=torch.float16) | ||||||
| sm_scale = 1.0 / (D_HEAD**0.5) | ||||||
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| downsample_factor = BLOCK | ||||||
| downsample_len = math.ceil(K_LEN / downsample_factor) | ||||||
| x_ds = torch.randn(BATCH, N_HEADS, downsample_len, downsample_len, device="npu", dtype=torch.float16) | ||||||
| x_ds[:, :, :, 0] = 100 | ||||||
| block_mask = get_sparse_attn_mask_from_topk(x_ds, topk=TOPK) | ||||||
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| kernel = blocksparse_flashattn_ascend(BATCH, N_HEADS, Q_LEN, K_LEN, D_HEAD, downsample_len, is_causal=True) | ||||||
| torch.npu.synchronize() | ||||||
| tilelang_output = kernel(q, k, v, block_mask.to(torch.int8)) | ||||||
| torch.npu.synchronize() | ||||||
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| past_len = K_LEN - Q_LEN | ||||||
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| attn = torch.einsum("bhsd,bhtd->bhst", q.float(), k.float()) * sm_scale | ||||||
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| full_mask_full = torch.kron(block_mask.float(), torch.ones(BLOCK, BLOCK, device="npu")).bool() | ||||||
| full_mask_full = full_mask_full[..., :K_LEN, :K_LEN] | ||||||
| effective_mask = full_mask_full[..., past_len:K_LEN, :] | ||||||
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| i_global = torch.arange(past_len, K_LEN, device=k.device).unsqueeze(1) | ||||||
| j_global = torch.arange(K_LEN, device=k.device).unsqueeze(0) | ||||||
| causal_mask = j_global <= i_global | ||||||
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| final_mask = effective_mask & causal_mask | ||||||
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| attn = attn.masked_fill(~final_mask, float("-inf")) | ||||||
| attn = F.softmax(attn, dim=-1) | ||||||
| ref_output = torch.einsum("bhst,bhtd->bhsd", attn, v.float()).to(torch.float16) | ||||||
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| torch.testing.assert_close(tilelang_output, ref_output, atol=1e-2, rtol=1e-2) | ||||||
| print("Pass topk sparse attention test with qlen < klen") | ||||||
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| def main(): | ||||||
| test_topk_sparse_attention() | ||||||
| test_topk_sparse_attention_qlen_lt_klen() | ||||||
| print("Test passed!") | ||||||
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| if __name__ == "__main__": | ||||||
| main() | ||||||
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The list of mandatory items is missing item 14 (which was just added) and item 13 (which is marked as mandatory on line 304). Please update the pass criteria to include all required checks.