import torch import tilelang as tl import tilelang.language as T from tilelang.profiler import do_bench import argparse from fla.ops.linear_attn import fused_chunk_linear_attn # We compare with FLA def chunk_linear_attn_bwd_kernel( B, S, H, DK, DV, dtype: str = 'float16', scale: float = None, ) -> torch.Tensor: if scale is None: scale = DK**-0.5 accum_dtype = 'float' chunk_size = 64 BK = BV = 64 assert S % chunk_size == 0 and DK % BK == 0 and DV % BV == 0 NK = tl.cdiv(DK, BK) NV = tl.cdiv(DV, BV) NT = tl.cdiv(S, chunk_size) @T.prim_func def main( Q: T.Tensor([B, S, H, DK], dtype), K: T.Tensor([B, S, H, DK], dtype), V: T.Tensor([B, S, H, DV], dtype), dO: T.Tensor([B, S, H, DV], dtype), dQ: T.Tensor([NV, B, S, H, DK], dtype), dK: T.Tensor([NV, B, S, H, DK], dtype), dV: T.Tensor([NK, B, S, H, DV], dtype), ): with T.Kernel(NV, NK, B * H) as (i_v, i_k, i_bh): i_b = i_bh // H i_h = i_bh % H ds = T.alloc_fragment([chunk_size, chunk_size], accum_dtype) ds_shared = T.alloc_shared([chunk_size, chunk_size], dtype) dq = T.alloc_fragment([chunk_size, BK], accum_dtype) dk = T.alloc_fragment([chunk_size, BK], accum_dtype) dv = T.alloc_fragment([chunk_size, BV], accum_dtype) q = T.alloc_shared([chunk_size, BK], dtype) k = T.alloc_shared([chunk_size, BK], dtype) v = T.alloc_shared([chunk_size, BV], dtype) do = T.alloc_shared([chunk_size, BV], dtype) h = T.alloc_fragment([BV, BK], accum_dtype) h_shared = T.alloc_shared([BV, BK], dtype) dh = T.alloc_fragment([BK, BV], accum_dtype) dh_shared = T.alloc_shared([BK, BV], dtype) T.clear(h) T.clear(dh) T.annotate_layout({ ds_shared: tl.layout.make_swizzled_layout(ds_shared), q: tl.layout.make_swizzled_layout(q), k: tl.layout.make_swizzled_layout(k), v: tl.layout.make_swizzled_layout(v), do: tl.layout.make_swizzled_layout(do), h_shared: tl.layout.make_swizzled_layout(h_shared), dh_shared: tl.layout.make_swizzled_layout(dh_shared) }) # Calculate dQ for i in T.Pipelined(0, NT, num_stages=1): T.copy(K[i_b, i * chunk_size:(i + 1) * chunk_size, i_h, i_k * BK:(i_k + 1) * BK], k) T.copy(V[i_b, i * chunk_size:(i + 1) * chunk_size, i_h, i_v * BV:(i_v + 1) * BV], v) T.copy(dO[i_b, i * chunk_size:(i + 1) * chunk_size, i_h, i_v * BV:(i_v + 1) * BV], do) T.gemm(do, v, ds, transpose_B=True, clear_accum=True) for row, col in T.Parallel(chunk_size, chunk_size): ds_shared[row, col] = T.if_then_else(row >= col, ds[row, col], 0) T.gemm(ds_shared, k, dq, clear_accum=True) T.copy(h, h_shared) T.gemm(do, h_shared, dq) T.gemm(v, k, h, transpose_A=True) for row, col in T.Parallel(chunk_size, BK): dq[row, col] *= scale T.copy( dq, dQ[i_v, i_b, i * chunk_size:(i + 1) * chunk_size, i_h, i_k * BK:(i_k + 1) * BK]) # Calculate dK, dV (reversely) for i in T.Pipelined(1, NT + 1, num_stages=1): start = NT - i for row, col in T.Parallel(chunk_size, BK): q[row, col] = Q[i_b, start * chunk_size + row, i_h, i_k * BK + col] * scale T.copy( K[i_b, start * chunk_size:(start + 1) * chunk_size, i_h, i_k * BK:(i_k + 1) * BK], k) T.copy( V[i_b, start * chunk_size:(start + 1) * chunk_size, i_h, i_v * BV:(i_v + 1) * BV], v) T.copy( dO[i_b, start * chunk_size:(start + 1) * chunk_size, i_h, i_v * BV:(i_v + 1) * BV], do) T.copy(dh, dh_shared) # Calculate dk T.gemm( v, do, ds, transpose_B=True, clear_accum=True ) # ds here actually means `s`, but we simply reuse the buffer `ds` for row, col in T.Parallel(chunk_size, chunk_size): ds_shared[row, col] = T.if_then_else(row <= col, ds[row, col], 0) T.gemm(ds_shared, q, dk, clear_accum=True) T.gemm(v, dh_shared, dk, transpose_B=True) # Calculate dv T.gemm(k, q, ds, transpose_B=True, clear_accum=True) for row, col in T.Parallel(chunk_size, chunk_size): ds_shared[row, col] = T.if_then_else(row <= col, ds[row, col], 0) T.gemm(ds_shared, do, dv, clear_accum=True) T.gemm(k, dh_shared, dv) # Update dh T.gemm(q, do, dh, transpose_A=True) T.copy( dk, dK[i_v, i_b, start * chunk_size:(start + 1) * chunk_size, i_h, i_k * BK:(i_k + 1) * BK]) T.copy( dv, dV[i_k, i_b, start * chunk_size:(start + 1) * chunk_size, i_h, i_v * BV:(i_v + 1) * BV]) return main def postprocess(dQ, dK, dV): dQ = dQ[0] if dQ.size(0) == 1 else dQ.sum(0) dK = dK[0] if dK.size(0) == 1 else dK.sum(0) dV = dV[0] if dV.size(0) == 1 else dV.sum(0) return dQ, dK, dV def main(): parser = argparse.ArgumentParser() parser.add_argument('--B', type=int, default=8, help='Batch size') parser.add_argument('--S', type=int, default=2048, help='Seq len') parser.add_argument('--H', type=int, default=64, help='Num heads') parser.add_argument('--D', type=int, default=256, help='Head dim') args = parser.parse_args() B, S, H, D = args.B, args.S, args.H, args.D q = torch.randn((B, S, H, D), device='cuda', dtype=torch.float16, requires_grad=True) k = torch.randn((B, S, H, D), device='cuda', dtype=torch.float16, requires_grad=True) v = torch.randn((B, S, H, D), device='cuda', dtype=torch.float16, requires_grad=True) do = torch.randn((B, S, H, D), device='cuda', dtype=torch.float16) fn = chunk_linear_attn_bwd_kernel(B, S, H, D, D) kernel = tl.compile(fn, out_idx=[4, 5, 6], target='cuda') dq, dk, dv = postprocess(*kernel(q, k, v, do)) o_ref, h_ref = fused_chunk_linear_attn(q, k, v, output_final_state=True, normalize=False) o_ref.backward(do, retain_graph=True) if torch.allclose(dq, q.grad) and torch.allclose(dk, k.grad) and torch.allclose(dv, v.grad): print('Passed all tests!✅') else: print('Failed some tests!❌') t1 = do_bench(lambda: o_ref.backward(do, retain_graph=True), warmup=25, rep=100) q.grad = k.grad = v.grad = None o_ref, h_ref = fused_chunk_linear_attn(q, k, v, output_final_state=True, normalize=False) t2 = do_bench(lambda: postprocess(*kernel(q, k, v, do)), warmup=25, rep=100) print(f'Triton latency: {t1:.3f} ms') print(f'TileLang latency: {t2:.3f} ms') print(f'Speedup: {t1/t2:.3f}x') if __name__ == '__main__': main()