[Example] Add MLA decode ws example (#928)

examples/deepseek_mla/example_mla_decode_ws.py

+import torch
+import torch.nn.functional as F
+import tilelang
+from tilelang.autotuner import *
+import tilelang.language as T
+from einops import rearrange, einsum
+import argparse
+
+
+@tilelang.jit(
+    out_idx=[6],
+    pass_configs={
+        tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True,
+    },
+    compile_flags=[
+        "-O3", "-Wno-deprecated-declarations", "-U__CUDA_NO_HALF_OPERATORS__",
+        "-U__CUDA_NO_HALF_CONVERSIONS__", "-U__CUDA_NO_HALF2_OPERATORS__",
+        "-U__CUDA_NO_BFLOAT16_CONVERSIONS__", "--expt-relaxed-constexpr", "--expt-extended-lambda",
+        "--ptxas-options=-v,--register-usage-level=10", "-DNDEBUG"
+    ],
+)
+def flashattn(batch, heads, kv_head_num, seqlen_kv, dim, pe_dim, block_N, block_H, num_split,
+              softmax_scale):
+    sm_scale = float(softmax_scale * 1.44269504)  # log2(e)
+    dtype = "float16"
+    accum_dtype = "float"
+    kv_group_num = heads // kv_head_num
+    VALID_BLOCK_H = min(block_H, kv_group_num)
+    assert kv_head_num == 1, "kv_head_num must be 1"
+
+    @T.macro
+    def flash_attn(
+            Q: T.Tensor([batch, heads, dim], dtype),
+            Q_pe: T.Tensor([batch, heads, pe_dim], dtype),
+            KV: T.Tensor([batch, seqlen_kv, kv_head_num, dim], dtype),
+            K_pe: T.Tensor([batch, seqlen_kv, kv_head_num, pe_dim], dtype),
+            Output: T.Tensor([batch, heads, dim], dtype),
+    ):
+        with T.Kernel(heads // min(block_H, kv_group_num), batch, threads=384) as (hid, bid):
+            Q_shared_l = T.alloc_shared([block_H, dim // 2], dtype)
+            Q_shared_r = T.alloc_shared([block_H, dim // 2], dtype)
+            Q_tail_shared = T.alloc_shared([block_H, pe_dim], dtype)
+            KV_shared_0_l = T.alloc_shared([block_N, dim // 2], dtype)
+            KV_shared_0_r = T.alloc_shared([block_N, dim // 2], dtype)
+            KV_shared_1_l = T.alloc_shared([block_N, dim // 2], dtype)
+            KV_shared_1_r = T.alloc_shared([block_N, dim // 2], dtype)
+            K_tail_shared_0 = T.alloc_shared([block_N, pe_dim], dtype)
+            K_tail_shared_1 = T.alloc_shared([block_N, pe_dim], dtype)
+            O_shared_l = Q_shared_l
+            O_shared_r = Q_shared_r
+
+            acc_o_l = T.alloc_fragment([block_H, dim // 2], accum_dtype)
+            acc_o_r = T.alloc_fragment([block_H, dim // 2], accum_dtype)
+            acc_s = T.alloc_fragment([block_H, block_N], accum_dtype)
+            S_shared = T.alloc_shared([block_H, block_N], dtype)
+            sumexp = T.alloc_fragment([block_H], accum_dtype)
+            sum_exp_shared = T.alloc_shared([block_H], accum_dtype)
+            sumexp_i = T.alloc_fragment([block_H], accum_dtype)
+            alpha_shared = T.alloc_shared([block_H], accum_dtype, scope="shared")
+            alpha_local = T.alloc_fragment([block_H], accum_dtype)
+            m_i = T.alloc_fragment([block_H], accum_dtype)
+            m_i_prev = T.alloc_fragment([block_H], accum_dtype)
+
+            # TODO: Multi buffer
+            bar_q = T.alloc_barrier(arrive_count=384)
+            bar_k_0_ready = T.alloc_barrier(arrive_count=128)
+            bar_k_1_ready = T.alloc_barrier(arrive_count=128)
+            bar_k_0_free = T.alloc_barrier(arrive_count=256)
+            bar_k_1_free = T.alloc_barrier(arrive_count=256)
+            bar_sScale_and_sS_ready = T.alloc_barrier(arrive_count=256)
+            bar_sScale_and_sS_free = T.alloc_barrier(arrive_count=256)
+
+            cur_kv_head = hid // (kv_group_num // block_H)
+            NI = T.ceildiv((seqlen_kv // num_split), block_N)
+
+            tx = T.get_thread_binding()
+
+            T.copy(Q[bid, hid * VALID_BLOCK_H:(hid + 1) * VALID_BLOCK_H, 0:dim // 2], Q_shared_l)
+            T.copy(Q[bid, hid * VALID_BLOCK_H:(hid + 1) * VALID_BLOCK_H, dim // 2:dim], Q_shared_r)
+            T.copy(Q_pe[bid, hid * VALID_BLOCK_H:(hid + 1) * VALID_BLOCK_H, :], Q_tail_shared)
+
+            T.barrier_arrive(bar_q)
+
+            if tx < 128:
+                T.set_max_nreg(240, 1)
+                T.fill(sumexp, 0)
+                T.fill(m_i, -2**30)  # avoid -inf - inf to cause nan
+                T.fill(acc_o_l, 0)
+                T.barrier_wait(bar_q, 0)
+
+                for i_i in T.serial(T.ceildiv(NI, 2)):
+                    # Buffer 0
+                    T.barrier_wait(bar_k_0_ready[0], (i_i & 1))
+
+                    T.clear(acc_s)
+                    T.gemm(Q_shared_l, KV_shared_0_l, acc_s, transpose_B=True, wg_wait=-1)
+                    T.gemm(Q_shared_r, KV_shared_0_r, acc_s, transpose_B=True, wg_wait=-1)
+                    T.gemm(Q_tail_shared, K_tail_shared_0, acc_s, transpose_B=True, wg_wait=-1)
+
+                    T.wait_wgmma(0)
+
+                    if i_i != 0:
+                        T.barrier_arrive(bar_sScale_and_sS_free)
+                        T.barrier_wait(bar_sScale_and_sS_free, ((i_i * 2) & 1) ^ 1)
+
+                    T.copy(m_i, m_i_prev)
+                    T.reduce_max(acc_s, m_i, dim=1, clear=False)
+                    for h_i in T.Parallel(block_H):
+                        alpha_local[h_i] = T.exp2((m_i_prev[h_i] - m_i[h_i]) * sm_scale)
+                    for h_i, bi_i in T.Parallel(block_H, block_N):
+                        acc_s[h_i, bi_i] = T.exp2(acc_s[h_i, bi_i] * sm_scale - m_i[h_i] * sm_scale)
+                    T.reduce_sum(acc_s, sumexp_i, dim=1)  # is this a accumulate operator?
+                    for h_i in T.Parallel(block_H):
+                        sumexp[h_i] = sumexp[h_i] * alpha_local[h_i] + sumexp_i[h_i]
+                    for h_i, d_i in T.Parallel(block_H, dim // 2):
+                        acc_o_l[h_i, d_i] *= alpha_local[h_i]
+                    T.copy(alpha_local, alpha_shared)
+
+                    T.copy(acc_s, S_shared)
+                    T.gemm(S_shared, KV_shared_0_l, acc_o_l)
+
+                    T.barrier_arrive(bar_sScale_and_sS_ready)
+                    T.barrier_arrive(bar_k_0_free[0])
+
+                    # Buffer 1
+                    T.barrier_wait(bar_k_1_ready[0], (i_i & 1))
+
+                    T.clear(acc_s)
+                    T.gemm(Q_shared_l, KV_shared_1_l, acc_s, transpose_B=True, wg_wait=-1)
+                    T.gemm(Q_shared_r, KV_shared_1_r, acc_s, transpose_B=True, wg_wait=-1)
+                    T.gemm(Q_tail_shared, K_tail_shared_1, acc_s, transpose_B=True, wg_wait=-1)
+
+                    T.wait_wgmma(0)
+
+                    T.barrier_arrive(bar_sScale_and_sS_free)
+                    T.barrier_wait(bar_sScale_and_sS_free, ((i_i * 2 + 1) & 1) ^ 1)
+
+                    T.copy(m_i, m_i_prev)
+                    T.reduce_max(acc_s, m_i, dim=1, clear=False)
+                    for h_i in T.Parallel(block_H):
+                        alpha_local[h_i] = T.exp2((m_i_prev[h_i] - m_i[h_i]) * sm_scale)
+                    for h_i, bi_i in T.Parallel(block_H, block_N):
+                        acc_s[h_i, bi_i] = T.exp2(acc_s[h_i, bi_i] * sm_scale - m_i[h_i] * sm_scale)
+                    T.reduce_sum(acc_s, sumexp_i, dim=1)  # is this a accumulate operator?
+                    for h_i in T.Parallel(block_H):
+                        sumexp[h_i] = sumexp[h_i] * alpha_local[h_i] + sumexp_i[h_i]
+                    for h_i, d_i in T.Parallel(block_H, dim // 2):
+                        acc_o_l[h_i, d_i] *= alpha_local[h_i]
+                    T.copy(alpha_local, alpha_shared)
+
+                    T.copy(acc_s, S_shared)
+                    T.gemm(S_shared, KV_shared_1_l, acc_o_l)
+
+                    T.barrier_arrive(bar_sScale_and_sS_ready)
+                    T.barrier_arrive(bar_k_1_free[0])
+
+                # Rescale
+                for h_i in T.Parallel(block_H):
+                    sum_exp_shared[h_i] = sumexp[h_i]
+                for h_i, d_i in T.Parallel(block_H, dim // 2):
+                    acc_o_l[h_i, d_i] /= sumexp[h_i]
+                for h_i in T.Parallel(block_H):
+                    sumexp[h_i] = T.log2(sumexp[h_i]) + m_i[h_i] * sm_scale
+                T.copy(acc_o_l, O_shared_l)
+                T.copy(O_shared_l, Output[bid, hid * VALID_BLOCK_H:(hid + 1) * VALID_BLOCK_H,
+                                          0:dim // 2])
+
+            elif tx >= 128 and tx < 256:
+                T.set_max_nreg(168, 1)
+                T.fill(acc_o_r, 0)
+                for i_i in T.serial(T.ceildiv(NI, 2)):
+                    # Buffer 0
+                    T.barrier_arrive(bar_sScale_and_sS_ready)
+                    T.barrier_wait(bar_sScale_and_sS_ready, ((i_i * 2) & 1))
+                    for h_i, d_i in T.Parallel(block_H, dim // 2):
+                        acc_o_r[h_i, d_i] *= alpha_shared[h_i]
+                    T.gemm(S_shared, KV_shared_0_r, acc_o_r)
+                    T.barrier_arrive(bar_k_0_free[0])
+                    T.barrier_arrive(bar_sScale_and_sS_free)
+
+                    # Buffer 1
+                    T.barrier_arrive(bar_sScale_and_sS_ready)
+                    T.barrier_wait(bar_sScale_and_sS_ready, ((i_i * 2 + 1) & 1))
+                    for h_i, d_i in T.Parallel(block_H, dim // 2):
+                        acc_o_r[h_i, d_i] *= alpha_shared[h_i]
+                    T.gemm(S_shared, KV_shared_1_r, acc_o_r)
+                    T.barrier_arrive(bar_k_1_free[0])
+                    if i_i != T.ceildiv(NI, 2) - 1:
+                        T.barrier_arrive(bar_sScale_and_sS_free)
+
+                # Rescale
+                for h_i, d_i in T.Parallel(block_H, dim // 2):
+                    acc_o_r[h_i, d_i] /= sum_exp_shared[h_i]
+
+                T.copy(acc_o_r, O_shared_r)
+                T.copy(O_shared_r, Output[bid, hid * VALID_BLOCK_H:(hid + 1) * VALID_BLOCK_H,
+                                          dim // 2:dim])
+
+            elif tx >= 256:
+                # producer
+                T.set_max_nreg(80, 0)
+                for i_i in T.serial(T.ceildiv(NI, 2)):
+                    # Buffer 0
+                    T.barrier_wait(bar_k_0_free[0], ((i_i & 1) ^ 1))
+                    for r in T.serial(4):
+                        kv_indices = (i_i * 2) * block_N + r * 16 + (tx - 256) // 8
+                        with T.attr("default", "async_scope", 1):
+                            for u in T.serial(4):
+                                for v in T.vectorized(8):
+                                    KV_shared_0_l[r * 16 + (tx - 256) // 8,
+                                                  64 * u + (tx - 256) % 8 * 8 +
+                                                  v] = KV[bid, kv_indices, cur_kv_head,
+                                                          64 * u + (tx - 256) % 8 * 8 + v]
+                                    KV_shared_0_r[r * 16 + (tx - 256) // 8,
+                                                  64 * u + (tx - 256) % 8 * 8 +
+                                                  v] = KV[bid, kv_indices, cur_kv_head, dim // 2 +
+                                                          64 * u + (tx - 256) % 8 * 8 + v]
+                        with T.attr("default", "async_scope", 1):
+                            for v in T.vectorized(8):
+                                K_tail_shared_0[r * 16 + (tx - 256) // 8, (tx - 256) % 8 * 8 +
+                                                v] = K_pe[bid, kv_indices, cur_kv_head,
+                                                          (tx - 256) % 8 * 8 + v]
+                    T.cp_async_barrier_noinc(bar_k_0_ready[0])
+
+                    # Buffer 1
+                    T.barrier_wait(bar_k_1_free[0], ((i_i & 1) ^ 1))
+                    for r in T.serial(4):
+                        kv_indices = (i_i * 2 + 1) * block_N + r * 16 + (tx - 256) // 8
+                        with T.attr("default", "async_scope", 1):
+                            for u in T.serial(4):
+                                for v in T.vectorized(8):
+                                    KV_shared_1_l[r * 16 + (tx - 256) // 8,
+                                                  64 * u + (tx - 256) % 8 * 8 +
+                                                  v] = KV[bid, kv_indices, cur_kv_head,
+                                                          64 * u + (tx - 256) % 8 * 8 + v]
+                                    KV_shared_1_r[r * 16 + (tx - 256) // 8,
+                                                  64 * u + (tx - 256) % 8 * 8 +
+                                                  v] = KV[bid, kv_indices, cur_kv_head, dim // 2 +
+                                                          64 * u + (tx - 256) % 8 * 8 + v]
+                        with T.attr("default", "async_scope", 1):
+                            for v in T.vectorized(8):
+                                K_tail_shared_1[r * 16 + (tx - 256) // 8, (tx - 256) % 8 * 8 +
+                                                v] = K_pe[bid, kv_indices, cur_kv_head,
+                                                          (tx - 256) % 8 * 8 + v]
+                    T.cp_async_barrier_noinc(bar_k_1_ready[0])
+
+    @T.macro
+    def flash_attn_split(
+            Q: T.Tensor([batch, heads, dim], dtype),
+            Q_pe: T.Tensor([batch, heads, pe_dim], dtype),
+            KV: T.Tensor([batch, seqlen_kv, kv_head_num, dim], dtype),
+            K_pe: T.Tensor([batch, seqlen_kv, kv_head_num, pe_dim], dtype),
+            glse: T.Tensor([batch, heads, num_split], dtype),
+            Output_partial: T.Tensor([batch, heads, num_split, dim], dtype),
+    ):
+        with T.Kernel(
+                batch, heads // min(block_H, kv_group_num), num_split,
+                threads=384) as (bid, hid, bz):
+            Q_shared_l = T.alloc_shared([block_H, dim // 2], dtype)
+            Q_shared_r = T.alloc_shared([block_H, dim // 2], dtype)
+            Q_tail_shared = T.alloc_shared([block_H, pe_dim], dtype)
+            KV_shared_0_l = T.alloc_shared([block_N, dim // 2], dtype)
+            KV_shared_0_r = T.alloc_shared([block_N, dim // 2], dtype)
+            KV_shared_1_l = T.alloc_shared([block_N, dim // 2], dtype)
+            KV_shared_1_r = T.alloc_shared([block_N, dim // 2], dtype)
+            K_tail_shared_0 = T.alloc_shared([block_N, pe_dim], dtype)
+            K_tail_shared_1 = T.alloc_shared([block_N, pe_dim], dtype)
+            O_shared_l = Q_shared_l
+            O_shared_r = Q_shared_r
+
+            acc_o_l = T.alloc_fragment([block_H, dim // 2], accum_dtype)
+            acc_o_r = T.alloc_fragment([block_H, dim // 2], accum_dtype)
+            acc_s = T.alloc_fragment([block_H, block_N], accum_dtype)
+            S_shared = T.alloc_shared([block_H, block_N], dtype)
+            sumexp = T.alloc_fragment([block_H], accum_dtype)
+            sum_exp_shared = T.alloc_shared([block_H], accum_dtype)
+            sumexp_i = T.alloc_fragment([block_H], accum_dtype)
+            alpha_shared = T.alloc_shared([block_H], accum_dtype, scope="shared")
+            alpha_local = T.alloc_fragment([block_H], accum_dtype)
+            m_i = T.alloc_fragment([block_H], accum_dtype)
+            m_i_prev = T.alloc_fragment([block_H], accum_dtype)
+
+            # TODO: Multi buffer
+            bar_q = T.alloc_barrier(arrive_count=384)
+            bar_k_0_ready = T.alloc_barrier(arrive_count=128)
+            bar_k_1_ready = T.alloc_barrier(arrive_count=128)
+            bar_k_0_free = T.alloc_barrier(arrive_count=256)
+            bar_k_1_free = T.alloc_barrier(arrive_count=256)
+            bar_sScale_and_sS_ready = T.alloc_barrier(arrive_count=256)
+            bar_sScale_and_sS_free = T.alloc_barrier(arrive_count=256)
+
+            cur_kv_head = hid // (kv_group_num // block_H)
+            NI = T.ceildiv((seqlen_kv // num_split), block_N)
+
+            tx = T.get_thread_binding()
+
+            T.copy(Q[bid, hid * VALID_BLOCK_H:(hid + 1) * VALID_BLOCK_H, 0:dim // 2], Q_shared_l)
+            T.copy(Q[bid, hid * VALID_BLOCK_H:(hid + 1) * VALID_BLOCK_H, dim // 2:dim], Q_shared_r)
+            T.copy(Q_pe[bid, hid * VALID_BLOCK_H:(hid + 1) * VALID_BLOCK_H, :], Q_tail_shared)
+
+            T.barrier_arrive(bar_q)
+
+            if tx < 128:
+                T.set_max_nreg(240, 1)
+                T.fill(sumexp, 0)
+                T.fill(m_i, -2**30)  # avoid -inf - inf to cause nan
+                T.fill(acc_o_l, 0)
+                T.barrier_wait(bar_q, 0)
+
+                for i_i in T.serial(T.ceildiv(NI, 2)):
+                    # Buffer 0
+                    T.barrier_wait(bar_k_0_ready[0], (i_i & 1))
+
+                    T.clear(acc_s)
+                    T.gemm(Q_shared_l, KV_shared_0_l, acc_s, transpose_B=True, wg_wait=-1)
+                    T.gemm(Q_shared_r, KV_shared_0_r, acc_s, transpose_B=True, wg_wait=-1)
+                    T.gemm(Q_tail_shared, K_tail_shared_0, acc_s, transpose_B=True, wg_wait=-1)
+
+                    T.wait_wgmma(0)
+
+                    if i_i != 0:
+                        T.barrier_arrive(bar_sScale_and_sS_free)
+                        T.barrier_wait(bar_sScale_and_sS_free, ((i_i * 2) & 1) ^ 1)
+
+                    T.copy(m_i, m_i_prev)
+                    T.reduce_max(acc_s, m_i, dim=1, clear=False)
+                    for h_i in T.Parallel(block_H):
+                        alpha_local[h_i] = T.exp2((m_i_prev[h_i] - m_i[h_i]) * sm_scale)
+                    for h_i, bi_i in T.Parallel(block_H, block_N):
+                        acc_s[h_i, bi_i] = T.exp2(acc_s[h_i, bi_i] * sm_scale - m_i[h_i] * sm_scale)
+                    T.reduce_sum(acc_s, sumexp_i, dim=1)  # is this a accumulate operator?
+                    for h_i in T.Parallel(block_H):
+                        sumexp[h_i] = sumexp[h_i] * alpha_local[h_i] + sumexp_i[h_i]
+                    for h_i, d_i in T.Parallel(block_H, dim // 2):
+                        acc_o_l[h_i, d_i] *= alpha_local[h_i]
+                    T.copy(alpha_local, alpha_shared)
+
+                    T.copy(acc_s, S_shared)
+                    T.gemm(S_shared, KV_shared_0_l, acc_o_l)
+
+                    T.barrier_arrive(bar_sScale_and_sS_ready)
+                    T.barrier_arrive(bar_k_0_free[0])
+
+                    # Buffer 1
+                    T.barrier_wait(bar_k_1_ready[0], (i_i & 1))
+
+                    T.clear(acc_s)
+                    T.gemm(Q_shared_l, KV_shared_1_l, acc_s, transpose_B=True, wg_wait=-1)
+                    T.gemm(Q_shared_r, KV_shared_1_r, acc_s, transpose_B=True, wg_wait=-1)
+                    T.gemm(Q_tail_shared, K_tail_shared_1, acc_s, transpose_B=True, wg_wait=-1)
+
+                    T.wait_wgmma(0)
+
+                    T.barrier_arrive(bar_sScale_and_sS_free)
+                    T.barrier_wait(bar_sScale_and_sS_free, ((i_i * 2 + 1) & 1) ^ 1)
+
+                    T.copy(m_i, m_i_prev)
+                    T.reduce_max(acc_s, m_i, dim=1, clear=False)
+                    for h_i in T.Parallel(block_H):
+                        alpha_local[h_i] = T.exp2((m_i_prev[h_i] - m_i[h_i]) * sm_scale)
+                    for h_i, bi_i in T.Parallel(block_H, block_N):
+                        acc_s[h_i, bi_i] = T.exp2(acc_s[h_i, bi_i] * sm_scale - m_i[h_i] * sm_scale)
+                    T.reduce_sum(acc_s, sumexp_i, dim=1)  # is this a accumulate operator?
+                    for h_i in T.Parallel(block_H):
+                        sumexp[h_i] = sumexp[h_i] * alpha_local[h_i] + sumexp_i[h_i]
+                    for h_i, d_i in T.Parallel(block_H, dim // 2):
+                        acc_o_l[h_i, d_i] *= alpha_local[h_i]
+                    T.copy(alpha_local, alpha_shared)
+
+                    T.copy(acc_s, S_shared)
+                    T.gemm(S_shared, KV_shared_1_l, acc_o_l)
+
+                    T.barrier_arrive(bar_sScale_and_sS_ready)
+                    T.barrier_arrive(bar_k_1_free[0])
+
+                # Rescale
+                for h_i in T.Parallel(block_H):
+                    sum_exp_shared[h_i] = sumexp[h_i]
+                for h_i, d_i in T.Parallel(block_H, dim // 2):
+                    acc_o_l[h_i, d_i] /= sumexp[h_i]
+                for h_i in T.Parallel(block_H):
+                    sumexp[h_i] = T.log2(sumexp[h_i]) + m_i[h_i] * sm_scale
+                T.copy(acc_o_l, O_shared_l)
+                T.copy(
+                    O_shared_l, Output_partial[bid, hid * VALID_BLOCK_H:(hid + 1) * VALID_BLOCK_H,
+                                               bz, 0:dim // 2])
+                T.copy(sumexp, glse[bid, hid * VALID_BLOCK_H:(hid + 1) * VALID_BLOCK_H, bz])
+
+            elif tx >= 128 and tx < 256:
+                T.set_max_nreg(168, 1)
+                T.fill(acc_o_r, 0)
+                for i_i in T.serial(T.ceildiv(NI, 2)):
+                    # Buffer 0
+                    T.barrier_arrive(bar_sScale_and_sS_ready)
+                    T.barrier_wait(bar_sScale_and_sS_ready, ((i_i * 2) & 1))
+                    for h_i, d_i in T.Parallel(block_H, dim // 2):
+                        acc_o_r[h_i, d_i] *= alpha_shared[h_i]
+                    T.gemm(S_shared, KV_shared_0_r, acc_o_r)
+                    T.barrier_arrive(bar_k_0_free[0])
+                    T.barrier_arrive(bar_sScale_and_sS_free)
+
+                    # Buffer 1
+                    T.barrier_arrive(bar_sScale_and_sS_ready)
+                    T.barrier_wait(bar_sScale_and_sS_ready, ((i_i * 2 + 1) & 1))
+                    for h_i, d_i in T.Parallel(block_H, dim // 2):
+                        acc_o_r[h_i, d_i] *= alpha_shared[h_i]
+                    T.gemm(S_shared, KV_shared_1_r, acc_o_r)
+                    T.barrier_arrive(bar_k_1_free[0])
+                    if i_i != T.ceildiv(NI, 2) - 1:
+                        T.barrier_arrive(bar_sScale_and_sS_free)
+
+                # Rescale
+                for h_i, d_i in T.Parallel(block_H, dim // 2):
+                    acc_o_r[h_i, d_i] /= sum_exp_shared[h_i]
+
+                T.copy(acc_o_r, O_shared_r)
+                T.copy(
+                    O_shared_r, Output_partial[bid, hid * VALID_BLOCK_H:(hid + 1) * VALID_BLOCK_H,
+                                               bz, dim // 2:dim])
+
+            elif tx >= 256:
+                # producer
+                T.set_max_nreg(80, 0)
+                for i_i in T.serial(T.ceildiv(NI, 2)):
+                    # Buffer 0
+                    T.barrier_wait(bar_k_0_free[0], ((i_i & 1) ^ 1))
+                    for r in T.serial(4):
+                        kv_indices = (seqlen_kv // num_split) * bz + (
+                            i_i * 2) * block_N + r * 16 + (tx - 256) // 8
+                        with T.attr("default", "async_scope", 1):
+                            for u in T.serial(4):
+                                for v in T.vectorized(8):
+                                    KV_shared_0_l[r * 16 + (tx - 256) // 8,
+                                                  64 * u + (tx - 256) % 8 * 8 +
+                                                  v] = KV[bid, kv_indices, cur_kv_head,
+                                                          64 * u + (tx - 256) % 8 * 8 + v]
+                                    KV_shared_0_r[r * 16 + (tx - 256) // 8,
+                                                  64 * u + (tx - 256) % 8 * 8 +
+                                                  v] = KV[bid, kv_indices, cur_kv_head, dim // 2 +
+                                                          64 * u + (tx - 256) % 8 * 8 + v]
+                        with T.attr("default", "async_scope", 1):
+                            for v in T.vectorized(8):
+                                K_tail_shared_0[r * 16 + (tx - 256) // 8, (tx - 256) % 8 * 8 +
+                                                v] = K_pe[bid, kv_indices, cur_kv_head,
+                                                          (tx - 256) % 8 * 8 + v]
+                    T.cp_async_barrier_noinc(bar_k_0_ready[0])
+
+                    # Buffer 1
+                    T.barrier_wait(bar_k_1_free[0], ((i_i & 1) ^ 1))
+                    for r in T.serial(4):
+                        kv_indices = (seqlen_kv // num_split) * bz + (
+                            i_i * 2 + 1) * block_N + r * 16 + (tx - 256) // 8
+                        with T.attr("default", "async_scope", 1):
+                            for u in T.serial(4):
+                                for v in T.vectorized(8):
+                                    KV_shared_1_l[r * 16 + (tx - 256) // 8,
+                                                  64 * u + (tx - 256) % 8 * 8 +
+                                                  v] = KV[bid, kv_indices, cur_kv_head,
+                                                          64 * u + (tx - 256) % 8 * 8 + v]
+                                    KV_shared_1_r[r * 16 + (tx - 256) // 8,
+                                                  64 * u + (tx - 256) % 8 * 8 +
+                                                  v] = KV[bid, kv_indices, cur_kv_head, dim // 2 +
+                                                          64 * u + (tx - 256) % 8 * 8 + v]
+                        with T.attr("default", "async_scope", 1):
+                            for v in T.vectorized(8):
+                                K_tail_shared_1[r * 16 + (tx - 256) // 8, (tx - 256) % 8 * 8 +
+                                                v] = K_pe[bid, kv_indices, cur_kv_head,
+                                                          (tx - 256) % 8 * 8 + v]
+                    T.cp_async_barrier_noinc(bar_k_1_ready[0])
+
+    @T.macro
+    def combine(
+            glse: T.Tensor([batch, heads, num_split], dtype),
+            Output_partial: T.Tensor([batch, heads, num_split, dim], dtype),
+            Output: T.Tensor([batch, heads, dim], dtype),
+    ):
+        with T.Kernel(heads, batch, threads=128) as (hid, bz):
+            po_local = T.alloc_fragment([dim], dtype)
+            o_accum_local = T.alloc_fragment([dim], accum_dtype)
+            lse_local_split = T.alloc_local([1], accum_dtype)
+            lse_logsum_local = T.alloc_local([1], accum_dtype)
+            lse_max_local = T.alloc_local([1], accum_dtype)
+            scale_local = T.alloc_local([1], accum_dtype)
+
+            T.annotate_layout({
+                lse_logsum_local: T.Fragment(lse_logsum_local.shape, forward_thread_fn=lambda i: i),
+            })
+
+            T.clear(lse_logsum_local)
+            T.clear(o_accum_local)
+            lse_max_local[0] = -T.infinity(accum_dtype)
+            for k in T.serial(num_split):
+                lse_max_local[0] = T.max(lse_max_local[0], glse[bz, hid, k])
+            for k in T.Pipelined(num_split, num_stages=1):
+                lse_local_split[0] = glse[bz, hid, k]
+                lse_logsum_local[0] += T.exp2(lse_local_split[0] - lse_max_local[0])
+            lse_logsum_local[0] = T.log2(lse_logsum_local[0]) + lse_max_local[0]
+            for k in T.serial(num_split):
+                for i in T.Parallel(dim):
+                    po_local[i] = Output_partial[bz, hid, k, i]
+                lse_local_split[0] = glse[bz, hid, k]
+                scale_local[0] = T.exp2(lse_local_split[0] - lse_logsum_local[0])
+                for i in T.Parallel(dim):
+                    o_accum_local[i] += po_local[i] * scale_local[0]
+            for i in T.Parallel(dim):
+                Output[bz, hid, i] = o_accum_local[i]
+
+    @T.prim_func
+    def main_split(
+            Q: T.Tensor([batch, heads, dim], dtype),
+            Q_pe: T.Tensor([batch, heads, pe_dim], dtype),
+            KV: T.Tensor([batch, seqlen_kv, kv_head_num, dim], dtype),
+            K_pe: T.Tensor([batch, seqlen_kv, kv_head_num, pe_dim], dtype),
+            glse: T.Tensor([batch, heads, num_split], dtype),
+            Output_partial: T.Tensor([batch, heads, num_split, dim], dtype),
+            Output: T.Tensor([batch, heads, dim], dtype),
+    ):
+        flash_attn_split(Q, Q_pe, KV, K_pe, glse, Output_partial)
+        combine(glse, Output_partial, Output)
+
+    @T.prim_func
+    def main_no_split(
+            Q: T.Tensor([batch, heads, dim], dtype),
+            Q_pe: T.Tensor([batch, heads, pe_dim], dtype),
+            KV: T.Tensor([batch, seqlen_kv, kv_head_num, dim], dtype),
+            K_pe: T.Tensor([batch, seqlen_kv, kv_head_num, pe_dim], dtype),
+            glse: T.Tensor([batch, heads, num_split], dtype),
+            Output_partial: T.Tensor([batch, heads, num_split, dim], dtype),
+            Output: T.Tensor([batch, heads, dim], dtype),
+    ):
+        flash_attn(Q, Q_pe, KV, K_pe, Output)
+
+    if num_split > 1:
+        return main_split
+    else:
+        return main_no_split
+
+
+def ref_program(q, q_pe, kv, k_pe, glse, Output_partial):
+    #     """
+    #     Inputs:
+    #     - q (Tensor): [batch, heads, dim]
+    #     - q_pe (Tensor): [batch, heads, pe_dim]
+    #     - kv (Tensor): [batch, seqlen_kv, kv_head_num, dim]
+    #     - k_pe (Tensor): [batch, seqlen_kv, kv_head_num, pe_dim]
+    #     - glse (Tensor): [batch, heads, num_split]
+    #     - Output_partial (Tensor): [batch, heads, num_split, dim]
+    #     Outputs:
+    #     - output (Tensor): [batch, heads, dim]
+    #     """
+    dim = q.shape[-1]
+    pe_dim = q_pe.shape[-1]
+    num_head_groups = q.shape[1] // kv.shape[2]
+    scale = (dim + pe_dim)**0.5
+    q = rearrange(
+        q, 'b (h g) d -> b g h d', g=num_head_groups)  # [batch_size, num_head_groups, groups, dim]
+
+    q_pe = rearrange(
+        q_pe, 'b (h g) d -> b g h d',
+        g=num_head_groups)  # [batch_size, num_head_groups, groups, pe_dim]
+
+    kv = rearrange(kv, 'b n h d -> b h n d')  # [batch_size, groups, seqlen_kv, dim]
+
+    k_pe = rearrange(k_pe, 'b n h d -> b h n d')  # [batch_size, num_head_groups, groups, pe_dim]
+
+    query = torch.concat([q, q_pe], dim=-1)
+    key = torch.concat([kv, k_pe], dim=-1)
+
+    scores = einsum(
+        query, key,
+        'b g h d, b h s d -> b g h s')  # [batch_size, num_head_groups, groups, seqlen_kv]
+
+    attention = F.softmax(
+        scores / scale, dim=-1)  # [batch_size, num_head_groups, groups, seqlen_kv]
+
+    out = einsum(attention, kv,
+                 'b g h s, b h s d -> b g h d')  # [batch_size, num_head_groups, groups, dim]
+    out = rearrange(out, 'b g h d -> b (h g) d')  # [batch_size, heads, dim]
+    return out
+
+
+def main(
+    batch=1,
+    heads=128,
+    kv_heads=1,
+    kv_ctx=8192,
+    dim=512,
+    pe_dim=64,
+):
+    qk_flops = 2 * batch * heads * kv_ctx * (dim + pe_dim)
+    pv_flops = 2 * batch * heads * kv_ctx * dim
+    total_flops = qk_flops + pv_flops
+    BLOCK_N = 64
+    BLOCK_H = min(64, heads // kv_heads)
+    num_split = 1
+    softmax_scale = (dim + pe_dim)**-0.5
+
+    kernel = flashattn(batch, heads, kv_heads, kv_ctx, dim, pe_dim, BLOCK_N, BLOCK_H, num_split,
+                       softmax_scale)
+    profiler = kernel.get_profiler(tensor_supply_type=tilelang.TensorSupplyType.Randn)
+    profiler.assert_allclose(ref_program, rtol=1e-4, atol=1e-4)
+    latency = profiler.do_bench(warmup=500)
+    print(f"Latency: {latency} ms")
+    print(f"TFlops: {total_flops / latency * 1e-9} TFlops")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--batch', type=int, default=132, help='batch size')
+    parser.add_argument('--heads', type=int, default=128, help='q heads number')
+    parser.add_argument('--kv_heads', type=int, default=1, help='kv heads number')
+    parser.add_argument('--kv_ctx', type=int, default=8192, help='kv context length')
+    parser.add_argument('--dim', type=int, default=512, help='head dim')
+    parser.add_argument('--pe_dim', type=int, default=64, help='pe head dim')
+    args = parser.parse_args()
+    batch, heads, kv_heads, kv_ctx, dim, pe_dim = args.batch, args.heads, args.kv_heads, args.kv_ctx, args.dim, args.pe_dim
+    main(batch, heads, kv_heads, kv_ctx, dim, pe_dim)