tile-ai
diff --git a/‎examples/warp_specialize/example_warp_specialize_flashmla.py‎
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diff --git a/‎examples/warp_specialize/example_warp_specialize_gemm.py‎
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+# Copyright (c) Tile-AI Corporation.
+# Licensed under the MIT License.
+# use default stage 1 template, not the optimal
+# schedule, please checkout examples/deepseek_mla
+import torch
+import torch.nn.functional as F
+import tilelang
+import tilelang.language as T
+from einops import rearrange, einsum
+import argparse
+
+
+def flashattn(batch, heads, kv_head_num, seqlen_kv, dim, pe_dim, block_N, block_H, num_split):
+    scale = (1.0 / (dim + pe_dim))**0.5 * 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(batch, heads // min(block_H, kv_group_num), threads=384) as (bx, by):
+            Q_shared = T.alloc_shared([block_H, dim], dtype)
+            S_shared = T.alloc_shared([block_H, block_N], dtype)
+            Q_pe_shared = T.alloc_shared([block_H, pe_dim], dtype)
+            KV_shared = T.alloc_shared([block_N, dim], dtype)
+            K_pe_shared = T.alloc_shared([block_N, pe_dim], dtype)
+            O_shared = T.alloc_shared([block_H, dim], dtype)
+            acc_s = T.alloc_fragment([block_H, block_N], accum_dtype)
+            acc_o = T.alloc_fragment([block_H, dim], accum_dtype)
+            scores_max = T.alloc_fragment([block_H], accum_dtype)
+            scores_max_prev = T.alloc_fragment([block_H], accum_dtype)
+            scores_scale = T.alloc_fragment([block_H], accum_dtype)
+            scores_sum = T.alloc_fragment([block_H], accum_dtype)
+            logsum = T.alloc_fragment([block_H], accum_dtype)
+
+            cur_kv_head = by // (kv_group_num // block_H)
+            T.use_swizzle(10)
+            T.annotate_layout({
+                O_shared: tilelang.layout.make_swizzled_layout(O_shared),
+            })
+
+            T.create_list_of_mbarrier(128, 128, 256, 128)
+
+            loop_range = T.ceildiv(seqlen_kv, block_N)
+            with T.ws(2):
+                T.dec_max_nreg(24)
+                T.copy(Q[bx, by * VALID_BLOCK_H:(by + 1) * VALID_BLOCK_H, :], Q_shared)
+                T.copy(Q_pe[bx, by * VALID_BLOCK_H:(by + 1) * VALID_BLOCK_H, :], Q_pe_shared)
+                T.mbarrier_arrive(T.get_mbarrier(3))
+                for k in T.serial(loop_range):
+                    T.mbarrier_wait_parity(
+                        T.FloorMod(k, 1) + 2, T.bitwise_xor(T.FloorDiv(k, 1) % 2, 1))
+                    T.copy(KV[bx, k * block_N:(k + 1) * block_N, cur_kv_head, :], KV_shared)
+                    T.mbarrier_arrive(T.FloorMod(k, 1))
+                    T.copy(K_pe[bx, k * block_N:(k + 1) * block_N, cur_kv_head, :], K_pe_shared)
+                    T.mbarrier_arrive(T.FloorMod(k, 1) + 1)
+            with T.ws(0, 1):
+                T.inc_max_nreg(240)
+                T.fill(acc_o, 0)
+                T.fill(logsum, 0)
+                T.fill(scores_max, -T.infinity(accum_dtype))
+                T.mbarrier_wait_parity(T.get_mbarrier(3), 0)
+                for k in T.serial(loop_range):
+                    T.clear(acc_s)
+                    T.mbarrier_wait_parity(T.get_mbarrier(T.FloorMod(k, 1)), T.FloorDiv(k, 1) % 2)
+                    T.gemm(
+                        Q_shared,
+                        KV_shared,
+                        acc_s,
+                        transpose_B=True,
+                        policy=T.GemmWarpPolicy.FullCol)
+                    T.mbarrier_wait_parity(
+                        T.get_mbarrier(T.FloorMod(k, 1) + 1),
+                        T.FloorDiv(k, 1) % 2)
+                    T.gemm(
+                        Q_pe_shared,
+                        K_pe_shared,
+                        acc_s,
+                        transpose_B=True,
+                        policy=T.GemmWarpPolicy.FullCol)
+                    T.copy(scores_max, scores_max_prev)
+                    T.fill(scores_max, -T.infinity(accum_dtype))
+                    T.reduce_max(acc_s, scores_max, dim=1, clear=False)
+                    for i in T.Parallel(block_H):
+                        scores_scale[i] = T.exp2(scores_max_prev[i] * scale - scores_max[i] * scale)
+                    for i, j in T.Parallel(block_H, block_N):
+                        acc_s[i, j] = T.exp2(acc_s[i, j] * scale - scores_max[i] * scale)
+                    T.reduce_sum(acc_s, scores_sum, dim=1)
+                    T.copy(acc_s, S_shared)
+                    for i in T.Parallel(block_H):
+                        logsum[i] = logsum[i] * scores_scale[i] + scores_sum[i]
+                    for i, j in T.Parallel(block_H, dim):
+                        acc_o[i, j] *= scores_scale[i]
+                    T.gemm(S_shared, KV_shared, acc_o, policy=T.GemmWarpPolicy.FullCol)
+                    T.mbarrier_arrive(T.get_mbarrier(T.FloorMod(k, 1) + 2))
+                for i, j in T.Parallel(block_H, dim):
+                    acc_o[i, j] /= logsum[i]
+                T.copy(acc_o, O_shared)
+                T.copy(O_shared, Output[bx, by * VALID_BLOCK_H:(by + 1) * VALID_BLOCK_H, :])
+
+    @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)
+
+    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():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--batch', type=int, default=128, 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
+    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 = 64
+    num_split = 1
+
+    program = flashattn(batch, heads, kv_heads, kv_ctx, dim, pe_dim, BLOCK_N, BLOCK_H, num_split)
+    kernel = tilelang.compile(program, out_idx=[6])
+
+    profiler = kernel.get_profiler(tensor_supply_type=tilelang.TensorSupplyType.Randn)
+    profiler.assert_allclose(ref_program, rtol=0.01, atol=0.01)
+    latency = profiler.do_bench(warmup=500)
+    print(f"Latency: {latency} ms")
+    print(f"TFlops: {total_flops / latency * 1e-9} TFlops")
+
+
+if __name__ == "__main__":
+    main()