[Enhancement] Deprecate split&sum in attn bwd examples on Hopper and migrate...

[Enhancement] Deprecate split&sum in attn bwd examples on Hopper and migrate to vectorized atomic add (#1065)

examples/flash_attention/example_gqa_bwd_wgmma_pipelined.py

@@ -113,41 +113,10 @@ def flashattn_bwd_preprocess(batch, heads, seq_len, dim_v):
     return flash_bwd_prep
 
 
-def make_dq_layout(dQ):
-    # atomicAdd can not be vectorized, so we need to reorder dq to match the 8x8 gemm fragment
-    return T.Layout(dQ.shape,
-                    lambda b, l, h, d: [b, l // 8, h, d // 8, (d % 2), 4 * (l % 8) + (d % 8) // 2])
-
-
-@tilelang.jit(
-    out_idx=[1], pass_configs={
-        tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True,
-    })
-def flashattn_bwd_postprocess(batch, heads, seq_len, dim_qk):
-    dtype = "float16"
-    accum_dtype = "float"
-    shape = [batch, seq_len, heads, dim_qk]
-    blk = 64
-
-    @T.prim_func
-    def flash_bwd_post(
-            dQ: T.Tensor(shape, accum_dtype),  # type: ignore
-            dQ_out: T.Tensor(shape, dtype),  # type: ignore
-    ):
-        with T.Kernel(T.ceildiv(seq_len, blk), heads, batch, threads=128) as (bx, by, bz):
-            T.annotate_layout({dQ: make_dq_layout(dQ)})
-            T.copy(
-                dQ[bz, bx * blk:(bx + 1) * blk, by, :],
-                dQ_out[bz, bx * blk:(bx + 1) * blk, by, :],
-            )
-
-    return flash_bwd_post
-
-
 @tilelang.jit(pass_configs={
     tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True,
 })
-def flashattn_bwd_atomic_add(batch,
+def flashattn_bwd(batch,
                   heads,
                   seq_len,
                   dim_qk,
@@ -196,10 +165,13 @@ def flashattn_bwd_atomic_add(batch,
             dq = T.alloc_fragment([block_N, dim_qk], accum_dtype)
             dk_shared = T.alloc_shared([block_M, dim_qk], accum_dtype)
             dv_shared = T.alloc_shared([block_M, dim_v], accum_dtype)
+            dq_shared = T.alloc_shared([block_N, dim_qk], accum_dtype)
 
             T.annotate_layout({
-                dQ: make_dq_layout(dQ),
                 K_shared: tilelang.layout.make_swizzled_layout(K_shared),
+                dq_shared: tilelang.layout.make_swizzled_layout(dq_shared),
+                dk_shared: tilelang.layout.make_swizzled_layout(dk_shared),
+                dv_shared: tilelang.layout.make_swizzled_layout(dv_shared),
             })
 
             T.copy(K[bz, by * block_M:(by + 1) * block_M, bx // groups, :], K_shared)
@@ -244,129 +216,12 @@ def flashattn_bwd_atomic_add(batch,
                 T.clear(dq)
                 T.gemm(dsT_shared, K_shared, dq, transpose_A=True, wg_wait=1)
                 T.wait_wgmma(0)
-                for i, j in T.Parallel(block_N, dim_qk):
-                    T.atomic_add(dQ[bz, k * block_N + i, bx, j], dq[i, j])
+                T.copy(dq, dq_shared)
+                T.atomic_add(dQ[bz, k * block_N:(k + 1) * block_N, bx, :], dq_shared)
             T.copy(dv, dv_shared)
             T.atomic_add(dV[bz, by * block_M:(by + 1) * block_M, bx // groups, :], dv_shared)
             T.copy(dk, dk_shared)
-            for i, j in T.Parallel(block_M, dim_qk):
-                T.atomic_add(dK[bz, by * block_M + i, bx // groups, j], dk_shared[i, j])
-
-    return flash_bwd
-
-
-@tilelang.jit(pass_configs={
-    tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True,
-})
-def flashattn_bwd_split(batch,
-                        heads,
-                        seq_len,
-                        dim_qk,
-                        dim_v,
-                        is_causal,
-                        block_M,
-                        block_N,
-                        threads=256,
-                        num_stages=2,
-                        groups=1):
-    sm_scale = (1.0 / dim_qk)**0.5
-    scale = (1.0 / dim_qk)**0.5 * 1.44269504  # log2(e)
-    head_kv = heads // groups
-    q_shape = [batch, seq_len, heads, dim_qk]
-    k_shape = [batch, seq_len, head_kv, dim_qk]
-    v_shape = [batch, seq_len, head_kv, dim_v]
-    dk_shape = [groups, batch, seq_len, head_kv, dim_qk]  # sum after kernel
-    dv_shape = [groups, batch, seq_len, head_kv, dim_v]  # sum after kernel
-    dtype = "float16"
-    accum_dtype = "float"
-
-    @T.prim_func
-    def flash_bwd(
-            Q: T.Tensor(q_shape, dtype),  # type: ignore
-            K: T.Tensor(k_shape, dtype),  # type: ignore
-            V: T.Tensor(v_shape, dtype),  # type: ignore
-            dO: T.Tensor([batch, seq_len, heads, dim_v], dtype),  # type: ignore
-            lse: T.Tensor([batch, heads, seq_len], accum_dtype),  # type: ignore
-            Delta: T.Tensor([batch, heads, seq_len], accum_dtype),  # type: ignore
-            dQ: T.Tensor(q_shape, accum_dtype),  # type: ignore
-            dK: T.Tensor(dk_shape, dtype),  # type: ignore
-            dV: T.Tensor(dv_shape, dtype),  # type: ignore
-    ):
-        with T.Kernel(heads, T.ceildiv(seq_len, block_M), batch, threads=threads) as (bx, by, bz):
-            K_shared = T.alloc_shared([block_M, dim_qk], dtype)
-            dsT_shared = T.alloc_shared([block_M, block_N], dtype)
-            q = T.alloc_shared([block_N, dim_qk], dtype)
-            V_shared = T.alloc_shared([block_M, dim_v], dtype)
-            qkT = T.alloc_fragment([block_M, block_N], accum_dtype)
-            dsT = T.alloc_fragment([block_M, block_N], accum_dtype)
-            qkT_cast = T.alloc_fragment([block_M, block_N], dtype)
-            dsT_cast = T.alloc_fragment([block_M, block_N], dtype)
-            lse_shared = T.alloc_shared([block_N], accum_dtype)
-            delta = T.alloc_shared([block_N], accum_dtype)
-            do = T.alloc_shared([block_N, dim_v], dtype)
-            dv = T.alloc_fragment([block_M, dim_v], accum_dtype)
-            dk = T.alloc_fragment([block_M, dim_qk], accum_dtype)
-            dq = T.alloc_fragment([block_N, dim_qk], accum_dtype)
-            dv_shared = T.alloc_shared([block_M, dim_v], dtype)
-            dk_shared = T.alloc_shared([block_M, dim_qk], dtype)
-
-            T.annotate_layout({
-                dQ: make_dq_layout(dQ),
-                K_shared: tilelang.layout.make_swizzled_layout(K_shared),
-                dv_shared: tilelang.layout.make_swizzled_layout(dv_shared),
-                dk_shared: tilelang.layout.make_swizzled_layout(dk_shared),
-            })
-
-            T.copy(K[bz, by * block_M:(by + 1) * block_M, bx // groups, :], K_shared)
-            T.copy(V[bz, by * block_M:(by + 1) * block_M, bx // groups, :], V_shared)
-            T.clear(dv)
-            T.clear(dk)
-            loop_st = T.floordiv(by * block_M, block_N) if is_causal else 0
-            loop_ed = T.ceildiv(seq_len, block_N)
-            for k in T.Pipelined(loop_st, loop_ed, num_stages=num_stages):
-                T.copy(Q[bz, k * block_N:(k + 1) * block_N, bx, :], q)
-                T.clear(qkT)
-                T.gemm(
-                    K_shared, q, qkT, transpose_B=True, policy=T.GemmWarpPolicy.FullRow, wg_wait=-1)
-                T.copy(dO[bz, k * block_N:(k + 1) * block_N, bx, :], do)
-                T.clear(dsT)
-                T.gemm(
-                    V_shared,
-                    do,
-                    dsT,
-                    transpose_B=True,
-                    policy=T.GemmWarpPolicy.FullRow,
-                    wg_wait=-1)
-                T.wait_wgmma(1)
-
-                T.copy(lse[bz, bx, k * block_N:(k + 1) * block_N], lse_shared)
-                for i, j in T.Parallel(block_M, block_N):
-                    qkT[i, j] = T.exp2(qkT[i, j] * scale - lse_shared[j])
-                if is_causal:
-                    for i, j in T.Parallel(block_M, block_N):
-                        qkT[i, j] = T.if_then_else(by * block_M + i <= k * block_N + j, qkT[i, j],
-                                                   0)
-                T.wait_wgmma(0)
-                T.copy(qkT, qkT_cast)
-                T.gemm(qkT_cast, do, dv, policy=T.GemmWarpPolicy.FullRow, wg_wait=-1)
-
-                T.copy(Delta[bz, bx, k * block_N:(k + 1) * block_N], delta)
-
-                for i, j in T.Parallel(block_M, block_N):
-                    dsT_cast[i, j] = qkT[i, j] * (dsT[i, j] - delta[j]) * sm_scale
-                T.gemm(dsT_cast, q, dk, policy=T.GemmWarpPolicy.FullRow, wg_wait=1)
-
-                T.copy(dsT_cast, dsT_shared)
-                T.clear(dq)
-                T.gemm(dsT_shared, K_shared, dq, transpose_A=True, wg_wait=1)
-                T.wait_wgmma(0)
-                for i, j in T.Parallel(block_N, dim_qk):
-                    T.atomic_add(dQ[bz, k * block_N + i, bx, j], dq[i, j])
-
-            T.copy(dv, dv_shared)
-            T.copy(dv_shared, dV[bx % groups, bz, by * block_M:(by + 1) * block_M, bx // groups, :])
-            T.copy(dk, dk_shared)
-            T.copy(dk, dK[bx % groups, bz, by * block_M:(by + 1) * block_M, bx // groups, :])
+            T.atomic_add(dK[bz, by * block_M:(by + 1) * block_M, bx // groups, :], dk_shared)
 
     return flash_bwd
 
@@ -403,11 +258,9 @@ class _attention(torch.autograd.Function):
         block_M = 128
         block_N = 32
         mod_prep = flashattn_bwd_preprocess(BATCH, H, N_CTX, D_HEAD_V)
-        mod_post = flashattn_bwd_postprocess(BATCH, H, N_CTX, D_HEAD_QK)
         delta = mod_prep(o, do)
 
-        if ctx.use_atomic:
-            kernel = flashattn_bwd_atomic_add(
+        kernel = flashattn_bwd(
             BATCH,
             H,
             N_CTX,
@@ -426,31 +279,9 @@ class _attention(torch.autograd.Function):
         dk = torch.zeros(shape_k, dtype=torch.float32, device=q.device)
         dv = torch.zeros(shape_v, dtype=torch.float32, device=q.device)
         kernel(q, k, v, do, lse, delta, dq, dk, dv)
-            dq = mod_post(dq)
+        dq = dq.to(torch.float16)
         dk = dk.to(torch.float16)
         dv = dv.to(torch.float16)
-        else:
-            kernel = flashattn_bwd_split(
-                BATCH,
-                H,
-                N_CTX,
-                D_HEAD_QK,
-                D_HEAD_V,
-                ctx.causal,
-                block_M,
-                block_N,
-                threads=256,
-                num_stages=2,
-                groups=groups)
-            shape_q = [BATCH, N_CTX, H, D_HEAD_QK]
-            shape_k = [groups, BATCH, N_CTX, HEAD_KV, D_HEAD_QK]  # sum after kernel
-            shape_v = [groups, BATCH, N_CTX, HEAD_KV, D_HEAD_V]  # sum after kernel
-            dq = torch.zeros(shape_q, dtype=torch.float32, device=q.device)
-            dk = torch.empty(shape_k, dtype=torch.float16, device=q.device)
-            dv = torch.empty(shape_v, dtype=torch.float16, device=q.device)
-            kernel(q, k, v, do, lse, delta, dq, dk, dv)
-            dq = mod_post(dq)
-            dk, dv = dk.sum(0), dv.sum(0)
 
         return dq, dk, dv, None, None, None
 
@@ -489,8 +320,7 @@ def main(BATCH: int = 1,
          D_HEAD_QK: int = 192,
          D_HEAD_V: int = 128,
          groups: int = 16,
-         causal: bool = False,
-         use_atomic: bool = True):
+         causal: bool = False):
     flops_per_qk = 2.0 * BATCH * H * N_CTX * N_CTX * D_HEAD_QK
     flops_per_v = 2.0 * BATCH * H * N_CTX * N_CTX * D_HEAD_V
     total_flops = 3 * flops_per_qk + 2 * flops_per_v
@@ -510,7 +340,7 @@ def main(BATCH: int = 1,
     dO = (
         torch.empty(BATCH, N_CTX, H, D_HEAD_V, dtype=torch.half,
                     device="cuda").normal_().requires_grad_())
-    O = attention(Q, K, V, causal, groups, use_atomic)
+    O = attention(Q, K, V, causal, groups)
     O.backward(dO, retain_graph=True)
     dQ, Q.grad = Q.grad.clone(), None
     dK, K.grad = K.grad.clone(), None
@@ -553,20 +383,6 @@ if __name__ == "__main__":
     parser.add_argument('--d_head_v', type=int, default=128, help='Head dimension for V')
     parser.add_argument('--causal', action='store_true', help='Causal flag')
     parser.add_argument('--groups', type=int, default=16, help='groups')
-    parser.add_argument(
-        '--use_atomic', action='store_true', default=False, help='Use atomic add for dK/dV')
-    parser.add_argument(
-        '--use_split', action='store_true', default=False, help='Use split for dK/dV')
     args = parser.parse_args()
 
-    # Handle backward compatibility and logic
-    if args.use_split:
-        use_atomic = False
-    elif args.use_atomic:
-        use_atomic = True
-    else:
-        # Default: use atomic
-        use_atomic = True
-
-    main(args.batch, args.h, args.n_ctx, args.d_head_qk, args.d_head_v, args.groups, args.causal,
-         use_atomic)
+    main(args.batch, args.h, args.n_ctx, args.d_head_qk, args.d_head_v, args.groups, args.causal)

examples/flash_attention/example_mha_bwd_wgmma_pipelined.py

 import torch
 import torch.nn.functional as F
 import tilelang
-from tilelang.autotuner import *
 import tilelang.language as T
+from tilelang.profiler import do_bench
 import argparse
 
 
@@ -112,37 +112,6 @@ def flashattn_bwd_preprocess(batch, heads, seq_len, dim):
     return flash_bwd_prep
 
 
-def make_dq_layout(dQ):
-    # atomicAdd can not be vectorized, so we need to reorder dq to match the 8x8 gemm fragment
-    return T.Layout(dQ.shape,
-                    lambda b, l, h, d: [b, l // 8, h, d // 8, (d % 2), 4 * (l % 8) + (d % 8) // 2])
-
-
-@tilelang.jit(
-    out_idx=[1], pass_configs={
-        tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True,
-    })
-def flashattn_bwd_postprocess(batch, heads, seq_len, dim):
-    dtype = "float16"
-    accum_dtype = "float"
-    shape = [batch, seq_len, heads, dim]
-    blk = 64
-
-    @T.prim_func
-    def flash_bwd_post(
-            dQ: T.Tensor(shape, accum_dtype),  # type: ignore
-            dQ_out: T.Tensor(shape, dtype),  # type: ignore
-    ):
-        with T.Kernel(T.ceildiv(seq_len, blk), heads, batch, threads=128) as (bx, by, bz):
-            T.annotate_layout({dQ: make_dq_layout(dQ)})
-            T.copy(
-                dQ[bz, bx * blk:(bx + 1) * blk, by, :],
-                dQ_out[bz, bx * blk:(bx + 1) * blk, by, :],
-            )
-
-    return flash_bwd_post
-
-
 @tilelang.jit(pass_configs={
     tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True,
 })
@@ -186,12 +155,13 @@ def flashattn_bwd(batch, heads, seq_len, dim, is_causal, block_M, block_N):
             dq = T.alloc_fragment([block_N, dim], accum_dtype)
             dv_shared = T.alloc_shared([block_M, dim], dtype)
             dk_shared = T.alloc_shared([block_M, dim], dtype)
+            dq_shared = T.alloc_shared([block_N, dim], accum_dtype)
 
             T.annotate_layout({
-                dQ: make_dq_layout(dQ),
                 K_shared: tilelang.layout.make_swizzled_layout(K_shared),
                 dv_shared: tilelang.layout.make_swizzled_layout(dv_shared),
                 dk_shared: tilelang.layout.make_swizzled_layout(dk_shared),
+                dq_shared: tilelang.layout.make_swizzled_layout(dq_shared),
             })
 
             T.copy(K[bz, by * block_M:(by + 1) * block_M, bx, :], K_shared)
@@ -237,8 +207,8 @@ def flashattn_bwd(batch, heads, seq_len, dim, is_causal, block_M, block_N):
                 T.clear(dq)
                 T.gemm(dsT_shared, K_shared, dq, transpose_A=True, wg_wait=1)
                 T.wait_wgmma(0)
-                for i, j in T.Parallel(block_N, dim):
-                    T.atomic_add(dQ[bz, k * block_N + i, bx, j], dq[i, j])
+                T.copy(dq, dq_shared)
+                T.atomic_add(dQ[bz, k * block_N:(k + 1) * block_N, bx, :], dq_shared)
             T.copy(dv, dv_shared)
             T.copy(dk, dk_shared)
             T.copy(dv_shared, dV[bz, by * block_M:(by + 1) * block_M, bx, :])
@@ -274,7 +244,6 @@ class _attention(torch.autograd.Function):
         block_M = 128
         block_N = 128 if D_HEAD <= 64 else 32
         mod_prep = flashattn_bwd_preprocess(BATCH, H, N_CTX, D_HEAD)
-        mod_post = flashattn_bwd_postprocess(BATCH, H, N_CTX, D_HEAD)
         delta = mod_prep(o, do)
         mod = flashattn_bwd(BATCH, H, N_CTX, D_HEAD, ctx.causal, block_M, block_N)
         shape = [BATCH, N_CTX, H, D_HEAD]
@@ -282,7 +251,7 @@ class _attention(torch.autograd.Function):
         dk = torch.empty(shape, dtype=torch.float16, device=q.device)
         dv = torch.empty(shape, dtype=torch.float16, device=q.device)
         mod(q, k, v, do, lse, delta, dq, dk, dv)
-        dq = mod_post(dq)
+        dq = dq.to(torch.float16)
         return dq, dk, dv, None
 
 
@@ -336,6 +305,7 @@ def main(
     assert torch.allclose(dV, dV_ref, rtol=1e-2, atol=1e-2)
     assert torch.allclose(dK, dK_ref, rtol=1e-2, atol=1e-2)
     assert torch.allclose(dQ, dQ_ref, rtol=1e-2, atol=1e-2)
+    print('All checks passed.✅')
 
     def run():
         O_ref.backward(dO, retain_graph=True)
@@ -343,8 +313,6 @@ def main(
     def run1():
         O.backward(dO, retain_graph=True)
 
-    from tilelang.profiler import do_bench
-
     latency = do_bench(run, warmup=500)
     print("torch: {:.2f} ms".format(latency))
     print("torch: {:.2f} TFlops".format(total_flops / latency * 1e-9))