tile-ai
diff --git a/‎examples/amd/example_amd_flash_attn_bwd.py‎
Lines changed: 363 additions & 0 deletions b/‎examples/amd/example_amd_flash_attn_bwd.py‎
Lines changed: 363 additions & 0 deletions
diff --git a/‎examples/amd/example_amd_flash_attn_fwd.py‎
Lines changed: 3 additions & 3 deletions b/‎examples/amd/example_amd_flash_attn_fwd.py‎
Lines changed: 3 additions & 3 deletions
diff --git a/‎examples/amd/test.sh‎
Lines changed: 10 additions & 0 deletions b/‎examples/amd/test.sh‎
Lines changed: 10 additions & 0 deletions
@@ -0,0 +1,363 @@
+import torch
+import torch.nn.functional as F
+import tilelang
+from tilelang.autotuner import *
+import tilelang.language as T
+import argparse
+
+
+@tilelang.jit(out_idx=[3, 4])
+def flashattn_fwd(batch, heads, seq_len, dim_qk, dim_v, is_causal, block_M, block_N, groups=1):
+    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]
+    dtype = "float16"
+    accum_dtype = "float"
+
+    @T.prim_func
+    def flash_fwd(
+            Q: T.Tensor(q_shape, dtype),  # type: ignore
+            K: T.Tensor(k_shape, dtype),  # type: ignore
+            V: T.Tensor(v_shape, dtype),  # type: ignore
+            Output: T.Tensor([batch, seq_len, heads, dim_v], dtype),  # type: ignore
+            lse: T.Tensor([batch, heads, seq_len], accum_dtype),  # type: ignore
+    ):
+        with T.Kernel(T.ceildiv(seq_len, block_M), heads, batch, threads=256) as (bx, by, bz):
+            Q_shared = T.alloc_shared([block_M, dim_qk], dtype)
+            K_shared = T.alloc_shared([block_N, dim_qk], dtype)
+            V_shared = T.alloc_shared([block_N, dim_v], dtype)
+            acc_s = T.alloc_fragment([block_M, block_N], accum_dtype)
+            acc_s_cast = T.alloc_fragment([block_M, block_N], dtype)
+            acc_o = T.alloc_fragment([block_M, dim_v], accum_dtype)
+            scores_max = T.alloc_fragment([block_M], accum_dtype)
+            scores_max_prev = T.alloc_fragment([block_M], accum_dtype)
+            scores_scale = T.alloc_fragment([block_M], accum_dtype)
+            scores_sum = T.alloc_fragment([block_M], accum_dtype)
+            logsum = T.alloc_fragment([block_M], accum_dtype)
+
+            T.copy(Q[bz, bx * block_M:(bx + 1) * block_M, by, :], Q_shared)
+            T.fill(acc_o, 0)
+            T.fill(logsum, 0)
+            T.fill(scores_max, -T.infinity(accum_dtype))
+            loop_range = (
+                T.ceildiv(
+                    (bx + 1) * block_M, block_N) if is_causal else T.ceildiv(seq_len, block_N))
+            for k in T.Pipelined(loop_range, num_stages=1):
+                T.copy(K[bz, k * block_N:(k + 1) * block_N, by // groups, :], K_shared)
+                if is_causal:
+                    for i, j in T.Parallel(block_M, block_N):
+                        acc_s[i, j] = T.if_then_else(bx * block_M + i >= k * block_N + j, 0,
+                                                     -T.infinity(acc_s.dtype))
+                else:
+                    T.clear(acc_s)
+                T.gemm(Q_shared, K_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullRow)
+                T.copy(V[bz, k * block_N:(k + 1) * block_N, by // groups, :], V_shared)
+                T.copy(scores_max, scores_max_prev)
+                T.reduce_max(acc_s, scores_max, dim=1, clear=False)
+                for i in T.Parallel(block_M):
+                    scores_scale[i] = T.exp2(scores_max_prev[i] * scale - scores_max[i] * scale)
+                for i, j in T.Parallel(block_M, dim_v):
+                    acc_o[i, j] *= scores_scale[i]
+                for i, j in T.Parallel(block_M, block_N):
+                    acc_s[i, j] = T.exp2(acc_s[i, j] * scale - scores_max[i] * scale)
+                T.copy(acc_s, acc_s_cast)
+                T.gemm(acc_s_cast, V_shared, acc_o, policy=T.GemmWarpPolicy.FullRow)
+                T.reduce_sum(acc_s, scores_sum, dim=1)
+                for i in T.Parallel(block_M):
+                    logsum[i] = logsum[i] * scores_scale[i] + scores_sum[i]
+            for i, j in T.Parallel(block_M, dim_v):
+                acc_o[i, j] /= logsum[i]
+            T.copy(acc_o, Output[bz, bx * block_M:(bx + 1) * block_M, by, :])
+            for i in T.Parallel(block_M):
+                logsum[i] = T.log2(logsum[i]) + scores_max[i] * scale
+            T.copy(logsum, lse[bz, by, bx * block_M:(bx + 1) * block_M])
+
+    return flash_fwd
+
+
+@tilelang.jit(out_idx=[2])
+def flashattn_bwd_preprocess(batch, heads, seq_len, dim_v):
+    dtype = "float16"
+    accum_dtype = "float"
+    shape = [batch, seq_len, heads, dim_v]
+    blk = 32
+
+    @T.prim_func
+    def flash_bwd_prep(
+            O: T.Tensor(shape, dtype),  # type: ignore
+            dO: T.Tensor(shape, dtype),  # type: ignore
+            Delta: T.Tensor([batch, heads, seq_len], accum_dtype),  # type: ignore
+    ):
+        with T.Kernel(heads, T.ceildiv(seq_len, blk), batch) as (bx, by, bz):
+            o = T.alloc_fragment([blk, blk], dtype)
+            do = T.alloc_fragment([blk, blk], dtype)
+            acc = T.alloc_fragment([blk, blk], accum_dtype)
+            delta = T.alloc_fragment([blk], accum_dtype)
+            T.clear(acc)
+            for k in range(T.ceildiv(dim_v, blk)):
+                T.copy(O[bz, by * blk:(by + 1) * blk, bx, k * blk:(k + 1) * blk], o)
+                T.copy(dO[bz, by * blk:(by + 1) * blk, bx, k * blk:(k + 1) * blk], do)
+                for i, j in T.Parallel(blk, blk):
+                    acc[i, j] += o[i, j] * do[i, j]
+            T.reduce_sum(acc, delta, 1)
+            T.copy(delta, Delta[bz, bx, by * blk:(by + 1) * blk])
+
+    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])
+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.copy(
+                dQ[bz, bx * blk:(bx + 1) * blk, by, :],
+                dQ_out[bz, bx * blk:(bx + 1) * blk, by, :],
+            )
+
+    return flash_bwd_post
+
+
+@tilelang.jit
+def flashattn_bwd(batch, heads, seq_len, dim_qk, dim_v, is_causal, block_M, block_N, 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]
+    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(k_shape, accum_dtype),  # type: ignore
+            dV: T.Tensor(v_shape, accum_dtype),  # type: ignore
+    ):
+        with T.Kernel(heads, T.ceildiv(seq_len, block_M), batch, threads=128) 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)
+
+            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=1):
+                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)
+                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.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)
+                T.copy(qkT, qkT_cast)
+                T.gemm(qkT_cast, do, dv, policy=T.GemmWarpPolicy.FullRow)
+
+                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)
+
+                T.copy(dsT_cast, dsT_shared)
+                T.clear(dq)
+                T.gemm(dsT_shared, K_shared, dq, transpose_A=True)
+                for i, j in T.Parallel(block_N, dim_qk):
+                    if k * block_N + i < seq_len:
+                        T.atomic_add(dQ[bz, k * block_N + i, bx, j], dq[i, j])
+
+            for i, j in T.Parallel(block_M, dim_v):
+                T.atomic_add(dV[bz, by * block_M + i, bx // groups, j], dv[i, j])
+            for i, j in T.Parallel(block_M, dim_qk):
+                T.atomic_add(dK[bz, by * block_M + i, bx // groups, j], dk[i, j])
+
+    return flash_bwd
+
+
+@torch.compile
+class _attention(torch.autograd.Function):
+
+    @staticmethod
+    def forward(ctx, q, k, v, causal, groups=1):
+        BATCH, N_CTX, H, D_HEAD_QK = q.shape
+        D_HEAD_V = v.shape[-1]
+        block_M = 128
+        block_N = 64
+        mod = flashattn_fwd(BATCH, H, N_CTX, D_HEAD_QK, D_HEAD_V, causal, block_M, block_N, groups)
+        o, lse = mod(q, k, v)
+        ctx.save_for_backward(q, k, v, o, lse)
+        ctx.causal = causal
+        return o
+
+    @staticmethod
+    def backward(ctx, do):
+        q, k, v, o, lse = ctx.saved_tensors
+        BATCH, N_CTX, H, D_HEAD_QK = q.shape
+        HEAD_KV, D_HEAD_V, = v.shape[-2], v.shape[-1]
+        groups = H // HEAD_KV
+
+        def maybe_contiguous(x):
+            if x.stride(-1) != 1:
+                return x.contiguous()
+            return x
+
+        do, q, k, v, o = [maybe_contiguous(x) for x in (do, q, k, v, o)]
+        block_M = 64
+        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)
+        kernel = flashattn_bwd(BATCH, H, N_CTX, D_HEAD_QK, D_HEAD_V, ctx.causal, block_M, block_N,
+                               groups)
+        shape_q = [BATCH, N_CTX, H, D_HEAD_QK]
+        shape_k = [BATCH, N_CTX, HEAD_KV, D_HEAD_QK]
+        shape_v = [BATCH, N_CTX, HEAD_KV, D_HEAD_V]
+        dq = torch.zeros(shape_q, dtype=torch.float32, device=q.device)
+        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)
+        return dq, dk, dv, None, None
+
+
+attention = _attention.apply
+
+
+def ref_program(Q, K, V, is_causal, groups=1):
+    # Q: [B, T, HQ, D_QK]
+    # K: [B, T, HK, D_QK]
+    # V: [B, T, HV, D_V]
+    # HQ = HKV * groups
+    assert Q.size(2) == K.size(
+        2) * groups, f"Q.size(2): {Q.size(2)}, K.size(2): {K.size(2)}, groups: {groups}"
+    assert Q.size(2) == V.size(
+        2) * groups, f"Q.size(2): {Q.size(2)}, V.size(2): {V.size(2)}, groups: {groups}"
+
+    dim_qk = Q.size(-1)
+    K = K.repeat_interleave(groups, dim=2)
+    V = V.repeat_interleave(groups, dim=2)
+    scores = torch.einsum('bqhd,bkhd->bhqk', Q, K)
+    scores = scores / torch.sqrt(torch.tensor(dim_qk, dtype=scores.dtype))
+    if is_causal:
+        seq_len = Q.size(1)
+        mask = torch.tril(torch.ones(seq_len, seq_len, device=scores.device))
+        mask = mask.unsqueeze(0).unsqueeze(0)
+        scores = scores.masked_fill(mask == 0, float('-inf'))
+    attention_weights = F.softmax(scores, dim=-1)
+    output = torch.einsum('bhqk,bkhd->bqhd', attention_weights, V)
+    return output
+
+
+def main(BATCH: int = 1,
+         H: int = 32,
+         N_CTX: int = 256,
+         D_HEAD_QK: int = 192,
+         D_HEAD_V: int = 128,
+         groups: int = 16,
+         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
+    if causal:
+        total_flops *= 0.5
+    Q = (
+        torch.empty(BATCH, N_CTX, H, D_HEAD_QK, dtype=torch.half,
+                    device="cuda").normal_().requires_grad_())
+
+    head_kv = H // groups
+    K = (
+        torch.empty(BATCH, N_CTX, head_kv, D_HEAD_QK, dtype=torch.half,
+                    device="cuda").normal_().requires_grad_())
+    V = (
+        torch.empty(BATCH, N_CTX, head_kv, D_HEAD_V, dtype=torch.half,
+                    device="cuda").normal_().requires_grad_())
+    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)
+    O.backward(dO, retain_graph=True)
+    dQ, Q.grad = Q.grad.clone(), None
+    dK, K.grad = K.grad.clone(), None
+    dV, V.grad = V.grad.clone(), None
+
+    O_ref = ref_program(Q, K, V, causal, groups)
+    O_ref.backward(dO, retain_graph=True)
+    dQ_ref, Q.grad = Q.grad.clone(), None
+    dK_ref, K.grad = K.grad.clone(), None
+    dV_ref, V.grad = V.grad.clone(), None
+
+    torch.testing.assert_close(O, O_ref, rtol=1e-2, atol=1e-2)
+    torch.testing.assert_close(dV, dV_ref, rtol=1e-2, atol=1e-2)
+    torch.testing.assert_close(dK, dK_ref, rtol=1e-2, atol=1e-2)
+    torch.testing.assert_close(dQ, dQ_ref, rtol=1e-2, atol=1e-2)
+
+    def run():
+        O_ref.backward(dO, retain_graph=True)
+
+    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))
+    latency = do_bench(run1, warmup=500)
+    print("tilelang: {:.2f} ms".format(latency))
+    print("tilelang: {:.2f} TFlops".format(total_flops / latency * 1e-9))
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--batch', type=int, default=8, help='Batch size')
+    parser.add_argument('--h', type=int, default=32, help='Number of heads')
+    parser.add_argument('--n_ctx', type=int, default=1024, help='Context size')
+    parser.add_argument('--d_head_qk', type=int, default=192, help='Head dimension for Q/K')
+    parser.add_argument('--d_head_v', type=int, default=128, help='Head dimension for V')
+    parser.add_argument('--causal', type=bool, default=False, help='Causal flag')
+    parser.add_argument('--groups', type=int, default=16, help='groups')
+    args = parser.parse_args()
+    main(args.batch, args.h, args.n_ctx, args.d_head_qk, args.d_head_v, args.groups, args.causal)
@@ -32,12 +32,12 @@ def get_configs():
     """Generates configurations for the autotuner, tailored for FA-2 style parallelism."""
     block_M = [32, 64, 128, 256]
     block_N = [32, 64, 128, 256]
-    threads = [64, 128, 192, 256, 512, 1024]
-    num_split_q = [32, 64, 128, 256, 256]
+    threads = [128, 256, 512]
+    num_split_q = [64, 128, 256]
     num_stages = [0]
     enable_rasterization = [True]
     k_pack = [2]
-    panel_size = [7, 8, 9, 10]
+    panel_size = [7, 8]
     qk_coalesced_width = [8]
     v_coalesced_width = [4]
 
 
@@ -0,0 +1,10 @@
+/root/miniconda3/envs/py312/bin/python3 examples/amd/example_amd_flash_attn_fwd.py \
+    --batch 2 \
+    --heads 16 \
+    --seq_len 4096 \
+    --dim 128 \
+    --is_causal \
+    --groups 2
+
+/root/composable_kernel/build/bin/tile_example_fmha_fwd  \
+-b=2 -h=16 -s=4096 -d=128 -mask=t -v=1 -warmup=5 -repeat=20