Optimize triton swa kernel by skipping computation (#8860)

benchmark/kernels/sliding_window_attention_triton/bench_triton_swa_kernel.py

@@ -0,0 +1,283 @@
+import itertools
+
+import torch
+import torch.nn.functional as F
+import triton.testing as tt
+
+from sglang.srt.layers.attention.triton_ops.extend_attention import extend_attention_fwd
+
+
+def extend_attention_fwd_torch(
+    q: torch.Tensor,  # [extend_tokens, H_Q, D]
+    k: torch.Tensor,  # [extend_tokens, H_KV, D]
+    v: torch.Tensor,  # [extend_tokens, H_KV, D]
+    o: torch.Tensor,  # [extend_tokens, H_Q, D]
+    k_cache: torch.Tensor,  # [total_tokens, H_KV, D]
+    v_cache: torch.Tensor,  # [total_tokens, H_KV, D]
+    qo_indptr: torch.Tensor,  # [B+1]
+    kv_indptr: torch.Tensor,  # [B+1]
+    kv_indices: torch.Tensor,  # [prefix_tokens]
+    sliding_window_size: int,
+):
+    B = qo_indptr.size(0) - 1
+    _, H_Q, D = q.shape
+    _, H_KV, _ = k.shape
+
+    group_size = H_Q // H_KV
+    scale = 1.0 / D**0.5
+
+    for i in range(B):
+        q_start = int(qo_indptr[i].item())
+        q_end = int(qo_indptr[i + 1].item())
+        kv_start = int(kv_indptr[i].item())
+        kv_end = int(kv_indptr[i + 1].item())
+
+        prefix_indices = kv_indices[kv_start:kv_end]
+        k_prefix = k_cache[prefix_indices]  # [prefix_len, H_KV, D]
+        v_prefix = v_cache[prefix_indices]  # [prefix_len, H_KV, D]
+
+        k_extend = k[q_start:q_end]  # [extend_len, H_KV, D]
+        v_extend = v[q_start:q_end]  # [extend_len, H_KV, D]
+        q_extend = q[q_start:q_end]  # [extend_len, H_Q,  D]
+
+        k_full = torch.cat([k_prefix, k_extend], dim=0)  # [total_len, H_KV, D]
+        v_full = torch.cat([v_prefix, v_extend], dim=0)  # [total_len, H_KV, D]
+
+        if group_size != 1:
+            k_full_hq = k_full.repeat_interleave(
+                group_size, dim=1
+            )  # [total_len, H_Q, D]
+            v_full_hq = v_full.repeat_interleave(
+                group_size, dim=1
+            )  # [total_len, H_Q, D]
+        else:
+            k_full_hq = k_full
+            v_full_hq = v_full
+
+        prefix_len = k_prefix.size(0)
+        extend_len = k_extend.size(0)
+        total_len = prefix_len + extend_len
+
+        # causal
+        pos_keys = torch.arange(total_len, device=q.device)
+        t = prefix_len + torch.arange(extend_len, device=q.device)  # [extend_len]
+        causal_mask = pos_keys.unsqueeze(0) <= t.unsqueeze(1)
+
+        # sliding window
+        if sliding_window_size is not None and sliding_window_size > 0:
+            start = (t - (sliding_window_size)).clamp_min(0)  # [extend_len]
+        else:
+            start = torch.zeros_like(t)
+        window_mask = pos_keys.unsqueeze(0) >= start.unsqueeze(1)
+
+        final_mask = causal_mask & window_mask
+
+        attn_scores = (
+            torch.einsum("qhd,khd->qhk", q_extend, k_full_hq) * scale
+        )  # [extend_len, H_Q, total_len]
+        attn_scores = attn_scores.masked_fill(~final_mask.unsqueeze(1), float("-inf"))
+
+        attn_weights = F.softmax(attn_scores, dim=-1)
+        o[q_start:q_end] = torch.einsum("qhk,khd->qhd", attn_weights, v_full_hq)
+
+
+def _build_batch(
+    B, N_CTX, H_Q, H_KV, D, WINDOW_SIZE, dtype=torch.bfloat16, device="cuda"
+):
+    b_seq_len_prefix = torch.randint(
+        1, max(2, N_CTX // 2), (B,), dtype=torch.int32, device=device
+    )
+    b_seq_len_extend = torch.randint(
+        1, max(2, N_CTX // 2), (B,), dtype=torch.int32, device=device
+    )
+    b_seq_len = b_seq_len_prefix + b_seq_len_extend
+
+    b_start_loc = torch.zeros((B,), dtype=torch.int32, device=device)
+    b_start_loc[1:] = torch.cumsum(b_seq_len[:-1], 0)
+    b_start_loc_extend = torch.zeros((B,), dtype=torch.int32, device=device)
+    b_start_loc_extend[1:] = torch.cumsum(b_seq_len_extend[:-1], 0)
+
+    kv_indptr = torch.zeros((B + 1,), dtype=torch.int32, device=device)
+    kv_indptr[1 : B + 1] = torch.cumsum(b_seq_len_prefix[:B], dim=0)
+
+    kv_indices = torch.zeros(
+        (int(b_seq_len_prefix.sum().item()),), dtype=torch.int32, device=device
+    )
+    for i in range(B):
+        s = kv_indptr[i].item()
+        e = kv_indptr[i + 1].item()
+        kv_indices[s:e] = torch.arange(
+            b_start_loc[i],
+            b_start_loc[i] + b_seq_len_prefix[i],
+            dtype=torch.int32,
+            device=device,
+        )
+
+    total_token_num = int(torch.sum(b_seq_len).item())
+    extend_token_num = int(torch.sum(b_seq_len_extend).item())
+
+    k_buffer = torch.empty(
+        (total_token_num, H_KV, D), dtype=dtype, device=device
+    ).normal_(mean=0.1, std=0.2)
+    v_buffer = torch.empty(
+        (total_token_num, H_KV, D), dtype=dtype, device=device
+    ).normal_(mean=0.1, std=0.2)
+
+    k_extend = torch.empty((extend_token_num, H_KV, D), dtype=dtype, device=device)
+    v_extend = torch.empty((extend_token_num, H_KV, D), dtype=dtype, device=device)
+    q_extend = torch.empty((extend_token_num, H_Q, D), dtype=dtype, device=device)
+
+    for i in range(B):
+        extend_start_in_buffer = b_start_loc[i] + b_seq_len_prefix[i]
+        extend_end_in_buffer = b_start_loc[i] + b_seq_len[i]
+        extend_start = b_start_loc_extend[i]
+        extend_end = b_start_loc_extend[i] + b_seq_len_extend[i]
+
+        k_extend[extend_start:extend_end] = k_buffer[
+            extend_start_in_buffer:extend_end_in_buffer
+        ]
+        v_extend[extend_start:extend_end] = v_buffer[
+            extend_start_in_buffer:extend_end_in_buffer
+        ]
+        q_extend[extend_start:extend_end] = torch.empty(
+            (int(b_seq_len_extend[i].item()), H_Q, D), dtype=dtype, device=device
+        ).normal_(mean=0.1, std=0.2)
+
+    o_extend_triton = torch.empty(
+        (extend_token_num, H_Q, D), dtype=dtype, device=device
+    )
+    o_extend_torch = torch.empty((extend_token_num, H_Q, D), dtype=dtype, device=device)
+
+    b_seq_len_extend = b_seq_len - b_seq_len_prefix
+    max_len_extend = int(torch.max(b_seq_len_extend, 0)[0].item())
+    qo_indptr = torch.zeros((B + 1,), dtype=torch.int32, device=device)
+    qo_indptr[1 : B + 1] = torch.cumsum(b_seq_len_extend[:B], dim=0)
+
+    inputs = dict(
+        q_extend=q_extend,
+        k_extend=k_extend,
+        v_extend=v_extend,
+        k_buffer=k_buffer,
+        v_buffer=v_buffer,
+        o_extend_triton=o_extend_triton,
+        o_extend_torch=o_extend_torch,
+        qo_indptr=qo_indptr,
+        kv_indptr=kv_indptr,
+        kv_indices=kv_indices,
+        max_len_extend=max_len_extend,
+        WINDOW_SIZE=WINDOW_SIZE,
+    )
+    meta = dict(
+        B=B, N_CTX=N_CTX, H_Q=H_Q, H_KV=H_KV, D=D, extend_token_num=extend_token_num
+    )
+    return inputs, meta
+
+
+def _run_triton(inputs):
+    extend_attention_fwd(
+        inputs["q_extend"],
+        inputs["k_extend"],
+        inputs["v_extend"],
+        inputs["o_extend_triton"],
+        inputs["k_buffer"],
+        inputs["v_buffer"],
+        inputs["qo_indptr"],
+        inputs["kv_indptr"],
+        inputs["kv_indices"],
+        custom_mask=None,
+        is_causal=True,
+        mask_indptr=None,
+        max_len_extend=inputs["max_len_extend"],
+        sliding_window_size=inputs["WINDOW_SIZE"],
+    )
+
+
+def _run_torch_ref(inputs):
+    extend_attention_fwd_torch(
+        inputs["q_extend"],
+        inputs["k_extend"],
+        inputs["v_extend"],
+        inputs["o_extend_torch"],
+        inputs["k_buffer"],
+        inputs["v_buffer"],
+        inputs["qo_indptr"],
+        inputs["kv_indptr"],
+        inputs["kv_indices"],
+        inputs["WINDOW_SIZE"],
+    )
+
+
+N_CTXS = [1024, 2048, 4096, 8192]
+WINDOW_SIZES = [-1, 127, 256, 512]
+
+CONFIGS = list(itertools.product(N_CTXS, WINDOW_SIZES))
+
+PROVIDERS = ["torch", "triton"]
+
+
+@tt.perf_report(
+    tt.Benchmark(
+        x_names=["N_CTX", "WINDOW_SIZE"],
+        x_vals=CONFIGS,
+        line_arg="provider",
+        line_vals=PROVIDERS,
+        line_names=PROVIDERS,
+        ylabel="Runtime (ms)",
+        plot_name="extend_attention_triton_vs_torch",
+        args={
+            "B": 32,
+            "H_Q": 64,
+            "H_KV": 8,
+            "D": 128,
+            "dtype": "bf16",
+            "device": "cuda",
+            "check_correctness": False,
+            "warmup": 25,
+            "rep": 100,
+        },
+    )
+)
+def bench(
+    N_CTX,
+    provider,
+    B,
+    H_Q,
+    H_KV,
+    D,
+    dtype,
+    device,
+    WINDOW_SIZE,
+    check_correctness,
+    warmup,
+    rep,
+):
+    torch.manual_seed(0)
+    torch.cuda.manual_seed(0)
+    dtype_map = {"bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}
+    dt = dtype_map[dtype]
+
+    inputs, _ = _build_batch(
+        B, N_CTX, H_Q, H_KV, D, WINDOW_SIZE, dtype=dt, device=device
+    )
+
+    if check_correctness and provider == "triton":
+        _run_triton(inputs)
+        _run_torch_ref(inputs)
+        torch.cuda.synchronize()
+        if not torch.allclose(
+            inputs["o_extend_triton"], inputs["o_extend_torch"], rtol=1e-3, atol=1e-3
+        ):
+            raise AssertionError("Mismatch between triton and torch reference.")
+
+    if provider == "triton":
+        ms = tt.do_bench(lambda: _run_triton(inputs), warmup=warmup, rep=rep)
+    elif provider == "torch":
+        ms = tt.do_bench(lambda: _run_torch_ref(inputs), warmup=warmup, rep=rep)
+    else:
+        raise ValueError(provider)
+
+    return ms
+
+
+if __name__ == "__main__":
+    bench.run(print_data=True, show_plots=False)

python/sglang/srt/layers/attention/triton_ops/extend_attention.py

@@ -134,38 +134,6 @@ def _fwd_kernel(
         start_n = tl.multiple_of(start_n, BLOCK_N)
         mask_n = (start_n + offs_n) < cur_seq_len_prefix
 
-        offs_kv_loc = tl.load(
-            kv_indices + cur_seq_kv_start_idx + start_n + offs_n, mask=mask_n, other=0
-        )
-
-        # load k in transposed way
-        offs_buf_k = (
-            offs_kv_loc[None, :] * stride_buf_kbs
-            + cur_kv_head * stride_buf_kh
-            + offs_d[:, None]
-        )
-        k = tl.load(
-            K_Buffer + offs_buf_k, mask=(mask_n[None, :]) & (mask_d[:, None]), other=0.0
-        )
-
-        qk = tl.dot(q.to(k.dtype), k)
-        if BLOCK_DPE > 0:
-            offs_kpe = (
-                offs_kv_loc[None, :] * stride_buf_kbs
-                + cur_kv_head * stride_buf_kh
-                + offs_dpe[:, None]
-            )
-            kpe = tl.load(
-                K_Buffer + offs_kpe,
-                mask=mask_n[None, :],
-                other=0.0,
-            )
-            qk += tl.dot(qpe.to(kpe.dtype), kpe)
-        qk *= sm_scale
-
-        if logit_cap > 0:
-            qk = logit_cap * tanh(qk / logit_cap)
-
         final_mask = mask_m[:, None] & mask_n[None, :]
         if USE_CUSTOM_MASK and not SKIP_PREFIX_CUSTOM_MASK:
             custom_mask = tl.load(
@@ -185,28 +153,72 @@ def _fwd_kernel(
                 cur_seq_len_prefix + cur_block_m * BLOCK_M + offs_m[:, None]
             ) <= (start_n + offs_n[None, :] + SLIDING_WINDOW_SIZE)
             final_mask &= window_mask
-        qk = tl.where(final_mask, qk, float("-inf"))
 
-        row_max = tl.max(qk, 1)
-        row_max_fixed = tl.where(row_max == float("-inf"), -1e20, row_max)
-        n_e_max = tl.maximum(row_max_fixed, e_max)
+        SKIP_TILE = False
+        if (USE_CUSTOM_MASK and not SKIP_PREFIX_CUSTOM_MASK) or SLIDING_WINDOW_SIZE > 0:
+            SKIP_TILE = tl.max(tl.max(final_mask.to(tl.int32), axis=1), axis=0) == 0
 
-        re_scale = tl.exp(e_max - n_e_max)
-        p = tl.exp(qk - n_e_max[:, None])
-        deno = deno * re_scale + tl.sum(p, 1)
+        if not SKIP_TILE:
+            offs_kv_loc = tl.load(
+                kv_indices + cur_seq_kv_start_idx + start_n + offs_n,
+                mask=mask_n,
+                other=0,
+            )
 
-        offs_buf_v = (
-            offs_kv_loc[:, None] * stride_buf_vbs
-            + cur_kv_head * stride_buf_vh
-            + offs_dv[None, :]
-        )
-        v = tl.load(
-            V_Buffer + offs_buf_v, mask=mask_n[:, None] & mask_dv[None, :], other=0.0
-        )
-        p = p.to(v.dtype)
-        acc = acc * re_scale[:, None] + tl.dot(p, v)
+            # load k in transposed way
+            offs_buf_k = (
+                offs_kv_loc[None, :] * stride_buf_kbs
+                + cur_kv_head * stride_buf_kh
+                + offs_d[:, None]
+            )
+            k = tl.load(
+                K_Buffer + offs_buf_k,
+                mask=(mask_n[None, :]) & (mask_d[:, None]),
+                other=0.0,
+            )
 
-        e_max = n_e_max
+            qk = tl.dot(q.to(k.dtype), k)
+            if BLOCK_DPE > 0:
+                offs_kpe = (
+                    offs_kv_loc[None, :] * stride_buf_kbs
+                    + cur_kv_head * stride_buf_kh
+                    + offs_dpe[:, None]
+                )
+                kpe = tl.load(
+                    K_Buffer + offs_kpe,
+                    mask=mask_n[None, :],
+                    other=0.0,
+                )
+                qk += tl.dot(qpe.to(kpe.dtype), kpe)
+            qk *= sm_scale
+
+            if logit_cap > 0:
+                qk = logit_cap * tanh(qk / logit_cap)
+
+            qk = tl.where(final_mask, qk, float("-inf"))
+
+            row_max = tl.max(qk, 1)
+            row_max_fixed = tl.where(row_max == float("-inf"), -1e20, row_max)
+            n_e_max = tl.maximum(row_max_fixed, e_max)
+
+            re_scale = tl.exp(e_max - n_e_max)
+            p = tl.exp(qk - n_e_max[:, None])
+            deno = deno * re_scale + tl.sum(p, 1)
+
+            offs_buf_v = (
+                offs_kv_loc[:, None] * stride_buf_vbs
+                + cur_kv_head * stride_buf_vh
+                + offs_dv[None, :]
+            )
+            v = tl.load(
+                V_Buffer + offs_buf_v,
+                mask=mask_n[:, None] & mask_dv[None, :],
+                other=0.0,
+            )
+            p = p.to(v.dtype)
+            acc = acc * re_scale[:, None] + tl.dot(p, v)
+
+            e_max = n_e_max
 
     # stage 2: compute the triangle part
 
@@ -219,35 +231,6 @@ def _fwd_kernel(
         start_n = tl.multiple_of(start_n, BLOCK_N)
         mask_n = (start_n + offs_n) < cur_block_m_end
 
-        # load k in transposed way
-        offs_k = (
-            (cur_seq_extend_start_idx + start_n + offs_n[None, :]) * stride_kbs
-            + cur_kv_head * stride_kh
-            + offs_d[:, None]
-        )
-        k = tl.load(
-            K_Extend + offs_k, mask=(mask_n[None, :]) & (mask_d[:, None]), other=0.0
-        )
-
-        qk = tl.dot(q, k, out_dtype=tl.float32)
-        if BLOCK_DPE > 0:
-            offs_kpe = (
-                (cur_seq_extend_start_idx + start_n + offs_n[None, :]) * stride_kbs
-                + cur_kv_head * stride_kh
-                + offs_dpe[:, None]
-            )
-            kpe = tl.load(
-                K_Extend + offs_kpe,
-                mask=mask_n[None, :],
-                other=0.0,
-            )
-            qk += tl.dot(qpe, kpe)
-
-        qk *= sm_scale
-
-        if logit_cap > 0:
-            qk = logit_cap * tanh(qk / logit_cap)
-
         final_mask = mask_m[:, None] & mask_n[None, :]
         if USE_CUSTOM_MASK:
             custom_mask = tl.load(
@@ -279,28 +262,62 @@ def _fwd_kernel(
             )
             final_mask &= window_mask
 
-        qk = tl.where(final_mask, qk, float("-inf"))
+        SKIP_TILE = False
+        if USE_CUSTOM_MASK or SLIDING_WINDOW_SIZE > 0:
+            SKIP_TILE = tl.max(tl.max(final_mask.to(tl.int32), axis=1), axis=0) == 0
 
-        row_max = tl.max(qk, 1)
-        row_max_fixed = tl.where(row_max == float("-inf"), -1e20, row_max)
-        n_e_max = tl.maximum(row_max_fixed, e_max)
+        if not SKIP_TILE:
+            # load k in transposed way
+            offs_k = (
+                (cur_seq_extend_start_idx + start_n + offs_n[None, :]) * stride_kbs
+                + cur_kv_head * stride_kh
+                + offs_d[:, None]
+            )
+            k = tl.load(
+                K_Extend + offs_k, mask=(mask_n[None, :]) & (mask_d[:, None]), other=0.0
+            )
 
-        re_scale = tl.exp(e_max - n_e_max)
-        p = tl.exp(qk - n_e_max[:, None])
-        deno = deno * re_scale + tl.sum(p, 1)
+            qk = tl.dot(q, k, out_dtype=tl.float32)
+            if BLOCK_DPE > 0:
+                offs_kpe = (
+                    (cur_seq_extend_start_idx + start_n + offs_n[None, :]) * stride_kbs
+                    + cur_kv_head * stride_kh
+                    + offs_dpe[:, None]
+                )
+                kpe = tl.load(
+                    K_Extend + offs_kpe,
+                    mask=mask_n[None, :],
+                    other=0.0,
+                )
+                qk += tl.dot(qpe, kpe)
 
-        offs_v = (
-            (cur_seq_extend_start_idx + start_n + offs_n[:, None]) * stride_vbs
-            + cur_kv_head * stride_vh
-            + offs_dv[None, :]
-        )
-        v = tl.load(
-            V_Extend + offs_v, mask=mask_n[:, None] & mask_dv[None, :], other=0.0
-        )
-        p = p.to(v.dtype)
-        acc = acc * re_scale[:, None] + tl.dot(p, v)
+            qk *= sm_scale
 
-        e_max = n_e_max
+            if logit_cap > 0:
+                qk = logit_cap * tanh(qk / logit_cap)
+
+            qk = tl.where(final_mask, qk, float("-inf"))
+
+            row_max = tl.max(qk, 1)
+            row_max_fixed = tl.where(row_max == float("-inf"), -1e20, row_max)
+            n_e_max = tl.maximum(row_max_fixed, e_max)
+
+            re_scale = tl.exp(e_max - n_e_max)
+            p = tl.exp(qk - n_e_max[:, None])
+            deno = deno * re_scale + tl.sum(p, 1)
+
+            offs_v = (
+                (cur_seq_extend_start_idx + start_n + offs_n[:, None]) * stride_vbs
+                + cur_kv_head * stride_vh
+                + offs_dv[None, :]
+            )
+            v = tl.load(
+                V_Extend + offs_v, mask=mask_n[:, None] & mask_dv[None, :], other=0.0
+            )
+            p = p.to(v.dtype)
+            acc = acc * re_scale[:, None] + tl.dot(p, v)
+
+            e_max = n_e_max
 
     if HAS_SINK:
         cur_sink = tl.load(sink_ptr + cur_head)