Optimize triton swa kernel by skipping computation

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)