feat: Add a unified merge_state API (#5428)

python/sglang/srt/layers/attention/merge_state.py

+from typing import Optional, Tuple
+
+import torch
+from sgl_kernel import merge_state_v2
+
+from sglang.srt.layers.attention.triton_ops.merge_state import merge_state_triton
+from sglang.srt.utils import is_cuda
+
+_is_cuda = is_cuda()
+
+
+# Automatically fallback to the Triton kernel in some cases
+# (e.g., for AMD GPUs, when the head dimension is not a multiple
+# of 4 or 8, and in FP8 precision)
+def _supported_dtypes(o: torch.Tensor) -> bool:
+    return o.dtype in [torch.float32, torch.half, torch.bfloat16]
+
+
+def _supported_headdim(o: torch.Tensor) -> bool:
+    headdim = o.shape[2]  # [NUM_TOKENS, NUM_HEADS, HEAD_SIZE]
+    if o.dtype == torch.float32:
+        return headdim % 4 == 0
+    return headdim % 8 == 0
+
+
+def merge_state(
+    prefix_output: torch.Tensor,
+    prefix_lse: torch.Tensor,
+    suffix_output: torch.Tensor,
+    suffix_lse: torch.Tensor,
+    output: Optional[torch.Tensor] = None,
+    output_lse: Optional[torch.Tensor] = None,
+) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
+    if (
+        _is_cuda
+        and _supported_dtypes(prefix_output)
+        and _supported_headdim(prefix_output)
+    ):
+        return merge_state_v2(
+            prefix_output, prefix_lse, suffix_output, suffix_lse, output, output_lse
+        )
+    else:
+        # Fallback to Triton kernel
+        return merge_state_triton(
+            prefix_output, prefix_lse, suffix_output, suffix_lse, output, output_lse
+        )

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

+from typing import Optional, Tuple
+
+import torch
+import triton
+import triton.language as tl
+
+
+@triton.jit
+def merge_state_kernel(
+    output,  # [NUM_TOKENS, NUM_HEADS, HEAD_SIZE] v_merged
+    output_lse,  # [NUM_TOKENS, NUM_HEADS] s_merged
+    prefix_output,  # [NUM_TOKENS, NUM_HEADS, HEAD_SIZE] v_a
+    prefix_lse,  # [NUM_TOKENS, NUM_HEADS] s_a
+    suffix_output,  # [NUM_TOKENS, NUM_HEADS, HEAD_SIZE] v_b
+    suffix_lse,  # [NUM_TOKENS, NUM_HEADS] s_b
+    HEAD_SIZE: tl.constexpr,
+    PADDED_HEAD_SIZE: tl.constexpr,
+    OUTPUT_LSE: tl.constexpr,
+):
+    token_idx = tl.program_id(0)
+    num_tokens = tl.num_programs(0)
+    head_idx = tl.program_id(1)
+    num_heads = tl.num_programs(1)
+
+    p_lse = tl.load(prefix_lse + token_idx * num_heads + head_idx)
+    s_lse = tl.load(suffix_lse + token_idx * num_heads + head_idx)
+    p_lse = float("-inf") if p_lse == float("inf") else p_lse
+    s_lse = float("-inf") if s_lse == float("inf") else s_lse
+
+    max_lse = tl.maximum(p_lse, s_lse)
+    p_lse = p_lse - max_lse
+    s_lse = s_lse - max_lse
+    out_se = tl.exp(p_lse) + tl.exp(s_lse)
+
+    if OUTPUT_LSE:
+        out_lse = tl.log(out_se) + max_lse
+        tl.store(output_lse + token_idx * num_heads + head_idx, out_lse)
+
+    head_arange = tl.arange(0, PADDED_HEAD_SIZE)
+    head_mask = head_arange < HEAD_SIZE
+    p_out = tl.load(
+        prefix_output
+        + token_idx * num_heads * HEAD_SIZE
+        + head_idx * HEAD_SIZE
+        + head_arange,
+        mask=head_mask,
+    )
+    s_out = tl.load(
+        suffix_output
+        + token_idx * num_heads * HEAD_SIZE
+        + head_idx * HEAD_SIZE
+        + head_arange,
+        mask=head_mask,
+    )
+
+    p_scale = tl.exp(p_lse) / out_se
+    s_scale = tl.exp(s_lse) / out_se
+    out = p_out * p_scale + s_out * s_scale
+    tl.store(
+        output + token_idx * num_heads * HEAD_SIZE + head_idx * HEAD_SIZE + head_arange,
+        out,
+        mask=head_mask,
+    )
+
+
+def merge_state_triton(
+    prefix_output: torch.Tensor,
+    prefix_lse: torch.Tensor,
+    suffix_output: torch.Tensor,
+    suffix_lse: torch.Tensor,
+    output: Optional[torch.Tensor] = None,
+    output_lse: Optional[torch.Tensor] = None,
+) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
+    # Avoid creating new tensors if they are already provided
+    if output is None:
+        output = torch.empty_like(prefix_output)
+    if output_lse is None:
+        output_lse = torch.empty_like(prefix_lse)
+
+    num_tokens = output.shape[0]
+    num_query_heads = output.shape[1]
+    head_size = output.shape[2]
+    padded_head_size = triton.next_power_of_2(head_size)
+
+    merge_state_kernel[(num_tokens, num_query_heads)](
+        output,
+        output_lse,
+        prefix_output,
+        prefix_lse,
+        suffix_output,
+        suffix_lse,
+        head_size,
+        padded_head_size,
+        output_lse is not None,
+    )
+    return output, output_lse