sglangv0.5.2 & support Qwen3-Next-80B-A3B-Instruct

sgl-kernel/python/sgl_kernel/attention.py

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+from typing import Optional, Tuple
+
+import torch
+
+
+def lightning_attention_decode(q, k, v, past_kv, slope, output, new_kv):
+    torch.ops.sgl_kernel.lightning_attention_decode.default(
+        q, k, v, past_kv, slope, output, new_kv
+    )
+
+
+def merge_state(
+    v_a: torch.Tensor,
+    s_a: torch.Tensor,
+    v_b: torch.Tensor,
+    s_b: torch.Tensor,
+    v_merged: Optional[torch.Tensor] = None,
+    s_merged: Optional[torch.Tensor] = None,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    s_a = s_a.to(torch.float32)
+    s_b = s_b.to(torch.float32)
+    # Avoid creating new tensors if they are already provided
+    if v_merged is None:
+        v_merged = torch.empty_like(v_a)
+    if s_merged is None:
+        s_merged = torch.empty_like(s_a)
+    torch.ops.sgl_kernel.merge_state.default(v_a, s_a, v_b, s_b, v_merged, s_merged)
+    return v_merged, s_merged
+
+
+def merge_state_v2(
+    v_a: torch.Tensor,
+    s_a: torch.Tensor,
+    v_b: torch.Tensor,
+    s_b: torch.Tensor,
+    v_merged: Optional[torch.Tensor] = None,
+    s_merged: Optional[torch.Tensor] = None,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    s_a = s_a.to(torch.float32)
+    s_b = s_b.to(torch.float32)
+    # TODO(DefTruth): Currently, the custom merge_attn_states kernel
+    # does not support the FP8 data type and non - CUDA devices.
+    # It may be necessary to fall back to using the Triton kernel.
+
+    # Avoid creating new tensors if they are already provided
+    if v_merged is None:
+        v_merged = torch.empty_like(v_a)
+    if s_merged is None:
+        s_merged = torch.empty_like(s_a)
+    torch.ops.sgl_kernel.merge_state_v2.default(v_a, s_a, v_b, s_b, v_merged, s_merged)
+    return v_merged, s_merged
+
+
+def cutlass_mla_decode(
+    q_nope: torch.Tensor,
+    q_pe: torch.Tensor,
+    kv_c_and_k_pe_cache: torch.Tensor,
+    seq_lens: torch.Tensor,
+    page_table: torch.Tensor,
+    workspace: torch.Tensor,
+    sm_scale: float,
+    num_kv_splits: int = 1,  # Set to 1 to avoid cuda_graph issue by default.
+) -> torch.Tensor:
+    assert q_nope.ndim == 3, f"q_nope must be a 3D tensor, but got {q_nope.ndim}"
+    assert q_pe.ndim == 3, f"q_pe must be a 3D tensor, but got {q_pe.ndim}"
+    assert (
+        kv_c_and_k_pe_cache.ndim == 3
+    ), f"kv_c_and_k_pe_cache must be a 3D tensor, but got {kv_c_and_k_pe_cache.ndim}"
+
+    B_q, H, D_q_nope = q_nope.shape
+    B_q_2, H_2, D_q_pe = q_pe.shape
+    assert (B_q == B_q_2) and (H == H_2)
+
+    _, PAGE_SIZE, D_ckv = kv_c_and_k_pe_cache.shape
+
+    D_latent = 512
+    D_rope = 64
+    assert D_q_nope == D_latent
+    assert D_q_pe == D_rope
+    assert D_ckv == D_latent + D_rope
+
+    MAX_HEADS = 128
+    assert H <= MAX_HEADS, f"H must be <= {MAX_HEADS}, but got {H}"
+    if H < MAX_HEADS:
+        q_nope_padded = q_nope.new_empty((B_q, MAX_HEADS, D_q_nope))
+        q_nope_padded[:, :H] = q_nope
+        q_nope = q_nope_padded
+
+        q_pe_padded = q_pe.new_empty((B_q, MAX_HEADS, D_q_pe))
+        q_pe_padded[:, :H] = q_pe
+        q_pe = q_pe_padded
+
+    assert len(page_table.shape) == 2
+    B_block_table, block_num = page_table.shape
+    assert B_block_table == B_q
+    assert block_num > 0, f"block num must be greater than 0, got {block_num}"
+    assert block_num % (128 / PAGE_SIZE) == 0
+
+    # TODO(kaixih@nvidia): support fp8
+    assert q_nope.dtype in (
+        torch.float16,
+        torch.bfloat16,
+    ), f"q_nope.dtype needs to be fp16 or bf16 but got {q_nope.dtype}."
+    assert q_nope.dtype == q_pe.dtype == kv_c_and_k_pe_cache.dtype
+    assert (
+        seq_lens.dtype == torch.int32
+    ), f"seq_lens.dtype needs to be int32 but got {seq_lens.dtype}."
+    assert (
+        page_table.dtype == torch.int32
+    ), f"page_table.dtype needs to be int32 but got {page_table.dtype}."
+
+    out = q_nope.new_empty((B_q, MAX_HEADS, D_latent))
+
+    torch.ops.sgl_kernel.cutlass_mla_decode.default(
+        out,
+        q_nope,
+        q_pe,
+        kv_c_and_k_pe_cache,
+        seq_lens,
+        page_table,
+        workspace,
+        sm_scale,
+        num_kv_splits,
+    )
+    return out[:, :H].contiguous()
+
+
+def cutlass_mla_get_workspace_size(
+    max_seq_len: int,
+    num_batches: int,
+    sm_count: int = 0,
+    num_kv_splits: int = 1,  # Set to 1 to avoid cuda_graph issue by default.
+) -> int:
+    assert max_seq_len > 0, f"max_seq_len must be greater than 0, got {max_seq_len}"
+    assert num_batches > 0, f"num_batches must be greater than 0, got {num_batches}"
+    return torch.ops.sgl_kernel.cutlass_mla_get_workspace_size.default(
+        max_seq_len, num_batches, sm_count, num_kv_splits
+    )